diff --git "a/data/curated_samples/s2orc_raw.json" "b/data/curated_samples/s2orc_raw.json" new file mode 100644--- /dev/null +++ "b/data/curated_samples/s2orc_raw.json" @@ -0,0 +1,496 @@ +[ + { + "corpusid": 264953628, + "externalids": { + "arxiv": null, + "mag": null, + "acl": null, + "pubmed": null, + "pubmedcentral": null, + "dblp": null, + "doi": "10.30890\/2709-1783.2023-28-01-006" + }, + "content": { + "source": { + "pdfurls": [ + "https:\/\/www.proconference.org\/index.php\/gec\/article\/download\/gec28-01-006\/1361" + ], + "pdfsha": "f2d6c54d49a176d57f546ac2d1e2de649d3b472d", + "oainfo": { + "license": "CCBY", + "openaccessurl": "https:\/\/www.proconference.org\/index.php\/gec\/article\/download\/gec28-01-006\/1361", + "status": "HYBRID" + } + }, + "text": "\nUSE OF INNOVATIVE TECHNOLOGIES IN APPLIED STUDENT TRAINING IN PHYSICAL EDUCATION \u0412\u0418\u041a\u041e\u0420\u0418\u0421\u0422\u0410\u041d\u041d\u042f \u0406\u041d\u041d\u041e\u0412\u0410\u0426\u0406\u0419\u041d\u0418\u0425 \u0422\u0415\u0425\u041d\u041e\u041b\u041e\u0413\u0406\u0419 \u0412 \u041f\u0420\u0418\u041a\u041b\u0410\u0414\u041d\u0406\u0419 \u041f\u0406\u0414\u0413\u041e\u0422\u041e\u0412\u0426\u0406 \u0421\u0422\u0423\u0414\u0415\u041d\u0422\u0406\u0412 \u0423 \u0424\u0406\u0417\u0418\u0427\u041d\u041e\u041c\u0423 \u0412\u0418\u0425\u041e\u0412\u0410\u041d\u041d\u0406\n2023 August 2023\n\nBalukhtina V V \/ \u0411\u0430\u043b\u0443\u0445\u0442\u0456\u043d\u0430 \nSHE\u0415 \u00abPriazovskyi State Technical University\u00bb, str. University\n7, Donetsk region87500MariupolUkraine\n\nUSE OF INNOVATIVE TECHNOLOGIES IN APPLIED STUDENT TRAINING IN PHYSICAL EDUCATION \u0412\u0418\u041a\u041e\u0420\u0418\u0421\u0422\u0410\u041d\u041d\u042f \u0406\u041d\u041d\u041e\u0412\u0410\u0426\u0406\u0419\u041d\u0418\u0425 \u0422\u0415\u0425\u041d\u041e\u041b\u041e\u0413\u0406\u0419 \u0412 \u041f\u0420\u0418\u041a\u041b\u0410\u0414\u041d\u0406\u0419 \u041f\u0406\u0414\u0413\u041e\u0422\u041e\u0412\u0426\u0406 \u0421\u0422\u0423\u0414\u0415\u041d\u0422\u0406\u0412 \u0423 \u0424\u0406\u0417\u0418\u0427\u041d\u041e\u041c\u0423 \u0412\u0418\u0425\u041e\u0412\u0410\u041d\u041d\u0406\n2023 August 20238250B6EAF17EB8B959D84F84DA91FBB410.30890\/2709-1783.2023-28-01-006studentinnovative technologiesphysical educationstudentsapplied trainingmodernization of educationobstacle course\nThe introduction of exercises on the obstacle course will increase the level of physical fitness of students, modernize the system of physical education of students in the applied direction in preparing young people for work and service in the Armed Forces.\n\n\u0410\u043d\u043e\u0442\u0430\u0446\u0456\u044f.\u041f\u0440\u043e\u0432\u0435\u0434\u0435\u043d\u043e \u0430\u043d\u0430\u043b\u0456\u0437 \u0441\u0443\u0447\u0430\u0441\u043d\u0438\u0445 \u0456\u043d\u043d\u043e\u0432\u0430\u0446\u0456\u0439\u043d\u0438\u0445 \u0442\u0435\u0445\u043d\u043e\u043b\u043e\u0433\u0456\u0439 \u0443 \u0441\u0444\u0435\u0440\u0456 \u0444\u0456\u0437\u0438\u0447\u043d\u043e\u0433\u043e \u0432\u0438\u0445\u043e\u0432\u0430\u043d\u043d\u044f \u0442\u0430 \u0457\u0445 \u0432\u043f\u0440\u043e\u0432\u0430\u0434\u0436\u0435\u043d\u043d\u044f \u0443 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u043d\u043e\u043c\u0443 \u043e\u0441\u0432\u0456\u0442\u043d\u044c\u043e\u043c\u0443 \u043f\u0440\u043e\u0446\u0435\u0441\u0456 \u0414\u0412\u041d\u0417 \u00ab\u041f\u0440\u0438\u0430\u0437\u043e\u0432\u0441\u044c\u043a\u0438\u0439 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\u0441\u0442\u0443\u0434\u0435\u043d\u0442, \u0456\u043d\u043d\u043e\u0432\u0430\u0446\u0456\u0439\u043d\u0456 \u0442\u0435\u0445\u043d\u043e\u043b\u043e\u0433\u0456\u0457, \u0444\u0456\u0437\u0438\u0447\u043d\u0435 \u0432\u0438\u0445\u043e\u0432\u0430\u043d\u043d\u044f, \u0441\u0442\u0443\u0434\u0435\u043d\u0442\u0438, \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u043d\u0430 \u043f\u0456\u0434\u0433\u043e\u0442\u043e\u0432\u043a\u0430, \u043c\u043e\u0434\u0435\u0440\u043d\u0456\u0437\u0430\u0446\u0456\u044f \u043e\u0441\u0432\u0456\u0442\u0438, \u0441\u043c\u0443\u0433\u0430 \u043f\u0435\u0440\u0435\u0448\u043a\u043e\u0434.\n\nAbstract.This article analyzes the modern innovative technologies in the field of physical education and their implementation in the applied educational process of the Azov State Technical University.\n\nAn attempt is made to talk about the modern educational standard of higher professional education, to present the obstacle course as one of the most effective means of using innovative technologies in the complex formation of vital applied skills in the process of comprehensive physical training of students in the cycle of general humanities.\n\nThis study provided a theoretical basis that illustrates innovation in applied physical education classes.Thus, one of the priorities of physical education teachers in universities is the search for innovative technologies that involve the use and development of non-traditional tools and methods in teaching physical education that would promote physical and spiritual education of students, developing habits to monitor the health of your body in general.Improving special motor skills involves mastering innovative technologies of scientific, practical and special knowledge necessary to understand the social processes of physical culture, their ability to adaptively,\n\n\n\n\u0442\u0430 \u0433\u0443\u043c\u0430\u043d\u0456\u0437\u0430\u0446\u0456\u0457 \u043e\u0441\u0432\u0456\u0442\u0438 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\u043e\u0440\u0433\u0430\u043d\u0456\u0437\u0430\u0446\u0456\u0457.\u041e.\u0406.\u0410\u043b\u0441\u0443\u0444'\u0454\u0432, \u0404.\u041a.\u0417\u0430\u0432'\u044f\u043b\u043e\u0432\u0430 .\/\/\u0412\u0456\u0441\u0442\u043d.\u0421.-\u041f.\u0443\u043d\u0442\u0430.\u0421\u0435\u0440. 8. 2014.\u2116 3. \u0421. 101-134.2. \u0410\u043d\u0456\u043a\u0454\u0454\u0432 \u0414. \u041c. \u041f\u0440\u043e\u0431\u043b\u0435\u043c\u0438 \u0444\u043e\u0440\u043c\u0443\u0432\u0430\u043d\u043d\u044f \u0437\u0434\u043e\u0440\u043e\u0432\u043e\u0433\u043e \u0441\u043f\u043e\u0441\u043e\u0431\u0443 \u0436\u0438\u0442\u0442\u044f \u0441\u0442\u0443\u0434\u0435\u043d\u0442\u0441\u044c\u043a\u043e\u0457 \u043c\u043e\u043b\u043e\u0434\u0456 \/ \u0414. \u041c. \u0410\u043d\u0456\u043a\u0454\u0454\u0432 \/\/ \u041f\u0435\u0434\u0430\u0433\u043e\u0433\u0456\u043a\u0430, \u043f\u0441\u0438\u0445\u043e\u043b\u043e\u0433\u0456\u044f \u0442\u0430 \u043c\u0435\u0434\u0438\u043a\u043e\u0431\u0456\u043e\u043b\u043e\u0433\u0456\u0447\u043d\u0456 \u043f\u0440\u043e\u0431\u043b\u0435\u043c\u0438 \u0444\u0456\u0437\u0438\u0447\u043d\u043e\u0433\u043e \u0432\u0438\u0445\u043e\u0432\u0430\u043d\u043d\u044f \u0456 \u0441\u043f\u043e\u0440\u0442\u0443 -2009.-\u2116 2 -\u0421.6-9.3\u00ab\u041f\u0440\u0438\u0430\u0437\u043e\u0432\u0441\u044c\u043a\u0438\u0439 \u0434\u0435\u0440\u0436\u0430\u0432\u043d\u0438\u0439 \u0442\u0435\u0445\u043d\u0456\u0447\u043d\u0438\u0439 \u0443\u043d\u0456\u0432\u0435\u0440\u0441\u0438\u0442\u0435\u0442\u00bb -\u043c.\u041c\u0430\u0440\u0456\u0443\u043f\u043e\u043b\u044c: 2017.215\u0441.6. \u0404\u0433\u043e\u0440\u0438\u0447\u0435\u0432 \u041e.\u041e., \u041f\u0435\u043d\u0446\u0438\u043a \u0411.\u041c., \u0421\u043c\u0438\u0440\u043d\u043e\u0432\u0430 \u042e.\u0410.\u0417\u0434\u043e\u0440\u043e\u0432'\u044f \u0441\u0442\u0443\u0434\u0435\u043d\u0442\u0456\u0432 \u0437 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"figureref": null, + "formula": null, + "paragraph": "[{\"end\":1484,\"start\":951},{\"end\":1955,\"start\":1486},{\"end\":2319,\"start\":1957},{\"end\":2555,\"start\":2321},{\"end\":2692,\"start\":2557},{\"end\":2894,\"start\":2694},{\"end\":3240,\"start\":2896},{\"end\":3914,\"start\":3242},{\"end\":9247,\"start\":3918},{\"end\":9708,\"start\":9252},{\"end\":11407,\"start\":10819}]", + "publisher": null, + "sectionheader": null, + "table": "[{\"end\":10818,\"start\":9709}]", + "tableref": null, + "title": "[{\"end\":174,\"start\":1},{\"end\":496,\"start\":323}]", + "venue": null + } + } + }, + { + "corpusid": 264955328, + "externalids": { + "arxiv": null, + "mag": null, + "acl": null, + "pubmed": null, + "pubmedcentral": null, + "dblp": null, + "doi": "10.1051\/e3sconf\/202344001003" + }, + "content": { + "source": { + "pdfurls": [ + "https:\/\/www.e3s-conferences.org\/articles\/e3sconf\/pdf\/2023\/77\/e3sconf_icenso2023_01003.pdf" + ], + "pdfsha": "a45dabca10942187caa9e6c59b7e4816bce9ea4a", + "oainfo": { + "license": "CCBY", + "openaccessurl": "https:\/\/www.e3s-conferences.org\/articles\/e3sconf\/pdf\/2023\/77\/e3sconf_icenso2023_01003.pdf", + "status": "GOLD" + } + }, + "text": "\nPublic Participation in Regulation on the Protection of Marine Resources\n\n\nFenty Puluhulawa fentypuluhulawa@ung.ac.id \nFaculty of Law\nUniversitas Negeri Gorontalo\nIndonesia\n\nAmanda Adelina Harun \nFaculty of Law\nUniversitas Negeri Gorontalo\nIndonesia\n\nKarlin Mamu \nFaculty of Law\nUniversitas Negeri Gorontalo\nIndonesia\n\nPublic Participation in Regulation on the Protection of Marine Resources\n3B6C1304965282E1950E25B8D8325DC410.1051\/e3sconf\/202344001003Marine ResourcesPublic ParticipationRegulation\nThis article discusses the significance of participation of the community as an el-ement of control\/supervision regarding the conservation of marine resources.This is particularly crucial, considering that Pohuwato Regency, which is located in the Tomini Bay area, is a strategic region with high potential for fishery re-sources.In addition, the region also boasts beautiful coastal area as well as other marine-related assets such as coral reefs and mangroves.As an imminent threat to the region's potentials, the technique of destruction fishing not only impacts to the fishery resources, but also damages the marine ecosystem.Therefore, public participation is of significance to maintain the sustainability of marine and coastal resources.The research was conducted using an empirical juridical approach.The data collection technique was carried out through a process of interviews and fo-cus group discussions with various relevant stakeholders as determined purpos-ively.Moreover, a descriptive method was employed to analyze the data.The re-sults show that the existing laws and regulations have provided opportunities for the public to participate in monitoring and reporting of any indications of unlaw-ful acts.Several factors are of importance to be highlighted to optimize the public participation.\n\nIntroduction\n\nIndonesia has been designated as an archipelagic State as stipulated in the 1982 UNCLOS convention.The State consists of 17.504 islands and 2\/3 of its territory is composed of waters.Such a vast sea territory shows that the potentials for marine resources are a plentiful natural wealth of Indonesia that requires an effective and sustainable management.Among these maritime potentials are the fishery resources.As based on the Food and Agricultural Organization (FAO) data in 2009, the potentials of capture fisheries ranked third globally after China and Peru; moreover, the aquaculture fisheries potentials ranked fourth worldwide after China, India and Vietnam.The potentials of capture fisheries in Indonesia soared up to 6.5 million tons with utilization rate of 5.71 tons per year.\n\nThe overall potentials of fishery resources in Indonesia up to 9.931 million tons per year.The presence of abundant fishery resources yielded a massive contribution to the national economy and the wealth of the community.This is in line with the man-date of Article 33 Section 3 of the 1945 Constitution of Republic of Indonesia, which mentions that the Earth, water, and natural resources contained therein are controlled by the State, and are utilized as much as possible for the prosperity of the people.Moreover, the Section 4 also mentions that the national economy is organized based on economic democracy with the principles of togetherness, efficiency, justice, sustainability, environmental insight, independence, and by maintaining a balance of progress and national economic unity.\n\nTomini Bay is an oceanic area with an ecosystem of mangrove forest, corals, small islands, and good circulation since it is traversed by the Indonesian traffic flow and the water flow of the Pacific Ocean and Indian Ocean.\n\nAs the largest bay in Indonesia, Tomini Bay has an area of 59,500 km2 and covers the territories of three provinces (North Sulawesi, Gorontalo, Central Sulawesi) and eleven cities\/regencies.The economic potentials in Tomini Bay had led to the exploitation activities conducted by both small-scale fishers and fishery enterprises.\n\nAs one of the regions in Gorontalo that is covered by Tomini Bay area, Pohuwato Regency features plentiful fishery potentials.The capture fisheries production in the region, as based on the data of Statistics Indonesia office in Gorontalo Province, was at 18,579 tons in 2015, and increased to 21,785 tons in 2017.Apart from the fishery resources, the region is also embedded with other marine resources, such as mangrove forest.Such potential marine resources require conservation and protection from unlawful, irresponsible acts.Illegal and destructive fishing are among the activities that inflict damage to the marine ecosystem.To cope with that, the government has formulated several laws and regulations regarding the management of natural re-sources by emphasizing on the notion of sustainability.\n\nThe data of Sub-directorate of Law Enforcement, Directorate of Marine and Aviation Police of Gorontalo in 2021 reported six alleged cases of destructive fishing during the period of 2016-2020 in Gorontalo Province.The data, however, do not include unreported cases.\n\nTherefore, the public participation is of significance in the monitoring, since the sea and its marine resources are collectively owned by the State to be utilized for the prosperity of Indonesian people.This article analyzes the importance of community participation in protecting the marine resources.Further, the article elaborates from a legal perspective regarding the role of community in protecting the marine resources in Pohuwato Regency as well as ensuring the sustainability of the maritime potentials in the region.\n\n\nMethod\n\nTherefore, the public participation is of significance in the monitoring, since the sea and its marine resources are collectively owned by the State to be utilized for the prosperity of Indonesian people.This article analyzes the importance of community participation in protecting the marine resources.Further, the article elaborates from a legal perspective regarding the role of community in protecting the marine resources in Pohuwato Regency as well as ensuring the sustainability of the maritime potentials in the region.\n\n\nRole of Community in Legal Perspective\n\nThe 2012 data from FAO reported that Indonesia sat in second rank of worldwide capture fisheries and in fourth rank of worldwide aquaculture fisheries.Indonesia also ranked second in global seaweed production.The utilization of abundant potentials of marine resources in Gorontalo Province is far from effective.It is indicated by the increased number from time to time.Aside from the fishery resources, the region also features an estimated coastal area of 590 km2 and sea area of 50,500 km2 with two Fisheries Management Areas (FMAs), i.e., Tomini Bay-Seram Sea and Sulawesi Sea-Pacific Ocean.These FMAs in total have fishery resources potentials of 1,226,090 tons\/year (19.5% of the total potential of Indonesia), with only 2.09% utilization rate.\n\nConsidering the massive potentials of the focused region, a good management for its sustainability is required and therefore expected to improve the economic level of the fishers community as well as the regional revenue.However, the principles of sustainability are to be integrated thoroughly in the resources management.The national government has formulated several policies to encourage the community participation to prevent the damages to the marine ecosystem from destructive fishing activities.The following are several regulations as the legal basis for a sustainable fishery management: The previous regulations are formulated as an acknowledgment of customary law communities, traditional communities, and the existing traditional values which are previously done by the community to meet their daily life and inherit it to the next generations [1].The community who lives in the area play important roles in imple-mented the existing values and would result to significant impacts [2].In this regard, the optimization of the sustainability of marine resources can be done by strengthening existing human resources.Such a conduct can also involve the indigenous communities, traditional communities, and local values that have long been practiced and recognized for their existence in the community, particularly those who reside nearby coastal areas.Regarding the present study, Pohuwato Regency is also able to implement the policies that focus on reinforcement and protection of marine resources.The Article 6 (Section 2) of Law of Fishery regulates that fishery management for the purpose of capture and cultivation requires to take into account the customary law and\/or local wisdom as well as community participation.In other words, the government has opened an opportunity to the development of local wisdom values as the basis of legal policies of protection of marine resources.The government of Gorontalo Province, through the Provincial Regulation No. 4 of 2018 concerning Zoning Plan of Coastal Areas and Small Islands in 2018-2038, has set the stage for the community participation in aspects as follows: 1) monitoring and control (Article 82), 2) reporting or whistleblowing (Article 83), 3) treatment and preservation of the functions of environment and natural resources (Article 90), and 4) community empowerment (Article 91).Viewed from legal and formal aspects, the government supports the community participation in monitoring all resources in the coastal area.\n\nThe prevention of misconducts such as fish bombing is of paramount importance, considering the impacts to the environment.Such misconducts are often committed by fishers from outside the region.Overall, the extent of coral reefs damage in Gorontalo is already at 40%.The damage to corals and other coastal resources is mainly due to the human misconducts.Acts such as illegal fishing and destructive fishing are the main cause of the damage.This situation will worsen if no measures are taken to prevent the illegal acts.That said, it will further damage the whole marine ecosystem.In this regard, a comprehensive approach is imminent to resolve the situation\n\n\nPublic Participation in Protecting Marine Resources by Preventing Destructive Fishing\n\nCommunity participation is very much needed in maintaining the sustainability of natural resources, including the coastal and marine resources.The coastal community mainly works as fisher.The Article 9 (Section 1) juncto of Law No. 45 of 2009 concerning Amendment of Law No. 31 of 2004 concerning Fishery stipulates that it is prohibited for anyone to own, control, carry, and\/or use fishing gear and\/or fishing aids that interfere with and damage the sustainability of fish resources in the territory of the Republic of Indonesia, which is punishable by a maximum imprisonment of 5 years and a maximum fine of Rp. 2,000,000,000.00 (two billion Rupiah).The above provision expressly regulates the prohibition accompanied by imprisonment and fines to enforce the law.As a preventive effort, community participation is also very much needed in maintaining the sustainability of existing resources as previously described; however, as based on the interview results, efforts to maximize the role of the community need to pay attention to the following notions: a. Coastal community mainly relies on the fishery resources.The income from fish catch is crucial to support the economy of the community.As based on the profile of Bajo Laut village in 2016, 61.35% of the community were fishers.Such a condition might hinder the investigation of destructive fishing activities.There is a tendency to cover each other's' misconducts because of solidarity; in addition, the fishers seek for ways of getting a large number of catches.This could lead to increasing numbers of law violations and unreported criminal acts.b.The community can also participate in illegal fishing by reporting any indications of law violations.This is feared to be less effective and backfire because of the sense of kinship and solidarity among fishermen, as well as the unwillingness to involve themselves in dealing with law enforcement because of fear and not wanting to be ostracized from fishing community groups or family members.c.The understanding and awareness of the coastal in maintaining marine ecosystems needs to be improved.Therefore, it is important to explain that marine resources deplete to prevent from over-fishing and other misconducts.Therefore, this program requires the formation of community groups that are pro-environmental sustainability and the implementation of alternative community business assistance programs.Therefore, the socialization of dangers of fish bombs and the law enforcement are to be carried out continuously.d.The limited number of law enforcement officers working in the site and inadequate equipment are the main challenges to the activities of operation.Public awareness is needed in assisting the task of law enforcers in the field.e. Cooperation between the government and the community in preserving marine resources needs to be continuously improved in maintaining the sustainability of collective resources.f.Law enforcement with strict sanctions is one solution in eradicating illegal fishing.\n\nHowever, the use of criminal sanctions needs to be limited only to the level of providing a deterrent effect, and so that others do not do the same.g.The supervisory authority by the regencial government, which was transferred to the provincial authority based on Law Number 23 of 2014 has an impact on the implementation of supervision.The findings show that the protection of marine resources is done by relying on legal solutions.Various factors are to be taken into account.In addition, a multi-factor approach is needed, both through preventive and repressive efforts.Community participation is seen as the effective multi-factor approach to tackle the complex issues of protection of marine resources.\n\n\nConclusion\n\nThe role of the community takes the form of supervision and efforts to preserve marine resources.Public supervision is carried out through the submission of reports and or complaints from the authorities.Based on the results of supervision, the role of the community in conducting supervision needs to pay attention to the following aspects that can promote or hinder the investigation and protection of fishery resources: (1) fishers' entailment of neighbor\/family relationship; (2) sense of solidarity among fishers; (3) limited number of apparatus working in the field, (4) use of criminal sanctions, (5) improvement of public awareness, and (6) regencial-wide monitoring.\n\nE3S\n\nWeb of Conferences 440, 01003 (2023) ICEnSO 2023 https:\/\/doi.org\/10.1051\/e3sconf\/202344001003 3 Discussion\n\n\n1 .\n1\nArticle 18 of 1945 Indonesian Constitution that regulates the traditional communities along with their traditional customary rights as long as these remain in existence and are in accordance with the societal development and the principles of the Unitary State of the Republic of Indonesia 2. Law No. 31of 2004 concerning Fishery that regulates that fishery management is required to take into account the traditional law and\/or local wisdom 3. Law No. 32 of 2014 that regulates the cultural values preservation, nautical vision, and revitalization of traditional law and local wisdom in maritime sector 4. Law No. 27of 2007 jo Law No. 1 of 2014 that regulates the local community, indigenous community, traditional community, and local wisdom 5. Regulation of Minister of Home Affairs No. 52 of 2014 as the basis of guidelines of recognition and protection of indigenous communities and traditional communities 6. Regulation of President of Republic Indonesia as based on the Instruction of PresidentNo.15 of 2011 regarding Protection of Fisher Communities that stipulates and grant the authority to the Coordinating Minister of Politics, Law, and Security in providing guarantees of legal certainty and protection for fisher communities, coordinating the steps needed in the context of preventing illegal, unreported, and unregulated fishing, as well as destructive fishing in the management area of the State.By the Instruction, it is expected that the measures taken are rather preventive and supervisory rather than repressive.\n\n\nE3S\n\nWeb of Conferences 440, 01003 (2023) ICEnSO 2023 https:\/\/doi.org\/10.1051\/e3sconf\/202344001003\n\n\nMembangun Kekuatan Laut Indonesia Dipandang Dari Pengawal Laut Dan Detterence Effect Indonesia. Yudi Listiyono, Jurnal Strategi Pertahanan Laut. 512019. June 13, 2020Building Indonesian Maritime Power based on Indonesia's Sea Guard and Detterent Effect\n\nPotensi Sumber Daya Alam Lautan. Arum Putri, Sutrisni, May 29, 2020. June 13, 2021Potentials of Ma-rine Resources\n\n10.1051\/e3sconf\/202344001003E3S Web of Conferences. 2023. 20234401003\n\nPotensi dan Tingkat Pemanfaatan Sumber Daya Ikan Di Wilayah Pengelolaan Perikanan Negara Republik Indonesia (WPP NRI) Tahun. Ali Suman, 2016. 2015\n\nPotentials and Utilization Rate of Fish Resources. Serta Opsi, Pengelolaannya , Fisheries Man-agement Area of Indonesian Republic (FMAs) and its Management Options. 2015\n\n. Jurnal Ke-Bijakan, Perikanan Indonesia, June 13, 20208\n\nKelimpahan , Komposisi, dan Sebaran Larva Ikan di Laut Seram, Laut Maluku dan Teluk Tomini (WPP 715) [Abundance, Composition, and Distribution of Fish Larvae. Gordon ; Dalam Karsono Wagiyo, Asep Priatna Dan Herlisman, Seram Sea, Sea, and Tomini Bay (FMA715). 2005. 2019. June 13, 202011Bawal Widya Riset Perikanan Tangkap\n\nPerkembangan Perikanan Pelagis Kecil di Teluk Tomini: Suatu Pendekatan Ke Arah Managemen yang Bertanggungjawab, [Develop-ment of Small Pelagic Fishery in Tomini Bay: An Approach to a Responsible Management. Sadhotomo B Suwarso, Wudianto , BAWAL. 162007. June 13, 2021\n\nKajian Strategi Pengelolaan Perikanan Berkelanjutan [A Strategic Study of Sustainable Fishery Manage-ment. Ppn Kementerian, Direktorat Bappenas, Kelautan Dan Perikanan, 2014. July 15, 2021\n\nThe news was quoted from the speech of Head of Environment Division of Environment, Research, and Technology of Gorontalo Province. See, Fenty U. Puluhulawa, dkk, 2013, Pemanfaatan Alat Penangkap Ikan Tradisional Buili Dan Peningkatan Kesadaran Hukum Masyarakat Nalayan Dalam Rangka Perlindungan Sumber Daya Ikan Di Desa Lamu Kecamatan Batudaa Pantai [Utilization of Traditional Fish Trap and Improvement of Legal Awareness of Fisher Com-munity in Protecting Fishery Resources in Lamu Village. Batudaa Pantai District. 22012. accessed December 7, 2013Kerusakan terumbu karang di Gorontalo mencapai 40% [Damage To coral reefs in Gorontalo reaches 40%. Proposal KKN-PPM yang didanai oleh DP2M Dikti\n\nDPK Gelar Rakor Penanganan Tindak Pidana Perikanan [DPK Organizes a Coordination Meeting of Law Enforcement of Criminal Acts. 2013on Fisher-ies\n\nPenyampaian Sekretaris daerah Provinsi Gorontalo pada acara dimaksud. April 5, 2014Speech of Regional Secretary of Gorontalo Province in the Mentioned Event\n\nKapal Perang TNI Angkatan Laut Berpatroli Tangkal Bom Ikan [Indonesian Naval Forces' War Ship Wards Off Fish Bombs. 2011. April 5, 2014\n\nProfile of Bajo Laut Village in 2016LNCS Homepage. 2016\/11\/21\n\n10.1051\/e3sconf\/202344001003E3S Web of Conferences. 2023. 20234401003\n", + "annotations": { + "abstract": "[{\"end\":1811,\"start\":500}]", + "author": "[{\"end\":174,\"start\":76},{\"end\":251,\"start\":175},{\"end\":319,\"start\":252}]", + "authoraffiliation": "[{\"end\":173,\"start\":120},{\"end\":250,\"start\":197},{\"end\":318,\"start\":265}]", + "authorfirstname": "[{\"end\":81,\"start\":76},{\"end\":181,\"start\":175},{\"end\":189,\"start\":182},{\"end\":258,\"start\":252}]", + "authorlastname": "[{\"end\":92,\"start\":82},{\"end\":195,\"start\":190},{\"end\":263,\"start\":259}]", + "bibauthor": 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"[{\"attributes\":{\"n\":\"1\"},\"end\":1825,\"start\":1813},{\"attributes\":{\"n\":\"2\"},\"end\":5575,\"start\":5569},{\"attributes\":{\"n\":\"3.1\"},\"end\":6145,\"start\":6107},{\"attributes\":{\"n\":\"3.2\"},\"end\":10141,\"start\":10056},{\"attributes\":{\"n\":\"4\"},\"end\":13884,\"start\":13874},{\"end\":14566,\"start\":14563},{\"end\":14680,\"start\":14677},{\"end\":16221,\"start\":16218}]", + "table": null, + "tableref": null, + "title": "[{\"end\":73,\"start\":1},{\"end\":392,\"start\":320}]", + "venue": null + } + } + }, + { + "corpusid": 264979274, + "externalids": { + "arxiv": null, + "mag": null, + "acl": null, + "pubmed": "37936832", + "pubmedcentral": "10627050", + "dblp": null, + "doi": "10.2147\/rmhp.s429157" + }, + "content": { + "source": { + "pdfurls": null, + "pdfsha": "d48a7c50d7193d63e38ff5cf8c6937dc8ff9bd20", + "oainfo": { + "license": "CCBYNC", + "openaccessurl": null, + "status": null + } + }, + "text": "\nValidation of an ICD-Based Algorithm to Identify Sepsis: A Retrospective Study\n2 November 2023\n\nShi-Tong Diao 0000-0002-4022-2519\nRun Dong \nJin-Min Peng \nYan Chen \nShan Li \nShu-Hua He \nYi-Fan Wang \nBin Du \nLi Weng wengli@gmail.com \n\nChina Critical Care Clinical Trials Group (CCCCTG) Medical Intensive Care Unit, State Key Laboratory of Complex Severe and Rare Diseases\nPeking Union Medical College Hospital\nPeking Union Medical College\nChinese Academy of Medical Sciences\nBeijingPeople's Republic of China\n\n\nMedical Intensive Care Unit\nState Key Laboratory of Complex Severe and Rare Diseases\nPeking Union Medical College Hospital\n\n\n\nPeking Union Medical College\nChinese Academy of Medical Sciences\n1 Shuai Fu Yuan100730Beijing, EmailPeople's Republic of China\n\nValidation of an ICD-Based Algorithm to Identify Sepsis: A Retrospective Study\n2 November 202327C806CB4D21AFC0BFF0457901B1BD0310.2147\/RMHP.S429157Received: 25 July 2023 Accepted: 25 October 2023sepsisvalidationInternational Classification of Diseasesadministrative health dataidentification\nObjective:The aim of the study was to validate a modified International Classification of Diseases (ICD)-10 based algorithm for identifying hospitalized patients with sepsis.Methods: We retrospectively analyzed a prospective, single-center cohort of adult patients who were consecutively admitted to one medical ICU ward and ten non-ICU wards with suspected or confirmed infections during a 6-month period.A modified ICD-10 based algorithm was validated against a reference standard of Sequential Organ Failure Assessment (SOFA) score based on Sepsis-3.Sensitivity (SE), specificity (SP), positive predictive value (PPV), negative predictive value (NPV), and areas under the receiver operating characteristic curves (AUROCs) were calculated for modified ICD-10 criteria, eSOFA criteria, Martin's criteria, and Angus's criteria.Results: Of the 547 patients in the cohort, 332 (61%) patients met Sepsis-3 criteria and 274 (50%) met modified ICD-10 criteria.In the ICU setting, modified ICD-10 criteria had SE (84.47%),SP (88.57%),PPV (95.60), and NPV (65.96).In non-ICU settings, modified ICD-10 had SE (64.19%),SP (80.00%),PPV (80.33), and NPV (63.72).In the whole cohort, the AUROCs of modified ICD-10 criteria, eSOFA, Angus's criteria, and Martin's criteria were 0.76, 0.75, 0.62, and 0.62, respectively.Conclusion: This study demonstrated that modified ICD-10 criteria had higher validity compared with Angus's criteria and Martin's criteria.Validity of the modified ICD-10 criteria was similar to eSOFA criteria.Modified ICD-10 algorithm can be used to provide an accurate estimate of population-based sepsis burden of China.\n\nBackground\n\n2][3][4] The scarcity of high-quality data in the rest of the world hampered the accuracy and generalizability of those estimates. 5eanwhile, it was estimated that 85.0% of incidences and 84.8% of case fatalities occurred in low-and middle-income countries (LMICs), which highlighted the priorities of sepsis epidemiology research in LMICs, particularly in China, the most populous country in the world. 1 As per the most recent definition of Sepsis-3, sepsis was defined as acute change in Sequential Organ Failure Assessment (SOFA) score \u22652 points consequent to any infections. 6However, the components of SOFA excluded the feasibility of sepsis surveillance, especially in LMICs. 7In 2018, the Centers for Disease Control and Prevention (CDC) developed a hospital toolkit, known as eSOFA, using simplified clinical data obtained from the electronic health records (EHRs) for sepsis surveillance in hospital. 8However, the limited availability and accessibility of EHRs in LMICs in China remained a challenge. 9Thereby, healthcare administrative claims data might be the most feasible way to estimate burden of sepsis in China.\n\nAs a common data source, the healthcare administrative claims were widely used for sepsis surveillance. 10The most cited international classification of diseases (ICD) identification algorithm was the combination of ICD codes for both infection and organ dysfunction. 11In the era of Sepsis-1, mainstream identification strategies including Angus's criteria and Martin's criteria focused mostly on narrow diagnoses of infection and organ failure.2][13] Similar findings were reported in the validation of the Sepsis-3 based algorithm. 7Nevertheless, a retrospective medical record review was limited to several methodological mistakes, 14 inadequate data collection, inter-rater or intra-rater unreliability, and compromised data abstraction.\n\nThe Chinese version of the International Classification of Diseases, Tenth Revision (ICD-10) diagnosis codes was expanded to a 10-digit version from the 4-digit WHO version. 15There were explicit sepsis cases that had an ICD-10 code that explicitly referenced sepsis, which differed from the traditional criteria. 16For instance, the category code 008 (3-digit) refers to \"complications following ectopic and molar pregnancy\", and its secondary category code 008.2 (4-digit) pertains to the specific condition \"embolism following ectopic and molar pregnancy\", while its tertiary category code 008.200x006 (10-digit) pertains to the particular condition \"embolism following abortion and ectopic and molar pregnancy (septicopyaemic)\" and was thus labeled as an explicit sepsis code.However, 008.200x006 was not included in the traditional criteria.\n\nSimilar to previous studies (Supplementary Table S1), the aim of this study was, therefore, using a prospective cohort of hospitalized patients with SOFA score confirmed sepsis or non-sepsis, to validate a modified Sepsis-3 based ICD identification algorithm in ICU and non-ICU settings, hopefully to provide a validated ICD-based algorithm for sepsis surveillance in China.\n\n\nMethods\n\n\nStudy Design and Data Collection\n\nThis is a retrospective analysis of a single-center, cohort study (NCT 02930070 registered in clinicaltrials.gov),which was designed to assess the diagnostic value of qSOFA for sepsis in a general ward. 17During the study period from October 1, 2016, to March 31, 2017, all adult patients admitted to one medical ICU ward and ten non-ICU wards with suspected or confirmed infection were eligible for enrollment.Patients were excluded if their age were less than 18 years or hospital length of stay (LOS) less than 24 hours.All patients were followed up until hospital discharge, death, or end of 28-day period, whichever occurred first.\n\nThe retrospective analysis was performed based on two databases.We retrieved the following data from the original trial's dataset: (1) Demographic data, chronic comorbidities, information of infections; (2) systolic and diastolic blood pressure, heart rate, saturation of pulse oxygen, respiratory rate, body temperature, urinary output; (3) laboratory results required for SOFA scores, microbiology findings, arterial blood gases; and (4) requirement for critical care resources, ie, ICU admission, respiratory support, vasopressors, and renal replacement therapy.The ICD codes were extracted from the discharge abstract dataset containing ICD-10 diagnosis codes and ICD-9 procedure codes.The protocol was approved by the institutional review board of Peking Union Medical College Hospital, and informed consent was waived (ZS-1142).\n\n\nDefinitions and Sepsis Identification Criteria Sepsis-3 Criteria\n\nThe Third International Consensus Definitions for Sepsis (Sepsis-3) were considered as the gold standard and sepsis was defined as a change in SOFA score \u22652 points consequent to an infection 6 (Supplementary Table S2).Infection was defined according to the predefined criteria (Supplementary Table S3).Baseline SOFA was defined as the estimated SOFA score on the hospital admission day.The baseline SOFA score was assumed to be zero if no history of organ dysfunction was present.The variables required for SOFA score were collected daily, including platelet count, serum bilirubin, serum creatinine, Glasgow Coma Score, saturation of pulse oxygen (SpO 2 ) or partial pressure of arterial oxygen (PaO 2 ), Fraction of Inspired Oxygen (FIO 2 ), blood pressure, and vasopressors.We preferentially used the PaO 2 to FIO 2 ratio (PaO 2 \/FIO 2 ) when arterial blood gases were obtained.If not available, the PaO 2 was estimated from the SpO 2 . 18dified ICD-10 Criteria Similar to previous studies, 11,13 our modified ICD-10 identification algorithm was based on a combination of ICD codes for both acute infection (Supplementary Table S4) and organ dysfunction.Organ dysfunction was defined based on organ dysfunction related ICD-10 diagnosis codes and ICD-9 procedure codes (Supplementary Tables S5 and S6).The identification algorithms of Angus and Martin were converted from ICD-9 to ICD-10 codes (Supplementary Tables S7-S10) according to the tool of Centers for Medicare & Medicaid Services. 19\n\n\nESOFA Criteria\n\nBased on the simplified organ dysfunction criteria (eSOFA) proposed by the US Centers for Disease Control and Prevention (CDC), sepsis was identified if there was presumed infection and concurrent eSOFA score \u22651 points. 7,8,20,21The presumed infection was defined as a medical record of both blood culture and new administration of antibiotics for at least 4 consecutive days.The eSOFA criteria was optimized for electronic health records including: 1) initiation of vasopressors; 2) initiation of mechanical ventilation; 3) doubling of serum creatinine or decrease by 50% of estimated glomerular filtration rate (eGFR) relative to baseline; 4) total bilirubin \u22652.0 mg\/dL and increase by 100% from baseline; 5) platelet count <100 cells\/\u03bcL and \u226550% decline from baseline; and 6) serum lactate \u22652.0 mmol\/L (Supplementary Table S2).\n\n\nStatistical Analysis\n\nContinuous variables were presented as medians (with interquartile range) and compared by Mann-Whitney U-test.Categorical variables were compared by Chi-square test or Fisher's exact test.\n\nThe Charlson Comorbidity Index was computed according to Supplementary Table S11.The accuracy of our ICD identification algorithm was evaluated by sensitivity (SE), specificity (SP), positive predictive value (PPV), negative predictive value (NPV), and Area Under the Receiver Operating Characteristic curve (AUROC).Confidence intervals (CI) were calculated by delta method.P-value <0.05 was considered as statistically significant.All statistical analyses were performed by statistical software IBM SPSS Statistics for Windows (IBM Corp. Released 2011, Version 20.0.Armonk, NY: IBM Corp.).\n\n\nResults\n\nA total of 547 patients were enrolled in the final cohort, including 138 patients in the ICU and 409 patients in the general wards (Figure 1).As shown in Table 1, in the ICU setting, there were 103 (75%) patients meeting Sepsis-3 criteria and 91 (66%) meeting modified ICD-10 criteria.In general wards, there were 229 (56%) patients meeting Sepsis-3 criteria and 183 (45%) meeting modified ICD-10 criteria.Clinical characteristics and outcomes were similar between patients meeting Sepsis-3 and modified ICD-10 criteria in both ICU and non-ICU settings.Compared with non-ICU sepsis patients, ICU patients had higher hospital acquired infections (54% vs 31%, p<0.01), blood stream infections (44% vs 20%, p<0.01), multiple infection rates (40% vs 24%, p<0.01), and expenses (142.06 vs 77.51 1000 CNY, p<0.01).Respiratory, blood stream and abdominal infection were the most common infection site.\n\nComparison of modified ICD-10, Angus's, Martin's, and eSOFA criteria for identification of sepsis were shown in Table 2.For both ICU and non-ICU setting, the sensitivity of modified ICD-10 (70.48%, 95% CI=65.20-75.27%)was higher than Angus's and Martin's criteria (30.12%, 95% CI=25.29-35.42%and 25.60%, 95% CI=21.07-30.72%,respectively), lower than eSOFA (80.12%, 95% CI=75.33-84.20%).On the contrary, the specificity of modified ICD-10 (81.39%, 95% CI=75.41-86.23%)was lower than Angus's and Martin's criteria (93.49%, 95% CI=89.09-96.26%and 98.14%, 95% CI=94.99-99.40%,respectively) and higher than eSOFA (69.77%, 95% CI=0.63-0.76).The corresponding AUROC value of modified ICD-10 criteria was the highest (0.76, 95% CI= 0.72-0.80).The modified ICD-10 criteria in the ICU setting had a higher sensitivity (84.47%, 95% CI=75.70-90.59%vs 64.19%, 95% CI=57.57-70.33%)and specificity (88.57%, 95% CI=72.32-96.27%vs 80.00%, 95% CI=73.67-85.68%)than th non-ICU setting.Positive and negative likelihood ratios of different identification strategies are shown in Supplementary Table S12.\n\nCharacteristics Sepsis-3 (+)\/ Modified ICD-10 (+), Sepsis-3 (+)\/ Modified ICD-10 (-), Sepsis-3 (-)\/ Modified ICD-10 (+), and Sepsis-3 (-)\/ Modified ICD-10 (-) Patients There were 234 (43%) patients who met both Sepsis-3 and modified ICD-10 criteria.In comparison, both the Sepsis-3 (+)\/modified ICD-10(-) (n=98) and the Sepsis-3 (-)\/modified ICD-10 (+) (N=40) (shown in Table 3) groups were less likely to have respiratory, blood stream, and two or more infections.Also, the SOFA score, qSOFA, eSOFA score, proportion of mechanical ventilation support and vasoactive agents, and mortality rates in both of the groups were significantly lower than the Sepsis-3+\/modified ICD-10+ group.Among 98 Sepsis-3 (+)\/modified ICD-10 (-) patients, 29 (30%) patients had abdominal infection and 43 (44%) patients had coagulation dysfunction.82 of 98 Sepsis-3 (+)\/modified ICD-10 (-) patients were not identified as sepsis by the modified ICD-10 algorithm because organ dysfunction ICD codes were not assigned.\n\n\nDiscussion\n\nReliable surveillance of sepsis was important for the estimation of disease burden, prevention initiatives.and national sepsis quality measures. 22In this study, we validated a sepsis surveillance algorithm based on ICD-10 codes.Our findings showed that the modified ICD-10 based algorithm could identify more than 95% of sepsis patients in the ICU and more than 80% of sepsis patients outside the ICU in a single medical center of China.Considering the availability and accessibility of ICD codes in China, 23 a modified ICD-10 based algorithm was a feasible way for population-based sepsis surveillance in China.5][26][27] EHRs-based criteria had several advantages such as objectivity, quantization, and real-time monitoring for hospitalized patients. 28CDC developed a Toolkit based on clinical data directly obtained from the EHRs, eSOFA, to track hospital-level sepsis incidence and outcome. 8,21In our study, a high eSOFA diagnosis proportion in the Sepsis-3 (+)\/modified ICD-10 (-) group showed the validity of EHRs.Although our previous validation study confirmed the performance of the toolkit in a hospitalized Chinese population, the feasibility to use the CDC sepsis surveillance toolkit was limited in most resource-limited areas where EHRs were not available or accessible. 29dministrative claims based modified ICD-10 criteria offered a feasible low-cost approach to extrapolate populationbased estimates of sepsis burden in LMICs, where most of the septic patients were admitted in non-ICU settings. 30In addition, our study showed the overall accuracy in sepsis identification was similar between the EHRs algorithm and modified ICD-10 algorithm.Even so, most patients were not identified by modified ICD-10 criteria because of the absence of the corresponding organ dysfunction codes, which could be explained by insufficient coding training.With improving coding practices and more standardized coding procedure, 7 modified ICD-10 criteria might have better performance in sepsis surveillance.A major strength of our study was the use of a prospective cohort which could accurately identify patients with sepsis and collect all components of SOFA score and other detailed clinical data.In 2019, Rhee et al 26 evaluated the accuracy of sepsis identification strategy based on claims data and clinical data from EHRs.The sensitivity of administrative claims strategy ranged from 5% to 54% for explicit sepsis codes, and from 42% to 80% for implicit codes.However, the robustness of the findings in the study was limited to the retrospectively confirmed diagnosis of sepsis, especially in non-ICU settings.Also, previous studies 7, [24][25][26] validating administrative data algorithms were based on retrospective cohorts which were inevitably influenced by missing data including Glasgow score, laboratory findings, and urinary output.Another strength of our study was the inclusion of patients with sepsis admitted to non-ICU settings, 31 allowing generalization of our findings to the hospitalized populations.\n\nThe study also had several limitations.First, our study was based on a cohort from a single center.Although the ICD coding practice might be different in hospitals across different regions, the data qualities of ICD coding were ensured by manual review (Supplementary Figure S1), quality control meetings, courses of staff training, and regulations for inspection in all public hospitals in China. 32Further validation was warranted for generalization of our algorithms in other hospitals in China.Second, we could not differentiate sepsis-related or non-sepsis related organ dysfunctions based on the ICD codes, which would lead to an overestimation of the sepsis rate.Lastly, we only enrolled patients with infection or suspected infection.Further investigation was needed for population with extremely low incidence of infection. 24,33\n\n\nConclusions\n\nThis study demonstrated that modified ICD-10 criteria had higher validity compared with Angus's criteria and Martin's criteria.Validity of the modified ICD-10 criteria was similar to eSOFA criteria.Modified ICD-10 algorithm can be used to provide an accurate estimate of population-based sepsis burden of China.\n\nFigure 1\n1\nFigure 1 Flowchart of the patients admitted to the non-ICU and ICU.Abbreviations: LOS, Length of stay; ICU, intensive care unit.\n\n\nTable 1\n1\nClinical Characteristics and Outcomes of Patients Enrolled in ICU and Non-ICU Settings\nICU (n=138)Non-ICU (n=409)Modified ICD-10Sepsis 3.0p-valueModified ICD-10Sepsis 3.0p-value(n=91)(n=103)(n=183)(n=229)Male, n (%)44 (48)51 (50)0.872100 (55)124 (54)0.920Age, median (IQR)58 (39-69)60 (46-69)0.47557 (37-68)58 (43-68)0.580Charlson comorbidity index, n (%)018 (20)20 (19)0.94956 (30)73 (32)0.781125 (27)26 (25)0.72530 (16)36 (16)0.853223 (25)28 (27)0.76357 (31)66 (29)0.6083 or more25 (27)29 (28)0.91640 (22)54 (24)0.679Emergency admission, n (%)37 (41)42 (41)0.98760 (38)76 (33)0.931Perioperative admission, n (%)10 (11)12 (12)0.88531 (17)39 (17)0.981Infected site, n (%)Blood stream41 (45)45 (44)0.84834 (19)46 (20)0.701Respiratory70 (77)80 (78)0.901118 (64)145 (63)0.922Abdominal18 (20)17 (17)0.55433 (18)53 (23)0.205Urinary4 (4)5 (5)0.86819 (10)19 (8)0.467Skin and soft tissue7 (8)10 (1)0.60514 (8)14 (6)0.538Neurological4 (4)3 (3)0.5907 (4)11 (5)0.629Two or more infections40 (44)41 (40)0.59042 (23)56 (24)0.722Nosocomial infection, n (%)51 (56)56 (54)0.81555 (30)71 (31)0.835qSOFA\u2265273 (80)82 (80)0.996100 (55)122 (53)0.782SOFA, median (IQR)8 (5-11)7 (5-11)0.8354 (3-6)4 (3-6)0.890eSOFA, median (IQR)2 (1-3)2 (1-3)0.4631 (0-2)0 (1-2)0.569Organ support, n (%)Renal replacement therapy16 (18)20 (19)0.7437 (4)7 (3)0.669Mechanical ventilation63 (69)68 (66)0.63424 (13)30 (13)0.997Vasoactive agent54 (59)56 (54)0.48638 (21)44 (19)0.695OutcomesHospital LOS, median (IQR)21 (11-37)21 (11-37)0.87624 (14-33)24 (13-32.5)0.766Hospital mortality, n (%)22 (24)23 (22)0.76131 (17)32 (14)0.40628-day mortality, n (%)15 (16)15 (15)0.71229 (16)30 (13)0.429Total cost, 1000 CNY (mean\u00b1SD)149.98\u00b1150.19142.06\u00b1144.750.71182.26\u00b191.2577.51\u00b187.28)0.594\n\nTable 2\n2\nComparison of Different ICD-Coded Algorithms and eSOFA Criteria for Identification of Sepsis in ICU and Non-ICU Settings\nSP, % (95% CI)SE, % (95% CI)PPV, % (95% CI)NPV, % (95% CI)AUC (95% CI)No-ICU (n=409)Modified ICD-1080.00 (73.67-85.68)64.19 (57.57-70.33)80.33 (73.67-85.68)63.72 (57.04-69.92)0.72 (0.67-0.77)Angus's criteria92.22 (87.04-95.52)31.44 (25.57-37.94)83.72 (73.85-90.50)51.39 (45.80-56.95)0.62 (0.56-0.67)Martin's criteria97.78 (94.04-99.29)21.40 (16.39-27.39)92.45 (80.93-97.55)49.44 (44.14-54.75)0.60 (0.54-0.65)eSOFA criteria68.33 (60.93-74.94)76.86 (70.75-82.05)75.54 (69.41-80.81)69.89 (62.45-76.44)0.73 (0.68-0.78)ICU (n=138)Modified ICD-1088.57 (72.32-96.27)84.47 (75.70-90.59)95.60 (88.50-98.58)65.96 (50.60-78.72)0.87 (0.79-0.94)Angus's criteria100.00 (87.68-100.00)27.18 (19.11-36.99)100.00 (84.98-100.00)31.82 (23.45-41.48)0.64 (0.54-0.73)Martin's criteria100.00 (87.68-100.00)34.95 (26.00-45.05)100.00 (87.99-100.00)34.31 (25.38-44.45)0.67 (0.58-0.76)eSOFA criteria77.14 (59.44-88.96)87.38 (79.03-92.84)91.84 (84.08-96.15)67.50 (50.76-80.93)0.82 (0.73-0.91)ICU and non-ICU (n=547)Modified ICD-1081.39 (75.41-86.23)70.48 (65.20-75.27)85.40 (80.53-89.25)64.10 (58.07-69.74)0.76 (0.72-0.80)Angus's criteria93.49 (89.09-96.26)30.12 (25.29-35.42)87.72 (79.93-92.88)46.42 (41.66-51.24)0.6 2(0.57-0.66)Martin's criteria98.14 (94.99-99.40)25.60 (21.07-30.72)95.51 (88.26-98.56)45.07 (41.45-50.76)0.62 (0.57-0.66)eSOFA criteria69.77 (63.08-75.73)80.12 (75.33-84.20)80.36 (75.58-84.42)69.44 (62.77-75.42)0.75 (0.71-0.79)\nhttps:\/\/doi.org\/10.2147\/RMHP.S429157 DovePress Risk Management and Healthcare Policy 2023:16\nPowered by TCPDF (www.tcpdf.org)\nRisk Management and Healthcare Policy 2023:16 https:\/\/doi.org\/10.2147\/RMHP.S429157 DovePress\nAbbreviations: SOFA, Sequential Organ Failure Assessment; eSOFA, Simplified Organ Dysfunction Criteria; IQR, inter quartile range; LOS, length of stay; CNY, Chinese yuan renminbi; SD, standard deviation. Risk Management and Healthcare Policy 2023:16 https:\/\/doi.org\/10.2147\/RMHP.S429157 DovePress 2253 Dovepress Diao et al Powered by TCPDF (www.tcpdf.org)\nAcknowledgmentsThe authors thank China Critical Care Clinical Trials Group: Shi-tong Diao, Run Dong, Jin-min Peng, Yan Chen, Shan Li, Shu-hua He, Yi-fan Wang, Bin Du, and Li Weng, MD.Data Sharing StatementThe datasets analyzed during the current study are available from the corresponding author on reasonable request.FundingThis work was supported by grants from CAMS Innovation Fund for Medical Sciences (CIFMS) from Chinese Academy of Medical Sciences 2021-I2M-1-062; National Key R&D Program of China from Ministry of Science and Technology of the People's Republic of China 2021YFC2500801; National key clinical specialty construction projects from National Health Commission.Abbreviations: SP, specificity; SE, sensitivity; PPV, positive predictive value; NPV, negative predictive value; CI, confidence interval; AUC, area under curve.Ethical Approval and Consent to ParticipateThe study was conducted in accordance with the Declaration of Helsinki and the protocol was approved by the institutional review board of Peking Union Medical College Hospital (ZS-1142).The data accessed complied with relevant data protection and privacy regulations.Author ContributionsAll authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis, and interpretation; took part in drafting, revising, or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.DovepressDiao et alPowered by TCPDF (www.tcpdf.org)DisclosureThe authors declare that they have no competing interests in this work.\nGlobal, regional, and national sepsis incidence and mortality, 1990-2017: analysis for the Global Burden of Disease Study. 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"source": { + "pdfurls": null, + "pdfsha": "9b488701f970170e43224bc45e436f676c4fb5bf", + "oainfo": { + "license": "CCBYNC", + "openaccessurl": null, + "status": null + } + }, + "text": "\nExploring the Effectiveness of Self-and Other-Focused Happiness: The Moderating Role of Job Resources\n3 November 2023\n\nYuping Xu 0000-0003-2602-3107\nSchool of Management\nHuazhong University of Science and Technology\nWuhanPeople's Republic of China\n\nYanyi Huang 0000-0002-7473-7713\nSchool of Management\nHuazhong University of Science and Technology\nWuhanPeople's Republic of China\n\nLin Sun \nSchool of History, Culture and Tourism\nGuangxi Normal University\nGuilinPeople's Republic of China\n\nJing Yang \nSchool of Management\nHuazhong University of Science and Technology\nWuhanPeople's Republic of China\n\n\nSchool of History, Culture and Tourism\nGuangxi Normal University\nNo. 1 Wangcheng\n\n\nXiufeng District\nGuilin, EmailPeople's Republic of China\n\nExploring the Effectiveness of Self-and Other-Focused Happiness: The Moderating Role of Job Resources\n3 November 2023AC01F46E2BC82109D4AD96DE45BAC05610.2147\/PRBM.S433888Received: 6 September 2023 Accepted: 17 October 2023self-focused happinessother-focused happinesswork-related vigorjob resourcescreativity\nThis paper aims to redefine happiness goals and explore the conditions and mechanisms through which these redefined happiness goals influence work-related outcomes.Methods:The study developed and validated scales for self-focused happiness and other-focused happiness through exploratory factor analyses of 244 employees and confirmatory factor analyses of 300 employees.The proposed theoretical model was subsequently tested using a time-lagged analysis with data from 556 supervisor-employee dyads.Results: The findings provide strong evidence for the categorization of happiness goals into self-focused happiness and other-focused happiness.Furthermore, both self-focused and other-focused happiness significantly contribute to work-related vigor, subsequently influencing employee creativity.Additionally, the impact of these happiness goals on vigor and creativity is contingent upon the availability of job resources.Conclusion:This study highlights the substantial role of self-focused and other-focused happiness in enhancing employee vigor and creativity.However, the extent of these effects depends on the level of available job resources.These outcomes carry notable implications for the fields of positive psychology, positive organizational behavior, and creativity.\n\nIntroduction\n\nThe pursuit of happiness has perennially been a central human aspiration. 1,2In academia, this pursuit has been found to be a predictor of some outcomes.For example, Lin and Chan demonstrated that happiness motives influenced well-being outcomes. 3Gentzler et al found a correlation between happiness goals and an array of social and emotional outcomes in youth. 4Jia et al found that eudaimonic motivation predicted adolescents' positive affect and life satisfaction. 5hile prior research offers invaluable insights, it predominantly focuses on individual happiness, such as happiness motives 3 and happiness goals, 4 overlooking instances where happiness goals revolve around the happiness of others.Additionally, prior research has largely examined the impact of the pursuit of happiness on life and affective outcomes, such as positive affect and life satisfaction, 5 while rarely delving into its influence on work-related outcomes.Furthermore, the scientific exploration of the circumstances and mechanisms through which the pursuit of happiness yields desirable outcomes remains in its early stages.Therefore, in the following sections, we synthesize insights from the fields of psychology and organizational behavior, offering a fresh perspective on defining happiness goals, and report our findings on the conditions and mechanisms through which these redefined happiness goals can influence work-related outcomes.\n\nResearchers posit that happiness comprises two components: an affective component and a meaning component. 6The affective component emphasizes individual needs, while the meaning component emphasizes transcending oneself. 6,7his paradigm suggests that aiding others in achieving happiness can become a personal objective.In light of this perspective, we propose that there are two goals toward which the pursuit of happiness can be directed: self-focused happiness, which pertains to personal well-being, and other-focused happiness, where fulfillment stems from the happiness of others.Furthermore, goals shape the subjective meaning and value people attribute to events, thus molding their experiences. 8In this study, we consider work-related vigor, a positive affective response to one's ongoing interactions with significant elements in one's job and work environment, 9 to be a work-related outcome.The study of work-related vigor has the potential to shed light on human goals at work, 10 and work-related vigor can often be considered an indicator of a person's optimal psychological functioning. 11,12We believe that in order to achieve happiness goals, people engage in a variety of activities that can increase their work-related vigor.\n\nWhile the pursuit of both self-focused and other-focused happiness can positively influence work-related vigor, we seek to ascertain under what circumstances one might have a stronger effect than the other.Prior research suggests that individual experiences within organizations are co-shaped by the interplay of individual characteristics and contextual factors. 13The concept of vigor is rooted in conservation of resources theory. 9,11One aim of this article is to discuss the differences in the impact of selffocused and other-focused happiness on work-related vigor when individuals have varying job resources.We argue that otherfocused happiness can only be achieved through other-focused behaviors, while self-focused happiness can be achieved through both self-focused and other-focused behaviors.Limited resources, such as time or energy, often lead to a competitive orientation among people, causing them to prioritize activities that are self-centered. 14,15At this point, self-focused happiness may have a greater impact on work-related vigor than other-focused behaviors.However, resourcefulness can stimulate other-focused behaviors. 16Under such conditions, the positive influence of other-focused happiness on work-related vigor significantly increases.Other-focused happiness can have a greater impact on work-related vigor than self-focused happiness.\n\nWhile we have primarily focused on work-related vigor as a proximal psychological outcome of happiness goals, it could also serve as a conduit, transmitting the effects of happiness goals to downstream outcomes.8][19] We argue creativity is an important outcome to consider because it naturally emanates from employee vigor and plays a crucial role in enhancing employee performance and facilitating enterprise development. 11,20,21The second objective of this article is to examine whether self-focused happiness and otherfocused happiness can influence employee creativity through work-related vigor.\n\nCombining the above discussions, we further propose that the third objective of this article is to explore the differential impact of self-focused happiness and other-focused happiness on employee creativity through work-related vigor when work resources vary.We argue that when job resources are plentiful, other-focused happiness has a stronger indirect effect on employee creativity than self-focused happiness.In contrast, when job resources are scarce, selffocused happiness has a stronger indirect effect on employee creativity than other-focused happiness.\n\nOur proposed model is presented in Figure 1.Through this research, we aim to make several significant theoretical and empirical contributions.Theory-wise, we utilize insights from psychology and organizational behavior to extend and enrich understanding of happiness experiences and innovation.Specifically, we introduce the concepts of self-focused and other-focused happiness, delineating their distinct processes and impacts, and thereby emphasizing the importance of differentiating between various means of pursuing happiness.On the empirical front, we investigate job resources as a moderating variable, underscoring that firms can indirectly influence the relationship between happiness goals and employees' work-related vigor and creativity by providing suitable job resources.\n\n\nLiterature Review and Model Development\n\n\nSelf-and Other-Focused Happiness\n\nWe define happiness goals as the objective of enhancing one's own happiness and that of others.Happiness is argued to be a comprehensive, subjective appraisal of the perceived quality of one's life. 6,22It comprises two essential components:\n\n(1) an affective facet, denoted by a predominance of positive emotions over negative ones, and (2) a meaning facet, denoted by a sense of purpose and value permeating one's life. 6,23,24he affective component appears to be deeply anchored in the satisfaction of one's personal needs and desires, 7 while the meaning component is often linked with engagement in causes that transcend the self. 6The meaning component is associated with performing positive acts for the benefit of others. 7Assisting others-including contributing to their happiness-instills a sense of meaning, which can indirectly boost the helper's own happiness.Therefore, individuals may perceive both personal happiness and the happiness of others as integral facets of their happiness goals.\n\nBased on this understanding, we categorize happiness goals into two types: (1) the goal of self-focused happiness, which is centered around cultivating one's own happiness, and (2) the goal of other-focused happiness, defined by the aim to augment others' happiness.Given the positive correlation between the affective and meaning components of happiness, 7 we propose that these two happiness goals are fundamentally intertwined.Individuals may simultaneously desire to enhance both their own and others' happiness.\n\nWhile self-focused and other-focused happiness goals are intrinsically related, they are distinct from the similar constructs of hedonic and eudaimonic motives.Hedonic motives, eudaimonic motives, and self-focused happiness goals are all centered around the actors' desire to benefit themselves.However, whereas hedonic motives prioritize the pursuit of enjoyment and pleasure and avoidance of discomfort, [25][26][27] eudaimonic motives underscore the strive for selfactualization and the realization of one's true potential, 26,27 and self-focused happiness emphasizes the pursuit of both enjoyment and personal development.In contrast to all three, other-focused happiness involves aspiring to elevate the pleasure and development of others.\n\nPrior research offers some preliminary support for this classification.Crocker and Canevello demonstrated that when individuals interact with others, they harbor self-image goals and compassion goals (eg, providing support for others). 28imilarly, motives in informal mentoring scenarios have been segregated into self-focused and other-focused motives.[31] Self-and Other-Focused Happiness and Work-Related Vigor: The Moderating Role of Job Resources Work-related vigor encapsulates three key facets: physical strength, emotional energy, and cognitive vitality. 11,32These facets interact and form a resource pool 11 whose level may be affected by subsequent resource-providing and resourcedraining processes and events. 33rawing from happiness research, we argue that other-focused happiness is primarily achieved through participation in prosocial or helping activities, 34,35 which impact resource-providing and resource-draining processes.Prosocial activities can foster high-quality relationships, 36 fortifying physiological resourcefulness by strengthening the cardiovascular, immune, and neuroendocrine systems, 37 and helping employees gain access to diverse information and viewpoints, 38 thereby enhancing their cognitive vitality.Additionally, focusing on how their actions benefit others can enhance employees' empathic abilities. 36,39Furthermore, the experience of helping others fosters other-focused attention, shielding employees from energy expenditure caused by personal problems, distress, and frustrations. 39Therefore, otherfocused happiness has the potential to increase employees' work-related vigor.\n\nConversely, self-focused happiness can be achieved through both self-focused and other-focused activities.It also influences employee vigor via resource provision and consumption processes.For instance, in their pursuit of personal growth, employees may engage in learning at work, which increases their exposure to many vital sources of information and enhances their cognitive vitality-a key component of vigor. 40Employees may also partake in restorative activities, thereby replenishing their physical and mental energy reserves for work. 33,41Additionally, employees can achieve self-focused happiness through positive reciprocal relationships, which in turn drive them to engage in other-focused behaviors.These other-focused behaviors can aid employees in gaining physical, cognitive, and emotional energy. 37,39,42Consequently, self-focused happiness can also enhance employees' work-related vigor.\n\nThe influence of job resources on the relationship between happiness goals and work-related vigor is also important.We propose that job resources have a stronger impact on the correlation between other-focused happiness and workrelated vigor than on the link between self-focused happiness and work-related vigor.This is because other-focused activities are more bounded by job resources and likely gives the individual more leeway. 43uilding upon our prior assertions, it's important to note that while other-focused behaviors are a crucial means to realize other-focused happiness, achieving self-focused happiness depends on both self-focused and other-focused behaviors.Situations where job resources are scarce incites a competitive orientation, prompting individuals to withdraw from other-focused behaviors. 14,15As a result, the positive impact of other-focused happiness on work-related vigor is considerably diminished, while the positive influence of self-focused happiness on work-related vigor declines only slightly.\n\nWhen job resources are abundant, the dynamics may shift.This is primarily because resourcefulness (ie, a perceived state of abundant resources) can stimulate other-focused behaviors. 16Under such conditions, the positive influence of the pursuit of otherfocused happiness on work-related vigor markedly increases, and the positive impact of self-focused happiness on work-related vigor only slightly increases.Therefore, when organizations furnish more job resources, other-focused happiness may have a stronger positive effect on work-related vigor than self-focused happiness.\n\nGiven the above considerations, we predict:\n\nHypothesis 1: The effects of self-or other-focused happiness on work-related vigor are moderated by job resources, such that (a) when job resources are plentiful, other-focused happiness has a stronger effect on work-related vigor than selffocused happiness, and (b) when job resources are scarce, self-focused happiness has a stronger effect on work-related vigor than other-focused happiness.\n\n\nWork-Related Vigor and Creativity\n\nCreativity is a function of cognitive flexibility as well as perseverance and persistence. 19Work-related vigor belongs to the family of positive affects. 11According to the broaden-and-build theory of positive affect, positive emotions are known to expand thought-action repertoires. 44,45In the workplace context, work-related vigor broadens cognitive scope and promotes flexibility, 45,46 which aids in generating creative solutions. 19,47In addition, work-related vigor entails the perceived abundance of physical, emotional, and cognitive resources, 48 thereby enabling employees to better handle the challenges inherent in creative activities. 47,49Furthermore, work-related vigor can help individuals gain or build additional resources by initiating an upward spiral, 44,50,51 which is conducive to persisting at creative tasks. 45For these reasons, we posit that work-related vigor is positively correlated with creativity.\n\nIntegrating the impacts of happiness goals on work-related vigor, we propose the following:\n\nHypothesis 2: Work-related vigor mediates the relationship between (a) self-and (b) other-focused happiness and employee creativity.\n\n\nThe Comprehensive Model\n\nHypothesis 1 proposes that job resources moderate the relationship between happiness goals and vigor.Specifically, when job resources are abundant, other-focused happiness exerts a stronger influence on work-related vigor than self-focused happiness.Conversely, when job resources are scarce, self-focused happiness has a stronger impact on work-related vigor than other-focused happiness.Hypothesis 2 proposes that work-related vigor mediates the relationship between happiness goals and employee creativity.Combining these predictions, we propose an overall model, which is as follows:\n\nHypothesis 3: The indirect effect of self-or other-focused happiness on employee creativity through work-related vigor is moderated by job resources, such that (a) when job resources are plentiful, other-focused happiness has a stronger indirect effect on employee creativity than self-focused happiness, and (b) when job resources are scarce, self-focused happiness has a stronger indirect effect on employee creativity than other-focused happiness.\n\n\nMethod\n\n\nSamples and Procedures\n\nWe conducted a three-wave survey (Time 1, Time 2, and Time 3) using data collected from employees and their direct supervisors.\n\nThe study was pre-registered at the following link: https:\/\/doi.org\/10.17605\/OSF.IO\/VF7X9.To recruit participants, we reached out to several high-tech companies in Central China through our social networks.These companies provided ideal sample pools because employee creativity was highly valued and observable within their organizations.With the assistance of the human resources department staff, all data were collected on-site during working hours by either the authors or research assistants.The participants were informed about the purpose of the study, in accordance with the Declaration of Helsinki.At Time 1, employees were asked to provide information regarding their self-focused happiness, other-focused happiness, job resources, and demographic details.A total of 701 completed questionnaires were received, constituting an 86% response rate.One month later, at Time 2, the same 701 employees were asked to report on their work-related vigor.We obtained 627 usable questionnaires devoid of missing data, corresponding to an 89% response rate.At Time 3, the immediate supervisors of these 627 employees were invited to evaluate the employees' creativity.We received usable responses from 165 supervisors, a response rate of 93%.By pairing the questionnaires using a unique identification code, we successfully matched the questionnaires of 556 employees with those of 165 direct supervisors.\n\nThe final sample of employees comprised 56.3% males, with 79.3% holding at least a college degree.Their average age was 31.98 years (SD age = 5.34), and their average organizational tenure was 4.91 years (SD tenure = 4.08).Among the supervisors, 60.0% were male, while 63.0% held at least a college degree.Their average age was 33.33 years (SD age = 8.14), and their average organizational tenure was 10.60 years (SD tenure = 8.36).No significant differences in terms of gender, age, education level, or organizational tenure were found between the final sample of employees and those who had dropped out at some point during the study.\n\n\nMeasures\n\nUnless stated otherwise, all survey items were originally formulated in English.To ensure consistency in meaning, we followed Brislin's translation and back-translation procedure. 52Participants responded to all items on a seven-point Likert scale, ranging from 1 (strongly disagree) to 7 (strongly agree).\n\n\nSelf-Focused Happiness\/Other-Focused Happiness (T1)\n\nDue to the unavailability of existing measures, we followed Hinkin's scale development procedure to develop and validate the measures for self-focused happiness and other-focused happiness. 53Drawing from the happiness literature [54][55][56] and subjective well-being literature, 4,5 we initially generated four items for each construct.The items for self-focused happiness included: \"I strive to enhance my emotional well-being\", \"I seek greater happiness in life\", \"I aim to avoid unpleasant experiences\", and \"I try not to feel bad\".The items for other-focused happiness were: \"I strive to enhance the emotional well-being of others\", \"I seek to bring happiness to others\", \"I aim to help others avoid unpleasant experiences\", and \"I try to prevent others from feeling bad\".\n\nIn the next phase, we enlisted the aid of 12 organizational behavior-specialized professors to assess the items' alignment with the constructs the items were intended to assess.These experts were asked to evaluate them using a 7-point scale, ranging from 1 (Item is an extremely poor match) to 7 (Item is an extremely good match).The results revealed strong alignment, with an average score of 6.13 for self-focused happiness items and 5.88 for other-focused happiness items.Furthermore, there was significant agreement among the evaluators for both measures, with interrater agreement values of 0.89 and 0.88 for self-focused happiness and other-focused happiness, respectively.This suggests that our measures possess robust content reliability.\n\nMoreover, a different sample (n = 244, with 42.2% male participants, average age = 31.25 years, average tenure = 5.89 years) was used to assess the scales' factor structure.This data was collected via the Credamo platform.An exploratory factor analysis (EFA) revealed two factors with eigenvalues over 1.00.Both the self-focused happiness items (with factor loadings ranging from 0.70 to 0.96) and the other-focused happiness items (with factor loadings ranging from 0.73 to 0.89) displayed satisfactory loadings.Additionally, the scales were highly internally consistency, with Cronbach's alpha coefficients of 0.868 for self-focused happiness and 0.881 for other-focused happiness.The correlation between the two measures was determined to be 0.35.Overall, these results corroborate the proposed factor structure of our measures.\n\nIn our final step, we tested the convergent and discriminant validity and the nomological network of the newly developed scales using a separate sample (n = 300).This sample was comprised of 47.3% male participants, with an average age of 32.75 years and an average tenure of 6.60 years.Data was collected via the Credamo platform.We first combined the self-focused happiness and other-focused happiness scales into a single factor for confirmatory factor analysis (CFA).Our findings indicated that a two-factor model fit the data significantly better than a one-factor model (\u0394\u03c7 2 (1) = 570.37,p < 0.001).\n\nSecond, we considered hedonic and eudaimonic motives (hedonic motives \u03b1 = 0.910, eudaimonic motives \u03b1 = 0.929). 25Consistent with expectations, the self-focused happiness scale displayed positive correlations with both hedonic motives (r = 0.31, p < 0.001) and eudaimonic motives (r = 0.17, p < 0.001), while the other-focused happiness scale did not show significant correlations with hedonic motives (r = 0.02, p > 0.01) or eudaimonic motives (r = 0.00, p > 0.01).Furthermore, confirmatory factor analysis demonstrated that a four-factor model fit the data significantly better than alternative models (p < 0.001 for all model comparisons).\n\nIn addition, we gathered data from employees regarding their perceptions of emotional support from coworkers (\u03b1 = 0.856), 57 self-sacrificial leadership (\u03b1 = 0.909), 58 and relational conflict (\u03b1 = 0.828). 59In line with our expectations, we found that emotional support was positively correlated with other-focused happiness (r = 0.29, p < 0.001), but showed no correlation with self-focused happiness (r = \u22120.10,p > 0.01).Likewise, self-sacrificial leadership was positively associated with other-focused happiness (r = 0.28, p < 0.001), yet bore no relation to self-focused happiness (r = \u22120.09,p > 0.001).Interestingly, relational conflict was negatively correlated with other-focused happiness (r = \u22120.38,p < 0.001), but was positively correlated with self-focused happiness (r = 0.16, p < 0.001).\n\nTo further validate our measurement model, alternative model tests were conducted.The results consistently demonstrated that the five-factor measurement model, which included self-focused happiness, other-focused happiness, emotional support, self-sacrificial leadership, and relational conflict, provided a significantly better fit to the data compared to alternative models (p < 0.001 for all model comparisons).Taken together, these findings provide strong evidence for the discriminant validity, convergent validity, and criterion validity of our measures.\n\nIn our main study, the Cronbach's \u03b1 coefficient for both scales was 0.915.Results of confirmatory factor analyses indicated that the two-factor measurement model fit the data well (\u03c7\u00b2 = 55.965;df = 19; CFI = 0.989, TLI = 0.983, RMSEA = 0.059, SRMR = 0.019) and better than a one-factor model (\u0394\u03c7 2 (1) = 1312.5,p < 0.001).\n\n\nJob Resources (T1)\n\nWe measured job resources using the four-item scale used by Du et al. 60 This scale includes two subscales: feedback and social support.A sample item from the feedback subscale is: \"I receive sufficient information about the results of my work\".A sample item of the social support subscale is: \"If necessary, I can ask my colleagues for help\".The Cronbach's \u03b1 of this scale was 0.894.\n\n\nWork-Related Vigor (T2)\n\nWe measured vigor using the Shirom-Melamed Vigor Measure (SMVM), 9 which asks respondents to indicate how often they experience each of several feeling states in the last 30 days.This scale consists of three subscales: physical strength (five items; eg, \"At work, I feel full of pep\"), emotional energy (four items; eg, \"I feel able to be sensitive to the needs of coworkers and customers\"), and cognitive liveliness (five items; eg, \"At work, I feel I can think rapidly\").Responses ranged from 1 (never or almost never) to 7 (always or almost always).The Cronbach's \u03b1 coefficient for this scale was 0.950.\n\n\nEmployee Creativity (T3)\n\nWe evaluated employee creativity using the 4-item scale developed by Farmer et al. 61 An example item from this scale is: \"This employee seeks new ideas and ways to solve problems\".The Cronbach's \u03b1 for this scale was 0.901.\n\n\nControl Variables (T1)\n\nIn our study, we controlled for employee gender (0 = male, 1 = female), age (in years), education level (rated from 1 for a college degree or lower to 4 for a doctoral degree), and organizational tenure (in years).This was based on the understanding that males and females may exhibit different average levels of physical vigor and emotional energy. 11,32urthermore, age has been found to be associated with vigor. 33,51Also, factors such as organizational tenure and level of education may potentially impact creativity. 42,49Notably, even though we factored in these variables while testing our hypothesized model, the results remained consistent when these variables were excluded.\n\n\nAnalytic Strategy\n\nWe utilized Mplus8 and RStudio software for hypothesis testing.Considering the nested structure of our data, we implemented a mixed-effects regression model to account for non-independent influences within teams. 62To evaluate whether self-focused and other-focused happiness exert different effects on work-related vigor conditionally, we conducted a difference test at both \"higher\" and \"lower\" values of job resources. 63This test essentially compares the disparity between coefficient estimates on dependent variables from an identical sample.\n\nTo assess the indirect effects, we incorporated estimates of both self-focused and other-focused happiness on vigor, along with vigor on employee creativity, into the Monte Carlo simulation.An indirect effect is deemed to be present if the resultant 95% confidence intervals (CIs) exclude zero.In order to investigate whether self-and other-focused happiness have distinct indirect effects on employee creativity, we applied the Monte Carlo simulation approach, running 20,000 repetitions, to examine the 95% CIs at both \"higher\" and \"lower\" job resource values.\n\n\nResults\n\nThe descriptive statistics for and correlations between the variables are presented in Table 1.\n\n\nPreliminary Analyses\n\nPrior to hypothesis testing, we conducted confirmatory factor analyses.The results showed that the hypothesized fivefactor model (self-focused happiness, other-focused happiness, job resources, work-related vigor, and employee creativity) demonstrated a good fit with the data (\u03c7\u00b2 = 361.325;df = 142; CFI = 0.974; TLI = 0.968; RMSEA = 0.053; SRMR = 0.053) and exhibited better fit than alternative models.These findings support the factorial validity of the measures.To address potential common method bias, we performed a common method bias test following Podsakoff et al. 64 We added a latent common method factor to the same-source four-factor model and found that the addition of this factor did not improve the fit (\u0394\u03c7\u00b2(15) = 19.779,p > 0.05).Thus, there is no evidence of common method bias in this study.\n\n\nTest of Hypotheses\n\nTo investigate Hypothesis 1, which suggests that the influence of self-or other-focused happiness on work-related vigor is contingent upon job resources, we conducted an analysis examining the effects of self-focused happiness, other- Notes: N=556; all variables are unstandardized.Gender: 0 = male; 1 = female.Education: 1 = college degree or lower; 2 = bachelor's degree; 3 = master's degree; 4 = doctorate degree.*p < 0.05, ** p < 0.01.\n\nfocused happiness, the interaction term between self-focused happiness and job resources, and the interaction term between other-focused happiness and job resources on work-related vigor.The results, presented in Table 2, support our hypotheses.We found significant positive relationships between both self-focused happiness and work-related vigor (\u03b2 = 0.279, p < 0.001) and other-focused happiness and work-related vigor (\u03b2 = 0.166, p < 0.01).Additionally, both interaction terms, that is, the interactions between self-focused happiness and job resources (\u03b2 = 0.132, p < 0.05) and between other-focused happiness and job resources (\u03b2 = 0.275, p < 0.01), significantly predicted work-related vigor.Additional analysis using a difference test demonstrated that under conditions of scarce job resources, the impact of self-focused happiness on work-related vigor was significantly stronger than the effect of other-focused happiness (difference = 0.243, p < 0.05; see Table 3).However, when job resources were abundant, there was no significant difference between the effects of other-focused happiness and self-focused happiness on work-related vigor (difference = 0.017, p > 0.05).As a result, Hypothesis 1a was not supported, while Hypothesis 1b received support.\n\nOur Hypothesis 2 proposed that the relationship between self-focused and other-focused happiness and employee creativity would be mediated by work-related vigor.As expected, a positive correlation was observed between workrelated vigor and employee creativity (\u03b2 = 0.237, p < 0.01).Utilizing Monte Carlo simulations, we found, consistent with Hypothesis 2a, a positive indirect effect of self-focused happiness on employee creativity, mediated by work-related vigor (indirect effect = 0.066, 95% CI = [0.032,0.107]).Likewise, the indirect effect of other-focused happiness on employee creativity via work-related vigor was also positive (indirect effect = 0.039, 95% CI = [0.013,0.073]).Hence, both Hypotheses 2a and 2b were corroborated.Hypothesis 3 suggested differential indirect effects of self-focused happiness and other-focused happiness on employee creativity.Specially, when job resources are plentiful, other-focused happiness has a stronger indirect effect on employee creativity than self-focused happiness.Conversely, when job resources are scarce, self-focused happiness has a stronger indirect effect on employee creativity than other-focused happiness.As shown in Table 3, under conditions of low job resources, self-focused happiness exerts a stronger indirect influence on creativity via work-related vigor than other-focused happiness does (difference = 0.058, 95% CI = [0.009,0.120]).However, in the context of high job resources, no significant difference was found between the indirect effects of self-focused happiness and other-focused happiness on employee creativity through work-related vigor (difference = 0.004, 95% CI = [\u22120.044,0.054]).Therefore, Hypothesis 3a was not substantiated, while Hypothesis 3b was supported.\n\n\nDiscussion\n\nIn this paper, we distinguish happiness goals into self-focused happiness and other-focused happiness.Through a multisource and multi-time point survey, we found that both self-focused and other-focused happiness have a positive impact on work-related vigor.The specific effects of these happiness goals on vigor are contingent upon the availability of job resources.Specifically, when job resources are scarce, self-focused happiness has a stronger effect on work-related vigor than other-focused happiness.However, when job resources are abundant, there is no significant difference between the effects of other-focused happiness and self-focused happiness on employee creativity.Work-related vigor mediates the relationship between self-focused happiness and creativity as well as the relationship between other-focused happiness and creativity.\n\nFurther, the indirect effect of self-or other-focused happiness on employee creativity through work-related vigor is moderated by job resources.Specifically, under conditions of low job resources, self-focused happiness exerts a stronger indirect influence on creativity via work-related vigor than other-focused happiness does.However, in the context of high job resources, no significant difference was found between the indirect effects of self-focused happiness and otherfocused happiness on employee creativity through work-related vigor.We seek explanations for two non-significant hypotheses and suggest that they may be due to the fact that when job resources are not considered, the influence of selffocused happiness on work-related vigor surpasses the impact of other-focused happiness.\n\n\nTheoretical Contributions\n\nOur research makes significant contributions to the fields of positive psychology, positive organizational behavior, and creativity.First, in the realm of positive psychology, our study introduces a new taxonomy of happiness goals.Previous research, such as the work of Crocker et al, 65 has highlighted the innate nature of both self-interest and concern for others.With the growing interest in positive psychology, there has been increased attention to the pursuit of happiness.However, prior categorizations of happiness goals, such as the distinction between hedonic and eudaimonic goals, 27 predominantly focus on self-centered pursuits and fail to capture individuals' concern for the happiness of others.In our study, we put forth a novel conceptualization of happiness goals, differentiating between self-focused happiness and other-focused happiness based on their distinct foci.Furthermore, we demonstrated the conceptual and psychological differences associated with these two types of happiness goals, thereby enriching the understanding of and development of research on happiness experiences.\n\nFurthermore, our research augments the currently underdeveloped literature regarding links between happiness goals and work-related outcomes.While happiness has been a subject of study for many years, 66 the impact of happiness goals on employee work-related outcomes remains a largely unexplored issue.Although previous research has investigated how happiness goals influence broader outcomes, 3,67 researchers have yet to incorporate an affective perspective into their theories about happiness goals.In addition, the conditions under which happiness goals substantially influence outcomes have not been thoroughly examined.The present research aims to illuminate the effect of happiness goals on employee creativity from the perspective of work-related vigor, and underscores the crucial moderating role of job resources.Thus, we are charting a new path for theory and research in the domain of happiness and work-related matters.\n\nThird, we answer calls to further investigate vigor's nomological network. 11,48Scholars have studied the antecedents of vigor from different perspectives. 45,49,68Vigor is related to motivational processes. 11,32,48While other researchers have argued that vigor is a prerequisite for work motivation, 11,32 the role of happiness goals in promoting work-related vigor has not been examined previously.Importantly, our study elucidates the differential contributions of self-and otherfocused happiness to work-related vigor through the introduction of job resources which sets the stage for further enhancing the understanding of the complex processes underlying work-related vigor.\n\nFinally, we make novel contributions to creativity research.While previous studies have identified numerous antecedents of employee creativity, there is a lack of understanding regarding the connections between non-workmotivated antecedents and creativity outcomes.We address this gap by specifically exploring the reasons why employees' happiness goals precipitate creativity.Additionally, we focus on investigating the role of intervening variables, such as job resources, and provide insights into how happiness goals may impact employee creativity in the workplace.It is important to note that our argument, which suggests that vigor as an activated positive affect state promotes creativity not only through promoting cognitive flexibility but also through promoting perseverance, is not in conflict with the two-path model of creativity proposed by De Dreu et al. 19 This is because the model acknowledges that, besides hedonic tone and activation, other dimensions of mood states (eg, regulatory focus) may also be relevant to creativity.\n\n\nPractical Implications\n\nOur study also has important implications for practice.First, employees and organizations should recognize the potential impact of happiness goals on work-related outcomes.Our findings suggest that both self-focused and other-focused happiness act as critical catalysts for fostering positive affects such as vigor, which subsequently enhance creativity.Therefore, it is essential for employees to prioritize the pursuit of happiness in their work, and organizations should cultivate a culture that underlines the significance of pursuing happiness.\n\nSecond, as self-focused happiness and other-focused happiness influence employee vigor and creativity in different ways depending on the availability of job resources, organizations aiming to boost employee creativity can strategically implement a mix of happiness goals and job resources.In particular, assigning fewer job resources to individuals with a high level of motivation to pursue self-focused happiness and extending more resources to those with high otherfocused happiness goals might help amplify overall innovation within the organization.\n\nLastly, the findings of this study underscore the significance of employees' vigor.Managers should take into account strategies to enhance and maintain high levels of vigor among employees.This can be achieved through various means, such as providing freedom and supervisor support, 50,69 fostering high-quality connections within the workplace, 70,71 and encouraging employees to take regular micro-breaks and utilize effective work-related strategies. 41,72mitations and Future Research Although our study possessed several strengths, we acknowledge certain limitations.First, it is important to recognize that culture exerts a significant influence on individuals' values, motives, and behaviors. 31Western cultures often emphasize individuality and self-actualization, while Chinese culture places greater emphasis on relationship harmony and interdependence. 6,27,50As our research was conducted in China, the cross-cultural applicability of our research findings is limited.In addition, our samples come from several high-tech companies in Central China, and we cannot ensure that the research hypothesis holds true for employees from other industries or organizations.Therefore, we encourage further studies to replicate these findings in diverse contexts.merit in distinguishing it into radical and incremental creativity 73 a differentiation not accounted for in this study.Consequently, future research should persist in probing the effect of happiness goals in the workplace.\n\n\nConclusion\n\nThe pursuit of happiness is an eternal theme.Despite a mounting body of research, this study has developed somewhat at odds with the happiness literature, in that it reclassifies happiness goals and explores their impact on work-related outcomes.Our key takeaway is that self-and other-focused happiness can indeed contribute to employee vigor and creativity, but the size of the effect depends on the level of job resources.Our results have valuable implications for theory as well as practice.\n\nFigure 1\n1\nFigure 1 Theoretical Model.\n\n\nTable 1\n1\nMeans, Standard Deviations, and Correlations Among Variables\nVariableMSD1234567891. Gender0.440.4961.0002. Age31.98 5.339 \u22120.0511.0003. Education1.940.5980.069\u22120.118**1.0004. Organizational tenure4.914.0830.0380.597**\u22120.125**1.0005. Self-focused happiness4.580.9170.016\u22120.012\u22120.0120.0461.0006. Other-focused happiness4.180.9010.0190.017\u22120.0500.092* 0.384**1.0007. Job resources4.260.908 \u22120.0380.0080.0150.0320.329** 0.370**1.0008. Work-related vigor4.280.803 \u22120.070\u22120.023\u22120.0460.0120.452** 0.386** 0.370**1.0009. Employee creativity5.000.9510.063\u22120.0330.0060.0210.221** 0.235** 0.206** 0.260** 1.000\n\nTable 2\n2\nPath Analysis Results\nVariableWork-Related VigorCreativity\u03b2SE\u03b2SEGender\u22120.1100.0630.1400.084Age\u22120.0050.009\u22120.0090.010Education\u22120.0360.0520.0230.068Organizational tenure0.0010.0130.0070.014Self-focused happiness0.279***0.0410.100*0.044Other-focused happiness0.166**0.0500.128**0.047Job resources0.126**0.0420.155**0.045Self-focused happiness * Job resources0.132*0.0600.0060.096Other-focused happiness * Job resources0.275***0.0600.0100.074Work-related vigor0.237***0.058\nNotes: N=556; \u03b2 coefficients are unstandardized.*p < 0.05.**p < 0.01.***p < 0.001.Abbreviation: SE, standard error.\n\n\nTable 3\n3\nSummary of Conditional Direct and Indirect Effects N = 556.*p < 0.05.***p < 0.001.P x-m = self-\/ other-focused happiness on work-related vigor; P m-y = work-related vigor on creativity.\nIndependent VariableModeratorStageEffectJob ResourcesFirst (P x-m ) Second (P m-y ) Indirect (P x-m \u00d7P m-y ) 95% CI of Indirect EffectSelf-Focused HappinessHigh (+1 SD)0.399***0.237***0.095[0.044, 0.155]Low (\u22121 SD)0.160*0.237***0.038[0.005, 0.080]Difference (high-low)0.239*0.057[0.0049, 0.123]Other-Focused HappinessHigh (+1 SD)0.416***0.237***0.099[0.046, 0.163]Low (\u22121 SD)\u22120.0830.237***\u22120.020[\u22120.060, 0.015]Difference (high-low)0.499***0.118[0.051, 0.204]Difference (High other -High self )0.0170.004[\u22120.044, 0.054]Difference (Low self -Low other )0.243*0.058[0.009, 0.120]\nNotes: Abbreviation: CI, Confidence interval.\n\nXu et al DovepressPowered by TCPDF (www.tcpdf.org)\nPsychology Research and Behavior Management 2023:16 https:\/\/doi.org\/10.2147\/PRBM.S433888 DovePress\nPowered by TCPDF (www.tcpdf.org)\nhttps:\/\/doi.org\/10.2147\/PRBM.S433888 DovePress Psychology Research and Behavior Management 2023:16\nSecond, although our study employed a longitudinal design to examine our hypothesis, we acknowledge that establishing causal relationships is fraught with difficulty in non-experimental research. In addition, because employees provided information regarding self-focused happiness, other-focused happiness, job resources, and work-related vigor, this method may lead to common method bias. Finally, the use of new scales and the selection of control variables may also affect the research results. Therefore, we suggest that future studies use more rigorous designs to test our hypotheses.Third, as previously discussed, our conceptual model prioritizes the influence of self-focused and other-focused happiness on employee creativity. Still, it's pertinent to recognize that these two types of happiness goals may also affect other work-related outcomes. Furthermore, while creativity is often examined as a singular concept, there is potential https:\/\/doi.org\/10.2147\/PRBM.S433888 DovePress Psychology Research and Behavior Management 2023:16\nAcknowledgmentsThe authors gratefully acknowledge financial support from the National Natural Science Foundation of China (71832004, 72202096, 72302112), the Key Project on Philosophy and Social Science Research of the Ministry of Education (21JZD056), the 2022 Guangxi Universities' Training Plan for Thousand Young and Middle-aged Backbone Teachers (2023QGRW011), and the Commissioned Project from the Research Institute of Zhujiang-Xijiang Economic Belt Development (ZX2023020).Data Sharing StatementThe data presented in this study are available on request from the corresponding author.Ethics StatementThe studies involving human participants were reviewed and approved by Ethics Committee of Huazhong University of Science and Technology.DisclosureThe authors report no conflicts of interest in this work.Psychology Research and Behavior ManagementDovepressPublish your work in this journalPsychology Research and Behavior Management is an international, peer-reviewed, open access journal focusing on the science of psychology and its application in behavior management to develop improved outcomes in the clinical, educational, sports and business arenas.Specific topics covered in the journal include: Neuroscience, memory and decision making; Behavior modification and management; Clinical applications; Business and sports performance management; Social and developmental studies; Animal studies.The manuscript management system is completely online and includes a very quick and fair peer-review system, which is all easy to use.Visit http:\/\/www.dovepress.com\/testimonials.php to read real quotes from published authors.\nWhat does it cost you to get there? 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"[{\"attributes\":{\"ref_id\":\"tab_0\"},\"end\":28670,\"start\":28669},{\"attributes\":{\"ref_id\":\"tab_1\"},\"end\":30191,\"start\":30190},{\"attributes\":{\"ref_id\":\"tab_2\"},\"end\":30945,\"start\":30944},{\"attributes\":{\"ref_id\":\"tab_2\"},\"end\":32426,\"start\":32425}]", + "title": "[{\"end\":102,\"start\":1},{\"end\":844,\"start\":743}]", + "venue": null + } + } + }, + { + "corpusid": 264990219, + "externalids": { + "arxiv": null, + "mag": null, + "acl": null, + "pubmed": "37937223", + "pubmedcentral": "10627070", + "dblp": null, + "doi": "10.2147\/ijwh.s428038" + }, + "content": { + "source": { + "pdfurls": null, + "pdfsha": "52fc7a37bbfc0e1f3a78ea8f38fdb99243d8fd4e", + "oainfo": { + "license": "CCBYNC", + "openaccessurl": "https:\/\/www.dovepress.com\/getfile.php?fileID=93963", + "status": "GOLD" + } + }, + "text": "\nStudy on the Relationship Between PTPRO Methylation in Plasma and Efficacy Neoadjuvant Chemotherapy in Patients with Early Breast Cancer\n2 November 2023\n\nXiang-Wei Liu \nDepartment of Breast Surgery\nThe First People's Hospital of Foshan\n528000FoshanPeople's Republic of China\n\nMei-Juan Hong \nUltrasound Diagnosis and Treatment Center\nThe First People's Hospital of Foshan\n528000FoshanPeople's Republic of China\n\nYan-Yu Qu \nDepartmentof Pathology\nThe Second People's Hospital of Foshan\n528000FoshanPeople's Republic of China\n\nStudy on the Relationship Between PTPRO Methylation in Plasma and Efficacy Neoadjuvant Chemotherapy in Patients with Early Breast Cancer\n2 November 20231A3037440BB92B5779A3A93DCB261DBD10.2147\/IJWH.S428038Received: 28 June 2023 Accepted: 7 October 2023breast cancerneoadjuvant chemotherapyPTPROmethylationliquid biopsy\nThis study aimed to explore the correlation between PTPRO methylation in plasma and the efficacy of neoadjuvant chemotherapy (NAC) for early breast cancer (BC).Methods: Eighty-two patients with early BC undergoing NAC were included.PTPRO methylation status in plasma before and after NAC was detected using methylation-specific PCR and the relationship between PTPRO methylation and NAC efficacy was analyzed.Results: The rate of pathologic complete response (pCR) was only 25.0% (12\/48) in patients with positive PTPRO methylation result before NAC, but 61 0.8% (21\/34) in pre-NAC methylation-negative patients (OR = 0.24, 95% CI: 0.09-0.65,P = 0.005).In addition, the pCR rate was 12.1% (4\/33) in patients with positive PTPRO methylation results both before and after NAC, but 53.3% (8\/15) in patients with pre-NAC positive methylation and post-NAC negative methylation results (OR = 0.12, 95% CI: 0.03-0.52,P = 0.004).Conclusion:Plasma PTPRO methylation is a potential biomarker for predicting the efficacy of NAC in early BC.\n\nIntroduction\n\nBreast cancer (BC) is the most prevalent malignant cancer among Chinese women, seriously threatening women's health and lives. 1 Chemotherapy is an important treatment for BC.Neoadjuvant chemotherapy (NAC) is a systemic therapy with cytotoxic agents administered prior to surgical treatment, mainly for patients with stage II and III BC and inflammatory BC. 2 The maximum lesions diameter and clinical stage of breast cancer patients decreased significantly after NAC than before, while the apparent diffusion coefficient increased considerably. 3Meanwhile, NAC could monitor the sensitivity of patients to chemotherapy regimens, thus converting inoperable BC into operable BC and giving non-breast-conserving patients the chance of breastconserving.NAC can also.A good prognosis is suggested if patients achieve a pathologic complete response (pCR) after completing NAC. 4 However, NAC is not effective for all BC patients, with pCR rates typically ranging from 5% to 38%. 5 A significant proportion of patients are insensitive to NAC and even experience tumor progression during NAC.Consequently, the timing of surgery is delayed, and the treatment opportunity is missed. 6Therefore, the prediction of whether NAC is effective for BC patients is highly meaningful for the precise treatment of BC.\n\nEpigenetics is a heritable molecular mechanism that regulates gene expression without altering the actual sequence of DNA. 7NA methylation, histone modification, chromatin remodeling, and RNA-mediated targeting regulate many biological processes that are critical to cancer development. 8A recent review by Sher et al provides a comprehensive overview of the important role of multiple epigenetic disorders in the progression and survival of BC. 9 A clinical study by Paydar et al showed that compared with benign breast tumors, malignant breast tumor samples displayed aberrant patterns of histones marks with hypomethylation of H4K20 and hypoacetylation of H3K18 and promoter methylation, and there was a negative significant correlation between H3K9ac levels and tumor size, indicating gene promoter hypermethylation along with histone modification may play an important role in the progression and prognosis of breast cancer. 10In addition, several clinical studies have shown the role of DNA methylation in early diagnosis of breast cancer, 11 chemotherapy response, 12 immune infiltration and survival. 13In conclusion, epigenetic modification plays an important role in the progression and prognosis of breast cancer.\n\nDNA methylation is an epigenetic mechanism, involving the transfer of methyl groups to cytosine bases by DNA methyltransferases. 14DNA methylation is a reversible process, occurring mainly in dinucleotide-rich CpG islands. 14ethylation in the promoter region of tumor-suppressor genes can lead to silencing of anti-oncogenes. 15Methylated circulating tumor DNA (met-ctDNA) can be detected in 20% to 47% of early BC, 16 with a significantly higher positive rate than that of traditional tumor markers (eg CEA, CA15-3).Met-ctDNA has been used to predict the response of BC to chemotherapy and has shown good specificity and sensitivity. 17rotein Tyrosine Phosphatase Receptor-type O (PTPRO) belongs to the R3 receptor-type protein tyrosine phosphatase family, which functions as an anti-oncogene in a variety of tumors. 18,191][22] Moreover, studies have shown that PTPRO gene promoter methylation and functional inhibition are frequently present in BC. 23 A study by Huang et al 24 demonstrated 74.3% of BC tissues had PTPRO methylation; when PTPRO methylation was observed in cancer tissues, methylated PTPRO was detectable in the plasma of 62.1% of patients.Similarly, Li et al 25 revealed 54.1% of BC tissues had PTPRO methylation and the overall survival rate was reduced in patients with higher methylation levels; when PTPRO methylation was present in cancer tissues, 54.7% of patients exhibited PTPRO methylation in plasma.At the cellular level, PTPRO phosphatase activity shortens the half-life of epidermal growth factor receptor 2 (HER2) by increasing intracellular degradation of HER2. 26These studies suggest that PTPRO methylation in plasma is a promising BC biomarker.\n\nBased on the above research background, this study is intended to examine PTPRO methylation in the plasma from BC patients before and after NAC and analyze its relationship with NAC efficacy.We aimed to explore whether PTPRO methylation in plasma could be used as a potential biomarker.\n\n\nStudy Subjects and Methods\n\n\nStudy Subjects\n\nEighty-two patients with early BC admitted to The First People's Hospital of Foshan between September 2016 and June 2018 were selected.The study was approved by the Ethics Committee of the First People's Hospital of Foshan and conducted in accordance with the approval guidelines (L-2023-5).\n\n\nInclusion and Exclusion Criteria\n\nInclusion criteria were as follows: (1) women aged >18 years; (2) a diagnosis of early intensive BC confirmed by histopathologic examination; (3) women who gave informed consent to NAC; (4) women who gave informed consent and volunteered to participate in this study.\n\nExclusion criteria were as follows: (1) severe cardiac, hepatic, or renal insufficiency or a combination of other highrisk diseases; (2) history of neurological disorders, psychiatric disorders, and recent use of psychiatric medications; (3) coagulation disorders or other contraindications to surgery; (4) incomplete clinical data.\n\n\nTreatment Methods\n\n\nChemotherapy Regimens\n\n\nEvaluation of Chemotherapy Efficacy\n\nPostoperative specimens from post-NAC patients were collected for pathological examination to clarify whether pCR was achieved.The pCR was defined as the absence of invasive BC at the primary site and a negative result of regional lymph nodes.\n\n\nExtraction of Circulating DNA\n\nBefore NAC and before surgery, 5 mL of venous blood from each patient was collected in anticoagulant tubes and mixed.Then the tubes were placed on ice, followed by centrifugation (4 \u00b0C, 1000 \u00d7 g, 15 min).The supernatant plasma was collected, divided, and stored at \u221280 \u00b0C or on ice until use.Circulating DNA in plasma was extracted according to the instructions of the Circulating DNA Nucleic Acid Extraction Kit (AmoyDx, Xiamen, China).\n\n\nBisulfite Modification of DNA and Reference Selection\n\nBisulfite modification of plasma DNA was performed using EZ DNA Methylation-Gold TM Kit (Zymo Research, Irvine, CA, USA).The modified DNA was stored at \u221220 \u00b0C.PTPRO-methylated MCF-7 human cancer cell line and PTPROunmethylated NE-2 immortalized normal cell line were used as positive and negative controls, respectively. 18thylation-Specific PCR Before NAC, the methylation-specific PCR (MSP) was used to detect PTPRO methylation status in the plasma of all patients.For patients with a positive result of PTPRO methylation in pre-NAC plasma, MSP was carried out again after NAC.For patients with a negative result of PTPRO methylation in pre-NAC plasma, PTPRO methylation status in post-NAC plasma was not tested again.Primers synthesized by Invitrogen (Shanghai, China) were added, and the primer sequences were referred to a published literature. 27A 20 \u03bcL MSP reaction system was built as follows: 2 \u00d7 premix 10 \u03bcL, forward and reverse primers (10 \u03bcmol\/L) 0.4 \u03bcL each, DNA 2 \u03bcL, and ddH 2 O 7.2 \u03bcL.The reaction conditions were: pre-denaturation at 94 \u00b0C for 2 min; 95 \u00b0C for 45s, 60 \u00b0C for 45s, 72 \u00b0C for 45s, 35 cycles; extension at 72 \u00b0C for 10 min.PCR products were electrophoresed on 2% agarose gels with ethidium bromide.Images were acquired using a bio-rad UV imaging system (Bio-Rad, Hercules, CA, USA) and Image J software (National Institutes of Health, Bethesda, MD, USA) was used for grayscale value analysis and result calculation.Each sample was amplified simultaneously with methylated primers and unmethylated primers.If the gene promoter was aberrantly methylated, it is denoted by M; if it was not methylated, it is denoted by U. The presence of only the M band indicated complete methylation, and that of both M and U bands indicated partial methylation; both partial and complete methylation were considered as methylationpositive.The presence of only the U band indicated a methylation-negative state.\n\n\nStatistical Analysis\n\nSPSS 21.0 software (SPSS, Chicago, IL, USA) was used for statistical analysis, and P < 0.05 was considered statistically significant.Measurement data were expressed as means \u00b1 standard deviation (SD) and count data were expressed as n (%).Univariate analysis was performed first.The t-test was used for comparison between two groups for measurement data, and \u03c7 2 test was used for the count data.Factors with P < 0.05 in univariate analysis were further included in logistic regression analysis, and the odds ratio (OR) was calculated.\n\n\nResults\n\n\nGeneral Clinical Data of Patients with Early Breast Cancer\n\nA total of 82 patients with early BC were included in this study, aged from 34 to 65 years (average age: 47.9 \u00b1 7.8 years).Preoperative clinical tumor stages were cT1 -4 in 7, 59, 6, and 10 patients, respectively; preoperative clinical lymph node stages were cN0 -3 in 19, 53, 8 and 2 cases, respectively.In terms of pathological classification, 79 patients had invasive ductal carcinoma, and 3 patients had other pathological types.Pathological histology was grade II in 64 cases and grade III in 18 cases.There were 34 estrogen receptor (ER)-negative patients and 48 ER-positive patients; there were 27 progesterone receptor (PR)negative patients and 55 PR-positive patients; there were 45 HER2-negative patients and 37 HER2-positive patients.Twentyeight (34.1%)BC patients with Ki67 \u2264 30% and 54 (65.9%) with Ki67 > 30%.Before NAC, plasma PTPRO methylation was negative in 34 (41.5%)patients, and it was positive in 48 (58.5%) patients (Table 1).\n\n\nUnivariate Analysis of NAC Efficacy in Patients with Early Breast Cancer\n\nWe evaluated the efficacy of NAC in BC patients using pathological response.Seventy-eight BC patients underwent mastectomy after NAC, and the other four patients underwent breast-conserving surgery; all patients received axillary lymph node dissection.pCR was achieved in 33 (40.2%), but not in the other 49 (59.8%)patients.Univariate analysis found that PTPRO methylation status before NAC (P = 0.001) and HER2 status (P = 0.01) were correlated factors for post-NAC pCR in patients with early BC.However, age, cT, cN, pathological type, histological grade, ER, PR, Ki67 were not significantly correlated with post-NAC pCR (Table 1).\n\n\nLogistic Regression Analysis of NAC Efficacy in Patients with Early Breast Cancer\n\nFurther logistic regression analysis was performed (Table 2).The pCR rate was only 25.0% (12\/48) in early BC patients with methylated PTPRO (methylation-positive) before NAC, while the pCR rate was 61.8% (21\/34) in those with unmethylated PTPRO (methylation-negative) before NAC (OR = 0.24, 95% CI: 0.09-0.65,P = 0.005).The pCR rate was 56.8% (21\/37) in HER2-positive patients and 26.7% (12\/45) in HER-negative patients (OR = 2.95, 95% CI: 1.11-7.87,\nP = 0.03).\nThe results suggested that plasma PTPRO methylation status before NAC and HER2 status were relevant factors for post-NAC pCR in BC patients.\n\n\nUnivariate Analysis of NAC Efficacy in Early Breast Cancer Patients with Positive Pre-NAC PTPRO Methylation\n\nAfter NAC treatment, we evaluated the factors affecting the efficacy of NAC in BC patients with PTPRO methylationpositive results in plasma (Table 3).Age, cT, cN, pathological type, histological grade, ER, PR, HER2 and Ki67 were not significantly associated with post-NAC pCR.Only PTPRO methylation status after NAC was a correlate of pCR (OR = 0.12, 95% CI: 0.03-0.52,P = 0.004).We found that after NAC treatment, PTPRO methylation status in plasma changed from positive to negative in 15 of 48 (31.3%) patients with early BC, and the pCR rate reached 53.3% (8\/15).Plasma PTPRO methylation status remained positive in the other 33 (68.7%) patients and their post-NAC pCR rate was only 12.1% (4\/33).\n\n\nDiscussion\n\nNAC is increasingly used in treating BC and has gradually become a new mode in BC treatment. 28However, not all BC patients can benefit from NAC, and the pCR rate of early BC patients after NAC treatment in this study was only 40.2% (33\/82).Therefore, it is necessary to identify patients who are not sensitive to NAC in advance to ensure more precise treatment.Liquid biopsy refers to the extraction of patient's body fluid (such as blood) by non-invasive means and analysis of tumor-derived biomarkers (such as met-ctDNA, ctDNA).Liquid biopsy is applied to early diagnosis, prognosis evaluation, and monitoring of treatment effects.Liquid biopsy has the advantages of non-invasiveness, repeatability, timeliness, which can realize the standardized process of diagnosis. 29The use of liquid biopsy for predicting the efficacy of NAC in BC is also increasingly studied. 30he present study showed that the pre-NAC positive rate of plasma PTPRO methylation in BC patients was 58.5%.This positive rate is basically equal to the positive rate of 54.1% 25 -62.2% 24 reported in previous research, but higher than the positive rate of 17.0% -46.2% for traditional tumor markers (CEA, CA125, CA153). 30Pre-NAC plasma PTPRO methylation status was an independent influencing factor of pCR (OR = 0.24, 95% CI: 0.09-0.65,P = 0.005).The pCR rate was only 25.0% in patients with a positive result of pre-NAC PTPRO methylation, which was significantly lower than the pCR rate of 61.8% in methylation-negative patients.This suggests that plasma PTPRO methylation is a promising predictor of NAC efficacy in BC.\n\nThis study showed that changes in plasma PTPRO methylation status before and after NAC were also significantly associated with NAC efficacy (OR = 0.12, 95% CI: 0.03-0.52,P = 0.004).The pCR rate was only 12.1% in patients with positive PTPRO methylation results before and after NAC, but 53.3% in patients with pre-NAC methylation-positive and post-NAC methylation-negative results.This result has some implications for the selection of surgical modality for patients after NAC.Patients with positive PTPRO methylation of the PTPRO gene both before and after NAC have a low pCR rate and thus breast-conserving surgery should be carefully selected.\n\nThis study also has some limitations.The number of cases was small and there may have been bias.We failed to detect the pre-NAC methylation status of PTPRO gene in the primary tumor specimens obtained by needle biopsy and did not study the concordance between the methylation of PTPRO gene in the primary tumor and blood.Additionally, the effect of plasma PTPRO methylation on the long-term survival of patients has not been observed because of short followup time.In future studies, we will continue to expand the sample size and observe the effect of PTPRO methylation on patient survival.\n\n\nConclusion\n\nIn conclusion, plasma PTPRO methylation status is a valid predictor of NAC efficacy in BC.The pCR rate is low in patients with positive methylation results before NAC, especially in patients with positive methylation results both before and after NAC.This study provides a new option for NAC efficacy prediction and precision medicine of BC.It is expected that the predictive efficiency can be further improved by combining other predictors in future studies.\n\nTable 1\n1\nUnivariate Analysis of Clinical Data and NAC Efficacy in Patients with Breast Cancer\nClinical FeaturesTotalNon-pCRpCR\u03c7 2 \/tP-valueNumber of patients, n (%)82 (100)49 (59.8)33 (40.2)-Age, years47.9\u00b17.847.6\u00b17.848.3\u00b17.8\u22120.410.68cT, n (%)4.0960.2517 (8.5)6 (7.2)1 (1.3)259 (72.0)32 (39.0)27 (33)36 (7.3)5 (6.1)1 (1.2)410 (12.2)6 (7.3)4 (4.9)cN, n (%)0.5620.91019 (23.2)11 (13.4)8 (9.8)153 (64.6)33 (40.2)20 (24.4)28 (9.8)4 (4.9)4 (4.9)32 (2.4)1 (1.2)1 (1.2)Pathological type, n (%)2.0970.15Invasive ductal carcinoma79 (96.3)46 (56.1)33 (40.2)Others3 (3.7)3 (3.7)0 (0)Histological grade, n (%)0.9130.34Grade II64 (78.0)40 (48.8)24 (29.2)Grade III18 (22.0)9 (11.0)9 (11.0)ER, n (%)2.2990.13Negative34 (41.4)17 (20.7)17 (20.7)Positive48 (58.6)32 (39.0)16 (19.6)PR, n (%)2.2560.13Negative27 (33.0)13 (15.9)14 (17.1)Positive55 (67.0)36 (43.9)19 (23.1)HER2, n (%)7.6450.01Negative45 (54.9)33 (40.2)12 (14.7)Positive37 (45.1)16 (15.9)21 (29.2)Ki67, n (%)1.160.28\u226430%28 (34.1)19 (23.2)9 (10.9)>30%54 (65.9)30 (36.6)24 (29.3)PTPRO methylation status before NAC, n (%)11.1860.001Negative34 (41.5)13 (15.9)21 (25.6)Positive48 (58.5)36 (43.9)12 (14.6)\nNote: Bold P-value indicates significance.Abbreviations: cT, clinical tumor stage; cN, clinical nodal stage; ER, estrogen receptor; PR, progesterone receptor; HER2, epidermal growth factor receptor 2; PTPRO, protein tyrosine phosphatase receptor-type O; NAC, neoadjuvant chemotherapy; pCR, pathologic complete response.https:\/\/doi.org\/10.2147\/IJWH.S428038 DovePress International Journal of Women's Health 2023:15 1676 Liu et al Dovepress Powered by TCPDF (www.tcpdf.org)\n\n\nTable 2\n2\nLogistic Regression Analysis of NAC Efficacy in Breast Cancer Patients\nClinical FeaturesOR value95% CIP-valueHER2 (positive vs negative)2.951.11-7.870.03PTPRO methylation status before NAC (positive vs negative)0.240.09-0.650.005Note: Bold P-value indicates significance.Abbreviations: HER2, epidermal growth factor receptor 2; PTPRO, protein tyrosine phosphatase receptor-type O; NAC,neoadjuvant chemotherapy.\n\nTable 3\n3\nUnivariate Analysis of NAC Efficacy in Early Breast Cancer Patients with Positive Pre-NAC PTPRO Methylation\nClinical FeaturesTotalNon-PCRpCR\u03c7 2 \/tP-valueNumber of patients, n (%)48 (100)36 (0.75)12 (0.25)-Age, years48.8\u00b17.648.4\u00b17.950.2\u00b16.7\u22120.7120.48cT, n (%)2.9710.4016 (\n\n\n\nBold P-value indicates significance.Abbreviations: cT, clinical tumor stage; cN, clinical nodal stage; ER, estrogen receptor; PR, progesterone receptor; HER2, epidermal growth factor receptor 2; PTPRO, protein tyrosine phosphatase receptor-type O; NAC, neoadjuvant chemotherapy; pCR, pathologic complete response.\n)0.3420.84013 (27.1)9 (18.8)4 (8.3)130 (62.5)23 (47.9)7 (14.6)25 (10.4)4 (8.3)1 (2.1)Pathological type, n (%)0.6960.40Invasive ductal carcinoma46 (95.8)34 (70.8)12 (25.0)Others2 (4.2)2 (4.2)0 (0)Histological grade, n (%)0.5930.44Grade II36 (75.0)28 (58.3)8 (16.7)Grade III12 (25.0)8 (16.7)4 (8.3)ER, n (%)0.2540.61Negative21 (43.8)15 (31.3)6 (12.5)Positive27 (56.2)21 (43.7)6 (12.5)PR, n (%)1.4880.22Negative17 (35.4)11 (22.9)6 (12.5)Positive31 (64.6)25 (52.1)6 (12.5)HER2, n (%)1.4880.22Negative31 (64.6)25 (52.1)6 (12.5)Positive17 (35.4)11 (22.9)6 (12.5)Ki67, n (%)0.1340.71\u226430%14 (29.2)11 (22.9)3 (6.3)>30%34 (70.8)25 (52.1)9 (18.7)PTPRO methylation status after NAC, n (%)9.3410.004Negative15 (31.3)7 (14.6)8 (16.7)Positive33 (68.7)29 (60.4)4 (8.3)\nNote:International Journal of Women's Health 2023:15\n\nAll patients received four cycles of anthracyclines (liposomal epirubicin or liposomal doxorubicin), followed by four cycles of taxanes (docetaxel or paclitaxel or albumin-bound paclitaxel). Chemotherapy was given every three weeks and recorded as a cycle. Patients received a total of eight chemotherapy cycles. For patients with HER2-positive BC, trastuzumab (Herceptin) targeted therapy was received in conjunction with taxanes-based chemotherapy. https:\/\/doi.org\/10.2147\/IJWH.S428038 DovePress International Journal of Women's Health 2023:15 1674 Liu et al Dovepress Powered by TCPDF (www.tcpdf.org)\nInternational Journal of Women's Health 2023:15 https:\/\/doi.org\/10.2147\/IJWH.S428038 DovePress\nPowered by TCPDF (www.tcpdf.org)\nhttps:\/\/doi.org\/10.2147\/IJWH.S428038 DovePress\nData Sharing StatementThe data used to support the findings of this study are available from the corresponding author upon request.Ethics Approval and Consent to ParticipateThe study was approved by the Ethics Committee of the First People's Hospital of Foshan and conducted in accordance with the approval guidelines (L-2023-5).All patients provided written informed consent prior to enrollment in the study.My study complies with the Declaration of Helsinki.DisclosureThe authors declare that they have no competing interests.International Journal of Women's HealthDovepressPublish your work in this journalThe International Journal of Women's Health is an international, peer-reviewed open-access journal publishing original research, reports, editorials, reviews and commentaries on all aspects of women's healthcare including gynecology, obstetrics, and breast cancer.The manuscript management system is completely online and includes a very quick and fair peer-review system, which is all easy to use.Visit http:\/\/www.dovepress.com\/testimonials.php to read real quotes from published authors.\nCancer statistics in China. 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NCfC\n\nClinical value of combined detection of CA15-3, CA125, and CEA in breast cancer. 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0000-0002-5347-837X\nBeijing Obstetrics and Gynecology Hospital\nCapital Medical University\n100026BeijingPeople's Republic of China\n\nGynecologic Oncology Department\nBeijing Maternal and Child Health Care Hospital\n100026BeijingPeople's Republic of China\n\nLei Huang \nBeijing Obstetrics and Gynecology Hospital\nCapital Medical University\n100026BeijingPeople's Republic of China\n\nGynecology department\nYanqing Maternal and Child Health Care Hospital\n102199BeijingPeople's Republic of China\n\nTong Wang 0000-0002-3413-8011\nBeijing Obstetrics and Gynecology Hospital\nCapital Medical University\n100026BeijingPeople's Republic of China\n\nGynecologic Oncology Department\nBeijing Maternal and Child Health Care Hospital\n100026BeijingPeople's Republic of China\n\nBeijing Obstetrics and Gynecology Hospital\nCapital Medical University\n100026BeijingPeople's Republic of China\n\nGynecologic Oncology Department\nBeijing Maternal and Child Health Care Hospital\n100026BeijingPeople's Republic of China\n\nJiandong Wang wangjiandongxy@ccmu.edu.cn \n\nDongcheng District\nBeijing Obstetrics and Gynecology Hospital\nCapital Medical University\nQihelou Street100006Beijing, EmailPeople's Republic of China\n\nThe Effect of Cervical Cold-Knife Conization (CKC) on HPV Infection in Patients with High-Grade Cervical Intraepithelial Neoplasia: A Retrospective Study\n2 November 2023AF6FF61D9FB052E61B9ED4A213107A6F10.2147\/IJWH.S429749Received: 31 July 2023 Accepted: 23 October 2023cold-knife conizationHPV infectionrecovery ratehigh-grade cervical intraepithelial neoplasia\nInvestigation of HPV infection treatment in women undergoing cervical cold-knife conization for advanced cervical intraepithelial neoplasia.Patients and Methods: A retrospective analysis was conducted on patients who underwent cervical cold-knife conization for cervical intraepithelial neoplasia grade II-III at Beijing Obstetrics and Gynecology Hospital from January 2017 to December 2018.The HPV infection status of the patients at 6 months, 1 year, and 2 years after surgery was collected.We use chi square analysis and binary logistic regression to evaluate various factors such as age, number of pregnancies, number of cesarean sections, number of vaginal deliveries, HPV type, size of surgical specimens (diameter and height), and the influence of specimen edge on HPV infection.Results: A total of 334 patients were included in the analysis.The patients are mainly infected with HPV 16\/58\/52.Age is a influencing factor for HPV recovery 12 months after CKC surgery (P=0.002).Based on the diagnosis of HPV one year after CKC, the recovery rate of HPV58 patients is significantly lower than HPV16.Age is a influencing factor for the recovery of HPV infection (P<0.05).Conclusion:The treatment of HPV infection by CKC is related to the patient's age and HPV subtype but not to number of pregnancies, number of pregnancies, number of vaginal deliveries, size of surgical specimens, and marginal conditions.The rate of HPV negative conversion is relatively high 24 months after the patient does not undergo surgery, but there is currently a lack of data on cervical lesions that match HPV results.\n\nIntroduction\n\nCervical cancer is the fourth most common cancer among women worldwide.In 2018, 57,000 new cases of cervical cancer occurred and caused 311,000 deaths. 1 Persistent HPV infection is the main driving factor for the occurrence of cervical cancer. 2The United States Preventive Services Working Group, the American Society for Colposcopy and Cervical Pathology, and the American College of Obstetrics and Gynecology all recommend cytological screening or HPV combined with Pap testing based on patient age to detect cervical precancerous lesions as soon as possible. 2At present, there is still no effective treatment for the persistence of HPV.Preventing health problems caused by HPV infection can only by injecting HPV vaccines, or frequent cervical HPV testing to detect diseases in advance. 3or patients who are HPV 16 or 18 positive during the HPV testing process, those who have been positive for one type of HPV for more than a year, or those with cervical cytology testing indicating LSIL or above, vaginal examination and pathological examination are required.If the patient is diagnosed with CINII or more advanced lesions, further treatment such as loop electrosurgical excision procedure (LEEP), cryotherapy (low grade CINs), laser therapy, and conization is required based on age and reproductive needs. 4ervical cold-knife conization (CKC) is a surgical method of removing the conical part of the cervix to eliminate the transformation zone of cervical lesions.When the pathological examination of the colposcopy indicates that cervical intraepithelial neoplasia (CIN) has persisted for more than 2 years or progressed to CIN 3, CKC is needed to treat cervical lesions and prevent the occurrence of cervical cancer. 5CKC can promote the recovery of HPV by removing a portion of cervical tissue and reducing the HPV-DNA load. 6It can be observed in clinical practice that CKC has a certain therapeutic effect on HPV, however, there are still patients who have doubts about the recovery of HPV after CKC surgery, and there is no comprehensive statistical data available to provide patients.\n\nThe aim of this study is to evaluate the relationship between CKC and the recovery rate of HPV patients, as well as the influencing factors.This result helps to provide a basis for HPV evaluation and treatment plans for postoperative CKC patients.\n\n\nMaterials and Methods Patients\n\nIn this retrospective study, we collected 899 patients who underwent cervical cold knife conization at Beijing Obstetrics and Gynecology Hospital from January 1, 2017 to December 31, 2018 due to pathological results of colposcopy biopsy CINII-CINIII.\n\nInclusion criteria: 1. Patients with pathological confirmation of CINII-CINIII under colposcopy; 2. Preoperative HPV positive and clear typing results.3. The patient underwent cervical cold knife conization; 4. Informed consent of the patient.\n\nExclusion criteria: 1.The patient undergoes total hysterectomy for any reason (uterine fibroids, other malignant tumors, etc.) within 2 years after surgery; 2. The patient underwent cervical conization again within two years after surgery due to any reason (long-term HPV infection, difficulty in follow-up, psychological stress, etc.); 3. The patient becomes pregnant within 2 years after surgery; 4. The patient who have undergone cervical surgery before this cervical cold knife conization surgery.\n\nWe collected HPV test results from all patients who met the standards at 6\/12\/24 months after surgery.Persistent infection of a certain type of HPV is considered when a patient is infected with the same type of HPV before surgery and still infected with the same type of HPV after surgery.Due to the current medical guidelines requiring patients with persistent high-risk HPV infection 12 months after surgery to undergo colposcopy again, some patients have undergone a second cervical surgery treatment.We divided the patients into two groups based on their HPV prognosis at 12 months after surgery, and separately listed the data of patients with persistent HPV infection for 24 months.Only when the type detected during preoperative HPV typing testing is consistent with the type detected during postoperative HPV typing testing, patients are considered to have sustained HPV infection.The impact of cervical cold knife conization on each HPV subtype of patients was analyzed.Patient consent was waived due to the retrospective nature of this study.We confirmed that the privacy of participants would be kept in strict confidence.The study was carried out in accordance with the ethical standards laid down in the Declaration of Helsinki, and was approved by the ethics committees of Beijing Obstetrics and Gynecology Hospital, Capital Medical University (No:2023-KY-021-01).\n\n\nTest Method HPV Testing\n\nCervical scrape specimens were gently collected from the squamocolumnar junction of the cervix using a sampling brush.The genotype of HPV was determined using a 23-HPV Genotyping Real-time PCR Kit (Hybribio, China) which detects 15 high-risk types (HPV 31, 33, 35, 39, 45, 51, 52, 53, 56, 58, 59, 66, 68, 73, 82) according to the manufacturer's instructions.\n\n\nCervical Liquid-Based Cytology Tests\n\nCells were collected from the ectocervix and the cervical canal using a cervical canal brush, and the cells attached to the small brush were eluted in vials containing cell preservation solution and sent to the pathology department (Beijing Obstetrics and Gynecology Hospital, Capital Medical University, China).The laboratory physician provided the final report.\n\n\nObservation Parameters\n\nWe collected and analyzed the age, number of pregnancies, number of deliveries completed through cesarean section, number of natural deliveries, preoperative HPV results, ThinPrep cytologic test (TCT) results, diameter and cone height of the cervical resection section in surgical records, and postoperative pathological indications of the incision margin.\n\n\nStatistical Method\n\nAll statistical analyses were conducted using SPSS version 26.0 and Excel.The grade data was described by frequency and composition ratio.We used ANOVA to analyze the possible influencing factors of patients in the cured and noncured groups, and binary logistic analysis was performed on the possible influencing factors of patients with HPV16, 52, and 58.The statistical significance is set to 0.05.\n\n\nResults\n\n465 out of 799 patients were excluded (21 patients had negative preoperative HPV testing, 79 patients did not undergo HPV testing or had unknown HPV test results, 116 patients had unclear HPV infection types, 36 patients underwent CKC surgery again within 2 years, 39 patients underwent hysterectomy or larger surgery due to various reasons, and 152 patients did not have follow-up data from our hospital.There are 334 patients included in this study.As patients may be infected with multiple HPV subtypes, we calculated the infection rates of different subtypes and their proportion to the number of infections of all types.Among all patients, 73.7% (n=246) had only one high-risk type of HPV, 20.4% (n=68) had two high-risk types of HPV, 3.6% (n=12) had three types, and 2.4% (n=8) had four or more types of HPV.We presented the proportion of different HPV infection types and the proportion of each HPV infection type to all HPV infection types.The two infection rates of each HPV subtype are shown in Table 1 shows the infection rates.\n\nAmong patients with multiple HPV subtypes, HPV16 combined with 31 infection was the most common (n=19), followed by 52 (n=12).We present the specific HPV infection types of all patients with multiple HPV types in Table 2.According to whether the HPV infection subtypes at 12 months after cervical conization were the same as before surgery, the patients were divided into a cured group and an untreated group.The patients were divided into an HPV untreated group (n=26) and an HPV cured group (n=308).The basic data of the patients are shown in Table 3.The age of patients in the cured group (37.38) was significantly lower than that in the untreated group (42.15) (P=0.002).The number of HPV infection types (P=0.057),number of pregnancies (P=0.717),number of vaginal deliveries (P=0.201),number of cesarean sections (P=0.488),TCT results (P=0.387),diameter of surgical pathological specimens (P=0.883),cone height (P=0.880), and edge positivity (P=0.697) were not related to the HPV cure rate.We compared the differences in HPV infection between patients 12 and 24 months after surgery, with 11 patients recovering from HPV infection and 2 patients relapsing.The statistical data is shown in Table 4. Two patients with recurrent HPV, aged 38\/36 years, were both infected with HPV16.The preoperative TCT was LSIL and Normal, and the size of the intraoperative specimen was not recorded.There was no residual pathological margin after surgery, and the subtypes of recurrent HPV were HPV16\/66, respectively.Due to the small number of patients who recovered for the first time within 24 months and experienced HPV recurrence, further analysis was not conducted.\n\nThe specific situation of different types of HPV recovery in patients is shown in Table 5.The number of people infected with each type of HPV is: HPV16 (n=231), HPV18 (n=22), HPV33 (n=17), 52 (n=39), 58 (n=44), others (n=10).Considering the small number of HPV59\/61\/67\/68 infections, this article only analyzes the HPV subtypes of over 10 patients infected with HPV in Table 6.The results showed that the recovery rate of HPV58 was lower than that of HPV16 at 12 and 24 months after cervical conization (P=0.006 at 12 months and P=0.025 at 24 months).\n\nWe analyzed the incidence rate 12-month after the surgery according to different HPV types, and used binary logistic regression to analyze the factors affecting the recovery of the three types of HPV with the highest incidence rate, HPV16, HPV52, and HPV58.The results showed that the recovery of the three types of HPV was unrelated to all factors in the statistics, as shown in Table 7.\n\n\n1687\n\nHPV58 infection is a risk factor for patients with poorly differentiated cervical cancer, 15 which may be due to the particularity of HPV58 protein structure.Some scholars have suggested that for patients with HPV58 positive, Colposcopy examination should be more frequent. 16 study has shown that over 50% of all CIN patients have multiple subtypes of HPV. 17 This study only focuses on CINII-CINIII patients who require cervical cold knife conization.In our study, the number of patients with multiple HPVs reached 20%.There are studies suggesting that infection with different types of HPV makes it easier for patients to acquire another type of HPV (such as HPV16 and HPV52). 18In this study, the number of HPV16 combined with 31 and 52 infections exceeded 10.Simultaneous infection of different types of HPV may lead to more severe cervical lesions.In the study by Spinillo et al, 19 the proportion of CIN3+in patients with HPV16 and HPV18 co infection was higher than the probability of HPV16 alone.However, in our study, the number of infection types was not related to the prognosis of HPV itself, indicating that the promoting effect of different HPVs can only occur on the basis of a certain HPV load.CKC surgery can directly reduce the virus load, and the HPV treatment effect on patients with multiple HPV infections is the same as that of patients with a single HPV infection.\n\nThe pathological margin condition after cone resection is usually considered one of the reasons for treatment failure of cervical cancer precancerous lesions. 20Studies have found that age, menopausal status, and abnormal cytological testing are common risk factors for positive margins. 21However, in our study, abnormal cytological testing and pathological margins were not associated with HPV infection status.Debaudrap et al 22 did a meta-analyses, showed that in the treatment of cervical precancerous lesions in HIV infected women, the failure rate of treatment with positive margins (47.2%, 95% CI 22.0-74.0)was significantly higher than that of patients with negative margins (19.4%, 95% CI 11.8-30.2).This may indicate that HPV infection is related to the body's autoimmune status, but due to the inability to monitor the patient's autoimmune status, there is currently a lack of objective indicators to determine the relationship between autoimmune status and HPV infection.\n\nPersistent long-term HPV infection is considered one of the influencing factors for cervical precancerous lesions, and the HPV viral load is closely related to the risk of high-grade cervical lesions. 23In 2001, a cohort study in Brazil suggested that the duration of HPV infection was related to the incidence rate of cervical precancerous lesions. 24In 2016, a study 25 showed that 54% of patients with persistent high-risk HPV infection had persistent vaginal and cervical lesions, while only 32.3% of patients with low-risk HPV infection had lesions, suggesting that further treatment is needed for persistent high-risk HPV infection after CKC.Byun et al 26 analyzed the HPV infection status of 172 CIN2, CIN3, and CIS patients after cold knife conization.It was mentioned in the analysis that 29.3% of all postoperative HPV infected patients were persistently infected with the same HPV subtype.In this experiment, our results showed that the postoperative recovery rate of HPV58 was significantly lower than that of the highest prevalent HPV16 at 12 and 24 months after surgery, and there was no significant difference compared to other high-risk HPV types.This suggests that for Chinese patients, Further research on the characteristics of HPV58 related infections is necessary.Due to the focus of this experiment on studying the recovery of HPV after CKC surgery and not paying attention to the condition of cervical lesions in patients, further statistical analysis of relevant data is needed.\n\n\nConclusion\n\nBased on the HPV test results one year after surgery, the age of patients in the HPV negative 12 months after surgery group was significantly lower than that in the HPV positive 12 months after surgery group.The number of HPV infection types, pregnancy frequency, vaginal delivery frequency, cesarean section frequency, TCT results, diameter and cone height of surgical pathological specimens were not related to the HPV cure rate.At 12 and 24 months after surgery, the recovery rate of HPV58 patients is significantly lower than HPV 16.Analyzing HPV16\/52\/58 infected patients separately, the above factors are not related to the cure rate of HPV.\n\nTable 1\n1\nInfection Rate and Proportion of Different Types of HPV\nTypesNumberProportion ofProportion ofTypesNumberProportion ofProportion ofof HPV(n=334)Population (%)Infection Types (%)of HPV(n=334)Population (%)Infection Types (%)610.3%0.2%5510.3%0.2%1130.9%0.7%5661.8%1.3%1623169.5%51.8%584413.2%9.8%18226.6%4.9%5951.5%1.1%31176.0%4.5%6110.3%0.2%33175.1%3.8%6410.3%0.2%3561.8%1.3%6651.5%1.1%3951.5%1.1%6710.3%0.2%4220.6%0.4%6851.5%1.1%4310.3%0.2%7410.3%0.2%4572.1%1.6%8130.9%0.7%51133.9%2.9%8261.8%1.3%523911.7%8.7%8310.3%0.2%\n\nTable 2\n2\nNumber of Patients with Multiple HPV Infections\nHPV TypesNumberHPV TypesNumberHPV16\/31\/33\/35\/52\/581 HPV16\/662HPV16\/52\/58\/59\/681 HPV16\/611HPV31\/33\/52\/58\/671 HPV16\/592HPV16\/56\/68\/821 HPV56\/581HPV16\/39\/51\/681 HPV39\/581HPV16\/43\/59\/661 HPV33\/581HPV16\/31\/58\/591 HPV16\/584HPV16\/31\/52\/581 HPV53\/561HPV16\/66\/811 HPV16\/561HPV16\/52\/811 HPV55\/581HPV\/16\/58\/661 HPV16\/532HPV16\/53\/581 HPV35\/521HPV51\/52\/581 HPV33\/521HPV16\/51\/581 HPV16\/528HPV16\/45\/581 HPV33\/511HPV16\/52\/561 HPV16\/516HPV16\/42\/531 HPV45\/581HPV16\/18\/451 HPV18\/453HPV16\/35\/391 HPV42\/521HPV16\/18\/391 HPV31\/391HPV16\/831 HPV16\/333HPV45\/821 HPV31\/581HPV18\/821 HPV18\/311HPV16\/821 HPV16\/3116HPV52\/811 HPV11\/163HPV64\/741 HPV6\/161HPV16\/682\n\nTable 3\n3\nBasic Data of HPV Positive 12 Months After Surgery Group (n=26) and HPV Negative 12 Months After Surgery Group (n=308)\nInfluence FactorHPV NegativeHPV PositiveP12 Months After12 Months AfterSurgery (n=308)Surgery (n=26)Age0.002>603 (0.9%)2 (7.7%)50-6015 (4.8%)5 (19.2%)40-5069 (22.4%)5 (19.2%)30-40155 (50.3%)11 (42.3%)\u22643066 (21.4%)\n\nTable 3 (\n3\nContinued).\nInfluence FactorHPV NegativeHPV PositiveP12 Months After12 Months AfterSurgery (n=308)Surgery (n=26)Number of types of HPV infections0.0571231 (75.0%)15 (57.7%)261 (19.8%)7 (27.0%)\u2265316 (5.20%)4 (15.4%)Number of pregnancy0.717031 (10.1%)2 (7.7%)156 (18.2%)3 (11.5%)283 (27.0%)6 (23.1%)365 (21.1%)8 (30.7%)444 (14.3%)3 (11.5%)\u2265529 (9.4%)4 (15.4%)Number of vaginal deliveries0.246056 (18.2%)6 (23.1%)1189 (61.4%)11 (42.3%)258 (18.8%)8 (30.8%)35 (1.6%)1 (3.8%)Number of cesarean sections0.1810234 (76.0%)19 (73.1%)168 (22.1%)5 (19.2%)26 (1.9%)2 (7.7%)The result of TCT0.681Normal or87 (28.2%)6 (23.1%)InflammatoryAUS-CS69 (22.4%)7 (26.9%)AUS-H17 (5.5%)0 (0%)LSIL64 (20.8%)4 (15.4%)HSIL70 (22.7%)8 (30.8%)AGC2 (0.6%)0 (0%)Specimen conditionSample sizeNot mentioned in86 (27.9%)5 (19.2%)the surgicalrecordsSpecimen diameter0.883<358 (18.8%)6 (23.1%)3-4148 (48.1%)13 (50.0%)\u2265416 (5.2%)2 (7.7%)Specimen height0.880<222 (7.1%)3 (11.5%)2101 (32.8%)9 (34.6%)2.596 (31.2%)9 (34.6%)33 (1.0%)0 (0%)Pathological status of surgical specimen edge0.697Positive31 (10.1%)2 (7.7%)Negative277 (89.9%)24 (92.3%)\nInternational Journal of Women's Health 2023:15\n\n\nTable 4\n4\nStatus of Patients Who Have Recovered from the First HPV Typing in 24 Months\nAgeNumber ofNumber ofNumberNumberTCTSpecimenPathologicalHPVTypes of HPVPregnancyof VaginalofDiameter*SpecimenStatus ofSubtypesInfectionsDeliveriesCesareanHeightSurgicalNot RecoverSectionsSpecimen12 MonthsEdgeAfterSurgery4111\/16200HSILNot mentionedNegative162716\/52000HSILNot mentionedNegative164431\/33\/52\/58\/67320Normal3*2.5Negative58\/675616510Normal3*2.5Negative162916310Normal3*2.5Negative164616320Normal3*2Negative165352620HSIL3*2Negative523616\/61211HSIL3*2Negative613258311AUS-3*2Negative58CS3742\/52000AUS-3*1.5Negative52CS4258211LSIL2*2.5Negative58\n\nTable 5\n5\nHealing Status of Different HPV Types\nType ofHPV Negative 12HPV Positive 12CureHPV Negative 24HPV Positive 24CureHPVMonths AfterMonths AfterRate (%)Months AfterMonths AfterRate (%)SurgerySurgerySurgerySurgery16222996.1%227498.2%1821195.5%21195.5%3316194.1%16194.1%5236392.3%38197.4%5837784.1%40490.9%\n\nTable 6\n6\nComparison of Cure Rates for Different HPV Subtypes at 12 and 24 Months After Surgery\nHPV Positive 24 Months After Surgery\n\nTable 7\n7\nComparison Between the HPV Positive 12 Months After Surgery Group and HPV Negative 12 Months After Surgery Group of HPV 16, 52, and 58\nNumber of0.3480.9990.211pregnancy0250314014418161259290101347210010442924060\u226551912111Number of vaginal0.2400.9980.174deliveries0462426111324290234240331823500000Number of0.1810.9990.378cesarean sections017072732651491901022510010TCT0.1280.9960.407Normal or7757061InflammatoryAUS-CS4728152(Continued)\nInternational Journal of Women's Health 2023:15 https:\/\/doi.org\/10.2147\/IJWH.S429749 DovePress\n\nhttps:\/\/doi.org\/10.2147\/IJWH.S429749 DovePress International Journal of Women's Health 2023:15\nPowered by TCPDF (www.tcpdf.org)\n1.8% 1.3% International Journal of Women's Health 2023:15 https:\/\/doi.org\/10.2147\/IJWH.S429749 DovePress 1683 Dovepress Gao et al Powered by TCPDF (www.tcpdf.org)\n(11.5%) (Continued) https:\/\/doi.org\/10.2147\/IJWH.S429749 DovePress International Journal of Women's Health 2023:15\nhttps:\/\/doi.org\/10.2147\/IJWH.S429749 DovePress\nhttps:\/\/doi.org\/10.2147\/IJWH.S429749 DovePress International Journal of Women's Health 2023:15\nInternational Journal of Women's Health 2023:15 https:\/\/doi.org\/10.2147\/IJWH.S429749 DovePress\nAcknowledgmentsNo contributors not mentioned in the text.FundingThere is no funding for this research.DiscussionThere is currently no reliable drug treatment for HPV virus, and it is possible to cure HPV infection through cervical photodynamic therapy,7local cryotherapy, 8 and surgical treatment.Cervical conization is a diagnostic and therapeutic method for CIN, which can directly reduce the HPV-DNA load by removing HPV infected lesions, thereby promoting the recovery of patients with HPV infection.Park6reported that the high level of cervical HR-HPV is the main reason why some patients still have persistent lesions after cervical conization.Due to the fact that only visible lesions were removed during the surgery, postoperative HPV may still remain positive, ultimately leading to the patient's hysterectomy.9atient age is considered an important factor in the duration of HPV infection.In a 2005 study, the proportion of persistent HPV infection gradually increased with age.10A study in 2017 suggested that the incidence rate of high-risk HPV in elderly women was lower than that in young people,11but the clearance rate of HPV after cervical conization decreased with age.In our study, similar to previous studies, the age of the untreated group was significantly higher than that of the cured group.The type of HPV infection is an important factor in patient prognosis,12and among cervical cancer patients, the prognosis of HPV18 infected patients is worse than that of HPV16 infected patients.13Statistics show that the incidence rate of HPV58 in Chinese women is the second of all HPV types.14For the prognosis of HPV after surgery, our study suggests that the cure rate of HPV58 is significantly lower than that of other HPV types.Chen et al's study suggests thatAuthor ContributionsAll authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.DisclosureThe authors declare no conflicts of interest.International Journal of Women's HealthDovepressPublish your work in this journalThe International Journal of Women's Health is an international, peer-reviewed open-access journal publishing original research, reports, editorials, reviews and commentaries on all aspects of women's healthcare including gynecology, obstetrics, and breast cancer.The manuscript management system is completely online and includes a very quick and fair peer-review system, which is all easy to use.Visit http:\/\/www.dovepress.com\/testimonials.php to read real quotes from published authors.\nA review of cervical cancer: incidence and disparities. 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J M Byun, D H Jeong, Y N Kim, 10.1097\/MD.0000000000013606Medicine. 9751e136062018\n", + "annotations": { + "abstract": "[{\"end\":3319,\"start\":1719}]", + "author": "[{\"end\":435,\"start\":172},{\"end\":668,\"start\":436},{\"end\":1162,\"start\":669},{\"end\":1204,\"start\":1163},{\"end\":1356,\"start\":1205}]", + "authoraffiliation": "[{\"end\":313,\"start\":204},{\"end\":434,\"start\":315},{\"end\":556,\"start\":447},{\"end\":667,\"start\":558},{\"end\":808,\"start\":699},{\"end\":929,\"start\":810},{\"end\":1040,\"start\":931},{\"end\":1161,\"start\":1042},{\"end\":1355,\"start\":1206}]", + "authorfirstname": "[{\"end\":179,\"start\":172},{\"end\":439,\"start\":436},{\"end\":673,\"start\":669},{\"end\":1171,\"start\":1163}]", + "authorlastname": "[{\"end\":183,\"start\":180},{\"end\":445,\"start\":440},{\"end\":678,\"start\":674},{\"end\":1176,\"start\":1172}]", + "bibauthor": 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"text": "\nA Rare Case of Metastatic Rectal Cancer to the Brain Presenting with Headaches and Memory Difficulties\nNovember 3, 2023\n\nMDPrabhat Kumar \nDepartment of Internal Medicine\nCleveland Clinic Foundation\nClevelandOH\n\nMDPearl Aggarwal pearl.aggarwal@uhhospitals.org \nDepartment of Internal Medicine\nUniversity Hospitals\nClevelandOH\n\nMDRajat Garg \nDepartment of Gastroenterology and Hepatology\nCleveland Clinic Foundation\nClevelandOH\n\nMDAmandeep Singh \nDepartment of Gastroenterology and Hepatology\nCleveland Clinic Foundation\nClevelandOH\n\nMDTalal Adhami \nDepartment of Gastroenterology and Hepatology\nCleveland Clinic Foundation\nClevelandOH\n\nA Rare Case of Metastatic Rectal Cancer to the Brain Presenting with Headaches and Memory Difficulties\nNovember 3, 20238BF25D84A005F4DD9BCC82DED8D7703A10.14309\/crj.0000000000001178\nColorectal cancer rarely spreads to the brain, but we report a unique case of rectal cancer with brain metastases.A 50-year-old White man presented with chronic headaches, memory difficulties, and occasional rectal bleeding after hard stools.His last colonoscopy was not successful because of poor preparation.Subsequently, he experienced dizziness, facial weakness, and seizures.Brain magnetic resonance imaging revealed metastatic deposits in the left thalamus.Partial tumor resection revealed adenocarcinoma positive for AE1\/AE3, CK7, and CDX2, indicating a pancreaticobiliary or gastrointestinal origin.Negative stains for SATB2, CK20, PAX 8, and S100 strengthened this assumption.The Ki-67 proliferative index was 75.0%.Abdominal\/pelvic and chest computed tomography scans showed no signs of cancer.A repeat colonoscopy found a nonobstructing 7 3 5 cm pedunculated polypoid rectal lesion (Figure1).It was successfully removed, and histology confirmed invasive moderately differentiated adenocarcinoma from tubulovillous adenoma without lymphovascular invasion.Metachronous brain metastasis of colorectal cancer is rare and carries a poor prognosis because chemotherapy does not penetrate the central nervous system. 1 Our case presents a rare scenario of undetected rectal cancer with isolated brain metastasis and neurological Figure 1.Brain MRI demonstrating metastasis to the left thalamus (black arrow).On the right, colonoscopy images showing a fungating mass in the rectum and postsnare colon.MRI, magnetic resonance imaging.\n\nmanifestations. 2 A thorough investigation is advised in similar cases. 3Treatment modalities encompass radiation therapy, anti-epidermal growth factor receptor antibody therapies, monoclonal antibody therapy, and chemotherapy. 4\n\n\nDISCLOSURES\n\nAuthor contributions: P. Kumar: substantial contributions to the conception, acquisition, analysis, and interpretation of data for the work and drafting and reviewing it critically for important intellectual content.P. Aggarwal: substantial contributions to the interpretation of data for the work and reviewing the work critically for important intellectual content.R. Garg: substantial contributions to the conception; drafting the work for important intellectual content; and final approval of the version to be published.A. Singh: substantial contributions to the conception, acquisition, analysis, and interpretation of data for the work and reviewing it critically for important intellectual content.A. Talal: substantial contributions to the analysis of data for the work and drafting the work.All authors gave final approval of the version to be published and agree to be accountable for all aspects of the work in ensuring questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.P. Kumar is the article guarantor.\n\nFinancial\n\ndisclosure: None to report.\n\n\n\n\n\n\nACG Case Reports Journal \/ Volume 10 acgcasereports.com 2\nPrevious presentation: Previously presented at the ACG Annual Scientific Meeting, October 2022, Charlotte, North Carolina.Informed consent was obtained for this case report.Received June 13, 2023; Accepted September 19, 2023\nAnnual report to the nation on the status of cancer, part 1: National cancer statistics. K A Cronin, S Scott, A U Firth, Cancer. 128242022\n\nCancer statistics, 2022. R L Siegel, K D Miller, H E Fuchs, A Jemal, CA Cancer J Clin. 7212022\n\nChanging patterns of bone and brain metastases in patients with colorectal cancer. M L Sundermeyer, N J Meropol, A Rogatko, H Wang, S J Cohen, Clin Colorectal Cancer. 522005\n\nMedical management of brain metastases. A Lauko, Y Rauf, M S Ahluwalia, Neurooncol Adv. 21152020\n\nPublished by Wolters Kluwer Health, Inc. on behalf of The American College of Gastroenterology. This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium. 2023 The Author(s). provided the original work is properly cited\n", + "annotations": { + "abstract": "[{\"end\":2353,\"start\":817}]", + "author": "[{\"end\":211,\"start\":122},{\"end\":326,\"start\":212},{\"end\":427,\"start\":327},{\"end\":532,\"start\":428},{\"end\":635,\"start\":533}]", + "authoraffiliation": "[{\"end\":210,\"start\":139},{\"end\":325,\"start\":261},{\"end\":426,\"start\":341},{\"end\":531,\"start\":446},{\"end\":634,\"start\":549}]", + "authorfirstname": "[{\"end\":131,\"start\":124},{\"end\":219,\"start\":214},{\"end\":334,\"start\":329},{\"end\":438,\"start\":430},{\"end\":540,\"start\":535}]", + "authorlastname": "[{\"end\":137,\"start\":132},{\"end\":228,\"start\":220},{\"end\":339,\"start\":335},{\"end\":444,\"start\":439},{\"end\":547,\"start\":541}]", + "bibauthor": 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null, + "tableref": null, + "title": "[{\"end\":103,\"start\":1},{\"end\":738,\"start\":636}]", + "venue": null + } + } + }, + { + "corpusid": 265004440, + "externalids": { + "arxiv": null, + "mag": null, + "acl": null, + "pubmed": null, + "pubmedcentral": "10631375", + "dblp": null, + "doi": "10.2147\/prbm.s438396" + }, + "content": { + "source": { + "pdfurls": null, + "pdfsha": "a3ac767b712b627ac2e57af7f76aba77f3792d49", + "oainfo": { + "license": "CCBYNC", + "openaccessurl": "https:\/\/www.dovepress.com\/getfile.php?fileID=94025", + "status": "GOLD" + } + }, + "text": "\nAcademic Motivation and Social Support: Mediating and Moderating the Life Satisfaction and Learning Burnout Link\n4 November 2023\n\nChunmei Chen 0000-0001-7763-0340\nTeachers College\nJimei University\n361021Xiamen, FujianPeople's Republic of China\n\nYujie Zhu 0000-0002-3154-708X\nSchool of Marine Culture and Law\nJimei University\n361021Xiamen, FujianPeople's Republic of China\n\nFanghao Xiao xiaofanghao@xit.edu.cn 0000-0003-2390-8399\nSchool of Foreign Languages\nXiamen Institute of Technology\n361021Xiamen, FujianPeople's Republic of China\n\nMingkun Que quemingkun@zju.edu.cn \nCollege of Education\nZhejiang University\n310058HangzhouZhejiangPeople's Republic of China\n\nAcademic Motivation and Social Support: Mediating and Moderating the Life Satisfaction and Learning Burnout Link\n4 November 2023FD69ABA1FB8581741B1DEF8366983CD810.2147\/PRBM.S438396Received: 3 September 2023 Accepted: 27 October 2023life satisfactionlearning burnoutacademic motivationsocial supportmediating effectmoderating effect\nBackground:The phenomenon of university students' learning burnout has attracted the research of many scholars because of its typicality.This study aims to explore the relationship between life satisfaction, academic motivation, social support and learning burnout among university students and its underlying mechanisms.Methods: A total of 1917 university students participated in this cross-sectional study.Research instruments included the Adolescent Student Life Satisfaction Scale, University Students' Academic Motivation Questionnaire, Adolescent Learning Burnout Scale and Adolescent Social Support Scale.The data analysis comprised descriptive statistics, correlation analyses, and assessment of multicollinearity through Variance Inflation Factor (VIF).Advanced analyses were conducted using Model 4 for mediation and Model 1 for moderation from the PROCESS macro.Results: (1) life satisfaction significantly and positively predicts academic motivation; (2) academic motivation significantly and negatively predicts learning burnout; and (3) life satisfaction significantly and negatively predicts learning burnout; (4) academic motivation partially mediates the effect of life satisfaction on learning burnout; and (5) social support plays a moderating role in the effect of academic motivation on learning burnout.Discussions: These results illuminate the complex web of relationships among life satisfaction, academic motivation, social support, and learning burnout.The partial mediating role of academic motivation underscores its significance in the link between life satisfaction and learning burnout.Additionally, the moderating impact of social support emphasizes its role in ameliorating or exacerbating the effects of academic motivation on learning burnout.Conclusion: These findings can help researchers and educators better understand the underlying mechanisms between life satisfaction and learning burnout.Meanwhile, the results of the study can provide practical and effective operational suggestions for preventing and intervening in university students' learning burnout and improving their academic motivation.\n\nIntroduction\n\nUniversity is the stage when individuals' social behavior and psychological quality tend to mature.The trajectory of changes in psychological ability and belief perception of university students at this stage and the factors influencing them deserve the attention of society. 1 By 2022, the gross enrollment rate of higher education in China has reached 59.6%, 2 which means that China's higher education has entered the stage of universal development from massification, and the path of internal development is the way to go. 3 However, the current learning situation of university students is not optimistic.Learning burnout is prevalent on campuses. 4In a study of an open-ended questionnaire survey of students in four universities in Wuhan, it is found that 72.2% of the students had varying degrees of study burnout. 5Learning burnout mainly refers to an individual's emotional exhaustion, depersonalization, and reduced sense of accomplishment during the learning process due to course stress, course load, or other psychological factors. 6Learning burnout has a significant impact on students' academic life, overall well-being, and future career development. 7It reduces students' enthusiasm for education and leads to lower motivation, higher absenteeism and dropout rates, etc. 8 It has been found that when college students are externally rewarded for learning, the higher their external motivation, the higher their selfefficacy.They are able to rely on high levels of academic motivation to enjoy the process of solving difficulties, thus showing lower learning burnout. 9Universities should provide students with a more conducive academic environment and more opportunities for professional practice; Society needs to encourage educators to provide more emotional support and recognition for students and to promote higher levels of engagement in learning by creating a positive academic and emotional atmosphere. 10At the same time, social support from families and schools enhances communication and exchanges between students and the community, thereby promoting students' physical and mental health and academic performance. 11A survey of 456 undergraduate students from freshmen to juniors at eight universities in Beijing finds that learning burnout can be related to life satisfaction through the mediating role of comprehension social support. 12In addition, a study of 573 university students from two universities in Jilin Province finds a negative correlation between learning burnout and social support. 13Moreover, a study of 454 university students from freshmen to seniors at Southwestern University also discovers that academic motivation is one of the internal factors that influence university students' learning burnout.University students' learning burnout is significantly related to academic motivation and attributional style plays a role in mediating the effect. 14To this end, this study will examine the relationship between learning burnout and life satisfaction, social support, and academic motivation among university students and the mechanisms that influence it.In the educational context of the transition from popularization to connotative development of higher education in the world, it is of great significance to explore the influence mechanism of university students' learning burnout in order to create a good learning atmosphere and improve the quality construction of higher education.\n\n\nThe Relationship Between Life Satisfaction and Academic Motivation\n\nLife satisfaction is a process by which individuals perceive and judge their quality of life in general.Through this process, people are able to reflect on their current life circumstances. 15Individuals gain life satisfaction by feeling important emotional experiences such as relationships, support, and companionship from others. 16Students with high life satisfaction are able to make appropriate adjustments to their behavior, which increases their academic motivation. 17ost of the existing studies have analyzed academic motivation as a dependent variable.In this study, academic motivation will be used as a mediating variable to explore the relationship between life satisfaction and learning burnout.Academic motivation is the driving force for learners to put in effort and engage deeply in learning. 18In one study, authoritative parents' frequent use of threats and\/or punishments to enforce rules and expectations have a negative impact on their children's mental health, decreasing their life satisfaction and affecting their self-emotional regulation system.This result in a bipolar state of weak or tight motivation for such children who are often limited by the demands of their parents. 191][22] In summary, hypothesis H1 is proposed: H1: There is a significant effect of life satisfaction on academic motivation.\n\n\nThe Relationship Between Academic Motivation and Learning Burnout\n\nAcademic motivation, which includes intrinsic and extrinsic motivation, is a key set of variables for understanding students' learning and performance (eg, grades and learning effectiveness). 23The former mainly refers to starting an activity without any external stimulus because of the fulfillment of an internal psychological need and can be directed towards knowledge, achievement and enjoyable experiences.The latter is goal-oriented. 24In order to learn something new, students need to have not only the required knowledge, skills and strategies, but also the inclination and willingness to learn.Academic motivation plays an important role in any task involving the acquisition, transfer and use of knowledge. 25Learning burnout, on the other hand, refers to a state in which students experience persistent negative feelings about learning, inappropriate learning behaviors, and low academic achievement. 26Students with learning burnout are tired of school-related activities and lose interest in academic learning. 27There are many studies directly exploring the relationship between academic motivation and learning burnout, but fewer studies have been conducted using academic motivation as a mediating variable.This study focuses on the mediating role of academic motivation in the relationship between life satisfaction and learning burnout.9][30][31] In summary, hypothesis H2 is proposed: H2: There is a significant effect of academic motivation on learning burnout.\n\n\nThe Relationship Between Life Satisfaction and Learning Burnout\n\nLife satisfaction is actually a psychological resource which is an important psychological factor influencing individuals' perception and control of stressful situations. 32The lower an individual's life satisfaction, the more anxiety, anger, and sadness can follow, which in turn can lead to burnout such as sleep disorders and headaches.Low happiness and high burnout levels are closely related. 33Students with higher life satisfaction have higher levels of hope, optimism, 34 and are less prone to burnout with the combined effects of self-efficacy, resilience, and optimism. 35Relatively little research has been done to explore life satisfaction and learning burnout.The role of other variables in this relationship will also be explored concurrently in this study.7][38][39] In summary, hypothesis H3 is proposed: H3: There is a significant effect of life satisfaction on learning burnout.\n\n\nMediating Effect of Learning Motivation\n\nThere are many studies on academic motivation, but few on academic motivation as a mediating variable to explore the relationship between life satisfaction and learning burnout.The acquisition and perception of life satisfaction can respond to the basic psychological needs of students.Individuals derive pleasure from learning something new.This pleasurable learning experience can ultimately further stimulate students' intrinsic motivation to learn. 40Students with low life satisfaction lack self-commitment and reflective learning exploration behaviors.This lack of academic motivation translates into a lack of self-consistency over time, which in turn affects students' commitment to learning. 41Such students are more likely to feel ambivalent psychological experiences such as guilt and confusion, which depress their academic motivation. 42Life satisfaction is closely related to academic motivation.In addition, a study of medical students at Islamic Azad University in Tehran finds that high levels of academic motivation provides meaning to students' behaviors.Students are able to facilitate or promote learned behaviors by imagining their desired state.Students under the guiding effect of personal goals grasp negative emotions more rapidly.The higher the level of academic motivation, the stronger their adjustment and adaptation to learning burnout.Academic motivation is negatively related to learning burnout. 43A survey of Danish university students reveals a direct positive correlation between life satisfaction and academic motivation, and a direct negative correlation between academic motivation and learning burnout. 20In summary, hypothesis H4 is proposed: H4: Academic motivation mediates the relationship between life satisfaction and learning burnout.\n\n\nModerating Effects of Social Support\n\nSocial support provides recipients with knowledge and information (informational support), eases difficulties in understanding and comforting (emotional support), provides feedback on accomplishments and problems (evaluative support) and creates tools to compensate for deficits (instrumental support). 44Establishing and strengthening social support systems is important for university students to cope with stressors and improve mental health. 45Social support is categorized into acquired social support and perceived social support.The former refers to actual support from those around the individual.The latter is the subjective perception and assessment of support from family, friends, and significant others, including emotional experiences and attitude formation. 46Perceived social support can give individuals a sense of being valued, cared for, and loved. 47There are many studies related to social support, but few studies have explored the relationship between life satisfaction and learning burnout using social support as a moderating variable.Research has found that high quality supportive social relationships create a psycho-social environment that meets the needs of students.Students who receive high levels of social support are able to have higher levels of academic motivation, bringing about lower levels of academic stress and anxiety.Social support is positively correlated with academic motivation. 480][51][52] In addition, an online learning study has found that the characteristics of online learning across time and space can lead to a lack of interaction between learners, which makes them susceptible to loneliness, distraction, and stress, and may further suffer from learning burnout.Without adequate social support and learning interactions during technology-assisted learning activities, students are more likely to experience burnout, and this burnout increases over time. 535][56] In summary, hypothesis H5 is proposed: H5: Social support moderates the relationship between academic motivation and learning burnout.\n\nThis study constructs a mediation model and a regulation model to explore the influence mechanism of life satisfaction on learning burnout in a group of university students, with a view to provide new ideas for university students to improve their academic motivation.The theoretical model is shown in Figure 1:\n\n\nMethod\n\n\nData Sources and Sample Characteristics\n\nThe data collection for this study was systematically executed during the months of June and July 2023.Students from Xiamen University, Jimei University, Xiamen Institute of Technology, Wuxi Taihu Lake College, Guangzhou University, Guangdong Institute of Petroleum and Chemical Technology, Zhongshan College of the University of Electronic Science and Technology, and Zhaoqing University (the source colleges and universities involve different levels of domestic colleges and universities and are geographically widely distributed) were engaged as participants, selected via a convenience sampling methodology.This non-probabilistic sampling technique was chosen due to its feasibility, considering the expansive participant pool and the study's time constraints.Before embarking on full-scale data collection, we piloted the questionnaire with a smaller subset of participants.This pilot served to validate the clarity, relevance, and reliability of the questionnaire.Feedback from this process informed adjustments to optimize the questionnaire's validity and reliability.\n\nA total of 2106 full-time university students were used as research subjects.Convenience sampling was employed to ensure diversity in terms of academic disciplines and demographics.After excluding invalid questionnaires, there were 1917 valid questionnaires, with a gender distribution of 56.91% males (n=1091) and 43.09% females (n=826).In terms of educational cultivation level, the vast majority, 93.74% (n=1797), were undergraduates, while the remainder, 6.26% (n=120), were postgraduates.When examining the distribution across academic majors, 77.67% (n=1489) of the participants were from Science and Engineering specialties, 18.73% (n=359) hailed from Arts disciplines, and the remaining 3.60% (n=69) were from other diverse academic domains.Inclusion criteria for participation included currently enrolled university students, while the exclusion criteria were limited to non-university participants and students who had previously taken the survey.The data acquisition process was facilitated through the Questionnaire Star online platform in Chinese, which eliminated geographical barriers and ensured broader coverage.Participants were given ample time to carefully complete the questionnaire, with an average expected completion time of 15 minutes.Given the digital nature of the survey, it was not confined to a physical location like a classroom, thus allowing participants the flexibility to respond at their convenience, further enhancing the likelihood of obtaining candid and comprehensive responses.\n\n\nResearch Instruments Adolescent Student Life Satisfaction Scale\n\nThe Adolescent Student Life Satisfaction Scale was developed by Zhang and He in 2004. 57The scale consists of 6 dimensions of friendship, family, academics, freedom, school and environment with 36 entries using 5-point scale.The KMO value of the scale was 0.956 and the study data was well suited for extracting information.The scale Cronbach's \u03b1 coefficient was 0.912.The scale had good consistency and the measure was valid.After reversing the scoring of the reverse questions, all the items were summed and averaged to obtain the variable life satisfaction, which was used to indicate the level of life satisfaction, with higher scores indicating a higher degree of life satisfaction.\n\n\nUniversity Students' Academic Motivation Questionnaire\n\nThe academic motivation questionnaire for university students was developed by Tian and Pan in 2006. 58The questionnaire contains 4 dimensions of interest in knowledge, competence pursuit, reputation acquisition and altruistic orientation with 34 entries using a 5-point scale.The KMO value of the questionnaire was 0.967 and the study data was well suited for extracting information.\n\nIn addition, the Cronbach's \u03b1 coefficient of the questionnaire was 0.891.The scale had good consistency and the measurements were valid.After reversing the scoring of the reverse questions, all the items were summed and averaged to obtain the variable of academic motivation, which was used to indicate the degree of academic motivation, with higher scores indicating a higher degree of academic motivation.\n\n\nAdolescent Learning Burnout Scale\n\nThe Adolescent Learning Burnout Scale was developed by Wu and Dai in 2007. 59The scale is a self-assessment scale that includes 3 dimensions of physical and mental exhaustion, academic detachment and low achievement, with 16 entries and a 5-point scale.The KMO value of the scale was 0.906, and the study data were well suited for extracting information.Moreover, the Cronbach's \u03b1 coefficient of the scale was 0.841.The scale had good consistency and the measurements were valid.After reversing the scoring of the reverse questions, all the items were summed and averaged to obtain the variable of learning burnout, which was used to indicate the degree of learning burnout, with higher scores indicating a higher degree of learning burnout.\n\n\nAdolescent Social Support Scale\n\nThe Adolescent Social Support Scale was developed by Yuemei Ye et al in 2008. 60The self-assessment scale consists of three dimensions, subjective support, objective support, and support utilization, with 17 entries on a five-point scale.The KMO value of the scale was 0.935, and the study data were well suited for extracting information.The Cronbach's \u03b1 coefficient of the scale was 0.901.The scale had good consistency and the measurements were valid.After reversing the scoring of the reverse questions, all the items were summed and averaged to obtain the variable of life satisfaction, which was used to indicate the degree of life satisfaction, with higher scores indicating a higher degree of life satisfaction.\n\n\nResearch Design\n\nOur study embraced a cross-sectional research design, strategically chosen to discern the relationships between university students' life satisfaction, academic motivation, social support, and learning burnout at a singular juncture.This design facilitated a comprehensive snapshot of the prevailing dynamics among the aforementioned variables within our sizable cohort of 1917 university students.\n\nThe data analysis was multifaceted.Preliminary analyses, including descriptive statistics and correlation analyses, were executed using SPSS, ensuring an initial understanding of data distribution and linear relationships.Subsequent advanced analyses delved into mediation and moderation effects, using Model 4 and Model 1 of the PROCESS macro, respectively.These analytical choices were tailored to extract nuanced insights into the underlying mechanisms between life satisfaction and learning burnout, with academic motivation and social support acting as potential mediators and moderators.\n\nThis design and methodological approach, rooted in established practices, aimed to provide a holistic understanding of the factors influencing university students' learning experiences, thereby advancing the field's knowledge base.\n\n\nData Processing\n\nDescriptive statistics and Pearson correlation analysis were performed using SPSS 26.0.In order to ensure the accuracy of the results, the variance inflation factor (VIF) method was used in the study for the covariance test (if VIF > 10, it means that there is a serious covariance problem between the variables, and the corresponding variables need to be excluded).Meanwhile, the study used model 4 and model 1 in the process plug-in prepared by Hayes 61 for chained mediation effect analysis and tested the significance of the mediation effect using the bias-corrected percentile Bootstrap method.It was considered statistically significant if the 99% confidence interval did not contain a value of zero. 62In addition, all variables were standardized beforehand to avoid bias in the moderating effects.\n\n\nFindings Common Method Bias Test\n\nThe issue of common method bias may arise when utilizing the self-report method of data collection.The common method bias test was performed using the Harman single-factor test. 63The results showed that there were nine principal components with eigenvalues greater than 1.The first principal component explained 39.63% of the variance, which was below the critical criterion of 40%.Therefore, there is no serious common method bias in this study.\n\n\nDescriptive Statistics and Correlation Analysis of the Variables\n\nFour variables, including learning burnout, academic motivation, life satisfaction and social support, were analyzed for correlation, and the Pearson correlation coefficient test was used, considering that the main variables were all continuous variables.It was found that there was a significant positive correlation between the four components of life satisfaction, academic motivation, learning burnout and social support.The results are shown in Table 1.\n\n\nTest of Mediating Effect\n\nModel 4 (Model 4 is a simple mediation model) in the SPSS macro developed by Hayes 61 was used to test the mediating effect of academic motivation in the relationship between life satisfaction and learning burnout.The results were shown in Table 2 and Table 3. Life satisfaction was a significant negative predictor of learning burnout (B= \u22120.472, p < 0. 001); And when the mediator variable was put in, life satisfaction remained a significant negative predictor of learning burnout, but the effect size was significantly lower (B = \u22120.304,p < 0. 001).Motivation was a significant negative predictor of burnout (B = \u22120.229,p < 0. 001); life satisfaction was a significant positive predictor of motivation (B = 0.733, p < 0. 001), and to this point, Hypotheses 1, 2, and 3 were validated; Academic motivation was a significant negative predictor of learning burnout (B = \u22120.229,p < 0. 001); life satisfaction was a significant positive predictor of academic motivation (B = 0.733, p < 0. 001), and to this point, Hypotheses 1, 2, and 3 were validated; In addition, the upper and lower bounds of the bootstrap 95% confidence intervals for the direct effect of life satisfaction on learning burnout and the mediating effect of academic motivation did not contain 0 (see Table 3), suggesting that the mediating effect existed and was partially mediated, and hypothesis H4 was tested.\n\nThe results of the data indicated that life satisfaction significantly negatively predicted learning burnout, that academic motivation significantly negatively predicted learning burnout, and that life satisfaction was able to negatively predict academic burnout through the mediating effect of academic motivation.The mediation model was shown in Figure 2.\n\n\nTest of Moderating Effects\n\nAgain, Model 1 in the SPSS plug-in macro PROCESS prepared by Hayes 61 was used with academic motivation as the independent variable, learning burnout as the dependent variable and social support as the moderating variable.The results were shown in Table 4, and the moderating effects were divided into three models.Model 1 included the independent variable (academic motivation).Model 2 included the moderating variable (social support) on the basis of model 1, and model 3 included the interaction term (the product term of the independent variable and the moderating variable) on the basis of model 2. The results showed that academic motivation significantly negatively predicted learning burnout (\u03b2= \u22120.386, t=\u221221.159,p<0.001), and the interaction term between academic motivation and social support showed significance (\u03b2= \u22120.046, t=\u22122.996,p<0.01).This meant that the magnitude of the effect of the moderating variable (social support) in the process of academic motivation in having an impact on learning burnout was significantly different at different levels, and this moderation was negative.\n\nIn order to test whether the pattern of this moderating effect is consistent with the hypothesis, we followed the suggestion of Aiken and West. 64A social support score above the mean plus one standard deviation was considered the high group, and below the mean minus one standard deviation was considered the low group.The moderating effect of different social support was shown in Table 5, and its simple slope diagram was shown in Figure 3.The negative predictive relationship between academic motivation and learning burnout was stronger in the high social support group (simple slope=\u22120.319,p<0.001) and weaker in the low social support group (simple slope=\u22120.256,p<0.001).In addition, at different levels of academic motivation, the low social support group had higher learning burnout than the high social support group, so hypothesis H5 was supported.\n\n\nDiscussion\n\nThe Effect of Life Satisfaction on Academic Motivation\n\nThe results of this study showed that life satisfaction positively predicted academic motivation, ie, university student groups with higher levels of life satisfaction would have higher academic motivation, and conversely, university student groups with low life satisfaction had lower academic motivation.Life satisfaction can provide students with a sense of fulfillment and predispose them to think positively about the results of their efforts to learn.With the accumulation of positive beliefs, students are able to mobilize and use intrinsic goal orientation (motivation based on challenge, curiosity, or proficiency) and extrinsic goal orientation (motivation based on achievement, rewards, evaluation by others, and competition) in learning tasks. 657][68] Another study also notes that higher life satisfaction can help students adopt adaptive learning behaviors to complete academic tasks and challenges.Students' desire for more academic, interpersonal resources further motivates them to learn. 69This study reaches similar conclusions.Students with high levels of life satisfaction mean that they show higher levels of engagement with self-care activities, have lower rates of perceived stress, and have a higher quality of life.The easier it is for such students to make a positive assessment of the current learning situation, which increases motivation.Students with lower life satisfaction are less able to easily transform negative emotions from the midst of academic stress, and the more difficult it is to accept and complete learning tasks. 70It is significant for related departments and persons to take measures to improve students' level of life satisfaction.This study enriches the research in this area by using academic motivation as a mediating variable in the relationship between life satisfaction and learning burnout.\n\n\nThe Effect of Academic Motivation on Learning Burnout\n\nThe results of this study showed that academic motivation negatively predicted learning burnout, ie, university student groups with higher academic motivation had lower levels of learning burnout, and conversely, university student groups with low academic motivation had higher levels of learning burnout.Higher levels of motivation and students' desire to succeed help them to utilize positive learning strategies to solve learning problems and to confront learning self-imposed barriers with a growth mindset, thus reducing the occurrence of learning burnout. 28,31These students become more pragmatic about school goals and future career directions and tend to achieve separable, reward-based outcomes.This allows them to circumvent burnout behaviors tinged with resentment, resistance, and disinterest to the greatest extent possible.The lower the level of motivation, the less willing students are to assume their social roles because they do not feel or want to be valued by others, and the more likely they are to induce behaviors such as dropping out, withdrawing from school, and avoiding. 71They are unable to connect their personal learning goals to their satisfaction in performing learning tasks and thus tend to hold a boredom, disinterestedness, and disinterestedness mindset towards learning, which in turn makes them prone to learning burnout such as physical and mental exhaustion, cynicism, and other phenomena. 72his study reaches similar conclusions.Therefore, in order to reduce the negative impacts caused by university students' learning burnout, stakeholders can take measures to stimulate, maintain and enhance their academic motivation.There are many studies exploring the correlation between academic motivation and learning burnout, but fewer exploring the relationship between learning burnout and other independent variables using academic motivation as a mediating variable.This study enriches the existing studies to some extent.\n\n\nThe Effect of Life Satisfaction on Academic Burnout\n\nThe results of this study showed that life satisfaction negatively predicted learning burnout, ie, university student groups with higher levels of life satisfaction had lower levels of learning burnout, and vice versa.Learning burnout is caused by external factors such as heavy academic loads and stressful learning environments, as well as internal factors such as the inability to effectively cope with learning pressure and inappropriate coping styles. 73Students with low life satisfaction are unable to cope effectively with stress and recover quickly from stressors.Over time, this can lead to a decline in academic performance and in turn induce burnout behaviors such as sleep disorders, risk of serious mental illness, and substance use disorders. 74Students with a high level of life satisfaction are able to self-adjust, calmly assess the situation, adapt and overcome adversity, and persevere in achieving their goals, even when under constant stress.In the process, life satisfaction buffers the negative effects of learning burnout. 34,75High life satisfaction during adolescence effectively moderates students' responses to stressful life events, protects them from adverse experiences, and serves as an effective buffer zone for learning burnout behaviors such as burnout, exhaustion, avoidance, and abandonment. 76This study reaches similar conclusions.In conclusion, the higher the life satisfaction of university students, the better they are able to adapt to their studies and reduce the occurrence of learning burnout.Relatively few studies have been conducted to explore life satisfaction and learning burnout.This study also explores the role of academic motivation and social support in this relationship.For this reason, this study can contribute to a more systematic and comprehensive understanding of the influencing mechanism.\n\n\nMediating Effect of Academic Motivation\n\nThe results of this study showed that academic motivation played a partial mediating role between life satisfaction and learning burnout.That is, the higher the life satisfaction of the university student group, the higher their academic motivation ability, and thus the lower their learning burnout level will be.Increased life satisfaction guides students in setting learning goals.Students are more motivated to engage in a wider range of learning engagement behaviors out of a desire to achieve higher levels of academic success.Students' perception of academic motivation then further improves their ability to capture interest in learning, which reduces the emergence of burnout. 77Students with higher levels of life satisfaction tend to perceive themselves as being in a positive, student-centered environment.Satisfaction with the educational environment, teacher-student relationships, and peer relationships can provide students with a sense of psychological security. 78The easier it is for such students to adopt problem-based coping strategies and actively engage their motivation to adjust their learning behaviors.Students are able to recover from learning burnout and emotional fatigue more quickly when they feel the pleasure of achieving academic success through their efforts. 79When students have high levels of life satisfaction, the higher their ability to perceive positive emotions, such as pride and joy from improved grades, and thus the higher their academic motivation.Such students exhibit fewer problem behaviors and are less likely to drop out of school. 80Conversely, when students' life satisfaction is low, students tend to lose interest and motivation in learning, 81 and instead focus on avoiding unwanted outcomes, placing their sense of self in anxiety.Unpleasant emotional experiences can cause students to develop self-doubt, making them more likely to believe that they lack sufficient learning ability to achieve a specific goal, which in turn causes them to be prone to more burnout behaviors of abandonment and avoidance. 82Although there are many studies on academic motivation and academic motivation and learning burnout, there are not many studies that explore the relationship between life satisfaction and learning burnout using academic motivation as a mediating variable.For this reason, this study enriches the existing research to some extent.\n\n\nModerating Effect of Social Support\n\nThis study found that as social support raised, the level of negative prediction of academic motivation on learning burnout would be higher, ie, relative to the group of university students with low social support, the inhibitory effect of their academic motivation on their learning burnout would be stronger for university students with high social support.\n\nThis suggests that in order to effectively reduce learning burnout, improving social support for university students at the same level of motivation is an effective measure.Students with too low academic motivation have an amplified perception of stress and their shallow learning strategies are used with increased frequency.Such students' burnout behaviors are more likely to be exacerbated by limited learning resources. 83When students feel socially supported, they are able to feel a sense of identity in their interactions with the social environment, have more opportunities to express and expand their abilities, and aspire to further academic development, which can fully mobilize their motivation to learn. 49In addition, a sense of social support helps students identify and maintain learning goals in complex learning environments, allowing them to experience less academic distress and reducing their complaining emotions, which reduces their symptoms of learning burnout such as anxiety, stress, and overwhelm. 84A number of studies have confirmed the impact of teachers' social support on university students' motivation and burnout.A study of 1048 Spanish university students shows that a teacher-supported teaching style is beneficial for motivating students to learn, helping students acquire knowledge in a reflexive way, and improving their self-esteem, confidence, and enthusiasm for learning.These beneficial results reflect a state of meaningful learning that helps to improve students' academic performance and reduces their probability of maladjustment, which in turn reduces learning burnout. 54Teacher social support is positively related to student academic motivation and negatively related to learning burnout.When students do not receive the desired teacher support, they are afraid to express themselves because of their \"immature\" ideas, which reduces their desire to share in the classroom.When students' academic motivation is undermined, they are unable to adopt effective and flexible learning coping strategies and are more prone to learning burnout such as mumbo-jumbo, timidity and avoidance. 48When teachers provide students with ample learning opportunities and timely feedback on learning in the classroom, the positive benefits students receive further stimulate their academic motivation.Learning behaviors motivated by this intrinsic motivation can lead to a stable approach to learning, which promotes deeper learning and a more positive view of students' future professional competence, thus acting as a better moderator of burnout. 85In addition to teachers, positive experiences from parental and peer support play an important role in students' cognitive, behavioral, and affective adjustment skills.The higher the level of social support, the more likely students are to develop their adjustment over time.These supportive relationships instill positive beliefs and competencies that motivate students to learn and help them apply viable learning strategies to cope with uncertain environmental conditions.These students are less likely to run away or give up even when they encounter difficulties in learning. 86 great deal of studies have focused on the important impact of university students' social support on their learning, and there have been a number of studies on the relationship between social support and learning burnout.However, there are few studies that explore the relationship between life satisfaction and learning burnout by using social support as a moderating variable along with academic motivation as a mediating variable.This study expands and enriches related research.\n\n\nConclusions\n\nThis study examines the mechanisms by which university students' life satisfaction influences learning burnout, and the mediating effect of academic motivation and the moderating effect of social support in this process.This study finds that (1) life satisfaction significantly and positively predicts academic motivation; (2) academic motivation significantly and negatively predicts learning burnout; (3) life satisfaction significantly and negatively predicts learning burnout; (4) academic motivation partially mediates the effect of life satisfaction on learning burnout; and (5) social support plays a moderating role in the effect of academic motivation on learning burnout.Learning burnout is one of the common challenges that affect student academic motivation and academic aspirations. 43These findings help stakeholders understand the mechanisms underlying the relationship between life satisfaction and learning burnout.At the same time, the results of the study can provide practical and effective operational suggestions for university workers to prevent and intervene in university students' learning burnout and improve their academic motivation.First of all, improve the life satisfaction of university students.Universities should, as far as possible, create a comfortable accommodation environment, a safe eating environment and a free and harmonious interpersonal atmosphere for students, and offer colorful extracurricular activities.Teachers and parents should be more proactive in approaching students to provide a comfortable physical and psychological environment for their learning and living.As individual university students, they should also learn to be proactive in building good relationships with others and integrating into the surrounding groups.These initiatives can help them to increase life satisfaction and thus reduce the occurrence of learning burnout.Students with higher life satisfaction tend to view success in college as socializing, rewarding, and aspirational, and they are more able to feel intrinsically motivated to attend college. 87Secondly, improve academic motivation of university students.Universities can help students understand the significance of learning and clarify their career development plans by offering relevant courses and organizing lectures to stimulate their academic motivation, reduce learning burnout, and enable them to be more actively engaged in learning.Teachers and parents should also try to identify students' potential developmental qualities as much as possible and give them more guidance, encouragement and praise.For example, teachers can help students make meaningful connections between students' personal strengths and academic work based on their life goals and interests, supporting the positive development of adolescents' academic motivation. 66Finally, provide more social support for university students.Universities should provide more platforms and more help for university students to study.Such as providing technical and methodological guidance for students' studies, internships and social practices.Students receive more appreciation, praise and support from teachers and peers, which enhances their sense of self-efficacy, stimulates their interest in learning, and makes them better able to accomplish their learning tasks.Parents should also give more positive guidance to university students, provide them with appropriate financial and spiritual support for their studies, and help them overcome the various obstacles encountered in the learning process.Through the concerted efforts of all parties, improve the life satisfaction and academic motivation of university students, and reduce their learning burnout as much as possible, so as to improve the learning quality of university students.\n\n\nContributions, Limitations and Prospects Contribution\n\nUniversity students' learning burnout can affect their healthy development of body and mind.9][90] Relatively few focus on the relationship between life satisfaction and learning burnout among university students, even fewer explore the mechanisms by which both academic motivation and social support play a role in it.However, both are important for university students' learning and deserve further investigation.The study confirms that university students' life satisfaction significantly and negatively predicts learning burnout and motivation plays a partial mediating role in this process.In addition, social support plays a moderating role in the influence pathway of academic motivation and learning burnout.The study further enriches the theoretical research on university students' learning burnout, and can help people more comprehensively understand the mechanism of life satisfaction's influence on university students' learning burnout.At the same time, the study also has realistic revelation significance.At a time when learning burnout is common among university students, universities and related departments should take relevant measures as much as possible to improve university students' life satisfaction, academic motivation and social support.To help university students overcome learning difficulties, buffer negative emotions and adopt effective learning strategies to better adapt to university students' learning, thus reducing university students' learning burnout phenomenon.\n\n\nLimitations and Prospects\n\nIn this study, 1917 students from different universities across the country were selected to conduct the research.There are some limitations of the study.On the one hand, the selection of the sample mainly follow the principle of convenience sampling, which lead to an uneven distribution of the sample across different grades, genders and schools.Meanwhile, different universities are located in different geographical areas.It is difficult to compare and analyze the situation of students in different universities.Subsequent related studies could use a variety of data collection methods to try to balance the sample across grades, genders, and schools, etc., to increase the likelihood of comparative analyses of the samples.On the other hand, the sample of this study was collected at one time, thus obtaining cross-sectional data, which leads to remain insufficient in the confirmation of inferences about the relationship of causal variables.Follow-up studies should collect data at different time periods whenever possible (eg, once every three months, three or four times in a row).Multiple data collections and analyses will be used to track the development of the mechanism of life satisfaction's influence on university students' learning burnout over time.\n\nFigure 1\n1\nFigure1The theoretical model.\n\n\nFigure 2\n2\nFigure 2 Intermediary model: effect size.Note: ***p<0.001.\n\n\nFigure 3\n3\nFigure 3 Moderating effects of high and low levels of social support.\n\n\nTable 1\n1\nCorrelation Analysis Between Variables\nItemsMeanStandardLearningAcademicLifeSocialvalueDeviationBurnoutMotivationSatisfactionSupportLearning burnout2.8950.6071Academic motivation3.6200.660\u22120.336**1Life satisfaction3.5410.575\u22120.447**0.812**1Social support3.6590.693\u22120.363**0.694**0.771**1Note: **Indicated p<0.01.\n\nTable 2\n2\nMediation Model Test for Academic Motivation\nPredictors (N=1061)Learning BurnoutAcademic BurnoutLearning Burnout\u03b2t\u03b2t\u03b2tConstant4.566***59.9661.010***13.1344.797***60.906Life satisfaction\u22120.472***\u221221.8690.733***34.206\u22120.304***\u221211.406Academic burnout\u22120.229***\u221210.195R 20.2000.3790.241Adjustment R 20.1990.3790.240F value478.241***1170.029***303.944***\nNote: ***Indicated p<0.001.\n\n\nTable 3\n3\nTotal Effect, Intermediary and Direct Effects\nEffect Value LL 95% CI UL 95% CI Efficacy PercentageTotal effect\u22120.472\u22120.51\u22120.430Intermediary effect\u22120.168\u22120.198\u22120.11835.5%Direct effect\u22120.304\u22120.357\u22120.25264.5%\nAbbreviations: LL 95% CI, 95% BootCI lower bound; UL 95% CI, 95% BootCI upper bound.\n\n\nTable 4\n4\nModerating Effect Test\nRegression Equation (N=1061)Fit IndicatorCoefficient of SignificanceOutcomePredictor VariableR 2Adjustment R 2F value\u03b2tVariableLearning burnoutConstant0.1890.189447.558***2.895***231.938Academic motivation\u22120.386***\u221221.156Learning burnoutConstant0.2060.206247.732***2.895***234.229Academic motivation\u22120.301***\u221213.332Social support\u22120.138***\u22126.247Learning burnoutConstant0.2090.208168.836***2.882***220.494Academic motivation\u22120.287***\u221212.482Social support\u22120.128***\u22125.636Academic motivation*\u22120.046**\u22122.996Social support\nNotes: **Indicated p<0.01, ***Indicated p<0.001.\n\n\nTable 5\n5\nModerating Role of Social Support\nLevels of Moderator Variables Regression Coefficients Standard Errorstp95% CIMean\u22120.2870.023\u221212.482 0.000 \u22120.332 \u22120.242Low level (\u22121SD)\u22120.2560.027\u22129.4120.000 \u22120.309 \u22120.203High level (+1SD)\u22120.3190.023\u221213.683 0.000 \u22120.365 \u22120.273\nhttps:\/\/doi.org\/10.2147\/PRBM.S438396 DovePress Psychology Research and Behavior Management 2023:16\n\nhttps:\/\/doi.org\/10.2147\/PRBM.S438396 DovePress Psychology Research and Behavior Management 2023:16\nPowered by TCPDF (www.tcpdf.org)\nPsychology Research and Behavior Management 2023:16 https:\/\/doi.org\/10.2147\/PRBM.S438396 DovePress\nDovepressChen et alPowered by TCPDF (www.tcpdf.org)\nhttps:\/\/doi.org\/10.2147\/PRBM.S438396 DovePress\nAcknowledgmentsThe authors would like to thank the participants for their involvement in this study.Data Sharing StatementThe raw data supporting the conclusions of this article will be available from the authors on reasonable requests.FundingResearch Program for College Counselors in Fujian Province (JSZF2020070).Ethics Approval and Consent to ParticipateThis study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of Jimei University.Informed consent was obtained from all participants involved in this study.DisclosureThe authors declare that the research was conducted in 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"openaccessurl": null, + "status": null + } + }, + "text": "\nLow interspecific variation and no phylogenetic signal in additive genetic variance in wild bird and mammal populations\n\n\nEuan A Young 0000-0001-9370-9681\nGroningen Institute for Evolutionary Life Sciences\nUniversity of Groningen\nGroningenThe Netherlands\n\nCentre for Ecology and Conservation\nUniversity of Exeter\nPenrynUK\n\nErik Postma e.postma@exeter.ac.uk 0000-0003-0856-1294\nCentre for Ecology and Conservation\nUniversity of Exeter\nPenrynUK\n\n\nCentre for Ecology and Conservation\nUniversity of Exeter\nPenrynUK\n\nLow interspecific variation and no phylogenetic signal in additive genetic variance in wild bird and mammal populations\n82BB8592C3CD91F6378FCB542B4102AA10.1002\/ece3.10693Received: 11 September 2023 | Revised: 18 October 2023 | Accepted: 19 October 2023evolvabilitygenetic varianceheritabilitymeta-analysisphylogenetic signalquantitative genetics\nEvolutionary adaptation through genetic change requires genetic variation and is a key mechanism enabling species to persist in changing environments.Although a substantial body of work has focused on understanding how and why additive genetic variance (V A ) differs among traits within species, we still know little about how they vary among species.Here we make a first attempt at testing for interspecific variation in two complementary measures of V A and the role of phylogeny in shaping this variation.To this end, we performed a phylogenetic comparative analysis using 1822 narrow-sense heritability (h 2 ) for 68 species of birds and mammals and 378 coefficients of additive genetic variance (CV A ) estimates for 23 species.Controlling for within-species variation attributable to estimation method and trait type, we found some interspecific variation in h 2 (~15%) but not CV A .Although suggestive of interspecific variation in the importance of non-(additive) genetic sources of variance, sample sizes were insufficient to test this hypothesis directly.Additionally, although power was low, no phylogenetic signal was detected for either measure.Hence, while this suggests interspecific variation in V A is probably small, our understanding of interspecific variation in the adaptive potential of wild vertebrate populations is currently hampered by data limitations, a scarcity of CV A estimates and a measure of their uncertainty in particular.\n\nin fitness exists across 19 wild bird and mammal populations (Bonnet et al., 2022).If representative, this suggests that birds and mammals are adapting to environmental change through natural selection.\n\nWhile it is also well established that variation in nearly all traits is underpinned to some degree by V A (Hill et al., 2008), it is less clear whether V A varies systematically across species.In a time of unprecedented levels of environmental change (Butchart et al., 2010;IPCC, 2013;Palumbi, 2001), understanding how species and populations vary in their ability to adapt to these changes is a key aim in evolutionary biology (Gienapp et al., 2008;Teplitsky et al., 2014;Visser, 2008).\n\nA meaningful comparison of V A across a variety of traits and species requires standardisation.One common standardisation is to divide the additive genetic variance by the total phenotypic variance to obtain the proportion of the phenotypic variance attributable to additive genetic effects, that is the narrow-sense heritability (h 2 ) (Falconer & Mackay, 1996).In the absence of information on the selection differential, however, h 2 is a poor measure of a trait's evolvability, that is its expected response to selection (Houle, 1992).Furthermore, as h 2 is a measure of the amount of additive genetic variance relative to the total amount of phenotypic variance, a low h 2 is indicative of either low levels of additive genetic variance or high levels of other sources of variation.Instead, it has been suggested that the coefficient of additive genetic variance (CV A ), a mean-standardised measure of additive genetic variation, does not suffer from these limitations and hence provides a superior measure of a trait's evolvability (Garcia-Gonzalez et al., 2012;Houle, 1992).However, CV A remains far less commonly reported than h 2 , among others, because it is only appropriate for specific traits (e.g.traits measured on a true ratio scale) (P\u00e9labon et al., 2020).\n\nAfter standardising V A as h 2 or CV A , substantial intraspecific variation between estimates within species exists owing to systematic differences among trait types, which has previously been described and discussed in detail Mousseau and Roff (1987), Postma (2014), Stirling et al. (2002).Broadly speaking, traits that are more closely related to an individual's fitness (e.g.lifetime reproductive success, a commonly used proxy of an individual's fitness) have been shown\n\nto have lower estimates of h 2 , but the fact that they do not have lower estimates of CV A tells us that this is because of a larger role for non-additive and\/or environmental effects in shaping variation in fitness traits (McCleery et al., 2004;Teplitsky et al., 2009).In fact, morphological traits are generally found to have a lower CV A than life-history traits (Mittell et al., 2015;Mousseau & Roff, 1987;Postma, 2014;Price & Schluter, 1991).This is interpreted as evidence in support of the target size hypothesis, which predicts higher levels of V A in traits more closely associated with fitness because they are assumed to be influenced by more genetic loci (Houle, 1991(Houle, , 1992(Houle, , 1998)).Having said this, it remains less well understood how behavioural and physiological traits fit into this framework (Stirling et al., 2002).This is unfortunate, as behavioural changes are often the first line of defence against environmental change (Charmantier et al., 2008), and physiology is thought to be an important driver of life-history evolution (Crespi et al., 2013).\n\nFar fewer studies have examined differences in V A among species (but see Dochtermann et al., 2019;Martinossi-Allibert et al., 2017;Mittell et al., 2015;Wood et al., 2016).Aside from applications to conservation, studying interspecific variation in V A can provide insight into the mechanisms shaping V A and contribute to our understanding of its stability (Arnold et al., 2008).This, however, is not a trivial task, owing to the variety of mechanisms by which interspecific variation may be generated (recently reviewed in P\u00e9labon et al., 2023).While species with larger populations are expected to have higher levels of V A (Hill, 1982), ultimately this will depend upon both the historical population sizes (e.g.genetic bottlenecks would reduce V A ) and the degree of gene flow both within and from outside the population (Lande, 1988).\n\nSelection also shapes V A in complex ways: While stabilising selection is expected to erode V A (Fisher, 1930), if this is heterogeneous across small (spatial or temporal) scales, it can increase V A through promoting different polymorphisms (Hedrick et al., 1976).Furthermore, single-species studies have generated a wide variety of hypotheses relating to how environmental conditions may affect both h 2 and V A (reviewed in Hoffman & Meril\u00e4, 1999).In studies of wild populations, for example, unfavourable conditions lead to lower h 2 owing to a relatively lower importance of genes compared with factors such as parental care or habitat quality (Charmantier & Garant, 2005;Gebhardt-Henrich & Van Noordwijk, 1991).\n\nHowever, examining any one of these hypotheses across species opens an array of methodological issues (e.g.how would one standardise a measure of environmental harshness across species?).Indeed, these complexities are one explanation for why Wood et al. (2016) found no evidence for h 2 to covary with adult census population size or the strength, direction or form of selection across 83 species.\n\nInstead, here we take a step back: Rather than searching for population-or species-specific correlates of h 2 or CV A , we quantify interspecific variation in V A and how much of this variation correlates with phylogeny.This provides an estimate of the opportunity for species-specific properties to shape absolute and relative levels of V A , some of which may be more similar among closely related species.For example, if environmental conditions shape h 2 and\/or CV A (Charmantier & Garant, 2005;Gebhardt-Henrich & Van Noordwijk, 1991) and more closely related species occupy more similar environments (Blomberg & Garland, 2002;Losos, 2008), this would give rise to a phylogenetic signal in measures of V A .\n\nTo our knowledge, only two studies have explicitly tested for a phylogenetic signal in V A across traits (Dochtermann et al., 2019;Martinossi-Allibert et al., 2017).Dochtermann et al. (2019) found no phylogenetic signal, but it was limited to behavioural traits, and Martinossi-Allibert et al. (2017) showed a small phylogenetic signal in V A but did not allow for other sources of interspecific variation in their model, which may have upwardly biased their estimate of the importance of phylogeny.Additionally, both studies primarily used the phylogeny to control for potential non-independence of species estimates while estimating other fixed and random effects of interest (Felsenstein, 1985;Nakagawa & Santos, 2012) and do not discuss interspecific variation (or its absence) in any detail.\n\nHere, we present a phylogenetic comparative analysis of univariate estimates of h 2 and CV A to quantify interspecific variation in V A in the wild.To do so, we used 1822 h 2 and 378 CV A estimates for, respectively, 68 and 23 species of birds and mammals obtained in the wild, while controlling for methodological and trait differences.For the latter, we classify traits as either behaviour, life history, morphology, physiology or fitness traits (measures of lifetime reproductive success).After controlling for these differences, we then present the level of interspecific V A according to each metric and to what extent it can be explained by phylogenetic relatedness.\n\n\n| ME THODS\n\n\n| Literature search and criteria\n\nWe used a collection of published estimates of variance-and meanstandardised additive genetic variance (narrow-sense heritability (h 2 ) and coefficients of additive genetic variance (CV A ), respectively) for wild populations of birds and mammals previously used in Postma (2014), covering studies published until 2012, updated to include studies published up to April 2020.Studies included in the original dataset were identified using the Web of Science database (https:\/\/ apps.webof knowl edge.com) with the following search terms in the 'Topic' field: ('wild population*' OR 'natural population*') AND ('heritabil*' OR 'genetic* estimate*').Additionally, estimates from all studies cited in Meril\u00e4 et al. (2001) and all publications that cited Kruuk (2004), Wilson et al. (2010) and Hadfield (2010) were included.These were complemented with estimates from studies published after 2012 that cited Wilson et al. (2010).As our primary goal was to gain insight into genetic variance in the wild, we included only studies carried out in a relevant ecological setting involving no breeding manipulations.We limited ourselves to birds and mammals, as they are the most widely studied taxa in the wild and the only two taxa for which the number of species was sufficient for the analyses outlined below.\n\n\n| Quantifying additive genetic variance\n\nWhenever possible, we recorded all reported estimates of h 2 and CV A .This includes estimates for the same trait but from different models (e.g. with different combinations of fixed effects, different covariates or single vs. multivariate models), unless the authors explicitly stated that they considered one estimate superior.\n\nIf h 2 was not provided, it was calculated from the additive genetic variance (V A ) and phenotypic variance (V P ) reported by the authors as:\n\nNote that whether this estimate of V P is conditioned on variation attributable to one or more fixed effects will vary among studies (Wilson, 2008).\n\nIf not provided by the authors, CV A was calculated as a percentage from V A and the trait mean (x) following Garcia-Gonzalez et al. (2012):\n\nEstimates of CV A were only included for traits on a true ratio scale (P\u00e9labon et al., 2020), and no log-transformed trait estimates were included.Estimates of I A (defined as V A \u2215 x 2 ; Houle, 1992) were converted to CV A by taking the square root of I A multiplied by 100.Standard errors (SEs) were recorded as a measure of the precision of estimates used for weighting estimates in the meta-analytic mixed models outlined below.If only 95% confidence or credible intervals were available, approximate SEs were calculated from these by dividing half the difference between the upper and lower 95% intervals by 1.96.As CV A estimates are rarely accompanied by an estimate of their precision (Garcia-Gonzalez et al., 2012), we approximated their SE from the SE accompanying the estimate of h 2 for that trait by assuming the z-value (estimate divided by SE) is the same for both.\n\n\n| Predictor variables\n\nPrevious analyses have suggested that larger sample sizes result in lower (less inflated) h 2 estimates (Postma, 2014).To control for this, sample sizes were recorded as the number of individual phenotypes used in the analysis, discounting multiple measurements of the same individual.\n\nWe classified traits as morphology, life history, fitness, physiology or behaviour traits.We acknowledge that these classifications are, to some degree, arbitrary and that some traits could be classified in multiple ways.In these instances, we followed the classification used in the original publication.In particular, we treat fitness traits as separate from other life histories, as traits more closely related to fitness are predicted to have different levels of genetic variance (Houle et al., 1996).Fitness traits included any measure of an individual's lifetime reproductive success (Brommer et al., 2004(Brommer et al., , 2007;;Hayward et al., 2018).\n\nThe publication year was recorded to account for temporal changes in the size of estimates, thought to be largely due to methodological differences (Postma, 2014).Consequently, the specific method used to estimate V A and the derived parameters h 2 and CV A were also recorded.The classes of methods used for this were: parent-offspring regression, animal model (MCMC) (i.e.Bayesian), animal model (REML), grandparent-offspring regression, half-sib and full-sib analyses.\n\nEach study was given its own unique ID to allow us to model any non-independence among estimates for different traits from the same study.\nh 2 = V A V P CV A = 100 \u00d7 \u221a V A x\n\n| Statistical analyses\n\nWe examined how h 2 and CV A vary among both traits and species using Bayesian generalised linear mixed effects models implemented in the R package MCMCglmm version 2.3.4 (Hadfield, 2010).This package has the option of implementing a random phylogenetic effect (Hadfield & Nakagawa, 2010) and allows us to obtain posterior distributions of all fixed and random effects rather than point estimates and SEs as provided by restricted maximum likelihood (REML) approaches (Morrissey et al., 2014).Although the distributions of h 2 and CV A are poorly understood (Wood et al., 2016), all models assumed Gaussian error distributions.\n\nTo test for a phylogenetic signal in h 2 and CV A , we built robust maximum clade credibility bird and mammal supertrees using\n\nTreeAnnotator, part of the BEAST version 1.10.4package (Rambaut et al., 2014).These were based on 1000 randomly sampled trees from a pseudo-posterior distribution of species-level phylogenies (available at: https:\/\/ vertl ife.org\/ phylo subse ts\/ ) and based on Jetz The tree was rooted at 315 million years based on the dating of Archerpeton anthracos and the origin of all amniotes (as recommended by Healy et al., 2014).\n\nWe fitted phylogenetic mixed models to both h 2 and CV A .Fixed effects included the study method (e.g.parent-offspring regression), trait category (behaviour, morphology, physiology, LRS, survival (i.e.\n\nfitness) and other life-history traits) and the sample size and study year as covariates.Both studies and species were included as random effects.The species effect controlled for repeated species measures and estimated the level of interspecific variation.Phylogenetic relatedness was accounted for following Hadfield and Nakagawa (2010) and modelled assuming Brownian motion.Measurement error was incorporated by inserting the squared SE of estimates into the mev argument in MCMCglmm().Thereby, this model provides both method-and trait-standardised estimates of interspecific variation in h 2 and CV A and method-and species-standardised estimates of h 2 and CV A for each trait category, while controlling for potential non-species independence.\n\nWe used the default weakly informative parameter expanded priors set to F 1,1 distributions (scale = 1000) for random effects, the Inverse-Wishart distribution for the residual variance, and non-informative priors for the fixed effects.We ran each model for 1,505,000 iterations, with a thinning interval of 1500 and a burn-in of 5000.This ensured effective sample sizes greater than 1000.We evaluated model convergence and mixing based on the visual examination of trace plots (Hadfield, 2010).This inclusion of a measure of sampling error for CV A in particular should help combat any scaling effects in the precision of the parameters (Garcia-Gonzalez et al., 2012).Fixed effects with MCMC p-values of less than .05were judged to be statistically significant.Estimates for fixed and random effects are obtained from the mode of the posterior distribution, and all estimates are accompanied by their 95% credible intervals.To aid interpretation, variance components were also expressed as percentages of the total amount of variance (with 95% credible intervals) excluding measurement error.Figures were created using the packages ggplot2 version 3.4.0(Wickham, 2016) and ggpubr version 0.4.0 (Kassambara, 2020).\n\n\n| RE SULTS\n\n\n| Heritability (h 2 )\n\nWe analysed 1822 h 2 estimates from 214 studies and 68 different species of birds and mammals (49 and 19, respectively).Estimates were based on 5 (M\u00f8ller, 1991) to 38,024 individual phenotypes (Garant et al., 2004) 1 and Figure 1a).The number of h 2 estimates per species ranged from 1 to 229 (Figure 2).There was also a clear bias in the number of estimates towards some species: The top five species with the most numerous estimates accounted for 42% of all estimates, and four out of these Estimates are lower in more recent studies and for larger sample sizes (Table 1).Furthermore, they are dependent on the estimation method used: full-sib analyses and grandparent-offspring regressions provide larger estimates than the animal model (Bayesian or REML) and parent-offspring regressions (Table 1).We also found 1a).Fitness traits had slightly lower h 2 than life-history traits (0.12 [95% CI 0.00, 0.24] and 0.21 [95% CI 0.07, 0.30], respectively; Figure 1a).\n\nThe variance in h 2 left unexplained by the fixed effects was 0.0348, of which 0.005 was explained by species, 0.015 by study and 0.014 remained unexplained (Table 1, Figure 3a).Although estimated with substantial uncertainty, phylogeny most likely explained little to no variation (0.001, Table 1).These estimates remained similar when the phylogenetic effect was removed from the model (Figure A1a and Table A1 in Appendix 1).However, when repeating the h 2 analysis using only estimates available for the CV A analysis, we found that the variance explained by species was similar when the phylogenetic was excluded from the model (0.006, Table A2 and Figure A2b in Appendix 1) but decreased when the phylogenetic was included (0.0005, Table A3 and Figure A2a in Appendix 1), even though the variance explained by phylogeny was very small too (0.0001), suggesting there was a lack of power to disentangle these two effects.Other estimates remained consistent across all models.\n\nPredicted method-and trait-standardised estimates for each species are presented in Figure 2. Of all species, the Song sparrow Note: Method and trait category were included as fixed categorical variables, and sample size and year of publication as fixed covariates.Sample size and study year were mean centred so that the intercept shows predicted values for estimates based on a parent-offspring regression for a morphological trait, a study published in 2002 and a sample size of 484.Species, phylogeny and study were included as random effects.For fixed effects, the posterior modes with 95% credible intervals and MCMC p-values (p MCMC ) are shown, with bold highlighting p MCMC < .05.For random effects, the posterior modes and the variation explained (as a percentage) are shown with 95% credible intervals.The variation explained was calculated as the posterior estimates of the variance divided by the total of all the posterior estimates (excluding measurement error).The posterior mode and credible intervals were then extracted from this.\n\nF I G U R E 1 Variation in h 2 and CV A among trait categories.Method-standardised and species-and study-independent estimates for (a) h 2 and (b) CV A for each trait category, assuming they are predicted using a parent-offspring regression, using mean values for sample size and study year and controlling for species, study and phylogenetic non-independence.Dots show the posterior mode of the predicted trait category estimate, with bars showing 95% credible intervals.The numbers show the number of estimates in the dataset for each trait category.No estimates of CV A were available for behavioural traits.\n\n\n| Coefficient of additive genetic variance (CV A )\n\nWith 378 estimates from 52 studies of 23 different species of birds and mammals, the dataset for CV A was substantially smaller than for h 2 .Estimates covered the years from 1989 to 2019, with the number of individual phenotypes on which estimates were based ranging from 19 (Larsson et al., 1997) to 38,024 (Garant et al., 2004).Morphological traits were most numerous (n = 286), with fewer life histories (n = 56), fitness (n = 22) and physiological traits (n = 14) (Figure 1b).As they are generally measured on a scale that lacks a natural zero, no estimates of CV A were available for behavioural traits.The weighted mean and variance for CV A were 7.33 [95% CI 6.19-8.49]and 145.62 [95% CI 123.83-165.69],\n\nrespectively.The dataset contained CV A estimates for 13 species of birds and 10 mammals.These ranged from 1 to 59 estimates per species.\n\nCV A estimates were not affected by study method, year or sample size (p MCMC -values > .05,Table 2).Morphological traits had the lowest predicted CV A , and fitness traits had the highest (posterior mode = 6.30[95% CI -2.30, 14.21] and posterior mode = 29.26[95% CI 18.94, 37.59], respectively; Figure 1b).Life-history traits had a lower CV A than fitness traits (posterior mode = 18.42 [95% CI 8.35, 26.08]), with physiology traits having moderate CV A estimates (posterior mode = 13.33 [95% CI 4.35, 24.00]; Figure 1b).Methodstandardised and species-independent CV A estimates for each trait category were predicted using a GLMM (Table 2) and are presented in Figure 1b.\n\nThe variance left unexplained by the fixed effects was 97.60, of which 0.62 was explained by species, 23.92 by study and 73.00 remained unexplained by the random effects (Table 2, Figure 3b).In line with the small amount of interspecific variance, there was little evidence for a phylogenetic signal of 0.06 (Table 2, Figure 2b).These effects remained unchanged when the phylogenetic effect was removed from the models (Table A4 and Figure A1b in Appendix 1).\n\n\n| DISCUSS ION\n\nTo better understand the mechanisms shaping additive genetic variances, we performed phylogenetic comparative analyses of h 2 and CV A estimates to quantify interspecific variation in additive genetic variances in the wild.After accounting for within-species differences (attributable to methodology and trait type), we found no interspecific variation in CV A (<1%; Figure 2), but we did in h 2 estimates (of around 15%).However, the latter variation was not associated with the phylogeny.Based on these findings, we cautiously present the following conclusions, acknowledging the low power of some of these analyses.\n\nThe low but non-zero level of interspecific variation in h 2 and the absence of interspecific variation in CV A agree with previous analyses (Mittell et al., 2015;Postma, 2014).Interspecific variation in h 2 but not CV A was unlikely to be due to a difference in power between the two analyses, as the difference persisted when the h 2 analysis was performed using only the estimates for which estimates for CV A were available too and when we did not fit a phylogenetic effect (Figure A2 in Appendix 1).However, when the phylogenetic effect was included in this model, the variance F I G U R E 2 Standardised h 2 estimates for each species across phylogeny.Phylogenetic tree (left) of birds and mammals with species names and predicted h 2 estimates (right) from a MCMC linear mixed model for each of the 68 species.Estimates for each species were predicted for a parent-offspring regression, a morphological trait and using mean values for sample size and study year.Dots show the posterior modes, with bars showing 95% credible intervals.The numbers show the number of estimates for each species included in the analysis.\n\nF I G U R E 3 Variance explained by random effects.Posterior distributions of the variance in (a) h 2 and (b) CV A explained by random effects from the MCMC generalised linear mixed models, conditioned on fixed effects.For posterior modes and 95% credible intervals, see Tables 1 and 2. explained by species was reduced, despite the phylogeny explaining no variation.This is suggestive of insufficient phylogenetic power in these models due to the smaller CV A dataset (Tables A2 and A3, and Figure A2 in Appendix 1).In fact, the credible intervals for both h 2 and CV A for the phylogenetic effect are large.\n\nNevertheless, these estimates do broadly agree with previous findings (Dochtermann et al., 2019;Martinossi-Allibert et al., 2017), although these analyses are likely to have been even more limited in terms of power.Clearly, future studies examining a phylogenetic effect on additive genetic variance should either wait for a considerable increase in the number of available estimates or broaden the scope of their study to include more taxa.None of the models (with or without a phylogenetic effect) revealed interspecific variation in CV A (Table A4 and Figure A1b in Appendix 1).While together this suggests that interspecific variation in h 2 may be larger than it is in CV A , the 95% credible intervals for both variances overlap, and thus no statistically significant difference can be inferred (Gelman & Stern, 2006).\n\nBearing in mind this caveat, the finding of a larger interspecific variation in h 2 than CV A would be in line with the level of environmental variance in traits being the driving factor in the interspecific variation in h 2 .Following this logic, h 2 estimates for Soay sheep (Ovis aries) would be among the lowest of all species included in this analysis, not due to particularly low levels of additive genetic variance, even if this could be expected based on their unique demographic characterised by bottlenecks and isolation (Clutton-Brock & Pemberton, 2004).Instead, they would be low because they inhabit a particularly variable environment and\/or they respond particularly strongly to any environmental variation (Robinson et al., 2009;Wilson et al., 2006).\n\nAside from the possibility of non-additive genetic effects (Mackay, 2014), which are likely to be small in wild populations regardless (Class & Brommer, 2020), h 2 between species may vary among species in a way that is similar to how environmentally induced variation (i.e.plasticity) drives variation in h 2 between trait types (Kruuk et al., 2000).For example, h 2 estimates may be lower in species exposed to unfavourable conditions, which could decrease h 2 through an increase in the importance of environmental factors (Charmantier & Garant, 2005;Gebhardt-Henrich & Van Noordwijk, 1991).Future studies could test this hypothesis, provided they are able to measure the quality of the environment in a manner that allows for across species comparisons.\n\nAlthough we currently do not have a good understanding of the role of (genetic or environmentally induced) variation in trait means in shaping variation in CV A among species, a more definitive test would be to compare the interspecific variation in CV A and CV E (the mean-scale non-additive genetic and\/or environmental variance).\n\nHowever, estimates of CV E are currently rarely published, and often without the estimates of precision that enable a formal meta-analysis.\n\nOverall, it is likely that further comparative studies of these trends in wild populations are limited by the availability of standardised estimates of V A .Not only are estimates extremely biased towards specific species with long-running field studies, but CV A still remains far less widely reported than h 2 , despite little difference in the difficulty of reporting the latter.Given the well-known difficulties of interpreting estimates of h 2 without knowledge of the absolute levels of Note: Estimation method and trait category were included as fixed categorical variables, and sample size and year of publication as fixed covariates.\n\nSample size and study year were mean centred so that the intercept shows predicted value estimates based on a parent-offspring regression for a morphological trait, a study published in 2006 and a sample size of 768.Species and studies were included as random effects.For fixed effects, the posterior modes with 95% credible intervals and MCMC p-values (p MCMC ) are shown, with bold highlighting p MCMC < .05.For random effects, the posterior modes and the variation explained (as a percentage) are shown with 95% credible intervals.The variation explained was calculated as the posterior estimates of the particular effect divided by the total of all the posterior estimates.The posterior mode and credible intervals were then extracted from this.\n\nadditive genetic and environmental variance, reporting both metrics together, whenever possible, would be an improvement to the literature.Nevertheless, because coefficients of variation are only meaningful for a subset of traits (as discussed below), sample sizes will always be smaller than for estimates of heritability.\n\nThe range of sample sizes across both trait types and species highlights the substantial biases in the literature.First, the vast majority of traits were morphology traits, and while the sample sizes for fitness and life-history traits were enough to confirm previous findings of the variation in V A among trait types (e.g.Mittell et al., 2015;Mousseau & Roff, 1987;Postma, 2014), for physiological and behavioural traits, the patterns are less clear due to data limitations.While technological advancements mean that physiological traits are now more often measured in wild populations (e.g.(B\u00e9ziers et al., 2019)), the CV A of behavioural traits is generally non-calculable as commonly reported behaviours, like boldness, do not fall on a true ratio scale (Hansen et al., 2011), limiting our understanding of the evolvability of these important traits.Despite its obvious advantages, the fact that CV A can only be calculated for non-random subsets of traits is an important limitation when it comes to comparative analyses of additive genetic variance.Second, the vast majority of estimates were restricted to a limited number of species and populations.While this highlights the importance of long-term, individual-based datasets (Clutton-Brock & Sheldon, 2010), the bias in the literature towards some species (birds in particular) is a concern.\n\nThe bias in methods is less problematic, with the literature moving towards the use of animal models, and our results confirm that they give smaller estimates of h 2 , presumably because they are less likely to be biased by confounding non-genetic effects (Mittell et al., 2015;Postma, 2014;Postma & Charmantier, 2007;Wood et al., 2016).\n\nHow h 2 and CV A vary across different methods and traits is relatively clear, with consistent findings between studies, but we still lack an understanding of what shapes estimates of coefficients of additive genetic variance between species.We show here that h 2 varies between species but that this appears not to relate to phylogeny.However, interpretation of our results is hampered by limited statistical power, in particular with respect to our analysis of CV A .\n\nThis also meant that although our interspecific variation in h 2 was higher than that of CV A (which was approximately zero), we were not able to demonstrate a statistically significant difference in the amount of interspecific variation in both measures of V A .There is therefore a need to move beyond this approach if we are to better Related to this, it should be noted that in this study we gathered data measured at the population level and aggregated them by species, meaning interpopulation differences could mask or drive interspecific variation.Second, there may be a need to move beyond univariate measures of additive genetic variance, as traits do not exist in isolation (Nicolaus et al., 2016;Poissant et al., 2016) and selection on one trait often affects the evolution of others (Gould & Lewontin, 1979;Lande, 1979;Phillips & Arnold, 1989).Although broad trends are yet to be determined, our hope is that in future years, when the number of estimates available is (even) larger andmost importantly-more diverse, we will be able to understand how additive genetic variance varies across species and the mechanisms responsible for this variation.\n\nA PPE N D I X 1 TA B L E A 1 Output from the MCMC generalised linear mixed model (GLMM) with h 2 as the dependent variable for all h 2 estimates, but no phylogenetic effect included.Note: Method and trait category were included as fixed categorical variables, and sample size and year of publication as fixed covariates.Sample size and study year were mean centred so that the intercept shows predicted values for parent-offspring regression method, morphological traits, study year 2002 and a sample size of 484.Species and studies were included as random effects.For fixed effects, the posterior modes with 95% credible intervals and MCMC p-values (p MCMC ) are shown, with bold highlighting p MCMC < .05.For random effects, the posterior modes and the variation explained (as a percentage) are shown with 95% credible intervals.The variation explained was calculated as the posterior estimates of the particular effect divided by the total of all the posterior estimates.The posterior mode and credible intervals were then extracted from this.\n\n\nF I G U R E A 1\n\nVariance explained by random effects in models without phylogenetic effect.Posterior distributions of the variance in (a) h 2 and (b) CV A are explained by random effects from the MCMC generalised linear mixed models containing no phylogenetic effect.For posterior modes and 95% credible intervals, see Tables A1 and A4.For random effects, the posterior modes and the variation explained (as a percentage) are shown with 95% credible intervals.The variation explained was calculated as the posterior estimates of the particular effect divided by the total of all the posterior estimates.The posterior mode and credible intervals were then extracted from this.\n\n\nTA B L E A 3\n\nOutput from the MCMC generalised linear mixed model (GLMM) with h 2 as the dependent variable using only data for which CV A estimates were available, with the phylogenetic effect included.Note: Method and trait category were included as fixed categorical variables, and sample size and year of publication as fixed covariates.Sample size and study year were mean centred so that the intercept shows predicted values for parent-offspring regression method, morphological traits, study year 2006 and a sample size of 768.Species and studies were included as random effects.For fixed effects, the posterior modes with 95% credible intervals and MCMC p-values (p MCMC ) are shown, with bold highlighting p MCMC < .05.For random effects, the posterior modes and the variation explained (as a percentage) are shown with 95% credible intervals.The variation explained was calculated as the posterior estimates of the particular effect divided by the total of all the posterior estimates.The posterior mode and credible intervals were then extracted from this.\n\n\nF I G U R E A 2\n\nVariance in h 2 explained by random effects using data for which only CV A estimates were available.Posterior distributions of the percentage of variance in h 2 explained by random effects from the MCMC generalised linear mixed models using only estimates for which CV A estimates were available and (a) with and (b) without a phylogenetic effect included in the model, conditioned on fixed effects.\n\nFor posterior modes and 95% credible intervals, see the text.Note: Method and trait category were included as fixed categorical variables, and sample size and year of publication as fixed covariates.Sample size and study year were mean centred so that the intercept shows predicted values for parent-offspring regression method, morphological traits, study year 2006 and a sample size of 768.Species and studies were included as random effects.For fixed effects, the posterior modes with 95% credible intervals and MCMC p-values (p MCMC ) are shown, with bold highlighting p MCMC < .05.For random effects, the posterior modes and the variation explained (as a percentage) are shown with 95% credible intervals.The variation explained was calculated as the posterior mode of the effect divided by the total of all the posterior modes.\n\n\n\net al. (2012) using theHackett et al. (2008) backbone for birds andUpham et al. (2019) for mammals.Both trees were then imported and combined in R version 4.2.1 (R Core Team, 2020) using the packages ape version 5.6.2(Paradis & Schliep, 2019), phytools version 1.2.0(Revell, 2012) and geiger version 2.0.10(Pennell et al., 2014).\n\n\n\n\nfive species were birds (Great tit (Parus major): n = 229; Collared flycatcher (Ficedula albicollis): n = 191; Darwin's medium ground finch (Geospiza fortis): n = 156; Bighorn sheep (Ovis canadensis): n = 99; Barnacle goose (Branta leucopsis); n = 98).\n\n\n(\n\nMelospiza melodia) had the lowest h 2 of 0.29 [95% CI 0.16-0.48](for a parent-offspring method, morphological trait and using median values for sample size, study length and study year), whereas the Darwin's medium ground finch (Geospiza fortis) had the highest h 2 of 0.51 [95% CI 0.38-0.65](Figure2).\n\n\n\n\nunderstand the factors shaping the coefficients of additive genetic variance.First, understanding how estimates vary within a species between populations (e.g.Mart\u00ednez-Padilla et al. (2017); Pennington et al. (2021); Volis et al. (2014)) could be a useful avenue and perhaps an important prerequisite for understanding interspecific variation.\n\n\n\n\nOutput from the MCMC generalised linear mixed model (GLMM) with CV A as the dependent variable and with no phylogenetic effect included.\n\n\n\n\nOutput from the MCMC generalised linear mixed model (GLMM) with h 2 as the dependent variable.\nFixed effectsPosterior mode [95% CrIs]p MCMC(Intercept)0.43 [0.31, 0.53]<.001Method, animal model (MCMC)\u22120.01 [\u22120.06, 0.04].859Method, animal model (REML)\u22120.01 [\u22120.06, 0.06].971Method, full-sib0.24 [0.17, 0.31]<.001Method, half-sib0.09 [\u22120.08, 0.32].252Method, grandparent-offspring regression0.26 [0.07, 0.44].009Trait category, fitness\u22120.29 [\u22120.35, \u22120.25]<.001Trait category, life history\u22120.22 [\u22120.26, \u22120.2]<.001Trait category, behaviour\u22120.1 [\u22120.17, \u22120.04]<.001Trait category, physiology\u22120.13 [\u22120.21, \u22120.07]<.001Study year\u22128.42 \u00d7 10 \u22123 [\u22125.63 \u00d7 10 \u22123 , \u22123.07 \u00d7 10 \u22123 ]<.001Sample size\u22127.85 \u00d7 10 \u22126 [\u22121.55 \u00d7 10 5 , \u22127.99 \u00d7 10 \u22127 ].033Random effectsPosterior mode [95% CrIs]Percentage variance explained [95% CrIs]Species0.0054 [0.0012, 0.0134]14.92% [3.48, 30.85]Phylogeny0.0001 [0.0000, 0.0214]0.23% [0.00, 39.00]Study0.0153 [0.0111, 0.0211]40.23% [23.34, 52.71]Residual0.0140 [0.0121, 0.0156]34.27% [22.90, 45.09]\nTA B L E 1\n\n\n\n\nOutput from the MCMC generalised linear mixed model (GLMM) with CV A as the dependent variable.\nFixed effectsPosterior mode [95% CrIs]p MCMC(Intercept)6.30 [\u22122.30, 14.21].148Method, animal model (REML)\u22120.48 [\u22124.45, 4.03].884Method, animal model (MCMC)\u22122.92 [\u221214.02, 5.18].457Method, full-sib\u22120.40 [\u221212.23, 11.03].883Trait category, fitness22.99 [18.61, 27.69]<.001Trait category, life history11.74 [8.55, 14.57]<.001Trait category, physiology7.37 [2.68, 15.04]<.004Year0.16 [\u22120.19, 0.48].453Sample size1.15 \u00d7 10 \u22124 [\u22124.63 \u00d7 10 \u22124 , 5.77 \u00d7 10 \u22124 ].825Random effectsPosterior mode [95% CrIs]Percentage variance explained [95% CrIs]Species0.62 [0.00, 105.4]0.25% [0.00, 49.91]Phylogeny0.06 [0.00, 19.18]0.03% [0.00, 14.31]Study23.92 [4.48, 52.22]13.35% [3.15, 35.24]Residual73.00 [64.99, 88.85]61.56% [31.67, 81.26]\nTA B L E 2\n\n\n\n\nOutput from the MCMC generalised linear mixed model (GLMM) with h 2 as the dependent variable using only data for which CV A estimates were available with no phylogenetic effect included.Method and trait category were included as fixed categorical variables, and sample size and year of publication as fixed covariates.Sample size and study year were mean centred so that the intercept shows predicted values for parent-offspring regression method, morphological traits, study year 2006 and a sample size of 768.Species and studies were included as random effects.For fixed effects, the posterior modes with 95% credible intervals and MCMC p-values (p MCMC ) are shown, with bold highlighting p MCMC < .05.\nTA B L E A 2 Fixed effectsPosterior mode [95% CrIs]p MCMC(Intercept)0.44 [0.33, 0.52].001Method, animal model (REML)\u22120.07 [\u22120.14, 0.04].235Method, animal model (MCMC)\u22120.1 [\u22120.26, 0.15].545Method, full-sib0.36 [0.04, 0.74].031Trait category, fitness\u22120.31 [\u22120.39, \u22120.22].001Trait category, life history\u22120.19 [\u22120.26, \u22120.13].001Trait category, physiology\u22120.11 [\u22120.22, 0].053Year\u22126.83 \u00d7 10 \u22126 [\u22121.14 \u00d7 10 \u22122 , 2.50 \u00d7 10 \u22123 ].201Sample size\u22126.83 \u00d7 10 \u22126 [\u22121.61 \u00d7 10 \u22125 , 1.71 \u00d7 10 \u22126 ].099Percentage varianceRandom effectsPosterior mode [95% CrIs]explained [95% CrIs]Species0.0063 [0, 0.0228]15.77% [0, 50.03]Study0.0098 [0.0033, 0.0223]32.5% [10.29, 59.21]Residual0.0132 [0.0106, 0.0176]37.65% [24.39, 55.95]Note:Probability densityRandom effect species study residualVariance explained\n\n, full-sib 0.45 [0.03, 0.72] .035 Trait category, fitness \u22120.31 [\u22120.36, \u22120.21] .001 Trait category, life history \u22120.2 [\u22120.25, \u22120.13] .001\nFixed effectsPosterior mode [95% CrIs]p MCMC(Intercept)0.42 [0.20, 0.62].005Method, animal model (REML)\u22120.06 [\u22120.15, 0.04].307Method, animal model (MCMC)\u22120.06 [\u22120.25, 0.13].657MethodTrait category, physiology\u22120.1 [\u22120.23, \u22120.01].056Year\u22124.31 \u00d7 10 \u22123 [\u22121.26 \u00d7 10 \u22122 , 1.87 \u00d7 10 \u22123 ].133Sample size\u22127.83 \u00d7 10 \u22126 [\u22121.60 \u00d7 10 \u22125 , 7.44 \u00d7 10 \u22126 ].067Percentage varianceRandom effectsPosterior mode [95% CrIs]explained [95% CrIs]Species0.0005 [0.0000, 0.0700]0.38% [0.00, 72.23]Phylogeny0.0001 [0.0000, 0.0167]0.18% [0.00, 36.13]Study0.0101 [0.0031, 0.0209]20.62% [5.45, 49.54]Residual0.0137 [0.0105, 0.0174]31.57% [10.53, 52.75]\n\nTrait category, fitness 22.72 [18.62, 27.49] <.001 Trait category, life history 10.93 [8.54, 14.72] <.001 Trait category, physiology 8.96 [2.86, 14.88] .007\nFixed effectsPosterior mode [95% CrIs]p MCMC(Intercept)5.56 [0.75, 9.64].019Method, animal model (REML)0.26 [\u22124.72, 4.04].875Method, animal model (MCMC)\u22122.40 [\u221214.01, 5.49].439Method, full-sib\u22121.62 [\u221212.44, 9.17].833Year0.03 [\u22120.24, 0.45].576Sample size\u22122.00 \u00d7 10 \u22125 [\u22124.98 \u00d7 10 \u22124 , 5.60 \u00d7 10 \u22124 ].984Percentage varianceRandom effectsPosterior mode [95% CrIs]explained [95% CrIs]Species0.17 [0.00, 28.73]0.12% [0.00, 21.73]Study25.98 [7.72, 61.18]25.20% [9.26, 44.72]Residual74.03 [63.67, 87.89]70.77% [47.23, 84.82]\nDATA AVA I L A B I L I T Y S TAT E M E N TAll code and data are deposited at: https:\/\/ doi.org\/ 10. 34894\/ RIVFHW.O RCI DEuan A. Young https:\/\/orcid.org\/0000-0001-9370-9681Erik Postma https:\/\/orcid.org\/0000-0003-0856-1294R E FE R E N C E SSchweizerischer Nationalfonds zur F\u00f6rderung der Wissenschaftlichen Forschung, Grant\/Award Number: 141110 and 159462; Natural Environment Research Council, Grant\/Award Number: NE\/W005867\/1ACK N O WLE D G E M ENTSAnja B\u00fcrkli and Franziska L\u00f6rcher compiled the first version of the dataset.We thank Alastair J. Wilson, Jarrod D Hadfield and one anonymous reviewer for their comments that significantly improved the manuscript, and Ben Longdon for help with the analysis.Finally, we thank the editor and associate editor for their constructive feedback and time during the submission process.FU N D I N G I N FO R M ATI O NSwiss National Science Foundation grants 141110 and 159462.CO N FLI C T O F I NTE R E S T S TATE M E NTThe authors declare no conflicts of interest.\nUnderstanding the evolution and stability of the G-matrix. 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J L A Wood, M C Yates, D J Fraser, 10.1111\/eva.12375Evolutionary Applications. 952016\n\nLow interspecific variation and no phylogenetic signal in additive genetic variance in wild bird and mammal populations. 10.1002\/ece3.10693Ecology and Evolution. 13e10693\n", + "annotations": { + "abstract": "[{\"end\":2319,\"start\":859}]", + "author": "[{\"end\":323,\"start\":123},{\"end\":444,\"start\":324},{\"end\":512,\"start\":445}]", + "authoraffiliation": "[{\"end\":255,\"start\":156},{\"end\":322,\"start\":257},{\"end\":443,\"start\":378},{\"end\":511,\"start\":446}]", + "authorfirstname": "[{\"end\":127,\"start\":123},{\"end\":129,\"start\":128},{\"end\":328,\"start\":324}]", + "authorlastname": "[{\"end\":135,\"start\":130},{\"end\":335,\"start\":329}]", + "bibauthor": 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"openaccessurl": null, + "status": null + } + }, + "text": "\nMutational analysis of severe acute respiratory syndrome coronavirus 2 in immunocompromised patients with persistent viral detection using whole genome sequencing\n\n\nSung-Han Kim kimsunghanmd@hotmail.com \nMan-Seong Park manseong.park@gmail.com \n\nDepartment of Infectious Diseases\nAsan Medical Center\nUniversity of Ulsan College of Medicine\nSeoulRepublic of Korea\n\n\nDepartment of Microbiology\nInstitute for Viral Diseases, Vaccine Innovation Center\nKorea University College of Medicine\nSeoulRepublic of Korea\n\n\nDivision of Emerging Virus and Vector Research\nCenter for Emerging Virus Research\nNational Institute of Infectious Diseases\nKorea Disease Control and Prevention Agency\nCheongjuRepublic of Korea\n\n\nDepartment of Infectious Diseases\nAsan Medical Center\nUniversity of Ulsan College of Medicine\n88, Olympic-ro-43-gil, Songpa-gu05505SeoulRepublic of Korea\n\n\nDepartment of Microbiology\nInstitute for Viral Diseases\nKorea University College of Medicine\n73 Goryeodae-ro, Seongbuk-gu02841SeoulRepublic of Korea\n\n\nCenter for Emerging Virus Research\nJoo-Yeon Lee\nKorea\n\n\nNational Institute of Health\nKorea Disease Control and Prevention Agency\nCheongjuRepublic of Korea\n\n\nO R C I D Euijin Chang https:\/\/orcid\n0000-0001-7417-0318Sung-Han Kim\n\nMutational analysis of severe acute respiratory syndrome coronavirus 2 in immunocompromised patients with persistent viral detection using whole genome sequencing\nECCF98E69D6C7C36112EF403967C4B7A10.1002\/ctm2.1462Received: 11 August 2023 Revised: 8 October 2023 Accepted: 13 October 2023\nDear Editor, During the coronavirus disease 2019 (COVID-19) pandemic of more than three years, several variants have evolved from the previously prevalent strains, being cate-F I G U R E 1 Nonsynonymous mutations of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) acquired by each patient.Each alphabet character represents the nonsynonymous mutations compared with the Wuhan-Hu-1 reference genome.The black boxes present the newly acquired nonsynonymous mutations compared with the initial SARS-CoV-2 genome in each patient.The day on which each specimen was collected is indicated to the left normed to the diagnosis day (D0).The results of the SARS-CoV-2 culture are displayed using red symbols: (+) for positive and (-) for negative.All nonsynonymous mutations were distributed throughout the entire SARS-CoV-2 genome, and each patient acquired unique and different mutations.Euijin Chang, Jungmin Lee and Jun-Won Kim contributed equally to this work.\n\ngorized as variants of concern (VOCs), variants of interest (VOIs), variants of high consequence and variants being monitored. 1The origin of new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants is unclear,\nF I G U R E 1 Continued\nbut one possible explanation is that they stem from immunocompromised patients. 2Whole-genome sequencing (WGS) is a useful tool for detecting new mutations and emerging SARS-CoV-2 variants. 3Here, we used WGS to investigate the features of nonsynonymous SARS-CoV-2 mutations that appeared in immunocompromised patients with persistent viral detection during the Omicron-prevalent era.\n\nThis prospective study was conducted at a 2732-bed tertiary teaching hospital from February to November 2022.We enrolled immunocompromised adults within 12 weeks of initial SARS-CoV-2 diagnosis and gathered nasopharyngeal swabs, saliva and blood samples on a weekly basis.We also performed real-time reverse transcriptionpolymerase chain reaction tests for SARS-CoV-2, viral cultures, plaque reduction neutralization tests, and WGS on at least two serial samples from each patient.The details of patient enrollment, sample collection, and laboratory procedures are explained in the Supporting Information.\n\nA total of 37 WGS results from 13 SARS-CoV-2 patients were included in the final analysis (Figure S1).The baseline features of the study subjects are described in Table S1.The WGS analysis results were obtained from each immunocompromised patient with a median frequency of three times (interquartile range [IQR] 2-3).The median interval between consecutive WGS analyses was 20 days (IQR 15-46 days).The patients acquired a median of two nonsynonymous mutations (IQR 1-7), excluding temporary mutations.The specific mutations compared with the Wuhan Hu-1 reference genome and the acquired mutations in subsequent WGS are presented for each patient in Figure 1 and Table 1.\n\nAmong the total 87 nonsynonymous mutations, 16 (18.4%)and 13 (14.9%)mutations were classified as persistent and temporary mutations, respectively.More than half of the mutations were detected in the ORF1ab region (Figure S2).There were 29 mutations in the S region, 12 of which were associated with immune evasion (see Supporting Information).Also, 13 mutations in the ORF1ab, S, and M regions were the defining mutations of the major variants, including Omicron BA.1, BA.2.75, BA.4\/5, and several XBB subvariants, 4 and eleven of these mutations occurred in the S region (Table 2).The proportion of acquired mutations that were defining mutations of other variants was higher in the S region (11\/29, 37.9%) than in the whole genomic region (13\/87, 14.9%).Table S2 outlines the number of nonsynonymous mutations associated with immune\nF I G U R E 1 Continued\nevasion and the defining mutations of the major variants for each patient.\n\nThe V792I mutation in the nsp12, also known as V5184I in the ORF1ab region, is reported to be associated with viral resistance to remdesivir. 5 Patient H acquired this mutation 142 days after SARS-CoV-2 diagnosis.Before the acquisition of this mutation, the patient had prolonged exposures to remdesivir, dexamethasone, and baricitinib for 28, 17 and 15 days, respectively (Figure 2).This patient also received high-dose steroids (\u2265 equivalent doses of prednisolone 0.3 mg\/kg daily) for more than two months.\n\nWhile B-cell depletion is considered the main factor affecting the period of SARS-CoV-2 shedding, 6 the presence of high neutralizing antibody titers does not always ensure eradication of SARS-CoV-2 infection. 7Patient J shed the virus persistently from days 72-79 despite maintaining a high titer of neutralizing antibodies since the initial COVID-19 diagnosis (Figure 2).WGS analyses were conducted on days 79 and 98, and the missense mutation S:L452Q, detected on day 79, seemed to have 'reverted' by day 98.This mutation has been reported to be associated with immune evasion and a decreased sensitivity to neutralizing antibodies. 8While we could not determine the exact duration of the presence of the S:L452Q mutation or assess the status of T-cell immunity for this patient, persistent viral shedding might be attributed to this mutation and its diminished sensitivity to neutralizing antibodies.\n\nThis study investigated the dynamics and characteristics of SARS-CoV-2 mutations in immunocompromised patients with persistent viral detection during the Omicron era.Each patient acquired a median of two amino acid substitutions over a median of 51 days, which equals 14.2 substitutions per year.In comparison, other studies from the pre-Omicron era reported nonsynonymous mutation rates of 24.4-52.4substitutions per year for immunocompromised patients. 7,9,10While our study involved a larger cohort of such patients, some exhibited a milder immunocompromised status than those in previous studies, potentially leading to variations in the mutation rates.\n\nSeveral mutations seem to have emerged sporadically, distributed throughout the whole SARS-CoV-2 genome.This distribution pattern mirrors findings from an earlier study that also reported a sporadic distribution of various mutations across the SARS-CoV-2 genome in immunocompromised individuals. 2,7,9,10The ORF1ab region, accounting for up to 21,290 nucleotides (71.2%) of the 29,900 total, housed more than half of the nonsyn-\nF I G U R E 1 Continued\nonymous mutations identified in this study.The S region, consisting of 3,822 nucleotides (12.8%), harboured about one-third of the mutations.The adjusted mutation numbers per kilobase were 2.1 for the ORF1ab region and 7.6 for the S region.Additionally, mutations known to contribute to immune escape, or those defining other variants designated as VOIs or VOCs, primarily arose in the S region.This observation aligns with findings from other studies. 7,9he immunocompromised patients in this study were predominantly infected with BA.2 or BA.2.3 sub-lineages.We identified mutations typical of BA.4\/5, BA.2.75, BQ.1 and various XBB subvariants in the SARS-CoV-2 genomes from these patients.Notably, during the pre-Omicron era, immunosuppressed patients were found to acquire nonsynonymous mutations linked to subsequent SARS-CoV-2 lineages. 2,7,9These observations suggest that persistent viral infections in immunocompromised patients could drive the acquisition of new mutations, leading to the adaptive evolution of SARS-CoV-2.Therefore, tracking these mutations might provide insights into viral adaptation and the advent of new SARS-CoV-2 variants.\n\nThis study has several limitations.Viral evolution within populations can be influenced by infection prevalence and immune landscapes, as well as ethnic genetic predispositions. 11Our study was conducted during the Omicron-prevalent era, and the lack of data from the pre-Omicron period limits the generalization of our findings.Also, the patients in our study might not fully represent the spectrum of immunity statuses in immunocompromised patients.Specifically, eleven out of the thirteen patients in our study had hematologic malignancies, and eight had not received SARS-CoV-2 vaccines.This particular immunity profile could give rise to mutations distinct from those observed in other immunocompromised populations. 11onsequently, there might be some potential for regional or immunological biases in our findings.\n\nIn conclusion, during the Omicron-prevalent era, SARS-CoV-2 genomes of immunocompromised individuals with persistent viral detection exhibited several mutations.These mutations have been reported to be associated with immune evasion, remdesivir resistance and new variant emergence.Given the rise of new subvariants with mutations associated with immune evasion or remdesivir resistance and the potential for immunocompromised individuals to shed viable viruses, decisions regarding the termination of isolation for immunocompromised patients with SARS-CoV-2 infection should be approached with caution.\n\nTA B L E 1 2\n12\nAcquired nonsynonymous mutations in the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes of immunocompromised patients.Changes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral loads, titers of neutralizing antibodies, and nonsynonymous mutations in sequenced SARS-CoV-2 genomes in each patient over time and the coronavirus disease 2019 (COVID-19) treatments used.Red and blue lines represent the changes in the amounts of genomic RNA and the 50% neutralization doses of neutralizing antibodies against SARS-CoV-2, respectively.Filled and empty circles on the red lines indicate the results of SARS-CoV-2 culture.Asterisks mark the days when whole-genome sequencing was conducted, with nonsynonymous mutation detection highlighted in yellow boxes.The identified SARS-CoV-2 lineages for each patient are displayed in the upper left olive-coloured boxes.Additionally, the time points and durations of treatments, including remdesivir, dexamethasone, baricitinib, tocilizumab, and evusheld, are indicated in each graph.F I G U R E 2 Continued\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nF I G U R E 2 Continued\n\nS:H245N (D64) ORF1ab:N2317D (D79) S:G446D (D64) ORF1ab:V2786I (D79) ORF1ab:G4287R (D79) ORF3:V259L (D79). \n\nC Ba, 10 ORF1ab:F2122L (D19) M:D3G (D34) ORF1ab:M3733T (D19) S:H505Y (D34) ORF1ab:P5360S (D19) ORF1ab:R6958K (D19). \n\nD Ba, .1.1ORF1ab:E102K (D68) ORF1ab:Q1365P (D159) ORF1ab:A372V (D208). \n\nORF1ab:I114T (D159) ORF1ab:V4101L (D159) ORF1ab:V1117I (D208). \n\nORF1ab:V1222A (D68) ORF7:T39I (D159) ORF1ab:I1367L (D208). \n\nORF1ab:R1404C (D159) ORF1ab:D3222N (D208). \n\nORF1ab:A2098T (D68) ORF1ab:L3919F (D208). \n\nORF1ab:S2352N (D159) ORF1ab:P3952S (D208). \n\nORF1ab:R2695S (D68) ORF1ab:Q4100R (D208). \n\nORF1ab:G2696A (D68) ORF1ab:A5017V (D208). \n\nS :I210V (D68) S:R273K (D208) S :V213E (D68) S:D936Y (D208). \n\nS:R346K (D68) M:Y71H (D208) ORF7:A105V (D68) N:D144H (D208). \n\nE Ba, 2 ORF1ab:V4558A (D19) ORF1ab:L3829F (D67). \n\nS:R493Q (D19) ORF1ab:A3969V (D67). \n\nS:R634H (D19) S:HV69-70Del (D67) S:K147E (D67) S:S408R (D67) S:L452R (D67) S:V483A (D67) ORF3:M1T (D67). \n\nF Ba, 2.3ORF1ab:G5063S (D56) S:Y248N (D56) S:S255F (D56) S:V445F (D56) M:L17F (D56). \n\n. G , A , 2A35V\n\nH Ba, ORF1ab:T1822I (D36) ORF1ab:I1505T (D142). 2\n\nORF1ab:D4165Y (D36) ORF1ab:P2046S (D142). \n\nXBB.1.16S :N405D (D36) ORF1ab:R3662H (D142) ORF1ab:N4358K (D142) ORF1ab:V5184I (D142) ORF1ab:Y5223H (D142) ORF1ab:M5557I (D142) (Continues) ORF1ab:L3829F BQ. 1\n\n(Lambda) S:L452R BA.4, BA.5, BQ.1, B.1.617.1 (Kappa. S:L452Q BA.2.12.1, C.37B.1.617.2 (Delta) S:R493Q BA.2.75, BQ.1, XBB, XBB.1.5, XBB.1.16\n\n1. World Health Organization. Updated working definitions and primary actions for SARS-CoV-2 variants. S:T547K BA.1 M:D3G BA.12023. June 27, 2023\n\nPersistence and evolution of SARS-CoV-2 in an immunocompromised host. B Choi, M C Choudhary, J Regan, 10.1056\/nejmc2031364N Engl J Med. 383232020\n\nDiagnostic tools for rapid screening and detection of SARS-CoV-2 infection. S K Pandey, G C Mohanta, V Kumar, K Gupta, 10.3390\/vaccines10081200Vaccines. 1082022\n\nSARS-CoV-2 Mutations and Variants of Interest. E Hodcroft, Covariants, 2021. April 14, 2023\n\nMutations in the SARS-CoV-2 RNA-dependent RNA polymerase confer resistance to remdesivir by distinct mechanisms. L J Stevens, A J Pruijssers, H W Lee, 10.1126\/scitranslmed.abo0718Sci Transl Med. 146562022\n\nSARS-CoV-2 in immunocompromised individuals. S Dewolf, J C Laracy, M A Perales, M Kamboj, Mrm Van Den Brink, S Vardhana, 10.1016\/j.immuni.2022.09.006Immunity. 55102022\n\nCumulative SARS-CoV-2 mutations and corresponding changes in immunity in an immunocompromised patient indicate viral evolution within the host. S T Sonnleitner, M Prelog, S Sonnleitner, 10.1038\/s41467-022-30163-4Nat Commun. 1312022\n\nNeutralization escape by SARS-CoV-2 Omicron subvariants BA.2.12.1, BA.4, and BA.5. N P Hachmann, J Miller, Y Collier A Ris, 10.1056\/nejmc2206576N Engl J Med. 38712022\n\nLong-term evolution of SARS-CoV-2 in an immunocompromised patient with non-hodgkin lymphoma. mSphere. V Borges, J Isidro, M Cunha, 10.1128\/msphere.00244-2120216\n\nDifferent within-host viral evolution dynamics in severely immunosuppressed cases with persistent sars-cov-2. L P\u00e9rez-Lago, T Ald\u00e1miz-Echevarr\u00eda, R Garc\u00eda-Mart\u00ednez, 10.3390\/biomedicines9070808Biomedicines. 972021\n\nThe evolution of SARS-CoV-2. 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