bluuebunny/crossref_metadata_2025 huggingface dataset:
The use of histopathological subtyping in patients with ampullary cancer: a nationwide analysis
Authors: de Bakker Jacob , Sommeijer Dirkje , Besselink Marc , Kazemier Geert , van Grieken Nicole
Published: December 2022

Background

Recent guidelines advise to subtype adenocarcinoma at the ampulla and papilla of Vater (here: ampullary cancer) as intestinal, pancreatobiliary, and mixed, because this has consequences for both prognosis and treatment. This nationwide study aimed to investigate how often histopathological subtyping is performed in daily clinical practice in patients with ampullary cancer.

Methods

Pathology reports of all patients with ampullary cancer were retrieved from the Dutch nationwide pathology database (PALGA, 1991-2020). Reports were assessed for the presence and methods used for the classification of these tumors into intestinal, pancreatobiliary, and mixed subtypes. The use of immunohistochemical markers was recorded.

Results

Overall, 5246 patients with ampullary cancer were included. In 1030 (19.6%) patients, a distinction between intestinal, pancreatobiliary, and mixed subtypes was made. Use of subtyping increased from 3% in 1991–1993 to 37% in 2018–2020. In 274 of the 1030 (26.6%) patients, immunohistochemistry was used to make this distinction. A gradual increase in the use of various immunohistochemical markers was seen over time since 2008, with cytokeratin 7, cytokeratin 20, and CDX2 being the most common. Staining of DPC4/SMAD4 was increasingly used since 2012.

Conclusion

Despite recent improvements in the use of subtyping in ampullary cancer, the distinction between intestinal, pancreatobiliary, and mixed subtypes is only made in a minority of patients. Nationwide efforts are required to standardize the pathological distinction of the various subtypes of ampullary cancer.
Prevalence and factors associated with substance use among HIV positive youth attending HIV care and treatment centers in Dodoma, Tanzania
Authors: Morawej Zahra , Nyundo Azan , Kinyaga Ally , Kirway Veneranda , Kagoye Sophia , Turiho Andrew , Nakasujja Noeline
Published: December 2022

Background

Substance use among people living with HIV is associated with poor health, social, and psychological outcomes. This study assessed the prevalence of substance use and associated factors among youth attending HIV care and treatment centers (CTCs) in Dodoma, Tanzania.

Methods

This cross-sectional study was carried out in Dodoma, Tanzania, from February to April 2020 among youth aged 15–24 attending HIV CTCs. Data was collected using sociodemographic, WHO ASSIST Version 3.0, BDI II, and SERAD questionnaires. Data analysis was done using Stata 17. Descriptive statistics were used to summarize continuous and categorical variables. Univariable and multivariable logistic regression analyses were conducted to determine factors independently associated with substance use.

Results

The prevalence of substance use was relatively low (6.6%). Older youth (20 to 24 years) were 2% less likely to use substances compared to the younger ones (15 to 19 years) (AOR: 0.07; 95% CI 0.01, 0.83). There were statistically significant decreasing odds of substance use with every year increase in age at HIV diagnosis (OR: 1.66; 95% CI 1.14, 2.41). Being unemployed was statistically significantly associated with decreased odds of substance use among this population (OR: 0.03; 95% CI 0.02, 0.33). Youth who had detectable viral loads were significantly more likely to use substances compared to those with undetectable viral loads (AOR: 12.9; 95% CI 1.07, 156.05).

Conclusions

Despite the low prevalence of substance use found in this study, it is important to note that late age of HIV diagnosis, employment, and detectable viral load negatively impacted HIV positive youth with regards to substance use. It is recommended that CTCs emphasize routine screening for substance use among youth who have detectable viral loads.
Integrating network pharmacology, UPLC-Q–TOF–MS and molecular docking to investigate the effect and mechanism of Chuanxiong Renshen decoction against Alzheimer's disease
Authors: Shen Zhuo Jun , Fu Yun Bo , Hou Jin Ling , Lin Lu Ning , Wang Xiao Yan , Li Chang Yu , Yang Yuan Xiao
Published: December 2022

Background and aim

Chuanxiong Renshen decoction (CRD) is a traditional Chinese medicine compound used to treat Alzheimer's disease (AD). However, the effects and active ingredients of CRD and its mechanism have not been clarified. We aimed to determine the neuroprotective effects of CRD in a triple-transgenic mouse model of AD (3 × Tg-AD) and investigate the possible active ingredients and their mechanisms.

Methods

Morris water maze (MWM) tests were used to determine the protective effect of CRD on learning and memory ability. Afterward, we used brain tissue staining, immunofluorescent staining and western blotting to detect the neuroprotective effects of CRD. Ultraperformance liquid-chromatography-quadrupole–time-of-flight tandem mass spectrometry (UPLC-Q–TOF–MS) was applied to determine the ingredients of CRD, and the potential AD targets were obtained from DisGeNET and the GeneCards database. The protein‒protein interaction (PPI) network was built with the additional use of STRING 11.0. Metascape was used in the pathway enrichment analysis. Discovery Studio 2016 (DS) software was used to analyze the binding ability of CRD and AD-related genes. Finally, we verified the regulatory effect of CRD on the predicted core targetsEGFRandCASP3by western blotting.

Results

Our study indicated that CRD can significantly improve learning and memory, reduce the expression of Aβ and protect neurons. A total of 95 ingredients were identified in the CRD. Then, 25 ingredients were identified in serum, and 5 ingredients were identified in the brain tissue homogenate. PPI network analysis identifiedCASP3,EGFR,APP,CNR1,HIF1A,PTGS2andMTORas hub targets. KEGG and GO analyses revealed that the TNF signaling pathway and MAPK signaling pathway were enriched in multiple targets. The results of molecular docking proved that the binding of the ingredients with potential key targets was excellent. The western blotting results showed that CRD could significantly reduce the expression ofCASP3andEGFRin the hippocampus of 3 × Tg-AD mice. Combined with literature analysis, we assumed the neuroprotective effect of CRD on AD may occur through regulation of the MAPK signaling pathway.

Conclusion

CRD significantly alleviated injury in 3 × Tg-AD mice. The possible active ingredients are ferulic acid, rutin, ginsenoside Rg1 and panaxydol. The therapeutic effect of CRD on AD is achieved through the downregulation ofCASP3andEGFR. The neuroprotective effect of CRD on AD may occur through regulation of the MAPK signaling pathway.
Cyclin G2 in macrophages triggers CTL-mediated antitumor immunity and antiangiogenesis via interferon-gamma
Authors: Liu Lu , Gao Jinlan , Xing Xuesha , Jiang Meixi , Liu Qi , Wang Shusen , Luo Yang
Published: December 2022

Background

IFN-γ is a key mediator of tumor immunity that can induce macrophage polarization to suppress tumor growth. Cyclin G2 functions as a tumor suppressor in various cancer cells; however, its role in macrophages remains unclear. This study aimed to investigate the role and underlying mechanisms of cyclin G2 in macrophages in vitro and in vivo.

Methods

Mouse tumor models were used to determine the effect of cyclin G2 in macrophages on tumor growth in vivo following IFN-γ treatment. Immunohistochemistry staining, immunofluorescence staining and flow cytometry were used to evaluate the number of cytotoxic T lymphocytes (CTLs) and blood vessels in the mouse tumors. Moreover, the biological roles of cyclin G2 in macrophages with regard to CTL chemotaxis, cytotoxic function, and vascular endothelial cell tube formation were assessed using in vitro functional experiments. Immunoprecipitation (IP), real-time PCR, and enzyme-linked immunosorbent assays (ELISAs) were conducted to investigate the underlying mechanisms by which cyclin G2 regulates CTLs and vascular endothelial cells.

Results

We found that cyclin G2 expression was upregulated in macrophages after IFN-γ treatment. Upregulated cyclin G2 inhibited lung and colon cancer growth by increasing the secretion of its downstream effector CXCL9, which promoted CTL chemotaxis and suppressed vascular endothelial cell tube formation. Moreover, cyclin G2 increased CXCL9 mRNA levels by promoting STAT1 nuclear translocation. In addition, cyclin G2 promoted the activation of the STAT1 signaling pathway, which was dependent on PP2Ac.

Conclusions

Cyclin G2 is upregulated by IFN-γ in macrophages, promotes the secretion of CXCL9 to increase CTL chemotaxis and inhibit angiogenesis to suppress tumor growth. Our findings suggest that targeting cyclin G2 could benefit future immunotherapy.
Preparing medical first responders for crises: a systematic literature review of disaster training programs and their effectiveness
Authors: Baetzner Anke S. , Wespi Rafael , Hill Yannick , Gyllencreutz Lina , Sauter Thomas C. , Saveman Britt-Inger , Mohr Stefan , Regal Georg , Wrzus Cornelia , Frenkel Marie O.
Published: December 2022

Background

Adequate training and preparation of medical first responders (MFRs) are essential for an optimal performance in highly demanding situations like disasters (e.g., mass accidents, natural catastrophes). The training needs to be as effective as possible, because precise and effective behavior of MFRs under stress is central for ensuring patients’ survival and recovery. This systematic review offers an overview of scientifically evaluated training methods used to prepare MFRs for disasters. It identifies different effectiveness indicators and provides an additional analysis of how and to what extent the innovative training technologies virtual (VR) and mixed reality (MR) are included in disaster training research.

Methods

The systematic review was conducted according to the PRISMA guidelines and focused specifically on (quasi-)experimental studies published between January 2010 and September 2021. The literature search was conducted via Web of Science and PubMed and led to the inclusion of 55 articles.

Results

The search identified several types of training, including traditional (e.g., lectures, real-life scenario training) and technology-based training (e.g., computer-based learning, educational videos). Most trainings consisted of more than one method. The effectiveness of the trainings was mainly assessed through pre-post comparisons of knowledge tests or self-reported measures although some studies also used behavioral performance measures (e.g., triage accuracy). While all methods demonstrated effectiveness, the literature indicates that technology-based methods often lead to similar or greater training outcomes than traditional trainings. Currently, few studies systematically evaluated immersive VR and MR training.

Conclusion

To determine the success of a training, proper and scientifically sound evaluation is necessary. Of the effectiveness indicators found, performance assessments in simulated scenarios are closest to the target behavior during real disasters. For valid yet inexpensive evaluations, objectively assessible performance measures, such as accuracy, time, and order of actions could be used. However, performance assessments have not been applied often. Furthermore, we found that technology-based training methods represent a promising approach to train many MFRs repeatedly and efficiently. These technologies offer great potential to supplement or partially replace traditional training. Further research is needed on those methods that have been underrepresented, especially serious gaming, immersive VR, and MR.
Improving access to extracorporeal membrane oxygenation for out of hospital cardiac arrest: pre-hospital ECPR and alternate delivery strategies
Authors: Song Changle , Dennis Mark , Burns Brian , Dyson Sophie , Forrest Paul , Ramanan Mahesh , Levinson David , Moylan Emily
Published: December 2022

Background

The use of extracorporeal membrane oxygenation (ECPR) in refractory out-of-hospital cardiac arrest (OHCA) patients is usually implemented in-hospital. As survival in ECPR patients is critically time-dependent, alternative models in ECPR delivery could improve equity of access.

Objectives

To identify the best strategy of ECPR delivery to provide optimal patient access, to examine the time-sensitivity of ECPR on predicted survival and to model potential survival benefits from different delivery strategies of ECPR.

Methods

We used transport accessibility frameworks supported by comprehensive travel time data, population density data and empirical cardiac arrest time points to quantify the patient catchment areas of the existing in-hospital ECPR service and two alternative ECPR strategies: rendezvous strategy and pre-hospital ECPR in Sydney, Australia. Published survival rates at different time points to ECMO flow were applied to predict the potential survival benefit.

Results

With an in-hospital ECPR strategy for refractory OHCA, five hospitals in Sydney (Australia) had an effective catchment of 811,091 potential patients. This increases to 2,175,096 under a rendezvous strategy and 3,851,727 under the optimal pre-hospital strategy. Assuming earlier provision of ECMO flow, expected survival for eligible arrests will increase by nearly 6% with the rendezvous strategy and approximately 26% with pre-hospital ECPR when compared to the existing in-hospital strategy.

Conclusion

In-hospital ECPR provides the least equitable access to ECPR. Rendezvous and pre-hospital ECPR models substantially increased the catchment of eligible OHCA patients. Traffic and spatial modelling may provide a mechanism to design appropriate ECPR service delivery strategies and should be tested through clinical trials.
Identification of hepatocellular carcinoma subtypes based on PcG-related genes and biological relevance with cancer cells
Authors: Fu Yunong , Yang Kaibo , Wu Kunjin , Wang Hai , Li Qinglin , Zhang Fengping , Yang Kun , Yao Qing , Ma Xiaohua , Deng Yujie , Zhang Jingyao , Liu Chang , Qu Kai
Published: December 2022

Background

Hepatocellular carcinoma (HCC) is an extensive heterogeneous disease where epigenetic factors contribute to its pathogenesis. Polycomb group (PcG) proteins are a group of subunits constituting various macro-molecular machines to regulate the epigenetic landscape, which contributes to cancer phenotype and has the potential to develop a molecular classification of HCC.

Results

Here, based on multi-omics data analysis of DNA methylation, mRNA expression, and copy number of PcG-related genes, we established an epigenetic classification system of HCC, which divides the HCC patients into two subgroups with significantly different outcomes. Comparing these two epigenetic subgroups, we identified different metabolic features, which were related to epigenetic regulation of polycomb-repressive complex 1/2 (PRC1/2). Furthermore, we experimentally proved that inhibition of PcG complexes enhanced the lipid metabolism and reduced the capacity of HCC cells against glucose shortage. In addition, we validated the low chemotherapy sensitivity of HCC in Group A and found inhibition of PRC1/2 promoted HCC cells’ sensitivity to oxaliplatin in vitro and in vivo. Finally, we found that aberrant upregulation of CBX2 in Group A and upregulation of CBX2 were associated with poor prognosis in HCC patients. Furthermore, we found that manipulation of CBX2 affected the levels of H3K27me3 and H2AK119ub.

Contributions

Our study provided a novel molecular classification system based on PcG-related genes data and experimentally validated the biological features of HCC in two subgroups. Our founding supported the polycomb complex targeting strategy to inhibit HCC progression where CBX2 could be a feasible therapeutic target.
Personal victimization experiences of autistic and non-autistic children
Authors: Libster Natalie , Knox Azia , Engin Selin , Geschwind Daniel , Parish-Morris Julia , Kasari Connie
Published: December 2022

Background

Autistic children report higher levels of bullying victimization than their non-autistic peers. However, autistic children with fewer social difficulties, as measured on the Autism Diagnostic Observation Schedule (ADOS), are more likely to report being bullied. Autistic children with stronger social skills may not only be more likely to identify and report incidents of bullying, but they may also be more likely to interact with their non-autistic peers, increasing their likelihood of being victimized. Autistic girls may be especially at-risk of experiencing bullying victimization, as a growing body of research suggests that autistic girls demonstrate fewer social difficulties and are more socially motivated than autistic boys. Here, we explored reported problems with peers and bullying victimization among a carefully matched sample of autistic and non-autistic boys and girls. Qualitative methods were further implemented to gain a more holistic understanding of the social experiences of autistic boys and girls.

Methods

This mixed-methods study analyzed the transcribed clinical evaluations of 58 autistic children (29 girls) matched to 42 non-autistic children (21 girls) on age and IQ. Within each diagnostic group, boys and girls were matched on ADOS severity score. We compared reported problems with peers and bullying victimization across sex and diagnosis. Among autistic children, we further examined whether ADOS social affect (SA), restricted repetitive behaviors, and severity scores predicted problems with peers and bullying victimization. We then identified themes related to personal experiences of victimization.

Results

Autistic children were more likely than non-autistic children to have experienced bullying victimization, and autistic children with lower ADOS severity and SA scores were more likely to report having been bullied. While autistic boys and girls reported similar levels of bullying victimization, qualitative analyses revealed sex differences in the underlying causes of peer conflict.

Limitations

This study was a secondary data analysis. The standardized set of questions on the ADOS limited the amount of information that children provided about their peer relationships, and variations in follow-up questions may have influenced children’s responses.

Conclusions

Although autism symptomatology places autistic children at greater risk for bullying victimization compared to their non-autistic peers, greater social challenges among autistic children are associated with lower rates of victimization. This study further highlights the importance of using mixed-methods approaches to discover nuances in the social experiences of autistic girls and boys that may become opportunities for support.
Autism spectrum disorder symptom expression in individuals with 3q29 deletion syndrome
Authors: Pollak Rebecca M. , Pincus Jordan E. , Burrell T. Lindsey , Cubells Joseph F. , Klaiman Cheryl , Murphy Melissa M. , Saulnier Celine A. , Walker Elaine F. , White Stormi Pulver , Mulle Jennifer G.
Published: December 2022

Background

The 1.6 Mb 3q29 deletion is associated with neurodevelopmental and neuropsychiatric phenotypes, including a 19-fold increased risk for autism spectrum disorder (ASD). Previous work by our team identified elevated social disability in this population via parent-report questionnaires. However, clinical features of ASD in this population have not been explored in detail.

Methods

Thirty-one individuals with 3q29 deletion syndrome (3q29del, 61.3% male) were evaluated using two gold-standard clinical ASD evaluations: the Autism Diagnostic Observation Schedule, Second Edition (ADOS-2), and the Autism Diagnostic Interview, Revised (ADI-R). Four matched comparators for each subject were ascertained from the National Database for Autism Research. Item-level scores on the ADOS-2 and ADI-R were compared between subjects with 3q29del and matched comparators.

Results

Subjects with 3q29del and no ASD (3q29del-ASD) had greater evidence of social disability compared to typically developing (TD) comparison subjects across the ADOS-2. Subjects with 3q29del and ASD (3q29del + ASD) were largely indistinguishable from non-syndromic ASD (nsASD) subjects on the ADOS-2. 3q29del + ASD performed significantly better on social communication on the ADI-R than nsASD (3q29 + ASD mean = 11.36; nsASD mean = 15.70; p = 0.01), and this was driven by reduced deficits in nonverbal communication (3q29 + ASD mean = 1.73; nsASD mean = 3.63; p = 0.03). 3q29del + ASD reported significantly later age at the first two-word phrase compared to nsASD (3q29del + ASD mean = 43.89 months; nsASD mean = 37.86 months; p = 0.01). However, speech delay was not related to improved nonverbal communication in 3q29del + ASD.

Limitations

There were not enough TD comparators with ADI-R data in NDAR to include in the present analysis. Additionally, our relatively small sample size made it difficult to assess race and ethnicity effects.

Conclusions

3q29del is associated with significant social disability, irrespective of ASD diagnosis. 3q29del + ASD have similar levels of social disability to nsASD, while 3q29del-ASD have significantly increased social disability compared to TD individuals. However, social communication is reasonably well preserved in 3q29del + ASD relative to nsASD. It is critical that verbal ability and social disability be examined separately in this population to ensure equal access to ASD and social skills evaluations and services.
Rare virilizing tumor: ovarian steroid cell tumor, not otherwise specified: a case report
Authors: Lobaton-Ginsberg Miry , Malanco-Hernández Luz María , Ferreira-Hermosillo Aldo
Published: December 2022

Background

Ovarian steroid cell tumors, not otherwise specified is a rare sex cord-stromal tumor. Almost 60% of all steroid cell tumors are categorized as not otherwise specified and represent less than 0.1% of all ovarian neoplasm. Some of them are endocrinologically active, producing virilization signs in young women. The recommended treatment is primarily surgical.

Case presentation

We present the case of a 20-year-old Mexican woman with secondary amenorrhea and virilization signs. She was treated with combined oral contraceptives from 13 years old, due to a misdiagnosis of polycystic ovarian syndrome. However, 4 months after stopping medication, amenorrhea and virilization signs worsened. Biochemically, she had high serum total testosterone and free testosterone levels, and a pelvic and transvaginal ultrasound followed by a pelvic tomography scan demonstrated a right adnexal tumor. She underwent right salpingo-oophorectomy and the histopathological and immunochemistry exams confirmed the diagnosis. The patient was followed for a year after surgery and until then, her menses were regular and she had no recurrence of virilization signs.

Conclusion

The purpose of this case report is to alert physicians to rule out ovarian steroid cell tumor, not otherwise specified diagnosis in young women with increased testosterone after discarding common causes such as polycystic ovarian syndrome. A multidisciplinary team including a gynecologist, endocrinologist, radiologist, and pathologist should be involved for correct diagnosis at the proper time.
Direct cost of cochlear implants in Germany – a strategic simulation
Authors: Thum Christin , Lenarz Thomas , Fleßa Steffen
Published: December 2022

Background

Despite the current undersupply of cochlear implants (CIs) with simultaneously increasing indication, CI implantation numbers in Germany still are at a relatively low level.

Methods

As there are hardly any solid forecasts available in the literature, we develop a System Dynamics model that forecasts the number and costs of CI implantations in adults for 40 years from a social health insurance (SHI) perspective.

Results

CI demand will grow marginally by demographic changes causing average annual costs of about 538 million €. Medical-technical progress with following relaxed indication criteria and patients’ increasing willingness for implantation will increase implantation numbers significantly with average annual costs of 765 million €.

Conclusion

CI demand by adults will increase in the future, thus will the costs for CI supply. Continuous research and development in CI technology and supply is crucial to ensure long-term financing of the growing CI demand through cost-reducing innovations.
Health state utility values for metastatic pancreatic cancer using a composite time trade-off based on the vignette-based approach in Japan
Authors: Takumoto Yuki , Sasahara Yuriko , Narimatsu Hiroto , Murata Tatsunori , Akazawa Manabu
Published: December 2022

Backgrounds

Limited information is available on the utility values of metastatic pancreatic cancer, focusing on different health statuses, selected chemotherapy, and related grades 1/2 and 3/4 adverse events (AEs). We evaluated Japanese societal-based health-related utility values for metastatic pancreatic cancer by considering different grade toxicities commonly associated with chemotherapy using the vignette-based method.

Methods

We developed health status scenarios for patients with metastatic pancreatic cancer undergoing chemotherapy and conducted utility research using the developed scenarios in four steps: ‘literature review,’ ‘exploratory interview,’ ‘content validation’, and ‘utility research’. In the development process, to consider the impact of AEs of chemotherapy for metastatic pancreatic cancer on health state utility values, we selected neutropenia, febrile neutropenia, diarrhea, nausea and vomiting, and neuropathy as representative AEs. Each AE was classified as either grade 1/2 or 3/4. We confirmed our created scenarios through cognitive interviews with the general population and clinical experts to validate the content. Finally, we developed 11 scenarios for using ‘utility research,’ evaluated in a societal-based valuation study using the face-to-face method. Participants for ‘utility research’ were the general population, and they evaluated these scenarios in the composite time trade-off (cTTO) and visual analog scale (VAS) of the European quality of life (EuroQol) valuation technology to derive health state utility scores.

Results

Of 220 responders who completed this survey, 201 were adapted into the analysis population. Stable disease with no AEs (reference state) had a mean utility value of 0.653 using cTTO. The lowest mean utility score in the stable state was 0.242 (stable disease + grade 3/4 vomiting). VAS results ranged from 0.189 to 0.468, depending on the various grades of AEs in stable disease. In addition, grade 3/4 AEs and grade 1/2 nausea/vomiting were associated with significantly greater disutility. Utility values were also strongly influenced by the direct impact of AE on physical symptoms, severity and their experience. In addition, 95.9% of the respondents agreed that they understood the questions in the post-response questionnaire.

Conclusions

We clarified the health state utility values of patients with metastatic pancreatic cancer based on the general population in Japan. The effect on utilities should be considered not only for serious AEs, but also for minor AEs.
Antibiotic use in township hospitals during the COVID-19 pandemic in Shandong, China
Authors: Wang Ting , Shen Liyan , Yin Jia , Zhou Liansheng , Sun Qiang
Published: December 2022

Background

The overuse of antibiotics in primary healthcare settings (PHSs) has caused a serious public health problem in China. The outbreak of the Coronavirus Disease-19 (COVID-19) pandemic brought about dramatic changes in the supply of and demand for medical services in PHSs, possibly resulting in unprecedented changes in antibiotic use.

Objective

This study aims to assess the immediate and long-term impacts of the COVID-19 pandemic on the changes in antibiotic consumption in PHSs.

Method

The data on antibiotic consumption were collected from selected township hospitals in Shandong, China from January 2019 to December 2021. Antibiotic consumption was quantified by using the defined daily doses (DDDs) and the WHO Access, Watch, Reserve category. A segmented regression model was established to analyze the immediate and long-term impacts of the COVID-19 pandemic on antibiotic use by using the interrupted time series analysis.

Results

The overall antibiotic consumption in all PHSs decreased by 32.04% and 16.69% in 2020 and 2021 respectively compared to the corresponding period in 2019. Over the entire study period, the use of penicillins (J01C) and cephalosporins (J01D) accounted for more than 50% of the total antibiotic consumption. The average annual consumption of Watch category antibiotics decreased by 42.02% and 33.47% in 2020 and 2021 respectively compared to that in 2019. According to the interrupted time series analysis, the total antibiotic consumption decreased significantly immediately after the COVID-19 pandemic outbreak (coef. = − 2.712, p = 0.045), but it then increased significantly over a long-term (coef. = 0.205, p = 0.005). Additionally, the consumption of Access category antibiotics increased significantly in PHSs in the long-term (coef. = 0.136, p = 0.018). However, the consumption of Watch category antibiotics declined sharply immediately after the pandemic (coef. = − 1.222, p < 0.001), but then it increased slightly over a long-term (coef. = 0.073, p < 0.001).

Conclusion

The extensive use of penicillin and cephalosporins should be of great concern. After the outbreak of COVID-19 pandemic, the total antibiotic consumption decreased generally and the use pattern was improved to some extent in the PHSs in Shandong, China. This provides an opportunity for improving the misuse of antibiotics in PHSs in China.
Sequence optimization and multiple gene-targeting improve the inhibitory efficacy of exogenous double-stranded RNA against pepper mottle virus in Nicotiana benthamiana
Authors: Kweon Yujin , Fang Miao , Shin Sang-Yoon , Lee Dowhan , Kim Kook-Hyung , Shin Chanseok
Published: December 2022

Double-stranded RNA (dsRNA)-induced RNA interference is a promising agricultural technology for crop protection against various pathogens. Recent advances in this field have enhanced the overall efficiency with which this approach inhibits pathogenic viruses. Our previous study verified that treatment ofNicotiana benthamianaplants with dsRNAs targeting helper component-proteinase (HC-Pro) and nuclear inclusion b (NIb) genes protected the plant from pepper mottle virus (PepMoV) infection. The aim of this study was to improve the inhibitory efficacy of dsRNAs by optimizing the target sequences and their length and by targeting multiple genes via co-treatment of dsRNAs. Each of the two targeting dsRNAs were divided into three shorter compartments and we found thatHC-Pro:mid-1st andNIb:mid-3rd showed significantly superior antiviral potency than the other fragments, including the parent dsRNA. In addition, we confirmed that the co-treatment of two dsRNAs targetingHC-ProandNIbproduced a greater inhibition of PepMoV replication than that obtained from individual dsRNA treatment. Complementing our previous study, this study will provide future directions for designing dsRNAs and enhancing their efficiency in dsRNA-mediated RNA interference technologies.
Authenti-City
Authors: Donner Luca , Sorcinelli Francesca
Published: December 2022

The new global cities and megacities, which are generally planned and built as result of political decisions and fast economic growth, are considered artificial and "false" in their nature. This widespread perception arises from a cognitive dissociation between the notions of authenticity and identity in relation to memory, time and the city’s own sedimentation. This perceived dichotomy is illustrated in the literature across the disciplines. Building on the work of Calvino, Burckhardt, Augé, Jacobs, Lynch, and others, this paper examines this question through the lens of sociology, architecture, literature, and philosophy. The goal of this dissertation, by means of dialectics, is to show how every city, even the most recent ones in terms of foundation and development, should be considered authentic. This paper argues that every city is a physical and socio-phenomenological expression of its residents. To support the study’s thesis, the city of Dubai will be closely examined. Dubai is a city that has all the elements that contribute to this collective vision, as well as the reasons for refuting it. In conclusion, this article will demonstrate that people are the driving force of a city, including more recent cities. It will further show that the built environment is only a base or matrix in support of the “Authentic-City,” a city made by people for people.
What do integrated STEM projects look like in middle school and high school classrooms? A systematic literature review of empirical studies of iSTEM projects
Authors: McLure Felicity I. , Tang Kok-Sing , Williams P. John
Published: December 2022

The past 20 years has seen a growing focus on the integration of Science, Technology, Engineering and Mathematics (iSTEM) disciplines in schools to provide students with authentic experiences in solving real-world problems. A frequently stated aim for iSTEM projects has been increasing engagement and interest in pursuing STEM subjects in senior high school and tertiary studies. In order to better understand the iSTEM projects’ landscape in school classes, this systematic literature review analysed empirical studies of integrated STEM projects carried out in secondary schools to answer the following questions: What are the characteristics of the projects described and to what extent do these projects reflect characteristics of effective STEM projects; and to what extent does research into iSTEM projects in classrooms investigate specific methods of integration of STEM domains? Thirty-five peer-reviewed publications were identified from database searches that met the following inclusion criteria: (a) integrating two or more of the STEM areas, (b) middle/high school education and (c) explicitly describing the research intervention. The review revealed a diversity of iSTEM approaches in the literature, with Engineering and Science, particularly Physics, the most commonly integrated fields. Concerns are raised about the degree to which projects are relevant to students and their context and address the diversity found within student cohorts. A gap was found in the literature in detailing how teachers and students enact integration of STEM skills in these projects.
Map-Reduce based Distance Weighted k-Nearest Neighbor Machine Learning Algorithm for Big Data Applications
Authors: Gothai E , Muthukumaran V , Valarmathi K , Sathishkumar V.E. , Thillaiarasu N. , Karthikeyan P.
Published: December 2022

With the evolution of Internet standards and advancements in various Internet and mobile technologies, especially since web 4.0, more and more web and mobile applications emerge such as e-commerce, social networks, online gaming applications and Internet of Things based applications. Due to the deployment and concurrent access of these applications on the Internet and mobile devices, the amount of data and the kind of data generated increases exponentially and the new era of Big Data has come into existence. Presently available data structures and data analyzing algorithms are not capable to handle such Big Data. Hence, there is a need for scalable, flexible, parallel and intelligent data analyzing algorithms to handle and analyze the complex massive data. In this article, we have proposed a novel distributed supervised machine learning algorithm based on the MapReduce programming model and Distance Weighted k-Nearest Neighbor algorithm called MR-DWkNN to process and analyze the Big Data in the Hadoop cluster environment. The proposed distributed algorithm is based on supervised learning performs both regression tasks as well as classification tasks on large-volume of Big Data applications. Three performance metrics, such as Root Mean Squared Error (RMSE), Determination coefficient (R2) for regression task, and Accuracy for classification tasks are utilized for the performance measure of the proposed MR-DWkNN algorithm. The extensive experimental results shows that there is an average increase of 3% to 4.5% prediction and classification performances as compared to standard distributed k-NN algorithm and a considerable decrease of Root Mean Squared Error (RMSE) with good parallelism characteristics of scalability and speedup thus, proves its effectiveness in Big Data predictive and classification applications.
Cognitive Perception for Scholastic Purposes using Innovative Teaching Strategies
Authors: Aruna S. , Swarna Kuchibhotla
Published: December 2022

The influence of emotion on attention is particularly strong, changing its selectivity in particular and motivating behavior and action. The degree to which a student participates in class determines their level of conceptual knowledge. Various teaching techniques have been developed over time to improve not only the attention of a student but also their engagement of a student. The level of engagement of a student can help us decide the amount of understanding a student can attain throughout the session. Though these techniques have been developed over time, the basic tests to determine the authenticity of these activities have been done mainly by the use of assessment-based methods. According to research in the field of neuroscience, a person's emotions can assist us to determine a student's level of participation. We also have the affective circumplex model to show us the correlation between emotions and the level of engagement of a person. Taking this into account, we developed an attentivity model with the help of an emotion recognition model (made with the help of VGG-16 architecture in CNN) and the eye tracking system to analyze the amount of engagement being displayed by the student in the class. This model applied to the students on the various teaching models helps us in deciding the effectiveness of various teaching methodologies for the primitive methods of teaching.
Gauging Stress, Anxiety, Depression in Student during COVID-19 Pandemic
Authors: Singh Astha , Kumar Divya
Published: December 2022

During the beginning of COVID-19 pandemic, studies came across the world concerning with health issues. Researches began to find the repercussions of the virus. The virus was found to be versatile as it changes its nature and targets the lungs of a person. Later, it was seen an astonishing massacre around the world due to the virus. Many people have lost their life but many more people are still suffering with bad psychological state. Researchers began to research on the nature virus but very few researches were made on the other side-effects of this pandemic. One such crucial subject to attend in contemporary world is the effect of COVID-19 on psychological state in general population. This side-effect may lead to raise an alarming situation in future that could result in more death cases. The proposed paper presents a study on the detection of stress and depression in people caused by the pandemic. The proposed methodology is based on perceived questionnaire method through which people’s responses are recorded in the form of text. COVID victims have been interrogated against a set of questions and their responses are recorded. The methodology performs text mining of their responses that also include the people’s reaction from social networking sites. The text processing of people’s responses is done by natural language processing (NLP). NLP is used to interpret textural facts into meaningful segments that must be understandable to machine. The refined data has been transformed into PSS (perceived stress scale) scaling factor that ranges from 0 to 4 showing various level of stress. The proposed system utilized artificial intelligence in which naive Bayes classifier, K-nearest neighbor (KNN), Decision tree and Random forest algorithms are applied to predict the emotional state of a person. The proposed system also uses data from social networking site for testing purpose. The model successfully shows a comparative study of such three classifiers for the classification of stress level into stress, anxiety and depression.
An Efficient Novel Approach with Multi Class Label Classification through Machine Learning Models for Pancreatic Cancer
Authors: Santosh P. Reddy , Sekhar M. Chandra
Published: December 2022

Pancreatic cancer is right now the fourth largest cause of cancer-related deaths. Early diagnosis is one good solution for pancreatic cancer patients and reduces the mortality rate. Accurate and earlier diagnosis of the pancreatic tumor is a demanding task due to several factors such as delayed diagnosis and absence of early warning symptoms. The conventional distributed machine learning techniques such as SVM and logistic regression were not efficient to minimize the error rate and improve the classification of pancreatic cancer with higher accuracy. Therefore, a novel technique called Distributed Hybrid Elitism gene Quadratic discriminant Reinforced Learning Classifier System (DHEGQDRLCS) is developed in this paper. First, the number of data samples is collected from the repository dataset. This repository contains all the necessary files for the identification of prognostic biomarkers for pancreatic cancer. After the data collection, the separation of training and testing samples is performed for the accurate classification of pancreatic cancer samples. Then the training samples are considered and applied to Distributed Hybrid Elitism gene Quadratic discriminant Reinforced Learning Classifier System. The proposed hybrid classifier system uses the Kernel Quadratic Discriminant Function to analyze the training samples. After that, the Elitism gradient gene optimization is applied for classifying the samples into multiple classes such as non-cancerous pancreas, benign hepatobiliary disease i.e., pancreatic cancer, and Pancreatic ductal adenocarcinoma. Then the Reinforced Learning technique is applied to minimize the loss function based on target classification results and predicted classification results. Finally, the hybridized approach improves pancreatic cancer diagnosing accuracy. Experimental evaluation is carried out with pancreatic cancer dataset with Hadoop distributed system and different quantitative metrics such as Accuracy, balanced accuracy, F1-score, precision, recall, specificity, TN, TP, FN, FP, ROC_AUC, PRC_AUC, and PRC_APS. The performance analysis results indicate that the DHEGQDRLCS provides better diagnosing accuracy when compared to existing methods.
Computer-aided Diagnosis applied to MRI images of Brain Tumor using Spatial Fuzzy Level Set and ANN Classifier
Authors: Virupakshappa S , Veerashetty Sachinkumar , Ambika N
Published: December 2022

The most vital organs in the human body are the brain, heart, and lungs. Because the brain controls and coordinates the operations of all other organs, normal brain function is vital. Brain tumour is a mass of tissues which interrupts the normal functioning of the brain, if left untreated will lead to the death of the subject. The classification of multiclass brain tumours using spatial fuzzy based level sets and artificial neural network (ANN) techniques is proposed in this paper. In the proposed method, images are preprocessed using Median Filtering technique, the boundaries of the Brain Tumor are obtained using Spatial Fuzzy based Level Set method, features are extracted using Gabor Wavelet and Gray-Level Run Length Matrix (GLRLM) methods. Finally ANN technique is used for the classification of the image into Normal or Benign Tumor or Malignant Tumor. The proposed method was implemented in the MATLAB working platform and achieved classification accuracy of 94%, which is significant compared to state-of-the-art classification techniques. Thus, the proposed method assist in differentiating between benign and malignant brain tumours, enabling doctors to provide adequate treatment.
A New Improved Binary Convolutional Model for Classification of Images
Authors: Hemalatha Putta , Shankar G. , Deepak Raj D.M.
Published: December 2022

There are numerous image classification strategies are developed in deep learning. However, due to the complexity of images, conventional image classification strategies have been incapable to meet real application needs. As the amount of pixel information rises, the classification becomes more difficult. However, CNN is widely used method for object identification in picture due to its simple and accurate, but still, it remains hazy which strategies are most supportive for analysing and distinguishing the objects in pictures. In this paper we introduced a CNN network and clustering-based technique called IBCNN to perform classification based on patch extraction. The proposed method can accomplish their goals in the following four different ways: a) Automatic Kernel selection; b) resilient patch size selection; c) CNN layer; and d) pooling layer modification. In addition, it also modifies the pooling layer with average value and calculate the pixel size. The proposed method was applied on ten different image datasets. Finally, the proposed model is compared to three benchmarking models: such as WCNN, MLP, and ELM-CNN to estimate its performance. The obtained results shows that the proposed method gives competitive results compared to the other models.
A Meta Heuristic Multi-View Data Analysis over Unconditional Labeled Material: An Intelligence OCMHAMCV
Authors: Kolli Srinivas , Praveen Krishna A.V. , Sreedevi M.
Published: December 2022

Artificial intelligence has been provided powerful research attributes like data mining and clustering for reducing bigdata functioning. Clustering in multi-labeled categorical analysis gives huge amount of relevant data that explains evaluation and portrayal of qualities as trending notion. A wide range of scenarios, data from many dimensions may be used to provide efficient clustering results. Multi-view clustering techniques had been outdated, however they all provide less accurate results when a single clustering of input data is applied. Numerous data groups are conceivable due to diversity of multi-dimensional data, each with its own unique set of viewpoints. When dealing multi-view labelled data, obtaining quantifiable and realistic cluster results may be challenge. This study provides unique strategy termed OCMHAMCV (Orthogonal Constrained Meta Heuristic Adaptive Multi-View Cluster). In beginning, OMF approach used to cluster similar labelled sample data into prototypes of dimensional clusters of low-dimensional data. Utilize adaptive heuristics integrate complementary data several dimensions complexity of computational analysis data representation data in appropriate orthonormality constrained viewpoint. Studies on massive data sets reveal that proposed method outperforms more traditional multi-view clustering techniques scalability and efficiency. The performance measures like accuracy 98.32%, sensitivity 93.42%, F1-score 98.53% and index score 96.02% has been attained, which was good improvement. Therefore it is proved that proposed methodology suitable for document summarization application for future scientific analysis.