bluuebunny/crossref_metadata_2025_split huggingface dataset:
Aspergillus dsRNA virus drives fungal fitness and pathogenicity in the mammalian host
Authors: Shlezinger Neta , Lerer Vanda , Rocha Marina , Adeoye John , Hayby Hilla 
Published: March 2024

Abstract
Fungal pathogens pose a significant threat to global health. Aspergillus fumigatus accounts for approximately 65% of all invasive fungal infections in humans, with mortality rates from aspergillosis reaching nearly 50%. Fungal virulence in plant pathogenic fungi can be modified by mycoviruses, viruses that infect fungi. However, their impact on fungal pathogenesis in mammals has remained largely unexplored. Here, utilizing an A. fumigatus strain naturally infected with Aspergillus fumigatus polymycovirus-1M (AfuPmV-1M), we found that the mycovirus confers a significant survival advantage to the fungus under conditions of oxidative stress, heat stress, and within the murine lung. Thus, AfuPmV-1M modulates fungal fitness, resulting in increased virulence and the progression of exacerbated fungal disease. Moreover, antiviral treatment reverses the exacerbated virus-mediated virulence, representing a promising "antipathogenicity" therapy against virus-bearing pathogenic fungi. Taken together, these data suggest that mycoviruses play a significant role as "backseat drivers" in human fungal diseases, presenting critical clinical implications.
Predictive Modeling of Pneumonia-Related Mortality in Long-Term Immunosuppressive Therapy Patients: A Machine Learning Approach with Interpretability
Authors: Xiong Xingyu , Tang Yongjiang 
Published: March 2024

Abstract
Background
The use of corticosteroids and immunosuppressive agents has become a cornerstone in the management of inflammatory and autoimmune diseases, but it comes with an increased risk of infections, particularly pneumonia. Machine learning (ML) and SHAP (SHapley Additive exPlanations) offer a promising approach to predict pneumonia-related mortality in patients on long-term immunosuppressive therapy.
Methods
Data from a retrospective cohort analysis of pneumonia patients undergoing glucocorticoid treatment were collected from six academic hospitals in China. Features such as demographics, clinical symptoms, disease severity, laboratory data, and treatment information were analyzed. Feature selection was performed, and three survival analysis models (Cox regression, Random Survival Forest, Fast Survival Support Vector Machine) were developed. Model interpretability was enhanced using SHAP.
Results
Among 716 patients, 74.02% survived, and 25.97% died within 90 days. Dyspnea, ventilation support, and certain laboratory values were associated with higher mortality. Eight predictors (Platelet, Albumin, Aspartate Aminotransferase, PH, Glucose, Blood Urea Nitrogen, Oxygenation index, Persistent lymphocytopenia) were identified for model development. The Random Survival Forest model outperformed others, showing a C-index of 0.754 and a Time-dependent AUC of 0.795. SHAP analysis revealed the impact of these predictors on patient outcomes.
Conclusion
Machine learning, coupled with SHAP analysis, identifies key predictors and enhances prediction accuracy for pneumonia-related mortality in patients on long-term immunosuppressive therapy. This approach facilitates risk stratification and informed clinical decision-making, potentially improving patient outcomes.
Key node identification for a network topology using hierarchical comprehensive importance coefficients
Authors: Qiu Fanshuo , Yu Chengpu , Feng Yunji , Li Yao 
Published: March 2024

Abstract
Key nodes are similar to important hubs in a network structure, which can directly determine the robustness and stability of the network. By effectively identifying and protecting these critical nodes, the robustness of the network can be improved, making it more resistant to external interference and attacks. There are various topology analysis methods for existing networks, but key node identification methods often only focus on local attributes or global attributes. Designing an algorithm that combines both attributes can improve the accuracy of key node identification. In this paper, the constraint coefficient of a weakly connected network is calculated based on the Salton indicator, and the hierarchical tenacity global coefficient is obtained by an improved K-Shell decomposition method. Then, a hierarchical comprehensive node importance identification algorithm is proposed which can comprehensively indicate the local and global attributes of the network nodes. Experimental results on real network datasets show that the proposed algorithm outperforms the other classic algorithms in terms of connectivity, average remaining edges, sensitivity and monotonicity.
Predictors of Mortality and Severe Illness from Escherichia coli Sepsis in Neonates
Authors: Mukherjee Devashis , Hoffman Adriana , Satyavolu Sriram , Muhanna Danah , Malay Sindhoosha , Raffay Thomas , Windau Anne , Ransom Eric 
Published: March 2024

Abstract

Background: Neonatal Escherichia coli (E. coli) sepsis is increasing. There is limited data on the factors which contribute to increased mortality and severity of illness in neonatal E. colisepsis.
Methods: Retrospective review of neonates (<30 days) admitted to a Level IV NICU in the United States from 2008 to 2022 with a diagnosis of E. coli bloodstream or cerebrospinal fluid infection was conducted. Primary outcome was defined as mortality from or severe illness during E. coliinfection (defined as need for inotropic support or metabolic acidosis).
Results: E. coli neonatal sepsis rate increased from 2008-2022 (average of 1.12 per 1,000 live births). The primary outcome, which occurred in 50% of cases, was independently associated with prematurity, neutropenia, and thrombocytopenia. Ampicillin resistance was not associated with the primary outcome.
Conclusion: GA, neutropenia, and thrombocytopenia but not ampicillin resistance, are associated with mortality or severe illness from E. colisepsis.
The diagnostic value of Near-Infrared Indocyanine Green Imaging for Endometriosis: A systematic review and meta-analysis
Authors: Zhuang Yuan , Ou Honghui , Xu Yue , Yang Hua 
Published: March 2024

Abstract

Objective: To evaluate the diagnostic effect of Near-Infrared Indocyanine Green (ICG) imaging for Endometriosis(EMs).
Methods: We searched Chinese National Knowledge Infrastructure (CNKI), Clinical Trials, WanFang Database, Web of Science, Cochrane Library, and Medline electronic databases up to January 2024. Reference lists of enrolled studies, Google Scholar, and reports of scientific meetings were also searched manually.
Results: Through systematic literature search and screening, six studies compared the diagnostic effect of ICG imaging to White Light imaging(WL) for EMs during laparoscopic surgery were enrolled. Two studies found that ICG imaging improved the diagnostic rate of EMs. The others found the diagnostic value of ICG imaging was minimal. Cumulative results found the sensitivity is 0.88 (95%CI: 0.81-0.93) for WL and 0.64 (95%CI: 0.36-0.84) for ICG respectively. The specificity is 0.85 (95%CI: 0.49-0.97) for WL and 0.88 (95%CI: 0.66-0.97) for ICG respectively. The positive likelihood ratio (LR+) is 5.8 (95%CI: 1.4-24.5) for WL and 5.4 (95%CI: 1.2-24.1) for ICG respectively. The negative likelihood ratio (LR-) is 0.14 (95%CI: 0.09-0.20) for WL and 0.41 (95%CI: 0.18-0.94) for ICG respectively. The diagnostic odds ratio (DOR) is 42 (95%CI: 10-182) for WL and 13(95%CI: 1-124) for ICG respectively.
Conclusion: Although ICG may be helpful for the visualization of occult EMs, the diagnostic value of ICG is nonsuperior to WL. However, the results should be minded with caution owing to the limited studies and small sample size. More reliable evidence based on larger simple-size studies and multicentric studies are needed urgently.
Prevalence, symbiosis with Rickettsia, and transmission of Tomato yellow leaf curl virus of invasive Bemisia tabaci MED Q2 in Japan
Authors: Fujiwara Akiko , Hagiwara Hiroki , Tsuchimoto Maiko , Tsuchida Tsutomu 
Published: March 2024

Abstract

The whitefly, Bemisia tabaci, is a notorious insect pest that transmits plant pathogenic viruses to a wide range of economically-important crops. An invasive genetic group of B. tabaci, Mediterranean Q2 (MED Q2), has recently spread to Europe, USA, and Asia. In this study, we investigated the prevalence of MED Q2 in Japanese agricultural sites and found that its distribution has expanded since it was first detected in 2013. Polymerase chain reaction analysis revealed that all MED Q2 individuals were infected with Rickettsia. Rickettsia titres increased during nymphal development, presumably in response to the nutritional needs of the host. Fluorescence in situ hybridisation analysis revealed that Rickettsia was densely located near Portiera-containing bacteriocytes at all growth stages. Rickettsiamay therefore play an important role, such as supplying nutrients to the host, in cooperation with Portiera. Transfer experiments indicated that MED Q2 was as effective a vector for Tomato yellow leaf curl virus as MED Q1 and is therefore a high-risk agricultural pest. These results provide important insights into the biology and ecology of the invasive MED Q2 to effectively control its spread and minimise its impact on crops.
Study on the risk of depression about nurses during the full liberalizationUsing machine learning method
Authors: Qi Xiao-yan , Xu Hong-ning 
Published: March 2024

Abstract

Background：COVID-19 is a rapidly spreading disease with high rates of infectivity, morbidity, and fatality, Nurses face heightened risks of infection since China published full liberalization policy .
Aim：To pinpoint the specific risk factors associated with depression among Chinese nurses during the comprehensive liberalization phase of the COVID-19 pandemic in 2022 and to formulate a predictive model for risk assessment.
Methods:a cross-sectional study from December 9, 2022, to March 26, 2023, recruiting 293 nurses from a tertiary hospital in Anhui Province. Participants were categorized into depression and without depression. The data of the two groups were analyzed using SPSS 23.0. Four predictive machine learning models—Logistic Regression, Support Vector Machine, Extreme Gradient Boosting Machine, and Adaptive Boosting —were developed.
Results：The AUC for the Logistic Regression, SVM, XGBoost, and AdaBoost models were 0.86, 0.88, 0.95, and 0.93 respectively, while their F1 scores were 0.79, 0.83, 0.90, and 0.89. The XGBoost model demonstrated the highest predictive accuracy. The Extreme Gradient Boosting Machine model, tailored to risk factors prevalent among Chinese nurses, offers a potent tool for predicting depression risks.
Conclusions：This model can aid clinical managers in accurately identifying and addressing potential risk factors during and post the comprehensive liberalization phase of the COVID-19 pandemic.
Finite-time command filter backstepping control of uncertain chaotic systems
Authors: Zhu Fang , Liu Yiyu , Shi Youwei 
Published: March 2024

Abstract

This article investigates the synchronization problem of a class of uncertain chaotic systems based on backstepping control technology and a finite-time command filter. Firstly, the virtual signal and its derivative are approximated by a finite-time command filter, which can guarantee that the estimation error converges to a small region within a finite-time. Meanwhile, a new error compensation mechanism is constructed, which effectively reduces the adverse impact of the filter error. Then, the designed controller ensures that the tracking error converges to a small region within a finite time by selecting appropriate parameters. Finally, two simulation examples validate the feasibility of the proposed method in this article.
Perceived Microaggressions and Quality of Life: The Mediating Role of Personal Resources and Social Support among People with African Migration Background in Germany
Authors: Adedeji Adekunle , Akintunde Tosin Yinka , Hanft-Robert Saskia , Witt Stefanie , Quitmann Julia , Buchcik Johanna , Boehnke Klaus 
Published: March 2024

Abstract

Empirical investigations into the predictive power of microaggressions have shed light on their profound and extensive detrimental effects on the life outcomes of minority groups. In contemporary discourse, microaggressions are not mere fleeting occurrences but rather pervasive, daily experiences that wield significant influence over individual and collective well-being. This current study delves into the role of personal resources and social support as mediators in the relationship between microaggressions and quality of life. The study analyses cross-sectional data from 604 African immigrants in Germany, employing Structural Equation Modelling techniques. Five direct associations were examined alongside three separate mediation analyses to evaluate the predictive effect of microaggressions on quality of life through personal resources, social support, and the combined influence of both. The results indicate a negative association between microaggressions, personal resources, social support, and quality of life. Microaggressions constrain personal resources and social support, thereby compromising quality of life, as evidenced by the attenuating effects observed in the mediation analyses. Furthermore, the serial mediation model highlights the distinct contributions of personal resources and social capital. The findings underscore the serialised nature of microaggression’s impact on quality of life, suggesting that neither personal resources nor social support can fully mitigate its effects. This study posits that microaggressions manifest through immigrants’ social interactions and exchanges, undermining personal resources and social support networks that are essential for enhancing their quality of life.
The association between the thickness of the hypoechoic halo of thyroid nodules and thyroid cancer: A retrospective study
Authors: Xu Yan , Zhang Ming , Ren Zhijun , Tang Weiyi , Li Liangyao , Tang Kui 
Published: March 2024

Abstract

Background
The relationship between hypoechoic halos and thyroid cancer in patients with thyroid nodules remains unknown. This study aimed to investigate the association between the thickness of the hypoechoic halo and thyroid cancer among patients with thyroid nodules.
Methods
Three hundred and twenty patients with thyroid nodules with a hypoechoic halo were enrolled for this retrospective analysis between January 2019 and December 2022. Univariable and multivariable logistic regression were used to explore the association between the thickness of the hypoechoic halo of thyroid nodules and thyroid cancer. Interaction and stratified analyses were conducted, including age, sex, thickness, tumor size and tumor location.
Results
After adjusting for multiple covariates, the odds ratios (ORs) (95% confidence intervals (CIs)) of thyroid cancer for participants with a thickness ≥ 1 mm were 3.99 (2.4 ~ 6.62), 3.73 (2.09 ~ 6.67), and 3.16 (1.61 ~ 6.19), compared with those with a thickness < 1 mm. The association between thyroid cancer and the thickness of the halo of the thyroid nodule was stable in different subgroups (all P for interaction > 0.05). The area under the curve (AUC) for the hypoechoic halo of nodules for the diagnosis of thyroid cancer was 0.821 (95% CI: 0.774–0.868), and the sensitivity and specificity were highest when the thickness cutoff value was 1.29 mm.
Conclusion
In a sample of single-center adults, the thickness of the halo was positively related to the risk of thyroid cancer. Our findings indicate that halo thickness may become a promising tool to predict the incidence of thyroid cancer.
Batch production prediction for the mechanical cutting industry based on process capability analysis: Experimental and modelling
Authors: Xu Guangtao , Liu Tianyi , Wang Weichuan , Qiao Zeyuan , Wang Gang , Peng Zhenlong , Zhao Minghao 
Published: March 2024

Abstract

In the mechanical cutting industry, trial production is used for predicting and evaluating the quality of product processes before batch production, and it can be expressed through the qualification rate. However, it cannot objectively and comprehensively evaluate the quality of product processes. This study optimizes the analysis of outliers and stability in mathematical statistics to better apply it in the mechanical cutting industry; then, it combines them with process capability analysis. Simultaneously, considering the non-normal distribution of process parameters, a batch production-prediction model is proposed. The diameter, roundness, and roughness of structural common samples are verified. The results show that after the diameter, roundness, and roughness stabilize, the process performance indices are 2.5157, 3.8883, and 3.5878, respectively, adhering to the standards for batch production. For other mechanical parts in the mechanical cutting industry, the model proposed in this paper can be used to quickly and accurately predict and evaluate batch production.
Active Hydrogen Free, Z-isomer selective, isatin derived “Turn on” fluorescent dual anions sensor
Authors: Shenbagapushpam Muthumanickam , Ashwin Bosco Christin Maria Arputham , Mareeswaran Paulpandian Muthu , Yuvaraj Paneerselvam , Kodirajan Selvakumar 
Published: March 2024

Abstract

An efficient 1-(prop-2-yn-1-yl)-3-(quinolin-3-ylimino)indolin-2-one (PQI) fluorescence "on-off" sensor for anions has been designed to detect dual F- and NO3- ions anions in an aqueous medium without interference. Superior UV light sensing activity against F- and NO3- ions is exhibited by the Z-isomer of the isatin-based π-conjugated quinoline isomer, which is free of Lewis acid and active hydrogen sites. The act of turning on the fluorescence is accomplished using the PET "on-off" mechanism. The non-covalent interactions that mediate the interaction between the probe molecule and anions are thought to be the low electron density covalently bonded isatin N-methylene moiety (-N-CH2-) of the propargyl group of the probe molecule for F- ions and the terminal acidic proton of propargyl for NO3- ions, respectively. The forms of anion binding with PQI are explained by conceivable mechanisms as shown by 1H and 13C NMR titrations. The selectivity of anions sensing may be affected by the bucked structure of the Z-isomer. The computed association constant values for PQI and F- and NO3- ions are 2.5 × 104 M-1 and 2.2 × 103 M-1, respectively, indicating strong interaction between PQI and anions. The relationship between anions and probes is investigated using a jobs plot analysis, and the findings indicate that F- and NO3- ions are in a 1:1 complexation with PQI. The calculation indicates that the probe's limit of detection (LOD) for F- and NO3- ions is 6.91 × 10-7 M-1 and 9.93 × 10-7 M-1, respectively. The low limit of detection (LOD) of the suggested PQI fluorophore is within the WHO-recommended limit for both F- and NO3- ions.
Turkish psychometric characteristics of the humanistic practice ability of nursing scale: differences by education, working year, and professional satisfaction
Authors: Sahin Ayse , Tarsuslu Bedia , Yilmaz Asli , Kuni Filiz , Durat Gulgun 
Published: March 2024

Abstract

Background
Humanistic nursing practices scientifically improve the knowledge structure of nursing, enrich its theoretical system and support its development. Therefore, it is crucial to evaluate the humanistic practice abilities of nurses.
Objective
This study aimed to test the psycholinguistic features, language and construct validity of the Humanistic Practice Ability of Nursing Scale and to examine it according to nurses' demographic characteristics.
Design and methods
This study was a methodological type of analytical research conducted with 397 clinical nurses working in a hospital. A questionnaire including demographic information and evaluating empathy and compassion adequacy was used. Data were analyzed using explanatory and confirmatory factor analysis, Cronbach's alpha, item-total score correlation, split-half analysis, t-test, analysis of variance and correlation analysis.
Results
The scale consists of 29 items and four factors, explaining 61.15% of the total variance. Factor loads were > 0.30. confirmatory factor analysis results were χ2/df: 2.58, GFI: 0.86, TLI: 0.91, IFI: 0.92, CFI: 0.92, RMSEA: 0.06, and SRMR: 0.03. The Cronbach alpha value for the full scale is 0.95. A significant relationship was found between the scale and empathy and compassion proficiency. It was observed that the scale scores differed according to the nurses' education level, working years and job satisfaction (p < 0.05).
Conclusion
This study shows that the Turkish version of the HPAN scale is valid and reliable for 29 items and four factors. The humanistic practice ability of nurses differ according to postgraduate education, years of working in the profession and professional satisfaction.
Investigating Long-Term Risk of Aortic Aneurysm and Dissection from Fluoroquinolones and the Key Contributing Factors Using Machine Learning Methods
Authors: Wang Hsiao-Wei , Huang Yen-Chun , Fang Yu-Wei , Jang Tsrang-Neng , Chen Mingchih , Tsai Ming-Hsien 
Published: March 2024

Abstract

The connection between fluoroquinolones and severe heart conditions, such as aortic aneurysm (AA) and aortic dissection (AD), has been acknowledged, but the full extent of long-term risks remains uncertain. Addressing this knowledge deficit, a retrospective cohort study was conducted in Taiwan, utilizing data from the National Health Insurance Research Database spanning from 2004 to 2010, with follow-up lasting until 2019. The study included 232,552 people who took fluoroquinolones and the same number of people who didn't, matched for age, sex, and index year. The Cox regression model was enlisted to calculate the hazard ratio (HR) for AA/AD onset. Additionally, five machine learning algorithms assisted in pinpointing critical determinants for AA/AD among those with fluoroquinolones. Intriguingly, within the longest follow-up duration of 16 years, exposed patients presented with a markedly higher incidence of AA/AD. After adjusting for multiple factors, exposure to fluoroquinolones was linked to a higher risk of AA/AD (HR 1.62). Machine learning identified ten factors that significantly affected AA/AD risk in those exposed. These results show a 62% increase in long-term AA/AD risk after fluoroquinolone use, highlighting the need for healthcare professionals to carefully consider prescribing these antibiotics due to the risks and factors involved.