bluuebunny/crossref_metadata_2025_split huggingface dataset:
Associations of pro-inflammatory factors and IL-10 levels with degree of suicide risk in adolescents with depression
Authors: Liu Wenyuan , Zheng Hongyu , Wen Xiaofei , Liu Longxing , Yang Yue , Zhong Hui 
Published: January 2025

BackgroundDepression and suicidal behavior are associated with pro-inflammatory status in adults. However, differences in inflammatory levels among adolescents with depression at different suicide risk levels are unclear, and the connection between anti-inflammatory factors, which serve as vital for the immune system, and suicide needs to be explored.MethodsThis study recruited 111 adolescent patients with depression aged 13-18 and 23 healthy controls. Patients were divided into three subgroups according to suicidal ideation within the past week and history of suicide attempts. Severity of depression, suicidal ideation, and suicide risk were assessed using the Hamilton Depression Scale-17 (HAMD-17) and the Chinese version of the Beck Suicide Ideation Scale (BSI-CV). Plasma levels of IL-6, TNF-α, IFN-γ, IL-1β, and IL-10 in all participants were measured.ResultsPlasma levels of IL-6, TNF-α, IFN-γ, and IL-10 differed between the suicide risk subgroups, and the differences remained significant after controlling for severity of depressive symptoms using covariance analysis. Pairwise comparison indicated that plasma levels of these four cytokines in the high suicide risk group were higher than those in the low suicide risk group (all p&lt;0.05), among which the level of IL-10 was significantly higher than that in the medium and low risk groups. IL-10 was positively correlated with the total score of the HAMD-17, BSI-CV, and suicidal ideation; the other four cytokines were also somewhat correlated with suicidal ideation (all p&lt;0.05). IL-10 correlated positively with these four pro-inflammatory factors. Multiple linear regression analysis showed that IL-10 levels significant were associated with BSI-CV (β = 0.270, t = 2.897, p = 0.005) and HAMA-17 (β = 0.285, t = 3.041, p = 0.003) total scores. In binary logistic regression, after controlling for depressive symptoms, gender, age, BMI, and duration of illness: IL-10 level remained a risk factor for suicidal behavior (OR = 3.224, 95% CI 1.571-6.619 p = 0.001).ConclusionAdolescents with different suicide risk levels differed in plasma levels of pro-inflammatory factors and the anti-inflammatory factor IL-10. These differences were independent of depressive symptoms; high IL-10 levels may be a risk factor for suicidal behavior in depressed patients. Further research is needed to explore the relationship between anti-inflammatory factors and suicide.
Determinants of readiness to implement forensic patient-oriented research: a study of barriers and facilitators in a high-secure hospital
Authors: Evans Cara , Deljavan Sevil , Zimmermann Kayla , Allen Kristy , Moghimi Elnaz , Canning Christopher 
Published: January 2025

IntroductionForensic mental health care is intended to promote recovery and reintegration, but is often experienced by patients as punitive and aversive. Forensic patients are rarely engaged in research to explore what matters most to them, and little guidance exists on how this engagement may be facilitated. In this paper, we explore perceived determinants of readiness to implement forensic patient-oriented research in a high-secure setting.MethodsFollowing a period of engagement with staff and patients in the high-secure setting, we conducted interviews with 30 staff members (including clinicians, researchers, and hospital leaders) and five patients. We analyzed interviews using a thematic analysis approach. Coding was initially informed by the Consolidated Framework for Implementation Research, and subsequent iterations of analysis extended beyond this framework to explore patterns of meaning encompassing multiple implementation domains.ResultsWe identified three themes in our data: “Navigating a climate of distrust, discrimination, and restricted autonomy”; “Hearing and interpreting patient voices”; and “Experiencing a slow shift in the tide.” The first two themes represent potential challenges, including distrust and stigma; inherent restrictions in forensic care, and perceptions that patient autonomy threatens staff safety; patient fears of repercussions; and barriers to valuing and understanding patient voices. The third theme describes the ongoing shift towards patient-centredness in this setting, and participants’ interest in proceeding with forensic patient-oriented research.DiscussionIncreased attention to relationship-building, trauma-informed principles, and epistemic injustice (i.e., unfair devaluing of knowledge) in high-secure settings can support the involvement of forensic patients in research.
Predicting conversion to psychosis using machine learning: response to Cannon
Authors: Smucny Jason , Cannon Tyrone D. , Bearden Carrie E. , Addington Jean , Cadenhead Kristen S. , Cornblatt Barbara A. , Keshavan Matcheri , Mathalon Daniel H. , Perkins Diana O. , Stone William , Walker Elaine F. , Woods Scott W. , Davidson Ian , Carter Cameron S. 
Published: January 2025

BackgroundWe previously reported that machine learning could be used to predict conversion to psychosis in individuals at clinical high risk (CHR) for psychosis with up to 90% accuracy using the North American Prodrome Longitudinal Study-3 (NAPLS-3) dataset. A definitive test of our predictive model that was trained on the NAPLS-3 data, however, requires further support through implementation in an independent dataset. In this report we tested for model generalization using the previous iteration of NAPLS-3, the NAPLS-2, using the identical machine learning algorithms employed in our previous study.MethodStandard machine learning algorithms were trained to predict conversion to psychosis in clinical high risk individuals on the NAPLS-3 dataset and tested on the NAPLS-2 dataset.ResultsNAPLS-2 and -3 individuals significantly differed on most features used in machine learning models. All models performed above chance, with Naive Bayes and random forest methods showing the best overall performance. Importantly, however, overall performance did not match those previously observed when using only NAPLS-3 data.ConclusionThe results of this study suggest that a machine learning model trained to predict conversion to psychosis on one dataset can be used to train an independent dataset. Performance on the test set was not in the range necessary for clinical application, however. Possible reasons that limited performance are discussed.
A cross-sectional study of parental perspectives on children about COVID-19 and classification using machine learning models
Authors: Kousar Fahmida , Sultana Arshiya , Albahar Marwan Ali , Shamkuwar Manoj , Heyat Md Belal Bin , Hayat Mohd Ammar Bin , Parveen Saba , Lira John Irish G. , Rahman Khaleequr , Alammari Abdullah , Sayeed Eram 
Published: January 2025

Background and objectiveThis study delves into the parenting cognition perspectives on COVID-19 in children, exploring symptoms, transmission modes, and protective measures. It aims to correlate these perspectives with sociodemographic factors and employ advanced machine-learning techniques for comprehensive analysis.MethodData collection involved a semi-structured questionnaire covering parental knowledge and attitude on COVID-19 symptoms, transmission, protective measures, and government satisfaction. The analysis utilised the Generalised Linear Regression Model (GLM), K-Nearest Neighbours (KNN), Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB), and AdaBoost (AB).ResultsThe study revealed an average knowledge score of 18.02 ± 2.9, with 43.2 and 52.9% of parents demonstrating excellent and good knowledge, respectively. News channels (85%) emerged as the primary information source. Commonly reported symptoms included cough (96.47%) and fever (95.6%). GLM analysis indicated lower awareness in rural areas (β = −0.137, p &lt; 0.001), lower attitude scores in males compared to females (β = −0.64, p = 0.025), and a correlation between lower socioeconomic status and attitude scores (β = −0.048, p = 0.009). The SVM classifier achieved the highest performance (66.70%) in classification tasks.ConclusionThis study offers valuable insights into parental attitudes towards COVID-19 in children, highlighting symptom recognition, transmission awareness, and preventive practices. Correlating these insights with sociodemographic factors underscores the need for tailored educational initiatives, particularly in rural areas, and for addressing gender and socioeconomic disparities. The efficacy of advanced analytics, exemplified by the SVM classifier, underscores the potential for informed decision-making in public health communication and targeted interventions, ultimately empowering parents to safeguard their children’s well-being amidst the ongoing pandemic.
Digital technologies as solutions to China’s aging population: a systematic review of their opportunities and challenges in rural development
Authors: Ming Yue , Li Yimin , Liu Yun 
Published: January 2025

IntroductionDifferences exist in the rate of aging between individuals residing in urban and rural areas in China, with rural areas experiencing a more pronounced impact. Smart aging represents a prevalent future trend in this regard, though its development will inevitably face challenges. However, studies focusing on rural areas are scarce. Future models of aging in less developed regions, including rural areas and townships, are expected to integrate and draw inspiration from the smart aging paradigm.MethodsWe present a systematic review of the current application of digital technologies in caring for older people in rural areas. We conducted extracted and screened 26 articles out of 823, sourced from five databases: Web of Science, IEEE Xplore, Engineering Village, PubMed/MEDLINE and CNKI.ResultsThe articles focus on digital technologies that cater to the daily life, medical care, spiritual comfort, and cultural entertainment needs of rural older people. In our review, we focused on four aspects of digital technology: mobile applications (apps), websites and platforms, mobile devices and terminals, and VR technologies or other unspecified technologies.DiscussionWe found that current digital technologies for smart aging have room for improvement in meeting the recreational and mental comfort needs of older adults. Digital technologies are predominantly applied to the smart community/rural and smart healthcare sectors, with limited applications in the smart home sector. Future studies should explore smart-home older adults care services to address digital cognitive barriers faced by older adults.
Development of a multi-laboratory integrated predictive model for silicosis utilizing machine learning: a retrospective case-control study
Authors: Sun Guo-kang , Xiang Yun-hui , Wang Lu , Xiang Pin-pin , Wang Zi-xin , Zhang Jing , Wu Ling 
Published: January 2025

ObjectiveDue to the high global prevalence of silicosis and the ongoing challenges in its diagnosis, this pilot study aims to screen biomarkers from routine blood parameters and develop a multi-biomarker model for its early detection.MethodsA case-control study was conducted to screen biomarkers for the diagnosis of silicosis using LASSO regression, SVM and RF. A sample of 612 subjects (half cases and half controls) were randomly divided into training and test groups in a 2:1 ratio. Logistic regression analysis and receiver operating characteristic (ROC) curves were used to construct a multiple biomarker-based model for the diagnosis of silicosis, which was applied to both the training and the testing datasets.ResultsThe training cohort revealed significant statistical differences (P &lt; 0.05) in multiple hematologic parameters between silicosis patients and healthy individuals. Based on machine learning, eight silicosis biomarkers were screened and identified from routine blood cell, biochemical and coagulation parameters. D-dimer (DD), Albumin/Globulin (A/G), lactate dehydrogenase (LDH) and white blood cells (WBC) were selected for constructing the logistic regression model for silicosis diagnostics. This model had a satisfactory performance in the training cohort with an area under the ROC curve (AUC) of 0.982, a diagnostic sensitivity of 95.4%, and a specificity of 92.2%. In addition, the model had a prediction accuracy of 0.936 with an AUC of 0.979 in the independent test cohort. Moreover, the diagnostic accuracies of the logistic model in silicosis stages 1, 2, and 3 were 88.0, 95.4, and 94.3% with an AUC of 0.968, 0.983, and 0.990 for silicosis, respectively.ConclusionA diagnostic model based on DD, A/G, LDH and WBC is successfully proposed for silicosis diagnostics. It is cheap, sensitive, specific, and preliminarily offers a potential strategy for the large-scale screening of silicosis.
The neutrophil–lymphocyte ratio as a risk factor for all-cause mortality among individuals with resolved HBV infection: evidence from the NHANES 1999–2018
Authors: Qiu Chen , Yu Chaojie , Yang Lanlan , Liu Siqi , Zhang Qian , Jia Shengnan , Wang Wenrui , Jin Zhenjing , Yu Dongdong 
Published: January 2025

BackgroundInflammation is a critical component in the process of resolved hepatitis B virus (HBV) infection. The neutrophil-to-lymphocyte ratio (NLR) serves as a sensitive indicator of systemic inflammation and immune activation. Our study aimed to investigate the correlation between elevated NLR levels and the risk of all-cause mortality in patients with resolved HBV infection. Additionally, we evaluated the potential mediating effect of diabetes mellitus (DM) on this correlation.MethodsOur study enrolled 1,146 adult patients with resolved HBV infection from the National Health and Nutrition Examination Survey (NHANES) between 1999 and 2018. We utilized the Restricted Cubic Splines (RCS) and Maximum Selection Rank Statistical Method (MSRSM) to analyze the relationship between the NLR and the risk of all-cause mortality. The impact of NLR was evaluated using a weighted multivariate Cox regression model, and the model’s predictive accuracy was assessed using time-dependent Receiver Operating Characteristic (ROC) curves. An intermediary analysis was conducted to explore the potential influence of DM on the observed relationship.ResultsDuring follow-up period of 103.54 ± 4.90 months, we recorded 207 deaths among the study participants. The analysis using the RCS method revealed a significant positive correlation between the NLR and the risk of all-cause mortality. Those with elevated NLR levels faced a substantially higher mortality risk compared to those with lower levels, as indicated by a Hazard Ratio (HR) of 1.84, with a 95% Confidence Interval (CI) of 1.17 to 2.89 (p &lt; 0.05). The predictive accuracy of the model was substantial, as evidenced by the Area Under the Curve (AUC) for ROC curves at 3, 5, and 10 years, which were 0.873, 0.870, and 0.862, respectively. Furthermore, mediation analysis indicated that DM significantly influenced the relationship between the NLR and mortality, with a mediation effect of 6.57% (95% Confidence Interval [CI]: 0.64 to 15%; p = 0.02).ConclusionElevated NLR is significantly associated with an increased risk of all-cause mortality in patients with resolved HBV infection. Concurrently, DM acts as a partial mediator of this association.
Prediction of tuberculosis-specific mortality for older adult patients with pulmonary tuberculosis
Authors: Wang Sihua , Gu Ruohua , Ren Pengfei , Chen Yu , Wu Di , Li Linlin 
Published: January 2025

BackgroundThis study aims to identify risk factors associated with tuberculosis-specific mortality (TSM) in older adult patients with pulmonary tuberculosis (TB) and to develop a competing risk nomogram for TSM prediction.MethodsWe conducted a retrospective cohort study and randomly selected 528 older adult pulmonary TB patients hospitalized in designated hospitals in Henan Province between January 2015 and December 2020. The cumulative incidence function (CIF) was calculated for both TSM and non-tuberculosis-specific mortality (non-TSM). A Fine and Gray proportional subdistribution hazards model and a competing risk nomogram were developed to predict TSM in older adult patients.ResultsThe 5-year cumulative incidence functions (CIFs) for TSM and non-TSM were 9.7 and 9.4%, respectively. The Fine and Gray model identified advanced age, retreatment status, chest X-rays (CXR) cavities, and hypoalbuminemia as independent risk factors for TSM. The competing risk nomogram for TSM showed good calibration and excellent discriminative ability, achieving a concordance index (c-index) of 0.844 (95% confidence interval [CI]: 0.830–0.857).ConclusionThe Fine and Gray model provided an accurate evaluation of risk factors associated with TSM. The competing risk nomogram, developed using the Fine and Gray model, provided accurate and personalized predictions of TSM.
Caregiver perceptions of usual care home programs for persons with acquired brain injury: a qualitative descriptive study
Authors: Donoso Brown Elena V. , Stepansky Kasey , Wallace Sarah E. , Bien Isabella , Buttino Emma 
Published: January 2025

ObjectiveThe study explores caregiver perceptions of home programs for clients with acquired brain injury based on current clinical care after transition to the community.DesignA qualitative descriptive study.SettingWithin the community, post inpatient rehabilitation.ParticipantsA convenience sample of eight caregivers of clients with acquired brain injury from one clinical site. All participants spoke English, were between the ages of 18 and 85 years, had no neurodegenerative disorders, and self-identified as caregivers.ProceduresTwo nested semi-structured interviews were completed post-discharge from an inpatient rehabilitation facility. All interviews were audio recorded and transcribed. Qualitative data analysis was performed utilizing MAXQDA© software, consensus coding, and abstraction of themes.ResultsTwo themes with subsequent subthemes were identified: (1) Systems, Roles, and Responsibilities Influenced Caregivers' Perceptions of Home Program and Recovery Outlook and (2) Caregivers' Home Program Experience. The first theme addresses topics of caregiver roles and responsibilities, system supports and barriers, and their general outlook on recovery. Within the second theme, results provide a chronological description of home program training, use, and modification.ConclusionsA caregiver’s outlook on the care receiver’s recovery and home program implementation is influenced by the burden of responsibilities, and system-level supports and barriers. The home program experience of the caregivers was reported to involve limited but satisfactory training. Caregivers saw the value in home programs and advised others to engage in them. Future programs should encourage healthcare providers to provide explicit instruction to the caregiver about their intrinsic value to home program implementation and adherence.
Factors affecting decision making among nurse managers working in government hospitals
Authors: Abeje Wubete , Tegegne Belachew , Tefera Zenebe , Zenebe Yosef , Yimam Wondwossen , Desu Birhanu , Andargie Yismaw , Amare Muluken , Kassa Molla , W/Selassie Mulugeta 
Published: January 2025

BackgroundEffective nursing management and leadership are essential for the provision of desired patient care that will contribute to the improvement of any country's health indicators. However, nurses' views and experiences on the multitude of personal and organizational factors which may impact their decision-making abilities are often neglected in the literature. The study aimed to assess magnitude of poor decision making and its associated factors among nurse managers in South Wollo Zone Governmental Hospitals, Amhara Regional State, Ethiopia, 2023.MethodsNon experimental cross-sectional study was conducted among 168 nurse managers in South Wollo Zone Public Hospitals from April 01 2023 to May 15/2023. Participants were selected by using a simple random sampling technique. The data were collected by using structured questionnaire from the study participants. Data were entered using EPI data version 4.6 and exported to SPSS version 26 for analysis. The bi-variable logistic regression analysis model was used to identify the potential predictor variable, with p-value &lt;0.25 was fitted into the multivariable logistic regression analysis model; p-value less than 0.05 and an adjusted odds ratio (AOR) with a 95% confidence interval (CI) was used to declare, factors associated with the outcome variable. The model fitness was checked by using the Hosmer and Lemeshow test. Data were presented with frequency tables, graphs, and pie charts.ResultsThe study found that the overall magnitude of poor decision-making among nurse managers was 35.7%. Being self-confident [AOR = 0.01, 95% CI: (00.002, 0.05)], receiving feedback [AOR = 0.24, 95% CI: 0.08, 0.76], and getting managerial support [AOR = 0.22, 95% CI: (0.06, 0.81)] were negatively associated with poor decision-making among nurse managers.ConclusionSelf-confidence, receiving feedback, and getting managerial support were variables significantly associated with poor decision-making. Authors strongly emphasize providing managerial support for nurse managers, increasing their habit of receiving feedback from colleagues, and encouraging them to build their self-confidence.