Background: Nonsuicidal self-injuries (NSSIs) are an important contributing factor to adolescent suicide, and various shared factors influence the risk of both NSSIs and suicide attempts (SAs). Both are important predictors of suicide and are part of a continuum of suicidal behaviors. Further exploration of the relationship between adolescent NSSI and SA may facilitate suicide prevention efforts. Methods: An online survey was conducted among 9,140 participants. Network analysis methods were used to explore expected influence (EI), bridge expected influence (BEI), edge weights, and differences between adolescents that have and have not attempted suicide (NSSI-SA and NSSI-NoSA, respectively). Results: Of the 9,140 participants, 7,030 completed the questionnaire, yielding a participation rate of 76.91%. Participants with at least one NSSI were retained, with 2,496 (35.50%) included in the network analysis. The strongest EI node for both networks was “emotion regulation strategies” (E = 1.389 and 1.393), and that for BEI was “personal distress” (Interpersonal Reactivity Index—personal distress; E = 0.497 and 0.492). Network comparisons revealed significant differences in NSSI 4 (“intentionally hitting walls, tables, and other hard objects”; E(Δ) = −0.384, P < 0.001), significant differences in BEI with regard to “perspective taking” (Interpersonal Reactivity Index—perspective taking; E(Δ) = −0.215, P < 0.001), and significant differences in edge weights between NSSI 4 and NSSI 5 (“intentionally hurting oneself by hitting with a fist, palm, or hard object”; E(Δr) = −0.173, P < 0.001). Conclusions: Our study suggests that interventions in the form of emotion regulation strategies can alleviate symptoms throughout the entire network. Attention should be paid to instances when NSSI 4 and NSSI 5 behaviors co-occur frequently.

Background: There is a substantial number of research exploring the application of artificial intelligence (AI) in identifying electrocardiogram (ECG) abnormalities related to heart rhythm or conduction with the 12-channel format. However, there is a scarcity of studies focusing on refined differentiation of serials of ECG abnormalities with wide QRS complexes in a simplified channel format. Methods: We constructed an ECG dataset (standard 10-s, 12-channel format) from adult patients from Tongji Hospital of Huazhong University of Science and Technology, Wuhan, China. This dataset was consisted of 5 kinds of ECG abnormalities with wide QRS complexes in the normal heartbeat (60 to 100 beats per minute) and the normal ECGs. Convolutional neural network was developed to classify these abnormalities. Four-channel (I, II, V1, and V5) and 8-channel (I, II, and V1 to V6) formats, compared to the standard 12-channel format (I, II, III, aVR, aVL, aVF, and V1 to V6), were chosen as the input channel format of the model. Other unreplicated ECGs from Tongji Hospital (TJ-Test set), annotated by a committee of board-certified cardiologists, served as the test dataset. The F1 score, area under the receiver operating characteristic curve (AUROC), and accuracy were calculated to assess the performance of the model, which were further compared with diagnoses of 6 ECG cardiologists who were informed that the final objective was classifying among 6 classes with the 12-channel format. In addition, a dataset of 291 ECGs from The First People’s Hospital of Jiangxia District (JX-Test set) and a public dataset of 64 ECGs were used to assess model generalizability Results: The dataset consisted of 11,808 ECGs from 8,542 patients from 2012 January 1 to 2020 November 30 and divided into training and validation datasets in the ratio of 9:1. The test dataset included unreplicated 480 ECGs from 480 new adult patients recorded from 2014 January 1 to 2017 November 30. The model shows a superior performance in the 8-channel format compared to that of 4- and 12-channel formats. As for the 8-channel format, the model obtained an accuracy of 95.0%, a mean F1 score of 0.969 (0.943 to 0.997), and a mean AUROC score of 0.997 (0.975 to 1.00) compared to an accuracy of 89.9%, an F1 score of 0.898 (0.863 to 0.932), and an AUROC score of 0.941 (0.918 to 0.963) of physicians assessing the same datasets. The model exhibited a mean F1 score of 0.917 (0.943 to 0.997) and a mean AUROC score of 0.994 (0.975 to 1.00) on the JX-Test set, and mean F1 scores of 0.708 for the left bundle branch block and 0.828 for the right bundle branch block for the external published validation data both with the 8-channel format. Conclusion: Our model distinguishes a range of distinct abnormalities focusing on abnormal morphology on QRS complexes in normal heartbeats with high accuracy, providing a foundation for AI-aided clinical decision-support systems in ECG differential diagnosis.

Background: The traditional manual literature screening approach is limited by its time-consuming nature and high labor costs. A pressing issue is how to leverage large language models to enhance the efficiency and quality of evidence-based evaluations of drug efficacy and safety. Methods: This study utilized a manually curated reference literature database—comprising vaccine, hypoglycemic agent, and antidepressant evaluation studies—previously developed by our team through conventional systematic review methods. This validated database served as the gold standard for the development and optimization of LitAutoScreener. Following the PICOS (Population, Intervention, Comparison, Outcomes, Study Design) principles, a chain-of-thought reasoning approach with few-shot learning prompts was implemented to develop the screening algorithm. We subsequently evaluated the performance of LitAutoScreener using 2 independent validation cohorts, assessing both classification accuracy and processing efficiency. Results: For respiratory syncytial virus vaccine safety validation title–abstract screening, our tools based on GPT (GPT-4o), Kimi (moonshot-v1-128k), and DeepSeek (deepseek-chat 2.5) demonstrated high accuracy in inclusion/exclusion decisions (99.38%, 98.94%, and 98.85%, respectively). Recall rates were 100.00%, 99.13%, and 98.26%, with statistically significant performance differences (χ2 = 5.99, P = 0.048), where GPT outperformed the other models. Exclusion reason concordance rates were 98.85%, 94.79%, and 96.47% (χ2 = 30.22, P < 0.001). In full-text screening, all models maintained perfect recall (100.00%), with accuracies of 100.00% (GPT), 100.00% (Kimi), and 99.45% (DeepSeek). Processing times averaged 1 to 5 s per article for title–abstract screening and 60 s for full-text processing (including PDF preprocessing). Conclusions: LitAutoScreener offers a new approach for efficient literature screening in drug intervention studies, achieving high accuracy and significantly improving screening efficiency.

Background: It is still uncertain how multimorbidity patterns affect transitions between fall states among middle-aged and older Chinese. Methods: Data were obtained from China Health and Retirement Longitudinal Study (CHARLS) 2011-2018. We utilized latent class analysis to categorize baseline multimorbidity patterns, Markov multi-state model to explore the impact of multimorbidity characterized by condition counts and multimorbidity patterns on subsequent fall transitions, and Cox proportional hazard models to assess hazard ratios of each transition. Results: A total of 14,244 participants aged 45 years and older were enrolled at baseline. Among these participants, 11,956 (83.9%) did not have a fall history in the last 2 years, 1,054 (7.4%) had mild falls, and 1,234 (8.7%) had severe falls. Using a multi-state model, 10,967 transitions were observed during a total follow-up of 57,094 person-times, 6,527 of which had worsening transitions and 4,440 had improving transitions. Among 6,711 multimorbid participants, osteo-cardiovascular (20.5%), pulmonary-digestive-rheumatic (30.5%), metabolic-cardiovascular (22.9%), and neuropsychiatric-sensory (26.1%) patterns were classified. Multimorbid participants had significantly higher risks of transitions compared with other participants. Among 4 multimorbidity patterns, osteo-cardiovascular pattern had higher transition risks than other 3 patterns. Conclusions: Multimorbidity, especially the "osteo-cardiovascular pattern" identified in this study, was associated with higher risks of fall transitions among middle-aged and older Chinese. Generally, the effect of multimorbidity is more significant in older adults than in middle-aged adults. Findings from this study provide facts and evidence for fall prevention, and offer implications for clinicians to target on vulnerable population, and for public health policymakers to allocate healthcare resources.

Importance: Precisely decoding brain dysfunction from high-dimensional functional recordings is crucial for advancing our understanding of brain dysfunction in brain disorders. Self-supervised learning (SSL) models offer a transformative approach for mapping dependencies in functional neuroimaging data. Leveraging the intrinsic organization of brain signals for comprehensive feature extraction, these models enable the analysis of critical neurofunctional features within a clinically relevant framework, overcoming challenges related to data heterogeneity and the scarcity of labeled data. Highlight: This paper provides a comprehensive overview of SSL techniques applied to functional neuroimaging data, such as functional magnetic resonance imaging and electroencephalography, with a specific focus on their applications in various neuropsychiatric disorders. We discuss 3 main categories of SSL methods: contrastive learning, generative learning, and generative-contrastive methods, outlining their basic principles and representative methods. Critically, we highlight the potential of SSL in addressing data scarcity, multimodal integration, and dynamic network modeling for disease detection and prediction. We showcase successful applications of these techniques in understanding and classifying conditions such as Alzheimer’s disease, Parkinson’s disease, and epilepsy, demonstrating their potential in downstream neuropsychological applications. Conclusion: SSL models provide a scalable and effective methodology for individual detection and prediction in brain disorders. Despite current limitations in interpretability and data heterogeneity, the potential of SSL for future clinical applications, particularly in the areas of transdiagnostic psychosis subtyping and decoding task-based brain functional recordings, is substantial.

Background: The burden of neonatal infections in low- and middle-income countries and territories (LMICs) is a critical public health challenge, while our understanding of specific burden and secular trends remains limited. Methods: We gathered annual data on 15 types of neonatal infections in LMICs from 1990 to 2019 from the Global Burden of Disease 2019. Numbers, rates, percent changes, and estimated annual percentage changes of incidence and deaths were calculated. We also explored the association between disease burden, socio-demographic index (SDI), and universal health coverage index (UHCI). Results: Enteric infections and upper respiratory infections owned the top highest incidence rates for neonates in 2019. Neonatal sepsis and other neonatal infections, as well as otitis media, demonstrated an increasing trend of incidence across all 3 low- and middle-income regions. The top 3 causes of neonatal mortality in 2019 were neonatal sepsis and other neonatal infections, lower respiratory infections, and enteric infections. Between 1990 and 2019, all of the neonatal infection-related mortality rates suggested an overall decline. Sex differences could be found in the incidence and mortality of some neonatal infections, but most disease burdens decreased more rapidly in males. SDI and UHCI were both negatively associated with most of the disease burden, but there were exceptions. Conclusions: Our study serves as a vital exploration into the realities of neonatal infectious diseases in LMICs. The identified trends and disparities not only provide a foundation for future research but also underscore the critical need for targeted policy initiatives to alleviate on a global scale.

Background: The cumulative effect of body mass index (BMI) on brain health remains ill-defined. The effects of overweight on brain health across different age groups need clarification. We analyzed the effect of cumulative BMI on neuroimaging features of brain health in adults of different ages. Methods: This study was based on a multicenter, community-based cohort study. We modeled the trajectories of BMI over 16 years to evaluate cumulative exposure. Multimodality neuroimaging data were collected once for volumetric measurements of the brain macrostructure, white matter hyperintensity (WMH), and brain microstructure. We used a generalized linear model to evaluate the association between cumulative BMI and neuroimaging features. Two-sample Mendelian randomization analysis was performed using summary level of BMI genetic data from 681,275 individuals and neuroimaging genetic data from 33,224 individuals to analyze the causal relationships. Results: Clinical and neuroimaging data were obtained from 1,074 adults (25 to 83 years). For adults aged under 45 years, brain volume differences in participants with a cumulative BMI of >26.2 kg/m2 corresponded to 12.0 years [95% confidence interval (CI), 3.0 to 20.0] of brain aging. Differences in WMH were statistically substantial for participants aged over 60 years, with a 6.0-ml (95% CI, 1.5 to 10.5) larger volume. Genetic analysis indicated causal relationships between high BMI and smaller gray matter and higher fractional anisotropy in projection fibers. Conclusion: High cumulative BMI is associated with smaller brain volume, larger volume of white matter lesions, and abnormal microstructural integrity. Adults younger than 45 years are suggested to maintain their BMI below 26.2 kg/m2 for better brain health. Trial Registration: This study was registered on clinicaltrials.gov (Clinical Indicators and Brain Image Data: A Cohort Study Based on Kailuan Cohort; No. NCT05453877; https://clinicaltrials.gov/ct2/show/NCT05453877).

Background: Early-onset type 2 diabetes (T2D) is an increasingly serious public health issue, particularly in China. This study aimed to analyze the characteristics of disease burden, secular trend, and attributable risk factors of early-onset T2D in China. Methods: Using data from the Global Burden of Disease (GBD) 2021, we analyzed the age-standardized rate (ASR) of incidence, disability-adjusted life years (DALYs), and mortality rates of T2D among individuals aged 15 to 39 years in China from 1990 to 2021. Joinpoint regression analysis was employed to analyze secular trend, calculating the average annual percent change (AAPC). We also examined changes in the proportion of early-onset T2D within the total T2D burden and its attributable risk factors. Results: From 1990 to 2021, the ASR of incidence of early-onset T2D in China increased from 140.20 [95% uncertainty interval (UI): 89.14 to 204.74] to 315.97 (95% UI: 226.75 to 417.55) per 100,000, with an AAPC of 2.67% (95% CI: 2.60% to 2.75%, P < 0.001). DALYs rose from 116.29 (95% UI: 78.51 to 167.05) to 267.47 (95% UI: 171.08 to 387.38) per 100,000, with an AAPC of 2.75% (95% CI: 2.64% to 2.87%, P < 0.001). Mortality rates slightly decreased from 0.30 (95% UI: 0.24 to 0.38) to 0.28 (95% UI: 0.23 to 0.34) per 100,000, with an AAPC of -0.22% (95% CI: -0.33% to -0.11%, P < 0.001). The 15 to 19 years age group showed the fastest increase in incidence (AAPC: 4.08%, 95% CI: 3.93% to 4.29%, P < 0.001). The burden was consistently higher and increased more rapidly among males compared to females. The proportion of early-onset T2D within the total T2D burden fluctuated but remained higher than global levels. In 2021, high body mass index (BMI) was the primary attributable risk factor for DALYs of early-onset T2D (59.85%, 95% UI: 33.54% to 76.65%), and its contribution increased substantially from 40.08% (95% UI: 20.71% to 55.79%) in 1990, followed by ambient particulate matter pollution (14.77%, 95% UI: 8.24% to 21.24%) and diet high in red meat (9.33%, 95% UI: -1.42% to 20.06%). Conclusion: The disease burden of early-onset T2D in China is rapidly increasing, particularly among younger populations and males. Despite a slight decrease in mortality rates, the continued rapid increase in incidence and DALYs indicates a need for strengthened prevention and management strategies, especially interventions targeting younger age groups. High BMI and environmental pollution emerge as primary risk factors and should be prioritized in future interventions.

Background: Blood transfusions, crucial in managing anemia and coagulopathy in intensive care unit (ICU) settings, require accurate prediction for effective resource allocation and patient risk assessment. However, existing clinical decision support systems have primarily targeted a particular patient demographic with unique medical conditions and focused on a single type of blood transfusion. This study aims to develop an advanced machine learning-based model to predict the probability of transfusion necessity over the next 24 h for a diverse range of non-traumatic ICU patients. Methods: We conducted a retrospective cohort study on 72,072 non-traumatic adult ICU patients admitted to a high-volume US metropolitan academic hospital between 2016 and 2020. We developed a meta-learner and various machine learning models to serve as predictors, training them annually with 4-year data and evaluating on the fifth, unseen year, iteratively over 5 years. Results: The experimental results revealed that the meta-model surpasses the other models in different development scenarios. It achieved notable performance metrics, including an area under the receiver operating characteristic curve of 0.97, an accuracy rate of 0.93, and an F1 score of 0.89 in the best scenario. Conclusion: This study pioneers the use of machine learning models for predicting the likelihood of blood transfusion receipt in a diverse cohort of critically ill patients. The findings of this evaluation confirm that our model not only effectively predicts transfusion reception but also identifies key biomarkers for making transfusion decisions.