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OBJECTIVE: This study aimed to develop and validate a nomogram for assessing the risk of nosocomial infections among obstetric inpatients, providing a valuable reference for predicting and mitigating the risk of postpartum infections. METHODS: A retrospective observational study was performed on a cohort of 28,608 obstetric patients admitted for childbirth between 2017 and 2022. Data from the year 2022, comprising 4,153 inpatients, were utilized for model validation. Univariable and multivariable stepwise logistic regression analyses were employed to identify the factors influencing nosocomial infections among obstetric inpatients. A nomogram was subsequently developed based on the final predictive model. The receiver operating characteristic (ROC) curve was utilized to calculate the area under the curve (AUC) to evaluate the predictive accuracy of the nomogram in both the training and validation datasets. RESULTS: The gestational weeks > = 37, prenatal anemia, prenatal hypoproteinemia, premature rupture of membranes (PROM), cesarean sction, operative delivery, adverse birth outcomes, length of hospitalization (days) > 5, CVC use and catheterization of ureter were included in the ultimate prediction model. The AUC of the nomogram was 0.828 (0.823, 0.833) in the training dataset and 0.855 (0.844, 0.865) in the validation dataset. CONCLUSION: Through a large-scale retrospective study conducted in China, we developed and independently validated a nomogram to enable personalized postpartum infections risk estimates for obstetric inpatients. Its clinical application can facilitate early identification of high-risk groups, enabling timely infection prevention and control measures.

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BACKGROUND: Cardiovascular progenitor cells (CPCs) derived from human embryonic stem cells (hESCs) are considered valuable cell sources for investigating cardiovascular physiology in vitro. Meeting the diverse needs of this application requires the large-scale production of CPCs in an in vitro environment. This study aimed to use an effective culture system utilizing signaling factors for the large-scale expansion of hESC-derived CPCs with the potential to differentiate into functional cardiac lineage cells. METHODS AND RESULTS: Initially, CPCs were generated from hESCs using a 4-day differentiation protocol with a combination of four small molecules (CHIR99021, IWP2, SB-431542, and purmorphamine). These CPCs were then expanded and maintained in a medium containing three factors (bFGF, CHIR, and A83-01), resulting in a > 6,000-fold increase after 8 passages. These CPCs were successfully cryopreserved for an extended period in late passages. The expanded CPCs maintained their gene and protein expression signatures as well as their differentiation capacity through eight passages. Additionally, these CPCs could differentiate into four types of cardiac lineage cells: cardiomyocytes, endothelial cells, smooth muscle cells, and fibroblasts, demonstrating appropriate functionality. Furthermore, the coculture of these CPC-derived cardiovascular lineage cells in rat tail collagen resulted in cardiac microtissue formation, highlighting the potential of this 3D platform for studying cardiovascular physiology in vitro. CONCLUSION: In conclusion, expandable hESC-derived CPCs demonstrated the ability to self-renewal and differentiation into functional cardiovascular lineage cells consistently across passages, which may apply as potential cell sources for in vitro cardiovascular studies.

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Large-scale climate indicators (LSCI) refer to the intricate connections between the atmosphere, oceans, and continents in specific regions. To comprehend the relationship between these vital indicators and atmospheric and climate variability, it is crucial to explore them in detail. The objective of the present study is to gather and review relevant research on LSCI in the Mediterranean area to gain a better understanding of their impacts on atmospheric variability, climate, air quality, ecosystems, and health in the region. Numerous studies have explored LSCI and their effects in the study area, and our work aims to contribute to the existing literature in this context. Our study concludes that LSCI are linked to spatial atmospheric variability in the Mediterranean region. They influence the spatial and temporal distribution of climate and environmental variability, including temperature, rainfall, extreme events, cyclones and storms, and air pollution. Some studies have demonstrated the effects of LSCI on ecosystems, such as forests and river basins in the region. However, research on their impacts on human health is limited. Additionally, the application of LSCI involves various formulations and explanations of their potential developments, primarily explaining atmospheric complex systems and the effort required to comprehend their implications for the environment and health. This review highlights recent progress made in defining, formulating, and calculating LSCI in the Mediterranean area. The most critical functions and characteristics of LSCI are also discussed. Understanding LSCI and their applications is the first step towards developing a health warning system, starting with monitoring atmospheric dynamics and culminating in managing human health responses.

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According to the coupled influence of climate variation and anthropogenic activities, hydro-meteorological variables are hard to keep stationary in a changing environment. Consequently, the efficacy of traditional standardized drought indices, predicated upon the assumption of stationarity, has been called into question. In China, the challenge of drought monitoring and declaration is exacerbated by the need for multiple drought indices covering meteorological, agricultural, hydrological, and groundwater aspects, often lacking real-time availability. To address these challenges, we developed a framework for drought monitoring and assessment from a drought propagation perspective. Central to this is the Nonstationary Integrated Drought Index (NIDI), which integrates responses from meteorological, agricultural, hydrological, and groundwater droughts, accounting for climate change and anthropogenic influences. First, we analyse the process of drought propagation to select the suitable time scale standardized drought index. Subsequently, significant large-scale climatic indices are selected through linear and nonlinear correlation analyses to identify climate anomalies. Anthropogenic influences are assessed using indicators such as the Normalized Difference Vegetation Index (NDVI), Impervious Surface Ratio (ISR), and population density (POP). Nonstationary probability models are then developed for precipitation, soil moisture, runoff, and groundwater series, incorporating climatic and human-induced factors. Finally, the NIDI is calculated using a D-vine copula model, with parameter estimation and updating facilitated by a genetic algorithm, representing the temporal dependence structure among the variables. A case study in the Hulu River Basin of western China validated the NIDI. Results showed that the NIDI effectively accounts for nonstationary hydro-meteorological variables due to climate change and human activities, accurately reproducing their time-dependent structure. Compared to conventional indices like SPI, SSI, SRI, and SGI, the NIDI identifies more extreme drought events. In conclusion, the presented NIDI offers a more comprehensive approach to drought identification, providing valuable insights for accurate drought detection and effective drought-related policy-making.

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The increase in extreme climate events in recent years has been considered as an important factor affecting forest pests. Understanding the responses of forest pests to climate is helpful for revealing the trends in forest pest dynamics and proposing effective control measures. In this study, the relationship between the dynamics of all forest pests, independent forest diseases, and forest insect pests with the climate was evaluated in China, and the corresponding differences among forest pests, diseases and insect pests were assessed. Based on cross-wavelet transform and wavelet coherence analysis, the influences of teleconnection factors on the relationship between climate and forest pests were quantitatively analyzed to determine the roles of these factors. The results indicate that (i) three types of disasters in most parts of China have decreased from 1979 to 2019, while forest pests and forest insect pests in the southwestern region have increased; (ii) the relationship among Forest Pest Occurrence Area Rate and climate factors such as the Multivariate ENSO index, Southern Oscillation index, Arctic Oscillation (AO), Atlantic Multidecadal Oscillation (AMO), and Sunspot is more significant; (iii) the cycle is short in most regions, with oscillations in 2-4 years bands being the main variation periods of disasters in East, Central, and South China; (iv) There is a significant correlation between climate and disasters in the periods of 2-4 or 8-10 years. The AO, AMO, and Sunspot were important driving factors affecting the relationship between climate and disasters. Specifically, the Sunspot had the greatest impact among these factors.

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Microstates analysis of electroencephalography (EEG) has gained increasing attention among researchers and clinicians as a valid tool for investigating temporal dynamics of large-scale brain networks with a millisecond time resolution. Although microstates analysis has been widely applied to elucidate the neurophysiological basis of various cognitive functions in both clinical and non-clinical samples, its application in relation to socio-affective processing has been relatively under-researched. Therefore, the main aim of the current study was to investigate the relationship between EEG microstates and mentalizing (i.e., the ability to understand the mental states of others). Eighty-two participants (thirty-six men; mean age: 24.28 ± 7.35 years; mean years of education: 15.82 ± 1.77) underwent a resting-state EEG recording and performed the Reading the Mind in the Eyes Test (RMET). The parameters of the microstates were then calculated using Cartool v. 4.09 software. Our results showed that the occurrence of microstate map C was independently and positively associated with the RMET total score and contributed to the prediction of mentalizing performance, even when controlling for potential confounding variables (i.e., age, sex, education level, tobacco and alcohol use). Since microstate C is involved in self-related processes, our findings may reflect the link between self-awareness of one's own thoughts/feelings and the enhanced ability to recognize the mental states of others at the neurophysiological level. This finding extends the functions traditionally attributed to microstate C, i.e. mind-wandering, self-related thoughts, prosociality, and emotional and interoceptive processing, to include mentalizing ability.

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This paper addresses the challenges posed by frequent memory access during simulations of large-scale spiking neural networks involving synaptic plasticity. We focus on the memory accesses performed during a common synaptic plasticity rule since this can be a significant factor limiting the efficiency of the simulations. We propose neuron models that are represented by only three state variables, which are engineered to enforce the appropriate neuronal dynamics. Additionally, memory retrieval is executed solely by fetching postsynaptic variables, promoting a contiguous memory storage and leveraging the capabilities of burst mode operations to reduce the overhead associated with each access. Different plasticity rules could be implemented despite the adopted simplifications, each leading to a distinct synaptic weight distribution (i.e., unimodal and bimodal). Moreover, our method requires fewer average memory accesses compared to a naive approach. We argue that the strategy described can speed up memory transactions and reduce latencies while maintaining a small memory footprint.

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INTRODUCTION: ChatGPT 4.0, a large-scale language model (LLM) developed by OpenAI, has demonstrated the capability to pass Japan's national medical examination and other medical assessments. However, the impact of imaging-based questions and different question types on its performance has not been thoroughly examined. This study evaluated ChatGPT 4.0's performance on Japan's national examination for physical therapists, particularly its ability to handle complex questions involving images and tables. The study also assessed the model's potential in the field of rehabilitation and its performance with Japanese language inputs. METHODS: The evaluation utilized 1,000 questions from the 54th to 58th national exams for physical therapists in Japan, comprising 160 general questions and 40 practical questions per exam. All questions were input in Japanese and included additional information such as images or tables. The answers generated by ChatGPT were then compared with the official correct answers. ANALYSIS: ChatGPT's performance was evaluated based on accuracy rates using various criteria: general and practical questions were analyzed with Fisher's exact test, A-type (single correct answer) and X2-type (two correct answers) questions, text-only questions versus questions with images and tables, and different question lengths using Student's t-test. RESULTS: ChatGPT 4.0 met the passing criteria with an overall accuracy of 73.4%. The accuracy rates for general and practical questions were 80.1% and 46.6%, respectively. No significant difference was found between the accuracy rates for A-type (74.3%) and X2-type (67.4%) questions. However, a significant difference was observed between the accuracy rates for text-only questions (80.5%) and questions with images and tables (35.4%). DISCUSSION: The results indicate that ChatGPT 4.0 satisfies the passing criteria for the national exam and demonstrates adequate knowledge and application skills. However, its performance on practical questions and those with images and tables is lower, indicating areas for improvement. The effective handling of Japanese inputs suggests its potential use in non-English-speaking regions. CONCLUSION: ChatGPT 4.0 can pass the national examination for physical therapists, particularly with text-based questions. However, improvements are needed for specialized practical questions and those involving images and tables. The model shows promise for supporting clinical rehabilitation and medical education in Japanese-speaking contexts, though further enhancements are required for a comprehensive application.

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AIMS: This study aimed to investigate the factors associated with the exacerbation of the severity of atherothrombotic brain infarction at discharge in patients with type 2 diabetes using a large-scale claims database. MATERIALS AND METHODS: This retrospective cross-sectional study utilized the Medical Data Vision administrative claims database, a nationwide database in Japan using acute care hospital data, and the Diagnosis Procedure Combination system. Diagnosis Procedure Combination data collected between April 1, 2008, and December 31, 2022, were extracted. Patients with type 2 diabetes were included. Severe atherothrombotic brain infarction was defined as a modified Rankin scale score of ≥3. RESULTS: Severe atherothrombotic brain infarction occurred in 43,916/99,864 (44.0%) patients with type 2 diabetes. The odds ratio for severe atherothrombotic brain infarction increased significantly per 10 year increments in age (odds ratio: 1.69, 95% confidence interval: 1.66-1.71). A body mass index of <25 kg/m2, with a body mass index of ≥25 kg/m2 as reference, also increased the risk for severe atherothrombotic brain infarction (odds ratio: 1.11, 95% confidence interval: 1.08-1.15). The odds ratios in insulin and dipeptidyl peptidase 4 inhibitor use were significantly higher than 1. In particular, statin use (odds ratio: 0.85, 95% confidence interval: 0.83-0.88), fibrate use (odds ratio: 0.68, 95% confidence interval: 0.59-0.78), aspirin use (odds ratio: 0.78, 95% confidence interval: 0.75-0.80), and P2Y12 inhibitor use (odds ratio: 0.88, 95% confidence interval: 0.85-0.91) were associated with a lower odds ratio for severe atherothrombotic brain infarction. CONCLUSIONS: The active management of lipid levels using statins and fibrates may be beneficial in preventing the exacerbation of atherothrombotic brain infarction in type 2 diabetes patients.

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Identifying protective factors that promote academic resilience is vital. Nevertheless, due to the variations in the operationalizations of academic resilience, timeframes, data sources, and employed research methods, it remains unclear whether the impact of protective factors identified across studies can be attributed to the factors themselves or to these variations. By addressing these uncertainties, this study aims to provide an overview of the protective factors that have been extensively investigated in academic resilience and their degree of influence. A literature search found 119 empirical studies on protective factors in education settings for children and adolescents. The review analyzed five protective factors groups (individual, family, school, peer, community), three operationalizations of academic resilience (simultaneous, progressive, instrumental), two timeframes (longitudinal, non-longitudinal), three data sources (self-collected, national/local assessments, international large-scale assessments), and commonly employed research methods. The studies analyzed in this review yielded mixed results regarding the impact of the examined protective factors, with measurement instruments and statistical power playing a significant role in explaining the variations. Individual and school-level characteristics emerged as the most well-studied protective factors; individual characteristics were often investigated through "instrumental" operationalization and structural equational models, whereas school-level characteristics were typically explored through "simultaneous" or "progressive" operationalizations and multilevel modeling. Approximately 31 and 16% of the studies utilized national assessments and international large-scale assessment data, respectively. Both data sources promoted the exploration of school-level factors, with the former facilitating the exploration of protective factors across time and the latter contributing to the investigation of teaching-related factors.

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Chromosomal Instability (CIN) is a common and evolving feature in breast cancer. Large-scale Transitions (LSTs), defined as chromosomal breakages leading to gains or losses of at least 10 Mb, have recently emerged as a metric of CIN due to their standardized definition across platforms. Herein, we report the feasibility of using low-pass Whole Genome Sequencing to assess LSTs, copy number alterations (CNAs) and their relationship in individual circulating tumor cells (CTCs) of triple-negative breast cancer (TNBC) patients. Initial assessment of LSTs in breast cancer cell lines consistently showed wide-ranging values (median 22, range 4-33, mean 21), indicating heterogeneous CIN. Subsequent analysis of CTCs revealed LST values (median 3, range 0-18, mean 5), particularly low during treatment, suggesting temporal changes in CIN levels. CNAs averaged 30 (range 5-49), with loss being predominant. As expected, CTCs with higher LSTs values exhibited increased CNAs. A CNA-based classifier of individual patient-derived CTCs, developed using machine learning, identified genes associated with both DNA proliferation and repair, such as RB1, MYC, and EXO1, as significant predictors of CIN. The model demonstrated a high predictive accuracy with an Area Under the Curve (AUC) of 0.89. Overall, these findings suggest that sequencing CTCs holds the potential to facilitate CIN evaluation and provide insights into its dynamic nature over time, with potential implications for monitoring TNBC progression through iterative assessments.

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To investigate the relationship between father involvement in parenting and mental health problems among children and adolescents in rural China. The Rural Children's Mental Health dataset includes mental health information from 2,489 children and adolescents aged 5-16 in seven provinces in China. The relationship between father involvement in children and adolescents depression risk and anxiety was analyzed by Spearman's correlation analysis, logistic regression analysis, and restricted cubic spline. Father involvement was significantly and negatively associated with depression scores (r = -0.38, P < 0.001) and anxiety scores (r = -0.18, P < 0.001) in rural Chinese children and adolescents. Both multivariate models indicate that the highest level of father involvement has a protective effect on the risk of depression among children and adolescents (OR = 0.268 and 0.303, 95% CI: 0.149~0.483 and 0.144~0.636), while the association with anxiety risk is only significant in the multivariate model 1 (OR = 0.570, 95% CI: 0.363~0.896). Father involvement is a protective factor for the risk of depression among children and adolescents in rural China. The level of father involvement should be increased, and active participation should be encouraged to reduce the risk of depression in their children and to further promote the mental health of children and adolescents in China.

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To contribute meaningfully to carbon dioxide (CO2) emissions reduction, CO2 electrolyzer technology will need to scale immensely. Bench-scale electrolyzers are the norm, with active areas <5 cm2. However, cell areas on the order of 100s or 1000s of cm2 will be required for industrial deployment. Here, we study the effects of increasing cell area, scaling over 2 orders of magnitude from a 5 cm2 lab-scale cell to an 800 cm2 pilot plant-scale cell. A direct scaling of the bench-scale cell architecture to the larger area results in a ∼20% drop in ethylene (C2H4) selectivity and an increase in the parasitic hydrogen (H2) evolution reaction (HER). We instrument an 800 cm2 electrolyzer cell to serve as a diagnostic tool and determine that nonuniformities in electrode compression and flow-influenced local CO2 availability are the key drivers of performance loss upon scaling. Machining of an initial 800 cm2 cell results in a standard deviation in MEA compression that is 7-fold that of a similarly produced 5 cm2 cell (0.009 mm). Using these findings, we redesign an 800 cm2 cell for compression tolerance and increased CO2 transport and achieve an H2 FE in the revised 800 cm2 cell similar to that of the 5 cm2 case (16% at 200 mA cm-2). These results demonstrate that by ensuring uniform compression and fluid flow, the CO2 electrolyzer area can be scaled over 100-fold and retain C2H4 selectivity (within 10% of small-scale selectivity).

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Output-only modal analysis using ambient vibration testing is ubiquitous for the monitoring of structural systems, especially for civil engineering structures such as buildings and bridges. Nonetheless, the instrumented nodes for large-scale structural systems need to cover a significant portion of the spatial volume of the test structure to obtain accurate global modal information. This requires considerable time and resources, which can be challenging in large-scale projects, such as the seismic vulnerability assessment over a large number of facilities. In many instances, a simple center-line (stairwell case) topology is generally used due to time, logistical, and economic constraints. The latter, though a fast technique, cannot provide complete modal information, especially for torsional modes. In this research, corner-line instrumented nodes layouts using only a reference and a roving sensor are proposed, which overcome this issue and can provide maximum modal information similar to that from 3D topologies for medium-rise buildings. Parametric studies are performed to identify the most appropriate locations for sensor placement at each floor of a medium-rise building. The results indicate that corner locations at each floor are optimal. The proposed procedure is validated through field experiments on two medium-rise buildings.

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BACKGROUND: Although involvement of toddlers in swimming activities has increased recently, information regarding the impact of swimming during toddlerhood on subsequent child motor competence development is scarce. This study aimed to determine how swimming experience, particularly the timing of initiation and the continuity of swimming activities up to the age of 3 years, affects motor competence development. METHODS: This prospective cohort study included data on children aged 1.5 and 3 years (100,286 mother-child pairs) from the Japan Environment and Children's Study. The outcomes measured were gross and fine motor function, using the Japanese version of the Ages and Stages Questionnaire (Third edition). We assessed how these functions correlated with the continuous pattern of swimming pool use frequency from age 1 up to 3 years. RESULTS: The group that used a swimming pool once a month or more from age 1-1.5 years but stopped from age 2-3 years showed consistently significant negative associations with gross motor development delay (minimum adjusted odds ratio [aOR]: 0.66, 95% confidence interval [CI]: 0.60-0.73) and fine motor development delay (minimum aOR: 0.66, 95% CI: 0.58-0.76). The group that continued swimming once a month or more from age 1-3 years showed consistently significant negative associations with gross motor development delay (minimum aOR: 0.64, 95% CI: 0.54-0.75) and fine motor development delay (minimum aOR: 0.42, 95% CI: 0.31-0.55). CONCLUSIONS: These results suggest that swimming experience starting around age 1 year is positively associated with gross and fine motor function development. The beneficial impact on gross motor function persisted from age 1-3 years. In contrast, the effects on fine motor function were not evident until age ≥ 2.5 years after starting swimming at approximately age 1 year. These findings underscore the potential benefits of early swimming experiences in enhancing overall motor skills development during early childhood.

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Large-scale multimodal neural recordings on high-density biosensing microelectrode arrays (HD-MEAs) offer unprecedented insights into the dynamic interactions and connectivity across various brain networks. However, the fidelity of these recordings is frequently compromised by pervasive noise, which obscures meaningful neural information and complicates data analysis. To address this challenge, we introduce DENOISING, a versatile data-derived computational engine engineered to adjust thresholds adaptively based on large-scale extracellular signal characteristics and noise levels. This facilitates the separation of signal and noise components without reliance on specific data transformations. Uniquely capable of handling a diverse array of noise types (electrical, mechanical, and environmental) and multidimensional neural signals, including stationary and non-stationary oscillatory local field potential (LFP) and spiking activity, DENOISING presents an adaptable solution applicable across different recording modalities and brain networks. Applying DENOISING to large-scale neural recordings from mice hippocampal and olfactory bulb networks yielded enhanced signal-to-noise ratio (SNR) of LFP and spike firing patterns compared to those computed from raw data. Comparative analysis with existing state-of-the-art denoising methods, employing SNR and root mean square noise (RMS), underscores DENOISING's performance in improving data quality and reliability. Through experimental and computational approaches, we validate that DENOISING improves signal clarity and data interpretation by effectively mitigating independent noise in spatiotemporally structured multimodal datasets, thus unlocking new dimensions in understanding neural connectivity and functional dynamics.

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BACKGROUND: The COVID-19 pandemic sparked a surge of research publications spanning epidemiology, basic science, and clinical science. Thanks to the digital revolution, large data sets are now accessible, which also enables real-time epidemic tracking. However, despite this, academic faculty and their trainees have been struggling to access comprehensive clinical data. To tackle this issue, we have devised a clinical data repository that streamlines research processes and promotes interdisciplinary collaboration. OBJECTIVE: This study aimed to present an easily accessible up-to-date database that promotes access to local COVID-19 clinical data, thereby increasing efficiency, streamlining, and democratizing the research enterprise. By providing a robust database, a broad range of researchers (faculty and trainees) and clinicians from different areas of medicine are encouraged to explore and collaborate on novel clinically relevant research questions. METHODS: A research platform, called the Yale Department of Medicine COVID-19 Explorer and Repository (DOM-CovX), was constructed to house cleaned, highly granular, deidentified, and continually updated data from over 18,000 patients hospitalized with COVID-19 from January 2020 to January 2023, across the Yale New Haven Health System. Data across several key domains were extracted including demographics, past medical history, laboratory values during hospitalization, vital signs, medications, imaging, procedures, and outcomes. Given the time-varying nature of several data domains, summary statistics were constructed to limit the computational size of the database and provide a reasonable data file that the broader research community could use for basic statistical analyses. The initiative also included a front-end user interface, the DOM-CovX Explorer, for simple data visualization of aggregate data. The detailed clinical data sets were made available for researchers after a review board process. RESULTS: As of January 2023, the DOM-CovX Explorer has received 38 requests from different groups of scientists at Yale and the repository has expanded research capability to a diverse group of stakeholders including clinical and research-based faculty and trainees within 15 different surgical and nonsurgical specialties. A dedicated DOM-CovX team guides access and use of the database, which has enhanced interdepartmental collaborations, resulting in the publication of 16 peer-reviewed papers, 2 projects available in preprint servers, and 8 presentations in scientific conferences. Currently, the DOM-CovX Explorer continues to expand and improve its interface. The repository includes up to 3997 variables across 7 different clinical domains, with continued growth in response to researchers' requests and data availability. CONCLUSIONS: The DOM-CovX Data Explorer and Repository is a user-friendly tool for analyzing data and accessing a consistently updated, standardized, and large-scale database. Its innovative approach fosters collaboration, diversity of scholarly pursuits, and expands medical education. In addition, it can be applied to other diseases beyond COVID-19.

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Background: The identification of compound-protein interactions (CPIs) is crucial for drug discovery and understanding mechanisms of action. Accurate CPI prediction can elucidate drug-target-disease interactions, aiding in the discovery of candidate compounds and effective synergistic drugs, particularly from traditional Chinese medicine (TCM). Existing in silico methods face challenges in prediction accuracy and generalization due to compound and target diversity and the lack of largescale interaction datasets and negative datasets for model learning. Methods: To address these issues, we developed a computational model for CPI prediction by integrating the constructed large-scale bioactivity benchmark dataset with a deep learning (DL) algorithm. To verify the accuracy of our CPI model, we applied it to predict the targets of compounds in TCM. An herb pair of Astragalus membranaceus and Hedyotis diffusaas was used as a model, and the active compounds in this herb pair were collected from various public databases and the literature. The complete targets of these active compounds were predicted by the CPI model, resulting in an expanded target dataset. This dataset was next used for the prediction of synergistic antitumor compound combinations. The predicted multi-compound combinations were subsequently examined through in vitro cellular experiments. Results: Our CPI model demonstrated superior performance over other machine learning models, achieving an area under the Receiver Operating Characteristic curve (AUROC) of 0.98, an area under the precision-recall curve (AUPR) of 0.98, and an accuracy (ACC) of 93.31% on the test set. The model's generalization capability and applicability were further confirmed using external databases. Utilizing this model, we predicted the targets of compounds in the herb pair of Astragalus membranaceus and Hedyotis diffusaas, yielding an expanded target dataset. Then, we integrated this expanded target dataset to predict effective drug combinations using our drug synergy prediction model DeepMDS. Experimental assay on breast cancer cell line MDA-MB-231 proved the efficacy of the best predicted multi-compound combinations: Combination I (Epicatechin, Ursolic acid, Quercetin, Aesculetin and Astragaloside IV) exhibited a half-maximal inhibitory concentration (IC50) value of 19.41 µM, and a combination index (CI) value of 0.682; and Combination II (Epicatechin, Ursolic acid, Quercetin, Vanillic acid and Astragaloside IV) displayed a IC50 value of 23.83 µM and a CI value of 0.805. These results validated the ability of our model to make accurate predictions for novel CPI data outside the training dataset and evaluated the reliability of the predictions, showing good applicability potential in drug discovery and in the elucidation of the bioactive compounds in TCM. Conclusion: Our CPI prediction model can serve as a useful tool for accurately identifying potential CPI for a wide range of proteins, and is expected to facilitate drug research, repurposing and support the understanding of TCM.

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Objective: The purpose of the present study was to develop and validate a brief screening instrument (F-SozU K-3) for the measurement of perceived social support in large scale surveys by shortening a well-established German questionnaire (F-SozU K-6). Method: First, a brief three-item version of the F-SozU was developed based on a representative sample of N = 2482 respondents using exploratory and confirmatory factor analysis. Second, the newly developed brief three-item questionnaire was evaluated and standardized in an independent second representative population sample (N = 2501). Results: A suitable three-item solution with a good internal consistency (α = 0.89, ω = 0.89) was identified. Full invariance across sex and partnership was established. Construct validity of the brief three-item form was established. Younger age, female sex, partnership status, and current employment were positively associated with higher social support scores. Norm values for the general sample and separately for sex and partnership status were reported. Conclusions: The newly developed F-SozU K-3 is a reliable and valid screening instrument. It can be used as an economical alternative to previous longer instruments, especially in large scale surveys.

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Introduction: sexual violence is currently a serious public health problem affecting women´s health. Globally, 1 in 3 women faces sexual violence in their lifetime. Female industry workers are at an increased risk of sexual violence. Assessing the magnitude and factors associated with sexual violence among female industrial workers is important for interventions. The objective was to assess the prevalence and factors associated with sexual violence among female large-scale industries workers in Bahir Dar, Ethiopia, 2021. Methods: institution-based cross-sectional study was conducted on 807 female industry workers from September to October 2021. Participants were selected by systematic random sampling. The data were collected by a structured questionnaire. Data entry and analysis were done by Epi data v.3.1 and SPSS v.23, respectively. Multivariable logistic regression analysis was done to identify factors. Adjusted odds ratios were computed at 95%CI. A P-value below 0.05 was used to declare association. Results: the prevalence of sexual violence were 59.4% (95% CI; 56.0%-62.6%). The significantly associated factors include; age less than twenty-five (AOR=4.01, 95%CI; 2.81, 10.83), never-married women (AOR=3.07, 95%CI; 1.11, 8.46), being secondary education (AOR=2.65, 95%CI; 1.51, 4.66), being contract employee (AOR=4.65, 95%CI; 1.92, 11.22), drinking alcohol (AOR=3.01, 95%CI; 1.49, 6.09), and night work shift (AOR=9.01, 95%CI; 4.53, 17.93). Conclusion: high rate (59.4%) of sexual violence was reported. Age, marital status, educational status, contract type of work agreement, drinking alcohol, and working night work shift were risk factors. Hence, emphasis on creating safe working environment & transportation, education on reproductive rights and reporting of sexual violence.

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