Objective: To explore the mechanism of action of hepatic leukemia factor (HLF) in diabetes mellitus and to construct a conditional animal model of mice with islet β ⁃cell⁃specific HLF gene knockout. Methods: At the cellular level , the effects of HLF inhibition or overexpression on the proliferation of MIN6 cells was verified by the CCK⁃8 assay. The effects of HLF inhibition or overexpression were detected at the mRNA level and protein level by Cre + / - mice (C57BL/6J) to obtain offspring mice. The genotypes of the mice were identified by the PCR method.The differences in the expression levels of the HLF gene at the mRNA and protein levels in islet β ⁃cell knockout mice (HLFflox/flox Cre + / - ) and control mice (HLFflox/flox ) were detected by RT⁃qPCR technology and Western blot technology to verify the knockout effect. At the same time , the islet tissues of the mice in two groups were taken to make paraffin sections and analyzed by hematoxylin⁃eosin (HE) staining. Results: HLF gene inhibition or overex⁃pression had no significant effect on the proliferation of MIN6 cells. When the HLF gene was inhibited in MIN6 cells , the mRNA expression level decreased by 74% compared with the control group , and the protein expression level decreased by 60% compared with the control group. After overexpressing the HLF gene , the mRNA expres⁃sion level was 2. 13 times compared with that of the control group , and the protein expression level was 1. 8 times compared with that of the control group. The mRNA expression level of the HLF gene in the knockout mice de⁃creased by 89% compared with the control group , and the protein expression level decreased by 65% compared with the control group. The results of HE staining showed that there was no significant difference in the cell mor⁃phology in the islet tissues between the knockout mice and the control mice. Inhibiting HLF increased the glycogen content in MIN6 cells by approximately 20% . Conclusion The HLF gene knockout mice are successfully con⁃structed , providing an animal model for studying the role of HLF in the pathogenesis of diabetes mellitus.

Computational analysis can accurately detect drug-gene interactions (DGIs) cost-effectively. However, transductive learning models are the hotspot to reveal the promising performance for unknown DGIs (both drugs and genes are present in the training model), without special attention to the unseen DGIs (both drugs and genes are absent in the training model). In view of this, this study, for the first time, proposed an inductive learning-based model for the precise identification of unseen DGIs. In our study, by integrating disease nodes to avoid data sparsity, a multi-relational drug-disease-gene (DDG) graph was constructed to achieve effective fusion of data on DDG intro-relationships and inter-actions. Following the extraction of graph features by utilizing graph embedding algorithms, our next step was the retrieval of the attributes of individual gene and drug nodes. In this way, a hybrid feature characterization was represented by integrating graph features and node attributes. Machine learning (ML) models were built, enabling the fulfillment of transductive predictions of unknown DGIs. To realize inductive learning, this study generated an innovative idea of transforming known node vectors derived from the DDG graph into representations of unseen nodes using node similarities as weights, enabling inductive predictions for the unseen DGIs. Consequently, the final model was superior to existing models, with significant improvement in predicting both external unknown and unseen DGIs. The practical feasibility of our model was further confirmed through case study and molecular docking. In summary, this study establishes an efficient data-driven approach through the proposed modeling, suggesting its value as a promising tool for accelerating drug discovery and repurposing.

Accurate prediction of drug-induced adverse drug reactions (ADRs) is crucial for drug safety evaluation, as it directly impacts public health and safety. While various models have shown promising results in predicting ADRs, their accuracy still needs improvement. Additionally, many existing models often lack interpretability when linking molecular structures to specific ADRs and frequently rely on manually selected molecular fingerprints, which can introduce bias. To address these challenges, we propose ToxBERT, an efficient transformer encoder model that leverages attention and masking mechanisms for simplified molecular input line entry system (SMILES) representations. Our results demonstrate that ToxBERT achieved area under the receiver operating characteristic curve (AUROC) scores of 0.839, 0.759, and 0.664 for predicting drug-induced QT prolongation (DIQT), rhabdomyolysis, and liver injury, respectively, outperforming previous studies. Furthermore, ToxBERT can identify drug substructures that are closely associated with specific ADRs. These findings indicate that ToxBERT is not only a valuable tool for understanding the mechanisms underlying specific drug-induced ADRs but also for mitigating potential ADRs in the drug discovery pipeline.

Efficient drug response prediction is crucial for reducing drug development costs and time, but current computational models struggle with limited experimental data and out-of-distribution issues between in vitro and in vivo settings. To address this, we introduced drug response prediction meta-learner (metaDRP), a novel few-shot learning model designed to enhance predictive accuracy with limited sample sizes across diverse drug-tissue tasks. metaDRP achieves performance comparable to state-of-the-art models in both genomics of drug sensitivity in cancer (GDSC) drug screening and in vivo datasets, while effectively mitigating out-of-distribution problems, making it reliable for translating findings from controlled environments to clinical applications. Additionally, metaDRP's inherent interpretability offers reliable insights into drug mechanisms of action, such as elucidating the pathways and molecular targets of drugs like epothilone B and pemetrexed. This work provides a promising approach to overcoming data scarcity and out-of-distribution challenges in drug response prediction, while promoting the integration of few-shot learning in this field.

This study aims to investigate the epidemic variation of porcine circovirus type2(PCV2)and its distribution in swine farms in Henan Province from 2022 to 2023.In this study,1 206 pig blood samples from 18 urban scale swine farms,and 318 pigs and environmental samples from 2 PCV2-positive swine farms in Henan Province were detected by fluorescence quantitative PCR.ORF2 gene sequencing and genetic evolution analysis were performed on the positive samples.The results showed that the positive rate of PCV2 was 7.05％(85/1 206)in Henan Province,11.15％in western Henan Province and 3.03％in southern Henan Province.The positive rate of PCV2 in spring was 11.60％(67/576).A total of 31 ORF2 gene sequences were obtained,of which 23 were PCV2d(74.19％),6 were PCV2a(19.35％),one was PCV2b(3.23％)and one was PCV2c(3.23％).The results of amino acid sequence comparison showed that the mutation sites mainly concentrated on amino acids at positions 40-68 and 180-209.The total positive rate of pig farm sam-ples was 37.42％(119/318),and the positive rate of pig samples from different types of pig houses was 60.71％(34/56),45.83％(11/24),35.09％(20/57),and 33.33(3/9)in order from high to low.The positive rate of environmental samples from high to low was 56.36％(31/55)in feeding system,46.67％(7/15)in feces system,12.50(1/8)in decontamination area,11.11％(2/18)in living area,and no PCV2 was detected in ventilation system.The highest positive rate was 45.00％(9/20)for the tools in the house,7.14％(1/14)for the surface and sole of the clothes after leaving the house,and no PCV2 was detected on the surface and sole of the clothes before entering the house.In this study,the epidemiological investigation of PCV2 in Henan Province and its distribu-tion in swine farms were carried out,and the main circulating strains,seasons,regions and gene mutations of PCV2 in H enan Province were identified.The distribution and transmission routes of PCV2 in positive pig farms were preliminarily analyzed and summarized,which provided data basis and reference for effective prevention and control of PCV and the establishment of accurate bio-safety prevention and control measures.

Accurate prediction of drug-induced adverse drug reactions(ADRs)is crucial for drug safety evaluation,as it directly impacts public health and safety.While various models have shown promising results in predicting ADRs,their accuracy still needs improvement.Additionally,many existing models often lack interpretability when linking molecular structures to specific ADRs and frequently rely on manually selected molecular fingerprints,which can introduce bias.To address these challenges,we propose ToxBERT,an efficient transformer encoder model that leverages attention and masking mechanisms for simplified molecular input line entry system(SMILES)representations.Our results demonstrate that ToxBERT achieved area under the receiver operating characteristic curve(AUROC)scores of 0.839,0.759,and 0.664 for predicting drug-induced QT prolongation(DIQT),rhabdomyolysis,and liver injury,respectively,outperforming previous studies.Furthermore,ToxBERT can identify drug substructures that are closely associated with specific ADRs.These findings indicate that ToxBERT is not only a valuable tool for understanding the mechanisms underlying specific drug-induced ADRs but also for mitigating potential ADRs in the drug discovery pipeline.

Computational analysis can accurately detect drug-gene interactions(DGIs)cost-effectively.However,transductive learning models are the hotspot to reveal the promising performance for unknown DGIs(both drugs and genes are present in the training model),without special attention to the unseen DGIs(both drugs and genes are absent in the training model).In view of this,this study,for the first time,proposed an inductive learning-based model for the precise identification of unseen DGIs.In our study,by integrating disease nodes to avoid data sparsity,a multi-relational drug-disease-gene(DDG)graph was constructed to achieve effective fusion of data on DDG intro-relationships and inter-actions.Following the extraction of graph features by utilizing graph embedding algorithms,our next step was the retrieval of the attributes of individual gene and drug nodes.In this way,a hybrid feature charac-terization was represented by integrating graph features and node attributes.Machine learning(ML)models were built,enabling the fulfillment of transductive predictions of unknown DGIs.To realize inductive learning,this study generated an innovative idea of transforming known node vectors derived from the DDG graph into representations of unseen nodes using node similarities as weights,enabling inductive predictions for the unseen DGIs.Consequently,the final model was superior to existing models,with significant improvement in predicting both external unknown and unseen DGIs.The practical feasibility of our model was further confirmed through case study and molecular docking.In summary,this study establishes an efficient data-driven approach through the proposed modeling,suggesting its value as a promising tool for accelerating drug discovery and repurposing.

Objective: To analyze the association between each regional fat mass and total body bone mineral density (BMD) in children and adolescents aged 7-17 years in Beijing, so as to provide theoretical basis and practical guidance for implementing interventions. Methods: From September to December 2020, a stratified cluster random sampling method was used to select 1 423 children and adolescents aged 7-17 years in Tongzhou District, Beijing. Dual energy X-ray absorptiometry (DXA) was employed to measure regional body composition and total body BMD. Multiple linear regression was used to analyze the association between regional fat mass and total body BMD. Results: The median (interquartile range) fat mass values for total body, upper limbs, abdomen, hips, and thighs were 13.51(8.84, 19.21), 1.59(1.08, 2.23), 0.73(0.39, 1.29), 2.32(1.46, 3.26), 5.29(3.59, 7.21)kg, respectively. After adjusting for covariates, the results of multiple linear regression analysis showed that total body fat mass (β=0.010), abdominal fat mass (β=-0.100), and hip fat mass (β=0.104) were significant associations with total body BMD (all P<0.01). Sexstratified analysis revealed that in boys, total body fat mass (β=0.008) and hip fat mass (β=0.058) were positively associated with BMD, while thigh fat mass (β=-0.038) showed a negative association with total body BMD (all P<0.05). In girls, total body fat mass (β=0.013), hip fat mass (β=0.163), and thigh fat mass (β=0.023) were positively associated with total body BMD, whereas abdominal fat mass (β=-0.196) showed a negative association with total body BMD (all P<0.05). Among children and adolescents with body fat percentage below the standard range, within the standard range and above the standard range, total body fat masses were positively associated with total body BMD (β=0.021, 0.016, 0.015); among children and adolescents with body fat percentage within the standard range while upper limb (β=-0.042), abdominal (β=-0.067), and thigh fat mass (β=-0.018) showed negative associations with total body BMD, and hip fat mass demonstrated a positive association with total body BMD (β=0.082) (all P<0.05). Conclusion Regional fat distribution is associated with total body BMD in children and adolescents, with the nature of these associations varying by sex and body fat percentage.

This study aims to investigate the epidemic variation of porcine circovirus type2(PCV2)and its distribution in swine farms in Henan Province from 2022 to 2023.In this study,1 206 pig blood samples from 18 urban scale swine farms,and 318 pigs and environmental samples from 2 PCV2-positive swine farms in Henan Province were detected by fluorescence quantitative PCR.ORF2 gene sequencing and genetic evolution analysis were performed on the positive samples.The results showed that the positive rate of PCV2 was 7.05％(85/1 206)in Henan Province,11.15％in western Henan Province and 3.03％in southern Henan Province.The positive rate of PCV2 in spring was 11.60％(67/576).A total of 31 ORF2 gene sequences were obtained,of which 23 were PCV2d(74.19％),6 were PCV2a(19.35％),one was PCV2b(3.23％)and one was PCV2c(3.23％).The results of amino acid sequence comparison showed that the mutation sites mainly concentrated on amino acids at positions 40-68 and 180-209.The total positive rate of pig farm sam-ples was 37.42％(119/318),and the positive rate of pig samples from different types of pig houses was 60.71％(34/56),45.83％(11/24),35.09％(20/57),and 33.33(3/9)in order from high to low.The positive rate of environmental samples from high to low was 56.36％(31/55)in feeding system,46.67％(7/15)in feces system,12.50(1/8)in decontamination area,11.11％(2/18)in living area,and no PCV2 was detected in ventilation system.The highest positive rate was 45.00％(9/20)for the tools in the house,7.14％(1/14)for the surface and sole of the clothes after leaving the house,and no PCV2 was detected on the surface and sole of the clothes before entering the house.In this study,the epidemiological investigation of PCV2 in Henan Province and its distribu-tion in swine farms were carried out,and the main circulating strains,seasons,regions and gene mutations of PCV2 in H enan Province were identified.The distribution and transmission routes of PCV2 in positive pig farms were preliminarily analyzed and summarized,which provided data basis and reference for effective prevention and control of PCV and the establishment of accurate bio-safety prevention and control measures.

Objective: To explore sex difference in the cardiovascular health (CVH) status of 6-8 year old children in Beijing, so as to inform the early intervention of CVH related lifestyles. Methods: Based on the Beijing Children s Growth and Health Cohort (PROC), baseline physical examination, sequential questionnaire survey, and laboratory tests were conducted among 1 914 grade 1 students. Children s CVH and its subscales (health behaviors and health factors) scores were calculated according to the Life s Essential 8 (LE 8) index and categorized into high, moderate, and low CVH. CVH scores were reported as medians and interquartile ranges; sex differences were compared using the Chi square test and Wilcoxon test. Results: Among the 1 914 participants, the percentages of high, moderate, and low CVH were 35.7%, 63.5%, and 0.8%, respectively, and the percentages of high, moderate, and low health behavior scores were 25.9%, 67.5%, and 6.6%, respectively, with no statistically significant differences between sex ( χ 2=2.30, 0.07, P >0.05). The rates of high, moderate, and low health factor scores for boys and girls were 61.1%, 36.0%, 2.9% and 71.1%, 28.4%, 0.5%, respectively, with a statistically significant sex difference ( χ 2=31.88, P < 0.01). The overall CVH score was 76.0(70.0, 83.0), 76.0(69.0, 82.0) for boys, and 77.0(71.0, 83.0) for girls. Among the health behavior metrics, sleep scores were the best and physical activity scores were the worst[100.0(90.0,100.0), 40.0(20.0, 80.0 )]; among the health factor metrics, blood glucose scores were the best and lipid scores were the worst[100.0(100.0,100.0), 60.0(40.0,100.0)]. In respect to health factors, there were significant gender differences in body mass index, blood lipids, blood sugar, and blood pressure scores ( Z =-6.92, 3.01, -6.60, -2.30, <0.05), but there were no significant gender differences in diet, physical activity, nicotine exposure, or sleep scores with regards to health behaviors ( Z =0.99, 0.88, -0.13, 0.36, P > 0.05 ). Compared to boys, girls in the low and moderate CVH groups had high health factor scores despite low health behavior scores. Conclusion Most 6 to 8-year-old children in Beijing were found to have relatively good CVH, and optimization of children s CVH status can be achieved by promoting healthier lifestyles and monitoring health factors, especially among boys.