Objective: To characterize longitudinal trajectories of testicular development in boys and breast development in girls, so as to provide reference data for understanding patterns of pubertal sexual maturation. Methods: Based on the Shanghai Pudong New Area Cohort Study on Growth, Development and Health in Children and Adolescents, a baseline survey was conducted in 2020 using a mult stage cluster random sampling method. A total of 2 184 children who completed all follow ups during the primary school period from 13 elementary schools in Pudong New Area,Shanghai,with annual follow ups during 2021-2025. Testicular volume and Tanner stage of breast development were assessed by professional physicians using standardized visual inspection and palpation. The age distribution of testicular volume and breast development was fitted by using cumulative link mixed models and Turnbull s nonparametric maximum likelihood estimation method. Results: Median ages for testicular volumes of 2, 3, 4 and 5 mL in boys were 7.07, 9.24, 10.29, and 11.57 years old, respectively. Median ages for Tanner breast stages Ⅱ, Ⅲ, Ⅳ, and Ⅴ in girls were 8.55 , 10.17, 11.18, and 13.78 years old, respectively. Based on overweight and obesity, stratified analysis showed that earlier pubertal onset among overweight/obesity children, and the key milestones for pubertal initiation were testicular volume reaching 4 mL in boys and breast Tanner II in girls for 10.29, 10.83; 8.18, 9.00 years. Conclusion Overweight and obesity are associated with earlier pubertal initiation,but there are certain gender and developmental stage specific patterns.

The interest in covalent drugs has resurged in recent decades, spurring the development of numerous specialized computational docking tools to facilitate covalent ligand design and screening. Herein, we present CarsiDock-Cov, a new paradigm distinguishing itself as the first deep learning (DL)-guided approach for covalent docking. CarsiDock-Cov retains the core components of its non-covalent predecessor, leveraging a DL model pretrained on millions of docking complexes to predict protein-ligand distance matrices, along with a dedicated-designed geometric optimization procedure to convert these distances into refined binding poses. Additionally, it incorporates several key enhancements specifically tailored to optimize the protocol for covalent docking applications. Our approach has been extensively validated on multiple public datasets regarding the docking and screening of covalent ligands, and the results indicate that our approach not only achieves comparably improved applicability compared to its non-covalent predecessor, but also exhibits competitive performance against various state-of-the-art covalent docking tools. Collectively, our approach represents a significant advance in covalent docking methodology, offering an automated and efficient solution that shows considerable promise for accelerating covalent drug discovery and design.

P-glycoprotein (P-gp) is a transmembrane protein widely involved in the absorption, distribution, metabolism, excretion, and toxicity (ADMET) of drugs within the human body. Accurate prediction of P-gp inhibitors and substrates is crucial for drug discovery and toxicological assessment. However, existing models rely on limited molecular information, leading to suboptimal model performance for predicting P-gp inhibitors and substrates. To overcome this challenge, we compiled an extensive dataset from public databases and literature, consisting of 5,943 P-gp inhibitors and 4,018 substrates, notable for their high quantity, quality, and structural uniqueness. In addition, we curated two external test sets to validate the model's generalization capability. Subsequently, we developed a multimodal graph contrastive learning (GCL) model for the prediction of P-gp inhibitors and substrates (MC-PGP). This framework integrates three types of features from Simplified Molecular Input Line Entry System (SMILES) sequences, molecular fingerprints, and molecular graphs using an attention-based fusion strategy to generate a unified molecular representation. Furthermore, we employed a GCL approach to enhance structural representations by aligning local and global structures. Extensive experimental results highlight the superior performance of MC-PGP, which achieves improvements in the area under the curve of receiver operating characteristic (AUC-ROC) of 9.82% and 10.62% on the external P-gp inhibitor and external P-gp substrate datasets, respectively, compared with 12 state-of-the-art methods. Furthermore, the interpretability analysis of all three molecular feature types offers comprehensive and complementary insights, demonstrating that MC-PGP effectively identifies key functional groups involved in P-gp interactions. These chemically intuitive insights provide valuable guidance for the design and optimization of drug candidates.

There is a large gap between production and demand of plant oil in China, which leads to the heavy reliance on imports. Diacylglycerol acyltransferase (DGAT) and phospholipid: diacylglycerol acyltransferase (PDAT) are two key enzymes responsible for the synthesis of triacylglycerol, thereby affecting the yield and quality of plant oil. This paper comprehensively reviews the research progress in DGAT and PDAT in terms of their biological functions in plant oil synthesis, the molecular mechanisms of regulating plant lipid metabolism, growth, and development under stress, and their roles in driving oil synthesis under the background of synthetic biology. Furthermore, future research and application of DGAT and PDAT are prospected. This review aims to provide a basis for deeply understanding the molecular mechanism of plant oil synthesis and improving the quality and productivity of oil crops by the utilization of DGAT and PDAT genes.
Diacylglycerol O-Acyltransferase/physiology* ; Plant Oils/metabolism* ; Acyltransferases/metabolism* ; Lipid Metabolism/genetics* ; Gene Expression Regulation, Plant ; Triglycerides/biosynthesis*

P-glycoprotein(P-gp)is a transmembrane protein widely involved in the absorption,distribution,metabolism,excretion,and toxicity(ADMET)of drugs within the human body.Accurate prediction of P-gp inhibitors and substrates is crucial for drug discovery and toxicological assessment.However,existing models rely on limited molecular information,leading to suboptimal model performance for predicting P-gp inhibitors and substrates.To overcome this challenge,we compiled an extensive dataset from public databases and literature,consisting of 5,943 P-gp inhibitors and 4,018 substrates,notable for their high quantity,quality,and structural uniqueness.In addition,we curated two external test sets to validate the model's generalization capability.Subsequently,we developed a multimodal graph contrastive learning(GCL)model for the prediction of P-gp inhibitors and substrates(MC-PGP).This framework integrates three types of features from Simplified Molecular Input Line Entry System(SMILES)sequences,molecular fingerprints,and molecular graphs using an attention-based fusion strategy to generate a unified mo-lecular representation.Furthermore,we employed a GCL approach to enhance structural representations by aligning local and global structures.Extensive experimental results highlight the superior perfor-mance of MC-PGP,which achieves improvements in the area under the curve of receiver operating characteristic(AUC-ROC)of 9.82％and 10.62％on the external P-gp inhibitor and external P-gp substrate datasets,respectively,compared with 12 state-of-the-art methods.Furthermore,the interpretability analysis of all three molecular feature types offers comprehensive and complementary insights,demonstrating that MC-PGP effectively identifies key functional groups involved in P-gp interactions.These chemically intuitive insights provide valuable guidance for the design and optimization of drug candidates.

Objective To investigate the prevalence of Echinococcus infections in small rodents around human residential areas in Yushu City, Qinghai Province in 2023, so as to provide insights into precision echinococcosis control. Methods One or two quadrats, each measuring 50 m × 50 m, were randomly assigned in Shanglaxiu Township and Longbao Township, Yushu City, Qinghai Province on June 2023, respectively, and 300 plate-type mouse traps, each measuring 12.0 cm × 6.5 cm, were assigned in each quadrat. Small rodents were captured during the period between 10 : 00 and 18 : 00 each day for 4 days. Then, all captured small rodents were identified and dissected, and liver specimens with suspected Echinococcus infections were subjected to pathological examinations. The Echinococcus cytochrome c oxidase 1 (cox1) gene was amplified using PCR assay, and the sequence of the amplified product was aligned to that was recorded in the GenBank to characterize the parasite species. In addition, a phylogenetic tree of Echinococcus was generated based on the cox1 gene sequence using the neighbor-joining method. Results A total of 236 small rodents were captured in Shanglaxiu and Longbao townships, Yushu City, including 65 Qinghai voles and 51 plateau pikas in Shanglaxiu Township, and 62 Qinghai voles and 58 plateau pikas in Longbao Township, and there was no significant difference in the constituent ratio of small rodents between the two townships (χ² = 0.294, P > 0.05). Seven plateau pikas and 12 Qinghai voles were suspected to be infected with Echinococcus by dissection, and pathological examinations showed unclear structure of hepatic lobules and disordered hepatocyte arrangement in livers of small rodents suspected of Echinococcus infections. PCR assay identified E. shiquicus DNA in 7 Qinghai voles, which were all captured from Shanglaxiu Township. Phylogenetic analysis showed that the cox1 gene sequence of Echinococcus in small rodents was highly homologous to the E. shiquicus cox1 gene sequence reported previously. Conclusion Plateau pika and Qinghai vole were predominant small rodents around human residential areas in Yushu City, Qinghai Province in 2023, and E. shiquicus infection was detected in Qinghai voles.

Objective To study the clinical efficacy of temporal small bone window microscopic craniot-omy in treating cerebral hemorrhage in basal ganglia region.Methods The retrospective analysis was adopt-ed.A total of 130 patients with intracerebral hemorrhage in the basal ganglia region treated in the Department of Neurosurgery of this hospital from January 2020 to January 2023 were selected as the study subjects.The patients treated by traditional frontotemporal large bone flap craniotomy hematoma removal were included in-to the control group (n=82) and the patients adopting temporal straight incision small bone window hemato-ma removal were included into the study group (n=48).The general data,surgical indicators,clinical efficacy,degree of neurological impairment,postoperative complications and postoperative quality of life of the patients were statistically analyzed.Results There were no statistically significant differences in preoperative Glasgow (GCS) score,gender,age and hematoma volume between the two groups (P>0.05);there was no statistically significant difference in the average hematoma clearance rate between the two groups (P>0.05);compared with the control group,the operation time and hospital stay of the study group were shorter,the incidence of severe edema and suboccipital fluid accumulation were lower,and the good prognosis rate was higher,with sta-tistical significance (P<0.05).Conclusion Temporal straight incision small bone window microscopic hema-toma removal in treating cerebral hemorrhage in basal ganglia region has mild edema reaction of brain tissue in operative area,the neurological function obtains the better protection and the good prognostic rate of the pa-tients is significantly increased.

Background Heavy metals may play an important role in environmental risk factors associated disorders of activities of daily living (ADL) in older adults. Objective To investigate the associations between plasma levels of six heavy metals (zinc, arsenic, cadmium, lead, manganese, and copper) and ADL disorders in older adults. Methods A cross-sectional survey was conducted from 2018 to 2019 among 1412 rural elderly people in Gongcheng Yao Autonomous County, Guangxi Zhuang Autonomous Region. The plasma metal concentrations were detected by inductively coupled plasma mass spectrometry (ICP-MS), and subsequently classified into three groups (T1-T3) based on tertiles, with the T1 group as the reference. The samples were assessed for ADL disorders using the ADL scale. Logistic regression and restricted cubic spline model (RCS) were used to estimate the odds ratio (OR) and 95% confidence interval (CI) to assess the associations between heavy metals and the prevalence of reporting ADL disorders. Results The mean age of the study population was (68.52 ± 5.92) years, 825 (58.4%) female and 587 (41.6%) male. Of these, 372 (26.34%) subjects reported ADL disorders, and most of them were female (74.30%). The results of logistic regression showed that the participants in the cadmium T2 group (0.15-0.25 µg·L⁻¹) had a higher risk of ADL disorders compared to the T1 group (≤0.15 μg·L⁻¹) (OR=1.552, 95%CI: 1.086, 2.134). After stratification by sex, the relative risk of ADL disorders was lower in the plasma copper T3 group (>1019.58 µg·L⁻¹) compared to the T1 group (≤868.12 μg·L⁻¹) in men (OR=0.481, 95%CI: 0.232, 0.998). The relative risk of ADL disorders was higher in the plasma cadmium T2 group (0.15-0.25 µg·L⁻¹) compared to the T1 group (≤0.15 μg·L⁻¹) in women (OR=1.758, 95%CI: 1.182, 2.616). The RCS results showed that the risk of ADL disorders in men was nonlinearly associated with copper (P_nonlinear=0.011, P_overall<0.05). Conclusion High levels of cadmium are positively associated with the risk of reporting ADL disorders, while high levels of copper are negatively associated with the risk of reporting ADL disorders in men.

Acid-base dissociation constant (pK_a) is a key physicochemical parameter in chemical science, especially in organic synthesis and drug discovery. Current methodologies for pK_a prediction still suffer from limited applicability domain and lack of chemical insight. Here we present MF-SuP-pK_a (multi-fidelity modeling with subgraph pooling for pK_a prediction), a novel pK_a prediction model that utilizes subgraph pooling, multi-fidelity learning and data augmentation. In our model, a knowledge-aware subgraph pooling strategy was designed to capture the local and global environments around the ionization sites for micro-pK_a prediction. To overcome the scarcity of accurate pK_a data, low-fidelity data (computational pK_a) was used to fit the high-fidelity data (experimental pK_a) through transfer learning. The final MF-SuP-pK_a model was constructed by pre-training on the augmented ChEMBL data set and fine-tuning on the DataWarrior data set. Extensive evaluation on the DataWarrior data set and three benchmark data sets shows that MF-SuP-pK_a achieves superior performances to the state-of-the-art pK_a prediction models while requires much less high-fidelity training data. Compared with Attentive FP, MF-SuP-pK_a achieves 23.83% and 20.12% improvement in terms of mean absolute error (MAE) on the acidic and basic sets, respectively.

Objective:To establish a decision tree model of pediatric complicated appendicitis (CA) based on Pediatric Appendicitis Score (PAS) combined with inflammatory indicators, and to evaluate its clinical application efficacy in pediatrics.Methods:The clinical data of 544 children diagnosed with appendicitis in Children′s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine from January 2018 to December 2021 was retrospectively analyzed. According to postoperative pathology, the children were divided into uncomplicated appendicitis group and CA group. The independent risk factors of CA were screened by univariate and multivariate logistic regression analysis, and these parameters were included to establish the decision tree model. The accuracy of the decision tree model was verified by receiver operating characteristic (ROC) curve.Results:Binary logistic regression analysis indicated that the PAS, C-reactive protein (CRP) and neutrophil to lymphocyte ratio (NLR) were identified as independent risk factors for complicated appendicitis in children (all P<0.05). PAS, CRP and NLR were included as covariables to construct the decision tree model and binary logistic regression model for predicting CA. The decision tree demonstrated an overall accuracy of 79.2% with a sensitivity of 86.7% and specificity of 71.9%, and achieved an area under curve (AUC) of 0.821(95% CI: 0.786-0.857). The binary logistic regression model had a sensitivity of 79.6% and specificity of 69.1%, with an overall accuracy of 75.1% and achieved an AUC of 0.808(95% CI: 0.770-0.845). Conclusions:The decision tree model based on PAS score combined with CRP, NLR is a simple, intuitive and effective tool , which can provide pediatric emergency physicians a reliable basis for diagnosis of pediatric CA.