Objective To investigate the species of sandflies and the prevalence of Leishmania infections in sandflies from selected areas of northern and northwestern China, so as to provide insights into identification of leishmaniasis vectors and assessment of epidemiological trends of leishmaniasis in China. Methods Sandfly samples were collected from Mentougou District of Beijing Municipality, Xiangning County in Linfen City of Shanxi Province, Ejin Banner in Alxa League of Inner Mongolia Autonomous Region, and Payzawat County of Karamay District of Karamay City, Gaochang District of Turpan City in Xinjiang Uygur Autonomous Region from July 2023 to July 2024. Approximately 100 intact female sandfly samples were randomly selected from each site and the species of sandflies was identified according to morphological characteristics and molecular assays. Female sandflies originating from the same habitat were grouped into pools of 10 individuals. Leishmania infection was detected using polymerase chain reaction (PCR) assay targeting the internal transcribed spacer 1 (ITS-1) gene, and the prevalence of Leishmania infection was calculated in sandflies from different sampling sites using the minimum infection rate (MIR) method. In addition, positive amplicons were sequenced and subjected to phylogenetic analysis. Results A total of 6 155 sandflies were collected from different environments at sampling sites across the six aforementioned regions from July 2023 to July 2024. Phlebotomus chinensis (96.00%) was the dominant sandfly species in Mentougou District, Beijing Municipality, with a small proportion of Ph. sergenti (4.00%), and only Ph. chinensis was found in Xiangning County, Linfen City, Shanxi Province. Ph. wui was the only sandfly species detected in Ejin Banner, Alxa League, Inner Mongolia Autonomous Region, and Payzawat County, Kashgar City, Xinjiang Uygur Autonomous Region, and Ph. caucasicus (97.70%) was the dominant sandfly species in Karamay District, Karamay City, Xinjiang Uygur Autonomous Region, with a small proportion of Ph. wui (2.30%), while Ph. alexandri was the only species in Gaochang District, Turpan City, Xinjiang Uygur Autonomous Region. A total of 40, 60, 34, 18, 18, and 22 pools of sandfly samples were tested from Mentougou District in Beijing Municipality, Xiangning County in Linfen City of Shanxi Province, Ejin Banner in Alxa League of Inner Mongolia Autonomous Region, Payzawat County in Kashgar City, Karamay District in Karamay City, and Gaochang District in Turpan City of Xinjiang Uygur Autonomous Region, respectively. L. infantum was detected in Ph. chinensis samples from Mentougou District in Beijing Municipality, and Xiangning County of Linfen City in Shanxi Province, with MIR of 0.25% to 1.00%, and L. donovani was detected in Ph. wui from Ejin Banner in Alxa League of Inner Mongolia Autonomous Region, and Payzawat County in Kashgar City of Xinjiang Uygur Autonomous Region, with MIR of 0.56% to 0.88%; however, no Leishmania infection was detected in Ph. caucasicus from Karamay District in Karamay City or Ph. alexandri from Gaochang District in Turpan City of Xinjiang Uygur Autonomous Region. Phylogenetic analysis showed that the Leishmania ITS-1 gene sequences obtained from Mentougou District in Beijing Municipality and Xiangning County in Linfen City of Shanxi Province were clustered into the same clade with the reference sequences of L. infantum ITS-1 gene, while the Leishmania ITS-1 gene sequences obtained from Ejin Banner in Alxa League of Inner Mongolia Autonomous Region and Payzawat County in Kashgar City of Xinjiang Uygur Autonomous Region were clustered into the same clade with the reference sequences of L. donovani ITS-1 gene. Conclusions There are variations in sandfly species in selected areas of northern and northwestern China, and variations in the species of Leishmania infecting sandflies. Improved surveillance of sandfly vectors and targeted control strategies with adaptations to geographical features and leishmaniasis vectors are recommended.

Objective: To translate the common metrics for donation attitude, subjective norm, perceived behavioral control, and intention for the blood donation context (BD-ASPI) into Chinese, and to test its reliability and validity among college students. Methods: A research team was established. Following Beaton's cross-cultural adaptation guidelines, the BD-ASPI was translated, culturally adapted, and pre-tested to develop the Chinese version. Using convenience sampling, 620 students from four universities in Wuhan were surveyed form August to November 2024 to test the scale's reliability and validity. Results: The Chinese version of the scale consisted of 21 items across four dimensions: attitude towards blood donation, subjective norm, perceived behavioral control, and intention. The item-level content validity index ranged from 0.89 to 1.00, and the average scale-level content validity index was 0.984. Confirmatory factor analysis indicated a good fit for the second-order factor model. The Criterion validity was 0.509 (P<0.001). The overall Cronbach's α coefficient was 0.965, with the coefficients for each dimension ranging from 0.891 to 0.974. The test-retest reliability was 0.894. Conclusion: The Chinese version of the BD-ASPI demonstrates good reliability and validity, and can serve as an effective tool for assessing the behavioral intention of voluntary blood donation among college students in China.

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.

Objective: To explore the value of contrast-enhanced computed tomography (CT) radiomics combined with machine learning algorithms in the preoperative prediction of perineural invasion (PNI) in oral squamous cell carcinoma (OSCC), aiming to provide evidence for assisting clinical treatment decision-making. Methods​: This study was approved by the Ethics Committee of the Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine. A total of 250 OSCC patients confirmed by postoperative pathology were included, comprising 128 PNI-positive and 122 PNI-negative cases. The dataset was randomly divided into training (n=175), validation (n=38), and independent testing (n=37) sets in a ratio of 7:1.5:1.5. Regions of interest were delineated on preoperative images, and radiomic features were extracted. After dimensionality reduction and feature selection using methods like Least Absolute Shrinkage and Selection Operator (LASSO) regression, multiple machine learning models, including support vector machine (SVM), random forest, Light gradient boosting machine (LightGBM), and a Stacking ensemble model, were constructed. Model performance was evaluated using metrics such as the area under the receiver operating characteristic curve (AUC), sensitivity, calibration curves, and decision curve analysis (DCA). Model interpretability was analyzed using Shapley additive explanations (SHAP) and grouped permutation feature importance analysis. Results ​: ​ Among the 250 samples analyzed, the LightGBM model based on radiomics demonstrated the best performance on the independent test set, with an AUC of 0.781, outperforming models like SVM (AUC = 0.730) and Random Forest (AUC = 0.691), as well as clinical models (AUCs ranging 0.549-0.711). The LightGBM model showed good calibration (Brier score 0.198), and DCA indicated high clinical net benefit over a wide threshold probability range. Paired DeLong tests revealed no statistically significant differences in AUC between the ensemble (Stacking) model and the corresponding best-performing radiomics-based model. SHAP analysis and grouped permutation feature importance analysis further indicated that the primary discriminative information for the model came from radiomic texture features. Conclusion ​ The LightGBM model based on contrast-enhanced CT radiomics demonstrated good discriminative ability for preoperative prediction of PNI in OSCC. In the independent test set, it achieved the highest AUC. This model holds promise as a non-invasive auxiliary tool for preoperative risk assessment. Given the limited sample size of the independent test set, these results require further validation in larger cohorts and external datasets.

In recent years， repetitive transcranial magnetic stimulation （rTMS） has garnered significant attention as a therapeutic approach for sleep disorders in the elderly. However， the prevailing rTMS protocols are predominantly developed based on normative neurophysiological data derived from young adults and fail to incorporate individualized parameters tailored to the brain characteristics of the elderly. To address this gap， the consensus development group synthesized the latest evidence from 2010 to 2025 and established a standardized rTMS protocol specifically for elderly patients with sleep disorders. Adhering to the Appraisal of Guidelines for Research and Evaluation II （AGREE II） framework， systematically screened randomized controlled trials （RCTs） and systematic reviews regarding rTMS in the treatment of sleep disorders across various conditions. Meanwhile， the Grading of Recommendations Assessment， Development and Evaluation （GRADE） system was employed to rigorously grade the quality of evidence and the strength of recommendations. This consensus guideline delineates precise rTMS protocols for the management of sleep disorders in the elderly， highlights the adjustment of stimulation intensity according to scalp-cortex distance recommends either MRI‑guided neuronavigation or the Beam F3/F4 heuristic approach for accurate target localization， thereby providing precise rTMS intervention protocol for sleep disorders in the elderly， aiming to enhance clinical efficacy while ensuring treatment safety. ［Funded by National Key Research and Development Program （number， 2023YFC3603200）； General Program of Shenzhen Science and Technology Innovation Commission （number， JCYJ20240813112859008， JCYJ20240813112900002）； Youth Program of Shenzhen Kangning Hospital （number， KN2023A004）； www.guidelines-registry.cn number， PREPARE-2026CN530］

ObjectiveTobacco-related diseases remain one of the leading preventable public health challenges worldwide and are among the primary causes of premature death. In recent years, accumulating evidence has supported the classification of nicotine addiction as a chronic brain disease, profoundly affecting both brain structure and function. Despite the urgency, effective diagnostic methods for smoking addiction remain lacking, posing significant challenges for early intervention and treatment. To address this issue and gain deeper insights into the neural mechanisms underlying nicotine dependence, this study proposes a novel graph neural network framework, termed TI-GNN. This model leverages functional magnetic resonance imaging (fMRI) data to identify complex and subtle abnormalities in brain connectivity patterns associated with smoking addiction. MethodsThe study utilizes fMRI data to construct functional connectivity matrices that represent interaction patterns among brain regions. These matrices are interpreted as graphs, where brain regions are nodes and the strength of functional connectivity between them serves as edges. The proposed TI-GNN model integrates a Transformer module to effectively capture global interactions across the entire brain network, enabling a comprehensive understanding of high-level connectivity patterns. Additionally, a spatial attention mechanism is employed to selectively focus on informative inter-regional connections while filtering out irrelevant or noisy features. This design enhances the model’s ability to learn meaningful neural representations crucial for classification tasks. A key innovation of TI-GNN lies in its built-in causal interpretation module, which aims to infer directional and potentially causal relationships among brain regions. This not only improves predictive performance but also enhances model interpretability—an essential attribute for clinical applications. The identification of causal links provides valuable insights into the neuropathological basis of addiction and contributes to the development of biologically plausible and trustworthy diagnostic tools. ResultsExperimental results demonstrate that the TI-GNN model achieves superior classification performance on the smoking addiction dataset, outperforming several state-of-the-art baseline models. Specifically, TI-GNN attains an accuracy of 0.91, an F1-score of 0.91, and a Matthews correlation coefficient (MCC) of 0.83, indicating strong robustness and reliability. Beyond performance metrics, TI-GNN identifies critical abnormal connectivity patterns in several brain regions implicated in addiction. Notably, it highlights dysregulations in the amygdala and the anterior cingulate cortex, consistent with prior clinical and neuroimaging findings. These regions are well known for their roles in emotional regulation, reward processing, and impulse control—functions that are frequently disrupted in nicotine dependence. ConclusionThe TI-GNN framework offers a powerful and interpretable tool for the objective diagnosis of smoking addiction. By integrating advanced graph learning techniques with causal inference capabilities, the model not only achieves high diagnostic accuracy but also elucidates the neurobiological underpinnings of addiction. The identification of specific abnormal brain networks and their causal interactions deepens our understanding of addiction pathophysiology and lays the groundwork for developing targeted intervention strategies and personalized treatment approaches in the future.

ObjectiveThe proteome of biological evidence contains rich genetic information, namely single amino acid polymorphisms (SAPs) in protein sequences. However, due to the lack of efficient and convenient analysis tools, the application of SAP in public security still faces many challenges. This paper aims to meet the application requirements of SAP analysis for forensic biological evidence’s proteome data. MethodsThe software is divided into three modules. First, based on a built-in database of common non-synonymous single nucleotide polymorphisms (nsSNPs) and SAPs in East Asian populations, the software integrates and annotates newly identified exonic nsSNPs as SAPs, thereby constructing a customized SAP protein sequence database. It then utilizes a pre-installed search engine—either pFind or MaxQuant—to perform analysis and output SAP typing results, identifying both reference and variant types, along with their corresponding imputed nsSNPs. Finally, SAPTyper compares the proteome-based typing results with the individual’s exome-derived nsSNP profile and outputs the comparison report. ResultsSAPTyper accepts proteomic DDA mass spectrometry raw data (DDA acquisition mode) and exome sequencing results of nsSNPs as input and outputs the report of SAPs result. The pFind and Maxquant search engines were used to test the proteome data of 2 hair shafts of2 individuals, and both obtained SAP results. It was found that the results of the Maxquant search engine were slightly less than those of pFind. This result shows that SAPTyper can achieve SAP fingding function. Moreover, the pFind search engine was used to test the proteome data of 3 hair shafts from 1 European person and 1 African person in the literature. Among the sites fully matched by the literature method, sites detected by SAPTyper are also included; for semi-matching sites, that is, nsSNPs are heterozygous, both literature method and SAPTyper method had the risk of missing detection for one type of the allele. Comparing the analysis results of SAPTyper with the SAP test results reported in the literature, it was found that some imputed nsSNP sites identified by the literature method but not detected by SAPTyper had a MAF of less than 0.1% in East Asian populations, and therefore they were not included in the common nsSNP database of East Asian populations constructed by this software. Since the database construction of this software is based on the genetic variation information of East Asian populations, it is currently unable to effectively identify representative unique common variation sites in European or African populations, but it can still identify SAP sites shared by these populations and East Asian populations. ConclusionAn automated SAP analysis algorithm was developed for East Asian populations, and the software named SAPTyper was developed. This software provides a convenient and efficient analysis tool for the research and application of forensic proteomic SAP and has important application prospects in individual identification and phenotypic inference based on SAP.

Colorectal cancer (CRC) is one of the leading causes of cancer-related morbidity and mortality worldwide, highlighting the urgent need for novel preventive and therapeutic strategies. Emerging research highlights the crucial role of the gut microbiota, including bacteria, fungi, viruses, and their metabolites, in the pathogenesis of CRC. Dysbiosis, characterized by an imbalance in microbial composition, contributes to tumorigenesis through immune modulation, metabolic reprogramming, and genotoxicity. Specific bacterial species, such as Fusobacterium nucleatum and enterotoxigenic Bacteroides fragilis , along with fungal agents like Candida species, have been implicated in CRC progression. Moreover, viral factors, including Epstein-Barr virus and human cytomegalovirus, are increasingly recognized for their roles in promoting inflammation and immune evasion. This review synthesizes the latest evidence on host-microbiome interactions in CRC, emphasizing microbial metabolites, such as short-chain fatty acids and bile acids, which may act as both risk factors and therapeutic agents. We further discuss the latest advances in microbiota-targeted clinical applications, including biomarker-assisted diagnosis, next-generation probiotics, and microbiome-based interventions. A deeper understanding of the role of gut microbiome in CRC pathogenesis could pave the way for diagnostic, preventive, and personalized therapeutic strategies.
Humans ; Dysbiosis/microbiology* ; Colorectal Neoplasms/metabolism* ; Gastrointestinal Microbiome/physiology* ; Animals