In recent years, China has been facing the dual challenges of declining fertility rates and births, with male reproductive health issues, especially the decline in semen quality, identified as a pivotal contributor to this phenomenon. Meanwhile, accumulating evidence indicates that air pollutants, an increasingly severe environmental problem, can damage semen quality not only directly through their biological toxicity but also indirectly by disrupting the composition of microbial communities in the gut and semen, thereby dysregulating immune function, endocrine homeostasis, and oxidative stress responses. The gut microbiota and semen microbiota, as important components of the human microecosystem, play crucial roles in maintaining reproductive health. This article comprehensively reviewed the research progress on the potential effects of air pollutants (particulate matter and gaseous pollutants), gut microbiota, and semen microbiota on semen quality. Specifically, it elucidated the mechanisms of interaction between these factors and explored how they affect male fertility.

With the emergence of deep learning techniques based on convolutional neural networks, artificial intelligence (AI) has driven transformative developments in the field of medical image analysis. Recently, large language models (LLMs) such as ChatGPT have also started to achieve distinction in this domain. Increasing research shows the undeniable role of AI in reshaping various aspects of medical image analysis, including processes such as image enhancement, segmentation, detection in image preprocessing, and postprocessing related to medical diagnosis and prognosis in clinical settings. However, despite the significant progress in AI research, studies investigating the recent advances in AI technology in the aforementioned aspects, the changes in research hotspot trajectories, and the performance of studies in addressing key clinical challenges in this field are limited. This article provides an overview of recent advances in AI for medical image analysis and discusses the methodological profiles, advantages, disadvantages, and future trends of AI technologies.
Artificial Intelligence ; Humans ; Image Processing, Computer-Assisted/methods* ; Neural Networks, Computer ; Deep Learning ; Diagnostic Imaging/methods*

Gut microbial communities are likely remodeled in tandem with accumulated physiological decline during aging, yet there is limited understanding of gut microbiome variation in advanced age. Here, we performed a metagenomics-based enterotype analysis in a geographically homogeneous cohort of 367 enrolled Chinese individuals between the ages of 60 and 94 years, with the goal of characterizing the gut microbiome of elderly individuals and identifying factors linked to enterotype variations. In addition to two adult-like enterotypes dominated by Bacteroides (ET-Bacteroides) and Prevotella (ET-Prevotella), we identified a novel enterotype dominated by Escherichia (ET-Escherichia), whose prevalence increased in advanced age. Our data demonstrated that age explained more of the variance in the gut microbiome than previously identified factors such as type 2 diabetes mellitus (T2DM) or diet. We characterized the distinct taxonomic and functional profiles of ET-Escherichia, and found the strongest cohesion and highest robustness of the microbial co-occurrence network in this enterotype, as well as the lowest species diversity. In addition, we carried out a series of correlation analyses and co-abundance network analyses, which showed that several factors were likely linked to the overabundance of Escherichia members, including advanced age, vegetable intake, and fruit intake. Overall, our data revealed an enterotype variation characterized by Escherichia enrichment in the elderly population. Considering the different age distribution of each enterotype, these findings provide new insights into the changes that occur in the gut microbiome with age and highlight the importance of microbiome-based stratification of elderly individuals.
Aged ; Aged, 80 and over ; Female ; Humans ; Male ; Middle Aged ; Bacteroides ; China ; Diabetes Mellitus, Type 2/microbiology* ; Escherichia coli/classification* ; Gastrointestinal Microbiome/genetics* ; Metagenomics ; East Asian People

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

Objective:To explore the correlation between the lesion imaging characteristics and the parent artery plaques detected by high-resolution magnetic resonance imaging (HR-MRI) in patients with recent small subcortical infarction (RSSI) in the lenticulostriate artery territory with mild stenosis of the parent artery.Methods:Consecutive patients with RSSI in the lenticulostriate artery territory admitted to Department of Neurology, the First Affiliated Hospital of Zhengzhou University from January 2020 to December 2022 were prospectively enrolled. Patients with stenosis of <50% in the ipsilateral middle cerebral artery (MCA) who had completed HR-MRI were included. The RSSI lesion characteristics, such as the lowest slice involved (LS), total number of slices involved (TNS) were evaluated based on serialized axial levels of diffusion weighted imaging. HR-MRI was utilized to assess the presence of dorsal superior plaques in the M1 segment of the responsible MCA. Patients were divided into plaque group and non-plaque group based on the presence or absence of plaque. Logistic regression analysis was carried out to identify the lesion characteristics associated with the presence of dorsal superior plaques in the MCA. A receiver operating characteristic (ROC) curve was employed to estimate the predictive efficacy of these parameters for MCA plaques using the area under the curve (AUC) and the optimal cut-off point.Results:A total of 112 patients were incorporated into the final analysis. The age of these patients was (57.08±11.90) years, and 78 patients (69.64%) were male. Plaques were detected on the dorsal superior wall of the MCA in 57 cases (50.89%) as the plaque group, and the other 55 cases (49.11%) were regarded as the non-plaque group. Multivariate Logistic regression revealed that LS was significantly associated with MCA plaques ( OR=0.674, 95% CI 0.485-0.937, P=0.019). The optimal cut-off point (LS≤1) was determined by ROC curve analysis (AUC=0.640, P=0.007). Conclusion:The findings suggest that in RSSI patients with mild stenosis of the parent artery, an LS≤1 is notably associated with the presence of dorsal superior plaques on MCA as detected by HR-MRI.

Objective:To evaluate the clinical outcomes of endoscopic-assisted polyether ether ketone (PEEK) patient-specific implant (PSI) revision for over-resected mandibles caused by the mandibular angle osteotomy.Methods:A retrospective analysis was conducted on 24 patients [8 males, 16 females, aged 19-57 (32.5±9.5) years] with 39 over-resected mandibles that underwent PEEK-PSI mandibular angle revision surgery at the Affiliated Friendship Plastic Surgery Hospital of Nanjing Medical University from January 2019 to December 2023. Preoperative cone-beam computed tomography (CBCT) data were used to design and fabricate customized PEEK PSIs based on individual anatomical requirements. An intraoral incision approach with endoscopic assistance was employed to meticulously dissect soft tissue attachment around the angle region, followed by the implantation of a customized PEEK PSI. Postoperative CBCT scans were performed for 3D reconstruction, with root mean square error (RMSE) and maximum deviation (MaxD) as accuracy metrics. Patients′ satisfaction was assessed preoperatively and ≥6 months postoperatively using the face questionnaire (FACE-Q) scores, which included overall facial appearance, lower face and jawline, appearance distress, psychological health and social function.Results:All 24 patients achieved satisfactory recovery with primary healing of intraoral incisions. No complications such as infection, nerve injury, or implant rejection occurred during follow-up period. Patients′ facial appearance and jaw line contouring were significantly improved. Fine anatomical fitting between PEEK-PSI and defect areas was observed: RMSE ranged from 0.117 to 0.315 mm, and MaxD was (5.485±1.300) mm. FACE-Q scores demonstrated significant improvements after surgery in overall facial appearance [(49.8±5.4) vs (65.0±5.3) scores], lower face and jawline [(42.5±5.3) vs (56.1±4.6) scores], appearance distress [(60.0±6.9) vs (70.6±6.5) scores], psychological health [(62.0±5.0) vs (70.8±5.3) scores], and social function [(60.3±4.3) vs (69.3±5.8) scores] (all P<0.001). Conclusion:Endoscopic-assisted PEEK-PSI revision for over-resected mandibles following mandibular angle osteotomy exhibits high surgical precision and safety, effectively restoring mandibular contour and significantly enhancing patients′ satisfaction.

Objective:To investigate the clinical outcomes of precapsular pocket repositioning for correcting implant malposition following augmentation mammoplasty.Methods:A retrospective analysis was conducted on 29 female patients aged 25-37 (28.8±3.4) years who underwent precapsular pocket repositioning at the Affiliated Friendship Plastic Surgery Hospital of Nanjing Medical University from December 2015 to August 2024. The surgical technique involved preserving the original capsule, creating a new implant pocket anterior to the capsule, and closing the original capsular space. Postoperative complications were recorded, and breast satisfaction was evaluated preoperatively and at 6 months postoperatively using the BREAST-questionnaire (BREAST-Q).Results:All 29 patients successfully underwent precapsular pocket repositioning with primary wound healing. During the follow-up period, all patients were satisfied with the correction of implant malposition. The mean BREAST-Q score improved significantly from (43.56±3.17) scores preoperatively to (72.56±13.49) scores at 6 months postoperatively ( P<0.001). No hematoma, implant rupture, recurrent malposition, capsular contracture, or surgical site infection occurred in any patient. Conclusion:Precapsular pocket repositioning provides favorable clinical outcomes for patients with implant malposition after augmentation mammoplasty, and there are no severe complications .

Objective:To investigate the clinical effect of the divisional design in composite labia minora and clitoral hood reduction.Methods:A retrospective analysis was performed on 57 patients diagnosed with labia minora and clitoral hood hypertrophy at Department of Plastic Surgery, Nanjing Medical University Friendship Plastic Surgery Hospital between July 2018 and May 2021. The ages of the patients ranged from 24 to 45 years with an average of (31.0±8.2) years. Preoperative symptoms and surgical demands included: appearance concerns in 57 cases (100%), irritation during friction in 49 cases (86.0%), and discomfort during intercourse in 22 cases (38.6%). The complex hypertrophy was divided into simple labia minora hypertrophy and clitoral hood hypertrophy. Then the reduction of clitoris hood was performed according to the prepuce morphology, and labiaplasty was performed using edge resection. The operative effects, patient satisfaction, and postoperative complications were assessed.Results:All incisions healed primarily with no flap necrosis or marginal infections. Hematoma occurred in 1 case which recovered well after timely treatment. All patients were followed up for 3-12 months. 54 cases were satisfied with the labia minora and clitoral hood shape and reported no sensory abnormality. Bilateral asymmetry occurred in 2 cases, one of which required further surgical repair. Scar hyperplasia occurred and was gradually softened after 6 months in 1 case. Of the 57 patients with appearance concerns, 55 (96.5%) patients reported improved appearance postoperatively. Of the 49 patients with preoperative friction discomfort, 47 (95.9%) patients achieved symptom relief. Of the 22 patients reporting sexual discomfort, 15 (68.1%) patients noted enhanced sexual satisfaction postoperatively. Overall, 54 (94.7%) patients were satisfied, 2 (3.5%) patients were moderately satisfied, and 1 (1.8%) patient was unsatisfied.Conclusions:For patients with labia minora and clitoral hood hypertrophy, the application of divisional design is surgically simple and safe. It achieves satisfactory outcomes with no severe adverse reactions.

The research on the cardiovascular toxicity of air pollutants is in urgent need of collaborative innovation across multiple models. This paper systematically reviewed the advantages and limitations of four principal research models of cardiotoxicity, including epidemiological model, mammalian model, zebrafish model, and in vitro model. Epidemiological models have been used to demonstrate a significant correlation between exposure to PM_2.5 and both the incidence and mortality of cardiovascular diseases within populations; however, these models face challenges in establishing causal inferences and interpreting individual mechanisms. Mammalian models have been applied to elucidate the pathogenic mechanisms of PM_2.5 at both the systemic and organ-specific levels, yet they encounter difficulties related to interspecies differences and throughput constraints. Zebrafish models, with their transparent embryos and observable development, offer a distinctive opportunity for high-throughput screening and mechanistic investigation of PM_2.5-induced cardiac developmental toxicity. Nonetheless, their cardiac physiological structure diverges from that of mammals, limiting their capacity to accurately model chronic conditions such as coronary heart disease. In vitro models, particularly human heart organoids and chip technologies, have provided profound insights into the direct toxic mechanisms of PM_2.5, including disruptions in calcium homeostasis, cellular senescence, and electrophysiological irregularities at the cellular and molecular levels. Despite these advancements, the complexity and developmental maturity of these models present challenges to their broader application. This paper proposed that the key to overcoming the bottlenecks of single models lies in the construction of an integrated evaluation system that combines “epidemiological studies, mammalian models, zebrafish models, and in vitro models”. By focusing on three aspects, namely model integration, technological convergence, and policy support, it is intended to collaboratively address issues such as standardization of multi-model data, simulation of complex exposure scenarios and susceptible life stages, and transformation pathways. This will provide innovative methodological support for the analysis of the cardiotoxic mechanisms of air pollutants, the assessment of environmental health impacts, and the formulation of precise prevention and control strategies.

Objective Focusing on gynecological surgery,we constructed a prediction model for surgical duration by extracting features from unstructured surgical planning texts and integrating multimodal data via artificial intelligence technology.Methods The clinical data of 34 614 patients who underwent gynecologic surgeries at West China Second University Hospital,Sichuan University between January 2022 and October 2024 were collected.An embedding-transformer model was constructed to convert surgical planning texts into a one-dimensional numerical feature,referred to as the step feature.The predictive value of the step feature was assessed by comparing the performance improvements of linear regression,random forest,eXtreme Gradient Boosting(XGBoost),support vector regression,K-nearest neighbor regression,and artificial neural network algorithms in two scenarios—with and without the step feature as an input.The out-of-sample prediction accuracy of the models was assessed using mean absolute error(MAE),root mean squared error(RMSE),and R-squared(R2).Furthermore,the model interpretability was examined using SHapley Additive exPlanations(SHAP)values.Results SHAP results showed that the step feature had the highest predictive contribution.Temporal factors in surgical scheduling also influenced gynecological surgery duration.The XGBoost model demonstrated optimal performance on the test set,significantly improving prediction accuracy with a 40.43%increase in R2,while reducing MAE and RMSE by 21.27%and 20.13%,respectively,compared to the baseline model without the step feature.Conclusion The embedding-transformer model developed in this study effectively extracts features from surgical planning texts and enhances the predictive performance of machine learning models.The XGBoost prediction model can assist hospital administrators in implementing more refined management of gynecological surgeries and improving the utilization efficiency of surgical resources.