Reproductive system diseases are among the primary contributors to the decline in social fertility rates and the intensification of aging, posing significant threats to both physical and mental health, as well as quality of life. Recent research has revealed the substantial potential of the gut microbiota in improving reproductive system diseases. Under healthy conditions, the gut microbiota maintains a dynamic balance, whereas dysfunction can trigger immune-inflammatory responses, metabolic disorders, and other issues, subsequently leading to reproductive system diseases through the gut-gonadal axis. Reproductive diseases, in turn, can exacerbate gut microbiota imbalance. This article reviews the impact of the gut microbiota and its metabolites on both male and female reproductive systems, analyzing changes in typical gut microorganisms and their metabolites related to reproductive function. The composition, diversity, and metabolites of gut bacteria, such as Bacteroides, Prevotella, and Firmicutes, including short-chain fatty acids, 5-hydroxytryptamine, γ-aminobutyric acid, and bile acids, are closely linked to reproductive function. As reproductive diseases develop, intestinal immune function typically undergoes changes, and the expression levels of immune-related factors, such as Toll-like receptors and inflammatory cytokines (including IL-6, TNF-α, and TGF-β), also vary. The gut microbiota and its metabolites influence reproductive hormones such as estrogen, luteinizing hormone, and testosterone, thereby affecting folliculogenesis and spermatogenesis. Additionally, the metabolism and absorption of vitamins can also impact spermatogenesis through the gut-testis axis. As the relationship between the gut microbiota and reproductive diseases becomes clearer, targeted regulation of the gut microbiota can be employed to address reproductive system issues in both humans and animals. This article discusses the regulation of the gut microbiota and intestinal immune function through microecological preparations, fecal microbiota transplantation, and drug therapy to treat reproductive diseases. Microbial preparations and drug therapy can help maintain the intestinal barrier and reduce chronic inflammation. Fecal microbiota transplantation involves transferring feces from healthy individuals into the recipient’s intestine, enhancing mucosal integrity and increasing microbial diversity. This article also delves into the underlying mechanisms by which the gut microbiota influences reproductive capacity through the gut-gonadal axis and explores the latest research in diagnosing and treating reproductive diseases using gut microbiota. The goal is to restore reproductive capacity by targeting the regulation of the gut microbiota. While the gut microbiota holds promise as a therapeutic target for reproductive diseases, several challenges remain. First, research on the association between gut microbiota and reproductive diseases is insufficient to establish a clear causal relationship, which is essential for proposing effective therapeutic methods targeting the gut microbiota. Second, although gut microbiota metabolites can influence lipid, glucose, and hormone synthesis and metabolism via various signaling pathways—thereby indirectly affecting ovarian and testicular function—more in-depth research is required to understand the direct effects of these metabolites on germ cells or granulosa cells. Lastly, the specific efficacy of gut microbiota in treating reproductive diseases is influenced by multiple factors, necessitating further mechanistic research and clinical studies to validate and optimize treatment regimens.

Following the release of the Technical Requirements on Quality Control and Standard Establishment of Traditional Chinese Medicine Formula Granules by the National Medical Products Administration in 2021, Chinese Pharmacopoeia Commission has promulgated 296 national drug standards so far, and most provinces have started the work of establishing provincial standards as supplements. The promulgation of standards fostered high-quality development of the industry. Since the implementation of national and provincial standards for more than three years, enterprises have gained deep understanding and hands-on experiences on the characteristics, technical requirements, production process, and quality control of traditional Chinese medicine(TCM) formula granules. Meanwhile, challenges have emerged restricting the high-quality development of this industry, including how to formulate quality control strategies for medicinal materials and decoction pieces, how to reduce manufacturing costs, and how to improve the pass rate and product stability under high standards. Based on the work experiences from standard management and process research, this article analyzed the distribution of sources, processing methods, dry extract rate ranges, process requirements for volatile oil-containing decoction pieces, control measures of safety indices, characteristics and trends of setting characteristic chromatograms or fingerprints, characteristics and trends of setting content ranges, and main differences between national standards and provincial standards. On the one hand, this article aims to present main characteristics for deeply understanding different indicators in standards and provide basic ideas for establishing quality and process control systems. On the other hand, from the perspective of industrial practice, suggestions are put forward on the important aspects that need to be focused on in the quality and process control of TCM formula granules.
Drugs, Chinese Herbal/chemistry* ; Quality Control ; Medicine, Chinese Traditional/standards* ; China ; Drug Industry/standards*

OpenSim is an open source, free motion simulation and gait analysis software, which can be used to dynamically simulate and analyze the complex motion of the human body, and is widely used in human biomechanical research. Since OpenSim can analyze multi-dimensional motion data such as muscle strength, joint torque, and muscle synergistic activation during human movement, it can be used to study the biomechanical mechanism of musculoskeletal imbalance diseases and various treatment methods in TCM orthopedics, and has a broad application prospect in the field of TCM orthopedics. By the analysis of the basic characteristics, elements, analysis process, and application prospects of OpenSim, it is concluded that OpenSim musculoskeletal model has a large application space in the field of traditional Chinese medicine orthopedic, which is helpful to explain the pathogenesis and mechanism of diseases, and promote the precision diagnosis and treatment of orthopedics diseases;the application of OpenSim musculoskeletal model can solve the problem that the previous research paid attention to the bone malalignment and not enough attention to the tendon, and provide a new method for the research of orthopedic diseases. At present, there are still problems in the promotion and application of OpenSim, such as large equipment requirements and high operation threshold. Therefore, multidisciplinary cooperation, clinical research, and data sharing are the basic research strategies in this field.
Humans ; Biomechanical Phenomena ; Orthopedics ; Traumatology ; Software ; Medicine, Chinese Traditional ; Musculoskeletal System ; Models, Biological

OBJECTIVE: To construct an integrated management model for early screening and diagnosis of PCa based on the Innovative Care for Chronic Conditions Framework (ICCC) and the 1+1 contract-based tiered diagnosis and treatment system (TDTS) in China. METHODS: Based on the 1+1 contract-based TDTS platform, we conducted PCa screening for the male residents aged 60 years and above during health check-ups in Pujin Community Health Center from January 1, 2023 to December 31, 2023. For those with abnormal total prostate-specific antigen (tPSA) ≥ 4 μg/L, we promptly referred them to higher-level hospitals for further diagnosis and treatment via the two-way referral green channel platform and information sharing service using the 1+1 contract model. We further analyzed the relevant data on screening and diagnosis. RESULTS: A total of 4 080 males aged 71.39±5.059 years received PCa screening from January to December 2023. PSA screening was performed in 43.96% of the male residents, revealing 654 cases of PSA abnormality, with a PSA positivity rate of 16.03%, which was higher than that found in the previous large-scale PCa screenings in other regions of China. Among the males with PSA abnormality, 292 (44.65%) expressed their willingness for medical referral, while the others did not seek further medical attention for reasons of being asymptomatic, low awareness of the disease, no accompany for medical visits, and concerns about further costs of diagnosis and treatment. Prostate biopsy was recommended to 154 cases after further examinations, which was accepted by 92 (59.74%). Fifty-eight cases were diagnosed with Pa, and thedetection rate reached 63.04%. CONCLUSION The integrated management model for PSA examination-based early screening and diagnosis of PCa using the 1+1 contract-based TDTS platform is plays a significant role in enhancing people's awareness and knowledge of PCa and improving the early detection rate of the malignancy.
Humans ; Male ; Prostatic Neoplasms/diagnosis* ; Early Detection of Cancer ; Prostate-Specific Antigen/blood* ; Aged ; China ; Mass Screening ; Middle Aged ; Chronic Disease

Objective To investigate the risk factors of in-hospital mortality and establish a risk prediction model for patients receiving venoarterial extracorporeal membrane oxygenation(VA-ECMO).Methods We retrospectively collected the data of 302 patients receiving VA-ECMO in ICU of 3 hospitals in Guangdong Province between January,2015 and January,2022 using a convenience sampling method.The patients were divided into a derivation cohort(201 cases)and a validation cohort(101 cases).Univariate and multivariate logistic regression analyses were used to analyze the risk factors for in-hospital death of these patients,based on which a risk prediction model was established in the form of a nomogram.The receiver operator characteristic(ROC)curve,calibration curve and clinical decision curve were used to evaluate the discrimination ability,calibration and clinical validity of this model.Results The in-hospital mortality risk prediction model was established based the risk factors including hypertension(OR=3.694,95%CI:1.582-8.621),continuous renal replacement therapy(OR=9.661,95%CI:4.103-22.745),elevated Na2+ level(OR=1.048,95%CI:1.003-1.095)and increased hemoglobin level(OR=0.987,95%CI:0.977-0.998).In the derivation cohort,the area under the ROC curve(AUC)of this model was 0.829(95%CI:0.770-0.889),greater than those of the 4 single factors(all AUC<0.800),APACHE Ⅱ Score(AUC=0.777,95%CI:0.714-0.840)and the SOFA Score(AUC=0.721,95%CI:0.647-0.796).The results of internal validation showed that the AUC of the model was 0.774(95%CI:0.679-0.869),and the goodness of fit test showed a good fitting of this model(χ2=4.629,P>0.05).Conclusion The risk prediction model for in-hospital mortality of patients on VA-ECMO has good differentiation,calibration and clinical effectiveness and outperforms the commonly used disease severity scoring system,and thus can be used for assessing disease severity and prognostic risk level in critically ill patients.

Objective:To understand the epidemiology of rubella and the genetic characteristics of the virus circulating during the period 2021-2022, providing basic scientific data for rubella prevention and control in China.Methods:National rubella incidence data for the period 2021-2022 were obtained from the Infectious Disease Surveillance System module and the Surveillance Report Management module of the China′s Disease Prevention and Control Information System. Positive rubella virus(RuV)isolates were obtained from the National Measles/Rubella Laboratory Network. Two nucleotide (nt) fragments [F1-480 (8 633-9 112 nt) and F2-633 (8 945-9 577 nt)] located in the E1 gene were amplified and determined by reverse transcription polymerase chain reaction (RT-PCR), and the target gene (E1-739) was obtained after collating and splicing. The sequences obtained in this study were used to construct a phylogenetic tree with the reported reference strains for genotype and lineage identification. Additionally, the phylogenetic analysis was performed to assess their genetic relatedness of RuV strains prevalent in China during 2018-2020 from GenBank database.Results:In 2021-2022, the rubella incidence in China was 0.06/100, 000 (2021: 840 cases; 2022: 784 cases), with cases primarily concentrated in the western and southern provinces. Age distribution analysis showed that rubella cases in 2021-2022 was mainly in children under 5 years of age (2021: 34.17%, 287/840; 2022: 42.09%, 330/784), with the highest proportion in children aged 0-2 years. Further analysis of the immunization history of cases revealed that in the 8-23 months age group, a significant proportion of cases had received only one dose of rubella containing vaccine (RCV); cases in the 2-14 years age group were mainly among children who had received two or more doses of RCV; however, cases over 15 years of age were primarily found in individuals who had not received RCV or had unknown immunization history. National virological surveillance data showed that totally 22 RuV virus isolates were obtained, from 6 provinces in China during 2021-2022, which belonged to lineage 1E-L2 (11 strains) and 2B-L2c (11 strains). And these viruses displayed high genetic homology with RuV prevalent from 2018 to 2020.Conclusions:The incidence of rubella in China was maintained at a low level during 2021-2022, and the prevalent RuV strains were lineage 1E-L2 and 2B-L2c.

Objective To investigate the risk factors of in-hospital mortality and establish a risk prediction model for patients receiving venoarterial extracorporeal membrane oxygenation(VA-ECMO).Methods We retrospectively collected the data of 302 patients receiving VA-ECMO in ICU of 3 hospitals in Guangdong Province between January,2015 and January,2022 using a convenience sampling method.The patients were divided into a derivation cohort(201 cases)and a validation cohort(101 cases).Univariate and multivariate logistic regression analyses were used to analyze the risk factors for in-hospital death of these patients,based on which a risk prediction model was established in the form of a nomogram.The receiver operator characteristic(ROC)curve,calibration curve and clinical decision curve were used to evaluate the discrimination ability,calibration and clinical validity of this model.Results The in-hospital mortality risk prediction model was established based the risk factors including hypertension(OR=3.694,95%CI:1.582-8.621),continuous renal replacement therapy(OR=9.661,95%CI:4.103-22.745),elevated Na2+ level(OR=1.048,95%CI:1.003-1.095)and increased hemoglobin level(OR=0.987,95%CI:0.977-0.998).In the derivation cohort,the area under the ROC curve(AUC)of this model was 0.829(95%CI:0.770-0.889),greater than those of the 4 single factors(all AUC<0.800),APACHE Ⅱ Score(AUC=0.777,95%CI:0.714-0.840)and the SOFA Score(AUC=0.721,95%CI:0.647-0.796).The results of internal validation showed that the AUC of the model was 0.774(95%CI:0.679-0.869),and the goodness of fit test showed a good fitting of this model(χ2=4.629,P>0.05).Conclusion The risk prediction model for in-hospital mortality of patients on VA-ECMO has good differentiation,calibration and clinical effectiveness and outperforms the commonly used disease severity scoring system,and thus can be used for assessing disease severity and prognostic risk level in critically ill patients.