Background: Recurrent pregnancy loss undermines the physical and mental health of women. Recent randomized controlled trials have reported some effects of traditional Chinese medicine (TCM); however, whether various TCM methods have different effectiveness remains unclear. Objective: To comprehensively evaluate the efficacy and adverse events of TCM for patients with RPL and to explore whether various TCM methods have different effectiveness. Methods: Ten databases were searched up to May 27, 2024. The risk of bias was assessed using the RoB2 tool. The certainty of the evidence was evaluated using the grading of Recommendations, Assessment, Development, and Evaluation tool. Pairwise and network analyses were conducted using Stata 18.0. Results: A total of 47 randomized controlled trials enrolling 6678 women with RPL were included. Pairwise analysis showed that use of TCM had a significantly lower miscarriage rate (RR 0.50 [95% CI 0.45, 0.55]), lower preterm birth rate (RR 0.81 [95% CI 0.67, 0.98), and lower adverse event rate (RR 0.46 [95% CI 0.37, 0.58]). Moreover, use of TCM was associated with a higher alive-fetus rate (RR 1.21 [95% CI 1.15, 1.26]), live-birth rate (RR 1.20 [95% CI 1.15, 1.25]), and full-term rate (RR 1.37 [95% CI 1.23, 1.53]) compared with nonuse of TCM. Network analysis demonstrated that Bushenshugan combined with conventional Western medicine was ranked the best for the reduction of miscarriage rate. Discussion: Use of TCM is more likely to improve pregnancy outcomes and reduce adverse events compared with nonuse of TCM in patients with RPL. Different TCM methods have differences in reducing the miscarriage rate. The Bushenshugan method might be a potential optimal TCM therapy, but more high-quality evidence is needed to further validate and evaluate the efficacy and safety.

OBJECTIVE To construct a closed-loop management model for externally dispensed intravenous prescriptions， and to provide reference for standardized management of externally dispensed intravenous prescriptions. METHODS Based on the Expert Consensus on Closed-loop Management of Externally Dispensed Intravenous Prescriptions in Guangxi Zhuang Autonomous Region previously formulated by our hospital， risk points during the entire process were systematically identified through multidisciplinary team brainstorming and a fishbone diagram. A series of strategies were subsequently formulated and implemented， including qualifying designated external dispensing pharmacies and the drug catalogs， operating and maintaining the hospital information system and the Pharmacy Intravenous Admixture Service （PIVAS） intelligent management platform， and strengthening differentiated training for staff in the whole workflow. A whole-process closed-loop management system was constructed with PIVAS as the co re hub and the daytime chemotherapy center as the safety terminal. RESULTS A total of 3 cooperating pharmacies and an initial drug list comprising 35 product specifications were selected. A closed‑loop management process encompassing hospital outpatient prescribing， patient drug purchase in designated pharmacies， PIVAS drug dispensing， and medication use in daytime chemotherapy center was successfully established. This system enabled the mandatory grouping and association of externally dispensed intravenous prescriptions with in-hospital diluents， full-process verification based on drug traceability codes， intelligent monitoring of infusion parameters， and whole-process data traceability. CONCLUSIONS The constructed model effectively resolves the coordination and safety oversight during the use of externally dispensed intravenous drugs from out-of-hospital circulation to in-hospital use， and has preliminarily enabled procedural standardization， whole-process information traceability， and proactive control of medication risks.

Mitochondrial dysfunction in chondrocytes is a key pathogenic factor in osteoarthritis (OA), but directly modulating mitochondria in vivo remains a significant challenge. This study is the first to verify a correlation between mitochondrial dysfunction and the downregulation of the FOXO3 gene in the cartilage of OA patients, highlighting the potential for regulating mitophagy via FOXO3 gene modulation to alleviate OA. Consequently, we developed a chondrocyte-targeting CRISPR/Cas9-based FOXO3 gene-editing tool (FoxO3) and integrated it within a nanoengineered 'truck' (NETT, FoxO3-NETT). This was further encapsulated in injectable hydrogel microspheres (FoxO3-NETT@SMs) to harness the antioxidant properties of sodium alginate and the enhanced lubrication of hybrid exosomes. Collectively, these FoxO3-NETT@SMs successfully activate mitophagy and rebalance mitochondrial function in OA chondrocytes through the Foxo3 gene-modulated PINK1/Parkin pathway. As a result, FoxO3-NETT@SMs stimulate chondrocytes proliferation, migration, and ECM production in vitro, and effectively alleviate OA progression in vivo, demonstrating significant potential for clinical applications.

Objective:To compare the results of plasma samples and histological/cytological samples for detection of lung cancer driver gene by next-generation sequencing (NGS), to provide reference for sampling selection of clinical patients.Methods:A retrospective analysis was performed on 220 patients with lung cancer who were admitted to Quanzhou First Hospital in Fujian Province from May 2017 to May 2024, and NGS detection of lung cancer driver gene was performed on both plasma samples and histological/cytological samples. Histological specimens included biopsy or surgical resection of lung cancer, cervical lymph nodes and pleural metastases; the cytological specimen was pleural fluid cell wax block. Specimens were divided into plasma group (experimental group) and matched histological and cytological group (control group). Eight gene variants recommended by the guidelines were EGFR mutation, ALK rearrangement, ROS1 rearrangement, BRAF V600 mutation, RET rearrangement, MET exon 14 jump mutation, KRAS mutation, and NTRK1/2/3 rearrangement. The detection results of the two groups of specimens were compared and analyzed.Results:Among the 220 cases, 183 were adenocarcinoma, 23 were squamous cell carcinoma and 14 were non-small cell lung cancer. There were 4 cases in stage Ⅰ, 3 cases in stage Ⅱ, 24 cases in stage Ⅲ, and 189 cases in stage Ⅳ. In the plasma group, 120 cases were positive, the detection rate was 54.5%; There were 152 positive cases in the control group, the detection rate was 69.1%; the detection rate in the plasma group was lower than that in the control group ( χ2=6.12, P<0.05). The detection rate of plasma in patients with stage Ⅰ/Ⅱ/Ⅲ was 12.9% (4/31), which was significantly lower than that in stage Ⅳ (61.4%; χ2=22.10, P<0.05). In the early clinical stage (stage Ⅰ/Ⅱ) of 7 cases, 3 cases were positive in the control group, while all were negative in the plasma group. There were 24 stage Ⅲ cases, 8 were positive in the control group and 4 were positive in the plasma group. Among the positive cases in the control group, 34 were negative and 4 were not detected in the matched plasma group. In the plasma positive cases, there were 2 negative cases and 4 partial mutations were not detected in the matched control group. Among these 6 cases, 5 were treated patients, and the mean mutation abundance of corresponding plasma positive genes was 1.5%. There were 110 cases with the same positive result (the same mutation site) and 66 cases with the same negative result, with agreement rate of 80.0% (176/220). The sensitivity and specificity of the plasma group were 75.0% (114/152) and 91.7% (110/120), respectively. Conclusions:When NGS is used for lung cancer driver gene detection, the positive rate of plasma samples is lower than that of tissue/cytology samples, but the consistency rate with the latter can reach 80%, and the sensitivity is higher than 70%, which has a good clinical detection efficiency, especially for patients with non-small cell lung cancer stage Ⅳ.

Primary biliary cholangitis(PBC)is a chronic progressive autoimmune liver disease that is often comorbid with other connective tissue diseases(CTDs),and such comorbidity can significantly alter the natural course or clinical phenotype of PBC or CTDs,limiting available therapeutic drugs and complicating clinical decision-making.Due to the involvement of the interdisciplinary subjects of hepatology,rheumatology,and clinical immunology and a paucity of large-scale cohort data and in-depth basic research,there is a limited understanding of such comorbidity in clinical practice,which increases the complexity of clinical diagnosis and treatment.This article summarizes the comorbidity of PBC with common CTDs such as Sj?gren's syndrome,systemic sclerosis,systemic lupus erythematosus,and idiopathic inflammatory myopathies,and analyzes related immune mechanisms,clinical manifestations,diagnostic challenges,treatment strategies,and prognosis.It is expected to establish PBC-CTD comorbidity cohorts through future multidisciplinary collaborations,focus on genetic background,immune mechanisms,and multi-omics approaches,elucidate pathogenesis and novel therapeutic targets,and improve the prognosis of patients by optimizing treatment strategies through precision medicine and artificial intelligence.

Primary biliary cholangitis （PBC） is a chronic progressive autoimmune liver disease that is often comorbid with other connective tissue diseases （CTDs）， and such comorbidity can significantly alter the natural course or clinical phenotype of PBC or CTDs， limiting available therapeutic drugs and complicating clinical decision-making. Due to the involvement of the interdisciplinary subjects of hepatology， rheumatology， and clinical immunology and a paucity of large-scale cohort data and in-depth basic research， there is a limited understanding of such comorbidity in clinical practice， which increases the complexity of clinical diagnosis and treatment. This article summarizes the comorbidity of PBC with common CTDs such as Sjögren’‍s syndrome， systemic sclerosis， systemic lupus erythematosus， and idiopathic inflammatory myopathies， and analyzes related immune mechanisms， clinical manifestations， diagnostic challenges， treatment strategies， and prognosis. It is expected to establish PBC-CTD comorbidity cohorts through future multidisciplinary collaborations， focus on genetic background， immune mechanisms， and multi-omics approaches， elucidate pathogenesis and novel therapeutic targets， and improve the prognosis of patients by optimizing treatment strategies through precision medicine and artificial intelligence.

The major obstacle to optimizing the design of rare disease coverage is the fragmented decision-making process among medical services, pharmaceuticals, and medical insurance departments. There is an urgent need to realize data sharing and digital empowerment, as well as to adopt top-level design and systematic decision-making. It is also crucial to establish mechanisms, facilitated by digital intelligence, for sharing power and responsibilities, and assessing rewards and punishments. Furthermore, there is an urgent need to incorporate the theories of collaborative governance, digital governance, and the full life cycle into the entire process, which includes patient classification, diagnosis and treatment, medical assistance, medication protection, and health insurance fund management for rare diseases. This integration aims to provide theoretical reference for the effective linkage of medical services, pharmaceuticals, and medical insurance, and to improve the efficiency and equity of resource allocation in the public sector.

Pharmacoepidemiology is an interdisciplinary field that applies epidemiological methods to study drug use,effectiveness,and associated risk in populations.Standardizing research methods in this field is crucial for ensuring research quality and promote the development of the discipline.Based on the Guide on Methodological Standards in Pharmacoepidemiology in China(2nd edition),this article interprets the relevant contents about classic research types and their derivative designs.It aims to clarify the types of study methodological designs in pharmacoepidemiology,systematically describe the characteristics and applications classical derivative designs,and compare these with research design frameworks outlined in international pharmacoepidemiological guidelines.Compared to the first edition,the second edition of the guideline has updated and detailed the types of research,updating the research design to original research(interventional research and non-interventional research),secondary research(systematic review,Meta-analysis,economic analysis,etc.),and tertiary research(umbrella review,etc.).Additionally,a variety of derivative designs have been added,including target trial emulation,nested case-control and case-cohort studies,case-crossover designs,self-controlled case series and self-controlled risk interval designs,case-population studies,interrupted time-series analysis,and case-coverage(ecological)designs for vaccine surveillance.This article strengthens the operability of the theoretical guidance of pharmacoepidemiological research methods in practice and provides a reference for conducting high-quality pharmacoepidemiological research.

Objective:To comprehensively evaluate the application of Common Data Model (CDM) of Observational Medical Outcomes Partnership (OMOP) in China, and provide reference for the implementation of data standardization and evidence sharing in China.Methods:PubMed, Embase, Web of Science, CNKI, VIP, WanFang and SinoMed databases were used for literature retrieval to collect the research papers of OMOP CDM application for data standardization in China until March 15, 2023. The information about institutions, types and numbers of patients were extracted.Results:A total of 14 research papers, including 9 in English and 5 in Chinese, were selected. The research papers published since 2018 were collected, which focused on patients with hypertension, diabetes, and depression. A total of 12 institutions or platforms transformed data into OMOP CDM. Jiangsu Provincial People's Hospital was the first one to apply the CDM and demonstrated its feasibility in China. Additionally, the regional information system in Yinzhou District of Ningbo, Zhejiang Province, standardized the multi-dimensional data of patients with diabetes and hypertension. Based on this platform, a series of prediction models for complications in patients with diabetes were constructed. Another major database in Beijing Anding Hospital applied OMOP CDM to analyze the characteristics of patients with late-life depression and dementia.Conclusions:This study analyzed the application of OMOP CDM in China. Through in-depth analysis of specific cases, the study provided guidance for the future cross-regional evidence sharing and collaboration.

Objective:To systematically review the progress in the method development and application of distributed learning in the estimation of epidemiological effect and provide methodological reference for multi-center studies.Methods:We conducted a literature retrieval for English papers published up to December 31, 2023 by using keywords of "health/medical big data" and "distributed/federated learning". After consulting experts, we set criteria of paper inclusion and exclusion and created a framework for data extraction. We collected information about basic study details, including method, application, and evaluation. Two researchers independently screened the papers and extracted information. We used EndNote 20 for the management of literatures and EpiData for the management of data.Results:A total of 3 444 papers were collected, and 29 papers were included in the final analysis. Most of the papers (25, 86.2%) were published in or after 2019, and the papers were mainly from the United States (21/29, 72.4%). For the estimation of epidemiological effects, 22 distributed learning methods had been developed, including methods for logistic regression (8), Cox regression (8), Poisson regression (2), and generalized linear mixed model (GLMM) (4), as well as three platforms for distributed analysis (VLP, Vantage6, AusCAT). The 29 papers described 45 applications, with 20 (44.4%) focusing on the establishment of prediction model and 25 (55.6%) on association analysis. Importantly, except for GLMM, current distributed learning methods can estimate effects with little bias in 1-3 rounds of communication. These methods show less bias compared with meta-analysis, especially in the address of data heterogeneity and rare outcomes. However, less studies examined how differences in data structure and sparse data affect results, an area that requires further research.Conclusion:While distributed learning shows promise in epidemiological effect estimation, it is still in early development, requiring further research on data heterogeneity handling and communication efficiency improvement.