OBJECTIVE: To explore the association between acupuncture during controlled ovarian hyperstimulation (COH) and the live birth rate (LBR) using different propensity score methods. METHODS: In this retrospective cohort study, eligible women who underwent a COH were divided into acupuncture and non-acupuncture groups. The primary outcome was LBR, as determined by propensity score matching (PSM). LBR was defined as the delivery of one or more living infants that reached a gestational age over 28 weeks after embryo transfer. The propensity score model encompassed 16 confounding variables. To validate the results, sensitivity analyses were conducted using three additional propensity score methods: propensity score adjustment, inverse probability weighting (IPW), and IPW with a "doubly robust" estimator. RESULTS: The primary cohort encompassed 9751 patients (1830 [18.76%] in the acupuncture group and 7921 [81.23%] in the non-acupuncture group). Following 1:1 PSM, a higher LBR was found in the acupuncture cohort (41.4% [755/1824] vs 36.4% [664/1824], with an odds ratio of 1.23 [95% confidence interval, 1.08-1.41]). Three additional propensity score methods produced essentially similar results. The risk of serious adverse events did not significantly differ between the two groups. CONCLUSION This retrospective study revealed an association between acupuncture and an increased LBR among patients undergoing COH, and that acupuncture is a safe and valuable treatment option. Please cite this article as: Zheng XY, Jiang ZY, Li YT, Li CL, Zhu H, Yu Z, Yu SY, Yang LL, Tang SY, Lü XY, Liang FR, Yang J. Association between acupuncture and live birth rates after fresh embryo transfer: A cohort study based on different propensity score methods. J Integr Med. 2025; 23(5):528-536.
Humans ; Female ; Propensity Score ; Embryo Transfer ; Adult ; Acupuncture Therapy ; Retrospective Studies ; Pregnancy ; Live Birth ; Birth Rate ; Cohort Studies

Alzheimer's disease (AD) is the major form of dementia in the elderly and is closely related to the toxic effects of microglia sustained activation. In AD, sustained microglial activation triggers impaired synaptic pruning, neuroinflammation, neurotoxicity, and cognitive deficits. Accumulating evidence has demonstrated that aberrant expression of deubiquitinating enzymes is associated with regulating microglia function. Here, we use RNA sequencing to identify a deubiquitinase YOD1 as a regulator of microglial function and AD pathology. Further study showed that YOD1 knockout significantly improved the migration, phagocytosis, and inflammatory response of microglia, thereby improving the cognitive impairment of AD model mice. Through LC-MS/MS analysis combined with Co-IP, we found that Myosin heavy chain 9 (MYH9), a key regulator maintaining microglia homeostasis, is an interacting protein of YOD1. Mechanistically, YOD1 binds to MYH9 and maintains its stability by removing the K48 ubiquitin chain from MYH9, thereby mediating the microglia polarization signaling pathway to mediate microglia homeostasis. Taken together, our study reveals a specific role of microglial YOD1 in mediating microglia homeostasis and AD pathology, which provides a potential strategy for targeting microglia to treat AD.

In protein engineering, while computational models are increasingly used to predict mutation effects, their evaluations primarily rely on high-throughput deep mutational scanning (DMS) experiments that use surrogate readouts, which may not adequately capture the complex biochemical properties of interest. Many proteins and their functions cannot be assessed through high-throughput methods due to technical limitations or the nature of the desired properties, and this is particularly true for the real industrial application scenario. Therefore, the desired testing datasets, will be small-size (∼10-100) experimental data for each protein, and involve as many proteins as possible and as many properties as possible, which is, however, lacking. Here, we present VenusMutHub, a comprehensive benchmark study using 905 small-scale experimental datasets curated from published literature and public databases, spanning 527 proteins across diverse functional properties including stability, activity, binding affinity, and selectivity. These datasets feature direct biochemical measurements rather than surrogate readouts, providing a more rigorous assessment of model performance in predicting mutations that affect specific molecular functions. We evaluate 23 computational models across various methodological paradigms, such as sequence-based, structure-informed and evolutionary approaches. This benchmark provides practical guidance for selecting appropriate prediction methods in protein engineering applications where accurate prediction of specific functional properties is crucial.

Ovulatory dysfunction (OD) is one of the main causes of infertility in women of childbearing age, which not only affects their reproductive ability, but also physical and mental health. Traditional treatment strategies have limited efficacies, and the emergence of biomedicines provides a promising alternative solution via the strategies of combining engineered design with modern advanced technology. This review explores the pathophysiological characteristics and related induction mechanisms of OD, and evaluates the current cutting-edge advances in its treatments. It emphasizes the potentials of biomedicines strategies such as hydrogels, nanoparticles and extracellular vesicles in improving therapeutic precision and efficacy. By mimicking natural physiological processes, and achieving controlled drug release, these advanced drug carriers are expected to address the challenges in ovarian microenvironment reprogramming, tissue repair, and metabolic and immune regulation. Despite the promising progress, there are still challenges in terms of biomedical complexity, differences between animal models and human physiology, and the demand for intelligent drug carriers in the therapy of OD. Future researches are mainly dedicated to developing precise personalized biomedicines in OD therapy through interdisciplinary collaboration, promoting the development of reproductive regenerative medicine.

Forensic medicine has been designated as a first-level discipline,presenting new opportunities and challenges for the development of forensic medicine.Since the 1980s,the establishment of foren-sic medicine discipline and the cultivation of high-level forensic talents have become hot topics in the development of forensic medicine in China.Since the 13th Five-Year Plan,the forensic team of Shanxi Medical University has been aiming at the forefront,proposing the development goals of"Five First-class"and the discipline development path"Six Major Achievements".It has selected benchmark disci-plines,identified gaps in disciplinary development,unified thoughts,formulated completion timelines,concentrated superior resources,assigned tasks to individuals,and created an"Organized Fill-in-the-Blank Format"forensic medicine discipline construction model with the characteristics of the new era.The construction model of forensic medicine has achieved good results in the goals,discipline frame-work,scientific research,talent cultivation,discipline team and platform construction,forming a rela-tively complete discipline construction and management system,and accumulating valuable experience for the construction of first-level discipline and high-level talent cultivation of forensic medicine.

Objective:To explore the predictive value of hepatitis B surface antibody (HBsAb) quantitative level for achieving hepatitis B surface antigen (HBsAg) seroclearance and serological conversion in patients with chronic hepatitis B (CHB) treated with nucleos(t)ide analogs (NAs) or interferon (IFN).Methods:A two-center prospective cohort study was conducted, including CHB patients from Nanfang Hospital Southern Medical University and Eastern Theater General Hospital treated with NAs and IFN. All patients were followed up once every three to six months. Basic clinical information and test results were collected at each follow-up. The presence or absence of HBsAg seroclearance and serological conversion rate was evaluated. HBsAg serological conversion was defined as HBsAg quantification continuously below the detection limit (<0.05 IU/mL) at two detection time points at least six months apart. HBsAg serological conversion was defined as HBsAb positivity (≥10 IU/L) at the same time as the first HBsAg seroclearance. The Kruskal-Wallis test was used to compare the quantitative data of multiple groups, and the Wilcoxon rank-sum test was used to compare the data between groups. The chi-square test was used for the count data, and the Fisher exact test was used when the chi-square test was not met. Univariate and multivariate Cox analysis was used to determine the predictors of the study endpoints, and stepwise regression was used for variable screening.Results:A total of 2 266 CHB cases were included, of which 86.5% (1 959/2 266) were NA antiviral-received population. The median treatment duration before baseline was 10.5 (2.5, 37.6) months, and the baseline HBsAg quantification was 3.1 (2.6, 3.5) log 10 IU/mL. A total of 68 cases (3.0%) had HBsAg seroclearance, and 44 cases (1.9%) achieved serological conversion after 85.0 (62.7, 97.3) months of prospective follow-up. The level and positivity rate of HBsAb showed a progressive increase 36 months before and significantly after HBsAg seroclearance. Cox regression analysis results showed that baseline HBsAb level was an independent predictor of HBsAg serological conversion ( HR=2.26, P=0.002) in the overall population, especially in the subgroup with HBsAg between 100 and 1 000 IU/mL, suggesting HBsAb level had important predictive value. In addition, the serological conversion development rate was significantly higher in the GOLDEN model favourable patients than in the unfavourable patients (11.5% vs. 0, P<0.001). Conclusion:The baseline HBsAb quantitative level can predict HBsAg seroclearance and serological conversion for patients with CHB receiving antiviral treatment, which is of significant value in long-term treatment monitoring.

Objective To explore the efficacy of a model combining clinicopathological characteristics and multiparametric MRI features for predicting BRCA-mutated breast cancer(BC).Methods A total of 256 BC patients were retrospectively selected and divided into BRCA mutation group(116 cases)and BRCA wild group(140 cases)based on the BRCA results.Chi-square tests or independ-ent sample t-tests were used to compare the differences in clinicopathological characteristics and multiparametric MRI features between the BRCA mutation group and the wild group.Risk factors for BRCA-mutated BC were identified through univariate and multivariate logistic regression ananlyses,and a combined predictive model was constructed.Receiver operating characteristic(ROC)curve was used to ana-lyze the diagnostic efficacy of the model.Results There were statistically significant differences in T stage,human epidermal growth factor receptor 2(HER-2),Ki-67,non-mass enhancement,enhancement pattern,time-signal intensity curve(TIC)type,and apparent diffusion coefficient(ADC)values between the BRCA mutation group and the wild group.Univariate logistic regression analysis showed that T stage,HER-2,Ki-67,non-mass enhancement,enhancement pattern,TIC type,and ADC values were risk factors for BRCA-mutated BC(P<0.05).Multivariate logistic regression analysis revealed that T stage,HER-2,Ki-67,enhancement pattern,and TIC type were independent risk factors for BRCA-mutated BC(P<0.05).The combined model incorporating T stage,HER-2,Ki-67,enhancement pattern,and TIC type had the best diagnostic efficacy in predicting BRCA-mutated BC,with an area under the curve(AUC)of 0.751.Conclusion The combined model integrating T stage,HER-2,Ki-67,enhancement pattern,and TIC type has good efficacy in predicting BRCA-mutated BC.

Bone metastasis,as a common terminal event in patients with advanced malignant tumors,significantly impacts patients'quality of life and survival prognosis,posing a major clinical challenge in the field of oncology.With the deepening understanding of tumor biology,the treatment paradigm for bone metastasis has progressively shifted from traditional palliative care to precise targeted interventions.Currently,clinical treatment strategies have demonstrated diversified development,with various therapeutic approaches-including radiotherapy,immunotherapy,bone-modifying agents,and small-molecule targeted drugs—achieving notable clinical progress.However,existing treatment regimens still face challenges such as significant interindividual variability in efficacy,frequent drug resistance,and prominent adverse effects.This article systematically reviews the latest advances in precision therapy for bone metastasis,provides an in-depth analysis of the strengths and limitations of different treatment strategies,and offers perspectives on future research directions and therapeutic prospects,aiming to provide reference for optimizing clinical practice and promoting translational research.

Objective To analyze the association between plasma circulating tumor DNA(ctDNA)TP53 mutation status and survival outcomes in breast cancer patients.Methods PubMed,Embase,and the Cochrane Library were searched for relevant literature published between 2000 and 2025,examining the impact of plasma ctDNA TP53 mutations on survival outcomes in breast cancer patients,including overall survival(OS),progression-free survival(PFS),or disease-free survival(DFS).Two researchers independently screened the literature according to predefined inclusion and exclusion criteria and extracted relevant data.The Newcastle-Ottawa scale and Cochrane Risk of Bias Assessment Tool were used to evaluate study quality.Meta-analysis,publication bias assessment,and sensitivity analysis were performed using Review Manager 5.3 and STATA 18.0 software.Results A total of 14 studies(13 cohort studies and 1 randomized controlled trial)in-volving 3521 breast cancer patients were included,among whom 921 had TP53 mutations.All studies were as-sessed as high-quality or low-risk.The random-effects model demonstrated that TP53 mutations were significantly associated with poorer OS(I2=77%,HR=1.82,95%CI:1.15-2.88,P=0.010),PFS(I2=63%,HR=1.68,95%CI:1.30-2.17,P＜0.001),and DFS(I2=85%,HR=1.98,95%CI:1.05-3.75,P=0.040).Funnel plots indicated no significant publication bias,and sensitivity analysis confirmed the stabil-ity and reliability of the results.Subgroup analyses based on study design,breast cancer stage and molecular subtype revealed that TP53 mutations were associated with worse prognosis in prospective studies(OS:HR=2.32,95%CI:1.84-2.92,P＜0.001;PFS:HR=1.83,95%CI:1.47-2.27,P＜0.001),metastatic/ad-vanced breast cancer(OS:HR=2.03,95%CI:1.44-2.87,P＜0.001;PFS:HR=1.90,95%CI:1.57-2.31,P＜0.001),and hormone receptor-positive and human epidermal growth factor receptor 2-negative(HR+HER2-)patients(OS:HR=2.11,95%CI:1.56-2.85,P＜0.001;PFS:HR=1.94,95%CI:1.62-2.33,P＜0.001).Among different treatment regimens,patients with TP53 mutations receiving trastuzumab combined with paclitaxel exhibited relatively better prognosis(PFS:HR=0.08,95%CI:0.02-0.30,P＜0.001).Conclusion Plasma ctDNA TP53 mutations are closely associated with survival outcomes in breast cancer patients and may serve as a potential predictor of poor prognosis,providing support for clinical manage-ment and prognostic assessment.