Objective: To evaluate the current situation of thermal environment in primary and secondary school classrooms during winter, and to analyze students thermal comfort needs, so as to provide a basis for improving classroom thermal environment. Methods: From December 16 to 26, 2024, a stratified cluster random sampling method was used to select 90 classrooms from 15 primary and secondary schools in centralized/air conditioned heating areas(Liaoning Province, Tianjin City, Shanghai City) and naturally ventilated areas(Anhui Province and Jiangxi Province)for on site environmental measurement. A questionnaire survey was conducted among 743 students. The differences between groups using the χ 2 test were compared. Based on actual measurement data, a predicted mean vote prepared percentage of dissatisfied (PMV-PPD) model for centralized/air conditioned classrooms and an adaptive model for naturally ventilated classrooms were established, and the thermal neutral temperature and comfort interval were calculated. Results: The average outdoor temperature during on site measurement was 4.00(0.20，7.00)℃. In classrooms with centralized or air conditioned heating systems, the measured average temperature was (19.33±2.59)℃, with a thermal comfort range of 20.35-25.35 ℃ and a thermal neutral temperature of 22.85 ℃. And 13.92% of students reported feeling cold, while 80.80% felt comfortable. In classrooms with natural ventilation, the measured average temperature was (12.26±1.83)℃, with a thermal neutral temperature of 19.67 ℃ and a thermal comfort range of 16.17-23.17 ℃. About 48.33% of students reported feeling cold, and 49.81 % felt comfortable.The results of univariate analysis showed that there were statistically significant differences in shoe thickness, temperature sensation, relative humidity sensation and wind speed sensation between centralized/air conditioned heating areas ( χ 2= 7.01 , 31.47, 13.57, 13.80,all P <0.05). There were also statistically significant differences in school stage for primary and secondary school students, body mass index, classroom location for seat, temperature sensation, relative humidity sensation and wind speed sensation between naturally ventilated areas ( χ 2=42.13, 11.13, 11.04, 60.39, 29.27, 38.46,all P <0.05). Conclusions There are differences in thermal environment and students subjective thermal comfort in primary and secondary schools under different ventilation modes in winter. The temperature standards for heated classrooms should be revised, and differentiated environmental regulation strategies should be adopted based on different ventilation methods to improve students health and comfort levels.

ObjectiveTo observe the effect of functional pelvic floor muscle training on pelvic floor muscle function, incontinence symptoms and quality of life in female patients with stress urinary incontinence (SUI). MethodsFrom February, 2024 to July, 2025, 40 female patients with SUI were recruited from Beijing Bo'ai Hospital and advertisements. They were randomly divided into control group (n = 20) and experimental group (n = 20). The control group received conventional pelvic floor muscle contraction and relaxation training, and the experimental group received functional pelvic floor muscle contraction and relaxation training, for eight weeks. The data of pelvic floor muscle strength, pelvic floor muscle surface electromyography (sEMG), 1-hour urine pad test and Incontinence Quality of Life questionnaire (I-QOL) were collected before and after training. The compliance rate was counted after training. ResultsOne case dropped down in the control group and three cases dropped down in the experimental group. After treatment, the pelvic floor muscle strength increased in both groups (|Z| > 3.317, P < 0.01), the pre-resting average value, sustained contraction average value and durable contraction average value of sEMG improved in the control group (|t| > 2.731, P < 0.05), the mass of 1-hour urine pad significantly reduced (t > 9.215, P < 0.001) and the I-QOL score significantly increased (|t| > 13.229, P < 0.001) in both groups; compared with the control group, the pelvic floor muscle strength significantly increased (Z = -2.281, P = 0.023), the mass of 1-hour urine pad significantly reduced (t = 4.215, P < 0.001), the I-QOL score significantly increased (t = -4.501, P < 0.001), and the compliance rate significantly increased (Z = -2.798, P < 0.01) in the experimental group . ConclusionFunctional pelvic floor muscle training can significantly improve pelvic floor muscle strength, incontinence symptoms and quality of life in female patients with SUI.

Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

Traditional Chinese medicine （TCM） therapy has unique clinical advantages in the treatment of atrial fibrillation， mainly reflected in five aspects， improving quality of life， enabling early diagnosis and treatment， promoting cardiac rehabilitation， making up for the limitations of Western medicine， and improving the success rate of catheter ablation. However， there is insufficient evidence in current clinical research. Based on the current status of TCM research in the treatment of atrial fibrillation， it is suggested that future studies should focus on standardized research on syndrome differentiation and classification. This can be achieved through clinical epidemiological surveys， expert consensus， and other methods to establish a unified syndrome differentiation and classification standard for atrial fibrillation. Clinical efficacy evaluation indicators should be standardized， and core outcome measures for clinical research on TCM treatment of atrial fibrillation should be developed through systematic reviews， patient interviews， and other methods. Additionally， clinical research design， implementation， and data management should be improved. By leveraging modern information technologies such as artificial intelligence， the scientific and standardized nature of TCM intervention research on atrial fibrillation can be enhanced， ultimately improving the quality of research.

Poly-(adenosine diphosphate-ribose)-polymerase(PARP)inhibitors are a novel group of small molecule targeted therapeutic drugs.It has become an effective treatment for ovarian cancer,prostate cancer and breast cancer carrying BRCA1/2 mutations.Studies have demonstrated that PARP inhibitors(PARPi)can improve treatment outcomes by targeting genetic defects in tumors with syner-gistic effects with standard therapies for glioma treatment,such as increasing radiation sensitivity and overcoming temozolomide resistance.Howev-er,the limited blood-brain barrier permeability,the adverse reactions of drug combination and the oc-currence of drug resistance are the key factors af-fecting the therapeutic effect.This article reviews the mechanism of action and research progress of PARP inhibitors involved in the treatment of brain glioma,and analyzes the challenges to be faced in clinical practice,in order to provide references for future research in this field.

Adverse pregnancy outcome (APO) refers to any pregnancy outcome other than a normal live birth, mainly including preeclampsia, fetal growth restriction, recurrent pregnancy loss, etc., which seriously threaten the health of both the mother and the fetus. Most APOs originate from abnormal uterine endometrial microenvironment before pregnancy. Once a woman confirms pregnancy, intervening in the uterine endometrium at this stage is often too late. The uterine endometrium, as a key site for embryo implantation and development, its abnormal microenvironment can lead to APO. Therefore, in-depth exploration of the relationship between the pre-pregnancy uterine endometrial microenvironment and APO is helpful for clarifying the mechanism of APO occurrence and providing key ideas and methods for early prevention and treatment. This article reviews the latest research on the mechanism of action between uterine endometrial microenvironment and APO, systematically summarizing the research progress and main conclusions in this field from several aspects such as immune imbalance, inflammatory response, abnormal energy metabolism, and senescence of uterine endometrial stromal cells.

Objective:To investigate the impact of Wenyang Ligong Decoction on pregnancy outcomes after transcervical resection of adhesions (TCRA) in patients with intrauterine adhesions (IUA).Methods:A retrospective cohort study was conducted to collect clinical data from 151 patients with IUA who underwent TCRA in the Reproductive Medicine Department of the First Affiliated Hospital of Tianjin University of Traditional Chinese Medicine between January 2020 and January 2023. Patients were divided into a Traditional Chinese medicine (TCM) group (79 patients) and a control group (72 patients) based on whether they received Wenyang Ligong Decoction after TCRA. The TCM group received estrogen and progesterone sequential therapy post-surgery, combined with Wenyang Ligong Decoction for 2-3 menstrual cycles. The control only received sequential treatment with estrogen and progesterone.Pregnancy outcomes one year after surgery were compared between the two groups. After adjusting for confounding factors using multivariate Cox regression analysis, the effect of Wenyang Ligong Decoction on pregnancy outcomes after TCRA in patients with IUA was observed.Results:The live birth rate [54.43%　(43/79)], the ongoing pregnancy rate [56.96% (45/79)], and the clinical pregnancy rate [52.03% (49/79)] were higher in the TCM group than in the control [26.39% (19/72), P<0.001; 30.56% (22/72), P=0.001;37.50% (27/72), P=0.003], with statistically significant differences. There were no statistically significant differences in early abortion rate and late abortion rate between the TCM group and the control (all P>0.05). According to the stratified analysis by preparation methods, in the natural conception group, the live birth rate [60.78% (31/51)], the ongoing pregnancy rate [62.75% (32/51)], and the clinical pregnancy rate [68.63% (35/51)] in the TCM group were significantly higher than those in control group [21.43% (12/56), P<0.001; 26.79% (15/56), P<0.001; 33.93% (19/56), P<0.001]; there were no statistically significant differences in early miscarriage rate and late miscarriage rate between the two groups (both P>0.05). In the assisted reproductive technology group, there were no statistically significant differences in live birth rate, ongoing pregnancy rate, clinical pregnancy rate, early miscarriage rate, and late miscarriage rate between the two groups (all P>0.05). According to the stratified analysis by age, in the <35-year-old patients, the live birth rate [66.00% (33/50)], the ongoing pregnancy rate [70.00% (35/50)], and the clinical pregnancy rate [74.00% (37/50)] in the TCM group were significantly higher than those in control group [41.30% (19/46), P=0.015; 47.83% (22/46), P=0.027; 54.35% (25/46), P=0.044]; there were no statistically significant differences in early miscarriage rate and late miscarriage rate between the two groups (both P>0.05). In the ≥35-year-old patients, the live birth rate [34.48% (10/29)], the ongoing pregnancy rate [34.48% (10/29)], and the clinical pregnancy rate [41.38% (12/29)] in the TCM group were significantly higher than those in control group [0%, P=0.001; 0%, P=0.001; 7.69% (2/26), P=0.004]; there were no statistically significant differences in early miscarriage rate and late miscarriage rate between the two groups (both P>0.05).Univariate Cox regression analysis showed that age, number of previous uterine cavity interventions, IUA score, degree of IUA, and endometrial thickness after TCRA were independent risk factors for live births, and age, IUA score, degree of IUA, intima thickness after TCRA, and treatment group were the influencing factors of persistent pregnancy (all P<0.05). After adjusting for confounding factors, multivariate Cox regression analysis showed that Wenyang Ligong Decoction significantly improved the live birth rate ( HR=3.19, 95% CI: 1.77-8.11, P=0.001) and the rate of continuous pregnancy ( HR=3.66, 95% CI: 1.80-7.48, P<0.001) in patients with IUA. Conclusion:Wenyang Ligong Decoction can significantly improve pregnancy outcomes after TCRA in patients with IUA.

Chronic hepatitis B（CHB）is one of the major challenges in the global public health field. As of 2022，approximately 254 million people worldwide were infected with the hepatitis B virus（HBV）. CHB is one of the main causes of liver cirrhosis and hepatocellular carcinoma（HCC）. Nucleos（t）ide analogs（NAs）and interferon therapy can delay the progression of liver fibrosis by inhibiting viral replication，but they cannot completely avoid the problem of heterogeneous treatment responses. Some patients are in a state of low-level viremia（LLV）during treatment. The persistent LLV state can induce chronic inflammation and the progression of liver fibrosis，ultimately increase the risk of HCC. In patients with poor treatment responses，the continuous active viral replication can induce immune disorders，accelerate the evolution of fibrosis to the decompensated stage of liver cirrhosis，and increase the risk of patient death. This article aims to review the definition，mechanisms，and impact on treatment outcomes of LLV and suboptimal response based on the latest research，provide a basis for optimizing antiviral therapy for CHB.