Objective: To understand the microclimate in primary and secondary school classrooms for the study period during the winter heating season, so as to provide a reference for the revision and improvement of relevant health standards. Methods: In December 2024, stratified random sampling was used to select 30 primary and secondary schools and 180 classrooms from the northern regions with centralized heating (Liaoning Province, Tianjin City) and the southern regions without centralized heating (Shanghai City, Anhui Province, and Jiangxi Province). Indoor temperature, relative humidity, wind speed, CO 2 and other indicators were measured on site. Variance analysis, t-test, Mann-Whitney U test and Kruskal-Wallis H test were used to analyze the differences in the microclimate of classrooms among regions and urban and rural differences. Results: The average temperature in the middle of the classrooms tested on site was (16.47±4.72)℃, and the variance analysis showed that the difference between the regions was statistically significant ( F=27.80, P <0.01). Among them, Tianjin had the highest average temperature of (20.43± 2.12 )℃, followed by Liaoning (19.03±2.23)℃, Shanghai (15.33±5.32)℃, Anhui (12.79±1.74)℃, and Jiangxi (11.69± 1.68 )℃. Horizontal temperature difference was 0.90 (0.50, 1.60)℃, the vertical temperature difference was 0.20 (0.10,0.60)℃, the average relative humidity was (44.39±16.16)%, the wind speed was 0.03(0.01,0.11)m/s, and the differences among different provinces and cities were statistically significant ( H/F =40.62, 82.69, 95.06, 55.28, all P <0.01). The average CO 2 volume concentration in urban areas of Tianjin, Liaoning, and Shanghai was 0.21(0.16,0.30)%, and there was no statistically significant difference ( H=4.65, P =0.10). There were grade differences in relative humidity ( F =3.71, 6.21) and CO 2 ( H =14.72, 12.92) in the north and the south (all P <0.05). In addition, the temperature, relative humidity, wind speed and CO 2 in the middle of the classroom were 42.8%, 67.8%, 100.0% and 22.2% respectively. Conclusions The temperature in the middle of the classroom in the non centralized heating area is lower than the standard, the relative humidity of classroom in the centralized heating area is lower than the standard，and the CO 2 in the classroom in winter is lower than the standard. It is recommended to install heating facilities in schools with low temperatures to increase the temperature and increase the frequency of ventilation in classrooms or adopt mechanical ventilation strategies to reduce CO 2 volume concentration.

AIM: To evaluate the safety and efficacy of low-dose transscleral cyclophotocoagulation(TSCP)in the management of persistent ocular hypertension after an acute attack of angle-closure glaucoma(AACG).METHODS:This retrospective study enrolled patients diagnosed with persistent ocular hypertension after an acute AACG attack at the No.988 Hospital of the Joint Logistics Support Force of the Chinese PLA between September 2023 and September 2024. All patients underwent low-dose TSCP using a semiconductor diode laser. Subsequent cataract surgery combined with goniosynechialysis was performed once intraocular pressure(IOP)was stabilized. Changes in anterior chamber depth(ACD), best-corrected visual acuity(VA), and IOP were compared before and after TSCP, as well as before and after phacoemulsification. Post-TSCP complications were also documented.RESULTS: A total of 21 patients(21 eyes)were enrolled, including 8 males and 13 females, with a mean age of 67.95±7.25 y. Compared with pre-cyclophotocoagulation values, ACD increased significantly at 3 d post-TSCP(1.49±0.18 vs 1.22±0.21 mm; P<0.001). BCVA and IOP decreased significantly at 1 d post-TSCP, pre-phacoemulsification, 1 wk post-phacoemulsification, and 1 mo post-phacoemulsification compared with pre-TSCP IOP(all P<0.01). Regarding postoperative complications, 2 eyes experienced pain on the day of the procedure, 5 eyes developed mild corneal endothelial folds, 2 eyes exhibited moderate anterior chamber inflammatory reaction, and 12 eyes showed shallow ciliary body detachment. No serious complications occurred during the 1-month follow-up period.CONCLUSION:Low-dose TSCP appears to be an effective bridging therapy for patients with persistent ocular hypertension following an AACG attack. It facilitates rapid IOP reduction, alleviates symptoms, and helps preserve visual function with a favorable safety profile, thereby reducing the risks associated with subsequent intraocular surgery.

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.

Objective To explore the prevalence of depressive symptoms in postoperative patients with ovarian cancer and to analyze its influencing factors from multiple dimensions, including clinical characteristics, psychological factors, and laboratory indicators. Methods A cross-sectional study was conducted, which enrolled 235 postoperative patients with ovarian cancer. Depressive status was assessed using the patient health questionnaire, and the demographic, pathological, and medical record data of the patients were collected using the generalized anxiety disorder scale, Pittsburgh sleep quality index, European organization for research and treatment of cancer quality of life questionnaire core 30, and ECOG performance status score. Peripheral blood tumor marker (CA125), routine blood test, lymphocyte subsets, and serum cytokine levels were measured. Univariate and multivariate binary logistic regression analysis were used for statistical analysis. Results The prevalence of depression in postoperative patients with ovarian cancer was 39.15% (92/235). Univariate analysis showed that ECOG score ≥ 2 points, pain, anxiety, poor sleep quality, low quality of life, low life satisfaction, tumor recurrence, six or more cycles of chemotherapy, as well as higher levels of CA125, NLR, and NAR, and lower hemoglobin levels were significantly associated with depression (all P<0.05). Multivariate binary Logistic regression analysis showed that anxiety (OR=1.975, 95%CI: 1.231-3.170), sleep efficiency (OR=4.181, 95%CI: 1.211-14.43), sleep latency (OR=34.806, 95%CI: 4.258-284.542), ECOG performance status score, cognitive function (OR=0.918, 95%CI: 0.868-0.97), and life satisfaction were independent risk factors for depression (all P<0.05). Laboratory indicators were not independent influencing factors in the multivariate Logistic regression model. Conclusion Depression in postoperative patients with ovarian cancer is influenced by physiological, psychological, and social factors. Clinical management should focus on patients with anxiety, sleep disorders, poor physical condition, and low life satisfaction, and a comprehensive prevention and treatment strategy centered on psychological intervention and taking into account symptom management and social support should be implemented.

Objective To introduce the calculation of the DISO index in multi-dimensional data ranking for disease surveillance, and evaluate its applicability. Methods Using the data from the quality supervision and inspection of infectious disease reporting in Jingzhou City in a certain year as an example, the DISO index was used to rank 16 investigated medical institutions and compared with the TOPSIS method. The consistency between the two evaluation results was analyzed using Spearman correlation. Then, The DISO index from different medical institutions was used to evaluate the ranking of 8 counties (cities, districts). The evaluation indicators for the supervision and inspection included six items: report rate, timely report rate, completeness of report card filling, accuracy of report card filling, consistency rate with network information report, and completeness of valid ID number filling. Results Among the 16 medical institutions, 8 institutions had all six evaluation indicators at 100%. Using the two methods to rank the 16 medical institutions, the two methods showed that the 8 medical institutions with all six evaluation indicators at 100% were ranked first. There was a strong correlation between the rankings of the 16 medical institutions using the two methods (r_s=0.992, P=0.000). The three counties (cities, districts) with all six evaluation indicators at 100% were all ranked first, while country G was ranked seventh and country E was ranked eighth. Conclusion The results of the DISO index and the TOPSIS method were similar in one-dimensional ranking. DISO can be used for multi-dimensional ranking of disease surveillance data and deserves to be promoted and applied.

BACKGROUND:The imbalance between bone resorption and bone formation in microgravity environments leads to significant bone loss in astronauts.Current research indicates that bone loss under microgravity conditions is the result of the combined effects of various cells,tissues,and systems. OBJECTIVE:To review different biological effects of microgravity on various cells,tissues,or systems,and summarize the mechanisms by which microgravity leads to the development of osteoporosis. METHODS:Databases such as PubMed,Web of Science,and the Cochrane Database were searched for relevant literature from 2000 to 2023.The inclusion criteria were all articles related to tissue engineering studies and basic research on osteoporosis caused by microgravity.Ultimately,85 articles were included for review. RESULTS AND CONCLUSION:(1)In microgravity environment,bone marrow mesenchymal stem cells tend to differentiate more into adipocytes rather than osteoblasts,and hematopoietic stem cells in this environment are more inclined to differentiate into osteoclasts,reducing differentiation into the erythroid lineage.At the same time,microgravity inhibits the proliferation and differentiation of osteoblasts,promotes apoptosis of osteoblasts,alters cell morphology,and reduces the mineralization capacity of osteoblasts.Microgravity significantly increases the number and activity of osteoclasts.Microgravity also hinders the differentiation of osteoblasts into osteocytes and promotes the apoptosis of osteocytes.(2)In a microgravity environment,the body experiences changes such as skeletal muscle atrophy,microvascular remodeling,bone microcirculation disorders,and endocrine disruption.These changes lead to mechanical unloading in the bone microenvironment,insufficient blood perfusion,and calcium cycle disorders,which significantly impact the development of osteoporosis.(3)At present,the mechanism by which microgravity causes osteoporosis is relatively complex.A deeper study of these physiological mechanisms is crucial to ensuring the health of astronauts during long-term space missions,and provides a theoretical basis for the prevention and treatment of osteoporosis.

Objective:To explore the effects three of anesthetics(tribromoethanol,isoflurane,and pentobarbital so-dium)on the outcome of mice with post-stroke dysphagia(PSD)induced by photothrombosis(PT)method,and to e-valuate which anesthetic is more suitable for the preparation of this model.Methods:Sixty-six male C57BL/6J mice were divided into Tribromoethanol group,Isoflurane group,Pentobarbital sodium group and Sham group.The post-stroke dysphagia model was established by PT.Before and 5 min after modeling,a laser speckle imager was used to measure the regional cerebral blood flow(rCBF)decrease rate of mice and record the wake-up time of mice.Forty-eight hours after modeling,the mortality rate of PSD mice in three groups was recorded and the rCBF decrease rate was meas-ured again.The neurological function of mice was evaluated using the neurological deficit score,the water intake of mice was recorded using the 4-min drinking test,the infarct volume ratio was measured using the TTC staining method,and the swallowing counts induced by water administration was recorded using the multichannel physiological recorder MP160 and the myoelectric area of the swallowing muscle was calculated.Results:There was no statistical difference in the percentage of decrease in rCBF,infarct volume ratio,neurological deficit score,water intake,swallowing counts,and myoelectric area of swallowing muscle among the three groups of PSD mice 48 h after modeling(P＞0.05).Com-pared with the Tribromoethanol group and the Pentobarbital sodium group,the rCBF of the mice in the Isoflurane group decreased rapidly within 5 min(P＜0.05),and the mortality rate of the mice was lower and the awakening time was shorter.(P＜0.05).Conclusion:The use of different anesthesia will affect the mortality rate,wake-up time and the downward trend of rCBF within 48 h after modeling of PSD mice.Among the three anesthetics,isoflurane is more suit-able as an anesthetic for the PSD mouse model.

Objective:To investigate the feasibility of flight fatigue being detected via photoplethysmography (PPG) and regional cerebral oxygen saturation (rScO 2) in order to address the challenges posed by flight fatigue during prolonged or multiple consecutive flights. Methods:A total of 16 healthy male volunteers were enrolled. A wireless cerebral oximetry monitor headband was employed to collect PPG and rScO 2 data from the forehead while a multi-lead physiological data acquisition system was used concurrently to record three-lead electrocardiograms (ECGs). After 18 h of sleep deprivation, each volunteer performed a flight-simulating task, which was divided into 4 stages: the baseline period (T1), relaxation period (T2), early fatigue period (T3) and severe fatigue period (T4). Five-minute data was collected from each stage for analysis using AcqKnowledge 6.0. Heart rate (HR) and 3 HR variability (HRV) metrics, namely standard deviation of NN intervals (SDNN), root mean square of successive differences (RMSSD), and low frequency to high frequency power ratio (LF/HF), were computed independently from both ECG and PPG traces. The mean rScO 2 value for each stage was used to represent the cerebral oxygen saturation during that stage. The intra-class correlation coefficient (ICC) was employed to assess the consistency of the measurements, and the differences in HR and HRV indicators of the volunteers in the 4 stages of the experiment were analyzed. Results:The HR measured by ECG and PPG was highly consistent across the 4 stages among the 14 volunteers ( ICC=0.951, 0.963, 0.962, 0.963, P=0.013, 0.011, 0.021, 0.015), so were SDNN, RMSSD and LF/HF values ( ICC=0.935-0.983, all P<0.05). HR values calculated with either method showed significant differences across the 4 stages in the 14 volunteers ( F=21.63, 20.52, P=0.007, 0.008). HR gradually declined from T1 to T4, and was significantly lower at T4 than at T1 ( P=0.011, 0.009). There were significant differences in SDNN ( F=22.31, 24.26, P=0.006, 0.003), RMSSD ( F=22.30, 22.26, P=0.006, 0.006), and LF/HF ( F=20.37, 25.13, P=0.009, 0.002) across the 4 stages among the 14 volunteers. SDNN and RMSSD kept increasing as fatigue was intensified, while LF/HF decreased correspondingly. Statistically significant differences were found in SDNN, RMSSD and LF/HF values between T4 and T1 (all P<0.01). rScO 2 measured during the flight-simulating trial also differed significantly across the 4 stages ( F=21.39, P=0.007). rScO? at both T3 and T4 was significantly lower than at T1 ( P=0.009, 0.007). Conclusions:PPG can replace ECG for monitoring HR and HRV indicators under flight fatigue. Furthermore, the combination of PPG with rScO 2 monitoring allows for earlier detection of flight fatigue. This study is expected to offer a user-friendly and non-invasive approach to management of pilot fatigue.