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.

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 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.

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 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.

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.

Objective To develop and validate a deep learning-based segmentation model for low-dose cervical spine X-ray,aiming to address the insufficient segmentation accuracy in low-dose protocols while balancing radiation protection and diagnostic accuracy.Methods A total of 1 363 patients cervical spine X-ray images data were collected.A dose-attenuation mathematical simulation sys-tem was constructed to generate 14 122 dynamic low-dose cervical spine images incorporating quantum noise,contrast degradation,and blur artifacts.A neural network model was developed for automated segmentation of low-dose cervical spine X-ray using this dataset.Results Within the dose range of 50%to 7.5%,the average reults of automatic segmentation by the neural network model and manual segmentation for each group were as follows:50%dose group,intersection over union(IoU)=0.98 vs 0.93(P=0.707)and Dice coefficient(Dice)=0.99 vs 0.96(P=0.749);10%dose group,IoU=0.97 vs 0.87(P=0.201)and Dice=0.99 vs 0.93(P=0.219);7.5%dose group,IoU=0.97 vs 0.67(P<0.01)and Dice=0.98 vs 0.80(P<0.01).Conclusion The developed deep learning model achieved robust cervical spine segmentation(IoU>0.96,Dice>0.98)below diagnostic dose thresholds[peak signal-to-noise ratio(PSNR)<38 dB,structural similarity index(SSIM)<0.65].Under ultra-low-dose conditions(PSNR=27.710 dB,SSIM=0.274),it demonstrated a 44.78%IoU improvement and 22.5%Dice improvement over manual segmentation.This model enables minimal radia-tion exposure while preserving diagnostic performance,confirming its theoretical feasibility for low-dose X-ray image analysis and clinical research potential.

Objective:To explore the effect of remote motor imagery therapy on rehabilitation of patients with severe coronary heart disease(CHD)after percutaneous coronary intervention(PCI).Methods:This randomized con-trolled study enrolled 164 patients with severe CHD undergoing PCI who admitted in the First Affiliated Hospital of Hebei North University between January 2022 and January 2024.Patients were divided into control group(n=82)and intervention group(n=82).Patients in the control group was given conventional home nursing intervention comparing to those in the intervention group receiving additional remote motor imagery therapy.Both groups were intervened for 8 weeks.Indexes of pain,psychological emotion,ability of daily living,motor function and fall were compared between two groups.Results:Compared to those in control group after intervention,patients in interven-tion group had significant lower scores of visual analogue scale(VAS)[(2.03±0.81)points vs.(5.14±0.84)points],the Hospital anxiety and depression scale(HADS)anxiety subscale(HADS-A)[(4.89±0.84)points vs.(9.05±0.78)points],HADS depression subscale(H ADS-D)[(5.31±1.10)points vs.(10.13±0.82)points],HADS total score[(10.20±1.39)points vs.(19.18±1.18)points](P＜0.001 all),and significant higher 6-min walking distance(6MWD)[(439.31±16.51)m vs.(364.94±12.76)m],peak oxygen uptake(VO2 peak)[(20.28±2.40)ml·kg-1·min-1 vs.(17.26±1.35)ml·kg-1·min-1],anaerobic threshold oxygen consumption(VO2AT)[(17.04±1.18)ml·kg-1·min-1 vs.(13.62±1.16)ml·kg-1·min-1],scores of Chinese modified Fall Efficacy Scale(MFES)[(107.28±4.84)points vs.(87.41±4.73)points],ability of daily living(ADL)scale[(78.95±3.92)points vs.(68.00±4.10)points]and Barthel index[(85.83±2.50)points vs.(69.79±3.42)points](P＜0.001 all).We detected significant lower incidence of falling(7.50％vs.21.25％)in the intervention group compared to control group(P=0.013).Conclusion:Remote motor imagery therapy may strengthen motor function and ability of daily living,and relieve pain and negative emotions in patients with severe CHD after PCI.