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.

Objective: To analyze the status of Treponema pallidum (TP) infection among blood donors in Hefei area by evaluating anti-TP reactive donors, and to provide data support for blood screening strategies, evaluation of reagent application, and public health prevention and control strategies. Methods: TPPA confirmation test were performed on 338 anti-TP positive samples of voluntary blood donors at Anhui Blood Center from February to April 2022, July to October 2022, February to June 2023. RPR tests were conducted on samples positive for TPPA. The test results, co-reactivity of TP with HBV, HCV, and HIV, and demographic characteristics of the donors were statistically analyzed. Results: The unqualified rate of anti-TP among blood donors in Hefei area was 0.30% (405/133 587), and the positive rate for TPPA was 67.46% (228/338). Among the TPPA-positive donors, 31.67% were RPR-positive. The co-positive rates of HBV, HCV and HIV in anti-TP reactive blood donors were 0.74% (3/405), 0.49% (2/405), and 1.73% (7/405), respectively, with HIV copositivity being the most common. Most co-positive donors were males aged 31-50 years with a high school education or lower, and all were first-time whole blood donors. Conclusion: The anti-TP unqualified rate among blood donors in Hefei area is at a low-to-mederate level. HIV is the most common co-infection with TP among anti-TP positive donors. The majority of co-infected donors are middle-aged men donating whole blood for the first time.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Objective:To evaluate the accuracy of L-shaped reduction malarplasty under the guidance of virtual surgical planning (VSP).Methods:The data of adult female patients who were diagnosed with zygomatic protrusion or hypertrophy at Department of Orthognathic and Temporo-mandibular Joint Surgery, West China Hospital of Stomatology, Sichuan University, from January 2018 to December 2020 were analyzed retrospectively. L-shaped reduction malarplasty with or without bone resection and with the mortice and tenon joint structure on the zygomatic arch was conducted either by digital procedures comprising VSP and three-dimensional printing titanium templates (digtal surgery group) or by conventional method (control group). The incidence of postoperative complications and the patient postoperative satisfaction [using a Likert scale with a score ranging from 1 to 5, representing very dissatisfied, dissatisfied, average, satisfied and very satisfied, satisfaction rate = (very satisfied + satisfied)/ total number of patients × 100%] were statistically analyzed in the two groups. The differences in the postoperative symmetry of the zygomatic complex between the digital group and the control group were analyzed by three-dimensional cephalometry. The accuracy of VSP in L-shaped reduction malarplasty was evaluated by comparing the preoperative design model with the actual postoperative model in the digital group. The statistical analyses were conducted using SPSS 24.0 software. The chi-square test was used in the comparison of surgical complications and patient satisfaction rates. The symmetry of bilateral landmarks in the three-dimensional direction between the two groups was evaluated using the independent t-test, and the pre- and post-operative measurements in the digital group were compared using paired t-test. Results:A total of 78 patients were included, with 36 in the digital group, aged (25.2±3.6) years, and 42 in the control group, aged (24.3±2.8) years. Satisfactory reduction of zygomatic protrusion or hypertrophy was recognized among all patients. Compared with the control group, the digital group had lower percentage of complications [25% (9/36) vs. 55% (23/42)] and higher postoperative satisfaction [78% (28/36) vs. 48% (20/42)], both of which were statistically significant (all P<0.01). Regarding the symmetry of bilateral zygomatic complexes, the average deviations of ΔZb (bottom point of zygoma) in the digital group in the horizontal, vertical, and sagittal directions [(1.05±0.24), (1.05±0.24), (1.00±0.88) mm] were significantly smaller than those in the control group [(2.03±0.58), (1.32±0.68), (1.47±0.47) mm], with statistically significant differences (all P<0.05). The bone segment movements of virtual plans and actual result in the digital surgery group were measured and showed no obvious difference for the inward movement [(5.42±0.98) mm vs. (5.33±0.93) mm] and the sagittal overlap [(4.87±1.21) mm vs. (4.77±1.32) mm] at the zygoma roots, along with the step length at the long-arm of the L-shaped osteotomy line [(2.43±1.11) mm vs. (2.39±0.89) mm] (all P>0.05). The mean differences of facial width and protrusion measurements between virtual simulations and actual result in the digital group ranged from (1.13±0.47) mm to (2.07±0.88) mm, with no significant differences( P>0.05). Meanwhile, the high resemblance between virtual plans and actual result was depicted via superimposition models, with a deviation controlled within ±0.5 mm. Conclusion:The application of VSP in reduction malarplasty significantly improved surgical accuracy and reduced difficulties in the operation, which would improve patients’ postoperative satisfaction.

Objective:To explore the diagnostic value of a clinical-radiomics model based on the T 1 mapping and apparent diffusion coefficient (ADC)-based radiomics, and the clinical indicator for renal fibrosis (RF) caused by chronic kidney disease (CKD). Methods:This cross-sectional study prospectively and consecutively enrolled 122 patients with CKD at the Second Affiliated Hospital of Soochow University from September 2021 to December 2023 who were randomly allocated to a training set ( n=85) or a validation set ( n=37) in an approximate 7∶3 ratio using simple random sampling. Patients underwent T 1 mapping and diffusion-weighted imaging scans. Renal biopsy was performed within 3 days after the MRI scans. Patients were categorized into three groups based on the degree of RF: no RF ( n=25), mild RF ( n=55), and moderate to severe RF ( n=42). To differentiate the presence of RF (no RF vs. any RF) and the severity of RF (mild RF vs. moderate to severe RF), univariate and multivariate logistic regression were used to optimize the independent clinical predictor, which constituted the clinical model. Radiomics features were extracted from regions of interest delineated within the renal parenchyma of the right kidney on T 1 mapping and ADC maps. Features were selected using least absolute shrinkage and selection operator regression to build the radiomics model. A clinical-radiomics model was subsequently constructed by integrating the independent clinical predictors with the selected radiomics features. Model diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC). Calibration curve was plotted to assess model calibration, and decision curve analysis was performed to evaluate clinical net benefit. Results:Univariate logistic regression analysis revealed that estimated glomerular filtration rate (eGFR), serum creatinine, and blood urea nitrogen exhibited statistically significant differences ( P0.05) in distinguishing both the presence and severity of RF. Multivariate analysis identified eGFR as an independent clinical predictor for both the presence of RF ( OR=0.939, 95% CI 0.898-0.982, P=0.006) and RF severity ( OR=0.956, 95% CI 0.917-0.997, P=0.037). From the MRI images, 7 radiomics features were selected to build the radiomics model for distinguishing the presence of RF, and 8 features were selected for the model assessing RF severity. These radiomics models were then combined with eGFR to construct the clinical-radiomics models. The clinical-radiomics models demonstrated the highest diagnostic performance, with an AUC of 0.935 (95% CI 0.859-0.977) for RF presence and 0.967 (95% CI 0.891-0.995) for RF severity in the training set, and 0.914 (95% CI 0.774-0.981) and 0.908 (95% CI 0.748-0.981) in the validation set. Calibration curves and decision curve analysis confirmed that the clinical-radiomics models exhibited excellent calibration and provided the highest clinical net benefit for assessing RF in CKD patients. Conclusion:The clinical-radiomics model integrating T 1 mapping and ADC-based radiomics and eGFR can effectively improve the diagnostic performance for RF in CKD patients.

Objective To organize and summarize the medication rules of GU Nai-fang in treating skin diseases through real-world data.Methods We collected traditional Chinese medicine prescriptions for GU Nai-fang's treatment of skin diseases from the outpatient medical record system of Shanghai Traditional Chinese Medicine Hospital to establish a database.Statistical analysis of disease types,performance,and efficacy was conducted,and association rules and systematic clustering analysis were performed using SPSS Modeler 18.0 and SPSS 26.0 software,respectively.Results A total of 5 020 patients were included,and 5 020 prescriptions were collected,involving 241 traditional Chinese medicines with a total frequency of 85 758 uses.The frequency of using heat clearing drugs,deficiency tonifying drugs,blood activating and stasis removing drugs,surface clearing drugs,and wind and dampness dispelling drugs was relatively high;most drugs tended to be cold and warm,mainly targeting the heart,lungs,and colon meridians.The top 15 Chinese medicines with the highest frequency of use were Smilacis Glabrae Rhixoma,Cortex Moutan,Radix Paeoniae Rubra,Rehmanniae Radix,Scutellariae Radix,Cynanchi Paniculati Radix et Rhizoma,Schisandrae Chinensis Fructus,Violsse Herba,Mume Fructus,Herba Pyrolae,Hedyotis Diffusae Herba,Lonicerae Japonicae Flos,Cicadae Periostracum,Bombyx Batryticatus,Radix Salviae.Association rule analysis obtained 15 high-frequency combinations of 2 traditional Chinese medicines and 3 traditional Chinese medicines.Cluster analysis resulted in 7 clustered prescriptions.Conclusion GU Nai-fang commonly used heat clearing drugs,deficiency tonifying drugs,blood activating and stasis removing drugs,surface resolving drugs,and wind and dampness dispelling drugs in the treatment of skin diseases,and Smilacis Glabrae Rhixoma,Cortex Moutan,Radix Paeoniae Rubra,Rehmanniae Radix,and Scutellariae Radix were the most frequently used drugs.

Objective To establish an expert consensus on the management of low anterior resection syndrome(LARS)in patients with rectal cancer post-surgery(hereinafter referred to as"consensus"),aiming to standardize the related work of medical institutions in the context of post-operative LARS.Methods A comprehensive search of domestic and international databases was conducted to collect guidelines,expert consensuses,systematic reviews,evidence summaries,and original research related to post-operative LARS in rectal cancer published from the establishment of the databases until August 2024.Based on clinical practice experience,a preliminary draft of the"consensus"was formed.From September to November 2024,22 experts were invited to participate in 2 rounds of expert consultations,during which the draft content was revised and improved,and the final version of the"consensus"was determined through expert validation.Results A total of 22 experts responded,achieving a response rate of 100％.The effective recovery rate of the consultation questionnaires in both rounds was 100％,with an expert authority coefficient of 0.89,a judgment coefficient of 0.97,and a familiarity degree of 0.84.The Kendall harmony coefficients for the 2 rounds of expert consultations were 0.122 and 0.136,respectively(P＜0.001).This consensus covers 5 main aspects:definition,assessment,prevention,treatment,and follow-up management of LARS.Conclusion This consensus demonstrates a high level of scientific rigor and can provide a strong reference for clinical nursing personnel in the specialized care of rectal cancer patients with post-operative LARS.

Objective:To evaluate the accuracy of L-shaped reduction malarplasty under the guidance of virtual surgical planning (VSP).Methods:The data of adult female patients who were diagnosed with zygomatic protrusion or hypertrophy at Department of Orthognathic and Temporo-mandibular Joint Surgery, West China Hospital of Stomatology, Sichuan University, from January 2018 to December 2020 were analyzed retrospectively. L-shaped reduction malarplasty with or without bone resection and with the mortice and tenon joint structure on the zygomatic arch was conducted either by digital procedures comprising VSP and three-dimensional printing titanium templates (digtal surgery group) or by conventional method (control group). The incidence of postoperative complications and the patient postoperative satisfaction [using a Likert scale with a score ranging from 1 to 5, representing very dissatisfied, dissatisfied, average, satisfied and very satisfied, satisfaction rate = (very satisfied + satisfied)/ total number of patients × 100%] were statistically analyzed in the two groups. The differences in the postoperative symmetry of the zygomatic complex between the digital group and the control group were analyzed by three-dimensional cephalometry. The accuracy of VSP in L-shaped reduction malarplasty was evaluated by comparing the preoperative design model with the actual postoperative model in the digital group. The statistical analyses were conducted using SPSS 24.0 software. The chi-square test was used in the comparison of surgical complications and patient satisfaction rates. The symmetry of bilateral landmarks in the three-dimensional direction between the two groups was evaluated using the independent t-test, and the pre- and post-operative measurements in the digital group were compared using paired t-test. Results:A total of 78 patients were included, with 36 in the digital group, aged (25.2±3.6) years, and 42 in the control group, aged (24.3±2.8) years. Satisfactory reduction of zygomatic protrusion or hypertrophy was recognized among all patients. Compared with the control group, the digital group had lower percentage of complications [25% (9/36) vs. 55% (23/42)] and higher postoperative satisfaction [78% (28/36) vs. 48% (20/42)], both of which were statistically significant (all P<0.01). Regarding the symmetry of bilateral zygomatic complexes, the average deviations of ΔZb (bottom point of zygoma) in the digital group in the horizontal, vertical, and sagittal directions [(1.05±0.24), (1.05±0.24), (1.00±0.88) mm] were significantly smaller than those in the control group [(2.03±0.58), (1.32±0.68), (1.47±0.47) mm], with statistically significant differences (all P<0.05). The bone segment movements of virtual plans and actual result in the digital surgery group were measured and showed no obvious difference for the inward movement [(5.42±0.98) mm vs. (5.33±0.93) mm] and the sagittal overlap [(4.87±1.21) mm vs. (4.77±1.32) mm] at the zygoma roots, along with the step length at the long-arm of the L-shaped osteotomy line [(2.43±1.11) mm vs. (2.39±0.89) mm] (all P>0.05). The mean differences of facial width and protrusion measurements between virtual simulations and actual result in the digital group ranged from (1.13±0.47) mm to (2.07±0.88) mm, with no significant differences( P>0.05). Meanwhile, the high resemblance between virtual plans and actual result was depicted via superimposition models, with a deviation controlled within ±0.5 mm. Conclusion:The application of VSP in reduction malarplasty significantly improved surgical accuracy and reduced difficulties in the operation, which would improve patients’ postoperative satisfaction.

Objective:To explore the diagnostic value of a clinical-radiomics model based on the T 1 mapping and apparent diffusion coefficient (ADC)-based radiomics, and the clinical indicator for renal fibrosis (RF) caused by chronic kidney disease (CKD). Methods:This cross-sectional study prospectively and consecutively enrolled 122 patients with CKD at the Second Affiliated Hospital of Soochow University from September 2021 to December 2023 who were randomly allocated to a training set ( n=85) or a validation set ( n=37) in an approximate 7∶3 ratio using simple random sampling. Patients underwent T 1 mapping and diffusion-weighted imaging scans. Renal biopsy was performed within 3 days after the MRI scans. Patients were categorized into three groups based on the degree of RF: no RF ( n=25), mild RF ( n=55), and moderate to severe RF ( n=42). To differentiate the presence of RF (no RF vs. any RF) and the severity of RF (mild RF vs. moderate to severe RF), univariate and multivariate logistic regression were used to optimize the independent clinical predictor, which constituted the clinical model. Radiomics features were extracted from regions of interest delineated within the renal parenchyma of the right kidney on T 1 mapping and ADC maps. Features were selected using least absolute shrinkage and selection operator regression to build the radiomics model. A clinical-radiomics model was subsequently constructed by integrating the independent clinical predictors with the selected radiomics features. Model diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC). Calibration curve was plotted to assess model calibration, and decision curve analysis was performed to evaluate clinical net benefit. Results:Univariate logistic regression analysis revealed that estimated glomerular filtration rate (eGFR), serum creatinine, and blood urea nitrogen exhibited statistically significant differences ( P0.05) in distinguishing both the presence and severity of RF. Multivariate analysis identified eGFR as an independent clinical predictor for both the presence of RF ( OR=0.939, 95% CI 0.898-0.982, P=0.006) and RF severity ( OR=0.956, 95% CI 0.917-0.997, P=0.037). From the MRI images, 7 radiomics features were selected to build the radiomics model for distinguishing the presence of RF, and 8 features were selected for the model assessing RF severity. These radiomics models were then combined with eGFR to construct the clinical-radiomics models. The clinical-radiomics models demonstrated the highest diagnostic performance, with an AUC of 0.935 (95% CI 0.859-0.977) for RF presence and 0.967 (95% CI 0.891-0.995) for RF severity in the training set, and 0.914 (95% CI 0.774-0.981) and 0.908 (95% CI 0.748-0.981) in the validation set. Calibration curves and decision curve analysis confirmed that the clinical-radiomics models exhibited excellent calibration and provided the highest clinical net benefit for assessing RF in CKD patients. Conclusion:The clinical-radiomics model integrating T 1 mapping and ADC-based radiomics and eGFR can effectively improve the diagnostic performance for RF in CKD patients.

Objective To establish an expert consensus on the management of low anterior resection syndrome(LARS)in patients with rectal cancer post-surgery(hereinafter referred to as"consensus"),aiming to standardize the related work of medical institutions in the context of post-operative LARS.Methods A comprehensive search of domestic and international databases was conducted to collect guidelines,expert consensuses,systematic reviews,evidence summaries,and original research related to post-operative LARS in rectal cancer published from the establishment of the databases until August 2024.Based on clinical practice experience,a preliminary draft of the"consensus"was formed.From September to November 2024,22 experts were invited to participate in 2 rounds of expert consultations,during which the draft content was revised and improved,and the final version of the"consensus"was determined through expert validation.Results A total of 22 experts responded,achieving a response rate of 100％.The effective recovery rate of the consultation questionnaires in both rounds was 100％,with an expert authority coefficient of 0.89,a judgment coefficient of 0.97,and a familiarity degree of 0.84.The Kendall harmony coefficients for the 2 rounds of expert consultations were 0.122 and 0.136,respectively(P＜0.001).This consensus covers 5 main aspects:definition,assessment,prevention,treatment,and follow-up management of LARS.Conclusion This consensus demonstrates a high level of scientific rigor and can provide a strong reference for clinical nursing personnel in the specialized care of rectal cancer patients with post-operative LARS.