Background Non-optimal temperatures pose significant threats to public health. Analyzing the association between temperature exposure and the number of emergency cases of acute alcohol intoxication can provide evidence for optimizing emergency resource allocation and response strategies. Objective To analyze the overall impact and lag effects of non-optimal temperatures on the number of 120 emergency calls for acute alcohol intoxication in Wuxi, and to assess the attributable risk, in order to provide empirical evidence for formulating climate-adaptive public health strategies. Methods Call records of acute alcohol intoxication from Wuxi's 120 emergency service, concurrent air pollutant data, and meteorological data (including daily mean temperature) were collected from January 1, 2014 to December 31, 2020. Distributed lag nonlinear modeling was used for time-series analysis, with cross-basis functions to capture the nonlinear relationship and lag effects between temperature and emergency volume. Confounding factors such as long-term trends, humidity, pollutants [ultimately including ozone (O₃) and fine particulate matter (PM_2.5)], day of the week, and holidays were controlled. The maximum lag period was set to 14 days. Single-day lag and cumulative lag effects of extreme temperatures were analyzed, followed by sensitivity analysis. Effects were quantified using relative risk (RR) and 95% confidence intervals (95%CI), and attributable fractions and numbers for different temperature ranges were calculated. Results A total of 10705 emergency cases of acute alcohol intoxication were included during the study period, with males accounting for 89.5% and individuals aged 18–50 years accounting for 83.4%. A non-linear "U-shaped" association was observed between daily mean temperature and the number of emergency calls, with an optimal temperature of 13.3 ℃. The effect of the extreme low temperature (1.88 ℃) was statistically significant from lag1 to lag3 after exposure (P<0.05), with the maximum effect at lag1 (RR=1.063, 95%CI: 1.002, 1.129). The extreme high temperature (32.3 ℃) showed an immediate effect, with maximum effect at lag0 (RR=1.374, 95%CI: 1.248, 1.512). The cumulative effect analysis showed that the risk associated with high temperature was the highest at lag0–3 (RR=2.000, 95%CI: 1.667, 2.399), while the risk associated with low temperature was the highest at lag0–14 (RR=1.462, 95%CI: 1.042, 2.051). The attribution analysis indicated that the mild heat (>24.7–30.8 ℃) and heat (>30.8–33.5 ℃) contributed the largest numbers of attributable cases, with 591 cases (95%CI: 56, 1032) and 190 cases (95%CI: 51, 309), respectively. The corresponding attributable fractions were 5.81% (95%CI: 0.80%, 9.94%) and 1.87% (95%CI: 0.49%, 2.96%), respectively. Conclusion Both extreme high and low temperatures significantly increase the number of 120 emergency calls for acute alcohol intoxication in Wuxi. High temperature exhibit an acute effect, while low temperature show a delayed effect. Mild heat have a significant impact on the emergency burden. It is recommended that emergency departments optimize resource allocation according to climatic characteristics and strengthen emergency responses during extreme weather to reduce alcohol-related health burden.

Objective To explore the preoperative diagnostic value of a radiomics nomogram based on intratumoral and peritumoral apparent diffusion coefficient(ADC)maps for prostate cancer.Methods A retrospective collection was conducted on MRI images of 503 patients with prostate lesions confirmed by pathology.The region of interest(ROI)was delineated on the ADC maps and extended 1-5 mm outward to form the peritumoral region.Radiomics features were extracted from both intratumoral and peritumoral regions,and radiomics models were established.A combined model integrating clinical model was constructed and a nomogram was drawn.The performance of each model and nomogram were evaluated.Results The combined model achieved the highest area under the curve(AUC)in the test set(AUC=0.823)at a peritumoral distance of 3 mm.The nomogram based on the combined model showed good predictive performance and clinical utility on both decision curve analysis(DCA)and calibration curve.Conclusion The radiomics nomogram based on intratumoral and peritumoral ADC maps has the greatest diagnostic value in distinguishing benign and malignant prostate cancer at a peritumoral distance of 3 mm before surgery.

Objective To explore the feasibility of a deep learning model based on early fundus lesions without manual segmentation in pan-retinal images for predicting diabetic kidney disease(DKD)and evaluating the enhancing effects of different binocular fusion strategies.Methods A retrospective cohort of 353 patients with type 2 diabetes mellitus(T2DM)admitted to the Endocrinology Department of Jiangdu People's Hospital Affiliated to Yangzhou University between December 2022 and March 2024 was analyzed.Patients were divided into DKD(n=114)and non-diabetic kidney disease(NDKD)(n=239)group based on the presence of DKD.First,a U-Net-based pre-trained automatic segmentation model was developed to process panoramic fundus retinal images.Subsequently,left and right eye deep learning models were constructed using ResNet152 under a five-fold cross-validation framework(70%training,30%validation).Three binocular fusion strategies were implemented:result fusion,feature fusion,and image fusion models.Model performance was evaluated using the area under the receiver operating characteristic(ROC)curve(AUC).DeLong test was used to compare AUC differences among models,while net reclassification index(NRI)and decision curve analysis(DCA)were used to assess clinical utility.Results Six prediction models were developed:clinical parameter model,left fundus model,right fundus model,binocular image fusion model,binocular result fusion model,and binocular feature fusion model.The Transformer-based binocular feature fusion model achieved the highest AUC in both training and validation sets(0.864 and 0.658,respectively).DeLong tests revealed significant AUC superiority of the Transformer model over the other five models in the training set(all P<0.001),though no significant differences were observed in the validation set(all P>0.05).NRI analysis showed negative values for all comparisons with the Transformer model(training set:-0.255,-0.244,-0.289,-0.426,-0.163;validation set:-0.060,-0.016,-0.028,-0.105,-0.033,respectively),indicating its optimal predictive performance.DCA further demonstrated greater net benefit for the Transformer-based fusion model.Conclusions The deep learning model constructed using early fundus lesions without manual segmentation in pan-retinal images can predict DKD.The Transformer-based fusion strategy present the best performance,providing a novel approach for further optimization and development of tools to predict DKD in the future.

Objective To analyze the current situation of the knowledge-attitude-practice among noise-exposed workers at an airport apron. Methods A total of 494 noise-exposed workers from an airport apron were selected as the study subjects using the judgmental sampling method. A self-designed "Occupational Protection Knowledge, Attitudes, and Practices Questionnaire" was used to assess the current situation of knowledge-attitude-practice on occupational protection. Results Regarding the awareness of noise hazards among the study subjects, the awareness rates of noise-induced impairment on digestive function and reproductive system were the lowest (44.9% and 37.7%, respectively). The awareness rate of noise-induced negative emotions increased with length of service (P<0.01). Regarding the occupational protection knowledge for noise, the awareness rate of occupational noise-induced deafness was “incurable” was the lowest (39.1%). The support rate for five kinds of occupational protection attitudes for noise was generally >85.0%, while only 58.3% of the study subjects consistently or frequently wearing earplugs during work. The most common source of noise hazard and protection knowledge was pre-employment training (76.9%), followed by occupational disease prevention and control campaigns (76.1%). Conclusion Noise-exposed workers in this airport apron have incomplete awareness of non-auditory system hazards caused by noise, and the awareness of knowledge of some occupational protection is relatively low. Although their attitudes toward occupational protection are positive, many workers still fail to consistently wear personal protective equipment at work.

Objective:To investigate the level of urinary serine protease(Corin) in early diabetic kidney disease(DKD) and its correlation with clinical stage.Methods:One hundred and seventy-three patients with type 2 diabetes mellitus(DM) from two tertiary A hospitals in Henan, diagnosed between April 2023 and December 2023 were selected as the research group, and 120 healthy subjects were selected as the control group. Basic clinical information and laboratory data were collected, and urinary Corin level was detected. DM patients were classified into G1-G5 stages based on estimated glomerular filtration rate(eGFR), and those in the early DKD stages(G1-G3) were further divided into A1-A3 subgroups based on urinary albumin/creatinine ratio(ACR). Spearman correlation analysis was performed to assess relationships between urinary Corin and other indicators, linear regression analysis identified factors influencing urinary Corin in early DKD patients, logistic regression analysis evaluated the risk factors for early DKD, and receiver operating characteristic(ROC) curve analysis determined the diagnostic value of urinary Corin in early DKD. Results:Urinary Corin levels were significantly higher in early DKD patients compared to healthy controls, with levels increasing as ACR rose( P<0.05). Urinary Corin was positively associated with serum creatinine( r=0.570), urea( r=0.458), cystatin C( r=0.693), ACR( r=0.616), urinary transferrin( r=0.448), urinary α1 microglobulin( r=0.507), urinary n-acetyl-β-D-glucosaminase( r=0.388) and A subgroup( r=0.692) while was negatively correlated with eGFR( r=-0.647), albumin( r=-0.312)(all P<0.05). eGFR was the only independent factor affecting urinary Corin. After adjusting for confounding factors in logistic regression analysis, urinary Corin was still an independent influencing factor for early DKD. ROC curve analysis indicated that urinary Corin had a diagnostic AUC of 0.842(95% CI 0.791-0.892, P<0.001), with a cut-off value of 2 226.04 pg/mL, sensitivity of 0.712, and specificity of 0.858 for early DKD diagnosis. Conclusions:Urinary Corin was elevated in early DKD patients and correlated with clinical stage. Urinary Corin is an independent factor of early DKD, and a reliable predictor of early DKD diagnosis.

Objective To explore the preoperative diagnostic value of a radiomics nomogram based on intratumoral and peritumoral apparent diffusion coefficient(ADC)maps for prostate cancer.Methods A retrospective collection was conducted on MRI images of 503 patients with prostate lesions confirmed by pathology.The region of interest(ROI)was delineated on the ADC maps and extended 1-5 mm outward to form the peritumoral region.Radiomics features were extracted from both intratumoral and peritumoral regions,and radiomics models were established.A combined model integrating clinical model was constructed and a nomogram was drawn.The performance of each model and nomogram were evaluated.Results The combined model achieved the highest area under the curve(AUC)in the test set(AUC=0.823)at a peritumoral distance of 3 mm.The nomogram based on the combined model showed good predictive performance and clinical utility on both decision curve analysis(DCA)and calibration curve.Conclusion The radiomics nomogram based on intratumoral and peritumoral ADC maps has the greatest diagnostic value in distinguishing benign and malignant prostate cancer at a peritumoral distance of 3 mm before surgery.

Objective To explore the feasibility of a deep learning model based on early fundus lesions without manual segmentation in pan-retinal images for predicting diabetic kidney disease(DKD)and evaluating the enhancing effects of different binocular fusion strategies.Methods A retrospective cohort of 353 patients with type 2 diabetes mellitus(T2DM)admitted to the Endocrinology Department of Jiangdu People's Hospital Affiliated to Yangzhou University between December 2022 and March 2024 was analyzed.Patients were divided into DKD(n=114)and non-diabetic kidney disease(NDKD)(n=239)group based on the presence of DKD.First,a U-Net-based pre-trained automatic segmentation model was developed to process panoramic fundus retinal images.Subsequently,left and right eye deep learning models were constructed using ResNet152 under a five-fold cross-validation framework(70%training,30%validation).Three binocular fusion strategies were implemented:result fusion,feature fusion,and image fusion models.Model performance was evaluated using the area under the receiver operating characteristic(ROC)curve(AUC).DeLong test was used to compare AUC differences among models,while net reclassification index(NRI)and decision curve analysis(DCA)were used to assess clinical utility.Results Six prediction models were developed:clinical parameter model,left fundus model,right fundus model,binocular image fusion model,binocular result fusion model,and binocular feature fusion model.The Transformer-based binocular feature fusion model achieved the highest AUC in both training and validation sets(0.864 and 0.658,respectively).DeLong tests revealed significant AUC superiority of the Transformer model over the other five models in the training set(all P<0.001),though no significant differences were observed in the validation set(all P>0.05).NRI analysis showed negative values for all comparisons with the Transformer model(training set:-0.255,-0.244,-0.289,-0.426,-0.163;validation set:-0.060,-0.016,-0.028,-0.105,-0.033,respectively),indicating its optimal predictive performance.DCA further demonstrated greater net benefit for the Transformer-based fusion model.Conclusions The deep learning model constructed using early fundus lesions without manual segmentation in pan-retinal images can predict DKD.The Transformer-based fusion strategy present the best performance,providing a novel approach for further optimization and development of tools to predict DKD in the future.

Objective:To investigate the level of urinary serine protease(Corin) in early diabetic kidney disease(DKD) and its correlation with clinical stage.Methods:One hundred and seventy-three patients with type 2 diabetes mellitus(DM) from two tertiary A hospitals in Henan, diagnosed between April 2023 and December 2023 were selected as the research group, and 120 healthy subjects were selected as the control group. Basic clinical information and laboratory data were collected, and urinary Corin level was detected. DM patients were classified into G1-G5 stages based on estimated glomerular filtration rate(eGFR), and those in the early DKD stages(G1-G3) were further divided into A1-A3 subgroups based on urinary albumin/creatinine ratio(ACR). Spearman correlation analysis was performed to assess relationships between urinary Corin and other indicators, linear regression analysis identified factors influencing urinary Corin in early DKD patients, logistic regression analysis evaluated the risk factors for early DKD, and receiver operating characteristic(ROC) curve analysis determined the diagnostic value of urinary Corin in early DKD. Results:Urinary Corin levels were significantly higher in early DKD patients compared to healthy controls, with levels increasing as ACR rose( P<0.05). Urinary Corin was positively associated with serum creatinine( r=0.570), urea( r=0.458), cystatin C( r=0.693), ACR( r=0.616), urinary transferrin( r=0.448), urinary α1 microglobulin( r=0.507), urinary n-acetyl-β-D-glucosaminase( r=0.388) and A subgroup( r=0.692) while was negatively correlated with eGFR( r=-0.647), albumin( r=-0.312)(all P<0.05). eGFR was the only independent factor affecting urinary Corin. After adjusting for confounding factors in logistic regression analysis, urinary Corin was still an independent influencing factor for early DKD. ROC curve analysis indicated that urinary Corin had a diagnostic AUC of 0.842(95% CI 0.791-0.892, P<0.001), with a cut-off value of 2 226.04 pg/mL, sensitivity of 0.712, and specificity of 0.858 for early DKD diagnosis. Conclusions:Urinary Corin was elevated in early DKD patients and correlated with clinical stage. Urinary Corin is an independent factor of early DKD, and a reliable predictor of early DKD diagnosis.

Objective To investigate the clinical manifestations,molecular genetics and gonadal pathol-ogy characteristics of patients with disorders of sex development(DSD),and to summarize the clinical experi-ence of identifying rare diseases from common symptoms.Methods The clinical data of 416 patients with DSD diagnosed and treated in the multidisciplinary center of DSD of Guangzhou Women and Children's Medical Cen-ter from May 2018 to August 2023 were retrospectively analyzed,summarized and discussed.Results Accord-ing to chromosome karyotype,416 cases of DSD were classified into three types:92 cases(22.1％)of abnormal sex chromosome karyotype,285 cases(68.5％)of 46,XY karyotype and 39 cases(9.4％)of 46,XX karyotype.Among the 92 patients with abnormal sex chromosome karyotype,59 cases were raised as males,18 cases(30.5％)complained of short penis with hypospadias and cryptorchidism.The most common karyotype was 45,X/46,XY(58 cases,63.0％).Among the 285 patients with 46,XY karyotype,238 cases were raised as males,and 63 cases(26.5％)complained of short penis and hypospadias;47 cases were raised as females,and 13 ca-ses(27.7％)complained of inguinal mass.A total of 216 patients with 46,XY karyotype were subjected to whole exome gene detection,and 155 cases(71.8％)were found to have molecular pathogenesis with the clinical phe-notype.Among the 39 patients with 46,XX karyotype,19 cases were raised as males,and 8 cases(42.1％)com-plained of short penis and hypospadias.In the 18 cases of gonad biopsy,17 cases showed testicular tissue in go-nads.Whole exome sequencing was performed in 14 cases.NR5A1 gene heterozygous mutation,SRY gene muta-tion and SOX3 gene mutation were found in 2 cases,respectively(14.3％).Twenty cases were raised as females,and 14 cases(70.0％)complained of clitoral hypertrophy.Gonad biopsy was performed in 8 cases,with 7 cases of ovotestis(87.5％)and 1 case of NR5A1 gene heterozygous mutation(14.3％).Conclusions The etiologies of DSD are complex and diverse,and the clinical manifestations are various,which can be manifested as hypospa-dias,micropenis,cryptorchidism and other common symptoms of the urinary system.Different etiologies have dif-ferent treatment options.Therefore,chromosome karyotype,molecular genetic testing and gonadal pathology can be used to clarify the cause of disease,especially for rare diseases,improve the detection rate,reduce the rate of missed diagnosis,and ensure reasonable treatment,especially sex selection.

Purpose To explore the value of deep learning feature prediction based on the ResNet50 deep residual network model for predicting early hematoma expansion in spontaneous intracerebral hemorrhage without traditional imaging manifestations. Materials and Methods A retrospective study was performed on 235 patients with spontaneous intracerebral hemorrhage in Jiangdu People's Hospital Affiliated to Yangzhou University from January 2019 and December 2022. These patients had undergone their initial plain cranial CT scan within 6 hours of symptom onset and a subsequent follow-up scan within 24 hours of admission. They were randomly assigned to a training set consisting of 188 cases and a test set of 47 cases,using an 8︰2 ratio. The region of interest (ROI) of hematoma was traced layer by layer on the first plain head CT,and image genomics features were extracted. The maximum two-dimensional cross-sectional ROI of the hematoma 3D-ROI,as well as ROI images at 1 mm and 2 mm above and below the maximum two-dimensional cross-sectional ROI,were then cut and input into the pre-trained ResNet50 model for feature extraction. The image genomics features were then fused with the extracted deep learning features using a least absolute shrinkage and selection operator regression model. A support vector machine classifier was used to construct a prediction model,which was evaluated using receiver operating characteristic curves and decision curve analysis. Results In the training set,the area under curve (AUC) of the deep learning feature model was 0.972,which was higher than that of the image genomics feature model (0.951) and the fused feature model (0.968),but this difference was not statistically significant (P>0.05). In the testing set,the AUCs of the deep learning feature model and the fused feature model were 0.867 and 0.895,respectively,which were significantly higher than that of the image genomics feature model (0.833),with statistically significant differences (Z=-1.794,-2.191,both P<0.05). The AUC of the fused feature model showed an improvement compared to the deep learning feature model,but the difference was not statistically significant (P>0.05). In the test set,decision curve analysis revealed that the fused feature model yielded greater benefits compared to both the deep learning feature model and the radiomic feature model. Conclusion The deep learning feature model based on ResNet50 deep residual network shows better performance in predicting early hematoma expansion than the image genomics feature model,and the fused feature model has a beneficial effect on predicting hematoma expansion. This deep learning approach provides a prediction tool with supervisory capability for clinical decision-making.