Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Objective To analyze the nonlinear relationship between hypothermic machine perfusion (HMP) parameters and delayed graft function (DGF) and optimize the construction of a predictive model for DGF. Methods The data of 923 recipients who underwent kidney transplantation from deceased donors were retrospectively analyzed. According to the occurrence of DGF, the recipients were divided into DGF group (n=823) and non-DGF group (n=100). Donor data, HMP parameters and recipient data were analyzed for both groups. The nonlinear relationship between HMP parameters and the occurrence of DGF was explored based on restricted cubic splines (RCS). Over-sampling, under-sampling and balanced sampling were used to address the imbalance in the proportion of DGF to construct logistic regression predictive models. The area under the curve (AUC) of each model was compared in the validation set, and a nomogram model was constructed. Results Donor BMI, cold ischemia time of the donor kidney, and HMP parameters (initial and final pressures, resistance, and perfusion time) were significantly different between the DGF and non-DGF groups (all P<0.05). The RCS analysis revealed a threshold-like nonlinear relationship between HMP parameters and the risk of DGF. Among the models constructed using different sampling methods, the balanced sampling model had the highest AUC. Using this model, a nomogram was constructed to stratify recipients based on risk scores. Recipients in the high-risk group had higher serum creatinine levels at 1, 6, and 12 months after kidney transplantation compared to those in the low-risk group (all P<0.05). Conclusions There is a nonlinear relationship between HMP parameters and the risk of DGF, and the threshold is helpful for organ quality assessment and monitoring of graft function after transplantation. The predictive model for DGF constructed on the base of balanced sampling algorithms helps perioperative decision-making and postoperative graft function monitoring of kidney transplantation.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

In order to further standardize the vaccination of kidney transplant candidates and recipients in China, the Branch of Organ Transplantation of Chinese Medical Association has organized experts in kidney transplantation and infectious diseases. Based on the "Vaccination of Solid Organ Transplant Candidates and Recipients: Guidelines from the American Society of Transplantation Infectious Diseases Community of Practice", and in combination with the clinical reality of infectious diseases and vaccination after organ transplantation in China, as well as referring to relevant recommendations from home and abroad in recent years, these guidelines are formulated from aspects such as epidemiology, types of vaccines, vaccination principles, target population, and specific vaccine administration. The "Guidelines for Vaccination of Kidney Transplant Candidates and Recipients in China" aims to provide theoretical reference for medical workers in the field of kidney transplantation in China, regarding the vaccination of kidney transplant candidates and recipients. It is expected to better guide the vaccination of kidney transplant candidates and recipients, reduce the risk of postoperative infection, and improve survival outcomes.

Objective:To evaluate the levels and source of cadmium exposure by dietary pathway among middle-aged and elderly people ≥40 in cadmium-contaminated areas of China.Methods:A total of 7 193 people aged 40-89 years from four typical cadmium-contaminated areas in China were selected as the study subjects. Food Frequency Questionnaire (FFQ), Total Diet Study (TDS) and a 3-day-24-hour dietary recall survey were conducted. Dietary cadmium intake and food sources through dietary pathways were assessed based on cadmium content in foods, consumption amounts and intake frequencies.Results:The mean age of the participants was 63.39±12.21 years, with 50.05% being males. The average monthly dietary cadmium intake was 7.39 μg/(kg·BW). Staple foods and vegetables were the primary sources of dietary cadmium intake, accounting for 57.51% and 32.48%, respectively. The monthly dietary cadmium intake in all surveyed regions did not exceed the Provisional Tolerable Monthly Intake (PTMI) recommended by the Joint FAO/WHO Expert Committee on Food Additives (JECFA).Conclusion:The monthly dietary cadmium intake among middle-aged and elderly people in cadmium-contaminated areas of China is relatively low, with the risk remaining at an acceptable level. Staple foods and vegetables are the most significant contributors to dietary cadmium intake.

Objective:To determine the optimal cut-off value of serum cotinine for distinguishing smoking status among Chinese adults based on a large-scale national sample.Methods:A cross-sectional study was conducted among 8 987 Chinese adults aged 20-79 years from 152 administrative counties across 31 provinces during 2017-2018. Sociodemographic characteristics, lifestyle, smoking status, and health status were collected via questionnaires and physical examinations. Blood samples were analyzed for serum cotinine levels using liquid chromatography-mass spectrometry and for blood creatinine levels using the picric acid method. Receiver operating characteristic (ROC) curve analysis was performed with serum cotinine concentration as the test variable and self-reported smoking status as the state variable. The optimal cut-off value was determined based on the maximum Youden′s index, and the bootstrap method was used for repeated sampling (2 000 times) to evaluate the confidence interval of the cut-off value. The net reclassification index (NRI) was used to evaluate the discrimination ability of the cut-off value of this study, the cut-off value of the American population 1 (total population: 3.3 μg/L, men: 4.1 μg/L, women: 3.0 μg/L) and the cut-off value of the American population 2 (the recommended value of the United States Centers for Disease Control and Prevention for the total population: 10.0 μg/L) against the smoking status of the Chinese population. Statistical analyses were conducted using IBM SPSS 27 and Python 3.11, with a significance level of α=0.05.Results:The age of the research subjects was (49.2±15.2) years. Among them, males accounted for 49.8% (4 477); smokers accounted for 28.8% (2 586); the detection rate of serum cotinine was 94.6% (8 501), and the M ( Q1, Q3) concentration of serum cotinine was 0.9 (0.3, 85.4) μg/L. The ROC curve analysis results showed that the cut-off value (95% CI) of serum cotinine in the total population was 8.8 (6.7-11.7) μg/L, with the specificity (95% CI) about 93.6%(92.7%-94.3%), the sensitivity (95% CI) about 91.0%(89.7%-92.3%) and the area under the curve (AUC) (95% CI) about 0.93 (0.92-0.94). The cut-off value (95% CI) of cotinine for males was 17.1 (8.8-21.8) μg/L, with the specificity (95% CI) about 90.7%(87.9%-92.0%), the sensitivity (95% CI) about 89.4%(88.4%-92.2%) and the AUC (95% CI) about 0.92 (0.91-0.93). The cut-off value (95% CI) of cotinine for females was 7.4 (3.3-15.0) μg/L, with the specificity (95% CI) about 95.6%(92.7%-96.8%), the sensitivity (95% CI) about 87.6%(81.6%-92.8%) and the AUC (95% CI) about 0.92 (0.87-0.95). The NRI analysis results showed that compared with the cut-off value of the American population 2, the NRI of this study′s cut-off values in the total population, males and females were 0.020 ( P=0.015), 0.033 ( P=0.015) and 0.011 ( P=0.380), respectively, indicating that this study′s cutoff value could have better classification performance in the total population and males. Compared with the cut-off value of the American population 2, the NRI of the total population in this study was 0.001 ( P=0.285). Conclusion:The serum cotinine cut-off value based on the analysis of large sample data in China is more suitable for distinguishing the smoking status of Chinese adults.

Objective:To investigate the association between blood selenium levels and sex hormones in Chinese men aged 18-79 years.Methods:Data were derived from the China National Human Biomonitoring survey conducted in 2017-2018, with a final sample size of 5 414 men. General demographic characteristics, behavioral habits, and dietary frequency were collected through questionnaires and physical examinations. Fasting blood samples were collected to measure blood lead, serum testosterone, and estradiol levels. Complex sampling linear regression models were used to analyze the associations between blood selenium levels and testosterone, estradiol, and the testosterone/estradiol ratio, adjusting for confounding factors including age, education level, marital status, smoking status, alcohol consumption, seafood intake, soy product intake, protein supplement intake, BMI, and diabetes status.Results:The mean age of the 5 414 participants was (46.85±27.91) years; 4 774 (91.65%) were of Han ethnicity and 4 505 (86.68%) were married. The median ( Q1, Q3) blood selenium concentration in men was 97.80 (80.64, 116.99) μg/L. After adjusting for confounding factors, the complex sampling linear regression model revealed negative associations between blood selenium levels and both testosterone levels and the testosterone/estradiol ratio, with a significant linear trend ( Ptrend<0.05). Compared with the Q1 group, the β (95% CI) values for testosterone in the Q2, Q3, and Q4 groups were -0.02 (-0.06 to 0.02), -0.03 (-0.08 to 0.01), and -0.06 (-0.09 to -0.02), respectively. Similarly, the β (95% CI) values for the testosterone/estradiol ratio in the Q2, Q3, and Q4 groups were -0.01 (-0.03 to 0.02), -0.01 (-0.04 to 0.04), and -0.03 (-0.06 to -0.01), respectively. Subgroup analysis indicated stronger associations between blood selenium levels and testosterone/estradiol levels in non-smoking and obese men (BMI≥28 kg/m2). Conclusion:Blood selenium levels are negatively associated with testosterone levels and the testosterone/estradiol ratio in Chinese adult males.

Objective:To investigate the utility of a computational pathology-based tumor microenvironment(TME)score derived from whole slide images(WSIs)in predicting the efficacy of epidermal growth factor receptor tyrosine kinase inhibitors(EGFR-TKIs)in patients with EGFR mutation-positive non-small cell lung cancer(NSCLC).Methods:This retrospective study collected 240 EGFR-mutant NSCLC pa-tients treated with EGFR-TKIs at The First Affiliated Hospital of Wannan Medical College and analyzed hematoxylin-eosin(H&E)-stained WSIs of biopsy specimens,along with clinical and imaging data.The patients were randomly assigned into a training cohort(n=160)and an inde-pendent validation cohort(n=80)in a 2:1 ratio.Treatment response was assessed based on CT findings at 3 months after EGFR-TKIs initi-ation.Computational pathology was employed to automatically quantify the proportions of four TME components(tumor epithelium,stroma,lymphocytes,and vasculature)within the tumor regions of WSIs.Multivariate Logistic regression in the training cohort identified TME components independently predictive of treatment response(P<0.05),which were then integrated into a TME-score.The predictive performance was evaluated using receiver operating characteristic(ROC)curve analysis and area under the curve(AUC).The TME-score model was compared with a clinical-feature-based model and a combined model(TME-score+clinical features).Finally,the models were val-idated in the independent cohort.Results:In the training cohort,the TME-score,incorporating epithelial and stromal proportions,achieved an AUC of 0.827(95%CI:0.749-0.892)for predicting treatment response,while the validation cohort yielded an AUC of 0.845(95%CI:0.735-0.937).Both outperformed the clinical model(AUCs=0.730[95%CI:0.645-0.804]and 0.712[95%CI:0.586-0.824],respectively).The combined model(TME-score+clinical features,including cytokeratin 19 fragment and non-contrast CT values)further improved predictive performance(AUCs=0.884[95%CI:0.827-0.932]and 0.882[95%CI:0.798-0.950],respectively).Delong's test for pairwise model comparis-ons showed significant differences(all P<0.05)except TME-score and the combined model in the validation cohort(P=0.289).Conclusions:TME-score outperformed clinical models in predicting EGFR-TKIs efficacy in EGFR mutation-positive NSCLC patients and may serve as a novel tool for identifying patients likely to benefit from targeted therapy.

Objective:To develop machine learning models for interpreting brain maturity in preterm infants based on electroencephalographic (EEG) features and analyze factors affecting interpretation accuracy.Methods:This prospective study enrolled preterm infants requiring bedside EEG monitoring in the Department of Neonatology at the First Hospital of Jilin University from January 2023 to March 2024. Data from each integer-corrected gestational age (GA) group were randomly split into training and testing sets (7∶3 ratio) using Python's sklearn.model_selection.train_test_split function. Three machine learning models, including support vector regression (SVR), random forest, and decision tree, were employed to analyze EEG signals. Model performance was evaluated against manually interpreted GA as the gold standard using prediction deviation, mean absolute error (MAE), root mean square error (RMSE), and correlation coefficient ( r). Accuracy was defined based on the difference between predicted and manually interpreted GA (categorized into accurate and inaccurate groups), with a difference less than one week considered accurate. Statistical analyses included Chi-square test (or Fisher's exact test), t-test, Mann-Whitney U test, and multivariate logistic regression. Results:Among 241 preterm infants (training set: n=168; testing set: n=73), the random forest model demonstrated optimal performance: concordance rate 90.4% (66/73) with MAE 0.378 weeks, RMSE 0.577 weeks, and r=0.932 ( P<0.001). The decision tree model achieved 87.7% concordance (64/73) with MAE 0.316 weeks, while SVR showed 64.2% concordance (47/73) and MAE 0.840 weeks. Stratified by GA, random forest performed best in the 34 weeks group [concordance 100.0% (52/52), MAE 0.269 weeks], followed by the 32-34 weeks group [89.0% (81/91), MAE 0.448 weeks] and <32 weeks group [88.8% (87/98), MAE 0.561 weeks]. Compared to the accurate group ( n=205), the inaccurate group ( n=36) had higher rates of vaginal delivery [41.7% (15/36) vs. 19.5% (40/205), χ2=8.53], grade ≥Ⅱ intracranial hemorrhage [11.1% (4/36) vs. 2.4% (5/205), χ2=4.22], and periventricular echogenicity [33.3% (12/36) vs. 7.8% (16/205), χ2=17.03] (all P<0.05). Multivariate analysis identified vaginal delivery ( OR=0.190, 95% CI: 0.068-0.527), lower corrected GA ( OR=0.678, 95% CI: 0.488-0.941), and periventricular echogenicity ( OR=11.339, 95% CI: 3.250-39.559) as independent factors affecting accuracy (all P<0.05). Conclusion:The random forest-based model shows optimal accuracy for predicting brain maturity in preterm infants. Vaginal delivery, lower corrected GA, and periventricular echogenicity reduce its predictive accuracy.