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.

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.

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 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 investigate the neuroprotective effect of TGP on PS rats and determine the impact on the Ste20-like proline/alanine-rich kinase/Na+-K+-Cl cotransport(SPAK/NKCC1)signaling pathway.Methods After PS model was successfully established in 60 male SD rats(7 weeks old),they were randomly divided into model group,low-and high-dose TGP groups,high-dose TGP+negative control group,and high-dose TGP+WNK3 overexpression group,with 12 rats in each group.Another 12 healthy rats served as the control group.After modeling,50 or 200 mg/kg TGP was given to the rats of corresponding groups intragastrically,the overexpression plasmids of WNK3 were given to the rats from the high-dose TGP+WNK3 overexpression group through tail vein injection,and same volume of normal saline was given to the control group.All of these agents were administrated once per day for 7 consecutive days.ELISA was applied to de-tect serum levels of IL-6,IL-1β,MDA and SOD.HE staining was applied to detect the pathological morphology of the substantia nigra region in brain tissue.TUNEL staining was used to observe neuronal apoptosis.Immunohistochemistry was conducted to measure the expression of α-synucle-in(α-syn),and Western blotting for the expression of Bax,Bcl-2 and SPAK/NKCC1 signaling pathway related proteins(WNK3,p-SPAK,SPAK,p-NKCC1 and NKCC1).Results Compared with the model group,the pathological damage of neurons in substantia nigra was reduced in low-and high dose TGP groups,reduced contents of IL-6,IL-1β and MDA,lower neuronal apoptotic rate,and declined expression of Bax,α-syn,WNK3,p-SPAK/SPAK,and p-NKCC1/NKCC1,but raised SOD content and Bcl-2 expression level(88.39±8.96 U/mg,119.57±12.01 U/mg vs 60.28±6.14 U/mg,P＜0.05;0.57±0.06,0.82±0.09 vs 0.38±0.04,P＜0.05).The intervention with WNK3 overexpression resulted in more severe pathological damage to neurons in the sub-stantia nigra,increased contents of IL-6,IL-1β and MDA,higher neuronal apoptotic rate,enhanced expression of Bax,α-syn,WNK3,p-SPAK/SPAK,and p-NKCC1/NKCC1,and reduced SOD con-tent and Bcl-2 expression level when compared with the high-dose TGP+negative control group(P＜0.05).Conclusion TGP exerts neuroprotective effects on PS rats,and its mechanism is re-lated to the inhibition of the SPAK/NKCC1 signaling pathway.

Objective To construct a prediction model of epidermal growth factor receptor(EGFR)gene mutation in patients with non-small cell lung cancer(NSCLC)based on spectral CT parameters,lymphocyte to monocyte ratio(LMR)and systemic inflam-mation response index(SIRI).Methods The spectral CT parameters,LMR and SIRI of EGFR mutant and wild types NSCLC patients were compared,respectively.The influencing factors of EGFR gene mutation were analyzed and a risk prediction model was estab-lished.Results The LMR,70 keV CT value in arterial phase and venous phase,normalized iodine concentration(NIC),slope of spectral curve(λHU)and venous phase ΔCT value in EGFR mutant type patients were significantly higher than those in EGFR wild type patients,while SIRI,arterial phase and venous phase normalized water concentration(NWC)were significantly lower than those in EGFR wild type patients(P<0.05).Female,adenocarcinoma,no smoking history,LMR,increased NIC,λHU,and ΔCT value in venous phase were the risk factors for EGFR gene mutation,and increased SIRI was a protective factor(P<0.05).The decision curve showed that when the risk threshold was 0.2-0.6,the prediction model had a good risk-benefit ratio.The P value of Hosmer-Lemeshow goodness of fit test was 0.519,and the area under the curve for predicting EGFR gene mutation in NSCLC patients was 0.911.Conclusion Spectral CT parameters,LMR and SIRI may be associated with EGFR gene mutation in NSCLC patients,the model constructed based on the above indicators has a high predictive efficacy for EGFR gene mutation.

Objective To investigate the neuroprotective effect of TGP on PS rats and determine the impact on the Ste20-like proline/alanine-rich kinase/Na+-K+-Cl cotransport(SPAK/NKCC1)signaling pathway.Methods After PS model was successfully established in 60 male SD rats(7 weeks old),they were randomly divided into model group,low-and high-dose TGP groups,high-dose TGP+negative control group,and high-dose TGP+WNK3 overexpression group,with 12 rats in each group.Another 12 healthy rats served as the control group.After modeling,50 or 200 mg/kg TGP was given to the rats of corresponding groups intragastrically,the overexpression plasmids of WNK3 were given to the rats from the high-dose TGP+WNK3 overexpression group through tail vein injection,and same volume of normal saline was given to the control group.All of these agents were administrated once per day for 7 consecutive days.ELISA was applied to de-tect serum levels of IL-6,IL-1β,MDA and SOD.HE staining was applied to detect the pathological morphology of the substantia nigra region in brain tissue.TUNEL staining was used to observe neuronal apoptosis.Immunohistochemistry was conducted to measure the expression of α-synucle-in(α-syn),and Western blotting for the expression of Bax,Bcl-2 and SPAK/NKCC1 signaling pathway related proteins(WNK3,p-SPAK,SPAK,p-NKCC1 and NKCC1).Results Compared with the model group,the pathological damage of neurons in substantia nigra was reduced in low-and high dose TGP groups,reduced contents of IL-6,IL-1β and MDA,lower neuronal apoptotic rate,and declined expression of Bax,α-syn,WNK3,p-SPAK/SPAK,and p-NKCC1/NKCC1,but raised SOD content and Bcl-2 expression level(88.39±8.96 U/mg,119.57±12.01 U/mg vs 60.28±6.14 U/mg,P＜0.05;0.57±0.06,0.82±0.09 vs 0.38±0.04,P＜0.05).The intervention with WNK3 overexpression resulted in more severe pathological damage to neurons in the sub-stantia nigra,increased contents of IL-6,IL-1β and MDA,higher neuronal apoptotic rate,enhanced expression of Bax,α-syn,WNK3,p-SPAK/SPAK,and p-NKCC1/NKCC1,and reduced SOD con-tent and Bcl-2 expression level when compared with the high-dose TGP+negative control group(P＜0.05).Conclusion TGP exerts neuroprotective effects on PS rats,and its mechanism is re-lated to the inhibition of the SPAK/NKCC1 signaling pathway.

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.