Objective To systematically review the studies on predictive models for delayed graft function (DGF) after kidney transplantation. Methods Databases including China Biology Medicine Database, China National Knowledge Infrastructure, Wanfang Database, VIP Database, PubMed, Web of Science and CINAHL were searched to collect studies on predictive models for DGF after kidney transplantation published from the establishment of each database to June 29, 2025. Two researchers screened the literatures according to the inclusion and exclusion criteria, evaluated the quality of the literatures using the prediction model risk of bias assessment tool (PROBAST), and conducted a meta-analysis of the common predictors of the models using R software. Results A total of 12 literatures were included, involving 14 predictive models with sample sizes ranging from 103 to 24 653 cases. Donor serum creatinine level, cold ischemia time, donor age and donor body mass index were the top four common predictors. All the predictive models were at high risk of bias and low in applicability. The results of meta-analysis showed that abnormal donor body mass index, advanced donor age, prolonged cold ischemia time and elevated donor serum creatinine level were all associated with an increased risk of DGF after transplantation (all P<0.01), but there was high heterogeneity among the studies. Fixed-effect model and random-effect model were used to re-pool the effect sizes separately. The results indicated that the fixed-effect model and random-effect model had good consistency in terms of donor body mass index, donor age and cold ischemia time, while there was a significant difference in the effect sizes of the two models for donor serum creatinine level. Conclusions The predictive models for DGF risk after kidney transplantation have good predictive performance, but the overall risk of bias is high. In the future, large-sample, multicenter and high-quality prospective clinical studies should be carried out to optimize the predictive models, so as to improve their predictive ability and clinical application value.

OBJECTIVE To predict and verify the mechanism of Jiawei qifang weitong granule in ameliorating precancerous lesions of gastric cancer （PLGC）. METHODS Transcriptomic data of PLGC-related were obtained from the GEO database to screen differentially expressed genes and conduct functional enrichment analysis. Targets related to gastric mucosal injury repair were screened via the GeneCards database. The active ingredient targets of Jiawei qifang weitong granule were predicted using the TCMSP and other databases. The intersection of differentially expressed genes， gastric mucosal repair targets and active ingredient targets was extracted. A protein-protein interaction network was constructed based on the STRING database， and the top 6 hub target genes with the highest connectivity were screened. A PLGC rat model was established by combining 1-methyl-3-nitro-1-nitrosoguanidine with alternating fasting and full feeding. Model rats were divided into model group， weifuchun group （positive control， 0.40 g/kg）， low-， medium- and high-dose Jiawei qifang weitong granule groups （9.73， 19.44， 38.90 g/kg）， with 10 rats in each group. Another 10 unmodeled rats were set as control group. Rats in each group were intragastrically administered corresponding liquid medicine or normal saline once a day for 30 consecutive days. After the last administration， pathological morphological changes of rat gastric tissues were observed， and the mRNA expression levels of hub target genes as well as partial protein expression levels in gastric tissues were detected. RESULTS Transcriptomics and network pharmacology analyses revealed that the top 6 hub target genes ranked by connectivity included MMP9 （matrix metalloproteinase 9）， BCL2（B-cell lymphoma 2）， IL10（interleukin 10），ICAM1（intercellular adhesion molecule 1）， PTGS2 （prostaglandin-endoperoxide synthase 2）， and HIF1A （hypoxia-inducible factor 1α subunit）. Animal experiments showed that compared with the model group， gastric mucosal pathological injury was significantly alleviated in medium- and high-dose Jiawei qifang weitong granule groups and weifuchun group. The pathological injury score of gastric mucosa， the mRNA expression levels of MMP9， PTGS2， ICAM1， BCL2 and HIF1A， as well as the protein expression levels of PTGS2 and BCL2 in gastric tissues were significantly decreased （P＜0.05）， while the mRNA expression level of IL10 was significantly increased （P＜0.05）. CONCLUSIONS Jiawei qifang weitong granule may ameliorate PLGC by targeted down-regulating the mRNA expression of MMP9， PTGS2， ICAM1， BCL2 and HIF1A， up-regulating IL10 mRNA expression， and regulating the imbalance between gastric mucosal inflammatory response and injury repair.

ObjectiveTo address the key challenges in breast electrical impedance tomography (EIT) reconstruction including the severe ill‑posedness of the inverse problem, limited measurement information from the electrodes, low contrast of small lesions, as well as the common drawbacks of traditional reconstruction methods such as boundary blurring, low spatial resolution, and weak small-target detection capability, and to overcome the limitations of existing deep learning approaches namely low training efficiency, insufficient physical consistency, and substantial information loss during the low‑to‑high dimensional mapping process, this study proposes a multi‑scale attention and dynamic sparse mask fusion network (MADS‑Net). The proposed method aims to enhance high‑resolution reconstruction accuracy, small-lesion detectability, noise robustness, and computational efficiency in breast‑screening-oriented EIT, thereby providing a feasible solution for the application of EIT in breast functional imaging. MethodsThe proposed network adopts a strategy of “locate first, reconstruct later”. It realizes adaptive allocation of computational resources through a dynamic mask module, alleviates information loss in the low-to-high dimensional mapping process via a multi-scale attention mechanism, and embeds physical constraints to improve the physical rationality of reconstruction results. The performance of the proposed method was validated through systematic numerical simulations, agar phantom experiments, and porcine tissue experiments. Comparisons were made with mainstream methods including the Gauss-Newton algorithm, convolutional neural networks, U-Net, and Deep D-Bar. The image correlation coefficient (ICC), root mean square error (RMSE), and peak signal-to-noise ratio (PSNR) were adopted as quantitative evaluation metrics. Furthermore, lesion‑level evaluation metrics were introduced to assess the capability of different methods in reconstructing abnormal target boundaries and small‑sized target regions. ResultsExperimental results demonstrate that MADS-Net outperforms all comparison methods across all test scenarios. In numerical simulations, it achieves higher accuracy in target morphology and spatial location recovery as well as better background suppression. In agar phantom experiments, its detection and imaging reliability for small targets is significantly superior to the comparison methods. In porcine tissue experiments, the model maintains good generalization performance and can adapt to complex biological tissue imaging scenarios. Quantitative results show that MADS-Net achieves an average ICC of 0.917 6±0.048 8, an RMSE as low as0.060 6±0.031 6, and a PSNR of (23.893 9±2.685 3) dB. It successfully detects tiny targets as small as 5 mm within a 100 mm diameter sensing field, reduces the total training duration by 23.5% compared with the baseline method, and maintains stable reconstruction performance even under strong noise interference. ConclusionThe results demonstrate that MADS-Net achieves favorable EIT reconstruction performance in numerical simulations, agar phantom experiments, and porcine tissue experiments, even under common practical imaging challenges including low signal-to-noise ratio and blurred tissue boundaries. This study provides a methodological reference for further research on EIT in breast-screening-related scenarios and other medical functional imaging tasks, and also offers a low-cost, radiation-free alternative for breast lesion screening that aligns with the clinical demand for non-invasive diagnostic tools. It should be noted that real clinical breast data have not yet been used for validation in this study, and the clinical applicability of the proposed method requires further investigation. Future work will prioritize collecting multi-center real clinical breast datasets, and further optimize the model’s generalization ability to adapt to individual differences in breast tissue composition to promote its clinical application.

Objective To investigate the species composition, distribution, density, and pathogen-carrying status of host-seeking ticks in urban environments of Jiangsu Province, and to provide a scientific basis for the prevention and control of tick-borne diseases in urban settings. Methods From April to September 2023, a survey on host-seeking ticks was conducted in three types of habitats (city parks, residential areas, and street greenbelts) across 54 counties (cities, districts) of 13 prefecture-level cities in Jiangsu Province by using the flagging method. RStudio 4.4.3 (Windows version) was employed to perform chi-square test analysis on the species composition of ticks across different months, and Friedman test (for multiple paired samples) was applied to analyze monthly changes in the average tick density index among different habitats. Quantitative real-time PCR was performed to detect pathogens in the captured ticks. Results A total of 863 host-seeking ticks were captured from 54 habitats in 24 counties (cities, districts) of 11 prefecture-level cities (Nanjing, Wuxi, Changzhou, Suzhou, Nantong, Huai'an, Yancheng, Yangzhou, Zhenjiang, Taizhou, and Suqian) in Jiangsu Province, whereas no ticks were captured in Xuzhou and Lianyungang. The collected ticks belonged to three species, with Haemaphysalis longicornis being the dominant species (accounting for 82.62%), followed by Haemaphysalis flava and Hae. phasiana. The average density index of ticks in positive habitats was 3.20 ticks/ (flag·100 m). In terms of tick density among prefecture-level cities, Wuxi had the highest density[13.55 ticks/(flag·100 m)], whereas Nanjing and Taizhou had the lowest[both,0.20 ticks/(flag·100 m)]. With respect to the types of positive habitats,46 positive city parks,7 positive residential areas, and 1 positive street greenbelt were found, with the average tick density index being 3.46, 1.86, and 3.60 ticks/flag · 100 m, respectively. No statistically significant differences were observed in the monthly variations in the average tick density index among different habitats (P＞0.05). Pathogen detection showed no Bandavirus dabieense, Rickettsia, or Borrelia burgdorferi in the collected ticks. Conclusions Host-seeking ticks have established populations in urban environments across most prefecture-level cities in Jiangsu Province, with Hae. longicornis identified as the dominant species. City parks serve as the primary breeding habitats for these ticks. Strengthening tick surveillance and control measures in densely populated and frequently visited areas, such as city parks and residential neighborhoods, is crucial for the effective prevention and control of urban tick-borne diseases.

The recombinant human thyroid-stimulating hormone (rhTSH) can be used for differentiated thyroid cancer (DTC) preparation before ¹³¹I therapy, and follow-up. Since its approval by the American Food and Drug Administration (FDA) in 1998, rhTSH has been clinically used in European countries, the United States, and other countries for nearly 30 years. In April 2024, China’s first independently developed rhTSH agent was approved. Based on clinical experience and evidence from domestic and international studies, an expert consensus on the application of rhTSH has been developed following thorough consultations among experts. The consensus offers evidence-based recommendations to guide the appropriate use of rhTSH in clinical practice.

[Objective] To explore the temperature parameters affecting the polybrene test and determine the optimal temperature conditions for detecting unexpected antibodies of the Rh system. [Methods] The reaction of IgG human anti-D antibody with different dilutions (undiluted, 1∶2, 1∶4, 1∶8, 1∶16, 1∶32，1∶64) with D antigen-positive red blood cells was detected by manual polybrene test (MPT). Different temperatures (25℃ and 37℃) were set, and the reaction time with low ionic medium was 4 minutes. The agglutination integral value of anti-D and red cell depolymerization time were compared to observe the effect of enhanced agglutination reaction, thereby establishing the test temperature reaction conditions for enhancing the MPT. The same reaction condition was applied to 36 blood samples containing unexpected antibodies of the Rh system, and the effect of enhanced MPT was observed in comparison with the polybrene method and the antiglobulin test (column agglutination). [Results] With all other conditions held constant, when low ionic medium was added, the incubation temperature of 25℃ and 37℃ resulted in different total agglutination integral values for anti-D (20.9±2.025 vs 25.5±2.635), and the comparison showed a significant difference (P<0.05). When the antibody dilution was 1∶16, the incubation temperature of 25℃ and 37℃ resulted in different agglutination integral values (3.9±0.738 vs 5.8±0.632), and the comparison showed a significant difference (P<0.05). Erythrocyte depolymerization time (62.8±8.149 vs 90.1±10.713) was significantly different (P<0.05). At a dilution of 1∶32, the incubation temperatures of 25℃ and 37℃ resulted in different agglutination integral values (2.5±0.527 vs 4.3±0.675), as well as different red blood cell dissociation times (35.4±7.792 vs 57.4±10.885)(P<0.05), and the comparison showed a significant difference (P<0.05), with no differences observed in the other groups. In the detection of 36 Rh system unexpected antibody samples, when the antibody titer was ≤2, the enhanced polybrene method had a higher positive rate, and when the antibody titer was ≥4, the detection rates of the three methods were consistent. [Conclusion] The reference temperature condition for the modified MPT is incubation at 37℃ for 4 min after the addition of low ionic medium. The application of this temperature condition to unexpected antibody samples of Rh system could achieve a significant enhancement effect, thereby increasing transfusion safety for the treatment of emergency patients, and is worth popularizing.

OBJECTIVE To evaluate the efficacy and safety of guideline-directed medical therapy （GDMT） combined with vericiguat in treating heart failure with reduced ejection fraction （HFrEF）. METHODS A retrospective study was conducted on 346 patients with HFrEF who received standardized diagnosis and treatment at the First People’s Hospital of Changzhou from January 2023 to May 2024. They were divided into standard treatment group （n＝215） and vericiguat group （n＝131）. Patients in the standard treatment group received GDMT， while patients in the vericiguat group received GDMT combined with vericiguat. Propensity score matching （PSM） was used to balance confounding factors between two groups， and the effectiveness （including outcome and prognostic indicators） and safety （occurrence of adverse events） of both groups were evaluated. Kaplan-Meier survival curves for primary and secondary outcome events were drawn， and the influential factors of primary outcome events were screened through univariate and multivariate Cox regression analysis. RESULTS After PSM， there were 100 patients in the standard treatment group and 100 patients in the vericiguat group， and there was no statistically significant differences in baseline data between two groups （P＞0.05）. During a 1-year follow-up， there were statistically significant differences in the cumulative incidence of major outcome events between the standard treatment group and the vericiguat group， cumulative incidence of hospitalization events due to heart failure， changes in N-terminal pro-B-type natriuretic peptide levels before and after treatment between the standard treatment group and the vericiguat group （P＜0.05）. There was no statistically significant difference in the incidence of adverse events between the two groups （P＞0.05）. Multivariate Cox regression analysis results showed that left ventricular ejection fraction ≤35% was a risk factor for the occurrence of major outcome events within 1 year [hazard ratio （HR）＝ 2.090， 95% confidence interval （CI）： 1.175-3.718， P＝0.012]， while the use of vericiguat was a protective factor for the occurrence of major outcome events within 1 year （HR＝0.505， 95%CI： 0.284-0.899， P＝0.020）. CONCLUSIONS Compared with GDMT， GDMT combined with vericiguat can improve the clinical symptoms and prognosis of HFrEF patients， and has good safety.

ObjectiveTo investigate the application value of a multimodal model integrating radiomics features， deep learning features， and clinical structured data in predicting severe acute pancreatitis （SAP）， and to provide more accurate tools for the early identification of SAP in clinical practice. MethodsThe patients with acute pancreatitis （AP） who attended The First Affiliated Hospital of Soochow University， Jintan Hospital Affiliated to Jiangsu University， and Suzhou Yongding Hospital from January 1， 2017 to December 31， 2023 were included. Related data were collected， including demographic information， previous medical history， etiology， laboratory test data， and systemic inflammatory response syndrome （SIRS） within 24 hours after admission， as well as imaging data within 72 hours after admission， while related scores were calculated， including Ranson score， modified CT severity index （MCTSI）， bedside index for severity in acute pancreatitis （BISAP）， and systemic inflammatory response syndrome， albumin， blood urea nitrogen and pleural effusion （SABP） score. The model was constructed in the following process： （1） three-dimensional CT images were used to extract and identify radiomics features， and a radiomics classification model was established based on the extreme gradient Boost （XGBoost） algorithm； （2） U-Net is used to perform semantic segmentation of three-dimensional CT images， and then the results of segmentation were imported into 3D ResNet50 to construct a deep learning classification model； （3） the predicted values of the above two models were integrated with clinical structured data to establish a multimodal model based on the XGBoost algorithm. The variable importance plot and local interpretability plot were used to perform visual interpretation of the model. The independent samples t-test was used for comparison of normally distributed continuous data between groups， and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between groups； the chi-square test or Fisher’s exact test was used for comparison of categorical data between groups. The receiver operating characteristic （ROC） curve was plotted for each model and existing scoring systems， and the area under the ROC curve （AUC） was calculated to assess their performance； the Delong test was used for comparison of AUC. ResultsA total of 609 patients who met the criteria were included， among whom 114 （18.7%） developed SAP. In this study， the data of 426 patients from The First Affiliated Hospital of Soochow University was used as the training set， and the data of 183 patients from Jintan Hospital Affiliated to Jiangsu University and Suzhou Yongding Hospital were used as the independent test set. The multimodal model had an AUC of 0.914 in the test set， which was significantly higher than the AUC of traditional scoring systems such as MCTSI （AUC=0.827）， Ranson score （AUC=0.675）， BISAP （AUC=0.791）， and SABP score （AUC=0.648）； in addition， the multimodal model showed a significant improvement in performance compared with the radiomics classification model （AUC=0.739） and the deep learning classification model （AUC=0.685） （the Delong test： Z=-3.23， -4.83， -3.48， -4.92， -4.31， and -4.59， all P <0.01）. The top 10 variables in terms of importance in the multimodal model were pleural effusion， predicted value of the deep learning model， predicted value of the radiomics model， triglycerides， calcium ions， SIRS， white blood cell count， age， platelets， and C-reactive protein， suggesting that the above variables had significant contributions to the performance of the model in predicting SAP. ConclusionBased on structured data， radiomic features， and deep learning features， this study constructs a multicenter prediction model for SAP based on the XGBoost algorithm， which has a better predictive performance than existing traditional scoring systems and unimodal models.

OBJECTIVE: This study aimed to determine the expression levels and prognostic significance of MYCN in bone marrow of adult patients with newly diagnosed acute myeloid leukemia (AML). METHODS: A total of 62 newly diagnosed patients with non-M3 AML were enrolled as the study group, and 20 healthy donors as the control group. Real-time quantitative reverse transcription-polymerase chain reaction (PCR) was performed to detect the expression level of MYCN, and the relationship between MYCN expression and prognosis of AML patients was analyzed. RESULTS: MYCN was up-regulated in newly diagnosed AML patients compared with normal controls (P < 0.001). Receiver operating characteristic (ROC) curve analysis revealed that MYCN could serve as a diagnostic biomarker for AML. Kaplan-Meier survival analysis showed that the patients with high MYCN expression had a shorter overall survival (OS) time than the patients with low MYCN expression (P =0.016). The expression level of MYCN was lower during the complete ressimion (CR) phase of AML compared to the initial diagnosis, but it returned to the initial diagnostic level or even higher during relapse phase. Multivariate Cox regression analysis showed that high expression of MYCN was an independent risk factor for OS of AML patients (P =0.021). CONCLUSION MYCN is highly expressed and associated with poor prognosis in de novo AML, which might be serve as a novel diagnostic and prognostic biomarker for adult AML.
Humans ; Leukemia, Myeloid, Acute/metabolism* ; Prognosis ; N-Myc Proto-Oncogene Protein ; Adult ; Female ; Male ; Middle Aged

Lung cancer is the deadliest form of cancer globally, with millions of new cases diagnosed each year. Although rapid advancements in surgical techniques, targeted therapies, and immunotherapy have significantly improved patient outcomes, the overall 5-year survival rate remains disappointingly low. Recent studies have highlighted the vital role of gut microbiota in maintaining host health and its close association with the onset and progression of lung cancer through various mechanisms. This article provides a systematic analysis of the role of gut microbiota in lung cancer, focusing on its immunomodulatory and metabolic functions, as well as its potential applications in treatment, while also exploring its prospects for clinical use.
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Humans ; Lung Neoplasms/therapy* ; Gastrointestinal Microbiome ; Animals