南方医科大学学报杂志最新文献

{"title":"[Gastrodin alleviates hypobaric hypoxia-induced brain injury in rats by reducing neuronal ferroptosis <i>via</i> the P53/SLC7A11/GPX4 signaling axis].","authors":"Jiping Yang, Yongzeng Gu, Zhenyi Fu, Yuexin Dong, Yihao Deng, Zheng Li, Liangli Liu, Tao Guo","doi":"10.12122/j.issn.1673-4254.2026.07.04","DOIUrl":"10.12122/j.issn.1673-4254.2026.07.04","url":null,"abstract":"<p><strong>Objectives: </strong>To investigate the neuroprotective effect of gastrodin (GAS) against hypobaric hypoxia (HH)-induced brain injury in rats and the underlying mechanism.</p><p><strong>Methods: </strong>Twenty-four adult SD rats were randomized equally into normoxic control group, HH model group, low-dose (100 mg/kg) GAS group (HH+GAS-L group), and high-dose (200 mg/kg) GAS group (HH+GAS-H group). In the latter 3 groups, the rats were exposed to HH in a hypobaric oxygen chamber for 24 h to simulate the condition at an altitude of 6000 m, and GAS was administered intraperitoneally once daily for 7 days. Cerebral cortex tissues were collected for analysis of P53, SLC7A11, and GPX4 protein expressions using Western blotting and for determination of the levels of reactive oxygen species (ROS), malondialdehyde (MDA), glutathione (GSH), and ferrous ion (Fe²⁺). In cultured HT22 neurons exposed to oxygen-glucose deprivation (OGD), the effects of GAS (500 μmol/L), nutlin-3 (a P53 agonist; 10 μmol/L) or their combination were examined on ferroptosis-related protein expressions, intracellular ROS, lipid peroxidation, MDA, GSH, cell viability, mitochondrial membrane potential, and Fe²⁺ levels.</p><p><strong>Results: </strong>In the rat models of HH, GAS treatment significantly inhibited P53 expression, upregulated SLC7A11 and GPX4 proteins, markedly reduced Fe²⁺, ROS, and MDA levels, and increased GSH content in the cerebral cortex. In cultured HT22 neurons, GAS treatment effectively alleviated OGD-induced cell ferroptosis as shown by decreased P53 expression, increased SLC7A11 and GPX4 expressions, and lowered levels of intracellular ROS generation, lipid peroxidation, and Fe²⁺ accumulation, along with obvious restoration of GSH levels, cell viability, and mitochondrial membrane potential. The protective effects of GAS was markedly attenuated by activation of the P53 pathway using nutlin-3.</p><p><strong>Conclusions: </strong>GAS produces neuroprotective effects against HH-induced brain injury in rats by inhibiting neuronal ferroptosis via regulating the P53/SLC7A11/GPX4 signaling pathway.</p>","PeriodicalId":18962,"journal":{"name":"南方医科大学学报杂志","volume":"46 7","pages":"1498-1508"},"PeriodicalIF":0.0,"publicationDate":"2026-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13391592/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148562534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[A frequency-adaptive implicit neural representation method for medical image compression].","authors":"Hanxiao Song, Huaxian Shi, Ying Li, Zhaoying Bian, Dong Zeng","doi":"10.12122/j.issn.1673-4254.2026.07.23","DOIUrl":"10.12122/j.issn.1673-4254.2026.07.23","url":null,"abstract":"<p><strong>Objectives: </strong>To address the limitations of data storage and transfer caused by exponential growth of medical imaging data size, we propose a frequency-adaptive implicit neural compression (FAINC) method for medical images based on optimized implicit neural networks (INRs).</p><p><strong>Methods: </strong>A retrospective analysis was conducted on abdominal CT data from 356 patients in the KiTS19 and AVT datasets. We developed the FAINC method, a multi-subnetwork collaborative compression framework, which first evaluates the frequency-domain complexity of image blocks using the Spectral Sparsity Index (SSI), and then dynamically allocates them to subnetworks of different capacities through a frequency-domain gating mechanism. Compression output is achieved by combining parameter quantization with entropy coding. To assess its performance, the proposed method was compared with mainstream commercial compression standards (H.265/HEVC and JPEG2000), the implicit neural representation method NeRV, and the deep-learning-based compression method DVC.</p><p><strong>Results: </strong>The FAINC method achieved the best reconstruction performance on both KiTS19 and AVT datasets at high compression ratios. At bitrates of BPV=0.32 and BPV=0.34, the FAINC method obtained the highest PSNR (47.03 and 50.76), the highest SSIM (0.9853 and 0.9930), and the lowest RMSE (0.0045 and 0.0029), achieving also a significantly higher subjective image quality score than other methods. Ablation studies demonstrated that the frequency-domain gating mechanism and dynamic parameter allocation contributed approximately 2.05 dB and 1.87 dB PSNR improvements, respectively.</p><p><strong>Conclusions: </strong>The proposed method substantially enhances image reconstruction quality at high compression ratios and outperforms the existing mainstream approaches in terms of structural fidelity and compression efficiency. The FAINC method provides a promising technical solution for efficient storage and low-bandwidth remote transfer of medical image data.</p>","PeriodicalId":18962,"journal":{"name":"南方医科大学学报杂志","volume":"46 7","pages":"1703-1713"},"PeriodicalIF":0.0,"publicationDate":"2026-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13391593/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148562350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Construction of an anxiety recognition model for patients with malignant gastrointestinal tumors based on multidimensional features and multiple interpretable machine learning].","authors":"Mengmeng Chen, Runfang Guo, Wenjie Zhao, Yongqi Wang, Lugen Zuo, Mulin Liu, Bin Li","doi":"10.12122/j.issn.1673-4254.2026.07.19","DOIUrl":"10.12122/j.issn.1673-4254.2026.07.19","url":null,"abstract":"<p><strong>Objectives: </strong>To explore the risk factors for concomitant anxiety in patients with malignant gastrointestinal tumors based on machine learning algorithms.</p><p><strong>Methods: </strong>A total of 280 patients with malignant gastrointestinal tumors admitted to the First Affiliated Hospital of Bengbu Medical University from November, 2022 to April, 2023 were enrolled, and the collected data were re-analyzed between November ,2024 and April, 2025. According to Hospital Anxiety and Depression Scale (HADS-A) anxiety scores, the patients were divided into anxiety group (HADS-A score≥8) and non-anxiety group (<8). Six machine learning algorithms including Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors, Support Vector Machine and Light Gradient Boosting Machine were used to predict anxiety. The performance of each model was assessed, and the factors affecting anxiety in these patients were identified by feature importance ranking and SHAP analysis by the optimal model.</p><p><strong>Results: </strong>Among the 6 machine learning algorithms for predicting anxiety in patients with malignant gastrointestinal tumors, the Random Forest model showed the best performance with an accuracy of 0.73, precision of 0.48, recall of 0.86, specificity of 0.69, F1-Score of 0.62, and the area under the ROC curve (AUC) of 0.85, and was more accurate for identifying and classifying anxiety status in the target patients. According to the feature importance ranking by the optimal model, the top 10 key factors affecting anxiety in these patients were total cholesterol (0.0406), gender (0.0224), albumin (0.0157), platelet count (0.0152), white blood cell count (0.0121), retinol-binding protein (0.0116), total protein (0.0115), body mass index (0.0098), C-reactive protein (0.0096), and platelet distribution width (0.0095).</p><p><strong>Conclusions: </strong>The Random Forest model has good performance for predicting anxiety and screening key factors affecting anxiety in patients with malignant gastrointestinal tumors to facilitate its early identification and targeted intervention.</p>","PeriodicalId":18962,"journal":{"name":"南方医科大学学报杂志","volume":"46 7","pages":"1660-1670"},"PeriodicalIF":0.0,"publicationDate":"2026-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13391589/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148562507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Interpretable machine learning models for preoperative precision prediction of perineural invasion in cervical cancer to support treatment decision: a multicenter retrospective study].","authors":"Mengxin Zhu, Xiao Liu, Shan Zhao, Xin Zhang, Chao Liu, Xiankong Liu, Haochen Qi, Tiesheng Han, Dong Ma","doi":"10.12122/j.issn.1673-4254.2026.07.05","DOIUrl":"10.12122/j.issn.1673-4254.2026.07.05","url":null,"abstract":"<p><strong>Objectives: </strong>To develop an interpretable machine learning model and web-based prediction tool for preoperative risk assessment of perineural invasion (PNI) in cervical cancer.</p><p><strong>Methods: </strong>A total of 845 cervical cancer patients undergoing radical surgery at Fourth Hospital of Hebei Medical University were retrospectively enrolled and divided into training and testing sets in a 7:3 ratio, with another 223 cervical cancer patients at Hebei Medical University Second Hospital during the same period serving as the external validation cohort. LASSO regression identified 13 preoperative predictors, which were incorporated into 7 machine learning algorithms. Model performance was evaluated using AUC and decision curve analysis. The optimal model was interpreted using SHAP values and deployed as a web-based prediction tool.</p><p><strong>Results: </strong>Of the total of 1068 patients enrolled, 192 (17.98%) were diagnosed to have PNI. Thirteen preoperative features, namely lymphovascular space invasion (LVSI), depth of stromal invasion, lymph node metastasis (LNM), colposcopy-directed biopsy (CDB), tumor maximum diameter, carcinoembryonic antigen, SCC-Ag, platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR), lymphocyte-albumin-neutrophil ratio (LANR), menopausal status, age, and histological type were selected. Comparison of model performance revealed that the Extreme Gradient Boosting (XGBoost) model resulted in the best efficacy in both the training and testing datasets with AUC of 0.962 and 0.923 (95% <i>CI</i>: 0.942-0.979 and 0.874-0.960), sensitivity of 0.873 and 0.767, and specificity of 0.939 and 0.942, respectively. Decision curve analysis demonstrated greater net benefit of the XGBoost model across a broader threshold range. The SHAP-XGBoost model showed excellent performance in external validation with an AUC of 0.924 and an accuracy of 0.933.</p><p><strong>Conclusions: </strong>The interpretable SHAP-XGBoost model effectively predicts PNI risk preoperatively. The predictive website derived from this model provides an useful tool to facilitate clinical decision-making in cervical cancer treatment.</p>","PeriodicalId":18962,"journal":{"name":"南方医科大学学报杂志","volume":"46 7","pages":"1509-1519"},"PeriodicalIF":0.0,"publicationDate":"2026-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13391591/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148562556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Isovanillic acid alleviates dextran sulfate sodium-induced ulcerative colitis in mice by improving mitochondrial function <i>via</i> activating the PPARγ pathway].","authors":"Tong Qiao, Longtao Zhang, Yu Zhang, Ju Huang, Qingqing Li, Zhijun Geng, Jianguo Hu, Jing Li","doi":"10.12122/j.issn.1673-4254.2026.07.03","DOIUrl":"10.12122/j.issn.1673-4254.2026.07.03","url":null,"abstract":"<p><strong>Objectives: </strong>To investigate the protective effect of isovanillic acid (IVA) against dextran sulfate sodium (DSS)-induced ulcerative colitis (UC) in mice and its underlying mechanism.</p><p><strong>Methods: </strong>Forty-eight male C57BL/6 mice were randomly divided into 6 groups (<i>n</i>=8), including a control group and 5 DSS model groups with daily gavage of saline containing 0.1% DMSO, low-, medium- or high-dose IVA (50, 100, and 150 mg/kg, respectively), or 5-ASA (100 mg/kg) for 10 days. Body weight and disease activity index (DAI) of the mice were monitored, and colon length and pathologies were assessed after the treatments. Immunofluorescence staining, Western blotting, TUNEL staining, and JC-1 staining were used to evaluate the effects of IVA on barrier function, apoptosis, and mitochondrial function in the mouse models and DSS-induced NCM460 cells. Network pharmacology was employed to predict potential signaling pathways.</p><p><strong>Results: </strong>The DSS-treated mice showed significantly decreased body weight, increased DAI score, shortened colon length, elevated colonic IL-6 and IL-1β expressions, and severe mucosal damage. IVA, especially at the medium and high doses, obviously improved these changes. Treatment with medium-dose IVA-M significantly increased colonic expressions of ZO-1 and claudin-1, decreased intestinal epithelial cell apoptosis rate and expressions of Bax and cleaved caspase-3, and increased Bcl-2 expression, TOMM20-positive cell counts, and activities of mitochondrial respiratory chain complexes I and IV. In NCM460 cells, IVA treatment obviously reversed DSS-induced mitochondrial impairment, reduced epithelial cell apoptosis, and enhanced expressions of ZO-1 and claudin-1. Network pharmacology analysis suggested that IVA potentially targeted the PPARγ pathway, which was confirmed by increased PPARγ protein expression in IVA-treated mice and NCM460 cells. Treatment with the PPARγ antagonist GW9662 significantly attenuated the protective effect of IVA in DSS-induced NCM460 cells.</p><p><strong>Conclusions: </strong>IVA alleviates DSS-induced colitis in mice by protecting mitochondrial function via activating the PPARγ pathway and suppressing inflammation and apoptosis.</p>","PeriodicalId":18962,"journal":{"name":"南方医科大学学报杂志","volume":"46 7","pages":"1487-1497"},"PeriodicalIF":0.0,"publicationDate":"2026-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13391598/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148562550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Multiple components in Astragalus Injection target CCL2/FOS/PPARG/CXCL8 to suppress cervical cancer progression].","authors":"Yuanyuan Ma, Yue Liu, Ning Gao, Yumeng Huo, Meini Chen, Jiawen Li, Jiahao Liu, Manqiu Zhang, Bingbing Yan, Nuo Xu, Jumei Zhao","doi":"10.12122/j.issn.1673-4254.2026.09.19","DOIUrl":"10.12122/j.issn.1673-4254.2026.09.19","url":null,"abstract":"<p><strong>Objectives: </strong>To predict the functional targets and molecular mechanisms of Astragalus Injection for suppressing cervical cancer.</p><p><strong>Methods: </strong>The overlapping targets between Astragalus Injection and cervical cancer were used to construct a protein-protein interaction network. GO functional annotation and KEGG pathway enrichment analysis were performed, and molecular docking study was conducted to evaluate the binding capacity between drug active components and the core targets. SiHa cells were treated with Astragalus Injection and the changes in cell proliferation, migration and invasion were examined. The expressions of the core targets (FOS, PPARG, CXCL8, and CCL2), key molecules in the IL-17 pathway (NF-κB p65, p-p65, STAT3, and p-STAT3), Bax, Bcl-2, caspase-3, and cleaved caspase-3 were detected in the treated cells. The changes in microfilament, mitochondrial membrane potential, cell apoptosis and membrane permeability were observed using Coomassie brilliant blue, phalloidin, JC-10 and AO/EB staining. In a mouse model bearing U14 cervical cancer xenograft, the effect of Astragalus Injection on tumor growth was observed, tumor histopathology was examined using HE staining, and CCL2, PPARG and FOS expressions were analyzed with immunohistochemistry.</p><p><strong>Results: </strong>Five major active components of Astragalus Injection were found to stably bind to 4 core targets (CCL2, CXCL8, FOS, and PPARG) with docking binding energies all below -5 kJ/mol. Astragalus Injection significantly inhibited the proliferation, migration and invasion of SiHa cells, disrupted cytoskeletal microfilament structure, and induced cell apoptosis, causing also significantly lowered protein levels of FOS, CXCL8, CCL2, PPARG, Bcl-2, p-p65 and p-STAT3 and increased expressions of Bax and cleaved caspase-3. In the tumor-bearing mice, Astragalus Injection significantly suppressed xenograft growth, induced tumor tissue necrosis, and reduced the expressions of CCL2, PPARG and FOS.</p><p><strong>Conclusions: </strong>Astragalus Injection inhibits cervical cancer progression by downregulating multiple target proteins, damaging microfilament structure, weakening tumor cell motility and promoting apoptosis.</p>","PeriodicalId":18962,"journal":{"name":"南方医科大学学报杂志","volume":"46 9","pages":"2206-2221"},"PeriodicalIF":0.0,"publicationDate":"2026-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13624993/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149081417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[The mitochondrial protein encoded by circUSP25 promotes colon cancer cell proliferation and migration].","authors":"Xiaomin Li, Jie Xu, Caiyue Gao, Jijun Zhu, Yu Wang, Dongdong Li, Xiaoyan Wang","doi":"10.12122/j.issn.1673-4254.2026.09.12","DOIUrl":"10.12122/j.issn.1673-4254.2026.09.12","url":null,"abstract":"<p><strong>Objectives: </strong>To investigate the regulatory role of the protein encoded by circUSP25 in proliferation and migration of colon cancer cells.</p><p><strong>Methods: </strong>The coding potential of circUSP25 was predicted using circRNADb and circBank databases, and the splice junction was validated by Sanger sequencing. circUSP25-Flag wild-type vector and mutant vector with start codon ATG mutation were constructed and transfected into SW620 and HCT116 colon cancer cells, and the expression of the encoded protein and its subcellular localization were analyzed using Western blotting, immunoprecipitation, mass spectrometry, and immunofluorescence staining. The signal peptide was predicted using TargetP-2.0. The effects of this protein on proliferation and migration of colon cancer cells were evaluated using CCK8 and Transwell migration assays. The interacting proteins were screened by co-immunoprecipitation coupled with mass spectrometry, and its co-localization with the candidate protein PHB was verified by immunofluorescence staining. The rescue effect of PHB2 knockdown on the activity of circUSP25-encoded protein for promoting colon cancer cell proliferation and migration were observed.</p><p><strong>Results: </strong>circUSP25 was formed by back-splicing of exons 2 and 3 of the parental gene USP25 and contained an open reading frame encoding 121 amino acids. circUSP25 encodes a protein localizing to the mitochondria of colon cancer cells, and its N-terminal 1-32 amino acids contain a mitochondrial transit peptide. Overexpression of the circUSP25-encoded protein significantly promoted colon cancer cell proliferation and migration. Co-immunoprecipitation coupled with mass spectrometry revealed that this protein binds to the mitochondrial proteins such as PHB2, and they co-localize in the mitochondria. Functional rescue experiments showed that PHB2 knockdown reversed the pro-proliferative and pro-migratory effects of this circUSP25-encoded protein.</p><p><strong>Conclusions: </strong>The circUSP25-encoded protein localizes to the mitochondria, and its overexpression promotes colon cancer cell proliferation and migration possibly by interacting with mitochondrial proteins.</p>","PeriodicalId":18962,"journal":{"name":"南方医科大学学报杂志","volume":"46 9","pages":"2123-2129"},"PeriodicalIF":0.0,"publicationDate":"2026-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13624988/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149081455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Rehmanniae radix preparata alleviates chronic kidney disease in mice through a multi-target mechanism].","authors":"Zaoqiang Lin, Mengyao Jiang, Liuchang Feng, Kunyang Fang, Pingjun Fang, Qinxiang Tan","doi":"10.12122/j.issn.1673-4254.2026.07.15","DOIUrl":"10.12122/j.issn.1673-4254.2026.07.15","url":null,"abstract":"<p><strong>Objectives: </strong>To explore the mechanisms of Rehmanniae radix preparate (RRP) for alleviating chronic kidney disease (CKD).</p><p><strong>Methods: </strong>The active components and targets of RRP were identified using TCMSP and BATMAN-TCM databases. A component-target-pathway network was constructed, followed by GO and KEGG enrichment analyses. Molecular docking was performed between the core targets (JUN and STAT3) and the active compounds using AutoDock Vina. For validation of the results, C57BL/6 mice were randomly divided into sham-operated group, unilateral renal ischemia-reperfusion injury (UIRI) model group, and UIRI+RRP treatment group (<i>n</i>=6). RRP treatment was administered by daily gavage (300, 600 and 1200 mg·kg) from 2 days before to 10 days after surgery. On postoperative day 30, serum creatinine, uric acid, blood urea nitrogen, and urinary protein levels were analyzed, and renal histopathology was assessed using HE, Masson, and Sirius Red staining. Immunohistochemistry was used to detect renal expressions of JUN, STAT3, collagen I, and collagen III.</p><p><strong>Results: </strong>A total of 25 active compounds and 307 CKD-related targets were identified, and protein-protein interaction analysis identified 19 hub genes with JUN and STAT3 as the key targets. Molecular docking showed strong binding affinities between the active compounds and JUN/STAT3. The mouse models of UIRI showed significant elevation of serum creatinine, uric acid, blood urea nitrogen, and urinary protein levels with marked tubular injury, interstitial fibrosis, and increased renal expressions of JUN, STAT3, collagen I, and collagen III. RRP treatment significantly reduced these biochemical parameters in the mouse models, improved renal histology, and downregulated JUN, STAT3, and fibrosis-related proteins.</p><p><strong>Conclusions: </strong>The ameliorative effect of RRP against renal fibrosis and CKD progression in mice is mediated by a multi-component, multi-target, and multi-pathway mechanism targeting the JUN/STAT3 signaling pathway.</p>","PeriodicalId":18962,"journal":{"name":"南方医科大学学报杂志","volume":"46 7","pages":"1611-1621"},"PeriodicalIF":0.0,"publicationDate":"2026-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13391530/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148562568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Efficient dental panoramic radiographic diagnosis under resource constraints <i>via</i> edge quantization and multi-modal synergy].","authors":"Taotao Zhao, Yuehao Jiao, Ming Ni, Youlu Luo, Shunxing Xia, Yingying Xu, Yating He","doi":"10.12122/j.issn.1673-4254.2026.09.24","DOIUrl":"10.12122/j.issn.1673-4254.2026.09.24","url":null,"abstract":"<p><strong>Objectives: </strong>To propose a full-scenario framework for edge-side quantization deployment and multi-modal collaborative diagnosis and treatment based on the prior high-precision algorithm YOLOv11-TDSP.</p><p><strong>Methods: </strong>A data-aware post-training quantization (PTQ) mechanism based on Kullback-Leibler divergence was constructed. The optimal distribution range of the features was determined using the calibration dataset for near-lossless compression of the FP32 model to INT8 precision. A cross-platform heterogeneous compilation pipeline was then established, with in-depth operator fusion and instruction set optimization for Intel Hybrid Architecture, Rockchip RK3588 and Ascend 310P to achieve extreme software-hardware decoupling. An integrated detection-diagnosis collaborative inference architecture for resource-constrained edge devices was constructed. The ultra-quantized detection model was used to free critical computing resources to drive the locally deployed lightweight large model Qwen2.5-3B, which, along with an expert rule base, allowed for an edge-side closed loop from visual detection to text report generation.</p><p><strong>Results: </strong>Experiments showed that the INT8-quantized model had a weight of only 6.1 MB (57.3% compression ratio), with mAP@0.5 of 95.4% and only a 0.4% precision loss. The model demonstrated excellent generalization on both standard office terminals and resource-limited mobile diagnostic devices. In the mobile diagnostic scenario constrained by computing power and power consumption (Intel i5-12450H), the inference speed of the model reached 22.91 FPS with a 3.64 speedup. The system achieved millisecond-level lesion detection and clinically compliant report generation on devices with only 8 GB RAM in a fully offline environment.</p><p><strong>Conclusions: </strong>This framework allows deployment of high-precision algorithms on low-end hardware and enables a paradigm shift of panoramic dental radiograph analysis from cloud dependence to edge inclusiveness while ensuring patient privacy, providing a low-cost, high-reliability engineering solution for intelligent upgrading of primary medical facilities.</p>","PeriodicalId":18962,"journal":{"name":"南方医科大学学报杂志","volume":"46 9","pages":"2266-2275"},"PeriodicalIF":0.0,"publicationDate":"2026-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13624990/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149081159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Plasma metabolomics combined with machine learning for classifying ischemic stroke subtypes and predicting the etiology of cryptogenic stroke].","authors":"Yinyu Zi, Zimiao Liu, Zhen Deng","doi":"10.12122/j.issn.1673-4254.2026.09.14","DOIUrl":"10.12122/j.issn.1673-4254.2026.09.14","url":null,"abstract":"<p><strong>Objectives: </strong>To analyze plasma metabolic profiles of different ischemic stroke subtypes and construct machine learning (ML)-based models to identify these subtypes for accurate diagnosis of cryptogenic stroke.</p><p><strong>Methods: </strong>A total of 277 patients with acute ischemic stroke (AIS) from 3 hospitals were prospectively enrolled and stratified according to the TOAST classification criteria. Venous blood samples were collected for ¹H-NMR metabolomics detection, resulting in the identification of 46 metabolites. Metabolomics and Mendelian randomization analyses were employed to identify the potential biomarkers, and PyCaret was used to develop and optimize the ML models. A Plasma Metabolite Machine Learning (PMML) model was developed to differentiate the ischemic stroke subtypes, and the efficacy of the model was validated using a cryptogenic stroke (SUE) cohort.</p><p><strong>Results: </strong>Significantly different metabolite profiles were observed among patients with large artery atherosclerosis (LAA), small vessel occlusion (SVO), and cardioembolism (CE). Mendelian randomization analysis indicated that glucose and proline may serve as potential biomarkers for SVO and LAA. The PMML model developed based on the metabolic profiles of LAA, SVO, and CE demonstrated a high predictive accuracy for distinguishing these ischemic stroke subtypes with an accuracy of 0.8630 and an area under the ROC curve of 0.9586. In the SUE patients (the validation cohort), 23.9% of the patients predicted to have LAA exhibited vulnerable plaques, as compared with a rate of 10.9% in those predicted to have SVO or CE. Five patients predicted to have CE were diagnosed with paroxysmal atrial fibrillation via long-term electrocardiographic monitoring.</p><p><strong>Conclusions: </strong>Different ischemic stroke subtypes have distinct plasma metabolic profiles. The PMML model developed based on plasma metabolic features of the 3 well-diagnosed stroke subtypes demonstrates a high accuracy in identifying these subtypes with a great potential for predicting the etiology of cryptogenic stroke.</p>","PeriodicalId":18962,"journal":{"name":"南方医科大学学报杂志","volume":"46 9","pages":"2149-2158"},"PeriodicalIF":0.0,"publicationDate":"2026-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13624998/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149081425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}