南方医科大学学报杂志最新文献

Objectives: To explore the transcriptomic characteristics of the endplate cartilage of the cervical intervertebral disc in cervical spondylosis (CS) and the effects of acupotomy on expressions of fibroblast growth factor 18 (FGF18)/protein kinase B (Akt) axis key molecules.

Methods: Transcriptomic analyses were performed using bioinformatics methods based on the GEO database. In the animal experiment, 24 New Zealand rabbits were randomized equally into control, CS model, and acupotomy groups. In the latter two groups, CS models were established followed 7 days later by acupotomy intervention (once a week for 3 weeks) or no particular treatment. Histopathological changes and cell apoptosis in the intervertebral discs were examined with HE staining and TUNEL assay, and the mRNA expressions of FGF18, FGFR3, and Akt in the intervertebral discs were detected using RT-qPCR; the localization and expressions of FGF18, p-Akt, and Akt proteins were examined with immunohistochemistry.

Results: The endplate cartilage in CS exhibited numerous differentially expressed genes enriched in the PI3K-Akt signaling, calcium signaling, and Rap1 signaling pathways. CS rabbits showed obvious changes in the cervical spine curvature and joint degenerations, changes in cervical intervertebral disc texture, thinning of the annulus fibrosus, shrinkage or even absence of the nucleus pulposus, and increased apoptotic cells in the endplate cartilage, which were all obviously alleviated after acupotomy. No significant differences were found in the mRNA expressions of FGF18, FGFR3, and Akt in the cervical intervertebral discs among the 3 groups. Acupotomy significantly increased FGF18 and p-Akt protein expressions and reduced the Akt/p-Akt ratio in the cervical endplate cartilage of CS rabbits.

Conclusions: The cervical endplate cartilage of CS show numerous differentially expressed genes. Acupotomy may delay degenerative changes of the intervertebral discs and improves CS by activating the FGF18/Akt axis to reduce apoptosis of endplate cartilage cells.

Objectives: To explore the association between the use of β-blockers and the risks of all-cause mortality and major adverse cardiovascular events (MACEs) in patients with stable coronary artery disease (SCAD) after percutaneous coronary intervention (PCI).

Methods: We performed secondary analyses of the data of 55 SCAD patients receiving post-PCI β-blocker treatment and 149 patients without post-PCI β‑blockers (control group) from the Dryad database. The clinical and coronary artery disease characteristics of the patients were analyzed, and propensity score matching was used to compare all-cause mortality and MACEs (including cardiovascular death, non-fatal myocardial infarction and non-fatal stroke) between the two groups.

Results: The overall patients (69.6% were male) had a mean age of 72.6±10.3 years with a median follow-up time of 783 days. A total of 18 patients (8.8%) died, and MACEs occurred in 19 patients (9.3%), including cardiovascular death in 6 cases (2.9%), non-fatal myocardial infarction in 3 cases (1.5%) and non-fatal stroke in 11 cases (5.4%). In the β‑blocker group, deaths occurred in 5 cases (9.1%), and MACEs in 4 cases (7.3%), including 2 cases with cardiovascular death (3.6%) and 2 cases with non-fatal stroke (3.6%). Kaplan-Meier survival curve analysis showed that the use of β-blockers after PCI was not associated with a reduced all-cause mortality (8.7% vs 9.1%, log-rank P=0.870) or incidence of MACEs (10.1% vs 7.3%, log-rank P=0.510) either before or after adjusting for age, sex, aspartate aminotransferase, estimated glomerular filtration rate, left ventricular ejection fraction, and history of atrial fibrillation (HR=0.81, 95% CI: 0.24-2.72; HR=0.62, 95% CI: 0.22-1.69). No significant differences were found in all-cause death or MACEs between the two groups after propensity score adjustment, matching, or IPTW inverse probability weighting (all P>0.05).

Conclusions: Routine use of β-blockers after PCI does not reduce the incidence of all-cause death or MACEs in patients with SCAD.

Objectives: To monitor the changes in oxygenation levels of rat kidneys under step oxygen stimulation by high temporal resolution dynamic T₂* weighted planar echo imaging (T₂*W-EPI).

Methods: Step oxygen stimulation was applied to SD rats (n=10) in the sequence of 2 min of hyperoxia (100% O₂) -10 min of hypoxia (10% O₂) -10 min of hyperoxia (100% O₂). Dynamic MRI data of the kidneys of multi-echo gradient echo (mGRE) sequence and gradient echo-planar imaging (EPI) sequence were continuously acquired on a 9.4T small animal magnetic resonance scanner. The time resolution of the two sequences were 9 s and 1 s, respectively. A second-order step response model was established for the dynamic time series curves of different regions of interest (ROIs) in rat kidneys, and the parameters of the step response model were obtained, including time delay ∆t, natural frequency ωn, damping constant D and oscillation period T. The performance of two MRI imaging methods with different temporal resolution in response to the step oxygen stimulation in the kidneys was compared.

Results: Compared with the control experiment results of mGRE, the dynamic T₂*W-EPI technology proposed in this study increased the temporal resolution of monitoring renal step oxygen stimulation by 8 folds and improved the goodness of fit of the step response model. The model showed a shorter time delay ∆t (shortened by 29.4%, 42.6%, 56.4%, and 47.4%, respectively, in the CO, OSOM, ISOM and IM), a larger natural frequency ωn (increased by 21.1%, 28.6%, 52.2%, and 61.9%, respectively), and oscillation of each ROI (damping constant D<1) under the step oxygen stimulation.

Conclusions: In a step oxygen stimulation model of rat kidneys, the high temporal resolution dynamic T₂*W-EPI technique proposed in this study is capable of real-time monitoring of the changes in renal oxygenation levels for detection of abnormal renal conditions.

Objectives: To investigate the role of transmembrane protein EVA1A in liver lipid metabolism and development of non-alcoholic fatty liver disease (NAFLD).

Methods: Eight-week-old male ob/ob mice were randomized into control group injected with AAV null vector via the tail vein (AAV-null group) and AAV-Eva1a group injected with recombinant vector AAV-Eva1a (n=8). HepG2 cells transfected with the lentiviral vector LV-EVA1A or the null vector were induced with oleic acid to construct a cell model of NAFLD. The expression levels of EVA1A, lipid metabolism-related and autophagy-related genes in mouse livers were detected with RT-qPCR, Western blotting, and immunofluorescence staining, and lipid accumulation in mouse livers and blood and in the treated cells was examined with HE and Oil Red O staining and lipid detection kits. Serum levels of ALT, AST, IL-6, IL-1β, and TNF-α of the mice were detected, and hepatic lipophagy was observed with transmission electron microscopy.

Results: The mouse livers in AAV-Eva1a group and LV-EVA1A-transfected cells showed significantly increased expression levels of EVA1A mRNA and protein. The liver weight and coefficient and lipid deposition of the mice with AAV-Eva1a injection and triglyceride (TG) content in LV-EVA1A-transfected cells were significantly decreased. The mice in AAV-Eva1a group showed significantly reduced serum total cholesterol, LDL-C, and HDL-C levels and hepatic TG levels with lowered serum levels of ALT, AST, IL-6 and TNF‑α. In both mouse livers in AAV-Eva1a group and LV-EVA1A-transfected HepG2 cells, acetyl-CoA carboxylase, fatty acid transport protein, and diacylglycerol acyltransferase expressions were all significantly decreased and adipose triglyceride lipase increased. Hepatic lipophagy, autophagosome numbers and LC3-II and ATG5 expressions were enhanced and p62 expression was lowered in the mice in AAV-Eva1a group and LV-EVA1A-transfected cells.

Conclusions: EVA1A overexpression alleviates fatty liver and inflammation in ob/ob mice by regulating lipid metabolism-related genes and enhancing lipophagy to promote clearance of accumulated hepatic lipids.

Objectives: To investigate the mechanism of poricoic acid A (PAA) for alleviating dextran sulfate sodium (DSS)-induced colitis in mice.

Methods: Eighteen C57BL/6 mice were randomly divided into control group, DSS-induced colitis model group, and PAA intervention (10 mg/kg) group. The changes in body weight, colon length, disease activity index (DAI), and histopathological scores of the mice were evaluated. In a DSS-induced Caco-2 cell model, the changes in expressions of ZO-1, claudin-1, Bcl-2, Bax, cleaved caspase-3, LC3-II/I, and P62 were detected. Molecular docking and Western blotting were used to analyze the mechanisms underlying the ameliorating effect of PAA on DSS-induced colitis.

Results: In the mouse models of DSS-induced colitis, PAA significantly ameliorated DSS-induced weight loss, colon shortening, and elevation of DAI scores while reducing colonic IL-1β and TNF-α levels. HE staining showed that PAA obviously alleviated colonic crypt damage, reduced inflammatory cell infiltration, and lowered histopathological scores of the colon. AB-PAS staining revealed significantly increased goblet cell counts in PAA-treated mice compared to those in DSS group. In DSS-induced Caco-2 cells, PAA treatment effectively inhibited DSS-induced downregulation of the tight junction proteins, reduced Bax and cleaved caspase-3 expressions, increased Bcl-2 expression and the LC3-II/I ratio, and decreased P62 expression. Mechanistic study suggested that PAA targeted the AMPK/mTOR pathway to activate autophagy and suppress cell apoptosis.

Conclusions: PAA protects intestinal barrier function and alleviates DSS-induced colitis in mice by activating AMPK/mTOR-mediated autophagy and inhibiting intestinal epithelial cell apoptosis.

Objectives: To improve the accuracy of machine learning models for preoperative prediction of high-intensity focused ultrasound (HIFU) ablation efficacy for uterine fibroids by correcting class imbalance in small sample datasets using undersampling methods.

Methods: Clinical and imaging data were collected from 140 patients with uterine fibroids undergoing HIFU treatment at Foshan Women and Children Hospital, including 104 with high ablation rates and 36 with low ablation rates. Radiomic features were extracted from MRI T2-weighted images (T2WI) of the patients, and machine learning models were constructed to predict HIFU treatment outcomes. Four machine learning algorithms, including k-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), and Multilayer Perceptron (MLP), were coupled with 7 undersampling methods, namely Random Undersampling (RUS), Repeated Edited Nearest Neighbors (RENN), All k-Nearest Neighbors (AllKNN), Neighborhood Cleaning Rule-3 (NM), Condensed Nearest Neighbor (CNN), Neighborhood Cleaning Rule (NCR), and Instance Hardness Threshold (IHT), for handling class imbalance in the datasets. The 28 prediction models were evaluated using 5-fold cross-validation for areas under the receiver operating characteristic curve (AUC), accuracy, recall, and specificity.

Results: The best combinations of undersampling methods and machine learning models CNN-RF, NM-SVM, CNN-KNN, and NM-MLP had AUCs of 0.772 (95% CI: 0.566-0.942), 0.797 (95% CI: 0.600-0.950), 0.822 (95% CI: 0.635-0.964), and 0.822 (95% CI: 0.632-0.960), respectively. The AUCs of the machine learning models significantly increased after coupling with undersampling methods, with the MLP model showing the most pronounced improvement. The recall rates of the 4 combined models also improved significantly (by 0.389 for CNN-RF, 0.836 for NM-SVM, 0.532 for CNN-KNN, and 0.372 for NM-MLP).

Conclusions: The use of undersampling methods can effectively correct class imbalance in small sample datasets to improve the accuracy of machine learning models for predicting the efficacy of HIFU ablation for uterine fibroids.

Objectives: To establish a pelvic active bone marrow (ABM) segmentation method based on diffusion cycle-consistent generative adversarial networks for improving individualized precision of conventional anatomical atlas-based methods.

Methods: We collected pelvic PET-CT data from 253 patients and constructed a 3-stage cascaded cross-modal learning framework for precise individualized ABM identification from CT images. The framework used cycle-consistent generative adversarial networks for bidirectional CT-PET mapping, conditional diffusion modules with 1000-step Markov chains for progressive denoising, and multi-scale progressive feature pyramid fusion networks for segmentation. The peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), normalized mean square error (NMSE), Dice similarity coefficient (DSC), and average symmetric surface distance (ASSD) were used for evaluation of the model performance for ABM segmentation.

Results: The proposed method outperformed the existing methods with a PSNR of 26.42±0.63 dB, an SSIM of 0.894±0.011, and an NMSE of 0.0235±0.0026. For ABM segmentation, the average Dice coefficient of the model reached 0.777±0.023 with an ASSD of 3.52±0.41 mm.

Conclusions: Compared with the conventional methods, the propose method significantly improves individualized segmentation accuracy of the ABM and is thus suitable use in individualized bone marrow protection radiotherapy for rectal cancer.

Objectives: Sepsis patients exhibit diverse immune states, making it crucial to identify subtypes with distinct inflammatory profiles through Th1/Th2 cytokine data for personalized treatment and improved prognosis.

Methods: We retrieved data from sepsis patients who underwent Th1/Th2 cytokine testing in Nanfang Hospital, Southern Medical University from June 1, 2020, to February 1, 2022. An unsupervised K-means clustering method classified participants based on Th1/Th2 cytokine levels, with the primary outcome being the 7-day mortality rate post-ICU admission. Cox proportional hazards and Restricted Mean Survival Time (RMST) analyses were utilized to explore survival outcomes.

Results: A total of 321 sepsis patients were included. IL-6 (HR 1.69, 95%CI: 1.22, 2.34) and IL-10 (HR 1.81, 95% CI: 1.37, 2.40) emerged as independent predictors of 7-day mortality. Unsupervised K-means clustering revealed 3 inflammatory/immune subgroups: Cluster 1 (n=166, low inflammatory response), Cluster 2 (n=99, moderate inflammatory response with immune suppression), and Cluster 3 (n=56, strong inflammatory and immune suppression). Compared to Cluster 1, Clusters 2 and 3 had higher 7-day mortality risks (14.4% vs 23.2%, HR=4.30, 95% CI: 1.51-12.26; 14.4% vs 35.7%, HR=7.32, 95% CI: 2.57-20.79).

Conclusions: Septic patients in a protective immune response state (Cluster 1) exhibit better short-term prognoses, suggesting the importance of understanding inflammatory/immune states for precise treatment and improved outcomes.

Objectives: To investigate the regulatory role of lncRNA SNHG12 in docetaxel (DTX) resistance of prostate cancer (PCa) cells.

Methods: Tumor-bearing male BALB/c nude mouse models were stablished by dorsal subcutaneous injection of PC-3 cells or DTX-resistant PC-3 (PC-3R) cells, either with or without transfection with sh-SNHG12 prior to the injection (n=5). The expressions of the key genes and proteins in the tumor tissues were detected using RT-qPCR, Western blotting, immunofluorescence staining or immunohistochemistry. The proliferation and migration of the treated cells were evaluated with CCK-8, clone formation and Transwell migration assays. RIP-qPCR technique was used to determine the binding between the RNAs and proteins.

Results: SNHG12 expression was significantly up-regulated in PC-3R cells. SNHG12 knockdown effectively inhibited proliferation and migration of PC-3R cells in vitro and suppressed tumor growth in nude mice. While 10 nmol/L DTX treatment alone did not significantly affect proliferation or migration of PC-3R cells, its combination with SNHG12 knockdown strongly inhibited cell proliferation and migration both in vitro and in the tumor-bearing mice. The expression of ELAVL1 was obviously up-regulated in PC-3R cells, and increased activation level of PI3K/AKT signaling pathway was detected in both PC-3R cells and the xenografts. The effect of SNHG12 knockdown was significantly weakened by treatment with the PI3K activator 740 Y-P. SNHG12 was found to bind to ELAVL1 in PC-3R cells, and mechanistic studies showed that their binding activated the PI3K/AKT signaling pathway to result in DTX resistance in PCa.

Conclusions: SNHG12 knockdown inhibits DTX resistance of PCa cells by reducing SNHG12 binding to ELAVL1 to inhibit the activation the PI3K/AKT signaling pathway.

Large language models (LLMs) are emerging artificial intelligence technologies with strong text and image processing capabilities, offering critical support for the intelligent transformation of healthcare and improving clinical efficiency and quality. This review summarizes the current applications, technical features, and future directions of LLMs in cancer diagnosis, focusing on two key scenarios: automated analysis of textual reports (e.g., imaging, pathology, and case summaries) and multimodal diagnosis combining text and medical images. Findings show that LLMs now perform at a level comparable to general resident physicians in cancer diagnosis but are still incapable of making specialized and precise judgments. They also exhibit application-specific traits, such as parameter-efficient models adapted for grassroots-level scenario and divergent versatility in multilingual report analysis. Future efforts should prioritize developing specialized, practical medical LLMs through optimized fine-tuning strategies, construction of high-quality Chinese medical datasets, and integration with vision-language models to promote the clinical application of these models and increase the accessibility of healthcare resources.