AIM: To investigate whether proanthocyanidins(PAC)can ameliorate ferroptosis in retinal pigment epithelium(RPE)cells via the Nrf2/GPX4 pathway, and to evaluate its protective effect in dry age-related macular degeneration(ARMD). METHODS: The protective effects of PAC against ferroptosis were evaluated using an in vitro model of H2O2-stimulated human RPE cells(ARPE-19)and an in vivo dry ARMD animal model induced by sodium iodate(NaIO3). CCK-8 assay was applied to assess cell viability. Calcein-AM/PI staining was applied to determine the level of cell death. Lipid peroxidation(LPO)levels were measured using a lipid peroxidation assay kit. CM-H2DCFDA was used to detect the level of reactive oxygen species(ROS)in cells. MitoSOX probe was employed to measure the mitochondrial ROS level. JC-1 staining was used to evaluate the changes in mitochondrial membrane potential. Western Blot was performed to detect the expression levels of proteins(Nrf2, GPX4, and HO-1)related to the Nrf2 pathway. Retinal flat mounts were used to evaluate the structure and function of RPE. Hematoxylin and eosin(H & E)staining was applied to assess the morphological changes in rat retinas. Rhodopsin and opsin staining was used to evaluate visual function in the retina. TUNEL staining was employed to detect apoptosis in retinal cells. RESULTS: H2O2 exacerbates ferroptosis in ARPE-19 cells, as evidenced by elevated levels of Fe2+, ROS, and LPO. PAC preconditioning ameliorates mitochondrial function, reduces intracellular Fe2+, ROS, and LPO levels, and suppresses ferroptosis in RPE cells via activation of the Nrf2/GPX4 signaling pathway. In a NaIO3-induced dry ARMD model, both PAC and the ferroptosis inhibitor Fer-1 reverse the downregulation of retinal Nrf2/GPX4 expression and attenuate retinal cell death.CONCLUSION:PAC inhibits ferroptosis via the Nrf2/GPX4 pathway, offering a novel potential therapeutic strategy for retinal degeneration.

OBJECTIVE To investigate the mechanism by which Shenfukang capsule （SFK） ameliorates renal interstitial fibrosis （RIF） by regulating macrophage-fibroblast crosstalk via the signal transducer and activator of transcription 3 （STAT3）/Yes-associated protein （YAP） pathway. METHODS RAW264.7 macrophages were induced to polarize with transforming growth factor-β 1 （TGF-β 1 ）. Subsequently， a non-contact co-culture system of macrophages with renal fibroblasts NRK-49F， as well as a non-contact co-culture system of macrophages with YAP-knockdown renal fibroblasts， were established. Cells were treated with low， medium， and high concentrations （4， 8， 16 μg/mL） of SFK， as well as a STAT3 inhibitor （STAT3-I，1.64 μg/mL） and losartan potassium tablets （positive control， 0.28 μg/mL）， for 48 h. After intervention， the protein and mRNA expression levels of CD86， CD163， and STAT3 in macrophages， as well as CD86， F4/80， and STAT3 mRNA， were detected. In co-cultured renal fibroblasts， the protein and mRNA expression levels of α -smooth muscle actin （ α -SMA）， Vimentin， collagen type Ⅰ （Col-Ⅰ）， matrix metalloproteinase-1 （MMP-1）， and YAP were detected. In co-cultured YAP-knockdown renal fibroblasts， the protein and mRNA expression levels of α -SMA， Vimentin， and MMP-1 were also detected. RESULTS Following TGF-β 1 induction， the protein expression levels of CD86， CD163， and STAT3， as well as the mRNA expression levels of CD86， F4/80， and STAT3 in macrophages were significantly increased （ P ＜0.05）. The conditioned medium from polarized macrophages activated renal fibroblasts， as evidenced by significantly increased protein and mRNA expression levels of α -SMA， Vimentin， Col-Ⅰ， and YAP， and significantly decreased protein and mRNA expression levels of MMP-1 in renal fibroblasts （ P ＜0.05）. Treatment with SFK and STAT3-I reversed the changes in the above indicators， with the medium concentration SFK group showing stronge r effects on some indicators than the low and high concentration groups. In the non-contact co-culture experiment of macrophages and YAP-knockdown renal fibroblasts， there were no statistically significant differences in the protein and mRNA expression of α-SMA， Vimentin， and MMP-1 among the groups. CONCLUSIONS SFK can inhibit macrophage-renal fibroblast crosstalk by blocking the STAT3/YAP pathway， thereby delaying the progression of RIF.

Objective To establish and internally validate a predictive model for poorly differentiated adenocarcinoma based on CT imaging and tumor marker results. Methods Patients with solid and partially solid lung nodules who underwent lung nodule surgery at the Department of Thoracic Surgery, the Affiliated Brain Hospital of Nanjing Medical University in 2023 were selected and randomly divided into a training set and a validation set at a ratio of 7:3. Patients' CT features, including average density value, maximum diameter, pleural indentation sign, and bronchial inflation sign, as well as patient tumor marker results, were collected. Based on postoperative pathological results, patients were divided into a poorly differentiated adenocarcinoma group and a non-poorly differentiated adenocarcinoma group. Univariate analysis and logistic regression analysis were performed on the training set to establish the predictive model. The receiver operating characteristic (ROC) curve was used to evaluate the model's discriminability, the calibration curve to assess the model's consistency, and the decision curve to evaluate the clinical value of the model, which was then validated in the validation set. Results A total of 299 patients were included, with 103 males and 196 females, with a median age of 57.00 (51.00, 67.25) years. There were 211 patients in the training set and 88 patients in the validation set. Multivariate analysis showed that carcinoembryonic antigen (CEA) value [OR=1.476, 95%CI (1.184, 1.983), P=0.002], cytokeratin 19 fragment antigen (CYFRA21-1) value [OR=1.388, 95%CI (1.084, 1.993), P=0.035], maximum tumor diameter [OR=6.233, 95%CI (1.069, 15.415), P=0.017], and average density [OR=1.083, 95%CI (1.020, 1.194), P=0.040] were independent risk factors for solid and partially solid lung nodules as poorly differentiated adenocarcinoma. Based on this, a predictive model was constructed with an area under the ROC curve of 0.896 [95%CI (0.810, 0.982)], a maximum Youden index corresponding cut-off value of 0.103, sensitivity of 0.750, and specificity of 0.936. Using the Bootstrap method for 1000 samplings, the calibration curve predicted probability was consistent with actual risk. Decision curve analysis indicated positive benefits across all prediction probabilities, demonstrating good clinical value. Conclusion For patients with solid and partially solid lung nodules, preoperative use of CT to measure tumor average density value and maximum diameter, combined with tumor markers CEA and CYFRA21-1 values, can effectively predict whether it is poorly differentiated adenocarcinoma, allowing for early intervention.

BACKGROUND:At present,osteoarthritis has become a major disease affecting the quality of life of the elderly,and the therapeutic effect is poor,often focusing on preventing the disease process,and the pathogenesis of osteoarthritis is still not fully understood.Bioinformatics analysis was carried out to explore the main pathogenesis of osteoarthritis and related mechanisms of gene coding regulation. OBJECTIVE:To screen core differential genes with a major role in osteoarthritis by gene expression profiling. METHODS:Datasets were downloaded from the Gene Expression Omnibus(GEO):GSE114007,GSE117999,and GSE129147.Differential genes in the GSE114007 and GSE117999 data collections were screened using R software,performing differential genes to weighted gene co-expression network analysis.The module genes most relevant to osteoarthritis were selected to perform protein interaction analysis.Candidate core genes were selected using the cytocape software.The candidate core genes were subsequently subjected to least absolute shrinkage and selection operator regression and COX analysis to identify the core genes with a key role in osteoarthritis.The accuracy of the core genes was validated using an external dataset,GSE129147. RESULTS AND CONCLUSION:(1)A total of 477 differential genes were identified,265 differential genes associated with osteoarthritis were obtained by weighted gene co-expression network analysis,and 8 candidate core genes were identified.The least absolute shrinkage and selection operator regression analysis finally yielded a differential gene ASPM with core value that was externally validated.(2)It is concluded that abnormal gene ASPM expression screened by bioinformatics plays a key central role in osteoarthritis.

Silent mutations within the RAS  gene have garnered increasing attention for their potential roles in tumorigenesis and therapeutic strategies. Kirsten-RAS ( KRAS ) mutations, predominantly oncogenic, are pivotal drivers in various cancers. While extensive research has elucidated the molecular mechanisms and biological consequences of active KRAS  mutations, the functional significance of silent mutations remains relatively understudied. This review synthesizes current knowledge on KRAS  silent mutations, highlighting their impact on cancer development. Silent mutations, which do not alter protein sequences but can affect RNA stability and translational efficiency, pose intriguing questions regarding their contribution to tumor biology. Understanding these mutations is crucial for comprehensively unraveling KRAS -driven oncogenesis and exploring novel therapeutic avenues. Moreover, investigations into the clinical implications of silent mutations in KRAS -mutant tumors suggest potential diagnostic and therapeutic strategies. Despite being in early stages, research on KRAS  silent mutations holds promise for uncovering novel insights that could inform personalized cancer treatments. In conclusion, this review underscores the evolving landscape of KRAS  silent mutations, advocating for further exploration to bridge fundamental biology with clinical applications in oncology.
Humans ; Mutation/genetics* ; Neoplasms/genetics* ; Proto-Oncogene Proteins p21(ras)/genetics* ; Animals

Objective: To investigate the application effectiveness of the artificial intelligence(AI) based Generative Pre-treatment tool of Skeletal Pathology (GPTSP) in measuring functional lumbar radiographic examinations. Methods: This is a retrospective case series study,reviewing the clinical and imaging data of 34 patients who underwent lumbar dynamic X-ray radiography at Department of Orthopedics, the Second Hospital of Shandong University from September 2021 to June 2023. Among the patients, 13 were male and 21 were female, with an age of (68.0±8.0) years (range:55 to 88 years). The AI model of the GPTSP system was built upon a multi-dimensional constrained loss function constructed based on the YOLOv8 model, incorporating Kullback-Leibler divergence to quantify the anatomical distribution deviation of lumbar intervertebral space detection boxes, along with the introduction of a global dynamic attention mechanism. It can identify lumbar vertebral body edge points and measure lumbar intervertebral space. Furthermore, spondylolisthesis index, lumbar index, and lumbar intervertebral angles were measured using three methods: manual measurement by doctors, predefined annotated measurement, and AI-assisted measurement. The consistency between the doctors and the AI model was analyzed through intra-class correlation coefficient (ICC) and Kappa coefficient. Results: AI-assisted physician measurement time was (1.5±0.1) seconds (range: 1.3 to 1.7 seconds), which was shorter than the manual measurement time ((2 064.4±108.2) seconds,range: 1 768.3 to 2 217.6 seconds) and the pre-defined annotation measurement time ((602.0±48.9) seconds,range: 503.9 to 694.4 seconds). Kappa values between physicians' diagnoses and AI model's diagnoses (based on GPTSP platform) for the lumbar slip index, lumbar index, and intervertebral angles measured by three methods were 0.95, 0.92, and 0.82 (all P<0.01), with ICC values consistently exceeding 0.90, indicating high consistency. Based on the doctor's manual measurement, compared with the predefined label measurement, altering AI assistance, doctors measurement with average annotation errors reduced from 2.52 mm (range: 0.01 to 6.78 mm) to 1.47 mm(range: 0 to 5.03 mm). Conclusions: The GPTSP system enhanced efficiency in functional lumbar analysis. AI model demonstrated high consistency in annotation and measurement results, showing strong potential to serve as a reliable clinical auxiliary tool.
Humans ; Female ; Retrospective Studies ; Male ; Lumbar Vertebrae/diagnostic imaging* ; Middle Aged ; Aged ; Aged, 80 and over ; Artificial Intelligence ; Radiography ; Spondylolisthesis/diagnostic imaging*

In this paper, near-infrared spectroscopy(NIRS) was employed to analyze 129 batches of commercial products of Reduning Injection. The batch reporting rate was estimated according to the report of Reduning Injection in the direct adverse drug reaction(ADR) reporting system of the drug marketing authorization holder of the Center for Drug Reevaluation of the National Medical Products Administration(National Center for ADR Monitoring) from August 2021 to August 2022. According to the batch reporting rate, the samples of Reduning Injection were classified into those with potential risks and those being safe. No processing, random oversampling(ROS), random undersampling(RUS), and synthetic minority over-sampling technique(SMOTE) were then employed to balance the unbalanced data. After the samples were classified according to appropriate sampling methods, competitive adaptive reweighted sampling(CARS), successive projections algorithm(SPA), uninformative variables elimination(UVE), and genetic algorithm(GA) were respectively adopted to screen the features of spectral data. Then, support vector machine(SVM), logistic regression(LR), k-nearest neighbors(KNN), naive bayes(NB), random forest(RF), and artificial neural network(ANN) were adopted to establish the risk prediction models. The effects of the four feature extraction methods on the accuracy of the models were compared. The optimal method was selected, and bayesian optimization was performned to optimize the model parameters to improve the accuracy and robustness of model prediction. To explore the correlations between potential risks of clinical use and quality test data, TreeNet was employed to identify potential quality parameters affecting the clinical safety of Reduning Injection. The results showed that the models established with the SVM, LR, KNN, NB, RF, and ANN algorithms had the F1 scores of 0.85, 0.85, 0.86, 0.80, 0.88, and 0.85 and the accuracy of 88%, 88%, 88%, 85%, 91%, and 88%, respectively, and the prediction time was less than 5 s. The results indicated that the established models were accurate and efficient. Therefore, near infrared spectroscopy combined with machine learning algorithms can quickly predict the potential risks of clinical use of Reduning Injection in batches. Three key quality parameters that may affect clinical safety were identified by TreeNet, which provided a scientific basis for improving the safety standards of Reduning Injection.
Spectroscopy, Near-Infrared/methods* ; Drugs, Chinese Herbal/administration & dosage* ; Machine Learning ; Algorithms ; Humans ; Quality Control

Guided by the theory of "the kidney storing essence, governing the bones, and producing marrow", this study explored the mechanism of icariin(ICA) in regulating the osteogenic differentiation of rat bone mesenchymal stem cells(BMSCs) through caveolin-1(Cav1) via in vitro and in vivo experiments, aiming to provide a theoretical basis for the prevention and treatment of postmenopausal osteoporosis with traditional Chinese medicine(TCM). Primary cells were obtained from 4-week-old female SD rats using the whole bone marrow adherent method. Flow cytometry was used to detect the expression of surface markers CD29, CD90, CD11b, and CD45. The potential for osteogenic and adipogenic differentiation was assessed. The effect of ICA on cell viability was determined using the CCK-8 assay, and the impact of ICA on the formation of mineralized nodules was verified by alizarin red staining. A stable Cav1-silenced cell line was constructed using lentivirus. The effect of Cav1 silencing on osteogenic differentiation was observed via alizarin red staining. Western blot analysis was conducted to detect the expression of Cav1, Hippo/TAZ, and osteogenic markers such as Runt-related transcription factor 2(RUNX2) and alkaline phosphatase(ALP). The results showed that primary cells were successfully obtained using the whole bone marrow adherent method, positively expressing surface markers of rat BMSCs and possessing the potential for both osteogenic and adipogenic differentiation. The CCK-8 assay and alizarin red staining results indicated that 1×10~(-7) mol·L~(-1) was the optimal concentration of ICA for intervention in this experiment(P<0.05). During osteogenic induction, ICA inhibited Cav1 expression(P<0.05) while promoting TAZ expression(P<0.05). Alizarin red staining demonstrated that Cav1 silencing significantly promoted the osteogenic differentiation of BMSCs. After ICA intervention, TAZ expression was activated, and the expression of osteogenic markers ALP and RUNX2 was increased. In conclusion, Cav1 silencing significantly promotes the osteogenic differentiation of BMSCs, and ICA promotes this differentiation by inhibiting Cav1 and regulating the Hippo/TAZ signaling pathway.
Animals ; Mesenchymal Stem Cells/metabolism* ; Caveolin 1/genetics* ; Osteogenesis/drug effects* ; Rats, Sprague-Dawley ; Rats ; Cell Differentiation/drug effects* ; Female ; Signal Transduction/drug effects* ; Flavonoids/administration & dosage* ; Protein Serine-Threonine Kinases/genetics* ; Drugs, Chinese Herbal/pharmacology* ; Cells, Cultured ; Humans