ObjectiveTo explore the material basis of the "interaction of blood stasis and toxin" mechanism in cerebral ischemia-reperfusion injury， as well as the protective role of Huoxue Huayu Jiedu prescription （HXHYJDF） against ferroptosis. MethodsSixty SPF-grade male SD rats were randomly divided into six groups： sham group， model group， deferoxamine （DFO） group （100 mg·kg^-1）， low-dose HXHYJDF group （4.52 g·kg^-1）， medium-dose HXHYJDF group （9.04 g·kg^-1）， and high-dose HXHYJDF group （18.07 g·kg^-1）， with ten rats in each group. Except for the sham group， the other groups were used to replicate the model of focal cerebral ischemia-reperfusion in the middle cerebral artery of rats by the reforming Longa method. Neurological function was assessed at 1^st， 3^rd， 5^th， and 7^th days post-reperfusion using the modified neurological severity scores （m-NSS）. Brain tissue pathology and the morphology of mitochondria were observed using hematoxylin-eosin （HE） staining and transmission electron microscopy. The contents of malondialdehyde （MDA）， glutathione （GSH）， divalent iron ions （Fe²⁺）， and reactive oxygen species （ROS） in the ischemic cerebral tissue were detected using enzyme-linked immunosorbent assay （ELISA）. Immunohistochemistry and Western blot （WB） were used to detect the expression of iron death marker proteins glutathione peroxidase 4 （GPX4）， ferroportin-1 （FPN1）， transferrin receptor protein 1 （TfR1）， and ferritin mitochondrial （FtMt） in brain tissue. ResultsCompared with the sham group， the mNSS score of the model group was significantly increased （P<0.01）. HE staining showed that the number of neurons in the cortex of brain tissue was seriously reduced， and the intercellular space was widened. The nucleus was fragmented， and the cytoplasm was vacuolated. The results of transmission electron microscopy showed that the mitochondria in the cytoplasm contracted and rounded， and the mitochondrial cristae decreased. The matrix was lost and vacuolated， and the density of the mitochondrial bilayer membrane increased. The results of ELISA showed that the content of GSH decreased significantly （P<0.01）， and the contents of MDA， Fe²⁺， and ROS increased significantly （P<0.01）. The results of immunohistochemistry and WB showed that the expression of GPX4 and FPN1 proteins was significantly decreased （P<0.01）， and the expression of FtMt and TfR1 proteins was significantly increased （P<0.01）. Compared with those of the model group， the m-NSS scores of the high-dose and medium-dose HXHYJDF groups began to decrease on the 3^rd and 5^th days， respectively （P<0.05， P<0.01）. The results of HE and transmission electron microscopy showed that the intervention of HXHYJDF improved the pathological changes of neurons and mitochondria. The results of ELISA showed that the content of GSH in the medium-dose and high-dose HXHYJDF groups increased significantly （P<0.01）， and the contents of MDA， Fe²⁺， and ROS decreased significantly （P<0.05， P<0.01）. The content of GSH in the low-dose HXHYJDF group increased significantly （P<0.01）， and the contents of MDA and ROS decreased significantly （P<0.01）. The results of immunohistochemistry showed that the expression of GPX4 and FPN1 in the high-dose HXHYJDF group increased significantly （P<0.01）， and the expression of FtMt and TfR1 decreased significantly （P<0.01）. The expression of GPX4 and FPN1 in the medium-dose HXHYJDF group increased significantly （P<0.05）， and the expression of TfR1 decreased significantly （P<0.01）. WB results showed that the expression levels of FPN1 and GPX4 proteins in the high-dose， medium-dose， and low-dose HXHYJDF groups were significantly up-regulated （P<0.01）， and the expression levels of FtMt and TfR1 proteins were significantly down-regulated （P<0.01）. ConclusionHXHYJDF can significantly improve neurological dysfunction symptoms in rats with cerebral ischemia-reperfusion injury， improve the pathological morphology of the infarcted brain tissue， and protect the brain tissue of rats with cerebral ischemia-reperfusion injury to a certain extent. Neuronal ferroptosis is involved in cerebral ischemia-reperfusion injury， with increased levels of MDA， Fe²⁺， ROS， and TfR1 and decreased levels of FtMt， FPN1， GPX4， and GSH potentially constituting the material basis of the interaction of blood stasis and toxin mechanism in cerebral ischemia-reperfusion injury. HXHYJDF may exert brain-protective effects by regulating iron metabolism-related proteins， promoting the discharge of free iron， reducing brain iron deposition， alleviating oxidative stress， and inhibiting ferroptosis.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

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

BACKGROUND: Arsenic trioxide (ATO) is indicated as a broad-spectrum medicine for a variety of diseases, including cancer and cardiac disease. While the role of ATO in hepatic ischemia/reperfusion injury (HIRI) has not been reported. Thus, the purpose of this study was to identify the effects of ATO on HIRI. METHODS: In the present study, we established a 70% hepatic warm I/R injury and partial hepatectomy (30% resection) animal models in vivo and hepatocytes anoxia/reoxygenation (A/R) models in vitro with ATO pretreatment and further assessed liver function by histopathologic changes, enzyme-linked immunosorbent assay, cell counting kit-8, and terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling (TUNEL) assay. Small interfering RNA (siRNA) for extracellular signal-regulated kinase (ERK) 1/2 was transfected to evaluate the role of ERK1/2 pathway during HIRI, followed by ATO pretreatment. The dynamic process of autophagic flux and numbers of autophagosomes were detected by green fluorescent protein-monomeric red fluorescent protein-LC3 (GFP-mRFP-LC3) staining and transmission electron microscopy. RESULTS: A low dose of ATO (0.75 μmol/L in vitro and 1 mg/kg in vivo ) significantly reduced tissue necrosis, inflammatory infiltration, and hepatocyte apoptosis during the process of hepatic I/R. Meanwhile, ATO obviously promoted the ability of cell proliferation and liver regeneration. Mechanistically, in vitro  studies have shown that nontoxic concentrations of ATO can activate both ERK and phosphoinositide 3-kinase-serine/threonine kinase (PI3K-AKT) pathways and further induce autophagy. The hepatoprotective mechanism of ATO, at least in part, relies on the effects of ATO on the activation of autophagy, which is ERK-dependent. CONCLUSION Low, non-toxic doses of ATO can activate ERK/PI3K-AKT pathways and induce ERK-dependent autophagy in hepatocytes, protecting liver against I/R injury and accelerating hepatocyte regeneration after partial hepatectomy.
Animals ; Arsenic Trioxide ; Autophagy/physiology* ; Reperfusion Injury/prevention & control* ; Mice ; Male ; Proto-Oncogene Proteins c-akt/physiology* ; Arsenicals/therapeutic use* ; Oxides/therapeutic use* ; Liver/metabolism* ; Extracellular Signal-Regulated MAP Kinases/metabolism* ; Mice, Inbred C57BL

The combination of Aidi Injection(ADI) and doxorubicin(DOX) is a common strategy in the treatment of cancer, which can achieve synergistic anti-tumor effects while attenuating the cardiotoxicity caused by DOX. This study aims to investigate the mechanism of ADI in attenuating DOX-induced cardiotoxicity by multi-omics. DOX was used to induce cardiotoxicity in mice, and the cardioprotective effects of ADI were evaluated based on biochemical indicators and pathological changes. Based on the results, transcriptomics, proteomics, and metabolomics were employed to analyze the changes of endogenous substances in different physiological states. Furthermore, data from multiple omics were integrated to screen key regulatory pathways by which ADI attenuated DOX-induced cardiotoxicity, and important target proteins were selected for measurement by ELISA kits and immunohistochemical analysis. The results showed that ADI significantly reduced the levels of cardiac troponin T(cTnT) and N-terminal pro-B-type natriuretic peptide(NT-proBNP) and effectively ameliorated myocardial fibrosis and intracellular vacuolization, indicating that ADI showed therapeutic effect on DOX-induced cardiotoxicity. The transcriptomics analysis screened out a total of 400 differentially expressed genes(DEGs), which were mainly enriched in inflammatory response, oxidative stress, and myocardial fibrosis. After proteomics analysis, 70 differentially expressed proteins were selected, which were mainly enriched in the inflammatory response, cardiac function, and energy metabolism. A total of 51 differentially expressed metabolites were screened by the metabolomics analysis, and they were mainly enriched in multiple signaling pathways, including the inflammatory response, lipid metabolism, and energy metabolism. The integrated data of multiple omics showed that linoleic acid metabolism, arachidonic acid metabolism, and glycerophosphate metabolism pathways played an important role in DOX-induced cardiotoxicity, and ADI may exert therapeutic effects by modulating these pathways. Target validation experiments suggested that ADI significantly regulated abnormal protein levels of cyclooxygenase-1(COX-1), cyclooxygenase-2(COX-2), prostaglandin H2(PGH2), and prostaglandin D2(PGD2) in the model group. In conclusion, ADI may attenuate DOX-induced cardiotoxicity by regulating linoleic acid metabolism, arachidonic acid metabolism, and glycerophosphate metabolism, thus alleviating inflammation of the body.
Doxorubicin/toxicity* ; Animals ; Mice ; Cardiotoxicity/genetics* ; Drugs, Chinese Herbal/administration & dosage* ; Male ; Proteomics ; Metabolomics ; Injections ; Humans ; Multiomics

Amber and subfossil resins are subjects of interdisciplinary research across multiple fields. However, due to their diverse origins and complex compositions, different disciplines vary in their definitions and functional interpretations. In traditional Chinese medicine(TCM), amber has been utilized as a medicinal material since ancient time, with extensive historical documentation. However, its classification, provenance, and nomenclature remain ambiguous, and authentic medicinal amber artifacts are exceedingly rare. This study employed Fourier-transform infrared spectroscopy(FTIR) to characterize amber and subfossil resins from various geological sources and commercially "medicinal amber". Additionally, historical literature and market surveys were analyzed to explore their provenance, composition, and functional attributes. The results indicate that amber and subfossil resins from different sources and with different compositions exhibit distinct fingerprint characteristics in the FTIR spectral range of 1 800-700 cm~(-1). "Medicinal amber" available in the market primarily consists of subfossil or modern resins, significantly differing in composition and structure from geological amber. This study highlights the importance of interdisciplinary research on amber identification and resource management. It is essential to establish a systematic database of amber and subfossil resin characteristics and integrate modern analytical techniques to enhance research on their composition, pharmacological mechanisms, and potential therapeutic effects, thereby promoting the standardized utilization of amber resources and advancing the modernization of TCM.
Amber/history* ; Archaeology ; Spectroscopy, Fourier Transform Infrared ; Medicine, Chinese Traditional