This study employed the drug affinity responsive target stability (DARTS) technique to investigate the molecular mechanism of the antipsychotic drug penfluridol against melanoma, revealing the biological pathway to exert its effect on the HSPA6/p53/p21 signaling axis. Experiments such as the methylthiazolyldiphenyl-tetrazolium bromide (MTT) assay and cell colony formation ability assay confirmed that penfluridol could significantly downregulate the expression of cyclin D1 and cyclin-dependent kinase 4 (CDK4) in melanoma A375 and B16 cells, induce cell cycle arrest in the G1 phase, and thus inhibit the proliferation of melanoma cells. Meanwhile, the results of Western blot, Hoechst 33342 staining and Annexin V-FITC/PI double staining experiments showed that penfluridol could significantly downregulate the expression of Bcl-2 and upregulate the expression of Bax and cleaved caspase-3, inducing cell apoptosis. Further, the DARTS technique was used to identify heat shock 70 kD protein 6 (HSPA6) as the key target bound by penfluridol. Penfluridol activates the p53/p21 pathway by upregulating HSPA6. Knocking down HSPA6 reverses not only the activation of the p53/p21 pathway mediated by penfluridol but also the associated cell cycle arrest and apoptosis. Animal experiments on tumor-bearing mice also confirmed that knocking down HSPA6 could reverse the in vivo anti-tumor activity of penfluridol. This study clarified that penfluridol can inhibit the progression of melanoma by targeting HSPA6 to activate the p53/p21 signaling axis, providing a new perspective for the repositioning of antipsychotic drugs in cancer treatment.

Neurocritical care involves complex pathophysiological mechanisms, and its incidence is higher, injuries are more severe, and treatment is more challenging in high-altitude environments. This consensus, based on the latest domestic and international evidence-based medical data, establishes a standardized, goal-oriented framework for neurocritical care management applicable in high-altitude regions and nationwide. The consensus was developed following international standards for evidence quality assessment and underwent two rounds of Delphi expert consultation, resulting in 32 recommendation statements covering three parts: management systems, monitoring and assessment, and core strategies. Key updates include: advocating for the establishment of independent neurocritical care units and implementing precise tiered diagnosis and treatment based on the "Five Differences in Critical Care" concept; constructing a "trinity" multimodal brain monitoring system centered on cerebral blood flow, cerebral oxygenation, and brain function, emphasizing routine bedside transcranial Doppler ultrasound, cerebral oximetry, and continuous electroencephalography monitoring; shifting management strategies from mild hypothermia therapy to targeted temperature management, and defining the "446" target management pathway for the supercritical stage; emphasizing the assessment of static and dynamic cerebrovascular autoregulation functions through multimodal methods to achieve individualized optimal mean arterial pressure management; elevating cerebrospinal fluid management goals to the level of "glymphatic system" function maintenance; implementing a multidisciplinary collaborative, whole-process management model focusing on patients' long-term neurological functional outcomes; de-escalation criteria include multidimensional indicators such as recovery of brain structure, restoration of cerebrovascular autoregulation, improvement in cerebrospinal fluid dynamics, and reduction in biomarker levels; and integrating cutting-edge technologies like artificial intelligence into post-critical care management and rehabilitation planning. This consensus systematically integrates the entire process of neurocritical care management, reflecting the modern connotation of goal-oriented, dynamic, and multimodal integration in neurocritical care medicine. It aims to adapt to new trends such as deepening understanding of pathophysiological mechanisms, the integration of medicine and engineering, and the empowerment of artificial intelligence, thereby further advancing the discipline of critical care medicine.

BACKGROUND:During diabetic wound healing,the sustained activation of M1 macrophages exacerbates the inflammatory response and hinders wound repair.Auranofin,an anti-inflammatory drug,has not been clearly studied for its effects on M1 macrophages and its potential role in diabetic wound healing.OBJECTIVE:To investigate the effects of different concentrations of auranofin on the biological function of M1 macrophages and evaluate its potential application in diabetic wound healing.METHODS:RAW264.7 and THP-1 cells were used as research models.M1 polarization was induced using different concentrations of interferon-γ and lipopolysaccharide.M1 macrophages were treated with 1 and 2 μmol/L auranofin.Cell counting kit-8 assay was used to evaluate the effect of auranofin on cell viability.Quantitative real-time PCR was performed to detect mRNA expression of interleukin-1β,interleukin-6,and tumor necrosis factor-α.ELISA was employed to measure the levels of interleukin-1β,interleukin-6,and tumor necrosis factor-α in the supernatant.Western blot analysis was used to assess the expression of nuclear factor-κB(p65),phosphorylated mitogen-activated protein kinases(MAPK),and total MAPK proteins.Additionally,6-8-week-old male C57BL/6J and db/db diabetic mice were used for wound healing experiments,with the mice divided into C57 control,db/db control and auranofin treatment groups,each containing six animals.Dorsal skin defect modeling and treatment with intraperitoneal injection of auranofin were performed to observe wound healing in mice.RESULTS AND CONCLUSION:(1)Cell experiments showed that co-treatment with interferon-y(10 ng/mL)and lipopolysaccharide(100 ng/mL)significantly induced M1 polarization in RAW264.7 and THP-1 cells,resulting in increased mRNA expression of interleukin-1β,interleukin-6,and tumor necrosis factor-α.Treatment with auranofin(1 and 2 μmol/L)reduced the mRNA expression of these inflammatory factors in the cells and inhibited the secretion of inflammatory factors in the cell supernatant.(2)Auranofin treatment significantly suppressed the activation of nuclear factor-κB(p65)and phosphorylated MAPK signaling pathways.(3)Animal experiments showed that auranofin promoted wound healing in db/db diabetic mice,suggesting that auranofin has strong anti-inflammatory effects and may facilitate the healing of wounds in diabetic mice.

BACKGROUND:During diabetic wound healing,the sustained activation of M1 macrophages exacerbates the inflammatory response and hinders wound repair.Auranofin,an anti-inflammatory drug,has not been clearly studied for its effects on M1 macrophages and its potential role in diabetic wound healing.OBJECTIVE:To investigate the effects of different concentrations of auranofin on the biological function of M1 macrophages and evaluate its potential application in diabetic wound healing.METHODS:RAW264.7 and THP-1 cells were used as research models.M1 polarization was induced using different concentrations of interferon-γ and lipopolysaccharide.M1 macrophages were treated with 1 and 2 μmol/L auranofin.Cell counting kit-8 assay was used to evaluate the effect of auranofin on cell viability.Quantitative real-time PCR was performed to detect mRNA expression of interleukin-1β,interleukin-6,and tumor necrosis factor-α.ELISA was employed to measure the levels of interleukin-1β,interleukin-6,and tumor necrosis factor-α in the supernatant.Western blot analysis was used to assess the expression of nuclear factor-κB(p65),phosphorylated mitogen-activated protein kinases(MAPK),and total MAPK proteins.Additionally,6-8-week-old male C57BL/6J and db/db diabetic mice were used for wound healing experiments,with the mice divided into C57 control,db/db control and auranofin treatment groups,each containing six animals.Dorsal skin defect modeling and treatment with intraperitoneal injection of auranofin were performed to observe wound healing in mice.RESULTS AND CONCLUSION:(1)Cell experiments showed that co-treatment with interferon-y(10 ng/mL)and lipopolysaccharide(100 ng/mL)significantly induced M1 polarization in RAW264.7 and THP-1 cells,resulting in increased mRNA expression of interleukin-1β,interleukin-6,and tumor necrosis factor-α.Treatment with auranofin(1 and 2 μmol/L)reduced the mRNA expression of these inflammatory factors in the cells and inhibited the secretion of inflammatory factors in the cell supernatant.(2)Auranofin treatment significantly suppressed the activation of nuclear factor-κB(p65)and phosphorylated MAPK signaling pathways.(3)Animal experiments showed that auranofin promoted wound healing in db/db diabetic mice,suggesting that auranofin has strong anti-inflammatory effects and may facilitate the healing of wounds in diabetic mice.

Objective To explore the predictive performance of machine learning models integrating clinico-pathological features and inflammatory markers for lymphovascular invasion (LVI) before gastric cancer surgery. Methods A retrospective cohort of 193 gastric cancer patients from The First Hospital of Lanzhou University (training set) and 185 patients from Zhongshan Hospital, Fudan University (validation set) was included. Preoperative clinical pathological characteristics, tumor markers, and inflammatory markers were collected to identify independent risk factors for LVI. Six machine learning models were established in the training set. Model performance was evaluated using area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, decision curve analysis (DCA), and Brier scores. Shapley additive explanations (SHAP) was applied for model interpretability. Results The multivariate logistic regression showed increased tumor invasion depth (T-stage), lymph node metastases (N-stage), and the systemic immune-inflammation index (SII) were independent risk factors for gastric cancer LVI (P＜0.05). Using these three indicators, 6 machine learning models were developed, all of which demonstrated favorable predictive performance, with 0.79 and 0.76 of minimum AUC values in the training set and the validation set, respectively. Among them, the light gradient boosting machine (LightGBM) model exhibited the best overall performance, achieving AUCs of 0.83 and 0.82 in the training set and the validation set, along with Brier scores of 0.163 and 0.187, respectively. Calibration and DCA curves further confirmed that the model possesses strong predictive accuracy and application value. SHAP analysis showed the feature importance in LightGBM model, identifying the N-stage as the top contributor, followed by the T-stage and the SII. Conclusion The machine learning models incorporating clinical pathological features and inflammatory indicators can effectively predict LVI status in gastric cancer, with the LightGBM model demonstrating optimal performance.

Metformin has been demonstrated to attenuate hyperglycaemia by modulating the gut microbiota. However, the mechanisms through which the microbiome mediates metformin monotherapy failure (MMF) are unclear. Herein, in a prospective clinical cohort study of newly diagnosed type 2 diabetes mellitus (T2DM) patients treated with metformin monotherapy, metagenomic sequencing of faecal samples revealed that Phocaeicola vulgatus abundance was approximately 12 times higher in nonresponders than in responders. P. vulgatus rapidly hydrolysed taurine-conjugated bile acids, leading to ceramide accumulation and reversing the improvements in glucose intolerance conferred by metformin in high-fat diet-fed mice. Interestingly, C22:0 ceramide bound to mitochondrial fission factor to induce mitochondrial fragmentation and impair hepatic oxidative phosphorylation in P. vulgatus-colonized hyperglycaemic mice, which could be exacerbated by metformin. This work suggests that metformin may be unsuitable for P. vulgatus-rich T2DM patients and that clinicians should be aware of metformin toxicity to mitochondria. Suppressing P. vulgatus growth with cefaclor or improving mitochondrial function using adenosylcobalamin may represent simple, safe, effective therapeutic strategies for addressing MMF.

Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by persistent inflammation and joint damage, accompanied by the accumulation of plasma cells, which contributes to its pathogenesis. Understanding the genetic alterations occurring during plasma cell differentiation in RA can deepen our comprehension of its pathogenesis and guide the development of targeted therapeutic interventions. Here, our study elucidates the intricate molecular mechanisms underlying plasma cell differentiation by demonstrating that PRDX1 interacts with DOK3 and modulates its degradation by the autophagy-lysosome pathway. This interaction results in the inhibition of plasma cell differentiation, thereby alleviating the progression of collagen-induced arthritis. Additionally, our investigation identifies Salvianolic acid B (SAB) as a potent small molecular glue-like compound that enhances the interaction between PRDX1 and DOK3, consequently impeding the progression of collagen-induced arthritis by inhibiting plasma cell differentiation. Collectively, these findings underscore the therapeutic potential of developing chemical stabilizers for the PRDX1-DOK3 complex in suppressing plasma cell differentiation for RA treatment and establish a theoretical basis for targeting PRDX1-protein interactions as specific therapeutic targets in various diseases.

The interest in covalent drugs has resurged in recent decades, spurring the development of numerous specialized computational docking tools to facilitate covalent ligand design and screening. Herein, we present CarsiDock-Cov, a new paradigm distinguishing itself as the first deep learning (DL)-guided approach for covalent docking. CarsiDock-Cov retains the core components of its non-covalent predecessor, leveraging a DL model pretrained on millions of docking complexes to predict protein-ligand distance matrices, along with a dedicated-designed geometric optimization procedure to convert these distances into refined binding poses. Additionally, it incorporates several key enhancements specifically tailored to optimize the protocol for covalent docking applications. Our approach has been extensively validated on multiple public datasets regarding the docking and screening of covalent ligands, and the results indicate that our approach not only achieves comparably improved applicability compared to its non-covalent predecessor, but also exhibits competitive performance against various state-of-the-art covalent docking tools. Collectively, our approach represents a significant advance in covalent docking methodology, offering an automated and efficient solution that shows considerable promise for accelerating covalent drug discovery and design.