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.

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.

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.

Supercooling preservation technology, as a groundbreaking innovation in the field of organ preservation, significantly reduces the metabolic rate of cells and inhibits ice crystal formation by placing organs in a low-temperature environment near or below the freezing point. This technology extends the preservation time of organs and maintains their biological activity. Compared with the traditional low-temperature preservation at 4 °C, supercooling preservation effectively avoids cell damage and the accumulation of metabolic products, demonstrating significant advantages in the preservation of cells, tissues and organs. In recent years, important progress has been made in the optimization of cryoprotectants, the application of antifreeze proteins, the improvement of vitrification technology, and the development of nanotechnology-based rewarming techniques. These advancements provide new pathways to address the challenges of toxicity, ice crystal formation and uneven rewarming rates during supercooling preservation. This review summarizes the basic principles of supercooling preservation, the application of key technologies, and their practical effects in organ transplantation. It also analyzes the challenges of toxicity and rewarming efficiency, aiming to provide theoretical support and research directions for the future optimization of organ low-temperature preservation technology and its clinical application.

Objective: To explore the feasibility of synthetic magnetic resonance imaging (syMRI) combined with clinicopathological characteristics for the pre-treatment prediction of chemoradiotherapy (CRT) response in advanced nasopharyngeal carcinoma (ANPC). Materials and Methods: Patients with ANPC treated with CRT between September 2020 and June 2022 were retrospectively enrolled and categorized into response group (RG, n = 95) and non RGs (NRG, n = 32) based on the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1. The quantitative parameters from pre-treatment syMRI (longitudinal [T1] and transverse [T2] relaxation times and proton density [PD]), diffusion-weighted imaging (apparent diffusion coefficient [ADC]), and clinicopathological characteristics were compared between RG and NRG. Logistic regression analysis was applied to identify parameters independently associated with CRT response and to construct a multivariable model. The areas under the receiveroperating characteristic curve (AUC) for various diagnostic approaches were compared using the DeLong test. Results: The T1, T2, and PD values in the NRG were significantly lower than those in the RG (all P < 0.05), whereas no significant difference was observed in the ADC values between these two groups. Clinicopathological characteristics (Epstein–Barr virus [EBV]-DNA level, lymph node extranodal extension, clinical stage, and Ki-67 expression) exhibited significant differences between the two groups. Logistic regression analysis showed that T1, PD, EBV-DNA level, clinical stage, and Ki-67 expression had significant independent relationships with CRT response (all P < 0.05). The multivariable model incorporating these five variables yielded AUC, sensitivity, and specificity values of 0.974, 93.8% (30/32), and 91.6% (87/95), respectively. Conclusion SyMRI may be used for the pretreatment prediction of CRT response in ANPC. The multivariable model incorporating syMRI quantitative parameters and clinicopathological characteristics, which were independently associated with CRT response, may be a new tool for the pretreatment prediction of CRT response.

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