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.

OBJECTIVE To systematically compare mature experiences of comprehensive drug value assessment in typical countries/regions and to provide decision-making references for China to establish a scientific and standardized comprehensive drug value assessment system for medical-insured drugs. METHODS The literature analysis was used to systematically review drug value assessment frameworks in 11 representative countries/regions， namely the UK， Canada， Italy， Australia， Germany， France， South Korea， Japan， the United States， as well as Taiwan （China） and Hong Kong （China）. Comparisons were made across three dimensions： assessment entities， value dimension， and application of results. RESULTS &CONCLUSIONS In most countries/regions， independent technical assessment institutions have been established as part of the drug value evaluation system， with the involvement of multiple stakeholders （e.g.， the UK， Canada）. The mainstream drug value assessment frameworks have generally transcended the traditional core dimensions of safety， efficacy， and cost-effectiveness， exhibiting two major trends： the continuous expansion of assessment dimensions and stricter evidence requirements. Assessment outcomes are closely integrated with payment policies， ranging from providing technical advice for decision-making （e.g.， Italy， France） to directly determining reimbursement eligibility （e.g.， the UK， Germany）. The following recommendations are proposed for China： first， establish an evaluation mechanism featuring multi-stakeholder participation and separation of evaluation from decision-making. Second， develop a comprehensive evaluation framework integrating clinical， economic， patient， and societal value， emphasizing quantitative indicator exploration and real-world evidence application. Third， promote direct linkage between value-based tiering outcomes and medical insurance reimbursement decisions or access negotiations to balance patient benefits， fund sustainability， and industrial innovation.

ObjectiveTo investigate the effects of metformin on the immune functions of immature dendritic cells （imDCs） and the underlying mechanisms. MethodsMouse bone marrow-derived imDCs were treated with different concentrations of metformin. The working concentration and treatment time of metformin in this study were determined based on the results of cell apoptosis and cell viability assays. The effects of metformin on the phagocytic capacity of imDCs was evaluated using an antigen endocytosis assay. The expression of cluster of differentiation 205 （CD205）， the polymerization of filamentous actin （F-actin）， and the underlying regulatory mechanisms were investigated through flow cytometry， laser confocal fluorescence microscopy， and Western blot. ResultsThe working concentrations of metformin were 1， 2， 4 mmol/L for 24 h determined by the apoptosis and cell viability assays.Metformin significantly suppressed the phagocytic capacity of imDCs， down-regulated the expression of the mannose receptor CD205 on the cell surface， which was closely associated with phagocytic function； metformin inhibited the RhoA-ROCK1-LIMK1-Cofilin signaling pathway， which inhibited the polymerization of F-actin and disturbed its dynamic remodeling of imDCs. ConclusionMetformin can inhibit the expression of CD205 and disrupt the remodeling of F-actin， thereby suppressing the antigen-capturing capacity of imDCs.

ObjectiveTo investigate the efficacy of selective internal radiation therapy （SIRT） in patients with unresectable hepatocellular carcinoma， and to provide a reference for the selection of clinical treatment regimens. MethodsA retrospective analysis was performed for the clinical data of 73 patients with unresectable hepatocellular carcinoma who received yttrium-90 microsphere SIRT in Eastern Hepatobiliary Surgery Hospital from May 1， 2023 to September 1， 2024. According to tumor characteristics， physical status， liver reserve function， laboratory tests， and SIRT treatment strategy， the patients were divided into radiation segmentectomy group with 9 patients， conversion therapy group with 47 patients， and palliative treatment group with 17 patients. Based on the results of postoperative follow-up， modified Response Evaluation Criteria in Solid Tumors were used to assess radiographic images. A one-way analysis of variance was used for comparison of normally distributed continuous data between three groups， and the chi-square test was used for comparison of categorical data between three groups； the Logistic regression model was used to perform the multivariate analysis. ResultsThere was a significant difference in postoperative outcome between the radiation segmentectomy group， the conversion therapy group， and the palliative treatment group （χ²=30.060， P<0.001）. The disease control rate was 100.0% （9/9） in the radiation segmentectomy group， 83.0% （39/47） in the conversion therapy group， and 29.4% （5/17） in the palliative treatment group， with a significant difference between the three groups （χ²=19.575， P<0.001）， and there was also a significant difference in objective response rate between the three groups （χ²=17.749， P<0.001）. The multivariate Logistic regression analysis showed that the number of tumors （odds ratio ［OR］=0.085， 95% confidence interval ［CI］： 0.008 — 0.906， P=0.041） and combined targeted immunotherapy （OR=18.808， 95%CI： 1.704 — 207.616， P=0.017） were independent influencing factors for achieving complete response. ConclusionThe number of tumors is an independent influencing factor for the efficacy of SIRT and is an important basis for selecting different treatment goals. SIRT combined with targeted immunotherapy may achieve better efficacy.

Atherosclerosis is a complex pathological condition resulting from lipid deposition， chronic inflammatory responses， and fibrosis， with a prolonged disease course and multifactorial etiology. Based on the traditional Chinese medicine （TCM） theory of accumulation syndrome， atherosclerosis can be classified under this category， with its pathogenesis involving phlegm， blood stasis， deficiency， and accumulation. This paper proposed a stage-based intervention strategy using the four therapeutic principles of "attacking， supplementing， dispersing， dissipating"， and divided into six stages based on the pathological progression， including the stage of accumulation before formation， the stage of accumulation already formed， the stage of nucleus accumulation， the stage of nucleus accumulation decay， the stage of nucleus accumulation consolidation， and the stage of severe stenosis of nucleus. At different stages， the intervention focuses on reinforcing healthy qi and consolidating the root， tonifying the kidneys and spleen， dispersing and removing turbidity， removing phlegm stagnation， promoting qi circulation， dispersing accumulations and removing stasis， attacking accumulation and expelling stasis， directing the turbid downward and dispersing accumulation， and treatment would be adjusted based on specific symptoms， which provides a theoretical framework for the prevention and treatment of atherosclerosis with TCM.

Objective:To investigate the prognostic factors for esophageal cancer after radical resection and the application value of machine learning prediction model.Methods:The retrospective cohort study was conducted. The clinicopatholigical data of 406 esophageal cancer patients who were admitted to Qilu Hospital of Shandong University from January 2018 to March 2022 were collected. There were 357 males and 49 females, aged (64±8)years. All patients underwent radical resection of esophageal cancer. The 406 patients were randomly divided into a training set of 285 cases and a validation set of 121 cases at a 7∶3 ratio based on a random number table. The training set was used to construct prediction model, and the validation set was used to validate prediction model. Patients were divided into high-risk group and low-risk group based on risk scores. Observation indicators: (1) follow-up of patients and analysis of influencing factors for prognosis; (2) construction and validation of machine learning prediction models. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Comparison of ordinal data between groups was conducted using the rank sum test. The Kaplan-Meier method was used to calculate survival rate and plot survival curve, and the Log-rank test was used for survival analysis. The Cox proportional hazard regression model was used for univariate and multivariate analyses. Independent influencing factors were included, and data processing, machine learning model construction, and visualization were performed using R packages including random survival forest (RSF), gradient boosting machine (GBM), least absolute shrinkage and selection operator Cox regression (LASSO-Cox), Cox proportional hazards model boosting (CoxBoost), survival support vector machine (survivalsvm), extreme gradient boosting (XGBoost), supervised principal component analysis (SuperPC), and Cox partial least squares regression (plsRcox). Receiver operating characteristic (ROC) curves were drawn, and sensitivity, specificity, and area under the curve (AUC) were calculated. The Delong test was used to assess the differences in AUC among different models in the training set, and the time-dependent ROC was used to compare the predictive performance of different models. Calibration curves were used to evaluate model accuracy, and decision curve analysis (DCA) was used to evaluate overall net benefit. Results:(1) Follow-up of patients and analysis of influencing factors for prognosis. All 406 patients were followed up postoperatively for 28(range, 6-36)months, with 1- and 3-year overall survival rate of 86.5% and 40.9%, respectively. The 285 patients in the training set were followed up postoperatively for 30(range, 6-36)months, with 1- and 3-year overall survival rate of 85.1% and 35.5%, respectively. The 121 patients in the validation set were followed up postoperatively for 25(range, 6-36)months, with 1- and 3-year overall survival rate of 87.0% and 43.2%, respectively. There was no significant difference in postoperative overall survival rate between the training set and the validation set ( χ2=3.20, P>0.05). Results of multivariate analysis showed that left thoracic surgical approach, preopera-tive neutrophil count, vascular invasion, perineural invasion, pathological T2-4 stage, pathological N2-3 stage, and postoperative pneumonia were independent risk factors affecting postoperative survival of 285 patients in the training set ( hazard ratio=1.466, 1.037, 1.482, 1.549, 5.268, 7.727, 22.202, 2.539, 2.686, 1.425, 95% confidence interval as 1.026-2.096, 1.003-1.073, 1.008-2.179, 1.105-2.170, 1.201-23.099, 1.833-32.576, 4.734-104.128, 1.577-4.087, 1.631-4.422, 1.018-1.994, P<0.05). (2) Construction and validation of machine learning prediction models. Independent risk factors affecting postoperative survival were included to construct RSF, GBM, LASSO-Cox, CoxBoost, survivalsvm, XGBoost, SuperPC, and plsRcox machine learning prediction models. Results of Delong test showed that there were significant differences in the AUC of RSF and GBM from the other six models ( P<0.05). Results of time-dependent ROC curve showed that all 8 machine learning predic-tion models had good discriminative ability in the training cohort, among which the RSF machine learning prediction model had the best predictive performance. Results of calibration curve showed that the RSF machine learning prediction model fitted well for predicting postoperative 1-, 2-, and 3-year overall survival in the training cohort, with high consistency with actual results. Results of decision curve analysis showed that within a threshold range of 0-0.80, the RSF machine learning prediction model provided a better overall net benefit. Further analysis showed that in the validation set, the AUC of RSF machine learning prediction model for postoperative 1-, 2-, and 3-year survival prediction were 0.786 (95% confidence interval as 0.609-0.962), 0.774 (95% confidence interval as 0.676-0.873), and 0.750 (95% confidence interval as 0.652-0.848), respectively. Results of calibration curve showed that the RSF machine learning prediction model fitted well for predicting postopera-tive 1-, 2-, and 3-year overall survival in the validation set, with high consistency with actual results. In the training set, the optimal cutoff value of the RSF machine learning prediction model risk score was 11.7. Patients with risk score ≥11.7 were classified as the high-risk group, and those with risk score <11.7 as the low-risk group. The median survival times of the two groups were 18.0 months and >36.0 months, respectively, showing a significant difference between them ( χ2=73.30, P<0.05). In the validation set, the optimal cutoff value of the RSF machine learning prediction model risk score was 11.7. Patients with risk score ≥11.7 were classified as the high-risk group, and those with risk score<11.7 as the low-risk group. The median survival times of the two groups were 17.0 months and>36.0 months for the high-risk and low-risk groups, respectively, showing a significant difference between them ( χ2=35.20, P<0.05). Conclusions:Left thoracic surgical approach, preoperative neutrophil count, vascular invasion, perineural invasion, pathological T2-4 stage, pathological N2-3 stage, and postoperative pneumonia are independent risk factors affecting survival of esophageal cancer patients after radical resection. The RSF machine learning prediction model constructed based on these factors can effectively distinguish the survival prognosis of high-risk and low-risk patients.

Background: Nasopharyngeal carcinoma (NPC) is characterized by high programmed death-ligand 1 (PD-L1) expression and abundant infiltration of non-malignant lymphocytes, which renders patients potentially suitable candidates for immune checkpoint blockade therapies. Palate, lung, and nasal epithelium clone (PLUNC) inhibit the growth of NPC cells and enhance cellular apoptosis and differentiation. Currently, the relationship between PLUNC (as a tumor-suppressor) and PD-L1 in NPC is unclear. Methods: We collected clinical samples of NPC to verify the relationship between PLUNC and PD-L1. PLUNC plasmid was transfected into NPC cells, and the variation of PD-L1 was verified by western blot and immunofluorescence. In NPC cells, we verified the relationship of PD-L1, activating transcription factor 3 (ATF3), and β-catenin by western blot and immunofluorescence. Later, we further verified that PLUNC regulates PD-L1 through β-catenin. Finally, the effect of PLUNC on β-catenin was verified by co-immunoprecipitation (Co-IP). Results: We found that PLUNC expression was lower in NPC tissues than in paracancer tissues. PD-L1 expression was opposite to that of PLUNC. Western blot and immunofluorescence showed that β-catenin could upregulate ATF3 and PD-L1, while PLUNC could downregulate ATF3/PD-L1 by inhibiting the expression of β-catenin. PLUNC inhibits the entry of β-catenin into the nucleus. Co-IP experiments demonstrated that PLUNC inhibited the interaction of DEAD-box helicase 17 (DDX17) and β-catenin. Conclusions PLUNC downregulates the expression of PD-L1 by inhibiting the interaction of DDX17/β-catenin in NPC.

Background: Nasopharyngeal carcinoma (NPC) is characterized by high programmed death-ligand 1 (PD-L1) expression and abundant infiltration of non-malignant lymphocytes, which renders patients potentially suitable candidates for immune checkpoint blockade therapies. Palate, lung, and nasal epithelium clone (PLUNC) inhibit the growth of NPC cells and enhance cellular apoptosis and differentiation. Currently, the relationship between PLUNC (as a tumor-suppressor) and PD-L1 in NPC is unclear. Methods: We collected clinical samples of NPC to verify the relationship between PLUNC and PD-L1. PLUNC plasmid was transfected into NPC cells, and the variation of PD-L1 was verified by western blot and immunofluorescence. In NPC cells, we verified the relationship of PD-L1, activating transcription factor 3 (ATF3), and β-catenin by western blot and immunofluorescence. Later, we further verified that PLUNC regulates PD-L1 through β-catenin. Finally, the effect of PLUNC on β-catenin was verified by co-immunoprecipitation (Co-IP). Results: We found that PLUNC expression was lower in NPC tissues than in paracancer tissues. PD-L1 expression was opposite to that of PLUNC. Western blot and immunofluorescence showed that β-catenin could upregulate ATF3 and PD-L1, while PLUNC could downregulate ATF3/PD-L1 by inhibiting the expression of β-catenin. PLUNC inhibits the entry of β-catenin into the nucleus. Co-IP experiments demonstrated that PLUNC inhibited the interaction of DEAD-box helicase 17 (DDX17) and β-catenin. Conclusions PLUNC downregulates the expression of PD-L1 by inhibiting the interaction of DDX17/β-catenin in NPC.

Objective:To investigate the current status of swallowing dysfunction in hospitalized very elderly patients and analyze the related influencing factors.Methods:A cross-sectional study was conducted, selecting data from 72 very elderly patients aged 85-100 years(mean age: 91.5±3.9 years)who met the inclusion criteria in the geriatrics department of a tertiary hospital in Guangzhou from July to December 2023.A comprehensive geriatric assessment was performed, including tools for orofacial function, nutrition, frailty, polypharmacy, comorbidity index, sarcopenia, cognition, and emotional/psychological status.Swallowing dysfunction was screened and its severity assessed using the EAT-10 and SSA scales, followed by analysis of related influencing factors.Results:Among the 72 very elderly patients, EAT-10 screening indicated a positive rate of swallowing dysfunction of 83.3%(60/72). Univariate analysis showed that age, body mass index, history of choking, nutritional status, cognitive function, frailty, comorbidity index, and calf circumference were associated with swallowing dysfunction, with statistically significant differences(all P<0.05). Multivariate logistic regression analysis revealed that age( OR=1.079, 95% CI: 1.011-1.151), nutritional status( OR=3.709, 95% CI: 1.825-7.540), impaired activities of daily living( OR=0.723, 95% CI: 0.578-0.905), frailty( OR=1.640, 95% CI: 1.274-2.110), and number of falls( OR=1.922, 95% CI: 1.050-2.984)were correlated with swallowing dysfunction(all P<0.05). Conclusions:The prevalence of swallowing dysfunction is high among hospitalized very elderly patients, with age, nutritional status, number of falls, activities of daily living, and frailty identified as independent risk factors.Early risk screening and intervention for swallowing function should be strengthened clinically to reduce complications associated with swallowing dysfunction.

Objective:To explore the application value of the double-low technique of auto-prescription technique combined with iterative reconstruction algorithm in CT pulmonary angiography (CTPA).Methods:A total of 86 patients who were clinically suspected of having pulmonary embolism and underwent CTPA examination in the First Affiliated Hospital of Dalian Medical University were prospectively collected and randomly assigned to a control group ( n = 45) and an observation group ( n = 41) according to the random number table method. In the control group, a tube voltage of 120 kVp was used with a standard iodine contrast agent dose of 60 ml, and images were reconstructed using the 40% adaptive statistical iterative reconstruction algorithm (ASIR-V). In the observation group, the tube voltage was set by auto-prescription technique, and 0.4 ml/kg of personalized low iodine contrast agent was used. Images were reconstructed with 40%, 60%, and 80% ASIR-V, respectively, and designated as observation 1, observation 2, and observation 3 respectively. The volume CT dose index (CTDI vol), dose-length product (DLP), and effective dose ( E) were recorded and compared among the four groups. The CT values and standard deviation (SD) of the main pulmonary artery, left and right pulmonary arteries, as well as the left and right pulmonary lobe arteries were measured, and the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of these arteries were calculated. Additionally, the SD value at the contrast medium concentration in the superior vena cava was measured, and the artifact index (AI) was subsequently calculated. Two observers independently assessed the visibility of the pulmonary arteries, image noise, and sclerosis artifacts in the superior vena cava using a blinded method. Results:The E in the observation group was 3.28 (2.08, 3.93) mSv, which was significantly lower than that in the control group [5.03 (4.86, 5.20)] mSv, and the difference was statistically significant ( Z = 174.00, P < 0.05). The contrast agent dosage in the observation group was 28 (25, 30) ml, which was lower than that in the control group (60 ml), and the difference was statistically significant ( Z = 0, P < 0.05). The CT values for the main pulmonary artery and the left and right pulmonary lobe arteries in the observation group were higher than those in the control group, and the differences were all statistically significant ( t = -3.65 to -3.89, P < 0.05). The SNR and CNR of the observation groups 2 and 3 were greater than those of the control group ( t = -9.20 to -2.98, P < 0.05). The consistency of subjective evaluations between the two observers was good ( Kappa = 0.729 - 0.879, P < 0.05). There was no statistically significant difference in the subjective score of pulmonary artery visibility between the control and observation group ( P > 0.05). The subjective scores for image noise in observation group 2 and group 3 were higher than those in the control group ( U =598.50, 654.00, P < 0.05). The presence of artifacts due to sclerosis in the superior vena cava was significantly lower in the observation group compared to the control group ( χ2 = 46.09, P < 0.001). Conclusions:The combination of auto-prescription technique with ASIR-V reconstruction algorithm and low contrast agent imaging protocol can reduce the radiation dose and contrast agent dose without compromising image quality, and enable personalized double low CTPA imaging.