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.

ObjectiveTo investigate the therapeutic effect of Astragali Radix-mediated changes in gut microbiota on treating type 2 diabetes （T2DM）. MethodsA 12-week randomized， placebo-controlled clinical trial enrolled eighty patients with newly diagnosed type 2 diabetes and poor glycemic control in the Qi deficiency type. All patients received insulin therapy. The observation group （40 cases） was administered with Astragali Radix Granules， while the control group （40 cases） received a placebo. Both treamtents were taken orally twice daily. Changes in gut microbiota were assessed by 16s rDNA sequencing. Serum glucagon-like peptide-1 （GLP-1） levels were measured using enzyme-linked immunosorbent assay （ELISA）. Glucose metabolism indicators including fasting blood glucose （FPG）， 2-hour postprandial blood glucose （2 h PG），glycated albumin（GA）， and glycated hemoglobin （HbA1c） were evaluated. Pancreatic function was evaluated using fasting C-peptide （FCP）， 2-hour postprandial C-peptide （2 h CP）， and C-peptide area under the curve （AUC_cp）. Traditional Chinese medicine （TCM） syndrome scores， clinical efficacy， and safety indicators were also observed. ResultsIn terms of glucose metabolism indicators， compared with the baseline， both groups exhibited significantly lower FPG， 2 h PG， GA and HbA1C （P<0.01），while FCP， 2 h CP and AUC_cp were significantly higher （P<0.01）. Compared with the control group after the treatment， the observation group showed significantly lower FPG， 2 h PG， GA and HbA1C（P<0.05， P<0.01），and significantly higher FCP， 2 h CP and AUC_cp （P<0.05， P<0.01）， indicating that Astragali Radix can improve glucose metabolism. In terms of the diversity of gut microbiota， no significant differences were detected in the Chao1， Shannon and Simpson indexes of the two groups compared with their respective baselines. However， compared with the post-treatment control group， the observation group demonstrated significant increases in the Chao1， Shannon and Simpson indexes （P<0.05， P<0.01）. The β-diversity analysis showed significant separation in gut microbiota composition before and after treatment in both groups， indicating that Astragali Radix can significantly alter the structure and improve the diversity of gut microbiota. At the phylum level， compared with the baseline， both groups showed a significant increase in the relative abundance of Bacteroidota（P<0.01）. The relative abundance of the potentially harmful phylum Proteobacteria was significantly lower in the observation Group after treatment （P<0.01）. Compared with the post-treatment control group， the observation group had a significantly higher relative abundance of Bacteroidota（P<0.01）. No significant difference was found in Firmicutes/Bacteroidota （F/B） ratio between the two groups after treatment， and other phyla showed no significant differences. At the genus level， compared with the baseline， the observation group exhibited a significant increase in Bacteroides （P<0.01） and a significant decrease in Escherichia-Shigella （P<0.01）， whereas no significant difference was seen in the control group . Compared with the control group after treatment， the observation group after treatment had a significantly higher relative abundance of Bacteroides （P<0.01）. No significant differences were seen in other genera. Linear discriminant analysis （LDA） identified potential characteristics taxa： in the observation group， Bacteroidota at the phylum level and Bacteroides and Dubosiella at the genus level， in the control group， Proteobacteria at the phylum level as well as Barnesiella and Staphylococcus at the genus level. Correlation analysis based on a heatmap revealed that GLP-1 levels were positively correlated with Firmicutes， F/B ratio and Fusobacterium， and negatively correlated with Bacteroidota， Proteobacteria， Bacteroides and Escherichia-Shigella. In terms of clinical efficacy， compared with the control group， the total effective rate of the observation group was significantly higher （P<0.05）. Compared with the baseline， the scores for shortness of breath， fatigue， weakness， spontaneous sweating and reluctance to speak significantly decreased in both groups （P<0.01）. Compared with the control group after treatment， the score for weakness was significantly lower in the observation group （P<0.01），indicating that Astragali Radix could improve clinical symptoms and alleviate weakness symptoms. In terms of safety， compared with the baseline， alanine aminotransferase （ALT） levels significantly decreased in both groups （P<0.05，P<0.01），indicating that Astragali Radix did not induce any significant abnormalities in liver and kidney functions. ConclusionAstragali Radix demonstrates the potential to significantly improve the gut microbiota environment in patients of newly diagnosed type 2 diabetes with Qi deficiency. The therapeutic effect may contribute to glycemic control， possibly mediated by an elevation in GLP-1 level. These findings may support its further clinical investigations and potential applications.

ObjectiveThis paper aims to investigate and evaluate the staged efficacy and safety of the representative empirical prescription of the “Zhu Ke Jiao” theory， Qijia Rougan prescription， combined with entecavir in the treatment of hepatic fibrosis in chronic hepatitis B. MethodsA multicenter randomized controlled clinical study was conducted， and 101 patients diagnosed with chronic hepatitis B-related hepatic fibrosis （CHB-HF） who met the diagnosis and inclusion criteria were randomly assigned to an observation group （Qijia Rougan prescription + entecavir） and a control group （entecavir）. The treatment duration was 24 weeks. Liver stiffness measurement （LSM）， fibrosis-4 index （FIB-4）， portal vein diameter， hepatitis B serology， biochemical indicators， hepatic fibrosis markers in serum ［hyaluronic acid （HA）， laminin （LN）， procollagen Ⅲ peptide （PⅢP）， and type Ⅳ collagen （Ⅳ-C）］， and traditional Chinese medicine syndrome scores were used as efficacy evaluation indicators. Efficacy assessments and explorations of different staged subgroups of Qijia Rougan prescription were conducted according to LSM values based on the Metavir pathological staging standard. ResultsA total of 98 cases were included for statistical analysis， with 49 cases in the observation group and 49 in the control group. The general data of the patients in both groups were comparable. Compared with the same group before treatment， the observation group showed a significant reduction in LSM and FIB-4 （P<0.01）， as well as notable improvements in LN， Ⅳ-C， and various TCM syndrome scores （P<0.05， P<0.01）. When compared to the control group after treatment， the observation group demonstrated significant improvements in LSM， FIB-4， and various TCM syndrome score indicators （P<0.05， P<0.01）， indicating that the observation group performed better than the control group. Subgroup analysis of the regression of hepatic fibrosis stages showed that compared to the same group before treatment， the observation group had better improvement in regression of stages F2 and F3 （P<0.05）. When compared to the control group after treatment， the observation group exhibited superior improvement in regression of stage F3 （P<0.05）. No adverse events occurred in either group during the treatment period. ConclusionCompared with entecavir alone， the combination of Qijia Rougan prescription and entecavir significantly improves the degree of hepatic fibrosis and clinical TCM symptoms in patients. The optimal intervention period is primarily during stage F3， which is a potential “interception” point of the “Zhu Ke Jiao” theory.

ObjectiveAngelicae Sinensis Radix， a commonly used medicinal herb with both medicinal and edible properties， is frequently adulterated in the market， severely affecting the clinical efficacy of preparations. While qualitative identification techniques for adulterants and counterfeits are now relatively mature， quantitative detection methods for adulterated processed products remain unexplored. Quantitative detection research of Angelicae Sinensis Radix and its primary closely related adulterant， "Tu Danggui" （Angelica gigas）， was conducted to establish a herbal quantitative molecular detection （Herb-Q） method for Angelicae Sinensis Radix and its processed products， providing a model for the establishment of quantitative detection technologies for Angelicae Sinensis Radix and related health products. MethodsThe specific single-nucleotide polymorphism （SNP） loci of Angelicae Sinensis Radix and Angelica gigas Nakai were screened based on the complete chloroplast genome sequence. The specific SNP loci of Angelicae Sinensis Radix were selected for quantitative methodological investigations （linearity， limit of quantification， limit of detection， and reproducibility） by mixing the powder of the herbs with different adulteration ratios. Huoxue Zhitong powder with three distinct adulteration ratios （15%， 25%， and 35%） was utilized to ascertain the precision of the Herb-Q method for the quantitative detection of Chinese patent medicines containing Angelicae Sinensis Radix. ResultsBy comparing the 123 chloroplast genome sequences of Angelicae Sinensis Radix， based on the principles of intraspecies conservation， interspecies specificity， and meeting the requirements of pyrophosphate high-throughput sequencing， it was determined that 9 674^th locus （A/G） in the chloroplast genome sequence NC_042826.1 and 38 592^nd locus （T/C） in the chloroplast genome sequence NC_029393.1 could be the exclusive molecular identification loci of Angelicae Sinensis Radix and Angelica gigas Nakai， respectively. The linear relationship R² of the Herb-Q method established by selecting the specific 9 674^th locus （A/G） of Angelicae Sinensis Radix was 0.997 4 （R²>0.99）， indicating an excellent linear relationship. The limits of quantification and detection were established at 2.0%， exhibiting excellent reproducibility ［relative standard deviation（RSD）<2.0%］. The established quantitative system based on the Herb-Q method detected the adulteration amount of counterfeit A. gigas in the Huoxue Zhitong powder， with an average deviation of 1.3% for three molecular quantitative replicates. ConclusionThis research demonstrates that the Herb-Q quantitative detection method established based on the 9 674^th locus （A/G） in the chloroplast genome sequence NC_042826.1 of Angelicae Sinensis Radix has good applicability， objectivity， and accuracy for Angelicae Sinensis Radix and A. gigas， and its processed products. This method has the capacity to provide technical support for the quantitative detection of commercially available Angelicae Sinensis Radix derivatives， including traditional Chinese medicinal preparations， dietary supplements， and nutraceuticals.

ObjectiveAngelicae Sinensis Radix， a commonly used medicinal herb with both medicinal and edible properties， is frequently adulterated in the market， severely affecting the clinical efficacy of preparations. While qualitative identification techniques for adulterants and counterfeits are now relatively mature， quantitative detection methods for adulterated processed products remain unexplored. Quantitative detection research of Angelicae Sinensis Radix and its primary closely related adulterant， "Tu Danggui" （Angelica gigas）， was conducted to establish a herbal quantitative molecular detection （Herb-Q） method for Angelicae Sinensis Radix and its processed products， providing a model for the establishment of quantitative detection technologies for Angelicae Sinensis Radix and related health products. MethodsThe specific single-nucleotide polymorphism （SNP） loci of Angelicae Sinensis Radix and Angelica gigas Nakai were screened based on the complete chloroplast genome sequence. The specific SNP loci of Angelicae Sinensis Radix were selected for quantitative methodological investigations （linearity， limit of quantification， limit of detection， and reproducibility） by mixing the powder of the herbs with different adulteration ratios. Huoxue Zhitong powder with three distinct adulteration ratios （15%， 25%， and 35%） was utilized to ascertain the precision of the Herb-Q method for the quantitative detection of Chinese patent medicines containing Angelicae Sinensis Radix. ResultsBy comparing the 123 chloroplast genome sequences of Angelicae Sinensis Radix， based on the principles of intraspecies conservation， interspecies specificity， and meeting the requirements of pyrophosphate high-throughput sequencing， it was determined that 9 674^th locus （A/G） in the chloroplast genome sequence NC_042826.1 and 38 592^nd locus （T/C） in the chloroplast genome sequence NC_029393.1 could be the exclusive molecular identification loci of Angelicae Sinensis Radix and Angelica gigas Nakai， respectively. The linear relationship R² of the Herb-Q method established by selecting the specific 9 674^th locus （A/G） of Angelicae Sinensis Radix was 0.997 4 （R²>0.99）， indicating an excellent linear relationship. The limits of quantification and detection were established at 2.0%， exhibiting excellent reproducibility ［relative standard deviation（RSD）<2.0%］. The established quantitative system based on the Herb-Q method detected the adulteration amount of counterfeit A. gigas in the Huoxue Zhitong powder， with an average deviation of 1.3% for three molecular quantitative replicates. ConclusionThis research demonstrates that the Herb-Q quantitative detection method established based on the 9 674^th locus （A/G） in the chloroplast genome sequence NC_042826.1 of Angelicae Sinensis Radix has good applicability， objectivity， and accuracy for Angelicae Sinensis Radix and A. gigas， and its processed products. This method has the capacity to provide technical support for the quantitative detection of commercially available Angelicae Sinensis Radix derivatives， including traditional Chinese medicinal preparations， dietary supplements， and nutraceuticals.

Objective To analyze the epidemiological characteristics of hand, foot, and mouth disease (HFMD) in Shanxi Province from 2012 to 2024, and predict the incidence trend for 2025, and to provide a scientific basis for the formulation of prevention and control strategies. Methods Based on the surveillance data of HFMD in Shanxi Province from 2012 to 2024, the spatial and temporal distribution characteristics and time trends of the disease were analyzed. The ARIMA model was constructed and used to predict the incidence trend in 2025. Results From 2012 to 2024, a total of 254 028 HFMD cases were reported in Shanxi Province, with an average annual incidence rate of 54.17 per 100 000 population, a severe case rate of 0.56%, and a case fatality rate (CFR) of 12.60 per 100 000 population. Joinpoint regression analysis showed that the incidence rate, severe case rate, mortality rate, and case fatality rate all presented a downward trend. The epidemic exhibited obvious seasonal distribution characteristics, with the peak period from April to November, and two incidence peaks in June-July and October-November. The male-to-female incidence ratio was 1.41:1. Children aged 1-5 years accounted for 89.24% of the total cases, among which scattered children (58.48%) and nursery children (33.54%) were the high-risk groups. Linfen City (96.06 /100 000) and Taiyuan City (88.54 /100 000) had relatively high incidence rates. After 2017, the proportion of enterovirus A71 (EV-A71) decreased, while coxsackievirus A16 (Cox-A16) and other enteroviruses became the main epidemic strains. The ARIMA(1,0,1)(0,1,1)₁₂ model predicted that the incidence of HFMD in 2025 would remain at the level of 2023-2024, and the dual-peak characteristic would continue. Conclusion From 2012 to 2024, the overall HFMD epidemic in Shanxi Province generally shows a significant downward trend. The high-risk population includes scattered children and nursery children under 5 years old, with high-incidence areas concentrated in the central and southern regions, requiring focused attention. The seasonal ARIMA model can effectively fit the evolutionary trend of HFMD incidence in Shanxi Province and possesses short-term predictive capability.

This article reports a rare case of autoinflammatory disease presenting initially with skin induration and swelling after trauma as the initial manifestation, followed by progressive limb weakness. The patient was a middle-aged female who developed skin induration and swelling after trauma, which gradually progressed to limb weakness, dysarthria and bilateral facial paralysis, accompanied by livedo reticularis of the lower extremities, diffuse skin induration of the limbs, and beaded subcutaneous nodules in the right upper limb. The patient had a susceptibility to infection since childhood and a history of chronic livedo reticularis. Skin pathological examination revealed panniculitis. A comprehensive etiological screening for special infections and autoimmune diseases was completed with an unremarkable results, and whole-exome sequencing showed no abnormal findings. Following a multidisciplinary discussion combined with RNA sequencing results, the patient was diagnosed with an autoinflammatory disease, with a suspected type Ⅰ interferonopathy. Treatment with tofacitinib resulted in gradual improvement of clinical symptoms. This case highlights the importance of detailed medical history collection, systematic physical examination and multidisciplinary collaborative diagnosis and treatment, and underscores the pivotal role of molecular diagnosis in the confirmation of rare diseases. It can provide a reference for the clinical diagnosis and management of similar rare cases.

Considering the complexity and diversity of breast ultrasound images,a deep learning model combining convolutional neural network and transfer learning is proposed to enhance the accuracy and efficiency of tumor discrimination.Specifically,a pre-trained convolutional neural network model is utilized to extract features from breast ultrasound images,and transfer learning is employed to adapt the pre-trained model to the breast ultrasound image dataset.The extracted features are then applied to multiple classification models to differentiate between benign and malignant tumors.Experimental results demonstrate that compared with traditional image processing and machine learning methods,the proposed deep learning model exhibits significant improvements in sensitivity,specificity,and precision.The deep learning-based feature extraction method for breast ultrasound images substantially enhances the accuracy and efficiency of discrimination between benign and malignant tumors,providing an effective technical tool for the early diagnosis and treatment of breast cancer and assisting clinicians in making more accurate clinical decisions.

Objective:To establish an early warning model of oral infection risk in patients with acute leukemia undergoing chemotherapy and to verify its predictive efficacy, so as to provide reference for formulating strategies to prevent oral infections.Methods:A retrospective study was conducted to select 288 patients with acute leukemia undergoing chemotherapy from January 2021 to January 2023 in Hematology Hospital of Chinese Academy of Medical Sciences (Institute of Hematology, Chinese Academy of Medical Sciences) as the training set. According to whether they developed oral infection after chemotherapy, they were divided into the infected group and the non-infected group. The risk factors of oral infection in patients with acute leukemia undergoing chemotherapy were investigated, and a risk warning model was established. A total of 246 acute leukemia undergoing chemotherapy patients admitted to the same hospital from February 2023 to February 2024 were selected as the validation set to conduct external verification of the model.Results:The oral infection rate was 19.44% (56/288) in the training set. There were 21 males and 35 females in the infected group (56 cases), with 49 cases<60 years old and 7 cases ≥60 years old. There were 102 males and 130 females in the non infected group (232 cases), with 196 cases<60 years old and 36 cases ≥60 years old. Multivariate analysis showed that neutrophil count <1.5×10 9/L, nutritional risk screening 2002≥3 points, high-dose of methotrexate, antibiotic types ≥3, poor oral self-cleaning habits, oral pH ≤6.5 were the risk factors for oral infection in patients with acute leukemia undergoing chemotherapy ( OR values were 2.716-10.074, all P<0.05). Based on this, the risk early warning model was as follows: Logit ( P)=-5.849+2.310× neutrophil count <1.5×10 9/L+1.363× nutritional risk screening 2002≥3 points +1.150× high-dose methotrexate +1.132× antibiotic types ≥3 + 1.044× oral pH ≤6.5 + 0.999× poor oral self-cleaning habits. The area under receiver operator characteristics curves (ROC) curve of this model Logit ( P) was 0.892, the maximum approximate entry index was 0.653, the sensitivity was 0.804, and the specificity was 0.849. Hosmer-Lemeshow test results indicated that χ2=4.91, P=0.768. For external validation, the goodness of fit test results were χ2=6.47, P=0.595. The area under ROC curve was 0.884, the sensitivity was 0.832, and the specificity was 0.825. Conclusions:The established early warning model of oral infection risk in patients with acute leukemia undergoing chemotherapy has good predictive value, which is helpful for medical staff to conduct early risk assessment of oral infection in such patients, and formulate countermeasures to reduce the incidence and improve the treatment effect of the disease.

Objective:To construct an evaluation system for the risk factors of PICC catheter malposition in patients with hematological diseases, providing a reference for the prevention of central venous access malposition in these patients.Methods:From July 2022, a literature review was conducted to retrieve literature published in database and website such as PubMed, Web of Science domestically and internationally related to the risk assessment system of PICC catheter malposition in hematology patients. After full-text screening and extraction, the articles were included in the system′s item pool. Through further discussions among the research group′s experts, in conjunction with evidence-based findings, the system′s consultation items were formulated. Using the Delphi expert consultation method, a standard for PICC catheter malposition risk assessment system applicable to hematology patients was ultimately formulated.Results:In this study, 25 experts were invited for consultation: 24 females and 1 male, with an average age of (48.36 ± 6.82) years. After two rounds of expert consultations and revisions, the risk factors for PICC catheter malposition in hematology patients were identified to include 4 first-level indicators: patient factors, treatment factors, catheter-related factors, and operational factors, along with 11 second-level indicators and 25 third-level indicators. The positive coefficients of the experts in the two rounds of consultations were 84% and 100%, respectively; the Kendall coordination coefficients were 0.22 and 0.55 (both P<0.05); and the expert authority coefficient was 0.93. Conclusions:The risk assessment scale for PICC catheter malposition in patients with hematological diseases has high expert recognition and good consistency, and has clinical practice and guiding value.