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 characterize the developmental trajectory and interdisciplinary integration of intelligent diagnosis in traditional Chinese medicine (TCM) through quantitative topic evolution analysis, we addressed the fragmentation of existing research and clarified the long-term research structure and evolutionary patterns of the field. Methods: A topic evolution analysis was performed on Chinese-language literature pertaining to intelligent diagnosis in TCM. Publications were retrieved from the China National Knowledge Infrastructure (CNKI), Wanfang Data, and China Science and Technology Journal Database (VIP), covering the period from database inception to July 3, 2025. A hybrid segmentation approach, based on cumulative publication growth trends and inflection point detection, was applied to divide the research timeline into distinct stages. Subsequently, the latent Dirichlet allocation (LDA) model was used to extract research topics, followed by alignment and evolutionary analysis of topics across different stages. Results: A total of 3 919 publications published between 2003 and 2025 were included, and the research trajectory was divided into five stages based on data-driven breakpoint detection. The field exhibited a clear evolutionary shift from early rule-based systems and tongue-pulse image and signal analysis (2006 – 2010), to machine-learning-based syndrome and prescription modeling (2011 – 2015), followed by deep-learning-driven pattern recognition and formula association (2016 – 2020). Since 2021, research has increasingly emphasized knowledge-graph construction, multimodal integration, and intelligent clinical decision-support systems, with recent studies (2024 – 2025) showing the emergence of large language models and agent-based diagnostic frameworks. Topic evolution analysis further revealed sustained cross-stage continuity in syndrome modeling and prescription association analysis, alongside the progressive consolidation of integrated intelligent diagnostic platforms. Conclusion By identifying key technological transitions and persistent core research themes, our findings offer a structured reference framework for the design of intelligent diagnostic systems, the construction of knowledge-driven clinical decision-support tools, and the alignment of AI models with TCM diagnostic logic. Importantly, the stage-based evolutionary insights derived from this analysis can inform future methodological choices, improve model interpretability and clinical applicability, and support the translation of intelligent TCM diagnosis from experimental research to real-world clinical practice.

ObjectiveThis paper aims to explore the risk factors for chronic atrophic gastritis （CAG） with turbidity toxin accumulation syndrome and establish a prediction model. MethodsClinical data of 180 patients with CAG who participated in the "clinical study of Xianglian Huazhuo Particles blocking CAG cancer transformation" of Hebei Sheng Zhong Yi Yuan from July 2021 to March 2022 were collected. After confounding factors were controlled by propensity score matching， patients were divided into a training set （namely dev） and a validation set （namely vad） in a seven to three ratio. The risk factors for CAG with turbidity toxin accumulation syndrome in the training set were investigated by using univariate Logistic regression analysis and least absolute shrinkage and selection operator （namely Lasso） regression algorithms. Subsequently， a model， named model 1se， was developed by using the training set data to predict the risk factors for CAG with turbidity toxin accumulation syndrome. The accuracy of the prediction model was assessed by using various methods， including the receiver operating characteristic （ROC） curve， Hosmer-Lemeshow test （H-L）， calibration plot， and decision curve analysis （DCA）. ResultsAge， body mass index （BMI）， family history of cancer， job and life satisfaction， yellow and greasy fur with slippery pulse， and heavy body sensation were independent risk factors of the model. The prediction model showed excellent predictive value for both the training and validation sets. ConclusionThe established prediction model for CAG with turbidity toxin accumulation syndrome has high discrimination and excellent calibration， which could provide an excellent clinical basis for disease diagnosis and individualized treatment of patients.

ObjectiveThis paper aims to explore the risk factors for chronic atrophic gastritis （CAG） with turbidity toxin accumulation syndrome and establish a prediction model. MethodsClinical data of 180 patients with CAG who participated in the "clinical study of Xianglian Huazhuo Particles blocking CAG cancer transformation" of Hebei Sheng Zhong Yi Yuan from July 2021 to March 2022 were collected. After confounding factors were controlled by propensity score matching， patients were divided into a training set （namely dev） and a validation set （namely vad） in a seven to three ratio. The risk factors for CAG with turbidity toxin accumulation syndrome in the training set were investigated by using univariate Logistic regression analysis and least absolute shrinkage and selection operator （namely Lasso） regression algorithms. Subsequently， a model， named model 1se， was developed by using the training set data to predict the risk factors for CAG with turbidity toxin accumulation syndrome. The accuracy of the prediction model was assessed by using various methods， including the receiver operating characteristic （ROC） curve， Hosmer-Lemeshow test （H-L）， calibration plot， and decision curve analysis （DCA）. ResultsAge， body mass index （BMI）， family history of cancer， job and life satisfaction， yellow and greasy fur with slippery pulse， and heavy body sensation were independent risk factors of the model. The prediction model showed excellent predictive value for both the training and validation sets. ConclusionThe established prediction model for CAG with turbidity toxin accumulation syndrome has high discrimination and excellent calibration， which could provide an excellent clinical basis for disease diagnosis and individualized treatment of patients.

This paper outlines the development of artificial intelligence （AI） and its applications in traditional Chinese medicine （TCM） research， and elucidates the roles and advantages of large language models， knowledge graphs， and natural language processing in advancing syndrome identification， prescription generation， and mechanism exploration. Using spleen-stomach diseases as an example， it demonstrates the empowering effects of AI in classical literature mining， precise clinical syndrome differentiation， efficacy and safety prediction， and intelligent education， highlighting an upgraded research paradigm that evolves from data-driven and knowledge-driven approaches to intelligence-driven models. To address challenges related to privacy protection and regulatory compliance in cross-institutional data collaboration， a "trusted federated evidence-based analysis platform for TCM spleen-stomach diseases" is proposed， integrating blockchain-based smart contracts， federated learning， and secure multi-party computation. The deep integration of AI with privacy-preserving computing is reshaping research and clinical practice in TCM spleen-stomach diseases， providing feasible pathways and a technical framework for building a high-quality， trustworthy TCM big-data ecosystem and achieving precision syndrome differentiation.

ObjectiveTo explore the effect of Shenge Bushen Capsules （SBC） on sexual development in normal 3-week-old mice. MethodsThe experiment consisted of two parts. In the first part， mice were divided into four groups： The control group and the low， medium， and high-dose SBC groups （234.7， 469.4， 938.7 mg·kg^-1， respectively）. In the second part， mice were divided into four groups： Control group， Pseudostellariae Radix polysaccharide （PRP） group， total flavonoids group， and SBC group， all receiving a dose of 469.4 mg·kg^-1. After 7 days of administration， the vaginal opening of female mice and the descent of testes and scrotum in male mice， as well as the ovarian and testicular organ indices， were observed. After 4 weeks of administration， female and male mice were housed together for 2 days， and the pregnancy rate of females was monitored. After delivery， the pregnant female mice continued receiving the treatment for 4 weeks， and the sexual development of their offspring， including vaginal opening， testicular descent， and organ indices of ovaries and testes， was observed. Serum sex hormones were measured by enzyme-linked immunosorbent assay （ELISA）， and the expression of gonadotropin-releasing hormone （GnRH） and growth hormone （GH） proteins in the hypothalamus was assessed by Western blot. ResultsCompared with the control group， there was no significant effect on the vaginal opening of female mice or the descent of testes in male mice after 7 days of SBC administration. After 4 weeks of administration， the pregnancy rate in the low-dose group was significantly reduced （P<0.05）， but no significant effects were observed in the other groups. The three doses of SBC did not significantly affect the ovarian or testicular organ indices， and there was no significant upregulation in the expression of GnRH or GH in the hypothalamus. The primary component of SBC， Pseudostellariae Radix polysaccharide， significantly reduced the vaginal opening in female mice after 7 days of administration （P<0.05）. After 4 weeks， the serum estradiol levels of non-pregnant female mice were decreased （P<0.05）， but there was no significant effect on the expression of GnRH or GH proteins in the hypothalamus of either male or female mice. Additionally， there were no significant effects on precocious puberty indicators， such as vaginal opening and testicular descent， in the offspring mice. ConclusionSBC does not significantly promote precocious puberty in young mice， and it does not have any noticeable effects on the pregnancy rate of adult mice or the sexual development of their offspring.

Bisphenol A(BPA)is an environmental endocrine disruptor commonly found in industri-al products such as plastics,resin coatings,and paper coatings,and it poses potential reproductive hazards.Despite extensive research,studies examining its effects on the F1 generation of rabbits are limited.This study established a BPA exposure model in pregnant female rabbits to investigate its impact on reproductive hormones,apoptosis,oxidative stress,inflammatory responses,and tissue integrity in weaned female offspring.The results indicate that BPA exposure induces oxidative stress and inflammation in F1 rabbits,disrupts hormonal balance,and affects antioxidant enzymes and inflammatory mediators through the modulation of the Nrf2 and NF-κB signaling pathways,ultimately leading to apoptosis and tissue damage.

Objective:To explore the human epidermal growth factor receptor 2 gene(HER2) expression amplification in endometrial cancer (EC) with different p53 expression patterns based on molecular typing, as well as their correlation and impact on prognosis.Methods:A retrospective analysis was conducted on 282 patients with EC who underwent surgical treatment at Dalian Women and Children′s Medical Center (Group) from January 2016 to December 2018 with complete clinical and pathological data. Immunohistochemistry and fluorescence in situ hybridization were used to detect the amplification of HER2 gene expression in paraffin- embedded tumor tissue of patients after surgery and confirmed the expression of p53. The clinical data were collected and tracked follow-up.Results:There were significant differences between HER2-positive patients and HER2-negative patients in the age of onset, pathological types of tumor tissue, depth of muscle infiltration, and tumor differentiation ( P< 0.05). The 5-year survival rate and 5-year progression-free survival rate of HER2-positive patients were lower than those of HER2-negative patients (45.45% vs. 95.16%, 45.45% vs. 95.11%), and there were statistical differences ( P<0.01). The positive expression of HER2 in EC tissues was positively correlated with p53 mutant expression ( r = 0.409, P<0.01). Among p53 wild-type EC patients, the 5-year survival rate was 98.4% for HER2-negative patients and 100.00% for HER2-positive patients. In the patients with p53 mutant EC, 58.05% and 37.50%, respectively, there was a statistical difference ( P<0.01). The 5-year progression-free survival rates were 97.50%, 100.00%, 60.20%, and 37.50%, respectively, and there was a statistical difference ( P<0.01). Conclusions:There is a significant positive correlation between HER2 positive overexpression and p53 mutant expression in EC tissue, and there is an inherent synergy between the two expressions. HER2 positive over expression is a key factor for poor prognosis in EC patients. Patients with HER2- positive over expression and p53 mutant expression have poorer 5-year overall survival and progression- free survival compared to other groups.

Bisphenol A(BPA)is an environmental endocrine disruptor commonly found in industri-al products such as plastics,resin coatings,and paper coatings,and it poses potential reproductive hazards.Despite extensive research,studies examining its effects on the F1 generation of rabbits are limited.This study established a BPA exposure model in pregnant female rabbits to investigate its impact on reproductive hormones,apoptosis,oxidative stress,inflammatory responses,and tissue integrity in weaned female offspring.The results indicate that BPA exposure induces oxidative stress and inflammation in F1 rabbits,disrupts hormonal balance,and affects antioxidant enzymes and inflammatory mediators through the modulation of the Nrf2 and NF-κB signaling pathways,ultimately leading to apoptosis and tissue damage.

Objective:To explore the human epidermal growth factor receptor 2 gene(HER2) expression amplification in endometrial cancer (EC) with different p53 expression patterns based on molecular typing, as well as their correlation and impact on prognosis.Methods:A retrospective analysis was conducted on 282 patients with EC who underwent surgical treatment at Dalian Women and Children′s Medical Center (Group) from January 2016 to December 2018 with complete clinical and pathological data. Immunohistochemistry and fluorescence in situ hybridization were used to detect the amplification of HER2 gene expression in paraffin- embedded tumor tissue of patients after surgery and confirmed the expression of p53. The clinical data were collected and tracked follow-up.Results:There were significant differences between HER2-positive patients and HER2-negative patients in the age of onset, pathological types of tumor tissue, depth of muscle infiltration, and tumor differentiation ( P< 0.05). The 5-year survival rate and 5-year progression-free survival rate of HER2-positive patients were lower than those of HER2-negative patients (45.45% vs. 95.16%, 45.45% vs. 95.11%), and there were statistical differences ( P<0.01). The positive expression of HER2 in EC tissues was positively correlated with p53 mutant expression ( r = 0.409, P<0.01). Among p53 wild-type EC patients, the 5-year survival rate was 98.4% for HER2-negative patients and 100.00% for HER2-positive patients. In the patients with p53 mutant EC, 58.05% and 37.50%, respectively, there was a statistical difference ( P<0.01). The 5-year progression-free survival rates were 97.50%, 100.00%, 60.20%, and 37.50%, respectively, and there was a statistical difference ( P<0.01). Conclusions:There is a significant positive correlation between HER2 positive overexpression and p53 mutant expression in EC tissue, and there is an inherent synergy between the two expressions. HER2 positive over expression is a key factor for poor prognosis in EC patients. Patients with HER2- positive over expression and p53 mutant expression have poorer 5-year overall survival and progression- free survival compared to other groups.