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 explore the effect of remote ischemic preconditioning (RIPC) on preoperative heart rate variability in patients with heart valves. Methods Patients scheduled to undergo on-pump cardiac valve surgery in the Department of Cardiovascular Surgery, General Hospital of Northern Theater Command, between January and July 2022 were initially enrolled. Eligible patients were randomly assigned at a 1 : 1 ratio to either the RIPC group or the control group. Relevant indicators of heart rate variability [standard deviation of NN interval (SDNN), standard deviation of mean value of NN interval in every five minutes (SDANN), mean square root of difference between consecutive NN intervals (RMSSD), percentage of adjacent RR interval>50 ms (PNN50), low frequency (LF) component, high frequency (HF) component and LF/HF] at 8 hours in the morning on the surgical day between two groups were compared. Results A total of 118 patients were initially assessed. After screening, 58 patients were excluded, and 60 patients provided written informed consent and were enrolled in the trial, with 30 allocated to the RIPC group and 30 to the control group. Seven patients in the control group and 5 patients in the RIPC group were subsequently excluded due to missing heart rate variability data resulting from cancelled operations. Finally, 23 patients in the control group and 25 patients in the RIPC group were included in the analysis. There was no statistical difference in baseline characteristics between the two groups, and there was no significant difference in heart rate variability 24 hours before intervention (P>0.05). After the intervention measures were taken, the comparison of the results of heart rate variability at 8 hours on the day of operation showed that SDNN and SDANN of patients in the RIPC group were higher than those in the control group, with statistical differences (P<0.05). Conclusion RIPC can stabilize the preoperative heart rate variability of patients undergoing cardiac valve surgery.

ObjectiveTo analyze the infection characteristics of human respiratory syncytial virus (HRSV) among children hospitalized with acute lower respiratory tract infection (ALRTI) in a specialized pediatric hospital in Shanghai, so as to provide evidence-based support for optimizing the prevention and control strategies and clinical diagnosis and treatment of respiratory tract infections in children in this region. MethodsA retrospective analysis was performed to the clinical and etiological data of 29 260 children hospitalized for ALRTI in Shanghai Children’s Hospital from January 2021 to December 2024. HRSV and 12 other common respiratory pathogens were detected with multiplex polymerase chain reaction (PCR) and capillary electrophoresis. Demographic and clinical data were collected for statistical analyses. A total of2 412 cases with positive HRSV were divided into the severe group and the non-severe group. Clinical characteristics between the two groups were compared using the Mann-Whitney U test and the chi- square (χ²) test. Additionally, the related influencing factors of severe HRSV infection were explored. ResultsThe overall positivity rate of HRSV from 2021 to 2024 was 8.24% (2 412/29 260), with statistically significant differences observed across the four years (χ²=389.42, P<0.001). The highest positivity rate was in 2021 (14.76%), with a high prevalence throughout the year. In 2022, when non-pharmaceutical interventions (NPIs) were implemented, the HRSV positivity rate was the lowest (4.93%), with a winter-dominant epidemic pattern. In 2023, after the NPIs were lifted, the HRSV positivity rate showed a slight rebound (8.14%), presenting a double-peak pattern. In 2024, the HRSV positivity rate slightly decreased compared to that in 2023 (6.29%), exhibiting a winter and spring-dominant epidemic pattern. Among the hospitalized children with ALRTI, the HRSV positivity rate in males (8.85%) was higher than that in females (7.51%), and the difference was statistically significant (χ²=17.33, P<0.001). Age distribution showed that 82.26% (1 984/2 412) of HRSV infections occurred in children aged 3 years old and below. Besides, as age increased, the infection rate of HRSV showed a gradually decreasing trend (P<0.001). Among the 2 412 children with HRSV infection, the proportion of severe cases was 22.31% (538/2 412), while the non-severe cases accounted for 77.69% (1 874/2 412). Compared with non-severe cases, severe cases were more frequently presented with high fever, longer duration of wheezing, as well as higher rates of underlying diseases or co-infection with Mycoplasma pneumoniae (P<0.001). ConclusionThe prevalence intensity of HRSV varied yearly from 2021 to 2024. After the removal of NPIs in 2023, a slight rebound with a double-peak epidemic pattern was observed. HRSV remained a common pathogen in children hospitalized for ARLTI, and children aged 3 years old and below constituted the highest proportion for infection. Compared with non-severe cases, those with severe HRSV infections were more prone to presenting with high fever and a longer duration of wheezing. Children with positive HRSV who had underlying diseases or co-infection with Mycoplasma pneumonia were more likely to develop severe conditions.

ObjectiveTo investigate the contamination levels of zearalenone (ZEN) in commercially available edible vegetable oils in Ningbo City and to assess its health risks to local residents. MethodsA total of 330 samples of commercially available edible vegetable oil samples (50 each of peanut oil, corn oil, and olive oil; 40 each of rapeseed oil and blended oil; 30 each of soybean oil, rice oil, and sunflower seed oil; and 10 of camellia oil) were collected in 2024. The samples were tested for ZEN using the first method specified in GB 5009.209‒2016 National Food Safety Standard―Determination of Zearalenone in Food, namely the liquid chromatography method, and the contamination status was analyzed. Additionally, combined with dietary consumption data of residents, the Monte Carlo simulation method was employed to evaluate the health risks of ZEN in edible vegetable oils. ResultsZEN was detected in 267 out of 330 samples, with a detection rate of 80.91%, and the median (P₅₀) and the 25th, 75th percentiles (P₂₅, P₇₅) of ZEN concentrations were 2.02 (0.37, 17.90) μg·kg^-1, with a maximum value of 342.00 μg·kg^-1. The ZEN detection rates in corn oil, peanut oil, and blended oil were all 100.00%. The daily average exposure (P₅₀) and daily high exposure (P₉₅) to ZEN via edible vegetable oils among Ningbo residents were 0.001 μg·kg^-1 (normalized to body weight, same below) and 0.060 μg·kg^-1, respectively. However, 1.22% of Ningbo residents had a daily ZEN exposure exceeding the tolerable daily intake (TDI) of 0.25 μg·kg^-1. The hazard quotients (HQ) for the daily average exposure (P₅₀) and daily high exposure (P₉₅) levels were 0.004 and 0.020, respectively, both substantially below 1. Nevertheless, the probability of health risk for Ningbo residents due to ZEN exposure from vegetable oil consumption remained at 1.02%. ConclusionEdible vegetable oils in Ningbo City were contaminated with ZEN, but the probability of ZEN exposure exceeding the TDI through edible vegetable oils was relatively low, and the associated health risk probability were also minimal, indicating an overall insignificant health risk.

Disease computable phenotype is a data model designed to identify specific clinical conditions or characteristics, which automatically extracts information from clinical databases such as electronic health records through algorithms. Phenotypic data for rare diseases often reside in unstructured text. Due to the scarcity of rare disease cases, atypical symptoms, and insufficient physician experience, misdiagnosis and underdiagnosis rates remain high. In this context, the application of computable phenotype technology holds promise for improving the accuracy and efficiency of rare disease diagnosis. This article reviews the current research status, challenges, and opportunities of computable phenotype technology in biomedicine, particularly in the field of rare diseases, and proposes a development and validation framework for rare disease computable phenotypes, aiming to provide research and development insights for computable phenotypes to empower the diagnosis and treatment of rare diseases.

ObjectiveTo investigate the characteristics and distribution of traditional Chinese medicine （TCM） syndromes in the patients with esophageal squamous cell carcinoma （ESCC）. MethodsAn electronic questionnaire was developed to collect the general data and four examination information of ESCC patients treated in 10 areas with high incidence of esophageal cancer in China from June 2020 to March 2021. Multiple analyses including frequency analysis， factor analysis， and hierarchical cluster analysis were performed to analyze the potential syndrome elements， disease location， and common syndromes of ESCC. ResultsA total of 2 027 patients with ESCC were included. Statistical analysis was performed on 113 symptoms， physical signs， 33 tongue manifestation variables， and 23 pulse manifestation variables of the patients’ four examination information. Factor analysis was performed on 55 variables with frequency>10%， extracting 19 common factors. According to clinical experience and expert opinions， the main lesions of patients with ESCC were in the spleen and stomach， and the main syndrome elements were Qi stagnation， blood stasis， phlegm， dampness， and Qi deficiency， with the syndrome element combination of phlegm obstruction + Qi stagnation + blood stasis being the most common. The syndromes can be classified into four categories of liver-stomach disharmony + combined phlegm and Qi obstruction， kidney-spleen dysfunction + combined phlegm and stasis， spleen-kidney Yang deficiency + obstinate phlegm and blood stasis， and liver-kidney Yin deficiency + obstinate phlegm and blood stasis. The main syndrome of ESCC was liver-stomach disharmony + combined phlegm and Qi obstruction in the early stage， liver-spleen dysfunction + combined phlegm and stasis in the middle stage， and spleen-kidney Yang deficiency + obstinate phlegm and blood stasis in the late stage. ConclusionESCC mainly has main pathological features of internal deficiency and external excess and combined deficiency and excess， with the key syndrome elements being phlegm obstruction， Qi stagnation， and blood stasis. The main disease locations are in the spleen and stomach， involving the liver， kidney， chest and diaphragm， heart， and lung. The main syndrome is liver-stomach disharmony + combined phlegm and Qi obstruction. In clinical practice， it is necessary to grasp the pathogenesis dynamics of the disease and use prescriptions according to patients’ syndromes.

ObjectiveTo evaluate the clinical efficacy of Maxing Loushi decoction in the treatment of acute exacerbation of chronic obstructive pulmonary disease （AECOPD） with phlegm turbidity obstructing lung syndrome， and to investigate its effects on inflammatory factors， immune function， and the programmed death-1（PD-1）/programmed death-ligand 1 （PD-L1） signaling pathway. MethodsA randomized controlled study was conducted， enrolling 90 hospitalized patients with AECOPD and phlegm turbidity obstructing lung syndrome in the Respiratory and Emergency Departments of Dongzhimen Hospital， Beijing University of Chinese Medicine， from April 2024 to December 2024. Patients were randomly assigned to a control group and an observation group using a random number table， with 45 patients in each group. The control group received conventional Western medical treatment， while the observation group received additional Maxing Loushi decoction for 14 days. Clinical efficacy， COPD Assessment Test （CAT） score， modified Medical Research Council Dyspnea Scale （mMRC）， 6-minute walk test （6MWT）， serum inflammatory factors， T lymphocyte subsets， and serum PD-1/PD-L1 levels were compared between the two groups before and after treatment. ResultsThe total clinical effective rate was 78.57% （33/42） in the control group and 95.35% （41/43） in the observation group， with the observation group showing significantly higher efficacy than that of the control group. The difference was statistically significant （χ² = 5.136， P<0.05）. After treatment， both groups showed significant reductions in CAT and mMRC scores （P<0.05， P<0.01） and significant increases in 6MWT compared to baseline （P<0.01）. The observation group demonstrated significantly greater improvements than the control group in this regard. Levels of inflammatory markers including C-reactive protein （CRP）， procalcitonin （PCT）， interleukin-6 （IL-6）， tumor necrosis factor-α （TNF-α）， monocyte chemoattractant protein-1（MCP-1）， and macrophage inflammatory protein-1α （MIP-1α） were significantly reduced in both groups （P<0.05， P<0.01）， with greater reductions in the observation group （P<0.05， P<0.01）. CD8⁺ levels were significantly reduced （P<0.01）， while CD3⁺， CD4⁺， and CD4⁺/CD8⁺ levels were significantly increased in both groups after treatment （P<0.05， P<0.01）， with more significant improvements observed in the observation group （P<0.05， P<0.01）. Serum PD-1 levels were reduced （P<0.05， P<0.01）， and PD-L1 levels were increased significantly in both groups after treatment （P<0.05， P<0.01）， with more pronounced changes in the observation group （P<0.05）. ConclusionMaxing Loushi decoction demonstrates definite therapeutic efficacy as an adjunctive treatment for patients with AECOPD and phlegm turbidity obstructing lung syndrome. It contributes to reducing serum inflammatory factors， improving immune function， and regulating the PD-1/PD-L1 signaling pathway.

ObjectiveTo evaluate the clinical efficacy of Maxing Loushi decoction in the treatment of acute exacerbation of chronic obstructive pulmonary disease （AECOPD） with phlegm turbidity obstructing lung syndrome， and to investigate its effects on inflammatory factors， immune function， and the programmed death-1（PD-1）/programmed death-ligand 1 （PD-L1） signaling pathway. MethodsA randomized controlled study was conducted， enrolling 90 hospitalized patients with AECOPD and phlegm turbidity obstructing lung syndrome in the Respiratory and Emergency Departments of Dongzhimen Hospital， Beijing University of Chinese Medicine， from April 2024 to December 2024. Patients were randomly assigned to a control group and an observation group using a random number table， with 45 patients in each group. The control group received conventional Western medical treatment， while the observation group received additional Maxing Loushi decoction for 14 days. Clinical efficacy， COPD Assessment Test （CAT） score， modified Medical Research Council Dyspnea Scale （mMRC）， 6-minute walk test （6MWT）， serum inflammatory factors， T lymphocyte subsets， and serum PD-1/PD-L1 levels were compared between the two groups before and after treatment. ResultsThe total clinical effective rate was 78.57% （33/42） in the control group and 95.35% （41/43） in the observation group， with the observation group showing significantly higher efficacy than that of the control group. The difference was statistically significant （χ² = 5.136， P<0.05）. After treatment， both groups showed significant reductions in CAT and mMRC scores （P<0.05， P<0.01） and significant increases in 6MWT compared to baseline （P<0.01）. The observation group demonstrated significantly greater improvements than the control group in this regard. Levels of inflammatory markers including C-reactive protein （CRP）， procalcitonin （PCT）， interleukin-6 （IL-6）， tumor necrosis factor-α （TNF-α）， monocyte chemoattractant protein-1（MCP-1）， and macrophage inflammatory protein-1α （MIP-1α） were significantly reduced in both groups （P<0.05， P<0.01）， with greater reductions in the observation group （P<0.05， P<0.01）. CD8⁺ levels were significantly reduced （P<0.01）， while CD3⁺， CD4⁺， and CD4⁺/CD8⁺ levels were significantly increased in both groups after treatment （P<0.05， P<0.01）， with more significant improvements observed in the observation group （P<0.05， P<0.01）. Serum PD-1 levels were reduced （P<0.05， P<0.01）， and PD-L1 levels were increased significantly in both groups after treatment （P<0.05， P<0.01）， with more pronounced changes in the observation group （P<0.05）. ConclusionMaxing Loushi decoction demonstrates definite therapeutic efficacy as an adjunctive treatment for patients with AECOPD and phlegm turbidity obstructing lung syndrome. It contributes to reducing serum inflammatory factors， improving immune function， and regulating the PD-1/PD-L1 signaling pathway.

ObjectiveTo analyze the epidemiological and clinical characteristics of surveillance cases in a sentinel hospital for pertussis in Jiangxi Province in 2019, and to provide corresponding references for the prevention and control of pertussis. MethodsCase investigation of pertussis was conducted among sentinel hospital surveillance cases, collecting their basic information, epidemiological characteristics, clinical characteristics, and other information. ResultsA total of 125 pertussis surveillance cases were investigated in 2019, including 73 clinically diagnosed cases (58.40%) and 52 confirmed cases (41.60%). The age of onset was mainly concentrated in children under 5 years old (108 cases, 86.40%), with the largest number of cases in infants aged less than 1-year-old (48 cases, 38.40%). Most cases had a history of receiving pertussis vaccine before onset (110 cases, 88.00%), and the intervals between the onset date and the date of last dose of pertussis vaccine in the 1‒2 doses group were significantly shorter than that in the 3‒4 doses group (U=-5.990, P<0.001). Probable household transmission of pertussis was found in 3 cases. All cases had cough symptoms, mainly manifested as whooping cough (77 cases, 61.60%), in addition to other main clinical manifestations, such as fever (76 cases, 60.80%), vomiting (30 cases, 24.00%), conjunctival congestion (27 cases, 21.60%), and inspiratory whoop (16 cases, 12.80%). A total of 73 cases (58.40%) experienced complications, including 1 death case. All the cases had multiple medical visit experiences before this visit, with an interval of 2 (0,3) days between the onset date and the first visit date. The misdiagnosis rate at the first medical visit was 88.00% (110/125), and the misdiagnosis rate of the first visit in secondary and primary hospitals was significantly higher than that in tertiary hospitals, exhibiting a statistically significant difference (χ²=21.582, P<0.001). ConclusionThe clinical symptoms of pertussis cases are often atypical, and the first diagnosis is prone to misdiagnosis, so it’s necessary to further strengthen the early diagnosis capabilities for pertussis cases in healthcare institutions, especially in the primary healthcare institutions.

In recent years, the application of artificial intelligence (AI) in the field of biology has witnessed remarkable advancements. Among these, the most notable achievements have emerged in the domain of protein structure prediction and design, with AlphaFold and related innovations earning the 2024 Nobel Prize in Chemistry. These breakthroughs have transformed our ability to understand protein folding and molecular interactions, marking a pivotal milestone in computational biology. Looking ahead, it is foreseeable that the accurate prediction of various physicochemical properties of proteins—beyond static structure—will become the next critical frontier in this rapidly evolving field. One of the most important protein properties is thermodynamic stability, which refers to a protein’s ability to maintain its native conformation under physiological or stress conditions. Accurate prediction of protein stability, especially upon single-point mutations, plays a vital role in numerous scientific and industrial domains. These include understanding the molecular basis of disease, rational drug design, development of therapeutic proteins, design of more robust industrial enzymes, and engineering of biosensors. Consequently, the ability to reliably forecast the stability changes caused by mutations has broad and transformative implications across biomedical and biotechnological applications. Historically, protein stability was assessed via experimental methods such as differential scanning calorimetry (DSC) and circular dichroism (CD), which, while precise, are time-consuming and resource-intensive. This prompted the development of computational approaches, including empirical energy functions and physics-based simulations. However, these traditional models often fall short in capturing the complex, high-dimensional nature of protein conformational landscapes and mutational effects. Recent advances in machine learning (ML) have significantly improved predictive performance in this area. Early ML models used handcrafted features derived from sequence and structure, whereas modern deep learning models leverage massive datasets and learn representations directly from data. Deep neural networks (DNNs), graph neural networks (GNNs), and attention-based architectures such as transformers have shown particular promise. GNNs, in particular, excel at modeling spatial and topological relationships in molecular structures, making them well-suited for protein modeling tasks. Furthermore, attention mechanisms enable models to dynamically weigh the contribution of specific residues or regions, capturing long-range interactions and allosteric effects. Nevertheless, several key challenges remain. These include the imbalance and scarcity of high-quality experimental datasets, particularly for rare or functionally significant mutations, which can lead to biased or overfitted models. Additionally, the inherently dynamic nature of proteins—their conformational flexibility and context-dependent behavior—is difficult to encode in static structural representations. Current models often rely on a single structure or average conformation, which may overlook important aspects of stability modulation. Efforts are ongoing to incorporate multi-conformational ensembles, molecular dynamics simulations, and physics-informed learning frameworks into predictive models. This paper presents a comprehensive review of the evolution of protein thermodynamic stability prediction techniques, with emphasis on the recent progress enabled by machine learning. It highlights representative datasets, modeling strategies, evaluation benchmarks, and the integration of structural and biochemical features. The aim is to provide researchers with a structured and up-to-date reference, guiding the development of more robust, generalizable, and interpretable models for predicting protein stability changes upon mutation. As the field moves forward, the synergy between data-driven AI methods and domain-specific biological knowledge will be key to unlocking deeper understanding and broader applications of protein engineering.