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 construct 5 machine-learning models and compare their performance in predicting the associations between pre-pregnancy socio-psycho-behavioral exposures of both spouses and preconception outcomes.Methods Based on Chongqing Preconception Reproductive Health and Birth Outcome Cohort of volunteers recruited from Chongqing Health Center for Women and Children during January 2019 and March 2022,5 447 couples were recruited and surveyed through interviewer-interview for the demographic and social-psychological-behavioral data of both spouses(221 variables).According to the inclusion and exclusion criteria,4 097 couples were finally included,and randomly assigned into a training set(n=2 867 spouses)and a validation set(n=1 230 spouses)at a ratio of 7∶3.Feature analysis and collinear screening were applied to select the potential exposure factors.In consideration of difficulty to carry out semen parameters analysis in primary healthcare institutions,feature Set 1 including sperm parameters and feature Set 2 excluding semen parameters were constructed by including or excluding sperm quality simultaneously in the training set and the validation set.Five algorithms,that is,Logistic Regression,Naive Bayes,Random Forest,Gradient Boosting Machine,and Support Vector Machine,were used to construct preconception outcome prediction models,and the parameters of each model were optimized using random search combined with grid search.The predictive performance of each model was compared using precision,recall,F1 score,area under the receiver operating characteristic curve(AUC),and calibration curve.The optimal model was then selected by comparing the changes in the predictive ability of the questionnaire data for fertility outcomes with or without semen parameters.Results There were 24 variables screened out in feature Set 1,and 16 variables in feature Set 2.In feature Set 1,the gradient boosting machine performed better,with a relatively higher AUC value(0.651)and better F1 score(0.61).The logistic regression model performed stably(AUC value=0.647)and was suitable as the reference model.The random forest(AUC value=0.641),Naive Bayes(AUC value=0.641),and support vector machine(AUC value=0.634)performed second-best.By utilizing the gradient boosting machine,comparable results were found between the predictions from feature sets with or without semen parameters,as in feature Set 1,the AUC value of its validation set was 0.651(95%CI:0.629～0.681),the prediction accuracy was 0.63,the recall rate was 0.65,and the average precision value F1 was 0.61;and in feature Set 2,the AUC value of its validation set was 0.649(95%CI:0.624～0.663),and both the calibration curves were close to the ideal curve.The prediction results indicated that in feature Set 1,the features highly negatively correlated with preconception outcomes were female age,male age,and no pregnancy within 1 year without contraception,while the features highly positively correlated with preconception outcomes were female pregnancy history,total sperm vitality,and use of contraceptive measures before enrollment.Conclusion Among the 5 machine-learning algorithms performed in this cohort data,the gradient boosting machine shows slightly better performance.There are 24 factors being associated with preconception outcomes in both spouses,and the performance of the simplified model excluding semen parameters is not significantly declined.It is feasible to use machine-learning methods to predict human preconception outcomes through social-psychological-behavioral questionnaires.

Iodine speciations in aquatic environments are affected by dissolved oxygen,redox potential,microbial activity,organic matter decomposition,light reaction,etc.Accurate quantification of iodine speciation can not only help to understand the geochemical cycle of iodine,but also help to trace and study environmental processes.Based on the combination of ion chromatography(IC)and inductively coupled plasma mass spectrometry(ICP-MS),a rapid and sensitive method was established for determining the speciations of iodine in environmental water samples including seawater,river water,lake water,rainwater,groundwater,etc.The results presented here showed that IO3?and I?in seawater were quickly separated and measured within 120 s when using guard column AG22 and 8 mmol/L(NH4)2CO3 as the mobile phase.While for lake water,river water and precipitation samples with high soluble organically bond iodine(SOI),an AS22 separation column(250 mm×4 mm)connected with a guard column and using 50 mmol/L(NH4)2CO3 as mobile phase could effectively separate unknown SOI from IO3? to achieve accurate quantification of IO3?.For accurate correction of iodine measurement signal fluctuations,133Cs was directly added to the(NH4)2CO3 mobile phase as an internal standard.The SOI content was calculated by the total iodine concentrations minus the sum of IO3?and I?.The precision of the established iodine speciation analytical method was better than 3.5%,and the standard addition experiment showed that the analytical method was accurate.When the injection volume was 25 μL,the detection limits were 0.011?0.025 μg/L for IO3? and 0.023?0.031 μg/L for I?,respectively.The method was successfully used to analyze IO3?,SOI and I? in environmental water samples,such as seawater,river water,rainwater and groundwater.

Accurate identification of endogenous and exogenous substances in food,particularly in competition supplies,is crucial for ensuring food safety and fair competition,as well as for protecting the legitimate rights and professional reputations of athletes.Testosterone(T)and dehydroepiandrosterone(DHEA)are important steroid hormones that can stimulate protein synthesis,increase the number and volume of muscle cells,and promote muscle growth and recovery.Both are often illegally used in the animal husbandry industry to promote animal growth and improve meat quality.However,current research in this area remains limited,and identification technologies require further investigation.This study focused on the techniques for identifying endogenous and exogenous hormones including T and DHEA in beef.A Soxhlet extraction method was established,reducing the pretreatment cycle to 110 min while achieving high extraction efficiency,with recovery rates of 102.5%for T and 91.9%for DHEA,respectively.Based on this,a gas chromatography-combustion-isotope ratio mass spectrometry(GC-C-IRMS)method was developed for analyzing carbon isotopes in T and DHEA,eliminating the need for derivatization.By adding reference materials to the extract,simultaneous measurement of reference materials and target analytes was achieved.The measurement of caffeine reference material,T and DHEA was completed within 40 min,with a measurement repeatability of 0.02‰.Theδ13C values of T and DHEA in standard substances,which may serve as exogenous additives,were determined using elemental analysis-isotope ratio mass spectrometry(EA-IRMS).The results indicated an average δ13C value of-29.44‰±0.81‰(k=1)for 10 T standards and-30.86‰±0.87‰(k=1)for 14 kinds of DHEA standards.This approach effectively distinguished between endogenous sources and exogenous addition of these two hormones in beef,thereby providing vital technical support for the assurance and supervision of food safety.

The cycling of iodine in the atmosphere and ocean plays a critical role in climate and environmental change.Ice cores and tree rings,as ideal climate archives,can provide essential data for paleoclimate reconstruction through accurate iodine analysis.However,iodine concentrations in ice core samples are as low as 0.01 μg/L,and as low as 0.01 μg/g in tree rings.Most currently reported analytical methods face challenges in accurately quantifying such low levels.In this study,a highly sensitive analytical method for trace iodine in environmental samples was developed based on inductively coupled plasma mass spectrometry(ICP-MS),incorporating a collision/reaction cell under the optimized collision/reaction gas conditions.Collision/reaction cell mode was employed to evaluate potential interferences in determination of trace iodine by ICP-MS.The results indicated that peak tailing from adjacent mass-to-charge ratios and polyatomic ion interferences could be considered negligible.However,residual iodine in ultrapure water constituted the major source of background,highlighting the need for strict control of sample pretreatment and procedural blanks in trace-level iodine analysis.By utilizing collision focusing effect of helium,the measurement sensitivity of iodine was enhanced to 3.5×105 cps(counts per second)/(μg/L),and the detection limit was reduced to 0.002 μg/L.The developed method exhibited a deviation of less than 2.3%in measurement of low-concentration standard solutions,indicating good accuracy.For actual samples and blanks,the precision of repeated measurements within 2 h was better than 1.6%,demonstrating excellent repeatability.No significant memory effect was observed during the measurements.The established method was successfully applied to determination of trace iodine in ice cores,tree rings,and other environmental samples,providing a robust technical foundation for investigating the role of iodine in climate and environmental change.

Objective:To explore the mechanism of Xuanfei Dahe Decoction in treating asthmatic mice through network pharmacology and animal experiments.Methods:The active components and their targets of Xuanfei Dahe Decoction were retrieved from TCMSP, and the asthma targets were obtained by searching the GeneCards, DisGeNet, OMIM, TTD and DrugBank databases. PPI network of intersection targets was constructed using string database and Cytoscape 3.9.0 software. Go function and KEGG pathway enrichment were analyzed by metascape database. The mice were divided into a blank group of 6 mice and a model group of 30 mice according to the random number table method. The asthma model was prepared in the model group. Totally 30 successfully modeled mice were divided into the model group, the dexamethasone group, and Xuanfei Dahe Decoction low-, medium- and high-dosage groups according to the random number table method, with 6 mice in each group. On the 5th day, gavage was initiated. Xuanfei Dahe Decoction low-, medium- and high-dosage groups were respectively gavaged with Xuanfei Dahe Decoction liquid at concentrations of 6.84, 13.68 and 27.36 g/kg. The dexamethasone group was gavaged with dexamethasone acetate tablets at concentrations of 2.73 mg/kg. The blank group and the model group were gavaged with the same volume of sterile normal saline once a day for 14 consecutive days. The pathological changes of lung tissue were observed by HE staining, the level of serum IL-17 was detected by ELISA, and the expression of IL-17 in lung tissue was detected by immunohistochemistry.Results:120 asthma targets and 13 key targets were obtained from Xuanfei Dahe Decoction. Pathway enrichment analysis suggested that IL-17 signaling pathway was one of the key pathways. Compared with the model group, the levels of IL-17 in the serum of the low-dose, medium-dose and high-dose Xuanfei Dahe Decoction groups and the expression of IL-17A in the lung tissue of the medium-dose Xuanfei Dahe Decoction group decreased ( P<0.05). Conclusion:Xuanfei Dahe Decoction may treat asthma by restrain airway inflammation mediated by Th17/IL-17.

Aging is characterized by a gradual deterioration of the physiological integrity of cells,tissues,and or-gans,resulting in a decrease in the body's physiological functions and an acceleration of the onset of age-related diseases,ultimately leading to death.The aging of the liver,which is a critical metabolic organ,is closely linked to various chronic liver diseases,such as hepatitis,liver fibrosis,and cirrhosis,and it ex-acerbates their prognosis and is a primary risk factor for their development at all stages.Therefore,a comprehensive understanding of the causes,mechanisms,and potential therapeutic targets associated with liver aging holds significant clinical importance for delaying or potentially reversing liver aging and for treating chronic liver diseases.Stem cells,which are potential anti-aging agents,present a promising and effective alternative for managing liver aging.In this review,we systematically assess the driving factors,characteristics,and underlying mechanisms of liver aging.We then discuss the current status of the use of stem cells to mitigate liver senescence and address related liver diseases.The review reveals that a stem cell-based approach represents a promising therapeutic strategy for combating liver aging and associated diseases.

ObjectiveAutism spectrum disorder (ASD) is a neurodevelopmental condition characterized by difficulties with communication and social interaction, restricted and repetitive behaviors. Previous studies have indicated that individuals with ASD exhibit early and lifelong attention deficits, which are closely related to the core symptoms of ASD. Basic visual attention processes may provide a critical foundation for their social communication and interaction abilities. Therefore, this study explores the behavior of children with ASD in capturing attention to changes in topological properties. MethodsOur study recruited twenty-seven ASD children diagnosed by professional clinicians according to DSM-5 and twenty-eight typically developing (TD) age-matched controls. In an attention capture task, we recorded the saccadic behaviors of children with ASD and TD in response to topological change (TC) and non-topological change (nTC) stimuli. Saccadic reaction time (SRT), visual search time (VS), and first fixation dwell time (FFDT) were used as indicators of attentional bias. Pearson correlation tests between the clinical assessment scales and attentional bias were conducted. ResultsThis study found that TD children had significantly faster SRT (P<0.05) and VS (P<0.05) for the TC stimuli compared to the nTC stimuli, while the children with ASD did not exhibit significant differences in either measure (P>0.05). Additionally, ASD children demonstrated significantly less attention towards the TC targets (measured by FFDT), in comparison to TD children (P<0.05). Furthermore, ASD children exhibited a significant negative linear correlation between their attentional bias (measured by VS) and their scores on the compulsive subscale (P<0.05). ConclusionThe results suggest that children with ASD have difficulty shifting their attention to objects with topological changes during change detection. This atypical attention may affect the child’s cognitive and behavioral development, thereby impacting their social communication and interaction. In sum, our findings indicate that difficulties in attentional capture by TC may be a key feature of ASD.

This study investigates the material basis and mechanism of Arisaematis Rhizoma Preparatum in the treatment of chronic obstructive pulmonary disease(COPD) using animal experiments, component analysis, network pharmacology, and molecular docking. A mouse model of COPD was constructed by cigarette smoke and lipopolysaccharide(LPS). Blood gas analysis was performed to measure the pH and partial pressure of carbon dioxide(PCO_2) in the blood of the mice. Lung tissue sections were analyzed using HE staining, and the effects of Arisaematis Rhizoma Preparatum water extract on inflammatory factors(TNF-α, IL-6, and IL-1β) and the PI3K/AKT signaling pathway in the lung tissue of COPD model mice were studied by qPCR and Western blot. The composition of the Arisaematis Rhizoma Preparatum water extract was analyzed using UPLC Q-Exactive Orbitrap MS. The SwissTargetPrediction database was used to predict the targets of the chemical components in Arisaematis Rhizoma Preparatum. GeneCards, OMIM, TTD, PharmGKB and DrugBank disease databases were used to screen for COPD targets, and the potential targets of Arisaematis Rhizoma Preparatum in treating COPD were identified. A protein-protein interaction(PPI) network of intersection targets was constructed and analyzed using the STRING database and Cytoscape 3.9.0, and core genes were screened. GO functional analysis and KEGG pathway enrichment analysis were performed using R language, and molecular docking verification was conducted using AutoDock Vina software. The results of the animal experiments showed that Arisaematis Rhizoma Preparatum water extract improved pulmonary ventilation function in COPD model mice, reduced lung inflammatory cells, decreased alveolar cavities, and improved lung tissue condition. The levels of inflammatory factors TNF-α, IL-6 and IL-1β were decreased, and the phosphorylation levels of PI3K and AKT were inhibited. Fifty-two chemical components were identified from Arisaematis Rhizoma Preparatum, and 440 intersection targets related to COPD were found. Nine key components were screened, including hydroxyphenylethylamine, L-tyrosine, L-tyrosyl-L-alanine, 3,4,5-trihydroxy-1-cyclohexene-1-carboxylic acid, methyl azelate, zingerone, 6-gingerol, linoleamide, and linoleoyl ethanolamine. Five core targets were identified, including AKT1, TNF, STAT3, ESR1, and IL1B. The PI3K/AKT pathway was identified as the key pathway for the treatment of COPD with Arisaematis Rhizoma Preparatum. Molecular docking results showed that 75% of the binding energies of key components and core targets were less than-5 kcal·mol~(-1), indicating good binding affinity. In conclusion, Arisaematis Rhizoma Preparatum may improve pulmonary ventilation function, enhance lung pathological morphology, and reduce pulmonary inflammation in COPD model mice by inhibiting the PI3K/AKT signaling pathway and downregulating TNF-α, IL-6, and IL-1β inflammatory factors. The material basis may be associated with L-tyrosyl-L-alanine, 3,4,5-trihydroxy-1-cyclohexene-1-carboxylic acid, zingerone and 6-gingerol, and AKT1 and TNF may be the primary targets.
Animals ; Pulmonary Disease, Chronic Obstructive/metabolism* ; Network Pharmacology ; Mice ; Drugs, Chinese Herbal/administration & dosage* ; Male ; Rhizome/chemistry* ; Humans ; Molecular Docking Simulation ; Chromatography, High Pressure Liquid ; Disease Models, Animal ; Signal Transduction/drug effects* ; Lung/metabolism* ; Phosphatidylinositol 3-Kinases/metabolism* ; Tumor Necrosis Factor-alpha/metabolism* ; Proto-Oncogene Proteins c-akt/metabolism* ; Interleukin-6/immunology*

Guided by the concept of quality by design(QbD), this study optimizes the calcination and quenching process of calcined Magnetitum and establishes the XRD characteristic spectra of calcined Magnetitum, providing a scientific basis for the formulation of quality standards. Based on the processing methods and quality requirements of Magnetitum in the Chinese Pharmacopoeia, the critical process parameters(CPPs) identified were calcination temperature, calcination time, particle size, laying thickness, and the number of vinegar quenching cycles. The critical quality attributes(CQAs) included Fe mass fraction, Fe~(2+) dissolution, and surface color. The weight coefficients were determined by combining Analytic Hierarchy Process(AHP) and the criteria importance though intercrieria correlation(CRITIC) method, and the calcination process was optimized using orthogonal experimentation. Surface color was selected as a CQA, and based on the principle of color value, the surface color of calcined Magnetitum was objectively quantified. The vinegar quenching process was then optimized to determine the best processing conditions. X-ray diffraction(XRD) was used to establish the characteristic spectra of calcined Magnetitum, and methods such as similarity evaluation, cluster analysis, and orthogonal partial least squares-discriminant analysis(OPLS-DA) were used to evaluate the quality of the spectra. The optimized calcined Magnetitum preparation process was found to be calcination at 750 ℃ for 1 h, with a laying thickness of 4 cm, a particle size of 0.4-0.8 cm, and one vinegar quenching cycle(Magnetitum-vinegar ratio 10∶3), which was stable and feasible. The XRD characteristic spectra analysis method, featuring 9 common peaks as fingerprint information, was established. The average correlation coefficient ranged from 0.839 5-0.988 1, and the average angle cosine ranged from 0.914 4 to 0.995 6, indicating good similarity. Cluster analysis results showed that Magnetitum and calcined Magnetitum could be grouped together, with similar compositions. OPLS-DA discriminant analysis identified three key characteristic peaks, with Fe_2O_3 being the distinguishing component between the two. The final optimized processing method is stable and feasible, and the XRD characteristic spectra of calcined Magnetitum was initially established, providing a reference for subsequent quality control and the formulation of quality standards for calcined Magnetitum.
X-Ray Diffraction/methods* ; Drugs, Chinese Herbal/chemistry* ; Quality Control ; Particle Size