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.

Esophageal cancer is a prevalent malignant tumor of the digestive tract in China, and radical surgery remains the cornerstone of its comprehensive treatment. However, multifactorial challenges such as postoperative gastrointestinal tract reconstruction, traumatic stress, and tumor-related metabolic disturbances render esophageal cancer patients highly susceptible to malnutrition. Perioperative nutritional support therapy plays a crucial role in enhancing surgical safety, improving clinical outcomes, and elevating patients' quality of life by regulating metabolic homeostasis, preserving organ function, and optimizing the immune microenvironment. This article reviews the mechanisms underlying malnutrition in esophageal cancer, methods for nutritional status assessment, and precision intervention pathways based on multi-omics evaluations. The aim is to strengthen clinicians' awareness of standardized perioperative nutritional management for esophageal cancer patients and promote its clinical implementation, thereby facilitating postoperative recovery and improving long-term quality of life.

ObjectiveTo investigate the effects of Anmeidan （AMD） on neuroinflammation in the hippocampus of sleep-deprived rats. MethodsSD rats were randomly divided into four groups （n = 10 per group）： control group， model group， AMD group， and melatonin group. A sleep deprivation model was established using the modified multiple platform water environment method. The AMD group received AMD at a dose of 18.18 g·kg^-1·d^-1， the melatonin group received melatonin at 100 mg·kg^-1·d^-1， and the control and model groups were given an equal volume of pure water. All treatments were administered by gavage for four weeks. Spontaneous activity was assessed using an animal behavior video system. Serum levels of interleukin-1β （IL-1β）， interleukin-6 （IL-6）， and tumor necrosis factor-α （TNF-α） were measured by enzyme-linked immunosorbent assay （ELISA）. Hippocampal pyramidal neuron morphology was examined using hematoxylin-eosin （HE） staining， and ultrastructural changes of hippocampal neurons were observed via transmission electron microscopy. Immunofluorescence was used to detect the expression of brain-derived neurotrophic factor （BDNF） and nerve growth factor （NGF） in the hippocampus. Western blot analysis was performed to measure the expression of nuclear factor-κB （NF-κB）， phosphorylated NF-κB （p-NF-κB）， NOD-like receptor protein 3 （NLRP3）， and Caspase-1 proteins. ResultsCompared with the control group， the model group showed a significant increase in activity duration and frequency （P<0.01）， increased hippocampal pyramidal cell structural damage and decreased cell count， aggravated hippocampal ultrastructural damage， mitochondrial cristae disruption， and exacerbated vacuolization. The expression of p-NF-κB p65， NLRP3， and Caspase-1 proteins was upregulated， serum IL-1β， IL-6， and TNF-α levels were significantly elevated （P<0.01）， and the fluorescence intensity of BDNF and NGF proteins was significantly reduced （P<0.01）. Compared with the model group， the AMD group showed a significant reduction in activity duration and frequency （P<0.01）， increased hippocampal pyramidal cell count with reduced structural damage， alleviated hippocampal ultrastructural damage， significantly downregulated p-NF-κB p65， NLRP3， and Caspase-1 protein expression （P<0.01）， decreased serum IL-1β， IL-6， and TNF-α levels （P<0.01）， and significantly increased the fluorescence intensity of BDNF and NGF proteins （P<0.01）. ConclusionAnmeidan alleviates hippocampal neuronal damage in sleep-deprived rats， potentially by downregulating the NLRP3 signaling pathway， reducing inflammatory cytokine release， and increasing neurotrophic factor levels.

BackgroundSevere mental disorders represent a major public health concern due to the high disability rates and substantial disease burden， which has garnered significant national attention and prompted their inclusion in public health project management systems. However， credible evidence regarding the current status of disease management and factors influencing disease stabilization among patients with severe mental disorders in Luzhou City， Sichuan Province， remains limited. ObjectiveTo investigate the current management status of patients with severe mental disorders in Luzhou City， Sichuan Province， and to analyze influencing factors of disease stabilization among patients under standardized care， so as to provide evidence-based insights for developing targeted management strategies to optimize clinical interventions for this patient population. MethodsIn March 2023， data were extracted from the Sichuan Mental Health Service Comprehensive Management Platform for patients with severe mental disorders in Luzhou City who received management between December 2017 and December 2022. Information on mental health service utilization and clinical status changes was collected. Trend analysis was conducted to evaluate temporal changes in key management indicators for severe mental disorders in Luzhou City. Logistic regression analysis was employed to identify factors influencing the disease stabilization or fluctuation of these patients. ResultsThis study enrolled a total of 20 232 patients. In Luzhou City， the stabilization rate and standardized management rate of severe mental disorders were 94.89% and 79.36% in 2017， respectively， which increased to 95.33% and 96.92% by 2022. The regular medication adherence rate rose from 34.42% in 2018 to 86.81% in 2022. In 2022， the regular medication adherence rate was 71.80% for schizophrenia， 55.26% for paranoid psychosis， and 51.43% for schizoaffective disorder. Multivariate analysis identified the following protective factors for disease stabilization： age of 18~39 years （OR=0.613， 95% CI： 0.409~0.918）， age of 40~65 years （OR=0.615， 95% CI： 0.407~0.931）， urban residence （OR=0.587， 95% CI： 0.478~0.720）， and regular medication adherence （OR=0.826， 95% CI： 0.702~0.973）. Risk factors for disease fluctuation included poor （OR=1.712， 95% CI： 1.436~2.040）， non-inclusion in care-support programs （OR=1.928， 95% CI： 1.694~2.193）， non-participation in community rehabilitation （OR=2.255， 95% CI： 1.930~2.634）， and intermittent medication adherence （OR=3.893， 95% CI： 2.548~5.946）. ConclusionThe stability rate， standardized management rate， and regular medication adherence rate of patients with severe mental disorders in Luzhou City have shown a year-by-year increase. Age， household registration status， economic condition， medication compliance， and community-based rehabilitation were identified as influencing factors for disease fluctuation in these patients. ［Funded by Luzhou Science and Technology Plan Project （number， 2022-ZRK-186）］

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Purpose: The aim of this study was to investigate the feasibility of an intelligent handheld ultrasound (US) device for assisting non-expert general practitioners (GPs) in detecting carotid plaques (CPs) in community populations. Methods: This prospective parallel controlled trial recruited 111 consecutive community residents. All of them underwent examinations by non-expert GPs and specialist doctors using handheld US devices (setting A, setting B, and setting C). The results of setting C with specialist doctors were considered the gold standard. Carotid intima-media thickness (CIMT) and the features of CPs were measured and recorded. The diagnostic performance of GPs in distinguishing CPs was evaluated using a receiver operating characteristic curve. Inter-observer agreement was compared using the intragroup correlation coefficient (ICC). Questionnaires were completed to evaluate clinical benefits. Results: Among the 111 community residents, 80, 96, and 112 CPs were detected in settings A, B, and C, respectively. Setting B exhibited better diagnostic performance than setting A for detecting CPs (area under the curve, 0.856 vs. 0.749; P<0.01). Setting B had better consistency with setting C than setting A in CIMT measurement and the assessment of CPs (ICC, 0.731 to 0.923). Moreover, measurements in setting B required less time than the other two settings (44.59 seconds vs. 108.87 seconds vs. 126.13 seconds, both P<0.01). Conclusion Using an intelligent handheld US device, GPs can perform CP screening and achieve a diagnostic capability comparable to that of specialist doctors.

Objective To explore the research progress and hotspots of intelligent ultrasound imaging in the diagnosis of thyroid nodules and clarify the research directions via the bibliometric method.Methods The relevant research articles on intelligent ultrasound imaging in the diagnosis of thyroid nodules were retrieved from the Web of Science Core Collection,covering the period from January 2004 to August 2024.Python was used to analyze the number of annual publications.VOSviewer was used to create the co-occurrence network of authors and the keyword density map.CiteSpace was used to demonstrate the dual-map overlays of the journals,as well as the bursts and clustering of co-citations and keywords.Results A total of 1 179 articles were included.The annual number of publications increased steadily.The involved journals demonstrated high quality,and the publications showed a trend of cross-research.Chinese researchers were the core research force in this field.Haugen et al.'s study on the guidelines for thyroid nodules had the most citations.The clustering of co-citations and keywords indicated studies in multiple fields.Thyroid nodules,cancer,and deep learning were the representative keywords in this field.Conclusions The continuous enrichment of research topics promotes the rapid development of intelligent ultrasound imaging for thyroid nodules.Intelligent diagnosis methods based on deep learning can provide diagnostic suggestions,while there are still challenges such as interpretation.One of the research directions is the deep combination of intelligent diagnosis algorithms and medical knowledge.
Thyroid Nodule/diagnostic imaging* ; Humans ; Ultrasonography ; Bibliometrics

Microplastics are widely present in various environments such as water bodies,land,and atmosphere,which pose threats to the ecological environment and human health through transmission and accumulation in the food chain.The existing detection techniques for microplastics face challenges such as complex preparation procedure of samples,low efficiency in processing large batches of samples,and difficulties in handling complex samples.Therefore,there is an urgent need for rapid and efficient detection techniques suitable for complex microplastics samples in the field of environmental monitoring.Raman spectroscopy,known for its advantages such as rapidity,accuracy,high sensitivity,non-destructiveness,and non-contact,demonstrates great application potential in detection of microplastics.Deep learning,an artificial intelligence method known for its large-scale data processing,nonlinear modeling and automatic feature extraction capabilities,is receiving increasing attention in the analysis of Raman spectroscopy signals.The application of deep learning-based Raman spectroscopy has significantly improved performance indicators such as detection efficiency and accuracy.This article introduced the existing Raman enhancement techniques,summarized the deep learning methods applied in Raman spectroscopy signal analysis,reviewed the recent research and application progress of deep learning-based Raman spectroscopy in detection of microplastics,and finally discussed the challenges and future prospects of deep learning-based Raman spectroscopy in detection of microplastics.

Objective To establish a highly sensitive and specific method for detecting human DNA based on real time quantitative PCR(qPCR)technique for the rapid detection of potential DNA con-tamination sources in DNA laboratories.Methods Primers and probes were designed with Primer Ex-pressTM software using the reference sequence of human 18S rRNA gene as a template,and the opti-mal prime-probe combination was screened by matrix method.The PCR products of the target se-quence of human 18S rRNA gene were used to construct the plasmid,and a plasmid standard was used to draw the standard curve of the qPCR system.According to the Minimum Information for Pub-lication of Quantitative Real-time PCR Experiments(MIQE)guidelines,the specificity,sensitivity,re-peatability and application effect of the qPCR system were evaluated.Results The sensitivity of the qPCR system established in this study was 5.3×10-5 ng/μL,which showed good specificity for human DNA samples.The correlation coefficient of the qPCR system was-0.999,and amplification efficiency was 100％.Both the intra-batch and inter-batch variation coefficients were less than 2％.Conclusion The established human DNA detection method based on qPCR technique has good specificity,high sen-sitivity,and robust stability.It can be used for rapid detection of DNA contamination and daily moni-toring of the accumulated human DNA in the laboratory environment.

Ketamine is a dissociative anesthetic.It is clinically used as a surgical anesthetic or anes-thetic inducer and has a certain degree of mental dependence.Its abuse can lead to nerve damage,ad-verse emotional reactions and other toxic side effects.The primary mechanism by which ketamine exerts its pharmacological effects is to block N-methyl-D-aspartate receptors(NMDAR).It also functions through pathways such as α-amino-3-hydroxy-5-methyl-4-isox-azolepropionic acid receptors(AMPAR),opioid receptors,γ-aminobutyric acid(GABA)receptors,monoaminergic receptors,cholinergic recep-tors,hyperpolarization-activated cyclic nucleotide-gated(HCN)channels,voltage-gated sodium channels,and L-type voltage-dependent calcium channels(VDCC).This article summarizes the molecular mecha-nism and toxic effects of ketamine's pharmacological functions,in order to provide a basis for foren-sic applications such as the identification of symptomatic phenotypes of ketamine toxic effects and the identification of ketamine abuse.