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.

Patients with Takayasu arteritis combined with aortic valve disease often have a poor prognosis following surgical valve replacement, frequently encountering complications such as perivalvular leakage, valve detachment, and anastomotic aneurysm. This article presents a high-risk case wherein severe aortic valve insufficiency associated with Takayasu arteritis was successfully managed through transcatheter aortic valve implantation via the transapical approach. The patient had satisfactory valve function with no complications observed during the six-month postoperative follow-up. This case provides a minimally invasive and feasible alternative for the clinical management of such high-risk patients.

Chemical constituents of n-butanol fraction of ethanol extract from the leaves of Cyclocarya paliurus (Batalin) Iljinskaja were studied. Ten compounds were purified by silica gel, MCI, ODS, Sephadex LH-20 column chromatography and semi-preparative high-performance liquid chromatography. Based on the physicochemical properties and spectroscopic data, these compounds were identified as (4S)-4,8-dihydroxy-α-tetralone-5-[6′-(3′′,5′′-dimethoxy-(E)-p-coumaroyl)]-β-D-glucopyranoside (1), (4S)-4,8-dihydroxy-α-tetralone-5-[6′-(E)-feruloyl]-β-D-glucopyranoside (2), (1S,3R,4S,5R)-4,5-di-O-caffeoyl quinic acid methyl ester (3), (1R,3R,4S,5R)-3,4-di-O-caffeoyl quinic acid methyl ester (4), (3R,5R)-3,4,5-tri-O-caffeoylquinic acid methyl ester (5), (1R,3R,4S,5R)-3-O-caffeoyl-5-O-p-coumaroyl quinic acid methyl ester (6), (1R,3R,4S,5R)-3-O-p-coumaroy-4-O-caffeoyl quinic acid methyl ester (7), (1R,3R,4S,5R)-3-O-caffeoyl-4-O-p-coumaroy quinic acid methyl ester (8), (3R,5R)-3,5-di-O-caffeoyl quinic acid methyl ester (9) and (1S,3R,4R,5R)-4-O-caffeoyl-5-O-feruloyl quinic acid methyl ester (10). Among them, compound 1 was a new compound, and compounds 3–10 were isolated from the genus Cyclocarya for the first time.

Currently, the drug delivery system (DDS) based on nanomaterials has become a hot interdisciplinary research topic. One of the core issues is drug loading and controlled release, in which the key lever is carriers. Vaterite, as an inorganic porous nano-material, is one metastable structure of calcium carbonate, full of micro or nano porous. Recently, vaterite has attracted more and more attention, due to its significant advantages, such as rich resources, easy preparations, low cost, simple loading procedures, good biocompatibility and many other good points. Vaterite, gained from suitable preparation strategies, can not only possess the good drug carrying performance, like high loading capacity and stable loading efficiency, but also improve the drug release ability, showing the better drug delivery effects, such as targeting release, pH sensitive release, photothermal controlled release, magnetic assistant release, optothermal controlled release. At the same time, the vaterite carriers, with good safety itself, can protect proteins, enzymes, or other drugs from degradation or inactivation, help imaging or visualization with loading fluorescent drugs in vitro and in vivo, and play synergistic effects with other therapy approaches, like photodynamic therapy, sonodynamic therapy, and thermochemotherapy. Latterly, some renewed reports in drug loading and controlled release have led to their widespread applications in diverse fields, from cell level to clinical studies. This review introduces the basic characteristics of vaterite and briefly summarizes its research history, followed by synthesis strategies. We subsequently highlight recent developments in drug loading and controlled release, with an emphasis on the advantages, quantity capacity, and comparations. Furthermore, new opportunities for using vaterite in cell level and animal level are detailed. Finally, the possible problems and development trends are discussed.

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.

Background: s/Aims: Sarcomatoid hepatocellular carcinoma (HCC) is a rare histological subtype of HCC characterized by extremely poor prognosis; however, its molecular characterization has not been elucidated. Methods: In this study, we conducted an integrated multiomics study of whole-exome sequencing, RNA-seq, spatial transcriptome, and immunohistochemical analyses of 28 paired sarcomatoid tumor components and conventional HCC components from 10 patients with sarcomatoid HCC, in order to identify frequently altered genes, infer the tumor subclonal architectures, track the genomic evolution, and delineate the transcriptional characteristics of sarcomatoid HCCs. Results: Our results showed that the sarcomatoid HCCs had poor prognosis. The sarcomatoid tumor components and the conventional HCC components were derived from common ancestors, mostly accessing similar mutational processes. Clonal phylogenies demonstrated branched tumor evolution during sarcomatoid HCC development and progression. TP53 mutation commonly occurred at tumor initiation, whereas ARID2 mutation often occurred later. Transcriptome analyses revealed the epithelial–mesenchymal transition (EMT) and hypoxic phenotype in sarcomatoid tumor components, which were confirmed by immunohistochemical staining. Moreover, we identified ARID2 mutations in 70% (7/10) of patients with sarcomatoid HCC but only 1–5% of patients with non-sarcomatoid HCC. Biofunctional investigations revealed that inactivating mutation of ARID2 contributes to HCC growth and metastasis and induces EMT in a hypoxic microenvironment. Conclusions We offer a comprehensive description of the molecular basis for sarcomatoid HCC, and identify genomic alteration (ARID2 mutation) together with the tumor microenvironment (hypoxic microenvironment), that may contribute to the formation of the sarcomatoid tumor component through EMT, leading to sarcomatoid HCC development and progression.

Purpose To explore the value of machine learning models based on synthetic MRI,dynamic contrast-enhanced MRI(DCE-MRI)and diffusion weighted imaging(DWI)parameters in identifying molecular subtypes of breast cancer.Materials and Methods A retrospective analysis was conducted on the data of 292 patients who underwent synthetic MRI,DCE-MRI and DWI examinations from September 2020 to September 2024 in Ningxia Medical University General Hospital before surgery and were pathologically confirmed to have breast cancer postoperatively.Patients were randomly divided into training and test sets using a ratio of 7:3.Multiple parameters were obtained from the synthetic MRI,DCE-MRI and DWI images.Variance analysis were used to screen the characteristic parameters among molecular subtype groups.Five machine learning models were established based on the selected characteristic parameters,and receiver operating characteristic curves were plotted to calculate the area under the curve among the molecular subtype groups.Results The support vector machine model exhibited the highest overall performance,with an area under the curve of 0.972,accuracy of 82.5％,specificity of 94.76％and sensitivity of 82.14％in the test set.This model's area under the curve values for differentiating luminal A,luminal B,human epidermal growth factor receptor-2 overexpression,and triple-negative groups in the training set were 0.979,0.925,0.971 and 0.982,respectively;in the test set,the area under the curve values were 0.973,0.873,0.956 and 0.955,respectively.Conclusion Machine learning models based on multimodal MRI parameters can assist clinicians in preoperatively determining the molecular subtypes of breast cancer and the support vector machine model shows relatively high comprehensive performance.

Purpose To explore the value of machine learning models based on synthetic MRI,dynamic contrast-enhanced MRI(DCE-MRI)and diffusion weighted imaging(DWI)parameters in identifying molecular subtypes of breast cancer.Materials and Methods A retrospective analysis was conducted on the data of 292 patients who underwent synthetic MRI,DCE-MRI and DWI examinations from September 2020 to September 2024 in Ningxia Medical University General Hospital before surgery and were pathologically confirmed to have breast cancer postoperatively.Patients were randomly divided into training and test sets using a ratio of 7:3.Multiple parameters were obtained from the synthetic MRI,DCE-MRI and DWI images.Variance analysis were used to screen the characteristic parameters among molecular subtype groups.Five machine learning models were established based on the selected characteristic parameters,and receiver operating characteristic curves were plotted to calculate the area under the curve among the molecular subtype groups.Results The support vector machine model exhibited the highest overall performance,with an area under the curve of 0.972,accuracy of 82.5％,specificity of 94.76％and sensitivity of 82.14％in the test set.This model's area under the curve values for differentiating luminal A,luminal B,human epidermal growth factor receptor-2 overexpression,and triple-negative groups in the training set were 0.979,0.925,0.971 and 0.982,respectively;in the test set,the area under the curve values were 0.973,0.873,0.956 and 0.955,respectively.Conclusion Machine learning models based on multimodal MRI parameters can assist clinicians in preoperatively determining the molecular subtypes of breast cancer and the support vector machine model shows relatively high comprehensive performance.

Objective To explore the current status,hotspots,and frontier directions in the field of hospital research management.Methods Literature related to hospital research management published from January 1987 to March 2025,as in-dexed in the China National Knowledge Infrastructure(CNKI),was retrieved.The visualization software CiteSpace was used to analyze the publication volume,authors,institutions,and keywords.Results A total of 250 Chinese-language articles were in-cluded.The overall publication volume showed a fluctuating upward trend,with the highest number of publications in 2023,reac-hing 26 articles.Notable institutions with higher publication volumes include Chongqing Traditional Chinese Medicine Hospital,the Medical Department of Nanjing General Hospital of the Nanjing Military Region,Tianjin Medical University Cancer Hospital,and Shengjing Hospital of China Medical University.Key authors with more publications include Zhou Laixin,Xu Dixiong,Liu Yuping,and Sun Meng.High-frequency keywords included"research management","hospital","research projects","re-search funding","public hospitals",and"project management",generating 11 clustering labels and 25 emerging terms.Con-clusion The research on hospital research management in China has evolved from conceptual definition and systematic construc-tion to the exploration of innovative strategies,with increasing depth and breadth.The field has yet to form a large-scale,cross-regional author and institutional cooperation network.High-frequency keywords such as"research management","hospital","research projects","research funding","public hospitals",and"project management"represent hotspots in the research.Keywords such as"public hospitals","design",and"research projects"may become the frontier directions for future research in this field.

Thoracic anesthesia with spontaneous respiration represents a form of precision anesthesia meticulously customized to individual patients.Considering the more stringent requirements this anesthesia approach imposes on the regulation of respiratory function,the writing group of the"Consensus of Experts on the Management of Thoracic Anesthesia with Spontaneous Respiration"has formulated elaborate guidelines regarding indications and contraindications,preoperative evaluation,anesthesia implementation,common complications,and treatment strategies.This was accomplished by referencing relevant domestic and international literature and integrating it with actual clinical requirements.The objective is to standardize the rational application of this anesthesia method.