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.

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.

ObjectiveThe present study aimed to investigate alterations in white matter microstructure and spontaneous neural activity in male college smokers, and to further explore their associations with nicotine dependence. Given that adolescence and early adulthood represent critical periods for brain maturation, particularly for white matter development, understanding the neural correlates of smoking behavior during this stage is of substantial importance for both neuroscience and public health. MethodsA total of 115 male undergraduate students were initially recruited for this study. After quality control and exclusion procedures, 52 male college smokers and 42 demographically matched healthy non-smokers were included in the final analysis. All participants underwent multimodal magnetic resonance imaging (MRI), including diffusion tensor imaging (DTI) and resting-state functional MRI (rs-fMRI). White matter fiber tracts were reconstructed using the automated fiber quantification (AFQ) method, which enables precise identification and quantification of major fiber bundles. Eighteen major white matter tracts were segmented for each participant. Along the core trajectory of each tract, 100 equidistant nodes were sampled. Fractional anisotropy (FA) was calculated at each node to assess white matter microstructural integrity, while amplitude of low-frequency fluctuation (ALFF) was computed to evaluate spontaneous neural activity within white matter tracts. Between-group differences in FA and ALFF were assessed using two-sample t-tests, with appropriate corrections applied for multiple comparisons. Furthermore, Pearson correlation analyses were conducted to examine the relationships between imaging-derived metrics (FA and ALFF values in regions showing significant group differences) and nicotine dependence severity, as measured by the Fagerström test for nicotine dependence (FTND). ResultsCompared with healthy non-smokers, male college smokers exhibited significantly increased FA values in several white matter tracts, including the left thalamic radiation, right corticospinal tract, forceps major of the corpus callosum, left uncinate fasciculus, and right arcuate fasciculus. These findings suggest altered microstructural organization or increased directional coherence within these pathways. In addition, smokers demonstrated significantly elevated ALFF values in the forceps major, right uncinate fasciculus, and left arcuate fasciculus, indicating enhanced spontaneous neural activity in these white matter regions. Correlation analyses revealed that FA values in the left thalamic radiation and right corticospinal tract were negatively correlated with FTND scores, suggesting that higher levels of nicotine dependence were associated with reduced microstructural integrity or altered fiber organization in these regions. In contrast, ALFF values in the forceps major and right uncinate fasciculus were positively correlated with FTND scores, indicating that greater nicotine dependence was associated with increased spontaneous neural activity in specific white matter pathways. ConclusionThe present study provides evidence that male college smokers exhibit distinct alterations in both white matter microstructure and functional activity. These abnormalities are not uniformly distributed but rather localized to specific fiber tracts implicated in sensorimotor processing, interhemispheric communication, and higher-order cognitive and emotional regulation. Importantly, the observed associations between imaging metrics and nicotine dependence severity suggest that these structural and functional alterations may reflect neurobiological mechanisms underlying addiction. The combination of AFQ-based tract profiling and multimodal MRI offers a sensitive approach for detecting subtle changes along white matter pathways, highlighting its potential utility in identifying neuroimaging biomarkers of nicotine dependence. Overall, these findings indicate that smoking during early adulthood may disrupt ongoing white matter maturation, potentially leading to long-term consequences for brain function. This study provides novel insights into the neural basis of nicotine dependence and underscores the importance of early intervention and prevention strategies targeting young smokers.

Objective: To evaluate the efficacy of a short video based health education model in enhancing e-cigarette awareness among junior high school students, so as to provide empirical evidence for optimizing school based health education in the digital era. Methods: In December 2024, a cluster sampling method was applied to recruit 465 eighth grade students from a middle school in Kunming for a survey. The intervention was implemented through playing short videos produced by the research team in the classroom, and a self designed adolescent e-cigarette questionnaire was used to conduct assessments before and after intervention, along with qualitative interviews with 60 teachers and students by purposive sampling. Pre and post intervention outcomes were compared by using paired t-test and Chi-square test. Qualitative data underwent a three stage coding process of "open, axial, and selective coding" to construct an explanatory framework. Results: Quantitative analysis showed significant post intervention increases in both baseline awareness scores（92.1±6.6）and deep awareness scores (70.0±17.6) compared with pre intervention( 78.2± 14.0，46.1±20.5)( t =23.54，26.72，both P <0.05), both meeting the criteria for large effect sizes (Cohen s d =1.09, 1.24). The improvement in deep awareness was particularly pronounced, specifically regarding addictive ingredients, illicit additives(tiletamine,metomidate,etomidate), and misconceptions about smoking cessation efficacy ( χ 2=150.54,113.27,117.40,112.92, 69.93 , all P <0.01). Qualitative insights revealed that narrative transportation and visual enhancement were the core mechanisms of efficacy: 87.3% of students preferred contextualized narratives, 85.5% of students decoded cognitive bottlenecks more effectively because of visual enhancement, and 72.7% relied on short videos for micro learning. Conclusion Short video based health education can effectively enhance e-cigarette awareness among junior high school students, offering a pragmatically viable digital pathway for school based health education.

Objective This study reports on the obligatory parasitism of water mites Arrenurus sp. on Anopheles messeae at the Manzhouli Port, Inner Mongolia, China. Methods Duing July 2024, a survey on the mosquito diversity was conducted at the Manzhouli Port. Captured mosquitoes and their ectoparasites were identified to species level. Results A total of 1840 adult mosquitoes were collected, representing species from three genera: Culex(Cx. modestus, Cx. pipiens pallens), Aedes(Ae. dorsalis, Ae. flavidorsalis, Ae. flavescens), and Anopheles (An. messeae). Among all the mosqutioes specimens,3 out of 150 captured An. messeae were found to carry ectoparasitic mites, with number of 2,4,27 mites separately. Morphological and molecular identification reached the same result as water mites(Hydrachnidiae, Hydracrina). COI gene sequence showed 94% similarity with the closest species Arrenurus truncatellus. Conlusions Literature review suggests water mites are host-specific parasitism of mosquito species and herein with the first record of Arrenurus sp. parasiting on An. Messeae in the most high-latitude region globally.

ObjectiveThis paper aims to investigate the intervention effect and molecular mechanism of Gancao Fuzitang （GCFZT） in knee osteoarthritis （KOA）. MethodsNetwork pharmacology was employed to predict the key targets and signaling pathways of GCFZT. Fifty SD rats were randomly divided into a sham group， a model group （KOA）， a low-dose GCFZT group （GCFZT-L， 2.8 g·kg^-1）， a high-dose GCFZT group （GCFZT-H， 5.6 g·kg^-1）， and a positive drug celecoxib group （Celecoxib， 20 mg·kg^-1）， with 10 rats in each group. The rats in the sham group and the model group were given an equal volume of distilled water. Except for the sham group， the other four groups underwent anterior cruciate ligament transection to establish the KOA rat model. After successful modeling， the rats in each group received intragastric administration once a day for eight consecutive weeks. The structural changes of cartilage and subchondral bone were assessed via micro-computed tomography （micro-CT） and histological staining. The levels of inflammatory factors in the serum， such as interleukin-1β （IL-1β）， IL-6， and tumor necrosis factor-α （TNF-α） were measured using an enzyme linked immunosorbent assay （ELISA） kit. Real-time quantitative polymerase chain reaction （Real-time PCR）， Western blot， and immunohistochemistry were used to detect changes in the expression of autophagy-related proteins such as microtubule-associated protein 1 light chain 3B （LC3B）， selective autophagy junction protein 62 （p62）， and autophagy key molecule yeast Atg6 homolog （Beclin-1） in articular cartilage， as well as key molecules of the phosphatidylinositol 3 kinase （PI3K）/protein kinase B （Akt）/mammalian target of rapamycin （mTOR） signaling pathway. ResultsNetwork pharmacology analysis suggests that GCFZT may exert anti-osteoarthritic effects through regulating the PI3K/Akt/mTOR pathway. In vivo experiment shows that GCFZT can significantly improve cartilage degeneration and attenuate subchondral bone destruction in KOA rats. Detection results of inflammatory factors in the serum reveal that， compared with those in the sham group， the levels of IL-6， IL-1β， and TNF-α in the serum of rats in the KOA group are significantly increased （P<0.05）. Compared with those in the KOA group， the levels of these three inflammatory factors in the GCFZT-L， GCFZT-H， and Celecoxib groups were significantly decreased （P<0.05）. However， there was no statistically significant difference in these three inflammatory factor levels between the GCFZT-H group and the celecoxib group. These findings suggest that GCFZT can effectively inhibit the systemic inflammatory response in KOA rats. Mechanism research indicates that GCFZT inhibits activation of the PI3K/Akt/mTOR pathway， thereby enhancing autophagic activity and improving chondrocyte function and matrix degeneration. ConclusionGCFZT improves cartilage and subchondral bone damage in KOA rats by inhibiting the PI3K/Akt/mTOR signaling pathway and promoting autophagy. These findings provide theoretical evidence for the application of GCFZT in the prevention and treatment of osteoarthritis.

Objective To analyze the clinical and epidemiological characteristics of influenza A(H3N2) in Tangshan City, Hebei Province, from 2023 to 2025, and to provide a scientific basis for precise prevention and control of local influenza. Methods The influenza-like cases and influenza outbreak cases in the influenza surveillance sentinel hospitals in Tangshan City from January 2023 to December 2025 were included in this study. The clinical characteristics and epidemic characteristics of H3N2 influenza patients were analyzed by descriptive epidemiological methods and chi-square test. Results A total of 11 567 influenza case samples were tested in the study, with a positive rate of 9.34% (1 080/11 567). Influenza A (H3N2) remained the predominant circulating subtype throughout the study period. January 2023 to February 2023 marked the tailing end of the previous season’s H3N2. Then, a minor peak dominated by H1N1 occurred from March to June, and H3N2 circulated again from October to December, peaking in December. From September 2024 to December 2024, H1N1 and H3N2 circulated concurrently, with H3N2 as the dominant strain, peaking in January of the following year. From March 2025 to September 2025, influenza activity remained at low level, and activity began to rise in October, with H1N1 peaking in November before declining, and H3N2 becoming the dominant strain in December. H3N2 mainly occurred in the 5-14 years old group. Cases in the 0–4-year-old group were primarily characterized by gastrointestinal symptoms, with high fever being common. Cases in the 5–59-year-old group were primarily characterized by systemic and respiratory symptoms, with better clinical outcomes. Cases aged 60 and older exhibited the highest rates of hospitalization, severe illness, and incidence of complications. Conclusion From 2023 to 2025, the influenza virus in Tangshan City, Hebei Province is dominated by influenza A (H3N2). The epidemic is characterized by a peak incidence during autumn and winter, co-circulation with H1N1, and fluctuations during the peak period. It is necessary to pay attention to the clinical management and prevention and control of influenza A (H3N2) in infants and the elderly.

Doctor digital twins， as a significant innovative form within the medical metaverse， integrate technologies such as large language models， natural language processing and multimodal learning. They can simulate doctors’ appearance， language style， and certain clinical reasoning， realizing diverse applications such as medical knowledge dissemination， intelligent consultation， health management， and medical education. While enhancing the accessibility and intelligence of healthcare services， doctor digital twins also give rise to multidimensional ethical challenges， such as data security and privacy breaches， algorithmic black box and bias， a crisis of doctor-patient trust， weakening of doctors’ subjectivity， and ambiguities in legal responsibility. This paper systematically reviewed the core concepts and current application status of doctor digital twins， constructing a tripartite analytical dimension centered on “technology， doctor-patient relationship， and law.” It focused on pivotal ethical issues encountered in practical application， including data security， patient informed consent and trust， legal responsibility delineation， and others. These aimed to provide ethical guidance and theoretical support for the sustainable development of doctor digital twins， promote the deep integration of smart healthcare and humanistic care， as well as contribute to building a safer， more trustworthy， and sustainable intelligent healthcare system.

The initiation of adaptive immune responses relies on the precise recognition and interpretation of antigenic information. In this process, the specific binding of T cell receptors (TCRs) to peptide-major histocompatibility complex (pMHC) molecules represents one of the key molecular events in the initiation of adaptive immune responses. Accordingly, the structural features of TCR-pMHC complexes provide a fundamental basis for dissecting antigen recognition mechanisms and support rational vaccine design, therapeutic target discovery in TCR-based immunotherapy, and TCR identification and optimization. However, experimental determination of TCR-pMHC structures remains costly, time-consuming, and limited in coverage, making computational approaches essential for rapidly obtaining reliable structural information. Computational methods for predicting the structures of TCR-pMHC complexes have advanced rapidly in recent years, driven by progress in deep learning-based modeling frameworks and the increasing availability of structural and sequence resources. Despite these developments, most existing tools do not adequately distinguish the key structural and biophysical differences between MHC class I (MHC-I) and MHC class II (MHC-II) complexes during model construction. As a consequence, their predictive performance differs substantially between class I and class II complexes. In general, structural predictions for class I complexes outperform those for class II complexes. This discrepancy may be related to several fundamental differences between the two systems, including the architecture of the peptide-binding groove, the distribution of peptide lengths, and the properties of peptide flanking residues (PFRs). Compared with MHC-I molecules, MHC-II molecules usually bind longer antigenic peptides, which typically range from 13 to 25 amino acids in length. PFRs at both termini of these peptides participate in regulating the overall conformation of TCR-pMHC class II complexes and exert a pronounced effect on the geometric and physicochemical characteristics of the TCR-pMHC binding interface. Furthermore, within the TCR recognition interface, the complementarity-determining regions (CDRs) consist of segments that differ markedly in conformational behavior. They commonly include regions that are relatively rigid and structurally stable, together with highly flexible segments exhibiting substantial conformational plasticity. These rigidity-flexibility features constitute an essential structural basis enabling TCRs to recognize diverse peptide-MHC ligands and to accommodate conformational heterogeneity at the interface. However, many current modeling tools, in an effort to enforce global conformational stability or reduce structural noise, tend to over-constrain intrinsically flexible regions. Such oversimplification may lead to inappropriate rigidification of flexible CDR loops, resulting in local structural distortions, compromised interface geometry, or even complete modeling failure for specific complexes. Against this background, the review approaches the field from the perspective of computational differences between MHC-I and MHC-II complexes. We first systematically organize and summarize available resources related to TCRs and pMHCs, including structural datasets, sequence databases, prediction tools, and benchmarking studies. We then focus on five representative tools capable of predicting both class I and class II complexes—AlphaFold2, AlphaFold3, TCRmodel2, tFold-TCR, and TCR-pHLA_ModellerS. After excluding structures present in the training sets of these tools, we constructed a benchmark dataset comprising 25 class I and 10 class II TCR-pMHC complexes in the bound state and conducted a systematic evaluation using this dataset. We first employ widely used general evaluation metrics, including All-Atom Root Mean Square Deviation (All-Atom RMSD), Backbone RMSD, Template Modeling score (TM-score), and DockQ, to assess the global conformational accuracy and interface modeling quality of class I and class II complexes. For class II complexes, we propose for the first time a peptide flanking residue deviation index, including the PFRs-Deviation Index (PFRs-DI), N-PFR-Deviation Index (N-PFR-DI), and C-PFR-Deviation Index (C-PFR-DI), to quantitatively characterize conformational deviations in PFRs. In addition, we propose the CDR conformational consistency index (CCC) designed to qualitatively evaluate the ability of prediction tools to capture TCR CDR conformational flexibility. These metrics collectively assess a tool’s ability to model both overall conformation and critical functional regions, thereby addressing the limitations of existing evaluation criteria that overemphasize global structure while inadequately capturing modeling quality in key functional areas. This establishes a unified analytical framework for MHC-I and MHC-II complexes to guide data resource selection, modeling strategy formulation, and evaluation system development. The framework further advances computational modeling and provides crucial support for multi-scale analysis of TCR-pMHC recognition mechanisms and their biological functions.

The initiation of adaptive immune responses relies on the precise recognition and interpretation of antigenic information. In this process, the specific binding of T cell receptors (TCRs) to peptide-major histocompatibility complex (pMHC) molecules represents one of the key molecular events in the initiation of adaptive immune responses. Accordingly, the structural features of TCR-pMHC complexes provide a fundamental basis for dissecting antigen recognition mechanisms and support rational vaccine design, therapeutic target discovery in TCR-based immunotherapy, and TCR identification and optimization. However, experimental determination of TCR-pMHC structures remains costly, time-consuming, and limited in coverage, making computational approaches essential for rapidly obtaining reliable structural information. Computational methods for predicting the structures of TCR-pMHC complexes have advanced rapidly in recent years, driven by progress in deep learning-based modeling frameworks and the increasing availability of structural and sequence resources. Despite these developments, most existing tools do not adequately distinguish the key structural and biophysical differences between MHC class I (MHC-I) and MHC class II (MHC-II) complexes during model construction. As a consequence, their predictive performance differs substantially between class I and class II complexes. In general, structural predictions for class I complexes outperform those for class II complexes. This discrepancy may be related to several fundamental differences between the two systems, including the architecture of the peptide-binding groove, the distribution of peptide lengths, and the properties of peptide flanking residues (PFRs). Compared with MHC-I molecules, MHC-II molecules usually bind longer antigenic peptides, which typically range from 13 to 25 amino acids in length. PFRs at both termini of these peptides participate in regulating the overall conformation of TCR-pMHC class II complexes and exert a pronounced effect on the geometric and physicochemical characteristics of the TCR-pMHC binding interface. Furthermore, within the TCR recognition interface, the complementarity-determining regions (CDRs) consist of segments that differ markedly in conformational behavior. They commonly include regions that are relatively rigid and structurally stable, together with highly flexible segments exhibiting substantial conformational plasticity. These rigidity-flexibility features constitute an essential structural basis enabling TCRs to recognize diverse peptide-MHC ligands and to accommodate conformational heterogeneity at the interface. However, many current modeling tools, in an effort to enforce global conformational stability or reduce structural noise, tend to over-constrain intrinsically flexible regions. Such oversimplification may lead to inappropriate rigidification of flexible CDR loops, resulting in local structural distortions, compromised interface geometry, or even complete modeling failure for specific complexes. Against this background, the review approaches the field from the perspective of computational differences between MHC-I and MHC-II complexes. We first systematically organize and summarize available resources related to TCRs and pMHCs, including structural datasets, sequence databases, prediction tools, and benchmarking studies. We then focus on five representative tools capable of predicting both class I and class II complexes—AlphaFold2, AlphaFold3, TCRmodel2, tFold-TCR, and TCR-pHLA_ModellerS. After excluding structures present in the training sets of these tools, we constructed a benchmark dataset comprising 25 class I and 10 class II TCR-pMHC complexes in the bound state and conducted a systematic evaluation using this dataset. We first employ widely used general evaluation metrics, including All-Atom Root Mean Square Deviation (All-Atom RMSD), Backbone RMSD, Template Modeling score (TM-score), and DockQ, to assess the global conformational accuracy and interface modeling quality of class I and class II complexes. For class II complexes, we propose for the first time a peptide flanking residue deviation index, including the PFRs-Deviation Index (PFRs-DI), N-PFR-Deviation Index (N-PFR-DI), and C-PFR-Deviation Index (C-PFR-DI), to quantitatively characterize conformational deviations in PFRs. In addition, we propose the CDR conformational consistency index (CCC) designed to qualitatively evaluate the ability of prediction tools to capture TCR CDR conformational flexibility. These metrics collectively assess a tool’s ability to model both overall conformation and critical functional regions, thereby addressing the limitations of existing evaluation criteria that overemphasize global structure while inadequately capturing modeling quality in key functional areas. This establishes a unified analytical framework for MHC-I and MHC-II complexes to guide data resource selection, modeling strategy formulation, and evaluation system development. The framework further advances computational modeling and provides crucial support for multi-scale analysis of TCR-pMHC recognition mechanisms and their biological functions.