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.

ObjectiveTo explore the complex network relationships among pain, kinesiophobia, social participation and knee function in patients after total knee arthroplasty (TKA), and to analyze the moderating effects of different socio-structural factors on the rehabilitation network from an ethical equity perspective. MethodsA convenience sampling method was used to select 291 patients who underwent TKA in Qilu Hospital of Shandong University from May to July, 2023. Pain was assessed using Numerical Rating Scale, kinesiophobia with Chinese short version of the Tampa Scale for Kinesiophobia, social participation with Impact on Participation and Autonomy Questionnaire, and knee function with Hospital for Special Surgery Knee Score. A partial correlation network among pain, kinesiophobia, social participation and knee function was constructed using Graphical Least Absolute Shrinkage and Selection Operator. Key variables were identified through node centrality and bridge centrality analysis. Network Comparison Tests (NCT) were used to analyze network differences among subgroups based on different socio-structural characteristics. ResultsIn the network model, the nodes with the highest strength centrality were indoor participation, activity behavior and activity pain. Bridge centrality analysis indicated that activity pain, knee function, indoor participation and activity cognition were key bridge nodes. NCT revealed no significant differences in overall network structure or global strength among subgroups based on residence, education level or payment method (P > 0.05). However, significant differences in edge weights were found for specific edges such as activity cognition-activity behavior and knee function-indoor participation (P < 0.05). ConclusionThere is a network of interactions among pain, kinesiophobia, social participation and knee function in patients after TKA, with nodes such as indoor participation and activity pain playing key roles in the rehabilitation process. Although the overall rehabilitation network is similar across different socio-structural groups, variations exist in specific relational pathways among patients from rural areas, those with lower education levels, and those with out-of-pocket payment. This suggests that clinical rehabilitation interventions should focus on these core nodes and implement targeted support strategies for socio-structurally disadvantaged groups to promote rehabilitation equity.

ObjectiveTo explore the complex network relationships among pain, kinesiophobia, social participation and knee function in patients after total knee arthroplasty (TKA), and to analyze the moderating effects of different socio-structural factors on the rehabilitation network from an ethical equity perspective. MethodsA convenience sampling method was used to select 291 patients who underwent TKA in Qilu Hospital of Shandong University from May to July, 2023. Pain was assessed using Numerical Rating Scale, kinesiophobia with Chinese short version of the Tampa Scale for Kinesiophobia, social participation with Impact on Participation and Autonomy Questionnaire, and knee function with Hospital for Special Surgery Knee Score. A partial correlation network among pain, kinesiophobia, social participation and knee function was constructed using Graphical Least Absolute Shrinkage and Selection Operator. Key variables were identified through node centrality and bridge centrality analysis. Network Comparison Tests (NCT) were used to analyze network differences among subgroups based on different socio-structural characteristics. ResultsIn the network model, the nodes with the highest strength centrality were indoor participation, activity behavior and activity pain. Bridge centrality analysis indicated that activity pain, knee function, indoor participation and activity cognition were key bridge nodes. NCT revealed no significant differences in overall network structure or global strength among subgroups based on residence, education level or payment method (P > 0.05). However, significant differences in edge weights were found for specific edges such as activity cognition-activity behavior and knee function-indoor participation (P < 0.05). ConclusionThere is a network of interactions among pain, kinesiophobia, social participation and knee function in patients after TKA, with nodes such as indoor participation and activity pain playing key roles in the rehabilitation process. Although the overall rehabilitation network is similar across different socio-structural groups, variations exist in specific relational pathways among patients from rural areas, those with lower education levels, and those with out-of-pocket payment. This suggests that clinical rehabilitation interventions should focus on these core nodes and implement targeted support strategies for socio-structurally disadvantaged groups to promote rehabilitation equity.

ObjectiveTo explore the complex network relationships among pain, kinesiophobia, social participation and knee function in patients after total knee arthroplasty (TKA), and to analyze the moderating effects of different socio-structural factors on the rehabilitation network from an ethical equity perspective. MethodsA convenience sampling method was used to select 291 patients who underwent TKA in Qilu Hospital of Shandong University from May to July, 2023. Pain was assessed using Numerical Rating Scale, kinesiophobia with Chinese short version of the Tampa Scale for Kinesiophobia, social participation with Impact on Participation and Autonomy Questionnaire, and knee function with Hospital for Special Surgery Knee Score. A partial correlation network among pain, kinesiophobia, social participation and knee function was constructed using Graphical Least Absolute Shrinkage and Selection Operator. Key variables were identified through node centrality and bridge centrality analysis. Network Comparison Tests (NCT) were used to analyze network differences among subgroups based on different socio-structural characteristics. ResultsIn the network model, the nodes with the highest strength centrality were indoor participation, activity behavior and activity pain. Bridge centrality analysis indicated that activity pain, knee function, indoor participation and activity cognition were key bridge nodes. NCT revealed no significant differences in overall network structure or global strength among subgroups based on residence, education level or payment method (P > 0.05). However, significant differences in edge weights were found for specific edges such as activity cognition-activity behavior and knee function-indoor participation (P < 0.05). ConclusionThere is a network of interactions among pain, kinesiophobia, social participation and knee function in patients after TKA, with nodes such as indoor participation and activity pain playing key roles in the rehabilitation process. Although the overall rehabilitation network is similar across different socio-structural groups, variations exist in specific relational pathways among patients from rural areas, those with lower education levels, and those with out-of-pocket payment. This suggests that clinical rehabilitation interventions should focus on these core nodes and implement targeted support strategies for socio-structurally disadvantaged groups to promote rehabilitation equity.

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.

Background Regulatory interactions between microRNAs (miRNAs) and messenger RNAs (mRNAs) are involved in the progression of pulmonary fibrosis, which can either promote or inhibit the development of this disease. Objective To explore the miRNA-mRNA regulatory network during the progression of silica (SiO₂)-induced pulmonary fibrosis in mice using integrated mRNA-seq and miRNA-seq analysis. Methods A mouse model of pulmonary fibrosis was established by dynamic SiO₂ dust exposure. The experimental design included a blank control group and four SiO₂-exposed groups (7, 14, 28, and 56 d, n=10 per group). Successful model induction was confirmed by histopathological analysis (HE and Masson staining), hydroxyproline (HYP) quantification, and expression of key fibrosis-related cytokines [fibroblast growth factor (FGF), interleukin-6 (IL-6), transforming growth factor-β (TGF-β), and tumor necrosis factor-α (TNF-α)]. Lung tissues from mice in each group were subjected to sequencing, and Mfuzz was used for time-series gene clustering to identify dynamic progression patterns. DESeq2 was utilized to identify differentially expressed genes (DEGs) and differentially expressed miRNAs. Enrichment analysis of DEGs was performed to identify critical signaling pathways and biological processes underlying pulmonary fibrosis progression. Expression of four selected miRNAs was subsequently validated by real-time quantitative polymerase chain reaction (RT-qPCR). The target mRNAs of key miRNAs were comprehensively predicted by integrating miRBase, starBase, and miRTarBase to construct the regulatory networks and investigate potential functions. Results SiO₂ exposure led to time-dependent aggravation of pulmonary fibrosis in mice, evidenced by increased fibrous deposition, elevated HYP levels (P < 0.01), and up-regulation of four kinds of pro-fibrotic cytokines (P < 0.01) compared with the NT group. Mfuzz clustering revealed the stage-specific characteristics. Compared to controls, 231, 662, 448, and 1020 DEGs were identified after SiO₂ exposure at 7, 14, 28, and 56 d, respectively, primarily enriched in immune responses and chemokine signaling. During critical fibrotic phases—7 d (acute inflammation and initiation) and 28 d (chronic inflammation and establishment)—18 differentially expressed miRNAs were identified; notably mmu-miR-135b-5p was significantly dysregulated at both time points. The expression trends of the four key miRNAs (mmu-miR-135b-5p, mmu-miR-708-5p, mmu-miR-21a-3p, and mmu-miR-205-5p) were consistent with the sequencing results. Furthermore, bioinformatics databases were used to predict the target mRNAs of key miRNAs. The constructed network highlighted critical miRNA-mRNA pairs—including mmu-miR-135b-5p and Meis1, mmu-miR-708-5p and Mmp25, mmu-miR-21a-3p and Cacna1d, mmu-miR-205-5p and Ereg which were closely associated with inflammatory response, extracellular matrix deposition, and fibroblast activation. Conclusion The progression of pulmonary fibrosis is accompanied by dynamic changes in miRNA-mRNA regulatory networks. The identified miRNA-target axes (e.g., miR-135b-5p and Meis1, mmu-miR-708-5p and Mmp25, mmu-miR-21a-3p and Cacna1d, and mmu-miR-205-5p and Ereg—) may play important roles in fibrogenesis and provide potential therapeutic targets for pulmonary fibrosis.

Background Regulatory interactions between microRNAs (miRNAs) and messenger RNAs (mRNAs) are involved in the progression of pulmonary fibrosis, which can either promote or inhibit the development of this disease. Objective To explore the miRNA-mRNA regulatory network during the progression of silica (SiO₂)-induced pulmonary fibrosis in mice using integrated mRNA-seq and miRNA-seq analysis. Methods A mouse model of pulmonary fibrosis was established by dynamic SiO₂ dust exposure. The experimental design included a blank control group and four SiO₂-exposed groups (7, 14, 28, and 56 d, n=10 per group). Successful model induction was confirmed by histopathological analysis (HE and Masson staining), hydroxyproline (HYP) quantification, and expression of key fibrosis-related cytokines [fibroblast growth factor (FGF), interleukin-6 (IL-6), transforming growth factor-β (TGF-β), and tumor necrosis factor-α (TNF-α)]. Lung tissues from mice in each group were subjected to sequencing, and Mfuzz was used for time-series gene clustering to identify dynamic progression patterns. DESeq2 was utilized to identify differentially expressed genes (DEGs) and differentially expressed miRNAs. Enrichment analysis of DEGs was performed to identify critical signaling pathways and biological processes underlying pulmonary fibrosis progression. Expression of four selected miRNAs was subsequently validated by real-time quantitative polymerase chain reaction (RT-qPCR). The target mRNAs of key miRNAs were comprehensively predicted by integrating miRBase, starBase, and miRTarBase to construct the regulatory networks and investigate potential functions. Results SiO₂ exposure led to time-dependent aggravation of pulmonary fibrosis in mice, evidenced by increased fibrous deposition, elevated HYP levels (P < 0.01), and up-regulation of four kinds of pro-fibrotic cytokines (P < 0.01) compared with the NT group. Mfuzz clustering revealed the stage-specific characteristics. Compared to controls, 231, 662, 448, and 1020 DEGs were identified after SiO₂ exposure at 7, 14, 28, and 56 d, respectively, primarily enriched in immune responses and chemokine signaling. During critical fibrotic phases—7 d (acute inflammation and initiation) and 28 d (chronic inflammation and establishment)—18 differentially expressed miRNAs were identified; notably mmu-miR-135b-5p was significantly dysregulated at both time points. The expression trends of the four key miRNAs (mmu-miR-135b-5p, mmu-miR-708-5p, mmu-miR-21a-3p, and mmu-miR-205-5p) were consistent with the sequencing results. Furthermore, bioinformatics databases were used to predict the target mRNAs of key miRNAs. The constructed network highlighted critical miRNA-mRNA pairs—including mmu-miR-135b-5p and Meis1, mmu-miR-708-5p and Mmp25, mmu-miR-21a-3p and Cacna1d, mmu-miR-205-5p and Ereg which were closely associated with inflammatory response, extracellular matrix deposition, and fibroblast activation. Conclusion The progression of pulmonary fibrosis is accompanied by dynamic changes in miRNA-mRNA regulatory networks. The identified miRNA-target axes (e.g., miR-135b-5p and Meis1, mmu-miR-708-5p and Mmp25, mmu-miR-21a-3p and Cacna1d, and mmu-miR-205-5p and Ereg—) may play important roles in fibrogenesis and provide potential therapeutic targets for pulmonary fibrosis.

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.