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.

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.

ObjectiveTobacco-related diseases remain one of the leading preventable public health challenges worldwide and are among the primary causes of premature death. In recent years, accumulating evidence has supported the classification of nicotine addiction as a chronic brain disease, profoundly affecting both brain structure and function. Despite the urgency, effective diagnostic methods for smoking addiction remain lacking, posing significant challenges for early intervention and treatment. To address this issue and gain deeper insights into the neural mechanisms underlying nicotine dependence, this study proposes a novel graph neural network framework, termed TI-GNN. This model leverages functional magnetic resonance imaging (fMRI) data to identify complex and subtle abnormalities in brain connectivity patterns associated with smoking addiction. MethodsThe study utilizes fMRI data to construct functional connectivity matrices that represent interaction patterns among brain regions. These matrices are interpreted as graphs, where brain regions are nodes and the strength of functional connectivity between them serves as edges. The proposed TI-GNN model integrates a Transformer module to effectively capture global interactions across the entire brain network, enabling a comprehensive understanding of high-level connectivity patterns. Additionally, a spatial attention mechanism is employed to selectively focus on informative inter-regional connections while filtering out irrelevant or noisy features. This design enhances the model’s ability to learn meaningful neural representations crucial for classification tasks. A key innovation of TI-GNN lies in its built-in causal interpretation module, which aims to infer directional and potentially causal relationships among brain regions. This not only improves predictive performance but also enhances model interpretability—an essential attribute for clinical applications. The identification of causal links provides valuable insights into the neuropathological basis of addiction and contributes to the development of biologically plausible and trustworthy diagnostic tools. ResultsExperimental results demonstrate that the TI-GNN model achieves superior classification performance on the smoking addiction dataset, outperforming several state-of-the-art baseline models. Specifically, TI-GNN attains an accuracy of 0.91, an F1-score of 0.91, and a Matthews correlation coefficient (MCC) of 0.83, indicating strong robustness and reliability. Beyond performance metrics, TI-GNN identifies critical abnormal connectivity patterns in several brain regions implicated in addiction. Notably, it highlights dysregulations in the amygdala and the anterior cingulate cortex, consistent with prior clinical and neuroimaging findings. These regions are well known for their roles in emotional regulation, reward processing, and impulse control—functions that are frequently disrupted in nicotine dependence. ConclusionThe TI-GNN framework offers a powerful and interpretable tool for the objective diagnosis of smoking addiction. By integrating advanced graph learning techniques with causal inference capabilities, the model not only achieves high diagnostic accuracy but also elucidates the neurobiological underpinnings of addiction. The identification of specific abnormal brain networks and their causal interactions deepens our understanding of addiction pathophysiology and lays the groundwork for developing targeted intervention strategies and personalized treatment approaches in the future.

Objective: To analyze the adverse treatment outcome status and spatio temporal characteristics of pulmonary tuberculosis cases among students in Qinghai Province, providing a reference basis for pulmonary tuberculosis prevention and control in schools. Methods: The data of student pulmonary tuberculosis cases during 2013-2023 in Qinghai Province were obtained through the "National Tuberculosis Management Information System", and the treatment outcome was retrospectively analyzed. The Joinpoint model was applied to analyze the adverse outcome rate trend. Global and local spatial autocorrelation analysis, and spatiotemporal scan cluster analysis were conducted on the adverse outcome rate of pulmonary tuberculosis among students in Qinghai Province. Results: During 2013-2023, 488 cases of adverse outcomes were reported among 6 155 students with pulmonary tuberculosis in Qinghai Province, with an adverse outcome rate of 7.93%. The reporting adverse outcome rate of pulmonary tuberculosis among students showed a downward trend from 2013 to 2023 (APC=-16.20, t =-3.89, P <0.05). The results of spatial autocorrelation showed that the adverse outcome rate of pulmonary tuberculosis was Moran s I >0 among students in Qinghai Province. Among them, there was a spatially positive correlation in the adverse outcome rate of pulmonary tuberculosis among students in 2020, 2021 and 2022(all Z >1.96, all P <0.05). The results of clustering and outlier analysis in local spatial autocorrelation showed that the areas with high high aggregation were mainly concentrated in Yushu Prefecture(Zhiduo County, Zaduo County, Nangqian County, Yushu City), Huangnan Prefecture (Zeku County, Henan County) and Hainan Prefecture (Tongde County). The low low concentration areas were distributed in Haidong City, Xining City, Haibei Prefecture (Gangcha County, Qilian County), Haixi Prefecture (Tianjun County, Ulan County), Hainan Prefecture (Gonghe County, Guide County) and Huangnan Prefecture (Tongren City, Jianzha County). The spatio temporal scanning showed that a total of two possible aggregation areas had been detected. Among them, the first level aggregation area composed of 10 counties and districts in Yushu Prefecture and Guoluo Prefecture of Qinghai Province, and the cluster radius was 658.09 km, the RR was 10.58 , and the LLR was 305.91; the second level aggregation area was composed of 16 counties and districts in Hainan Prefecture, Haixi Prefecture, Huangnan Prefecture and Guoluo Prefecture, and the cluster radius was 407.02 km, the RR was 9.83, and the LLR was 152.48 (both P <0.05). Conclusions The reporting rates of adverse treatment outcome of pulmonary tuberculosis cases among students in Qinghai Province remain relatively high and unevenly distribute throughout the province. Supervision should be strengthened to improve cases compliance,and to reduce student pulmonary tuberculosis adverse treatment outcomes incidence.

OBJECTIVE: To explore the improvement effect of electroacupuncture (EA) based on Xingnao Kaiqiao acupuncture (acupuncture for regaining consciousness and opening orifices) on cognitive impairment in mice with Parkinson's disease (PD), and to explore its regulatory mechanisms on the kinesin family member 5A (KIF5A)/mitochondrial Rho GTPase 1 (Miro1) pathway and mitophagy in prefrontal cortical neurons. METHODS: A total of 70 male C57BL/6J mice of clean grade were randomly divided into a normal group (12 mice), a sham operation group (12 mice), and a model pre-screening group (46 mice). Unilateral stereotaxic injection of 6-hydroxydopamine (6-OHDA) into the medial forebrain bundle was adopted to establish the PD model in the model pre-screening group. Twenty-four mice after successful modeling were randomly selected and divided into a model group and an EA group, 12 mice in each one. In the EA group, acupuncture was applied at "Shuigou" (GV26) and bilateral "Sanyinjiao" (SP6) and "Neiguan" (PC6), ipsilateral "Sanyinjiao" (SP6) and "Neiguan" (PC6) were connected to EA respectively, with disperse-dense wave, 5 Hz/20 Hz in frequency, 0.5 mA in current intensity, 20 min a time, 6 times a week for 30 days. Cognitive function was assessed by Y-maze and Morris water maze tests; morphology of prefrontal cortex was observed by H.E. staining; reactive oxygen species (ROS) level in prefrontal cortex was detected by fluorescence probe method; mitochondrial morphology and autophagosome ultrastructure were observed by transmission electron microscopy; the mRNA expression of tyrosine hydroxylase (TH) was detected by quantitative real-time PCR; the protein expression of TH, KIF5A, Miro1, p62, Parkin and PTEN induced kinase 1 (PINK1) was detected by Western blot. RESULTS: Compared with the sham operation group, both the model group and the EA group exhibited increased rotation number of per minute (P<0.001). Compared with the sham operation group, in the model group, the novel arm exploration time of Y-maze test was shortened (P<0.001), the escape latency of Morris water maze test was prolonged (P<0.05) and the platform crossing number of Morris water maze test was reduced (P<0.01); in the prefrontal cortex, the number of cellular vacuole and neurons with karyopyknosis was increased (P<0.001), and mitochondrial autophagosomes could be observed; in the prefrontal cortex, the relative expression of ROS was increased (P<0.001), the protein and mRNA expression of TH was decreased (P<0.001), the protein expression of Miro1, PINK1, Parkin was increased (P<0.001, P<0.01), the protein expression of KIF5A and p62 was decreased (P<0.001). Compared with the model group, in the EA group, the novel arm exploration time of Y-maze test was prolonged (P<0.01), the escape latency of Morris water maze test was shortened (P<0.05) and the platform crossing number of Morris water maze test was increased (P<0.05); in the prefrontal cortex, the number of cellular vacuole and neurons with karyopyknosis was decreased (P<0.001), and the number of mitochondrial autophagosomes reduced and the mitochondrial morphology was improved; in the prefrontal cortex, the relative expression of ROS was decreased (P<0.01), the protein and mRNA expression of TH was increased (P<0.001, P<0.01), the protein expression of Miro1, PINK1, Parkin was decreased (P<0.001, P<0.01, P<0.05), the protein expression of KIF5A and p62 was increased (P<0.01, P<0.05). CONCLUSION Xingnao Kaiqiao electroacupuncture effectively alleviates cognitive impairment and damage of neuronal function in PD mice, its mechanism may be related to the regulation of KIF5A/Miro1 pathway, hence reducing the mitophagy in prefrontal cortical neurons.
Animals ; Electroacupuncture ; Male ; Mice ; Parkinson Disease/physiopathology* ; Cognitive Dysfunction/psychology* ; Kinesins/genetics* ; Humans ; Mitophagy ; Mice, Inbred C57BL ; rho GTP-Binding Proteins/genetics* ; Mitochondria/genetics* ; Prefrontal Cortex/metabolism*

Objective To explore the relationship between peripheral blood mitochondrial DNA(mtDNA)copy number and urinary microalbumin(MAlb)in patients with type 2 diabetes mellitus(T2DM).Methods 87 patients with T2DM from the Endocrinology Department of East Hospital of Tongji University were selected based on inclusion criteria.According to urinary MAlb levels,patients were divided into T2DM group(MAlb＜30 mg/L)and MAlb group(MAlb＞30 mg/L).General patient information was collected,and clinical indicators such as blood glucose,lipid profile,glycated hemoglobin and insulin were measured.Peripheral blood mtDNA was extracted and the copy number was detected using real-time quantitative polymerase chain reaction(RT-PCR).Logistic regression analysis was used to identify the risk factors for albuminuria.Results Compared with T2DM group,DM duration,serum creatinine,serum uric acid and copy number of mtDNA in MAlb group increased(P＜0.05),while high density lipoprotein cholesterol decreased(P＜0.05).Logistic analysis showed that the copy number of mtDNA was the influencing factor of MAlb in T2DM patients.Conclusions The peripheral blood mtDNA copy number in T2DM patients is positively correlated with urinary microalbumin,thus providing a potential target for early detection of DKD.

Aim To explore the underlying molecular mechanism of Alisol A 24-acetate(24A)in improving oxygen-glucose deprivation/reoxygenation(OGD/R)injury in brain microvascular endothelial cells(BMECs)and its correlation with miR-98-5p/transi-ent receptor potential melastatin-2(TRPM2).Meth-ods The ischemia-reperfusion injury in brain micro-vascular endothelial cells(BMECs)was established u-sing bEnd.3 cells subjected to 8 h of oxygen-glucose deprivation followed by 16 h of re-oxygenation.The cells were intervened by miR-98-5p mimics and/or 18.77 μmol·L-1 24A for 24 h and divided into the control group,OGD/R group,OGD/R+24A group,OGD/R+24A+miR-98-5p mimics group and OGD/R+miR-98-5p mimics group.The mRNA levels of miR-98-5p and TRPM2 were detected by qPCR.IL-1 β and TNF-α levels were detected by ELISA.The expression levels of TRPM2,p-AKT,p-GSK3 β,AKT,GSK3 β,Bcl-2,Bax,ZO-1,Occludin,Claudin-5 were detected by Western blot.Apoptosis and reactive oxygen species(ROS)levels were detected by flow cytometry.The targeting relationship between miR-98-5p and TRPM2 was verified using dual luciferase assay.Results Compared with the control group,the apoptosis of OGD/R group was obvious,Bcl-2/Bax decreased,ZO-1,Occludin,Claudin-5 decreased,IL-1 β,TNF-α and ROS increased,miR-98-5p,p-AKT/AKT,p-GSK3β/GSK3β decreased but TRPM2 increased.But com-pared with the OGD/R group,except the control group,the other three groups showed the opposite trend in the above aspects;compared with the OGD/R+24A group,OGD/R+24A+miR-98-5p mimics group showed decreased apoptosis,decreased degradation of ZO-1,Occludin and Claudin-5,and decreased inflam-mation and ROS.miR-98-5p,p-AKT/AKT,p-GSK3β/GSK3β increased and TRPM2 decreased.However,compared with the OGD/R+24A+miR-98-5p mimics group,the OGD/R+miR-98-5p mimics group reversed this trend.Dual luciferase confirmed that miR-98-5p targeted regulation of TRPM2.Conclusion 24A in-hibits the expression of TRPM2 in BMECs through miR-98-5p,regulates AKT/GSK3β signal pathway,re-duces OGD/R inflammation and oxidative stress-medi-ated apoptosis,prevents the degradation of ZO-1,Oc-cludin and Claudin-5,and improves BBB permeability.

Objective To evaluate the efficacy and safety of a novel intravascular lithotripsy(IVL)balloon—Vesscrack shockwave balloon—for vascular preparation before stent implantation in patients with severe coronary artery calcification(CAC).Methods This was a prospective,single-arm,multicenter study conducted in China from June 2022 to October 2022.Patients with severe CAC were treated with the Vesscrack shockwave balloon for lesion preparation,followed by drug-eluting stent(DES)implantation.Of these,33 patients underwent optical coherence tomography(OCT).The primary endpoint was procedural success,defined as successful stent implantation with residual stenosis≤30％and the absence of in-hospital major adverse events,including cardiac death,target vessel-related myocardial infarction,or target lesion revascularization.Results A total of 170 patients[mean age:(65.9±7.9)years,116 males]were enrolled.After treatment with IVL and DES,the minimum lumen diameter increased significantly compared to baseline[(2.34±0.40)mm vs.(0.95±0.33)mm,P＜0.001],the degree of stenosis was significantly reduced[(13.24±6.60)％vs.(65.18±10.59)％,P＜0.001].Procedural success was achieved in 100％of cases,and device success was 98.8％.The 30-day patient-related cardiovascular clinical composite endpoint(POCE)rate was 0.0,with no target lesion failure,no confirmed or potential thrombotic events were observed.The shockwave energy generator demonstrated excellent stability and ease of use.Among the 33 patients assessed with OCT,after IVL intervention,the maximum calcified area of the lumen[(3.51±1.51)mm2 vs.(2.85±1.80)mm2,P＜0.001],and the minimum lumen area within the target lesion[(3.08±1.04)mm2 vs.(2.02±0.75)mm2,P＜0.001],and after DES intervention,the luminal area of the largest calcified site[(6.59±1.64)mm2 vs.(2.85±1.80)mm2,P＜0.001]and the minimum luminal area within the target lesion[(6.19±1.45)mm2 vs.(2.02±0.75)mm2,P＜0.001]were significantly increased,and the differences were statistically significant.Conclusions The Vesscrack shockwave balloon is effective and safe for vascular preparation in patients with severe CAC prior to stent implantation.It achieves significant calcified plaque modification,high procedural success rates,and minimal complications.