ObjectiveTo construct a multifunctional liposomal delivery system by replacing cholesterol（Chol） in conventional liposomes with saikosaponin D（SSD） and modifying with poloxamer 407（P407） for co-delivery of curcumin（Cur）. The system was evaluated for in vivo tumor targeting and inhibitory effects on mouse subcutaneous solid tumors. MethodsSingle-factor and orthogonal tests combined with information entropy weighting were used to optimize the formulation process of the liposome with encapsulation efficiency and absolute Zeta potential as indexes， and validation studies and liposomal characterization were performed. A subcutaneous solid tumor model was established by injecting H22 hepatocellular carcinoma cells subcutaneously into the dorsal surface of the right forelimb of mice. DiR-loaded traditional Chol liposomes（P407-DiR-Chol-LPs， PDCL） and novel SSD-based liposomes（P407-DiR-SSD-LPs， PDSL） were prepared by the optimized formulation process， and tail vein injection was performed to investigate the impact of SSD on liposome tumor targeting with small animal in vivo imaging. Mice were randomly divided into eight groups， including blank group， model group， free doxorubicin（DOX） group（2 mg·kg^-1）， free Cur group（8 mg·kg^-1）， free SSD group（10 mg·kg^-1）， P407-Cur-Chol-LPs（PCCL） group， P407-SSD-LPs（PSL） group， and P407-Cur-SSD-Lps（PCSL） group. Treatments were administered intraperitoneally every other day for seven doses. Antitumor efficacy and biocompatibility were evaluated by monitoring body weight change， organ indices， tumor volume and mass， relative tumor proliferation rate（T/C）， and tumor growth inhibition rate（TGI）. Histopathological analysis of liver， kidney， and tumor tissues was performed using hematoxylin-eosin（HE） staining. Serum levels of aspartate aminotransferase（AST）， alanine aminotransferase （ALT）， blood urea nitrogen（BUN）， and creatinine（Crea）in mice were quantified by fully automated biochemical analyzer. ResultsOrthogonal test yielded optimal ratios of Cur， SSD， and P407 to soybean phosphatidylcholine（SPC） as 1∶25， 1∶20， and 1∶4. The optimized PCSL exhibited spherical morphology with a particle size of 179.15 nm， a Zeta potential of -47.25 mV， and an encapsulation efficiency of 96.40%. Its in vitro release profile conformed to first-order kinetics， demonstrating excellent storage stability and hemocompatibility. In vivo imaging revealed that the fluorescence signal in tumor tissues and the fluorescence intensity ratio between tumors and organs were significantly higher in the PDSL group than in the PDCL group（P<0.05， P<0.01）. Among the treatment groups， PCSL group showed superior efficacy over free Cur group， free SSD group， PCCL group， and PSL group， with TGI>40% and T/C<60%， indicating pronounced anti-hepatocellular carcinoma effects（P<0.05， P<0.01）. Histopathology and serum biochemistry indicated minimal hepatorenal toxicity and improved hepatic and renal function in PCSL-treated mice. ConclusionReplacing Chol with SSD in preparing multifunctional drug delivery systems not only stabilizes liposomes but also yields superior anti-hepatocellular carcinoma efficacy， achieving the effect of drug-excipient integration. Co-delivery of Cur via this system can be used for treating subcutaneous solid tumors in hepatocellular carcinoma， providing new insights and technical approaches for anti-hepatocellular carcinoma research and the meridian-guiding and messenger-directing theory in traditional Chinese medicine.

ObjectiveTo compare the clinical efficacy of traditional Chinese medicine （TCM） manipulative reduction combined with small splint fixation versus surgical treatment for type A distal radius fracture （DRF） and to explore the factors influencing the choice of treatment. MethodsA multi-center retrospective study was conducted， collecting data from 1237 type A DRF patients treated in 11 hospitals in Jiangsu province from September， 2023 to April， 2025. Among them， 851 patients in the TCM group received manipulative reduction combined with small splint fixation， and 386 patients in the surgical group underwent open reduction and internal fixation. Visual analog scale （VAS） scores for pain and radiographic indicators including palmar tilt， ulnar deviation， and radial height were compared before treatment， 5-7 days after treatment， and 4-6 weeks after treatment. The wrist joint function scores including Dienst and Gartland-Werley scores at 12 weeks after treatment were recorded. Subgroup analysis was conducted for the excellent rate of Dienst and Gartland-Werley scores， stratified by age （＜50， 50-59， 60-69， ≥70 years old） and AO subtypes （A1， A2， A3）. A multivariate logistic regression model was used to identify independent factors influencing treatment choice. ResultsOn 5-7 days after treatment， the surgical group had lower VAS scores than the TCM group， while 4-6 weeks after treatment， the TCM group showed lower VAS scores than the surgical group （P＜0.01）. In terms of radiographic indicators， except for the palmar tilt before treatment being higher in the surgical group than in the TCM group （P＜0.01）， there were no significant differences in palmar tilt， ulnar deviation， and radial height at other timepoints （P＞0.05）. Twelve weeks after treatment， the surgical group had a higher average Gartland-Werley score and the excellent rate than the TCM group （P＜0.01）. Subgroup analysis showed that in patients with A2 type DRF aged 50-59 and 60-69 years old， the excellent rates of Dienst and Gartland-Werley scores in the TCM group were higher than those in the surgical group （P＜0.05）. Multivariate logistic regression analysis revealed that age， palmar tilt， ulnar deviation， and the degree of swelling on the affected side were independent factors influencing the choice of treatment （P＜0.05）. ConclusionBoth TCM manipulative reduction combined with small splint fixation and surgical treatment for type A DRF can achieve good therapeutic effects. TCM manipulative reduction combined with small splint fixation has certain advantages in medium- and long-term pain relief， especially in elderly patients， where wrist joint function recovery is more stable. Age， palmar tilt， ulnar deviation， and swelling degree are the main factors influencing the treatment choice.

ObjectiveTo observe the clinical efficacy and radiographic outcomes of manipulative reduction combined with small splint fixation in the treatment of distal radius fractures. MethodsThe clinical data of 1051 patients with distal radius fractures were retrospectively collected from five hospitals included in the Jiangsu Diagnosis and Treatment Data Platform for Traditional Chinese Medicine（TCM） Dominant Diseases. Propensity score matching at a 1∶4 ratio was applied， resulting in 580 cases selected for final analysis， which comprised 448 patients in the TCM group（manipulative reduction plus small splint fixation） and 132 in the surgical treatment group（open reduction and internal fixation）. Each group was further stratified into type A， B， and C subgroups based on AO fracture classification. Radiographic indicators including palmar tilt， radial inclination， and radial height were compared between groups before treatment and 1 day， 1 week， and 4-6 weeks after treatment， and pain visual analog scale（VAS） scores before treatment and 1 week and 4-6 weeks after treatment were also compared. Wrist joint function was assessed 12 weeks after treatment， using the Dienst wrist function score and the Gartland and Werley（G-W） wrist function score. Additionally， the radiographic indicators at different timepoints and the 12-week wrist function levels were compared between groups across different fracture types. ResultsNo statistically significant difference was observed in radiographic indicators and VAS scores at all timepoints before and after treatment， as well as wrist joint function grades assessed by the Dienst score and the G-W score at 12 weeks after treatment （P＞0.05）. Compared to those before treatment， both groups showed increased palmar tilt， radial inclination， and radial height 1 week and 4-6 weeks after treatment， and decreased VAS scores （P＜0.05）. Compared to those 1 week after treatment， both groups showed a decrease in palmar tilt， an increase in radial inclination and radial height， and a reduction in VAS score 4-6 weeks after treatment（P＜0.05）. In type A and B subgroups， the surgical treatment group had a higher radial inclination than the TCM group 4-6 weeks after treatment， while in the type C subgroup， a higher radial height was shown in the surgical treatment group than in the TCM group 4-6 weeks after treatment（P＜0.05）. In type C subgroup， there was significant difference between groups in the wrist joint function by G-W scores 12 weeks after treatment（P＜0.05）. ConclusionManipulative reduction combined with small splint fixation can maintain fracture alignment and alleviate pain in treating distal radius fractures， which achieves therapeutic outcomes comparable to surgical treatment. It is particularly suitable for type A and B fractures and can be considered an effective treatment option for distal radius fractures.

ObjectiveTo construct an automatic measurement model for palmar tilt and radial inclination suitable for traditional Chinese medicine （TCM） clinical scenarios， and to validate its accuracy and efficiency in TCM manipulative reduction settings. MethodsData on anteroposterior （AP） and lateral X-rays of distal radius fractures were collected from patients admitted to 18 TCM/ integrated TCM and western medicine hospitals in Jiangsu province between September 1st， 2023， and September 1st， 2024， via the Jiangsu Diagnosis and Treatment Big Data Platform for TCM Dominant Diseases. A medical image segmentation framework based on multi-scale feature fusion and edge-awareness was employed， combined with anatomical knowledge specific to TCM orthopedics， to optimize the feature extraction strategy of an artificial intelligence （AI） model. This framework enabled automatic segmentation of fracture regions and measurement of distal radius palmar tilt and radial inclination. The accuracy of the AI model in measuring radial inclination and volar tilt was validated， and the measurement time and average time gain rate of the AI model were compared to those of manual measurement. ResultsA total of 15,444 AP and lateral X-ray images of distal radius fractures were collected， and were divided into a training set （11,144 images， 5066 AP and 6078 lateral）， a validation set （3700 images， 1840 AP and 1860 lateral）， and an independent test set （600 images， 300 AP and 300 lateral） after preprocessing. In the measurement of 300 AP X-rays in the independent test set for radial inclination， when the degree error between AI measurement and manual measurement was ＜3° and ＜5°， AI measurement accuracy was 83% and 93%， respectively. In 300 lateral X-rays in the test set for palmar tilt， when AI measurements had an error of ＜3° and ＜5° compared to manual measurements， corresponding accuracy rate was 78% and 90%， respectively. For 50 X-ray images， AI measurement time was （1.37±0.05） min for radial inclination while manual measurement time was （22.57±2.52） min （P＜0.001）； in terms of palmar tilt， the AI measurement time was （1.33±0.14） min， shorter than （23.70±2.80） min for manual measurement time （P＜0.001）. Average time gain rates for manual and AI measurements were 93.93% and 94.39% respectively. ConclusionAn automatic measurement model for palmar tilt and radial inclination in distal radius fractures has been established， enabling more accurate and efficient assessment as well as providing a tool to support the quantitative evaluation of the efficacy of TCM manipulative reduction and large-sample clinical research.

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.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

Objective To analyze the correlation between cardiac function and degree of coronary artery lesion in elderly patients with coronary heart disease. Methods A total of 110 elderly patients with coronary heart disease and 30 elderly people without coronary heart disease (non-coronary heart disease group) in the Third People's Hospital of Chengdu were selected for the study. Based on Gensini score, the patients with coronary heart disease were classified into mild, moderate and severe lesion groups. The echocardiographic indicators and myocardial injury indicators [cardiac troponin I (cTnI), creatine kinase (CK), creatine kinase isoenzyme (CK-MB)] were compared among the groups. The correlation between cardiac function indicators and degree of coronary artery lesion and the evaluation value of various indicators on severe coronary artery lesion were analyzed. Results The left ventricular ejection fraction (LVEF) was represented as severe lesion groupmoderate lesion group>mild lesion group> non-coronary heart disease group (P<0.05). LVEF showed a negative correlation with degree of coronary artery lesion (P<0.05), while LVEDD, LVESD, cTnI, CK and CK-MB all exhibited positive correlations with degree of coronary artery lesion (P<0.05). The combined detection of the above indicators demonstrated superior evaluation efficiency on severe coronary artery lesion compared to any single indicator (P<0.05). Conclusion There is a significant correlation between cardiac function and degree of coronary artery lesion in elderly patients with coronary heart disease. Comprehensive evaluation of these indicators is conducive to clinical disease risk stratification and individualized treatment.

Despite that sleep disturbance and poor neurocognitive performance are common complaints among coronavirus disease 2019 (COVID-19) survivors, few studies have focused on the effect of post-COVID-19 sleep disturbance (PCSD) on cognitive function. This study aimed to identify the impact of PCSD on neurocognitive function and explore the associated risk factors for the worsening of this condition. This cross-sectional study was conducted via the web-based assessment in Chinese mainland. Neurocognitive function was evaluated by the modified online Integrated Cognitive Assessment (ICA) and the Number Ordering Test (NOT). Propensity score matching (PSM) was utilized to match the confounding factors between individuals with and without PCSD. Univariate analyses were performed to evaluate the effect of PCSD on neurocognitive function. The risk factors associated with worsened neurocognitive performance in PCSD individuals were explored using binary logistic regression. A total of 8692 individuals with COVID-19 diagnosis were selected for this study. Nearly half (48.80%) of the COVID-19 survivors reported sleep disturbance. After matching by PSM, a total of 3977 pairs (7954 individuals in total) were obtained. Univariate analyses revealed that PCSD was related to worse ICA and NOT performance (P<0.05). Underlying disease, upper respiratory infection, loss of smell or taste, severe pneumonia, and self-reported cognitive complaints were associated with worsened neurocognitive performance among PCSD individuals (P<0.05). Furthermore, aging, ethnicity (minority), and lower education level were found to be independent risk factors for worsened neurocognitive performance in PCSD individuals (P<0.05). PCSD was related to impaired neurocognitive performance. Therefore, appropriate prevention and intervention measures should be taken to minimize or prevent PCSD and eliminate its potential adverse effect on neurocognitive function.
Humans ; COVID-19/epidemiology* ; Male ; Female ; Sleep Wake Disorders/epidemiology* ; Propensity Score ; Middle Aged ; Cross-Sectional Studies ; Adult ; SARS-CoV-2 ; Aged ; Risk Factors ; China/epidemiology* ; Cognition ; Cognitive Dysfunction/etiology* ; Neuropsychological Tests

The catalytic activity of 3-mercaptopyruvate (3MP) sulfurtransferase (MPST) converts 3MP to hydrogen sulfide (H₂S). However, the regulatory mechanisms governing MPST and its impact on the brain remain largely unexplored. Our study reveals the neuroprotective role of endothelial MPST-generated H₂S, regulated by protein phosphatase 2A (PP2A). Bioinformatics analysis and RNA sequencing demonstrated that endothelial PP2A is associated with neurodegenerative disease pathways. Cerebral ischemic mice exhibited significant inactivation of endothelial PP2A, evidenced by the reduction of PP2Acα in the brain endothelium. Mice with endothelium-specific null PP2A (PP2A^EC-cKO) exhibited neuronal loss, cognitive dysfunction, and long-term potentiation deficits. Postnatal inactivation of endothelial PP2A also contributes to cognitive dysfunction and neuronal loss. However, regaining endothelial PP2A activity by overexpressing Ppp2ca rescued neuronal dysfunction. Mechanistically, PP2A deficiency is intricately linked to the MPST-H₂S signaling pathway. A robust reduction in endothelial MPST-dependent H₂S production followed PP2A deficiency. Exogenous H₂S treatment and AAV-mediated overexpression of MPST in brain endothelial cells significantly mitigated neuronal dysfunction in PP2A^EC-cKO mice. Furthermore, PP2A deficiency promotes an increase in calcium influx and calpain2 phosphorylation, subsequently leading to MPST degradation. The PP2A activator (FTY720) and MPST activator (3MP sodium) both remarkably restored endothelial MPST-dependent H₂S production, subsequently rescuing ischemia-induced neurological deficits. In conclusion, our study demonstrates that endothelial PP2A deficiency leads to MPST degradation by activating calpain2, thus damaging neuronal function.