Objective To explore the construction of heart preservation model of empty beating donor based on extracorporeal membrane oxygenation (ECMO). Methods From January 2022 to August 2023, 20 Guangxi Bama miniature pigs weighing 25-30 kg were selected, half male and half female. Under general anesthesia and heparinization, a midline thoracotomy was performed. The pericardium was cut after freeing the anterior and posterior vena cavae, and a perfusion needle was inserted near the brachiocephalic artery in the ascending aorta, connected to a blood collection bag to collect 500-600 mL of blood. The anterior and posterior vena cavae were ligated, the aorta was blocked and perfused with HTK solution to stop the heart beating. The superior and inferior vena cavae were cut off, the right pulmonary vein was decompressed, the aorta and left and right pulmonary arteries and veins were cut off, and the whole heart was removed. An ECMO device was used to continuously perfuse a cardioprotective solution mainly composed of oxygenated warm blood, maintaining the isolated pig heart beating for 8 hours, monitoring (once/hour) ECMO perfusion parameters, blood gas indicators, perfusate electrolytes, inflammatory factors, myocardial enzymes, myoglobin, and troponin levels. Myocardial tissue was taken for hematoxylin-eosin (HE) staining to observe myocardial cell damage and evaluate the quality of heart preservation. Results Among the 20 isolated beating pig hearts, 17 successfully resumed beating, 3 experienced ventricular fibrillation, resuscitated after intracardiac electrical defibrillation, and all 20 pig hearts successfully beat for 8 hours. There was no statistical difference in ECMO perfusion parameters, blood gas indicators, perfusate electrolytes, and inflammatory factors at each time point (P>0.05). There were statistical increases in myocardial enzymes, myoglobin, and troponin levels (P<0.05). HE staining results suggested that there was no severe myocardial damage. Conclusion ECMO technology can be used for pig heart preservation with good results, and this study provides experimental evidence for improving heart preservation research in clinical heart transplantation.

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.

ObjectiveTo investigate the relationship between the vasodilation and hydrogen sulfide （H₂S） mechanism of total flavonoids of chrysanthemum （TFCC） and the large conductance Ca²⁺- activated K⁺ （BK_Ca） channels on vascular smooth muscle cells （VSMCs） of the middle cerebral artery in rats. In addition， this study will also investigate the effect of TFCC on the expression of BK_Ca channel alpha protein in rat middle cerebral artery VSMCs. MethodsThe primary method employed was acute digestion to isolate VSMCs from the middle cerebral artery of rats； whole-cell patch-clamp techniques were used to measure BK_Ca channel currents； primary tissue adherence culture was utilized to cultivate VSMCs from the middle cerebral artery of rats； Western blot were employed to determine protein expression levels. ResultsIn whole-cell patch-clamp experiments， both the H₂S donor NaHS （100 μmol/L） and endogenous H₂S enhanced BK_Ca channel currents. TFCC （30， 90， and 270 mg/L） also activatedBK_Ca channels and exhibited a certain concentration-dependent effect. Even after blocking endogenous H₂S production， TFCC （270 mg/L） still activated BK_Ca channels in VSMCs of the middle cerebral artery in rats. In Western blot experiments， the α-subunit of BK_Ca channel proteins was expressed in all groups of cells， but TFCC （30， 90， and 270 mg/L） and inhibitor IBTX group did not affect the expression of channel protein content.Conclusion TFCC can promote the opening of BK_Ca channels by promoting the generation of endogenous H₂S， or directly activate BK_Ca channels， thereby playing a role in relaxing cerebral blood vessels. However， TFCC had no significant effect on the expression of BK_Ca channel proteins.

ObjectiveTo explore the vascular endothelial injury in male mice caused by exposure to polystyrene microplastics （PS-MPs） and the intervention effect of luteolin on vascular remodeling. Additionally， to investigate the mechanism through the oxidative system and metabolomics. MethodsThirty-two C57BL/6 mice （6-8 weeks old） were randomly divided into the saline group （saline group）， the 0.1 mg/kg PS-MPs exposure group （0.1PS-MPs group）， the 1 mg/kg PS-MPs exposure group （1PS-MPs group）， and the 1 mg/kg PS-MPs + luteolin treatment group （1PS-MPs + Lut group）， with 8 mice in each group. After 8 weeks of intervention， the body weight， blood pressure， aortic organ coefficient， and aortic histopathological changes of mice in each group were detected； the total cholesterol （TC）， triglyceride （TG）， and high-density lipoprotein cholesterol （HDL-C） lipid metabolism-related indicators in the aorta of mice were detected； the reactive oxygen species （ROS）， glutathione （GSH）， and malondialdehyde （MDA） oxidative stress-related indicators were detected； the endothelin （ET-1）， nitric oxide （NO）， vascular endothelial growth factor A （VEGF-A）， vascular cell adhesion molecule-1 （VCAM-1/CD106）， and intercellular adhesion molecule-1 （ICAM-1/CD54） endothelial function-related indicators and serum metabolomics were detected. ResultsCompared to the saline group， exposure to PS-MPs resulted in pathological thickening of the mouse aorta， increased aortic organ coefficient， and elevated blood pressure. Lipid metabolism-related indicators， including TC and TG， were elevated， while HDL-C was reduced， indicating lipid metabolism disorder in mice. Oxidative stress markers such as ROS and MDA increased， whereas GSH decreased， demonstrating oxidative damage. Vascular endothelial inflammation and injury markers， including ET-1， VEGF-A， VCAM-1， and ICAM-1， were upregulated， while the vasodilatory substance NO was downregulated， confirming endothelial injury. Furthermore， serum metabolomics results revealed that PS-MPs exposure induced endothelial damage by disrupting metabolic pathways such as the citrate cycle. Compared to the PS-MPs group， luteolin significantly reversed these effects， attenuating oxidative stress and lipid metabolism disorders， and effectively repairing endothelial injury. ConclusionPS-MPs induce vascular toxicity through oxidative stress and lipid metabolism. Luteolin effectively alleviates endothelial damage and vascular remodeling.

ObjectiveTo investigate the mechanism by which Shaoyaotang regulates the miRNA-155-mediated suppressor of cytokine signaling 1 （SOCS1）/Janus kinase 1 （JAK1）/signal transducer and activator of transcription 1 （STAT1） signaling pathway and thereby affects macrophage polarization. MethodsThe cell-counting kit-8 （CCK-8） assay was used to detect the effect of drug-containing serum of Shaoyaotang at different concentrations on the viability of RAW 264.7 cells. A cell model of inflammation was established by stimulating RAW264.7 cells with lipopolysaccharide （LPS） at a concentration of 10 mg·L^-1 The modeled cells were assigned by the random number table method into seven groups： LPS-induced M1 polarization （model）， M1+miRNA-155 mimics， M1+miRNA-155 inhibitor， M1+Shaoyaotang-containing serum， M1+miRNA-155 mimics+Shaoyaotang-containing serum， M1+miRNA-155 inhibitor+Shaoyaotang-containing serum， and M1+blank serum. Enzyme-linked immunosorbent assay was employed to measure the levels of inflammatory factors ［tumor necrosis factor-α （TNF-α）， interleukin-6 （IL-6）， and interleukin-1β （IL-1β）］. Immunofluorescence assay was used to detect the expression of macrophage polarization markers ［inducible nitric oxide synthase （iNOS） and macrophage mannose receptor 1 （CD206）］. Real-time PCR was employed to measure the expression of miRNA-155 in cells. Western blot was performed to determine the protein levels of SOCS1， STAT1， and JAK1. ResultsCompared with the LPS-induced M1 polarization （model） group， the M1+miRNA-155 mimics group showed up-regulated expression of miRNA-155， JAK1， STAT1， TNF-α， IL-6， IL-1β， and iNOS （P<0.05） and down-regulated expression of CD206 （P<0.05）. In both the M1+miRNA-155 inhibitor group and the M1+Shaoyaotang-containing serum group， the expression levels of miRNA-155， JAK1， STAT1， TNF-α， IL-6， IL-1β， and iNOS were down-regulated （P<0.05）， while those of SOCS1 and CD206 were up-regulated （P<0.05）. Compared with the M1+miRNA-155 mimics group， the M1+miRNA-155 mimics+Shaoyaotang-containing serum group showed down-regulated expression of miRNA-155， JAK1， STAT1， TNF-α， IL-6， IL-1β， and iNOS （P<0.05） and up-regulated expression of SOCS1 and CD206 （P<0.05）. Compared with the M1+miRNA-155 inhibitor group， the M1+miRNA-155 inhibitor+Shaoyaotang-containing serum group showed down-regulated expression of miRNA-155， JAK1， STAT1， TNF-α， IL-6， IL-1β， and iNOS （P<0.05） and up-regulated expression of SOCS1 and CD206 （P<0.05）. ConclusionShaoyaotang regulates macrophage polarization by modulating miRNA-155 expression and interfering with the SOCS1/JAK1/STAT1 signaling pathway. The findings provide new experimental evidence for the treatment of ulcerative colitis with Shaoyaotang.

ObjectiveTo investigate the potential role and underlying mechanisms of Shaoyaotang in intervening macrophage glycolytic reprogramming in ulcerative colitis （UC）. MethodsForty-eight C57BL/6 mice were randomly divided into six groups： Normal control group， model group， mesalazine group （0.39 g·kg^-1）， Shaoyaotang group （15.54 g·kg^-1）， 2-deoxy-D-glucose （2-DG） group （glycolysis inhibitor， 100 mg·kg^-1）， and 2-DG + Shaoyaotang combined group （100 mg·kg^-1+15.54 g·kg^-1）. Except for the normal control group， mice in the other five groups were induced to establish UC models using dextran sulfate sodium （DSS）. The normal control group was administered pure water via intragastric gavage， while the other groups received intragastric gavage of mesalazine solution， intragastric gavage of Shaoyaotang， and the 2-DG group was treated with 2-DG via intraperitoneal injection. After 7 consecutive days of treatment， colonic tissues were extracted. Hematoxylin and eosin （HE） staining was performed to evaluate histopathological changes and tissue injury in the colon. Enzyme-linked immunosorbent assay （ELISA） was used to detect the expression of interleukin-10 （IL-10） and tumor necrosis factor-α （TNF-α） in colonic tissues. Western blot analysis was employed to determine the expression levels of hypoxia-inducible factor-1α （HIF-1α）， glucose transporter （GLUT1）， lactate dehydrogenase A （LDHA）， pyruvate kinase M2 （PKM2）， and 6-phosphofructo-2-kinase/fructose-2，6-biphosphatase 3 （PFKFB3） in colonic tissues. Immunofluorescence was conducted to detect the expression of CD206 and inducible nitric oxide synthase （iNOS） in colonic tissues. Liquid chromatography-mass spectrometry （LC-MS） was utilized to measure lactate and citrate levels in colonic tissues. ResultsCompared with the normal control group， mice in the model group exhibited a significant increase in disease activity index （DAI） scores， accompanied by colonic mucosal congestion， edema， and inflammatory cell infiltration， significantly elevated expression of the inflammatory cytokine TNF-α （P<0.05）， significantly decreased IL-10 expression （P<0.05）， significantly increased levels of HIF-1α， GLUT1， LDHA， PKM2， and PFKFB3 in colonic tissues （P<0.05）， markedly elevated iNOS expression （P<0.05）， significantly decreased CD206 expression （P<0.05）， and significantly elevated lactate and citrate levels in colonic tissues （P<0.05）. In contrast to the model group， the Shaoyaotang group， inhibitor group， and Shaoyaotang combined with inhibitor group demonstrated amelioration of mucosal injury in colonic tissues， markely decreased expression levels of the inflammatory cytokine TNF-α （P<0.05）， elevated IL-10 expression levels， significantly decreased expression of HIF-1α， GLUT1， LDHA， PKM2， and PFKFB3 （P<0.05）， markedly reduced iNOS expression levels （P<0.05）， significantly increased CD206 expression （P<0.05） and significantly decreased lactate and citrate levels （P<0.05）. ConclusionShaoyaotang ameliorates symptoms of DSS-induced UC in mice， and its therapeutic mechanism may be associated with regulating macrophage glycolytic reprogramming via modulation of the HIF-1α signaling pathway.

ObjectiveTo investigate the role of microRNA-155-5p （miR-155-5p） in ulcerative colitis （UC） and study the molecular mechanism of Shaoyaotang in the treatment of UC by regulating miR-155-5p. MethodsForty-eight SPF-grade male C57BL/6 mice were selected and assigned via the random number table method into 6 groups （n=8）： A blank control group， a model group， a mesalazine （0.39 g·kg^-1） group， a Shaoyaotang （31.08 g·kg^-1） group， a Janus kinase 1 （JAK1） inhibitor （baricitinib， 10 mg·kg^-1） group， and a Shaoyaotang combined with inhibitor （baricitinib 10 mg·kg^-1 + Shaoyaotang 31.08 g·kg^-1） group. After successful modeling of UC by gavage of 3% dextran sulphate sodium solution， each group received corresponding drug intervention for 7 days. Shaoyaotang and mesalazine were administered by gavage， and baricitinib by intraperitoneal injection. Twenty-four hours after the last administration， mice were anesthetized by intraperitoneal injection of pentobarbital sodium， and blood was collected for determination of white blood cell count and erythrocyte sedimentation rate （ESR）. Mice were then sacrificed for measurement of colon length. Hematoxylin-eosin staining was used to observe colonic pathological changes and perform pathological scoring. Real-time fluorescence quantitative polymerase chain reaction （Real-time PCR） was employed to determine the relative expression of miR-155-5p in the colonic tissue， and Western blot was used to determine the protein levels of JAK1， phosphorylated JAK1 （p-JAK1）， suppressor of cytokine signaling 1 （SOCS1）， signal transducer and activator of transcription 1 （STAT1）， and phosphorylated STAT1 （p-STAT1）. ResultsCompared with the blank control group， the model group showed increased disease activity index （DAI） score and pathological score， shortened colon， upregulated relative expression of miR-155-5p and protein levels of p-JAK1 and p-STAT1， downregulated protein level of SOCS1 in the colonic tissue， prolonged time of erythrocyte sedimentation， and increased white blood cell count （P<0.01）. Compared with the model group， all drug-treated groups exhibited improvements in the above indicators （P<0.01）. Moreover， the Shaoyaotang group showed better therapeutic effects than the mesalazine group in regulating miR-155-5p expression， related protein levels， DAI score， and colonic pathological score （P<0.01）. ConclusionShaoyaotang may downregulate miR-155-5p to relieve its inhibition on SOCS1， thereby suppressing the excessive activation of the JAK1/STAT1 signaling pathway and ultimately alleviating intestinal inflammatory damage.

ObjectiveTo investigate the role of the Toll-like receptor 4 （TLR4）/myeloid differentiation factor 88 （MyD88）/nuclear factor-κB （NF-κB） signaling pathway in intestinal mucosal barrier damage in ulcerative colitis， as well as the intervention mechanism of Shaoyaotang. MethodsSixty SD rats were allocated into a blank group， a model group， a mesalazine （0.42 g·kg^-1） group， and low-， medium-， and high-dose （11.1， 22.2， 44.4 g·kg^-1， respectively） Shaoyaotang groups. A model of ulcerative colitis was induced by 2，4，6-trinitrobenzenesulfonic acid （TNBS）. After successful modeling， rats were administrated with corresponding agents via gavage for 7 days. Changes in colon length and colon weight were observed. Hematoxylin-eosin staining was performed to examine the pathological changes of the colon， and immunohistochemistry was employed to detect the expression of the inflammatory cytokine interleukin-8 （IL-8）， cyclooxygenase-2 （COX-2）， junction adhesion molecule-1 （JAM-1）， and claudin-1 in the colon. Western blot analysis was performed to determine the protein levels of TLR4， MyD88， and NF-κB in the colon. ResultsCompared with the blank group， the model group showed elevated DAI score （P<0.01）， reduced colon length and colon weight （P<0.01）， down-regulated protein levels of JAM-1 and claudin-1 （P<0.01）， and up-regulated protein levels of IL-8， COX-2， TLR4， MyD88， and NF-κB p65 （P<0.01） in the colon tissue. Compared with the model group， each treatment group showed decreased DAI score （P<0.05， P<0.01）， increased colon length and colon weight （P<0.05， P<0.01）， up-regulated protein levels of JAM-1 and claudin-1 （P<0.01）， and down-regulated protein levels of IL-8， COX-2， TLR4， MyD88， and NF-κB p65 （P<0.01） in the colon tissue. ConclusionShaoyaotang alleviates intestinal inflammation and intestinal mucosal damage to protect intestinal barrier integrity by regulating the TLR4/MyD88/NF-κB signaling pathway.

Background As climate change intensifies and extreme temperature events become more frequent, non-optimal temperature has emerged as a significant contributor to the global disease burden, representing a pressing public health challenge. Objective To analyze the disease burden, temporal trends, and health inequalities attributable to non-optimal, high, and low temperatures in China from 1990 to 2021, and to compare these findings with global levels to provide a scientific basis for targeted prevention strategies. Methods Using data from the Global Burden of Disease 2021 (GBD 2021), we extracted mortality rates and disability-adjusted life year (DALY) rates, and other indicators attributable to non-optimal, high, and low temperatures by sex, age, region, and cause. Joinpoint regression was applied to examine temporal trends. Decomposition analysis identified driving factors of change, while the slope index of inequality (SII) and concentration index (CI) quantified disparities across socio-demographic index (SDI) levels. Results From 1990 to 2021, the age-standardized mortality rates (ASMR) and age-standardized DALY rates (ASDR) attributable to non-optimal temperature in China exhibited a downward trend, decreasing from 66.48 (95%UI: 58.09, 76.56) to 32.70 (95%UI: 27.26, 39.26) per 100000 population, and from 1219.59 (95%UI: 1056.28, 1418.37) to 493.22 (95%UI: 403.88, 609.32) per 100000 population, respectively. Burdens attributable to non-optimal temperature and low temperature were higher than the global average, whereas the high temperature burden was lower. Males consistently experienced higher ASMR and ASDR attributable to non-optimal temperature than females. Cardiovascular diseases, chronic respiratory diseases, and respiratory infections and tuberculosis were the top three causes of non-optimal temperature-attributable burdens. Decomposition analysis revealed that population aging and growth were the primary drivers of increased burden, while epidemiological changes primarily drove the decline. Health inequalities were most predominant between extreme SDI regions but narrowed over time. Conclusion Despite the overall decline in burden attributable to non-optimal temperature in China, significant challenges remain, including high risks from cold exposure, gender disparities, and the compounding effects of an aging population with cardiovascular or respiratory diseases. Policy makers should prioritize climate change adaptation, focusing on elderly health and regional equity while strengthening the public health workforce.