ObjectiveHepatocellular carcinoma (HCC) represents 90% of all primary liver cancers. The main risk factors associated with HCC include viral hepatitis (B and/or C), alcohol abuse, and metabolic dysfunction-associated steatotic liver disease (MASLD), which progressively advance to liver fibrosis, cirrhosis, and ultimately evolve into HCC. Surgical resection represents the most effective treatment for HCC, while recent advances in immunotherapy, including immune checkpoint inhibitors and adoptive cell therapies, have provided improved treatment prospects for patients with unresectable HCC. However, the complex metabolic heterogeneity of HCC limits the therapeutic efficacy. Metabolic intermediates acyl-CoA not only provide energy and substrates for numerous biochemical reactions but also serve as donors for protein lysine acylation, a major class of post-translational modification (PTM). Therefore, a deeper understanding of the molecular mechanisms underlying protein lysine acylation and hepatocarcinogenesis is urgently needed. MethodsThe levels of protein lysine acylation and silence information regulator 5 (SIRT5) expression levels in clinical HCC samples were analyzed by Western blot. Quantitative malonylome and succinylome of HCC samples were analyzed by antibody-based affinity enrichment coupled with tandem mass spectrometry. The proliferation of HCC cells was analyzed with Cell Counting Kit-8 (CCK-8) assays, the apoptosis was quantified by Annexin V-FITC/propidium iodide (PI) staining coupled with flow cytometry, and the ability of cells to migrate was assayed by Transwell assays. The enzymatic activity of glutathione S-transferase Mu 1 (GSTM1) was quantified. Transgenic mice with hepatic overexpression of SIRT5 were constructed using CRISPR-Cas9, and primary hepatocarcinogenesis was induced by administration of diethylnitrosamine. ResultsWestern blot analysis indicated that the expression level of SIRT5 was elevated in clinical samples from HCC patients, and the levels of lysine malonylation, glutarylation, and succinylation were significantly reduced in HCC tissues. Knockout of SIRT5 in MHCC-97H and MHCC-97L hepatoma cells suppressed cell proliferation, and increased the percentage of apoptotic cells significantly. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the differentially malonylome and succinylome of HCC samples revealed significant enrichment in two major classes of biological processes: core energy metabolism (e.g., glycolysis/gluconeogenesis, tricarboxylic acid metabolic process, fatty acid beta oxidation) and detoxification and oxidative stress response (e.g., response to toxic substance, chemical carcinogenesis, reactive oxygen species (ROS)). SIRT5 removes malonylation from lysine residues in GSTM1 and restores its detoxification activity, which is crucial for the survival of hepatocytes under stressed conditions. More importantly, in vivo experiment indicated that hepatic-specific overexpression of SIRT5 in mice accelerated diethylnitrosamine-induced liver fibrosis and hepatocarcinogenesis, indicating the critical role of SIRT5 in HCC progression. ConclusionThis study highlights the previously unrecognized SIRT5-GSTM1 axis as a key regulator in hepatocarcinogenesis, and suggests a potential target for the treatment of patients with HCC.

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 evaluate the impact of yang-reinforcing and blood-activating therapy on the long-term prognosis for patients with dilated cardiomyopathy （DCM） of yang deficiency and blood stasis syndrome. MethodsA retrospective cohort study was conducted involving 371 DCM patients with yang deficiency and blood stasis syndrome. The yang-reinforcing and blood-activating therapy was defined as the exposure factor. Patients were categorized into exposure group （186 cases） and non-exposure group （185 cases） according to whether they received yang-reinforcing and blood-activating therapy combined with conventional western medicine for 6 months or longer. The follow-up period was set at 48 months， and the Kaplan-Meier survival analysis was used to assess the cumulative incidence of major adverse cardiovascular events （MACE） in both groups. Cox regression analysis was used to explore the impact of yang-reinforcing and blood-activating therapy on the risk of MACE， and subgroup analysis was performed. Changes in traditional Chinese medicine （TCM） syndrome score， left ventricular ejection fraction （LVEF）， left ventricular fractional shortening （LVFS）， left ventricular end-diastolic diameter （LVEDD）， and Minnesota Living with Heart Failure Questionnaire （MLHFQ） score were compared between groups at the time of first combined use of yang-reinforcing and blood-activating therapy （before treatment） and 1 year after receiving the therapy （after treatment）. ResultsMACE occurred in 31 cases （16.67%） in the exposure group and 47 cases （25.41%） in the non-exposure group. The cumulative incidence of MACE in the exposure group was significantly lower than that in the non-exposure group ［HR=0.559， 95%CI（0.361，0.895）， P=0.014］. Cox regression analysis showed that yang-reinforcing and blood-activating therapy was an independent factor for reducing the risk of MACE in DCM patients ［HR=0.623， 95%CI（0.396，0.980）， P=0.041］， and consistent results were observed in different subgroups. Compared with pre-treatment， the exposure group showed decreased TCM syndrome score and MLHFQ score， reduced LVEDD， and increased LVEF and LVFS after treatment （P＜0.05）； in the non-exposure group， TCM syndrome score decreased， LVEF and LVFS increased， and LVEDD reduced after treatment （P＜0.05）. After treatment， the exposure group had higher LVEF and LVFS， smaller LVEDD， and lower TCM syndrome score and MLHFQ score compared with the non-exposure group （P＜0.05）. ConclusionCombining yang-reinforcing and blood-activating therapy with conventional western medicine can reduce the risk of MACE in DCM patients with yang deficiency and blood stasis syndrome， meanwhile improving their clinical symptoms， cardiac function， and quality of life.

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.

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.

Diabetic retinopathy (DR) is one of the most prevalent and vision-threatening microvascular complications of diabetes mellitus, yet its pathogenesis extends far beyond vascular injury alone. As the retina is among the most energy-demanding tissues in the body, its neurons, glial cells, pigment epithelial cells, pericytes, and endothelial cells are highly dependent on mitochondrial oxidative phosphorylation to maintain visual signal transduction, ionic homeostasis, and neurovascular integrity. This review summarizes current evidence indicating that mitochondrial dysfunction is not merely a downstream consequence of chronic hyperglycemia, but a central pathogenic hub that initiates, amplifies, and perpetuates retinal neurovascular degeneration in DR. Persistent hyperglycemia activates multiple abnormal metabolic pathways, including the polyol pathway, hexosamine pathway, protein kinase C signaling, advanced glycation end-product formation, and angiotensin II-related responses. Although these pathways differ mechanistically, they converge on excessive reactive oxygen species (ROS) generation, antioxidant depletion, and mitochondrial injury. Under diabetic stress, electron transport chain overload promotes mitochondrial ROS leakage, damages mitochondrial DNA, disrupts membrane potential, and impairs the transcription of key respiratory chain components. In parallel, mitochondrial quality-control systems become progressively compromised. The balance between fusion and fission shifts toward pathological fragmentation through reduced MFN1/2 and OPA1 activity and enhanced DRP1-mediated fission. Mitochondrial biogenesis is suppressed through inhibition of the AMPK/SIRT1/PGC-1α/NRF1/TFAM axis, while mitophagy changes from an early compensatory response to a later state of autophagic flux blockade and accumulation of dysfunctional mitochondria. Importantly, damaged mitochondria serve as signal amplifiers linking metabolic stress to inflammation and programmed cell death. Mitochondrial ROS, oxidized mitochondrial DNA, calcium overload, cardiolipin exposure, and membrane permeabilization activate interrelated death pathways, including intrinsic apoptosis, ferroptosis, and pyroptosis. Cytochrome C and apoptosis-inducing factor promote caspase-dependent and caspase-independent apoptosis; iron dyshomeostasis, glutathione depletion, GPX4 dysfunction, and lipid peroxidation drive ferroptosis; and mitochondrial danger signals activate the NLRP3 inflammasome and gasdermin-dependent pyroptosis. These pathways jointly damage the retinal neurovascular unit and contribute to pericyte loss, endothelial barrier breakdown, Müller cell dysfunction, retinal ganglion cell apoptosis, retinal pigment epithelial injury, and photoreceptor degeneration. This review also emphasizes the role of epigenetic regulation in stabilizing mitochondrial pathology. DNA methylation, histone modifications, and non-coding RNAs interact to silence mitochondrial protective genes, alter antioxidant responses, and maintain the “metabolic memory” of DR even after glycemic normalization. Therefore, mitochondrial dysfunction should be understood as a dynamic, multidimensional network rather than a single pathological event. Current clinical approaches, such as laser photocoagulation, intravitreal anti-VEGF therapy, and vitrectomy, mainly target advanced vascular lesions and are limited by invasiveness, incomplete responsiveness, recurrence, and potential adverse effects. Therapeutically, strategies targeting mitochondrial ROS, restoring mitochondrial dynamics, enhancing biogenesis, regulating mitophagy, inhibiting inflammasome activation, correcting epigenetic abnormalities, and improving targeted delivery systems show promising potential. However, major translational barriers remain, including retinal cell heterogeneity, stage-specific mitochondrial responses, insufficient organelle-specific drug delivery, and long-term safety concerns. A deeper understanding of mitochondrial regulatory networks may support earlier, more precise, and multi-target interventions for preventing or slowing DR progression.

ObjectiveTo investigate the relationship between periodontitis severity and brain functional indices-amplitude of low-frequency fluctuations （ALFF） and fractional ALFF （fALFF） in cognitively normal middle-aged and older adults， and to examine their associations with cognitive performance. Methods42 cognitively normal middle-aged and older participants were enrolled. Periodontal data—including the number of remaining teeth （NTP）， bleeding on probing positivity rate （BOP%）， probing depth （PD）， and clinical attachment loss （CAL） as well as resting-state functional magnetic resonance imaging data were collected. Participants were divided into a mild periodontitis group and a moderate-to-severe periodontitis group according to periodontitis severity. Group differences in ALFF and fALFF were compared， and correlation analyses were performed. ResultsCompared with the mild periodontitis group， the moderate-to-severe group showed decreased ALFF values in the right lingual gyrus， superior occipital gyrus， paracentral lobule， and in the left calcarine cortex as well as the precentral and postcentral gyri. Decreased fALFF values were observed in the right lingual gyrus， cuneus， and left calcarine cortex. Indicators related to periodontitis progression ［BOP%， PD=4-5 mm （%）， PD≥6 mm （%）， CAL=3-4 mm （%） and CAL≥5 mm （%）］ were negatively correlated with ALFF， fALFF and cognitive performance， whereas indicators reflecting better periodontal function ［PD=1-3 mm （%） and CAL=0-2 mm （%）］ were positively correlated with these measures. ConclusionIn cognitively normal middle-aged and older adults， periodontitis severity is associated with alterations in brain functional activity and cognitive performance.

Alzheimer’s disease (AD) is a central neurodegenerative disease characterized by progressive cognitive decline and memory impairment in clinical. Currently, there are no effective treatments for AD. In recent years, a variety of therapeutic approaches from different perspectives have been explored to treat AD. Although the drug therapies targeted at the clearance of amyloid β-protein (Aβ) had made a breakthrough in clinical trials, there were associated with adverse events. Neuroinflammation plays a crucial role in the onset and progression of AD. Continuous neuroinflammatory was considered to be the third major pathological feature of AD, which could promote the formation of extracellular amyloid plaques and intracellular neurofibrillary tangles. At the same time, these toxic substances could accelerate the development of neuroinflammation, form a vicious cycle, and exacerbate disease progression. Reducing neuroinflammation could break the feedback loop pattern between neuroinflammation, Aβ plaque deposition and Tau tangles, which might be an effective therapeutic strategy for treating AD. Traditional Chinese herbs such as Polygonum multiflorum and Curcuma were utilized in the treatment of AD due to their ability to mitigate neuroinflammation. Non-steroidal anti-inflammatory drugs such as ibuprofen and indomethacin had been shown to reduce the level of inflammasomes in the body, and taking these drugs was associated with a low incidence of AD. Biosynthetic nanomaterials loaded with oxytocin were demonstrated to have the capability to anti-inflammatory and penetrate the blood-brain barrier effectively, and they played an anti-inflammatory role via sustained-releasing oxytocin in the brain. Transplantation of mesenchymal stem cells could reduce neuroinflammation and inhibit the activation of microglia. The secretion of mesenchymal stem cells could not only improve neuroinflammation, but also exert a multi-target comprehensive therapeutic effect, making it potentially more suitable for the treatment of AD. Enhancing the level of TREM2 in microglial cells using gene editing technologies, or application of TREM2 antibodies such as Ab-T1, hT2AB could improve microglial cell function and reduce the level of neuroinflammation, which might be a potential treatment for AD. Probiotic therapy, fecal flora transplantation, antibiotic therapy, and dietary intervention could reshape the composition of the gut microbiota and alleviate neuroinflammation through the gut-brain axis. However, the drugs of sodium oligomannose remain controversial. Both exercise intervention and electromagnetic intervention had the potential to attenuate neuroinflammation, thereby delaying AD process. This article focuses on the role of drug therapy, gene therapy, stem cell therapy, gut microbiota therapy, exercise intervention, and brain stimulation in improving neuroinflammation in recent years, aiming to provide a novel insight for the treatment of AD by intervening neuroinflammation in the future.

Objective We aimed to evaluate the efficacy and safety of Shengxuebao Mixture in the treatment of cancer-related anemia(CRA)presenting with syndrome of deficiency of liver and kidney combined with syndrome of deficiency of both qi and blood.Methods A randomized,double-blind,placebo-controlled,multicenter clinical trial was conducted.Eligible patients with malignant tumors meeting the inclusion and exclusion criteria were enrolled from 26 hospitals,including Dongzhimen Hospital,Beijing University of Chinese Medicine,Xiaogan Central Hospital,and Yangzhou Hospital of Traditional Chinese Medicine,from June 1,2022,to September 30,2024.Patients were allocated 1:1 to either the experimental group receiving Shengxuebao Mixture or the control group receiving its simulator(placebo)using a block randomization method under double-blind conditions.Both groups received 15 mL orally three times daily for 28 consecutive days.The primary efficacy indicators included the hemoglobin(Hb)improvement rate(RHb)and the traditional Chinese medicine(TCM)syndrome improvement rate(RTCM)at week 4 of treatment.The secondary efficacy indicators encompassed Hb and red blood cell(RBC)count,Karnofsky Performance Status(KPS)score,TCM syndrome score,individual TCM symptom scores,and changes in each of these indicators compared to the baseline period at weeks 2,4,and 6 of treatment.Safety evaluations were conducted at week 4 of treatment.Results A total of 239 patients were enrolled,with 225 cases included in the Full Analysis Set(FAS)(109 in the experimental group vs.116 control group),163 in the Per Protocol Set(PPS)(77 vs.86),and 225 in the Safety Set(SS)(109 vs.116).Baseline characteristics between groups showed no significant differences.Significant differences were observed between the experimental and control groups in RHb at week 4(FAS:49.51%vs.35.24%,P<0.05;PPS:53.25%vs.36.05%,P<0.05)and RTCM at week 4(FAS:61.54%vs.39.62%,P<0.01;PPS:64.94%vs.40.70%,P<0.01).At weeks 2,4,and 6,the experimental group showed greater improvements in Hb and RBC counts than the control group.Additionally,the TCM syndrome scores were lower in the experimental group than in the control group at these time points.Except for week 2 in PPS,the KPS improvement was better in the experimental group than in the control group(P<0.05).The experimental group also demonstrated a greater reduction in scores for individual TCM symptoms such as spiritlessness and weakness,poor appetite and reduced food intake at weeks 4 and 6 compared to the control group(P<0.05,P<0.01).Furthermore,the reduction in vertigo score was more pronounced in the experimental group at week 6(P<0.01).For the score of pale and lusterless complexion,only in the PPS was the reduction from baseline more significant in the experimental group than in the control group at weeks 4 and 6(P<0.05).No significant differences were observed between the experimental and control groups in the incidence of all adverse events or drug-related adverse reactions.Conclusion Shengxuebao Mixture demonstrates significant efficacy in patients with CRA presenting syndrome of deficiency of liver and kidney combined with syndrome of deficiency of both qi and blood,effectively increasing Hb levels,ameliorating TCM syndromes,alleviating clinical symptoms,and enhancing functional status,with no significant difference in adverse drug reactions compared to the placebo.

Objective:To evaluate the ability of ENDOANGEL artificial intelligence system to predict invasion depth and differentiation status of early gastric cancer using more diverse multi-center videos, and to test the performance of the new system upgraded from ENDOANGEL.Methods:Based on the completed 2020 man-machine competition for early gastric cancer diagnosis using single-center videos, the second man-machine competition was conducted in 2022, involving 30 endoscopists from 30 hospitals across 10 Chinese provinces. A multi-center video cohort was retrospectively collected from 12 institutions in 8 provinces/municipalities in China. The study proceeded in 3 stages. First, the ENDOANGEL was re-tested on multi-center videos, its performance on single and multi-center videos was compared, then the ENDOANGEL was upgraded to ENDOANGEL-2022. Second, the second man-machine competition was conducted between ENDOANGEL-2022 and 30 endoscopists using multi-center videos, and the performance between ENDOANGEL-2022, ENDOANGEL and endoscopists on multi-center videos were compared. Third, the ENDOANGEL-2022 was re-tested on the single-center videos previously collected in 2020, its performance on single and multi-center videos was also compared.Results:Compared with the performance on single-center videos, the sensitivity of ENDOANGEL for predicting submucosal invasion of early gastric cancer decreased significantly [18.18% (2/11) VS 70.00% (7/10), P=0.030], but demonstrated comparable ability to predict undifferentiated type of early gastric cancer ( P>0.05). On multi-center videos, in the respect of predicting submucosal invasion of early gastric cancer, the sensitivity of ENDOANGEL-2022 was higher than that of ENDOANGEL [40.00% (4/10) VS 18.18% (2/11), P=0.361], but inferior to that of 30 endoscopists [40.00% VS 52.04% (95% CI: 43.70%-60.38%), P<0.001]. The specificity of ENDOANGEL-2022 was lower than that of ENDOANGEL [82.86% (29/35) VS 100.00% (34/34), χ2=4.41, P=0.036] and higher than that of 30 endoscopists [82.86% VS 68.97% (95% CI: 60.83%-77.11%), P=0.018], the accuracy of ENDOANGEL-2022 was lower than that of ENDOANGEL [73.33% (33/45) VS 80.00% (36/45), χ2=0.56, P=0.455] and higher than that of 30 endoscopists [73.33% VS 65.30% (95% CI: 60.61%-69.99%), P=0.018]. In the respect of predicting undifferentiated type of early gastric cancer, the sensitivity of ENDOANGEL-2022 was higher than that of ENDOANGEL [71.43% (5/7) VS 57.14% (4/7), P>0.999] and 30 endoscopists [71.43% VS 63.11% (95% CI: 55.58%-70.64%), P=0.031], the specificity of ENDOANGEL-2022 was lower than that of ENDOANGEL [76.32% (29/38) VS 78.95% (30/38), χ2=0.08, P=0.783] and higher than that of 30 endoscopists [76.32% VS 65.27% (95% CI: 59.10%-71.44%), P=0.004],the accuracy of ENDOANGEL-2022 was similar to that of ENDOANGEL [75.56% (34/45) VS 75.56% (34/45), χ2=0.00, P>0.999] and higher than that of 30 endoscopists [75.56% VS 65.10% (95% CI: 59.96%- 70.24%), P<0.001]. Compared with performance in single center videos, the sensitivity [40.00% VS 60.00%(6/10), P=0.656], specificity [82.86% VS 93.75% (15/16), χ2=0.37, P=0.542] and accuracy [73.33% VS 80.77% (21/26), χ2=0.50, P=0.479] of ENDOANGEL-2022 for predicting submucosal invasion of early gastric cancer decreased; in predicting undifferentiated type of early gastric cancer, the sensitivity of ENDOANGEL-2022 increased [71.43% VS 37.50% (3/8), P=0.315], while the specificity [76.32% VS 100.00% (18/18), χ2=3.48, P=0.062] and accuracy [75.56% VS 80.77% (21/26), χ2=0.26, P=0.612] decreased. Conclusion:Multi-center cases introduce greater heterogeneity that may reduce artificial intelligence prediction accuracy, but the artificial intelligence system still outperforms endoscopists.