Annexin A3（ANXA3）is a member of the membrane associated protein family. It has two subtypes of 36 kDa and 33 kDa. Its gene is located on the fourth chromosome of human. ANXA3, widely expressed in human bone marrow, lung, placenta, prostate and thyroid, is closely related to several biological processes such as exoplasmosis, vascular production, fat cell maturity, and white blood cell migration. Studies have found that ANXA3 is abnormally expressed in various diseases including cancer, cardiovascular disease and inflammation. It can regulate multiple signaling pathways such as JNK, NF-κB, PI3K/AKT, and may become a potential drug target for treatment of related diseases. The structure, functions, the link with diseases and related mechanisms of ANXA3 were summarized in this paper, which could provide reference for ANXA3 related research.

BACKGROUND: G protein-coupled receptor kinase 2 (GRK2) could participate in the regulation of diverse cells via interacting with non-G-protein-coupled receptors. In the present work, we explored how paroxetine, a GRK2 inhibitor, modulates the differentiation and activation of immune cells in rheumatoid arthritis (RA). METHODS: The blood samples of healthy individuals and RA patients were collected between July 2021 and March 2022 from the First Affiliated Hospital of Anhui Medical University. C57BL/6 mice were used to induce the collagen-induced arthritis (CIA) model. Flow cytometry analysis was used to characterize the differentiation and function of dendritic cells (DCs)/T cells. Co-immunoprecipitation was used to explore the specific molecular mechanism. RESULTS: In patients with RA, high expression of GRK2 in peripheral blood lymphocytes, accompanied by the increases of phosphatidylinositol 3 kinase (PI3K), protein kinase B (AKT), and mammalian target of rapamycin (mTOR). In animal model, a decrease in regulatory T cells (T regs ), an increase in the cluster of differentiation 8 positive (CD8 + ) T cells, and maturation of DCs were observed. Paroxetine, when used in vitro and in CIA mice, restrained the maturation of DCs and the differentiation of CD8 + T cells, and induced the proportion of T regs . Paroxetine inhibited the secretion of pro-inflammatory cytokines, the expression of C-C motif chemokine receptor 7 in DCs and T cells. Simultaneously, paroxetine upregulated the expression of programmed death ligand 1, and anti-inflammatory cytokines. Additionally, paroxetine inhibited the PI3K-AKT-mTOR metabolic pathway in both DCs and T cells. This was associated with a reduction in mitochondrial membrane potential and changes in the utilization of glucose and lipids, particularly in DCs. Paroxetine reversed PI3K-AKT pathway activation induced by 740 Y-P (a PI3K agonist) through inhibiting the interaction between GRK2 and PI3K in DCs and T cells. CONCLUSION Paroxetine exerts an immunosuppressive effect by targeting GRK2, which subsequently inhibits the metabolism-related PI3K-AKT-mTOR pathway of DCs and T cells in RA.
G-Protein-Coupled Receptor Kinase 2/metabolism* ; Arthritis, Rheumatoid/immunology* ; Animals ; Dendritic Cells/metabolism* ; Paroxetine/therapeutic use* ; Proto-Oncogene Proteins c-akt/metabolism* ; Mice ; Humans ; Mice, Inbred C57BL ; Signal Transduction/drug effects* ; Male ; Phosphatidylinositol 3-Kinases/metabolism* ; Lymphocyte Activation/drug effects* ; Female ; T-Lymphocytes/metabolism* ; Middle Aged

Natural product-based drug combinations (NPDCs) present distinctive advantages in treating complex diseases. While high-throughput screening (HTS) and conventional computational methods have partially accelerated synergistic drug combination discovery, their applications remain constrained by experimental data fragmentation, high costs, and extensive combinatorial space. Recent developments in artificial intelligence (AI), encompassing traditional machine learning and deep learning algorithms, have been extensively applied in NPDC identification. Through the integration of multi-source heterogeneous data and autonomous feature extraction, prediction accuracy has markedly improved, offering a robust technical approach for novel NPDC discovery. This review comprehensively examines recent advances in AI-driven NPDC prediction, presents relevant data resources and algorithmic frameworks, and evaluates current limitations and future prospects. AI methodologies are anticipated to substantially expedite NPDC discovery and inform experimental validation.
Artificial Intelligence ; Biological Products/chemistry* ; Humans ; Drug Combinations ; Drug Discovery/methods* ; Machine Learning ; Algorithms

OBJECTIVE: To investigate the association between fluid balance trajectories within the first 3 days of intensive care unit (ICU) admission and 28-day mortality as well as the incidence of continuous renal replacement therapy (CRRT) in patients with severe acute pancreatitis (SAP). METHODS: Clinical data of SAP patients were extracted from the Medical Information Mart of Intensive Care-IV (MIMIC-IV). Group-based trajectory modeling (GBTM) was used to analyze the daily fluid balance of patients within 3 days of ICU admission, and grouping them accordingly. Univariate and multivariate Logistic regression analyses were performed to assess the association between fluid balance trajectory and 28-day mortality and ICU CRRT in SAP patients. RESULTS: A total of 251 SAP patients were included, with 33 deaths within 28 days, and a 28-day mortality of 13.15%; 49 patients (19.52%) continued to receive bedside CRRT after 3 days of ICU admission. The fluid balance on the 3rd day, cumulative fluid balance within 3 days of ICU admission, and incidence of CRRT in the death group were significantly higher than those in the survival group. According to GBTM groups, there were 127 cases in the moderate fluid resuscitation with rapid reduction (MF group), 44 cases in the large fluid resuscitation with rapid reduction (LF group), 20 cases in the moderate fluid resuscitation with slow reduction (MS group), and 60 cases in the small fluid resuscitation with slow reduction (SS group). The cumulative fluid balance within 3 days of ICU admission of the MF group, LF group, MS group, and SS group were 8.60% (5.15%, 11.70%), 16.70% (13.00%, 21.02%), 23.40% (19.38%, 25.45%), and 0.65% (-2.35%, 2.20%), respectively, and the incidence of CRRT during ICU hospitalization were 11.02%, 29.55%, 85.00%, and 8.33%, respectively, with statistically significant differences among the groups (both P < 0.05); the 28-day mortality were 11.02%, 18.18%, 20.00%, and 11.67%, respectively, with no statistically significant difference among the groups (P > 0.05). Kaplan-Meier survival curve analysis showed there was no statistically significant difference in 28-day cumulative survival rate among groups with different fluid balance trajectories (Log-rank test: χ ² = 2.31, P = 0.509). Multivariate Logistic regression analysis showed that cumulative fluid balance within 3 days of ICU admission was an independent risk factor for 28-day mortality [odds ratio (OR) = 1.071, 95% confidence interval (95%CI) was 1.005-1.144, P = 0.040] and CRRT requirement (OR = 1.233, 95%CI was 1.125-1.372, P < 0.001); early aggressive fluid resuscitation on day 1 reduced CRRT risk (OR = 0.866, 95%CI was 0.756-0.978, P = 0.030). CONCLUSIONS Dynamic fluid management is essential in SAP patients. While early aggressive fluid resuscitation may reduce CRRT demand, excessive cumulative fluid balance is associated with increased 28-day mortality and CRRT incidence.
Humans ; Male ; Female ; Middle Aged ; Water-Electrolyte Balance ; Continuous Renal Replacement Therapy ; Intensive Care Units ; Aged ; Adult ; Pancreatitis/mortality* ; Logistic Models ; Retrospective Studies ; Renal Replacement Therapy

Abstract In recent years, the prevalence of overweight and obesity among children and adolescents has risen rapidly, posing a serious threat to their physical and mental health. To provide scientific, systematic, and standardized weight management guidance for overweight and obese children and adolescents, the study focuses on the core concept of healthy lifestyle intervention, integrates multidisciplinary expert opinions and research findings,and proposes a comprehensive multidisciplinary intervention framework covering scientific exercise intervention, precise nutrition and diet, optimized sleep management, and standardized psychological support. It calls for the establishment of a multi agent collaborative management mechanism led by the government, implemented by families, fostered by schools, initiated by individuals, optimized by communities, reinforced by healthcare, and coordinated by multiple stakeholders. Emphasizing a child and adolescent centered approach, the consensus advocates for comprehensive, multi level, and personalized guidance strategies to promote the internalization and maintenance of a healthy lifestyle. It serves as a reference and provides recommendations for the effective prevention and control of overweight and obesity, and enhancing the health level of children and adolescents.

The increasing prevalence of metabolic diseases poses a significant threat to public health,high-lighting the urgent need to identify effective therapeutic targets.Humanin(HN),a 24-amino acid linear peptide synthe-sized from mitochondrial DNA,has been previously acknowledged for its neuroprotective properties.Recent research indi-cates that HN can regulate glucose and lipid metabolism and has shown promise in treating metabolic disorders in animal models using either HN or its analogs.This review outlines the fundamental biological functions and signal transduction mechanisms of HN,and comprehensively evaluates its role and therapeutic potential in type 2 diabetes mellitus,nonalco-holic fatty liver disease,atherosclerosis and polycystic ovary syndrome,aiming to examine the potential of HN as a target for the prevention and treatment of metabolic diseases.

Objective:To investigate the value of systemic inflammatory response syndrome (SIRS), pediatric sequential organ failure assessment (pSOFA) and pediatric critical illness score (PCIS) in predicting mortality of pediatric sepsis in pediatric intensive care units (PICU) from Southwest China.Methods:This was a prospective multicenter observational study. A total of 447 children with sepsis admitted to 12 PICU in Southwest China from April 2022 to March 2023 were enrolled. Based on the prognosis, the patients were divided into survival group and non-survival group. The physiological parameters of SIRS, pSOFA and PCIS were recorded and scored within 24 h after PICU admission. The general clinical data and some laboratory results were recorded. The area under the curve (AUC) of the receiver operating characteristic curve was used to compare the predictive value of SIRS, pSOFA and PCIS in mortality of pediatric sepsis.Results:Amongst 447 children with sepsis, 260 patients were male and 187 patients were female, aged 2.5 (0.8, 7.0) years, 405 patients were in the survival group and 42 patients were in the non-survival group. 418 patients (93.5%) met the criteria of SIRS, and 440 patients (98.4%) met the criteria of pSOFA≥2. There was no significant difference in the number of items meeting the SIRS criteria between the survival group and the non-survival group (3(2, 4) vs. 3(3, 4) points, Z=1.30, P=0.192). The pSOFA score of the non-survival group was significantly higher than that of the survival group (9(6, 12) vs. 4(3, 7) points, Z=6.56, P<0.001), and the PCIS score was significantly lower than that of the survival group (72(68, 81) vs. 82(76, 88) points, Z=5.90, P<0.001). The predictive value of pSOFA (AUC=0.82) and PCIS (AUC=0.78) for sepsis mortality was significantly higher than that of SIRS (AUC=0.56) ( Z=6.59, 4.23, both P<0.001). There was no significant difference between pSOFA and PCIS ( Z=1.35, P=0.176). Platelet count, procalcitonin, lactic acid, albumin, creatinine, total bilirubin, activated partial thromboplastin time, prothrombin time and international normalized ratio were all able to predict mortality of sepsis to a certain degree (AUC=0.64, 0.68, 0.80, 0.64, 0.68, 0.60, 0.77, 0.75, 0.76, all P<0.05). Conclusion:Compared with SIRS, both pSOFA and PCIS had better predictive value in the mortality of pediatric sepsis in PICU.

OBJECTIVE To analyze the chemical constituents and components absorbed into plasma of the extract of Ardisia crenata and to elucidate its possible pharmacodynamic material basis. METHODS Overall， 12 rats were randomly assigned to the blank group （n＝6） and A. crenata group （n＝6） by the paired comparison method. The drug was administered once daily in the morning and afternoon for three days. Serum samples were prepared from serum after redosing on 4th day. The UPLC-QE-HF-MS/ MS was used to analyze and identify the chemical constituents in A. crenata extract and serum samples. Compound Discoverer 3.0 was employed for retention time correction， peak identification， and peak extraction. According to the secondary mass spectrometry information， the Thermo mzCloud online and Thermo mzVault local databases， referring to the relevant literature and control quality spectrum information were used to preliminarily identify the chemical constituents and components absorbed into plasma of A. crenata. RESULTS A total of 34 compounds were identified from the extract of A. crenata， mainly coumarins， flavonoids， organic acids， amino acids， including bergenin， quercetin， gallic acid， L-pyroglutamic acid， etc. Besides， 5 components absorbed into plasma were identified from serum samples: L-pyroglutamic acid， syringic acid， bergenin， cinnabar root saponin A， and mycophenolic acid. CONCLUSIONS L-pyroglutamic acid， syringic acid， bergenin， cinnabar root saponin A， and mycophenolic acid may act as the pharmacodynamic material basis of A. crenata.

Objective To predict the core targets and action pathways of Hedysari Radix based on UPLC-MS/MS and network pharmacology methods,and to verify the results of network pharmacology by molecular docking and molecular dynamics techniques.This article aims to investigate immune regulation mechanism of effective components absorbed into blood from Hedysari Radix.Methods Qualitative quantification of effective components absorbed into blood from Hedysari Radix were operated by using UPLC-MS/MS technique.The corresponding targets of effective components absorbed into blood from Hedysari Radix were screened by TCMSP and HERB databases.Targets of immune-related disease were obtained through DisGeNET,OMIM,TTD,and MalaCards databases.The network of"components absorbed into blood from Hedysari Radix-immune-related diseases"was then constructed.GO and KEGG enrichment analysis and mapped the PPI network were performed.Molecular docking and molecular dynamics techniques were applied for validation.Results A total of 8 prototype components absorbed into blood,synergistically acting on 101 targets,were identified by UPLC-MS/MS.They mediated 538 biological processes including immune response,positive regulation of gene expression,receptor binding,and cytokine activity.Meanuhile,116 signaling pathways,such as HIF-1,Toll-like receptor,JAK-STAT,T cell receptor,PI3K-Akt,and FoxO etc.were involved.The core targets were MAPK14,PTGS2,MMP9,PPARG,CCND1,etc..The results of molecular docking showed that formononetin and calycosin had strong docking binding activity with MAPK14.And molecular dynamics simulations further demonstrated that the binding between MAPK14 and formononetin or calycosin had good structural stability and binding affinity.Conclusion The results of serum pharmacochemistry,network pharmacology and molecular dynamics were verified to reveal the material basis and mechanism of Hedysari Radix in regulating immunity.The aim of this study is to provide scientific basis for its immunomodulatory mechanism.