BACKGROUND:At present,osteoarthritis has become a major disease affecting the quality of life of the elderly,and the therapeutic effect is poor,often focusing on preventing the disease process,and the pathogenesis of osteoarthritis is still not fully understood.Bioinformatics analysis was carried out to explore the main pathogenesis of osteoarthritis and related mechanisms of gene coding regulation. OBJECTIVE:To screen core differential genes with a major role in osteoarthritis by gene expression profiling. METHODS:Datasets were downloaded from the Gene Expression Omnibus(GEO):GSE114007,GSE117999,and GSE129147.Differential genes in the GSE114007 and GSE117999 data collections were screened using R software,performing differential genes to weighted gene co-expression network analysis.The module genes most relevant to osteoarthritis were selected to perform protein interaction analysis.Candidate core genes were selected using the cytocape software.The candidate core genes were subsequently subjected to least absolute shrinkage and selection operator regression and COX analysis to identify the core genes with a key role in osteoarthritis.The accuracy of the core genes was validated using an external dataset,GSE129147. RESULTS AND CONCLUSION:(1)A total of 477 differential genes were identified,265 differential genes associated with osteoarthritis were obtained by weighted gene co-expression network analysis,and 8 candidate core genes were identified.The least absolute shrinkage and selection operator regression analysis finally yielded a differential gene ASPM with core value that was externally validated.(2)It is concluded that abnormal gene ASPM expression screened by bioinformatics plays a key central role in osteoarthritis.

Type 2 diabetes mellitus(T2DM) is a prevalent metabolic and endocrine disorder. Long-term hyperglycemia can lead to severe chronic complications, imposing substantial economic burdens on both society and patients. Despite the availability of various hypoglycemic agents for clinical use, these agents often fail to meet the therapeutic needs of T2DM and its complications. Consequently, there is an urgent need for novel therapeutic strategies and drugs. Lithocarpus litseifolius(L. litseifolius), commonly referred to as "cordyceps on trees", has a long history of use in traditional medicine and can be applied in tea, sugar, and medicine. Research indicates that L. litseifolius extracts are rich in dihydrochalcones, including trilobatin, phloridzin, and phloretin, which exhibit a range of pharmacological activities, such as anti-inflammatory, antioxidant, hypoglycemic, hypolipidemic, hepatoprotective, and cardioprotective effects. These properties suggest potential applications in the treatment of T2DM and its complications. This review systematically compiled and organized the relevant literature from the past decade on dihydrochalcones(trilobatin, phloridzin, and phloretin) from L. litseifolius extracts. It highlighted recent research progress regarding their role in treating T2DM and its complications through mechanisms such as reducing insulin resistance, regulating glucose transport, improving glucose and lipid metabolism, modulating enzyme activity, regulating gut microbiota, and alleviating inflammation and oxidative damage. The purpose of this review is to provide a reference and basis for future research on the prevention and treatment of T2DM and its complications using dihydrochalcones(trilobatin, phloridzin, and phloretin) from L. litseifolius extracts.
Chalcones/chemistry* ; Diabetes Mellitus, Type 2/metabolism* ; Humans ; Animals ; Elaeocarpaceae/chemistry* ; Drugs, Chinese Herbal/therapeutic use* ; Hypoglycemic Agents/chemistry* ; Plant Extracts/chemistry*

OBJECTIVE: To explore the functional rehabilitation of patients with rheumatoid arthritis (RA) after total knee arthroplasty (TKA). METHODS: A retrospective analysis was conducted on 101 patients who needed TKA due to rheumatoid arthritis (RA) involving both knees from January 2017 to December 2020, including 16 males and 85 females, aged from 41 to 65 years old with an average of (58.13±5.53) years old;body mass index (BMI) ranged from 16.88 to 33.33 kg·m^-2 with an average of (23.16±3.49) kg·m^-2;63 patients with grade 1, 29 patients with grade 2, and 9 patients with grade 3 according to classification of American Society of Anesthesiologists (ASA). According to the latest follow-up results at 12 months after operation, 82 patients returned to work and 19 patients did not return to work. Visual analogue scale(VAS) was used to evaluate the degree of pain relief before operation and 12 months after operation, and work, osteoarthritis and joint replacement questionnaire (WORQ) was used to evaluate knee joint activity status of all patients before and after operation, and the working ability index was used to evaluate working ability of all patients before operation and 12 months after operation. For the 82 patients who returned to work, the labor time stopped before operation and within 12 months after operation was compared, and the changes in labor grades, types of work and labor hours of patients before and after operation were recorded. For the 19 patients who did not return to work, the specific reasons for their non-return to work was analyzed;the postoperative satisfaction of patients was evaluated by using Likert satisfaction scale. All patients were followed up for at least 12 months. VAS was decreased from (6.49±0.59) before operation to (1.10±0.43) at 12 months after operation (P<0.05);for WORQ questionnaire survey, scores of walking, sitting posture, standing and stair climbing were increased from (1.07±0.35), (1.05±0.29), (1.06±0.34) and (1.14±0.42) before operation to (3.00±0.00), (2.87±0.33), (2.95±0.21) and (2.95±0.21) after operation, respectively, had statistically significant (P<0.05);the labor work index of all patients increased from 1.11±0.46 before operation to 2.99±0.10 at 12 months after operation, and the difference was statistically significant (P<0.05). Among the 82 patients who returned to work after operation, regarding the time of stopping labor, 81 patients stopped working within 3 months before operation, 1 patient stopped working for 4 to 6 months after operation, and the number of patients who stopped working was 81, 1, and 0 respectively. Forty patients returned to work within 3 months after operation, 4 to 6 months after operation for 29 patients, and 12 months after operation for 13 patients. 95.1% (78/82) of patients engaged in light labor before operation, and 85.4% (70/82) of patients engaged in moderate labor after operation. At 12 months after operation, the types of jobs and working hours available to all patients increased compared with those before operation. Among 19 patients who did not return to work after TKA, 7 patients had poor control of rheumatoid arthritis, 5 patients still felt pain, swelling and numbness on knee joint, 2 patients had retired, and 5 patients had other reasons. Eighty-six patients (85%) expressed great satisfaction with the postoperative working ability, 8 patients (8%) expressed satisfaction with the postoperative working ability, 6 patients (6%) expressed acceptance of postoperative working ability, and 1 patient (1%) expressed dissatisfaction with postoperative working ability. CONCLUSION TKA is an effective treatment option for patients with RA. After undergoing TKA, patients could significantly improve pain and functional activities of knee joint, and effectively enhance the quality of life and working ability. For patients whose rehabilitation labor capacity is not fully met, postoperative management and personalized rehabilitation treatment need to be strengthened to achieve the best rehabilitation effect.
Humans ; Female ; Male ; Arthroplasty, Replacement, Knee/rehabilitation* ; Arthritis, Rheumatoid/physiopathology* ; Middle Aged ; Aged ; Retrospective Studies ; Adult

The interest in covalent drugs has resurged in recent decades, spurring the development of numerous specialized computational docking tools to facilitate covalent ligand design and screening. Herein, we present CarsiDock-Cov, a new paradigm distinguishing itself as the first deep learning (DL)-guided approach for covalent docking. CarsiDock-Cov retains the core components of its non-covalent predecessor, leveraging a DL model pretrained on millions of docking complexes to predict protein-ligand distance matrices, along with a dedicated-designed geometric optimization procedure to convert these distances into refined binding poses. Additionally, it incorporates several key enhancements specifically tailored to optimize the protocol for covalent docking applications. Our approach has been extensively validated on multiple public datasets regarding the docking and screening of covalent ligands, and the results indicate that our approach not only achieves comparably improved applicability compared to its non-covalent predecessor, but also exhibits competitive performance against various state-of-the-art covalent docking tools. Collectively, our approach represents a significant advance in covalent docking methodology, offering an automated and efficient solution that shows considerable promise for accelerating covalent drug discovery and design.

Background::Findings on the association of genetic factors and colorectal cancer (CRC) survival are limited and inconsistent, and revealing the mechanism underlying their prognostic roles is of great importance. This study aimed to explore the relationship between functional genetic variations and the prognosis of CRC and further reveal the possible mechanism.Methods::We first systematically performed expression quantitative trait locus (eQTL) analysis using The Cancer Genome Atlas (TCGA) dataset. Then, the Kaplan-Meier analysis was used to filter out the survival-related eQTL target genes of CRC patients in two public datasets (TCGA and GSE39582 dataset from the Gene Expression Omnibus database). The seven most potentially functional eQTL single nucleotide polymorphisms (SNPs) associated with six survival-related eQTL target genes were genotyped in 907 Chinese CRC patients with clinical prognosis data. The regulatory mechanism of the survival-related SNP was further confirmed by functional experiments.Results::The rs71630754 regulating the expression of endoplasmic reticulum aminopeptidase 1 ( ERAP1) was significantly associated with the prognosis of CRC (additive model, hazard ratio [HR]: 1.43, 95% confidence interval [CI]: 1.08-1.88, P = 0.012). The results of dual-luciferase reporter assay and electrophoretic mobility shift assay showed that the A allele of the rs71630754 could increase the binding of transcription factor 3 (TCF3) and subsequently reduce the expression of ERAP1. The results of bioinformatic analysis showed that lower expression of ERAP1 could affect the tumor immune microenvironment and was significantly associated with severe survival outcomes. Conclusion::The rs71630754 could influence the prognosis of CRC patients by regulating the expression of the immune-related gene ERAP1. Trial Registration::No. NCT00454519 (https://clinicaltrials.gov/)

Objective To explore the metastasis rate and related risk factors of T1 stage colonic neuroendocrine tumor(C-NET),and to compare the long-term survival outcomes of patients with non-metastatic(T1N0M0 stage)C-NET after local excision(LE)or radical surgery(RS).Methods Clinical information of 433 patients diagnosed with C-NET in the SEER database from January 1,2004 to December 31,2015 were analyzed.Cox regression was used to analyze the influencing factors of metastasis of C-NET.The patients without metastasis were divided into LE group and RS group,and assigned in a 1∶1 ratio using propensity score matching(PSM)according to gender,age,tumor largest diameter,and infiltration depth,with a caliper value set to 0.02.Kaplan-Meier survival curve was used to analyze 5-year cancer-specific survival(CSS)and overall survival(OS)of patients.Cox regression analysis was used to evaluate the influence of metastasis on survival.Results Among 419 C-NET patients,19(4.52%)had distant metastases.Cox regression analysis showed that 11-20 mm of tumor large diameter(HR=9.264,95%CI 3.322-25.835,P＜0.001),right colon location(HR=0.116,95%CI 0.042-0.321,P＜0.001),and submucosal invasion(HR=5.842,95%CI 1.858-18.371,P=0.003)were independent risk factors for distant metastasis of T1 stage C-NET.The 5-year OS rates of non-metastatic and metastatic patients were 94.5%and 47.4%,respectively(χ2=79.762,P＜0.001),and their 5-year CSS rates were 99.5%and 55.7%,respectively(χ2=164.604,P＜0.001).Before PSM,the 5-year OS rates of non-metastatic C-NET patients after LE and RS were 95.8%and 90.1%(χ2=2.679,P=0.063),and the 5-year CSS rates were 100.0%and 97.2%(χ2=0.579,P=0.038);after PSM,the 5-year OS rates of non-metastatic patients after LE and RS were 96.8%and 92.1%(χ2=3.606,P=0.058),and the 5-year CSS rates were 100.0%and 98.5%(χ2=1.015,P=0.314).After PSM,there was no significant difference in the 5-year OS and CSS of patients with defferent tumor location,tumor large diameter,or submucosal invasion between the LE and RS groups.Conclusions 11-20 mm of tumor diameter,right colon location,and submucosal invasion might be independent risk factors for distant metastasis of T1 stage C-NET,and LE could be an appropriate treatment option for non-metastatic C-NET.

Objective To establish a nomogram model for efficiently predicting overall survival(OS)and cancer-specific survival(CSS)in patients with CRCHBM.Method 2239 patients from 2010 to 2019 were retrospectively analyzed from the Surveillance,Epidemiology,and End Results Program(SEER)databases and Wuhan Union Hospital Cancer Center.SEER is randomly assigned to the training and internal validation cohorts,and the Wuhan database serves as the external validation.Cox regression analyses were used to determine the independent clinicopathological prognosis factors affecting OS and CSS,and a nomogram was constructed to predict OS and CSS.The clinical utility of columnar plots was assessed using calibration curves,area under the curve(AUC),and decision curve analysis(DCA).Result OS column line graphs were constructed based on nine independent predictors:age,tumor location,degree of differentiation,tumor size,TNM stage,chemotherapy,primary focus surgery,number of lymph nodes sampled,and serum carcinoembryonic antigen(CEA)level.The C-index of the nomogram to predict the 1-,3-,and 5-year OS were 0.764,0.790,and 0.805 in the training group,0.754,0.760,and 0.801 in the internal validation group,and 0.822,0.874,and 0.906 in the external validation group.CSS column line graphs were constructed based on 3 independent predictors of TNM staging,radiotherapy and chemotherapy.The 1-,3-,and 5-year CSS AUROC values of the training group were 0.791,0.757,and 0.782,respectively.0.682,0.709,0.625 in the internal validation group and 0.759,0.702,0.755 in the external validation group,respectively.The results of receiver operating characteristic curve(ROC),ROC and DCA showed that the use of our model was more effective in predicting OS and CSS than other single clinicopathological features.Conclusion In summary,the nomogram based on significant clinicopathological features can be conveniently used to predict OS and CSS individually in patients with CRCHBM.