Background: The rerupture or need for revision after anterior cruciate ligament reconstruction (ACLR) is a serious complication. Preventive strategies that target the early identification of risk factors are important to reduce the incidence of additional surgery. Purpose: To perform a systematic review and meta-analysis to investigate risk factors for revision or rerupture after ACLR. Study Design: Systematic review and meta-analysis; Level of evidence, 4. Methods: Literature searches were performed in PubMed, Embase, and Web of Science from database inception to November 2021 and updated in January 2022. Quantitative, original studies reporting potential adjusted risk factors were included. Odds ratios (ORs) were calculated for potential risk factors. Results: A total of 71 studies across 13 countries with a total sample size of 629,120 met the inclusion criteria. Fifteen factors were associated with an increase in the risk of revision or rerupture after ACLR: male sex (OR, 1.27; 95% CI, 1.14-1.41), younger age (OR, 1.07; 95% CI, 1.05-1.08), lower body mass index (BMI) (OR, 1.03; 95% CI, 1.00-1.06), family history (OR, 2.47; 95% CI, 1.50-4.08), White race (OR, 1.32; 95% CI, 1.08-1.60), higher posterolateral tibial slope (OR, 1.15; 95% CI, 1.05-1.26), preoperative high-grade anterior knee laxity (OR, 2.30; 95% CI, 1.46-3.64), higher baseline Marx activity level (OR, 1.07; 95% CI, 1.02-1.13), return to a high activity level/sport (OR, 2.03; 95% CI, 1.15-3.57), an ACLR within less than a year after injury (OR, 2.05; 95% CI, 1.81-2.32), a concomitant medial collateral ligament (MCL) injury (OR, 1.62; 95% CI, 1.31-2.00), an anteromedial portal or transportal technique (OR, 1.36; 95% CI, 1.22-1.51), hamstring tendon (HT) autografts (vs bone–patellar tendon–bone [BPTB] autografts) (OR, 1.60; 95% CI, 1.40-1.82), allografts (OR, 2.63; 95% CI, 1.65-4.19), and smaller graft diameter (OR, 1.21; 95% CI, 1.05-1.38). The other factors failed to show an association with an increased risk of revision or rerupture after ACLR. Conclusion: Male sex, younger age, lower BMI, family history, White race, higher posterolateral tibial slope, preoperative high-grade anterior knee laxity, higher baseline Marx activity level, return to a high activity level/sport, an ACLR within less than a year from injury, a concomitant MCL injury, an anteromedial portal or transportal technique, HT autografts (vs BPTB autografts), allografts, and smaller graft diameter may increase the risk of revision or rerupture after ACLR. Raising awareness and implementing effective preventions/interventions for risk factors are priorities for clinical practitioners to reduce the incidence of revision or rerupture after ACLR.

ObjectivesThe primary aim was to evaluate risk factors for surgical site infections after anterior cruciate ligament reconstruction (ACLR). The secondary aim was to investigate the surgical site infection incidence rate and the mean time to postoperative surgical site infection symptoms.DesignSystematic review and meta-analysis.Data sourcesPubMed, Embase and Web of Science were searched from database inception to September 2021 and updated in April 2022.Eligibility criteriaQuantitative, original studies reporting potential risk factors for surgical site infections after ACLR were included.ResultsTwenty-three studies with 3871 infection events from 469 441 ACLRs met the inclusion criteria. Male sex (OR 1.78, p< 0.00001), obesity (OR 1.82, p=0.0005), tobacco use (OR 1.37, p=0.01), diabetes mellitus (OR 3.40, p=0.002), steroid use history (OR 4.80, p<0.00001), previous knee surgery history (OR 3.63, p=0.02), professional athlete (OR 4.56, p=0.02), revision surgery (OR 2.05, p=0.04), hamstring autografts (OR 2.83, p<0.00001), concomitant lateral extra-articular tenodesis (OR 3.92, p=0.0001) and a long operating time (weighted mean difference 8.12, p=0.005) were identified as factors that increased the risk of surgical site infections (superficial and deep) after ACLR. Age, outpatient or inpatient surgery, bone-patellar tendon-bone autografts or allografts and a concomitant meniscus suture did not increase the risk of surgical site infections. The incidence of surgical site infections after ACLR was approximately 1% (95% CI 0.7% to 1.2%). The mean time from surgery to the onset of surgical site infection symptoms was approximately 17.1 days (95% CI 13.2 to 21.0 days).ConclusionMale sex, obesity, tobacco use, diabetes mellitus, steroid use history, previous knee surgery history, professional athletes, revision surgery, hamstring autografts, concomitant lateral extra-articular tenodesis and a long operation time may increase the risk of surgical site infections after ACLR. Although the risk of surgical site infections after ACLR is low, raising awareness and implementing effective preventions for risk factors are priorities for clinicians to reduce the incidence of surgical site infections due to its seriousness.

Osteoarthritis (OA) is thought to be the most prevalent chronic joint disease. The incidence of OA is rising because of the ageing population and the epidemic of obesity. This research was designed for the identification of novel diagnostic biomarkers for OA and analyzing the possible association between critical genes and infiltrated immune cells. 10 OA samples from patients with spinal OA and 10 normal samples were collected. GSE55235 and GSE55457 datasets including human OA and normal samples were downloaded from the GEO datasets. Differentially expressed genes (DEGs) were identified between 20 OA and 20 controls. SVM-RFE analysis and LASSO regression model were carried out to screen possible markers. The compositional patterns of the 22 types of immune cell fraction in OA were determined by the use of CIBERSORT. The expression level of the biomarkers in OA was examined by the use of RT-PCR. In this study, an overall 44 DEGs were identified: 18 genes were remarkably upregulated and 26 genes were distinctly downregulated. KEGG pathway analyses revealed that pathways were significantly enriched including IL-17 signal path, rheumatoid arthritis, TNF signal path, and lipid and atherosclerosis. Based on the results of machine learning, we identified APOLD1 and EPYC as critical diagnostic genes in OA, which were further confirmed using ROC assays. Immune cell infiltration analysis revealed that APOLD1 was correlated with mastocytes stimulated, NK cells resting, T cells CD4 memory resting, DCs stimulated, T cells gamma delta, macrophages M0, NK cells stimulated, and mastocytes resting. Moreover, we found that EPYC was correlated with mastocytes stimulated, NK cells resting, T cells CD4 memory resting, DCs stimulated, T cells gamma delta, macrophages M0, NK cells stimulated, and mastocytes resting. Overall, our findings might provide some novel clue for the exploration of novel markers for OA diagnosis. The critical genes and their associations with immune infiltration may offer new insight into understanding OA developments.

ObjectiveTo explore the effective components and mechanism of Polygonati Rhizoma (PR) in the treatment of osteoporosis (OP) based on network pharmacology and molecular docking methods.MethodsThe effective components and predicted targets of PR were obtained through the Traditional Chinese Medicine Systems Pharmacology and Analysis Platform (TCMSP) database. The disease database was used to screen the disease targets of OP. The obtained key targets were uploaded to the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database for protein-protein interaction (PPI) network analysis. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of key targets. Analysis and docking verification of chemical effective drug components and key targets were performed with IGEMDOCK software.ResultsA total of 12 chemically active components, 84 drug target proteins and 84 common targets related to drugs and OP were obtained. Key targets such as JUN, TP53, AKT1, ESR1, MAPK14, AR and CASP3 were identified through PPI network analysis. The results of enrichment analysis showed that the potential core drug components regulate the HIF-1 signaling pathway, PI3K-Akt signaling pathway, estrogen signaling pathway and other pathways by intervening in biological processes such as cell proliferation and apoptosis and estrogen response regulation, with an anti-OP pharmacological role. The results of molecular docking showed that the key targets in the regulatory network have high binding activity to related active components.ConclusionsPR may regulate OP by regulating core target genes, such as JUN, TP53, AKT1, ESR1, AR and CASP3, and acting on multiple key pathways, such as the HIF-1 signaling pathway, PI3K-Akt signaling pathway, and estrogen signaling pathway.

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