The Gut Microbiome and Joint Microbiome Show Alterations in Patients with Knee Osteoarthritis Versus Controls: A Systematic Review.

PURPOSE: To assess the current scientific literature on the microbiome's relationship with knee osteoarthritis (OA), with specific focuses on the gut microbiome-joint axis and joint microbiome-joint axis. METHODS: A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines, searching PubMed, Embase, and Cochrane databases for relevant English-language clinical studies on the gut and/or joint microbiomes' association with knee OA in humans. Bias was evaluated using the methodological index for non-randomized studies score. RESULTS: Thirty-five thousand bacterial species comprise the gut microbiome; approximately 90% are members of the phyla Bacteroides and Firmicutes. Symbiosis between the gut microbiome and host under normal physiological conditions positively affects host growth, development, immunity, and longevity. Gut microbiome imbalance can negatively influence various physiological processes, including immune response, inflammation, metabolism, and joint health including development of knee OA. In addition, next generation gene sequencing suggests the presence of microorganisms in the synovial fluid of osteoarthritic knees, and distinct microbiome profiles detected are presumed to play a role in the development of OA. With regard to the gut microbiome, consistent alterations in microbial composition between OA patients and controls are noted, in addition to several associations between certain gut bacteria with OA-related knee pain, patient-reported outcome measure performance, imaging findings, and changes in metabolic and inflammatory pathways. Regarding the joint microbiome, studies revealed increased levels of lipopolysaccharide (LPS) and LPS-binding protein in synovial fluid are associated with activated macrophages, and correlated with worsened osteophyte severity, joint space narrowing, and pain scores in knee OA patients. In addition, studies demonstrated various microbial composition differences in OA patients compared to control, with certain joint microbes directly associated with OA pathogenesis, inflammation, and metabolic dysregulation. CONCLUSIONS: The gut microbiome-joint axis and joint microbiome shows alterations in microbial composition between osteoarthritic patients and controls. These alterations are associated with perturbations of metabolic and inflammatory pathways, imaging findings, osteoarthritis-related pain, and patient reported outcome measure performance. LEVEL OF EVIDENCE: Systematic Review; Level III.

Arthroscopic Debridement of Mild and Moderate Knee Osteoarthritis Results in Clinical Improvement at Short-Term Follow-Up: A Systematic Review.

PURPOSE: To report the clinical outcomes of arthroscopic debridement for the treatment of Kellgren-Lawrence (KL) grade I and II (mild) and III (moderate) knee osteoarthritis (OA) at a minimum 1-year follow-up. METHODS: A systematic review of primary literature was performed in concordance with Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines using the Medline, Embase, and Cochrane databases for studies regarding arthroscopic debridement/chondroplasty for management of knee OA at a minimum 1-year follow-up. Studies were included if they included KL grades I to III or dichotomized clinical outcomes by KL grade. The primary outcome was patient-reported outcome measures (PROMs) at the final follow-up. Bias was assessed using the Methodological Index for Non-Randomized Studies (MINORS) score. RESULTS: Eight studies including a total of 773 patients met inclusion criteria (range of patients in each study, 31-214). Mean age of patients ranged from 35.5 to 64 years, with most studies having a mean patient age of 55 to 65 years. Mean follow-up ranged from 1.5 to 10 years. Seven of the 8 (87.5%) studies reported good to excellent PROMs at a minimum 1- to 4-year follow-up after arthroscopic debridement. Improvements in PROMs were superior in patients with less severe knee OA (KL I-II) in comparison to KL III in most studies. Conversion to arthroplasty ranged from 7.6% to 50% in KL III patients compared with 0% to 4.5% in KL I-II patients after arthroscopic debridement. Two of the 3 studies with at least a 4-year clinical follow-up reported that clinical improvements diminished with time (improvements no longer significant in total Western Ontario and McMaster Universities Osteoarthritis Index score). The lone randomized controlled trial was the only investigation that did not find a benefit of arthroscopic debridement over quality nonoperative care. MINORS scores ranged from 6 to 10 (mean, 8.0) for the 5 nonrandomized studies without controls. CONCLUSIONS: Arthroscopic debridement for the management of mild to moderate knee OA is effective at short-term follow-up in patients who have exhausted conservative care. There is limited evidence demonstrating the durability of improvement following arthroscopic debridement after 2 years. LEVEL OF EVIDENCE: Level IV, systematic review of Level I to IV studies.

Orthopaedic Musculoskeletal Biologics Research Impacts Patient Care: The Second Annual Arthroscopy Orthobiologics Virtual Special Issue.

The field of orthobiologics is rapidly evolving, offering clinicians a shift in treatment from symptom relief to the potential for disease modification and tissue repair. These agents, derived from autologous tissues, components of blood, and growth factors, are used as surgical adjuncts or as standalone treatments. Their clinical applications are expanding to encompass a variety of conditions, supported by a growing base of research efforts. Arthroscopy and its companion publications are committed to evidence-based research with a robust history of publications that enhance clinical decision-making and impact patient care. This curated collection of articles highlights the year's most compelling advancements in orthopaedic musculoskeletal biologics research.

Computational Modeling of Virally-encoded Ion Channel Structure.

Viroporins are ion channels encoded within a virus's genome, that facilitate a range of devastating infectious diseases such as COVID-19, HIV, and rotavirus. The non-structural protein 4 (NSP4) from rotavirus includes a viroporin domain that disrupts cellular Ca2+ homeostasis, initiating viral replication, and leading to life-threatening vomiting and diarrhea. Though the structure of soluble segments of NSP4 has been determined, membrane-associated regions, including the viroporin domain, remain elusive when utilizing well-established available experimental methods such as x-ray crystallography. However, two recently published protein folding algorithms, AlphaFold2 and trRosetta, demonstrated a high degree of accuracy, when determining the structure of membrane proteins from their primary amino acid sequences, though their training datasets are known to exclude proteins from viral systems. We tested the ability of these non-viral algorithms to predict functional molecular structures of the full-length NSP4 from SA11 rotavirus. We also compared the accuracy of these structures to predictions of other experimental structures of eukaryotic proteins from the Protein Data Banks (PDB), and show that the algorithms predict models more similar to corresponding experimental data than what we saw for the viroporin structure. Our data suggest that while AlphaFold2 and trRosetta each produced distinct NSP4 models, constructs based on either model showed viroporin activity when expressed in E. coli, consistent with that seen from other historical NSP4 sequences.

Global convergence of the EM algorithm for unconstrained latent variable models with categorical indicators.

Convergence of the expectation-maximization (EM) algorithm to a global optimum of the marginal log likelihood function for unconstrained latent variable models with categorical indicators is presented. The sufficient conditions under which global convergence of the EM algorithm is attainable are provided in an information-theoretic context by interpreting the EM algorithm as alternating minimization of the Kullback-Leibler divergence between two convex sets. It is shown that these conditions are satisfied by an unconstrained latent class model, yielding an optimal bound against which more highly constrained models may be compared.