A flexible and highly sensitive organic electrochemical transistor-based biosensor for continuous and wireless nitric oxide detection.

As nitric oxide (NO) plays significant roles in a variety of physiological processes, the capability for real-time and accurate detection of NO in live organisms is in great demand. Traditional assessments of NO rely on indirect colorimetric techniques or electrochemical sensors that often comprise rigid constituent materials and can hardly satisfy sensitivity and spatial resolution simultaneously. Here, we report a flexible and highly sensitive biosensor based on organic electrochemical transistors (OECTs) capable of continuous and wireless detection of NO in biological systems. By modifying the geometry of the active channel and the gate electrodes of OECTs, devices achieve optimum signal amplification of NO. The sensor exhibits a low response limit, a wide linear range, high sensitivity, and excellent selectivity, with a miniaturized active sensing region compared with a conventional electrochemical sensor. The device demonstrates continuous detection of the nanomolar range of NO in cultured cells for hours without significant signal drift. Real-time and wireless measurement of NO is accomplished for 8 d in the articular cavity of New Zealand White rabbits with anterior cruciate ligament (ACL) rupture injuries. The observed high level of NO is associated with the onset of osteoarthritis (OA) at the later stage. The proposed device platform could provide critical information for the early diagnosis of chronic diseases and timely medical intervention to optimize therapeutic efficacy.

Artificial intelligence in osteoarthritis detection: A systematic review and meta-analysis.

OBJECTIVES: As an increasing number of studies apply artificial intelligence (AI) algorithms in osteoarthritis (OA) detection, we performed a systematic review and meta-analysis to pool the data on diagnostic performance metrics of AI, and to compare them with clinicians' performance. MATERIALS AND METHODS: A search in PubMed and Scopus was performed to find studies published up to April 2022 that evaluated and/or validated an AI algorithm for the detection or classification of OA. We performed a meta-analysis to pool the data on the metrics of diagnostic performance. Subgroup analysis based on the involved joint and meta-regression based on multiple parameters were performed to find potential sources of heterogeneity. The risk of bias was assessed using Prediction Model Study Risk of Bias Assessment Tool reporting guidelines. RESULTS: Of the 61 studies included, 27 studies with 91 contingency tables provided sufficient data to enter the meta-analysis. The pooled sensitivities for AI algorithms and clinicians on internal validation test sets were 88% (95% confidence interval [CI]: 86,91) and 80% (95% CI: 68,88) and pooled specificities were 81% (95% CI: 75,85) and 79% (95% CI: 80,85), respectively. At external validation, the pooled sensitivity and specificity for AI algorithms were 94% (95% CI: 90,97) and 91% (95% CI: 77,97), respectively. CONCLUSION: Although the results of this meta-analysis should be interpreted with caution due to the potential pitfalls in the included studies, the promising role of AI as a diagnostic adjunct to radiologists is indisputable.

Is there a role for autologous conditioned serum injections in osteoarthritis? A systematic review and meta-analysis of randomised controlled trials.

OBJECTIVE: To assess whether patient reported outcome measures (PROMs) improve after autologous conditioned serum (ACS) administration in patients with osteoarthritis. METHODS: Databases and clinical trial registers were searched to March 2024 for randomised controlled trial (RCTs) comparing ACS vs comparators/controls. Primary outcomes were pain, function and stiffness measured with Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and visual analogue scale (VAS). Secondary outcome was complications. Risk of bias (RoB) and certainty of evidence were assessed using RoB 2 and the Grades of Recommendation, Assessment, Development and Evaluation Working Group (GRADE) respectively. Meta-analysis was undertaken using RevMan v5.4. Results are presented as standardised mean differences (SMD) or mean differences (MD) with 95% confidence intervals (CI). Sensitivity analysis compared all comparators and saline control. RESULTS: Five RCTs were identified (n = 741 participants); two (n = 529 participants) compared ACS against saline (placebo). Three studies were "some concern" and two studies "high risk" for bias. Analysis comparing ACS with all comparators significantly favoured ACS at 6 months for WOMAC: SMD -0.61 (95% CI -1.01 to -0.21; p = 0.003); and VAS: SMD -1.24 (95% CI -2.11 to -0.38; p = 0.005); with high heterogeneity. Comparing ACS with saline, there was no significant difference in WOMAC or VAS at 6 months: SMD -0.40 (95% CI -0.93 to 0.12; p = 0.13) and MD -9.87 (95% CI -27.73 to 7.98, p = 0.28). Complications were similar: ACS (24.8%) vs saline (24.4%), with serious complications rare. CONCLUSION: There is currently insufficient data to support the use of ACS in osteoarthritis with conflicting results when compared to alternative therapies and saline control, with high heterogeneity. Before consideration as a potential treatment, a high-quality multicentre RCT is required to assess the efficacy of ACS.

Identification of ion channel-related genes as diagnostic markers and potential therapeutic targets for osteoarthritis through bioinformatics and machine learning-based approaches.

BACKGROUND: Osteoarthritis (OA) is a debilitating joint disorder characterized by the progressive degeneration of articular cartilage. Although the role of ion channels in OA pathogenesis is increasingly recognized, diagnostic markers and targeted therapies remain limited. METHODS: In this study, we analyzed the GSE48556 dataset to identify differentially expressed ion channel-related genes (DEGs) in OA and normal controls. We employed machine learning algorithms, least absolute shrinkage and selection operator(LASSO), and support vector machine recursive feature elimination(SVM-RFE) to select potential diagnostic markers. Then the gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were performed to explore the potential diagnostic markers' involvement in biological pathways. Finally, weighted gene co-expression network analysis (WGCNA) was used to identify key genes associated with OA. RESULTS: We identified a total of 47 DEGs, with the majority involved in transient receptor potential (TRP) pathways. Seven genes (CHRNA4, GABRE, HTR3B, KCNG2, KCNJ2, LRRC8C, and TRPM5) were identified as the best characteristic genes for distinguishing OA from healthy samples. We performed clustering analysis and identified two distinct subtypes of OA, C1, and C2, with differential gene expression and immune cell infiltration profiles. Then we identified three key genes (PPP1R3D, ZNF101, and LOC651309) associated with OA. We constructed a prediction model using these genes and validated it using the GSE46750 dataset, demonstrating reasonable accuracy and specificity. CONCLUSIONS: Our findings provide novel insights into the role of ion channel-related genes in OA pathogenesis and offer potential diagnostic markers and therapeutic targets for the treatment of OA.

As society ages, the incidence of knee osteoarthritis continues to rise, bringing with it a series of social impacts and medical pressure. Despite the increasing recognition of the role of ion channels in the pathogenesis of OA, diagnostic markers and targeted therapies remain limited.This study investigated the role of TRP as possible diagnostic tools for OA.Seven TRP-related genes were identified as the best traits to distinguish OA from healthy samples, and then we constructed and validated risk scores for three key genes (PPP1R3D, ZNF101, and LOC651309) relevant to OA ion channel gene modules.Our findings provide novel insights into the role of ion channel-related genes in OA pathogenesis and offer a reference for further clinical diagnosis.

Systemic inflammation response index as an emerging biomarker in osteoarthritis patients: a bibliometric and large sample retrospective investigation.

OBJECTIVES: To determine the value of the whole-blood inflammatory response index as an emerging biomarker for the assessment of disease activity in osteoarthritis (OA). METHODS: Extensive analysis of the literature on OA and whole-blood inflammatory indicators were provided through a bibliometric approach. Clinical characteristics and indicators of OA patients and healthy controls (HC) were retrospectively analysed. Four whole-blood inflammatory response indices - neutrophil/lymphocyte count (NLR), platelet/lymphocyte count (PLR), monocyte/lymphocyte count (MLR), and systemic inflammation response index (SIRI), as well as clinical traits like the OA patient's self-perception and immune-inflammatory indicators were analysed for correlations. Cut-off values were determined using receiver operating characteristic (ROC) curves, and they were subsequently employed in logistic regression models to work out whole-blood inflammatory indices and disease activity. RESULTS: The pathophysiology of osteoarthritis has received most of the spotlight in literature studies of OA and whole-blood inflammation indicators. The "inflammation", "osteoarthritis" and "disease activity" were the top 3 key word clusters. Retrospective analysis showed that MLR, NLR, PLR, and SIRI were markedly higher in OA subjects compared to HC subjects. ROC curve consequences manifested that SIRI and NLR could separate OA from healthy controls. NLR, PLR, MLR, and SIRI proved to be related to immune-inflammatory markers, visual analogue scale (VAS) scores, and short-form (SF)-36 scores with regard to correlation analysis and association criteria. Logistic regression manifested that SIRI, NLR, and C-reactive protein (CRP) forecasted disease activity, however, the model that combined SIRI and CRP was superior to CRP alone. CONCLUSIONS: SIRI may serve as a non-invasive, appropriate biomarker to correlate with disease activity.

Risk for cardiovascular disease development in rheumatoid arthritis.

BACKGROUND: Patients with rheumatoid arthritis have significant cardiovascular mortality and morbidity. OBJECTIVE: To investigate the effects of chronic inflammation in rheumatoid arthritis on cardiovascular morbidity association with cardiovascular risk factors risk factors. Mortality report is secondary just to show trends without sufficient statistical power as it is accidental endpoint. METHODS: A total of 201 individuals without previous cardiovascular disease, 124 with rheumatoid arthritis (investigation group) and 77 with osteoarthritis (control group), were included in the study and followed up for an average of 8 years to assess the development of fatal or non-fatal cardiovascular diseases. The incidence and prevalence of cardiovascular risk factors were also investigated. RESULTS: The total incidence of one or more fatal or nonfatal cardiovascular events was 43.9% in the investigation group and 37.5% in the control group. Of these patients, 31.7% and 30.9% survived cardiovascular events in the investigation and control groups, respectively. The most common cardiovascular disease among participants who completed the study and those who died during the study was chronic heart failure. The results of the subgroup analysis showed that strict inflammation control plays a central role in lowering cardiovascular risk. CONCLUSION: A multidisciplinary approach to these patients is of paramount importance, especially with the cooperation of immunologists and cardiologists for early detection, prevention, and management of cardiovascular risks and diseases.

Development and validation of a frailty index for use in the osteoarthritis initiative.

BACKGROUND: The Osteoarthritis Initiative (OAI) evaluates the development and progression of osteoarthritis. Frailty captures the heterogeneity in aging. Use of this resource-intensive dataset to answer aging-related research questions could be enhanced by a frailty measure. OBJECTIVE: To: (i) develop a deficit accumulation frailty index (FI) for the OAI; (ii) examine its relationship with age and compare between sexes, (iii) validate the FI versus all-cause mortality and (iv) compare this association with mortality with a modified frailty phenotype. DESIGN: OAI cohort study. SETTING: North America. SUBJECTS: An FI was determined for 4,755/4,796 and 4,149/4,796 who had a valid FI and frailty phenotype. METHODS: Fifty-nine-variables were screened for inclusion. Multivariate Cox regression evaluated the impact of FI or phenotype on all-cause mortality at follow-up (up to 146 months), controlling for age and sex. RESULTS: Thirty-one items were included. FI scores (0.16 ± 0.09) were higher in older adults and among females (both, P < 0.001). By follow-up, 264 people had died (6.4%). Older age, being male, and greater FI were associated with a higher risk of all-cause mortality (all, P < 0.001). The model including FI was a better fit than the model including the phenotype (AIC: 4,167 vs. 4,178) and was a better predictor of all-cause mortality than the phenotype with an area under receiver operating characteristic curve: 0.652 vs. 0.581. CONCLUSION: We developed an FI using the OAI and validated it in relation to all-cause mortality. The FI may be used to study aging on clinical, functional and structural aspects of osteoarthritis included in the OAI.

Comprehensive Analysis of Sphingolipid Metabolism-Related Genes in Osteoarthritic Diagnosis and Synovial Immune Dysregulation.

BACKGROUND Osteoarthritis (OA) is a chronic degenerative disease characterized by synovitis and has been implicated in sphingolipid metabolism disorder. However, the role of sphingolipid metabolism pathway (SMP)-related genes in the occurrence of OA and synovial immune dysregulation remains unclear. MATERIAL AND METHODS In this study, we obtained synovium-related databases from GEO (n=40 for both healthy controls and OA) and analyzed the expression levels of SMP-related genes. Using 2 algorithms, we identified hub genes and developed a diagnostic model incorporating these hub genes to predict the occurrence of OA. Subsequently, the hub genes were further validated in peripheral blood samples from OA patients. Additionally, CIBERSORT and MCP-counter analyses were employed to explore the correlation between hub genes and immune dysregulation in OA synovium. WGCNA was used to determine enriched modules in different clusters. RESULTS Overall, the expression levels of SMP genes were upregulated in OA synovium. We identified 6 hub genes of SMP and constructed an excellent diagnostic model (AUC=0.976). The expression of re-confirmed hub genes showed associations with immune-related cell infiltration and levels of inflammatory cytokines. Furthermore, we observed heterogeneity in the expression patterns of hub genes across different clusters of OA. Notably, older patients displayed increased susceptibility to elevated levels of pain-related inflammatory cytokines and infiltration of immune cells. CONCLUSIONS The SMP-related hub genes have the potential to serve as diagnostic markers for OA patients. Moreover, the 4 hub genes of SMP demonstrate wide participation in immune dysregulation in OA synovium. The activation of different pathways is observed among different populations of patients with OA.

Community ambulation in older adults and people with OA - a model verification using Canadian Longitudinal Study on Aging (CLSA) data.

BACKGROUND: There are health and well-being benefits of community ambulation; however, many older adults do not regularly walk outside of their home. Objectives were to estimate the associations between latent constructs related to community ambulation in older adults aged 65-85 (65+), and in adults with osteoarthritis (OA) aged 45-85. METHODS: Secondary data analysis of the comprehensive baseline and maintaining contact questionnaire data from the Canadian Longitudinal Study of Aging (CLSA) was completed. Based on a previous model of community ambulation post-stroke, structural equation modeling (SEM) was used to develop measurement and structural models for two groups: older adults 65+ and people with OA. Multi-group SEM was conducted to test measurement invariance across sex and age groups. Measurement models were developed for the following latent factors: ambulation (frequency of walking outside/week, hours walked/day, ability to walk without help, frequency and aids used in different settings); health perceptions (general health, pain frequency/intensity); timed functional mobility (gait speed, timed up-and-go, sit-to-stand, balance). Variables of depression, falls, age, sex, and fear of walking alone at night were covariates in the structural models. RESULTS: Data were used from 11,619 individuals in the 65+ group (mean age 73 years ±6, 49% female) and 5546 individuals in the OA group (mean age 67 ± 10, 60% female). The final 65+ model had a close fit with RMSEA (90% CI) = 0.018 (0.017, 0.019), CFI = 0.91, SRMR = 0.09. For the OA group, RMSEA (90% CI) = 0.021 (0.020, 0.023), CFI = 0.92, SRMR = 0.07. Health perceptions and timed functional mobility had a positive association with ambulation. Depression was associated with ambulation through negative associations with health perceptions and timed functional mobility. Multi-group SEM results reveal the measurement model was retained for males and females in the 65+ group, for males and females and for age groups (65+, < 65) in the OA group. CONCLUSIONS: The community ambulation model post-stroke was verified with adults aged 65+ and for those with OA. The models of community ambulation can be used to frame and conceptualize community ambulation research and clinical interventions.

Co-development and testing of an extended community pharmacy model of service delivery for managing osteoarthritis: protocol for a sequential, multi-methods study (PharmOA).

BACKGROUND: Osteoarthritis is a common, painful and disabling long-term condition. Delivery of high-quality guideline-informed osteoarthritis care that successfully promotes and maintains supported self-management is imperative. However, osteoarthritis care remains inconsistent, including under use of core non-pharmacological approaches of education, exercise and weight loss. Community pharmacies are an accessible healthcare provider. United Kingdom government initiatives are promoting their involvement in a range of long-term conditions, including musculoskeletal conditions. It is not known what an enhanced community pharmacy role for osteoarthritis care should include, what support is needed to deliver such a role, and whether it would be feasible and acceptable to community pharmacy teams. In this (PharmOA) study, we aim to address these gaps, and co-design and test an evidence-based extended community pharmacy model of service delivery for managing osteoarthritis. METHODS: Informed by the Theoretical Domains Framework, Normalisation Process Theory, and the Medical Research Council (MRC) framework for developing complex interventions, we will undertake a multi-methods study involving five phases: 1. Systematic review to summarise currently available evidence on community pharmacy roles in supporting adults with osteoarthritis and other chronic (non-cancer) pain. 2. Cross-sectional surveys and one-to-one qualitative interviews with patients, healthcare professionals and pharmacy staff to explore experiences of current, and potential extended community pharmacy roles, in delivering osteoarthritis care. 3. Stakeholder co-design to: a) agree on the extended role of community pharmacies in osteoarthritis care; b) develop a model of osteoarthritis care within which the extended roles could be delivered (PharmOA model of service delivery); and c) refine existing tools to support community pharmacies to deliver extended osteoarthritis care roles (PharmOA tools). 4. Feasibility study to explore the acceptability and feasibility of the PharmOA model of service delivery and PharmOA tools to community pharmacy teams. 5. Final stakeholder workshop to: a) finalise the PharmOA model of service delivery and PharmOA tools, and b) if applicable, prioritise recommendations for its wider future implementation. DISCUSSION: This novel study paves the way to improving access to and availability of high-quality guideline-informed, consistent care for people with osteoarthritis from within community pharmacies.

Global burden of osteoarthritis in adults aged 30 to 44 years, 1990 to 2019: results from the Global Burden of Disease Study 2019.

BACKGROUND: Osteoarthritis (OA) is a common orthopedic disorder, and its incidence has been increasing among young adults in recent years. The purpose of this study is to investigate the global, regional, and national trends in OA burden and variation among individuals aged 30 to 44 from 1990 to 2019. METHODS: Data on the incidence, prevalence, and years lived with disability (YLDs) related to OA were sourced from the Global Burden of Disease Study 2019 among individuals aged 30 to 44. These measures were stratified by gender, region, country, and socio-demographic index (SDI). Additionally, we analyzed YLDs attributable to risk factors. RESULTS: In 2019, there were a total of 32,971,701 cases of OA among individuals aged 30 to 44 years worldwide, with an additional 7,794,008 new incident cases reported. OA of the knee was the primary contributor to both incidence and prevalence rates over the past three decades. From 1990 to 2019, both males and females in countries with high SDI and high-middle SDI showed upward trends in age-standardized incidence, prevalence, and YLDs rates. In 2019, the United States of America had the highest age-standardized incidence, prevalence, and YLDs rates. Elevated body-mass index (BMI) was found to be the most prevalent risk factor for osteoarthritis-related YLDs. Age-standardized YLDs rates were positively associated with SDI. CONCLUSIONS: OA remains a significant disease burden on individuals aged 30 to 44, with modifiable risk factors such as unhealthy lifestyle and obesity representing key targets for future interventions aimed at reducing the impact of this condition on younger generations.

Risk of carpal tunnel syndrome among patients with osteoarthritis: a US population-based study.

BACKGROUND: Carpal tunnel syndrome (CTS), an entrapment neuropathy caused by pressure of the median nerve, is a progressive condition that can lead to a decreased quality of life. Studies suggest an association between CTS and arthritis; however, previous studies examining osteoarthritis (OA) and CTS are limited in number, scope and study design. This study estimated the incidence and risk of CTS among patients with OA, both overall and by specific joints, in a large population-based cohort in the United States. METHODS: Patients from the Optum claims database aged ≥ 45 years and diagnosed with OA between January 1, 2018, and December 31, 2022, were eligible for the OA cohort. The non-OA cohort included those without a diagnosis of OA at the index date and no history of OA for 12 months pre-index. Baseline characteristics were balanced using propensity score matching. The risk of CTS in the OA and non-OA cohort were evaluated using incidence rates and adjusted hazard ratios that were estimated using Cox regression. RESULTS: After applying the inclusion/exclusion criteria, 3,610,240 of the 6,023,384 adults with a diagnosis of OA remained in the OA cohort. After propensity-score matching, each cohort included 1,033,439 individuals. The incidence rates for CTS per 1000 person-years were 7.35 (95% confidence interval [CI] 7.21-7.49) in the OA cohort and 1.44 (95% CI 1.38-1.50) in the non-OA cohort. The risk of developing CTS in patients with OA was ~ 4 times that of patients without (hazard ratio = 3.80; 95% CI 3.54-4.07). This increased risk was found across all OA joint types, with OA of the hand/wrist having the highest risk for CTS. Additionally, multiple OA joints presented a higher risk compared with a single affected joint. CONCLUSIONS: OA increases the risk of CTS, but this is not limited to patients with hand/wrist OA, suggesting a systemic impact of OA on CTS. While the risk appears highest for patients with hand/wrist OA, patients with more distant affected joints like knee or hip also have an increased risk of CTS.

Patterns of pain medication usage and self-reported pain in older Irish adults with osteoarthritis: A latent class analysis of data from the Irish Longitudinal Study on Ageing.

BACKGROUND: This study aimed to identify and describe links between pain medication use and self-reported pain among people aged ≥ 50 years with osteoarthritis (OA) in an Irish population, and to examine the relationships between pain, medication usage and socioeconomic and clinical characteristics. METHODS: Secondary data analysis of wave 1 cross-sectional data from The Irish Longitudinal Study on Ageing (TILDA) was undertaken of 1042 people with self-reported doctor-diagnosed OA. We examined use of medications typically included in OA clinical guidelines, including non-opioid analgesics (e.g. paracetamol), topical and oral non-steroidal anti-inflammatory drugs (NSAIDs), opioids and nutraceuticals. Latent Class Analysis (LCA) was used to identify underlying clinical subgroups based on medication usage patterns, and self-reported pain severity. Multinomial logistic regression was used to explore sociodemographic and clinical characteristic links to latent class membership. RESULTS: A total of 358 (34.4%) of the 1042 people in this analysis were taking pain medications including oral NSAIDs (17.5%), analgesics (11.4%) and opioids (8.7%). Nutraceutical (glucosamine/chondroitin) use was reported by 8.6% and topical NSAID use reported by 1.4%. Three latent classes were identified: (1) Low medication use/no pain (n = 382, 37%), (2) low medication use/moderate pain (n = 523, 50%) and (3) moderate medication use/high pain (n = 137, 13%). Poorer self-rated health and greater sleep disturbance were associated with classes 2 and 3; depressive symptoms and female gender were associated with class 2, and retirement associated with class 3. CONCLUSIONS: Whilst pain medication use varied with pain severity, different medication types reported broadly aligned with OA guidelines. The two subgroups exhibiting higher pain levels demonstrated poorer self-rated health and greater sleep disturbance.

Identification of HTRA1, DPT and MXRA5 as potential biomarkers associated with osteoarthritis progression and immune infiltration.

BACKGROUND: Our study aimed to identify potential specific biomarkers for osteoarthritis (OA) and assess their relationship with immune infiltration. METHODS: We utilized data from GSE117999, GSE51588, and GSE57218 as training sets, while GSE114007 served as a validation set, all obtained from the GEO database. First, weighted gene co-expression network analysis (WGCNA) and functional enrichment analysis were performed to identify hub modules and potential functions of genes. We subsequently screened for potential OA biomarkers within the differentially expressed genes (DEGs) of the hub module using machine learning methods. The diagnostic accuracy of the candidate genes was validated. Additionally, single gene analysis and ssGSEA was performed. Then, we explored the relationship between biomarkers and immune cells. Lastly, we employed RT-PCR to validate our results. RESULTS: WGCNA results suggested that the blue module was the most associated with OA and was functionally associated with extracellular matrix (ECM)-related terms. Our analysis identified ALB, HTRA1, DPT, MXRA5, CILP, MPO, and PLAT as potential biomarkers. Notably, HTRA1, DPT, and MXRA5 consistently exhibited increased expression in OA across both training and validation cohorts, demonstrating robust diagnostic potential. The ssGSEA results revealed that abnormal infiltration of DCs, NK cells, Tfh, Th2, and Treg cells might contribute to OA progression. HTRA1, DPT, and MXRA5 showed significant correlation with immune cell infiltration. The RT-PCR results also confirmed these findings. CONCLUSIONS: HTRA1, DPT, and MXRA5 are promising biomarkers for OA. Their overexpression strongly correlates with OA progression and immune cell infiltration.

The Diagnostic Value of miR-124a Expression in Peripheral Blood and Synovial Fluid of Patients with Rheumatoid Arthritis.

INTRODUCTION: Rheumatoid arthritis (RA), a chronic autoimmune disorder, is currently a severe health threat. Previous studies have documented the altered expression of various miRNAs in RA patients. This study determined the expression of miR-124a in RA patients and estimated its diagnostic value for RA. METHODS: A total of 80 RA patients were enrolled as the study subjects, and 36 patients with osteoarthritis were included, with another 36 healthy people as the controls. miR-124a expression levels in peripheral blood plasma, peripheral blood mononuclear cells (PBMCs), and synovial fluid were measured using reverse transcription quantitative polymerase chain reaction, followed by Pearson correlation analysis. Additionally, the association between miR-124a and major clinical indicators was assessed, such as rheumatoid factor (RF), erythrocyte sedimentation rate (ESR), and disease activity score of 28 joints (DAS28). The diagnostic efficacy of miR-124a expression in plasma, PBMCs, and synovial fluid for RA was evaluated by the receiver operating characteristic curve, and the difference in the area under the curve (AUC) was analyzed. RESULTS: miR-124a was downregulated in RA patients, and the expression levels of miR-124a in plasma, PBMCs, and synovial fluid showed a certain degree of positive correlation. miR-124a was inversely linked with RF, ESR, and DAS28. For the diagnosis of RA patients, the AUC of plasma miR-124a was 0.899 and the cut-off value was 0.800, with 68.75% sensitivity and 94.44% specificity; the AUC of miR-124a in PBMCs was 0.937 and the cut-off value was 0.805, with 82.50% sensitivity and 91.67% specificity; the AUC of miR-124a in plasma combined with PBMCs was 0.961, with a higher diagnostic value than independent plasma or PBMCs; the AUC of miR-124a in synovial fluid was 0.929 and the cut-off value was 0.835, with 80.00% sensitivity and 88.89% specificity. CONCLUSION: miR-124a expression is downregulated in the plasma, PBMCs, and synovial fluid of RA patients and has a high diagnostic value for RA.

Exploring m6A-linked aging genes in osteoarthritis and broad cancer spectrum: Prospects for diagnostic and therapeutic advancements.

Osteoarthritis (OA) is a prevalent degenerative joint disease that significantly impacts individuals and healthcare systems worldwide. However, the exploration of N6-methyladenosine (m6A)-related aging genes in OA pathogenesis remains largely underexplored. This study aimed to elucidate the role of m6A-related aging genes in OA and to develop a robust diagnostic model based on their expression profiles. Leveraging publicly available gene expression datasets, we conducted consensus clustering to categorize OA into distinct subtypes, guided by the expression patterns of m6A-related aging genes. Utilizing XGBoost, a cutting-edge machine learning approach, we identified key diagnostic genes and constructed a predictive model. Our investigation extended to the immune functions of these genes, shedding light on potential therapeutic targets and underlying regulatory mechanisms. Our analysis unveiled specific OA subtypes, each marked by unique expression profiles of m6A-related aging genes. We pinpointed a set of pivotal diagnostic genes, offering potential therapeutic avenues. The developed diagnostic model exhibited exceptional capability in distinguishing OA patients from healthy controls. To corroborate our computational findings, we performed quantitative real-time polymerase chain reaction analyses on two cell lines: HC-OA (representing adult osteoarthritis cells) and C-28/I2 (representative of normal human chondrocytes). The gene expression patterns observed were consistent with our bioinformatics predictions, further validating our initial results. In conclusion, this study underscores the significance of m6A-related aging genes as promising biomarkers for diagnosis and prognosis, as well as potential therapeutic targets in OA. Although these findings are encouraging, further validation and functional analyses are crucial for their clinical application.

Efficacy analysis of clinical serological indicators in the diagnosis of postoperative periprosthetic joint infection in patients with rheumatoid arthritis or osteoarthritis.

PURPOSE: To investigate the incidence of periprosthetic joint infection (PJI) in patients with rheumatoid arthritis (RA) or osteoarthritis (OA) after primary joint arthroplasty; to analyze the optimal cut-off values of clinical serum markers C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), and D-dimer for the diagnosis of PJI in RA patients; and to explore their diagnostic efficacy and clinical significance. METHODS: Clinical data of 15,702 patients with RA (578) or OA (15,124) who underwent total joint arthroplasty from 2013 to 2021 were retrospectively analyzed. Serum CRP, ESR, and D-dimer were recorded for each patient, and subject characteristic curves were used to determine the optimal threshold values of CRP, ESR, and D-dimer for RA-PJI and OA-PJI and to compare the areas under the curves to assess the diagnostic efficacy of the optimal threshold values of serologic indices for RA-PJI. RESULTS: The five year incidence of PJI was 6.92% in RA patients and 0.67% in OA patients. The optimal thresholds of CRP, ESR, and D-dimer for the diagnosis of RA-PJI were respectively 13.85 mg/L, 33.02 mm/h, and 796.50 ng/mL. The sensitivities of the optimal thresholds were respectively 67.6%, 62.2%, and 56.8%, and the specificities were 74.7%, 60.4%, and 74.4%. CONCLUSION: RA patients have a higher incidence of PJI than OA patients. The optimal thresholds for CRP, ESR, and d-dimer for the diagnosis of PJI were higher in RA patients than in OA patients, but the sensitivity and specificity of the diagnosis were not as good as in OA patients.

Rheumatoid Arthritis and Osteoarthritis in Adult Women: A Functional Approach to the Stomatognathic System.

Rheumatoid arthritis and osteoarthritis both affect the articular cartilage, and are characterized by signs and symptoms that can affect the functions of the human body. This cross-sectional observational study evaluated electromyographic activity in the masseter and temporalis muscles, molar bite force, and mandibular mobility in adult women with rheumatoid arthritis or osteoarthritis. A total of 42 women were distributed into 3 groups: rheumatoid arthritis group (ARG, n=14); osteoarthritis group (OAG, n=14); and a healthy control group (CG, n=14). Electromyography was used to evaluate mandibular tasks at rest, right and left laterality, protrusion, and dental clenching during maximum voluntary contraction, with and without parafilm, and a dynamometer was used to analyse the right and left molar bite forces. A digital caliper was used to measure the range of mandibular movement for maximum mouth opening, right and left laterality, and protrusion. Statistical analyses were performed, including analysis of variance and Tukey's test (P<0.05). Electromyography showed no significant differences between the groups when evaluating the masticatory muscles during the mandibular tasks. Significant difference was observed between the ARG and CG, however, in the maximum right (P=0.007) and left (P=0.02) molar bite forces. Significant difference was observed in the maximum mouth opening of the ARG and OAG groups compared with that of the CG (P=0.009), suggesting that adult women with rheumatoid arthritis or osteoarthritis experience functional alterations in the stomatognathic system, particularly in molar bite force and maximum mouth opening.

Prevalence of diagnosed and undiagnosed osteoarthrosis and associated factors in the adult general Spanish population.

OBJECTIVE: To determine the prevalence and related factors of diagnosed osteoarthrosis (DO) and undiagnosed osteoarthrosis (UO) in the general Spanish adult population. SETTING: Cross-sectional study with data from the Spanish National Health Survey 2017. PARTICIPANTS: N=23,089 adults. Three groups of people were defined: DO, UO, and no osteoarthrosis (NO). MAIN MEASUREMENTS: Sociodemographic information, lifestyle (tobacco, alcohol, physical activity, body mass index) and health factors (intensity of pain, pain drug consumption, mental health, self-perceived health status, pain involvement in daily living) were collected. Descriptive and bivariate analyses were performed, and a multinomial logistic regression model for the factors associated with each group. RESULTS: The prevalence of DO was 22.4% (95%CI=21.8;22.9) and 0.9% (95%CI=0.8;1) of UO. With respect to NO, risk factors for DO and UO included higher pain levels and pain drug consumption. Better self-perceived health status was inversely related with both. More pain involvement in daily living was associated with increased risk of DO, but reduced risk of UO. CONCLUSIONS: The prevalence of DO and UO was similar to that reported in Europe, but slightly higher than in low/middle-income countries. It was more prevalent in females, older people, people with worse perceived health status and worse mental health. Higher pain levels and pain drug consumption were risk factors for DO and UO. Better self-perceived health status was protective. Pain involvement in daily living was a risk factor for DO, but protective for UO. Different public health strategies should be considered in view of this.

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Objective: To identify inflamm-aging related biomarkers in osteoarthritis (OA). Methods: Microarray gene profiles of young and aging OA patients were obtained from the Gene Expression Omnibus (GEO) database and aging-related genes (ARGs) were obtained from the Human Aging Genome Resource (HAGR) database. The differentially expressed genes of young OA and older OA patients were screened and then intersected with ARGs to obtain the aging-related genes of OA. Enrichment analysis was performed to reveal the potential mechanisms of aging-related markers in OA. Three machine learning methods were used to identify core senescence markers of OA and the receiver operating characteristic (ROC) curve was used to assess their diagnostic performance. Peripheral blood mononuclear cells were collected from clinical OA patients to verify the expression of senescence-associated secretory phenotype (SASP) factors and senescence markers. Results: A total of 45 senescence-related markers were obtained, which were mainly involved in the regulation of cellular senescence, the cell cycle, inflammatory response, etc. Through the screening with the three machine learning methods, 5 core senescence biomarkers, including FOXO3, MCL1, SIRT3, STAG1, and S100A13, were obtained. A total of 20 cases of normal controls and 40 cases of OA patients, including 20 cases in the young patient group and 20 in the elderly patient group, were enrolled. Compared with those of the young patient group, C-reactive protein (CRP), interleukin (IL)-6, and IL-1ß levels increased and IL-4 levels decreased in the elderly OA patient group (P<0.01); FOXO3, MCL1, and SIRT3 mRNA expression decreased and STAG1 and S100A13 mRNA expression increased (P<0.01). Pearson correlation analysis demonstrated that the selected markers were associated with some indicators, including erythrocyte sedimentation rate (ESR), IL-1ß, IL-4, CRP, and IL-6. The area under the ROC curve of the 5 core aging genes was always greater than 0.8 and the C-index of the calibration curve in the nomogram prediction model was 0.755, which suggested the good calibration ability of the model. Conclusion: FOXO3, MCL1, SIRT3, STAG1, and S100A13 may serve as novel diagnostic biomolecular markers and potential therapeutic targets for OA inflamm-aging.