Increased risk of knee osteoarthritis in patients using oral N-acetylcysteine: a nationwide cohort study

Exploring the association between knee osteoarthritis outpatient visits and Asian dust storms: a time-series analysis

Osteoarthritis (OA) is one of the most prevalent musculoskeletal diseases in Taiwan, posing a significant public health challenge. In recent years, outdoor air pollution has become an increasingly critical global health issue. Asian Dust Storms (ADS) are known to exacerbate various health conditions due to elevated levels of particulate matter and other pollutants. However, the relationship between ADS and knee OA remains insufficiently explored. This study investigates the association between ADS occurrences and knee OA outpatient visits from January 2006 to December 2012, aiming to understand the potential health impacts of dust storms on OA patients. Using data from the National Health Insurance Research Database (NHIRD), the Taiwan Environmental Protection Agency (TEPA), and the Taiwan Central Weather Bureau, we conducted a time-series analysis employing the autoregressive moving average with exogenous variables (ARMAX) model. This approach accounted for daily outpatient visits related to knee OA, ADS events, and various environmental and meteorological factors. The results revealed a significant increase in knee OA outpatient visits on days immediately following ADS events, with peaks observed one to two days after the event. This increase was most pronounced among females, individuals aged 61 and above, and residents in the western regions. The study demonstrates an association between ADS and increased knee OA outpatient visits, highlighting the need for public health strategies to mitigate the health impacts of dust storms.

Mechanistic target of rapamycin (mTOR): a potential new therapeutic target for rheumatoid arthritis

Rheumatoid arthritis (RA) is an autoimmune disease characterized by systemic synovitis and bone destruction. Proinflammatory cytokines activate pathways of immune-mediated inflammation, which aggravates RA. The mechanistic target of rapamycin (mTOR) signaling pathway associated with RA connects immune and metabolic signals, which regulates immune cell proliferation and differentiation, macrophage polarization and migration, antigen presentation, and synovial cell activation. Therefore, therapy strategies targeting mTOR have become an important direction of current RA treatment research. In the current review, we summarize the biological functions of mTOR, its regulatory effects on inflammation, and the curative effects of mTOR inhibitors in RA, thus providing references for the development of RA therapeutic targets and new drugs.

N-Acetyl-L-cysteine in human rheumatoid arthritis and its effects on nitric oxide (NO) and malondialdehyde (MDA): analytical and clinical considerations

N-Acetyl-L-cysteine (NAC) is an endogenous cysteine metabolite. The drug is widely used in chronic obstructive pulmonary disease (COPD) and as antidote in acetaminophen (paracetamol) intoxication. Currently, the utility of NAC is investigated in rheumatoid arthritis (RA), which is generally considered associated with inflammation and oxidative stress. Besides clinical laboratory parameters, the effects of NAC are evaluated by measuring in plasma or serum nitrite, nitrate or their sum (NOx) as measures of nitric oxide (NO) synthesis. Malondialdehyde (MDA) and relatives such as 4-hydroxy-nonenal and 15(S)-8-iso-prostaglandin F_2α serve as measures of oxidative stress, notably lipid peroxidation. In this work, we review recent clinico-pharmacological studies on NAC in rheumatoid arthritis. We discuss analytical, pre-analytical and clinical issues and their potential impact on the studies outcome. Major issues include analytical inaccuracy due to interfering endogenous substances and artefactual formation of MDA and relatives during storage in long-term studies. Differences in the placebo and NAC groups at baseline with respect to these biomarkers are also a serious concern. Modern applied sciences are based on data generated using commercially available instrumental physico-chemical and immunological technologies and assays. The publication process of scientific work rarely undergoes rigorous peer review of the analytical approaches used in the study in terms of accuracy/trueness. There is pressing need of considering previously reported reference concentration ranges and intervals as well as specific critical issues such as artefactual formation of particular biomarkers during sample storage. The latter especially applies to surrogate biomarkers of oxidative stress, notably MDA and relatives. Reported data on NO, MDA and clinical parameters, including C-reactive protein, interleukins and tumour necrosis factor α, are contradictory in the literature. Furthermore, reported studies do not allow any valid conclusion about utility of NAC in RA. Administration of NAC patients with rheumatoid arthritis is not recommended in current European and American guidelines.

A Deep Learning Model to Predict Knee Osteoarthritis Based on Nonimage Longitudinal Medical Record

PURPOSE

To develop deep learning model (Deep-KOA) that can predict the risk of knee osteoarthritis (KOA) within the next year by using the previous three years nonimage-based electronic medical record (EMR) data.

PATIENTS AND METHODS

We randomly selected information of two million patients from the Taiwan National Health Insurance Research Database (NHIRD) from January 1, 1999 to December 31, 2013. During the study period, 132,594 patients were diagnosed with KOA, while 1,068,464 patients without KOA were chosen randomly as control. We constructed a feature matrix by using the three-year history of sequential diagnoses, drug prescriptions, age, and sex. Deep learning methods of convolutional neural network (CNN) and artificial neural network (ANN) were used together to develop a risk prediction model. We used the area under the receiver operating characteristic (AUROC), sensitivity, specificity, and precision to evaluate the performance of Deep-KOA. Then, we explored the important features using stepwise feature selection.

RESULTS

This study included 132,594 KOA patients, 83,111 females (62.68%), 49,483 males (37.32%), mean age 64.2 years, and 1,068,464 non-KOA patients, 545,902 females (51.09%), 522,562 males (48.91%), mean age 51.00 years. The Deep-KOA achieved an overall AUROC, sensitivity, specificity, and precision of 0.97, 0.89, 0.93, and 0.80 respectively. The discriminative analysis of Deep-KOA showed important features from several diseases such as disorders of the eye and adnexa, acute respiratory infection, other metabolic and immunity disorders, and diseases of the musculoskeletal and connective tissue. Age and sex were not found as the most discriminative features, with AUROC of 0.9593 (-0.76% loss) and 0.9644 (-0.25% loss) respectively. Whereas medications including antacid, cough suppressant, and expectorants were identified as discriminative features.

CONCLUSION

Deep-KOA was developed to predict the risk of KOA within one year earlier, which may provide clues for clinical decision support systems to target patients with high risk of KOA to get precision prevention program.