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Toll-like receptor (TLR)-4 and TLR9 are known to play important roles in the immune system, and several studies have shown their association with the development of rheumatoid arthritis (RA) and regulation of tumor necrosis factor alpha (TNF-α). However, studies that investigate the association between TLR4 or TLR9 gene polymorphisms and remission of the disease in RA patients taking TNF-α inhibitors have yet to be conducted. In this context, this study was designed to investigate the effects of polymorphisms in TLR4 and TLR9 on response to TNF-α inhibitors and to train various models using machine learning approaches to predict remission. A total of six single nucleotide polymorphisms (SNPs) were investigated. Logistic regression analysis was used to investigate the association between genetic polymorphisms and response to treatment. Various machine learning methods were utilized for prediction of remission. After adjusting for covariates, the rate of remission of T-allele carriers of TLR9 rs352139 was about 5 times that of the CC-genotype carriers (95% confidence interval (CI) 1.325–19.231, p = 0.018). Among machine learning algorithms, multivariate logistic regression and elastic net showed the best prediction with the area under the receiver-operating curve (AUROC) value of 0.71 (95% CI 0.597–0.823 for both models). This study showed an association between a TLR9 polymorphism (rs352139) and treatment response in RA patients receiving TNF-α inhibitors. Moreover, this study utilized various machine learning methods for prediction, among which the elastic net provided the best model for remission prediction.

Introduction Various subtypes of severe acute respiratory syndrome coronavirus 2 and variations among immune systems in different ethnicities need to be considered to understand the outcomes of coronavirus disease 2019 (COVID-19). This study aimed to provide evidence for the association between the use of antidepressants and the severity of COVID-19. Methods We used the National Health Information Data-COVID database. Patients with one or more prescriptions of any antidepressant were selected as the exposure group. Detailed analyses were performed to determine the type of medication associated with the prognosis. Results The use of selective serotonin reuptake inhibitors (SSRIs) was associated with a lower risk of severe outcomes of COVID-19, whereas the use of tricyclic antidepressants (TCAs) increased the risk of poor prognosis of COVID-19. Detailed analyses showed that escitalopram was significantly associated with better clinical outcomes, and nortriptyline was linked to more severe COVID-19 outcomes. Conclusion This study revealed an association between antidepressants and COVID-19 prognosis. SSRIs were significantly associated with a lower risk of severe outcomes, whereas TCAs were related to the poor prognosis of COVID-19.

Studies that investigate the association between toll-like receptor (TLR)-4 or TLR9 gene polymorphisms and remission from the disease in RA patients taking tumor necrosis factor alpha (TNF-α) inhibitors have yet to be conducted. In this context, this study was designed to investigate the effects of polymorphisms in TLR4 and TLR9 on response to TNF-α inhibitor and develop various machine learning approaches to predict remission. A total of six single nucleotide polymorphisms (SNPs) were investigated. Logistic regression analysis was used to investigate the association between genetic polymorphisms and response to treatment. Various machine learning methods were utilized for prediction of remission. After adjusting for covariates, the rate of remission of T-allele carriers of TLR9 rs352139 was about 5 times that of the CC-genotype carriers (95% confidence interval (CI) 1.325–19.231, p = 0.018). Among machine learning algorithms, multivariate logistic regression and elastic net showed the best prediction with the AUROC value of 0.71 (95% CI 0.597 - 0.823 for both models). This study showed an association between a TLR9 polymorphism (rs352139) and treatment response in RA patients receiving TNF-α inhibitors. Moreover, this study developed various machine learning methods for prediction, among which the elastic net provided the best model for remission prediction.

Background In March 2020, the US Food and Drug Administration decided that the dangers related to neuropsychiatric events (NPEs) of montelukast, one of the leukotriene modifying agents (LTMAs), should be communicated through ‘boxed warning’. In case of NPEs, the prevalence has been the highest in elderly people. Because the characteristics of the elderly such as old age itself can act as risk factors. Therefore, an investigation on safety of LTMAs related to NPEs in elderly using LTMAs is needed. Method A nested case-control study using an elderly sample cohort from the Korean National Health Insurance Service database was used. The asthma cohort included asthma patients newly diagnosed between 2003 and 2013. Within the asthma cohort, the case group was defined as patients who were diagnosed with NPEs. Among patients who had never been diagnosed with NPEs, the control group was selected by matching 1:1 by propensity score. Patients who were prescribed LTMAs for 1 year prior to index date were defined as the exposure group. The logistic regression model was used to measure the effect of LTMAs on NPEs. Results We identified 141,165 patients with newly diagnosed asthma, and selected 31,992 patients per each case and control group. Exposure to LTMAs significantly increased the risk of overall NPEs about in comparison with the absence of exposure (crude odds ratio [OR] 1.58, 95% CI 1.50–1.68). After adjusting for confounding factors, the overall NPEs risk increased (adjusted OR, 1.67, 95% CI 1.58–1.78). Conclusion This study suggests that elderly asthma patients prescribed LTMAs had a higher risk of NPEs than patients who were not treated with LTMAs. Therefore, clinicians should be aware of the potential risks of LTMAs.