Objective: To investigate the effects of anti-tumor necrosis factor (TNF) treatment on lipid profiles and identify risk factors for an increase in total cholesterol (TC) after the anti-TNF treatment in ankylosing spondylitis (AS) patients. Methods: This retrospective cohort study analyzed AS patients who received the first-line anti-TNF treatment. Patients with at least nine months of follow-up were included; those who were under 18 years or on any lipid-lowering agent were excluded. A linear mixed model was used to assess the impact of anti-TNF inhibitors on disease activity and lipid profile (TC, low-density lipoprotein [LDL], high-density lipoprotein [HDL], and triglycerides [TG]). Univariable and multivariable linear regression were used to identify risk factors for an increase in TC after 3 months of anti-TNF treatment. Results: A total of 315 AS patients were enrolled (78.1% male, median age 32.0 [26.0~41.0]). TC, HDL, and TG levels significantly increased particularly within the first 3 months of anti-TNF treatment, while LDL level did not show significant changes.Changes in inflammatory markers and lipid particles (TC, LDL, TG) were correlated over time, but HDL showed no significant correlation. Older age, higher baseline erythrocyte sedimentation rate, and lower baseline LDL level were related to an increase in TC after 3 months of the anti-TNF treatment. Conclusion In AS patients, anti-TNF treatment has been found to increase lipid particles, potentially due to its anti-inflammatory effects. Future research should explore the underlying mechanism and the clinical implications of dyslipidemia, particularly the occurrence of cardiovascular events, following anti-TNF treatment in AS patients.

Mycobacterium avium complex (MAC) is the most common pathogen in nontuberculous mycobacterial lung diseases, but vertebral osteomyelitis caused by MAC is rare. We experienced a case of vertebral osteomyelitis with epidural abscess in a rheumatoid arthritis patient who received immunosuppressive agents. Initial assessment was tuberculous vertebral osteomyelitis, and then treated with antituberculous drugs. Fifty-six days later, Mycobacterium intracellulare was identified from abscess culture and drugs were altered to clarithromycin, rifabutin, and ethambutol. After 3 months of M. intracellulare treatment, the radiological findings showed increases of epidural abscess. According to the suseptibility, the patient received intravenous amikacin for four weeks, and then, oral ciprofloxacin in addition to clarithromycin, rifabutin, and ethambutol. The patient is being treated with the medication for 13 months and currently showing slow improvements.
Abscess ; Amikacin ; Arthritis, Rheumatoid ; Ciprofloxacin ; Clarithromycin ; Epidural Abscess ; Ethambutol ; Humans ; Immunosuppressive Agents ; Lung Diseases ; Mycobacterium ; Mycobacterium avium Complex ; Nontuberculous Mycobacteria ; Osteomyelitis ; Rifabutin

Objective: . We investigated Asian mitochondrial DNA (mtDNA) haplogroups associated with knee osteoarthritis (OA) progression in a prospective community-based cohort comprised of Koreans. Methods: . Epidemiologic data and Kellgren-Lawrence (K/L) scores of knee radiographs were obtained from the second (2005∼2006) and sixth (2013∼2014) follow-up, and patient DNA was analyzed. The mtDNA haplogroup frequencies (M, G, D, D4, D5, M7, M8, M9, M10, N, A, N9, R, F, and B) were compared between the progression (K/L score change on either knee ≥2 or arthroplasty) and non-progression (K/L score change on both knee ≤1) groups at the sixth follow-up. Multiple logistic regression was performed to determine relative risk (RRs) of mtDNA haplogroups for OA. Results: . In total, 1,115 participants were included, 405 of whom had early OA (higher K/L score on both knees of 1 or 2). Among them, 143 and 166 patients were classified in non-progression and progression groups, respectively, at the sixth follow-up. The most frequent haplogroups, B and D4, in Koreans also showed a high frequency in our study. There were no significantly different haplogroups between the non-progression and progression groups. However, the frequency of haplogroup D4 was likely higher in the non-progression group than in the progression group, although not significantly (13.3% vs. 7.2%, RR=0.51, p=0.081 in the unadjusted model and RR=0.56, p=0.149 in the adjusted model). Conclusion . No significant haplogroups are related to OA progression. Large-scaled studies are needed to reveal the association between mtDNA haplogroups and OA.

Background: and Purpose The prevalence of Alzheimer’s dementia (AD) is increasing as populations age, causing immense suffering for patients, families, and communities. Unfortunately, no treatments for this neurodegenerative disease have been established. Predicting AD is therefore becoming more important, because early diagnosis is the best way to prevent its onset and delay its progression. Methods: Mild cognitive impairment (MCI) is the stage between normal cognition and AD, with large variations in its progression. The disease can be effectively managed by accurately predicting the probability of MCI progressing to AD over several years. In this study we used the Alzheimer’s Disease Neuroimaging Initiative dataset to predict the progression of MCI to AD over a 3-year period from baseline. We developed and compared various recurrent neural network (RNN) models to determine the predictive effectiveness of four neuropsychological (NP) tests and magnetic resonance imaging (MRI) data at baseline. Results: The experimental results confirmed that the Preclinical Alzheimer’s Cognitive Composite score was the most effective of the four NP tests, and that the prediction performance of the NP tests improved over time. Moreover, the gated recurrent unit model exhibited the best performance among the prediction models, with an average area under the receiver operating characteristic curve of 0.916 Conclusions Timely prediction of progression from MCI to AD can be achieved using a series of NP test results and an RNN, both with and without using the baseline MRI data.

Background: and Purpose The prevalence of Alzheimer’s dementia (AD) is increasing as populations age, causing immense suffering for patients, families, and communities. Unfortunately, no treatments for this neurodegenerative disease have been established. Predicting AD is therefore becoming more important, because early diagnosis is the best way to prevent its onset and delay its progression. Methods: Mild cognitive impairment (MCI) is the stage between normal cognition and AD, with large variations in its progression. The disease can be effectively managed by accurately predicting the probability of MCI progressing to AD over several years. In this study we used the Alzheimer’s Disease Neuroimaging Initiative dataset to predict the progression of MCI to AD over a 3-year period from baseline. We developed and compared various recurrent neural network (RNN) models to determine the predictive effectiveness of four neuropsychological (NP) tests and magnetic resonance imaging (MRI) data at baseline. Results: The experimental results confirmed that the Preclinical Alzheimer’s Cognitive Composite score was the most effective of the four NP tests, and that the prediction performance of the NP tests improved over time. Moreover, the gated recurrent unit model exhibited the best performance among the prediction models, with an average area under the receiver operating characteristic curve of 0.916 Conclusions Timely prediction of progression from MCI to AD can be achieved using a series of NP test results and an RNN, both with and without using the baseline MRI data.

Objective: Ankylosing spondylitis (AS) is chronic inflammatory arthritis causing structural damage and radiographic progression to the spine due to repeated and continuous inflammation over a long period. This study establishes the application of machine learning models to predict radiographic progression in AS patients using time-series data from electronic medical records (EMRs). Methods: EMR data, including baseline characteristics, laboratory findings, drug administration, and modified Stoke AS Spine Score (mSASSS), were collected from 1,123 AS patients between January 2001 and December 2018 at a single center at the time of first (T1 ), second (T2 ), and third (T3 ) visits. The radiographic progression of the (n+1)th visit (Pn+1 =(mSASSSn+1 –mSASSSn )/(Tn+1 – Tn )≥1 unit per year) was predicted using follow-up visit datasets from T1 to Tn . We used three machine learning methods (logistic regression with the least absolute shrinkage and selection operation, random forest, and extreme gradient boosting algorithms) with three-fold cross-validation. Results: The random forest model using the T1 EMR dataset best predicted the radiographic progression P2 among the machine learning models tested with a mean accuracy and area under the curves of 73.73% and 0.79, respectively. Among the T1 variables, the most important variables for predicting radiographic progression were in the order of total mSASSS, age, and alkaline phosphatase. Conclusion Prognosis predictive models using time-series data showed reasonable performance with clinical features of the first visit dataset when predicting radiographic progression.

Background: and Purpose The prevalence of Alzheimer’s dementia (AD) is increasing as populations age, causing immense suffering for patients, families, and communities. Unfortunately, no treatments for this neurodegenerative disease have been established. Predicting AD is therefore becoming more important, because early diagnosis is the best way to prevent its onset and delay its progression. Methods: Mild cognitive impairment (MCI) is the stage between normal cognition and AD, with large variations in its progression. The disease can be effectively managed by accurately predicting the probability of MCI progressing to AD over several years. In this study we used the Alzheimer’s Disease Neuroimaging Initiative dataset to predict the progression of MCI to AD over a 3-year period from baseline. We developed and compared various recurrent neural network (RNN) models to determine the predictive effectiveness of four neuropsychological (NP) tests and magnetic resonance imaging (MRI) data at baseline. Results: The experimental results confirmed that the Preclinical Alzheimer’s Cognitive Composite score was the most effective of the four NP tests, and that the prediction performance of the NP tests improved over time. Moreover, the gated recurrent unit model exhibited the best performance among the prediction models, with an average area under the receiver operating characteristic curve of 0.916 Conclusions Timely prediction of progression from MCI to AD can be achieved using a series of NP test results and an RNN, both with and without using the baseline MRI data.

Background: and Purpose The prevalence of Alzheimer’s dementia (AD) is increasing as populations age, causing immense suffering for patients, families, and communities. Unfortunately, no treatments for this neurodegenerative disease have been established. Predicting AD is therefore becoming more important, because early diagnosis is the best way to prevent its onset and delay its progression. Methods: Mild cognitive impairment (MCI) is the stage between normal cognition and AD, with large variations in its progression. The disease can be effectively managed by accurately predicting the probability of MCI progressing to AD over several years. In this study we used the Alzheimer’s Disease Neuroimaging Initiative dataset to predict the progression of MCI to AD over a 3-year period from baseline. We developed and compared various recurrent neural network (RNN) models to determine the predictive effectiveness of four neuropsychological (NP) tests and magnetic resonance imaging (MRI) data at baseline. Results: The experimental results confirmed that the Preclinical Alzheimer’s Cognitive Composite score was the most effective of the four NP tests, and that the prediction performance of the NP tests improved over time. Moreover, the gated recurrent unit model exhibited the best performance among the prediction models, with an average area under the receiver operating characteristic curve of 0.916 Conclusions Timely prediction of progression from MCI to AD can be achieved using a series of NP test results and an RNN, both with and without using the baseline MRI data.