Patients and Methods: MS and NMOSD patients older than 40 were identified from neurology records from hospitals in Malaysia. The diagnoses were based on the Revised McDonald (2010) and Wingerchuk (2015) criteria. Controls were sampled from Malaysia's normal population. Individuals were interviewed telephonically or face-to-face. The age inclusion criterion (over 40) differentiated high or low lifetime risk of appendicitis, as appendicitis incidence is rare after 40.
Results: 49 MS, 71 NMOSD, and 880 controls met the inclusion criteria. Seventy-two individuals (9 MS, 4 NMOSD, 59 control) had undergone appendectomy. Appendectomy rates were 18.37% in the MS group (95% CI 7.5-29.2%), 5.6% in the NMOSD group (0.3%, 11%), and 6.7% among controls (5.1%, 8.4%), (MS vs NMOSD P = 0.036, MS vs controls P = 0.007). Binary regression analysis showed that MS was an independent risk factor for appendectomy (OR 2.938, 95% CI 1.302, 6.633, P = 0.009). NMOSD showed no association with appendectomy.
Conclusion: MS is positively associated with appendectomy, unlike ulcerative colitis, which is negatively associated. We hypothesize that there is a commonality in the microflora in persons who have had these two illnesses.
Objective: We aimed to study the prevalence of visual memory dysfunction among epilepsy patients and identify the predictors that could contribute to the impairment.
Materials and Methods: This was a cross-sectional study. We analyzed 250 patients with epilepsy from neurology clinic at our tertiary center. Assessment of visual memory was done using Wechsler Memory Scale-IV (WMS-IV) with scores from subsets of visual reproduction I, II and designs I, II contributing to visual memory index (VMI) score. The correlation between continuous variables was analyzed using Pearson correlation; whereas the VMI scores of different factors were analyzed via a 1-way ANOVA test. The statistical significance was set at P < 0.05.
Results: The prevalence of visual memory dysfunction in our epilepsy population was 37.2%. Analysis of individual predictors showed that older patients, lower educational level, combined generalized and focal types of epilepsy, longer duration of epilepsy, greater number of antiepileptic drugs (AEDs) used, and abnormal neuroimaging contributed to poor visual memory. Multiple logistic regression analysis showed that educational level, types of epilepsy, and the number of AEDs used were significant predictors for visual memory impairment.
Conclusion: Visual memory dysfunction in patients with epilepsy was due to manifold confounding factors. Our findings enabled us to identify patients with visual memory dysfunction and modifiable factors that contribute to it. WMS-IV is a suitable assessment tool to determine visual memory function, which can help clinicians to optimize the patients' treatment.
METHODS: We evaluated the expression patterns of 11 candidate miRNAs using quantitative real-time PCR in whole blood (n = 10) and muscle biopsy samples (n = 9) of DM1 patients, and compared them to those of normal control samples (whole blood, n = 10; muscle, n = 9).
RESULTS: In DM1 whole blood, miRNA-133a, -29b, and -33a were significantly upregulated, whereas miRNA-1, -133a, and -29c were significantly downregulated in the skeletal muscles compared to controls.
CONCLUSIONS: Our findings align to those reported in other studies and point towards pathways that potentially contribute toward pathogenesis in DM1. However, the currently available data is not sufficient for these miRNAs to be made DM1-specific biomarkers because they seem to be common to many muscle pathologies. Hence, they lack specificity, but reinforce the need for further exploration of DM1 biomarkers.