Coexistence of IgLON5-IgG and SOX1-IgG in a Patient with Progressive Brainstem Dysfunction.

PURPOSE: The coexistence of IgLON5-IgG and SOX1-IgG is rare. Previous reports have shown that patients with IgLON5-IgG spectrum disease present with sleep disorders, bulbar involvement, and autonomic abnormality, while SOX1-IgG positive patients present with peripheral nervous system symptoms such as the Lambert-Eaton Myasthenic Syndrome (LEMS). CASE REPORT: We report a patient who presented with progressive ophthalmoplegia, ptosis, oropharyngeal dysphagia, gait instability, and sleep disorders. The paraneoplastic antibody screening tested doublepositive for IgLON5-IgG and SOX1-IgG. However, there was no clinical sign of LEMS in this patient. After extensive cancer screening, only lung nodules with hilar adenopathy were noted. CONCLUSION: The coexistence of IgLON5-IgG with onconeuronal SOX1-IgG would suggest an underlying immune-mediated paraneoplastic process rather than secondary autoimmunity because of neurodegeneration. This is the first IgLON5-IgG case reported in Thailand, with a case of doublepositive IgLON5-IgG and SOX1-IgG as well. Keyword: IgLON5-IgG, SOX1-IgG, Paraneoplastic process, case report.

Effects of Combined Physical Movement Activity and Multifaceted Cognitive Training in Older People with Mild Neurocognitive Disorder in a Rural Community: A Randomized Control Trial.

INTRODUCTION: Cognitive deterioration in older people with mild neurocognitive disorders (mNCD) increases the risk of progress to major NCD. Health professionals worldwide are trying to find strategies for prevention. There is a limited number of studies that deal with cultural conditions in northern Thailand. OBJECTIVES: This study aimed to investigate the effects of a combination of physical movement activity and multifaceted cognitive training on cognitive function in older people with mNCD. METHODS: A randomized control trial involving 70 mNCD people, according to DSM-5 criteria, was conducted on an intervention group and a control group (n = 35 each). The program for the intervention group included 24 sessions (twice a week). The outcome measures on cognitive function were assessed before and after the intervention by means of composite cognitive measures for older people, i.e., the Trail-Making Test (TMT), Digit Span (DS), Verbal Fluency (VF), Word-List Learning (WLL), and Block Design (BD). RESULTS: The combined intervention enhanced cognitive function. TMT-A was significantly improved in the intervention group. There were significant improvements in DS sequence scores, letter verbal fluency (LVF), and category verbal fluency (CVF). Comparing the groups, there were significant differences in LVF including immediate and delayed recall. BD improved significantly in the intervention group. CONCLUSIONS: The combined intervention appeared to be effective in delaying/preventing cognitive deterioration and cognitive functional decline in people with mNCD. Further studies on a variation of treatments suited to cultural conditions and their effects are needed. Trials in other communities are also recommended.

Classification of elderly pain severity from automated video clip facial action unit analysis: A study from a Thai data repository.

Data from 255 Thais with chronic pain were collected at Chiang Mai Medical School Hospital. After the patients self-rated their level of pain, a smartphone camera was used to capture faces for 10 s at a one-meter distance. For those unable to self-rate, a video recording was taken immediately after the move that causes the pain. The trained assistant rated each video clip for the pain assessment in advanced dementia (PAINAD). The pain was classified into three levels: mild, moderate, and severe. OpenFace© was used to convert the video clips into 18 facial action units (FAUs). Five classification models were used, including logistic regression, multilayer perception, naïve Bayes, decision tree, k-nearest neighbors (KNN), and support vector machine (SVM). Out of the models that only used FAU described in the literature (FAU 4, 6, 7, 9, 10, 25, 26, 27, and 45), multilayer perception is the most accurate, at 50%. The SVM model using FAU 1, 2, 4, 7, 9, 10, 12, 20, 25, and 45, and gender had the best accuracy of 58% among the machine learning selection features. Our open-source experiment for automatically analyzing video clips for FAUs is not robust for classifying pain in the elderly. The consensus method to transform facial recognition algorithm values comparable to the human ratings, and international good practice for reciprocal sharing of data may improve the accuracy and feasibility of the machine learning's facial pain rater.