Vitamin D Level and Vitamin D Receptor Gene Polymorphisms in Iranian Azeri Turkish Patients With Autoimmune Thyroid Diseases

The Autoimmune thyroid diseases (AITDs) are among the most common endocrine disorders. Vitamin D as an immunomodulator and Vitamin D receptor (VDR) gene polymorphisms may be effective in AITDs pathogenesis. The aim of this study was to evaluate the vitamin D level and VDR BsmI and TaqI polymorphisms in Iranian Azeri Turkish patients with AITDs. This case-control study included 121 adults with AITDs and 117 non-AITDs controls. Serum level of 25-hydroxyvitamin D was measured by electrochemiluminescence (ECL) immunoassay. BsmI and TaqI polymorphisms were assessed by polymerase chain reaction fragment length polymorphism technique. The serum level of 25-hydroxyvitamin D in AITDs patients were lower than controls (P=0.03). The frequencies of TT, TC, CC, T and C genotypes/alleles at TaqI (rs731236) marker were 52.1%, 34.7%, 13.2% , 69.4% and 30.6% in AITDs and 44.4%, 41.9%, 13.7%, 65.4% and 34.6% in controls, respectively. The frequencies of AA, AG, GG, A and G genotypes/alleles at BsmI (rs1544410) marker were 14%, 64.5%, 21.5% , 46.3% and 53.7% in AITDs and 26.5%, 58.1%, 15.4%, 55.6% and 44.4% in controls, respectively. BsmI (rs1544410) GG+AG genotypes and G allele were more frequent among patients with Hashimoto compared with control group (86.6% vs. 73.5% (OR: 2.34, 95% CI: 1.16-4.70, P = 0.014) and 54.29% vs. 44.44% (OR: 1.48, 95% CI: 1.02-2.15, P = 0.038), respectively). Vitamin D status can be related to AITDs pathogenesis. BsmI (rs1544410) GG+AG genotypes and G allele may play an important role in the predisposition to Hashimoto.

A tensor decomposition scheme for EEG-based diagnosis of mild cognitive impairment

Mild Cognitive Impairment (MCI) is the primary stage of acute Alzheimer's disease, and early detection is crucial for the person and those around him. It is difficult to recognize since this mild stage does not have clear clinical signs, and its symptoms are between normal aging and severe dementia. Here, we propose a tensor decomposition-based scheme for automatically diagnosing MCI using Electroencephalogram (EEG) signals. A new projection is proposed, which preserves the spatial information of the electrodes to construct a data tensor. Then, using parallel factor analysis (PARAFAC) tensor decomposition, the features are extracted, and a support vector machine (SVM) is used to discriminate MCI from normal subjects. The proposed scheme was tested on two different datasets. The results showed that the tensor-based method outperformed conventional methods in diagnosing MCI with an average classification accuracy of 93.96% and 78.65% for the first and second datasets, respectively. Therefore, it seems that maintaining the spatial topology of the signals plays a vital role in the processing of EEG signals.</p

A tensor decomposition scheme for EEG-based diagnosis of mild cognitive impairment

Mild Cognitive Impairment (MCI) is the primary stage of acute Alzheimer's disease, and early detection is crucial for the person and those around him. It is difficult to recognize since this mild stage does not have clear clinical signs, and its symptoms are between normal aging and severe dementia. Here, we propose a tensor decomposition-based scheme for automatically diagnosing MCI using Electroencephalogram (EEG) signals. A new projection is proposed, which preserves the spatial information of the electrodes to construct a data tensor. Then, using parallel factor analysis (PARAFAC) tensor decomposition, the features are extracted, and a support vector machine (SVM) is used to discriminate MCI from normal subjects. The proposed scheme was tested on two different datasets. The results showed that the tensor-based method outperformed conventional methods in diagnosing MCI with an average classification accuracy of 93.96% and 78.65% for the first and second datasets, respectively. Therefore, it seems that maintaining the spatial topology of the signals plays a vital role in the processing of EEG signals

The global prevalence of depression, anxiety, and sleep disorder among patients coping with Post COVID-19 syndrome (long COVID): a systematic review and meta-analysis

Abstract Background Post COVID-19 syndrome, also known as "Long COVID," is a complex and multifaceted condition that affects individuals who have recovered from SARS-CoV-2 infection. This systematic review and meta-analysis aim to comprehensively assess the global prevalence of depression, anxiety, and sleep disorder in individuals coping with Post COVID-19 syndrome. Methods A rigorous search of electronic databases was conducted to identify original studies until 24 January 2023. The inclusion criteria comprised studies employing previously validated assessment tools for depression, anxiety, and sleep disorders, reporting prevalence rates, and encompassing patients of all age groups and geographical regions for subgroup analysis Random effects model was utilized for the meta-analysis. Meta-regression analysis was done. Results The pooled prevalence of depression and anxiety among patients coping with Post COVID-19 syndrome was estimated to be 23% (95% CI: 20%—26%; I2 = 99.9%) based on data from 143 studies with 7,782,124 participants and 132 studies with 9,320,687 participants, respectively. The pooled prevalence of sleep disorder among these patients, derived from 27 studies with 15,362 participants, was estimated to be 45% (95% CI: 37%—53%; I2 = 98.7%). Subgroup analyses based on geographical regions and assessment scales revealed significant variations in prevalence rates. Meta-regression analysis showed significant correlations between the prevalence and total sample size of studies, the age of participants, and the percentage of male participants. Publication bias was assessed using Doi plot visualization and the Peters test, revealing a potential source of publication bias for depression (p = 0.0085) and sleep disorder (p = 0.02). However, no evidence of publication bias was found for anxiety (p = 0.11). Conclusion This systematic review and meta-analysis demonstrate a considerable burden of mental health issues, including depression, anxiety, and sleep disorders, among individuals recovering from COVID-19. The findings emphasize the need for comprehensive mental health support and tailored interventions for patients experiencing persistent symptoms after COVID-19 recovery