Downregulation of miR-29a as a possible diagnostic biomarker for schizophrenia.

BACKGROUND: MicroRNAs (miRNAs) are epigenetic factors regulating many genes involved in brain development. Dysregulation of miRNA could result in dysregulation of genes which may contribute to diseases affecting the brain and behavior (e.g., schizophrenia). miR-29 family is a miRNA family contributing to brain maturation. miR-29 knockout in animal studies is reported to correlate with psychiatric disorders very similar to those seen in schizophrenia. In this study, we aimed to evaluate the miR-29a level in patients with schizophrenia and its potential value in the diagnosis of schizophrenia. MATERIALS AND METHODS: The serum sample of 42 patients with schizophrenia and 40 healthy subjects were obtained from the Azeri Recent onset/Acute phase psychosis Survey (ARAS) Cohort study. After preparations, the expression level of miR-29a was investigated by real-time PCR. The SPSS and GraphPad prism software were used to analyze the relation between miR-29a level and clinical parameters and its potential as a biomarker for the diagnosis of schizophrenia. RESULTS: Our study showed a significantly lower miR-29a level in patients compared to healthy controls (p = 0.0012). Furthermore, miR-29a level was significantly lower in some types of schizophrenia (p = 0.024). miR-29a level was not related to sex, age, or heredity (p > 0.05). miR-29a also showed 80% specificity and 71.43% sensitivity in the diagnosis of schizophrenia. CONCLUSION: Downregulation of miR-29a in schizophrenia is significantly related to the development of this illness. It might have the potential as a biomarker for schizophrenia.

Prevalence of Urogenital Injury following Road Accident: A Systematic Review.

INTRODUCTION AND HYPOTHESIS: Trauma complications have been one of the most serious public health concerns worldwide. In most reports, urogenital injuries (UGIs) are seen in approximately 10% of adult traumatic patients and less than 3% of children with multiple/severe trauma to the abdomen or pelvis. Traffic accidents are the most common cause of UGIs. The purpose of this study is to systematically determine the prevalence and types of UGIs after car accidents. METHODS: The search strategy was aimed at finding relevant studies in October 2023. No restrictions on language or date were applied. The following criteria were considered eligibility criteria: reporting at least one epidemiological aspect of UGIs in people with road traffic injury (RTI) and a separate epidemiological analysis of RTIs in UGI (we also included those articles that pointed out all RTIs but separately mentioned UGIs). Two experts assessed the reporting quality of articles using standardized critical appraisal instruments from the Joanna Briggs Institute. Statistical analysis for this study was conducted using the CMA statistical software version 3.2.0. RESULTS: A total of 1,466,024 cases following RTIs through 107 studies were included in our review. Of these, 29 studies were related to children (20,036), and the others reported RTIs in adults (1,445,988). The total prevalence was 4.7%, and car accidents were responsible in 36 studies, followed by motorcycle accidents in 25, bicycles in 17 studies, and automobile-pedestrian collisions in 23 related studies. In subgroup analysis based on the damaged organ, the rate of bladder injury was 3.5%. This rate was 5.3% for kidneys. CONCLUSION: This systematic review and meta-analysis found that the prevalence of UGI following RTIs was 4.7%, with car accidents being the most common cause. UGIs were more prevalent in adults than in children, and bladder and kidney injuries were the most commonly reported types. The prevalence of UGI varied by country and study design.

Retinal imaging and Alzheimer's disease: a future powered by Artificial Intelligence.

Alzheimer's disease (AD) is a neurodegenerative condition that primarily affects brain tissue. Because the retina and brain share the same embryonic origin, visual deficits have been reported in AD patients. Artificial Intelligence (AI) has recently received a lot of attention due to its immense power to process and detect image hallmarks and make clinical decisions (like diagnosis) based on images. Since retinal changes have been reported in AD patients, AI is being proposed to process images to predict, diagnose, and prognosis AD. As a result, the purpose of this review was to discuss the use of AI trained on retinal images of AD patients. According to previous research, AD patients experience retinal thickness and retinal vessel density changes, which can occasionally occur before the onset of the disease's clinical symptoms. AI and machine vision can detect and use these changes in the domains of disease prediction, diagnosis, and prognosis. As a result, not only have unique algorithms been developed for this condition, but also databases such as the Retinal OCTA Segmentation dataset (ROSE) have been constructed for this purpose. The achievement of high accuracy, sensitivity, and specificity in the classification of retinal images between AD and healthy groups is one of the major breakthroughs in using AI based on retinal images for AD. It is fascinating that researchers could pinpoint individuals with a positive family history of AD based on the properties of their eyes. In conclusion, the growing application of AI in medicine promises its future position in processing different aspects of patients with AD, but we need cohort studies to determine whether it can help to follow up with healthy persons at risk of AD for a quicker diagnosis or assess the prognosis of patients with AD.

Transfer Learning in Cancer Genetics, Mutation Detection, Gene Expression Analysis, and Syndrome Recognition.

Artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL), has revolutionized medical research, facilitating advancements in drug discovery and cancer diagnosis. ML identifies patterns in data, while DL employs neural networks for intricate processing. Predictive modeling challenges, such as data labeling, are addressed by transfer learning (TL), leveraging pre-existing models for faster training. TL shows potential in genetic research, improving tasks like gene expression analysis, mutation detection, genetic syndrome recognition, and genotype-phenotype association. This review explores the role of TL in overcoming challenges in mutation detection, genetic syndrome detection, gene expression, or phenotype-genotype association. TL has shown effectiveness in various aspects of genetic research. TL enhances the accuracy and efficiency of mutation detection, aiding in the identification of genetic abnormalities. TL can improve the diagnostic accuracy of syndrome-related genetic patterns. Moreover, TL plays a crucial role in gene expression analysis in order to accurately predict gene expression levels and their interactions. Additionally, TL enhances phenotype-genotype association studies by leveraging pre-trained models. In conclusion, TL enhances AI efficiency by improving mutation prediction, gene expression analysis, and genetic syndrome detection. Future studies should focus on increasing domain similarities, expanding databases, and incorporating clinical data for better predictions.

Growth-Associated Protein 43 and Tensor-Based Morphometry Indices in Mild Cognitive Impairment.

Growth-associated protein 43 (GAP-43) is found in the axonal terminal of neurons in the limbic system, which is affected in people with Alzheimer's disease (AD). We assumed GAP-43 may contribute to AD progression and serve as a biomarker. So, in a two-year follow-up study, we assessed GAP-43 changes and whether they are correlated with tensor-based morphometry (TBM) findings in patients with mild cognitive impairment (MCI). We included MCI and cognitively normal (CN) people with available baseline and follow-up cerebrospinal fluid (CSF) GAP-43 and TBM findings from the ADNI database. We assessed the difference between the two groups and correlations in each group at each time point. CSF GAP-43 and TBM measures were similar in the two study groups in all time points, except for the accelerated anatomical region of interest (ROI) of CN subjects that were significantly greater than those of MCI. The only significant correlations with GAP-43 observed were those inverse correlations with accelerated and non-accelerated anatomical ROI in MCI subjects at baseline. Plus, all TBM metrics decreased significantly in all study groups during the follow-up in contrast to CSF GAP-43 levels. Our study revealed significant associations between CSF GAP-43 levels and TBM indices among people of the AD spectrum.