A personalized classification of behavioral severity of autism spectrum disorder using a comprehensive machine learning framework.

Autism Spectrum Disorder (ASD) is characterized as a neurodevelopmental disorder with a heterogeneous nature, influenced by genetics and exhibiting diverse clinical presentations. In this study, we dissect Autism Spectrum Disorder (ASD) into its behavioral components, mirroring the diagnostic process used in clinical settings. Morphological features are extracted from magnetic resonance imaging (MRI) scans, found in the publicly available dataset ABIDE II, identifying the most discriminative features that differentiate ASD within various behavioral domains. Then, each subject is categorized as having severe, moderate, or mild ASD, or typical neurodevelopment (TD), based on the behavioral domains of the Social Responsiveness Scale (SRS). Through this study, multiple artificial intelligence (AI) models are utilized for feature selection and classifying each ASD severity and behavioural group. A multivariate feature selection algorithm, investigating four different classifiers with linear and non-linear hypotheses, is applied iteratively while shuffling the training-validation subjects to find the set of cortical regions with statistically significant association with ASD. A set of six classifiers are optimized and trained on the selected set of features using 5-fold cross-validation for the purpose of severity classification for each behavioural group. Our AI-based model achieved an average accuracy of 96%, computed as the mean accuracy across the top-performing AI models for feature selection and severity classification across the different behavioral groups. The proposed AI model has the ability to accurately differentiate between the functionalities of specific brain regions, such as the left and right caudal middle frontal regions. We propose an AI-based model that dissects ASD into behavioral components. For each behavioral component, the AI-based model is capable of identifying the brain regions which are associated with ASD as well as utilizing those regions for diagnosis. The proposed system can increase the speed and accuracy of the diagnostic process and result in improved outcomes for individuals with ASD, highlighting the potential of AI in this area.

Transient Cell Cycle Induction in Cardiomyocytes to Treat Subacute Ischemic Heart Failure.

BACKGROUND: The regenerative capacity of the heart after myocardial infarction is limited. Our previous study showed that ectopic introduction of 4 cell cycle factors (4F; CDK1 [cyclin-dependent kinase 1], CDK4 [cyclin-dependent kinase 4], CCNB [cyclin B1], and CCND [cyclin D1]) promotes cardiomyocyte proliferation in 15% to 20% of infected cardiomyocytes in vitro and in vivo and improves cardiac function after myocardial infarction in mice. METHODS: Using temporal single-cell RNA sequencing, we aimed to identify the necessary reprogramming stages during the forced cardiomyocyte proliferation with 4F on a single cell basis. Using rat and pig models of ischemic heart failure, we aimed to start the first preclinical testing to introduce 4F gene therapy as a candidate for the treatment of ischemia-induced heart failure. RESULTS: Temporal bulk and single-cell RNA sequencing and further biochemical validations of mature human induced pluripotent stem cell-derived cardiomyocytes treated with either LacZ or 4F adenoviruses revealed full cell cycle reprogramming in 15% of the cardiomyocyte population at 48 hours after infection with 4F, which was associated mainly with sarcomere disassembly and metabolic reprogramming (n=3/time point/group). Transient overexpression of 4F, specifically in cardiomyocytes, was achieved using a polycistronic nonintegrating lentivirus (NIL) encoding 4F; each is driven by a TNNT2 (cardiac troponin T isoform 2) promoter (TNNT2-4Fpolycistronic-NIL). TNNT2-4Fpolycistronic-NIL or control virus was injected intramyocardially 1 week after myocardial infarction in rats (n=10/group) or pigs (n=6-7/group). Four weeks after injection, TNNT2-4Fpolycistronic-NIL-treated animals showed significant improvement in left ventricular ejection fraction and scar size compared with the control virus-treated animals. At 4 months after treatment, rats that received TNNT2-4Fpolycistronic-NIL still showed a sustained improvement in cardiac function and no obvious development of cardiac arrhythmias or systemic tumorigenesis (n=10/group). CONCLUSIONS: This study provides mechanistic insights into the process of forced cardiomyocyte proliferation and advances the clinical feasibility of this approach by minimizing the oncogenic potential of the cell cycle factors owing to the use of a novel transient and cardiomyocyte-specific viral construct.