Background: There exist functional deficits in motor, sensory, and olfactory abilities in dementias. Measures of these deficits have been discussed as potential clinical markers. Objective: We measured the deficit of motor, sensory, and olfactory functions on both the left and right body side, to study potential body lateralizations. Methods: This IRB-approved study (N = 84) performed left/right clinical tests of gross motor (dynamometer test), sensory (Von Frey test), and olfactory (peppermint oil test) ability. The Mini-Mental Status Exam was administered to determine level of dementia; medical and laboratory data were collected. Results: Sensory and olfactory deficits lateralized to the left side of the body, while motor deficits lateralized to the right side. We found clinical correlates of motor lateralization: female, depression, MMSE <15, and diabetes. While clinical correlates of sensory lateralization: use of psychotherapeutic agent, age ≥85, MMSE <15, and male. Lastly, clinical correlates of olfactory lateralization: age <85, number of medications >10, and male. Conclusion: These lateralized deficits in body function can act as early clinical markers for improved diagnosis and treatment. Future research should identify correlates and corresponding therapies to strengthen at-risk areas.

The mutagenic chain reaction (MCR) is a genetic tool to use a CRISPR-Cas construct to introduce a homing endonuclease, allowing gene drive to influence whole populations in a minimal number of generations 1,2,3 . The question arises: if an active genetic terror event is released into a population, could we prevent the total spread of the undesired allele 4 ? Thus far, MCR protection methods require knowledge of the terror locus 5 . Here we introduce a novel approach, an autocatalytic-Protection for an Unknown Locus (a-PUL), whose aim is to spread through a population and arrest and decrease an active terror event's spread without any prior knowledge of the terror-modified locus, thus allowing later natural selection and ERACR drives to restore the normal locus 6 . a-PUL, using a mutagenic chain reaction, includes (i) a segment encoding a non-Cas9 endonuclease capable of homology-directed repair suggested as Type II endonuclease Cpf1 (Cas12a), (ii) a ubiquitously-expressed gene encoding a gRNA (gRNA1) with a U4AU4 3′-overhang specific to Cpf1 and with crRNA specific to some desired genomic sequence of non-coding DNA, (iii) a ubiquitously-expressed gene encoding two gRNAs (gRNA2/gRNA3) both with tracrRNA specific to Cas9 and crRNA specific to two distinct sites of the Cas9 locus, and (iv) homology arms flanking the Cpf1/gRNA1/gRNA2/gRNA3 cassette that are identical to the region surrounding the target cut directed by gRNA1 7 . We demonstrate the proof-of-concept and efficacy of our protection construct through a Graphical Markov model and computer simulation.

Nocturnal blood pressure (BP) profile shows characteristic abnormalities in OSA, namely acute postapnea BP surges and nondipping BP. These abnormal BP profiles provide prognostic clues indicating increased cardiovascular disease risk. We developed a deep neural network model to perform computerized analysis of polysomnography data and predict nocturnal BP profile. Methods: We analyzed concurrently performed polysomnography and noninvasive beat-to-beat BP measurement with a deep neural network model to predict nocturnal BP profiles from polysomnography data in 13 patients with severe OSA. Results: A good correlation was noted between measured and predicted postapnea systolic and diastolic BP (Pearson r ≥ .75). Moreover, Bland-Altman analyses showed good agreement between the 2 values. Continuous systolic and diastolic BP prediction by the deep neural network model was also well correlated with measured BP values (Pearson r ≥ .83). Conclusions: We developed a deep neural network model to predict nocturnal BP profile from clinical polysomnography signals and provide a potential prognostic tool in OSA. Validation of the model in larger samples and examination of its utility in predicting CVD risk in future studies is warranted.

COVID-19 was declared by the World Health Organization in 2020 to be a pandemic. Analysis of COVID-19 related genetic pathways allows for a better understanding of the possible effects and sequelae of the disease. Using 6178 scRNA sequenced human cells, having a status of control/mild/severe COVID-19 disease status, differential expression of genes and pathways was analyzed. Using Gene Set Enrichment Analysis (GSEA), mild COVID-19 was found to over-express the Influenza Pathway. In order to identify genes important in COVID-19 severity, a deep learning classifier was trained. Classifiers were repeatedly trained for this task using 10 randomly selected genes from the total number of 18,958 genes. The highest performing classifier (AUC = 0.748) was trained using: AC008626.1, SGO1, RHOBTB2, RBM41, NDUFAF4P1, COX5A, ZDHHC17, STX11, IPP, NUDT5 genes. These results further illustrate the other factors contributing to mild versus severe COVID-19, as well as evidence of potential misdiagnosis or overlapping pathway effects of Influenza and COVID-19.

Essential genes have been studied by copy number variants and deletions, both associated with introns. The premise of our work is that introns of essential genes have distinct characteristic properties. We provide support for this by training a deep learning model and demonstrating that introns alone can be used to classify essentiality. The model, limited to first introns, performs at an increased level, implicating first introns in essentiality. We identify unique properties of introns of essential genes, finding that their structure protects against deletion and intron-loss events, especially centered on the first intron. We show that GC density is increased in the first introns of essential genes, allowing for increased enhancer activity, protection against deletions, and improved splice site recognition. We find that first introns of essential genes are of remarkably smaller size than their nonessential counterparts, and to protect against common 3′ end deletion events, essential genes carry an increased number of (smaller) introns. To demonstrate the importance of the seven features we identified, we train a feature-based model using only these features and achieve high performance.