Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy revisited: Genotype-phenotype correlations of all published cases.

OBJECTIVE: The aim of this study was to evaluate the correlation between the various NOTCH3 mutations and their clinical and genetic profile, along with the presentation of a novel mutation in a patient. METHODS: Here, we describe the phenotype of a patient with cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) harboring a novel mutation. We also performed an extensive literature research for NOTCH3 mutations published since the identification of the gene and performed a systematic review of all published cases with NOTCH3 mutations. We evaluated the mutation pathogenicity in a great number of patients with detailed clinical and genetic evaluation and investigated the possible phenotype-genotype correlations. RESULTS: Our patient harbored a novel mutation in the NOTCH3 gene, the c.3084 G > C, corresponding to the aminoacidic substitution p.Trp1028Cys, presenting with seizures as the first neurologic manifestation. We managed to find a correlation between the pathogenicity of mutations, severity of the phenotype, and age at onset of CADASIL. Significant differences were also identified between men and women regarding the phenotype severity. CONCLUSIONS: The collection and analysis of these scarce data published since the identification of NOTCH3 qualitatively by means of a systematic review and quantitatively regarding genetic profile and pathogenicity scores, highlight the significance of the ongoing trend of investigating phenotypic genotypic correlations.

Streaming updates for heart rate variability algorithms.

Heart rate variability (HRV) quantifies the fluctuations of the lengths of consecutive heart beat intervals, and is a reliable descriptor of many physiological factors modulating the normal rhythm of the heart. As the heart rate signal is nonstationary, indicators deduced from it may be present at all times, but may also occur episodically at nonpredetermined time instances. The potential for real-time feedback long-term ambulatory recordings is thus apparent. Numerous methods for measuring HRV have been standardized and are in active use, but are typically not designed to operate at real time. In this paper, we study the most popular HRV quantification methods and propose streaming algorithms that maximally utilize previously computed information without altering the output of the methods. We demonstrate speedups of more than two orders of magnitude for typical use-case scenarios. Using our algorithms on embedded systems that compute HRV leads to dramatic decreases in power consumption and in some cases allows for computation of metrics that were not previously possible at real time.