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There were 7 acetylation sites in α and β chains, and none in fibrinogen γ chain. Two acetylation sites were identical with FL sites (αK-195 and β-247), while one with CML site (βK-353). In 7 patients with low post-ASA TXB2, intensity of acetylation, as well as clot properties were unaffected by ASA. This study identifies glycation and acetylation sites on fibrinogen in plasma fibrin clot of T2DM and supports the view that low-dose ASA does not increase fibrinogen acetylation in T2DM. Our findings suggest that glycation may block sites previously identified to be acetylated in vitro.Glaucoma, the group of eye diseases is characterized by increased intraocular pressure, optic neuropathy and visual field defect patterns. Early and correct diagnosis of glaucoma can prevent irreversible vision loss and glaucomatous structural damages to the eye. However, greater chances of misdiagnosis by the currently used conventional methods for diagnosis open up ways for more advanced techniques like the use of artificial intelligence (AI). Artificial intelligence coupled with optical coherence tomography imaging creates an algorithm that can be effectively used to make a model of complex data for detection as well as diagnosis of glaucoma. The present review is an attempt to provide state-of-the-art information on various AI techniques used in the diagnosis and assessment of glaucoma. buy Oxaliplatin The second part of the review is focused on understanding how the AI along with machine learning (ML) can be potentially used to be subjected for software as a medical device (SaMD) in precise diagnosis or early detection of disease conditions.Glaucoma is a disease that affects the optic nerve and can lead to blindness. The cup-to-disc ratio (CDR) measurement is one of the key clinical indicators for glaucoma assessment. However, the CDR only evaluates the relative sizes of the cup and optic disc (OD) via their diameters, and does not characterize local morphological changes that can inform clinicians on early signs of glaucoma. In this work, we propose a novel glaucoma score based on a statistical atlas framework that automatically quantifies the deformations of the OD region induced by glaucoma. A deep-learning approach is first used to segment the optic cup with a dedicated atlas-based data augmentation strategy. The segmented OD region (disc, cup and vessels) is then registered to the statistical OD atlas and the deformation is projected onto the atlas eigenvectors. The atlas glaucoma score (AGS) is then obtained by a linear combination of the principal modes of deformation of the atlas with linear discriminant analysis. The AGS performs better than the CDR on the three datasets used for evaluation, including RIM-ONE and ORIGA650. Compared to the CDR measurement, which yields an area under the ROC curve (AUC) of 91.4% using the expert segmentations, the AGS achieves an AUC of 98.2%. Our novel glaucoma score captures more complex deformations within the optic disc region than the CDR can. Such morphological changes are the first cue of glaucoma onset, before the visual field is affected. The proposed approach can thus significantly improve early detection of glaucoma.Biallelic loss of function of TELO2 gene cause a severe syndromic disease mainly characterized by global developmental delay with poor motor and language acquisitions, microcephaly, short stature, minor facial and limbs anomalies, sleep disorder, spasticity, and balance impairment up to ataxia. TELO2-related syndrome, also known as You-Hoover-Fong Syndrome, is extremely rare and since its first description in 2016 only 8 individuals have been reported, all showing a severe disability. The causative gene is member of the big molecular family of genes responsible for cells proliferation and DNA stability. We describe the case of two sisters, carrying the homozygous p. Arg609His variant of the gene, who present a milder phenotype of TELO2-related syndrome. Such variant has been reported once in a more severely affected patient, in compound heterozygous state associated with the p. Pro260Leu variant, suggesting a possible role of the p. Arg609His variant in determining milder phenotypes. Comparing the siblings with all previously reported cases, we offer an overview on the condition and discuss TELO2 genetic interactions, in order to further explore the molecular bases of this recently described disorder.The EEF1A2 gene encodes eukaryotic translation elongation factor 1α2, an integral component of the elongation factor complex. Heterozygous pathogenic variants in EEF1A2 are associated with neurodevelopmental disorders characterized by epilepsy, global developmental delay, and autism. To date, dilated cardiomyopathy has only been reported in two siblings with neurodevelopmental phenotypes and a homozygous missense variant in EEF1A2. This report describes a nine-year-old female patient who presented with neurodevelopmental phenotypes and dilated cardiomyopathy. Analysis of 193 epilepsy genes by focused exome sequencing revealed a novel heterozygous variant c.46G > C (p.Val16Leu; NM_001958.3) in EEF1A2. The variant was not detected in either parent, confirming its de novo origin. No additional variants that explain the patient's phenotypes were found by subsequent whole exome analysis. Copy number analysis of the exome data and exon-level microarray excluded a deletion in the other allele of EEF1A2. We present the first patient with a heterozygous pathogenic EEF1A2 variant who had dilated cardiomyopathy as well as neurodevelopmental phenotypes, suggesting that this cardiac phenotype may be associated with the autosomal dominant form of the EEF1A2-related disorder.Spike sorting refers to the technique of detecting signals generated by single neurons from multi-neuron recordings and is a valuable tool for analyzing the relationships between individual neuronal activity patterns and specific behaviors. Since the precision of spike sorting affects all subsequent analyses, sorting accuracy is critical. Many semi-automatic to fully-automatic spike sorting algorithms have been developed. However, due to unsatisfactory classification accuracy, manual sorting is preferred by investigators despite the intensive time and labor costs. Thus, there still is a strong need for fully automatic spike sorting methods with high accuracy. Various machine learning algorithms have been developed for feature extraction but have yet to show sufficient accuracy for spike sorting. Here we describe a deep learning-based method for extracting features from spike signals using an ensemble of auto-encoders, each with a distinct architecture for distinguishing signals at different levels of resolution.