Pattern recognition in data as a diagnosis tool

Medical data often appear in the form of numerical matrices or sequences. We develop mathematical tools for automatic screening of such data in two medical contexts: diagnosis of systemic lupus erythematosus (SLE) patients and identification of cardiac abnormalities. The idea is first to implement adequate data normalizations and then identify suitable hyperparameters and distances to classify relevant patterns. To this purpose, we discuss the applicability of Plackett-Luce models for rankings to hyperparameter and distance selection. Our tests suggest that, while Hamming distances seem to be well adapted to the study of patterns in matrices representing data from laboratory tests, dynamic time warping distances provide robust tools for the study of cardiac signals. The techniques developed here may set a basis for automatic screening of medical information based on pattern comparison.

Modeling and predicting the overlap of B- and T-cell receptor repertoires in healthy and SARS-CoV-2 infected individuals

Adaptive immunity's success relies on the extraordinary diversity of protein receptors on B and T cell membranes. Despite this diversity, the existence of public receptors shared by many individuals gives hope for developing population wide vaccines and therapeutics. Yet many of these public receptors are shared by chance. We present a statistical approach, defined in terms of a probabilistic V(D)J recombination model enhanced by a selection factor, that describes repertoire diversity and predicts with high accuracy the spectrum of repertoire overlap in healthy individuals. The model underestimates sharing between repertoires of individuals infected with SARS-CoV-2, suggesting strong antigen-driven convergent selection. We exploit this discrepancy to identify COVID-associated receptors, which we validate against datasets of receptors with known viral specificity. We study their properties in terms of sequence features and network organization, and use them to design an accurate diagnosis tool for predicting SARS-CoV-2 status from repertoire data.

Is Lung Ultrasound Helpful in COVID-19 Neonates?—A Systematic Review

Background: The SARS-CoV-2 infection has occurred in neonates, but it is a fact that radiation exposure is not recommended given their age. The aim of this review is to assess the evidence on the utility of lung ultrasound (LUS) in neonates diagnosed with COVID-19. Methods: A systematic literature review was performed so as to find a number of published studies assessing the benefits of lung ultrasound for newborns diagnosed with COVID and, in the end, to make a comparison between LUS and the other two more conventional procedures of chest X-rays or CT exam. The key terms used in the search of several databases were: “lung ultrasound”, “sonography”, “newborn”, “neonate”, and “COVID-19′. Results: In total, 447 studies were eligible for this review, and after removing the duplicates, 123 studies referring to LU were further examined, but only 7 included cases of neonates. These studies were considered for the present research paper. Conclusions: As a non-invasive, easy-to-use, and reliable method for lung lesion detection in neonates with COVID-19, lung ultrasound can be used as a useful diagnosis tool for the evaluation of COVID-19-associated lung lesions. The benefits of this method in this pandemic period are likely to arouse interest in opening new research horizons, with immediate practical applicability.

Practical experience condition diagnosis gained from oil-immersed power transformers voltage class 10 kV

Dissolved Gas Analysis (DGA) for oil samples has been the most widely used diagnosis tool for transformer condition assessment for many years. However, DGA use to oil-filled transformers with a voltage class up to 100 kV. The aim of this paper is to address the issue of DGA interpretation to oil-filled transformers with a voltage class of 10 kV. This paper will present DGA tests results from 57 power transformers and will propose a maintenance decision making procedure using the IEC 60599-2015 Ratio Method, IEEE Std C57.104-2008 include Dornenberg Ratio Method and Rogers Ratio Method, and Russian Std CTO 56947007-29.180.010.094-2011 and Russian Std RD 153-34.0-46.302-00.

Analysis of Graphomotor Tests with Machine Learning Algorithms for an Early and Universal Pre-Diagnosis of Dysgraphia

Five to ten percent of school-aged children display dysgraphia, a neuro-motor disorder that causes difficulties in handwriting, which becomes a handicap in the daily life of these children. Yet, the diagnosis of dysgraphia remains tedious, subjective and dependent to the language besides stepping in late in the schooling. We propose a pre-diagnosis tool for dysgraphia using drawings called graphomotor tests. These tests are recorded using graphical tablets. We evaluate several machine-learning models and compare them to build this tool. A database comprising 305 children from the region of Grenoble, including 43 children with dysgraphia, has been established and diagnosed by specialists using the BHK test, which is the gold standard for the diagnosis of dysgraphia in France. We performed tests of classification by extracting, correcting and selecting features from the raw data collected with the tablets and achieved a maximum accuracy of 73% with cross-validation for three models. These promising results highlight the relevance of graphomotor tests to diagnose dysgraphia earlier and more broadly.

Dynamic Changes in the Levels of Amyloid-β42 Species in the Brain and Periphery of APP/PS1 Mice and Their Significance for Alzheimer’s Disease

Although amyloid-β42 (Aβ42) has been used as one of the core biomarkers for Alzheimer’s disease (AD) diagnosis, the dynamic changes of its different forms in the brain, blood, and even intestines and its correlation with the progression of AD disease remain obscure. Herein, we screened Aβ42-specific preferred antibody pairs 1F12/1F12 and 1F12/2C6 to accurately detect Aβ42 types using sandwich ELISA, including total Aβ42, Aβ42 oligomers (Aβ42Os), and Aβ42 monomers (Aβ42Ms). The levels of Aβ42 species in the brain, blood, and intestines of different aged APP/PS1 mice were quantified to study their correlation with AD progression. Total Aβ42 levels in the blood were not correlated with AD progression, but Aβ42Ms level in the blood of 9-month-old APP/PS1 mice was significantly reduced, and Aβ42Os level in the brain was significantly elevated compared to 3-month-old APP/PS1, demonstrating that the levels of Aβ42Ms and Aβ42Os in the blood and brain were correlated with AD progression. Interestingly, in 9-month-old APP/PS1 mice, the level of Aβ42 in the intestine was higher than that in 3-month-old APP/PS1 mice, indicating that the increased level of Aβ42 in the gastrointestinal organs may also be related to the progression of AD. Meanwhile, changes in the gut microbiota composition of APP/PS1 mice with age were also observed. Therefore, the increase in Aβ derived from intestinal tissues and changes in microbiome composition can be used as a potential early diagnosis tool for AD, and further used as an indicator of drug intervention to reduce brain amyloid.