Cardiac troponins: Mechanisms of release and role in healthy and diseased subjects.

The cardiac troponins (cTns), cardiac troponin C (cTnC), cTnT, and cTnI are key elements of myocardial apparatus, fixed as protein complex on the thin filament of sarcomere and are involved in the regulation of excitation-contraction coupling of cardiomyocytes in the presence of Ca2+ . Circulating cTnT and cTnI (cTns) increase following cardiac tissue necrosis, and they are consolidated biomarkers of acute myocardial infarction (AMI). However, the use of high sensitivity (hs)-immunoassay tests for cTnT and cTnI has made it possible to identify a multitude of other clinical conditions associated with increased circulating levels of cTns. cTns can be measured also in the peripheral circulation of healthy subjects or athletes, suggesting that different mechanisms are involved in the release of cTns in the blood independently of cardiac cell necrosis. In this review, the molecular/cellular mechanisms involved in cTns release in blood and the exploitation of cTnI and cTnT as biomarkers of cardiac adverse events, in addition to cardiac necrosis, are discussed.

COVID-19 Detection from Cough Recording by means of Explainable Deep Learning

The new coronavirus disease (COVID-19), declared a pandemic on 11 March 2020 by the World Health Organization, has caused over 6 million victims worldwide. Because of the rapid spread of the virus, with the aim to perform screening we exploit deep learning model to quickly diagnose altered respiratory conditions. In this paper, we propose a method to recognize and classify cough audio files into three classes to distinguish patients with COVID-19 disease, symptomatic ones and healthy subjects, with the use of a convolutional neural network (CNN). Cough audios were recorded by using a smartphone and its built-in microphone. From cough recordings, we generate spectrogram images and we obtain an accuracy equal to 0.82 with a deep learning network developed by authors. Our method also provides heatmaps, which show the relevant input areas used by the model for the final forecast, and this aspect ensures the explainability of the method. © 2022 IEEE.

Screening for COVID-19 via Acoustics Using Artificial Intelligence

This research presents deep learning concepts used on crowd-sourced audio files of people who have had or are having positive COVID-19 symptoms to help determine how to diagnose them just by analyzing their telephone calls to the artificial intelligence machine as compared to healthy subjects for medical screening. First the (.wav file) samples are processed by audio means, using the python code library librosa and the resulting output is then converted to images, specifically log-power spectrograms. Then those images are processed using the Convolutional Neural Networks (CNN) computer vision methods. The proposed model, trained on 70% of the data, validated on 20% of the data and finally tested on 10% of the data, gave good initial results of 85% Area Under Receiver Operating Characteristics (ROC) Curve (AUC). The Coswara crowd-sourced dataset contains 1433 healthy and 681 positive samples. This proposed method may improve patient outcomes, reduce cost of testing, and prevent false negative test results. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Evaluating the Performance of Hough based Moments to Classify Healthy and COVID Subjects in Comparison with Traditional Shape Measures

The aim of this analysis is to measure and analyse the shape changes in Lung CT scans using orthogonal Zernike moments in comparison with traditional shape measures. Materials and Methods: A total sample size of 176 scans are acquired for this analysis, by assigning parameters such as the effect size = 0.3, standard error rate = 0.05 and algorithm power = 0.80 are predefined in Gpower software. In this analysis, the comparison between traditional shape measures and Hough Transform algorithms in classifying normal and COVID-19 is performed. Results: It is observed that there is no shape change in the lungs of the normal subjects and in COVID subjects the shape of the lungs reduces due to tissue loss. The feature values obtained from Hough Transform are found to be statistically important (p<0.05). The statistical values (Mean ± standard deviation) of normal and COVID subjects are 0.18 ± 0.13 and 0.10 ± 0.13. The significant features for the Zernike moment were M13,9, M10,8. The extracted values from the Computed Tomography images are consistent in displaying a considerable difference between healthy subject and COVID CT- scan images. The proposed Hough Transform based Zernike Moments algorithm has significantly better accuracy (97%) than the Traditional shape measures with accuracy (78%). Conclusion: The Hough transform based Zernike moments algorithm gives a significantly better result oriented to extraction of shape changes and manifestation of a significant difference in the healthy subject and COVID subject CT scan images than Traditional shape measures algorithm. © 2022 IEEE.

The Coronavirus Footprint on Dual-Task Performance in Post-Acute Patients after Severe COVID-19: A Future Challenge for Rehabilitation.

Recent studies suggest that also the non-critical form of COVID-19 infection may be associated with executive function impairments. However, it is not clear if they result from cognitive impairments or by COVID-19 infection per se. We aimed to investigate if patients in the post-acute stage of severe COVID-19 (PwCOVID), without manifest cognitive deficits, reveal impairments in performing dual-task (DT) activities compared to healthy controls (HS). We assessed balance in 31 PwCOVID vs. 30 age-matched HS by stabilometry and the Timed Up and Go (TUG) test with/without a cognitive DT. The DT cost (DTC), TUG test time and sway oscillations were recorded; correct cognitive responses (CCR) were calculated to evaluate cognitive performance. Results show a significant difference in overall DT performance between PwCOVID and HS in both stabilometry (p < 0.01) and the TUG test (p < 0.0005), although with similar DTCs. The main difference in the DTs between groups emerged in the CCR (effect size > 0.8). Substantially, PwCOVID gave priority to the motor task, leaving out the cognitive one, while HS performed both tasks simultaneously. Our findings suggest that PwCOVID, even without a manifest cognitive impairment, may present a deficit in executive function during DTs. These results encourage the use of DTs and CCR in PwCOVID.

Deep learning and machine learning-based voice analysis for the detection of COVID-19: A proposal and comparison of architectures.

Alongside the currently used nasal swab testing, the COVID-19 pandemic situation would gain noticeable advantages from low-cost tests that are available at any-time, anywhere, at a large-scale, and with real time answers. A novel approach for COVID-19 assessment is adopted here, discriminating negative subjects versus positive or recovered subjects. The scope is to identify potential discriminating features, highlight mid and short-term effects of COVID on the voice and compare two custom algorithms. A pool of 310 subjects took part in the study; recordings were collected in a low-noise, controlled setting employing three different vocal tasks. Binary classifications followed, using two different custom algorithms. The first was based on the coupling of boosting and bagging, with an AdaBoost classifier using Random Forest learners. A feature selection process was employed for the training, identifying a subset of features acting as clinically relevant biomarkers. The other approach was centered on two custom CNN architectures applied to mel-Spectrograms, with a custom knowledge-based data augmentation. Performances, evaluated on an independent test set, were comparable: Adaboost and CNN differentiated COVID-19 positive from negative with accuracies of 100% and 95% respectively, and recovered from negative individuals with accuracies of 86.1% and 75% respectively. This study highlights the possibility to identify COVID-19 positive subjects, foreseeing a tool for on-site screening, while also considering recovered subjects and the effects of COVID-19 on the voice. The two proposed novel architectures allow for the identification of biomarkers and demonstrate the ongoing relevance of traditional ML versus deep learning in speech analysis.

Analysis of coupling between autonomic control loops of blood circulation in patients with Covid-19

This work aims to analyze the coupling between autonomic control loops of blood circulation in patients with Covid-19. In this work, we assessed the degree of coupling between the mechanisms of autonomic control using noninvasive signals of the cardiovascular system - RR-intervals signals and photoplethysmogram signals. Statistical evaluation of the study results using the methods of phase synchronization analysis did not reveal significant differences between the sample of patients with Covid-19 and healthy subjects of the corresponding age group. © 2021 IEEE

Safety, tolerability, and pharmacokinetics of VV116, an oral nucleoside analog against SARS-CoV-2, in Chinese healthy subjects.

VV116 (JT001) is an oral drug candidate of nucleoside analog against SARS-CoV-2. The purpose of the three phase I studies was to evaluate the safety, tolerability, and pharmacokinetics of single and multiple ascending oral doses of VV116 in healthy subjects, as well as the effect of food on the pharmacokinetics and safety of VV116. Three studies were launched sequentially: Study 1 (single ascending-dose study, SAD), Study 2 (multiple ascending-dose study, MAD), and Study 3 (food-effect study, FE). A total of 86 healthy subjects were enrolled in the studies. VV116 tablets or placebo were administered per protocol requirements. Blood samples were collected at the scheduled time points for pharmacokinetic analysis. 116-N1, the metabolite of VV116, was detected in plasma and calculated for the PK parameters. In SAD, AUC and Cmax increased in an approximately dose-proportional manner in the dose range of 25-800 mg. T1/2 was within 4.80-6.95 h. In MAD, the accumulation ratio for Cmax and AUC indicated a slight accumulation upon repeated dosing of VV116. In FE, the standard meal had no effect on Cmax and AUC of VV116. No serious adverse event occurred in the studies, and no subject withdrew from the studies due to adverse events. Thus, VV116 exhibited satisfactory safety and tolerability in healthy subjects, which supports the continued investigation of VV116 in patients with COVID-19.

Interpretable AI Model-Based Predictions of ECG changes in COVID-recovered patients

COVID-19 has caused immense social and economic losses throughout the world. Subjects recovered from COVID are learned to have complications. Some studies have shown a change in the heart rate variability (HRV) in COVID-recovered subjects compared to the healthy ones. This change indicates an increased risk of heart problems among the survivors of moderate-to-severe COVID. Hence, this study is aimed at finding HRV features that get altered in COVID-recovered subjects compared to healthy subjects. Data of COVID-recovered and healthy subjects were collected from two hospitals in Delhi, India. Seven ML models have been built to classify healthy versus COVID-recovered subjects. The best-performing model was further analyzed to explore the ranking of altered heart features in COVID-recovered subjects via AI interpretability. Ranking of these features can indicate cardiovascular health status to doctors, who can provide support to the COVID-recovered subjects for timely safeguard from heart disorders. To the best of our knowledge, this is the first study with an in-depth analysis of the heart status of COVID-recovered subjects via ECG analysis. © 2021 IEEE.

Erythrocytes Induce Vascular Dysfunction in COVID-19.

Current knowledge regarding mechanisms underlying cardiovascular complications in patients with COVID-19 is limited and urgently needed. We shed light on a previously unrecognized mechanism and unravel a key role of red blood cells, driving vascular dysfunction in patients with COVID-19 infection. We establish the presence of profound and persistent endothelial dysfunction in vivo in patients with COVID-19. Mechanistically, we show that targeting reactive oxygen species or arginase 1 improves vascular dysfunction mediated by red blood cells. These translational observations hold promise that restoring the redox balance in red blood cells might alleviate the clinical complications of COVID-19-associated vascular dysfunction.

Corrigendum: Age-Related Differences in the Expression of Most Relevant Mediators of SARS-CoV-2 Infection in Human Respiratory and Gastrointestinal Tract.

[This corrects the article DOI: 10.3389/fped.2021.697390.].

Age-Related Differences in the Expression of Most Relevant Mediators of SARS-CoV-2 Infection in Human Respiratory and Gastrointestinal Tract.

Background: Clinical features of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection seem to differ in children compared to that in adults. It has been hypothesized that the lower clinical severity in children could be influenced by differential expression of the main host functional receptor to SARS-CoV-2, the angiotensin-converting enzyme 2 (ACE2), but data are still conflicting. To explore the origin of age-dependent clinical features of coronavirus disease 2019 (COVID-19), we comparatively evaluated the expression in children and adult subjects of the most relevant mediators of the SARS-CoV-2 infection: ACE2, angiotensin-converting enzyme 1 (ACE1), transmembrane serine protease-2 (TMPRSS2), and neuropilin-1 (NRP1), at upper respiratory tract and small intestine level. Methods: The expression of ACE2, ACE1, TMPRSS2, and NRP1 in nasal epithelium and in small intestine epithelium was investigated by quantitative real-time PCR analysis. Results: We found no differences in ACE2, ACE1, and TMPRSS2 expression in the nasal epithelium comparing children and adult subjects. In contrast, nasal epithelium NRP1 expression was lower in children compared to that in adults. Intestinal ACE2 expression was higher in children compared to that in adults, whereas intestinal ACE1 expression was higher in adults. Intestinal TMPRSS2 and NRP1 expression was similar comparing children and adult subjects. Conclusions: The lower severity of SARS-CoV-2 infection observed in children may be due to a different expression of nasal NRP1, that promotes the virus interaction with ACE2. However, the common findings of intestinal symptoms in children could be due to a higher expression of ACE2 at this level. The insights from these data will be useful in determining the treatment policies and preventive measures for COVID-19.