A brief molecular insight of COVID-19: epidemiology, clinical manifestation, molecular mechanism, cellular tropism and immuno-pathogenesis

Vein wall thickness and severity of pulmonary involvement due to sars n-cov2 virus infection

BACKGROUND

An observational study involving patients recovered from COVID-19 was conducted in order to evaluate the presence/absence of vein wall thickness increasing, according to the severity of pulmonary involvement quantified with a CT-scoring system.

METHODS

The venous wall thickness (VWT) of 31 patients (23 males and 8 females) with COVID 19 previously admitted to Federico II University Hospital of Naples was evaluated through ultrasound measurement of the common femoral Vein 1 cm proximal to the saphenous-femoral junction and the popliteal Vein 1 cm distal to the confluence of gemellary veins. Measurements were taken with an automated tool to avoid human error. All patients were evaluated in the supine position. Patients were then stratified into two groups, VWT > 1 mm and VWT < 1 mm. Lung damage was assessed through thoracic High Resolution Computer Tomography and subsequently quantified using the scoring system set out by Chung et al. CEAP-C class was calculated for all patients.

RESULTS

The mean value of COVID score in VWT > 1 mm group was 7.4 (S.D. 4.83), whilst the mean value of the COVID score in the VWT < 1 mm group was 3.82 (S.D 3.34). These findings were determined to be statistically significant in a two-tie Student-T test. The linear regression test between VWT and Covid score values demonstrated a direct relationship between the two variables.

CONCLUSION

These results demonstrate a link between two different aspects of the pathological effects on the vessels during a SARS-COV 2 infection. As such a common primum movens can be hypothesized in both micro-thrombotic and inflammatory processes relating to COVID 19.

Hybrid feature engineering of medical data via variational autoencoders with triplet loss: a COVID-19 prognosis study

Medical machine learning frameworks have received much attention in recent years. The recent COVID-19 pandemic was also accompanied by a surge in proposed machine learning algorithms for tasks such as diagnosis and mortality prognosis. Machine learning frameworks can be helpful medical assistants by extracting data patterns that are otherwise hard to detect by humans. Efficient feature engineering and dimensionality reduction are major challenges in most medical machine learning frameworks. Autoencoders are novel unsupervised tools that can perform data-driven dimensionality reduction with minimum prior assumptions. This study, in a novel approach, investigated the predictive power of latent representations obtained from a hybrid autoencoder (HAE) framework combining variational autoencoder (VAE) characteristics with mean squared error (MSE) and triplet loss for forecasting COVID-19 patients with high mortality risk in a retrospective framework. Electronic laboratory and clinical data of 1474 patients were used in the study. Logistic regression with elastic net regularization (EN) and random forest (RF) models were used as final classifiers. Moreover, we also investigated the contribution of utilized features towards latent representations via mutual information analysis. HAE Latent representations model achieved decent performance with an area under ROC curve of 0.921 (±0.027) and 0.910 (±0.036) with EN and RF predictors, respectively, over the hold-out data in comparison with the raw (AUC EN: 0.913 (±0.022); RF: 0.903 (±0.020)) models. The study aims to provide an interpretable feature engineering framework for the medical environment with the potential to integrate imaging data for efficient feature engineering in rapid triage and other clinical predictive models.

Mild or Moderate COVID-19 during Pregnancy Does Not Affect the Content of CD34+ Hematopoietic Stem Cells in Umbilical Cord Blood of Newborns

The study included umbilical cord blood samples (n=64) intended for cryogenic storage of hematopoietic stem cells and obtained from patients with a history of mild and moderate forms of COVID-19 during pregnancy. The control group was composed of samples (n=746) obtained from healthy women in labor. A comparative analysis of the volume of cord blood collected, the total leukocyte count, the relative and absolute content of cells with the CD34⁺/CD45⁺ phenotype revealed no significant differences between the groups.

Host cell entry mediators implicated in the cellular tropism of SARS‑CoV‑2, the pathophysiology of COVID‑19 and the identification of microRNAs that can modulate the expression of these mediators (Review)

The pathophysiology of coronavirus disease 2019 (COVID-19) is mainly dependent on the underlying mechanisms that mediate the entry of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) into the host cells of the various human tissues/organs. Recent studies have indicated a higher order of complexity of the mechanisms of infectivity, given that there is a wide-repertoire of possible cell entry mediators that appear to co-localise in a cell- and tissue-specific manner. The present study provides an over-view of the 'canonical' SARS-CoV-2 mediators, namely angiotensin converting enzyme 2, transmembrane protease serine 2 and 4, and neuropilin-1, expanding on the involvement of novel candidates, including glucose-regulated protein 78, basigin, kidney injury molecule-1, metabotropic glutamate receptor subtype 2, ADAM metallopeptidase domain 17 (also termed tumour necrosis factor-α convertase) and Toll-like receptor 4. Furthermore, emerging data indicate that changes in microRNA (miRNA/miR) expression levels in patients with COVID-19 are suggestive of further complexity in the regulation of these viral mediators. An in silico analysis revealed 160 candidate miRNAs with potential strong binding capacity in the aforementioned genes. Future studies should concentrate on elucidating the association between the cellular tropism of the SARS-CoV-2 cell entry mediators and the mechanisms through which they might affect the clinical outcome. Finally, the clinical utility as a biomarker or therapeutic target of miRNAs in the context of COVID-19 warrants further investigation.

Does acute and persistent metabolic dysregulation in COVID19 point to novel biomarkers and future therapeutic strategies?

When the coronavirus disease 2019 (COVID-19) pandemic first appeared in December of 2019, the pathophysiological underpinnings of the disease were largely unknown. Scientists, physicians and government institutions from around the globe took an “all-hands on deck” approach with the hope of identifying potential therapies to treat as well as understand the pathophysiology of the disease [1]. Currently, more than 4800 clinical trials listed on clinicaltrials.gov have been performed or proposed around the world, many with subjects from vastly different ethnic and racial backgrounds, as well as different standard-of-care strategies [2]. Despite this effort, apart from monoclonal antibodies, few therapies have emerged as effective treatments of COVID-19; vaccines remain the best approach to control and mitigate the pandemic [3].

Metabolomics changes in COVID-19 predict acute patient outcomes and suggest a role for a bioenergetic crisis. Thus, metabolomics changes in COVID-19 may serve as a biomarker and provide insight into pathogenic mechanisms and pharmacologic targets.https://bit.ly/2XkJeU8