Aerosolized sulfated hyaluronan derivatives prolong the survival of K18 ACE2 mice infected with a lethal dose of SARS-CoV-2.

Despite several vaccines that are currently approved for human use to control the pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), there is an urgent medical need for therapeutic and prophylactic options. SARS-CoV-2 binding and entry in human cells involves interactions of its spike (S) protein with several host cell surface factors, including heparan sulfate proteoglycans (HSPGs), transmembrane protease serine 2 (TMPRSS2), and angiotensin-converting enzyme 2 (ACE2). In this paper we investigated the potential of sulphated Hyaluronic Acid (sHA), a HSPG mimicking polymer, to inhibit the binding of SARS-CoV-2 S protein to human ACE2 receptor. After the assessment of different sulfation degree of sHA backbone, a series of sHA functionalized with different hydrophobic side chains were synthesized and screened. The compound showing the highest binding affinity to the viral S protein was further characterized by surface plasmon resonance (SPR) towards ACE2 and viral S protein binding domain. Selected compounds were formulated as solutions for nebulization and, after being characterized in terms of aerosolization performance and droplet size distribution, their efficacy was assessed in vivo using the K18 human (h)ACE2 transgenic mouse model of SARS-CoV-2 infection.

Aid or Antagonize: Nuclear Long Noncoding RNAs Regulate Host Responses and Outcomes of Viral Infections.

Long noncoding RNAs (lncRNAs) are transcripts measuring >200 bp in length and devoid of protein-coding potential. LncRNAs exceed the number of protein-coding mRNAs and regulate cellular, developmental, and immune pathways through diverse molecular mechanisms. In recent years, lncRNAs have emerged as epigenetic regulators with prominent roles in health and disease. Many lncRNAs, either host or virus-encoded, have been implicated in critical cellular defense processes, such as cytokine and antiviral gene expression, the regulation of cell signaling pathways, and the activation of transcription factors. In addition, cellular and viral lncRNAs regulate virus gene expression. Viral infections and associated immune responses alter the expression of host lncRNAs regulating immune responses, host metabolism, and viral replication. The influence of lncRNAs on the pathogenesis and outcomes of viral infections is being widely explored because virus-induced lncRNAs can serve as diagnostic and therapeutic targets. Future studies should focus on thoroughly characterizing lncRNA expressions in virus-infected primary cells, investigating their role in disease prognosis, and developing biologically relevant animal or organoid models to determine their suitability for specific therapeutic targeting. Many cellular and viral lncRNAs localize in the nucleus and epigenetically modulate viral transcription, latency, and host responses to infection. In this review, we provide an overview of the role of nuclear lncRNAs in the pathogenesis and outcomes of viral infections, such as the Influenza A virus, Sendai Virus, Respiratory Syncytial Virus, Hepatitis C virus, Human Immunodeficiency Virus, and Herpes Simplex Virus. We also address significant advances and barriers in characterizing lncRNA function and explore the potential of lncRNAs as therapeutic targets.

Validation of a Deep Learning Model for Detecting Chest Pathologies from Digital Chest Radiographs.

Purpose: Manual interpretation of chest radiographs is a challenging task and is prone to errors. An automated system capable of categorizing chest radiographs based on the pathologies identified could aid in the timely and efficient diagnosis of chest pathologies. Method: For this retrospective study, 4476 chest radiographs were collected between January and April 2021 from two tertiary care hospitals. Three expert radiologists established the ground truth, and all radiographs were analyzed using a deep-learning AI model to detect suspicious ROIs in the lungs, pleura, and cardiac regions. Three test readers (different from the radiologists who established the ground truth) independently reviewed all radiographs in two sessions (unaided and AI-aided mode) with a washout period of one month. Results: The model demonstrated an aggregate AUROC of 91.2% and a sensitivity of 88.4% in detecting suspicious ROIs in the lungs, pleura, and cardiac regions. These results outperform unaided human readers, who achieved an aggregate AUROC of 84.2% and sensitivity of 74.5% for the same task. When using AI, the aided readers obtained an aggregate AUROC of 87.9% and a sensitivity of 85.1%. The average time taken by the test readers to read a chest radiograph decreased by 21% (p < 0.01) when using AI. Conclusion: The model outperformed all three human readers and demonstrated high AUROC and sensitivity across two independent datasets. When compared to unaided interpretations, AI-aided interpretations were associated with significant improvements in reader performance and chest radiograph interpretation time.

Evaluating the Association Between Comorbidities and COVID-19 Severity Scoring on Chest CT Examinations Between the Two Waves of COVID-19: An Imaging Study Using Artificial Intelligence

Background Coronavirus disease 2019 (COVID-19) has accounted for over 352 million cases and five million deaths globally. Although it affects populations across all nations, developing or transitional, of all genders and ages, the extent of the specific involvement is not very well known. This study aimed to analyze and determine how different were the first and second waves of the COVID-19 pandemic by assessing computed tomography severity scores (CT-SS). Methodology This was a retrospective, cross-sectional, observational study performed at a tertiary care Institution. We included 301 patients who underwent CT of the chest between June and October 2020 and 1,001 patients who underwent CT of the chest between February and April 2021. All included patients were symptomatic and were confirmed to be COVID-19 positive. We compared the CT-SS between the two datasets. In addition, we analyzed the distribution of CT-SS concerning age, comorbidities, and gender, as well as their differences between the two waves of COVID-19. Analysis was performed using the SPSS version 22 (IBM Corp., Armonk, NY, USA). The artificial intelligence platform U-net architecture with Xception encoder was used in the analysis. Results The study data revealed that while the mean CT-SS did not differ statistically between the two waves of COVID-19, the age group most affected in the second wave was almost a decade younger. While overall the disease had a predilection toward affecting males, our findings showed that females were more afflicted in the second wave of COVID-19 compared to the first wave. In particular, the disease had an increased severity in cases with comorbidities such as hypertension, diabetes mellitus, bronchial asthma, and tuberculosis. Conclusions This assessment demonstrated no significant difference in radiological severity score between the two waves of COVID-19. The secondary objective revealed that the two waves showed demographical differences. Hence, we iterate that no demographical subset of the population should be considered low risk as the disease manifestation was heterogeneous.

6-Thioguanine blocks SARS-CoV-2 replication by inhibition of PLpro.

The emergence of SARS-CoV-2 has led to a global health crisis that, in addition to vaccines and immunomodulatory therapies, calls for the identification of antiviral therapeutics. The papain-like protease (PLpro) activity of nsp3 is an attractive drug target as it is essential for viral polyprotein cleavage and for deconjugation of ISG15, an antiviral ubiquitin-like protein. We show here that 6-Thioguanine (6-TG), an orally available and widely available generic drug, inhibits SARS-CoV-2 replication in Vero-E6 cells with an EC50 of approximately 2 µM. 6-TG also inhibited PLpro-catalyzed polyprotein cleavage and de-ISGylation in cells and inhibited proteolytic activity of the purified PLpro domain in vitro. We therefore propose that 6-TG is a direct-acting antiviral that could potentially be repurposed and incorporated into the set of treatment and prevention options for COVID-19.