Understanding the Excitation Wavelength Dependence and Thermal Stability of the SARS-CoV-2 Receptor-Binding Domain Using Surface-Enhanced Raman Scattering and Machine Learning

COVID-19 has cost millions of lives worldwide. The constant mutation of SARS-CoV-2 calls for thorough research to facilitate the development of variant surveillance. In this work, we studied the fundamental properties related to the optical identification of the receptor-binding domain (RBD) of SARSCoV-2 spike protein, a key component of viral infection. The Raman modes of the SARS-CoV-2 RBD were captured by surface enhanced Raman spectroscopy (SERS) using gold nanoparticles (AuNPs). The observed Raman enhancement strongly depends on the excitation wavelength as a result of the aggregation of AuNPs. The characteristic Raman spectra of RBDs from SARS-CoV-2 and MERS-CoV were analyzed by principal component analysis that reveals the role of secondary structures in the SERS process, which is corroborated with the thermal stability under laser heating. We can easily distinguish the Raman spectra of two RBDs using machine learning algorithms with accuracy, precision, recall, and F1 scores all over 95%. Our work provides an in-depth understanding of the SARS-CoV-2 RBD and paves the way toward rapid analysis and discrimination of complex proteins of infectious viruses and other biomolecules.

Sirt1 overexpression improves senescence-associated pulmonary fibrosis induced by vitamin D deficiency through downregulating IL-11 transcription.

Determining the mechanism of senescence-associated pulmonary fibrosis is crucial for designing more effective treatments for chronic lung diseases. This study aimed to determine the following: whether Sirt1 and serum vitamin D decreased with physiological aging, promoting senescence-associated pulmonary fibrosis by activating TGF-ß1/IL-11/MEK/ERK signaling, whether Sirt1 overexpression prevented TGF-ß1/IL-11/MEK/ERK signaling-mediated senescence-associated pulmonary fibrosis in vitamin D-deficient (Cyp27b1-/- ) mice, and whether Sirt1 downregulated IL-11 expression transcribed by TGF-ß1/Smad2 signaling through deacetylating histone at the IL-11 promoter in pulmonary fibroblasts. Bioinformatics analysis with RNA sequencing data from pulmonary fibroblasts of physiologically aged mice was conducted for correlation analysis. Lungs from young and physiologically aged wild-type (WT) mice were examined for cell senescence, fibrosis markers, and TGF-ß1/IL-11/MEK/ERK signaling proteins, and 1,25(OH)2 D3 and IL-11 levels were detected in serum. Nine-week-old WT, Sirt1 mesenchymal transgene (Sirt1Tg ), Cyp27b1-/- , and Sirt1Tg Cyp27b1-/- mice were observed the pulmonary function, aging, and senescence-associated secretory phenotype and TGF-ß1/IL-11/MEK/ERK signaling. We found that pulmonary Sirt1 and serum vitamin D decreased with physiological aging, activating TGF-ß1/IL-11/MEK/ERK signaling, and promoting senescence-associated pulmonary fibrosis. Sirt1 overexpression improved pulmonary dysfunction, aging, DNA damage, senescence-associated secretory phenotype, and fibrosis through downregulating TGF-ß1/IL-11/MEK/ERK signaling in Cyp27b1-/- mice. Sirt1 negatively regulated IL-11 expression through deacetylating H3K9/14ac mainly at the region from -871 to -724 of IL-11 promoter, also the major binding region of Smad2 which regulated IL-11 expression at the transcriptional level, and subsequently inhibiting TGF-ß1/IL-11/MEK/ERK signaling in pulmonary fibroblasts. This signaling in aging fibroblasts could be a therapeutic target for preventing senescence-associated pulmonary fibrosis induced by vitamin D deficiency.

Accurate virus identification with interpretable Raman signatures by machine learning.

Rapid identification of newly emerging or circulating viruses is an important first step toward managing the public health response to potential outbreaks. A portable virus capture device, coupled with label-free Raman spectroscopy, holds the promise of fast detection by rapidly obtaining the Raman signature of a virus followed by a machine learning (ML) approach applied to recognize the virus based on its Raman spectrum, which is used as a fingerprint. We present such an ML approach for analyzing Raman spectra of human and avian viruses. A convolutional neural network (CNN) classifier specifically designed for spectral data achieves very high accuracy for a variety of virus type or subtype identification tasks. In particular, it achieves 99% accuracy for classifying influenza virus type A versus type B, 96% accuracy for classifying four subtypes of influenza A, 95% accuracy for differentiating enveloped and nonenveloped viruses, and 99% accuracy for differentiating avian coronavirus (infectious bronchitis virus [IBV]) from other avian viruses. Furthermore, interpretation of neural net responses in the trained CNN model using a full-gradient algorithm highlights Raman spectral ranges that are most important to virus identification. By correlating ML-selected salient Raman ranges with the signature ranges of known biomolecules and chemical functional groups­for example, amide, amino acid, and carboxylic acid­we verify that our ML model effectively recognizes the Raman signatures of proteins, lipids, and other vital functional groups present in different viruses and uses a weighted combination of these signatures to identify viruses.

Quadruple Augmented Pyramid Network for Multi-class COVID-19 Segmentation via CT.

COVID-19, a new strain of coronavirus disease, has been one of the most serious and infectious disease in the world. Chest CT is essential in prognostication, diagnosing this disease, and assessing the complication. In this paper, a multi-class COVID-19 CT segmentation is proposed aiming at helping radiologists estimate the extent of effected lung volume. We utilized four augmented pyramid networks on an encoder-decoder segmentation framework. Quadruple Augmented Pyramid Network (QAP-Net) not only enable CNN capture features from variation size of CT images, but also act as spatial inter-connections and down-sampling to transfer sufficient feature information for semantic segmentation. Experimental results achieve competitive performance in segmentation with the Dice of 0.8163, which outperforms other state-of-the-art methods, demonstrating the proposed framework can segment of consolidation as well as glass, ground area via COVID-19 chest CT efficiently and accurately.

Associations Between the Use of Renin-Angiotensin System Inhibitors and the Risks of Severe COVID-19 and Mortality in COVID-19 Patients With Hypertension: A Meta-Analysis of Observational Studies.

Angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) share a target receptor with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The use of ACEIs/ARBs may cause angiotensin-converting enzyme 2 receptor upregulation, facilitating the entry of SARS-CoV-2 into host cells. There is concern that the use of ACEIs/ARBs could increase the risks of severe COVID-19 and mortality. The impact of discontinuing these drugs in patients with COVID-19 remains uncertain. We aimed to assess the association between the use of ACEIs/ARBs and the risks of mortality and severe disease in patients with COVID-19. A systematic search was performed in PubMed, EMBASE, Cochrane Library, and MedRxiv.org from December 1, 2019, to June 20, 2020. We also identified additional citations by manually searching the reference lists of eligible articles. Forty-two observational studies including 63,893 participants were included. We found that the use of ACEIs/ARBs was not significantly associated with a reduction in the relative risk of all-cause mortality [odds ratio (OR) = 0.87, 95% confidence interval (95% CI) = 0.75-1.00; I 2 = 57%, p = 0.05]. We found no significant reduction in the risk of severe disease in the ACEI subgroup (OR = 0.95, 95% CI = 0.88-1.02, I 2 = 50%, p = 0.18), the ARB subgroup (OR = 1.03, 95% CI = 0.94-1.13, I 2 = 62%, p = 0.48), or the ACEI/ARB subgroup (OR = 0.83, 95% CI = 0.65-1.08, I 2 = 67%, p = 0.16). Moreover, seven studies showed no significant difference in the duration of hospitalization between the two groups (mean difference = 0.33, 95% CI = -1.75 to 2.40, p = 0.76). In conclusion, the use of ACEIs/ARBs appears to not have a significant effect on mortality, disease severity, or duration of hospitalization in COVID-19 patients. On the basis of the findings of this meta-analysis, there is no support for the cessation of treatment with ACEIs or ARBs in patients with COVID-19.