Lack of Benefit of High Flow Nasal Oxygen Therapy as Ceiling of Treatment for Severe COVID-19 Pneumonitis in Elderly Frail Patients: A Single Centre Observational Study.

Background: Severe COVID-19 pneumonitis in elderly frail patients is associated with poor outcomes, and therefore invasive mechanical ventilation is often deemed an inappropriate course of action. Some evidence suggests high-flow nasal oxygen (HFNO) may prevent the need for invasive ventilation in other groups of patients, but whether it is an appropriate ceiling of care for older frail patients is unknown. Methods: We retrospectively identified patients with severe COVID-19 pneumonitis requiring FiO2>60% who were deemed inappropriate for invasive ventilation or non-invasive continuous positive airway pressure ventilation (CPAP). Our local protocol based on national guidance suggested these patients should be considered for HFNO. We observed whether the patients received HFNO or standard oxygen therapy (SOT) and compared mortality and survival time in these groups. Results: We identified 81 patients meeting the inclusion criteria. From this group, 24 received HFNO and 57 received SOT. The HFNO group was similar in age, BMI and co-morbidities to the SOT group but less frail, as determined by the Clinical Frailty Scale (CFS). All 24 patients that received HFNO died in comparison to 46 patients (80.7%) in the SOT group. Mortality in the HFNO group was significantly higher than in the SOT group. Conclusion: Elderly frail patients with severe COVID-19 pneumonitis deemed inappropriate for invasive ventilation and did not benefit from HFNO. Further, HFNO may have been associated with harm in this group.

A novel CT image de-noising and fusion based deep learning network to screen for disease (COVID-19).

A COVID-19, caused by SARS-CoV-2, has been declared a global pandemic by WHO. It first appeared in China at the end of 2019 and quickly spread throughout the world. During the third layer, it became more critical. COVID-19 spread is extremely difficult to control, and a huge number of suspected cases must be screened for a cure as soon as possible. COVID-19 laboratory testing takes time and can result in significant false negatives. To combat COVID-19, reliable, accurate and fast methods are urgently needed. The commonly used Reverse Transcription Polymerase Chain Reaction has a low sensitivity of approximately 60% to 70%, and sometimes even produces negative results. Computer Tomography (CT) has been observed to be a subtle approach to detecting COVID-19, and it may be the best screening method. The scanned image's quality, which is impacted by motion-induced Poisson or Impulse noise, is vital. In order to improve the quality of the acquired image for post segmentation, a novel Impulse and Poisson noise reduction method employing boundary division max/min intensities elimination along with an adaptive window size mechanism is proposed. In the second phase, a number of CNN techniques are explored for detecting COVID-19 from CT images and an Assessment Fusion Based model is proposed to predict the result. The AFM combines the results for cutting-edge CNN architectures and generates a final prediction based on choices. The empirical results demonstrate that our proposed method performs extensively and is extremely useful in actual diagnostic situations.

In silico designed mRNA vaccines targeting CA-125 neoantigen in breast and ovarian cancer.

Somatic mutation-derived neoantigens are associated with patient survival in breast and ovarian cancer. These neoantigens are targets for cancer, as shown by the implementation of neoepitope peptides as cancer vaccines. The success of cost-effective multi-epitope mRNA vaccines against SARS-Cov-2 in the pandemic established a model for reverse vaccinology. In this study, we aimed to develop an in silico pipeline designing an mRNA vaccine of the CA-125 neoantigen against breast and ovarian cancer, respectively. Using immuno-bioinformatics tools, we predicted cytotoxic CD8+ T cell epitopes based on somatic mutation-driven neoantigens of CA-125 in breast or ovarian cancer, constructed a self-adjuvant mRNA vaccine with CD40L and MHC-I -targeting domain to enhance cross-presentation of neoepitopes by dendritic cells. With an in silico ImmSim algorithm, we estimated the immune responses post-immunization, showing IFN-γ and CD8+ T cell response. The strategy described in this study may be scaled up and implemented to design precision multi-epitope mRNA vaccines by targeting multiple neoantigens.

'Striking' racial disparities observed in surgical care of gastrointestinal tract cancer

Gastrointestinal cancer accounts for approximately one-third of all cancer deaths," Baylee F. Bakkila, BA, BS, and Sajid A. Khan, MD, FACS, of Yale School of Medicine, and Caroline H. Johnson, PhD, of Yale School of Public Health, told Healio ;HemOnc Today in a joint statement. "Given our team's expertise at Yale in gastrointestinal surgical oncology, we wanted to examine whether race-specific treatment disparities exist with curativeintent surgery and the impact this might have on clinical outcome for patients across the U.S" The retrospective cohort study included 565,124 adults (10.9% Black, 83.5% white;54.7% men;50.7% with Medicare coverage) diagnosed with gastrointestinal tract cancers between 2004 to 2017 who underwent surgical resection. In a related editorial, Shervin Assari, MD, MPH, associate professor of family medicine at Charles R. Drew University of Medicine and Science, and Helena Hansen, MD, PhD, chair of research theme in translational social science and health equity and associate director of the Center for Social Medicine at UCLAs David Geffen School of Medicine, wrote the Yale study leaves questions about the systemic mechanisms driving inequalities unanswered.

A Deep Learning and Handcrafted Based Computationally Intelligent Technique for Effective COVID-19 Detection from X-ray/CT-scan Imaging.

The world has witnessed dramatic changes because of the advent of COVID19 in the last few days of 2019. During the last more than two years, COVID-19 has badly affected the world in diverse ways. It has not only affected human health and mortality rate but also the economic condition on a global scale. There is an urgent need today to cope with this pandemic and its diverse effects. Medical imaging has revolutionized the treatment of various diseases during the last four decades. Automated detection and classification systems have proven to be of great assistance to the doctors and scientific community for the treatment of various diseases. In this paper, a novel framework for an efficient COVID-19 classification system is proposed which uses the hybrid feature extraction approach. After preprocessing image data, two types of features i.e., deep learning and handcrafted, are extracted. For Deep learning features, two pre-trained models namely ResNet101 and DenseNet201 are used. Handcrafted features are extracted using Weber Local Descriptor (WLD). The Excitation component of WLD is utilized and features are reduced using DCT. Features are extracted from both models, handcrafted features are fused, and significant features are selected using entropy. Experiments have proven the effectiveness of the proposed model. A comprehensive set of experiments have been performed and results are compared with the existing well-known methods. The proposed technique has performed better in terms of accuracy and time.

Kynurenic acid may underlie sex-specific immune responses to COVID-19.

Coronavirus disease 2019 (COVID-19) has poorer clinical outcomes in males than in females, and immune responses underlie these sex-related differences. Because immune responses are, in part, regulated by metabolites, we examined the serum metabolomes of COVID-19 patients. In male patients, kynurenic acid (KA) and a high KA-to-kynurenine (K) ratio (KA:K) positively correlated with age and with inflammatory cytokines and chemokines and negatively correlated with T cell responses. Males that clinically deteriorated had a higher KA:K than those that stabilized. KA inhibits glutamate release, and glutamate abundance was lower in patients that clinically deteriorated and correlated with immune responses. Analysis of data from the Genotype-Tissue Expression (GTEx) project revealed that the expression of the gene encoding the enzyme that produces KA, kynurenine aminotransferase, correlated with cytokine abundance and activation of immune responses in older males. This study reveals that KA has a sex-specific link to immune responses and clinical outcomes in COVID-19, suggesting a positive feedback between metabolites and immune responses in males.

In-silico network-based analysis of drugs used against COVID-19: Human well-being study.

INTRODUCTION: Researchers worldwide with great endeavor searching and repurpose drugs might be potentially useful in fighting newly emerged coronavirus. These drugs show inhibition but also show side effects and complications too. On December 27, 2020, 80,926,235 cases have been reported worldwide. Specifically, in Pakistan, 471,335 has been reported with inconsiderable deaths. PROBLEM STATEMENT: Identification of COVID-19 drugs pathway through drug-gene and gene-gene interaction to find out the most important genes involved in the pathway to deal with the actual cause of side effects beyond the beneficent effects of the drugs. METHODOLOGY: The medicines used to treat COVID-19 are retrieved from the Drug Bank. The drug-gene interaction was performed using the Drug Gene Interaction Database to check the relation between the genes and the drugs. The networks of genes are developed by Gene MANIA, while Cytoscape is used to check the active functional association of the targeted gene. The developed systems cross-validated using the EnrichNet tool and identify drug genes' concerned pathways using Reactome and STRING. RESULTS: Five drugs Azithromycin, Bevacizumab, CQ, HCQ, and Lopinavir, are retrieved. The drug-gene interaction shows several genes that are targeted by the drug. Gene MANIA interaction network shows the functional association of the genes like co-expression, physical interaction, predicted, genetic interaction, co-localization, and shared protein domains. CONCLUSION: Our study suggests the pathways for each drug in which targeted genes and medicines play a crucial role, which will help experts in-vitro overcome and deal with the side effects of these drugs, as we find out the in-silico gene analysis for the COVID-19 drugs.

nCOV-19 peptides mass fingerprinting identification, binding, and blocking of inhibitors flavonoids and anthraquinone of Moringa oleifera and hydroxychloroquine.

An rare pandemic of viral pneumonia occurs in December 2019 in Wuhan, China, which is now recognized internationally as Corona Virus Disease 2019 (COVID-19), the etiological agent classified as Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2). According to the World Health Organization (WHO), it has so far expanded to more than 213 countries/territories worldwide. Our study aims to find the viral peptides of SARS-COV-2 by peptide mass fingerprinting (PMF) in order to predict its novel structure and find an inhibitor for each viral peptide. For this reason, we calculated the mass of amino acid sequences translated from the SARS-CoV2 whole genome and identify the peptides that may be a target for inhibition. Molecular peptide docking with Moringa oleifera, phytochemicals (aqueous and ethanolic) leaf extracts of flavonoids (3.56 ± 0.03), (3.83 ± 0.02), anthraquinone (11.68 ± 0.04), (10.86 ± 0.06) and hydroxychloroquine present therapy of COVID-19 in Pakistan for comparative study. Results indicate that 15 peptides of SARS-CoV2 have been identified from PMF, which is then used as a selective inhibitor. The maximum energy obtained from AutoDock Vina for hydroxychloroquine is -5.1 kcal/mol, kaempferol (flavonoid) is -6.2 kcal/mol, and for anthraquinone -6 kcal/mol. Visualization of docking complex, important effects are observed regarding the binding of peptides to drug compounds. In conclusion, it is proposed that these compounds are effective antiviral agents against COVID-19 and can be used in clinical trials.Communicated by Ramaswamy H. Sarma.