Machine learning-driven blood transcriptome-based discovery of SARS-CoV-2 specific severity biomarkers.

The Coronavirus disease 2019 (COVID-19) pandemic, caused by rapidly evolving variants of severe acute respiratory syndrome coronavirus (SARS-CoV-2), continues to be a global health threat. SARS-CoV-2 infection symptoms often intersect with other nonsevere respiratory infections, making early diagnosis challenging. There is an urgent need for early diagnostic and prognostic biomarkers to predict severity and reduce mortality when a sudden outbreak occurs. This study implemented a novel approach of integrating bioinformatics and machine learning algorithms over publicly available clinical COVID-19 transcriptome data sets. The robust 7-gene biomarker identified through this analysis can not only discriminate SARS-CoV-2 associated acute respiratory illness (ARI) from other types of ARIs but also can discriminate severe COVID-19 patients from nonsevere COVID-19 patients. Validation of the 7-gene biomarker in an independent blood transcriptome data set of longitudinal analysis of COVID-19 patients across various stages of the disease showed that the dysregulation of the identified biomarkers during severe disease is restored during recovery, showing their prognostic potential. The blood biomarkers identified in this study can serve as potential diagnostic candidates and help reduce COVID-19-associated mortality.

A case report on anesthetic challenges in the post-Covid complication of active mucormycosis: Patient preparedness for family physicians.

Mucormycosis is a well-known yet complicated illness that seems benign but behaves malignantly. This article discusses the anesthesia challenges in providing care for a 49-year-old male who presented with post-Covid pneumonia and uncontrolled diabetes along with active mucormycosis and scheduled for functional endoscope sinus surgery (FESS) and debridement of necrotic tissue. We want to illustrate the importance of anticipated difficult airway, while highlighting the toxicity of intravenous amphotericin-B and its combination against anesthesia drugs.

Essential role of Rnd1 in innate immunity during viral and bacterial infections.

Intracellular and cell surface pattern-recognition receptors (PRRs) are an essential part of innate immune recognition and host defense. Here, we have compared the innate immune responses between humans and bats to identify a novel membrane-associated protein, Rnd1, which defends against viral and bacterial infection in an interferon-independent manner. Rnd1 belongs to the Rho GTPase family, but unlike other small GTPase members, it is constitutively active. We show that Rnd1 is induced by pro-inflammatory cytokines during viral and bacterial infections and provides protection against these pathogens through two distinct mechanisms. Rnd1 counteracts intracellular calcium fluctuations by inhibiting RhoA activation, thereby inhibiting virus internalisation. On the other hand, Rnd1 also facilitates pro-inflammatory cytokines IL-6 and TNF-α through Plxnb1, which are highly effective against intracellular bacterial infections. These data provide a novel Rnd1-mediated innate defense against viral and bacterial infections.

Dilemmas in corona crisis: responsibilities of orthopedic surgeons clearly defined?

COVID-19 has come up as most difficult problem as of today faced by whole world especially by the medical community. All kinds of measures are being done by various governments and health officials to deal with this present pandemic. Intensivists, physicians, and anesthetists have defined role in primary and ICU care. Although the orthopedic community is not involved in first-line management of COVID-19-infected patients, we have equal responsibility to provide emergency care to all trauma patients and various other orthopedic emergencies. While delivering services, it's essential to restrict the consumption of various resources. It's our collective responsibility to prevent the spread of infection among healthcare workers and patients. Lockdown implemented by various governments has impacted the global economy, which in turn resulted in reduction of purchasing power of patients, so we may have to choose more cost-effective implants and curtail the number of investigations. While performing operations, certain procedures like using diathermy and saw blade generate aerosols. So, it is our responsibility to understand these procedures and to make use of them only when absolutely essential. We may experience second wave of cases later this year. Relaxation of lockdown may not mean relaxation from the virus.

Comparative transcriptome analysis of SARS-CoV, MERS-CoV, and SARS-CoV-2 to identify potential pathways for drug repurposing.

The ongoing COVID-19 pandemic caused by the coronavirus, SARS-CoV-2, has already caused in excess of 1.25 million deaths worldwide, and the number is increasing. Knowledge of the host transcriptional response against this virus and how the pathways are activated or suppressed compared to other human coronaviruses (SARS-CoV, MERS-CoV) that caused outbreaks previously can help in the identification of potential drugs for the treatment of COVID-19. Hence, we used time point meta-analysis to investigate available SARS-CoV and MERS-CoV in-vitro transcriptome datasets in order to identify the significant genes and pathways that are dysregulated at each time point. The subsequent over-representation analysis (ORA) revealed that several pathways are significantly dysregulated at each time point after both SARS-CoV and MERS-CoV infection. We also performed gene set enrichment analyses of SARS-CoV and MERS-CoV with that of SARS-CoV-2 at the same time point and cell line, the results of which revealed that common pathways are activated and suppressed in all three coronaviruses. Furthermore, an analysis of an in-vivo transcriptomic dataset of COVID-19 patients showed that similar pathways are enriched to those identified in the earlier analyses. Based on these findings, a drug repurposing analysis was performed to identify potential drug candidates for combating COVID-19.

Prediction and analysis of COVID-19 positive cases using deep learning models: A descriptive case study of India.

In this paper, Deep Learning-based models are used for predicting the number of novel coronavirus (COVID-19) positive reported cases for 32 states and union territories of India. Recurrent neural network (RNN) based long-short term memory (LSTM) variants such as Deep LSTM, Convolutional LSTM and Bi-directional LSTM are applied on Indian dataset to predict the number of positive cases. LSTM model with minimum error is chosen for predicting daily and weekly cases. It is observed that the proposed method yields high accuracy for short term prediction with error less than 3% for daily predictions and less than 8% for weekly predictions. Indian states are categorised into different zones based on the spread of positive cases and daily growth rate for easy identification of novel coronavirus hot-spots. Preventive measures to reduce the spread in respective zones are also suggested. A website is created where the state-wise predictions are updated using the proposed model for authorities,researchers and planners. This study can be applied by other countries for predicting COVID-19 cases at the state or national level.