COVID-associated rhinocerebral mucormycosis: a retrospective analysis of presentation and outcomes.

OBJECTIVES: To comprehensively analyse the disease presentation and mortality of COVID-associated rhino-orbito-cerebral mucormycosis. METHODS: A retrospective analysis of the demographics, clinical and radiographic findings was performed. A binary logistic regression analysis was performed to examine the survival of patients with mucormycosis from hypothesised predictors. RESULTS: A total of 202 patients were included in this study. Statistical significance was demonstrated in the predilection to the male gender, recent history of SARS-COV-2, history of use of corticosteroid and hyperglycemia in this cohort of CAM. The mortality rate was 18.31%. Advanced age, raised HbA1c and intra-orbital extension were found to be predictors adversely affecting survival. CONCLUSION: Early diagnosis, aggressive surgical therapy, early and appropriate medical therapy can help improve outcomes. LEVEL OF EVIDENCE: Level 4.

COVID-19 Fake News Detection Using Ensemble-Based Deep Learning Model

Fake news on various medicines, foods, and vaccinations relating to the COVID-19 pandemic has increased dramatically. These fake news reports lead individuals to believe in false and sometimes harmful claims and stories, and they also influence people’s vaccination opinions. Immediately detecting COVID-19 false news can help to reduce the spread of fear, confusion, and potential health risks among citizens. An ensemble-based deep learning model for detecting COVID-19-related fake news on Twitter is proposed in this article. CT-BERT, BERTweet, and roberta are three different models that are fine-tuned on COVID-19-linked text data to separate fake and authentic news. In addition, the proposed ensemble-based model is compared to a variety of standard machine learning and deep learning models. In the detection of COVID-19 fake news from Twitter, the proposed ensemble-based deep learning model achieved state-of-the-art performance with a weighted $F_1$F1-score of 0.99.

Accelerating the discovery of antifungal peptides using deep temporal convolutional networks.

The application of machine intelligence in biological sciences has led to the development of several automated tools, thus enabling rapid drug discovery. Adding to this development is the ongoing COVID-19 pandemic, due to which researchers working in the field of artificial intelligence have acquired an active interest in finding machine learning-guided solutions for diseases like mucormycosis, which has emerged as an important post-COVID-19 fungal complication, especially in immunocompromised patients. On these lines, we have proposed a temporal convolutional network-based binary classification approach to discover new antifungal molecules in the proteome of plants and animals to accelerate the development of antifungal medications. Although these biomolecules, known as antifungal peptides (AFPs), are part of an organism's intrinsic host defense mechanism, their identification and discovery by traditional biochemical procedures is arduous. Also, the absence of a large dataset on AFPs is also a considerable impediment in building a robust automated classifier. To this end, we have employed the transfer learning technique to pre-train our model on antibacterial peptides. Subsequently, we have built a classifier that predicts AFPs with accuracy and precision of 94%. Our classifier outperforms several state-of-the-art models by a considerable margin. The results of its performance were proven as statistically significant using the Kruskal-Wallis H test, followed by a post hoc analysis performed using the Tukey honestly significant difference (HSD) test. Furthermore, we identified potent AFPs in representative animal (Histatin) and plant (Snakin) proteins using our model. We also built and deployed a web app that is freely available at https://tcn-afppred.anvil.app/ for the identification of AFPs in protein sequences.

Deep-AVPpred: Artificial Intelligence Driven Discovery of Peptide Drugs for Viral Infections.

Rapid increase in viral outbreaks has resulted in the spread of viral diseases in diverse species and across geographical boundaries. The zoonotic viral diseases have greatly affected the well-being of humans, and the COVID-19 pandemic is a burning example. The existing antivirals have low efficacy, severe side effects, high toxicity, and limited market availability. As a result, natural substances have been tested for antiviral activity. The host defense molecules like antiviral peptides (AVPs) are present in plants and animals and protect them from invading viruses. However, obtaining AVPs from natural sources for preparing synthetic peptide drugs is expensive and time-consuming. As a result, an in-silico model is required for identifying new AVPs. We proposed Deep-AVPpred, a deep learning classifier for discovering AVPs in protein sequences, which utilises the concept of transfer learning with a deep learning algorithm. The proposed classifier outperformed state-of-the-art classifiers and achieved approximately 94% and 93% precision on validation and test sets, respectively. The high precision indicates that Deep-AVPpred can be used to propose new AVPs for synthesis and experimentation. By utilising Deep-AVPpred, we identified novel AVPs in human interferons- α family proteins. These AVPs can be chemically synthesised and experimentally verified for their antiviral activity against different viruses. The Deep-AVPpred is deployed as a web server and is made freely available at https://deep-avppred.anvil.app, which can be utilised to predict novel AVPs for developing antiviral compounds for use in human and veterinary medicine.

Deep-AFPpred: identifying novel antifungal peptides using pretrained embeddings from seq2vec with 1DCNN-BiLSTM.

Fungal infections or mycosis cause a wide range of diseases in humans and animals. The incidences of community acquired; nosocomial fungal infections have increased dramatically after the emergence of COVID-19 pandemic. The increase in number of patients with immunodeficiency / immunosuppression related diseases, resistance to existing antifungal compounds and availability of limited therapeutic options has triggered the search for alternative antifungal molecules. In this direction, antifungal peptides (AFPs) have received a lot of interest as an alternative to currently available antifungal drugs. Although the AFPs are produced by diverse population of living organisms, identifying effective AFPs from natural sources is time-consuming and expensive. Therefore, there is a need to develop a robust in silico model capable of identifying novel AFPs in protein sequences. In this paper, we propose Deep-AFPpred, a deep learning classifier that can identify AFPs in protein sequences. We developed Deep-AFPpred using the concept of transfer learning with 1DCNN-BiLSTM deep learning algorithm. The findings reveal that Deep-AFPpred beats other state-of-the-art AFP classifiers by a wide margin and achieved approximately 96% and 94% precision on validation and test data, respectively. Based on the proposed approach, an online prediction server is created and made publicly available at https://afppred.anvil.app/. Using this server, one can identify novel AFPs in protein sequences and the results are provided as a report that includes predicted peptides, their physicochemical properties and motifs. By utilizing this model, we identified AFPs in different proteins, which can be chemically synthesized in lab and experimentally validated for their antifungal activity.

Pattern of liver function and clinical profile in COVID-19: A cross-sectional study of 91 patients.

BACKGROUND: - COVID-19 caused by SARS-CoV-2 leads to myriad range of organ involvement including liver dysfunction. AIM: To analyse the liver function in patients with COVID-19 and their association with respect to age, sex, severity of disease and clinical features. MATERIALS AND METHODS: This study was a cross-sectional study done at Rajendra Institute of Medical Sciences, Ranchi. 91 patients admitted with confirmed SARS-CoV-2 infection were included in this study and divided into asymptomatic, mild, moderate and severe groups. Liver function tests were compared among different severity groups. RESULTS: Of 91 patients with COVID-19, 70 (76.9%) had abnormal liver function. Aspartate transaminase (AST), alanine transaminase (ALT), alkaline phosphatase (ALP), total bilirubin levels was 1-2 × ULN in 33(36.3%), 34(37.3%), 12(13.2%), 6(6.6%) cases and >2 × ULN in 20(22%), 18(19.8%), 7(7.7%) and 2 (2.2%) cases respectively. Mean AST and ALP levels among different severity groups of COVID-19 was statistically significant (p < 0.05) whereas mean ALT and total bilirubin levels was statistically non-significant (p > 0.05). There was no statistical difference between males and females with regard to abnormal liver function. Liver injury was seen in 64.3% cases of hypertension and 73.3% cases of diabetes. Fever, myalgia, headache and breathlessness were found to be correlated significantly with severity of disease. CONCLUSION: Liver injury is common in SARS-CoV-2 infection and is more prevalent in the severe disease group. Aspartate transaminase and alkaline phosphatase are better indicators of covid-19 induced liver injury than alanine transaminase and total bilirubin.

Using 164 Million Google Street View Images to Derive Built Environment Predictors of COVID-19 Cases.

The spread of COVID-19 is not evenly distributed. Neighborhood environments may structure risks and resources that produce COVID-19 disparities. Neighborhood built environments that allow greater flow of people into an area or impede social distancing practices may increase residents' risk for contracting the virus. We leveraged Google Street View (GSV) images and computer vision to detect built environment features (presence of a crosswalk, non-single family home, single-lane roads, dilapidated building and visible wires). We utilized Poisson regression models to determine associations of built environment characteristics with COVID-19 cases. Indicators of mixed land use (non-single family home), walkability (sidewalks), and physical disorder (dilapidated buildings and visible wires) were connected with higher COVID-19 cases. Indicators of lower urban development (single lane roads and green streets) were connected with fewer COVID-19 cases. Percent black and percent with less than a high school education were associated with more COVID-19 cases. Our findings suggest that built environment characteristics can help characterize community-level COVID-19 risk. Sociodemographic disparities also highlight differential COVID-19 risk across groups of people. Computer vision and big data image sources make national studies of built environment effects on COVID-19 risk possible, to inform local area decision-making.

Clinical and Epidemiological Features of SARS-CoV-2 Patients in SARI Ward of a Tertiary Care Centre in New Delhi.

IMPORTANCE: Rapid spread of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in Wuhan, China, prompted heightened surveillance in India. Since the first laboratory confirmed case of SARS-CoV-2 was reported from Kerala on January 30, 2020 novel coronavirus infected pneumonia (NCIP) has been presenting to the hospital emergencies as severe acute respiratory illness (SARI). We aim to find out the rate of SARS-CoV-2 positivity in SARI cases and further clarify the epidemiological and clinical characteristics of NCIP in New Delhi, India. AIMS AND OBJECTIVES: To find out the rate of SARS-CoV-2 positivity in SARI cases presenting to the hospital emergency and describe the epidemiological and clinical characteristics of NCIP. DESIGN, SETTING AND PARTICIPANTS: Retrospective, single-center case series of the 82 consecutive hospitalized patients with SARI and subsequent confirmed NCIP cases at Dr Ram Manohar Lohia Hospital, New Delhi between 10th April 2020 and 30th April 2020. MAIN OUTCOMES AND MEASURES: Epidemiological, demographic, clinical, laboratory, radiological, and treatment data were collected and analyzed. The primary composite end-point was admission to an intensive care unit (ICU), the use of mechanical ventilation or death. Patients were categorized as severe pneumonia and non-severe pneumonia at time of admission and outcome data was compared. RESULTS: Of the 82 SARI cases, 32(39%) patients were confirmed to be SARS-CoV-2 positive. The median age of NCIP cases was 54.5 years (IQR, 46.25 - 60) and 19(59.3%) of them were males. 24(75%) cases were categorized as severe pneumonia on admission. 22(68.8%) patients had 1 or more co-morbidities. Diabetes mellitus 16(50%), hypertension 11(34.4%) and chronic obstructive airway disease 5(15.6%) were the most common co-existing illnesses. Compared with the patients who did not meet the primary outcome, patients who met the primary outcome were more likely to be having at least 1 underlying comorbidity (p-0.03), diabetes (p-0.003) and hypertension (p-0.03). Common symptoms included dyspnea 29(90.6%) followed by cough 27(84.4%), fever 22(68%), bodyache and myalgias 14(43.75%). Median time from symptom onset to hospital admission was 3 days. The most common pattern on chest X-ray was bilateral patchy nodular or interstitial infiltration seen in 30(93.8%) patients. Leucopenia was present in 10(31.2%) of the patients, with majority of patients presenting with lymphocytopenia, 24(75%) [lymphocyte count (1106 cells/ dL), interquartile range {IQR}, (970-1487)]. Thrombocytopenia was seen in 14(43.8%) patients, pancytopenia in 10(31.2%) patients and anemia was seen in 14(43.8%) patients. Hypoalbuminemia was present in 22(68.8%) cases. Raised CK-MB was seen in 7(21.9%) patients. The primary composite end-point occurred in 12(37.5%) patients, including 9(28.13%) patients who required mechanical ventilation and subsequently expired. 3(9.3%) of these patients who recovered, were subsequently shifted to COVID-19 ward from the ICU. The patients who met the primary outcome were older in age (56.5 years vs 50 years), had significantly higher SOFA scores (6 vs 3.5), were in shock (41.7% vs 5%), in higher respiratory distress (66.7% vs 10%), had lower mean arterial oxygen saturation (85% vs 89.5%), had higher CK-MB values (66 vs 26)U/L [6(54.5%) vs 2(9.5%)], had hypoalbuminemia (100% vs 50%) and acute kidney injury 8(72.7%) vs 5(23.8%) on admission. Of the 50 non-COVID-19 SARI patients in our study cohort, 13 (26%) patients met the primary composite outcome. Of them 9 (18%) patients expired and remaining 4 patients have subsequently recovered. As on 17th May 2020, 23 patients were still hospitalized, recovering in COVID-19 ward. CONCLUSION AND RELEVANCE: In this single-center case series from New Delhi, out of 82 patients of SARI, 32 patients were confirmed NCIP, with a COVID-19 positivity of 39%. 75% of NCIP presented in severe pneumonia and 37.5% required ICU care. The case fatality rate was 28%.