Assessment of GO-Based Protein Interaction Affinities in the Large-Scale Human-Coronavirus Family Interactome.

SARS-CoV-2 is a novel coronavirus that replicates itself via interacting with the host proteins. As a result, identifying virus and host protein-protein interactions could help researchers better understand the virus disease transmission behavior and identify possible COVID-19 drugs. The International Committee on Virus Taxonomy has determined that nCoV is genetically 89% compared to the SARS-CoV epidemic in 2003. This paper focuses on assessing the host-pathogen protein interaction affinity of the coronavirus family, having 44 different variants. In light of these considerations, a GO-semantic scoring function is provided based on Gene Ontology (GO) graphs for determining the binding affinity of any two proteins at the organism level. Based on the availability of the GO annotation of the proteins, 11 viral variants, viz., SARS-CoV-2, SARS, MERS, Bat coronavirus HKU3, Bat coronavirus Rp3/2004, Bat coronavirus HKU5, Murine coronavirus, Bovine coronavirus, Rat coronavirus, Bat coronavirus HKU4, Bat coronavirus 133/2005, are considered from 44 viral variants. The fuzzy scoring function of the entire host-pathogen network has been processed with ~180 million potential interactions generated from 19,281 host proteins and around 242 viral proteins. ~4.5 million potential level one host-pathogen interactions are computed based on the estimated interaction affinity threshold. The resulting host-pathogen interactome is also validated with state-of-the-art experimental networks. The study has also been extended further toward the drug-repurposing study by analyzing the FDA-listed COVID drugs.

Effect of glucocorticoids on the development of COVID-19-associated pulmonary aspergillosis: a meta-analysis of 21 studies and 5174 patients (preprint)

Objective: COVID-19-associated pulmonary aspergillosis (CAPA) remains a high mortality mycotic infection throughout the pandemic, and glucocorticoids (GCs) may be its root cause. We aimed to evaluate the effect of systemic GC treatment on the development of CAPA. Methods: We systematically searched the PubMed, Google Scholar, Scopus, and Embase databases to collect eligible studies published until December 31, 2022. The pooled outcome of CAPA development was calculated as the log odds ratio (LOR) with 95% confidence intervals (CI) using a random effect model. Results: A total of 21 studies with 5174 patients were included. Of these, 20 studies with 4675 patients consisting of 2565 treated with GC but without other immunomodulators (GC group) and 2110 treated without GC and other immunomodulators (controls) were analyzed. The pooled LOR of CAPA development was higher for the GC group than for the control group (0.54; 95% CI: 0.22, 0.86; p<0.01). In the subgroups, the pooled LOR was higher for high-dose GC (0.90; 95% CI: 0.17, 1.62: p=0.01) and dexamethasone (0.71; 95% CI: 0.35, 1.07; p<0.01)-treated patients, but there was no significant difference for low-dose GC (0.41; 95% CI: -0.07, 0.89; p=0.09)- and non-dexamethasone GC (0.21; 95% CI: -0.36, 0.79; p=0.47)-treated patients versus controls. Conclusion: GC treatment increased the risk of CAPA development, and the risk was associated with the use of high-dose GC or dexamethasone therapy.

Molecular and Therapeutic Insights of Alpha-Lipoic Acid as a Potential Molecule for Disease Prevention.

Alpha-lipoic acid is an organic, sulfate-based compound produced by plants, humans, and animals. As a potent antioxidant and a natural dithiol compound, it performs a crucial role in mitochondrial bioenergetic reactions. A healthy human body, on the other hand, can synthesize enough α-lipoic acid to scavenge reactive oxygen species and increase endogenous antioxidants; however, the amount of α-lipoic acid inside the body decreases significantly with age, resulting in endothelial dysfunction. Molecular orbital energy and spin density analysis indicate that the sulfhydryl (-SH) group of molecules has the greatest electron donating activity, which would be responsible for the antioxidant potential and free radical scavenging activity. α-Lipoic acid acts as a chelating agent for metal ions, a quenching agent for reactive oxygen species, and a reducing agent for the oxidized form of glutathione and vitamins C and E. α-Lipoic acid enantiomers and its reduced form have antioxidant, cognitive, cardiovascular, detoxifying, anti-aging, dietary supplement, anti-cancer, neuroprotective, antimicrobial, and anti-inflammatory properties. α-Lipoic acid has cytotoxic and antiproliferative effects on several cancers, including polycystic ovarian syndrome. It also has usefulness in the context of female and male infertility. Although α-lipoic acid has numerous clinical applications, the majority of them stem from its antioxidant properties; however, its bioavailability in its pure form is low (approximately 30%). However, nanoformulations have shown promise in this regard. The proton affinity and electron donating activity, as a redox-active agent, would be responsible for the antioxidant potential and free radical scavenging activity of the molecule. This review discusses the most recent clinical data on α-lipoic acid in the prevention, management, and treatment of a variety of diseases, including coronavirus disease 2019. Based on current evidence, the preclinical and clinical potential of this molecule is discussed. Supplementary Information: The online version contains supplementary material available at 10.1007/s43450-023-00370-1.

Surgical Challenges in the Management of Post COVID-19 Midface Mucormycosis (PCoMM): An Institutional Protocol.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has posed another serious threat, mucormycosis infection, affecting the maxilla and orbitocerebral region. This condition has not spared world population from its merciless claws. This article addresses the challenges faced by the maxillofacial surgeons in setting the protocols from preoperative diagnosis, surgical management to postoperative care, including short-term and long-term rehabilitation. To manage this relentlessly progressing condition, a multispecialty team approach is to be activated in diagnosing, managing, and rehabilitating the patients. PURPOSE: The purpose of this clinical study is to document and analyze the clinical and demographic data, presentation of the lesion, the diagnostic methods followed for early clinical detection, and management of post COVID-19 midface mucormycosis. The article also discusses postoperative medical management and prosthetic rehabilitation. RESULTS: Most of the mucormycosis cases reporting to our center were treated and recovered patients of Severe Acute Respiratory Syndrome Coronavirus 2 infection. Thirty-four (n=34) case were operated for post COVID-19 midface mucormycosis between October 2020 and December 2021. Male to Female ratio is 1:42. The average age of the patients was 57.5 years. Maximum patients were in fifth and sixth decade of life. Maxilla was the involved bone. Treatment was primarily surgical debridement to extended or radical maxillectomy. All patients were treated with Liposomal Amphotericin B and tab posaconazole for 3 to 4 weeks depending upon the age, weight, and physiological state of the patients to attain an optimal cumulative load. Three patients succumbed to illness postoperatively (n=3, 1.02%). Average duration of hospital stay was 47 days. The average review period was 5.1 months.

Molecular and Therapeutic Insights of Alpha-Lipoic Acid as a Potential Molecule for Disease Prevention

Alpha-lipoic acid is an organic, sulfate-based compound produced by plants, humans, and animals. As a potent antioxidant and a natural dithiol compound, it performs a crucial role in mitochondrial bioenergetic reactions. A healthy human body, on the other hand, can synthesize enough α-lipoic acid to scavenge reactive oxygen species and increase endogenous antioxidants;however, the amount of α-lipoic acid inside the body decreases significantly with age, resulting in endothelial dysfunction. Molecular orbital energy and spin density analysis indicate that the sulfhydryl (-SH) group of molecules has the greatest electron donating activity, which would be responsible for the antioxidant potential and free radical scavenging activity. α-Lipoic acid acts as a chelating agent for metal ions, a quenching agent for reactive oxygen species, and a reducing agent for the oxidized form of glutathione and vitamins C and E. α-Lipoic acid enantiomers and its reduced form have antioxidant, cognitive, cardiovascular, detoxifying, anti-aging, dietary supplement, anti-cancer, neuroprotective, antimicrobial, and anti-inflammatory properties. α-Lipoic acid has cytotoxic and antiproliferative effects on several cancers, including polycystic ovarian syndrome. It also has usefulness in the context of female and male infertility. Although α-lipoic acid has numerous clinical applications, the majority of them stem from its antioxidant properties;however, its bioavailability in its pure form is low (approximately 30%). However, nanoformulations have shown promise in this regard. The proton affinity and electron donating activity, as a redox-active agent, would be responsible for the antioxidant potential and free radical scavenging activity of the molecule. This review discusses the most recent clinical data on α-lipoic acid in the prevention, management, and treatment of a variety of diseases, including coronavirus disease 2019. Based on current evidence, the preclinical and clinical potential of this molecule is discussed. Graphical Supplementary Information The online version contains supplementary material available at 10.1007/s43450-023-00370-1.

ML-DTD: Machine Learning-Based Drug Target Discovery for the Potential Treatment of COVID-19.

Recent research has highlighted that a large section of druggable protein targets in the Human interactome remains unexplored for various diseases. It might lead to the drug repurposing study and help in the in-silico prediction of new drug-human protein target interactions. The same applies to the current pandemic of COVID-19 disease in global health issues. It is highly desirable to identify potential human drug targets for COVID-19 using a machine learning approach since it saves time and labor compared to traditional experimental methods. Structure-based drug discovery where druggability is determined by molecular docking is only appropriate for the protein whose three-dimensional structures are available. With machine learning algorithms, differentiating relevant features for predicting targets and non-targets can be used for the proteins whose 3-D structures are unavailable. In this research, a Machine Learning-based Drug Target Discovery (ML-DTD) approach is proposed where a machine learning model is initially built up and tested on the curated dataset consisting of COVID-19 human drug targets and non-targets formed by using the Therapeutic Target Database (TTD) and human interactome using several classifiers like XGBBoost Classifier, AdaBoost Classifier, Logistic Regression, Support Vector Classification, Decision Tree Classifier, Random Forest Classifier, Naive Bayes Classifier, and K-Nearest Neighbour Classifier (KNN). In this method, protein features include Gene Set Enrichment Analysis (GSEA) ranking, properties derived from the protein sequence, and encoded protein network centrality-based measures. Among all these, XGBBoost, KNN, and Random Forest models are satisfactory and consistent. This model is further used to predict novel COVID-19 human drug targets, which are further validated by target pathway analysis, the emergence of allied repurposed drugs, and their subsequent docking study.

Do Operational Facilitating Conditions and Subjective Norms Improve the Intention to use Social Distancing Nudge during a Pandemic: A Case of SARS-CoV-2(COVID-19) Outbreak in India (preprint)

The COVID-19 outbreak reached a critical stage where it became imperative for public health systems to act decisively and design potential behavioral operational strategies that aim at containment of the pandemic. Isolation through social distancing plays a key role in achieving this objective. This research study is conducted to examine the factors affecting the intention of individuals towards social distancing in India. Correlation survey study is conducted on the samples of Pan Indian (N = 499) residents. Online questionnaire was floated consisting of Health Belief Model, and Theory of Planned Behavior Model, with respect to social distancing behavior at an initial occasion. Finally, Structural equation modeling is used to test the hypotheses. The results show that Perceived susceptibility, Facilitating Conditions and Subjective Norms are the major predictors of Attitude towards social distancing with the effect size of 0.277, 0.132, and 0.551 respectively. The result also confirms that the Attitude towards social distancing, perceived Usefulness of social distancing, and Subjective Norms significantly predicted the Intention to use the social distancing with the effect size of 0.355, 0.197, and 0.385 respectively. The non-significant association of Perceived Susceptibility(PS) with Social Distancing Intention (IN) (H1b) is rendering the fact that Attitude (AT) mediates the relationship between PS and IN similarly, the non-significant association of Facilitating Conditions (FC) with IN (H5) is rendering the fact that AT mediates the relationship between FC and IN. Results of the study is helpful to the policy makers to handle operations management of nudges like social distancing. The research is one of its kind that explores the behavioral aspects of handling social nudges.

An accessible infrastructure for artificial intelligence using a docker-based Jupyterlab in Galaxy (preprint)

Artificial intelligence (AI) programs that train on a large amount of data require powerful compute infrastructure. Jupyterlab notebook provides an excellent framework for developing AI programs but it needs to be hosted on a powerful infrastructure to enable AI programs to train on large data. An open-source, docker-based, and GPU-enabled jupyterlab notebook infrastructure has been developed that runs on the public compute infrastructure of Galaxy Europe for rapid prototyping and developing end-to-end AI projects. Using such a notebook, long-running AI model training programs can be executed remotely. Trained models, represented in a standard open neural network exchange (ONNX) format, and other resulting datasets are created in Galaxy. Other features include GPU support for faster training, git integration for version control, the option of creating and executing pipelines of notebooks, and the availability of multiple dashboards for monitoring compute resources. These features make the jupyterlab notebook highly suitable for creating and managing AI projects. A recent scientific publication that predicts infected regions of COVID-19 CT scan images is reproduced using multiple features of this notebook. In addition, colabfold, a faster implementation of alphafold2, can also be accessed in this notebook to predict the 3D structure of protein sequences. Jupyterlab notebook is accessible in two ways - first as an interactive Galaxy tool and second by running the underlying docker container. In both ways, long-running training can be executed on Galaxy’s compute infrastructure. The scripts to create the docker container are available under MIT license at https://github.com/anuprulez/ml-jupyter-notebook . Contact kumara@informatik.uni-freiburg.de anup.rulez@gmail.com

A Pathway for Multi-Stage Cyclone-Induced Hazard Tracking – Case Study for Yaas (preprint)

A Very Severe Cyclonic Storm “Yaas” developed over the Bay of Bengal (BoB) on May 23, 2021, and crossed over the Odisha coast on May 26 with maximum sustained wind speed of 75 kts. Herein, a pathway has been developed and exemplified for ‘Yaas’ through three-stage cyclone-induced hazard tracking. Days before the cyclone formation, Cyclone Genesis Potential Parameter, Sea Surface Temperature (SST) (> 30 0C) and Tropical Cyclone Heat Potential (anomaly of 40–80 kJ/cm2) indicated a strong possibility of cyclogenesis in the BoB. A Lagrangian Advection Model used for its track prediction with 24-hour lead-time provided an accuracy of ~ 19 km and ~ 6 hour in its landfall location and time. Further, intensity prediction was done using Numerical Weather Prediction model. Geostationary Satellites, INSAT-3D/3DR, were used to visualise cyclone structure. Passing of cyclone had its reverbarations in oceans, which are observed in SST drop of ~ 3o C, salinity and density increase by ~ 1 psu and ~ 2 Kg/m3, respectively. During the period, 23–26 May 2021, the Ekman suction velocity and chlorophyll concentration were found significantly high at ~ 5m/day and > 0.5 mg/m3, respectively. Forecast of storm surge was found to be between 3.5-4 m at coastal locations. Significant wave height was found to be 5.5 to 9.2 m. The coastal inundation forecast for 24 May 2021 provided its quantitative maximum inland extent. Finally, loss of the crop, fishery and forest areas by strong winds and inundation/ingress of saline water associated with storm surge were examined using SAR and optical data.

Molecular and pharmacological aspects of piperine as a potential molecule for disease prevention and management: evidence from clinical trials.

BACKGROUND: Piperine is a type of amide alkaloid that exhibits pleiotropic properties like antioxidant, anticancer, anti-inflammatory, antihypertensive, hepatoprotective, neuroprotective and enhancing bioavailability and fertility-related activities. Piperine has the ability to alter gastrointestinal disorders, drug-metabolizing enzymes, and bioavailability of several drugs. The present review explores the available clinical and preclinical data, nanoformulations, extraction process, structure-activity relationships, molecular docking, bioavailability enhancement of phytochemicals and drugs, and brain penetration properties of piperine in the prevention, management, and treatment of various diseases and disorders. MAIN BODY: Piperine provides therapeutic benefits in patients suffering from diabetes, obesity, arthritis, oral cancer, breast cancer, multiple myeloma, metabolic syndrome, hypertension, Parkinson's disease, Alzheimer's disease, cerebral stroke, cardiovascular diseases, kidney diseases, inflammatory diseases, and rhinopharyngitis. The molecular basis for the pleiotropic activities of piperine is based on its ability to regulate multiple signaling molecules such as cell cycle proteins, anti-apoptotic proteins, P-glycoprotein, cytochrome P450 3A4, multidrug resistance protein 1, breast cancer resistance protein, transient receptor potential vanilloid 1 proinflammatory cytokine, nuclear factor-κB, c-Fos, cAMP response element-binding protein, activation transcription factor-2, peroxisome proliferator-activated receptor-gamma, Human G-quadruplex DNA, Cyclooxygenase-2, Nitric oxide synthases-2, MicroRNA, and coronaviruses. Piperine also regulates multiple signaling pathways such as Akt/mTOR/MMP-9, 5'-AMP-activated protein kinase-activated NLR family pyrin domain containing-3 inflammasome, voltage-gated K+ current, PKCα/ERK1/2, NF-κB/AP-1/MMP-9, Wnt/ß-catenin, JNK/P38 MAPK, and gut microbiota. SHORT CONCLUSION: Based on the current evidence, piperine can be the potential molecule for treatment of disease, and its significance of this molecule in the clinic is discussed.

Model evolution in SARS-CoV-2 spike protein sequences using a generative neural network (preprint)

Modelling evolutionary elements inherent in protein sequences, emerging from one clade into another of the SARS-CoV-2 virus, would provide insights to augment our understanding of its impact on public health and may help in formulating better strategies to contain its spread. Deep learning methods have been used to model protein sequences for SARS-CoV-2 viruses. A few significant drawbacks in these studies include being deficient in modelling end-to-end protein sequences, modelling only those genomic positions that show high activity and upsampling the number of sequences at each genomic position for balancing the frequency of mutations. To mitigate such drawbacks, the current approach uses a generative model, an encoder-decoder neural network, to learn the natural progression of spike protein sequences through adjacent clades of the phylogenetic tree of Nextstrain clades. Encoder transforms a set of spike protein sequences from the source clade (20A) into its latent representation. Decoder uses the latent representation, along with Gaussian distributed noise, to generate a different set of protein sequences that are closer to the target clade (20B). The source and target clades are adjacent nodes in the phylogenetic tree of different evolving clades of the SARS-CoV-2 virus. Sequences of amino acids are generated, for the entire length, at each genomic position using the latent representation of the amino acid generated at a previous step. Using trained models, protein sequences from the source clade are used to generate sequences that form a collection of evolved sequences belonging to all children clades of the source clade. A comparison of this predicted evolution (between source and generated sequences) of proteins with the true evolution (between source and target sequences) shows a high pearson correlation (> 0.7). Moreover, the distribution of the frequencies of substitutions per genomic position, including high- and low-frequency positions, in source-target sequences and source-generated sequences exhibit a high resemblance (pearson correlation > 0.7). In addition, the model partially predicts a few substitutions at specific genomic positions for the sequences of unseen clades (20J (Gamma)) where they show little activity during training. These outcomes show the potential of this approach in learning the latent mechanism of evolution of SARS-CoV-2 viral sequences.

Time versus tissue: Timely identification of Scedosporium Rhinosinusitis in a post-COVID-19 case by MALDI-TOF MS leading to successful management.

COVID-19 (Coronavirus Disease 2019), illness with associated comorbidities and corticosteroid therapy makes the host immunocompromised and prone to opportunistic microbial infections. As the world continues to struggle with the pandemic of COVID-19, an increase in cases of opportunistic fungal infections have been reported from all over the world during the second wave of COVID-19 like aspergillosis, mucormycosis, and candidiasis. Scedosporium apiospermum is an emerging pathogen that is usually associated with mycetoma, pulmonary infection, and central nervous infections. It has been rarely associated with fungal rhinosinusitis (FRS). In this study, a rare case of FRS caused by S.apiospermum in an immunocompromised post-Covid-19 diabetic woman is reported.

Critical Care Management of a Severe Acute Respiratory Distress Syndrome COVID-19 Patient With Control Cesarean Section.

We share our experience of one 29-year-old female, G2 P1, with acute respiratory distress syndrome (ARDS) and at 30 weeks of pregnancy. The 30-week gravid uterus in combination with a poor ventilation-perfusion ratio creates a restrictive lung pattern that may prove to be lethal for both the mother and baby. Due to her rapid deterioration and increased hemodynamic instability we opted for controlled delivery in the operating room with an ICU physician, a Neonatologist, and an Obstetric team. At 3.27 minutes from induction, the baby was born with Apgar scores of 7 and 8. The mother was placed on a RotoProne® bed, treated with remdesivir, steroids, and was subsequently extubated seven days later. The newborn was admitted to the Neonatal Intensive Care Unit (NICU) after delivery. We have reviewed the literature and provided a concise set of recommendations based on our field experience and current world literature review. Prompt delivery in a controlled environment with multiple resuscitating teams provided expeditious treatment of both patients, maintaining oxygenation and perfusion while keeping hemodynamic stability. The controlled environment and the proximity of all teams avoided deleterious consequences to the unborn baby. This is an example where the risk of keeping the baby in the womb outweighs the premature delivery into a NICU. Both mother and baby were downgraded from their respective Intensive Care Units (ICUs) and discharged home in one month.

Rare Thrombotic Complications of COVID-19: A Case Series.

Coronavirus disease 2019 (COVID-19) is known to manifest with bilateral pneumonia and acute respiratory distress syndrome. This infection with severe acute respiratory syndrome coronavirus 2 (SAR-CoV-2) is alarming because it not only affects the respiratory system but may also cause thromboembolic events. Multiple studies have reported procoagulation/hypercoagulable complications in COVID-19. This case series is a valuable addition to the literature because it reflects unique presentations of thrombotic events in COVID-19 patients. We report two cases in which patients presented with thromboembolic complications secondary to COVID-19 infection: one with severe bowel ischemia and the other with blue toe syndrome. To formulate management strategies to prevent fatal outcomes for patients with COVID-19, physicians must be vigilant in identifying life-threatening thromboembolic complications from this disease.

Computational modeling of human-nCoV protein-protein interaction network.

Novel coronavirus(SARS-CoV2) replicates the host cell's genome by interacting with the host proteins. Due to this fact, the identification of virus and host protein-protein interactions could be beneficial in understanding the disease transmission behavior of the virus as well as in potential COVID-19 drug identification. International Committee on Taxonomy of Viruses (ICTV) has declared that nCoV is highly genetically similar to the SARS-CoV epidemic in 2003 (∼89% similarity). With this hypothesis, the present work focuses on developing a computational model for the nCoV-Human protein interaction network, using the experimentally validated SARS-CoV-Human protein interactions. Initially, level-1 and level-2 human spreader proteins are identified in the SARS-CoV-Human interaction network, using Susceptible-Infected-Susceptible (SIS) model. These proteins are considered potential human targets for nCoV bait proteins. A gene-ontology-based fuzzy affinity function has been used to construct the nCoV-Human protein interaction network at a ∼99.98% specificity threshold. This also identifies 37 level-1 human spreaders for COVID-19 in the human protein-interaction network. 2474 level-2 human spreaders are subsequently identified using the SIS model. The derived host-pathogen interaction network is finally validated using six potential FDA-listed drugs for COVID-19 with significant overlap between the known drug target proteins and the identified spreader proteins.

Drug repurposing for COVID-19 using computational screening: Is Fostamatinib/R406 a potential candidate?

With the gradual increase in the COVID-19 mortality rate, there is an urgent need for an effective drug/vaccine. Several drugs like Remdesivir, Azithromycin, Favirapir, Ritonavir, Darunavir, etc., are put under evaluation in more than 300 clinical trials to treat COVID-19. On the other hand, several vaccines like Pfizer-BioNTech, Moderna, Johnson & Johnson's Janssen, Sputnik V, Covishield, Covaxin, etc., also evolved from the research study. While few of them already gets approved, others show encouraging results and are still under assessment. In parallel, there are also significant developments in new drug development. But, since the approval of new molecules takes substantial time, drug repurposing studies have also gained considerable momentum. The primary agent of the disease progression of COVID-19 is SARS-CoV2/nCoV, which is believed to have ~89% genetic resemblance with SARS-CoV, a coronavirus responsible for the massive outbreak in 2003. With this hypothesis, Human-SARS-CoV protein interactions are used to develop an in-silico Human-nCoV network by identifying potential COVID-19 human spreader proteins by applying the SIS model and fuzzy thresholding by a possible COVID-19 FDA drugs target-based validation. At first, the complete list of FDA drugs is identified for the level-1 and level-2 spreader proteins in this network, followed by applying a drug consensus scoring strategy. The same consensus strategy is involved in the second analysis but on a curated overlapping set of key genes/proteins identified from COVID-19 symptoms. Validation using subsequent docking study has also been performed on COVID-19 potential drugs with the available major COVID-19 crystal structures whose PDB IDs are: 6LU7, 6M2Q, 6W9C, 6M0J, 6M71 and 6VXX. Our computational study and docking results suggest that Fostamatinib (R406 as its active promoiety) may also be considered as one of the potential candidates for further clinical trials in pursuit to counter the spread of COVID-19.

Profile of Mucormycosis Cases from a Network of Hospitals in North India Amidst COVID-19 Pandemic (preprint)

Incidence of mucormycosis suddenly surged in India after the second wave of COVID-19. This is a crippling disease and needs to be studied in detail to understand the disease, its course, and the outcomes. Between 1st March and 15th July 2021, our network of hospitals in North India received a total of 155 cases of COVID-associated mucormycosis cases as all of them reported affliction by COVID-19 earlier or concurrent. Their records were retrieved from the Electronic Health Records system of the hospitals and their demographics, clinical features, treatments, and outcomes were studied. More than 80% (125 cases) had proven disease and the remaining 30 were categorized as possible mucormycosis as per the EORTC criteria. More than two-thirds (69.0%) of the cases were males and the mean age was 53 years for either sex. Nearly two-thirds (64.5%) had symptoms of nose and jaws and 42.6% had eye involvement. Some had multiple symptoms. As many as 78.7% had diabetes and 91.6% gave history of use of steroids during COVID-19 treatment. The primary surgery was functional endoscopic sinus surgery (FESS) (83.9%). Overall mortality was 16.8%, which is one-and-a-half times the mortality in hospitalized COVID-19 patients in the corresponding population. Occurrence of mucormycosis was associated with diabetes and use of steroids, but mortality was not associated with either of them. Cases undergoing surgery and on antifungal had steeply lower mortality (11.9% vs. 50.0%, P < 0.001) than those who were exclusively on antifungal drugs. Treatment by different drugs did not make much of a difference in mortality.

Sentiment Analysis of Covid-19 Tweets using Evolutionary Classification-Based LSTM Model (preprint)

As the Covid-19 outbreaks rapidly all over the world day by day and also affects the lives of million, a number of countries declared complete lock-down to check its intensity. During this lockdown period, social media plat-forms have played an important role to spread information about this pandemic across the world, as people used to express their feelings through the social networks. Considering this catastrophic situation, we developed an experimental approach to analyze the reactions of people on Twitter taking into ac-count the popular words either directly or indirectly based on this pandemic. This paper represents the sentiment analysis on collected large number of tweets on Coronavirus or Covid-19. At first, we analyze the trend of public sentiment on the topics related to Covid-19 epidemic using an evolutionary classification followed by the n-gram analysis. Then we calculated the sentiment ratings on collected tweet based on their class. Finally, we trained the long-short term network using two types of rated tweets to predict sentiment on Covid-19 data and obtained an overall accuracy of 84.46%.

Predicting the pandemic: sentiment evaluation and predictive analysis from large-scale tweets on Covid-19 by deep convolutional neural network.

Engaging deep neural networks for textual sentiment analysis is an extensively practiced domain of research. Textual sentiment classification harnesses the full computational potential of deep learning models. Typically, these research works are carried either with a popular open-source data corpus, or self-extracted short phrase texts from Twitter, Reddit, or web-scrapped text data from other resources. Rarely do we see a large amount of data on a current ongoing event is being collected and cultured further. Also, an even more complex task would be to model the data from a currently ongoing event, not only for scaling the sentiment accuracy but also for making a predictive analysis for the same. In this paper, we propose a novel approach for achieving sentiment evaluation accuracy by using a deep neural network on live-streamed tweets on Coronavirus and future case growth prediction. We develop a large tweet corpus exclusively based on the Coronavirus tweets. We split the data into train and test sets, alongside we perform polarity classification and trend analysis. The refined outcome from the trend analysis helps to train the data to provide an incremental learning curvature for our neural network, and we obtain an accuracy of 90.67%. Finally, we provide a statistical-based future prediction for Coronavirus cases growth. Not only our model outperforms several previous state-of-art experiments in overall sentiment accuracy comparison for similar tasks, but it also maintains a throughout performance stability among all the test cases when tested with several popular open-source text corpora.