Mycological profile of acute invasive fungal rhinosinusitis during COVID-19 pandemic at a tertiary care hospital

Introduction: During the coronavirus disease-19 (COVID-19) pandemic, a surge in acute invasive fungal rhinosinusitis (AIFRS) cases with high mortality was reported in India. Objective: The objective was to study the spectrum of fungus associated with AIFRS during the pandemic of COVID-19. Materials and Methods: A total of 51 patients who were clinically diagnosed as cases of acute invasive rhinosinusitis in the department of ear, nose and throat (ENT) were included in the study. The clinical data along with demographic details were noted, and fungal identification was done using a conventional method. Results: Out of 51 patients, 66.6% were males and 33.4% females. Predominantly affected age group was 41-50 years. Out of 51 patients, 92.15% (47/51) had suffered from COVID-19 and 7.8% (4/51) did not have COVID-19 infection previously. Thirty-seven patients out of 51 (72.54%) were diabetics. Out of 51 samples collected from patients, 94.11% (48/51) were fungal culture-positive and only 5.8% (3/51) were culture-negative. A total of 52 fungi were isolated from the 48 culture-positive samples. Mucormycetes were predominantly isolated from the samples followed by Aspergillus species and Candida species. Among mucormycetes, Rhizopus species was the predominantly isolated. Conclusion: Patients with COVID-19, especially those at high risk, need to undergo an ENT examination once they recover because an early identification of AIFRS and a strong clinical suspicion of the disease are crucial for a successful course of treatment and to improve patient prognosis. © 2022 Medical Journal of Babylon ;Published by Wolters Kluwer - Medknow.

Increased Medical Severity of Trauma Admissions to the Picu during the Covid-19 Pandemic

INTRODUCTION: It has been suggested that the coronavirus-19 disease (COVID-19) pandemic and associated community containment measures led to hesitancy among patients and their parents/guardians to report to hospitals for care. We hypothesized that the clinical condition of trauma patients admitted to the PICU was more severe during the COVID-19 pandemic in 2020 and early 2021 compared to previous years. METHOD(S): We completed a retrospective, crosssectional, study at a tertiary children's hospital by comparing admissions to our PICU between March 2020 - March 2021, during which COVID-19 and community pandemic mitigation measures occurred, to those during the same period in the previous three years. All patients admitted to the PICU for trauma were included. Severity was measured using the pediatric risk of mortality (PRISM) score estimated probability of death. Trauma admissions between COVID-19 and non-COVID-19 epochs were compared using negative binomial regression. Demographic and clinical characteristics of patients admitted during pre-COVID-19 and COVID-19 epochs were compared using Fisher exact and Mann-Whitney U rank sum tests. The normality of data was assessed using the Shapiro-Wilk test. RESULT(S): Total trauma PICU admissions during COVID-19 pandemic months were similar to the same months in the preceding three years (mean 4.5/month, 95%-CI: 3.5 - 5.9/ month vs. mean 3.6/month 95%-CI: 3.0 - 4.3/month, P = 0.133), although overall PICU admissions per month were lower (-19%, P< 0.001). Trauma patients admitted during COVID-19 had estimated median mortality more than twice as high as patients admitted during the non-COVID epoch (1.1%, interquartile range: 0.6 - 1.8% vs. 0.5%, interquartile range: 0.3 - 1.3%, P = 0.002). Age, sex, race and type of trauma (motor vehicle accident/gunshot wound/fall/all-terrain vehicle or bike accident/assault) were similar between the two time periods (all P > 0.05). CONCLUSION(S): These findings suggest that the COVID-19 pandemic was associated with increased severity in pediatric traumatic injuries, and possible hesitation to seek care earlier. These findings add to existing knowledge about increased trauma severity at presentation during a period of communicable disease spread and mitigation measures;these results may provide insight for future outbreak management.

EffViT-COVID: A dual-path network for COVID-19 percentage estimation

The first case of novel Coronavirus (COVID-19) was reported in December 2019 in Wuhan City, China and led to an international outbreak. This virus causes serious respiratory illness and affects several other organs of the body differently for different patient. Worldwide, several waves of this infection have been reported, and researchers/doctors are working hard to develop novel solutions for the COVID diagnosis. Imaging and vision-based techniques are widely explored for the prediction of COVID-19;however, COVID infection percentage estimation is under explored. In this work, we propose a novel framework for the estimation of COVID-19 infection percentage based on deep learning techniques. The proposed network utilizes the features from vision transformers and CNN (Convolutional Neural Networks), specifically EfficientNet-B7. The features of both are fused together for preparing an information-rich feature vector that contributes to a more precise estimation of infection percentage. We evaluate our model on the Per-COVID-19 dataset (Bougourzi et al., 2021b) which comprises labeled CT data of COVID-19 patients. For the evaluation of the model on this dataset, we employ the most widely-used slice-level metrics, i.e., Pearson correlation coefficient (PC), Mean absolute error (MAE), and Root mean square error (RMSE). The network outperforms the other state-of-the-art methods and achieves 0 . 9886 +/- 0 . 009 , 1 . 23 +/- 0 . 378 , and 3 . 12 +/- 1 . 56 , PC, MAE, and RMSE, respectively, using a 5-fold cross-validation technique. In addition, the overall average difference in the actual and predicted infection percentage is observed to be < 2 % . In conclusion, the detailed experimental results reveal the robustness and efficiency of the proposed network.

The INTERSPEECH 2021 Computational Paralinguistics Challenge: COVID-19 Cough, COVID-19 Speech, Escalation & Primates

The INTERSPEECH 2021 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the COVID-19 Cough and COVID-19 Speech Sub-Challenges, a binary classification on COVID-19 infection has to be made based on coughing sounds and speech;in the Escalation Sub-Challenge, a three-way assessment of the level of escalation in a dialogue is featured;and in the Primates Sub-Challenge, four species vs background need to be classified. We describe the Sub-Challenges, baseline feature extraction, and classifiers based on the 'usual' COMPARE and BoAW features as well as deep unsupervised representation learning using the AUDEEP toolkit, and deep feature extraction from pre-trained CNNs using the DEEP SPECTRUM toolkit;in addition, we add deep end-to-end sequential modelling, and partially linguistic analysis.

Concerns of patients (pts) with melanoma: Gaining insights from social media listening (SML) research on symptoms and quality of life (QoL)

Social media (SM) provides a platform to learn from pts and the public to better understand pts' needs. This research examined SM discussions on symptoms, impact of the COVID-19 pandemic, and QoL of pts with melanoma. A SML approach using melanoma-specific terminology from publicly-available blogs, forums, and SM sites, collected data retrospectively over 2 years (Nov 2018-Sep 2020) across 15 European countries. Manual and automated relevancy approaches filtered the extracted data for content that provided pt-centric insights. This contextualized data was then mined to gain insights on symptoms, QoL, and the impact of the COVID-19 pandemic. Of 182.4K mentions of melanoma, Twitter was the primary channel used (71% of conversations), followed by blogs (17%), and forums (11%). Pts were the predominant contributors to conversations (62%), with caregivers (22%), and family/friends (12%) also contributing. The top 5 regions where conversations took place were the UK (38%), Spain (16%), Italy (13%), Germany (11%), and France (11%). Female-led conversations were more common (55%), and malignant and metastatic disease accounted for 77% of the types of melanoma discussed. Of the 864 insightful conversations identified, QoL was mentioned in 255 (30%);emotional burden was the most frequent topic mentioned (70%), followed by physical (24%), social (17%), and financial (4%) impacts. Symptoms were mentioned in only 2% of conversations, with pain (36%), hardened nodules under the skin (21%), and itchy skin (14%) the most common. 5% of discussions highlighted treatments being postponed, rescheduled, or cancelled, which was often attributed to the COVID-19 pandemic. Emotional burden was the main impact on QoL identified in this study. SML is a useful tool to understand concerns surrounding the QoL of pts with melanoma.

EXPLORING AUTOMATIC COVID-19 DIAGNOSIS VIA VOICE AND SYMPTOMS FROM CROWDSOURCED DATA

The development of fast and accurate screening tools, which could facilitate testing and prevent more costly clinical tests, is key to the current pandemic of COVID-19. In this context, some initial work shows promise in detecting diagnostic signals of COVID-19 from audio sounds. In this paper, we propose a voice-based framework to automatically detect individuals who have tested positive for COVID-19. We evaluate the performance of the proposed framework on a subset of data crowdsourced from our app, containing 828 samples from 343 participants. By combining voice signals and reported symptoms, an AUC of 0.79 has been attained, with a sensitivity of 0.68 and a specificity of 0.82. We hope that this study opens the door to rapid, low-cost, and convenient pre-screening tools to automatically detect the disease.

Vocal for local through krishi vigyan kendras

The World Health Organization (WHO) declared corona virus outbreak a pandemic. World is fighting hard and preparing each day to face new challenge for dual situation;one is to save lives from the pandemic caused due to COVID -19 and other is its impact as a giant forthcoming situation of unemployment, hunger and depression. Saxena, 2020 reported that data from various studies show that about 1.3 billion people are affected due to ongoing lockdown and about 10 Crore Indians lost their jobs. On 13.5.2020, the Prime Minister of India, Sh. Narendra Modi Ji appealed 'vocal for local' during his corona virus lockdown address (Tripathi, 2020). Make in India has become a necessity to support and sustain the population and India's economy. The crisis situation in India may be turned into an opportunity by making need based products at local level, for the local markets. There is also an urgent need to involve women in economic activities. Krishi Vigyan Kendras (KVKs) is established all over the country by the Indian Council of Agricultural Research, New Delhi. The integrated work of this team is expected to cater to the need of farmer, farm women, rural youth and school drop outs from farm to fork. KVKs system may be the mile stone to turn the slogan, "vocal for local" by generating entrepreneurs, employment and addressing the problem of malnutrition. The motto of "vocal the local" may not only initiate a mindset to change dreams into realities but also cater to people's changed choices. The KVKs system may show tremendous success rate in the development of micro and large scale entrepreneurs provided training is planned meticulously and jointly by all the subject matter specialists. It is imperative to understand the core strength and spirit of KVKs system by the subject matter specialists and coordinators and pursue it effectively and as a team. The aim of each KVKs employee should be to treat each person (farmer, farm women, youth and school dropout) as a potential entrepreneur.

Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data

Audio signals generated by the human body (e.g., sighs, breathing, heart, digestion, vibration sounds) have routinely been used by clinicians as indicators to diagnose disease or assess disease progression. Until recently, such signals were usually collected through manual auscultation at scheduled visits. Research has now started to use digital technology to gather bodily sounds (e.g., from digital stethoscopes) for cardiovascular or respiratory examination, which could then be used for automatic analysis. Some initial work shows promise in detecting diagnostic signals of COVID-19 from voice and coughs. In this paper we describe our data analysis over a large-scale crowdsourced dataset of respiratory sounds collected to aid diagnosis of COVID-19. We use coughs and breathing to understand how discernible COVID-19 sounds are from those in asthma or healthy controls. Our results show that even a simple binary machine learning classifier is able to classify correctly healthy and COVID-19 sounds. We also show how we distinguish a user who tested positive for COVID-19 and has a cough from a healthy user with a cough, and users who tested positive for COVID-19 and have a cough from users with asthma and a cough. Our models achieve an AUC of above 80% across all tasks. These results are preliminary and only scratch the surface of the potential of this type of data and audio-based machine learning. This work opens the door to further investigation of how automatically analysed respiratory patterns could be used as pre-screening signals to aid COVID-19 diagnosis. © 2020 Owner/Author.