Cataract Surgery Trends during Lockdown and Unlocking Periods of COVID-19 Pandemic: A Cross-sectional Hospital-based Study

Introduction: Coronavirus Disease-2019 (COVID-19) has affected healthcare access to population around the world. India also had its own set of problems for patients with disruption of healthcare services during the pandemic. This also brought in unique challenges for ophthalmologists who adapted to new challenges to provide quality care to the patients including those reporting for cataract surgery. Aim: To find out cataract surgery trends and demographic variables during lockdown and unlocking periods of COVID-19 pandemic. Materials and Methods: This cross-sectional hospital-based study was conducted at Ophthalmology department of a tertiary care centre in eastern India, from January 2020 to March 2022. Trends of cataract surgery including numbers, demographic factors, visual acuity at presentation, difference during first and second lock and unlock periods etc were compared during various lock and unlock period over more than two years. Results: A total of 3,843 patients were planned for surgery and 3,594 patients underwent cataract surgery. A total of 218 patients reported being positive for COVID-19 preoperatively and voluntarily dropped out from surgery. A total of 24 patients were found to be positive during preoperative Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) workup and were excluded from surgery. Seven patients didn't report for the surgery. A total of 59 patients reported febrile illness during one month postoperative period. There was dip in cataract surgery during lockdown periods (from 178.33 every month in pre COVID-19 period to near zero during first lockdown period) but recovery was much faster during second unlock period compared to first unlock period. Conclusion: The study concludes that there was drastic decrease in number of patients undergoing cataract surgery during COVID-19 pandemic. Predominantly young, male patients who had advanced morphology of cataracts with poor visual acuity accessed healthcare set-up for cataract surgery during initial lock and unlock period. Similar trend was seen during second lock and unlock period with rapid recovery of numbers and demography of cataract surgery patients to pre-COVID-19 levels. [ FROM AUTHOR] Copyright of Journal of Clinical & Diagnostic Research is the property of JCDR Research & Publications Private Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Ensemble Deep Learning Derived from Transfer Learning for Classification of COVID-19 Patients on Hybrid Deep-Learning-Based Lung Segmentation: A Data Augmentation and Balancing Framework.

BACKGROUND AND MOTIVATION: Lung computed tomography (CT) techniques are high-resolution and are well adopted in the intensive care unit (ICU) for COVID-19 disease control classification. Most artificial intelligence (AI) systems do not undergo generalization and are typically overfitted. Such trained AI systems are not practical for clinical settings and therefore do not give accurate results when executed on unseen data sets. We hypothesize that ensemble deep learning (EDL) is superior to deep transfer learning (TL) in both non-augmented and augmented frameworks. METHODOLOGY: The system consists of a cascade of quality control, ResNet-UNet-based hybrid deep learning for lung segmentation, and seven models using TL-based classification followed by five types of EDL's. To prove our hypothesis, five different kinds of data combinations (DC) were designed using a combination of two multicenter cohorts-Croatia (80 COVID) and Italy (72 COVID and 30 controls)-leading to 12,000 CT slices. As part of generalization, the system was tested on unseen data and statistically tested for reliability/stability. RESULTS: Using the K5 (80:20) cross-validation protocol on the balanced and augmented dataset, the five DC datasets improved TL mean accuracy by 3.32%, 6.56%, 12.96%, 47.1%, and 2.78%, respectively. The five EDL systems showed improvements in accuracy of 2.12%, 5.78%, 6.72%, 32.05%, and 2.40%, thus validating our hypothesis. All statistical tests proved positive for reliability and stability. CONCLUSION: EDL showed superior performance to TL systems for both (a) unbalanced and unaugmented and (b) balanced and augmented datasets for both (i) seen and (ii) unseen paradigms, validating both our hypotheses.

Multi-Modal Point-of-Care Diagnostics for COVID-19 Based on Acoustics and Symptoms.

BACKGROUND: The COVID-19 pandemic has highlighted the need to invent alternative respiratory health diagnosis methodologies which provide improvement with respect to time, cost, physical distancing and detection performance. In this context, identifying acoustic bio-markers of respiratory diseases has received renewed interest. OBJECTIVE: In this paper, we aim to design COVID-19 diagnostics based on analyzing the acoustics and symptoms data. Towards this, the data is composed of cough, breathing, and speech signals, and health symptoms record, collected using a web-application over a period of twenty months. METHODS: We investigate the use of time-frequency features for acoustic signals and binary features for encoding different health symptoms. We experiment with use of classifiers like logistic regression, support vector machines and long-short term memory (LSTM) network models on the acoustic data, while decision tree models are proposed for the symptoms data. RESULTS: We show that a multi-modal integration of inference from different acoustic signal categories and symptoms achieves an area-under-curve (AUC) of 96.3%, a statistically significant improvement when compared against any individual modality ([Formula: see text]). Experimentation with different feature representations suggests that the mel-spectrogram acoustic features performs relatively better across the three kinds of acoustic signals. Further, a score analysis with data recorded from newer SARS-CoV-2 variants highlights the generalization ability of the proposed diagnostic approach for COVID-19 detection. CONCLUSION: The proposed method shows a promising direction for COVID-19 detection using a multi-modal dataset, while generalizing to new COVID variants.

Detection of COVID-19 Infection in CT and X-ray images using transfer learning approach.

BACKGROUND: The infection caused by the SARS-CoV-2 (COVID-19) pandemic is a threat to human lives. An early and accurate diagnosis is necessary for treatment. OBJECTIVE: The study presents an efficient classification methodology for precise identification of infection caused by COVID-19 using CT and X-ray images. METHODS: The depthwise separable convolution-based model of MobileNet V2 was exploited for feature extraction. The features of infection were supplied to the SVM classifier for training which produced accurate classification results. RESULT: The accuracies for CT and X-ray images are 99.42% and 98.54% respectively. The MCC score was used to avoid any mislead caused by accuracy and F1 score as it is more mathematically balanced metric. The MCC scores obtained for CT and X-ray were 0.9852 and 0.9657, respectively. The Youden's index showed a significant improvement of more than 2% for both imaging techniques. CONCLUSION: The proposed transfer learning-based approach obtained the best results for all evaluation metrics and produced reliable results for the accurate identification of COVID-19 symptoms. This study can help in reducing the time in diagnosis of the infection.

Segmentation-Based Classification Deep Learning Model Embedded with Explainable AI for COVID-19 Detection in Chest X-ray Scans.

BACKGROUND AND MOTIVATION: COVID-19 has resulted in a massive loss of life during the last two years. The current imaging-based diagnostic methods for COVID-19 detection in multiclass pneumonia-type chest X-rays are not so successful in clinical practice due to high error rates. Our hypothesis states that if we can have a segmentation-based classification error rate <5%, typically adopted for 510 (K) regulatory purposes, the diagnostic system can be adapted in clinical settings. METHOD: This study proposes 16 types of segmentation-based classification deep learning-based systems for automatic, rapid, and precise detection of COVID-19. The two deep learning-based segmentation networks, namely UNet and UNet+, along with eight classification models, namely VGG16, VGG19, Xception, InceptionV3, Densenet201, NASNetMobile, Resnet50, and MobileNet, were applied to select the best-suited combination of networks. Using the cross-entropy loss function, the system performance was evaluated by Dice, Jaccard, area-under-the-curve (AUC), and receiver operating characteristics (ROC) and validated using Grad-CAM in explainable AI framework. RESULTS: The best performing segmentation model was UNet, which exhibited the accuracy, loss, Dice, Jaccard, and AUC of 96.35%, 0.15%, 94.88%, 90.38%, and 0.99 (p-value <0.0001), respectively. The best performing segmentation-based classification model was UNet+Xception, which exhibited the accuracy, precision, recall, F1-score, and AUC of 97.45%, 97.46%, 97.45%, 97.43%, and 0.998 (p-value <0.0001), respectively. Our system outperformed existing methods for segmentation-based classification models. The mean improvement of the UNet+Xception system over all the remaining studies was 8.27%. CONCLUSION: The segmentation-based classification is a viable option as the hypothesis (error rate <5%) holds true and is thus adaptable in clinical practice.

Dry eye disease survey among schoolteachers and children using visual display terminals during COVID-19 lockdown-CODE study ( Co vid and d ry e ye study).

Background: Lockdown during COVID-19 led to teachers and children shifting to online classes, using visual display terminals (VDTs) for education, resulting in increased screen time. The present study was done to assess and understand the nature and magnitude of the problem and to suggest preventive or remedial measures. Methods: A questionnaire-based cross-sectional study was conducted. The questionnaire was prepared for an online survey (using Google Forms) and circulated among school children belonging to different schools across India using multiple groups on social media. Results: A total of 3327 participants from 46 schools across India participated in the survey. We found a marked rise in cumulative screen time for both teachers and students before and during the lockdown. There was a threefold increase in the number of participants with a cumulative screen time 6 h or more compared to the pre-COVID era. Teachers (older participants) had worse symptom scores than students. Larger screens, like televisions, were better VDTs compared to smartphones, tablets, or laptops. Conclusions: School administrators and policymakers should pay due attention to institutionalizing the guidelines about class duration, appropriate screens, and stipulating break duration during online classes, which will continue to remain the predominant mode of education for teachers and students alike, at least in the near future.

Black Pepper an Ideal Choice against Corona Virus: A Systemic study against Pandemic Covid-19

According to Various surveys has conducted on home remedies during Covid-19, among a wide range of group of peoples in different age group from various country, found taking Kadha of Black pepper for combating infection and boosting immunity. [...]we conclude from survey and available literature that spice Black pepper plays an important role against viral infections and boosting immunity more significantly. [...]according to the Ayurveda, the king of the medicine, Black Pepper is an important candidate consists of the dried, unripe fruit of Piper nigrum L. belongs to Piperaceae. With advancement in chemistry and in biology we have several ways for obtaining powerful and specific drugs. [...]presentlyNDDS new drug discovery system are facing major challenges.

Four Types of Multiclass Frameworks for Pneumonia Classification and Its Validation in X-ray Scans Using Seven Types of Deep Learning Artificial Intelligence Models.

BACKGROUND AND MOTIVATION: The novel coronavirus causing COVID-19 is exceptionally contagious, highly mutative, decimating human health and life, as well as the global economy, by consistent evolution of new pernicious variants and outbreaks. The reverse transcriptase polymerase chain reaction currently used for diagnosis has major limitations. Furthermore, the multiclass lung classification X-ray systems having viral, bacterial, and tubercular classes-including COVID-19-are not reliable. Thus, there is a need for a robust, fast, cost-effective, and easily available diagnostic method. METHOD: Artificial intelligence (AI) has been shown to revolutionize all walks of life, particularly medical imaging. This study proposes a deep learning AI-based automatic multiclass detection and classification of pneumonia from chest X-ray images that are readily available and highly cost-effective. The study has designed and applied seven highly efficient pre-trained convolutional neural networks-namely, VGG16, VGG19, DenseNet201, Xception, InceptionV3, NasnetMobile, and ResNet152-for classification of up to five classes of pneumonia. RESULTS: The database consisted of 18,603 scans with two, three, and five classes. The best results were using DenseNet201, VGG16, and VGG16, respectively having accuracies of 99.84%, 96.7%, 92.67%; sensitivity of 99.84%, 96.63%, 92.70%; specificity of 99.84, 96.63%, 92.41%; and AUC of 1.0, 0.97, 0.92 (p < 0.0001 for all), respectively. Our system outperformed existing methods by 1.2% for the five-class model. The online system takes <1 s while demonstrating reliability and stability. CONCLUSIONS: Deep learning AI is a powerful paradigm for multiclass pneumonia classification.

Pancytopenia Secondary to SARS-CoV-2 Infection-a Case Report.

Pancytopenia is a condition when a person has a low count of all three types of blood cells, causing a triage of anaemia, leukopenia and thrombocytopenia. It should not be considered a disease in itself but rather a sign of a disease that needs to be further evaluated. Among the various causes, viral infections like the human immunodeficiency virus, cytomegalovirus, Epstein-Barr virus and parvovirus B19 have been implicated. Pancytopenia is a rare complication and is not commonly seen in patients with COVID-19 disease. Here, we report a case of pancytopenia in a previously immunocompetent elderly male patient with SARS-CoV-2 infection.

Pancytopenia Secondary to SARS-CoV-2 Infection—a Case Report

Pancytopenia is a condition when a person has a low count of all three types of blood cells, causing a triage of anaemia, leukopenia and thrombocytopenia. It should not be considered a disease in itself but rather a sign of a disease that needs to be further evaluated. Among the various causes, viral infections like the human immunodeficiency virus, cytomegalovirus, Epstein-Barr virus and parvovirus B19 have been implicated. Pancytopenia is a rare complication and is not commonly seen in patients with COVID-19 disease. Here, we report a case of pancytopenia in a previously immunocompetent elderly male patient with SARS-CoV-2 infection.

Towards sound based testing of COVID-19-Summary of the first Diagnostics of COVID-19 using Acoustics (DiCOVA) Challenge.

The technology development for point-of-care tests (POCTs) targeting respiratory diseases has witnessed a growing demand in the recent past. Investigating the presence of acoustic biomarkers in modalities such as cough, breathing and speech sounds, and using them for building POCTs can offer fast, contactless and inexpensive testing. In view of this, over the past year, we launched the "Coswara" project to collect cough, breathing and speech sound recordings via worldwide crowdsourcing. With this data, a call for development of diagnostic tools was announced in the Interspeech 2021 as a special session titled "Diagnostics of COVID-19 using Acoustics (DiCOVA) Challenge". The goal was to bring together researchers and practitioners interested in developing acoustics-based COVID-19 POCTs by enabling them to work on the same set of development and test datasets. As part of the challenge, datasets with breathing, cough, and speech sound samples from COVID-19 and non-COVID-19 individuals were released to the participants. The challenge consisted of two tracks. The Track-1 focused only on cough sounds, and participants competed in a leaderboard setting. In Track-2, breathing and speech samples were provided for the participants, without a competitive leaderboard. The challenge attracted 85 plus registrations with 29 final submissions for Track-1. This paper describes the challenge (datasets, tasks, baseline system), and presents a focused summary of the various systems submitted by the participating teams. An analysis of the results from the top four teams showed that a fusion of the scores from these teams yields an area-under-the-receiver operating curve (AUC-ROC) of 95.1% on the blind test data. By summarizing the lessons learned, we foresee the challenge overview in this paper to help accelerate technological development of acoustic-based POCTs.

Portal Vein Thrombosis-a Rare Complication of SARS-CoV-2 Infection.

The SARS-CoV-2 is the causative organism for COVID-19 disease. It primarily affects the respiratory system. With time, some new extra-pulmonary manifestations of COVID-19 disease have been identified. Recent studies have shown that patients with SARS-CoV-2 infection may have a hypercoagulable state which explains the increased incidence of thrombotic events in these patients without any known risk factors. The most common thrombotic event described in these patients is pulmonary embolism. Intra-abdominal thrombosis is a rare thrombotic complication of COVID-19 disease. Here, we report a case of COVID-19 disease associated with acute portal vein thrombosis.

Hospital Preparedness and Resilience in Public Health Emergencies at District Hospitals and Community Health Centres

Background: Public health emergencies (PHE) caused by natural hazards spread from one particular locality to adjacent geographic areas and then encompass the entire planet in today's fast global connectivity mode. Each country, including India, has its own set of potential disasters based on the hazards present as well as the unique vulnerabilities of the community and community's preparedness to respond to particular disasters. Currently, human history is observing a very critical time fighting an invisible enemy-COVID-19. Therefore, in this study, we seek to understand the standardised measures of public hospital preparedness and resilience at times of health emergencies, including a pandemic, the most current one being COVID-19. Methods: We conducted a descriptive, cross-sectional study among health officials of district hospitals (DHs) and community health centres (CHCs) of Rajasthan using a semi-structured online questionnaire, with COVID-19 in mind, and sending it to those who had attended a training programme on disaster preparedness in hospitals. Results: In all, questionnaires were sent to 80 health officials of DHs and CHCs, of which 58 responded, with a response rate of 72.5 per cent. We collected responses on public health emergency preparedness, training-related issues, the capacity to deal with emergencies and prior experience in managing an emergency. Conclusion: The resilience and preparedness of DHs and CHCs in Rajasthan appear to be limited. From the studies it has been revealed that proper training and education on disasters like the current COVID-19, which is of significant importance for healthcare workers, is limited to only 37.9 per cent of healthcare workers. It also emerges that the staff members whenever required could mark and perform in the triage area, but the Isolation room haven't got the request facilities and equipped to stabilise a critical patient despite availability of emergency stock of medicine. The stated functional status of DHs and CHCs reveals that the level of emergency preparedness is between low and medium and also varies from hospital to hospital and from CHC to CHC. Hence, it is time to reassess and upgrade emergency preparedness plans, which include mitigation, preparedness, response and recovery. Federal-, state- and local-level emergency management agencies' functioning has to be effective and well-coordinated with the local level of operation.

Targeting TMPRSS2 in SARS-CoV-2 Infection.

Severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) has rapidly caused a global pandemic associated with a novel respiratory infection: coronavirus disease-19 (COVID-19). Angiotensin-converting enzyme-2 (ACE2) is necessary to facilitate SARS-CoV-2 infection, but-owing to its essential metabolic roles-it may be difficult to target it in therapies. Transmembrane protease serine 2 (TMPRSS2), which interacts with ACE2, may be a better candidate for targeted therapies. Using publicly available expression data, we show that both ACE2 and TMPRSS2 are expressed in many host tissues, including lung. The highest expression of ACE2 is found in the testes, whereas the prostate displays the highest expression of TMPRSS2. Given the increased severity of disease among older men with SARS-CoV-2 infection, we address the potential roles of ACE2 and TMPRSS2 in their contribution to the sex differences in severity of disease. We show that expression levels of ACE2 and TMPRSS2 are overall comparable between men and women in multiple tissues, suggesting that differences in the expression levels of TMPRSS2 and ACE2 in the lung and other non-sex-specific tissues may not explain the gender disparities in severity of SARS CoV-2. However, given their instrumental roles for SARS-CoV-2 infection and their pleiotropic expression, targeting the activity and expression levels of TMPRSS2 is a rational approach to treat COVID-19.

Understanding genomic diversity, pan-genome, and evolution of SARS-CoV-2.

Coronovirus disease 2019 (COVID-19) infection, which originated from Wuhan, China, has seized the whole world in its grasp and created a huge pandemic situation before humanity. Since December 2019, genomes of numerous isolates have been sequenced and analyzed for testing confirmation, epidemiology, and evolutionary studies. In the first half of this article, we provide a detailed review of the history and origin of COVID-19, followed by the taxonomy, nomenclature and genome organization of its causative agent Severe Acute Respiratory Syndrome-related Coronavirus-2 (SARS-CoV-2). In the latter half, we analyze subgenus Sarbecovirus (167 SARS-CoV-2, 312 SARS-CoV, and 5 Pangolin CoV) genomes to understand their diversity, origin, and evolution, along with pan-genome analysis of genus Betacoronavirus members. Whole-genome sequence-based phylogeny of subgenus Sarbecovirus genomes reasserted the fact that SARS-CoV-2 strains evolved from their common ancestors putatively residing in bat or pangolin hosts. We predicted a few country-specific patterns of relatedness and identified mutational hotspots with high, medium and low probability based on genome alignment of 167 SARS-CoV-2 strains. A total of 100-nucleotide segment-based homology studies revealed that the majority of the SARS-CoV-2 genome segments are close to Bat CoV, followed by some to Pangolin CoV, and some are unique ones. Open pan-genome of genus Betacoronavirus members indicates the diversity contributed by the novel viruses emerging in this group. Overall, the exploration of the diversity of these isolates, mutational hotspots and pan-genome will shed light on the evolution and pathogenicity of SARS-CoV-2 and help in developing putative methods of diagnosis and treatment.

Nasopharyngeal wash in preventing and treating upper respiratory tract infections: Could it prevent COVID-19?

Rapid transmission of the severe acute respiratory syndrome coronavirus 2 has led to the novel coronavirus disease 2019 (COVID-19) pandemic. The current emphasis is on preventive strategies such as social distancing, face mask, and hand washing. The technique of nasopharyngeal wash to prevent the virus from inhabiting and replicating in the nasal and pharyngeal mucosa has been suggested to be useful in reducing symptoms, transmission, and viral shedding in cases of viral acute respiratory tract infections. In rapid systematic review, we found studies showing some improvement in prevention and treatment of upper respiratory tract infections. We postulate that hypertonic saline gargles and nasal wash may be useful in prevention and for care of patients with COVID-19. The present evidence emphasizes the need of randomized controlled trials to evaluate the role and mechanism of nasopharyngeal wash in COVID-19.

Personal Protective Equipment: Challenges and Strategies to Combat COVID-19 in India: A Narrative Review

The current outbreak of the coronavirus disease (COVID-19) has become a pandemic. All COVID-19-affected countries in the world are implementing containment interventions and trying their best to fight against the disease to halt the further spread of the infection and to reduce mortality. The public health workforce and healthcare staff in clinical settings are playing a crucial role in the early detection of cases, contact tracing and treatment of patients. The availability of personal protective equipment (PPE) and their consistent, proper use by healthcare providers and public health professionals is a crucial factor in combating any infectious disease in a crisis. The requirement of PPE has exponentially increased, as more and more countries are experiencing the COVID-19 pandemic. The rapid spread of the pandemic has created a temporary shortage of PPE in many countries, including India. The lack of PPE has affected the morale of healthcare workers (HCWs) and other frontline warriors in fighting the coronavirus disease, as more than 22,000 health workers in 56 countries have suffered from COVID-19. Some of them have succumbed to it across all countries, including India (WHO). We have reviewed the available literature to understand the challenges in ensuring adequate availability and consistent use of PPE and the strategies for the rational use of PPE in India. Our study reveals that India has responded swiftly to enhance the accessibility of PPE and put in place strategies for the judicious use of PPE to reduce the incidence of the COVID-19 infection to a bare minimum in healthcare settings. In the present article, we report the current status of COVID-19 among HCWs. We have reviewed the challenges and the surge strategies adopted by India to produce or procure good-quality PPE and supply it to all service delivery points in adequate quantities.