Current Scenario Of COVID-19 Pandemic Research Articles

Gender differences in comorbidities of patients with COVID-19: An Italian local register-based analysis

In the current COVID-19 pandemic scenario, it is still necessary to understand if differences exist between genders in terms of patients’ characteristics and clinical outcomes. For this reason, we retrospectively analyzed data obtained from a local register-based dataset of all SARS-CoV-2 positive patients diagnosed in the province of Catania (Italy). The main aim of this analysis was to understand any differences between genders in the distribution of previous medical conditions, and to evaluate which of them posed individuals at higher risk of death.With this purpose, we analyzed data from 1424 patients with at least one underlying medical condition, who were tested positive for SARS-CoV-2 infection from February 2020 to December 2021. Overall, males were 59.5% of the total population and significantly younger than females (median ages: 68 years vs. 72 years; p = 0.011). The age distribution of cases by gender confirms that individuals from 70 to 79 years were the most affected in both genders. The comparison of underlying comorbidities by gender shows significant differences for diabetes (p < 0.001), other metabolic diseases (p = 0.006), and obesity (p = 0.019). Accordingly, multivariable logistic regression analysis confirmed that diabetes was more likely to be present in males than in females (p = 0.001), while other metabolic diseases and obesity were less likely to be present (p = 0.003 and p = 0.005, respectively). Although no difference in mortality was evident between genders (p = 0.141), both male and female COVID-19 patients had a significantly higher risk of death if they had comorbidities such as CVDs, kidney diseases, or chronic neurological diseases. Moreover, diabetes and chronic respiratory diseases were significant risk factors for COVID-19 mortality among men, whereas cancer was a significant contributor among women. Our findings confirm gender-differences in pre-existing medical conditions of COVID-19 patients, which may influence the risk of death. Further studies, however, are needed to understand physiological and pathological mechanisms underpinning these differences.

Post COVID-19 Pandemic Generalized Anxiety Status of Health Professional undergraduate students

Aim: To assess the psychological impact of COVID-19 on mental status of undergraduate medical students, after reopening of educational institutions. Methods: This descriptive, online cross-sectional study was conducted on medical students of Combined Military Hospital Lahore Medical College &amp; Institute of Dentistry, Lahore Pakistan for 6 months from 1st June to 30th November, 2021. Generalized Anxiety Disorder Scale (GAD-7) was used as the study tool. Data was analyzed by using statistical software SPSS - 23. Frequencies and percentages were used for descriptive variables. A univariate analysis was utilized to identify the noticeable associations between traits of sample and the anxiety level in current scenario of COVID-19 pandemic. Finally multivariate logistic regression analyses, odds ratio (OR), and Spearman's correlation coefficient, r, was used to evaluate the association between COVID-19-related stressors and anxiety level. Results: Of the 324 respondents, majority were females residing in urban areas with their parents. Severe anxiety was experienced by 23.8% of students. Female respondents were found to have more anxiety as compared to males (OR = 1.81; 95% CI = 1.173 – 2.815). Moreover, respondents having a relative or an acquaintance infected with COVID-19 reported to have more anxiety (OR = 3.007, 95% CI = 2.377 - 3.804). Conclusion: A significant number of students are experiencing anxiety in the post COVID-19 phase, especially those that had an acquaintance infected with COVID-19. There is a need to look after student's mental health and to implement appropriate psychological strategies and interventions to deal with this level of psychological distress in the students at this phase of pandemic for optimum training of future health professionals. Keywords: Anxiety, COVID-19, healthcare professionals, mental health, medical students, pandemic

P135 Immunological correlation with clinicomycological aspects of fungal rhinosinusitis during COVID-19 pandemic

Poster session 1, September 21, 2022, 12:30 PM - 1:30 PM Background and ObjectivesTo determine the relation between disease spectrum, etiology, and immunological markers of fungal rhinosinusitis.Material and MethodsDiagnostic nasal endoscopy was used for the collection of sinus and nasal tissue samples which were processed as per standard mycological procedure for microscopy, culture and histopathological examination (HPE). Enzyme-linked immunosorbent assay was used for total IgE, serum IL-6, IL-10, TNF-α levels detection.ResultsOut of 148 samples, 70% were diagnosed as FRS. COVID-19 infection 46.9% was the most common comorbid condition. Use of steroid therapy 46.9% was the most common predisposing factor. Nasal obstruction 62.2% was the most common clinical feature but headache (P-value .002) and eye discharge were statistically significant (P-value .005). KOH mount revealed findings in 84.69% patients in this study. Combination of KOH, HPE, and fungal culture revealed findings in 62.2% cases. Rhizopus arrhizus 27.6% was the most common isolate Aspergillus flavus and Rhizopus arrhizus were statistically significant (P-value .003, .026 respectively). IL-6 59.2% was the most common serological marker raised in this study. PNS imaging revealed that 100% of the patients had single or multiple sinus involvement and maxillary sinus (P-value .038) was significantly associated. Acute invasive fungal rhinosinusitis 75.5% was the most common type of FRS. Mortality was 17.34% in this study.ConclusionFRS is an important emerging public health problem and a cause of mortality and morbidity, particularly in immunocompromised patients. Furthermore, in the current scenario of COVID-19 pandemic, FRS has become more fatal and an entity of immediate and grave concern than ever before.

Social Representations of LGBT Old Age and Sex Workers in Brazilian Adults

Background: The number of elderly people in the world is growing significantly, so there is an urgent need to ensure that this population can live a full life, especially taking into account the current COVID-19 pandemic scenario. Objective: To analyze the social representations of LGBT old age for male and female sex workers through the pandemic. Method: A qualitative, descriptive, and exploratory research conducted with 10 male and 10 female sex workers aged between 18 and 48. Sociodemographic questionnaires and semi-structured interviews were administered using Google Forms and then analyzed by descending hierarchical classification through the IRaMuTeQ software. Results: The analysis showed three classes: (1) «Social understanding of the topic LGBT old age,» (2) «Stigma of genders and sexual orientations,» and (3) «Physical transformations and their accompanying obstacles». Conclusions: The representations are associated with the difficulties that elderly people have with aging in society, as well as debating LGBT old age. There are also greater obstacles regarding aging of female sex workers. In this way, the study seeks to contribute to broaden the knowledge of the psychosocial aspects of LGBT aging and old age, especially those of sex workers.

Cloud of Things in Crowd Engineering: A Tile-Map-Based Method for Intelligent Monitoring of Outdoor Crowd Density.

Managing citizen and community safety is one of the most essential services that future cities will require. Crowd analysis and monitoring are also a high priority in the current COVID-19 pandemic scenario, especially because large-scale gatherings can significantly increase the risk of infection transmission. However, crowd tracking presents several complex technical challenges, including accurate people counting and privacy preservation. In this study, using a tile-map-based method, a new intelligent method is proposed which is integrated with the cloud of things and data analytics to provide intelligent monitoring of outdoor crowd density. The proposed system can detect and intelligently analyze the pattern of crowd activity to implement contingency plans, reducing accidents, ensuring public safety, and establishing a smart city. The experimental results demonstrate the feasibility of the proposed model in detecting crowd density status in real-time. It can effectively assist with crowd management tasks such as monitoring, guiding, and managing crowds to ensure safety. In addition, the proposed algorithm provides acceptable performance.

Service robots are an option for contactless services due to the COVID-19 pandemic in the hotels

The development of robotics in the current COVID-19 pandemic scenario can change the face of the industries. Robots are becoming more prominent in the hospitality industry. In this scenario, the usage of service robots for hotels is the best option. This study is performed using TAM and TRI theories. The constructs selected for the study are perceived ease of use, perceived usefulness, attitudes, behavioural intention, discomfort, insecurity, and trust. Survey-based research is carried out with the help of a questionnaire. The target population are the employees working in the hotels. Ten hypotheses are proposed for the study. This study highlights the acceptance of service robots in the hotels of India. Out of ten proposed hypothesis, five hypotheses were accepted, and the rest were rejected. For data analysis, structural equation modelling in AMOS 20.0 was carried out. This study will help the managers and the top management in the adoption of service robots.

Docking and molecular dynamics simulation for therapeutic repurposing in small cell lung cancer (SCLC) patients infected with COVID-19

Cancer care has become a challenge with the current COVID-19 pandemic scenario. Specially, cancers like small cell lung cancers (SCLC) are difficult to treat even in the normal situation due to their rapid growth and early metastasis. For such patients, treatment can’t be compromised and care must be taken to ensure their minimum exposure to the ongoing spread of COVID-19 infection. For this reason, in-house treatments are being suggested for these patients. Another issue is that symptoms of SCLC match well with that of COVID-19 infection. Hence, the detection of COVID-19 may also get delayed leading to unnecessary complications. Thus, we have tried to investigate if the therapeutics that is currently used in lung cancer treatment can also act against SARS-CoV-2. If it is so, the same treatment protocols can be continued even if the SCLC patient had contracted COVID-19 without compromising the cancer care. For this, RNA dependent RNA polymerase (RdRP) from SARS-CoV-2 has been selected as drug target. Both docking and molecular dynamicssimulation analysis have indicated that Paclitaxel and Dacomitinib may be explored as multi-target drugs for both SCLC and COVID-19. Communicated by Ramaswamy H. Sarma

PNS13 Comparative Network Efficiency Analysis of Brazil Response to COVID-19 at State Level

Efficiency analyses of health systems are often limited to hospital applications or macroeconomic evaluations considering a single production process. This application using a two-stage prevention and cure network perspective highlights nonparametric frontier estimations more coherently with the current COVID-19 pandemic scenario. This two-stage Data Envelopment Analysis methodology estimates cost efficiencies of preventing an increasing propagation of COVID-19 using the Federal Government COVID-19 expenditures as input and the inverse rate of cases as output (standardized by population) in the first stage. The second stage for the technical efficiency (cure model) has the number of ICUs, ventilators and medical staff (nurses, assistants and physicians) as input and success rate (the difference between the number of infected and deaths) as output. Two efficient states on both prevention and cure models (Acre and Roraima). Pernambuco is deemed efficient in preventing COVID-19 (3 states total), and São Paulo, Mato Grosso, Amapá, Santa Catarina and Rio Grande do Sul (7 states) in cure. About 1400 ICU admissions, 590 ventilators and more than 100 thousand medical staff can be spared directed by best practices from these seven efficient states. The most inefficient states in preventing the pandemic are São Paulo (0.028), Bahia (0.076), Rio Grande do Sul (0.087) and Paraná (0.098), also reporting low social isolation. The most inefficient states in cure are Rio de Janeiro (0.231), Pernambuco (0.277), and Rio Grande do Norte (0.439). The proposed methodology offers an interesting framework combining both public health perspectives as responses to the COVID-19 pandemic. Brief comments can be made on Pernambuco's mapping technologies available for the general population; the cooperation between the government and NGOs in Roraima for providing EPIs, tests, food and hygiene items for indigenous districts (reducing potential propagations without expanding governments expenditures); and Acre's campaigns for basic hygiene, and pandemic control.

A Pilot Study Comparing Teletherapy with the Conventional Face-to-Face Therapy for Speech-Language Disorders.

Speech-language therapists along with affected individuals face various challenges for accomplishing the rehabilitation services. In the current COVID19 pandemic scenario, telerehabilitation has emerged as a substitute to the traditional face-to-face therapy, and is the only option possible in some cases. To subjectively assess the feasibility & acceptability of telerehabilitation provided by speech-language pathologist to patients of speech and language disorders. This qualitative study includes 20 patients suffering from disorders of fluency, voice, swallowing and neurogenic disorders. The participants included were undergoing face-to-face therapy at our institute. After the completion of face-to-face session series, telerehabilitation services were provided through a video calling app. The outcomes of teletherapy were assessed subjectively using a structured questionnaire on 11 parameters using a Likert scale. Of the included 20 patients, after completion of teletherapy, four patients chose the physical interaction as the preferred mode of therapy while 16 chose teletherapy as the preferred mode. Except three clients who rated their overall satisfaction as ‘3’, others rated as ‘4’ or ‘5’.The therapists were satisfied with the outcomes in 17 cases, and were pleased with the overall progress of all the clients (rated 4 or 5). Telerehabilitation is a reliable method to deliver speech and language services at community level, on long-term basis, as is proven by the high satisfaction scores among the clients as well as the service providers.Clinical Trial RegistrationThe trial has been registered in Clinical Trials Registry of India (CTRI) vide number CTRI/2018/04/ 012,922 (http://ctri.nic.in/Clinicaltrials/login.php) on 02/04/2018.

Modified full-face snorkeling mask for thoracic surgery and otolaryngology surgical use: comfort and usability assessment during the COVID-19 pandemic.

Background:A worldwide personal protection equipment (PPE) shortage has emerged during COVID-19 pandemic, contributing to the high incidence of SARS-CoV-2 infection among health care providers. To address this lack of PEE, new solutions have been researched. Among those, full-face snorkeling masks demonstrated to be an interesting option. Among surgical specialties otolaryngologists and thoracic surgeons are at high risk of infection, due to the close contact with airway secretions.Objectives:We tested the comfort and usability of a modified full-face snorkeling mask (Ocean Reef Mask Aria QR+) as a protective device for otolaryngologic and thoracic surgeries.Methods:The mask was customized with a 3D-printed adaptor supporting many industrial filter types, including FFP3 and heat and moisture exchangers (HME). We evaluated surgical performances of the mask, both subjectively, with a questionnaire filled in by the surgeons, as well as objectively, monitoring transcutaneous PCO2 and PO2 values of surgeons during surgical procedures.Results:The modified full-face snorkeling mask was tested during 9 otolaryngologic and 15 thoracic surgery procedures. The device demonstrated very good overall vision quality with some limitations regarding lateral vision and almost no difficulties in usability. Water condensation into the mask was absent in almost every case. Both PO2 and PCO2 parameters remained within normal ranges during every procedure.Discussion:The modified full-face snorkeling mask can be an innovative PPE. In the current COVID-19 pandemic scenario, the worldwide shortage of protective masks and goggles may exploit this ready-to-use and low-cost solution, especially for high-risk surgical procedures.

On contact tracing in COVID-19 (SARS-CoV-2) pandemic using lowest common ancestor in m-ary data aggregation tree in the fog-computing enhanced internet of things

PurposeThe purpose of this study is to exploit the lowest common ancestor technique in an m-ary data aggregation tree in the fog computing-enhanced IoT to assist in contact tracing in COVID-19. One of the promising characteristics of the Internet of Things (IoT) that can be used to save the world from the current crisis of COVID-19 pandemic is data aggregation. As the number of patients infected by the disease is already huge, the data related to the different attributes of patients such as patient thermal image record and the previous health record of the patient is going to be gigantic. The authors used the technique of data aggregation to efficiently aggregate the sensed data from the patients and analyse it. Among the various inferences drawn from the aggregated data, one of the most important is contact tracing. Contact tracing in COVID-19 deals with finding out a person or a group of persons who have infected or were infected by the disease.Design/methodology/approachThe authors propose to exploit the technique of lowest common ancestor in an m-ary data aggregation tree in the Fog-Computing enhanced IoT to help the health-care experts in contact tracing in a particular region or community. In this research, the authors argue the current scenario of COVID-19 pandemic, finding the person or a group of persons who has/have infected a group of people is of extreme importance. Finding the individuals who have been infected or are infecting others can stop the pandemic from worsening by stopping the community transfer. In a community where the outbreak has spiked, the samples from either all the persons or the patients showing the symptoms are collected and stored in an m-ary tree-based structure sorted over time.FindingsContact tracing in COVID-19 deals with finding out a person or a group of persons who have infected or were infected by the disease. The authors exploited the technique of lowest common ancestor in an m-ary data aggregation tree in the fog-computing-enhanced IoT to help the health-care experts in contact tracing in a particular region or community. The simulations were carried randomly on a set of individuals. The proposed algorithm given in Algorithm 1 is executed on the samples collected at level-0 of the simulation model, and to aggregate the data and transmit the data, the authors implement Algorithm 2 at the level-1. It is found from the results that a carrier can be easily identified from the samples collected using the approach designed in the paper.Practical implicationsThe work presented in the paper can aid the health-care experts fighting the COVID-19 pandemic by reducing the community transfer with efficient contact tracing mechanism proposed in the paper.Social implicationsFighting COVID-19 efficiently and saving the humans from the pandemic has huge social implications in the current times of crisis.Originality/valueTo the best of the authors’ knowledge, the lowest common ancestor technique in m-ary data aggregation tree in the fog computing-enhanced IoT to contact trace the individuals who have infected or were infected during the transmission of COVID-19 is first of its kind proposed. Creating a graph or an m-ary tree based on the interactions/connections between the people in a particular community like location, friends and time, the authors can attempt to traverse it to find out who infected any two persons or a group of persons or was infected by exploiting the technique of finding out the lowest common ancestor in a m-ary tree.

Fever Time Series Analysis Using Slope Entropy. Application to Early Unobtrusive Differential Diagnosis.

Fever is a readily measurable physiological response that has been used in medicine for centuries. However, the information provided has been greatly limited by a plain thresholding approach, overlooking the additional information provided by temporal variations and temperature values below such threshold that are also representative of the subject status. In this paper, we propose to utilize continuous body temperature time series of patients that developed a fever, in order to apply a method capable of diagnosing the specific underlying fever cause only by means of a pattern relative frequency analysis. This analysis was based on a recently proposed measure, Slope Entropy, applied to a variety of records coming from dengue and malaria patients, among other fever diseases. After an input parameter customization, a classification analysis of malaria and dengue records took place, quantified by the Matthews Correlation Coefficient. This classification yielded a high accuracy, with more than 90% of the records correctly labelled in some cases, demonstrating the feasibility of the approach proposed. This approach, after further studies, or combined with more measures such as Sample Entropy, is certainly very promising in becoming an early diagnosis tool based solely on body temperature temporal patterns, which is of great interest in the current Covid-19 pandemic scenario.

Vital Role of Ultrasound in the Era of COVID-19: Arriving at the Right Diagnosis Real Time.

Ultrasound has become an integral part for assessment of critically ill patients. It has helped in diagnosing and treating critically ill patients. The added advantage of ultrasonography is that it is a fantastic diagnostic tool that is easily available at the bedside, repeatable, more objective, and has a steep learning curve. It has become fifth vital assessment along with inspection, palpation, percussion, and auscultation. In the current scenario of COVID-19 pandemic, the disease caused by virus ranges from mild influenza-like illness to severe acute respiratory illness (SARI). Among the patients developing SARI, few require hospitalization and might need intensive care management. As a critical care specialist, we need to keep our antenna up to look for other causes for SARI due to non-COVID etiology as well. This article describes algorithmic approach and vital role of ultrasonography while managing patients with respiratory distress.How to cite this articleHavaldar AA. Vital Role of Ultrasound in the Era of COVID-19: Arriving at the Right Diagnosis Real Time. Indian J Crit Care Med 2020;24(7):563–564.

Is dental practice in the current scenario in India safe or unsafe: A questionnaire study

Aim: This study was intended to evaluate whether it is safe or unsafe for the clinician as well as the patient to carry out dental procedures in the current scenario of COVID-19 pandemic in India. Materials and Methods: This study was designed based on a cross-sectional survey. An self-administered questionnaire survey was used to evaluate whether it is safe or unsafe for the clinician as well as the patient to carry out dental procedures in the current scenario of COVID-19 pandemic in India. In this regard, a questionnaire study with a convenience sample of 124 dentists working in various clinics in India was conducted. This study, while limited in sample size, benefits the general practitioners as target readers to assess the awareness pertaining to safety of the dental practice. Results: The results of this study reveal that there exists a good knowledge among the dental health professionals pertaining to the COVID-19 virus and its oral manifestations in addition to the precautions to be taken for the prevention of COVID-19 in a clinical setup. Most of the dental professionals follow proper infection control biomedical waste disposal protocols, but their clinical areas are not properly ventilated. In addition to this, there exists an apprehension as well as an uncertainty pertaining to the current scenario to carry out their professional work. Conclusion: The results of this study suggest that it is relatively unsafe to carry out most of the dental procedures at this juncture in spite of taking all necessary precautions.

Telemedicine for chronic pain management during COVID-19 pandemic.

Treatment of chronic pain is an essential service. Due to lockdown, travel restrictions, social and physical distancing requirements or fear that health care facilities may be infected; patients may avoid visiting health care facilities in person. It is also imperative to decrease the risk of exposure of the health care workers (HCWs) to severe acute respiratory syndrome corona virus 2 (SARS CoV2) and to ease the overtly burdened health care system. But any disruption in pain practice will have alarming consequences for individuals, society, and whole of health care system and providers. In the current scenario of COVID-19 pandemic, telemedicine is emerging as a key technology for efficient communication and sustainable solution to provide essential health care services and should be considered for chronic pain patients (CPPs). Recently, Board of Governors in supersession of Medical Council of India along with National Institution for Transforming India (NITI Aayog) released “Telemedicine Practice Guidelines” enabling registered medical practitioners to provide healthcare using telemedicine. This article describes the challenges in CPPs during COVID-19 pandemic and the use of telemedicine as the rescue management vehicle for CPPs in current scenario.

“Artificial intelligence &amp; COVID-19: (bio)ethical aspects of end of life

The disease caused by the SARS-CoV-2 virus has brought significant challenges to clinical medicine. In this scenario, there is, naturally, the need to make decisions when faced with questions such as: defining which patients will be given priority in intensive care units and whether or not to submit COVID-19 patients to mechanical ventilation. The use of mathematical modeling could help to determine what type of screening policy could be useful in ICUs during the SARS-CoV-2. Another possible way of applying these techniques concerns the use of AI in determining personalized sedation and analgesia in the case of mechanical ventilation and extubation. AI techniques, especially those belonging to the machine learning area, allow for the construction and extraction of behavioral patterns implicit in decision histories, regarding a problem situation. Assuming the existence of a database of previous decisions that comprise, for example, the type of care to be used at the end of life or the therapy to be pre scribed to a patient, the patterns underlying this data can assist in choosing the most appropriate conduct to be adopted in each situation, provided it is prop erly extracted. In the current COVID-19 pandemic scenario, in which these decisions become even more pressing, this type of support can be of great value for ethically, more responsible, and fair conducts.