Coagulopathy and Covid-19: A quantitative review on the influence of coronavirus on the human blood coagulation parameters

Covid 19 is an illness caused by the recently discovered Coronavirus. The virus causes an unparalleled range of coagulopathy related disorders in affected patients. In this paper, we aim to understand the linkage between abnormal clot formation and the Coronavirus. The clotting parameters of Covid infected patients were studied to elucidate a better understanding of the coagulation disorder. A comparative analysis of the diagnostic reports collected from five different labs across the world aids us comprehend the elevated levels of coagulation parameters observed in these Covid infected patients. Initial coagulopathy of COVID 19 has been characterized as increased levels of prothrombin time, activated partial thromboplastin time, and platelet counts followed by elevated concentrations of D dimer and fibrin degradation products. Coagulation assays used to determine these clotting factors were through invasive methods. Conventional coagulation assays being invasive had their drawbacks taken care of with the advent of non-invasive methods. With the current scenario of this pandemic, the necessity for technological improvements in non-invasive and point of care testing methods are substantial. © 2023 Author(s).

A novel adaptive class weight adjustment-based multiclass segmentation error minimization technique for COVID-19 X-ray image analysis

Coronavirus outbreaks during the last couple of years created a huge health disaster for human lives. Diagnosis of COVID-19 infections is, thus, very important for the medical practitioners. For a quick detection, analysis of the COVID-19 chest X-ray images is inevitable. Therefore, there is a strong need for the development of a multiclass segmentation method for the purpose. Earlier techniques used for multiclass segmentation of images are mostly based on entropy measurements. Nonetheless, entropy methods are not efficient when the gray-level distribution of the image is nonuniform. To address this problem, a novel adaptive class weight adjustment-based multiclass segmentation error minimization technique for COVID-19 chest X-ray image analysis is investigated. Theoretical investigations on the first-hand objective functions are presented. The results on both the biclass and multiclass segmentation of medical images are enlightened. The key to our success is the adjustment of the pixel counts of different classes adaptively to reduce the error of segmentation. The COVID-19 chest X-ray images are taken from the Kaggle Radiography database for the experiments. The proposed method is compared with the state-of-the-art methods based on Tsallis, Kapur's, Masi, and Renyi entropy. The well-known segmentation metrics are used for an empirical analysis. Our method achieved a performance increase of around 8.03% in the case of PSNR values, 3.01% for FSIM, and 4.16% for SSIM. The proposed technique would be useful for extracting dots from micro-array images of DNA sequences and multiclass segmentation of the biomedical images such as MRI, CT, and PET.

COVID-19 and Its Impact on Diverse Aspects of Women's Lives

The objective of this article is to study the impact of COVID-19 on the lives of women by exploring different aspects like their daily work patterns, hygiene practices, psychological effects and nutritional status during the pandemic. 510 women participated in the online survey. The majority of the respondents belonged to the age group of 20-29 years and were either graduates or above. 37.3% of the working respondents reported increased professional responsibilities during the pandemic. Cooking and cleaning occupied most of the time during the lockdown. Anxiety, lack of concentration and frequent arguments with the family members were reported by the respondents. Many of the respondents took up physical activities to maintain their fitness. They also believed that usage of masks would prevent them from catching the infection. 75.2% of women included vitamin-rich sources in their diet. This level of consciousness might be linked to the educational profile of the respondents.

MCH Leadership Training Program: An Innovative Application of an Implementation Science Framework.

INTRODUCTION: MCH training programs in schools of public health provide specialized training to develop culturally competent and skilled MCH leaders who will play key roles in public health infrastructure. Previous literature has reported on the effectiveness of MCH training programs (e.g., number of trainees, improvement in knowledge/skills); less attention has been devoted to understanding factors influencing program implementation during times of rapid change, while considering internal and external contexts (e.g., global pandemic, social unrest, uncertainty of funding, mental health issues, and other crises). PURPOSE: This article describes a graduate-level MCH leadership training program and illustrates how an implementation science framework can inform the identification of determinants and lessons learned during one year of implementation of a multi-year program. ASSESSMENT: Findings reveal how CFIR can be applicable to a MCH training program and highlight how constructs across domains can interact and represent determinants that serve as both a barrier and facilitator. Key lessons learned included the value of accountability, flexibility, learner-centeredness, and partnerships. CONCLUSION: Findings may apply to other programs and settings and could advance innovative training efforts that necessitate attention to the multi-level stakeholder needs (e.g., student, program, institution, community, and local/regional/national levels). Applying CFIR could be useful when interpreting process and outcome evaluation data and transferring findings and lessons learned to other organizations and settings. Integrating implementation science specifically into MCH training programs could contribute to the rigor, adaptability, and dissemination efforts that are critical when learning and sharing best practices to expand leadership capacity efforts that aim to eliminate MCH disparities across systems.

A Study on Metaverse in Education

Most nations have used online meeting software platforms for distant education in some capaci ty during the COVID-19 epidemic. These software applications do, however, have substantial drawbacks that hinder engagement and fall short of simulating the classroom environment. Many of these restrictions are resolved by the newly forming Metaverse. In education in Metaverse, learners have the opportunity to engage with digital content and each other in a more interactive and immersive way. For example, learners can participate in virtual simulations, role -playing activities, and collaborative projects with other learners from around the world. They can also access a wide range of digital resources, such as virtual textbooks, lectures, and assessments, all within the same platform. This paper reviews different Metaverse models, frameworks for applying Metaverse in the field of education. © 2023 IEEE.

Engagement, Advance Care Planning, and Hospice Use in a Telephonic Nurse-Led Palliative Care Program for Persons Living with Advanced Cancer.

Persons living with advanced cancer have intensive symptoms and psychosocial needs that often result in visits to the Emergency Department (ED). We report on program engagement, advance care planning (ACP), and hospice use for a 6-month longitudinal nurse-led, telephonic palliative care intervention for patients with advanced cancer as part of a larger randomized trial. Patients 50 years and older with metastatic solid tumors were recruited from 18 EDs and randomized to receive nursing calls focused on ACP, symptom management, and care coordination or specialty outpatient palliative care (ClinicialTrials.gov: NCT03325985). One hundred and five (50%) graduated from the 6-month program, 54 (26%) died or enrolled in hospice, 40 (19%) were lost to follow-up, and 19 (9%) withdrew prior to program completion. In a Cox proportional hazard regression, withdrawn subjects were more likely to be white and have a low symptom burden compared to those who did not withdraw. Two hundred eighteen persons living with advanced cancer were enrolled in the nursing arm, and 182 of those (83%) completed some ACP. Of the subjects who died, 43/54 (80%) enrolled in hospice. Our program demonstrated high rates of engagement, ACP, and hospice enrollment. Enrolling subjects with a high symptom burden may result in even greater program engagement.

Explainable Misinformation Detection across Multiple Social Media Platforms

Web Information Processing (WIP) has enormously impacted modern society since a huge percentage of the population relies on the internet to acquire information. Social Media platforms provide a channel for disseminating information and a breeding ground for spreading misinformation, creating confusion and fear among the population. One of the techniques for the detection of misinformation is machine learning-based models. However, due to the availability of multiple social media platforms, developing and training AI-based models has become a tedious job. Despite multiple efforts to develop machine learning-based methods for identifying misinformation, there has been very limited work on developing an explainable generalized detector capable of robust detection and generating explanations beyond black-box outcomes. Knowing the reasoning behind the outcomes is essential to make the detector trustworthy. Hence employing explainable AI techniques is of utmost importance. In this work, the integration of two machine learning approaches, namely domain adaptation and explainable AI, is proposed to address these two issues of generalized detection and explainability. Firstly the Domain Adversarial Neural Network (DANN) develops a generalized misinformation detector across multiple social media platforms. DANN is employed to generate the classification results for test domains with relevant but unseen data. The DANN-based model, a traditional black-box model, cannot justify and explain its outcome, i.e., the labels for the target domain. Hence a Local Interpretable Model-Agnostic Explanations (LIME) explainable AI model is applied to explain the outcome of the DANN model. To demonstrate these two approaches and their integration for effective explainable generalized detection, COVID-19 misinformation is considered a case study. We experimented with two datasets and compared results with and without DANN implementation. It is observed that using DANN significantly improves the F1 score of classification and increases the accuracy by 5% and AUC by 11%. The results show that the proposed framework performs well in the case of domain shift and can learn domain-invariant features while explaining the target labels with LIME implementation. This can enable trustworthy information processing and extraction to combat misinformation effectively. Author

Audiological Assessment in Post COVID-19 Patients- A Cross-sectional Study

Introduction: Hearing loss following a viral infection is a common entity. In recent studies, hearing loss has been seen among Coronavirus Disease 2019 (COVID-19) infected patients, but its association is yet to be established. Aim: To determine the presence of hearing loss and its type in patients after COVID-19 infection. Materials and Methods: A cross-sectional study was conducted at a tertiary health centre, Department of Otorhinolaryngology at Chettinad Academy of Research and Education, Chettinad Hospital and Research Institute, Chennai, from October 2021 to April 2022. Total of 125 patients, who had a positive history of COVID-19 infection, were reviewed in the Otorhinolaryngology Department, one month after they were tested Real Time-Polymerase Chain Reaction (RT-PCR) positive. After obtaining proper clinical history and examination, Pure Tone Audiometry (PTA) were done. Audiological report was assessed and analysed. Qualitative variables will be expressed in proportions and quantitative variables in Mean±SD/ Median (IQR), Chi- square test was applied. Results: This study included 65 males (52%) and 60 females (48%), and the mean age was 38.44±10.9 years years. Among the 125 patients, 12 (9.6%) were diabetic, 14 (11.2%) were hypertensive, 5 (4%) had dyslipidaemia, 3 (2.4%) were hypothyroid, while remaining 91 patients (72.8%) had no co-morbidities. Sensorineural Hearing Loss (SNHL) was found among 45 patients (34 with unilateral and 11 with bilateral involvement). Out of them, 2 (4.5%) (4.5%) were in the age group of 18-30 years, 19 (42.2%) in 31-45 years and 24 (53.3%) between 46-60 years age group. Based on the World Health Organization (WHO) classification of hearing loss, 27 patients had mild sensorineural hearing loss, 12 patients with moderate, and 6 patients with moderately severe sensorineural hearing loss. Conclusion: SNHL were found among patients who had COVID-19 infection, but due to the absence of a pre COVID-19 documented audiogram, it was difficult to conclude whether the hearing loss had occurred due to COVID-19, pre-existing hearing loss, or age-related. Further studies are required for proper understanding and correlation.

About the use of the term "synbiotics " in Spanish language

Lockdown and social distancing due to COVID-19 affected the mental health and lifestyle of the population. However, there is insufficient evidence of alterations in eating behavior. Our study seeks to describe the relationship between eating behavior and eating habits among Chilean adults during the confinement period. A sample of 760 Chilean subjects was analyzed, who answered surveys using Google Forms, considering demographic characteristics, social distancing, dietary habits and EB. More than half of the participants consider that their dietary intake increased during confinement. Changes in dietary intake were analyzed according to food group, and a decrease in the consumption of fish, fruits and dairy pro- ducts was observed, while legumes, processed foods and soft drinks showed an increase, which represents risk factors for the development of cardiovascular diseases. When analyzing eating behavior, a greater difficulty in stopping eating was observed when faced with external stimuli;increased intake associated with complex emotional situations, and when isolating the group that decreased their intake of unhealthy foods, a greater ability to limit their intake for weight control was reported. Our results are similar to other studies, and they reinforce that confinement is related to eating behavior, leading to changes in eating habits, which indicates that, at the public health level, post-pandemic nutritional strategies, should be focused on regulating eating behavior in order to guide habits towards healthy eating. Keywords: COVID-19;Dietary intake;Eating behavior;Food intake;Lockdown.

Severe Air Leak Syndrome Associated with Sars-Cov-2 and Corona-Oc43 Infections

BACKGROUND AND AIM: Air leak syndrome is an uncommon complication for viral infections in pediatric patients and has been associated with pneumothoraces, empyemas, necrotizing pneumonias, barotrauma, and other underlying lung diseases. We present a case series of three patients with Coronavirus infections that developed severe air leak syndrome, two of which were placed on venovenous-extracorporeal membrane oxygenation (VV-ECMO). METHOD(S): Patient 1 (Pt1) is a 6-month-old male with a history of prematurity presenting with fever, cough, and respiratory failure with severe air leak syndrome requiring VV-ECMO support with SARS-CoV2. Patient 2 (Pt2) is a previously healthy 19-month-old female presenting with fever, cough, and respiratory failure with multiple pneumatoceles and pneumothoraces in the setting of coronavirus-OC43 requiring VV-ECMO support. Patient 3 (Pt3) is a previously healthy 25-day-old infant presenting with shock, cyanosis, apnea, multiple pneumothoraces and pneumatoceles, and subsequent respiratory failure with SARS-CoV2. RESULT(S): Pt1 and Pt2 developed multiple pneumothoraces with tension physiology and severe hypoxemia from necrotizing pneumonia with severe air leak, requiring multiple chest tubes, JET ventilation, and ultimately VV-ECMO support (see Figure 1). Pt3 developed multiple loculated pneumothoraces that necessitated surgically-placed chest tubes for decompression and JET ventilation for a 3+ week course. CONCLUSION(S): These cases highlight severe air leak syndromes as an infrequent and life-threatening complication correlated with Coronavirus infections. Viral illnesses such as SARS-CoV2 and Corona-OC43 and their associated multiorgan system disease have more recently impacted a larger number of pediatric patients and must be further evaluated to better understand underlying etiologies and compare management strategies. (Figure Presented).

Predictive Factors for Picu Admission in Patients with Mis-C

BACKGROUND AND AIM: Multisystem inflammatory syndrome in children (MIS-C) associated with a recent SARS-CoV2 infection is a rare complication of the infection that often requires admission to the pediatric intensive care unit (PICU). Currently, there are no prediction models to assess severity in MIS-C patients as tools of risk assessment that may prevent decompensation out of the ICU setting. METHOD(S): We performed a retrospective chart review from 5/2020 to 6/2021 to collect presenting symptoms, lab and imaging results, and treatment regimens for all patients presenting to our institution with MIS-C. We imputed missing values using KNN Imputation and used a random forest algorithm to identify three lab test features for our model: ferritin, procalcitonin, and platelets. The echocardiogram ejection fraction (EF) was included for clinical relevance. We modeled these variables using multiple logistic regression and evaluated it through the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. RESULT(S): 78 patients were analyzed, of which 50 (64%) were admitted to the ICU. Platelet concentration was significant (OR 0.9930, CI 0.986-0.998, p-value = 0.021). However, procalcitonin, ferritin, and EF showed no significance. The AUC of our ROC curve was significant at 0.8043 (p-value < 0.0001). Fig1 shows the mean and standard deviation of platelets and EF to ICU admission and AUROC of the model. The negative predictive value (NPV) 75.0%, positive predictive value (PPV) 77.6%. CONCLUSION(S): Platelet concentration on admission was a significant predictor of ICU admission. Larger studies are required to develop predictive PICU admission models. (Figure Presented).

Crowd Maps: A Crowd Density Control System to Address Contagious Pandemics

In an ordered community, a society's population is evident in public gathering spaces. Meeting people in such places is done deliberately on particular events, but in times when it is not needed or when the capacity of a place is met, it becomes difficult to mass plan a population's visit. There must be a mechanism to distribute the population over space and time. A collaborative effort to make sure that the crowd density is as low as it can be for the sake of certain problems such as traffic, parking space, pandemics, or natural disasters needs to be made by crowdsourcing information. To overcome these problems, showing the population a real-time map of the crowd density in an area over time is one way to curb crowds by voluntary action. In this paper, we present a system of two applications for this purpose. A desktop application which, with the help of CCTV cameras, counts the people in an area and projects that number onto a map, and a mobile application which, with the help of location sensors, will project each user's location alone on the same map. The crowding is evident on a map and hence tells the users the crowd density in an area. © 2022 American Institute of Physics Inc.. All rights reserved.

Potential sources of moral distress during COVID-19: Perspectives of hospice interdisciplinary teams.

OBJECTIVE: This study aimed to examine the impact of COVID-19 on hospice Interdisciplinary team (IDT) members' self-reported stress and identify possible sources of moral distress. METHODS: A cross-sectional survey was conducted using Qualtrics to understand the impact of COVID-19 on quality improvement initiative implementation and hospice IDT members' general and dementia-specific care provision. Directed qualitative content analysis was used to analyze hospice IDT members' responses from five open-ended survey questions that were indicative of stress and possible moral distress. RESULTS: The final sample consisted of 101 unique respondents and 175 comments analyzed. Three categories related to sources of moral distress based on hospice IDT member survey responses were identified: (1) impact of telehealth, personal protective equipment (PPE), and visit restrictions on relationships; (2) lack of COVID-19-specific skills; and (3) organizational climate. Sources of moral distress were categorized in 40% of all responses analyzed. SIGNIFICANCE OF RESULTS: This study is one of the first to document and confirm evidence of potential stress and moral distress amongst hospice IDT members during COVID-19. It is imperative given the possible negative impact on patient care and clinician well-being, that future research and interventions incorporate mechanisms to support clinicians' emotional and ethical attunement and support organizations to actively engage in practices that address clinician moral distress resulting from restrictive environments, such as the one necessitated by COVID-19.

Liver Transplant and Global Surgery: Experience Establishing a Global Liver Transplantation Mentoring Program

Introduction: Liver transplantation (LT) is the second most common solid organ transplantation, however, less than 10% of global transplantation needs are achieved. Low- and middle- income countries (LMIC's) are the most affected. A university- based Center for Global Surgery and our LT team joined efforts in 2017 to create an international alliance for clinical care and academic endeavors. Here we describe our experience establishing a LT mentoring group within an academic Center for Global Surgery. Method(s): This is a retrospective observational study. We evaluated the number of clinical, research and educational activities that our program did with LMICs from 2009 to 2022. Surgeries, patient evaluation, and follow-up were done in a multidisciplinary fashion with protocols from our LT program in partnership with LMIC's teams and via telehealth. Most educational and research activities were done online. Result(s): We performed 15 surgeries in pediatric and adult patients, including cadaveric, living donor LT, portosystemic shunts, and resections, and evaluated 27 patients from LMIC's. We have submitted 2 articles, presented 5 s, and obtained 1 grant. Our group received support to sponsor 1 research scholar per year and we had 15 bilateral exchange visits and organized 27 online multidisciplinary education sessions in collaboration with centers from LMIC'S. Conclusion(s): Our data shows that global transplant efforts with a multidisciplinary model can have clinical and academic impact. It is feasible to partner and mentor LT programs in LMICs through telehealth and exchange programs. Funding of these efforts remains challenging, and COVID-19 has limited academic and clinical activities.

Air Pollution Trend and Variation during a Mega Festival of Firecrackers (Diwali) in Context to COVID-19 Pandemic

The present study was conducted in Lucknow city to assess the impact of firecracker burning during Diwali, from 2 November 2021-6 November 2021 including the pre and post-Diwali days. The concentrations of PM10, PM2.5, SO2, NO2, CO, O3, benzene and toluene, were monitored from the Central Pollution Control Board site on an hourly basis. The Air Quality Index was also recorded for PM10, PM2.5, SO2 and NO2. A questionnaire survey was done with 51 doctors to know the reported complaints post-Diwali. On Diwali night the PM2.5 value reached 262 μg m-3 around 22:00 hours and the maximum value (900 μg m-3) was obtained on 5 November, reported from the Central School monitoring station. From Gomti Nagar highest PM2.5 value obtained on Diwali day was 538 μg m-3 at 23:00 hours reaching 519 μg m-3 post-Diwali. Areas belonging to the old part of the city witnessed higher variations as PM2.5 crossed 900 μg m-3, in Lalbagh and Talkatora areas. The multivariate analysis showed that on Diwali night there was an increase of 204, 386, 344 and 341 in the PM2.5 concentration reported from Gomtinagar, Central School, Talkatora and Lalbagh stations, showing that firecracker burning resulted in a significant increase in air pollution. The Toluene/Benzene ratio was mostly more than 1 indicating that toluene and benzene may be emitted from other sources as well including the mobile sources. Around 50-75% rise was seen in the number of patients post-Diwali. 57.1% of the reported cases had respiratory issues, followed by allergic reactions. The data obtained from Lalbagh, Talkatora and Central School showed that although the values remained high, a decreasing trend was seen in the AQI compared to previous years which is a good sign and may be attributed to public awareness and the ongoing pandemic making people conscious © 2022 by Asian Association for Atmospheric Environment

Lessons from COVID-19: Impact on Healthcare Systems and Technology

Lessons from COVID-19: Impact on Healthcare Systems and Technology uncovers the impact that COVID-19 has made on healthcare and technology industries. State-of-the-art case studies, empirical research, and new trends in technology-mediated solution are discussed to help inform and guide readers in understanding the effects that the COVID-19 outbreak has had across healthcare and technology industries. The book discusses challenges to identify vaccines, changes in legislation on clinical trials and re-purposing of licensed drugs, effects on primary healthcare, best practices adopted by different countries to control the pandemic, and different effects on patients within diverse age groups and comorbidities. In addition, the book covers technology-mediated solutions and infrastructures applied, digital transformations, modeling techniques, statistical projections, and the benefits and use of cloud computing and artificial intelligence. This is a valuable resource for healthcare professionals, medical doctors, researchers and graduate students from both biomedical and technological fields who are interested in learning more about the use of new technologies to fight a pandemic. © 2022 Elsevier Inc. All rights reserved.

Towards Sustainable Living through Sentiment Analysis during Covid19

Artificial intelligence is the process of the machine to perform with the simulation of human intelligence. Computing within the field of emotions paves the recognitions to sentiment analysis. Sentiment analysis is the method of capturing the emotions behind a text whether or not it's positive, negative or neutral. Sentiment Analysis (SA) or Opinion Mining (OA) is the process to provide computational treatment to unstructured data to categorize and identify the sentiments or emotions expressed in a piece of text. It combines Natural Language Processing Techniques and Machine Learning Techniques. This technology is additionally referred to as opinion mining or feeling computing. Sentiment Analysis uses the ideas of machine learning alongside an AI based process called NLP to extract and analyse the data, emotions, information from the text. This work explores the impact of social media during covid 19 and possible link between sustainable living and health care with the usage of sentiments. This paper address the sustainable development goal 3 (good health and wellbeing) of SDG 2030 and a possible way towards sustainable living through sentiment analysis. © The Electrochemical Society