Supervised Machine Learning Strategy for detection of covid19 patients

The current severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) public health catastrophe, both human lives have been lost and the economy has disrupted severely the current scenario. In this paper, we develop a detection module using a series of steps that involves pre-processing, feature extraction and detection of covid-19 patients based on the images collected from the computerized tomography (CT) images. The images are initially pre-processed and then the features are extracted using Gray Level Co-occurrence Matrix (GLCM) and then finally classified using back propagation neural network (BPNN). The simulation is conducted to test the efficacy of the model against various CT image datasets of numerous patients. The results of simulation shows that the proposed method achieves higher detection rate, and reduced mean average percentage error (MAPE) than other existing methodologies. © 2023 IEEE.

Association of Smoking and Covid Severity-A Cross Sectional Study

Background: Novel Corona virus (SARS-COV2) infection discovered in late 2019 in china, became a pandemic and caused mortality due to severe acute respiratory syndrome (SARS) around worldwide. Smoking is well known to cause acute and chronic injury to respiratory epithelium and parenchyma leading to chronic obstructive pulmonary disease (COPD). The relationship of Cigarette smoking and coronavirus infection is paradoxical. There is no clear conclusion regarding the relationship between smoking and covid infection and its severity. Objective(s): The purpose of the current study was to determine how smoking affected Covid severity amongst patients admitted to Covid-designated tertiary care hospitals. Material(s) and Method(s): In a tertiary care institution that has been designated by COVID, this retrospective, cross-sectional study was conducted. The patients who were admitted for covid illnesses between Jan 2021 and June 2021 were included in research after receiving clearance from the institute's ethics committee for human studies, and the medical records department reviewed their case files. Patients hospitalized for observation for fewer than 24 hours, patients with missing or untraceable data, and patients for whom a CT scan of the chest was not performed were all excluded from our research. To ensure the integrity of the data, the acquired information was put into Epicollect. Using SPSS, descriptive statistics were used to analyze the data (version 24). Result(s): The majority of the participants in our study (n=1109) were men, 848 (76%). The ratio of men to women was 3:1. The majority of patient data (715, or 64.47%), fell between age range of 30 to 60 yrs. Majority of patients (363;32.7%) had mild CT severity. The most prevalent 369 (33.27%) comorbidity in the study population was diabetes. 987 (89%) of the patients who were hospitalized were nonsmokers. The majority of smokers and non-smokers had CT severity that was normal or mild to severe. Smokers and non-smokers both had comparable distributions of CT severity. Conclusion(s): The majority of individuals who needed hospital admission for the management of chronic illnesses were nonsmokers. In our investigation, there was no correlation between smoking and the severity of the COVID condition. For further clarification of our findings, more research with a larger sample size is required. Copyright © 2022, Dr Yashwant Research Labs Pvt Ltd. All rights reserved.

Semiconductor Supply Chain: A 360-Degree View of Supply Chain Risk and Network Resilience Based on GIS and AI

The semiconductor industry is essential to modern global economies, as chips and other components are crucial in consumer and industrial goods. In the past decade, the explosion of new technologies, such as the Internet of Things (IoT), Big Data, Artificial Intelligence (AI), and 5G telecommunication infrastructure has created a sustained demand for semiconductors that is reshaping every industry on its path to digitization and automation. We first characterize the supply chain of semiconductors, which over time have been honed to deliver maximum efficiency and speed, and we examine the drivers of past disruptions, especially due to the COVID-19 pandemic, natural hazards, and increasing geopolitical tensions between China and the West. Second, we present the rationale for resiliency management in this critical sector by probing what countries and companies plan to do given these disruptions. Finally, we are examining the role of Geographic Information Systems (GIS), spatial analysis, and AI in semiconductors supply chain management. While the specific tools and analytics for supply chain analysis remain an open question for researchers and managers, in order to model, predict, and plan for larger scale disruptions, such as pandemics, they definitely will rely heavily on data-driven approaches. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Relationship between Alcoholism and Severity of COVID-19 Infection: An Institution Based Study

Background: Pandemic caused by Covid 19 infection witnessed several patients suffering from severe acute respiratory syndrome (SARS) due to severe inflammatory response. Risk factors that contributed to severe covid 19 infection include age, diabetes, previous lung disease, liver and kidney disease. Several other risk factors like smoking, alcoholism, hypertension and obesity are also being studied to understand their contribution in causing severe covid 19 infection and death. Alcohol is well known to cause immunosuppression and multiple organ injury including liver, pancreas and lung. It is a necessity to understand the effect of alcoholism on covid severity and risk of infection to create better awareness and understand the prognosis of covid 19 infection among alcoholics. The relationship of Alcoholism and COVID-19 infection is still controversial. In the literature, fewer studies are done to assess alcoholism and covid severity. Objective: The present study is done to find out the relationship of alcohol consumption on Covid severity among individuals admitted at Covid designated tertiary care hospital. Materials and Methods: This retrospective, cross sectional study was carried out in a covid designated tertiary care hospital. After approval from Institute Ethics Committee (Human studies), the patients admitted for covid illness from January 2021 to June 2021 were taken up for the study and their case records from medical records department were studied. Telephonic conversation was also done for patients with inadequate data. The collected data was entered in google forms and MS excel sheet and analysis done using descriptive statistics involving SPSS (version 24). Result: A total of 1109 patients were included in the study. Most of the patients in our study were males-851(76.73). The male:female ratio was 3.2:1. The maximum patient belonged to the age range 30 to 60 years. CT severity was mild in most of the patients (32.82%). Diabetes was the most common (33.18%) comorbidity among the study population. Majority of the admitted patients were Non-alcoholics 884 (79.7%). Among alcoholics and non-alcoholics, majority showed normal, mild to moderate CT severity. There is no statistically significant association between alcoholism and CT severity score compared to non-alcoholics (p value=0.947). Also, there is no significant association between alcoholism and Covid severity among Diabetic and Hypertensive patients compared to patients without these medical morbidities. Conclusion: In conclusion, majority of the patients who required admission in hospital for covid illness treatment were non-alcoholics. There is no significant association between alcoholism and covid-19 severity compared to general population. Also, there is no significant association between alcoholism and Covid severity among Diabetic and Hypertensive patients. Further human and experimental studies with more sample size is needed for further clarification of our findings.

Transformational Impact of COVID-19 on Savings and Spending Patterns of Indian Rural Households

COVID-19 has spread across the globe at a shocking level and significantly affects the world economy. The pandemic has significantly impacted rural households, the primary workforce for industrialized urban areas, in every sector of rural businesses, including agriculture. Furthermore, the dearth of employment in the primary industry has also adversely influenced rural inhabitants' livelihood and financial decisions. COVID-19 changed the perception of people regarding their income and expenditure. This study is intended to analyse the transformation of savings and spending of rural households during COVID-19. A questionnaire was developed using a Likert scale to elicit study variables, and the collected data were analysed using structural equation modelling. The results showed that all types of savings had a positive and significant relationship with the savings motive of rural households during COVID-19. Further, customary and spontaneous spending had a positive and significant relationship spending pattern of rural households. Rural inhabitants were interested in compromising their spending and other forms of savings to have more emergency savings. Earlier studies have examined either the savings or the spending pattern of rural households, and studies on both savings and spending by rural households are very few. The present study thus adds to the existing literature in the field.

Demographic analysis of prevalence of oral lesions in patients with deleterious habits– an institutional based descriptive observational study

Aim:Tobacco consumption in various forms has become common social habit in India. The increasing use of tobacco has led to an increased incidence of oral potentially malignant disorders and oral malignancies. The aim of this study was to assess the demographic details and prevalence of tobacco and other deleterious habits, and the relative risk of oral mucosal lesions among the patients reporting to the department of Oral Medicine and Radiology at Meenakshi Ammal Dental College, Chennai. Materials And Methods: This study was conducted on individuals with deleterious habits such as tobacco and alcohol. A total number of 1000 subjects were included in this study. Patients were interviewed through questionnaire in relation to their demographic details, tobacco habits and clinical examination was done to evaluate tobacco-related oral lesions. This study was conducted during the period of COVID pandemic following COVID standard operating protocols. Results: Among the 1000 participants, 973 (97.3%) were male and 27 (2.7%) were female. The most common smoked form of tobacco was cigarette (51.3%), and the smokeless form was betal quid (9.3%). The oral mucosal lesions had a prevalence rate of 30.6%. Among them, 18.9% subjects consumed alcohol. 33.7% of subjects confessed that they developed the habit due to peer pressure. The most common oral mucosal lesion was leukoplakia (7.5%). Conclusion: Tobacco consumption is a common cause of preventable illness and death. This study may serve as a useful tool in understanding the socio-demographic association among different forms of tobacco and associated oral lesions along with other deleterious habits. It also serves the patients by educating about its ill effects.

Impact of COVID-19 pandemic on income and opportunities of ophthalmologists in India: Ophthalmologists' Workplace Expectations and Satisfaction Survey (OWESS) Report 1.

Purpose: To evaluate the impact of COVID-19 pandemic on the income and surgical training opportunities among the ophthalmologists in India and their opinion on salary reduction during this period. Methods: A questionnaire in the form of a Google survey was sent to ophthalmologists across India on May 1, 2021. The data collected until May 11, 2021 was analyzed. Results: A total of 1057 ophthalmologists all over India participated in the survey. Of the respondents, 559 (52.9%) were women and 730 (69.1%) were young ophthalmologists (below the age of 40 years). Salary reduction was reported by 569 (53.8%) of the respondents. The categories suffering the maximum salary reduction were - young ophthalmologists (407, 55.8%) (P < 0.001), women (304, 54.4%) (P < 0.001), and private sector employees (457, 67%) (P < 0.001). More women ophthalmologists (438, 78.4%) felt it was unfair to reduce the salary during the pandemic, as compared to men (330, 66.3%) (P < 0.001). A reduction in surgical training opportunities was reported by 689 (65.2%) of ophthalmologists. The categories who suffered the maximum loss of surgical training opportunities were young ophthalmologists (565, 77.4%) (P < 0.001), women ophthalmologists (415, 74.2%) (P < 0.001), and ophthalmologists in the government sector (147, 82.6%) (P < 0.001). Conclusion: Ophthalmologists in India, especially women and the younger professionals, had to face salary reduction and loss of surgical training opportunities during the COVID-19 pandemic. Most ophthalmologists in India do not favor a reduction in salary. There is a need to formulate policies to safeguard ophthalmologists, especially women and younger generation from future crises in training, employment, and income.

The prognostic significance of neutrophil-lymphocyte ratio (NLR) in patients with COVID-19

INTRODUCTION: Neutrophil lymphocyte ratio (NLR) is elevated in response to stressful stimuli and has been shown to be associated with poor prognosis in both benign & malignant disorders. Literature regarding NLR as a prognostic marker in COVID19 are limited. Our study was aimed to investigate the relationship between NLR & survival outcomes in patients hospitalized with Coronavirus disease 2019 (COVID19). METHODS: Ours was a single center, retrospective observational study, which included 472 nasopharyngeal swab SARS-CoV-2 RT-PCR positive patients. NLR was derived from the admission complete blood count & was divided into 5 sub-groups as (0-0.99, 1-2.99, 3-9.99, 10-19.99, >20). Demographics, comorbid conditions, and outcomes such as need for mechanical ventilation, length of stay and inpatient mortality were assessed. Statistics were performed using STATA. Significance was assigned at p<0.05. RESULTS: The mean age was 71.16 years in NLR >10 group as compared to 60.3 years in patients with normal NLR 1-2.99. Male patients were found to have much higher NLR than females (65.12% vs 34.88% in NLR 10-19.99, 64.86% vs 35.14% in NLR>20;p-value: 0.05). Among comorbidities, COPD patients were found to have higher NLR (18.92% of NLR>20 vs 10.71% of NLR 1-2.99;p-value:0.02). Rate of endotracheal intubation and need for mechanical ventilation was significantly higher with increasing NLR (0% vs 7% vs 14% vs 17% vs 32%;p-value: 0.03). Inpatient mortality was significantly higher in patients who had NLR>20 (70.27% of NLR>20 vs 16.07% of NLR 1-3 p-value <0.0001). On multivariate regression, patients with NLR>20 had 4 times higher odds of mortality;however, the p-value was not significant (4.07±2.78 p-value: 0.175). CONCLUSIONS: Increasing NLR in COVID19 patients is associated with increased ICU admission, intubation & inpatient mortality. Further studies are warranted to establish NLR, which is readily available & inexpensive, as a potential prognostic indicator in COIVD19 patients.

The prognostic significance of absolute lymphocyte count in patients with COVID-19

INTRODUCTION: The host immune responses try to confront Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with all the potential cells and cytokines. Eventually, natural killer cells and T cells become exhausted, decreasing their counts, leading to lymphopenia. This study aims to assess the clinical utility of the absolute lymphocyte count (ALC) at admission in predicting outcome in patients with COVID-19. METHODS: Ours was a single-center, retrospective observational study, which included 463 nasopharyngeal swabs SARS-CoV-2 RT-PCR positive patients. Absolute lymphocyte count was retrieved from the admission complete blood count & was divided into 3 sub-groups (<500, <1000, and >1000 cells/μL). Demographics, comorbid conditions, and outcomes such as the need for mechanical ventilation, length of stay, and inpatient mortality were assessed. Statistics were performed using STATA. Significance was assigned at p<0.05. RESULTS: 13.82% of patients had ALC count<500, 44.71% had <1000 and 41.25% had more than 1000. Mean age in ALC group<500 was higher (71±1 years vs 65± 1.1 years in ALC group <1000 and 59.9+/-1.3 in ALC group >1000). Profound lymphopenia (<500 cells/μL) was more common in males compared to females (71.88 % vs 28% p value 0.01). ALC count <500, was associated with higher rate of non-invasive (45.31% vs 26.56% for ALC <1000, p-value: 0.01) as well as invasive ventilation (26.5% with ALC <500 vs 19% with ALC <1000 vs 10.4% with ALC with >1000;p-value: 0.01). Inpatient mortality was significantly higher in cohort with ALC <500 (51.56% with ALC <500 vs 33.3% with ALC <1000 vs 24.08% with ALC >1000;p-value 0.05). On multivariate regression, ALC was not a independent predictor of mortality (ALC<500, OR: 1.56±0.75, p-value: 0.44). CONCLUSIONS: Lymphopenia at admission in COVID19 patients is associated with an increased need for non-invasive & invasive ventilation & inpatient mortality. Currently, clinical trials assessing GM-CSF as a possible therapeutic option is underway.

Can age alone be used as a guide for decision-making in COVID-19 patients? a retrospective study

INTRODUCTION: Coronavirus disease 2019 (COVID- 19) caused by the SARS-CoV-2 virus has emerged as one of the greatest challenges to humanity in recent history. Older people have shown to have poor outcomes in recent studies. Our study looks at the characters and outcomes in patients of different age groups admitted to our center. METHODS: Our study is a single-center, retrospective, observational study of 471 COVID-19 patients (confirmed with a positive nasopharyngeal swab for SARS-CoV2 RT PCR) admitted to our hospital. Patients were divided into 3 groups based on Age (0-45 years, 46-65 years, and >65 years). Demographic characteristics and in-hospital outcomes were compared between these groups. STATA was used to perform statistics. Statistical significance was assigned at p=<0.05. RESULTS: 471 patients were included in the study of which 79 (16.77%), 159 (33.76%), and 233 (49.47%) belonged to the age group of 0-45 years (Group A), 46- 65 years (Group B) and >65 years (Group C) respectively. On comparison of pre-existing comorbidities, patients in group B and group C had a higher incidence of baseline comorbidities (Diabetes, Hypertension, Heart failure, COPD rates were 33.96% vs 43.1%, 55.35% vs 81.12%, 9.01% vs 20.59%, 2.5% vs 11.21% respectively). On comparing in-hospital outcomes, the mean time to mechanical ventilation from admission was 3.25 (±1.31) days, 2.42 (±0.68) days and 2.75 (±0.53) days for group A, B and C respectively. 74 (15.71%) patients required intubation during hospitalization of which 7.5%, 32.5%, and 60% belonged to groups A, B, and C respectively. The overall mortality rate among intubated patients was 90.54% among which 8.15%, 31.08%, and 60.81% belonged to groups A, B, and C respectively. The inhospital mortality rate was 32.48% of which 3.27%, 17.65%, and 79.08% belonged to groups A, B, and C respectively. In-hospital mortality rate for group A, B and C were 6.33%, 16.98% and 51.93% respectively (p <0.0001). However, on multivariate regression analysis, age was not an independent predictor of in-hospital mortality for any age group. CONCLUSIONS: Patients >65 years of age have higher co-morbidities and worse in-hospital outcomes. However, age is not an independent predictor of mortality and each patient should be evaluated individually while making an important treatment decision.

The prognostic significance of lymphocyte-to-crp ratio (Lcr) in patients with COVID-19

INTRODUCTION: Systemic inflammation elicited by a cytokine storm is considered a hallmark of coronavirus disease 2019 (COVID-19). This study aims to assess the clinical utility of the lymphocyte-to-C-reactive protein (CRP) ratio (LCR), typically used for gastric & colorectal cancer prognostication. METHODS: Ours was a single center, retrospective observational study, which included 321 nasopharyngeal swab SARS-CoV-2 RT-PCR positive patients. LCR was derived from the admission complete blood count & was divided into 2 sub-groups (<99.99 vs >100). Demographics, comorbid conditions, and outcomes such as need for mechanical ventilation, length of stay and inpatient mortality were assessed. Statistics were performed using STATA. Significance was assigned at p<0.05. RESULTS: LCR <99.99 group had more elderly patients as compared to LCR >100 group (67.74% vs 54.01% of patients >60 years of age). Male patients were found to have lower LCR than females (60.75% vs 39.25% with LCR <99.99;p-value: 0.03). Among comorbidities, patients with history of cancer were found to have higher LCR (7.53% of LCR <99.99 vs 13.24% of LCR >100;p-value:0.03). Lower LCR was associated with higher rate of non-invasive (36.56% with LCR <99.99 vs 19.12% with LCR >100;p-value: 0.01) as well as invasive ventilation (17.74% with LCR <99.99 vs 11.76 with LCR >100;p-value: 0.01). Inpatient mortality was significantly higher in patients who had LCR <99.99 (39.25% with LCR <99.99 vs 22.63% with LCR >100;p-value <0.03). On multivariate regression, patients with LCR <99.99 had 2 times higher odds of mortality;however, this finding did not reach statistical significance. (2.27± 0.81 p-value: 0.15). CONCLUSIONS: Decreasing LCR in COVID19 patients is associated with increased need for non-invasive & invasive ventilation & inpatient mortality. Further studies are warranted to establish LCR, which is readily available & inexpensive, as a potential prognostic indicator in COIVD19 patients.

Outcomes of novel coronavirus disease in patients with diabetes mellitus: A retrospective analysis

INTRODUCTION: Coronavirus disease 2019 (COVID-19) is a multisystem infection caused by SARS-CoV-2 Virus. Recent studies have demonstrated poor outcomes in patients with diabetes mellitus (DM). We sought to assess the in-hospital outcomes of COVID19 patients with DM at our centre. METHODS: Ours was a single centre, retrospective, observational study of 470 COVID-19 patients admitted to our hospital. We divided these patients into 2 groups;those with DM and those without. We compared demographic characteristics, comorbid conditions, and in-hospital outcomes between the two groups. Statistics were performed using STATA. Statistical significance was assigned at p<0.05. RESULTS: Out of the 470 patients included in the study, 35.53% of patients had DM. Mean age of patients with and without DM was 68.35years±1.08 vs 61.71±1.05years respectively. 8.72% of patients were on pharmacological therapy. The diabetic cohort had a higher prevalence of hypertension, heart failure compared to the non-diabetic cohort (88.02 vs49.5% p-value:0.004, 22.9% vs 9.31% p-value: 0.04). Other comorbidities such as OSA, CKD, COPD, Asthma were comparable between both groups. The DM group had a higher level of inflammatory markers during the course of hospitalisation (D-dimer 3802.68± 1499 vs 3448.13 ±1139, CRP: 12.60±0.76 vs 11.85±0.60, ESR: 73.66±10.41 vs 58.04±7.10). The DM group had a significantly higher need for mechanical ventilation (18.56% vs 13.29%, p<0.03), and subsequent in-hospital mortality (43.35% vs 25.74% p<0.05). On multivariate regression, diabetics had 2.64 higher odds of in-hospital mortality, however, the p-value was not significant (Write ODDS Ratio and Confidence interval p-value: 0.116). CONCLUSIONS: Overall inpatient mortality was higher in patients with DM, likely driven by an increased need for mechanical ventilation. Our study positively adds to the existing literature that DM is a significant risk factor for higher morbidity and mortality in COVID-19 patients.

A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence.

COVID-19 has infected 77.4 million people worldwide and has caused 1.7 million fatalities as of December 21, 2020. The primary cause of death due to COVID-19 is Acute Respiratory Distress Syndrome (ARDS). According to the World Health Organization (WHO), people who are at least 60 years old or have comorbidities that have primarily been targeted are at the highest risk from SARS-CoV-2. Medical imaging provides a non-invasive, touch-free, and relatively safer alternative tool for diagnosis during the current ongoing pandemic. Artificial intelligence (AI) scientists are developing several intelligent computer-aided diagnosis (CAD) tools in multiple imaging modalities, i.e., lung computed tomography (CT), chest X-rays, and lung ultrasounds. These AI tools assist the pulmonary and critical care clinicians through (a) faster detection of the presence of a virus, (b) classifying pneumonia types, and (c) measuring the severity of viral damage in COVID-19-infected patients. Thus, it is of the utmost importance to fully understand the requirements of for a fast and successful, and timely lung scans analysis. This narrative review first presents the pathological layout of the lungs in the COVID-19 scenario, followed by understanding and then explains the comorbid statistical distributions in the ARDS framework. The novelty of this review is the approach to classifying the AI models as per the by school of thought (SoTs), exhibiting based on segregation of techniques and their characteristics. The study also discusses the identification of AI models and its extension from non-ARDS lungs (pre-COVID-19) to ARDS lungs (post-COVID-19). Furthermore, it also presents AI workflow considerations of for medical imaging modalities in the COVID-19 framework. Finally, clinical AI design considerations will be discussed. We conclude that the design of the current existing AI models can be improved by considering comorbidity as an independent factor. Furthermore, ARDS post-processing clinical systems must involve include (i) the clinical validation and verification of AI-models, (ii) reliability and stability criteria, and (iii) easily adaptable, and (iv) generalization assessments of AI systems for their use in pulmonary, critical care, and radiological settings.

Responding to “COVID-19”pandemic: Quality management and preventive strategies of a hospital

The newly emerged public health crisis threatening the world with emergence or spread of a novel coronavirus named SARS-CoV-2 associated with higher infection rates and deaths especially elderly people and people with Hypertension, Diabetes Mellitus, Cerebrovascular and Cardiovascular diseases throughout the world. As of 17thJune 2020, 216 countries were affected with 83,26,825 confirmed cases including 4,48,081 total deaths. India has reported around 3.6 lakh confirmed cases with more than 12 thousand total deaths. To respond this pandemic, India needs to set-up an adequate, well equipped and dedicated health care facility to contain the spread of infection and manage the infected patients as well as providing protection to the healthcare workers (HCW). Quality management and preventive strategies of a hospital plays very important role for this purpose. Quality management is the fundamental feature of a hospital to establish customer satisfactions and desired outcomes. High quality care health services involve doing the right things, for the right patient, at the right time, in the right way to minimise the harm and resource waste. Preventive strategies especially infrastructure development & Infection prevention and control policies (IPC) are very crucial. The effective, safe, people centred, timely, equitable, integrated and efficient heath care delivery improvement with appropriate quality management system and preventive strategies will be helpful to combat with “COVID-19”. This study highlights the “Quality management and Preventive Strategies of a Hospital responding to COVID-19” for providing better healthcare services in Indian healthcare management system.