Location Scheme of Routine Nucleic Acid Testing Sites Based on Location-Allocation Models: A Case Study of Shenzhen City

Since late 2019, the explosive outbreak of Coronavirus Disease 19 (COVID-19) has emerged as a global threat, necessitating a worldwide overhaul of public health systems. One critical strategy to prevent virus transmission and safeguard public health, involves deploying Nucleic Acid Testing (NAT) sites. Nevertheless, determining the optimal locations for public NAT sites presents a significant challenge, due to the varying number of sites required in different regions, and the substantial influences of population, the population heterogeneity, and daily dynamics, on the effectiveness of fixed location schemes. To address this issue, this study proposes a data-driven framework based on classical location-allocation models and bi-objective optimization models. The framework optimizes the number and location of NAT sites, while balancing various cost constraints and adapting to population dynamics during different periods of the day. The bi-objective optimization process utilizes the Knee point identification (KPI) algorithm, which is computationally efficient and does not require prior knowledge. A case study conducted in Shenzhen, China, demonstrates that the proposed framework provides a broader service coverage area and better accommodates residents' demands during different periods, compared to the actual layout of NAT sites in the city. The study's findings can facilitate the rapid planning of primary healthcare facilities, and promote the development of sustainable healthy cities.

Novel coronavirus disease-19:An overviwe of pathogenesis, health influences and measures

The coronavirus disease-19 (COVID-19) signs mostly include fever and respiratory symptoms (unusual viral pneumonia by SARS-Coronaviruses 2 or SARS-CoV-2). The Receptor-Binding Domain (RBD) of COVID-19 and SARS-CoV are similar, causing cross-reactivity of anti-SARSCoV antibodies with associated spike protein, exerting promising implications for rapid development of vaccines and therapeutic antibodies against COVID-19. ACE2 is the SARS TMPRSS2 for spike (S) protein receptor for initiation of infection;hence, it is a target for pharmacological intervention. Furthermore, designing novel monoclonal antibodies binding specifically to COVID-19 RBD is essential. A viral S proteins (TMPRSS2) was proposed for clinical use by blocking the viral intake by cell.Copyright © 2020, Health Biotechnology and Biopharma. All rights reserved.

Multi-branch sustainable convolutional neural network for disease classification

Pandemic and natural disasters are growing more often, imposing even more pressure on life care services and users. There are knowledge gaps regarding how to prevent disasters and pandemics. In recent years, after heart disease, corona virus disease-19 (COVID-19), brain stroke, and cancer are at their peak. Different machine learning and deep learning-based techniques are presented to detect these diseases. Existing technique uses two branches that have been used for detection and prediction of disease accurately such as brain hemorrhage. However, existing techniques have been focused on the detection of specific diseases with double-branches convolutional neural networks (CNNs). There is a need to develop a model to detect multiple diseases at the same time using computerized tomography (CT) scan images. We proposed a model that consists of 12 branches of CNN to detect the different types of diseases with their subtypes using CT scan images and classify them more accurately. We proposed multi-branch sustainable CNN model with deep learning architecture trained on the brain CT hemorrhage, COVID-19 lung CT scans and chest CT scans with subtypes of lung cancers. Feature extracted automatically from preprocessed input data and passed to classifiers for classification in the form of concatenated feature vectors. Six classifiers support vector machine (SVM), decision tree (DT), K-nearest neighbor (K-NN), artificial neural network (ANN), naïve Bayes (NB), linear regression (LR) classifiers, and three ensembles the random forest (RF), AdaBoost, gradient boosting ensembles were tested on our model for classification and prediction. Our model achieved the best results on RF on each dataset. Respectively, on brain CT hemorrhage achieved (99.79%) accuracy, on COVID-19 lung CT scans achieved (97.61%), and on chest CT scans dataset achieved (98.77%). © 2023 Wiley Periodicals LLC.

Impact of mass media on the population as a precautionary prevention from COVID-19 in Iraq

The aim of this study was to identify the impact of both traditional mass media and social digital media on the population to prevent the Corona virus disease-19 (COVID-19). Three hundred twenty participants were included. A questionnaire was prepared consisting of socio-demographic characteristics and the effect of traditional mass media and mobile digital media on the population. The sources used for information included TV (72.8 %), Facebook (71.2 %), health professionals (64.4 %), Instagram (28.1 %), Twitter (16.8 %), Radio (14.4 %) and mobile Apps (Viber and WhatsApp being 30.9 %). Social Media could be blamed for aiding the spread of stress and hysteria among people.Copyright © 2021 Health Biotechnology And Biopharma. All Rights Reserved.

ANFIS for prediction of epidemic peak and infected cases for COVID-19 in India.

Corona Virus Disease 2019 (COVID-19) is a continuing extensive incident globally affecting several million people's health and sometimes leading to death. The outbreak prediction and making cautious steps is the only way to prevent the spread of COVID-19. This paper presents an Adaptive Neuro-fuzzy Inference System (ANFIS)-based machine learning technique to predict the possible outbreak in India. The proposed ANFIS-based prediction system tracks the growth of epidemic based on the previous data sets fetched from cloud computing. The proposed ANFIS technique predicts the epidemic peak and COVID-19 infected cases through the cloud data sets. The ANFIS is chosen for this study as it has both numerical and linguistic knowledge, and also has ability to classify data and identify patterns. The proposed technique not only predicts the outbreak but also tracks the disease and suggests a measurable policy to manage the COVID-19 epidemic. The obtained prediction shows that the proposed technique very effectively tracks the growth of the COVID-19 epidemic. The result shows the growth of infection rate decreases at end of 2020 and also has delay epidemic peak by 40-60 days. The prediction result using the proposed ANFIS technique shows a low Mean Square Error (MSE) of 1.184 × 10-3 with an accuracy of 86%. The study provides important information for public health providers and the government to control the COVID-19 epidemic.

Awareness, Knowledge and Attitude of Teledentistry Among Dentists in Kanchipuram District - A Cross-Sectional Survey

Introduction: A highly infectious and life-threatening novel coronavirus Corona Virus Disease (COVID-19) has been spreading worldwide, causing severe medical complications and practising dentistry is becoming difficult. To reduce the risk of spread of coronavirus infection between dentist and patient, teledentistry, an innovative digital tool, has the potential to reach patients straightforward without direct contact. Materials and Methods: A self-structured standard questionnaire was framed and distributed among dentists from July 2021 to August 2021. The survey consisted of 15 closed-ended and multiple-choice questions related to awareness, knowledge and attitude of teledentistry during this COVID 19 pandemic. After proper validation of the questionnaire from the experts and evaluating reliability, the survey was conducted by forwarding the link of the Google Form through social media. Totally 520 participants responded to the survey. The statistical analysis was performed using SPSS statistical software version 21. All statistical analyses were carried out at a significance level of P < 0.05. The descriptive data were analysed and compared using the Chi-square test. Results: Among specialists, general practitioners, postgraduate students and undergraduate students, specialists have better awareness, knowledge and attitude of teledentistry. Almost all participants have 50% knowledge about teledentistry and have a high (80%) attitude towards teledentistry. Conclusion: From this study, it is clearly understood that it is high time to increase the use of teledentistry practice by spreading knowledge among dentists and dental students. It is potentially an innovative digital tool in this new era of dentistry. It is an effective tool not only in the current pandemic situation but also in emergencies. Thus, teledentistry is a satisfied boon in the field of dentistry through the use of digital technology.

Lockdown-induced Urban Aerosol Change over Changchun, China During COVID-19 Outbreak with Polarization LiDAR.

Depending on various government policies, COVID-19 (Corona Virus Disease-19) lockdowns have had diverse impacts on global aerosol concentrations. In 2022, Changchun, a provincial capital city in Northeast China, suffered a severe COVID-19 outbreak and implemented a very strict lockdown that lasted for nearly two months. Using ground-based polarization Light Detection and Ranging (LiDAR), we detected real-time aerosol profile parameters (EC, extinction coefficient; DR, depolarization ratio; AOD, aerosol optical depth), as well as air-quality and meteorological indexes from 1 March to 30 April in 2021 and 2022 to quantify the effects of lockdown on aerosol concentrations. The period in 2022 was divided into three stages: pre-lockdown (1-10 March), strict lockdown (11 March to 10 April), and partial lockdown (11-30 April). The results showed that, during the strict lockdown period, compared with the pre-lockdown period, there were substantial reductions in aerosol parameters (EC and AOD), and this was consistent with the concentrations of the atmospheric pollutants PM2.5 (particulate matter with an aerodynamic diameter ≤ 2.5 µm) and PM10 (particulate matter with an aerodynamic diameter ≤ 10 µm), and the O3 concentration increased by 8.3%. During the strict lockdown, the values of EC within 0-1 km and AOD decreased by 16.0% and 11.2%, respectively, as compared to the corresponding period in 2021. Lockdown reduced the conventional and organized emissions of air pollutants, and it clearly delayed the time of seasonal emissions from agricultural burning; however, it did not decrease the number of farmland fire points. Considering meteorological factors and eliminating the influence of wind-blown dust events, the results showed that reductions from conventional organized emission sources during the strict lockdown contributed to a 30% air-quality improvement and a 22% reduction in near-surface extinction (0-2 km). Aerosols produced by urban epidemic prevention and disinfection can also be identified using the EC. Regarding seasonal sources of agricultural straw burning, the concentrated burning induced by the epidemic led to the occurrence of heavy pollution from increased amounts of atmospheric aerosols, with a contribution rate of 62%. These results indicate that there is great potential to further improve air quality in the local area, and suggest that the comprehensive use of straw accompanied by reasonable planned burning is the best way to achieve this.

The Comparison For Epidemiological Investigation Of Corona Virus-19 Pandemic

The corona virus is currently in a pandemic. At this point, the most pressing challenge in the health care field is the coronavirus disease (COVID-19) epidemic. "In Korea, the first laboratory confirmed case was confirmed on January 20, 2020[1]". One way to identify these diseases is building the computational model. And then it needs a way to check whether the model worked or not. In this paper, We explain how to check the model using several programs.

Cooccurrence of anti-N-methyl-D-aspartate receptor encephalitis and COVID-19 infection – Case series of two patients with a brief review of literature

Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is a clinical condition characterized by a myriad of psychiatric symptoms, abnormal movements, autonomic instability, seizures, and encephalopathy. Coronavirus disease-19 (COVID-19) infection is infrequently present with altered mental status. As the days of the COVID-19 pandemic pass, more and more awareness of its different types of immunological reactions are unveiled. A wide spectrum of clinical, pathological, and radiological manifestations has been reported. However, there have only been a few cases where anti-NMDAR antibodies have been found in people who have COVID-19. Herein, we reported two cases with simultaneous anti-NMDAR antibody and COVID-19 infection detection. Both cases clinically responded after treatment with an immunomodulator, showing significant improvement, and were discharged in a conscious and ambulatory state. Autoimmune encephalitis should be thought about if there are neurological symptoms associated with SARS-CoV-2 infection, and immunomodulators should be given to such patients. [ FROM AUTHOR] Copyright of Asian Journal of Medical Sciences is the property of Manipal Colleges of Medical Sciences 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.)

COVID-19 Prediction, Diagnosis and Prevention Through Computer Vision

Recent Corona Virus Disease (COVID) outbreak, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV2), has been posing a big threat to global health since December 2019. In response, research community from all over the world has shifted all their efforts to contribute in this global war by providing crucial solutions. Various computer vision (CV) technologies along with other artificial intelligence (AI) subsets have significant potential to fight in frontline of this turbulent war. Normally radiologists and other clinicians are using reverse transcript polymerase chain reaction (RT-PCR) for diagnosing COVID-19, which requires strict examination environment and a set of resources. Further, this method is also prone to false negative errors. One of the potential solutions for effective and fast screening of doubtful cases is the intervention of computer vision-based support decision systems in healthcare. CT-scans, X-rays and ultrasound images are being widely used for detection, segmentation and classification of COVID-1. Computer vision is using these modalities and is providing the fast, optimal diagnosis at the early-stage controlling mortality rate. Computer vision-based surveillance technologies are also being used for monitoring physical distance, detecting people with or without face masks, screening infected persons, measuring their temperature, tracing body movements and detecting hand washing. In addition to these, it is also assisting in production of vaccine and contributing in administrative tasks and clinical management. This chapter presents an extensive study of some computer vision-based technologies for detection, diagnosis, prediction and prevention of COVID. Our main goal here is to draw a bigger picture and provide the role of computer vision in fight against COVID-19 pandemic. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Assessment of online teaching–learning during coronavirus disease-19 pandemic: Medical students’ perspective

Background: During the corona virus disease 19 (COVID-19) pandemic, most of the medical schools across the world has started to transfer the curriculum from face-to-face to online delivery using various virtual platforms for undergraduate teaching without any uniformity. It is imperative to understand the students’ outlook about the current online teaching in order to make it more effective. This study was planned to gain an insight into the medical students’ perspective toward online teaching–learning program and the challenges faced by them toward the same. Materials and Methods: It was a cross-sectional descriptive study conducted for 3 months at a medical college in Chengalpet district, Tamil Nadu, among 351 medical students across all professional years. The Google form platform was used to administer a semi-structured questionnaire to all the participants to obtain information related to various parameters of online teaching-learning. The statistical analysis was done using frequency and percentages. Results: A total of 351 students participated in this study, including 134 males and 217 females. Almost 318 (90.6%) perceived that online classes were able to cover academic portion amidst COVID-19 pandemic. The most common challenges found were network related issues, lack of practical sessions, including dissection and lack of exposure to clinical cases cited by 181 (90%), 77 (38.3%) and 72 (35.8%) participants, respectively. Conclusions: The study reveals the perspectives of medical students on online teaching–learning sessions and identifies important challenges pertaining to it. However, the positive overall experience by the students provides confidence to the medical education fraternity in the entire process of online teaching and learning amidst the COVID-19 pandemic. © The Authors.

Myasthenia gravis with ocular symptoms following a ChAdOx1 nCoV-19 vaccination: A case report.

Purpose: We report on the case of a 35-year-old man who developed myasthenia gravis with ocular symptoms following a ChAdOx1 nCoV-19 vaccine injection. Observations: A 35-year-old man complained of binocular diplopia one month following ChAdOx1 nCoV-19 vaccination. He had weak infraduction of the left eye. Upper and lower extremity strength was normal on presentation. A serum antiacetylcholine receptor antibody titer was elevated at 1.60 nmol/L. His diplopia improved temporarily following the application of an ice pack for 2 min. Conclusions and importance: This case report describes a rare occurrence of myasthenia gravis with ocular symptoms as a potential complication of ChAdOx1 nCoV-19 vaccination.

The impact of clinical pharmacist implemented education on the incidence of prescribing errors in COVID-19 patients.

Background: Clinical pharmacists have a vital role during COVID-19 pandemic in mitigating medication errors, particularly prescribing errors in hospitals. That is owing to the fact that prescribing errors during the COVID-19 pandemic has increased. Aim: This study aimed to evaluate the impact of the clinical pharmacist on the rate of prescribing errors on COVID-19 patients in a governmental hospital. Methods: The study was a pre-post study conducted from March 2020 till September 2020. It included the pre-education phase P0; a retrospective phase where all the prescriptions for COVID-19 patients were revised by the clinical pharmacy team and prescription errors were extracted. Followed by a one-month period; the clinical pharmacy team prepared educational materials in the form of posters and flyers covering all prescribing errors detected to be delivered to physicians. Then, the post-education phase P1; all prescriptions were monitored by the clinical pharmacy team to assess the rate and types of prescribing errors and the data extracted was compared to that from pre-education phase. Results: The number of prescribing errors in P0 phase was 1054 while it was only 148 in P1 Phase. The clinical pharmacy team implemented education phase helped to significantly reduce the prescribing errors from 14.7/1000 patient-days in the P0 phase to 2.56/1000 patient-days in the P1 phase (p-value <0.001). Conclusion: The clinical pharmacist significantly reduced the rate of prescribing errors in patients with COVID-19 which emphasizes the great role of clinical pharmacists' interventions in the optimization of prescribing in these stressful conditions.

Lifestyle Behaviours among General Population During Lockdown Period during Corona Virus Disease-19 Pandemic.

BACKGROUND: Healthy lifestyle behaviours have been consistently associated with reduced non-communicable disease related morbidity, mortality and wellbeing. Unhealthy behaviours are major contributors to the global burden of disease. The main aim of this study is to access lifestyle behaviours in adults during the corona virus disease-19 pandemic. METHODS: Cross sectional study was conducted among general population residing in Nepal. Online questionnaire was developed using Google Forms. Questionnaire comprised of three validated tools regarding the following lifestyle behaviours: Physical activity, Nutrition, Sleep. The collected data was analysed using SPSS version 20. To test the differences between changes in dietary and physical activity behaviours in relation to changes in body weight a Chi-square test was used. RESULTS: During Covid -19 lockdown, 124(42%) participants performed moderate level of physical activity. Of those participated, 127(43.1%) and 44(14.9%) reported an increase and decrease of weight, respectively. Among 110(37.3%) who reported snacking in lockdown led to weight gain in 68(61.8%). Availability of more time for meal preparation (24.1%) and feelings of boredom (17.4%) were the main reasons for changing dietary habits. The subjective sleep quality of participants was as follows: very good-40.3%; fairly good-45.4 %; fairly bad-11.2%; very bad 3.1%. There was significant positive correlation between sleep quality and sleep duration (R=0.261; P<0.001), sleep latency (R=0.362; P<0.001), sleeping medications (R=0.174; P<0.003) and daytime dysfunction (R=0.308; P<0.001). CONCLUSIONS: Life style behaviours were affected during lockdown period. Higher amounts of food intake and snaking were increased. Physical activity was at a moderate level, increased sedentary behaviour was reported by most participants during lockdown. However, sleep quality was not negatively affected.

Clinical Profile and Outcome of Corona Virus Disease-19 Infection in Pregnant Women.

BACKGROUND: The corona virus disease 19 pandemic has affected the whole world with pregnant ladies being more vulnerable population. This study aimed to evaluate characteristics of corona virus disease 19 infection in pregnancy and neonates and whether close proximity to the mother increases the incidence of corona virus disease infection in neonates.. METHODS: This is a hospital based prospective cross sectional observational study done among pregnant women presenting to Paropakar maternity and womens hospital from 1st September 2020 to 31st march 2021 with confirmed corona virus disease 19 infection. RESULTS: The total 160 cases were included in study. Most of the women (33.8%) were of 20- 25 years of age, 55 % were multigravida and 77.6 % were full term. Around 74 % of cases were symptomatic with predominant symptoms being cough, fever and sore throat present in 33.1 %, 18% and 14% respectively. Out of 125 deliveries 71 % of cases underwent cesarean section of which fetal distress was most common indication. Six newborns were positive for corona virus disease 19 infection within 48 hours of life. Mortality was seen in four mothers and three neonates. CONCLUSIONS: The clinical presentation of corona virus disease infection in pregnant ladies is similar to general population. There is no increased risk of vertical transmission to the baby.

Anxiety, Perceived Stress and Coping Strategies among the Nepalese Nurses Working around the World During a Corona Virus Disease -19 Outbreak.

BACKGROUND: Nurses are the frontline health professional of healthcare delivery system prone to have psychosocial problems. This study aimed to explore anxiety, stress and coping strategies among the Nepalese Nurses working around the World during a corona virus disease -19 Outbreak. METHODS: A web based cross sectional study was conducted for a period of three month among 240 nurses from Nepal and working abroad. They were invited to participate via various web based networks. Anxiety Self rating scale, perceived stress Scale and coping strategies were used for data collection. Chisquare, spearman rho and Manwhitnney was used for data analysis. RESULTS: More than half 58.8% were <30 years of age,mean age was 31±7.29 ,range was 20-56 years.Only17.5%were having Mild to Extreme Anxiety and, 62.5% Nepalese nurses were having stress. Regarding coping strategies mean score is higher in positive reframing followed by acceptance.There was significant association between stress and demographic variables marital status and country.Nepalese Nurses working in Nepal were having more anxiety and stress mean score than Nepalese nurses working abroad. CONCLUSIONS: Nepalese nurses working in Nepal were having more anxiety and stress mean score than Nepalese nurses working in abroad. Mean score of coping strategies was higher in avoidant coping (Maladaptive coping) in nurses working in Nepal whereas mean score is higher in Approach coping (Adaptive coping) in Nepalese nurses working abroad.

What Has Changed in Patients Aged 65 and over Diagnosed with Breast Cancer during the COVID-19 Pandemic: A Single-Center Experience.

Introduction: The COVID-19 pandemic has a worldwide negative impact on healthcare systems. This study aims to determine how the diagnosis, clinicopathological features, and treatment approaches of patients with breast cancer (BC) diagnosed at ≥65 years old were affected during the pandemic. This survey has shown that patients, especially the elderly, had to postpone their BC health problems or delay their routine controls due to the risk of COVID-19 transmission, high mortality rates due to comorbidity, and restrictions. Materials and Methods: The medical records of 153 patients with BC diagnosed at ≥65 years old before (January-December 2019; group A, n = 61) and during (March 2020-May 2021; group B, n = 92) the COVID-19 pandemic were retrospectively analyzed. In addition, clinicopathological features of patients, including age, admission form, clinical stage, tumor (T) size-grade-histology-subtype, lymph node involvement, surgery type, and treatment protocols, were evaluated. Results: Patients mostly applied for screening purposes were included in group A and patients who frequently applied for diagnostic purposes due to their existing BC or other complaints were included in group B (p = 0.009). Group B patients had a higher clinical stage (p = 0.026) and had commonly larger (p = 0.020) and high-grade (p = 0.001) Ts. Thus, mastectomy and neoadjuvant systemic therapy were more commonly performed in group B (p = 0.041 and p = 0.005). Conclusion: The survey showed significant changes in BC diagnosis and treatment protocols for patients diagnosed at ≥65 years old during the COVID-19 pandemic. Postponing screening and delaying treatment leads to more advanced BC stages in elderly patients.

Study of Acute Kidney Injury in Patients Hospitalized with COVID-19: A Single Center Study in Bangladesh

Introduction: The incidence of acute kidney injury (AKI) associated with hospitalized corona virus disease -19(Covid-19) patients and associated outcomes are not well determined. This study describes the presentation, risk factors and outcomes of AKI in patients hospitalized with Covid-19. Material & Methods: In this cross sectional study, we reviewed the health records for all conveniently selected patients hospitalized with Covid-19 irrespective of co morbidity from 1st May to 31st July, 2020, at combined military hospital Dhaka, Bangladesh. Patients younger than 18 years, end stage kidney disease or with a kidney transplant recipient were excluded from the study. AKI was deûned according to kidney disease improving global outcome (KDIGO) criteria. Results: A total of 470 Covid-19 patients were recruited in this current study, out of them 67.02% were male and 32.98% of were female;with male to female ratio was 2:1. The mean age of the study population was 54.71(±14.31) years. AKI developed among 106 (22.55%) patients of whom 50 patients had CKD. The peak stages of AKI were stage 3 in 58(12.34%), followed by stage 1 in 37(7.87%), and stage 2 in 11(2.34%) patients. Renal replacement therapy was required (RRT) for 37(7.87%) patients. Risk factors included older age, hypertension, diabetes mellitus, cardiovascular disease, and chronic kidney disease and those who presented with prolong fever and breathlessness.AKI was commonly seen in patients with severe disease. Considerable number of patient had proteinuria 222(47.23%) and haematuria in 63 (13.40%) and were significantly associated with AKI. Elevated level of ferritin, D-dimer and procalcitonin were observed among 249(52.98%), 179(38.08%) and 138(35, 88%) patients respectively which were substantially correlated with AKI. COVID-19 patients complicated to acute kidney injury were strongly associated with higher mortality19 of 23 (82.60%). Conclusion: Renal involvement in COVID-19 (Corona virus-nephropathy) has a complex etiology. It is closely associated with severity of disease and indicating poor prognosis. Further study will be needed for better understanding the causes of AKI and patient outcomes. [ FROM AUTHOR] Copyright of Journal of Bangladesh College of Physicians & Surgeons is the property of Bangladesh College of Physicians & Surgeons 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.)

Machine Intelligent Techniques for COVID-19 Detection: A Critical Review and Analysis

Every human being is discussing a highly addressed topic in the current days which is about the COrona VIrus Disease (COVID) in 2019-2020. The outbreak of corona has affected the human race all over the world, the patient count is increasing day by day, and doctors are in a critically need of computer-aided diagnosis with machine learning (ML) algorithms that will discover and diagnose the coronavirus for a large number of patients. Also, it is more complicated to estimate the discharge time and the criticalness of the patient during treatment. Chest computed tomography (CT) scan was the best tool for the corona diagnosis. Also survival analysis methods in ML outperform better in predicting discharge time. In this, we survey on the COVID 19 diagnosis with a chain of CT scan pictures mined from the COVID-19 data set by using ML algorithms like marine predator, simplified suspected infected recovered (SIR), image acquisition, and some more techniques and also survival analysis techniques of ML. The survey clearly explains the models used up to now which are highly defined for the diagnosis of COVID-19 Virus. © 2021 IEEE.

The Combined Use of Neuropsychiatric and Neuropsychological Assessment Tools to Make a Differential Dementia Diagnosis in the Presence of "Long-Haul" COVID-19.

The longer term neurocognitive/neuropsychiatric consequences of moderate/severe COVID-19 infection have not been explored. The case herein illustrates a complex web of differential diagnosis. The onset, clinical trajectory, treatment course/response, serial neuroimaging findings, and neuropsychological test data were taken into account when assessing a patient presenting 8 months post-COVID-19 (with premorbid attention-deficit hyperactivity disorder, diabetes mellitus, mood difficulties, and a positive family history of vascular dementia). Her acute COVID-19 infection was complicated by altered mental status associated with encephalopathy and bacterial pneumonia. After recovery from COVID-19, the patient continues to experience persisting cognitive and emotive difficulties despite an ongoing psychopharmacotherapy regimen (16 + years), psychotherapy (15 + sessions), and speech-language pathology SLP; 2 × week/for 12 weeks). The purpose of her most recent and comprehensive neuropsychological evaluation was to determine the presence/absence of neurocognitive disorder. The patient is a 62-year-old Caucasian woman. Cognitive screening was completed 3 months post-acute COVID-19 as part of an SLP evaluation, and a full neuropsychological evaluation was conducted 8 months post-COVID-19 recovery on an outpatient basis (in person). The patient had serial neuroimaging. Initial neurological evaluation during acute COVID-19 included unremarkable brain computed tomography (CT)/magnetic resonance imaging. However, follow-up CT (without contrast) revealed, in part, "asymmetric perisylvian atrophy on the left." Full neuropsychological evaluation at 8 months post-COVID-19 recovery revealed a dysexecutive syndrome characterized by language dysfunction and affective theory-of-mind deficit, consistent with dementia. There is need for careful use of differential diagnosis in COVID-19 patients with multiple risk factors that make them more susceptible to long-term neurological complications post-COVID-19. Differential diagnosis should involve multidisciplinary assessment (e.g., neuropsychology, SLP, neurology, and psychiatry).