Immunological Response to Conventional COVID-19 Vaccines in a Cohort of Pakistani Healthy Recipients

Coronavirus disease 2019 mass vaccination has led to drastic reduction in hospitalizations and mortality. A number of case reports have emerged reporting coronavirus disease 2019 infection within days following vaccination. There is a need to understand development of immune antibodies in the early post-vaccination period. A prospective analysis of immunoglobulin M and immunoglobulin G kinetics was conducted during the first 28 d following vaccination with either CanSino or Sinovac vaccines in a cohort of 40 healthy volunteers. Serial blood samples were collected from the volunteers right before the first dose of vaccine (d 0) and then on d 4, d 7, d 14, d 21, d 24 and d 28 post-vaccination. Using enzyme-linked immunosorbent assay, circulating anti-severe acute respiratory syndrome coronavirus 2 receptor binding domain immunoglobulin M and immunoglobulin G antibodies were analyzed. Most vaccine recipients (31/40) did not develop any circulating immunoglobulin M. The remaining 9 recipients showed a typical immunoglobulin M curve with antibodies appearing on d 4, peaking on d 7 and declining on d 21 and beyond. Immunoglobulin G response was more typical within 38/40 recipients showing the appearance of immunoglobulin G on d 4, which continued till the end of the study period. This study demonstrates that vaccine-induced immunoglobulin M-based immunity cannot be relied during the first few days following vaccination and more time is needed to have a better picture of the real situation.

Evaluation of Factors Causing a Delay in Management and Hospital Stay in Diabetic Foot Amputation Patients: A Prospective Cohort Study

OBJECTIVE: To determine the rate of different amputation levels in diabetic foot patients and the incidence of repetitive foot surgeries and evaluate the factors causing a delay in hospital stay and amputation of patients. METHODOLOGY: This prospective cohort study was conducted in Dr. Ruth K.M. Pfau, Civil Hospital Karachi, Pakistan. The study selected 375 participants from the clinic's daily patient inflow from October 2021 to March 2022 using a non-probability consecutive sampling technique. Those who had a delay in hospital stay and amputation were further followed up from May-October 2022. The chi-square test and Kruskal Wallis test (p-value <0.05) were used to correlate the effect of the level of lower limb amputation and the cause of delay in amputation using SPSS version 24.0. RESULT(S): Total 246(65.60%) were males and 129(34.40%) were females. Toe amputation was the most commonly seen amputation in 173(46.1%) participants. About 168(44.8%) patients had some in-hospital delay stay during their treatment. Preoperative hurdles (Uncontrolled RBS, Osteomyelitis, etc.) were the most common factor causing an in-hospital delay in 92(24.5%) patients. The level of amputation performed was found to be statistically significant with factors causing a delay in hospital stay through chi-square (p=0.003*) and Kruskal Wallis test H (2) statistic= 13.3, df = 3, H (2), P=0.004*). CONCLUSION(S): Diabetic foot is a frequent cause of amputation globally, majorly in developing countries like Pakistan. On-time provision of treatment to these patients can decline the global amputation rate due to diabetic foot ulcers.Copyright © 2023 Syeda Anjala Tahir.

Modelling and Analysis of Hospital Inventory Policies during COVID-19 Pandemic

The global coronavirus pandemic (COVID-19) started in 2020 and is still ongoing today. Among the numerous insights the community has learned from the COVID-19 pandemic is the value of robust healthcare inventory management. The main cause of many casualties around the world is the lack of medical resources for those who need them. To inhibit the spread of COVID-19, it is therefore imperative to simulate the demand for desirable medical goods at the proper time. The estimation of the incidence of infections using the right epidemiological criteria has a significant impact on the number of medical supplies required. Modeling susceptibility, exposure, infection, hospitalization, isolation, and recovery in relation to the COVID-19 pandemic is indeed crucial for the management of healthcare inventories. The goal of this research is to examine the various inventory policies such as reorder point, periodic order, and just-in-time in order to minimize the inventory management cost for medical commodities. To accomplish this, a SEIHIsRS model has been employed to comprehend the dynamics of COVID-19 and determine the hospitalized percentage of infected people. Based on this information, various situations are developed, considering the lockdown, social awareness, etc., and an appropriate inventory policy is recommended to reduce inventory management costs. It is observed that the just-in-time inventory policy is found to be the most cost-effective when there is no lockdown or only a partial lockdown. When there is a complete lockdown, the periodic order policy is the best inventory policy. The periodic order and reorder policies are cost-effective strategies to apply when social awareness is high. It has also been noticed that periodic order and reorder policies are the best inventory strategies for uncertain vaccination efficacy. This effort will assist in developing the best healthcare inventory management strategies to ensure that the right healthcare requirements are available at a minimal cost. © 2023 by the authors.

COVID-19 Impact on Educational Institutions: A Detailed Analysis

This study draws attention to the global impact of e-learning during the Covid 19 era which is still not over completely. As part of the preventive measures and to counter the spread of the coronavirus;lockdowns and social distancing have been implemented, causing total paralysis of global activities including educational activities. As a result of the closures of the Universities, there is a shift from traditional learning to electronic learning (e-learning). This shift was necessary for the students to continue their studies and enabling them to graduate on time. However, such a shift created a significant increase in online classes, conferences, and meetings in fact entire world operations became online and completely dependent on the IT tools and related technology. Through the use of different electronic media, e-learning conveys preparation, training, and cooperation that has impacted students' perception, critical thinking, and other factors. This impact can be positive and or negative. This study offers an overview and related details of the process of e-learning as well as its advantages and disadvantages and also discusses the future of e-learning in Higher Education Institutes. It is evident from analysis and literature review that Covid created a vast gap between student learning, faculty teaching, and knowledge sharing. E-learning in the era of Covid-19 created more difficulties than ease of operations. To the best of the knowledge of researchers, there are limited studies conducted on this topic and this study will contribute to the enhancement of the existing body of knowledge. This study will be beneficial to the Higher Education Institutes, related educational ministries, and other regulatory bodies. © 2023 selection and editorial matter, Joanna Rosak Szyrocka, Justyna Zywiolek, Anand Nayyar and Mohd Naved;individual chapters, the contributors.

Lightweight Cost Effective Deep Learning Model for COVID-19 Detection using CXR Images

In the field of medical imaging, deep learning techniques have already proven to be quite a success.The global population's health and well being continue to be severely impacted by the Coronavirus Disease 2019 (COVID-19) pandemic, healthcare systems are unable to examine and diagnose patients as soon as they ought to. The various post COVID complications which may have been dealt in a better way if the virus was detected at an earlier stage and given appropriate clinical support. Chest radiography imaging is essential for detecting and tracking COVID-19 because of its effects on pulmonary tissues. Chest X-Ray(CXR) imaging is even more readily available than chest computed tomography(CT) imaging, especially in developing countries where CT scanners are too costly due to high equipment and maintenance costs. In this work we propose a very lightweight convolutional neural network (CNN), in which the chest X-Ray samples comprising of COVID-19, Non-COVID and Normal cases are analyzed without any human intervention. Our model gives comparable accuracy to other COVID-19 detection models proposed earlier while having significantly fewer parameters than them, which makes our model optimal for deployment on machines with low computing power. © 2022 IEEE.

Overcoming High Flow Line Pressure by Successful Installation of First Surface Jet Pump

The year 2020 has been challenging for the whole world due to the COVID pandemic. The unprecedented impacts of the world's recent lockdown and volatility of oil and gas markets tested the leadership and resilience of business models and repositioned strategies that will shape the industry for the next decade. Business survival has become critical during these unprecedented times, especially in the energy industry which has taken a significant hit due to the oil price fall and supply gluts. Existing plans are under observation, as it is vital to identify more efficient approaches and solutions for building future business resilience. Modern technologies can be used to support energy transition. The surface jet pump is one of the newest technologies that help lower the wellhead pressure of the well. It also reduces back pressure on the well. Thus, it enhances the flow rate. Additionally, the surface jet pump overcomes the flow line pressure into the existing pressurized flow lines without creating back pressure on the reservoirs. This technology can be utilized for many applications, including well-bore cleanup after completions, de-liquefying gas wells, producing heavy, viscous, or corrosive liquids, producing CO2 and natural gas wells. This paper is about the successful installation of the first-ever surface jet pump in the country. The pump was deployed in the Northern Iraq region to reduce the backpressure on the wells caused by the central production facility. The pump parameters were designed on Jet Evaluation and Modeling Software (JEMS) and 13G was selected as the optimum nozzle and throat combination for this project. The pump worked successfully, and the wellhead increased from 310 to 355 psi. And gross production from a single well was increased to around 850 BPD from 550 BPD generating about 0.4 Million USD/Month additional revenue. With the long-term impact of the recent pandemic on the energy market, it's clear that companies are more focused than ever on picking the best solutions to ensure the long-term viability and survival of their operations. The surface jet pump is one of the technological advancements for such solutions that have been successfully tested at this location. This paper goes on the technicalities of the technology and project. Copyright © 2022, Offshore Technology Conference.

A review on biochemical constituents of pumpkin and their role as pharma foods, a key strategy to improve health in post COVID 19 period

Progression of today's world has been given setback due to the adversity of a novel, viral, deadly outbreak COVID 19, which raised the concerns of the scientists, researchers and health related officials about the inherent and adaptive immune system of the living body and its relation with healthy diet balanced with pharma foods. Now world is coming out of the destructive pandemic era, the choice of right food can help to build and boost adaptive immunity and pumpkin due to excellent profile of functional and nutraceutical constituents could be the part of both infected and non-infected person's daily diet. Vitamins like A, C and E, minerals like zinc, iron and selenium, essential oils, peptides, carotenoids and polysaccharides present in pumpkin could accommodate the prevailing deficiencies in the body to fought against the viral pathogens. In current post COVID 19 scenario adequate supply of healthy diet, balanced with pharma foods could play a basic role in boosting immune system of the populations. This review covers the pharmacological activities of pumpkin functional constituents in relation with COVID 19 pandemic. Pumpkins are well equipped with nutraceuticals and functional bioactives like tocopherols, polyphenols, terpenoids and lutein therefore, consumption and processing of this remarkable vegetable could be encouraged as pharma food due to its antihyperlipidemic, antiviral, anti-inflammatory, antihyperglycemic, immunomodulatory, antihypertensive, antimicrobial and antioxidant potential. Need of healthy eating in current post COVID 19 period is very crucial for healthy population, and medicinal foods like pumpkin could play a vital role in developing a healthy community around the globe. Graphical : [Figure not available: see fulltext.]. © 2023, The Author(s).

COVID-19 Outbreak Prediction by Using Machine Learning Algorithms

COVID-19 is a contagious disease and its several variants put under stress in all walks of life and economy as well. Early diagnosis of the virus is a crucial task to prevent the spread of the virus as it is a threat to life in the whole world. However, with the advancement of technology, the Internet of Things (IoT) and social IoT (SIoT), the versatile data produced by smart devices helped a lot in overcoming this lethal disease. Data mining is a technique that could be used for extracting useful information from massive data. In this study, we used five supervised ML strategies for creating a model to analyze and forecast the existence of COVID-19 using the Kaggle dataset” COVID-19 Symptoms and Presence.” RapidMiner Studio ML software was used to apply the Decision Tree (DT), Random Forest (RF), K-Nearest Neighbors (K-NNs) and Naive Bayes (NB), Integrated Decision Tree (ID3) algorithms. To develop the model, the performance of each model was tested using 10-fold cross-validation and compared to major accuracy measures, Cohan's kappa statistics, properly or mistakenly categorized cases and root means square error. The results demonstrate that DT outperforms other methods, with an accuracy of 98.42% and a root mean square error of 0.11. In the future, a devised model will be highly recommendable and supportive for early prediction/diagnosis of disease by providing different data sets. © 2023 Tech Science Press. All rights reserved.

Association of Body Mass Index with the Severity of SARS-COVID-19 Pneumonia in Hospitalized Patients

Objective: To determine the association of body mass index with the severity of COVID-19 pneumonia in hospitalized patients. Study Design: Cross-sectional study. Place and Duration of Study: Pak Emirates Military Hospital, Rawalpindi Pakistan, form May to Jun 2021. Methodology: Patients diagnosed with COVID-19 pneumonia on PCR and chest imaging and admitted to our hospital were included in the study. Body mass index was calculated on the first day of hospital admission, and they were followed up for two weeks during the disease. Increased oxygen demand, duration of admission, CT severity score and use of non-invasive ventilation were compared in patients with normal and increased body mass index. Results: A total of 800 COVID-19 patients admitted to the hospital were included in the final analysis. The mean age of the study participants was 41.36±4.55 years. Out of 800 patients, 337(42.1%) had normal BMI, 420(52.5%) were classed in the category of overweight and 43(5.4%) were obese. Furthermore, it was seen that increased demand for oxygen, high CT severity score and longer duration of hospital admission had a statistically significant relationship (p-value<0.05) with high body mass index. Conclusion: More than half of the patients admitted after diagnosis of COVID-19 had higher than normal body mass index. A significant association was found between increased demand for oxygen, high CT severity score, longer hospital admission duration, and high body mass index. © 2022, Army Medical College. All rights reserved.

Sustaining innovation during downsizing strategy through knowledge coupling, business process digitization, and market capitalizing agility

COVID-19 has developed chaos and uncertainty for small and medium enterprises (SMEs) causing mass downsizing. Under such uncertainty, innovation is key to survival and agility to growth. This study examines role of knowledge coupling and business process digitization (BPD) in sustaining innovation through market capitalizing agility (MCA) besides downsizing strategy. Data have been collected from top and mid management of Chinese manufacturing SMEs and analyzed with knowledge-based view and self-tuning model through Smart-PLS 4. Knowledge coupling positively contributes to MCA and innovation irrespective of downsizing strategy. Effect of BPD on innovation performance is same;however, insignificant on MCA in no-downsizing sample. Likewise, MCA positively influences innovation performance and positively mediates between knowledge coupling (BPD) and innovation performance only during downsizing. This study is first of its kind to establish mediating effect of MCA between knowledge coupling (BPD) and innovation performance during downsizing phase and offers significant theoretical and practical implications. JEL Classification: J63, O31

SMEs' sustainable innovation performance during pandemic: impact of knowledge coupling and parallel-mediation of ambidexterity and market capitalising agility

Covid-19 pandemic has caused crisis and uncertainty which adversely influenced innovation performance of small and medium enterprises (SMEs). Innovation is a primary source of survival and growth in SMEs;therefore, this study aims to develop and empirically analyse conceptual model that sustains innovation performance of SMEs during the pandemic. This study integrates knowledge-based view (KBV) with a self-tuning model to examine Chinese manufacturing SMEs. Survey data of 306 responses were collected from the top and mid management and analysed through Smart-PLS 3. Results indicate a positive effect of knowledge coupling on innovation performance;besides, ambidexterity and market capitalising agility (MCA) positively mediate between knowledge coupling and SME innovation performance. This study contributes to KBV, self-tuning, strategy, innovation and crisis management literature. To practitioners, this study advises developing dynamic capabilities to sustain innovations during pandemic crisis. This is the earliest empirical examination to mediate the effect of knowledge coupling on innovation performance through ambidexterity and MCA during a crisis.

Maritime Labour Market Dynamics (MLMD) and Futuristic Approach in Developing Skilled Global Maritime Labour (SGML)

Oceans are very vital for humans because holding 97% of water, 70% of the oxygen we breathe, ecosystem, food, energy, trade and leisure. The globalized maritime industry with more than 74,000 merchant ships transporting 90% of the world's cargo with around 1.89 million seafarers. The world will be experiencing a few megatrends demanding high skilled workforce. Sustainable development is impossible without upskilled force. LMD is always changing, attributable to demand and supply, matching efficiency, innovations, high-tech systems, education level, productivity, unemployment etc. Maritime labour market data shows a decline in job offers. Supply and demand affected during recent times due to Covid -19 pandemic and the Russian/Ukraine conflict. This paper highlights MLMD and SGML and suggests a futuristic approach for remodeling maritime labour skills. A survey through IAMU member universities will present a very clear picture of the issue. Paper suggests approaching IMO/IAMU to introduce MLMS Course in collaboration with the ILO and other maritime stakeholders. It also suggests IAMU Maritime Skilled Labour Data Program (MSLDP), IAMU Maritime Labour Market Data Program (MLMDP), and IAMU Maritime Skilled Labour Standards (MSLS) according to maritime industry requirements. © 2022 IAMUC. All Rights Reserved.

Dynamics of Coronavirus and Malaria Diseases: Modeling and Analysis

COVID-19 has been declared a pandemic by the WHO on the 11th of Mar. 2020. This virus is believed to be born in China in 2019. The study of this disease is very complicated and challenging. In this manuscript, a fractional-order epidemic model to study the impact of COVID-19 and malaria disease has been proposed and analysed. The model is formulated with the help of fractional order by using the Caputo-Fabrizio derivative. The model is solved numerically with the help of the ABM method. The parameters which characterize the disease transmission are taken from real data of India [1]. The qualitative and quantitative behaviour of the proposed model is examined. The numerical work is performed to authenticate the analytic solutions. It is observed that the malaria disease acts as a launching pad for the COVID-19 dynamics as it weakens of humans immune system. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

COVID-19 Outbreak Prediction by Using Machine Learning Algorithms

COVID-19 is a contagious disease and its several variants put under stress in all walks of life and economy as well. Early diagnosis of the virus is a crucial task to prevent the spread of the virus as it is a threat to life in the whole world. However, with the advancement of technology, the Internet of Things (IoT) and social IoT (SIoT), the versatile data produced by smart devices helped a lot in overcoming this lethal disease. Data mining is a technique that could be used for extracting useful information from massive data. In this study, we used five supervised ML strategies for creating a model to analyze and forecast the existence of COVID-19 using the Kaggle dataset” COVID-19 Symptoms and Presence.” RapidMiner Studio ML software was used to apply the Decision Tree (DT), Random Forest (RF), K-Nearest Neighbors (K-NNs) and Naive Bayes (NB), Integrated Decision Tree (ID3) algorithms. To develop the model, the performance of each model was tested using 10-fold cross-validation and compared to major accuracy measures, Cohan’s kappa statistics, properly or mistakenly categorized cases and root means square error. The results demonstrate that DT outperforms other methods, with an accuracy of 98.42% and a root mean square error of 0.11. In the future, a devised model will be highly recommendable and supportive for early prediction/diagnosis of disease by providing different data sets. © 2023 Tech Science Press. All rights reserved.

Anxiety of Caregivers in Telerehabilitation Sessions during Covid 19

Introduction: Pandemics affect people in a defeatist manner and become stressful for people with relatives which need specific forms of care and attention. The study was conducted to find out if anxiety prevails among caretakers during the Covid-19 Pandemic as according to the literature review caregivers experience burden and fears related to their care-recipients and telerehabilitation. Material and Methods: The study used cross sectional survey and quantitative research.50 care-givers participated in the research where they filled online questionnaires inspired and derived from care-giver burden scale and beck anxiety inventory. Anxiety was clearly evident as most of the care-givers agreed to have feelings of nervousness 19 (38%), feeling anxious 18 (36%), feeling distressed 22 (44%), complaints about emotional burden 23 (43%) and 23 (46%) constant immersion in duties towards care-recipients. Results: SPSS tables depict the analyzed results and their interpretation. The results show 36%of the care-givers agreed that they face anxiety when a situation gets out of control, 44% were distressed about not getting enough help from healthcare team and other family and friends, 55% are apprehensive about their present condition and 46% are emotionally challenged and constantly immersed in duties owing to their family members. Conclusions: Anxiety and depression as a result of caregiving burden is common among care-givers and needs to be addressed as soon as possible. This makes it essential that health professionals pay heed and attention to develop interventions for care-givers and provide them with pertinent knowledge.

The Impact of COVID-19 on Training Opportunities and Long-Term Career Aspirations of UK Based Doctors

Background: The onset of the COVID-19 pandemic in November 2019 has witnessed a reactive 'covidisation' of NHS care services. Training opportunities for junior doctors have suffered and psycho-social impacts are anticipated. This study will seek to assess these factors and their impact on doctors' career aspirations. Method: A five question survey was disseminated to 350 junior doctors in the Yorkshire and Humber deanery. 115 responses were gained;78 Foundation doctors (FY1-2), 24 Core/Early specialist trainees (CT 1-2/ ST1-2) and 13 Specialist Registrars. Results: Negative impacts on training include teaching commitments (61.7%) and work-based assessments (48%). A change in treatment protocols (67%) and acute referrals (58%) were also identified. Concerns regarding personal wellbeing were highlighted by 65%, with a greater willingness to consider taking time out of training (32%) or leave UK medical practice altogether (22%). Discussion: The number of doctors immediately progressing into specialist training has almost halved. COVID era practice has impacted on training opportunities, clinical experience, and the ability to meet ARCP requirements. It has also led to high levels of personal concern amongst junior doctors surveyed. These factors may stimulate a greater willingness to take time out of training (1/3) or leave UK based medical practice (1/4). Conclusions: It is evident that new and existing concerns amongst junior doctors have been heightened by COVID-19. This data provides a useful snapshot and may be utilised in ensuring the continued health and robustness of the UK's junior doctor workforce and its ability to meet the evolving challenges of global healthcare.

Attitudes Towards COVID-19 Testing in Pediatric Emergency Department Settings in Pakistan

Background: The COVID-19 global pandemic has shed light on the importance of testing to stop the spread of disease. For a developing country with a large population of over 200 million inhabitants such as Pakistan, widespread testing can be difficult. To date, 957,371 cases have been confirmed and over 14 million tests have been performed in Pakistan, with only 1% of the population vaccinated. In a country already burdened by health disparities with little to no resources, the challenges became ever apparent as case numbers grew. According to the WHO, complacency among the population in cooperating with public protective measures is a rising challenge. Several violent incidents have occurred in hospital wards in Pakistan, prompting medical staff to fear for their lives and demand extra security not only from the virus, but from volatile patients and families. The incidents are thought to be rooted in a mix of anger at a lack of resources, and mistrust of the medical system. The objective of this study was to survey Pediatric emergency medicine (PEM) physicians in Pakistan on their ability to test for COVID-19 and their limitations experienced. Methods: An anonymous prospective survey was performed from February to March 2021 in association with the ChildLife Foundation, a nonprofit organization that operates and manages Pediatric EDs in 10 government teaching hospitals in the province of Sindh. 170 PEM providers were surveyed on their experiences with COVID-19 testing, reasoning for why testing was not performed when infection was suspected and reasoning for patient refusals. Results: 68% of respondents had COVID-19 on their differential for patients under their care in the week prior to survey. However, 49% of respondents did not order any COVID-19 testing. 37% of those providers had at least one patient in whom COVID-19 was on the differential. 81% of providers claimed to collect COVID-19 testing every time when suspected. When surveying reasoning for not acquiring COVID-19 testing, providers listed patient refusal as the top reason, followed by limited availability and cost, mild presentation of disease, patient leaving AMA, fear of violence against healthcare professionals, social stigma/fear from patients of being labelled as COVID-19 positive and denial of the diagnosis. Conclusion: According to this survey, PEM providers in Pakistan were not always able to send COVID-19 testing, when indicated, due to a variety of factors. Testing limitations despite suspicion for disease can be a major hurdle in identifying cases and limiting spread in unvaccinated populations.

Covid-19 Detection by using Deep learning-based Custom Convolution Neural Network (CNN)

The early diagnosis and treatment of COVID-19 has been a challenge all over the world. It is challenging to manufacture many testing kits and even then, their accuracy rate is very low. Studies carried out recently show that chest x-ray images are of great help in the diagnosis of COVID-19. In this study, we have developed a COVID-19 detection model that by observing the chest x-ray images of the patient, detects that either the patient is affected by COVID-19 or not. The model is developed using a custom Convolutional Neural Network (CNN) that differentiates between COVID-19 and healthy x-ray images so that the patient can be diagnosed and quarantined on time to prevent the spread of the pandemic. We used two different datasets which are publicly available for the training and validation of this model. Upon completion, the proposed model yields an accuracy of almost 98%. Upon further training, our model will be able to be used as a COVID-19 detection tool in hospitals worldwide and will play a vital role in early detection and timely containment of the pandemic.

COVID-19 HEALTH CRISIS: UNEXPECTED MISERIES FOR MANY

Background:As the pandemic progresses, we are growing increasingly aware that COVID-19 affects multiple parts of the body beyond the lungs. Objective: We aimed to review the literature to outline the COVID-19 effect on hair, vision, thinking, hearing, fertility, taste and smell, skin and gastro-intestine (GI), and its health crisis among COVID-19 infected patients.Method: We searched the database «PubMed» which included studies that measured COVID-19 effect on hair, vision, thinking, hearing, fertility, taste and smell, skin, and GI. Results: A total of 60 studies were reviewed and screened based on titles and s. Of these, only 15 studies were determined to meet the eligibility criteria for discussion. The health crisis associated with hair, vision, thinking, hearing, fertility, taste and smell, skin, and GI were baldness, hair shedding, conjunctivitis, pink-eye syndrome, sore-eyes, brain fog, short-term memory loss, reduction in male sperm concentration, altered sperm cell shape, morbidity, tinnitus, loss of hearing, reduce taste and loss of smell, acne, eczema, psoriasis, and rosacea, lacy and dusky rashes on the skin, loss of appetite, nausea, vomiting diarrhea, and abdomen pain. Conclusion: Scientists, researchers and clinicians are still learning, observing and knowledge is evolving daily related to COVID-19 infection.