A Novel SARS-COV-2 Variant Omicron Disseminating Evaluation

Omicron is a relatively new form of COVID-19 that has created an unavoidable and life-threatening situation to the entire world since late 2021. Absence of appropriate vaccination, medication, the epidemiological cycle has become more complex. This study primarily concentrates on the analysis of genome sequence for COVID-19 variants. To conduct such analysis, two datasets are collected from Kaggle and GISAID. Using these datasets, the globally existing genome sequences are identified and insights regarding the countries that are carrying significantly higher genome sequence count are provided. This investigation analyzes the worldwide virus variants and further identifies that the United States and United Kingdom are the countries where proper inspection should be provided because of the genome sequence count. An adequate idea regarding the mutations of the Omicron virus is also considered in this study. To address this issue, recent genome sequence data ranging from February, 2022 to 10th March, 2022 is analyzed to understand how the latest arrival, Omicron, is perturbing the world. This study emphasizes on the constant surveillance of genome sequences among all the countries which in turn will benefit the health care professionals and frontline healthcare workers as well as the Governments can take necessary policies and precautions to combat such pandemic. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Predictors of in-hospital mortality in COVID-19: a study across two peripheral district general hospitals in UK

Aim: The mortality from Coronavirus Disease 2019 (COVID-19) has remained a significant medical challenge. Internationally, patient demographics and pre-existing co-morbidities are significant determinants of mortality from COVID-19. The mortality-risk in a local population is difficult to determine. The objective of our study is to examine the risk posed by epidemiological and demographic variables, and co-morbidities in our local population. Method: A retrospective, observational study was conducted on confirmed COVID-19 patients, identified from the local microbiology database. A search of the electronic patient records was performed to collect demographic details and co-morbidities. Chi-square test and logistic regression analysis of the demographic variables and co-morbidities were utilised to calculate the predictive-risk for in-hospital mortality of adult COVID-19 patients. Results: Final analysis included 263 samples. Univariate logistic regression analysis was performed using age as an independent categorical predictor with two cohorts - those <60 and those 60 years old. Age (X2 =17.12, p<0.001) was found to be an independent predictor of mortality - this was independent of sex (X2 =1.784, p<0.182). Charlson Comorbidity Index (CCI) score was found to be a significant predictor of adverse outcome. The odds of death for patients with CCI scores 0-4 was less than half (44.8%) of those with CCI scores 5 (p=0.005). Patients with no pre-existing medical conditions had a lower mortality-risk (OR=0.181, p=0.022) than those with known medical conditions. Pre-existing renal disease predicted a poor outcome (OR=1.996, p=0.027). The odds of death for the patients coming from their own-home was only 26% of the odds for those from a longterm care-home. Long-term care facility, advanced age (OR=1.058, p <0.001), and long-term oral steroid (OR=3.412, p=0.016) use were all associated with a poor prognosis. Conclusion: People aged 60 years, residence in a long-term care-home, pre-existing renal diseases, a high CCI score and long-term oral steroids use were associated with an increased mortality-risk.

Global Level of Harm experienced by Bariatric Surgeons for Bariatrics surgical interventions: An exploration of predictors

Background: COVID-19 pandemic has taken the world by surprise with the depth and breadth of its effect on all walks of life, bariatric surgery being no exception. With the scientific literature hitherto unable to comment and ascertain the influence of the COVID-19 pandemic on bariatric surgery and the level of harm experienced by bariatric surgeons, we- TUGS 'Level of Harm' collaborative group- attempted to gauge the effect of the said pandemic on bariatrics surgery specifically vis a vis the level of harm experienced by bariatric surgeons due to the pandemic. Method(s): A virtual questionnaire- developed on both: Google forms and Survey Monkey- was circulated via TUGS social media platforms to reach bariatric consultant surgeons, fellows and residents practising throughout the world in a bid to explore the influence of the COVID-19 pandemic on their surgical practice including but not limited to the annual surgical volume including re-do surgeries volume and postoperative complications. Moreover, they were also requested to categorise their respective level of harm vis a vis bariatric surgical interventions they undertake. After de-identification of the data, SPSS (V.26) was adopted to undergo statistical analysis. After exploring the dataset by descriptive analyses, the Chi-square test was applied to pursue the association of categorical variables with the reported level of harm. A double-sided p-value of less than 0.05 was considered statistically significant. Result(s): 16.8% of the respondents (21/125) indicated no harm vis a vis bariatrics surgery work whereas a comparative 18.4% of the respondents (23/125) reported moderate harm with significant worsening of symptoms. None of those who indicated less than 10% increase in surgery waitlisted patients being subjected to endoscopic interventions (0/14) reported Moderate Harm for bariatrics surgery work with significant worsening of symptoms whereas 1 in every 3 of those who indicated between 10% to 25% increase in surgery waitlisted patients being subjected to endoscopic interventions (5/15) reported such level of harm for bariatrics surgery work. (p < 0.001) Upon exhaustive sub-group analysis, it was uncovered that 33.6% of bariatrics surgical professionals perceived no harm (no evidence of change in clinical condition) during gastric band or related surgery work with only 4% perceiving Moderate Harm (significant worsening of symptoms/ comorbidities control/ minor increase in medications) for such surgical interventions. All of those who reported No harm for gastric band or related surgical work reported that Single anastomosis duodeno-ileal bypass (SADI-S) accounts for 10% of their practice whereas none of those who indicated that SADI-S accounts for more than 10% of their practice reported No harm for such surgical work. (p = 0.019) Conclusion(s): The global snapshot illustrates a trend of low harm vis a vis bariatrics surgery work in surgical professionals practising in the private sector with a lesser number of patients developing COVID-19 postoperatively and no postoperative COVID-19 related mortality. The patient being subjected to endoscopic intervention portends a higher level of harm for bariatrics surgical work- strict adherence to criteria and safety protocols being a logical inference. For gastric band and related surgery work, preoperative COVID-19 testing appears to be influenced by confounders in its effect on the surgeon's level of harm for the said interventions warranting further exploration. SADI-S, at a cut-off of 10%, exhibits strong interaction with the surgeon's level of harm for gastric band insertion and relation surgery work. Women surgical professionals came out to exhibit equivalent mental resilience and technical prowess at par with their male colleagues when it came to bariatrics surgical intervention.

Pharmacy Impact for Distinguishing Normal Face from Abnormal Face Due to COVID-19

In today's world face detection is the most important task. Due to the chromosomes disorder sometimes a human face suffers from different abnormalities. In the recent scenario, the entire globe is facing enormous health risks occurred due to Covid-19. To fight against this deadly disease, consumption of drugs is essential. Consumption of drugs may provide some abnormalities to human face. For example, one eye is bigger than the other, cliff face, different chin-length, variation of nose length, length or width of lips are different, etc. To assess these human face abnormalities, the application of computer vision is favoured in this study. This work analyses an input image of human's frontal face and performs a segregation method to separate the abnormal faces. In this research work, a method has been proposed that can detect normal or abnormal faces from a frontal input image due to COVID-19. This method has used Fast Fourier Transformation (FFT) and Discrete Cosine Transformation offrequency domain and spatialdomain analysis to detect those faces.

Emergency surgery on patients receiving dual antiplatelet therapy — review of a challenging surgical problem

Patients on Dual Antiplatelet therapy often suffer from surgical problems that necessitate urgent surgery. Platelet dysfunction induced by the medications exposes them to the risk of major perioperative haemorrhage. Cessation of antiplatelet agents on the other hand increases the risk of adverse outcome due to their concomitant medical illnesses. We report our experience of performing major surgeries in two patients receiving dual antiplatelet therapy whose medications had to be continued perioperatively. The multidisciplinary care involved in optimisation and monitoring of the patients ensured a successful immediate recovery. We lost one of the patients who contracted COVID-19 later and succumbed from his medical problems but could discharge the other patient successfully.

Predictors of in-hospital mortality in covid-19: A study across two peripheral district general hospitals in UK

Aim-The mortality from Coronavirus Disease 2019 (COVID-19) has remained a significant medical challenge. Internationally, patient demographics and pre-existing co-morbidities are significant determinants of mortality from COVID-19. The mortality-risk in a local population is difficult to determine. The objective of our study is to examine the risk posed by epidemiological and demographic variables, and co-morbidities in our local population. Method-A retrospective, observational study was conducted on confirmed COVID-19 patients, identified from the local microbiology database. A search of the electronic patient records was performed to collect demographic details and co-morbidities. Chi-square test and logistic regression analysis of the demographic variables and co-morbidities were utilised to calculate the predictive-risk for in-hospital mortality of adult COVID-19 patients. Results-Final analysis included 263 samples. Univariate logistic regression analysis was performed using age as an independent categorical predictor with two cohorts – those <60 and those ≥60 years old. Age (2 =17.12, p<0.001) was found to be an independent predictor of mortality – this was independent of sex (2 =1.784, p<0.182). Charlson Comorbidity Index (CCI) score was found to be a significant predictor of adverse outcome. The odds of death for patients with CCI scores 0-4 was less than half (44.8%) of those with CCI scores ≥5 (p=0.005). Patients with no pre-existing medical conditions had a lower mortality-risk (OR=0.181, p=0.022) than those with known medical conditions. Pre-existing renal disease predicted a poor outcome (OR=1.996, p=0.027). The odds of death for the patients coming from their own-home was only 26% of the odds for those from a long-term care-home. Long-term care facility, advanced age (OR=1.058, p <0.001), and long-term oral steroid (OR=3.412, p=0.016) use were all associated with a poor prognosis. Conclusion-People aged ≥60 years, residence in a long-term care-home, pre-existing renal diseases, a high CCI score and long-term oral steroids use were associated with an increased mortality-risk.

Pharmacy Impact on Covid-19 Vaccination Progress Using Machine Learning Approach

The novel coronavirus disease (COVID-19) has created immense threats to public health on various levels around the globe. The unpredictable outbreak of this disease and the pandemic situation are causing severe depression, anxiety and other mental as physical health related problems among the human beings. This deadly disease has put social, economic condition of the entire world into an enormous challenge. To combat against this disease, vaccination is essential as it will boost the immune system of human beings while being in the contact with the infected people. The vaccination process is thus necessary to confront the outbreak of COVID-19. The worldwide vaccination progress should be tracked to identify how fast the entire economic as well as social life will be stabilized. The monitor of the vaccination progress, a machine learning based Regressor model is approached in this study. This vaccination tracking process has been applied on the data starting from 14th December, 2020 to 24th April, 2021. A couple of ensemble based machine learning Regressor models such as Random Forest, Extra Trees, Gradient Boosting, AdaBoost and Extreme Gradient Boosting are implemented and their predictive performance are compared. The comparative study reveals that the Extra trees Regressor outperforms with minimized mean absolute error (MAE) of 6.465 and root mean squared error (RMSE) of 8.127. The uniqueness of this study relies on assessing as well as predicting vaccination intake progress by utilizing automated process offered by machine learning techniques. The innovative idea of the method is that the vaccination process and their priority are considered in the paper. Among several existing machine learning approaches, the ensemble based learning paradigms are employed in this study so that improved prediction efficiency can be delivered.