Implementing Simulation Based Learning (SBL) to Reduce the Gap in Surgical Training, A Theoretical Evaluation Approach

Background The current surgical training is severely affected by COVID-19 pandemic with redeployment and reduced number of elective procedure across NHS hospitals, this has affected both core and higher surgical trainees, rendering the traditional apprenticeship model obsolete. It became evident that the future of Surgical training and innovation will require a combination of simulation and operative exposure to overcome the obstacle of reduced exposure in surgical education and operative training. Discussion In our theoretical analysis, we will discuss the efficacy, safety and impact of relying on SBL to fill the gaps in surgical training. Clinical exposure alone will not be sufficient to train procedure based speciality trainees to their highest proficiency. SBL is one design that is supported by learning theories such as Transformational Learning and Experiential Learning Theory. In a high fidelity simulation, such as laparoscopic simulation courses, all concepts of facilitated learning are fulfilled which strongly supports our hypothesis. On balance, given the complexity of skills learnt, it remains difficult to measure the efficacy of transferring the learnt capabilities into practice and standardise this among learners. SBL also leaves non-technical skills un-assessed in depth. Conclusion The disruption of training due to COVID-19 affected our procedure based learning, this leaves us with a dilemma to catch-up with these unmet training needs. SBL could be one of the adjuncts that fill in the gaps on the short and medium term. Implementing SBL in surgical training curriculum, should be evaluated for efficacy and cost effectiveness.

From HIV to COVID-19, Molecular mechanisms of pathogens' trade-off and persistence in the community, potential targets for new drug development.

Background: On the staggering emergence of the Omicron variant, numerous questions arose about the evolution of virulence and transmissibility in microbes. Main body of the abstract: The trade-off hypothesis has long speculated the exchange of virulence for the sake of superior transmissibility in a wide array of pathogens. While this certainly applies to the case of the Omicron variant, along with influenza virus, various reports have been allocated for an array of pathogens such as human immunodeficiency virus (HIV), malaria, hepatitis B virus (HBV) and tuberculosis (TB). The latter abide to another form of trade-off, the invasion-persistence trade-off. In this study, we aim to explore the molecular mechanisms and mutations of different obligate intracellular pathogens that attenuated their more morbid characters, virulence in acute infections and invasion in chronic infections. Short conclusion: Recognizing the mutations that attenuate the most morbid characters of pathogens such as virulence or persistence can help in tailoring new therapies for such pathogens. Targeting macrophage tropism of HIV by carbohydrate-binding agents, or targeting the TMPRSS2 receptors to prevent pulmonary infiltrates of COVID-19 is an example of how important is to recognize such genetic mechanisms.

COVID-19 and myocarditis: a brief review.

Cardiovascular complications (especially myocarditis) related to COVID-19 viral infection are not well understood, nor do they possess a well recognized diagnostic protocol as most of our information regarding this issue was derived from case reports. In this article we extract data from all published case reports in the second half of 2020 to summarize the theories of pathogenesis and explore the value of each diagnostic test including clinical, lab, ECG, ECHO, cardiac MRI and endomyocardial biopsy. These tests provide information that explain the mechanism of development of myocarditis that further paves the way for better management.

Early assessment of lung function in coronavirus patients using invariant markers from chest X-rays images.

The primary goal of this manuscript is to develop a computer assisted diagnostic (CAD) system to assess pulmonary function and risk of mortality in patients with coronavirus disease 2019 (COVID-19). The CAD system processes chest X-ray data and provides accurate, objective imaging markers to assist in the determination of patients with a higher risk of death and thus are more likely to require mechanical ventilation and/or more intensive clinical care.To obtain an accurate stochastic model that has the ability to detect the severity of lung infection, we develop a second-order Markov-Gibbs random field (MGRF) invariant under rigid transformation (translation or rotation of the image) as well as scale (i.e., pixel size). The parameters of the MGRF model are learned automatically, given a training set of X-ray images with affected lung regions labeled. An X-ray input to the system undergoes pre-processing to correct for non-uniformity of illumination and to delimit the boundary of the lung, using either a fully-automated segmentation routine or manual delineation provided by the radiologist, prior to the diagnosis. The steps of the proposed methodology are: (i) estimate the Gibbs energy at several different radii to describe the inhomogeneity in lung infection; (ii) compute the cumulative distribution function (CDF) as a new representation to describe the local inhomogeneity in the infected region of lung; and (iii) input the CDFs to a new neural network-based fusion system to determine whether the severity of lung infection is low or high. This approach is tested on 200 clinical X-rays from 200 COVID-19 positive patients, 100 of whom died and 100 who recovered using multiple training/testing processes including leave-one-subject-out (LOSO), tenfold, fourfold, and twofold cross-validation tests. The Gibbs energy for lung pathology was estimated at three concentric rings of increasing radii. The accuracy and Dice similarity coefficient (DSC) of the system steadily improved as the radius increased. The overall CAD system combined the estimated Gibbs energy information from all radii and achieved a sensitivity, specificity, accuracy, and DSC of 100%, 97% ± 3%, 98% ± 2%, and 98% ± 2%, respectively, by twofold cross validation. Alternative classification algorithms, including support vector machine, random forest, naive Bayes classifier, K-nearest neighbors, and decision trees all produced inferior results compared to the proposed neural network used in this CAD system. The experiments demonstrate the feasibility of the proposed system as a novel tool to objectively assess disease severity and predict mortality in COVID-19 patients. The proposed tool can assist physicians to determine which patients might require more intensive clinical care, such a mechanical respiratory support.