Issue 2 (April)

In December 2019, a novel coronavirus, now named as SARS-CoV-2, caused a series of acute atypical respiratory diseases in Wuhan, Hubei Province, China. The disease caused by this virus was termed COVID-19. The virus is transmittable between humans and has caused pandemic worldwide. The number of death tolls continues to rise and a large number of countries have been forced to do social distancing and lockdown. Lack of targeted therapy continues to be a problem. Epidemiological studies showed that elder patients were more susceptible to severe diseases, while children tend to have milder symptoms. Here we reviewed the current knowledge about this disease and considered the potential explanation of the different symptomatology between children and adults.

Background: Meta-analysis research was implemented to appraise the effect of resident participation (RP) in ophthalmic surgery (OS) on wound dehiscence (WD). Methods: Inclusive literature research until April 2023 was done, and 645 interconnected researches were revised. The 7 picked researches, enclosed 4224 OS persons were in the utilized researchers' starting point, 2189 of them were utilizing RP, and 2035 were no RP. Odds ratio (OR) and 95% confidence intervals (CIs) were utilized to appraise the consequence of RP in OS on WD by the dichotomous approach and a fixed or random model. Results: RP had significantly higher WD (OR, 1.69; 95% CI, 1.04-2.76, p=0.03) compared to no RP in OS persons. Conclusions: RP had significantly higher WD compared to no RP in OS persons. However, caution needs to be taken when interacting with its values since there was a low sample size of some of the chosen researches and a low number of researches found for the comparison in the meta-analysis.

An artificial intelligence-driven Chatbot called Chat Generative Pre-trained Transformer, created by Open artificial intelligence, is making waves in many industries. Its foundation in the Generative Pre-trained Transformer language model is where its name originates. The most promising aspect of Chat Generative Pre-trained Transformer is that, compared to other artificial intelligence models, it can provide responses to text input that are almost human-like through the use of deep learning techniques. The public's increasing reliance on artificial intelligence technology is indicated by its quick integration across a range of businesses. Therefore, it is crucial to assess Chat Generative Pre-trained Transformer's possible effects on clinical and translational medicine research in academic settings seriously.

Worldwide, medical education has been impacted by the coronavirus pandemic. As academic institutions endeavor to impart medical knowledge through innovative approaches, this ongoing education guarantees the future workforce of the National Health Service (NHS) is well-versed. When developing new medical curriculum, innovative approaches to online instruction and online delivery of practical skills to medical students should be taken into account.

Every academic institution is shaken by the recent development of Chat Generative Pre-trained Transformer, and although we still don't fully grasp all of its potential and hazards, it is worthwhile to offer some first analysis. Academic prospects for Chat Generative Pre-trained Transformer are unparalleled because of its remarkable human-like skills, which outperform the majority of recently released tools. Over the past few months, Chat Generative Pre-trained Transformer has received extraordinary interest from the news and academic community (as of 23/02/2023, there were 650,000,000 results in a Google search for the term). Since it is unlikely that the chatbot was designed with the intention of serving as a stand-in for academic writing, its use in academic writing is a byproduct of clever artificial intelligence. If given the chance, students everywhere would find a way around assessments, thus we are all worried that some students would take advantage of it despite its advantages. Although there isn't now a problem involving assessment integrity in academics, it is impossible to overlook the development of powerful artificial intelligence and tools that could facilitate cheating. Some of us think that the use of Chat Generative Pre-trained Transformer in assessments has some epistemic consequences; yet, these risks do not indicate that we will give up. Although we now know that certain university programs—like management studies and information technology—have a higher risk of academic dishonesty, educators are not new to the practice; rather, they are still learning about Chat Generative Pre-trained Transformer. I don't see any strong arguments in favor of allowing its usage in assessments, even though it is unavoidable in certain academic contexts. Instead than teaching students to "copy and paste," they teach them to "think and write critically." Therefore, the fact that Chat Generative Pre-trained Transformer passed the MBA and medical school exams should raise red flags.