RESUMO
Celiac disease is receiving much attention due to the gluten-free diet trend. Many health-conscious individuals practice a gluten-free diet, even if they do not have celiac disease. As it is an autoimmune disorder, it is associated with many other autoimmune diseases. We were interested in one skin condition, another autoimmune disorder lichen planus as a correlative factor for celiac disease. The following systematic review may give some clues. We searched online resources including PubMed, PubMed Central, Cochrane library, and Google scholar for systematic reviews, traditional reviews, randomized controlled trials, and meta-analysis on celiac disease and lichen planus. We included human studies published in peer-reviewed journals in the English language. After reviewing 2389 initial results of our search, we excluded 1250 duplicates, 1108 abstracts, 42 irrelevant articles. We assessed the remaining 26 articles for their quality using various quality assessment tools. After the quality assessment, we included nine final articles in our systematic review. Out of these nine studies, there were four systematic reviews, one traditional review, two case reports, and two observational studies. Only two articles had exclusively studied the specific association between celiac and lichen planus. The remaining studies included data that gave an overall association between other skin manifestations of celiac disease. From our study, we could not establish the relationship between celiac disease and lichen planus. We need more case-control studies and clinical trials with a larger population to get conclusive data. From current data, we can conclude that both immunological processes correlate but there is no causation. There is also a need for clinical trials to explore the exacerbation of lichen planus due to celiac disease.
RESUMO
Marijuana is one of the most abused substances in the world. Marijuana is getting legalized around the world. So, it is crucial to understand its effect on our mental health. Its impact on the schizophrenia spectrum needs our special attention. Even though marijuana has been around for a long time, its exact effects are still unknown. Schizophrenia is a chronic illness affecting approximately 20 million people worldwide. Schizophrenia and cannabis seem to have a close relationship, and we want to explore this. We want to know if marijuana is causing, exacerbating, or treating schizophrenia. This systematic review explores this question. We searched online resources like PubMed, PubMed Central, Cochrane Library, and Google Scholar for systematic reviews, traditional reviews, randomized controlled trials, and meta-analysis on cannabis and schizophrenia/ psychosis. We included human studies published in peer-reviewed journals in the English language in the last five years. After reviewing 96 initial results of our search, we excluded 25 duplicates, 29 abstracts, and 18 irrelevant articles. We did a quality assessment for the remaining 24 studies using various quality assessment tools. After the quality assessment, we found 12 articles were of low quality and excluded those. We included the remaining 12 final studies in our systematic review. Out of these 12 studies, five were traditional reviews, two systematic reviews, two meta-analysis, and three observational studies. Six of the articles were on cannabis's effect on just schizophrenia or psychotic disorder. The other six included schizophrenia plus other psychiatric or neurological illnesses. Ten of the studies had data supporting the causative link between cannabis and schizophrenia. Eight records had data supporting the exacerbating effect of marijuana. Six studies had data supporting the therapeutic effect of the cannabidiol (CBD) component of cannabis. From the current data, we can conclude that the tetrahydrocannabinol (THC) component of cannabis can be the main culprit causing psychosis and schizophrenia in the at-risk population. THC can also be the one exacerbating symptoms and causing an adverse prognosis in already diagnosed patients. Even though CBD shows therapeutic effects and THC opposing effects, the data is minimal and low safety and efficacy warrants more research. The relation between cannabis and schizophrenia needs further investigation. We need more case-control studies and clinical trials with a larger population to get conclusive data.
RESUMO
The pandemic of type 2 diabetes mellitus (T2DM) has been largely attributed to the increasing prevalence of worldwide obesity at a geometric rate. However, the number of non-obese patients with T2DM is also on the rise, and it is as high as 60-80% in some Asian countries. These non-obese individuals have certain peculiarities and have a higher mortality rate compared with obese individuals. The pathophysiology of T2DM in non-obese individuals remains poorly understood, and this has an impact on defining its management. This review discusses the current understanding of the pathophysiology of T2DM in non-obese individuals. The definition of T2DM in non-obese individuals remains controversial because of the limited clinical measurements, and the current definition of obesity using body mass index (BMI) is not very helpful as these individuals have BMIs of <25K g/m3, which is considered normal. Many authors have argued that the so-called non-obese people are actually metabolically obese; however, in terms of the measurements clinically available, they are non-obese. The simplistic understanding of the mechanism of the pathophysiology sees it in terms of the balance between insulin secretion and insulin resistance. The pathogenesis of insulin resistance in a lean patient has been proven to be the same as what is seen in an obese individual, but most studies confirm more severe functional insulin secretory defects in lean individuals compared to the obese phenotype. The mechanism underlying this form of T2DM is still poorly defined, and more research is required to understand the mechanism of sarcopenic obesity, which some studies have revealed.
RESUMO
Workplace violence against doctors is not new, but in recent times, it has grown up in epidemic proportions. Doctors are more worried about their safety and life in the workplace. Meager government spending on healthcare associated with the poor socioeconomic status of the patient and the ever-rising cost of treatment had worsened the situation in present times. The article aims to address this critical issue and try to find possible ways to prevent it.
RESUMO
Artificial Intelligence (AI) has taken radiology by storm, in particular, mammogram interpretation, and we have seen a recent surge in the number of publications on potential uses of AI in breast radiology. Breast cancer exerts a lot of burden on the National Health Service (NHS) and is the second most common cancer in the UK as of 2018. New cases of breast cancer have been on the rise in the past decade, while the survival rate has been improving. The NHS breast cancer screening program led to an improvement in survival rate. The expansion of the screening program led to more mammograms, thereby putting more work on the hands of radiologists, and the issue of double reading further worsens the workload. The introduction of computer-aided detection (CAD) systems to help radiologists was found not to have the expected outcome of improving the performance of readers. Unreliability of CAD systems has led to the explosion of studies and development of applications with the potential use in breast imaging. The purported success recorded with the use of machine learning in breast radiology has led to people postulating ideas that AI will replace breast radiologists. Of course, AI has many applications and potential uses in radiology, but will it replace radiologists? We reviewed many articles on the use of AI in breast radiology to give future radiologists and radiologists full information on this topic. This article focuses on explaining the basic principles and terminology of AI in radiology, potential uses, and limitations of AI in radiology. We have also analysed articles and answered the question of whether AI will replace radiologists.