Intermittent Fasting: A User-Friendly Method for Type 2 Diabetes Mellitus.

Type 2 diabetes mellitus (T2DM) is an alarmingly rising disorder characterized mainly by insulin resistance and hyperglycemia. Due to the impairment of glucose homeostasis, most subjects present with elevated serum glucose levels, which can lead to several complications, including hospitalizations and even death. Diet quality and quantity are at the heart of its pathogenesis; hence, for the management of this condition, a technique known as intermittent fasting (IF) has been an area of interest for researchers. Different fasting regimens, including alternate-day fasting, religious fasting, and time-restricted fasting, have proven to be of strategic importance for glycemic control due to their physiologic effects. According to case studies and randomized trials, therapeutic fasting has been shown to reverse insulin resistance, resulting in the discontinuance of insulin therapy while maintaining blood sugar levels. Studies on IF have demonstrated their efficacy in glycemic control and other metabolic parameters, including reducing visceral fat and controlling inflammatory mediators and markers such as C-reactive protein (CRP) and interleukin-6 (IL-6), but control in obesity is its most significant effect as it acts as a risk factor for T2DM. Several case studies have shown a reduction in elevated hemoglobin A1c (HbA1c) levels in subjects after fasting, which some believe is due to sirtuin-6 (SIRT6) proteins. SIRT6 proteins are believed to be responsible for blood glucose homeostasis and insulin resistance reversal by increasing its sensitivity. This family of proteins is increased after fasting; hence, further research in this area will help researchers better understand its mechanism of action and potential therapeutic effects on T2DM. With an alarming increase in the incidence of T2DM around the world, a cost-effective strategy is required to control the disease with easy patient compliance, and IF might prove to be the solution.

Association Between Myasthenia Gravis and Systemic Lupus Erythematosus as a Comorbid State.

Systemic lupus erythematosus (SLE) and myasthenia gravis (MG) are autoimmune states which have presentational similitude. Both conditions test serologically positive for anti-nuclear antibodies and require exceptional differential diagnostic acumen to segregate one from the other. The hypothesized factors provoking these diseases may be immunological, genetic, hormonal, or environmental and can be better understood by large-scale controlled epidemiological studies. Biochemical factors such as variation in CXC (an α chemokine subfamily), CXCL13, and granulocyte-macrophage colony-stimulating factor levels are assumed to play a pivotal role in the pathogenesis of SLE and MG; however, further studies are required to understand their exact mechanism and effect on the underlying autoimmune diseases. Following this, another precipitating factor for this overlap is believed to be thymectomy which is performed to eliminate MG symptoms. Although thymectomy is the effective treatment modality in MG patients, other findings and data support the view that this procedure may lead to the development of other autoimmune states such as SLE. It is evident from previously published data and case reports that patients with one autoimmune disease who underwent thymectomy contracted SLE and became more susceptible to other autoimmune diseases compared to the general population. Post-thymectomy follow-up of patients provides us with mechanistic clues for understanding the development of SLE-MG overlap; hence, in MG patients who have undergone thymectomy, any clinical and immune serological SLE suspicion should be carefully evaluated.

Risk Assessment Using the Association Between Renin-Angiotensin Genes Polymorphisms and Coronary Artery Disease.

Coronary artery disease (CAD) is a multifactorial disease that involves genetic and environmental interaction. In addition to the well-known CAD risk factors, such as diabetes mellitus, hypertension, hyperlipidemia, and atherosclerosis, it has a genetic component that predisposes to its occurrence even in young people. One of the most commonly studied genes that increase the susceptibility to CAD is renin-angiotensin system (RAS) genes polymorphisms mainly angiotensin-converting enzyme gene (ACE) polymorphisms, angiotensinogen polymorphisms, angiotensin- II type 1 receptor gene polymorphisms, and many other genes. These genetic polymorphisms have a direct association with CAD development or indirect association through causing atherosclerosis and hypertension which, in turn, are complicated by CAD later on. The difference between genetic mutations and polymorphisms lies in the frequency of the abnormal genotype. If the frequency is 1% and more in the general population, it is called polymorphism and if it is less than 1%, then it is called a mutation. According to our findings, after thorough searching, which support the association of RAS genes polymorphisms with premature CAD, hypertension, hypertrophic cardiomyopathy, and atherosclerosis, we recommend additional studies in the form of clinical trials and meta-analyses aiming to create a specific diagnostic tool for CAD risk assessment and discovering the high-risk people as early as possible. Targeted gene therapy, being the future of medicine, needs to be taken into researchers' consideration. It can have promising results in these cases.

An Unusual Case of Gastrointestinal Bleeding in a Patient With COVID-19.

Coronavirus disease 2019 (COVID-19) is predominantly a respiratory disease that often presents with fever, cough, dyspnea, and myalgia or fatigue. Digestive symptoms such as nausea, vomiting, diarrhea, and abdominal pain may accompany respiratory symptoms. However, gastrointestinal (GI) bleeding among COVID-19 patients is a rare and unusual presentation, since these patients are frequently hypercoagulable and are less likely to bleed and more likely to clot. In this report, we present a case of an 80-year-old male with a history of type 2 diabetes mellitus, hypertension, and obesity who presented with GI bleed and was subsequently found to have COVID-19.

Physiological Effects of High-Flow Nasal Cannula Therapy and Its Use in Acute Cardiogenic Pulmonary Edema.

High-flow nasal cannula (HFNC) is an open oxygen delivery system, which provides heated and humidified oxygen at a high flow (up to 60 L/min). This effect can improve mucociliary function, airway clearance, and level of comfort to the patient. It can provide controlled and adequate fraction of inspired oxygen (FiO2) between 21% and 100%. Generation of end-expiratory pressure helps in carbon dioxide washout, reduction of anatomical dead space, and recruitment of collapsed alveoli, ultimately improving tissue oxygenation. The use of HFNC in acute hypoxemic respiratory failure, post-extubation period, pre-intubation period, respiratory infection, and obstructive airway disease has been extensively studied, but there are very few studies regarding its use in cardiogenic pulmonary edema. This review provides the current understanding of the physiological effect of HFNC and its application in acute cardiogenic pulmonary edema (ACPE). We conducted a literature search on PubMed using appropriate terms and reviewed relevant articles published within the last 10 years. We found that initial therapy with HFNC in ACPE patients can improve oxygenation and respiratory rate. HFNC can potentially be an alternative to non-invasive positive-pressure ventilation in terms of initial oxygen therapy in patients with ACPE. There is a need for larger prospective studies to evaluate and develop guidelines to consider the use of HFNC in patients with ACPE. We also highlight the fact that if there is no improvement in arterial blood gas parameters after HFNC therapy, initiation of invasive ventilation should not be delayed.

Can Big Data and Machine Learning Improve Our Understanding of Acute Respiratory Distress Syndrome?

Acute respiratory distress syndrome (ARDS) accounts for 10% of all diagnoses in the Intensive Care Unit, and about 40% of the patients succumb to the disease. Clinical methods alone can result in the under-recognition of this heterogeneous syndrome. The purpose of this study is to evaluate the role that big data and machine learning (ML) have played in understanding the heterogeneity of the disease and the development of various prediction algorithms. Most of the work in the field of ML in ARDS has been in the development of prediction models that have comparable efficacies to that of traditional models. Prediction algorithms have been useful in identifying new variables that may be important to consider in the future, supplementing the unknown information with the help of available noninvasive parameters, as well as predicting mortality. Phenotype identification using an unsupervised ML algorithm has been pivotal in classifying the heterogeneous population into more homogenous classes. Big data generated from ventilators in the form of ventilator waveform analysis and images in the form of radiomics have also been leveraged for the identification of the syndrome and can be incorporated into a clinical decision support system. Although the results are promising, lack of generalizability, "black box" nature of algorithms and concerns about "alarm fatigue" should be addressed for more mainstream adoption of these models.