No abstract

Type 1 Diabetes Mellitus (T1DM) is a common hyperglycemic disease characterized by the autoimmune destruction of insulin-producing beta cells of the pancreas. Various attempts have been made to understand the complex interplay of genetic and environmental factors which lead to the development of the autoimmune response in an individual. T1DM is frequently associated with other autoimmune illnesses, the most common being autoimmune thyroid disorders affecting more than 90% of people with T1D and autoimmune disorders. Antithyroid antibodies are present in around 20% of children with T1D at the start of the illness and are more frequent in girls. Patients with T1DM often have various other co-existing multi-system autoimmune disorders including but not limited to thyroid diseases, parathyroid diseases, celiac disease, vitiligo, gastritis, skin diseases, and rheumatic diseases. It is a consistent observation in clinics that T1DM patients have other autoimmune disorders which in turn affect their prognosis. Concomitant autoimmune illness might affect diabetes care and manifest itself clinically in a variety of ways. A thorough understanding of the complex pathogenesis of this modern-day epidemic and its association with other autoimmune disorders has been attempted in this review in order to delineate the measures to prevent the development of these conditions and limit the morbidity of the afflicted individuals as well. The measures including antibody screening in susceptible individuals, early identification and management of other autoimmune disorders, and adoption of personalized medicine can significantly enhance the quality of life of these patients. Personalized medicine has recently gained favor in the scientific, medical, and public domains, and is frequently heralded as the future paradigm of healthcare delivery. With the evolution of the ‘omics’, the individualization of therapy is not only closer to reality but also the need of the hour.

The evolution of AI and data science has aided in mechanizing several aspects of medical care requiring critical thinking: diagnosis, risk stratification, and management, thus mitigating the burden of physicians and reducing the likelihood of human error. AI modalities have expanded feet to the specialty of pediatric cardiology as well. We conducted a scoping review searching the Scopus, Embase, and PubMed databases covering the recent literature between 2002–2022. We found that the use of neural networks and machine learning has significantly improved the diagnostic value of cardiac magnetic resonance imaging, echocardiograms, computer tomography scans, and electrocardiographs, thus augmenting the clinicians’ diagnostic accuracy of pediatric heart diseases. The use of AI-based prediction algorithms in pediatric cardiac surgeries improves postoperative outcomes and prognosis to a great extent. Risk stratification and the prediction of treatment outcomes are feasible using the key clinical findings of each CHD with appropriate computational algorithms. Notably, AI can revolutionize prenatal prediction as well as the diagnosis of CHD using the EMR (electronic medical records) data on maternal risk factors. The use of AI in the diagnostics, risk stratification, and management of CHD in the near future is a promising possibility with current advancements in machine learning and neural networks. However, the challenges posed by the dearth of appropriate algorithms and their nascent nature, limited physician training, fear of over-mechanization, and apprehension of missing the ‘human touch’ limit the acceptability. Still, AI proposes to aid the clinician tomorrow with precision cardiology, paving a way for extremely efficient human-error-free health care.

IntroductionCoronavirus disease 2019 (COVID-19) disease attributed to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has shown associations with various fungal opportunistic infections such as mucormycosis, invasive candidiasis, and aspergillosis, which have contributed to the mortality of the disease. In India, the incidence of mucormycosis had risen rapidly during the second wave. There is ample literature demonstrating the role of iron in the pathogenesis of mucormycosis. The hyperferritinemia associated with COVID-19 may have played a significant role in promoting the invasion and extent of the fungus.

Acute hepatitis has always been a public health concern, but the recent clustering of cases in various parts of the world has drawn some special attention. The sudden rise in cases has mainly been among the pediatric population of around 35 countries around the world, including developed countries such as the United States, the United Kingdom, and European countries. The outbreaks have had a devastating impact, with around 10% of the affected patients developing liver failure. The clinical presentation of patients resembles any other case of acute hepatitis, with the major symptoms being: jaundice (68.8%), vomiting (57.6%), and gastrointestinal symptoms such as abdominal pain (36.1%) and nausea (25.7%). Interestingly, the cases have tested negative for hepatotropic viruses Hep A, B, C, and E, thus giving rise to the terms Hepatitis of Unknown Origin or non-HepA–E hepatitis. Many causes have been attributed to the disease, with major evidence seen for adenovirus and SARS-CoV-2. International agencies have stressed on establishing diagnostic and management protocols to limit these outbreaks. As the understanding has evolved over time, diagnostic and management faculties have found more shape. The current review was designed to comprehensively compile all existing data and whittle it down to evidence-based conclusions to help clinicians.

Cardiac diseases form the lion’s share of the global disease burden, owing to the paradigm shift to non-infectious diseases from infectious ones. The prevalence of CVDs has nearly doubled, increasing from 271 million in 1990 to 523 million in 2019. Additionally, the global trend for the years lived with disability has doubled, increasing from 17.7 million to 34.4 million over the same period. The advent of precision medicine in cardiology has ignited new possibilities for individually personalized, integrative, and patient-centric approaches to disease prevention and treatment, incorporating the standard clinical data with advanced “omics”. These data help with the phenotypically adjudicated individualization of treatment. The major objective of this review was to compile the evolving clinically relevant tools of precision medicine that can help with the evidence-based precise individualized management of cardiac diseases with the highest DALY. The field of cardiology is evolving to provide targeted therapy, which is crafted as per the “omics”, involving genomics, transcriptomics, epigenomics, proteomics, metabolomics, and microbiomics, for deep phenotyping. Research for individualizing therapy in heart diseases with the highest DALY has helped identify novel genes, biomarkers, proteins, and technologies to aid early diagnosis and treatment. Precision medicine has helped in targeted management, allowing early diagnosis, timely precise intervention, and exposure to minimal side effects. Despite these great impacts, overcoming the barriers to implementing precision medicine requires addressing the economic, cultural, technical, and socio-political issues. Precision medicine is proposed to be the future of cardiovascular medicine and holds the potential for a more efficient and personalized approach to the management of cardiovascular diseases, contrary to the standardized blanket approach.

Broken Heart Syndrome, also known as Takotsubo Syndrome (TS), is sudden and transient dysfunction of the left and/or right ventricle which often mimics Acute Coronary Syndrome (ACS). Japan was the first country to describe this syndrome in the 1990s, and since then it has received a lot of attention from researchers all around the world. Although TS was once thought to be a harmless condition, recent evidence suggests that it may be linked to serious complications and mortality on par with Acute Coronary Syndrome (ACS). The understanding of TS has evolved over the past few years. However, its exact etiology is still poorly understood. It can be classified into two main types: Primary and Secondary TS. Primary TS occurs when the symptoms of myocardial damage, which is typically preceded by emotional stress, are the reason for hospitalization. Secondary TS is seen in patients hospitalized for some other medical, surgical, obstetric, anesthetic, or psychiatric conditions, and the dysfunction develops as a secondary complication due to the activation of the sympathetic nervous system and the release of catecholamines. The etiopathogenesis is now proposed to include adrenergic hormones/stress, decreased estrogen levels, altered microcirculation, endothelial dysfunction, altered inflammatory response via cardiac macrophages, and disturbances in the brain-heart axis. The role of genetics in disease progression is becoming the focus of several upcoming studies. This review focuses on potential pathophysiological mechanisms for reversible myocardial dysfunction observed in TS, and comprehensively describes its epidemiology, clinical presentation, novel diagnostic biomarkers, and evolving principles of management. We advocate for more research into molecular mechanisms and promote the application of current evidence for precise individualized treatment.

In motor learning, augmented feedback (AF) is the information provided by sources outside the body and encompasses visual feedback, auditory feedback, and multimodal augmented feedback. This review aims to examine the most recent evidence on these different modality types in healthy and diseased populations and athletes. The reporting of this review was guided by the standards of the “Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA)” statement with the aim to examine the most recent evidence on these feedback types in healthy and diseased populations and athletes. The literature search for this review has been limited to electronic journals with the search engines ISI Web of Knowledge, OvidSP EMBASE, and PubMed databases. This review considers visual feedback as the cornerstone of all augmented feedback types by citing its superiority in learning complex skills by medical students and balance maintenance by older adults. The review also deciphers the role of auditory augmented feedback in encumbering already burdened visual connections in the rehabilitation of patients with Parkinson’s disease (PD) and prosthetic limbs and further argues why the multimodal feedback model seems to be the most efficient way to offer feedback in learning motor tasks by alluding to multifaceted “skill training communication model” in the prevention of sports injuries such as anterior cruciate ligament tears. Multimodal augmented feedback seems to be the most effective and appropriate way to give feedback during motor learning in healthy and diseased populations and athletes as its stimuli are perceived faster and tend to be retained longer compared with the unimodal stimuli of auditory and visual feedback mechanisms.