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.

Neuroendocrine neoplasms (NENs) are a group of heterogeneous tumors with neuroendocrine differentiation that can arise from any organ. They account for 2% of all malignancies in the United States. A significant proportion of NEN patients experience endocrine imbalances consequent to increased amine or peptide hormone secretion, impacting their quality of life and prognosis. Over the last decade, pathologic categorization, diagnostic techniques and therapeutic choices for NENs—both well-differentiated neuroendocrine tumors (NETs) and poorly differentiated neuroendocrine carcinomas (NECs)—have appreciably evolved. Diagnosis of NEN mostly follows a suspicion from clinical features or incidental imaging findings. Hormonal or non-hormonal biomarkers (like serum serotonin, urine 5-HIAA, gastrin and VIP) and histology of a suspected NEN is, therefore, critical for both confirmation of the diagnosis and classification as an NET or NEC. Therapy for NENs has progressed recently based on a better molecular understanding, including the involvement of mTOR, VEGF and peptide receptor radionuclide therapy (PRRT), which add to the growing evidence supporting the possibility of treatment beyond complete resection. As the incidence of NENs is on the rise in the United States and several other countries, physicians are more likely to see these cases, and their better understanding may support earlier diagnosis and tailoring treatment to the patient. We have compiled clinically significant evidence for NENs, including relevant changes to clinical practice that have greatly updated our diagnostic and therapeutic approach for NEN patients.

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.

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.

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.

Smartphone use, particularly at night, has been shown to provoke various circadian sleep–wake rhythm disorders such as insomnia and excessive daytime tiredness. This relationship has been mainly scrutinized among patient groups with higher rates of smartphone usage, particularly adolescents and children. However, it remains obscure how smartphone usage impacts sleep parameters in adults, especially undergraduate college students. This study sought to (1) investigate the association between smartphone use (actual screen time) and four sleep parameters: Pittsburgh sleep quality score (PSQI), self-reported screen time, bedtime, and rise time; (2) compare the seven PSQI components between good and poor sleep quality subjects. In total, 264 undergraduate medical students (aged 17 to 25 years) were recruited from the Government Doon Medical College, Dehradun, India. All participants completed a sleep questionnaire, which was electronically shared via a WhatsApp invitation link. Hierarchical and multinomial regression analyses were performed in relation to (1) and (2). The average PSQI score was 5.03 ± 0.86, with approximately one in two respondents (48.3%) having a poor sleep index. Smartphone use significantly predicted respondents’ PSQI score (β = 0.142, p = 0.040, R² = 0.027), perceived screen time (β = 0.113, p = 0.043, R² = 343), bedtime (β = 0.106, p = 0.042, R² = 045), and rise time (β = 0.174, p = 0.015, R² = 0.028). When comparing poor-quality sleep (PSQI ≥ 5) to good-quality sleep (PSQI < 5), with good-quality sleep as the reference, except sleep efficiency and sleep medications (p > 0.05), five PSQI components declined significantly: subjective sleep quality (β = −0.096, p < 0.001); sleep latency (β = −0.034, p < 0.001); sleep duration (β = −0.038, p < 0.001); sleep disturbances (β = 1.234, p < 0.001); and sleep dysfunction (β = −0.077, p < 0.001). Consequently, public health policymakers should take this evidence into account when developing guidelines around smartphone use—i.e., the when, where, and how much smartphone use—to promote improved sleep behaviour and reduce the rate of sleep–wake rhythm disorders.