Tumor metabolism in pheochromocytomas: clinical and therapeutic implications.

Pheochromocytomas and paragangliomas (PPGLs) have emerged as one of the most common endocrine tumors. It epitomizes fascinating crossroads of genetic, metabolic, and endocrine oncology, providing a canvas to explore the molecular intricacies of tumor biology. Predominantly rooted in the aberration of metabolic pathways, particularly the Krebs cycle and related enzymatic functionalities, PPGLs manifest an intriguing metabolic profile, highlighting elevated levels of oncometabolites like succinate and fumarate, and furthering cellular malignancy and genomic instability. This comprehensive review aims to delineate the multifaceted aspects of tumor metabolism in PPGLs, encapsulating genetic factors, oncometabolites, and potential therapeutic avenues, thereby providing a cohesive understanding of metabolic disturbances and their ramifications in tumorigenesis and disease progression. Initial investigations into PPGLs metabolomics unveiled a stark correlation between specific genetic mutations, notably in the succinate dehydrogenase complex (SDHx) genes, and the accumulation of oncometabolites, establishing a pivotal role in epigenetic alterations and hypoxia-inducible pathways. By scrutinizing voluminous metabolic studies and exploiting technologies, novel insights into the metabolic and genetic aspects of PPGLs are perpetually being gathered elucidating complex interactions and molecular machinations. Additionally, the exploration of therapeutic strategies targeting metabolic abnormalities has burgeoned harboring potential for innovative and efficacious treatment modalities. This review encapsulates the profound metabolic complexities of PPGLs, aiming to foster an enriched understanding and pave the way for future investigations and therapeutic innovations in managing these metabolically unique tumors.

Applying AI and Guidelines to Assist Medical Students in Recognizing Patients With Heart Failure: Protocol for a Randomized Trial.

BACKGROUND: The integration of artificial intelligence (AI) into clinical practice is transforming both clinical practice and medical education. AI-based systems aim to improve the efficacy of clinical tasks, enhancing diagnostic accuracy and tailoring treatment delivery. As it becomes increasingly prevalent in health care for high-quality patient care, it is critical for health care providers to use the systems responsibly to mitigate bias, ensure effective outcomes, and provide safe clinical practices. In this study, the clinical task is the identification of heart failure (HF) prior to surgery with the intention of enhancing clinical decision-making skills. HF is a common and severe disease, but detection remains challenging due to its subtle manifestation, often concurrent with other medical conditions, and the absence of a simple and effective diagnostic test. While advanced HF algorithms have been developed, the use of these AI-based systems to enhance clinical decision-making in medical education remains understudied. OBJECTIVE: This research protocol is to demonstrate our study design, systematic procedures for selecting surgical cases from electronic health records, and interventions. The primary objective of this study is to measure the effectiveness of interventions aimed at improving HF recognition before surgery, the second objective is to evaluate the impact of inaccurate AI recommendations, and the third objective is to explore the relationship between the inclination to accept AI recommendations and their accuracy. METHODS: Our study used a 3 × 2 factorial design (intervention type × order of prepost sets) for this randomized trial with medical students. The student participants are asked to complete a 30-minute e-learning module that includes key information about the intervention and a 5-question quiz, and a 60-minute review of 20 surgical cases to determine the presence of HF. To mitigate selection bias in the pre- and posttests, we adopted a feature-based systematic sampling procedure. From a pool of 703 expert-reviewed surgical cases, 20 were selected based on features such as case complexity, model performance, and positive and negative labels. This study comprises three interventions: (1) a direct AI-based recommendation with a predicted HF score, (2) an indirect AI-based recommendation gauged through the area under the curve metric, and (3) an HF guideline-based intervention. RESULTS: As of July 2023, 62 of the enrolled medical students have fulfilled this study's participation, including the completion of a short quiz and the review of 20 surgical cases. The subject enrollment commenced in August 2022 and will end in December 2023, with the goal of recruiting 75 medical students in years 3 and 4 with clinical experience. CONCLUSIONS: We demonstrated a study protocol for the randomized trial, measuring the effectiveness of interventions using AI and HF guidelines among medical students to enhance HF recognition in preoperative care with electronic health record data. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/49842.

Teaching artificial intelligence as a fundamental toolset of medicine.

Artificial intelligence (AI) is transforming the practice of medicine. Systems assessing chest radiographs, pathology slides, and early warning systems embedded in electronic health records (EHRs) are becoming ubiquitous in medical practice. Despite this, medical students have minimal exposure to the concepts necessary to utilize and evaluate AI systems, leaving them under prepared for future clinical practice. We must work quickly to bolster undergraduate medical education around AI to remedy this. In this commentary, we propose that medical educators treat AI as a critical component of medical practice that is introduced early and integrated with the other core components of medical school curricula. Equipping graduating medical students with this knowledge will ensure they have the skills to solve challenges arising at the confluence of AI and medicine.

Pharmacotherapy of Painful Diabetic Neuropathy: A Clinical Update.

The rising prevalence of diabetes mellitus (DM) leads on to an increase in chronic diabetic complications. Diabetic peripheral neuropathies (DPNs) are common chronic complications of diabetes. Distal symmetric polyneuropathy is the most prevalent form. Most patients with DPN will remain pain-free; however, painful DPN (PDPN) occurs in 6-34% of all DM patients and is associated with reduced health-related-quality-of-life and substantial economic burden. Symptomatic treatment of PDPN and diabetic autonomic neuropathy is the key treatment goals. Using certain patient related characteristics, subjects with PDPN can be stratified and assigned targeted therapies to produce better pain outcomes. The aim of this review is to discuss the various pathogenetic mechanisms of DPN with special reference to the mechanisms leading to PDPN and the various pharmacological and non-pharmacological therapies available for its management. Recommended pharmacological therapies include anticonvulsants, antidepressants, opioid analgesics, and topical medications.

The Role of Spirituality and Religion in Physician and Trainee Wellness.

Burnout in medicine is a substantial problem with adverse consequences for both physicians and the patients who they treat. In our efforts to combat burnout, we must consider every tool at our disposal, since a complex problem requires a multifaceted approach. Recognizing that many physicians derive meaning from spirituality and religion, attempts to improve physician and trainee wellness should acknowledge the importance of religion and spirituality for self-care more than has heretofore been the case.

Machine Learning: The Next Paradigm Shift in Medical Education.

Machine learning (ML) algorithms are powerful prediction tools with immense potential in the clinical setting. There are a number of existing clinical tools that use ML, and many more are in development. Physicians are important stakeholders in the health care system, but most are not equipped to make informed decisions regarding deployment and application of ML technologies in patient care. It is of paramount importance that ML concepts are integrated into medical curricula to position physicians to become informed consumers of the emerging tools employing ML. This paradigm shift is similar to the evidence-based medicine (EBM) movement of the 1990s. At that time, EBM was a novel concept; now, EBM is considered an essential component of medical curricula and critical to the provision of high-quality patient care. ML has the potential to have a similar, if not greater, impact on the practice of medicine. As this technology continues its inexorable march forward, educators must continue to evaluate medical curricula to ensure that physicians are trained to be informed stakeholders in the health care of tomorrow.

Severe Mitral Regurgitation in a Child With Henoch-Schönlein Purpura and Pulmonary Hemorrhage.

Introduction: Henoch-Schönlein purpura (HSP) is the most common vasculitis of childhood. The classic triad of HSP consists of nonthrombocytopenic purpura, arthritis/arthralgia, and gastrointestinal complaints. Pulmonary hemorrhage and cardiac involvement are rare complications of HSP. Case Report: We report the case of a 10-year-old girl with HSP complicated by both severe mitral regurgitation and pulmonary hemorrhage. Discussion: HSP is typically a self-limited illness with an excellent prognosis in children. Pulmonary hemorrhage is a rare complication that increases morbidity and mortality; it generally indicates the presence of severe vasculitis. Cardiac involvement in HSP is extremely rare and associated with a poor prognosis. Conclusion: Cardiac involvement in HSP may be more common than believed. Because of the increased morbidity and mortality associated with HSP complicated by pulmonary hemorrhage and cardiac involvement, it is important for clinicians to be aware of these potential complications.