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Most people are now familiar with the concepts of big data, deep learning, machine learning, and artificial intelligence (AI) and have a vague expectation that AI using medical big data can be used to improve the quality of medical care. However, the expectation that big data could change the field of medicine is inconsistent with the current reality. The clinical meaningfulness of the results of research using medical big data needs to be examined. Medical staff needs to be clear about the purpose of AI that utilizes medical big data and to focus on the quality of this data, rather than the quantity. Further, medical professionals should understand the necessary precautions for using medical big data, as well as its advantages. No doubt that someday, medical big data will play an essential role in healthcare; however, at present, it seems too early to actively use it in clinical practice. The field continues to work toward developing medical big data and making it appropriate for healthcare. Researchers should continue to engage in empirical research to ensure that appropriate processes are in place to empirically evaluate the results of its use in healthcare.
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Evidence has emerged that endocrine-disrupting chemicals (EDCs) can produce adverse effects, even at low doses that are assumed safe. However, systemic reviews and meta-analyses focusing on human studies, especially of EDCs with short half-lives, have demonstrated inconsistent results. Epidemiological studies have insuperable methodological limitations, including the unpredictable net effects of mixtures, non-monotonic dose-response relationships, the non-existence of unexposed groups, and the low reliability of exposure assessment. Thus, despite increases in EDC-linked diseases, traditional epidemiological studies based on individual measurements of EDCs in bio-specimens may fail to provide consistent results. The exposome has been suggested as a promising approach to address the uncertainties surrounding human studies, but it is never free from these methodological issues. Although exposure to EDCs during critical developmental periods is a major concern, continuous exposure to EDCs during non-critical periods is also harmful. Indeed, the evolutionary aspects of epigenetic programming triggered by EDCs during development should be considered because it is a key mechanism for developmental plasticity. Presently, living without EDCs is impossible due to their omnipresence. Importantly, there are lifestyles which can increase the excretion of EDCs or mitigate their harmful effects through the activation of mitohormesis or xenohormesis. Effectiveness of lifestyle interventions should be evaluated as practical ways against EDCs in the real world.
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Intraoperative parathyroid hormone monitoring (IPM) has been shown to be a useful adjunct during parathyroidectomy to ensure operative success at many specialized medical centers worldwide. Using the Miami or “>50% intraoperative PTH drop” criterion, IPM confirms the complete excision of all hyperfunctioning parathyroid tissue before the operation is finished, and helps guide the surgeon to identify additional hyperfunctioning parathyroid glands that may necessitate further extensive neck exploration when intraoperative parathyroid hormone (PTH) levels do not drop sufficiently. The intraoperative PTH assay is also used to differentiate parathyroid from non-parathyroid tissues during operations using fine needle aspiration samples and to lateralize the side of the neck harboring the hypersecreting parathyroid through differential jugular venous sampling when preoperative localization studies are negative or equivocal. The use of IPM underscores the recognition and understanding of sporadic primary hyperparathyroidism (SPHPT) as a disease of function rather than form, where the surgeon is better equipped to treat such patients with quantitative instead of qualitative information for durable long-term operative success. There has been a significant paradigm shift over the last 2 decades from conventional to focused parathyroidectomy guided by IPM. This approach has proven to be a safe and rapid operation requiring minimal dissection performed in an ambulatory setting for the treatment of SPHPT.
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Since the identification of succinate's receptor in 2004, studies supporting the involvement of succinate signaling through its receptor in various diseases have accumulated and most of these investigations have highlighted succinate's pro-inflammatory role. Taken with the fact that succinate is an intermediate metabolite in the center of mitochondrial activity, and considering its potential regulation of protein succinylation through succinyl-coenzyme A, a review on the overall multifaceted actions of succinate to discuss whether and how these actions relate to the cellular locations of succinate is much warranted. Mechanistically, it is important to consider the sources of succinate, which include somatic cellular released succinate and those produced by the microbiome, especially the gut microbiota, which is an equivalent, if not greater contributor of succinate levels in the body. Continue learning the critical roles of succinate signaling, known and unknown, in many pathophysiological conditions is important. Furthermore, studies to delineate the regulation of succinate levels and to determine how succinate elicits various types of signaling in a temporal and spatial manner are also required.
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In the current era of effective antiretroviral therapies (ARTs), human immunodeficiency virus (HIV) infection became a chronic disorder that requires long term follow-up. Among other medical issues, these patients may develop endocrine problems, specific to HIV infection and its treatment. The purpose of this review is to give an overview of common endocrine complications associated with HIV infection, and to propose diagnostic and therapeutic strategies. HIV can affect the endocrine system at several levels. Adrenal and gonadal dysfunction, osteoporosis with increased fracture risk, dyslipidemia with increased cardiovascular risk, are some of the endocrine disorders prevalent in HIV-infected patients that may negatively influence quality of life, and increase morbidity and mortality. While ARTs have dramatically increased life expectancy in the HIV-infected population, they are not devoid of adverse effects, including endocrine dysfunction. Physicians caring for HIV-infected patients should be knowledgeable and exercise a high index of suspicion for the diagnosis of endocrine abnormalities, and in particular be aware of those that can be life threatening. Endocrine evaluation should follow the same strategies as in the general population, including prevention, early detection, and treatment.
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Machine learning (ML) applications have received extensive attention in endocrinology research during the last decade. This review summarizes the basic concepts of ML and certain research topics in endocrinology and metabolism where ML principles have been actively deployed. Relevant studies are discussed to provide an overview of the methodology, main findings, and limitations of ML, with the goal of stimulating insights into future research directions. Clear, testable study hypotheses stem from unmet clinical needs, and the management of data quality (beyond a focus on quantity alone), open collaboration between clinical experts and ML engineers, the development of interpretable high-performance ML models beyond the black-box nature of some algorithms, and a creative environment are the core prerequisites for the foreseeable changes expected to be brought about by ML and artificial intelligence in the field of endocrinology and metabolism, with actual improvements in clinical practice beyond hype. Of note, endocrinologists will continue to play a central role in these developments as domain experts who can properly generate, refine, analyze, and interpret data with a combination of clinical expertise and scientific rigor.
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