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Context HNF4A–maturity-onset diabetes of the young (MODY1) is a relatively rare subtype of monogenic diabetes caused by loss of function of the HNF4A gene, which encodes the transcription factor HNF4α. HNF4α is known to form heterodimers, and the various combinations of isoforms that make up these heterodimers have been reported to result in a diversity of targeted genes. However, the function of individual HNF4α variant isoforms and the heterodimers comprising both wild-type (WT) and variant HNF4α have not yet been assessed. Objective In this study, we analyzed the functional consequence of the HNF4A D248Y variant in vitro. Methods We investigated the case of a 12-year-old Japanese girl who developed diabetes at age 11 years. Genetic sequencing detected a novel heterozygous missense HNF4A variant (c.742G > T, p.Asp248Tyr; referred as "D248Y") in the patient and her relatives who presented with diabetes. Results Although the WT HNF4α isoforms (HNF4α2, HNF4α3, HNF4α8, HNF4α9) enhanced the INS gene promoter activity in HepG2 cells, the promoter activity of D248Y was consistently low across all isoforms. The presence of D248Y in homodimers and heterodimers, comprising either HNF4α8 or HNF4α3 or a combination of both isoforms, also reduced the INS promoter activity in Panc-1 cells. Conclusion We report the clinical course of a patient with HNF4A-MODY and the functional analysis of novel HNF4A variants, with a focus on the isoforms and heterodimers they form. Our results serve to improve the understanding of the dominant-negative effects of pathogenic HNF4A variants. [ABSTRACT FROM AUTHOR]

Non-Intrusive Load Monitoring (NILM), which provides sufficient load for the energy consumption of an entire building, has become crucial in improving the operation of energy systems. Although NILM can decompose overall energy consumption into individual electrical sub-loads, it struggles to estimate thermal-driven sub-loads such as occupants. Previous studies proposed Non-Intrusive Thermal Load Monitoring (NITLM), which disaggregates the overall thermal load into sub-loads; however, these studies evaluated only a single building. The results change for other buildings due to individual building factors, such as floor area, location, and occupancy patterns; thus, it is necessary to analyze how these factors affect the accuracy of disaggregation for accurate monitoring. In this paper, we conduct a fundamental evaluation of NITLM in various realistic office buildings to accurately disaggregate the overall thermal load into sub-loads, focusing on occupant thermal load. Through experiments, we introduce NITLM with deep learning models and evaluate these models using thermal load datasets. These thermal load datasets are generated by a building energy simulation, and its inputs for the simulation were derived from realistic data like HVAC on/off data. Such fundamental evaluation has not been done before, but insights obtained from the comparison of learning models are necessary and useful for improving learning models. Our experimental results shed light on the deep learning-based NITLM models for building-level efficient energy management systems. [ABSTRACT FROM AUTHOR]

Wolfram syndrome (OMIM 222300) is a rare autosomal recessive disease with a devastating array of symptoms, including diabetes mellitus, optic nerve atrophy, diabetes insipidus, hearing loss, and neurological dysfunction. The discovery of the causative gene, WFS1, has propelled research on this disease. However, a comprehensive understanding of the function of WFS1 remains unknown, making the development of effective treatment a pressing challenge. To bridge these knowledge gaps, disease models for Wolfram syndrome are indispensable, and understanding the characteristics of each model is critical. This review will provide a summary of the current knowledge regarding WFS1 function and offer a comprehensive overview of established disease models for Wolfram syndrome, covering animal models such as mice, rats, flies, and zebrafish, along with induced pluripotent stem cell (iPSC)-derived human cellular models. These models replicate key aspects of Wolfram syndrome, contributing to a deeper understanding of its pathogenesis and providing a platform for discovering potential therapeutic approaches. [ABSTRACT FROM AUTHOR]

Some patients with developmental and epileptic encephalopathy (DEE) respond to adrenocorticotropic hormone (ACTH) therapy but relapse soon after. While long‐term ACTH therapy (LT‐ACTH) has been attempted for these patients, no previous studies have carefully assessed adrenal function during LT‐ACTH. We evaluated the effectiveness of LT‐ACTH, as well as adverse effects (AE), including their adrenal function in three DEE patients. Patients underwent a corticotropin‐releasing hormone (CRH) stimulation test during LT‐ACTH, and those with peak serum cortisol below 15 μg/dL were considered to be at high risk of adrenal insufficiency (AI). Two of three responded, and their life‐threatening seizures with postgeneralized electroencephalogram (EEG) suppression decreased. Although no individuals had serious AE, CRH stimulation test revealed relatively weak responses, without reaching normal cortisol peak level (18 μg/dL). Hydrocortisone replacement during stress was prepared in a case with lower cortisol peak than our cutoff level. LT‐ACTH could be a promising treatment option for cases of DEE that relapse soon after effective ACTH treatment. The longer duration and larger cumulative dosage in LT‐ACTH than in conventional ACTH could increase the relative risk of AI. Careful evaluation with pediatric endocrinologists, including hormonal stimulation tests, might be useful for continuing this treatment safely. [ABSTRACT FROM AUTHOR]

A novel sensing technique is developed that makes use of evanescent terahertz (THz) waves without directly detecting the THz wave. The technique is demonstrated for measuring changes in the electric field of near-infrared light, transcribed from changes in the electric field of THz waves. The method will enable sensing of various materials in the THz-frequency region, to provide knowledge about chemical reactions that require real-time tracking. [ABSTRACT FROM AUTHOR]