박현태 교수

Menopause is an aging process and an important time equivalent to one-third of a woman's lifetime. Menopause significantly increases the risk of cardiometabolic diseases, such as obesity, type 2 diabetes, cardiovascular diseases, non-alcoholic liver disease (NAFLD)/metabolic associated fatty liver disease (MFFLD), and metabolic syndrome (MetS). Women experience a variety of symptoms in the perimenopausal period, and these symptoms are distressing for most women. Many factors worsen a woman's men

This study uses artificial intelligence for testing (1) whether the comorbidity of diabetes and its comorbid condition is very strong in the middle-aged or old (hypothesis 1) and (2) whether major determinants of the comorbidity are similar for different pairs of diabetes and its comorbid condition (hypothesis 2). Three pairs are considered, diabetes-cancer, diabetes-heart disease and diabetes-mental disease. Data came from the Korean Longitudinal Study of Ageing (2016-2018), with 5527 participa

Stellera chamaejasme L. (SCL) is a perennial herb with demonstrated bioactivities against inflammation and metabolic dysfunction. Adipocyte differentiation is a critical regulator of metabolic homeostasis and a promising target for the treatment of metabolic diseases, so we examined the effects of SCL on adipogenesis. A methanol extract of SCL dose-dependently suppressed intracellular lipid accumulation in adipocyte precursors cultured under differentiation induction conditions and reduced expre

This work proposes a proof-of-concept ultrasound blood-flow-monitoring circuit system using a single-element transducer. The circuit system consists of a single-element ultrasonic transducer, an analog interface circuit, and a field-programmable gate array (FPGA). Since the system uses a single-element transducer, an ultrasound image cannot be reconstructed unless scanning with mechanical movement is used. An ultrasound blood-flow monitor basically needs to acquire a Doppler sample volume by pos

Recent advancements in zero-shot commonsense reasoning have empowered Pre-trained Language Models (PLMs) to acquire extensive commonsense knowledge without requiring task-specific fine-tuning. Despite this progress, these models frequently suffer from limitations caused by human reporting biases inherent in textual knowledge, leading to understanding discrepancies between machines and humans. To bridge this gap, we introduce an additional modality to enrich the reasoning capabilities of PLMs. We

Recent advancements in zero-shot commonsense reasoning have empowered Pre-trained Language Models (PLMs) to acquire extensive commonsense knowledge without requiring task-specific fine-tuning. Despite this progress, these models frequently suffer from limitations caused by human reporting biases inherent in textual knowledge, leading to understanding discrepancies between machines and humans. To bridge this gap, we introduce an additional modality to enrich the reasoning capabilities of PLMs. We

Molecule and text representation learning has gained increasing interest due to its potential for enhancing the understanding of chemical information.However, existing models often struggle to capture subtle differences between molecules and their descriptions, as they lack the ability to learn fine-grained alignments between molecular substructures and chemical phrases.To address this limitation, we introduce MolBridge, a novel molecule-text learning framework based on substructure-aware alignm

Antibody function is an important topic for the understanding of disease, but it would be quite challenging to make an accurate prediction of a paratope position from very limited information such as a B cell receptor’s (BCR’s) amino acid sequence alone. In this context, this study presents a knowledge-based Bidirectional Encoder Representation from Transformers (K-BERT) to deliver a precise prediction of a paratope position from a B cell receptor’s amino acid sequence alone. Here, the knowledge