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Purpose: Despite the advent of heated tobacco products (HTPs), their relationship to mental health remains unclear. This study aimed to determine associations between the use of combustible cigarettes (CCs) and HTPs with depressive symptoms. Methods: This descriptive-analytical cross-sectional study was conducted in March 2023. Using the 8th Korea National Health and Nutrition Examination Survey, 5,349 adults aged 19 years or older were classified into four groups: non-users, CC-only users, HTP-only users, and dual users. Relationships between exclusive or dual use of CCs and HTPs and depressive symptoms were analyzed using item scores and total scores of the Patient Health Questionnaire-9 (PHQ-9). To examine associations between exclusive or dual use of CCs and HTPs and depressive symptoms, a multinomial regression analysis was performed using the PHQ-9 total score. Results: HTP-only users had the highest proportion of those with anhedonia and depressed mood. CC-only users had the highest proportion of individuals with trouble sleeping, while dual users had a higher proportion of those with fatigue and appetite problems. After adjusting for general characteristics, compared to non-users, CC-only users were more likely to have mild and moderate to severe depressive symptoms. HTP-only users and dual users were also more likely to have moderate to severe depressive symptoms. Conclusions: All smokers have a higher risk of depression than non-smokers. Health care providers should closely monitor depressive symptoms, especially in HTP users and dual users of tobacco products. [ABSTRACT FROM AUTHOR]

We aimed to examine the association between metabolic syndrome (MetS), depressive symptoms, and health-related quality of life in postmenopausal women. We conducted a descriptive cross-sectional study in naturally postmenopausal women aged 45–65 years using data from the 8th Korea National Health and Nutrition Examination Survey. We classified the participants into four groups: normal (neither MetS nor depressive symptoms), MetS, depressive symptoms, and MetS + depressive symptoms. Compared to the other three groups, the MetS + depressive symptoms group had the worst self-rated health. High fasting glucose, high triglyceride and low high-density lipoprotein-cholesterol levels were the most common in the MetS + depressive symptoms group. After adjusting for covariates, the MetS + depressive symptoms group was more likely to have problems with usual activities and pain/discomfort than the normal group. [ABSTRACT FROM AUTHOR]

Predicting Bitcoin prices is crucial because they reflect trends in the overall cryptocurrency market. Owing to the market's short history and high price volatility, previous research has focused on the factors influencing Bitcoin price fluctuations. Although previous studies used sentiment analysis or diversified input features, this study's novelty lies in its utilization of data classified into more than five major categories. Moreover, the use of data spanning more than 2,000 days adds novelty to this study. With this extensive dataset, the authors aimed to predict Bitcoin prices across various timeframes using time series analysis. The authors incorporated a broad spectrum of inputs, including technical indicators, sentiment analysis from social media, news sources, and Google Trends. In addition, this study integrated macroeconomic indicators, on-chain Bitcoin transaction details, and traditional financial asset data. The primary objective was to evaluate extensive machine learning and deep learning frameworks for time series prediction, determine optimal window sizes, and enhance Bitcoin price prediction accuracy by leveraging diverse input features. Consequently, employing the bidirectional long short-term memory (Bi-LSTM) yielded significant results even without excluding the COVID-19 outbreak as a black swan outlier. Specifically, using a window size of 3, Bi-LSTM achieved a root mean squared error of 0.01824, mean absolute error of 0.01213, mean absolute percentage error of 2.97%, and an R-squared value of 0.98791. Additionally, to ascertain the importance of input features, gradient importance was examined to identify which variables specifically influenced prediction results. Ablation test was also conducted to validate the effectiveness and validity of input features. The proposed methodology provides a varied examination of the factors influencing price formation, helping investors make informed decisions regarding Bitcoin-related investments, and enabling policymakers to legislate considering these factors. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive exploration of topic modeling methods tailored for large language model (LLM) using data obtained from Web of Science and LexisNexis from June 1, 2020, to December 31, 2023. The data collection process involved queries focusing on LLMs, including "Large language model," "LLM," and "ChatGPT." Various topic modeling approaches were evaluated based on performance metrics, including diversity and coherence. latent Dirichlet allocation (LDA), nonnegative matrix factorization (NMF), combined topic models (CTM), and bidirectional encoder representations from Transformers topic (BERTopic) were employed for performance evaluation. Evaluation metrics were computed across platforms, with BERTopic demonstrating superior performance in diversity and coherence across both LexisNexis and Web of Science. The experiment result reveals that news articles maintain a balanced coverage across various topics and mainly focus on efforts to utilize LLM in specialized domains. Conversely, research papers are more concise and concentrated on the technology itself, emphasizing technical aspects. Through the insights gained in this study, it becomes possible to investigate the future path and the challenges that LLMs should tackle. Additionally, they could offer considerable value to enterprises that utilize LLMs to deliver services. [ABSTRACT FROM AUTHOR]

Context It is not clear how to integrate artificial intelligence (AI)-based models into diagnostic workflows. Objective To develop and validate a deep-learning–based AI model (AI-Thyroid) for thyroid cancer diagnosis, and to explore how this improves diagnostic performance. Methods The system was trained using 19 711 images of 6163 patients in a tertiary hospital (Ajou University Medical Center; AUMC). It was validated using 11 185 images of 4820 patients in 24 hospitals (test set 1) and 4490 images of 2367 patients in AUMC (test set 2). The clinical implications were determined by comparing the findings of six physicians with different levels of experience (group 1: 4 trainees, and group 2: 2 faculty radiologists) before and after AI-Thyroid assistance. Results The area under the receiver operating characteristic (AUROC) curve of AI-Thyroid was 0.939. The AUROC, sensitivity, and specificity were 0.922, 87.0%, and 81.5% for test set 1 and 0.938, 89.9%, and 81.6% for test set 2. The AUROCs of AI-Thyroid did not differ significantly according to the prevalence of malignancies (>15.0% vs ≤15.0%, P =.226). In the simulated scenario, AI-Thyroid assistance changed the AUROC, sensitivity, and specificity from 0.854 to 0.945, from 84.2% to 92.7%, and from 72.9% to 86.6% (all P <.001) in group 1, and from 0.914 to 0.939 (P =.022), from 78.6% to 85.5% (P =.053) and from 91.9% to 92.5% (P =.683) in group 2. The interobserver agreement improved from moderate to substantial in both groups. Conclusion AI-Thyroid can improve diagnostic performance and interobserver agreement in thyroid cancer diagnosis, especially in less-experienced physicians. [ABSTRACT FROM AUTHOR]