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The demand for psychological counselling has grown significantly in recent years, particularly with the global outbreak of COVID-19, which heightened the need for timely and professional mental health support. Online psychological counselling emerged as the predominant mode of providing services in response to this demand. In this study, we propose the Psy-LLM framework, an AI-based assistive tool leveraging large language models (LLMs) for question answering in psychological consultation settings to ease the demand on mental health professions. Our framework combines pre-trained LLMs with real-world professional questions-and-answers (Q&A) from psychologists and extensively crawled psychological articles. The Psy-LLM framework serves as a front-end tool for healthcare professionals, allowing them to provide immediate responses and mindfulness activities to alleviate patient stress. Additionally, it functions as a screening tool to identify urgent cases requiring further assistance. We evaluated the framework using intrinsic metrics, such as perplexity, and extrinsic evaluation metrics, including human participant assessments of response helpfulness, fluency, relevance, and logic. The results demonstrate the effectiveness of the Psy-LLM framework in generating coherent and relevant answers to psychological questions. This article discusses the potential and limitations of using large language models to enhance mental health support through AI technologies. [ABSTRACT FROM AUTHOR]

There are limited studies investigating the association between short-term exposure to PM2.5 and incident cardiovascular disease (CVD) cases in China. This study aims to examine the short-term effects of PM2.5 on the incidence of cardiovascular diseases. A combination of Poisson-distribution generalized linear model and distributed lag non-linear model was used to examine the association between short-term exposure to PM2.5 and incident cases of CVD. The results revealed that per 10 µg/m3 increment of PM2.5 would increase the incident CVD cases by 0.147% (Relative Risk: 1.00147, 95% Confidence Interval: 1.00008–1.00286) at a lag of 2 days. The stratified analyses showed higher effects risk in females, older residents (aged 60–75 years), and acute myocardial infarction group (p-value for difference <0.05). This study indicates that short-term exposure to PM2.5 may increase the risk of CVD and highlights the necessity for a higher air quality standard in Yantai, China. [ABSTRACT FROM AUTHOR]

Few studies have investigated the association between PM2.5 and hypertension among floating populations. We therefore examined the relationship using binary logistic regression. Each grade of increment in the annual average PM2.5 (grade one: ≤15 µg/m3; grade two: 15–25 µg/m3; grade three: 25–35 µg/m3 [Excluding 25]; grade four: ≥35 µg/m3) was associated with an increased risk of hypertension (odds ratio [OR] = 1.081, 95% confidence interval (CI): 1.034–1.129). Among the female floating population (OR = 1.114, 95% CI: 1.030–1.204), those with education level of primary school and below (OR = 1.140, 95% CI: 1.058–1.229), construction workers (OR = 1.228, 95% CI: 1.058–1.426), and those living in the eastern region of China (OR = 1.241, 95% CI: 1.145–1.346) were more vulnerable to PM2.5. These results indicate that PM2.5 is positively associated with hypertension in floating populations. Floating populations who are female, less educated, construction workers, and living in the eastern region of China are more vulnerable to the adverse impacts of PM2.5. [ABSTRACT FROM AUTHOR]

Fusulinid foraminifera are among the most common microfossils of the Late Palaeozoic and act as key fossils for stratigraphic correlation, paleogeographic and paleoenvironmental indication, and evolutionary studies of marine life. Accurate and efficient identification forms the basis of such research involving fusulinids but is limited by the lack of digitized image datasets. This article presents the first large image dataset of fusulinids containing 2,400 images of individual samples subjected to 16 genera of all six fusulinid families and labelled to species level. These images were collected from the literature and our unpublished samples through an automatic segmentation procedure implementing BlendMask, a deep learning model. The dataset shows promise for the efficient accumulation of fossil images through automated procedures and will facilitate taxonomists in future morphologic and systematic studies. [ABSTRACT FROM AUTHOR]