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This research presents a novel method for non-invasive arterial blood pressure (ABP) prediction using speech signals, employing a BERT -based regression model. Arterial blood pressure is a vital indicator of cardiovascular health, and accurate monitoring is essential in preventing hypertension-related complications. Traditional cuff-based methods often yield inconsistent results due to factors like white-coat and masked hypertension. Our approach leverages the acoustic characteristics of speech, capturing voice features to establish correlations with blood pressure levels. Utilizing advanced deep learning techniques, we analyze speech signals to extract relevant patterns, enabling real-time monitoring without the discomfort of conventional methods.

Photoplethysmography (PPG) is emerging as a crucial tool for monitoring human hemodynamics, with recent studies highlighting its potential in assessing vascular aging through deep learning. However, real-world age distributions are often imbalanced, posing significant challenges for deep learning models. In this paper, we introduce a novel, simple, and effective loss function named the Dist Loss to address deep imbalanced regression tasks. We trained a one-dimensional convolutional neural network (Net1D) incorporating the Dist Loss on the extensive UK Biobank dataset (n=502,389) to estimate vascular age from PPG signals and validate its efficacy in characterizing cardiovascular health. The model's performance was validated on a 40% held-out test set, achieving state-of-the-art results, especially in regions with small sample sizes. Furthermore, we divided the population into three subgroups based on the difference between predicted vascular age and chronological age: less than -10 years, between -10 and 10 years, and greater than 10 years. We analyzed the relationship between predicted vascular age and several cardiovascular events over a follow-up period of up to 10 years, including death, coronary heart disease, and heart failure. Our results indicate that the predicted vascular age has significant potential to reflect an individual's cardiovascular health status. Our code will be available at https://github.com/Ngk03/AI-vascular-age.

From the title character in Napoleon Dynamite to McLovin in Superbad, stereotypical'nerds' are often depicted wearing glasses. But a new study suggests that if you want people to think you're intelligent, you should consider swapping your glasses for contact lenses. Researchers from the University of Jordan found that people are seen as less attractive, less confident, and less intelligent when wearing glasses. A new study suggests that if you want people to think you're intelligent, you should consider swapping your glasses for contact lenses (stock image) From the title character in Napoleon Dynamite (pictured) to McLovin in Superbad, stereotypical'nerds' are often depicted wearing glasses Children as young as five perceive thinner people as happier and more attractive than overweight people, a study has revealed. Researchers from the University of Gdańsk showed preschool boys and girls images of men and women with various body types, and asked them to rate who was the most attractive and happiest.

Machine learning analysis of stool samples may provide a helpful first pass for the mass screening for any type of cardiovascular disease (CVD) in patients, researchers claimed. Various machine learning algorithms were fed gut microbiota data and, with training, were subsequently able to distinguish between people with and without CVD, with ROC curves as high as 0.70, reported a group led by Sachin Aryal, an MS student in bioinformatics at the University of Toledo, Ohio. "While this demonstrates the promising potential of applying microbiome-based ML [machine learning] for predicting CVD, in the future, it will be of interest to further calibrate and improve predictive capability of ML modeling by including more samples from different sources or stratifying specific types of CVD incorporated with combinatorial features such as health records, in addition to gut microbiome data," the authors said. Their study was presented as a poster at the virtual Hypertension meeting, sponsored by the American Heart Association, and was simultaneously published online in the November 2020 issue of Hypertension. Investigators claimed theirs as the first study to apply existing knowledge of dysbiosis of gut microbiota in CVD patients to a machine learning approach to CVD screening.

Individuals create and consume more diverse data about themselves today than any time in history. Sources of this data include wearable devices, images, social media, geospatial information and more. A tremendous opportunity rests within cross-modal data analysis that leverages existing domain knowledge methods to understand and guide human health. Especially in chronic diseases, current medical practice uses a combination of sparse hospital based biological metrics (blood tests, expensive imaging, etc.) to understand the evolving health status of an individual. Future health systems must integrate data created at the individual level to better understand health status perpetually, especially in a cybernetic framework. In this work we fuse multiple user created and open source data streams along with established biomedical domain knowledge to give two types of quantitative state estimates of cardiovascular health. First, we use wearable devices to calculate cardiorespiratory fitness (CRF), a known quantitative leading predictor of heart disease which is not routinely collected in clinical settings. Second, we estimate inherent genetic traits, living environmental risks, circadian rhythm, and biological metrics from a diverse dataset. Our experimental results on 24 subjects demonstrate how multi-modal data can provide personalized health insight. Understanding the dynamic nature of health status will pave the way for better health based recommendation engines, better clinical decision making and positive lifestyle changes.

If you suffer with a short temper – you're probably not as smart as you think you are, a new study has found. Angry people are more likely to overestimate their intelligence levels than those with a calmer disposition, scientists have found. That's because being angry is linked with high levels of narcissism, as well as a greater belief in their abilities and competence. Those with a short fuse were also more likely to have problems maintaining a stable relationship, according to the latest findings. This is because individuals with high levels of narcissism struggle to establish bonds with others as they are always trying to dominate them.

The eyes may be the window to the soul, but they're also a window into heart health thanks to breakthrough health technology that combines artificial intelligence (AI) and machine learning. In a study published in February 2018 in Nature Biomedical Engineering, Google AI researchers and Verily (a health tech subsidiary of Google) teamed up to develop a non-invasive procedure to assess future heart health with retinal imaging techniques. Scientists used AI cameras to observe the blood vessels behind the eyes of more than 250,000 individuals from the United Kingdom and United States. From the collected data, an algorithm predicted the next five years of cardiovascular health for each patient with over 70 percent accuracy. "This discovery is particularly exciting because it suggests we might discover even more ways to diagnose health issues from retinal images," Lily Peng, a product manager for the Google Brain AI research group, said on their blog.