Archive for month: May, 2024

Home OCT device useful tool for self-monitoring of neovascular age-related macular degeneration suggests a study published in the Ophthalmology Retina.

New research has found a significant association between participating in low to moderate intensity exercise and reduced rates of depression.

Researchers from Anglia Ruskin University (ARU) carried out an umbrella review of studies carried out across the world to examine the potential of physical activity as a mental health intervention.

The analysis, published in the journal Neuroscience and Biobehavioural Reviews, found that physical activity reduced the risk of depression by 23% and anxiety by 26%. A particularly strong association was found between low and moderate physical activity, which included activities such as gardening, golf and walking, and reduced risk of depression.

However, this was not strongly observed for high intensity exercise.

Physical activity was also significantly associated with reduced risk of severe mental health conditions, including a reduction in psychosis/schizophrenia by 27%.

The results were consistent in both men and women, and across different age groups and across the world.

Lead author Lee Smith, Professor of Public Health at Anglia Ruskin University (ARU), said: “Preventing mental health complications effectively has emerged as a major challenge, and an area of paramount importance in the realm of public health. These conditions can be complex and necessitate a multi-pronged approach to treatment, which may encompass pharmacological interventions, psychotherapy, and lifestyle changes.

“These effects of physical activity intensity on depression highlight the need for precise exercise guidelines. Moderate exercise can improve mental health through biochemical reactions, whereas high-intensity exercise may worsen stress-related responses in some individuals.

“Acknowledging differences in people’s response to exercise is vital for effective mental health strategies, suggesting any activity recommendations should be tailored for the individual.

“The fact that even low to moderate levels of physical activity can be beneficial for mental health is particularly important, given that these levels of activity may be more achievable for people who can make smaller lifestyle changes without feeling they need to commit to a high-intensity exercise programme.”

Reference:

Masoud Rahmati, San Lee, Dong Keon Yon, Seung Won Lee, Raphael Udeh, Mark McEvoy, Hans Oh, Laurie Butler, Helen Keyes, Yvonne Barnett, Ai Koyanagi, Jae Il Shin, Lee Smith. Physical activity and prevention of mental health complications: An umbrella review. Neuroscience & Biobehavioral Reviews, 2024; 160: 105641 DOI: 10.1016/j.neubiorev.2024.105641.

Atrial fibrillation (Afib), a common type of arrhythmia that is on the rise in people under the age of 65, is more dangerous in this increasingly younger population than previously thought, according to a new study published today in Circulation Arrhythmia and Electrophysiology and authored by physician-scientists at the UPMC Heart and Vascular Institute.

The study, which is among the first to examine a large group of Afib patients younger than 65 in the U.S., found that these younger patients were more likely to be hospitalized for heart failure, stroke or heart attack and had significantly higher rates of comorbidity and mortality, compared to similarly aged and gender-matched people who do not have Afib.

“Common knowledge among cardiologists is that, in people under 65, Afib is extremely uncommon and not detrimental. But there really hasn’t been any data to back that up,” said lead author Dr. Aditya Bhonsale, M.D., M.H.S, a UPMC cardiac electrophysiologist in HVI’s Division of Cardiology who is also an assistant professor of medicine at the University of Pittsburgh.

“At UPMC, we’ve been seeing a lot more young patients with Afib in recent years and have been interested in understanding the real-world clinical course of these individuals. As a payer-provider with patient records across more than 40 hospitals, UPMC was uniquely positioned to ask this question, which no one has been able to ask before,” Bhonsale added.

Drawing from the electronic health records of 67,221 UPMC patients seeking care for Afib from 2010 through 2019, the researchers found that more than a quarter of them (17,335) were under the age of 65, a stark contrast to the 2% prevalence commonly estimated. The high proportion likely reflects an increasing burden of cardiovascular risk factors in younger Americans, said Bhonsale.

The UPMC team found that over the course of a decade, survival rates for those with the arrythmia were 1.3 to 1.5 times worse for men with Afib, and 1.82 to 3.16 times worse for women, compared to similarly aged patients who did not have Afib. The patients studied also had high rates of cardiovascular disease risk factors, including smoking, obesity, hypertension and sleep apnea, which contribute to damaging structural and electrical changes in the heart over time.

“We are optimistic that data from this study will foster future investigation to evaluate optimal therapies for patients with Afib,” said senior author Sandeep Jain, M.D., director of cardiac electrophysiology in HVI’s Division of Cardiology who is also a professor of medicine at the University of Pittsburgh.

Reference:

Aditya Bhonsale, Jianhui Zhu, Floyd Thoma, Steve Koscumb, Krishna Kancharla, Andrew Voigt, Jared Magnani, N.A. Estes, Samir Saba, Oscar Marroquin, Suresh Mulukutla and Sandeep Jain, Mortality, Hospitalization, and Cardiac Interventions in Patients With Atrial Fibrillation Aged <65 Years, Circulation: Arrhythmia and Electrophysiology, https://doi.org/10.1161/CIRCEP.123.012143.

Japan: A recent study published in Cells has identified prenatal nicotine exposure (PNE) to be a risk factor for comorbidity with attention-deficit/hyperactive disorder (ADHD) and autism spectrum disorder (ASD) in newborns. 

Previous studies have linked smoking during pregnancy with neurodevelopmental disorders, the results of behavioral experiments in mice prenatally exposed to nicotine have been inconsistent. In the new study, scientists from Japan developed a deep learning-based framework to automatically observe and classify mice behavior in such experiments, producing more accurate and unbiased results. 

The fact that smoking is a risk factor for several diseases, including cancer, stroke, and diabetes, has been known for approximately half a century. However, over the past few decades, scientists have brought to light many of the detrimental effects of smoking during pregnancy, linking this habit to high infant mortality, failed delivery, and low body weight at birth. In addition, recent studies suggest that prenatal nicotine exposure (PNE) may be related to neurodevelopmental disorders, such as attention deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD).

For a long time now, scientists have used animal models (like rodents) to understand how PNE leads to neurodevelopmental disorders. By carefully analyzing the behavior of rodents, they can infer whether PNE is causing neurological changes and the brain regions affected by it; this can later be confirmed through histological analyses.

Unfortunately, thus far, studies on behavioral changes induced by PNE in mice have shown varied results, some of which are contradictory. Although there could be multiple reasons behind these discrepancies, human error and bias are prime suspects. In general, the assessment of complex animal behaviors, especially social interactions, relies on the efforts of human observers, which introduces a baseline level of subjectivity that is hard to dispel. But what if we can leverage artificial intelligence (AI) to produce more accurate and unbiased results from observations of PNE mice behavior?

In the study, researchers from the Department of Molecular and Cellular Physiology at the Shinshu University School of Medicine, including graduate student Mengyun Zhou, Assistant Professor Takuma Mori, and Professor Katsuhiko Tabuchi, developed and trained a deep learning-based system to automatically analyze footage from behavioral experiments on mice. They used this tool to explore the behavioral changes induced by PNE in mice without observer biases, seeking to shed light on the link between nicotine and neurodevelopmental disorders.

The proposed AI-based framework relied on a combination of two well-established open-source toolkits, namely DeepLabCut and Simple Behavioral Analysis (SimBA). “AI tools can label the body parts of animals in a markerless video footage and precisely estimate their poses using supervised machine learning,” explains Prof. Tabuchi. “Since animal behaviors are defined as a specific arrangement of body parts over a short period of time, deep-learning toolkits like SimBA can use the pose estimations obtained with DeepLabCut to classify different types of animal behaviors.”

After reaching an optimal training protocol for their framework using manually labeled data, the researchers conducted several experiments using PNE and control mice, looking for indicators of ADHD- and ASD-like behaviors. First, they carried out cliff avoidance reaction tests, which are used to test impulsivity. In these tests, they placed the subject mouse on top of a slightly elevated platform and took note—both manually and with the AI system—of how long the mouse waited before jumping down the platform. The test results suggested that PNE mice are more impulsive, a behavioral feature of ADHD in humans.

They also tested the working memory of mice using a Y-shaped maze and counted the number of times each mouse spontaneously switched from one arm of the maze to another. “We observed a decrease in the spontaneous alteration in PNE mice, suggesting that their working memory was altered, which is another behavioral feature of ADHD,” comments Mengyun Zhou. “These results suggest prenatal exposure to nicotine may cause ADHD in mice, which is consistent with clinical reports in humans.”

Finally, the researchers conducted open-field and social-interaction experiments, which represented the main challenge for their AI-based system. In these experiments, the researchers observed either one or two mice behaving freely in a large enclosure and looked for indicators of anxiety and social behaviors, such as grooming and following. Interestingly, PNE mice exhibited social behavioral deficits and increased anxiety which are features of ASD. Subsequent histological analysis of hippocampal brain tissue confirmed decreased neurogenesis, a hallmark of ASD. Thus, it appears that smoking may not only increase the risk of ADHD, but also ASD.

Worth noting, the results obtained using the AI-based system were highly reliable, as Prof. Tabuchi highlights: “We validated the accuracy of our behavioral analysis framework by drawing a careful comparison between the results generated by the model and behavior assessments made by multiple human annotators, which is considered the gold standard.” These analyses cement the potential of the proposed approach and showcase its capabilities for many types of behavioral studies.

With any luck, further efforts will pave the way to a solid understanding of mechanisms behind neurodevelopmental disorders like ASD and ADHD, ultimately leading to better diagnostic tools and therapeutic methods.

Reference:

Zhou, M.; Qiu, W.; Ohashi, N.; Sun, L.; Wronski, M.-L.; Kouyama-Suzuki, E.; Shirai, Y.; Yanagawa, T.; Mori, T.; Tabuchi, K. Deep-Learning-Based Analysis Reveals a Social Behavior Deficit in Mice Exposed Prenatally to Nicotine. Cells 2024, 13, 275. https://doi.org/10.3390/cells13030275.

Researchers have found that the use of antibiotics early in life is linked to an increased risk of developing atopic dermatitis, a chronic inflammatory skin condition, likely due to gut microbiome disruption. This association was observed in data from the Canadian CHILD birth cohort study. This study was published in The Journal of Allergy and Clinical Immunology by Hoskinson C. and colleagues.