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The potential of satellite-based synthetic biology and genetic engineering to revolutionize healthcare is becoming increasingly clear. Recent advances in the field have opened up a world of possibilities for medical professionals and researchers, allowing them to diagnose and treat diseases more effectively and efficiently than ever before.

Satellite-based synthetic biology and genetic engineering have already been used to develop treatments for a variety of conditions, including cancer, heart disease, and neurological disorders. By using satellite-based techniques, researchers can quickly and accurately identify genetic mutations and other abnormalities in a patient’s DNA. This allows them to develop personalized treatments that are tailored to the individual’s specific needs.

The use of satellite-based synthetic biology and genetic engineering also has the potential to reduce healthcare costs. By identifying genetic mutations and other abnormalities at an early stage, doctors can avoid costly and unnecessary treatments. This could lead to significant savings for both patients and healthcare providers.

Summary: Experts see a bright future in the complementary use of artificial intelligence (AI) and structure-based drug discovery for drug discovery. Researchers explain how computational methods will streamline drug discovery by predicting which drug molecules are most likely to bind with the target receptor. The structure-based and AI-based approaches complement each other and can save time and money while yielding better results than traditional trial-and-error methods.

Source: USC

Artificial intelligence can generate poems and essays, create responsive game characters, analyze vast amounts of data and detect patterns that the human eye might miss. Imagine what AI could do for drug discovery, traditionally a time-consuming, expensive process from the bench to the bedside.

Chronic anxiety is prevalent and associated with an increased risk of cardiovascular disease. Prior studies that have reported a relationship between muscle sympathetic nerve activity (MSNA) and anxiety have focused on participants with anxiety disorders and/or metabolic syndrome. The present study leverages a large cohort of healthy adults devoid of cardiometabolic disorders to examine the hypothesis that trait anxiety severity is positively associated with resting MSNA and blood pressure. Resting blood pressure (BP) (sphygmomanometer and finger plethysmography), MSNA (microneurography), and heart rate (HR; electrocardiogram) were collected in 88 healthy participants (52 males, 36 females, 25 ± 1 yr, 25 ± 1 kg/m²). Multiple linear regression was performed to assess the independent relationship between trait anxiety, MSNA, resting BP, and HR while controlling for age and sex. Trait anxiety was significantly correlated with systolic arterial pressure (SAP; r = 0.251, P = 0.018), diastolic arterial pressure (DAP; r = 0.291, P = 0.006), mean arterial pressure (MAP; r = 0.328, P = 0.002), MSNA burst frequency (BF; r = 0.237, P = 0.026), and MSNA burst incidence (BI; r = 0.225, P = 0.035). When controlling for the effects of age and sex, trait anxiety was independently associated with SAP (β = 0.206, P = 0.028), DAP (β = 0.317, P = 0.002), MAP (β = 0.325, P = 0.001), MSNA BF (β = 0.227, P = 0.030), and MSNA BI (β = 0.214, P = 0.038). Trait anxiety is associated with increased blood pressure and MSNA, demonstrating an important relationship between anxiety and autonomic blood pressure regulation.

NEW & NOTEWORTHY Anxiety is associated with development of cardiovascular disease. Although the sympathetic nervous system is a likely mediator of this relationship, populations with chronic anxiety have shown little, if any, alteration in resting levels of directly recorded muscle sympathetic nerve activity (MSNA). The present study is the first to reveal an independent relationship between trait anxiety, resting blood pressure, and MSNA in a large cohort of healthy males and females devoid of cardiometabolic comorbidities.

Listen to this article’s corresponding podcast at https://ajpheart.podbean.com/e/anxiety-and-muscle-sympathetic-nerve-activity/.

A recent study published in Current Biology provides evidence that people suffering from nightmare disorder can experience an acceleration in the remission of their symptoms when treated with behavioral therapy and Targeted Memory Reactivation.

Nightmare disorder (ND) is a condition characterized by recurrent nightmares that significantly affect daytime functioning. Imagery Rehearsal Therapy (IRT) is the most recommended treatment for ND, whereby patients imagine a more positive outcome for their nightmare, and then recall the new dream. However, “approximately 30% of patients are unresponsive to this treatment,” the authors of the new study explained.

A growing body of evidence indicates that Rapid Eye Movement (REM) sleep can aid in decreasing negative emotions and consolidating the recall of positive emotional memories. With this in mind, the authors sought to investigate whether manipulating memory processing during REM could accelerate the remission of ND when combined with Imagery Rehearsal Therapy.

Menno van Dijk/iStock.

This according to a study reported by The National Post on Friday.

According to a recent study, ChatGPT surpasses the quality and empathy of physicians when responding to online queries. However, there are some caveats.

A recent study published in JAMA Internal Medicine reveals that ChatGPT surpasses physicians in terms of quality and empathy when responding to online queries. The study evaluated ChatGPT’s performance compared to physicians in answering patient questions from Reddit’s r/AskDocs forum.

The cross-sectional study involved 195 randomly selected questions and found that chatbot responses were preferred over physician responses. ChatGPT received significantly higher ratings for both quality and empathy.

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Maybe you can’t tell a book from its cover, but according to researchers at MIT you may now be able to do the equivalent for materials of all sorts, from an airplane part to a medical implant. Their new approach allows engineers to figure out what’s going on inside simply by observing properties of the material’s surface.

The team used a type of machine learning known as deep learning to compare a large set of simulated data about materials’ external force fields and the corresponding internal structure, and used that to generate a system that could make reliable predictions of the interior from the surface data.

The results have been published in the journal Advanced Materials, in a paper by doctoral student Zhenze Yang and professor of civil and environmental engineering Markus Buehler.

MIT engineers have designed a two-component system that can be injected into the body and help form blood clots at the sites of internal injury. These materials, which mimic the way that the body naturally forms clots, could offer a way to keep people with severe internal injuries alive until they can reach a hospital.

In a mouse model of internal injury, the researchers showed that these components—a nanoparticle and a polymer—performed significantly better than hemostatic nanoparticles that were developed earlier.

“What was especially remarkable about these results was the level of recovery from severe injury we saw in the animal studies. By introducing two complementary systems in sequence it is possible to get a much stronger clot,” says Paula Hammond, an MIT Institute Professor, the head of MIT’s Department of Chemical Engineering, a member of the Koch Institute for Integrative Cancer Research, and one of the senior authors of a paper on the study.