Lifeboat Foundation

The investigators carried out animal trials with the engineered AsCas12f system, partnering it with other genes and administering it to live mice. The encouraging results indicated that engineered AsCas12f has the potential to be used for human gene therapies, such as treating hemophilia.

The team discovered numerous potentially effective combinations for engineering an improved AsCas12f gene-editing system, and acknowledged the possibility that the selected mutations may not have been the most optimal of all the available mixes. As a next step, computational modeling or machine learning could be used to sift through the combinations and predict which might offer even better improvements.

And as the authors noted, by applying the same approach to other Cas enzymes, it may be possible to generate efficient genome-editing enzymes capable of targeting a wide range of genes. “The compact size of AsCas12f offers an attractive feature for AAV-deliverable gRNA and partner genes, such as base editors and epigenome modifiers. Therefore, our newly engineered AsCas12f systems could be a promising genome-editing platform … Moreover, with suitable adaptations to the evaluation system, this approach can be applied to enzymes beyond the scope of genome editing.”

Australian researchers have developed a molecular-sized, more efficient version of a widely used electronic sensor, in a breakthrough that could bring widespread benefits.

Piezoresistors are commonly used to detect vibrations in electronics and automobiles, such as in smartphones for counting steps, and for airbag deployment in cars. They are also used in medical devices such as implantable pressure sensors, as well as in aviation and space travel.

Breakthrough in Piezoresistor Technology.

Aging is a major risk factor for most chronic conditions, evidence shows, yet much of current research focuses on addressing specific diseases. The new translational geroscience initiative at Yale School of Medicine (YSM) seeks to change that approach by studying the effects of aging on various ailments.

“Yale School of Medicine has a long legacy in studying aging, but with this new initiative we are bolstering our ability to delineate basic mechanisms of healthy and accelerated aging,” said Nancy J. Brown, MD, Jean and David W. Wallace Dean of the Yale School of Medicine and C.N.H. Long Professor of Internal Medicine.

The mechanisms underlying the aging process are often also driving the development and progression of chronic conditions, explains Thomas Gill, MD, Humana Foundation Professor of Medicine (Geriatrics) and professor of epidemiology, Yale School of Public Health, and of investigative medicine at YSM, who leads the Yale Pepper Older Americans Independence Center.

Small and precise: These are the ideal characteristics for CRISPR systems, the Nobel-prize winning technology used to edit nucleic acids like RNA

Ribonucleic acid (RNA) is a polymeric molecule similar to DNA that is essential in various biological roles in coding, decoding, regulation and expression of genes. Both are nucleic acids, but unlike DNA, RNA is single-stranded. An RNA strand has a backbone made of alternating sugar (ribose) and phosphate groups. Attached to each sugar is one of four bases—adenine (A), uracil (U), cytosine ©, or guanine (G). Different types of RNA exist in the cell: messenger RNA (mRNA), ribosomal RNA (rRNA), and transfer RNA (tRNA).

ROCHESTER, Minn. — A recent study based on real-world community patient data confirms the effectiveness of the Pooled Cohort Equation (PCE), developed by the American Heart Association and the American College of Cardiology in 2013. The PCE is used to estimate a person’s 10-year risk of developing clogged arteries, also known as atherosclerosis, and guide heart attack and stroke prevention efforts. Study findings are published in the Journal of the American College of Cardiology.

The new study highlights to patients and clinicians the continued reliability and effectiveness of the PCE as a tool for assessing cardiovascular risk, regardless of statin use to lower cholesterol.

The PCE serves as a shared decision-making tool for a clinician and patient to evaluate their current status in preventing atherosclerotic cardiovascular disease. The calculator considers input in the categories of gender, age, race, total cholesterol, HDL cholesterol, systolic blood pressure, treatment for high blood pressure, diabetes status, and smoking status.

Advances in imaging technologies are giving physicians unprecedented insights into disease states, but fragmented and siloed information technology systems make it difficult to provide the personalized, coordinated care that patients expect.

In the field of medical imaging, health care providers began replacing radiographic films with digital images stored in a picture and archiving communication system (PACS) in the 1980s. As this wave of digitization progressed, individual departments—ranging from cardiology to pathology to nuclear medicine, orthopedics, and beyond—began acquiring their own, distinct IT solutions.

View questions to ask your health care team, ways to manage your self-care, treatment and support resources, and hear from patients and caregivers with rare brain and spine tumors to guide you through your journey.

The study shows that two liquid biopsy techniques can detect the presence of human papillomavirus in blood.

For more information on liver cancer treatment or #YaleMedicine, visit: https://www.yalemedicine.org/stories/artificial-intelligence-liver-cancer.

With liver cancer on the rise (deaths rose 25% between 2006 and 2015, according to the CDC), doctors and researchers at the Yale Cancer Center are highly focused on finding new and better treatment options. A unique collaboration between Yale Medicine physicians and researchers and biomedical engineers from Yale’s School of Engineering uses artificial intelligence (AI) to pinpoint the specific treatment approach for each patient. First doctors need to understand as much as possible about a particular patient’s cancer. To this end, medical imaging techniques such as computed tomography (CT) and magnetic resonance imaging (MRI) are valuable tools for early detection, accurate diagnosis, and effective treatment of liver cancer. For every patient, physicians need to interpret and analyze these images, along with a multitude of other clinical data points, to make treatment decisions likeliest to lead to a positive outcome. “There’s a lot of data that needs to be considered in terms of making a recommendation on how to manage a patient,” says Jeffrey Pollak, MD, Robert I. White, Jr. Professor of Radiology and Biomedical Imaging. “It can become quite complex.” To help, researchers are developing AI tools to help doctors tackle that vast amount of data. In this video, Julius Chaprio, MD, PhD, explains how collaboration with biomedical engineers like Lawrence Staib, PhD, facilitated the development of specialized AI algorithms that can sift through patient information, recognize important patterns, and streamline the clinical decision-making process. The ultimate goal of this research is to bridge the gap between complex clinical data and patient care. “It’s an advanced tool, just like all the others in the physician’s toolkit,” says Dr. Staib. “But this one is based on algorithms instead of a stethoscope.”