Researchers identify new genes that may contribute to Alzheimer’s disease

Researchers from Boston University School of Medicine, working with scientists across the nation on the Alzheimer’s Disease Sequencing Project (ADSP), have discovered new genes that will further current understanding of the genetic risk factors that predispose people to the development of Alzheimer’s disease (AD). The ADSP was developed by the National Institutes of Health (NIH) in response to the National Alzheimer’s Project Act milestones to fight AD.

The incidence of AD is increasing each year and is the most common cause of dementia. Also, it is the fifth leading cause of death in those 65-years and older, according to the CDC. AD is characterized by the formation of senile plaques (extracellular deposits of β-amyloid protein) and neurofibrillary tangles (aggregates of hyper-phosphorylated tau protein) in the brain, leading to neurodegeneration and decline in memory, and eventually death. Despite the growing prevalence of AD and cost to society, the genetic and environmental factors that make some more susceptible to the development of AD is still not well understood.

“This large and deep gene sequencing study is an important part of identifying which variations may play a part in risk of getting Alzheimer’s or protection against it,” said Eliezer Masliah, MD, director of the Division of Neuroscience at the National Institute on Aging, part of NIH. “Big data efforts like the ADSP are really helping research move forward. Identifying rare variants could enhance our ability to find novel therapeutic targets and advance precision medicine approaches for Alzheimer’s disease.”

By comparing the exomes (gene-coding portions of entire genetic sequences) of nearly 6,000 individuals with AD and 5,000 cognitively healthy older adults, the researchers were able to find rare variations in genes that they believe may contribute to the development of common AD. These newly discovered genes may suggest an inflammatory response and changes in the protein production. These combined changes are thought to contribute to the overall neurodegeneration witnessed in AD.

The researchers hope their work will help bridge the knowledge gaps of the genetic architecture related to AD, which is a necessary step toward a better understanding of mechanisms leading to AD and eventual therapeutic treatments. “Many of our findings will provide insight into disease mechanisms and targets for biological experiments to gain further understanding about the role of these genes in AD pathogenesis,” explained corresponding author Lindsay A. Farrer, Ph.D., Chief of Biomedical Genetics and a professor of Medicine, Neurology, Ophthalmology, Epidemiology and Biostatistics at Boston University Schools of Medicine and Public Health.

The research team emphasizes that further research will need to be done to find other genes hidden throughout the genome, as the current paradigm is that many genes contribute to the development of AD.

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Team finds missing immune cells that could fight lethal brain tumors

Glioblastoma brain tumors can have an unusual effect on the body’s immune system, often causing a dramatic drop in the number of circulating T-cells that help drive the body’s defenses.

Where the T-cells go has been unclear, even as immunotherapies are increasingly employed to stimulate the body’s natural ability to fight invasive tumors.

Now researchers at Duke Cancer Institute have tracked the missing T-cells in glioblastoma patients. They found them in abundance in the bone marrow, locked away and unable to function because of a process the brain stimulates in response to glioblastoma, to other tumors that metastasize in the brain and even to injury.

The findings, published online Aug. 13 in the journal Nature Medicine, open a new area of exploration for adjunct cancer drugs that could free trapped T-cells from the bone marrow, potentially improving the effectiveness of existing and new immunotherapies.

“Part of the problem with all these immunotherapies—particularly for glioblastoma and other tumors that have spread to the brain—is that the immune system is shot,” said lead author Peter E. Fecci, M.D., Ph.D., director of the Brain Tumor Immunotherapy Program in Duke’s Department of Neurosurgery. “If the goal is to activate the T-cells and the T-cells aren’t there, you’re simply delivering therapy into a black hole.”

Fecci said the research team began its search for the missing T-cells after observing that many newly diagnosed glioblastoma patients have the equivalent immune systems of people with full-blown AIDS, even before they undergo surgery, chemotherapy and radiation.

Where most people have a CD-4 “helper” T-cell count upwards of 700-1,000, a substantial proportion of untreated glioblastoma patients have counts of 200 or less, marking poor immune function that makes them susceptible to all manner of infections and potentially to progression of their cancer.

Initially, the researchers hunted for the missing T-cells in the spleen, which is known to pathologically harbor the cells in certain disease states. But the spleens were abnormally small, as were the thymus glands—another potential T-cell haven. They decided to check the bone marrow to see if production was somehow stymied and instead found hordes of T-cells.

“It’s totally bizarre—this is not seen in any disease state,” Fecci said. “This appears to be a mechanism that the brain possesses for keeping T-cells out, but it’s being usurped by tumors to limit the immune system’s ability to attack them.”

When examining the stashed T-cells, Fecci and colleagues found that they lacked a receptor on the cell surface called S1P1, which essentially serves as a key that enables them to leave the bone marrow and lymph system. Lacking that key, they instead get locked in, unable to circulate and fight infections, let alone cancer.

Fecci said the research team is now working to learn exactly how the brain triggers the dysfunction of this S1P1 receptor. He said the current theory is that the receptor somehow is signaled to retract from the cell surface into the cell interior.

“Interestingly, when we restore this receptor to T-cells in mice, the T-cells leave the bone marrow and travel to the tumor, so we know this process is reversible,” Fecci said.

His team is collaborating with Duke scientist Robert Lefkowitz, M.D., whose 2012 Nobel Prize in Chemistry honored discovery of the class of receptors to which S1P1 belongs. They are working to develop molecules that would restore the receptors on the cells’ surface.

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Machine-learning system determines the fewest, smallest doses that could still shrink brain tumors

MIT researchers are employing novel machine-learning techniques to improve the quality of life for patients by reducing toxic chemotherapy and radiotherapy dosing for glioblastoma, the most aggressive form of brain cancer.

Glioblastoma is a malignant tumor that appears in the brain or spinal cord, and prognosis for adults is no more than five years. Patients must endure a combination of radiation therapy and multiple drugs taken every month. Medical professionals generally administer maximum safe drug doses to shrink the tumor as much as possible. But these strong pharmaceuticals still cause debilitating side effects in patients.

In a paper being presented next week at the 2018 Machine Learning for Healthcare conference at Stanford University, MIT Media Lab researchers detail a model that could make dosing regimens less toxic but still effective. Powered by a “self-learning” machine-learning technique, the model looks at treatment regimens currently in use, and iteratively adjusts the doses. Eventually, it finds an optimal treatment plan, with the lowest possible potency and frequency of doses that should still reduce tumor sizes to a degree comparable to that of traditional regimens.

In simulated trials of 50 patients, the machine-learning model designed treatment cycles that reduced the potency to a quarter or half of nearly all the doses while maintaining the same tumor-shrinking potential. Many times, it skipped doses altogether, scheduling administrations only twice a year instead of monthly.

“We kept the goal, where we have to help patients by reducing tumor sizes but, at the same time, we want to make sure the quality of life—the dosing toxicity—doesn’t lead to overwhelming sickness and harmful side effects,” says Pratik Shah, a principal investigator at the Media Lab who supervised this research.

The paper’s first author is Media Lab researcher Gregory Yauney.

Rewarding good choices

The researchers’ model uses a technique called reinforced learning (RL), a method inspired by behavioral psychology, in which a model learns to favor certain behavior that leads to a desired outcome.

The technique comprises artificially intelligent “agents” that complete “actions” in an unpredictable, complex environment to reach a desired “outcome.” Whenever it completes an action, the agent receives a “reward” or “penalty,” depending on whether the action works toward the outcome. Then, the agent adjusts its actions accordingly to achieve that outcome.

Rewards and penalties are basically positive and negative numbers, say +1 or -1. Their values vary by the action taken, calculated by probability of succeeding or failing at the outcome, among other factors. The agent is essentially trying to numerically optimize all actions, based on reward and penalty values, to get to a maximum outcome score for a given task.

The approach was used to train the computer program DeepMind that in 2016 made headlines for beating one of the world’s best human players in the game “Go.” It’s also used to train driverless cars in maneuvers, such as merging into traffic or parking, where the vehicle will practice over and over, adjusting its course, until it gets it right.

The researchers adapted an RL model for glioblastoma treatments that use a combination of the drugs temozolomide (TMZ) and procarbazine, lomustine, and vincristine (PVC), administered over weeks or months.

The model’s agent combs through traditionally administered regimens. These regimens are based on protocols that have been used clinically for decades and are based on animal testing and various clinical trials. Oncologists use these established protocols to predict how much doses to give patients based on weight.

As the model explores the regimen, at each planned dosing interval—say, once a month—it decides on one of several actions. It can, first, either initiate or withhold a dose. If it does administer, it then decides if the entire dose, or only a portion, is necessary. At each action, it pings another clinical model—often used to predict a tumor’s change in size in response to treatments—to see if the action shrinks the mean tumor diameter. If it does, the model receives a reward.

However, the researchers also had to make sure the model doesn’t just dish out a maximum number and potency of doses. Whenever the model chooses to administer all full doses, therefore, it gets penalized, so instead chooses fewer, smaller doses. “If all we want to do is reduce the mean tumor diameter, and let it take whatever actions it wants, it will administer drugs irresponsibly,” Shah says. “Instead, we said, ‘We need to reduce the harmful actions it takes to get to that outcome.'”

This represents an “unorthodox RL model, described in the paper for the first time,” Shah says, that weighs potential negative consequences of actions (doses) against an outcome (tumor reduction). Traditional RL models work toward a single outcome, such as winning a game, and take any and all actions that maximize that outcome. On the other hand, the researchers’ model, at each action, has flexibility to find a dose that doesn’t necessarily solely maximize tumor reduction, but that strikes a perfect balance between maximum tumor reduction and low toxicity. This technique, he adds, has various medical and clinical trial applications, where actions for treating patients must be regulated to prevent harmful side effects.

Optimal regimens

The researchers trained the model on 50 simulated patients, randomly selected from a large database of glioblastoma patients who had previously undergone traditional treatments. For each patient, the model conducted about 20,000 trial-and-error test runs. Once training was complete, the model learned parameters for optimal regimens. When given new patients, the model used those parameters to formulate new regimens based on various constraints the researchers provided.

The researchers then tested the model on 50 new simulated patients and compared the results to those of a conventional regimen using both TMZ and PVC. When given no dosage penalty, the model designed nearly identical regimens to human experts. Given small and large dosing penalties, however, it substantially cut the doses’ frequency and potency, while reducing tumor sizes.

The researchers also designed the model to treat each patient individually, as well as in a single cohort, and achieved similar results (medical data for each patient was available to the researchers). Traditionally, a same dosing regimen is applied to groups of patients, but differences in tumor size, medical histories, genetic profiles, and biomarkers can all change how a patient is treated. These variables are not considered during traditional clinical trial designs and other treatments, often leading to poor responses to therapy in large populations, Shah says.

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Together, big data, bench science and genome-wide diagnostics predict genomic instability that can lead to disease

They are the most common repeated elements in the human genome; more than a million copies are scattered among and between our genes. Called Alu elements, these relatively short (approximately 300 Watson-Crick base pairs), repetitive non-coding sequences of DNA have been implicated in the rapid evolution of humans and non-human primate species. Unfortunately, these repeats also cause genomic structural variation that can lead to disease.

Disease-causing Alu elements do not work alone. To cause structural variations, pairs of elements (Alu/Alu) mediate genomic rearrangements that result in either gene copy number gains or losses, and these changes can have profound consequences for an individual’s health.

For instance, the first Alu-mediated rearrangement was described 30 years ago in a patient with familial hypercholesterolemia or very high levels of cholesterol in the blood. The patient carried a small deletion—8-kilobase long—of the gene for the low-density lipoprotein (LDL) receptor that binds to low-density lipoprotein particles, which are the primary carriers of cholesterol in the blood. Alu/Alu-mediated rearrangements had resulted in the small deletion of the LDL receptor in this patient, rendering it unfit to capture LDL-cholesterol particles and remove them from the blood.

Years later, other similarly severe medical conditions were linked to Alu/Alu-mediated structural variations, such as spastic paraplegia 4 and Fanconi anemia. Scientists have estimated that Alu/Alu-associated copy number variants cause approximately 0.3 percent of human genetic diseases.

In their laboratories at Baylor College of Medicine, Dr. James R. Lupski and Dr. Chad A. Shaw have been studying the mechanisms mediating a number of structural variations for many years; Dr. Lupski’s research interest in structural variant mutagenesis has spanned decades. Among other things, his lab and the findings from other labs pointed at Alu element-mediated variation as the cause of a significant portion of some pediatric genetic diseases.

“The Alu elements we are talking about are thought to be completely inert, they are not actively producing proteins, but problems arise when the machinery that repairs broken DNA incorrectly replicates a genomic segment flanked by a pair of repetitive Alu elements. The machinery ‘gets confused’ by the repetitive Alu sequences and responds in a way that leads to either duplication or deletion of the sequence between the Alu elements, and this can lead to disease,” said Shaw, who is a statistician, a computational scientist and an associate professor of molecular and human genetics at Baylor College of Medicine, as well as senior director of bioinformatics at Baylor Genetics.

The situation would be analogous to reading a text that has the same sentence repeated twice at intervals. In this analogy, the gene is represented by a paragraph of text flanked by the two same short phrase of words. The reader would see the repetition, get confused and probably skip that section, possibly missing important information between the repeats. Conversely, the reader would read the same sentences multiple times by returning to the first sentence. In the genome, ‘missing’ a section that includes important genes—a deletion copy number variant—or repeating a segment—causing a duplication or copy gain—can both have serious health consequences.

Given the relevance of Alu elements in human genetic diseases as well as genome evolution, the researchers wanted to find a way to predict which genes are susceptible to Alu/Alu-mediated rearrangements. Current clinically applied methods for measuring genome variation have limitations to achieve this goal, such as insufficient resolution or great cost, so the researchers developed a novel approach.

“We began by conducting a comprehensive statistical study to identify the characteristics of the Alu pairs known to cause diseases,” said Xiaofei Song, a graduate student in the Lupski lab. “This would enable us to build a machine-learning model to predict genes that would likely be susceptible to changes due to Alu/Alu-mediated rearrangements.”

How to build and test a machine-learning model to predict disease-causing genes

The researchers applied a comprehensive and unbiased computational approach to identify the features of the Alu pairs that make genes susceptible to copy number gain or loss.

“We analyzed a training data set composed of 219 Alu pairs that are known to contribute to diseases by affecting specific genes,” Song said. ‘First, we identified the sequence features of the Alu elements in those 219 pairs; then, we looked on the entire human genome, using the current human genome reference sequence to which the Baylor Human Genome Sequencing Center (HGSC) contributed significantly, for other Alu pairs with similar characteristics. So, if we found a region including a number of Alu pairs with these specific features, then we would consider it to be a ‘hotspot’ of genomic instability associated with Alu pairs.”

“We also looked at other features, such as the characteristics of the DNA section surrounding two Alu elements,” said Shaw, who also is adjunct associate professor of statistics at Rice University. “If the pairs are at a certain distance from each other and are oriented in a certain way, then this is a risk factor. Having a high similarity level on the DNA sequence is another clue that an Alu pair may confuse the replication machinery and mediate rearrangements.”

The researchers conducted an extensive computational analysis of the human genome and approximately 78 million Alu pairs using the BlueGene supercomputer at Rice University that integrated all these data and built a comprehensive model. They used the model to evaluate the whole genome, characterizing the risk of Alu/Alu-mediated rearrangement for each gene.

“In addition, we carried out computational work to test our model in real human genome data—more than 54 thousand personal genome samples. For each of these samples, the copy number variation has been determined and is available as anonymized genomic variation information at the Baylor Genetics diagnostic laboratory,” Song said. “This analysis predicted that a number of known disease genes were at risk of Alu/Alu mediated copy number gain or loss.”

The researchers selected 89 of the predicted cases and, using PCR and genomic sequencing in the Lupski lab, tested for the presence of Alu-mediated rearrangements, confirming the prediction in 94 percent of the cases.

“These are all new discoveries of copy number variations caused by Alu-mediated rearrangements,” Shaw said. “We also identified the junction, the piece of DNA between Alu elements, which may include one or more genes that have been rearranged.”

The work also enabled Song to produce an AluAluCNVpredictor, a web-based tool that allows researchers around the world to predict the risk of Alu/Alu-mediated rearrangements for the genes of their interest. This tool can be accessed at http://alualucnvpredictor.research.bcm.edu:3838/.

Interdisciplinary collaboration uncovers hidden clues in the DNA

This work shows the power of collaboration between experimental geneticists, genomicists and computational scientists. Years of research have produced extensive knowledge of the genetic basis of disease as well as vast amounts of genomic data that, thanks to the computational teams that built sophisticated computational tools, can now be analyzed to uncover hidden clues in the DNA. The results are a deeper understanding of the structure of the genome, the ability to elucidate novel disease-gene associations, improved molecular diagnosis and the revelation of further insights into genomic instability, human gene structure and human genome evolution.

“Our approach allows us to visualize evidence for genomic rearrangements at very high resolution,” Shaw said. “One of the things Song’s work has helped us learn is that a large portion of human variation, including both variants associated and not associated with disease, is driven by small scale Alu/Alu-mediated events.”

This research marks another important chapter in more than a decade of collaboration between wet-bench science in the Lupski laboratory, genomics in the Baylor HGSC and computational science in the Shaw laboratory, as well as the rich data for research provided by Baylor Genetics. This work highlights the unparalleled environment for interdisciplinary research at Baylor College of Medicine.

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The Meal Prep Trick That Mindy Kaling Swears By

Mindy Kaling adores being a mom, and she took to Instagram Stories to reveal her latest way to show her love for her 6-month-old daughter, Katherine: by meal prepping.

What exactly is the showrunner and actress whipping up in her kitchen on Sundays? DIY baby food, which looks absolutely delicious.

“Hi guys, one of the most rewarding parts of my weekend is doing meal prep for my daughter because she’s now eating foods,” she shared on Sunday. “I never did this for myself before I had a kid.”

Kaling is a newbie to food prepping, but she’s taken on some tricky recipes and ingredients, cooking sweet potatoes and string beans, chopping up mango chunks and poaching salmon. She packed them all in reusable containers and labels each container with the date they were prepped.

“I know that there’s so many good, prepared organic baby foods out there that you can buy, but because I work, I like making them on the weekends because it makes me feel like I’m a part of my daughter’s life,” Kaling told her Instagram followers, writing “I just love being a mom” over the clip.

Kaling gives credit to two sources for the fresh ingredients she uses to prep Katherine’s meals. 

“So I prepped sweet potatoes, green beans from the farmer’s market, salmon from the farmer’s market and mango that my dad got from the Indian store,” she said in her latest video. “It’s frickin’ Urth Caffé over here.”

Even if baby food isn’t on your household menu, you can still use Kaling’s inspo to kick your meal prep game into gear. With a few fresh ingredients and a few hours over the weekend, try this routine in your own kitchen this Sunday.

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Scientists identify mechanism that may explain why males are more at risk than females for neurodevelopmental disorders

Researchers have recently begun to realize that biological sex plays a key role in disease risk. Sex plays a role in hypertension, diabetes, arthritis—and in many neurological and psychiatric disorders. Depression and anxiety affect females more, while neurodevelopmental disorders, including autism spectrum disorders, early onset schizophrenia, and attention deficit hyperactivity, affect more males. Males are also more sensitive to prenatal insults, such as gestational stress, maternal infection and drug exposure.

To better understand the molecular underpinnings of this disparity, Tracy Bale of the University of Maryland School of Medicine, along with several colleagues, focused on a molecule that plays a key role in placental health. In a study of mice, they found that the molecule, O-linked N-acetylglucosamine transferase (OGT) works by establishing sex-specific patterns of gene expression.

The study was published this week in the journal Nature Communications.

OGT seems to work via an epigenetic modification that broadly controls transcription, H3K27me3. Epigenetics is the study of changes in how genes are expressed. Dr. Bale showed that high levels of H3K27me3 in the female placenta produce resilience to stress experienced by the mother. This indicates at least one molecular pathway that allows females to be more resilient to maternal stress than males.

“This pathway could help explain why we see this profound neurodevelopmental difference in humans,” said Dr. Bale. “OGT and H3K27me3 in the placenta are crucial to a lot of protein encoding that occurs during pregnancy, and so this process has a lot of downstream effects. The OGT gene is on the X chromosome, and seems to provide a level of protection for the female fetus to perturbations in the maternal environment.”

Dr. Bale has focused much of her research on the links between stress and subsequent risk for neurodevelopmental disorders, including autism and schizophrenia in offspring. Her previous work on the placenta has found novel sex differences that may predict increased prenatal risk for disease in males.

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Drugs that block structural changes to collagen could prevent lung fibrosis

Scientists have found that it is the structure of collagen, rather than the amount, that leads to the devastating condition of lung fibrosis, according to a report in the journal eLife.

The study provides the first evidence in humans that altered collagen structure affects tissue stiffness during progression of lung fibrosis and identifies a potential new target for drugs to prevent the condition.

It is widely thought that fibrosis occurs when components that hold together a tissue’s architecture (called the extracellular matrix (ECM)) build up in the tissue and lead to tissue stiffness. But recently evidence has suggested that this increased stiffness causes the build-up of yet more ECM components, resulting in a cycle that causes more scar tissue.

“We knew that stiffness is an important factor in the build-up of scar tissue in the lung,” explains lead author Mark Jones, NIHR Clinical Lecturer in Respiratory Medicine at the NIHR Southampton Biomedical Research Centre and University of Southampton, UK. “But we didn’t understand what specifically causes increased stiffness in diseased human tissue. Given that excessive build-up of collagen is considered a hallmark of fibrosis, we wanted to see whether this molecule has a role in tissue stiffness.”

They started by looking at the biological and mechanical features of lung tissue from people with lung fibrosis and compared this to healthy lung tissue. They found that the lung fibrosis samples were much stiffer than those from healthy people but, surprisingly, had similar levels of collagen.

However, when they looked at enzymes that give collagen its unique ‘cross-linked’ structure within the ECM, they found that a family of these enzymes (the LOXL family) was more abundant in the fibrosis samples. This led them to further investigate the types of collagen structures found in the fibrosis samples—which are broadly grouped into immature and mature collagen cross-links. They found that increased lung tissue stiffness only occurred where there were higher amounts of the mature cross-linked collagen and that, in these samples, the structure of each collagen building block—or fibril—was altered. This suggested that it is collagen structure, controlled by the LOXL family, that determines tissue stiffness.

Having made this discovery, the team tested whether they could alter the structure of collagen by blocking the LOXL enzymes, with a view to preventing lung fibrosis. They tested a compound called PXS-S2A that blocks LOXL-2 and LOXL-3 in lung tissue cells isolated from people with fibrosis. They found that the number of cross-linked collagen molecules declined with an increasing dose of PXS-S2A.The compound also reduced tissue stiffness, even at low concentrations, suggesting that blocking LOXL-2/LOXL-3 could be an effective way to reduce tissue stiffness.

Finally, they tested the LOXL-2/3 inhibitor in rats with lung fibrosis and found that although there was no effect on total collagen content in the lungs, the treated rats had reduced fibrosis and improved lung function, with no adverse effects.

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Princess Eugenie Opens Up About Living With Scoliosis — Here's Why That Matters

The British royal family is known for keeping personal details out of the headlines, but Princess Eugenie recently bucked tradition in favor of opening up about what it’s like living with the spinal condition scoliosis.

To mark International Scoliosis Awareness Day, Eugenie took to Instagram to share an X-ray of her spine in addition to writing: "Today is International Scoliosis Awareness Day and I’m very proud to share my X Rays for the very first time. I also want to honor the incredible staff at The Royal National Orthopaedic Hospital who work tirelessly to save lives and make people better. They made me better and I am delighted to be their patron of the Redevelopment Appeal."

According to Mayo Clinic, scoliosis is a sideways curvature of the spine that typically occurs during a growth spurt just before puberty. If someone is diagnosed with scoliosis, they are monitored via regular X-rays and may have to wear a corrective brace or have surgery to fix it.

Eugenie says that she had surgery for the condition at the age of 12 and provides more details of her eight-hour procedure in a three-minute video she made with the Royal National Orthopaedic Hospital. 

"They put metal rods in my neck and 8-inch screws up my back, which have now fused together and keep me straight," she says in the video. "I’m living proof that all these young people who have the same thing as I have… I have done it and been through it and I want to be able to help as much as I can for everyone."

Being a 12-year-old human is hard enough without having to worry about having a curved spine, wearing a brace or having surgery, and Eugenie speaking out about her experience may help others who are also going through it feel less alone.

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Study Reveals That Regular Sex Has One Major Health Benefit For Women

Getting it on will help you live longer, according to a new study.

Researchers from the University of California documented the sexual activity of 129 women between 20 and 50, before taking blood tests to determine their long-term health prospects.

They found that participants who had regular sex had significantly longer telomeres – the protective caps on the ends of DNA that can predict a person’s biological age.

Without these telomeres, DNA strands become damaged and their abilities diminished. Shorter telomeres have been associated with ageing, disease and higher risk of death. So basically, the longer they are the longer you’re likely to live.

“This is an important finding,” Researcher Dr Aric Prather said. “It provides new evidence that sexual intimacy within long-term relationships has health-enhancing benefits.’’

“The comparison was between women who had sexual intimacy in the previous week and those who had not,” he explained.

“It is possible the greater the frequency of sexual intimacy, the stronger the effect, and we plan to investigate that at a later date.”

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Research Has Found That These Type Of People Are More Likely To Cheat

Just like an impressive job, a dazzling smile or a Tinder profile pic featuring a cute dog can signify a winning suitor, the mention of a university degree can often be an appealing attribute to the opposite sex.

But if it is your date’s higher education that has got you weak at the knees then you need to read this.

Research from extra-marital dating site Victoria Milan has found that those who have graduated from university were more likely to be unfaithful.

Yep, the website surveyed nearly 80,000 of their Australian users and found that women with a bachelor’s degree were 22.5 per cent more likely to stray while men with the same education level were 17.6 per cent more likely to cheat.

Recent statistics show that 44% of the Australian population has a BA or higher, so that’s a whole lot of infidelity.

“Aussies with university credentials are the ones most likely to have an affair and those with limited education are the most faithful,” said Victoria Milan CEO, Sigurd Vedal.

“What this tells us is that highly educated people are likely to have more time on their hands, while the working class are too busy making ends meet to have time for a sexy fling.”

But it’s not all bad news for schooling, the study also found that men and women with doctorates were the groups least likely to commit adultery (1.3 per cent and 1.4 per cent respectively).

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