Same-gender couples interact better than heterosexual couples: study

Same-gender couples have higher-quality interactions with one another than heterosexual couples in Southern California, a new UC Riverside study finds.

The study also holds that couples with two men have the smallest social networks.

Researcher Megan Robbins says the recently published study is the first to compare same- and different-sex couples’ social networks and daily interactions with one another.

Past research shows that same-gender couples enjoy strengths including appreciation of individual differences, positive emotions, and effective communication. But research hasn’t compared the quality of their daily interactions—inside and outside the couple dynamic—to those of heterosexual couples.

“The comparison is important because there is so much research linking the quality of romantic relationships and other social ties to health and well-being, yet it is unclear if this applies similarly or differently to people in same-gender romantic relationships because they have been historically excluded from past research,” said Robbins, who is an associate professor of psychology at UCR. Reasons for potential differences include the stigma sexual minorities face, and also their resilience.

For the study, Robbins and her team recruited same-gender and different-gender couples throughout Southern California. The couples had to be in a married or “married-like” committed relationship; living together for at least a year; and have no physical or mental health conditions that impeded their daily functioning.

Among those who applied to be in the study, 78 couples were found to be eligible, 77 of which provided enough data to be used. Twenty-four of the couples were woman-woman; 20 were man-man, and 33 were man-woman.

Participants met with the researchers on two separate Fridays, a month apart, completing surveys. They received text or email prompts several times in the days following the in-person meetings. In the text/email prompts, participants were asked whether they had an interaction with their partner, a family member, or a friend in the past 10 minutes, then asked to rate the quality of the social interaction using a five-point scale—one being unpleasant; three, neutral; five, pleasant.

In terms of social networks, the study found couples in man-man relationships had smaller social networks than woman-woman and man-woman couples. On the other end of the results spectrum, women in relationships with men were most likely to have the largest social networks.

Robbins said the finding is consistent with previous research showing men with men experience the least acceptance among family members.

“We hypothesized that one model for how the social life of people in same-gender couples might differ from those in different-gender couples was a honing model, where people in same-gender couples reduce their social networks down to only those people who are supportive. We found some support for this by learning that the men with men had the smallest social networks in our sample,” Robbins said.

The quality of interactions with families was reported to be greatest by same-gender couples. There was no difference for interaction quality with friends.

In terms of the quality of interactions with their partners, the study found same-gendered relationships had better-quality interactions than found in different-gendered relationships.

Robbins said that may be due to greater similarity between partners when they share a gender identity, and greater equality within the couple, compared to people in different-sex couples.

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High-intensity resistance training in post-acute care produced better outcomes and patient experience

Today, researchers from the University of Colorado Anschutz Medical Campus released a new study evaluating the effectiveness and safety of high-intensity rehabilitation for older adults in skilled nursing facilities.

The study was published today in Physical Therapy.

Skilled nursing facilities provide medical and rehabilitation services to individuals post-hospitalization to help facilitate the transition to home or the next level of care. However, recent research has shown the trajectory of functional recovery following hospitalization and skilled nursing facilities care is generally poor, with less than 25 percent of patients returning to pre-hospitalization levels of function.

“Our study identified an impactful opportunity to improve the way we care for patients in skilled nursing facilities.” said lead author Allison Gustavson, PT, DPT, Ph.D., at the CU Anschutz Medical Campus. “Our findings demonstrate that high-intensity resistance training is safe, effective, and preferable in caring for medically complex older adults in skilled nursing facilities.

The researchers implemented a study consisting of 103 participants split into two nonrandomized independent groups (usual care and high-intensity) within a single skilled nursing facility. For both groups, physical therapists administered the Short Physical Performance Battery and gait speed at evaluation and discharge. Additionally, an observational checklist and documentation audits were used to assess treatment fidelity and regression analyses evaluated the response of functional change by group.

For the high-intensity training, the physical therapists used the i- STRONGER program (Intensive Therapeutic Rehabilitation for Older Skilled Nursing Home Residents). The results showed that patients participating in the high-intensity program benefited by increasing their function, specifically by significantly increasing their walking speed from evaluation to discharge by 0.13 m/s which exceeds clinically meaningful changes in walking speed. Also, their stay at the skilled nursing facility was reduced by 3.5 days.

The researchers advocate that their findings signal the need to fundamentally change the intensity of rehabilitation provided to patients with medically complex conditions to promote greater value and patient experience within post-acute care.

The Principal Investigator Jennifer Stevens-Lapsley, MPT, Ph.D., FAPTA, adds, “Our study shows that the quality of rehabilitation compared to the quantity drives better outcomes. These findings provide a timely solution to address rehabilitation value in the context of recent post-acute care changes by policymakers who are looking to raise the bar on the quality and efficiency of post-acute care services.”

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AI to better diagnose and treat renal cancer and COVID-19

USC researchers are using AI to fuel more confident diagnosis of renal tumors, as well as more customized treatment for cancer patients and patients infected with COVID-19.

Kidney cancer is among the 10 most common cancers. In 2019, the American Cancer Society estimated 73,820 new cases of kidney cancer and 14,770 deaths from this disease. The five-year survival rate reduces from 93% in low-risk groups to 69% in high risk groups of patients with localized kidney cancer. However, following the spread of cancer, these rates plummet to 12%.

For radiologists, a fundamental driver of diagnosing renal cancer remains visual and qualitative, meaning CT scans (images of a mass) are largely evaluated based on individual knowledge and experience. To improve accuracy, this visual analysis has been supplemented by quantitative assessment of renal masses through radiomics, the extraction of quantifiable characteristics from the images.

Researchers at the University of Southern California, including Vinay Duddalwar, director of the USC Radiomics Laboratory and Professor of Clinical Radiology, Urology and Biomedical Engineering at the Keck School of Medicine of USC, and Assad Oberai, Hughes Professor in the Department of Aerospace and Mechanical Engineering and Interim Vice Dean for Research at the USC Viterbi School of Engineering, are combining deep learning with existing contrast CT scanning to help radiologists make more confident diagnoses. Their research was published in the British Journal of Radiology.

The widespread use of contrast enhanced CT, where an intravenous contrast agent like a dye is injected into the tumor and imaged over four distinct points in time, has led to the increased detection of kidney cancers that would have otherwise remained undetected. While many of the tumors identified this way can be labeled benign fairly easily, a significant portion prove more complicated, requiring further invasive testing, the researchers said. Such testing might include biopsies, which might also be inconclusive, pushing many patients to prefer going straight to surgery to remove the tumor in case it is malignant.

“Using a purely visual qualification, 20-25% of all tumors taken out in the U.S. today in the range of 3-5 cm are benign, and didn’t need to come out,” Duddalwar said.

Oberai and Duddalwar recognized this process could be improved by better utilizing existing data. “We wanted to combine what Assad’s group does in deep learning with what my group does in radiomics to improve accuracy of diagnosis,” Duddalwar said.

The researchers also hope such advances could help better understand individual patients’ prognosis in dealing with renal cancer, as well as in addressing diseases such as COVID-19, where individuals report widely varied reactions to infection and treatment.

The research team also includes: from the Keck School of Medicine Associate Professor of Clinical Pathology Manju Aron, Associate Professor of Research Neurology Steven Cen, Executive Director of the USC Institute of Urology Inderbir Gill, medical student researcher at the Department of Radiology Christopher Lau and Assistant Professor in Research Radiology Bino Varghese; and from USC Viterbi computer science student Tomas Angelini and Assistant Professor of Research Radiology and Biomedical Engineering Darryl Hwang.

Contrast Enhanced CT Scans Used to Identify Variations in Tumors

Contrast enhanced CT scans can help diagnose specific cancers, like renal cancer, because of the changes in vascularity seen in such cancers. In a usual workflow, Duddalwar’s group would look at the images of a tumor taken at four different points in time: pre-injection of the contrast agent, 30-40 seconds after injection, 80-90 seconds after injection and then about five minutes after injection. The contrast agent helps identify characteristics related to vascularity, for example, how much blood supply is flowing through the tumor. How early the tumor enhances and washes out compared to the rest of the kidney can help indicate what sort of tumor the patient might have, the researchers said.

“Imagine if you’re sitting at the bank of a river and someone injects a dye further upriver. If the dye gets to where you are quickly, then you know that the current is moving faster. If the dyes spread out, then you know that the flow is turbulent. So you can say a lot about the flow by observing what happens to the dye. Think of the vascular system in the same way. It’s a closed loop fluid system, so if you inject a fluid somewhere, you can watch for it somewhere else,” Oberai said.”For example, if you inject the dye into a blood vessel, but do not observe it downstream, you might be dealing with a tumor that is blocking the vessel and thwarting the flow of blood.”

How the dye diffuses through tissue reveals a lot about the underlying pathophysiology and can help determine a more accurate diagnosis. Instead of recommending more tests and procedures, the deep learning algorithm relies on data collected in the four contrast CT scans. “We are not doing any extra imaging,” Duddalwar said. “We’re using the images already collected and then evaluating them in a different way, so it is no extra expense to the patient or to the healthcare system.” In this way, images collected have the opportunity to convey more data to experts than previously accessible.

Building on Quantitative Evaluations in Radiomics

Radiomics computations can take three to four people about 30-40 minutes to produce results on one patient’s CT scans. An AI algorithm working with the same data can produce results in a matter of seconds.

But efficiency isn’t the most important factor, Oberai said. “More than the time, it’s the effort of experts trying to subjectively figure out where the tumor is, where the boundary is and get the correct margins. What we want to do is save experts’ time for more important tasks, such as evaluating other images and studies, conducting research and teaching and ultimately contributing to improved clinical care through optimized workflow.”

Incorporating deep learning can also help identify new markers that might not otherwise have been discovered. Said Duddalwar: “When you utilize radiomics, you pre-judge, by choosing which element(s) (for example uniformity or asymmetry) you want to evaluate. But with deep learning, you make no such assumption. You let the algorithm figure out what the important characteristic is going to be, which might be an element you never imagined would be significant to diagnosis.”

In the study, the deep learning algorithm demonstrated a 78% accuracy rate in diagnosing the most challenging scans, a rate on par with results produced using radiomics.

Integrating Patient History with Imaging Data

Next the researchers hope to integrate information about a patient’s medical history and clinical examination to help not only improve the accuracy of a diagnosis, but also an individual’s prognosis in treatment.

“We’re looking at using all the imaging information and combining it with clinical data (patient health history, blood tests, symptomology) to make an even more accurate prediction,” Oberai said. “It’s about more than just giving an answer about whether the tumor is benign or malignant, but also producing a number based on all the informational and image inputs that shares how confident the algorithm is about its results.”

He added: “Additionally, we want to be able to have a dynamic model, which can be updated as newer information comes in. For example, a cancer patient might be scanned every three months. We want to see the model updated based on newer data and help better understand the trajectory of the illness for each individual.”

The researchers are looking to apply this beyond diagnosis of renal cancer to its treatment. “We’re trying to find potential markers to help us identify the best treatment straight away instead of wasting months on trial and error,” Duddalwar said. “At the same time, we want to see if deep learning algorithms can help identify which tumors have a better versus worse prognosis for our patients.”

One of the more urgent adaptations the researchers are pursuing is how to leverage this work to better diagnose and treat COVID-19. “Putting together patient symptomology and clinical data with images, you can get a more accurate sense not just of diagnosis but of prognosis. In the case of COVID-19, the data collected can help the model predict how the patient might do—not just whether or not they will recover or get sicker, but also whether or not the patient will need to go to the ICU or require a ventilator.”

The group is going to look at data from COVID-19 patients initially from the USC Health Science campus, which includes the LA County Medical Center. Their research group includes other radiologists, epidemiologists and biostatisticians.

The coronavirus behaves differently in varying locations due to a variety of factors, which are difficult for doctors to access and apply during treatment. However, an algorithm trained on such data can bring in these disparate factors and help link everything together, the researchers said.

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New test better predicts which babies will develop type 1 diabetes

A new approach to predicting which babies will develop type 1 diabetes moves a step closer to routine testing for newborns which could avoid life-threatening complications.

Scientists at seven international sites have followed 7,798 children at high risk of developing type 1 diabetes from birth, over nine years, in The Environmental Determinants of Diabetes in the Young (TEDDY) Study. The TEDDY Study is a large international study funded primarily by the US National Institutes of Health and U.S. Centers for Disease Control, as well as by the charity JDRF.

In research published in Nature Medicine, scientists at the University of Exeter and the Pacific Northwest Research Institute in Seattle used the TEDDY data to develop a method of combining multiple factors that could influence whether a child is likely to develop type 1 diabetes. The combined risk score approach incorporates genetics, clinical factors such as family history of diabetes, and their count of islet autoantibodies—biomarkers known to be implicated in type 1 diabetes.

The research team found that the new combined approach dramatically improved prediction of which children would develop type 1 diabetes, potentially allowing better diabetes risk counseling of families. Most importantly, the new approach doubled the efficiency of programs to screen newborns to prevent the potentially deadly condition of ketoacidosis, a consequence of type 1 diabetes in which insulin deficiency causes the blood to become too acidic. Identifying which children are at highest risk will also benefit clinical trials on drugs that are showing promise in preventing the condition.

Dr. Lauric Ferrat at the University of Exeter Medical School, said: “At the moment, 40 per cent of children who are diagnosed with type 1 diabetes have the severe complication of ketoacidosis. For the very young this is life-threatening, resulting in long intensive hospitalizations and in some cases even paralysis or death. Using our new combined approach to identify which babies will develop diabetes can prevent these tragedies, and ensure children are on the right treatment pathway earlier in life, meaning better health.”

Professor William Hagopian of the Pacific Northwest Research Institute, said: “We’re really excited by these findings. They suggest that the routine heel prick testing of babies done at birth, could go a long way towards preventing early sickness as well as predicting which children will get type 1 diabetes years later. We’re now putting this to the test in a trial in Washington State. We hope it will ultimately be used internationally to identify the condition as early as possible, and to power efforts to prevent the disease.”

Researchers believe the combined approach can also be rolled out to predict the onset of other diseases with a strong genetic component that are identifiable in childhood, such as celiac disease.

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Fauci suggests goggles, eye shield for better protection against coronavirus

Dr. Fauci says he’s ‘cautiously optimistic’ about COVID vaccine trials, guarantees no corners are being cut

Dr. Anthony Fauci, director of the National Institute of Allergy and Infectious Diseases, joins John Roberts with insight on ‘Special Report.’

Dr. Anthony Fauci, the nation’s top infectious disease expert, this week said wearing goggles or an eye shield in addition to a face mask would provide more complete protection against the coronavirus, according to a report.

“Theoretically you should protect all of the mucosal surfaces [eyes, nose, mouth], so if you have goggles or an eye shield, you should use it,” he said in an interview with ABC News on Instagram Wednesday.

The Centers for Disease Control and Prevention already recommends wearing a face mask that covers the nose and mouth in public but the virus can also enter through the eyes.

Fauci recommended goggles in addition to a face mask for those who want “perfect protection” from the COVID-19 but admitted it’s not “universally recommended.”

He added one of the reasons eyewear hasn’t been recommended yet is “it’s so easy for people to just make a cloth mask.”

Heading into fall, Fauci said he encourages people to get a flu vaccine and hopes face masks will protect people from the flu as well as the coronavirus, ABC reported.

Not everyone responded favorably on social media to the idea of adding eyewear to facemasks. Some remarked the next step would be hazmat suits or living inside of a bubble, according to Market Watch. 

The United States is still outpacing every other country in the number of cases with more than 4.3 million and upwards of 150,000 deaths.

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