New technology that optimizes DNA sequencing using nanophysics and electric currents has been developed by scientists. Their work offers a method for loading DNA into sequencing wells with orders of magnitude higher efficiencies.
Researchers have created the first patient-derived laboratory model of macular degeneration, the leading cause of vision loss in older adults. With the new model, the team has identified possible drug targets for the disease, which they hope will help lead to an effective treatment.
Using open data from four previously conducted clinical trials, teams of international researchers designed mathematical models predicting the likelihood that a patient will discontinue docetaxel treatment due to adverse events.
Hydrogels, also known as soft matter in the medical world, are leading materials for biomedical applications such as drug delivery and stem cell therapy. But traditional hydrogels, used in products such as facial masks and contact lenses, are made up of either synthetic polymers or biological extracts such as animal collagen, are likely to cause allergies. They cannot fully mimic the complex biological environment needed for cell growth and development.
People tend to believe that others will come around to their point of view over time, according to new findings. The findings show that this ‘belief in a favorable future’ holds across various contexts and cultures, shedding light on some of the causes and consequences of the political polarization evident today.
By developing a new technique for labeling the gene segments of influenza viruses, researchers now know more about how influenza viruses enter the cell and establish cell co-infections — a major contributing factor to potential pandemic development.
Seasonal influenza viruses are estimated to cause 3-5 million cases of severe illness each year. Since the most severe infections are caused by influenza type A and type B viruses, the available vaccines provide coverage against these two types. However, influenza viruses are constantly evolving, which requires that vaccines are designed to match the circulating variants of the virus each year.
Influenza viruses evolve by acquiring mutations in the viral genome or by a process called reassortment. Reassortment, which was responsible for the 2009 pandemic virus, occurs when one or more of the eight genome segments are exchanged between two different influenza viruses.
With current techniques it is not easy to make comparative analysis of influenza viruses with single mutations in their genomes, and it is extremely difficult to identify factors that limit the reassortment process between two influenza genomes that have infected the same cell. Through a collaborative effort, scientists from Stockholm University, SciLifeLab, Karolinska Institute and the Leibniz Institute developed a procedure to analyze influenza virus infections in cells and lung tissue by labeling and visualizing the viral genome.
The specificity of the approach enabled the researchers to visualize the delivery of the eight influenza genome segments to the cell nucleus where the virus replicates, and to analyze co-infections by two influenza viruses that differed by single mutations. Using this technique, the researchers concluded that productive cell co-infections, which are necessary for reassortment, only occur when both viruses enter the same cell within two hours.
“This unique approach will make it easier to evaluate how new mutations affect influenza pathogenicity and help to identify the underlying properties that enable or restrict influenza gene segment reassortment,” says Robert Daniels, the lead researcher from Stockholm University. “This can help the community predict the possibility of two strains reassorting into a potential pandemic virus. While further research is needed to achieve these goals, the current approach can already help to characterize and assess treatments aimed at inhibiting influenza entry into cells. Through additional improvements the technique also has potential diagnostic applications for identifying influenza virus infections as well as many other pathogens.”
The research is published in the scientific journal Cell Reports.
Materials provided by Stockholm University. Note: Content may be edited for style and length.
Professionally active doctors increasingly hesitate to take on the task of tutoring students from undergraduate medical education. Stress and pressure from higher up, and sometimes also from colleagues, contributes to this ambivalence, according to a thesis at Sahlgrenska Academy.
“If you don’t have clear support from management, a mistrust of the tutors can arise; you’re viewed as someone who doesn’t take care of patients and is just a burden. Tutoring should provide academic merits and a useful qualification in the same way as when somebody conducts research on the side,” says Bernhard von Below, MD/PhD, Researcher at the Institute of Medicine with a base in primary health care as a specialist in general medicine at the Närhälsan health care center in Floda.
As the educational director for the Early Professional Contact course, he has monitored developments concerning medical students’ in-service training during undergraduate studies for several years. Periods of in-service training at health care centers and hospitals are held throughout the medical program’s 11 semesters. In the first two years, it involves three to four days a semester, and they increase considerably later in the program.
“Learning the profession at the workplace is an incredibly important part of their education. They have to have well-educated tutors, otherwise it hurts the medical student’s training and, in the long term, the medical profession itself,” says Bernhard von Below.
A stimulating task
The thesis shows that clinical tutorship is appreciated by the tutoring physicians and gives them joy and stimulation. The tutors are motivated and driven by a desire to give the students their best, better than what they themselves received once upon a time. Another driving force is the loyalty to their own professional specialty.
However, junior doctors more often seem to consider whether the conditions are reasonable before they take on the task. These are the findings of a study that Bernhard von Below did with doctors training for to become clinical tutors.
“Today’s healthcare system is being pressured from many directions, with high demands on production and detailed management that mean that tutoring is at risk of becoming the exception rather than the rule. Doctors are loyal to their patients and if they feel that they don’t have the support of managers and colleagues, they often choose to leave the tutoring role and not take it on the next time,” he says.
“This is a trend that has been happening for eight to ten years and is associated with increasing pressure in the healthcare system and physicians’ difficulties deciding over their own workdays,” continues Bernhard von Below.
Conflicts on the factory floor
Important factors that make it easier for the doctors to be tutors are clear support from management and time allocated for the task. They also need support from the others at the workplace and understanding for there being fewer patient contacts. Training and feedback on how the tutoring was carried out are also important.
According to Bernhard von Below, the structures are often in place. How a health care center or clinic should accept students is decided and set, but in reality things can nonetheless slip.
“There’s a positive development in terms of agreements. What’s hard is securing the time for tutoring. In theory, it’s going in one direction, in practice it’s going in another. There can be an agreement in principle, but on the factory floor conflicts still arise,” he notes.
Materials provided by University of Gothenburg. Note: Content may be edited for style and length.
One stroke is dangerous, and a second, even more so. One important risk factor for that perilous second stroke is an irregular heart beat called atrial fibrillation.
If doctors could identify the stroke patients who are most likely to experience atrial fibrillation, they could start treatments that would help prevent a second stroke.
But which stroke patients are at risk for the condition has been hard to predict without costly 24/7 monitoring for the hundreds of thousands of people who have a first stroke every year.
Now, a team led by researchers at the Stanford University School of Medicine and Santa Clara Valley Medical Center has used electronic medical records to predict the likelihood of a person experiencing atrial fibrillation after either of two kinds of strokes: a cryptogenic stroke or a transient ischemic attack.
A paper describing their findings will be published online June 28 in Cardiology. The senior authors are Nigam Shah, MBBS, PhD, associate professor of biomedical data science at Stanford, and Susan Zhao, MD, of Valley Medical Center. Stanford graduate student Albee Ling and Valley Medical Center internist Calvin Kwong, MD, share lead authorship.
“This work resulted from a unique collaboration,” said Shah, “where a need for risk stratification was identified by Dr. Susan Zhao, and followed up jointly by an informatics student and a clinical fellow to derive a risk estimate for a population for which we don’t have good scoring methods.”
A need to rank stroke patients by risk
Stroke patients are typically monitored for atrial fibrillation while they’re in the hospital. “But once they go home — after about a week — clinicians aren’t usually too vigilant about monitoring them for atrial fibrillation,” said Kwong. But if doctors monitor stroke patients for even 30 days after they go home, atrial fibrillation can be picked up if it’s happening. And, indeed, the American Heart Association recommends 30 days of heart rhythm monitoring to detect atrial fibrillation within six months of an initial stroke. The problem, said Kwong, is that such monitoring is expensive and not appropriate for every patient.
Shah and his colleagues decided they needed a way to predict which patients should be monitored. There had to be a way to tell the patients who were at high risk for atrial fibrillation and should be monitored from the ones who were at low risk and didn’t need to be monitored.
List of seven risk factors
The team did a retrospective cohort study using data from thousands of stroke patients from Stanford’s Translational Research Integrated Database Environment. Of the 9,589 stroke patients in the database, 482 of them, or 5 percent, went on to be diagnosed with atrial fibrillation.
The team had already developed a text-processing pipeline for analyzing clinical data and clinical-diagnosis coding. Using that pipeline, the team extracted information from clinical notes, flagging, for example, phrases such as “ruled out stroke” and classifying data according to whether it referred to the patient or came from a family history section. The result was a list of biomedical facts about each patient — including age, body mass index and so on.
Then, by ranking the clinical attributes of patients whose medical records indicated they went on to be diagnosed with atrial fibrillation, the team was able to assemble a set of seven risk factors that, when combined, predicted which stroke patients were the most likely to develop the condition and should be monitored after hospitalization. The risk factors — age, obesity, congestive heart failure, hypertension, coronary artery disease, peripheral vascular disease and disease of the heart valves — are the basis of a scoring system that assigns patients to one of three risk groups.
Scoring system online
“The scoring system we developed is simple to use and the results could help physicians tailor treatment to individual patients,” said Ling.
It can help physicians decide which patients to monitor. Once it’s known that patients have a high risk of atrial fibrillation, they can wear a heart monitor at home to see if they actually are experiencing bouts of atrial fibrillation and then, if they are, treated with the appropriate drugs to try to prevent a second stroke.
“Our system needs to be further validated in studies using other independent data sources,” said Ling. She said she expects that clinicians and researchers will further validate and improve the scoring system and that, hopefully, it will one day be adopted in everyday practice. “On the other hand, there will surely be more clinical studies conducted using electronic health records, not just at Stanford but in other medical institutions, as well,” she added.
The study is an example of Stanford Medicine’s focus on precision health, the goal of which is to anticipate and prevent disease in the healthy and precisely diagnose and treat disease in the ill.
Studies like this one can be done quickly using preexisting patient data in just a matter of days, and provide a way to score patients’ individual risk so that treatment can be partly customized.
Long-term exposure to a noisy environment, particularly at night, is linked to infertility in men, according to a study in Environmental Pollution. The researchers found that exposure above the WHO night noise level (55 dB — equivalent to the noise of a suburban street) is linked to a significant increase in infertility.
The scientists behind the study, from Seoul National University in the Republic of Korea, say it is important to consider noise when assessing environmental conditions that contribute to infertility.
Noise can be annoying — it breaks your concentration and disrupts your sleep. But noise has also been linked to health problems, such as heart disease and mental illness, and has been shown to change social behavior and interfere with the performance of complex tasks. Previous research focused on fertility in women has shown a link between exposure to noise and birth-related problems, such as premature birth, spontaneous abortion and congenital malformations.
The new study reveals that long-term exposure to relatively low levels of noise, particularly at night, may contribute to the development of infertility in men.
“Infertility is becoming a significant public health issue because of unexpected adverse effects on the health and quality of life and heavy expenditures on the health system,” said Dr. Jin-Young Min, the study’s co-author. “We know noise exposure has an effect on male fertility in animals, but our study is the first to show the risk of exposure to environmental noise on male infertility in humans.”
Worldwide infertility problems affect one in six couples at least once in their lifetime, either temporarily or permanently. This may be down to a variety of causes, such as genetic abnormalities, infectious disease, environmental agents or certain behaviors. Dr. Min wanted to find out whether environmental exposure to noise, for example at work, has an impact on male infertility.
The researchers analyzed a health insurance dataset, focusing on 206,492 men aged 20-59. They calculated the levels of noise exposure using information from the National Noise Information System combined with the men’s postal codes. In the eight years covered by the study (2006-2013), 3,293 had an infertility diagnosis.
After adjusting the data for variables like age, income, BMI and smoking, they found the chances of being diagnosed infertile were significantly higher in men exposed to noise over 55 dB at night (about as noisy as a suburban street or an air conditioner).
“One of the biggest problems the world is facing today is environmental pollution; my special concern is what Theo Colborn described in her book Our Stolen Future: that the rapid decline in men’s sperm counts in the 20th century was due to environmental pollution,” commented Dr. Min. “If this trend continues, humans in the future will not be able to have normal pregnancy and childbirth. If you are a man and suffer from infertility, you need to consider exposure to environmental pollution as a risk factor.”
Materials provided by Elsevier. Note: Content may be edited for style and length.