Not indoors, say researchers in the Penn State Department of Architectural Engineering. The team found that indoor distances of two meters — about six and a half feet — may not be enough to sufficiently prevent transmission of airborne aerosols. Their results were made available online ahead of the October print edition of Sustainable Cities and Society.

“We set out to explore the airborne transport of virus-laden particles released from infected people in buildings,” said Gen Pei, first author and doctoral student in architectural engineering at Penn State. “We investigated the effects of building ventilation and physical distancing as control strategies for indoor exposure to airborne viruses.”

The researchers examined three factors: the amount and rate of air ventilated through a space, the indoor airflow pattern associated with different ventilation strategies and the aerosol emission mode of breathing versus talking. They also compared transport of tracer gas, typically employed to test leaks in air-tight systems, and human respiratory aerosols ranging in size from one to 10 micrometers. Aerosols in this range can carry SARS-CoV-2.

“Our study results reveal that virus-laden particles from an infected person’s talking — without a mask — can quickly travel to another person’s breathing zone within one minute, even with a distance of two meters,” said Donghyun Rim, corresponding author and associate professor of architectural engineering. “This trend is pronounced in rooms without sufficient ventilation. The results suggest that physical distance alone is not enough to prevent human exposure to exhaled aerosols and should be implemented with other control strategies such as masking and adequate ventilation.”

The researchers found that aerosols traveled farther and more quickly in rooms with displacement ventilation, where fresh air continuously flows from the floor and pushes old air to an exhaust vent near the ceiling. This is the type of ventilation system installed in most residential homes, and it can result in a human breathing zone concentration of viral aerosols seven times higher than mixed-mode ventilation systems. Many commercial buildings use mixed-mode systems, which incorporate outside air to dilute the indoor air and result in better air integration — and tempered aerosol concentrations, according to the researchers.

“This is one of the surprising results: Airborne infection probability could be much higher for residential environments than office environments,” Rim said. “However, in residential environments, operating mechanical fans and stand-alone air cleaners can help reduce infection probability.”

According to Rim, increasing the ventilation and air mixing rates can effectively reduce the transmission distance and potential accumulation of exhaled aerosols, but ventilation and distance are only two options in an arsenal of protective techniques.

“Airborne infection control strategies such as physical distancing, ventilation and mask wearing should be considered together for a layered control,” Rim said.

The researchers are now applying this analysis technique to various occupied spaces, including classrooms and transportation environments.

Mary Taylor, a graduate student at Penn State at the time of the research, also contributed to this work, which was supported by the National Science Foundation.

Story Source:

Materials provided by Penn State. Original written by Ashley J. WennersHerron. Note: Content may be edited for style and length.

The term ‘DNA’ immediately calls to mind the double-stranded helix that contains all our genetic information. But the individual units of its two strands are pairs of molecules bonded with each other in a selective, complementary fashion. Turns out, one can take advantage of this pairing property to perform complex mathematical calculations, and this forms the basis of DNA computing.

Since DNA has only two strands, performing even a simple calculation requires multiple chemical reactions using different sets of DNA. In most existing research, the DNA for each reaction are added manually, one by one, into a single reaction tube, which makes the process very cumbersome. Microfluidic chips, which consist of narrow channels etched onto a material like plastic, offer a way to automate the process. But despite their promise, the use of microfluidic chips for DNA computing remains underexplored.

In a recent article — made available online in ACS Nano on 7 July 2021 and published in Volume 15 Issue 7 of the journal on 27 July 2021 — a team of scientists from Incheon National University (INU), Korea, present a programmable DNA-based microfluidic chip that can be controlled by a personal computer to perform DNA calculations. “Our hope is that DNA-based CPUs will replace electronic CPUs in the future because they consume less power, which will help with global warming. DNA-based CPUs also provide a platform for complex calculations like deep learning solutions and mathematical modelling,” says Dr. Youngjun Song from INU, who led the study.

Dr. Song and team used 3D printing to fabricate their microfluidic chip, which can execute Boolean logic, one of the fundamental logics of computer programming. Boolean logic is a type of true-or-false logic that compares inputs and returns a value of ‘true’ or ‘false’ depending on the type of operation, or ‘logic gate,’ used. The logic gate in this experiment consisted of a single-stranded DNA template. Different single-stranded DNA were then used as inputs. If part of an input DNA had a complementary Watson-Crick sequence to the template DNA, it paired to form double-stranded DNA. The output was considered true or false based on the size of the final DNA.

What makes the designed chip extraordinary is a motor-operated valve system that can be operated using a PC or smartphone. The chip and software set-up together form a microfluidic processing unit (MPU). Thanks to the valve system, the MPU could perform a series of reactions to execute a combination of logic operations in a rapid and convenient manner.

This unique valve system of the programmable DNA-based MPU paves the way for more complex cascades of reactions that can code for extended functions. “Future research will focus on a total DNA computing solution with DNA algorithms and DNA storage systems,” says Dr. Song.

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Materials provided by Incheon National University. Note: Content may be edited for style and length.

Researchers analyzed nearly 130 subjects with PCR-confirmed COVID-19 illness between three and six months after initial infection. Three patients were hospitalized while the rest were treated as outpatients and experienced mild infection, with symptoms including headaches, chills and loss of taste or smell.

The results, published in Microbiology Spectrum, reveal approximately 90% of participants produced spike and nucleocapsid antibody responses, and all but one had persistent antibody levels at follow up.

“Previously, there was a lot of concern that only those with severe COVID-19 produced strong antibody responses to infection,” said Charles Schuler, M.D., lead author of the paper and clinical assistant professor of allergy and immunology at Michigan Medicine. “We’re showing that people with mild bouts of COVID-19 did really well after their infection, made antibodies, and kept them.”

The prospective study’s participants were either Michigan Medicine health care workers or patients with a high risk of exposure to COVID-19. Most subjects took part in the same research team’s previous study, which found that COVID antibody tests are effective at predicting prior infection.

During the observation period, none of the subjects who produced antibodies were re-infected, compared to 15 antibody-negative patients. Schuler’s team also found that the antibodies’ ability to neutralize COVID-19 did not differ significantly from the first visit, which occurred three months after infection, to the second visit at the six-month mark.

“While some studies have suggested antibodies against COVID-19 wane over time, these findings provide strong prospective evidence for longer-term immunity for those who produce an immune response to mild infection,” said James Baker Jr., M.D., senior author of the paper and founding director of the Mary H. Weiser Food Allergy Center at Michigan Medicine. “To our knowledge, this is the first prospective study that demonstrates such a risk reduction for clinical reinfection in this specific type of population.”

Impact on COVID vaccination

The team of researchers is now analyzing samples of this subject group taken up to a year after infection to further evaluate antibody responses. Meanwhile, they concluded that individuals with COVID-19 can delay vaccination for 90 days after infection ends. The Centers for Disease Control and Prevention recommends those treated with monoclonal antibodies or convalescent plasma wait 90 days after receiving treatment before getting vaccinated, and others should wait until they have recovered from COVID-19 and “have met the criteria to discontinue isolation.”

A study conducted in Kentucky that found that unvaccinated people who already had COVID-19 were 2.34 times more likely than fully vaccinated people to be infected again, suggesting “vaccination provides additional protection against reinfection.”

Additionally, the research was conducted between March 2020 and Feb. 2021, months before the highly transmissible Delta variant became the dominant strain of COVID in the United States.

Amid rising cases and hospitalizations, Schuler said, remaining unvaccinated comes with “a high price” for immunity.

“These results are encouraging for those who have already run the gauntlet of COVID-19 infection,” he said. “However, I do not recommend citing this study as a reason not to be vaccinated for those never previously infected. Vaccination decreases infectiousness, the risk of hospitalization and deaths from COVID-19, without having the actual infection. Achieving natural immunity by deferring vaccination in favor of infection is not worth going through the discomfort, risk to yourself and risk to others.”

This approach to weight management is based on the century-old energy balance model which states that weight gain is caused by consuming more energy than we expend. In today’s world, surrounded by highly palatable, heavily marketed, cheap processed foods, it’s easy for people to eat more calories than they need, an imbalance that is further exacerbated by today’s sedentary lifestyles. By this thinking, overeating, coupled with insufficient physical activity, is driving the obesity epidemic. On the other hand, despite decades of public health messaging exhorting people to eat less and exercise more, rates of obesity and obesity-related diseases have steadily risen.

The authors of “The Carbohydrate-Insulin Model: A Physiological Perspective on the Obesity Pandemic,” a perspective published in The American Journal of Clinical Nutrition, point to fundamental flaws in the energy balance model, arguing that an alternate model, the carbohydrate-insulin model, better explains obesity and weight gain. Moreover, the carbohydrate-insulin model points the way to more effective, long-lasting weight management strategies.

According to lead author Dr. David Ludwig, Endocrinologist at Boston Children’s Hospital and Professor at Harvard Medical School, the energy balance model doesn’t help us understand the biological causes of weight gain: “During a growth spurt, for instance, adolescents may increase food intake by 1,000 calories a day. But does their overeating cause the growth spurt or does the growth spurt cause the adolescent to get hungry and overeat?”

In contrast to the energy balance model, the carbohydrate-insulin model makes a bold claim: overeating isn’t the main cause of obesity. Instead, the carbohydrate-insulin model lays much of the blame for the current obesity epidemic on modern dietary patterns characterized by excessive consumption of foods with a high glycemic load: in particular, processed, rapidly digestible carbohydrates. These foods cause hormonal responses that fundamentally change our metabolism, driving fat storage, weight gain, and obesity.

When we eat highly processed carbohydrates, the body increases insulin secretion and suppresses glucagon secretion. This, in turn, signals fat cells to store more calories, leaving fewer calories available to fuel muscles and other metabolically active tissues. The brain perceives that the body isn’t getting enough energy, which, in turn, leads to feelings of hunger. In addition, metabolism may slow down in the body’s attempt to conserve fuel. Thus, we tend to remain hungry, even as we continue to gain excess fat.

To understand the obesity epidemic, we need to consider not only how much we’re eating, but also how the foods we eat affect our hormones and metabolism. With its assertion that all calories are alike to the body, the energy balance model misses this critical piece of the puzzle.

While the carbohydrate-insulin model is not new — its origins date to the early 1900s — The American Journal of Clinical Nutrition perspective is the most comprehensive formulation of this model to date, authored by a team of 17 internationally recognized scientists, clinical researchers, and public health experts. Collectively, they have summarized the growing body of evidence in support of the carbohydrate-insulin model. Moreover, the authors have identified a series of testable hypotheses that distinguish the two models to guide future research.

Adoption of the carbohydrate-insulin model over the energy-balance model has radical implications for weight management and obesity treatment. Rather than urge people to eat less, a strategy which usually doesn’t work in the long run, the carbohydrate-insulin model suggests another path that focuses more on what we eat. According to Dr. Ludwig, “reducing consumption of the rapidly digestible carbohydrates that flooded the food supply during the low-fat diet era lessens the underlying drive to store body fat. As a result, people may lose weight with less hunger and struggle.”

The authors acknowledge that further research is needed to conclusively test both models and, perhaps, to generate new models that better fit the evidence. Toward this end, they call for constructive discourse and “collaborations among scientists with diverse viewpoints to test predictions in rigorous and unbiased research.”