Researchers Develop Wearable Sensor for Detecting Atrial Fibrillation

Advanced devices developed by a mechanical engineering team at the University of Hong Kong (HKU) has proven to be useful for detecting potential stroke patients and helping machines mimic human brain functions.

In a collaboration with Nanjing University, Dr Paddy K.L. Chan, Associate Professor at the Department of Mechanical Engineering, developed a novel wearable electrocardiogram (ECG) sensor by integrating flexible, ultra-thin organic semiconductors into a flexible polyimide substrate. Powered by button battery, the sensor has outstanding signal amplification properties with a gain larger than 10,000, which allows it to detect electrophysiological signal, or f-wave with a frequency of 357 beats per minute (BPM), which indicates atrial fibrillation.

Conventional portable ECG sensors cannot easily detect the f-wave due to its weak amplitude. Atrial fibrillation is the most common arrhythmia associated with the increased risk of stroke or heart failure. The high signal detection capability stems from the ultralow subthreshold swing (SS) in the organic field effect transistors (OFETs).

Dr Chan’s study showed the ECG sensor managed to pick up unusual signals from patients with atrial fibrillation, while conventional electrodes could not.

“People wearing the new sensors can also enjoy freedom of movement, run around or even take a shower if they want, not being attached to a machine. We have seen a breakthrough in application with the use of a new device structure,” he said. The finding has been published in Nature Communications, in the article entitled “Sub-thermionic, ultra-high-gain organic transistors and circuits.”

Dr Chan’s previous breakthrough in developing the staggered structure monolayer OFETs, the material used in the latest experiment, was published in Advanced Materials. A US patent was also filed for the innovation. In the latest work, his team has advanced the application of the monolayer OFETs to flexible substrate for wearable electronic applications.

“The subthreshold swing is an important parameter in transistor or inverter operation as it implies how much voltage change is needed to turn the device from “off” state to “on” state. Our devices provide a record low subthreshold swing device which ensures low operating power and high sensitivity,” Dr Chan said.

His team also succeeded in adding ‘memory’ or collected signal, information to an organic transistor, which paves the way for advanced machine learning to mimic human brain functions.

The work has been published in Nature Communications, in another article entitled “Mimicking associative learning using an ion-trapping non-volatile synaptic organic electrochemical transistor”.

“Our paper explains the physics behind how information can be stored in a device,” said Dr Chan. “It sets the stage for the next generation of computer learning through the enhancement of the ‘learning function’ of a device. For example, we can integrate the memory transistors with optical sensors for image processing and computation at the same time. The memory transistors are building blocks for the artificial neural network that can perform signal recognition or learn like a human brain.”

Dr Chan’s team successfully added the “ion retainer” polytetrahydrofuran (PTHF) into a conductive organic polymer PEDOT:TOS. The PTHF can significantly slow down the move in-and-out of the ions in the PEDOT:TOS channel layer and maintain them at the desired conductance state. Multi-conductance levels, which can be considered as “memory levels”, were achieved. The experiment was held jointly with Northwestern University.

There is vast room for research in this area of human-machine interface, with unthinkable benefits for mankind. “There are unlimited possibilities when it comes to the applications of such interface,” added Dr Chan. In the meantime, however, he said that his focus would be on developing sophisticated circuit using advanced materials.

Source: HKU

Researchers Find Potential Path to a Broadly Protective COVID-19 Vaccine Using T Cells

Rachel Leeson wrote . . . . . . . . .

Gaurav Gaiha, MD, DPhil, a member of the Ragon Institute of MGH, MIT and Harvard, studies HIV, one of the fastest-mutating viruses known to humankind. But HIV’s ability to mutate isn’t unique among RNA viruses — most viruses develop mutations, or changes in their genetic code, over time. If a virus is disease-causing, the right mutation can allow the virus to escape the immune response by changing the viral pieces the immune system uses to recognize the virus as a threat, pieces scientists call epitopes.

To combat HIV’s high rate of mutation, Gaiha and Elizabeth Rossin, MD, PhD, a Retina Fellow at Massachusetts Eye and Ear, a member of Mass General Brigham, developed an approach known as structure-based network analysis. With this, they can identify viral pieces that are constrained, or restricted, from mutation. Changes in mutationally constrained epitopes are rare, as they can cause the virus to lose its ability to infect and replicate, essentially rendering it unable to propagate itself.

When the pandemic began, Gaiha immediately recognized an opportunity to apply the principles of HIV structure-based network analysis to SARS-CoV-2, the virus that causes COVID-19. He and his team reasoned that the virus would likely mutate, potentially in ways that would allow it to escape both natural and vaccine-induced immunity. Using this approach, the team identified mutationally constrained SARS-CoV-2 epitopes that can be recognized by immune cells known as T cells. These epitopes could then be used in a vaccine to train T cells, providing protective immunity. Recently published in Cell, this work highlights the possibility of a T cell vaccine which could offer broad protection against new and emerging variants of SARS-CoV-2 and other SARS-like coronaviruses.

From the earliest stages of the COVID-19 pandemic, the team knew it was imperative to prepare against potential future mutations. Other labs already had published the protein structures (blueprints) of roughly 40% of the SARS-CoV-2 virus, and studies indicated that patients with a robust T cell response, specifically a CD8+ T cell response, were more likely to survive COVID-19 infection.

Gaiha’s team knew these insights could be combined with their unique approach: the network analysis platform to identify mutationally constrained epitopes and an assay they had just developed, a report on which is currently in press at Cell Reports, to identify epitopes that were successfully targeted by CD8+ T cells in HIV-infected individuals. Applying these advances to the SARS-CoV-2 virus, they identified 311 highly networked epitopes in SARS-CoV-2 likely to be both mutationally constrained and recognized by CD8+ T cells.

“These highly networked viral epitopes are connected to many other viral parts, which likely provides a form of stability to the virus,” says Anusha Nathan, a medical student in the Harvard-MIT Health Sciences and Technology program and co–first author of the study. “Therefore, the virus is unlikely to tolerate any structural changes in these highly networked areas, making them resistant to mutations.”

You can think of a virus’s structure like the design of a house, explains Nathan. The stability of a house depends on a few vital elements, like support beams and a foundation, which connect to and support the rest of the house’s structure. It is therefore possible to change the shape or size of features like doors and windows without endangering the house itself. Changes to structural elements, like support beams, however, are far riskier. In biological terms, these support beams would be mutationally constrained — any significant changes to size or shape would risk the structural integrity of the house and could easily lead to its collapse.

Highly networked epitopes in a virus function as support beams, connecting to many other parts of the virus. Mutations in such epitopes can risk the virus’s ability to infect, replicate, and ultimately survive. These highly networked epitopes, therefore, are often identical, or nearly identical, across different viral variants and even across closely related viruses in the same family, making them an ideal vaccine target.

The team studied the identified 311 epitopes to find which were both present in large amounts and likely to be recognized by the vast majority of human immune systems. They ended up with 53 epitopes, each of which represents a potential target for a broadly protective T cell vaccine. Since patients who have recovered from COVID-19 infection have a T cell response, the team was able to verify their work by seeing if their epitopes were the same as ones that had provoked a T cell response in patients who had recovered from COVID-19. Half of the recovered COVID-19 patients studied had T cell responses to highly networked epitopes identified by the research team. This confirmed that the epitopes identified were capable of inducing an immune reaction, making them promising candidates for use in vaccines.

“A T cell vaccine that effectively targets these highly networked epitopes,” says Rossin, who is also a co–first author of the study, “would potentially be able to provide long-lasting protection against multiple variants of SARS-CoV-2, including future variants.”

By this time, it was February 2021, more than a year into the pandemic, and new variants of concern were showing up across the globe. If the team’s predictions about SARS-CoV-2 were correct, these variants of concerns should have had little to no mutations in the highly networked epitopes they had identified.

The team obtained sequences from the newly circulating B.1.1.7 Alpha, B.1.351 Beta, P1 Gamma, and B.1.617.2 Delta SARS-CoV-2 variants of concern. They compared these sequences with the original SARS-CoV-2 genome, cross-checking the genetic changes against their highly networked epitopes. Remarkably, of all the mutations they identified, only three mutations were found to affect highly networked epitopes sequences, and none of the changes affected the ability of these epitopes to interact with the immune system.

“Initially, it was all prediction,” says Gaiha, an investigator in the MGH Division of Gastroenterology and senior author of the study. “But when we compared our network scores with sequences from the variants of concern and the composite of circulating variants, it was like nature was confirming our predictions.”

In the same time period, mRNA vaccines were being deployed and immune responses to those vaccines were being studied. While the vaccines induce a strong and effective antibody response, Gaiha’s group determined they had a much smaller T cell response against highly networked epitopes compared to patients who had recovered from COVID-19 infections.

While the current vaccines provide strong protection against COVID-19, Gaiha explains, it’s unclear if they will continue to provide equally strong protection as more and more variants of concern begin to circulate. This study, however, shows that it may be possible to develop a broadly protective T cell vaccine that can protect against the variants of concern, such as the Delta variant, and potentially even extend protection to future SARS-CoV-2 variants and similar coronaviruses that may emerge.

Source: Massachusetts General Hospital

The Science of Picky Shoppers

Katie Bohn wrote . . . . . . . . .

There are hard-to-please customers in almost every industry, with certain people being picky about which clothes, houses and even romantic partners they will consider.

A new series of studies has found that shopper pickiness can go beyond shopping for the “best” option. The researchers define what it means to be “picky” and also developed a scale for measuring shopper pickiness.

Margaret Meloy, department chair and professor of marketing at Penn State, said the findings could help companies devise the best strategies for satisfying their pickier customers.

“If a company knows they have a lot of picky customers, they may need to change the way they reward salespeople or dedicate specific salespeople to their pickiest customers, because picky shoppers have very narrow preferences and they see perceived flaws in products others wouldn’t notice,” Meloy said. “Alternatively, a company may allow picky shoppers to customize their products to satisfy their idiosyncratic preferences. It’s not just about offering the best products, but offering the products that are best for the picky customers.”

Meloy added that even the most robust promotional strategies, like offering a free gift with purchase, may fail with picky customers.

Previous research has found that about 40% of people have family or friends they would consider “picky,” suggesting the trait is common. The researchers said it might be helpful for retailers to have a better understanding of what being “picky” means for their customer base, and what those customers may need from a product or shopping experience.

Meloy said that while pickiness affects a customer’s shopping habits and therefore affects a company’s business, there hasn’t been much research done on defining pickiness or investigating how it influences a customer’s behavior.

“In marketing, we call customers who want the absolute best version of a product ‘maximizers,’” Meloy said. “But with picky customers, the best is more idiosyncratic. For them, it might not be about getting the best quality, but getting the precise version of a product they have in their head — a shirt in a very precise shade of black, for example. We wanted to explore this a bit more.”

For the paper — recently published in the Journal of Consumer Psychology — the researchers performed a series of studies to create a scale for measuring shopper pickiness and to identify the consequences of that pickiness on customer behavior.

The first series of studies focused on developing the scale. The researchers said they created a series of questions that would help uncover the psychological dimensions of pickiness while also avoiding using the word “picky,” since the word tends to have negative connotations. Once the researchers were confident the scale accurately measured pickiness, they conducted additional studies to examine the possible consequences of pickiness.

The researchers found that people who scored higher on the picky shopper scale tend to have a small window of what they consider acceptable, which the researchers described as having a small latitude of acceptance and a wide latitude for rejection. These shoppers were more likely to reject a free gift when offered as a thank you for participating in a survey.

“This may seem irrational to some people who may not understand why a person would reject things that come at no cost,” said Andong Cheng, an assistant professor of marketing at the University of Delaware who earned her doctorate at Penn State. “We speculate that it could be psychologically costly for picky shoppers to take free items that they don’t like because possessing these items is a source of irritation for these individuals.”

Additionally, the researchers found that picky people didn’t change their opinions based on a product’s popularity. When they were informed that their top choice of a product was less popular than other options, people who scored high on the picky scale weren’t swayed by that information. They stuck with their original selection.

Meloy said the results support the theory that being picky is a general personality trait that isn’t just present in one situation or area of a person’s life.

“We looked at a range of contexts to see whether being picky in one domain meant you were likely to be picky in others,” Meloy said. “Sure enough, individuals who were picky in one domain were picky in other domains. For example, if you tend to be picky while shopping for groceries, you’ll probably be picky shopping for clothes, as well.”

Meloy said the findings also illustrate the importance of a company understanding and tailoring their business practices to their customer base.

“If you know you have a lot of picky customers, you might not want to bother with offering free products or promoting products by saying how popular they are with other people,” Meloy said. “It’s just not going to work as well with picky customers. These companies will need to come up with strategies that give customers more control to better align their idiosyncratic preferences with the company’s offerings.”

Source: The Pennsylvania State University

Suppression of COVID-19 Waves Reflects Time-Dependent Social Activity, Not Herd Immunity


Scientists modeling the spread of COVID-19 showed that a temporary state of immunity arises due to individual differences in social behaviors. This “transient collective immunity”— referring to when the susceptible or more social groups collectively have been infected—gets destroyed as people modify their social behaviors over time. For example, someone who isolated in the early days of the epidemic may at some point renew their social networks, meeting with small groups or large crowds.


Scientists at the U.S. Department of Energy’s (DOE) Brookhaven National Laboratory and the University of Illinois Urbana-Champaign (UIUC) have developed a new mathematical model for predicting how COVID-19 spreads. This model not only accounts for individuals’ varying biological susceptibility to infection but also their levels of social activity, which naturally change over time. Using their model, the team showed that a temporary state of collective immunity—what they coined “transient collective immunity”—emerged during early, fast-paced stages of the epidemic. However, subsequent “waves,” or surges in the number of cases, continued to appear because of changing social behaviors. Their results are published in the Proceedings of the National Academy of Sciences.

The COVID-19 epidemic reached the United States in early 2020, rapidly spreading across several states by March. To mitigate disease spread, states issued stay-at-home orders, closed schools and businesses, and put in place mask mandates. In major cities like New York City (NYC) and Chicago, the first wave ended in June. In the winter, a second wave broke out in both cities. Understanding why initial waves end and subsequent waves begin is key to being able to predict future epidemic dynamics.

Here’s where modeling can help. But classical epidemiological models were developed almost 100 years ago. While these models are mathematically robust, they don’t perfectly capture reality. One of their flaws is failing to account for the structure of person-to-person contact networks, which serve as channels for the spread of infectious diseases.

“Classical epidemiological models tend to ignore the fact that a population is heterogenous, or different, on multiple levels, including physiologically and socially,” said Alexei Tkachenko, a physicist in the Theory and Computation Group at the Center for Functional Nanomaterials (CFN), a DOE Office of Science User Facility at Brookhaven Lab. “We don’t all have the same susceptibility to infection because of factors such as age, preexisting health conditions, and genetics. Similarly, we don’t have the same level of activity in our social lives. We differ in the number of close contacts we have and in how often we interact with them throughout different seasons. Population heterogeneity—these individual differences in biological and social susceptibility—is particularly important because it lowers the herd immunity threshold.”

Herd immunity is the percentage of the population who must achieve immunity in order for an epidemic to end.

“Herd immunity is a controversial topic,” said Sergei Maslov, a CFN user and professor and Bliss Faculty Scholar at UIUC, with faculty appointments in the Departments of Physics and Bioengineering and at the Carl R. Woese Institute for Genomic Biology. “Since early on in the COVID-19 pandemic, there have been suggestions of reaching herd immunity quickly, thereby ending local transmission of the virus. However, our study shows that apparent collective immunity reached in this way would not last.”

“What was missing prior to this work was that people’s social activity waxes and wanes, especially due to lockdowns or other mitigations,” added Nigel Goldenfeld, Swanlund Professor of Physics and director of the NASA Astrobiology Institute for Universal Biology at UIUC. “So, a wave of the epidemic can seem to die away due to mitigation measures when the susceptible or more social groups collectively have been infected—what we call transient collective immunity. But once these measures are relaxed and people’s social networks are renewed, another wave can start, as we’ve seen with states and countries opening up too soon, thinking the worst was behind them.”

Ahmed Elbanna, a Donald Biggar Willett Faculty Fellow and professor of civil and environmental engineering at UIUC, noted transient collective immunity has profound implications for public policy.

“Mitigation measures, such as mask wearing and avoiding large gatherings, should continue until the true herd immunity threshold is achieved through vaccination,” said Elbanna. “We can’t outsmart this virus by forcing our way to herd immunity through widespread infection because the number of infected people and number hospitalized who may die would be too high.”

The nuts and bolts of predictive modelling

Over the past year, the Brookhaven-UIUC team has been carrying out various projects related to a broader COVID-19 modeling effort. Previously, they modeled how the epidemic would spread through Illinois and the UIUC campus, and how mitigation efforts would impact that spread. Last May, they began this project to calculate the effect of population heterogeneity on the spread of COVID-19.

Several approaches already exist for modeling the effect of heterogeneity on epidemic dynamics, but they typically assume heterogeneity remains constant over time. So, for example, if you’re not socially active today, you won’t be socially active tomorrow or in the weeks and months ahead.

“Basic epidemiological models only have one characteristic time, called the generation interval or incubation period,” said Tkachenko. “It refers to the time when you can infect another person after becoming infected yourself. For COVID-19, it’s roughly five days. But that’s only one timescale. There are other timescales over which people change their social behavior.”

In this work, the team incorporated time variations in individual social activity into existing epidemiological models. While a complicated, multidimensional model is needed to describe each group of people with different susceptibilities to disease, they compressed this model into only three equations, developing a single parameter to capture biological and social sources of heterogeneity.

“We call this parameter the immunity factor, which tells you how much the reproduction number drops as susceptible individuals are removed from the population,” explained Maslov.

The reproduction number indicates how transmissible an infectious disease is. Specifically, the quantity refers to how many people one infected person will in turn infect. To estimate the social contribution to the immunity factor, the team leveraged previous studies in which scientists actively monitored people’s social behavior. They also looked at actual epidemic dynamics, determining the immunity factor most consistent with data on COVID-19-related hospitalizations, intensive care unit admissions, and daily deaths in NYC and Chicago. For example, when the susceptible number dropped by 10 percent during the early, fast-paced epidemic in NYC and Chicago, the reproduction number fell by 40 to 50 percent—corresponding to an estimated immunity factor of four to five.

“That’s a fairly large immunity factor, but it’s not representative of lasting herd immunity,” said Tkachenko. “On a longer timescale, we estimate a much lower immunity factor of about two. The fact that a single wave stops doesn’t mean you’re safe. It can come back.”

This temporary state of immunity arises because population heterogeneity is not permanent; people change their social behavior over time. For instance, individuals who self-isolated during the first wave—staying home, not having visitors over, ordering groceries online—subsequently start relaxing their behaviors. Any increase in social activity means additional exposure risk.

“The epidemic has been with us a year now,” said Maslov. “It’s important to understand why it has been here for such a long time. The gradual change in social behavior among individuals partially explains why plateaus and subsequent waves are occurring. For example, both cities avoided a summer wave but experienced a winter wave. We attribute the winter wave to two factors: the change in season and the waning of transient collective immunity.”

With vaccination becoming more widespread, the team hopes we will be spared from another wave. In their most recent work, they are studying epidemic dynamics in more detail. For example, they are feeding statistics from “superspreader” events—gatherings where a single infected person causes a large outbreak among attendees—into the model. They are also applying their model to different regions across the country to explain overall epidemic dynamics from the end of lockdown to early March 2021.

Source: Brookhaven National Laboratory

New Research Identifies Biological Causes of Muscle Weakness in Later Life

A new largescale genetic analysis has found biological mechanisms that contribute to making people more susceptible to muscle weakness in later life, finding that diseases such as osteoarthritis and diabetes may play a large role in susceptibility.

As we get older we lose muscle strength, and in some people this severe weakness impacts their ability to live everyday lives, a condition called sarcopenia. Around 10 per cent of people over 50 experience sarcopenia. Many causes thought to impact likelihood of developing this weakness, which is linked to higher death rates.

In a genetic analysis of over 250,000 people aged over 60 from UK Biobank and 21 other cohorts, an international team led by researchers at the University of Exeter looked at handgrip strength, using thresholds of loss of muscle function derived from international definitions of sarcopenia.

The team, including collaborators from the USA and the Netherlands, then conducted a genetic analysis, and found specific biological mechanisms push some people towards sarcopenia, whilst protecting others. The study, published in Nature Communications identified 15 areas of the genome, or loci, associated with muscle weakness, including 12 loci not implicated in previous analyses of continuous measures of grip strength.

Biomarkers in the blood including red blood cells and inflammation may also share causal pathways with sarcopenia. Together, these results highlight specific areas for intervention or for identifying those at most risk.

Lead author Garan Jones said: “The strongest associations we found were close to regions of the genome regulating the immune system, and growth and development of the musclo-skeletal system. However we also discovered associations with regions not previously known to be linked to musclo-skeletal traits.

“We found that our analysis of muscle weakness in older people shared common genetic pathways with metabolic diseases such as type-2 diabetes, and auto-immune conditions such as osteoarthritis and rheumatoid arthritis. In subgroups of people with increased risk of these conditions, sarcopenia may be a key outcome to look out for and prevent.

“We hope that by understanding the genetic contributions to muscle weakness with age, we will be able to highlight possible therapeutic interventions earlier in life, which would lead to a happier and healthier old age.”

Source: University of Exeter