Posted on Autism

Are autistic adults more vulnerable to criminal exploitation?

Researchers at Flinders University tested the belief that autistic adults are more likely than non-autistic adults to be criminally exploited due to difficulties in recognizing criminal intent.

“It is not uncommon for defence lawyers, often with the backing of testimony from ‘expert’ witnesses, to claim that autistic adults struggle to interpret the intentions of others or understand their thoughts. This difficulty can make them more susceptible to being lured into criminal activity,” says Professor Neil Brewer, Matthew Flinders Distinguished Emeritus Professor of Psychology in the College of Education, Psychology, and Social Work.

“Such arguments reflect the widely-held perspective that difficulties reading others’ intentions, emotions, and motivations are fundamental features of autism.

However, this perspective may not withstand scrutiny, and we found that, in general, autistic adults are no more vulnerable to being involved in criminal acts than non-autistic adults.

“Furthermore, the difficulties in mindreading often associated with autism are not universally present among autistic adults.”

In a study published in the American Psychological Society’s journal, Law and Human Behavior, former PhD student Zoe Michael and her supervisor, Professor Neil Brewer, developed a new and realistic approach called the Suspicious Activity Paradigm (SAP). This paradigm was designed to evaluate how effectively adults can recognize and respond to cues that indicate social interactions may lead to criminal behaviour.

The study included 197 participants: 102 autistic adults and 95 non-autistic adults, who role-played in scenarios that progressively indicated criminal intent from their interactions.

They were asked about their reactions at different stages as the scenarios developed to evaluate their ability to recognize and respond to suspicious actions from others, thus gauging their susceptibility to being unknowingly drawn into criminal activities.

“We found that, overall, both autistic and non-autistic adults responded in similar ways to suspicious behaviour across various scenarios,” says Professor Brewer.

“Importantly, autistic adults did not show lower rates of suspicion or adaptive responses when compared to their non-autistic counterparts as the scenarios unfolded. Nor did they take longer to recognise the potentially problematic nature of the interaction.”

Building on previous research, the study found that verbal intelligence and Theory of Mind (ToM) – a term used to describe the ability to take the perspective or read the mind of others – predicted someone’s ability to recognise and respond to suspicious activity.

“Our findings indicate that the ability to understand others’ perspectives and intentions – and not the presence of an autism diagnosis – was a critical factor influencing their vulnerability to crime,” he says.

In other words, while autistic individuals who had difficulty discerning others’ intentions were vulnerable, the same was true of their non-autistic peers.

It is important to note, however, that a relatively small proportion of autistic individuals’ performance on the mindreading measure was below that of any of the non-autistic sample, a finding consistently replicated by the Flinders research team that developed the measure.

This indicates that there will be some autistic individuals who will likely be particularly vulnerable because of mindreading difficulties – but such challenges cannot be assumed.

“Thus, rather than defence lawyers and clinicians assuming, and arguing, that a diagnosis of autism automatically signals a particular vulnerability to being lured into crime, it is important to formally assess and demonstrate that a criminal suspect or defendant has significant mindreading difficulties that likely have rendered them vulnerable,” he adds.

Posted on Diabetes

Enhancing the accuracy of wearables that measure blood glucose levels

Diabetes is an increasingly pervasive disease, currently affecting over 500 million adults worldwide. Since there is as yet no cure for type 1 or type 2 diabetes, patients must regularly monitor their BGLs to keep them in check. Though BGL-measuring devices relying on painful finger pricks have been the gold standard for decades, modern technology is slowly opening doors to better alternatives. Many researchers have proposed noninvasive methods to monitor BGLs using widely available wearable devices, such as smartwatches. For example, by placing the LEDs and photodetectors present in certain smartwatches against the skin, oxyhemoglobin and hemoglobin pulse signals can be measured to calculate a metabolic index, from which BGLs can be estimated. However, given the small size and limited power of smartwatches and similar wearables, the data quality of the measured signals tends to be quite low. Moreover, as these devices are worn on extremities, daily movements introduce measurement errors. These problems limit the accuracy and clinical applicability of such wearables for diabetes management. A team from Hamamatsu Photonics K.K., Japan, has been actively researching this issue, looking for effective solutions. In a recent study led by Research and Development Engineer Tomoya Nakazawa, published in the Journal of Biomedical Optics (JBO), they conducted an in-depth theoretical analysis of the sources of errors in the metabolic-index-based method. Based on this analysis, they implemented a novel signal quality index to filter out low-quality data as a preprocessing step and thereby enhance the accuracy of estimated BGLs. “As smartwatches are widely adopted across different regions and age groups, and with the global rise in diabetes cases, a signal quality enhancing method that is easy to implement and apply regardless of personal and individual differences is absolutely essential for meeting the increasing worldwide demand for noninvasive glucose monitoring devices,” remarks Nakazawa, explaining the motivation behind the study. First, the researchers mathematically showed that discrepancy between the two types of phase delays in the oxyhemoglobin and hemoglobin pulse signal calculated by different methods provides a good measure of the influence of noise. They then considered two main sources of phase error, namely, a background noise level and the estimation errors introduced via sampling at discrete intervals. After formalizing these sources of errors, they calculated the effect on the estimated metabolic index. The proposed screening approach involves implementing thresholds for the phase estimation and metabolic index errors. Data chunks that exceed the set thresholds are discarded, and the missing values are approximated using other means based on the rest of the data. To test this strategy, the researchers conducted a long-term experiment in which the sensors in a commercial smartwatch were used to monitor the BGLs of a healthy individual during “oral challenges.” In each of the 30 tests conducted over four months, the subject would fast for two hours before consuming high-glucose foods. Their BGLs were measured using the smartwatch and a commercial continuous glucose monitoring sensor, the latter of which was used to capture the reference values. Notably, preprocessing the data with the proposed screening method led to a notable increase in accuracy. Using the Parkes error grid technique to categorize measurement errors, a substantially higher percentage of data points ended up in Zone A when screening was applied. This refers to clinically accurate values that would lead to correct treatment decisions. “Adopting the screening process improved BGL estimation accuracy in our smartwatch-based prototype,” remarks Nakazawa, “Our technique could facilitate the integration of wearable and continuous BGL monitoring into devices such as smartwatches and smart rings, which are typically constrained in terms of size and signal quality,” he adds, highlighting the impact of the research work. The research team also noted some of the current limitations of smartwatches that lead to inferior performance compared to smartphone camera-based techniques. Though the proposed method could certainly help enhance the performance of the former, hardware improvements in the photodetector and amplifier circuits could go a long way to make wearable electronics a more attractive and clinically acceptable option to monitor BGLs.

Diabetes is a growing global issue, currently affecting over 500 million adults. As there is still no cure for either type 1 or type 2 diabetes, patients need to regularly monitor their blood glucose levels (BGLs) to manage their condition. While traditional BGL-measuring devices that require painful finger pricks have been the standard for many years, modern technology is beginning to offer better alternatives.

Many researchers have proposed noninvasive methods to monitor blood glucose levels (BGLs) using commonly available wearable devices, such as smartwatches. For instance, by positioning the LEDs and photodetectors found in certain smartwatches against the skin, it is possible to measure the pulse signals of oxyhemoglobin and haemoglobin. This data can then be used to calculate a metabolic index, which can help estimate BGLs. However, due to the small size and limited power of these smartwatches and similar wearables, the quality of the measured signals is often low. Additionally, daily movements can introduce measurement errors because these devices are typically worn on the extremities. These issues hinder the accuracy and clinical applicability of wearables for managing diabetes.

A team from Hamamatsu Photonics K.K. in Japan has been actively researching solutions to a pressing issue. In a recent study led by Research and Development Engineer Tomoya Nakazawa and published in the Journal of Biomedical Optics (JBO), they conducted a thorough theoretical analysis of the errors associated with the metabolic index-based method. Based on their findings, they developed a novel signal quality index to filter out low-quality data as a preprocessing step, which enhances the accuracy of estimated blood glucose levels (BGLs).

“As smartwatches are widely adopted across different regions and age groups, and with the global rise in diabetes cases, a signal quality enhancing method that is easy to implement and apply regardless of personal and individual differences is essential for meeting the increasing worldwide demand for noninvasive glucose monitoring devices,” remarks Nakazawa, explaining the motivation behind the study.

First, the researchers mathematically showed that the discrepancy between the two types of phase delays in the oxyhemoglobin and haemoglobin pulse signal calculated by different methods provides a good measure of the influence of noise. They then considered two primary sources of phase error: a background noise level and the estimation errors introduced via sampling at discrete intervals. After formalizing these sources of errors, they calculated the effect on the estimated metabolic index.

The proposed screening approach involves implementing thresholds for the phase estimation and metabolic index errors. Data chunks that exceed the set thresholds are discarded, and the missing values are approximated using other means based on the rest of the data.

To test this strategy, the researchers conducted a long-term experiment in which the sensors in a commercial smartwatch were used to monitor the BGLs of a healthy individual during “oral challenges.” In each of the 30 tests conducted over four months, the subject would fast for two hours before consuming high-glucose foods. Their BGLs were measured using the smartwatch and a commercial continuous glucose monitoring sensor, which was used to capture the reference values.

Notably, preprocessing the data with the proposed screening method led to a notable increase in accuracy. Using the Parkes error grid technique to categorize measurement errors, a substantially higher percentage of data points ended up in Zone A when screening was applied. This refers to clinically accurate values that would lead to correct treatment decisions. “Adopting the screening process improved BGL estimation accuracy in our smartwatch-based prototype,” remarks Nakazawa, “Our technique could facilitate the integration of wearable and continuous BGL monitoring into devices such as smartwatches and smart rings, which are typically constrained in terms of size and signal quality,” he adds, highlighting the impact of the research work.

The research team also noted some of the current limitations of smartwatches that lead to inferior performance compared to smartphone camera-based techniques. Though the proposed method could certainly help enhance the former’s performance, hardware improvements in the photodetector and amplifier circuits could go a long way toward making wearable electronics a more attractive and clinically acceptable option for monitoring BGLs.

Posted on A to Z of Medical Conditions, Diabetes

Understanding how mutations affect diseases such as diabetes

Natália Ružičková Institute of Science and Technology Austria

PhD student Natália Ružičková

Many statistical models and algorithms scientists use can be imagined as a “black box.” These powerful models give accurate predictions, but their internal workings are not easily understood. In an era dominated by deep learning, where an ever-increasing amount of data can be processed, Natália Ružičková, a physicist and PhD student at the Institute of Science and Technology Austria (ISTA), chose to take a step back at least in the context of genomic data analysis.

Ružičková, along with recent ISTA graduate Michal Hledík and Professor Gašper Tkačik, has proposed a model to analyze polygenic diseases—conditions where multiple regions of the genome contribute to dysfunction. This model also aids in understanding the role of these identified genomic regions in developing these diseases. Their research provides valuable findings by integrating advanced genome analysis with fundamental biological insights. The results have been published in the Proceedings of the National Academy of Sciences (PNAS).

Decoding the human genome

In 1990, the Human Genome Project was launched to decode human DNA fully—the genetic blueprint that defines humanity. By 2003, the project was completed, leading to numerous scientific, medical, and technological breakthroughs. By deciphering the human genetic code, scientists aimed to learn more about diseases linked to specific mutations and variations in this genetic map. The human genome comprises approximately 20,000 genes and even more base pairs, which are the letters of the blueprint. This complexity made ample statistical power essential, resulting in the development of “genome-wide association studies” (GWAS).

GWAS approach the issue by identifying genetic variants potentially linked to organismal traits such as height. Notably, they also include the propensity for various diseases. The underlying statistical principle is relatively straightforward: participants are divided into two groups—healthy and sick individuals. Their DNA is then analyzed to detect variations—changes in their genome—that are more prominent in those affected by the disease.  

An interplay of genes

When genome-wide association studies emerged, scientists expected to find just a few mutations in known genes linked to a disease that would explain the difference between healthy and sick individuals. The truth, however, is much more complicated. “Sometimes, hundreds or thousands of mutations are linked to a specific disease,” says Miss Ružičková. “It was a surprising revelation and conflicted with our understanding of biology.”

Each individual mutation contributes only minimally to the risk of developing a disease. However, when combined, these mutations can provide a better—though not complete—understanding of why some individuals develop the disease. Such diseases are known as “polygenic.” For instance, type 2 diabetes is considered polygenic because it cannot be attributed to a single gene; rather, it involves hundreds of mutations. Some of these mutations influence insulin production, insulin action, or glucose metabolism, while many others are found in genomic regions that have not been previously linked to diabetes or have unknown biological functions.

The omnigenic model

In 2017, Evan A. Boyle and colleagues from Stanford University proposed a new conceptual framework called the “omnigenic model.” They proposed an explanation for why so many genes contribute to diseases: cells possess regulatory networks that link genes with diverse functions.

“Since genes are interconnected, a mutation in one gene can impact others, as the mutational effect spreads through the regulatory network,” Ružičková explains. Due to these networks, many genes in the regulatory system contribute to a disease. However, until now, this model has not been formulated mathematically and has remained a conceptual hypothesis that was difficult to test. In their latest paper, Ružičková and her colleagues introduce a new mathematical formalization based on the omnigenic model named the “quantitative omnigenic model” (QOM).

Combining statistics and biology

To demonstrate the new model’s potential, they needed to apply the framework to a well-characterized biological system. They chose the typical lab yeast model Saccharomyces cerevisiae, better known as the brewer’s yeast or the baker’s yeast. It is a single-cell eukaryote, meaning its cell structure is similar to that of complex organisms such as humans. “In yeast, we have a fairly good understanding of how regulatory networks that interconnect genes are structured,” Miss Ružičková says.

Using their model, the scientists predicted gene expression levels—the intensity of gene activity, indicating how much information from the DNA is actively utilized—and how mutations spread through the yeast’s regulatory network. The predictions were highly efficient: The model identified the relevant genes and could clearly pinpoint which mutation most likely contributed to a specific outcome.

The puzzle pieces of polygenic diseases

The scientists’ goal was not to outdo the standard GWAS in prediction performance but rather to go in a different direction by making the model interpretable. Whereas a standard GWAS model works as a “black box,” offering a statistical account of how frequently a particular mutation is linked to a disease, the new model also provides a chain-of-events causal mechanism for how that mutation may lead to disease.

In medicine, understanding the biological context and such causal pathways has huge implications for finding new therapeutic options. Although the model is far from any medical application, it shows potential, especially for learning more about polygenic diseases. “If you have enough knowledge about the regulatory networks, you could also build similar models for other organisms. We looked at the gene expression in yeast, which is just the first step and proof of principle. Now that we understand what is possible, one can start thinking about applications to human genetics,” says Miss Ružičková.

Posted on Autism

“Human mini-brains” reveal autism biology

“Human mini-brains” reveal autism biology and potential treatments

A cerebral organoid showing rosettes (or whirls) of newly developing and migrating new neurons (shown in purplish-red). Credit Scripps Research

Researchers at Scripps Research have created personalized “mini-brains” (or organoids) from stem cells derived from patients with a rare and severe form of autism spectrum disorder and intellectual disability. These lab-grown organoids have enabled the team to gain deeper insights into how a specific genetic mutation contributes to autism spectrum disorder. Additionally, they found that an experimental drug called NitroSynapsin can reverse some brain dysfunction associated with autism in these models.

“Our research demonstrates how a genetic mutation linked to autism disrupts the normal balance of brain cells during development,” says Stuart A. Lipton, MD, PhD, Step Family Foundation Endowed Professor and co-director of the Neurodegeneration New Medicines Center at Scripps Research. However, we have also found potential ways to address this imbalance later in life.”

Learning from patients

Autism is a neurological and developmental disorder that impacts social interactions and communication and leads to repetitive interests and behaviours. The exact causes of autism are still not fully understood; various genetic variants have been linked to the disorder, but each accounts for only a small percentage of cases. For many years, research on autism has focused on creating models of the disorder in mice or examining isolated human brain cells. However, neither of these methods accurately captures the complexity of an interconnected human brain.

Lipton and his colleagues studied MEF2C haploinsufficiency syndrome (MHS), a rare and severe form of autism and intellectual disability caused by a genetic variation in the MEF2C gene. They isolated skin cells from patients with MHS and applied modern stem cell biology techniques to transform these cells into human stem cells. Subsequently, they grew them into small, millimetre-sized “mini-brain” organoids, allowing the researchers to investigate how the various types of brain cells interact with one another.

“We were able to replicate key features of patients’ brains to study their electrical activity and other properties,” says Lipton. “We even invited children into the lab to see their own mini-brains, which was an emotional experience for both the kids and their families.”

In healthy human brains and brain organoids, neural stem cells develop into nerve cells, or neurons, which communicate messages throughout the brain. They also differentiate into various types of glial cells, which support neurons and play important roles in signalling and immune function. A healthy brain maintains a balance between excitatory neurons, which enhance electrical signalling, and inhibitory neurons, which suppress it. Autism is associated with an imbalance between these types of neurons, often resulting in excessive excitation.

In the organoids created from cells of children with MHS, researchers found that neural stem cells more frequently developed into glial cells, resulting in a higher proportion of glial cells compared to neurons. Notably, the MHS organoids had fewer inhibitory neurons than normal, leading to excessive electrical signaling in these mini-brains, similar to what is observed in many human brains affected by autism.

A role for microRNA

When Lipton’s group probed exactly how MEF2C mutations could lead to this imbalance between cell types, they found nearly 200 genes directly controlled by the MEF2C gene. Three of these genes stood out—rather than encoding DNA that led to messenger (m)RNA and then protein expression, they encoded genes for microRNA molecules.

MicroRNAs (miRNAs) are small RNA molecules that bind to DNA to control gene expression rather than producing proteins themselves. This month, two scientists won the 2024 Nobel Prize in Physiology or Medicine for their work describing the discovery of miRNA molecules and how they can impact cell development and behaviour.

“In our study, a few specific miRNAs appear to be important in telling developing brain cells whether to become glial cells, excitatory neurons, or inhibitory neurons,” says Lipton. “Mutations in MEFC2 alter the expression of these miRNAs which, in turn, prevent the developing brain from making proper nerve cells and proper connections or synapses between nerve cells.”

Lipton’s group found that early-developing brain cells from patients with MHS have lower levels of three specific miRNAs. When the researchers added extra copies of these miRNA molecules to the patient-derived brain organoids, the mini-brains developed more normally, with a standard balance of neurons and glial cells.

A potential treatment

Since autism is generally not diagnosed during fetal brain development, treatments that aim to alter initial development—such as correcting a mutated gene or adding miRNA molecules to stop the imbalance of cell types—are not currently feasible. However, even after development, Lipton was already developing another drug that could help promote the balance between excitatory and inhibitory neurons.

Lipton’s group recently tested a drug they invented and patented called NitroSynapsin (also known as EM-036) for its ability to restore brain connections in “mini-brains” derived from cells affected by Alzheimer’s disease.

In the new paper, they tested whether the drug could also help treat the MHS form of autism. Using the patient-derived brain organoids, Lipton and his colleagues showed that in fully developed brain organoids that had an imbalance between cell types, NitroSynapsin could partially correct the imbalance, preventing the hyperexcitability of the neurons and restoring excitatory/inhibitory balance in the mini-brain. This also protects nerve cell connections or synapses.

More work is needed to show whether the drug improves the symptoms of patients with MHS or influences other types of autism spectrum disorder that are not caused by mutations in the MEF2C gene. Lipton hypothesizes this could be the case since MEF2C is known to influence many other genes associated with autism.

“We are continuing to test this drug in animal models to get it into people soon,” says Lipton. “This is an exciting step in that direction.”

Posted on Fibromyalgia, Pain Management, Women's Health

Researchers explore new methods for quantifying chronic pain in women

Measuring pain

Over 70% of chronic pain cases are women. Effective treatment of pain has been hampered by an entirely subjective protocol for measuring pain severity, with variation introduced in patient assessments and physician biases. Credit Arocamora, CC BY-SA 4.0

Chronic pain affects millions of people, with women experiencing more severe and frequent pain than men. Over 70% of chronic pain cases involve women. However, measuring and managing pain remains a complex challenge. There is currently no objective method to quantify pain, which makes it difficult to tailor treatments effectively. Additionally, there are significant variations in how patients experience pain and how physicians respond. A new research initiative aims to address these issues.

Tufts University, in collaboration with external partners, has been selected by the Advanced Research Projects Agency for Health (ARPA-H) as an award recipient for the Sprint for Women’s Health. This initiative aims to develop new technologies for quantitatively measuring pain in patients, to improve care and accelerate the development of new treatments. The team will receive $3.03 million in funding over the next two years.

Various factors, such as inflammation, damaged nerves, or conditions like fibromyalgia, can cause chronic pain. Each of these causes may require a different treatment approach. Regardless of its origin, pain is highly subjective and can be influenced by psychological, social, and other factors. While elite athletes and soldiers often train to tolerate high levels of pain, individual reactions to pain can vary significantly among those who experience it.

Standard practice in assessing pain in the clinical setting is entirely subjective—something most of us have experienced if asked to measure it on a chart using smiling to frowning emoticons.

Subjectivity in assessing pain is not just on the patient’s side. Bias also occurs on the treatment side, with some minority groups being undertreated for managing pain compared to the general population.

“Having an objective, quantitative tool to assess pain will help eliminate subjective variables and provide a more rational basis for treatment,” said Sameer Sonkusale, a professor of electrical and computer engineering at the Tufts School of Engineering and the principal investigator on the project. The project includes collaborators from the Uniformed Services University of Health Sciences (USU), The Henry M. Jackson Foundation for the Advancement of Military Medicine (HJF), and Northwestern Medicine.

The research team plans to screen more than 30 biomarkers, including stress hormones, inflammation markers, and neurotransmitters in the interstitial fluid that circulates between skin cells. Additionally, they will monitor physiological responses such as fluctuations in heart rate, galvanic skin response, and breathing patterns.

These biomarkers were identified in earlier studies as linked to a patient’s experience of pain, but this is the first effort to create a composite panel of markers to generate a quantitative score for pain.

The biomarker data will be merged with answers to pain questionnaires collected from women at several sites, including the Defense and Veterans Center for Integrative Pain Management and Northwestern Medicine. Shuchin Aeron, an electrical and computer engineering associate professor at Tufts, will apply artificial intelligence and machine learning to combine these factors into an objective and quantitative pain score.

The researchers will narrow the panel to five or more of the most reliable pain-linked biomarkers. These biomarkers can be monitored on a portable, wearable device for clinical site and remote pain assessment. The results would instantly be reported to the physician or the patient on a smartwatch or ring.

The availability of such devices would not only improve pain management. Still, it could also accelerate the development of new drugs and treatments, which could benefit from an objective measure of their effectiveness.

“While pain reporting is subjective and dependent on many extraneous factors, for the same pain level, the measurable physiological markers and signals are expected to be similar from one individual to the next,” said Sonkusale. “Considering an observed gender bias in the prevalence and approach to treatment of chronic pain, this technology addresses a large unmet medical need for women, creating a path to more effective pain management.”

“It has been extremely challenging to objectively quantify nociplastic pain—the type of pain involving nervous system sensitization in conditions like fibromyalgia that are quite common in women. This study could provide a way to objectively quantify pain in a way that will greatly help their treatment,” said Steven P Cohen, Edmond I Eger Professor of Anesthesiology and Pain Medicine at Northwestern Medicine.