But if the model mistakenly identifies reports with both conditions, the most likely diagnosis could be quite different: If a patient has tissue swelling and an enlarged heart, the condition is very likely to be cardiac related, but with no enlarged heart there could be several underlying causes.

In a new study, MIT researchers have found that vision-language models are extremely likely to make such a mistake in real-world situations because they don’t understand negation — words like “no” and “doesn’t” that specify what is false or absent.

“Those negation words can have a very significant impact, and if we are just using these models blindly, we may run into catastrophic consequences,” says Kumail Alhamoud, an MIT graduate student and lead author of this study.

The researchers tested the ability of vision-language models to identify negation in image captions. The models often performed as well as a random guess. Building on those findings, the team created a dataset of images with corresponding captions that include negation words describing missing objects.

They show that retraining a vision-language model with this dataset leads to performance improvements when a model is asked to retrieve images that do not contain certain objects. It also boosts accuracy on multiple choice question answering with negated captions.

But the researchers caution that more work is needed to address the root causes of this problem. They hope their research alerts potential users to a previously unnoticed shortcoming that could have serious implications in high-stakes settings where these models are currently being used, from determining which patients receive certain treatments to identifying product defects in manufacturing plants.

“This is a technical paper, but there are bigger issues to consider. If something as fundamental as negation is broken, we shouldn’t be using large vision/language models in many of the ways we are using them now — without intensive evaluation,” says senior author Marzyeh Ghassemi, an associate professor in the Department of Electrical Engineering and Computer Science (EECS) and a member of the Institute of Medical Engineering Sciences and the Laboratory for Information and Decision Systems.

Ghassemi and Alhamoud are joined on the paper by Shaden Alshammari, an MIT graduate student; Yonglong Tian of OpenAI; Guohao Li, a former postdoc at Oxford University; Philip H.S. Torr, a professor at Oxford; and Yoon Kim, an assistant professor of EECS and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT. The research will be presented at Conference on Computer Vision and Pattern Recognition.

Neglecting negation

Vision-language models (VLM) are trained using huge collections of images and corresponding captions, which they learn to encode as sets of numbers, called vector representations. The models use these vectors to distinguish between different images.

A VLM utilizes two separate encoders, one for text and one for images, and the encoders learn to output similar vectors for an image and its corresponding text caption.

“The captions express what is in the images — they are a positive label. And that is actually the whole problem. No one looks at an image of a dog jumping over a fence and captions it by saying ‘a dog jumping over a fence, with no helicopters,'” Ghassemi says.

Because the image-caption datasets don’t contain examples of negation, VLMs never learn to identify it.

To dig deeper into this problem, the researchers designed two benchmark tasks that test the ability of VLMs to understand negation.

For the first, they used a large language model (LLM) to re-caption images in an existing dataset by asking the LLM to think about related objects not in an image and write them into the caption. Then they tested models by prompting them with negation words to retrieve images that contain certain objects, but not others.

For the second task, they designed multiple choice questions that ask a VLM to select the most appropriate caption from a list of closely related options. These captions differ only by adding a reference to an object that doesn’t appear in the image or negating an object that does appear in the image.

The models often failed at both tasks, with image retrieval performance dropping by nearly 25 percent with negated captions. When it came to answering multiple choice questions, the best models only achieved about 39 percent accuracy, with several models performing at or even below random chance.

One reason for this failure is a shortcut the researchers call affirmation bias — VLMs ignore negation words and focus on objects in the images instead.

“This does not just happen for words like ‘no’ and ‘not.’ Regardless of how you express negation or exclusion, the models will simply ignore it,” Alhamoud says.

This was consistent across every VLM they tested.

“A solvable problem”

Since VLMs aren’t typically trained on image captions with negation, the researchers developed datasets with negation words as a first step toward solving the problem.

Using a dataset with 10 million image-text caption pairs, they prompted an LLM to propose related captions that specify what is excluded from the images, yielding new captions with negation words.

They had to be especially careful that these synthetic captions still read naturally, or it could cause a VLM to fail in the real world when faced with more complex captions written by humans.

They found that finetuning VLMs with their dataset led to performance gains across the board. It improved models’ image retrieval abilities by about 10 percent, while also boosting performance in the multiple-choice question answering task by about 30 percent.

“But our solution is not perfect. We are just recaptioning datasets, a form of data augmentation. We haven’t even touched how these models work, but we hope this is a signal that this is a solvable problem and others can take our solution and improve it,” Alhamoud says.

At the same time, he hopes their work encourages more users to think about the problem they want to use a VLM to solve and design some examples to test it before deployment.

In the future, the researchers could expand upon this work by teaching VLMs to process text and images separately, which may improve their ability to understand negation. In addition, they could develop additional datasets that include image-caption pairs for specific applications, such as health care.

Takotsubo cardiomyopathy is a stress-related heart condition in which part of the heart temporarily enlarges and doesn’t pump well. It is thought to be a reaction to a surge of stress hormones that can be caused by an emotionally or physically stressful event, such as the death of a loved one or a divorce. It can lead to severe, short-term failure of the heart muscle and can be fatal. Takotsubo cardiomyopathy may be misdiagnosed as a heart attack because the symptoms and test results are similar.

This study is one of the largest to assess in-hospital death rates and complications of the condition, as well as differences by sex, age and race over five years.

“We were surprised to find that the death rate from Takotsubo cardiomyopathy was relatively high without significant changes over the five-year study, and the rate of in-hospital complications also was elevated,” said study author M. Reza Movahed, M.D., Ph.D., an interventional cardiologist and clinical professor of medicine at the University of Arizona’s Sarver Heart Center in Tucson, Arizona. “The continued high death rate is alarming, suggesting that more research be done for better treatment and finding new therapeutic approaches to this condition.”

Researchers reviewed health records in the Nationwide Inpatient Sample database to identify people diagnosed with Takotsubo cardiomyopathy from 2016 to 2020.

The analysis found:

• The death rate was considered high at 6.5%, with no improvement over period.
• Deaths were more than double in men at 11.2% compared to the rate of 5.5% among women.
• Major complications included congestive heart failure (35.9%), atrial fibrillation (20.7%), cardiogenic shock (6.6%), stroke (5.3%) and cardiac arrest (3.4%).
• People older than age 61 had the highest incidence rates of Takotsubo cardiomyopathy. However, there was a 2.6 to 3.25 times higher incidence of this condition among adults ages 46-60 compared to those ages 31-45 during the study period.
• White adults had the highest rate of Takotsubo cardiomyopathy (0.16%), followed by Native American adults (0.13%) and Black adults (0.07%).
• In addition, socioeconomic factors, including median household income, hospital size and health insurance status, varied significantly.

“Takotsubo cardiomyopathy is a serious condition with a substantial risk of death and severe complications,” Movahed said. “The health care team needs to carefully review coronary angiograms that show no significant coronary disease with classic appearance of left ventricular motion, suggesting any subtypes of stress-induced cardiomyopathy. These patients should be monitored for serious complications and treated promptly. Some complications, such as embolic stroke, may be preventable with an early initiation of anti-clotting medications in patients with a substantially weakened heart muscle or with an irregular heart rhythm called atrial fibrillation that increases the risk of stroke.”

He also noted that age-related findings could serve as a useful diagnostic tool in discriminating between heart attack/chest pain and Takotsubo cardiomyopathy, which may prompt earlier diagnosis of the condition and could also remove assumptions that Takotsubo cardiomyopathy only occurs in the elderly.

Among the study’s limitations is that it relied on data from hospital codes, which could have errors or overcount patients hospitalized more than once or transferred to another hospital. In addition, there was no information on outpatient data, different types of Takotsubo cardiomyopathy or other conditions that may have contributed to patients’ deaths.

Movahed said further research is needed about the management of patients with Takotsubo cardiomyopathy and the reason behind differences in death rates between men and women.

Study details, background and design:

• The analysis included 199,890 U.S. adults from across the nation (average age 67; 83% of cases were among women). White adults comprised 80% of the Takotsubo cardiomyopathy patients, while 8% were Black adults, 6% were Hispanic adults, 2% were Asian/Pacific Islander adults, 0.64% were Native American adults and 2.2% were reported as Other.
• The Nationwide Inpatient Sample database is the largest publicly available source detailing publicly and privately paid hospital care in the U.S. It produces estimates of inpatient utilization, access, cost, quality and outcomes for about 35 million hospitalizations nationally annually.

To take the guesswork out of breastfeeding, an interdisciplinary team of engineers, neonatologists and pediatricians at Northwestern University has developed a new wearable device that can provide clinical-grade, continuous monitoring of breast milk consumption.

The unobtrusive device softly and comfortably wraps around the breast of a nursing mother during breastfeeding and wirelessly transmits data to a smartphone or tablet. The mother can then view a live graphical display of how much milk her baby has consumed in real time.

By eliminating uncertainty, the device can provide peace of mind for parents during their baby’s first days and weeks. In particular, the new technology could help reduce parental anxiety and improve clinical management of nutrition for vulnerable babies in the neonatal intensive care unit (NICU).

The study will be published on Wednesday (May 14) in the journal Nature Biomedical Engineering. To ensure its accuracy and practicality, the device endured several stages of rigorous assessments, including theoretical modeling, benchtop experiments and testing on a cohort of new mothers in the hospital.

“Knowing exactly how much milk an infant is receiving during breastfeeding has long been a challenge for both parents and healthcare providers,” said Northwestern’s John A. Rogers, who led the device development. “This technology eliminates that uncertainty, offering a convenient and reliable way to monitor milk intake in real time, whether in the hospital or at home.”

“Uncertainty around whether an infant is getting sufficient nutrition can cause stress for families, especially for breastfeeding mothers with preterm infants in the NICU,” said Dr. Daniel Robinson, a Northwestern Medicine neonatologist and co-corresponding author of the study. “Currently, only cumbersome ways exist for measuring how much milk a baby has consumed during breastfeeding, such as weighing the baby before and after they have fed. We expect this sensor to be a big advance in lactation support, reducing stress for families and increasing certainty for clinicians as infants make progress with breastfeeding but still need nutritional support. Reducing uncertainty and helping families achieve their breastfeeding goals will lead to healthier children, healthier mothers and healthier communities.”

A bioelectronics pioneer, Rogers is the Louis Simpson and Kimberly Querrey Professor of Materials Science and Engineering, Biomedical Engineering and Neurological Surgery at Northwestern — where he has appointments in the McCormick School of Engineering and Feinberg School of Medicine — and the director of the Querrey Simpson Institute for Bioelectronics (QSIB). Robinson is an associate professor of pediatrics at Feinberg and an attending physician in the division of neonatology at Ann & Robert H. Lurie Children’s Hospital of Chicago. Rogers and Robinson co-led the study with Dr. Craig Garfield, a professor of pediatrics at Feinberg and attending physician at Lurie Children’s, and Dr. Jennifer Wicks, a pediatrician at Lurie Children’s.

Three postdoctoral researchers at QSIB contributed equally to the project, each of whom is now a faculty member in Korea: Jiyhe Kim, an assistant professor at Ajou University, led the device design and supported clinical trials; Seyong Oh, an assistant professor at Hanyang University, engineered the wireless electronics; and Jae-Young Yoo, an assistant professor at Sungkyunkwan University, developed methods for data analytics. Kim and Oh are co-first authors with Raudel Avila, an assistant professor of mechanical engineering at Rice University and Northwestern Ph.D. graduate, who led the computational modeling.

Addressing an unmet need

The project started four years ago, when neonatologists and pediatricians at Lurie Children’s approached Rogers’ team with a critical unmet need. Because the transfer of milk from mother to baby during breastfeeding is not visible and the flow of milk varies, it’s nearly impossible to know the precise volume of milk a baby consumes in one sitting.

“Currently, there are no reliable ways to know how much babies are eating when they are breastfeeding,” said Wicks, who is a mother of three. “Some pediatricians and lactation consultants will use scales to weigh a baby before and after feeding, and that measurement gives a decent estimate of the amount of milk the baby drank. But unfortunately, baby scales are not small, and most people do not own baby scales. So, while that can provide an estimate, it’s not convenient.”

As another option, mothers can pump breastmilk into a bottle. While bottle-feeding offers precise volume measurements and visual reassurance that the baby is consuming milk, it removes the benefits of skin-to-skin contact. And the extra steps of pumping, storing and handling milk are time-consuming and can even increase the risk of bacterial contamination.

“There are several advantages to breastfeeding at the breast compared to feeding breast milk with a bottle,” Wicks said. “First and foremost, that skin-to-skin bond is beneficial for both babies and moms. Additionally, milk production is oftentimes stimulated better by actual breastfeeding.”

Although other academic researchers and small startup companies have explored technologies to monitor aspects of breast milk and feeding, peer-reviewed studies are scarce.

“Based on our reviews of the scientific literature and our discussions with pediatricians and neonatologists, there are no clinically validated technologies that address this important medical need,” Rogers said. “Our work fills that gap.”

Pinpointing the right strategy

Rogers’ team previously developed soft, flexible wireless body sensors for monitoring babies in the NICU as well as wearable sensors for tracking the drainage of fluid flow through shunts, which are commonly used to treat patients with hydrocephalus. With experience working with vulnerable populations and developing devices capable of measuring fluid flow, Rogers and his team were ideal candidates for the project.

“Our clinical colleagues asked us whether we could develop a sensor that would allow new mothers to determine how much milk their babies are consuming during a nursing session,” Rogers said. “At first, we weren’t sure how to approach the problem. The strategies we used to track flow through shunts as they pass through locations superficially below the skin don’t work because milk ducts lie too far beneath the skin’s surface.”

After years of failed attempts based on methods to monitor the optical properties of the breast, to quantify suckling motions, to track swallowing events and several others, the engineers finally settled on a remarkably simple technique. The device sends a tiny, safe electrical current through the breast using two small pads, or electrodes, placed on the skin. Another pair of electrodes captures the voltage difference associated with that current.

As the baby drinks milk, the amount of milk in the breast decreases. This reduction leads to a change in the electrical properties of the breast in a subtle but measurable manner. These changes directly relate to the amount of milk removed from the breast. The larger the amount, the bigger the change in electrical properties. Though subtle, that change can be accurately calibrated and quantified for real-time display on a smartphone during breastfeeding.

“This is a concept called bioimpedance, and it’s commonly used to measure body fat,” Rogers said. “Because muscle, fat, bone and tissues conduct electricity differently, bioimpedance can yield an accurate measurement of fat content. In a conceptually similar way, we can quantify the change in milk volume within the breast. This was the last strategy we tried, unfortunately. But fortunately, we found that we were able to make it work really well.”

Rigorous testing

After designing initial prototypes, the engineering team optimized it through several stages of testing and modeling. First, they built simplified models of a breast using materials that mimic the electrical properties of skin, fat and milk. By precisely controlling the amount of “milk” in these models, the researchers could see how the device’s data changed as the volume of “milk” changed.

Led by Avila at Rice, the team then created detailed computer models of the breast, based on real anatomy. Their physics-based computer simulations monitored the physiological changes that occur during breastfeeding. Using bioimpedance, Avila linked the flow of electrical signals to the amount of milk leaving the breast in real time. His team’s anatomically correct computer models incorporate patient-specific breast shapes and tissue distributions, enabling them to test how sensor placement and tissue variation affect readings.

“Our simulation results matched the trends of experiments and human clinical studies,” Avila said. “Connecting our models to impact in the real world is always a highlight, and it’s only possible through the collaboration among experimental, modeling and clinical teams.”

Personalized for all shapes and sizes

The resulting device is a thin, soft, pliable cord that lightly wraps around the outer circumference of the breast. Electrodes, which gently adhere to the skin, are integrated into each end of the cord. A small, lightweight “base station,” which also softly mounts onto the skin, sits in the middle of the cord between the electrodes. Enclosed in a soft, silicone case, the base station holds a small rechargeable battery, Bluetooth technology for wireless data transfer and a memory chip.

Because every mother has differences in breast density, shape and size, the device can be personalized through a single calibration. To calibrate the system, the mother wears the device while using a breast pump connected to a bottle with volume markings. This enables the user to know the precise volume of milk being expressed over a specific period of time. Meanwhile, the device records the breast’s electrical properties throughout the pumping process. This calibration scheme teaches the device how to interpret the changes in electrical signals for each specific mother.

After developing prototypes, the team tested the device on 12 breastfeeding mothers — both in the NICU and at home. To assess whether the device was consistent and reliable over time, the researchers took multiple measurements from the same mothers, spans of time as long as 17 weeks.

In this first stage of testing, mothers wore the sensor while they pumped as this important step required knowing precisely the amount of milk mothers expressed. In one testing session, the researchers compared the device’s data to the difference in the baby’s weight before and after breastfeeding. Overall, with the testing during pumping, the results between amounts in the bottle and amounts detected by the sensor were strikingly similar.

Improving care in the NICU

While the device would provide reassurance and useful information to all parents, Robinson and Wicks say NICU babies would benefit the most from careful monitoring. Knowing exactly how much a baby in the NICU is eating is even more critical than for healthy, full-term infants.

These babies often have precise nutritional needs. Premature babies, for example, may have underdeveloped digestive systems, making them more vulnerable to feeding intolerance. Precise feeding volumes can help minimize the risks of developing intestinal disorders and reflux.

“Some babies are limited to a certain number of feeds at a time,” Wicks said. “For babies who are born prematurely or who are recovering from a surgery, they can only eat small amounts of milk very slowly. Oftentimes, we cannot allow them to breastfeed because there’s no way for us to know how much milk they are getting from mom. Having a sensor to monitor this would enable these babies to breastfeed more successfully with their mom.”

Future directions

To become even more user-friendly, the researchers envision the technology eventually could be integrated into comfortable undergarments like breastfeeding bras. This would further enhance the device’s ease of use and overall experience for mothers.

The researchers still plan to complete comprehensive comparisons to the pre- and post-feed weighing. The team also aims to ensure the sensor is usable for mothers with a wide range of skin tones. While the current version of the device detects the amount of milk flowing out of the breast, future iterations could measure milk refilling into the breast. Then mothers could track changes in milk production over time. The team also plans to continue optimizing the device so it can glean even more insights, such as milk quality and fat content.

“Breastfeeding can be extremely emotional for mothers, in part due to the uncertainty surrounding how much milk their babies are getting,” Wicks said. “It can come with a lot of sadness because mothers feel anxious and like they aren’t doing a good job. Oftentimes, mothers experience anxiety, frustration or symptoms of depression and give up on breastfeeding altogether.

“There are many factors that make breastfeeding difficult. Being able to remove one piece of uncertainty and being able to help reassure them that they are producing enough milk will really help decrease some of that stress and anxiety. For all moms around the world — who are in all different stages of their breastfeeding journeys — this device will be incredibly helpful. We’re looking forward to bringing it to more people.”

The findings suggest that social difficulties often faced by autistic people are more about differences in how autistic and non-autistic people communicate, rather than a lack of social ability in autistic individuals, experts say.

Researchers hope the results of the study will help reduce the stigma surrounding autism, and lead to more effective communication support for autistic people.

Autism is a lifelong neurodivergence, and influences how people experience and interact with the world.

Autistic people often communicate more directly and may struggle with reading social cues and body language, leading to differences in how they engage in conversation compared to non-autistic people.

The study, led by experts from the University of Edinburgh, tested how effectively information was passed between 311 autistic and non-autistic people.

Participants were tested in groups where everyone was autistic, everyone was non-autistic, or a combination of both.

The first person in the group heard a story from the researcher, then passed it along to the next person. Each person had to remember and repeat the story, and the last person in the chain recalled the story aloud.

The amount of information passed on at each point in the chain was scored to discern how effective participants were at sharing the story. Researchers found there were no differences between autistic, non-autistic, and mixed groups.

After the task, participants rated how much they enjoyed the interaction with the other participants, based on how friendly, easy, or awkward the exchange was.

Researchers found that non-autistic people preferred interacting with others like themselves, and autistic people preferred learning from fellow autistic individuals. This is likely down to the different ways that autistic and non-autistic people communicate, experts say.

The findings confirm similar findings from a previous smaller study undertaken by the same researchers. They say the new evidence should lead to increased understanding of autistic communication styles as a difference, not a deficiency.

Dr Catherine Crompton, Chancellor’s Fellow at the University of Edinburgh’s Centre for Clinical Brain Sciences, said: “Autism has often been associated with social impairments, both colloquially and in clinical criteria. Researchers have spent a lot of time trying to ‘fix’ autistic communication, but this study shows that despite autistic and non-autistic people communicating differently it is just as successful. With opportunities for autistic people often limited by misconceptions and misunderstandings, this new research could lead the way to bridging the communication gap and create more inclusive spaces for all.”

“This is a global trend that is expected to increase in the coming years. In Europe alone, one million IVF treatments are carried out each year; in Sweden, the corresponding number is 25,000,” says Yvonne Lundberg Giwercman, professor at Lund University who led the research. She has been researching fertility in both men and women for many years.

IVF treatment involves stimulating the woman’s ovaries to mature many eggs, which are then retrieved and fertilised with sperm in the laboratory before being returned to the uterus. There are two different types of hormone treatments to choose from for egg maturation: biological or synthetic. But the powerful hormone therapy also carries the risk of serious side effects, sometimes requiring women to go into intensive care — and many attempts at IVF fail. In Sweden, the government subsidises up to three IVF cycles.

“There is an over-reliance on IVF treatments. Around 75 per cent of all attempts fail and up to 20 per cent of women experience side effects, some serious enough to require emergency treatment. The choice of hormone therapy is a contributing factor, and a major challenge is that healthcare today to some extent has to guess which treatment is best for the woman,” says Ida Hjelmér, PhD and laboratory researcher at Lund University and first author of the study.

To find out who responds best to which hormone treatment, the researchers turned to genetics. A total of 1,466 women undergoing IVF treatment at the Reproductive Medicine Centre at Skåne University Hospital in Malmö, Sweden were included in the study. Women with endometriosis or polycystic ovary syndrome (PCOS) were excluded. Of the 1,466 women, 475 were randomised to two different hormone treatments while the rest were controls. One candidate gene that is involved in fertilisation by mediating the action of follicle-stimulating hormone (FSH), which is known to play an important role in egg maturation, was of particular interest and mapped by gene sequencing.

The study identified that women with a particular variant of the FSH receptor (FSHR) gene that mediates the action of the hormone responded best to the biological hormone treatment, while others benefited from receiving the synthetic type of hormone. By knowing the woman’s genetic profile in advance, we can increase the number of successful pregnancies, says Yvonne Lundberg Giwercman.

“We see an increase in the number of pregnancies and a relative number of 38% more babies born among women who received hormone therapy that matched their gene variation compared with those who did not. This means that for every 1,000 women undergoing IVF treatment, the equivalent of four more school classes are born: 110 more babies,” says Yvonne Lundberg Giwercman.

But mapping genes is costly and takes time. That is why the researchers have now developed a simple oral swab test, which within an hour shows which hormone therapy is most suitable. The result can be seen with the naked eye as a pink or yellow coulour.

The researchers have applied for a patent for the test, set up the company Dx4Life AB and are supported in the process by LU Innovation, LU Ventures and the SmiLe Incubator with a view to commercialising the product.

“Our hope is that this will reduce the risk of suffering for women, increase the number of successful treatments and cut costs for taxpayers. Our goal is for the test to be available by the start of 2026,” says Yvonne Lundberg Giwercman, who is also the CEO of the company that developed the oral swab test.

Empa researchers from the Cellulose and Wood Materials laboratory have now developed a bio-based material that cleverly avoids this compromise. Not only is it completely biodegradable, it is also tear-resistant and has versatile functional properties. All this with minimal processing steps and without chemicals — you can even eat it. Its secret: It’s alive.

Optimized by nature

As the basis for their novel material, the researchers used the mycelium of the split-gill mushroom, a widespread edible fungus that grows on dead wood. Mycelia are root-like filamentous fungal structures that are already being actively researched as potential sources of materials. Normally, the mycelial fibers — known as hyphae — are cleaned and, if necessary, chemically processed, which brings about the above-mentioned trade-off between performance and sustainability.

The Empa researchers chose a different approach. Instead of treating the mycelium, they use it as a whole. As it grows, the fungus not only forms hyphae, but also a so-called extracellular matrix: a network of various fiber-like macromolecules, proteins and other biological substances that the living cells secrete. “The fungus uses this extracellular matrix to give itself structure and other functional properties. Why shouldn’t we do the same?” explains Empa researcher Ashutosh Sinha. “Nature has already developed an optimized system,” adds Gustav Nyström, head of the Cellulose and Wood Materials lab.

With a bit of additional optimization, the researchers gave nature a helping hand. From the enormous genetic diversity of the split-gill, they selected a strain that produces particularly high levels of two specific macromolecules: the long-chain polysaccharide schizophyllan and the soap-like protein hydrophobin. Due to their structure, hydrophobins collect at interfaces between polar and apolar liquids, for example water and oil. Schizophyllan is a nanofiber: less than a nanometer thick, but more than a thousand times as long. Together, these two biomolecules give the living mycelium material properties that make it suitable for a wide range of applications.

A living emulsifier

The researchers demonstrated the versatility of their material in the laboratory. In their study, which was published recently in the journal Advanced Materials, they showcased two possible applications for the living material: a plastic-like film and an emulsion. Emulsions are mixtures of two or more liquids that normally do not mix. All you have to do to see an example is open the fridge: Milk, salad dressing or mayonnaise are all emulsions. And various cosmetics, paints and varnishes also take the form of emulsions.

One challenge is to stabilize such mixtures so that they do not separate into the individual liquids over time. This is where the living mycelium shows its strengths: Both the schizophyllan fibers and the hydrophobins act as emulsifiers. And the fungus keeps releasing more of these molecules. “This is probably the only type of emulsion that becomes more stable over time,” says Sinha. Both the fungal filaments themselves and their extracellular molecules are completely non-toxic, biologically compatible and edible — the split-gill mushroom is routinely eaten in many parts of the world. “Its use as an emulsifier in the cosmetics and food industry is therefore particularly interesting,” says Nyström.

From compost bags to batteries

The living fungal network is also suitable for classic material applications. In a second experiment, the researchers manufactured the mycelium into thin films. The extracellular matrix with its long schizophyllan fibers gives the material very good tensile strength, which can be further enhanced by targeted alignment of the fungal and polysaccharide fibers within it.

“We combine the proven methods for processing fiber-based materials with the emerging field of living materials,” explains Nyström. Sinha adds: “Our mycelium is a living fiber composite, so to speak.” The researchers can control the fungal material’s properties by changing the conditions under which the fungus grows. It would also be conceivable to use other fungal strains or species that produce other functional macromolecules.

Working with the living material also presents certain challenges. “Biodegradable materials always react to their environment,” says Nyström. “We want to find applications where this interaction is not a hindrance but maybe even an advantage.” However, its biodegradability is only part of the story for the mycelium. It is also a biodegrader: The split-gill mushrooms can actively decompose wood and other plant materials. Sinha sees another potential application here: “Instead of compostable plastic bags, it could be used to make bags that compost the organic waste themselves,” says the researcher.

There are also promising applications for the mycelium in the field of sustainable electronics. For example, the fungal material shows a reversible reaction to moisture and could be used to produce biodegradable moisture sensors. Another application that Nyström’s team is currently working on combines the living material with two other research projects from the Cellulose and Wood Materials laboratory: the fungal biobattery and the paper battery. “We want to produce a compact, biodegradable battery whose electrodes consist of a living ‘fungal paper’,” says Sinha.