Publisher Health

Degenerative arthritis is no longer exclusive to the elderly population. According to the National Health Insurance Service report covering the years from 2012 to 2022, there has been a 22.8% increase in the prevalence of degenerative arthritis among people in their 20s and 30s. This rise is attributed to prolonged periods of desk sitting and the excessive lifting of heavy sports equipment, both of which can lead to significant cartilage damage. While artificial joints are a common treatment, bacterial infections have posed challenges. However, a recent study has proposed an intriguing solution involving the use of mussels.
A collaborative research team, comprising of Professor Hyung Joon Cha from the Department of Chemical Engineering and the School of Convergence Science and Technology and Dr. Hyun Sun Choi from the Department of Chemical Engineering at Pohang University of Science and Technology (POSTECH), and Professor Yun Kee Jo from the Department of Biomedical Convergence Science and Technology of the College of Advanced Technology Convergence at the Kyungpook National University, has successfully developed a coating material for implants. This material, based on mussel adhesion proteins, is designed to release antibiotics in response to bacterial invasion. The research has been recently published in the online edition of Biomaterials, a prominent international journal in the field of biomaterials.
In implant procedures, bacterial infections not only compromise the stability of the implant but also give rise to various complications. Moreover, highly antibiotic-resistant bacteria often lead to recurrent infections even after antibacterial treatment, requiring additional procedures. While there has been active exploration of implant coating materials with antibiotics, numerous challenges have emerged including physical damage to the material during the procedure and potential leakage of antibiotics inside.
In this research, the team directed their attention to DOPA, one of the amino acids found in mussel adhesion proteins. DOPA, crucial for the robust adhesion observed in mussels, forms potent bonds with metal ions. Its interaction with ferrous metal ions is notable because it weakens as the acidity (pH) decreases. Recognizing that bacterial invasion alters the body’s acidity, the team developed a novel implant coating material.
This material contains antibiotics under normal conditions, but in the event of a bacterial infection and subsequent acidification, it releases 70 percent of the antibiotics within eight hours, effectively eliminating the bacteria. Notably, the material exhibits remarkable durability, showcasing immediate antibacterial efficacy even during the bone regeneration phase (approximately four weeks) following the implant procedure.
The quantity of antibiotics discharged by the material corresponds to the extent of bacterial infection, and the researchers additionally validated the antibacterial efficacy of the coating material based on varying bacterial concentrations. Particularly, the bond between DOPA and iron ions showed remarkable resilience to external physical stimuli, rendering it resistant to abrasion and mechanical loads encountered during the implantation process.
Professor Hyung Joon Cha of the POSTECH who led the study expressed his expectation by saying, “The immediate and sustained antimicrobial effect of the adhesive implant coating material has the potential to significantly enhance the success rate of implant procedures.” Professor Yun Kee Jo of the Kyungpook National University added, explaining the significance of the research, “By releasing antibiotics selectively in response to actual need, this could represent a groundbreaking technology in preventing the emergence of superbacteria in the future.”
The research was conducted with support from the Korea Health Technology R&D Project and the Dentistry Technology R&D Project of the Ministry of Health and Welfare, the Mid-Career Research Program and the Young Researcher Program of the Ministry of Science and ICT, and POSCO Holdings.

People who experience sleep apnea may be more likely to also have memory or thinking problems, according to a preliminary study released today, March 3, 2024, that will be presented at the American Academy of Neurology’s 76th Annual Meeting taking place April 13-18, 2024, in person in Denver and online. The study shows a positive association but did not determine whether sleep apnea causes cognitive decline.
Sleep apnea is when people stop and restart breathing repeatedly during sleep which can lower oxygen levels in the blood. Symptoms include snorting, gasping and breathing pauses. People with the disorder may also experience morning headaches or have trouble focusing on tasks.
“Sleep apnea is a common disorder that is often underdiagnosed, yet treatments are available,” said study author Dominique Low, MD, MPH, of Boston Medical Center in Massachusetts, and a member of the American Academy of Neurology. “Our study found participants who had sleep apnea symptoms had greater odds of having memory or thinking problems.”
The study involved 4,257 people. Participants completed a questionnaire asking about sleep quality as well as memory and thinking problems. For sleep, participants were asked about snorting, gasping or breathing pauses in their sleep. For memory and thinking, participants were asked questions related to difficulty remembering, periods of confusion, difficulty concentrating or problems with decision making.
Of all participants, 1,079 reported symptoms of sleep apnea. Of those with symptoms, 357 people, or 33%, reported memory or thinking problems compared to 628 people, or 20% of people without sleep apnea symptoms.
After adjusting for other factors that could affect memory and thinking problems, such as age, race, gender and education, researchers found that people who reported sleep apnea symptoms were about 50% more likely to also report having memory or thinking problems compared to people who did not have sleep apnea symptoms.
“These findings highlight the importance of early screening for sleep apnea,” said Low. “Effective treatments like continuous positive airway pressure (CPAP) machines are readily available. Quality sleep, along with eating a healthy diet, regular exercise, social engagement and cognitive stimulation, may ultimately reduce a person’s risk of thinking and memory problems, improving their quality of life.”
Limitations of the study include that the data was sourced from one survey and participants reported their symptoms instead of being assessed by medical professionals. Additional studies are needed following people’s sleep apnea, memory and thinking symptoms over time.

Medical diagnostics expert, doctor’s assistant, and cartographer are all fair titles for an artificial intelligence model developed by researchers at the Beckman Institute for Advanced Science and Technology.
Their new model accurately identifies tumors and diseases in medical images and is programmed to explain each diagnosis with a visual map. The tool’s unique transparency allows doctors to easily follow its line of reasoning, double-check for accuracy, and explain the results to patients.
“The idea is to help catch cancer and disease in its earliest stages — like an X on a map — and understand how the decision was made. Our model will help streamline that process and make it easier on doctors and patients alike,” said Sourya Sengupta, the study’s lead author and a graduate research assistant at the Beckman Institute.
This research appeared in IEEE Transactions on Medical Imaging.
Cats and dogs and onions and ogres
First conceptualized in the 1950s, artificial intelligence — the concept that computers can learn to adapt, analyze, and problem-solve like humans do — has reached household recognition, due in part to ChatGPT and its extended family of easy-to-use tools.
Machine learning, or ML, is one of many methods researchers use to create artificially intelligent systems. ML is to AI what driver’s education is to a 15-year-old: a controlled, supervised environment to practice decision-making, calibrating to new environments, and rerouting after a mistake or wrong turn.

Deep learning — machine learning’s wiser and worldlier relative — can digest larger quantities of information to make more nuanced decisions. Deep learning models derive their decisive power from the closest computer simulations we have to the human brain: deep neural networks.
These networks — just like humans, onions, and ogres — have layers, which makes them tricky to navigate. The more thickly layered, or nonlinear, a network’s intellectual thicket, the better it performs complex, human-like tasks.
Consider a neural network trained to differentiate between pictures of cats and pictures of dogs. The model learns by reviewing images in each category and filing away their distinguishing features (like size, color, and anatomy) for future reference. Eventually, the model learns to watch out for whiskers and cry Doberman at the first sign of a floppy tongue.
But deep neural networks are not infallible — much like overzealous toddlers, said Sengupta, who studies biomedical imaging in the University of Illinois Urbana-Champaign Department of Electrical and Computer Engineering.
“They get it right sometimes, maybe even most of the time, but it might not always be for the right reasons,” he said. “I’m sure everyone knows a child who saw a brown, four-legged dog once and then thought that every brown, four-legged animal was a dog.”
Sengupta’s gripe? If you ask a toddler how they decided, they will probably tell you.

“But you can’t ask a deep neural network how it arrived at an answer,” he said.
The black box problem
Sleek, skilled, and speedy as they may be, deep neural networks struggle to master the seminal skill drilled into high school calculus students: showing their work. This is referred to as the black box problem of artificial intelligence, and it has baffled scientists for years.
On the surface, coaxing a confession from the reluctant network that mistook a Pomeranian for a cat does not seem unbelievably crucial. But the gravity of the black box sharpens as the images in question become more life-altering. For example: X-ray images from a mammogram that may indicate early signs of breast cancer.
The process of decoding medical images looks different in different regions of the world.
“In many developing countries, there is a scarcity of doctors and a long line of patients. AI can be helpful in these scenarios,” Sengupta said.
When time and talents are in high demand, automated medical image screening can be deployed as an assistive tool — in no way replacing the skill and expertise of doctors, Sengupta said. Instead, an AI model can pre-scan medical images and flag those containing something unusual — like a tumor or early sign of disease, called a biomarker — for a doctor’s review. This method saves time and can even improve the performance of the person tasked with reading the scan.
These models work well, but their bedside manner leaves much to be desired when, for example, a patient asks why an AI system flagged an image as containing (or not containing) a tumor.
Historically, researchers have answered questions like this with a slew of tools designed to decipher the black box from the outside in. Unfortunately, the researchers using them are often faced with a similar plight as the unfortunate eavesdropper, leaning against a locked door with an empty glass to their ear.
“It would be so much easier to simply open the door, walk inside the room, and listen to the conversation firsthand,” Sengupta said.
To further complicate the matter, many variations of these interpretation tools exist. This means that any given black box may be interpreted in “plausible but different” ways, Sengupta said.
“And now the question is: which interpretation do you believe?” he said. “There is a chance that your choice will be influenced by your subjective bias, and therein lies the main problem with traditional methods.”
Sengupta’s solution? An entirely new type of AI model that interprets itself every time — that explains each decision instead of blandly reporting the binary of “tumor versus non-tumor,” Sengupta said.
No water glass needed, in other words, because the door has disappeared.
Mapping the model
A yogi learning a new posture must practice it repeatedly. An AI model trained to tell cats from dogs studying countless images of both quadrupeds.
An AI model functioning as doctor’s assistant is raised on a diet of thousands of medical images, some with abnormalities and some without. When faced with something never-before-seen, it runs a quick analysis and spits out a number between 0 and 1. If the number is less than .5, the image is not assumed to contain a tumor; a numeral greater than .5 warrants a closer look.
Sengupta’s new AI model mimics this setup with a twist: the model produces a value plus a visual map explaining its decision.
The map — referred to by the researchers as an equivalency map, or E-map for short — is essentially a transformed version of the original X-ray, mammogram, or other medical image medium. Like a paint-by-numbers canvas, each region of the E-map is assigned a number. The greater the value, the more medically interesting the region is for predicting the presence of an anomaly. The model sums up the values to arrive at its final figure, which then informs the diagnosis.
“For example, if the total sum is 1, and you have three values represented on the map — .5, .3, and .2 — a doctor can see exactly which areas on the map contributed more to that conclusion and investigate those more fully,” Sengupta said.
This way, doctors can double-check how well the deep neural network is working — like a teacher checking the work on a student’s math problem — and respond to patients’ questions about the process.
“The result is a more transparent, trustable system between doctor and patient,” Sengupta said.
X marks the spot
The researchers trained their model on three different disease diagnosis tasks including more than 20,000 total images.
First, the model reviewed simulated mammograms and learned to flag early signs of tumors. Second, it analyzed optical coherence tomography images of the retina, where it practiced identifying a buildup called Drusen that may be an early sign of macular degeneration. Third, the model studied chest X-rays and learned to detect cardiomegaly, a heart enlargement condition that can lead to disease.
Once the mapmaking model had been trained, the researchers compared its performance to existing black-box AI systems — the ones without a self-interpretation setting. The new model performed comparably to its counterparts in all three categories, with accuracy rates of 77.8% for mammograms, 99.1% for retinal OCT images, and 83% for chest x-rays compared to the existing 77.8%, 99.1%, and 83.33.%
These high accuracy rates are a product of the deep neural network, the non-linear layers of which mimic the nuance of human neurons.
To create such a complicated system, the researchers peeled the proverbial onion and drew inspiration from linear neural networks, which are simpler and easier to interpret.
“The question was: How can we leverage the concepts behind linear models to make non-linear deep neural networks also interpretable like this?” said principal investigator Mark Anastasio, a Beckman Institute researcher and the Donald Biggar Willet Professor and Head of the Illinois Department of Bioengineering. “This work is a classic example of how fundamental ideas can lead to some novel solutions for state-of-the-art AI models.”
The researchers hope that future models will be able to detect and diagnose anomalies all over the body and even differentiate between them.
“I am excited about our tool’s direct benefit to society, not only in terms of improving disease diagnoses, but also improving trust and transparency between doctors and patients,” Anastasio said.

Lactating mothers who get the COVID-19 booster pass along the antibodies to their children via their breast milk — and potentially protect babies too young to receive the vaccine, a study from the University of Florida Institute of Food and Agricultural Sciences (UF/IFAS) and the UF College of Medicine found.
The study is the third in a series that looks at antibody protection being transferred via breast milk from mothers who received their first two COVID-19 vaccinations and, now, the booster shot. The second publication reported the same antibody transfer via breast milk.
“We think that breast milk may play an important role in protecting the infants during the first six months of life from COVID,” said Dr. Vivian Valcarce, a former UF College of Medicine researcher who worked on this study. She now is an assistant professor at the University of Alabama at Birmingham. “We continue to see babies being hospitalized from COVID-19 infections.”
The study was published in February in Frontiers in Nutrition, and the study was funded by the Gerber Foundation and the Children’s Miracle Network.
The study looked at how breast milk antibody protection changed when a mother received their first COVID-19 booster shot, said Joseph Larkin, UF/IFAS associate professor of microbiology and cell science and part of UF’s Emerging Pathogens Institute. Researchers looked at the antibody response and antibody functionality in breast milk and tested to see if antibodies were present after the babies drank breast milk with COVID-19 antibodies.
Larkin said this study suggests that breastfeeding can provide COVID-19 antibodies for infants too young to receive a vaccination — and that the antibodies wane in people’s bodies over time, so getting a booster can provide prolonged protection to babies that drink breast milk.
“When babies are born, they have an immature immune system, so they rely heavily on mom’s immune system,” he said. “Breastfeeding can serve as a gap in between while babies are building their own immune system.”
Larkin said some antibodies are transferred to fetuses through the placenta, as well, but that initial protection also lessens over time.

In this study, 14 lactating mothers and their babies were followed from before they received their COVID-19 booster until after they received their booster shots, Larkin said. Researchers tested the mothers’ blood to confirm their bodies made COVID-19 antibodies after a booster shot, tested breast milk to confirm the milk had antibodies in it and tested babies’ poop to confirm antibodies were present in the babies’ bodies.
To see if the breast milk’s antibodies worked against COVID-19, breast milk was placed in a 96-well plate with a lab-safe COVID virus strain, and researchers found these antibodies from the mother disable the virus, said Lauren Stafford, a UF/IFAS graduate research assistant and Ph.D. candidate in microbiology and cell science.
The study was a collaboration between UF/IFAS and the UF College of Medicine and included Dr. Josef Neu, professor of pediatrics within the division of neonatology at the UF College of Medicine.
“This shows how important breast milk and breastfeeding is for infant health during a pandemic,” Valcarce said.

New cases fell by half in just over a year, San Francisco health officials reported.A single dose of doxycycline, a widely used antibiotic, taken after sex halved the incidence of chlamydia and early syphilis among gay and bisexual men and transgender women in San Francisco, city health officials announced on Monday. The findings offered a glimmer of hope amid a rising tide of sexually transmitted infections nationwide.The strategy is called doxy-PEP, shorthand for doxycycline post-exposure prophylaxis. In San Francisco, gay and bisexual men and transgender women who had a history of S.T.I.s or multiple sex partners were given a supply of the antibiotic and asked to take two 100-milligram pills within 72 hours of unprotected sex.New cases of chlamydia and early syphilis — but not gonorrhea — dropped over the course of about a year. The results were presented at the Conference on Retroviruses and Opportunistic Infections in Denver.“It’s not subtle, it is very fast and we’re seeing the beginning of it, not the end,” Dr. Hyman Scott, a medical director at the San Francisco Department of Public Health, said in an interview. “This is what we want for S.T.I. prevention.”Strategies to stem S.T.I.s are sorely needed.Syphilis, once nearly eliminated in the United States, has reached the highest rate of new infections recorded since 1950, the Centers for Disease Control and Prevention reported in January. Left untreated, syphilis can damage the heart and brain and cause blindness, deafness and paralysis.Rates of chlamydia remained flat nationwide in 2022, compared with the number in 2021, but at nearly 1.7 million cases, infections were common. (Gonorrhea cases decreased in 2022, but experts cautioned that the trend might have been the result of a decrease in testing.)We are having trouble retrieving the article content.Please enable JavaScript in your browser settings.Thank you for your patience while we verify access. If you are in Reader mode please exit and log into your Times account, or subscribe for all of The Times.Thank you for your patience while we verify access.Already a subscriber? Log in.Want all of The Times? Subscribe.

She discovered new ways to promote regeneration in the nervous system, offering hope to countless paralyzed patients worldwide.Mary Bartlett Bunge, who with her husband, Richard, studied how the body responds to spinal cord injuries and continued their work after his death in 1996, ultimately discovering a promising treatment to restore movement to millions of paralyzed patients, died on Feb. 17, at her home in Coral Gables, Fla. She was 92.The Miami Project to Cure Paralysis, a nonprofit research organization with which Dr. Bunge (pronounced BUN-ghee) was affiliated, announced the death.“She definitely was the top woman in neuroscience, not just in the United States but in the world,” Dr. Barth Green, a co-founder and dean at the Miami Project, said in a phone interview.Dr. Bunge’s focus for much of her career was on myelin, a mix of proteins and fatty acids that coats nerve fibers, protecting them and boosting the speed at which they conduct signals.Early in her career, she and her husband, who she met as a graduate student at the University of Wisconsin in the 1950s, used new electron microscopes to describe the way that myelin developed around nerve fibers, and how, after because of injury or illness, it receded, in a process called demyelination.Dr. Bunge in the early 1990s with her husband, Richard Bunge, at the Miami Project to Cure Paralysis research facility. They worked as a team, and she continued their research after his death in 1996.via The Miami Project to Cure ParalysisWe are having trouble retrieving the article content.Please enable JavaScript in your browser settings.Thank you for your patience while we verify access. If you are in Reader mode please exit and log into your Times account, or subscribe for all of The Times.Thank you for your patience while we verify access.Already a subscriber? Log in.Want all of The Times? Subscribe.

The NewsThe first nonprescription birth control pill in the United States will be available in stores and online in the coming weeks at a price of $19.99 a month, the manufacturer, Perrigo Company, announced on Monday.Perrigo Company, via Associated PressWhy It MattersThe medication, called Opill, which was approved for over-the-counter sale by the Food and Drug Administration last year, will be the most effective birth control method available without a prescription, research shows — more effective than condoms, spermicides and other nonprescription methods.Reproductive health experts said that its availability could be especially useful for teenagers, young women, and others who have difficulty dealing with the time, costs or logistical hurdles involved in visiting a doctor to obtain a prescription.Some experts said they thought it might be a particularly good option for teenagers, who might otherwise rely on condoms.Lupe M. Rodriguez, the executive director of the National Latina Institute for Reproductive Justice, said in a statement Monday that “over-the-counter access to birth control will greatly reduce the barriers like transportation, cost, language, and documentation.”Opill is not a new medication — it was approved for prescription use 50 years ago. Reproductive health experts and members of an F.D.A. advisory panel cited its long history of safety and efficacy. It is 93 percent effective at preventing pregnancy with typical use. Women with certain conditions — primarily breast cancer or undiagnosed vaginal bleeding — should not take Opill. But for most women, “the risk is very low, and almost nonexistent if they read and follow the labeling,” Karen Murry, the deputy director of the F.D.A.’s office of nonprescription drugs, said in a memo explaining the approval decision.Since the Supreme Court overturned the national right to an abortion in 2022, the accessibility of contraception has become an increasingly urgent issue. But long before that, the move to make a nonprescription pill available for all ages had received widespread support from specialists in reproductive and adolescent health and groups.The approval of Opill faced very little public opposition from conservative groups that are often critical of measures that increase access to abortion, emergency contraception and sex education. Opposition appeared to come primarily from some Catholic organizations and Students for Life Action.In a survey in 2022 by the health care research organization KFF, more than three-quarters of women of reproductive age said they favored an over-the-counter pill, primarily because of convenience.The DetailsWe are having trouble retrieving the article content.Please enable JavaScript in your browser settings.Thank you for your patience while we verify access. If you are in Reader mode please exit and log into your Times account, or subscribe for all of The Times.Thank you for your patience while we verify access.Already a subscriber? Log in.Want all of The Times? Subscribe.

Fat tissue holds the key to 3D printing layered living skin and potentially hair follicles, according to researchers who recently harnessed fat cells and supporting structures from clinically procured human tissue to precisely correct injuries in rats. The advancement could have implications for reconstructive facial surgery and even hair growth treatments for humans.
The team’s findings published today (March 1) in Bioactive Materials. The U.S. Patent and Trademark Office granted the team a patent in February for the bioprinting technology it developed and used in this study.
“Reconstructive surgery to correct trauma to the face or head from injury or disease is usually imperfect, resulting in scarring or permanent hair loss,” said Ibrahim T. Ozbolat, professor of engineering science and mechanics, of biomedical engineering and of neurosurgery at Penn State, who led the international collaboration that conducted the work. “With this work, we demonstrate bioprinted, full thickness skin with the potential to grow hair in rats. That’s a step closer to being able to achieve more natural-looking and aesthetically pleasing head and face reconstruction in humans.”
While scientists have previously 3D bioprinted thin layers of skin, Ozbolat and his team are the first to intraoperatively print a full, living system of multiple skin layers, including the bottom-most layer or hypodermis. Intraoperatively refers to the ability to print the tissue during surgery, meaning the approach may be used to more immediately and seamlessly repair damaged skin, the researchers said. The top layer — the epidermis that serves as visible skin — forms with support from the middle layer on its own, so it doesn’t require printing. The hypodermis, made of connective tissue and fat, provides structure and support over the skull.
“The hypodermis is directly involved in the process by which stem cells become fat,” Ozbolat said. “This process is critical to several vital processes, including wound-healing. It also has a role in hair follicle cycling, specifically in facilitating hair growth.”
The researchers started with human adipose, or fat, tissue obtained from patients undergoing surgery at Penn State Health Milton S. Hershey Medical Center. Collaborator Dino J. Ravnic, associate professor of surgery in the Division of Plastic Surgery at Penn State College of Medicine, led his lab in obtaining the fat for extraction of the extracellular matrix — the network of molecules and proteins that provides structure and stability to the tissue — to make one component of the bioink.
Ravnic’s team also obtained stem cells, which have the potential to mature into several different cell types if provided the correct environment, from the adipose tissue to make another bioink component. Each component was loaded into one of three compartments in the bioprinter. The third compartment was filled with a clotting solution that helps the other components properly bind onto the injured site.

“The three compartments allow us to co-print the matrix-fibrinogen mixture along with the stem cells with precise control,” Ozbolat said. “We printed directly into the injury site with the target of forming the hypodermis, which helps with wound healing, hair follicle generation, temperature regulation and more.”
They achieved both the hypodermis and dermis layers, with the epidermis forming within two weeks by itself.
“We conducted three sets of studies in rats to better understand the role of the adipose matrix, and we found the co-delivery of the matrix and stem cells was crucial to hypodermal formation,” Ozbolat said. “It doesn’t work effectively with just the cells or just the matrix — it has to be at the same time.”
They also found that the hypodermis contained downgrowths, the initial stage of early hair follicle formation. According to the researchers, while fat cells do not directly contribute to the cellular structure of hair follicles, they are involved in their regulation and maintenance.
“In our experiments, the fat cells may have altered the extracellular matrix to be more supportive for downgrowth formation,” Ozbolat said. “We are working to advance this, to mature the hair follicles with controlled density, directionality and growth.”
According to Ozbolat, the ability to precisely grow hair in injured or diseased sites of trauma can limit how natural reconstructive surgery may appear. He said that this work offers a “hopeful path forward,” especially in combination with other projects from his lab involving printing bone and investigating how to match pigmentation across a range of skin tones.

“We believe this could be applied in dermatology, hair transplants, and plastic and reconstructive surgeries — it could result in a far more aesthetic outcome,” Ozbolat said.
“With the fully automated bioprinting ability and compatible materials at the clinical grade, this technology may have a significant impact on the clinical translation of precisely reconstructed skin.”
Ravnic and Ozbolat also are affiliated with the Huck Institutes of the Life Sciences and the Penn State Cancer Institute. Ozbolat has additional affiliations with the Penn State Materials Research Institute and the Department of Medical Oncology at Cukurova University in Turkey, where he is currently on sabbatical leave. Other contributors include Yogendra Pratap Singh, postdoctoral scholar, and Mecit Altan Alioglu, graduate student, both in the Penn State Department of Engineering Science and Mechanics; Youngnam Kang and Miji Yeo, both researchers, and Irem Deniz Derman, graduate student, in the Huck Institutes of the Life Sciences; Yang Wu, Harbin Institute of Technology in China; and Jasson Makkar and Ryan R. Driskell, College of Veterinary Medicine at Washington State University. Kang, Yeo, Singh, Alioglu and Derman also are affiliated with Penn State Department of Engineering Science and Mechanics.
The National Institutes of Health and the Scientific and Technological Research Council of Türkiye supported this work.

Accurate “time of death” estimates are a mainstay of murder mysteries and forensic programs, but such calculations in the real world are often complex and imprecise.
In a first-of-its-kind study, Arizona State University researchers and their colleagues have discovered a group of common microbes that work together specifically to decompose flesh. These microorganisms serve as a biological clock and allow scientists to investigate the post-mortem breakdown of tissue with unprecedented precision.
Researchers explored a network of some 20 microbes responsible for cadaver decomposition across different climates and seasons. The findings demonstrate that a microbial orchestration of decay operates according to a strict timetable. This predictable behavior allows for accurate time-of-death estimates, regardless of environmental conditions.
The study also shows that these “universal decomposers” are largely unique to decomposition environments and are not commonly found in other soil or host-associated microbial communities.
“High-throughput technologies like DNA sequencing and mass spectrometry generate large volumes of data,” said Qiyun Zhu, part of the team of researchers. “Such approaches often yield deeper insights into fundamental questions than traditional methods do.”
Zhu is a researcher with the Biodesign Center for Fundamental and Applied Microbiomics at ASU and an assistant professor with the School of Life Sciences.
The research appears in the current issue of the journal Nature Microbiology.
The three research sites used for the study, located within two divergent climate zones — temperate forest and semi-arid steppe — showed a consistent pattern of results. The same set of 20 decomposing microbes, including rare bacteria and fungi, were found across all cadavers. This group of microbes demonstrated consistent consumption behavior that appeared to be unaffected by variations in environmental conditions.

The researchers also applied machine learning to the field data to develop a predictive model for time of death, based on microbial activity. Shedding new light on the microbial ecology of decomposition, the research represents a significant advance for forensic science, and it could also impact agricultural and food industries by enhancing the understanding of nutrient recycling and soil health.
Unearthing vertebrate decay
The decomposition of dead biological material is one of nature’s most fundamental phenomena. Most decaying matter consists of organic plant waste, and such processes are relatively well understood. Much less is known about the ecology of vertebrate decomposition, including that of humans, and a better understanding of this progression is of primary concern to forensic scientists.
This multiyear study involved the decomposition of 36 cadavers at three different forensic anthropological facilities — the University of Tennessee, Knoxville; Sam Houston State University; and Colorado Mesa University. The bodies, collected from three willed-body donation facilities, were decomposed in different climates and during all four seasons.
During the first 21 days for each decomposing body, researchers collected skin and soil samples from the cadavers and graves undergoing decomposition. The research team also took samples from unaffected soil for comparison and recorded daily environmental conditions, including temperature, humidity and other atmospheric data.
The team then constructed an overall picture of the microbial community, or microbiome, present at each site. This demonstrated which microbes were present, where they came from and how their activities changed over time.

Timely arrival
Surprisingly, researchers found the same set of approximately 20 microbes that specialize in decomposition on all 36 bodies, regardless of climate or soil type. Further, these microbes arrived punctually at specific time points throughout the 21-day observation period. This living network is not just a random assembly of microorganisms but a structured community that plays a critical role in breaking down the body.
Notably, this specialized suite of microbes was not found in soil microbiome databases or human skin and gut microbiome catalogs. Instead, insects — particularly carrion beetles and blowflies — act as microbe carriers. Their interaction with cadavers facilitates the spread of these microorganisms, thereby accelerating the decomposition process.
Data derived from universal decomposing microbes offer several advantages during forensic analysis of death scenes. In addition to their consistent behavior over a broad range of seasonal conditions, these primary decomposing microbes are guaranteed to appear at every scene, unlike fingerprints, bloodstains, photographs or other forms of conventional evidence. The arrival of a particular microbe during decomposition depends on the state of the cadaver.
The team used machine learning to develop a predictive model based on the microbial ecology found on decomposing bodies. This model demonstrated high accuracy and was validated using an independent set of samples from various climates, confirming its reliability in predicting time of death within three calendar days. Future efforts aim to refine models and improve the accuracy of predictions.

In order to record coffee consumption in nutrition and health studies, researchers usually rely on self-reporting by participants. However, this is not always reliable. It would therefore be desirable to conduct additional studies to objectively verify individual consumption using biomarkers. A research team led by the Leibniz Institute for Food Systems Biology at the Technical University of Munich has now validated the suitability of a specific roasted coffee compound and proposes it as a new, practical food biomarker.
Millions of people around the world drink coffee every day. The beverage contains a large number of bioactive substances, and its health effects on the human metabolism are therefore frequently subjects of scientific studies. In many of these studies, however, the data on coffee consumption is largely based on self-reporting by the participants and is therefore not always accurate. This can affect the scientific validity of nutritional studies.
Biomarkers could provide a remedy
Reliable biomarkers could remedy this problem by using biological samples to objectively distinguish between coffee drinkers and non-coffee drinkers. “So far, however, only a few substances are known that could be used as coffee markers,” says principal investigator Roman Lang from the Leibniz Institute. “However, these are not yet sufficiently validated or available in sufficient quantities to serve as reference substances for comparative measurements in nutritional studies,” he continues.
The research team, which also includes the nutritional physician Thomas Skurk and first author Beate Brandl from the ZIEL — Institute for Food & Health at the Technical University of Munich, has therefore comprehensively validated the roast coffee compound N-methylpyridinium as one such biomarker candidate for its suitability. Researchers at the Technical University of Munich first proposed the substance as a biomarker candidate in 2011 as part of a pilot study.
Data from over 460 people analyzed
As part of the scientific validation, the team analyzed existing literature data. It also analyzed urine, blood and plasma samples from more than 460 people from Freising and Nuremberg who had participated in a nutrition study conducted by the BMBF-funded enable cluster.
As the study shows, N-methylpyridinium is a compound that is specific to roasted Arabica and Robusta coffee. The substance is chemically very stable and its absorption into the organism is concentration-dependent. The substance can also be easily and reproducibly detected in various body fluids after coffee consumption, before leaving the body unchanged in the urine within a few hours to days.
Roman Lang, who heads the Biosystems Chemistry & Human Metabolism research group at the Leibniz Institute, explains: “As we have shown, N-methylpyridinium fulfills all the criteria that science demands of a biomarker to control food intake. Even if we cannot draw direct conclusions about the amount of coffee consumed due to various factors, the roasting substance is still suitable as a marker. This is because it allows us to distinguish objectively and practically between people who have drunk coffee and those who have not. We therefore propose it as a reliable qualitative biomarker for coffee consumption.”