PurposeAutism is a common neurodevelopmental condition (NDC) that is characterized by restricted, repetitive behaviors and social communication differences that can impact the daily functioning of individuals. The clinical diagnosis of autism can be challenging, mainly due to its behavioral variability and frequent co-occurrence with other NDCs. This study investigates the ability of machine learning-based classification models trained using multimodal neuroimaging data combined with feature-importance analyses to identify development-specific brain characteristics associated with autism.ApproachA total of 144 participants aged 5 to 18 years with structural MRI (sMRI), diffusion MRI (dMRI), and resting-state functional MRI (rs-fMRI) data available were obtained from the Autism Brain Imaging Data Exchange (ABIDE) database. Radiomic features were extracted from each MRI data modality and used to train support vector machine (SVM) classifiers to identify neuroimaging patterns associated with autism. Single MRI modality classifiers, as well as one combining all three modalities, were trained for comparison purposes. To investigate age-specific effects, the same approach was followed for three age sub-groups: younger children (5–11 years), adolescents (12–18 years), and the entire 5–18 years age cohort. Model performance was evaluated using leave-one-out cross-validation across 30 diagnosis-balanced data splits. Feature-importance analyses were conducted to identify the most important neuroimaging features for classification.ResultsThe classification accuracies of the unimodal models ranged from 68.3% to 75.3% for sMRI, from 69.3% to 77.6% for dMRI, and from 66.3% to 69.9% for rs-fMRI data across age groups. Among all single imaging modalities and age groups, dMRI showed the highest performance with a 77.6% accuracy in younger children (5–11 years). The multimodal approach improved classification performance when compared to the unimodal models in all age groups, achieving accuracies of 78.9%, 76.7%, and 70.5% in the younger, adolescent, and entire age cohorts, respectively. Our findings indicate that multimodal classifiers integrating complementary structural, microstructural, and functional imaging features result in a more comprehensive representation of brain features that strengthens model performance. The most informative brain regions for classification differed between children and adolescents while several diffusion-derived features significantly correlated with social responsiveness scores, emphasizing the clinical importance of studying white and gray matter microstructure in autism.ConclusionsThis study demonstrates the potential of multimodal neuroimaging-based machine learning models to identify development-specific biomarkers associated with autism. The results highlight the value of integrating age-stratified analyses of multimodal neuroimaging to better capture autism-associated developmental brain characteristics. The framework adopted in this study could be extended to explore other NDCs in the future.
New Antibody Biomarkers May Improve Early Detection of Lyme Disease, Predict Persistent Symptoms
Researchers have identified a distinct set of anti-lipid antibodies that could improve the early diagnosis of Lyme disease while also helping distinguish patients who develop persistent symptoms after treatment from those who recover fully.
The study, published in Infection and Immunity, found that antibodies against specific phospholipids appear before conventional Lyme disease antibodies in some patients and persist in a subset of individuals with post-treatment Lyme disease (PTLD). The findings suggest these immune markers could eventually complement existing diagnostic tests and provide new clues about the biology underlying chronic symptoms.
Lyme disease, caused by the spirochete Borrelia burgdorferi, is the most common vector-borne infection in North America and Europe. Although most patients respond well to a two- to three-week course of antibiotics, an estimated 10% to 20% continue to experience fatigue, pain, cognitive impairment, and other nonspecific symptoms long after treatment. The biological basis for these persistent symptoms remains poorly understood, and clinicians currently lack objective biomarkers to identify affected patients.
Current diagnostic testing also has significant limitations. Standard two-tier serologic testing relies on antibodies directed against Borrelia proteins, but these antibodies often do not appear until several weeks after infection and can remain detectable long after the bacteria have been eliminated. As a result, existing tests have limited sensitivity during early infection and cannot reliably distinguish active infection from previous exposure or persistent post-treatment illness.
The new study builds on earlier work showing that B. burgdorferi scavenges lipids from its human host and incorporates them into its outer membrane. The researchers hypothesized that this unusual biology could trigger immune responses against host lipids that might serve as biomarkers of disease activity.
To test that idea, investigators analyzed serum samples from patients with acute Lyme disease and PTLD collected from two independent biobanks. Together, the cross-sectional and longitudinal cohorts allowed researchers to follow antibody responses from the day of diagnosis through one year after treatment.
Among numerous lipid targets examined, only three antiphospholipid antibodies were consistently elevated following infection. Two antibodies—anti-phosphatidic acid (αPA) and anti-phosphatidylserine (αPS)—were significantly elevated at diagnosis, including in patients with erythema migrans before they had seroconverted on conventional Lyme disease testing.
Longitudinal analyses showed that antibody levels followed distinct patterns over time. Both αPA and αPS peaked approximately three to six months after diagnosis before declining in most patients. However, αPS remained persistently elevated in a subset of individuals with PTLD, distinguishing them from healthy controls and from patients with autoimmune and chronic illnesses that often resemble post-treatment Lyme disease, including systemic lupus erythematosus, multiple sclerosis, fibromyalgia, long COVID, and chronic fatigue syndrome.
The researchers propose that persistent αPS elevations may reflect an abnormal immune response rather than ongoing infection. While the precise mechanism remains unknown, they suggest that dysregulated activation of innate-like B cells or continued exposure to phosphatidylserine antigens could sustain antibody production in susceptible individuals.
Importantly, the authors note that whether these antibodies actively contribute to disease or simply mark immune dysfunction remains uncertain. “The presence of autoantibodies does not always drive autoimmune disease,” the authors write, emphasizing that further mechanistic studies are needed.
The investigators also draw parallels with syphilis, another spirochete infection. In syphilis, lipid-directed antibody tests are routinely used alongside pathogen-specific assays to monitor disease activity and treatment response. The authors suggest a similar strategy could eventually enhance Lyme disease diagnostics.
“The addition of antilipid antibodies to these panels may improve sensitivity while retaining the more specific diagnostic antibodies,” the authors write. They note that anti-lipid antibodies could potentially function both as adjuncts for early diagnosis and as biomarkers for monitoring recovery following treatment.
The study has several limitations, including relatively modest sample sizes and the use of specimens collected from multiple biobanks with differing collection protocols. Nevertheless, the consistency of findings across independent cohorts strengthens confidence in the observations.
Ultimately, the authors conclude that “the antibodies described here may be valuable biomarkers of early or persistent disease and suggest another mechanism linking B. burgdorferi infection and pathologic autoimmunity.” Larger prospective studies will be needed to determine whether these antibodies can be incorporated into clinical practice for diagnosing Lyme disease or identifying patients at risk for persistent symptoms.
The post New Antibody Biomarkers May Improve Early Detection of Lyme Disease, Predict Persistent Symptoms appeared first on Inside Precision Medicine.
Associations of TNF-α, MIF, and cortisol with cognitive function in patients with bipolar disorder during acute manic episodes: a short-term follow-up study
BackgroundBipolar disorder (BD) is frequently accompanied by cognitive impairment, and growing evidence suggests that immune-inflammatory activation and hypothalamic-pituitary-adrenal axis dysregulation may contribute to its pathophysiology. This study aimed to examine the associations of tumor necrosis factor-α (TNF-α), macrophage migration inhibitory factor (MIF), and cortisol (COR) with cognitive function in patients with BD during manic episodes and to characterize their short-term changes.MethodsIn this short-term follow-up study, 53 patients with BD during manic episodes and 53 healthy controls (HCs) were enrolled. Plasma TNF-α, MIF, and COR levels were measured using enzyme-linked immunosorbent assay. Cognitive function was assessed using the Chinese Brief Cognitive Test, including information processing speed (IPS), executive function (EF), sustained attention (SAT), and working memory (WM). Patients were evaluated at baseline and after 8 weeks of treatment, whereas HCs were assessed once at baseline. Group comparisons and biomarker–cognition correlation analyses were performed. Multiple testing in the correlation analyses was controlled using the Benjamini–Hochberg false discovery rate (FDR) procedure.ResultsAt both baseline and follow-up, patients with BD had significantly lower IPS, EF, SAT, and WM scores, but significantly higher plasma TNF-α, MIF, and COR levels, than HCs. After 8 weeks of treatment, cognitive scores in the BD group improved significantly, whereas reductions in TNF-α, MIF, and COR did not reach statistical significance. In exploratory unadjusted Pearson analyses, several biomarker–cognition associations survived FDR correction. However, in the primary adjusted partial correlation analyses, only the negative association between TNF-α and WM remained significant after adjustment for covariates and FDR correction at both baseline and follow-up.ConclusionPatients with BD during manic episodes exhibited widespread cognitive impairment accompanied by elevated inflammatory and neuroendocrine markers. TNF-α showed the most robust association with working memory after adjustment for covariates and correction for multiple comparisons. Associations involving MIF or cortisol and executive function should be interpreted as exploratory and require validation in larger longitudinal studies.
Blood Protein May Signal Dementia Risk Decades Before Symptoms Appear
A blood protein long associated with dementia in older adults may also identify people at increased risk decades before symptoms develop, according to a large international study published in Science Advances.
Analyzing data from six large longitudinal cohorts, researchers from the National Institute on Aging found that elevated levels of growth differentiation factor-15 (GDF15)—a circulating cytokine involved in inflammation and cellular stress responses—in adults younger than 55 years were associated with a significantly greater risk of developing dementia later in life, particularly vascular dementia. The findings suggest that molecular changes associated with neurodegeneration may be detectable years before cognitive symptoms emerge.
“Our findings extend existing evidence by demonstrating that elevated GDF15 levels are detectable in midlife—before age 55—in individuals who later develop dementia,’” the authors write.
The study included approximately 500,000 participants from the UK Biobank, more than 15,000 from the Atherosclerosis Risk in Communities (ARIC) study, nearly 5,700 from the AGES-Reykjavik Study, and three additional cohorts. Participants were followed for 15 to 25 years, enabling investigators to determine whether plasma GDF15 levels measured in midlife predicted future dementia.
Across nearly all cohorts, elevated plasma GDF15 was associated with increased risk for all-cause dementia. However, the relationship was strongest for vascular dementia, with effect sizes approximately two to five times greater than those observed for Alzheimer’s disease.
The distinction suggests GDF15 may be particularly useful for identifying individuals at risk for vascular cognitive impairment rather than the amyloid-driven pathology typically associated with Alzheimer’s disease. As the authors note, “the association was particularly pronounced for vascular dementia,” supporting the protein’s potential as an early marker of vascular brain injury.
To investigate whether GDF15 might play a biological role in disease rather than simply reflect ongoing pathology, the researchers performed Mendelian randomization analyses using genetic data. The analyses supported a potential causal relationship between elevated circulating GDF15 and Alzheimer’s disease and related dementias.
Additional analyses linked higher plasma GDF15 concentrations with several established indicators of neurodegeneration, including cerebral small vessel disease, elevated phosphorylated tau (pTau-181) in both plasma and cerebrospinal fluid, and increased neurofilament light, a marker of neuronal injury. In contrast, GDF15 was not associated with amyloid pathology, suggesting that it may reflect alternative disease mechanisms.
Instead, multiple lines of evidence pointed toward inflammation and immune dysregulation. Individuals with elevated GDF15 exhibited cerebrospinal fluid protein signatures consistent with neuroimmune activation, including complement activation, inflammatory signaling pathways, and disease-associated microglial responses.
To better understand these mechanisms, the investigators exposed cultured human macrophages to recombinant GDF15. The protein altered cellular pathways involved in interferon signaling, energy metabolism, and heme scavenging—processes that have all been implicated in dementia risk. Together, the experimental and clinical findings suggest that GDF15 may actively influence neurodegeneration through immune and vascular pathways rather than acting solely as a marker of biological aging.
The authors conclude that “these findings support circulating GDF15’s role as an early biomarker—particularly for vascular dementia and neuroinflammation—and identify the mechanisms by which it may drive dementia risk.”
The post Blood Protein May Signal Dementia Risk Decades Before Symptoms Appear appeared first on Inside Precision Medicine.
Prediction of cardiac cycle duration for cardiac-gated closed-loop auricular vagus nerve stimulation
Auricular vagus nerve stimulation (aVNS) is a neuromodulation technology that establishes balance in the autonomic nervous system and, in turn, provides therapy for numerous chronic ailments. Personalized aVNS adapts the stimulation parameters in accordance with the time-varying physiological state of the body, and is suggested to improve the therapeutic outcomes and reduce side effects. The physiological state is estimated via recorded biomarkers such as the electrocardiogram (ECG). aVNS can be delivered in synchrony with any phase of the cardiac cycle before and after the R-peak. This paper proposes the prediction of the duration of the next cardiac cycle after the detected R-peak for the realization of the personalized cardiac-gated closed-loop aVNS applied at any time point during the predicted cardiac cycle. We propose and explore the feasibility of four different prediction methods for predicting the duration of the next cardiac cycle. Two methods are respiration-insensitive, last value and averaging, and the other two are respiration-sensitive, extrapolation and interpolation. Offline recorded ECG waveforms were used to evaluate the different methods. Subsequently, three of the four methods (last value, averaging, and extrapolation) were implemented in real-time on a proprietary aVNS hardware setup, with the data acquisition performed across normal and paced deep breathing. Offline evaluation of the methods revealed that extrapolation and interpolation achieved lower prediction errors during deep breathing with the median absolute error (MdAE) of 32.09 ms (interquartile range 16.07–56.61 ms) and 31.71 ms (15.5–54.06 ms), respectively, as compared with the averaging and last-value methods with 88.75 ms (58.73–124.15 ms) and 40.85 ms (19.7–68.4 ms), respectively. During normal breathing, all evaluated methods yielded lower prediction errors relative to the averaging method 28.5 ms (15.2–43.7 ms). Real-time implementation validated these methods for closed-loop cardiac-gated aVNS, with the best performance achieved by the extrapolation method with 31.4 ms (15.17–55.9 ms) during paced deep breathing. During normal breathing, comparable performance across prediction methods favors the computationally simple last-value approach (MdAE: 31.6 ms). Proposed methods establish the potential of ECG-based R-peak prediction in real-time as a reliable and individual biomarker for the personalized cardiac-gated aVNS, creating a foundation for future clinical applications of aVNS.
Elevated NHHR at admission is independently associated with elevated post-stroke depressive symptoms
BackgroundElevated post-stroke depressive symptoms are common among acute ischemic stroke (AIS) survivors and are associated with poor functional recovery. However, readily available biomarkers reflecting lipid–inflammation status remain limited. The ratio of non–high-density lipoprotein cholesterol to high-density lipoprotein cholesterol (NHHR) may integrate atherogenic burden and inflammatory status and thus be associated with depressive symptom burden after stroke.MethodsWe retrospectively analyzed a cohort of 518 Chinese AIS patients. Admission NHHR was calculated from routine lipid panels. Depressive symptoms at 90 days were assessed using the Hamilton Depression Rating Scale (HAMD), and elevated post-stroke depressive symptoms were defined according to the prespecified HAMD threshold. Multivariable logistic regression, restricted cubic spline (RCS) analysis, and subgroup analyses were conducted to examine the association between NHHR and elevated depressive symptoms.ResultsAmong the 518 patients, 179 exhibited elevated post-stroke depressive symptoms at 90 days. Higher NHHR levels were independently associated with increased odds of elevated depressive symptoms after adjustment for demographics, stroke severity, cognitive function, inflammatory markers, and coagulation parameters (OR = 1.35, P = 0.029). Dose–response trends were observed, and RCS analysis suggested a linear relationship without significant nonlinearity. No significant interactions were found across sex, smoking, alcohol use, hypertension, or diabetes subgroups.ConclusionsElevated NHHR at admission was independently associated with elevated post-stroke depressive symptoms at 90 days in Chinese AIS patients. As a simple, cost-effective, and readily obtainable biomarker, NHHR may facilitate early risk stratification and guide individualized interventions.
Cliramitug for depletion of cardiac amyloid transthyretin: long-term follow-up of the NI006-101 trial
Nature Medicine, Published online: 26 June 2026; doi:10.1038/s41591-026-04487-3
In an open-label extension study of the NI006-101 trial, in which patients with transthyretin amyloidosis cardiomyopathy were treated with the monoclonal antibody cliramitug, which targets misfolded transthyretin, long-term follow-up for a median of 29.3 months showed favorable safety, further reduction in cardiac amyloid burden and improvements in structural, functional and biomarker endpoints.
Pump You Up: Epigenetic Editor Drives Muscle Growth in FSHD Patients
A first-in-human clinical trial of an experimental epigenetic therapy has produced an outcome long considered elusive in the progressive genetic muscle degeneration disease facioscapulohumeral muscular dystrophy (FSHD): measurable gains in lean muscle volume.
Epicrispr Biotechnologies announced updated interim results from its ongoing Phase I/II study of EPI-321, reporting that all three FSHD patients who reached the six-month evaluation point experienced increases in lean muscle mass following a single intravenous (IV) infusion of the therapy. The findings represent what the company says is the first clinical evidence that a treatment may be capable of increasing muscle volume in FSHD, which affects an estimated 870,000 people worldwide and is one of the most common forms of muscular dystrophy.
Although the study remains in its early stages with only nine total patients across two dosing cohorts, the promising findings were supported by imaging data, functional measurements, and biomarker changes that together suggest the therapy may be altering the biological drivers of the disease.
FSHD lacks disease-modifying treatments to intervene in the characteristic loss of skeletal muscle function, starting in the face, shoulders, and upper arms and spreading throughout the body. “We were pretty blown away,” Epicrispr CEO Amber Salzman told Inside Precision Medicine. “Every single patient gained lean muscle volume. No one has seen that.”
A silencing GEM
FSHD is caused by hypomethylation of the D4Z4 region, which is a polymorphic variable number tandem repeat (VNTR) array made up of 3.3 kilobase units, with each unit encoding the DUX4 gene. This hypomethylation leads to abnormal activation of the DUX4 gene, triggering muscle cell death and tissue degeneration, resulting in progressive muscle weakness, asymmetry, and fat infiltration.
Unlike conventional gene-editing therapies that permanently alter DNA sequences, Epicrispr’s therapy uses their Gene Expression Modulation System (GEMS). Epicrispr’s EPI-321 is an adeno-associated virus (AAV)-delivered epigenetic gene therapy that restores D4Z4 methylation and suppresses DUX4 expression, and preclinical studies show improved muscle function and reduced muscle cell death. “We don’t cut DNA,” Salzman said. “We’re not using Cas9 in the traditional editing sense. We use a dead Cas protein and an epigenetic mechanism to silence the gene.”
According to Epicrispr, preclinical studies demonstrated that the epigenetic modifications persist through many rounds of cell division, suggesting long-term suppression may be possible after a single treatment.
In addition to the therapeutic innovation, Epicrispr has also made headway into the long-standing challenge of identifying reliable molecular biomarkers for FSHD to evaluate activity downstream of DUX4. Traditional muscle biopsies provide information from only a small tissue sample and can be difficult to interpret when substantial fat replacement has already occurred.
Instead, Epicrispr collaborated with researchers at the University of Colorado investigating circulating cell-free DNA signatures associated with DUX4-driven disease activity. The biomarker, known as CBT17, emerged from studies comparing blood samples from approximately 50 healthy individuals and 50 FSHD patients. The biomarker may provide a whole-body readout of disease activity, offering evidence that DUX4 suppression is occurring beyond individual muscles examined through biopsy.
As of the May 12, 2026 data cutoff, EPI-321 has demonstrated a manageable safety profile, with nine patients treated across two dose cohorts. One IV infusion was administered to six patients in the first cohort at a target dose of 2×10¹³ vg/kg and to three patients in the second cohort at a target dose of 4×10¹³ vg/kg.
The company reported a “manageable” safety profile, with no severe dose-limiting toxicities disclosed to date. Salzman was careful not to overstate the findings. “I never want to be hubristic when it comes to AAV,” she said. “You’re giving people a lot of virus.”
She described the treatment experience as somewhat analogous to vaccine-related immune responses, noting that prophylactic immunosuppression is used as part of the protocol. “It’s manageable,” she said. “I’m not going to say it’s favorable. It’s manageable.” No serious grade 3 or grade 4 treatment-related safety concerns have been reported so far.
Baseline levels of CBT17 in treated patients fell substantially after therapy. “When we looked at baseline and three months, our patients’ levels came down so that it was just about in the healthy range,” she said.
Functional measures match MRI
The headline result comes from whole-body MRI analyses conducted six months after treatment. Among the first three evaluable patients, lean muscle volume increased in every individual compared with baseline measurements. On average, patients gained approximately 370 milliliters of lean muscle tissue, equivalent to roughly 0.8 pounds of muscle mass. Individual gains ranged from about 0.5 to 1.3 pounds. For a disease characterized by chronic muscle loss, even stabilizing muscle mass would be considered a meaningful achievement. Actual gains are potentially more significant.
The MRI analyses were performed in collaboration with Springbok Analytics, a company specializing in AI-powered muscle imaging. In addition to being widely used in professional sports to monitor injury recovery and rehabilitation, as well as a growing number of clinical settings, FSHD is one of Springbok’s primary clinical focus areas, along with disorders such as Charcot-Marie-Tooth disease and Duchenne muscular dystrophy. Using a specialized MRI protocol that runs on standard scanners, the company can quantify changes across as many as 140 individual muscles throughout the body, measuring muscle volume, muscle composition, and fat infiltration, producing objective data on muscle health.
According to Salzman, Springbok compared patients’ baseline scans against machine-learning models trained on longitudinal imaging datasets from more than 100 FSHD patients. “They can predict what it’s going to look like in six months—how much muscle patients will lose and where,” she said. “Then we sent them the six-month scans, and every patient gained lean muscle volume.”
The gains were not evenly distributed throughout the body. Patients appeared to benefit most in muscles that still retained substantial healthy tissue before treatment. “If you have too much fat in that muscle, there’s not that much we can do,” Salzman explained. “But if you have some residual muscle, it can not only stop getting worse because we’re cutting off the poison, but you can also regenerate muscle.”
One participant, a post-menopausal woman, gained approximately 1.3 pounds of lean muscle mass according to the MRI analysis. The increase was particularly notable because age-related muscle loss would normally be expected in that population. Earlier data released by Epicrispr showed favorable trends across several functional and strength assessments at the three-month mark. These measures included tests commonly used in neuromuscular disease studies, such as the Timed Up and Go test, 10-meter walk/run assessments, and quantitative muscle testing.
Because many functional endpoints depend on patient effort, interpreting changes in small open-label studies can be challenging. Investigators and investors alike often question whether participants simply perform better because they know they received treatment. Salzman acknowledged those concerns. “You could say maybe they had a good day, and that’s why the functional measures were better,” she said.
MRI measurements, however, are not subject to effort-dependent variability. “You can’t fake MRI,” she added. Importantly, the regions showing muscle growth on imaging appeared to correspond with areas where patients demonstrated improved performance.
One participant who gained substantial upper-body muscle volume showed corresponding gains on upper-body strength assessments, while lower-body improvements were more limited in areas where muscle loss had already become severe. “The whole story is holding together,” Salzman said. “Their functional measures are getting better; their lean muscle is increasing.”
Taken together, the MRI, biomarker, and functional data provide multiple independent signals supporting biological activity. “You can’t fake blood; you can’t fake MRIs,” Salzman said. “Those are totally supportive.”
Epitome of epigenetic editing?
Beyond FSHD, researchers are closely watching EPI-321 because it represents one of the first clinical tests of an epigenetic CRISPR-based therapeutic strategy. “This is an important milestone not just for FSHD but for epigenetic medicine,” Salzman said. The approach could potentially be adapted to other diseases caused by harmful gene activation or insufficient gene silencing.
Still, significant hurdles remain before EPI-321 can be considered a proven therapy. The current dataset includes only three patients with six months of follow-up. Epicrispr plans to present additional data from six patients at the World Muscle Society Annual Congress in September 2026.
The company expects the initial Phase I/II study to complete enrollment and generate a full readout in mid-2027. Yet, larger studies will be needed to determine whether muscle gains persist, whether they translate into meaningful long-term functional benefits, and whether safety remains acceptable over time.
Regulators will also require evidence linking increases in lean muscle volume to tangible improvements in patient function and quality of life. To that end, Epicrispr is preparing discussions with the U.S. Food and Drug Administration regarding potential future endpoints and whether MRI-based muscle measurements could eventually serve as surrogate markers of clinical benefit.
Whether those early signals hold up in larger populations remains to be seen. But for a disease in which muscle loss has long been viewed as inevitable, the possibility of reversing that trend, even modestly, marks a notable moment for the FSHD field.
The post Pump You Up: Epigenetic Editor Drives Muscle Growth in FSHD Patients appeared first on Inside Precision Medicine.
Light Sensor Detects Ultra-Low Levels of Traumatic Brain Injury Biomarkers
Researchers in China have developed a biosensor chip that uses light to detect extremely low concentrations of biomarkers of traumatic brain injury (TBI) at concentrations as low as femtograms per milliliter. The technology could one day be used to make faster diagnoses after a head injury, helping doctors choose the best treatment course and providing early warning of complications.
“Although several biomarkers have been validated as indicators of TBI, current methods for measuring them are time-consuming and require multiple complex laboratory steps,” said Guangyuan Li, PhD, professor at the Beijing Institute of Technology. “To address this challenge, we developed metasurface biosensors that are exceptionally sensitive, allowing them to produce clear, reliable optical signals even when only tiny amounts of a biomarker are present.”
The biosensor achieves its sensitivity thanks to metasurfaces—ultra-thin materials with microscopic patterns etched on them, which enable the device to manipulate light very precisely. For this study, Li and colleagues coated a gold metasurface with antibodies that specifically target TBI biomarkers. When the target molecules bind to the antibodies on the metasurface, the light wavelengths it reflects change slightly, indicating the presence of the biomarker even at extremely low concentrations.
To test this approach, the researchers built two separate sensors targeting two key biomarkers of TBI: the glial fibrillary acidic protein (GFAP) and S100 calcium-binding protein β (S100β). Results showed that the sensor could accurately detect subtle wavelength shifts depending on the biomarker concentration, with a sensitivity as low as under a femtogram per milliliter. This response was highly sensitive to the target biomarker, even when other biomarkers were present in the sample.
In recent years, light-based sensors have been increasingly gaining traction as diagnostic tools thanks to their potential to make biomarker detection much more precise compared to conventional methods, with promising applications currently being explored in early cancer diagnosis and real-time monitoring of diabetes.
However, more work will be needed before this technology can be routinely used in a clinical setting. With further development, the platform could be adapted to create metasurface sensors capable of detecting multiple biomarkers simultaneously to offer a more complete picture of a patient’s state in a short period of time. Going forward, Li and colleagues plan to continue working to reduce the costs of manufacturing the sensor, adapting fluid handling and packaging for clinical use, and ultimately validating the technology in clinical trials to assess its performance in a real-world setting.
“If developed into a point‑of‑care format, this technology could help provide faster and accurate answers after brain injury—perhaps using just a finger prick,” said Yunhui Liu, PhD, associate professor at the Shenzhen Institutes of Advanced Technology. “This could potentially reduce unnecessary CT scans for low‑risk cases while flagging higher‑risk patients earlier. It could also enable more accessible biomarker detection in ambulances, rural clinics, sports settings or emergency departments where time matters.”
The post Light Sensor Detects Ultra-Low Levels of Traumatic Brain Injury Biomarkers appeared first on Inside Precision Medicine.
Enhancing hematoma expansion prediction in hypertensive intracerebral hemorrhage based on habitat and perihematomal edema radiomics from non-contrast CT: a dual-center study
ObjectivesCharacterizing the microenvironmental habitats within the hematoma may yield crucial imaging biomarkers and improve the early prediction of hematoma expansion (HE) in patients with hypertensive intracerebral hemorrhage (HICH). Our objective was to construct and validate a combined model that integrates clinical data with whole-hematoma radiomics, habitat radiomics of the hematoma, and perihematomal edema (PHE) radiomics features extracted from non-contrast computed tomography (NCCT) images for preoperative HE prediction.MethodsThis retrospective dual-center cohort of 353 HICH patients. Based on baseline NCCT images, radiomics features were extracted from the whole hematoma, three distinct habitats within the hematoma, and the PHE region. Five models were constructed: a clinical model, a whole-hematoma radiomics model, a habitat-based radiomics model, a PHE radiomics model, and a combined model. Model performance was evaluated using receiver operating characteristic (ROC) curve analysis.ResultsThe combined model integrated with smoking history, island sign, maximum distance of the PHE, and the whole-hematoma, habitat, and PHE radiomics models, achieved the best predictive performance. In the training, testing, and validation sets, the combined model predicted the area under the curve for HE as 0.951 (95% CI: 0.915–0.986), 0.937 (95% CI: 0.883–0.991), and 0.939 (95% CI: 0.888–0.989), respectively.ConclusionThe NCCT-based combined model integrating clinical data, whole-hematoma radiomics, habitat radiomics, and PHE radiomics improves HE prediction in patients with HICH, providing a noninvasive tool with potential for guiding treatment strategies.