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.
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.”
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Transcutaneous auricular vagus nerve stimulation: mechanisms, applications, and research progress
This review systematically examines the mechanisms of action, optimization of stimulation parameters and targets, and the research progress in the application of taVNS for neurological disorders and systemic conditions. Rather than merely cataloging existing findings, this review critically synthesizes recent advances with a focused emphasis on two core aspects: (1) the mechanistic convergence and divergence among neuroimaging, autonomic, and molecular pathways underlying taVNS effects; and (2) the methodological challenges and biomarker-driven strategies for optimizing stimulation parameters and personalizing treatment. By prioritizing these key directions over exhaustive enumeration of clinical applications, this review aims to provide a conceptually structured framework that distinguishes genuine progress from descriptive accumulation. Moreover, compared with previously published reviews on similar topics, the present work offers a distinctive contribution by integrating multi-modal mechanistic evidence into a testable model of taVNS action and critically evaluating the translational gap between parametric optimization in research settings and standardized clinical implementation. Furthermore, it provides an outlook on future research directions and technological developments, aiming to offer a comprehensive theoretical framework to inform both clinical translation and foundational research in this rapidly evolving field.
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.
Contemporary pharmacological strategies for acute peripheral facial palsy: a narrative review with clinical decision considerations
Acute peripheral facial palsy (APFP) sometimes known as Bell’s palsy is a common neurological condition and is marked by the acute onset of lower motor weakness on one side of the face. Whereas spontaneous recovery is widespread, there is a significant rate of incomplete recovery, synkinesis, or enduring functional and psychosocial disability of patients. The past decades witnessed the improvement of the diagnostic and therapeutic plans, particularly the pharmacological and adjunctive ones, due to the developments in pathophysiological and clinical trials and the creation of guidelines. This narrative review summarizes the existing evidence on the classification, diagnosis, and treatment of APFP, particularly corticosteroid disease treatment, antiviral medication, combination therapy, adjunct, and rehabilitative therapies, and the future of precision medicine. Randomised controlled trials and high-quality systematic reviews have shown evidence in support of the early initiation of systemic corticosteroids within 72 h of symptom onset as the foundation of treatment practice, improving the likelihood of achieving full facial nerve recovery and less morbidity in the long run. Conversely, antiviral monotherapy has not demonstrated significant clinical benefit with combination therapy with antivirals potentially presenting some benefit to older patients and with severe cases of paralysis. New data highlight the significance of risk stratification, electrophysiological testing, and focal rehabilitation to maximize the results and reduce the sequelae. The developments in artificial intelligence, the work on biomarkers and adaptive clinical trial designs will likely enable more personalized prognostication and treatment choice. In general, a shift towards precision risk-based approaches to the management of acute peripheral facial palsy is also being considered and emerging diagnostic strategies, promotion of the use of corticosteroids as early as possible and focused adjunctive operations that are tailored to a child are becoming the key to improving functional outcomes in the long term.
Bridging the gap from mechanism to clinic: a translational perspective on taVNS for gastrointestinal disorders
In recent years, multiple clinical and translational studies have investigated the application and mechanisms of transcutaneous auricular vagus nerve stimulation (taVNS) in gastrointestinal disorders (GIDs), with consideration given to pathophysiology, stimulation parameters, and patient-specific factors. In this review, we systematically synthesize recent evidence from clinical trials and preclinical models published in leading gastroenterology and neurology journals. Our focus is on taVNS-mediated modulation of the brain–gut axis, particularly its role in improving autonomic balance, reducing visceral sensitivity, and attenuating inflammatory responses, with the aim of enhancing therapeutic outcomes in functional and inflammatory GIDs. There is a need to optimize stimulation protocols through mechanistic insights and to promote the use of this non-invasive, well-tolerated neuromodulation approach. These advances are essential for expanding taVNS accessibility in clinical practice, especially for patients with refractory symptoms, comorbid psychological conditions, and in settings where conventional treatments are limited or contraindicated. Personalized taVNS strategies and biomarker-guided dosing represent emerging trends in neuromodulation therapy. However, standardized protocols and predictive models have yet to be established for widespread clinical implementation.
Editorial: Advancing neurodegenerative disease biomarkers: the role of neuroimaging in TDP-43 and tau proteinopathies
Vibrating Pill Predicts Anorexia Relapse
A vibrating capsule that assesses how nerve signals from the stomach are registered by the brain could help identify patients with anorexia at risk of relapse, a clinical trial suggests.
The findings, in JAMA Psychiatry, offers an insight into how the eating disorder affects the nervous system as well as a way to personalize and assess the success of treatment.
The results suggest that disruptions in gastrointestinal (GI) interoception—involving the detection, transmission, and processing of signals in the digestive tract—can increase the risk of anorexia returning, even if weight has normalized.
Gastric signals could therefore act as scalable biomarkers to identify who is at increased risk of relapse and track whether treatment is improving gut-brain signaling.
“People with anorexia nervosa do not simply ignore signals from the body,” explained researcher Sahib Khalsa, PhD, at the University of California, Los Angeles.
“Rather, their nervous system may process gut sensations differently, making those signals harder to detect, trust and learn from. Over time, that may contribute to the persistence of symptoms even after weight is restored.”
Khalsa and co-workers asked 62 girls and women with anorexia nervosa whose weight had been restored to healthy levels to swallow the vibrating capsule in their crossover trial.
The capsule could be remotely controlled by the researchers to produce mechanosensory stimulation of different intensities in the stomach.
Participants were asked to press a button upon sensing a vibration, with 54 patients followed up at six months after discharge from hospital and their results compared with 57 healthy control individuals.
Computational modeling was used to estimate how strongly participants anticipated feeling sensations in their stomachs, how heavily their brains depended on body signals, and how quickly they adjusted their expectations as to the presence or absence of signals.
The researchers found that participants with anorexia had abnormal GI interoception across behavioral and computational domains compared with healthy participants, despite having intact neural and physiological responses.
Participants with anorexia were less able to detect subtle stomach sensations and were more likely than others to believe there were no sensations when the pill was vibrating. They were also less likely to update those expectations when gastric signals occurred.
Patients who were more likely to ignore the stomach signals were also at increased likelihood of relapsing to the eating disorder.
“One of the most striking findings was that these differences persisted even after weight restoration,” said Khalsa.
“Recovery from anorexia nervosa isn’t just about restoring body weight. The underlying brain–body communication problems may remain and could contribute to relapse.”
The researchers conclude that their results support the use of ingestible mechanosensory probes and computer modeling as scalable tools to monitor treatment response and guide relapse prevention in eating disorders.
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