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Real-time closed-loop control of neuromodulation devices requires long-term monitoring of neural activity in the peripheral nervous system. Although many signal extraction methods exist, few are both clinically viable and designed for extracting small signals from fragile peripheral visceral nerves. Here, we report that our minimally invasive recording and analysis technology extracts low to negative signal to noise ratio (SNR) neural activity from a visceral nerve with a high degree of specificity for fiber type and class. Complex activity was recorded from the rat pelvic nerve that was physiologically evoked during controlled bladder filling and voiding, in an extensively characterized model that provided an excellent test bed to validate our technology. Urethane-anesthetized male rats (n = 12) were implanted with a four-electrode planar array and the bladder instrumented for continuous-flow cystometry, which measures urodynamic function by recording bladder pressure changes during constant infusion of saline. We demonstrated that differential bipolar recordings and cross-correlation analyses extracts afferent and efferent activity, and discriminated between subpopulations of fibers based on conduction velocity. Integrated Aδ afferent fiber activity correlated with bladder pressure during voiding (r: 0.66 ± 0.06) and was not affected by activating nociceptive afferents with intravesical capsaicin (r: 0.59 ± 0.14, = 0.54, and n = 3). Collectively, these results demonstrate our minimally invasive recording and analysis technology is selective in extracting mixed neural activity with low/negative SNR. Furthermore, integrated afferent activity reliably correlates with bladder pressure and is a promising first step in developing closed-loop technology for bladder control.
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http://dx.doi.org/10.1063/5.0164951 | DOI Listing |
J Craniofac Surg
September 2025
Department of Oral and Maxillofacial Surgery, University of Ulsan Hospital, University of Ulsan College of Medicine.
This study aimed to develop a deep-learning model for the automatic classification of mandibular fractures using panoramic radiographs. A pretrained convolutional neural network (CNN) was used to classify fractures based on a novel, clinically relevant classification system. The dataset comprised 800 panoramic radiographs obtained from patients with facial trauma.
View Article and Find Full Text PDFTraffic Inj Prev
September 2025
School of Safety Engineering, Beijing Institute of Petrochemical Technology, Beijing, China.
Objective: To clarify the potential risks and causative mechanisms of glare from nighttime road fill lights on driving safety, this study investigates the dual interference of glare-induced visual cognitive load and physiological stress.
Methods: A field driving experiment involving 20 drivers was conducted, with real-time collection of visual data (e.g.
J Appl Physiol (1985)
September 2025
Women's Heart Health Laboratory, Institute for Exercise and Environmental Medicine at Texas Health Presbyterian Hospital Dallas, TX, USA.
We investigated the impact of short-term dietary nitrate supplementation on sympathetic neural responses to isometric exercise in postmenopausal women. Ten healthy women aged 64±2 (SD) years participated in this randomized, placebo-controlled, double-blind, crossover study. All participants underwent two-week beetroot juice (BRJ: 800 mg nitrate/day) and placebo (nitrate-depleted BRJ) interventions with ≥14 days of wash-out.
View Article and Find Full Text PDFSci Adv
September 2025
State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Science, Beijing 100101, China.
Insects, unlike vertebrates, use heteromeric complexes of odorant receptors and co-receptors for olfactory signal transduction. However, the secondary messengers involved in this process are largely unknown. Here, we use the olfactory signal transduction of the aggregation pheromone 4-vinylanisole (4VA) as a model to address this question.
View Article and Find Full Text PDFPLoS Comput Biol
September 2025
Faculty of Science, Cognitive and Systems Neuroscience Group, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, the Netherlands.
Predictive coding (PC) proposes that our brains work as an inference machine, generating an internal model of the world and minimizing predictions errors (i.e., differences between external sensory evidence and internal prediction signals).
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