Biophysical Journal

Understanding how cells migrate through confined environments is crucial for elucidating fundamental biological processes, including cancer invasion, immune surveillance, and tissue morphogenesis. The nucleus, as the largest and stiffest cellular organelle, often limits cellular deformability, making it a key factor in migration through narrow pores or highly constrained spaces. In this work, we introduce a geometric surface partial differential equation (GS-PDE) model in which the cell plasma membrane and nuclear envelope are described as evolving energetic closed surfaces governed by force-balance equations. We replicate the results of a biophysical experiment, where a microfluidic device is used to impose compressive stresses on cells by driving them through narrow microchannels under a controlled pressure gradient. The model is validated by reproducing cell entry into the microchannels. A parametric sensitivity analysis highlights the dominant influence of specific parameters, whose accurate estimation is essential for faithfully capturing the experimental setup. We found that surface tension and confinement geometry emerge as key determinants of translocation efficiency. Although tailored to this specific setup for validation purposes, the framework is sufficiently general to be applied to a broad range of cell mechanics scenarios, providing a robust and flexible tool for investigating the interplay between cell mechanics and confinement. It also offers a solid foundation for future extensions integrating more complex biochemical processes such as active confined migration.

Network-based epidemic models have been instrumental in understanding how contact structure shapes infectious disease dynamics, yet widely used frameworks such as Erd[o]s-Renyi, configuration-model, and stochastic block networks do not explicitly capture the combination of fully accessible (saturated) within-group interactions and constrained between-group connectivity characteristic of many real-world settings. Here, we introduce the Multi-Clique (MC) network model, a generative framework in which individuals are organised into fully connected cliques representing stable contact groups (e.g., households, classrooms, or workplaces), with a limited number of external connections governing inter-group transmission. Using stochastic susceptible-infectious-recovered (SIR) simulations on degree-matched networks, we compare epidemic dynamics on MC networks with those on classical random graph models. Despite having an identical mean degree, MC networks exhibit systematically distinct behaviour, including slower epidemic growth, reduced peak prevalence, increased fade-out probability, and delayed time to peak. These effects arise from rapid within but constrained between clique transmission, creating structural bottlenecks that standard models do not capture. The MC framework provides an interpretable, data-driven representation of recurrent contact structure, with parameters that map directly to observable quantities such as household and classroom sizes. By isolating the role of intergroup connectivity, the model offers a basis for evaluating targeted intervention strategies that reduce between-group mixing while preserving within-group interactions. Our results highlight the importance of explicitly representing the real-life clique-based network structure in epidemic models and suggest that classical degree-matched networks may systematically overestimate epidemic speed and intensity in structured populations.

While the need for continuous blood pressure (BP) monitoring in Japan is high, there are no commercially available cuffless devices for personal daily monitoring use. Fingertip-based sensors are a promising alternative as they eliminate the discomfort of repeated cuff inflation. However, their reliability during winter has been a major technical limitation due to cold-induced peripheral vasoconstriction. This study aimed to address this issue by validating a novel fingertip-based continuous BP monitor used by exercising adults during summer and winter. Eleven community-dwelling older adults (mean age, 73.1 {+/-} 8.8 years) were included in this seasonal comparative study. During exercise, we compared a personal fingertip-based continuous monitor (ArteVu) with a standard oscillometric cuff device (Omron) in summer (mean, 26.5{degrees}C) and winter (mean, 7.4{degrees}C). The study also evaluated the device's accuracy during exercise-induced BP fluctuations and seasonal environmental changes. Awareness of the participants regarding BP management was also assessed using questionnaires. There were strong correlations for systolic BP (SBP) between summer and winter (r = 0.93 in summer; r = 0.88 in winter). Although the mean difference for the SBP was higher in winter than in summer (3.1 {+/-} 11.2 mmHg vs. 0.2 {+/-} 9.4 mmHg), the values remained within a clinically acceptable range for personal monitoring. Notably, 72.7% of participants reported that the ease of using the fingertip-based device significantly increased their awareness and motivation for daily BP management. This study confirms the feasibility of cuffless fingertip-based continuous BP monitoring across different seasons, including in winter. By overcoming the seasonal limitations, this device fills a critical gap in the Japanese health-monitoring market. Our findings support the development of smaller and more portable models, representing a shift from traditional "snapshot" cuff measurements to continuous and integrated lifestyle monitoring for older adults.

Fluorescent in situ hybridization (FISH) enables highly sensitive, high-resolution detection of gene transcripts. Moreover, by employing multiple probes, this technique allows for multiplexed, simultaneous detection of distinct gene expression patterns spatiotemporally, making it a valuable spatial transcriptomics approach. Owing to these advantages, FISH techniques are rapidly being adopted across diverse areas of basic biology. However, conventional protocols often rely on volatile, toxic reagents such as formalin or methanol, posing potential health risks to researchers. Here, we present a safer protocol that replaces these chemicals with low-toxicity alternatives, without compromising the high detection sensitivity of FISH. We validated this protocol using both in situ hybridization chain reaction (HCR) and signal amplification by exchange reaction (SABER)-FISH in frozen sections of various model organisms, including mouse (Mus musculus), amphibians (Xenopus laevis and Pleurodeles waltl), and medaka (Oryzias latipes). Our results demonstrate successful multiplexed detection of morphogenetic and cell-type marker genes in these model animals using this safer protocol. The protocol has the additional advantage of requiring no proteolytic enzyme treatment, thus preserving tissue integrity. Furthermore, we show that this protocol is fully compatible with EGFP immunostaining, allowing for the simultaneous detection of mRNAs and reporter proteins in transgenic animals. This protocol retains the benefits of highly sensitive, multiplexed, and multimodal detection afforded by integrating in situ HCR and SABER-FISH with immunohistochemistry, while providing a safer option for researchers, thereby offering a valuable tool for basic biology.

Infectious disease surveillance systems in tropical countries show that respiratory disease incidence generally manifests as year-round activity with weak fluctuations and irregular seasonality. Previously, using a ten-year time series of influenza-like illness (ILI) collected from outpatient clinics in Ho Chi Minh City (HCMC), Vietnam, we found a combination of nonannual and annual signals driving these dynamics, but with unknown mechanisms. In this study, we use seven stochastic dynamical models incorporating humidity, temperature, and school term to investigate plausible mechanisms behind these annual and nonannual incidence trends. We use iterated filtering to fit the models and evaluate the models by comparing how well they replicate the combination of annual and nonannual signals. We find that a model including specific humidity, temperature, and school term best fits our observed data from HCMC and partially reproduces the irregular seasonality. The estimated effects from specific humidity and temperature on transmission are nonlinearly negative but weak. School dismissal is associated with decreased transmission, but also with low magnitude. Under these weak external drivers, we hypothesize that stochasticity makes a strong sub-annual cycle more likely to be observed in ILI disease dynamics. Our study shows a possible mechanism for respiratory disease dynamics in the tropics. When the external drivers are weak, the seasonality of respiratory disease dynamics is prone to the influence of stochasticity.

Brain activity comprises both rhythmic (periodic) and arrhythmic (aperiodic) components. These signal elements vary across healthy aging, and disease, and may make distinct contributions to conscious perception. Despite pioneering techniques to parameterize rhythmic and arrhythmic neural components based on power spectra, the methodology for quantifying rhythmic activity remains in its infancy. Previous work has relied on parametric estimates of rhythmic power extracted from specparam, or estimates of rhythmic power obtained after detrending neural spectra. Variation in analytical choices for isolating brain rhythms from background arrhythmic activity makes interpreting findings across studies difficult. Whether these current approaches can accurately recover the independent contribution of these neural signal elements remains to be established. Here, using simulation and parameter recovery approaches, we show that power estimates obtained from detrended spectra conflate these two neurophysiological components, yielding spurious correlations between spectral model parameters. In contrast, modelled rhythmic power obtained from specparam, which detrends the power spectra and parametrizes brain rhythms, independently recovers the rhythmic and arrhythmic components in simulated neural time series, minimising spurious relationships. We validate these methods using resting-state recordings from a large cohort. Based on our findings, we recommend modelled rhythmic power estimates from specparam for the robust independent quantification of rhythmic and arrhythmic signal components for cognitive neuroscience.

Class imbalance in clinical electrocardiogram (ECG) datasets limits the diagnostic sensitivity of automated arrhythmia classifiers, particularly for rare but clinically significant beat types. We propose a three-stage hybrid generative pipeline that combines a spectral-guided conditional Variational Autoencoder (cVAE), a class-conditional latent Denoising Diffusion Probabilistic Model (DDPM), and a Quantum Latent Refinement (QLR) module built on parameterized quantum circuits to augment minority arrhythmia classes in the MIT-BIH Arrhythmia Database. The QLR module applies a bounded residual correction guided by Maximum Mean Discrepancy minimization to align synthetic latent distributions with real class-specific latent banks. A lightweight 1D MobileNetV2 classifier evaluated over five independent random seeds and four augmentation ratios serves as the downstream benchmark. Our findings establish latent diffusion augmentation as an effective strategy for imbalanced ECG classification and motivate further investigation of quantum-classical hybrid methods in cardiac diagnostics.

Objective detection of evoked potentials (EPs) is central to digital diagnostics in hearing assessment and clinical neurophysiology, yet current approaches remain time-intensive and sensitive to inter-individual noise variability. Many existing detection methods rely on population-based assumptions or computationally demanding procedures, limiting robustness and efficiency in real-world clinical settings. We present Fmpi, a digital EP detection framework enabling individualised, real-time response detection through analytical modelling of the spectral colour and temporal dynamics of background noise within each recording. Using extensive simulations and large-scale human electroencephalography datasets spanning brainstem, steady-state, and cortical EPs recorded in adults and infants, we demonstrate performance comparable or superior to state-of-the-art bootstrapped methods while operating at a fraction of the computational cost and maintaining well-controlled sensitivity with improved specificity. Importantly, Fmpi incorporates a futility detection mechanism enabling early termination of uninformative recordings, reducing testing time without compromising diagnostic reliability.

Infectious disease dynamics are strongly shaped by human mobility, social structure, and heterogeneous contact patterns, yet many epidemic models do not jointly capture these features. This study develops a spatial metapopulation epidemic model incorporating recurrent group-switch interactions to represent real-world transmission processes. Building on the Movement-Interaction-Return framework, the model integrates household structure, age-stratified contacts, and mobility between locations within a single SEIR framework. Using UK demographic, mobility, and social contact data, the model quantifies how within- and between-group interactions, mobility rates, and location connectivity influence epidemic spread. Both deterministic and stochastic simulations are implemented to analyse outbreak dynamics, variability, and fade-out probabilities for COVID-19-like and Ebola-like infections. Results shows that highly connected locations drive faster transmission, earlier epidemic peaks, and greater difficulty in containment, whereas larger but less connected locations tend to produce slower, more localised outbreaks despite their population size. Comparative analysis reveals that COVID-19-like infections spread rapidly and remain difficult to control even under interventions, while Ebola-like infections exhibit slower dynamics and are more effectively contained, particularly under targeted measures. Non-pharmaceutical interventions, particularly widespread closures, substantially reduce infections, hospitalisations, and deaths, although effectiveness depends on timing and pathogen characteristics. These findings highlight the importance of integrating mobility, clustering, and demographic heterogeneity to inform targeted and effective epidemic control strategies.

Symptom propagation occurs when the symptoms of secondary cases are related to those of the primary case as a result of epidemiological mechanisms. Determining whether - and to what extent - symptom propagation occurs requires data-driven methods. Here we quantify the strength of symptom propagation as the increase in risk of a secondary case developing severe symptoms if the primary case has severe symptoms. We first used synthetic results to determine the data requirements to robustly estimate the strength of symptom propagation and to investigate the effect of severity-dependent reporting bias. Categorising symptom severity into two group (mild or severe; asymptomatic or symptomatic), our estimation requires only four summary statistics - the number of primary-secondary case pairs of each combination of symptom presentations. Our analysis showed that a relatively small number (100) of synthetic primary-secondary case pairs was sufficient to obtain a reasonable estimate of the strength of symptom propagation and 1,000 pairs meant errors were consistently small across replicates. Our estimates were robust to severity-dependent reporting bias. We also explored how symptom propagation can be separated from other individual-level factors affecting severity, using age dependence as an example. Although synthetic data generated from an age-structured model led to overestimations of the strength of symptom propagation, allowing disease severity to be age-dependent restored the accuracy of parameter estimation. Finally, we applied our methodology to estimate the strength of symptom propagation from three publicly available data collected during the COVID-19 pandemic with data on presence or absence of symptoms: England households, Israel households, and Norway contact tracing. Our age-free methodology indicated a 12-17% increase in the risk of being symptomatic if infected by someone symptomatic. Our positive estimates for the strength of symptom propagation persisted when applying our age-dependent methodology to the two household data sets with age-structured information (England and Israel). These findings demonstrate evidence for symptom propagation of SARS-CoV-2 and provide consistent estimates for its strength. Our synthetic data analysis supports the conclusion that these correlations are not a result of reporting bias or age-dependent effects. This work provides a practical tool for estimating the strength of symptom propagation that has minimal data requirements, enabling application across a wide range of pathogens and epidemiological settings.

How embryonic cells generate large clones of cells in the adult represents a fundamental question in biology. Here, using melanocyte stem cells (McSCs) in the zebrafish as a model, we explore the function of the master melanocyte transcription factor (MITF) in safeguarding McSCs in embryonic development and their potential to pigment large clones in the adult. MITF is well known is for its role in the specification of melanoblasts from the neural crest (NC) and their differentiation into melanocytes, yet little is known about how this activity shapes the stem cell lineages. Here, we use live imaging coupled with single-cell transcriptomics and lineage tracing to show that MITF (mitfa in zebrafish) protects the melanocyte stem cell (McSC) fate in zebrafish. Utilizing a temperature sensitive mitfavc7 mutant, we show loss of Mitfa leads to a surprising premature and aberrant expansion of McSC progeny at the niche during embryogenesis, coupled with novel emergent transcriptional cell states. Linage tracing of McSCs from the embryonic to juvenile stages reveals Mitfa activity is subsequently required in regeneration by Schwann cell-like and melanocyte stem cell progenitors that serve as a reservoir for fast-responding pigment progenitors. Thus, the impact of Mitfa loss on the melanocyte lineage is cell-state and stage-specific. The emergent cell states upon mitfa loss may have important implications for our understanding the loss of MITF activity in human genetic disease and melanoma.

ObjectivesEpidural spinal cord stimulation (SCS) is an emerging therapy for motor rehabilitation following spinal cord injury (SCI) and other motor disorders. Conventionally, SCS leads are placed along the dorsal spinal cord (SCSD), where stimulation activates large diameter afferent fibers, which indirectly activate motoneurons through reflex pathways. This leads to broad activation of flexor and extensor muscles and limited fine-tuned control of motor output. Targeting the ventral spinal cord (SCSV) may enable more direct activation of motoneuron pools, potentially improving the specificity of muscle activation; however, there is currently no established method to place leads ventrally. To address this, we evaluated the feasibility of four modified percutaneous implantation techniques to target the ventrolateral thoracolumbar spinal cord. Materials and methodsPercutaneous SCSV implantation was performed in three human cadaver torso specimens under fluoroscopic guidance. The following approaches were evaluated: sacral hiatus, transforaminal, interlaminar contralateral, and interlaminar ipsilateral. The leads in the latter 3 approaches were inserted between L1 and L5. Eighteen implants were attempted, with nine leads retained for analysis. Lead and electrode position were assessed using computed tomography (CT) with three-dimensional reconstruction, along with anatomical dissection to verify lead and electrode placement within the epidural space. ResultsSuccessful ventral epidural lead placement was achieved using all four implantation approaches. The sacral hiatus (16/16 electrodes) and transforaminal (8/8 electrodes) approaches resulted in exclusively ventrolateral placement. The interlaminar contralateral approach led to 27/32 electrodes positioned ventrolaterally and 5/32 dorsally. The interlaminar ipsilateral implantation approach led to 14/32 electrodes positioned ventrolaterally and 18/32 positioned ventromedially. ConclusionsThese findings demonstrate that ventral epidural SCS lead placement can be achieved using modified percutaneous implant techniques. The four approaches outlined here provide a clinically feasible pathway to SCSV and establishes a foundation for future clinical studies investigating SCSV for motor rehabilitation following SCI.

Objective: The study aims to evaluate the real-world effectiveness of Highly Aspherical Lenslets spectacle (HAL; Essilor(R) Stellest(R)) in slowing myopia progression among Indian children and adolescents aged between 4 and 16 years. Methods and analysis: This was a multicentre retrospective study conducted across 10 leading ophthalmic centers. The study participants comprised children aged between 4 and 16 years who were prescribed HAL spectacles (Essilor(R) Stellest(R)). Data were extracted from electronic medical records at three time points: T1: One year prior to intervention; T2: Baseline at HAL spectacle prescription; T3: 6 to 24 months after prescription. The primary endpoint was the myopia progression and axial elongation in the year following prescription, compared with the untreated year and with published meta-regression models. Only data from the right eye were analysed, with the expected physiological progression estimated based on the individual progression trajectory after adjusting for age-related slowing as reported in published meta-regression models. Results: A total of 372 myopic children were included in the study. The annual myopia progression was -0.72 {+/-} 0.47 D/year during the untreated period, reducing to -0.11 {+/-} 0.29 D/year with HAL spectacle wear. The expected progression without treatment was -0.65 D/year, based on trajectory-adjusted modelling, indicating a treatment effect of 0.54 D/years and an estimated 83% slowing in myopia progression compared to expected progression. The expected axial elongation under physiological conditions was 0.29 mm/year, estimated using age-adjusted meta-regression models; with HAL lens wear, axial elongation was 0.11 {+/-} 0.16 mm/year, corresponding to a [~]62% relative slowing of elongation. Conclusion: The present study demonstrates the real-world evidence validating the efficacy of HAL lenses as an effective myopia control intervention in Indian children and adolescents. The retrospective design and limited follow-up period warrant future prospective, long-term studies to validate these findings.

Genome-wide association studies (GWAS) have identified numerous genetic variants associated with complex traits. However, linkage disequilibrium (LD) confounds these associations, leading to false positives where non-causal variants appear associated because they are correlated with nearby causal variants. This is particularly the case in highly polygenic traits where the genome can be saturated in causal variants. To address this issue, we propose LDeconv a method based on truncated singular value decomposition (SVD) that adjust GWAS summary statistics without requiring individual-level genotype data. This approach accounts for LD structure, isolates causal variants in high-LD regions, and improve the reliability of effect size estimates. We assess its performance through simulations across various LD scenarios, conduct extensive sensitivity analyses, and apply them to real GWAS data from the UK Biobank. Our results demonstrate that LDeconv effectively reduces false discoveries while preserving true associations, offering a robust framework for post-GWAS analysis.

Purpose: To identify the ocular biometric parameters associated with refractive outcomes in Chinese Primary angle closure glaucoma (PACG) patients receiving phacoemulsification and intraocular lens (IOL) implantation (PEI) surgery. Methods: 165 Chinese PACG patients receiving PEI and goniosynechialysis (GSL) and 53 cataract patients as controls only receiving PEI surgery were recruited. The prediction accuracy of IOL power calculation was assessed by the prediction error (PE), mean absolute error (MAE), median absolute error (MedAE), and proportions of eyes with a PE within {+/-} 0.25 diopters (D), {+/-} 0.50 D, {+/-} 0.75 D, and {+/-} 1.00 D. The association of different ocular biometric parameters with the PE of IOL calculation were evaluated. Results: The PACG patients had significantly higher absolute of PE as compared to the control subjects, especially the acute PACG patients. The axial length (AL), changes in aqueous depth pre- and post-surgery ({bigtriangleup}AD), and the ratio of {bigtriangleup}AD/AL were significantly associated with the PE in acute PACG patients. The association of {bigtriangleup}AD with the PE of IOL power calculation was found in PACG patients with AL [≥] 22 mm. Conclusions: This study revealed the association of AL and {bigtriangleup}AD with the PE of IOL calculation in Chinese PACG patients. Precisely predict the {bigtriangleup}AD is necessary for acute PACG patients, especially for those with AL [≥] 22 mm, to improve the refractive outcomes.

ImportanceAge-related eye diseases, such as cataract, glaucoma, age-related macular degeneration (AMD), and diabetic retinopathy (DR), are leading causes of irreversible vision loss globally. Chronic inflammation is a shared pathogenic pathway, but the role of systemic inflammatory drivers like clonal hematopoiesis of indeterminate potential (CHIP) is unknown. ObjectiveTo investigate the association of CHIP, including its major genetic subtypes and clone sizes, with the risk of four major age-related eye diseases. Design, Setting, and ParticipantsThis was a prospective cohort study conducted using data from the UK Biobank, a large-scale, population-based cohort. A total of 436,469 participants free of the four eye diseases at baseline were included in the analysis. Data were collected from 2006 to 2010, with follow-up extending to March 2022. ExposuresCHIP status was ascertained from whole-exome sequencing data, defined by the presence of a somatic driver mutation with a variant allele fraction of 2% or greater. Main Outcomes and MeasuresThe primary outcomes were incident cases of cataract, glaucoma, AMD, and DR, identified through linked electronic health records. Associations were assessed using multivariable Cox proportional hazards regression models. ResultsOf 436,469 participants (mean [SD] age, 56.4 [8.1] years; 54.5% women), 14,110 (3.2%) had CHIP. Over a median follow-up of 13.1 years, CHIP was significantly associated with an increased risk of incident cataract (Hazard Ratio [HR], 1.08; 95% CI, 1.03-1.14), AMD (HR, 1.12; 95% CI, 1.04-1.21), and DR (HR, 1.41; 95% CI, 1.20-1.64). No significant association was found with glaucoma (HR, 1.08; 95% CI, 0.99-1.17). The risk for AMD was primarily associated with smaller clones (VAF <10%), while the risk for DR was highest with non-DNMT3A mutations. Systemic inflammation, particularly neutrophil count, partially mediated the associations. Conclusions and RelevanceIn this study, CHIP was independently associated with a higher risk of developing cataract, AMD, and DR, but not glaucoma. These findings establish a link between hematopoietic somatic mutations and the pathogenesis of several major age-related eye diseases, suggesting that CHIP-driven inflammation is a potential target for risk stratification and prevention. Key PointsO_ST_ABSQuestionC_ST_ABSIs clonal hematopoiesis of indeterminate potential (CHIP) associated with the risk of major age-related eye diseases? FindingsIn this cohort study of 436,469 participants, CHIP was associated with an increased risk of incident cataract (HR, 1.08; 95% CI, 1.03-1.14), age-related macular degeneration (HR, 1.12; 95% CI, 1.04-1.21), and diabetic retinopathy (HR, 1.41; 95% CI, 1.20-1.64), but not glaucoma. MeaningThese findings identify CHIP as an independent, non-ocular risk factor for cataract, AMD, and diabetic retinopathy, suggesting that systemic inflammation driven by CHIP contributes to the pathogenesis of these conditions and may represent a novel target for preventive strategies.

Background: Ribavirin is a guanosine analogue with clinical antiviral activity against a range of RNA viruses including hepatitis C virus (HCV), respiratory syncytial virus and Lassa virus. Several potential mechanisms of action have been proposed, but there is limited data supporting them clinically. Methods: We studied 196 HCV-infected participants from a trial of short-course directly antiviral therapy (STOPHCV-1) which included a factorial randomisation to ribavirin versus no ribavirin. Deep sequencing of the HCV genome was performed on samples with detectable viremia from three time-points: baseline (n = 191), day 3 of treatment (n = 25) and post-treatment failure (n = 47). Results: Ribavirin exposure significantly increased total mutational load at treatment failure (P = 0.0065) and enriched classical ribavirin-associated transitions, including G->A (P = 0.026) and C[-&gt;]U (P = 0.004), along with other key changes including A->G (P = 0.005), U->C (P = 0.023), C->G (P = 0.010), and U->A (P = 0.026). The resulting mutational signature was broad, not dominated by G-related changes. Region-specific analyses demonstrated this increase was broadly distributed across the viral genome, without strong evidence for protection of specific regions. Non-synonymous to synonymous mutation ratios (dN/dS) rose at day 3 (P = 5.5e-5) before declining at failure (P = 8.5e-7), with trends toward higher dN/dS in the ribavirin group at day 3 (P = 0.06). Conclusions: Ribavirin acts as a potent in vivo mutagen, driving viral populations toward genome-wide diversity rather than selecting a few highly fit drug-resistant clones. These findings support an error-catastrophe model.

Background: Deep brain stimulation (DBS) is a treatment option for Parkinson disease (PD). However, the effect of DBS on the arterial pressure (AP) remains unexplored. We aimed to develop an artificial baroreflex system for treating orthostatic hypotension (OH) due to central baroreflex failure in patients with PD. To achieve this, we developed an appropriate algorithm after estimating the dynamic responses of the AP to DBS using a white noise system identification method. Methods: We randomly performed DBS while measuring the AP tonometrically in 3 trials involving 3 patients with PD treated with DBS. We calculated the frequency response of the AP to the DBS using a fast Fourier transform algorithm. Finally, the feedback correction factors were determined via numerical simulation. Results: The frequency responses of the systolic AP to random DBS were identifiable in all 3 trials, and the steady state gain was 8.24 mmHg/STM. Based on these results, the proportional correction factor was set to 0.12, and the integral correction factor was set to 0.018. The computer simulation revealed that the system could quickly and effectively attenuate a sudden AP drop induced by external disturbances such as head-up tilting. Conclusion: An artificial baroreflex system with DBS may be a novel therapeutic approach for OH caused by central baroreflex failure.

Objective: Skin sympathetic nerve activity (SKNA) has emerged as a promising non-invasive surrogate measure of sympathetic drive, but its relevant physiological characteristics remain ill-defined. This observational study aims to investigate its regulatory patterns during rest and Valsalva maneuver (VM) in healthy participants. Method: Using a two-layer strategy integrating signal analysis and physiological modelling, we analyzed data recorded from 41 subjects performing repeated VMs. The observational layer includes time-domain feature comparisons using linear mixed-effect models, and time-varying spectral coherence analysis. The mechanistic layer proposes a mathematical model to investigate whether baroreflex and respiratory modulation are sufficient to reproduce the observed HR and average SKNA (aSKNA) dynamics. Main Results: Mean integrated SKNA (iSKNA) showed more significant change than HRV for VM induced effects. We also found mean iSKNA increase during VM varies with BMI and sex. The coherence analysis indicated that iSKNA strongly synchronized with EDR under resting conditions. The proposed model successfully reproduced main characteristics of aSKNA dynamics, yielding a high median Pearson correlation coefficient of 0.80 ([Q1, Q3] = [0.60, 0.91]). In contrast, HR dynamics were only partially captured, with a median PCC of 0.37 ([Q1, Q3] = [0.16, 0.55]). These results likely suggest SKNA provides a more direct representation of sympathetic burst dynamics during VM in healthy subjects. Significance: This study provides convergent evidence that SKNA reflects known autonomic regulatory influences in healthy subjects. These findings strengthen the physiological interpretability of SKNA while clarifying its appropriate use as a practical biomarker of sympathetic function.

Gene networks (GNs) encode diverse molecular relationships and are central to interpreting cellular function and disease. The heterogeneity of interaction types has led to computational methods specialized for particular network contexts. Large language models (LLMs) offer a unified, language-based formulation of GN inference by leveraging biological knowledge from large-scale text corpora, yet their effectiveness remains sensitive to prompt design. Here, we introduce Gene-Relation Adaptive Soft Prompt (GRASP), a parameter-efficient and trainable framework that conditions inference on each gene pair through only three virtual tokens. Using factorized gene-specific and relation-aware components, GRASP learns to map each pair's biological context into compact soft prompts that combine pair-specific signals with shared interaction patterns. Across diverse GN inference tasks, GRASP consistently outperforms alternative prompting strategies. It also shows a stronger ability to recover unannotated interactions from synthetic negative sets, suggesting its capacity to identify biologically meaningful relationships beyond existing databases. Together, these results establish GRASP as a scalable and generalizable prompting framework for LLM-based GN inference.