Using neural biomarkers to personalize dosing of vagus nerve stimulation.

BACKGROUND: Vagus nerve stimulation (VNS) is an established therapy for treating a variety of chronic diseases, such as epilepsy, depression, obesity, and for stroke rehabilitation. However, lack of precision and side-effects have hindered its efficacy and extension to new conditions. Achieving a better understanding of the relationship between VNS parameters and neural and physiological responses is therefore necessary to enable the design of personalized dosing procedures and improve precision and efficacy of VNS therapies. METHODS: We used biomarkers from recorded evoked fiber activity and short-term physiological responses (throat muscle, cardiac and respiratory activity) to understand the response to a wide range of VNS parameters in anaesthetised pigs. Using signal processing, Gaussian processes (GP) and parametric regression models we analyse the relationship between VNS parameters and neural and physiological responses. RESULTS: Firstly, we illustrate how considering multiple stimulation parameters in VNS dosing can improve the efficacy and precision of VNS therapies. Secondly, we describe the relationship between different VNS parameters and the evoked fiber activity and show how spatially selective electrodes can be used to improve fiber recruitment. Thirdly, we provide a detailed exploration of the relationship between the activations of neural fiber types and different physiological effects. Finally, based on these results, we discuss how recordings of evoked fiber activity can help design VNS dosing procedures that optimize short-term physiological effects safely and efficiently. CONCLUSION: Understanding of evoked fiber activity during VNS provide powerful biomarkers that could improve the precision, safety and efficacy of VNS therapies.

Online Bayesian optimization of vagus nerve stimulation.

Objective.In bioelectronic medicine, neuromodulation therapies induce neural signals to the brain or organs, modifying their function. Stimulation devices capable of triggering exogenous neural signals using electrical waveforms require a complex and multi-dimensional parameter space to control such waveforms. Determining the best combination of parameters (waveform optimization or dosing) for treating a particular patient's illness is therefore challenging. Comprehensive parameter searching for an optimal stimulation effect is often infeasible in a clinical setting due to the size of the parameter space. Restricting this space, however, may lead to suboptimal therapeutic results, reduced responder rates, and adverse effects.Approach. As an alternative to a full parameter search, we present a flexible machine learning, data acquisition, and processing framework for optimizing neural stimulation parameters, requiring as few steps as possible using Bayesian optimization. This optimization builds a model of the neural and physiological responses to stimulations, enabling it to optimize stimulation parameters and provide estimates of the accuracy of the response model. The vagus nerve (VN) innervates, among other thoracic and visceral organs, the heart, thus controlling heart rate (HR), making it an ideal candidate for demonstrating the effectiveness of our approach.Main results.The efficacy of our optimization approach was first evaluated on simulated neural responses, then applied to VN stimulation intraoperatively in porcine subjects. Optimization converged quickly on parameters achieving target HRs and optimizing neural B-fiber activations despite high intersubject variability.Significance.An optimized stimulation waveform was achieved in real time with far fewer stimulations than required by alternative optimization strategies, thus minimizing exposure to side effects. Uncertainty estimates helped avoiding stimulations outside a safe range. Our approach shows that a complex set of neural stimulation parameters can be optimized in real-time for a patient to achieve a personalized precision dosing.

Tumour heterogeneity promotes collective invasion and cancer metastatic dissemination.

Heterogeneity within tumour cell populations is commonly observed in most cancers. However, its impact on metastatic dissemination, one of the primary determinants of the disease prognosis, remains poorly understood. Working with a simplified numerical model of tumour spheroids, we investigated the impact of mechanical heterogeneity on the onset of tumour invasion into surrounding tissues. Our work establishes a positive link between tumour heterogeneity and metastatic dissemination, and recapitulates a number of invasion patterns identified in vivo, such as multicellular finger-like protrusions. Two complementary mechanisms are at play in heterogeneous tumours. A small proportion of stronger cells are able to initiate and lead the escape of cells, while collective effects in the bulk of the tumour provide the coordination required to sustain the invasive process through multicellular streaming. This suggests that the multicellular dynamics observed during metastasis is a generic feature of mechanically heterogeneous cell populations and might rely on a limited and generic set of attributes.

Behavioral risk factors and Latino body mass index: a cross-sectional study in Missouri.

Obesity is the fastest-growing cause of disease and death in the United States, with minority populations suffering some of the most severe consequences. Latinos constitute 16% of the U.S. population as of 2010, and have a higher proportion of the population that is overweight and obese compared with their non-Hispanic Black and White counterparts. Although there are over 15.8 million Latino residents living in non-gateway states (outside California, Texas, Arizona, Illinois, and New York), there is little research exploring obesity factors among Latinos outside of gateway states. The aim of this paper was to study socio-economic characteristics, mental health, insurance status, physical activity, and fruit and vegetable consumption, in relation to body mass index (BMI) among Latinos living in a non-gateway state. The results showed that income, employment status, marital status, insurance status, physical activity, fruit and vegetable consumption, and mental health were all associated with BMI.