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.

Clarifying and Expanding the Role of Narrative in Ethics Consultation.

Understanding a patient's story is integral to providing ethically supportable and practical recommendations that can improve patient care. Important skills include how to elicit an individual's story, how to weave different narrative threads together, and how to assist the care team, patients, and caregivers to resolve difficult decisions or moral dilemmas. Narrative approaches to ethics consultation deepen dialogue and stakeholders' engagement to reveal important values, preferences, and beliefs that may prove critical in resolving care challenges. Recognizing barriers to narrative inquiry, such as patients who are unable or refuse to share their story, is also important. In this article we offer specific steps and guidelines that ethicists can follow to systematically elicit and construct patients' stories. We provide a case example to illustrate how a narrative approach to ethics consultation illuminates salient ethical issues that may otherwise go unnoticed. We argue that this approach should be part of every consultant's tool kit.

Demonstrating Value Through Tracking Ethics Program Activities Beyond Ethics Consultations.

Demonstrating value is an ongoing process and requirement for institutional survival for ethics programs. Although our ethics program has significantly increased our ethics consultation volume and maintains a robust database that tracks ethics consultation data, these data regarding ethics consultations alone do not accurately represent the program's overall activities and value to the institution. The roles and responsibilities of clinical ethicists extend beyond clinical ethics consultation, and there are many other ways that clinical ethicists contribute and add value to their institutions. This article describes our ethics program's early efforts to systematically track ethics program activities outside of ethics consultations as a way to demonstrate additional value to the institution that goes beyond ethics consultation. By systematically tracking activities such as internal ethics education sessions, conference presentations, publications, grants, committee/policy work, and other activities, our ethics program has been able to gather substantial quantitative data that highlight our program's numerous activities and outreach, both within and outside the institution, that provide additional value to the institution beyond our ethical consultation activities.

Next-Generation Bioelectric Medicine: Harnessing the Therapeutic Potential of Neural Implants.

Bioelectric medicine leverages natural signaling pathways in the nervous system to counteract organ dysfunction. This novel approach has potential to address conditions with unmet needs, including heart failure, hypertension, inflammation, arthritis, asthma, Alzheimer's disease, and diabetes. Neural therapies, which target the brain, spinal cord, or peripheral nerves, are already being applied to conditions such as epilepsy, Parkinson's, and chronic pain. While today's therapies have made exciting advancements, their open-loop design-where stimulation is administered without collecting feedback-means that results can be variable and devices do not work for everyone. Stimulation effects are sensitive to changes in neural tissue, nerve excitability, patient position, and more. Closing the loop by providing neural or non-neural biomarkers to the system can guide therapy by providing additional insights into stimulation effects and overall patient condition. Devices currently on the market use recorded biomarkers to close the loop and improve therapy. The future of bioelectric medicine is more holistically personalized. Collected data will be used for increasingly precise application of neural stimulations to achieve therapeutic effects. To achieve this future, advances are needed in device design, implanted and computational technologies, and scientific/medical interpretation of neural activity. Research and commercial devices are enabling the development of multiple levels of responsiveness to neural, physiological, and environmental changes. This includes developing suitable implanted technologies for high bandwidth brain/machine interfaces and addressing the challenge of neural or state biomarker decoding. Consistent progress is being made in these challenges toward the long-term vision of automatically and holistically personalized care for chronic health conditions.

Management of cancer pain: 1. Wider implications of orthodox analgesics.

In this review, the first of two parts, we first provide an overview of the orthodox analgesics used commonly against cancer pain. Then, we examine in more detail the emerging evidence for the potential impact of analgesic use on cancer risk and disease progression. Increasing findings suggest that long-term use of nonsteroidal anti-inflammatory drugs, particularly aspirin, may reduce cancer occurrence. However, acetaminophen may raise the risk of some hematological malignancies. Drugs acting upon receptors of gamma-aminobutyric acid (GABA) and GABA "mimetics" (eg, gabapentin) appear generally safe for cancer patients, but there is some evidence of potential carcinogenicity. Some barbiturates appear to slightly raise cancer risks and can affect cancer cell behavior in vitro. For cannabis, studies suggest an increased risk of squamous cell carcinoma of the tongue, larynx, and possibly lung. Morphine may stimulate human microvascular endothelial cell proliferation and angiogenesis; it is not clear whether this might cause harm or produce benefit. The opioid, fentanyl, may promote growth in some tumor cell lines. Opium itself is an emerging risk factor for gastric adenocarcinoma and possibly cancers of the esophagus, bladder, larynx, and lung. It is concluded that analgesics currently prescribed for cancer pain can significantly affect the cancer process itself. More futuristically, several ion channels are being targeted with novel analgesics, but many of these are also involved in primary and/or secondary tumorigenesis. Further studies are needed to elucidate possible cellular and molecular effects of orthodox analgesics and their possible long-term impact, both positive and negative, and thus enable the best possible clinical gain for cancer patients.

Host perception and signal transduction studies in wild-type Blumeria graminis f. sp. hordei and a quinoxyfen-resistant mutant implicate quinoxyfen in the inhibition of serine esterase activity.

BACKGROUND: Quinoxyfen is a potent and effective fungicide, hitherto considered to control powdery mildew disease by perturbing signal transduction during early germling differentiation. The aim of this paper is to understand the mode of action of quinoxyfen by comparing the perception of host-derived signals and signal relay in a wild-type Blumeria graminis f. sp. hordei EM Marchal (Bgh) (WT/IM82) and a quinoxyfen-resistant field isolate (QR/2B11). RESULTS: QR/2B11 germinates more promiscuously on host-like and artificial surfaces than the quinoxyfen-sensitive WT/IM82. The pivotal role of host cuticle deprivation in the formation of hooked appressorial germ tubes (hAGTs) in WT/IM82 and a dramatic drop in germling differentiation in the presence of the mildewicide are demonstrated. QR/2B11 strain shows a dependence on host cuticle-like features for hAGT formation but no significant difference between germling differentiation in the presence or absence of quinoxyfen. PKC-inhibitor Ro 318220 induces morphological changes similar to those seen in quinoxyfen-treated germlings. PKC1 transcript accumulation is equivalently upregulated by quinoxyfen in QR/2B11 and WT/IM82 strains, but Bgh cutinase CUT1 transcript is 8 times more abundant in QR/2B11 conidia than in WT/IM82 conidia. Quinoxyfen inhibits serine esterase activity in WT/IM82, but not in QR/2B11. CONCLUSION: Collectively, these data suggest that quinoxyfen interferes with the perception of host-derived signals required for full germling differentiation, and that QR/2B11 bypasses the need for such signals. Moreover, quinoxyfen appears to target serine esterase activity, with a downstream perturbation in signal transduction; this represents the first demonstrable biochemical difference between the quinoxyfen-resistant and -sensitive isolates.