Accepted

While cancer patients may have chemotherapeutics to thank for being cured of their malignancy, they are often left to suffer a disabling neuropathy induced by that same cancer treatment. This neuropathy, known as chemotherapy-induced peripheral neuropathy, or CIPN, is one of the most debilitating survivorship concerns for patients, with many citing hallmark symptoms of hyperalgesia, allodynia, and numbness, and subsequently reducing their dose or even ceasing treatment altogether. Investigations into this interplay between the antineoplastic activity of chemotherapeutic agents and the preservation of peripheral nerve health are therefore crucial for the development of CIPN treatment and prevention methods. Responding to need, current literature is inundated with varying preclinical models of CIPN. This chapter thus seeks to provide a detailed and reliable methodology for the induction and assessment of CIPN in mice, using a preclinical model that is both reproducible and translatable to several aspects of the clinical phenotype. Specifically, this chapter lays out a model for intermittent low-dose paclitaxel induction of CIPN in C57BL/6J mice, and a testing of this induction via von Frey filament mechanical hypersensitivity assays, a mechanical hyposensitivity (numbness) assay, and a cold-thermal allodynia assay (acetone test). These protocols can easily be adjusted to fit the needs of individual CIPN experiments, as stated throughout the chapter.

Alpha-calcitonin gene-related peptide (α-CGRP) is a vasodilator neuropeptide of the calcitonin gene family. Pharmacological and gene knock-out studies have established a significant role of α-CGRP in normal and pathophysiological states, particularly in cardiovascular disease and migraines. α-CGRP knock-out mice with transverse aortic constriction (TAC)-induced pressure-overload heart failure have higher mortality rates and exhibit higher levels of cardiac fibrosis, inflammation, oxidative stress, and cell death compared to the wild-type TAC-mice. However, administration of α-CGRP, either in its native- or modified-form, improves cardiac function at the pathophysiological level, and significantly protects the heart from the adverse effects of heart failure and hypertension. Similar cardioprotective effects of the peptide were demonstrated in pressure-overload heart failure mice when α-CGRP was delivered using an alginate microcapsules-based drug delivery system. In contrast to cardiovascular disease, an elevated level of α-CGRP causes migraine-related headaches, thus the use of α-CGRP antagonists that block the interaction of the peptide to its receptor are beneficial in reducing chronic and episodic migraine headaches. Currently, several α-CGRP antagonists are being used as migraine treatments or in clinical trials for migraine pain management. Overall, agonists and antagonists of α-CGRP are clinically relevant to treat and prevent cardiovascular disease and migraine pain, respectively. This review focuses on the pharmacological and therapeutic significance of α-CGRP-agonists and -antagonists in various diseases, particularly in cardiac diseases and migraine pain.

The field of neurostimulation for the treatment of chronic pain is a rapidly developing area of medicine. Although neurostimulation therapies have advanced significantly as a result of technologic improvements, surgical planning, device placement, and postoperative care are of equal importance to optimize outcomes. This Neurostimulation Appropriateness Consensus Committee (NACC) project intends to provide evidence-based guidance for these often-overlooked areas of neurostimulation practice.

Chronic low back pain (cLBP) afflicts 8. 2% of adults in the United States, and is the leading global cause of disability. Neuropsychiatric co-morbidities including anxiety, depression, and substance abuse- are common in cLBP patients. In particular, cLBP is a risk factor for opioid addiction, as more than 50% of opioid prescriptions in the United States are for cLBP. Misuse of these prescriptions is a common precursor to addiction. While associations between cLBP and neuropsychiatric disorders are well established, causal relationships for the most part are unknown. Developing effective treatments for cLBP, and associated co-morbidities, requires identifying and understanding causal relationships. Rigorous methods for causal inference, a process for quantifying causal effects from observational data, have been developed over the past 30 years. In this review we first discuss the conceptual model of cLBP that current treatments are based on, and how gaps in causal knowledge contribute to poor clinical outcomes. We then present cLBP as a "Big Data" problem and identify how advanced analytic techniques may close knowledge gaps and improve clinical outcomes. We will focus on causal discovery, which is a data-driven method that uses artificial intelligence (AI) and high dimensional datasets to identify causal structures, discussing both constraint-based (PC and Fast Causal Inference) and score-based (Fast Greedy Equivalent Search) algorithms.