Human Studies

People with chronic low back pain (LBP) exhibit changes in postural control. Stereotypical muscle activations resulting from external perturbations include anticipatory (APAs) and compensatory (CPAs) postural adjustments. The aim and objective of this study was to determine differences in postural control strategies (peak amplitude, APAs and CPAs) between symptomatic and asymptomatic adults with and without Lumbar Disc Degeneration (LDD) using surface electromyography during forward postural perturbation. Ninety-seven subjects participated in the study (mean age 50 years (SD 12)). 3T MRI was used to acquire T2 weighted images (L1-S1). LDD was determined using Pfirrmann grading. A bespoke translational platform was designed to deliver horizontal perturbations in sagittal and frontal planes. Electromyographic activity was analysed bilaterally from 8 trunk and lower limb muscles during four established APA and CPA epochs. A Kruskal-Wallis H test with Bonferroni correction for multiple comparisons was conducted. Four groups were identified: no LDD no pain (n = 19), LDD no pain (n = 38), LDD pain (n = 35) and no LDD pain (n = 5). There were no significant differences in age or gender between groups. The most significant difference between groups was observed during forward perturbation. In the APA and CPA phases of predictable forward perturbation there were significant differences ankle strategy between groups (p = 0.007-0.008); lateral gastrocnemius and tibialis anterior activity was higher in the LDD pain than the LDD no pain group. There were no significant differences in the unpredictable condition (p>0.05). These findings were different from the remaining groups, where significant differences in hip strategy were observed during both perturbation conditions (p = 0.004-0.006). Symptomatic LDD patients exhibit different electromyographic strategies to asymptomatic LDD controls. Future LBP electromyographic research should benefit from considering assessment of both lower limbs in addition to the spine. This approach could prevent underestimation of postural control deficits and guide targeted rehabilitation.

Animal studies have shown that high-frequency stimulation (HFS) of peripheral C-fibres induces long-term potentiation (LTP) within spinal nociceptive pathways. The aim of this replication study was to assess if a perceptual correlate of LTP can be observed in humans. In 20 healthy volunteers, we applied HFS to the left or right volar forearm. Before and after applying HFS, we delivered single electrical test stimuli through the HFS electrode while a second electrode at the contra-lateral arm served as a control condition. Moreover, to test the efficacy of the HFS protocol, we quantified changes in mechanical pinprick sensitivity before and after HFS of the skin surrounding both electrodes. The perceived intensity was collected for both electrical and mechanical stimuli. After HFS, the perceived pain intensity elicited by the mechanical pinprick stimuli applied on the skin surrounding the HFS-treated site was significantly higher compared to control site (heterotopic effect). Furthermore, we found a higher perceived pain intensity for single electrical stimuli delivered to the HFS-treated site compared to the control site (homotopic effect). Whether the homotopic effect reflects a perceptual correlate of homosynaptic LTP remains to be elucidated.

Fibromyalgia and myalgic encephalomyelitis / chronic fatigue syndrome (ME/CFS) are poorly understood conditions with overlapping symptoms, fuelling debate as to whether they are manifestations of the same spectrum or separate entities. Both are associated with hypermobility, but this remains significantly undiagnosed, despite impact on quality of life.

Opioids are a class of drugs that are known for their use as pain relievers. They bind to opioid receptors on nerve cells in the brain and the nervous system to mitigate pain. Addiction is one of the chronic and primary adverse events of prolonged usage of opioids. They may also cause psychological disorders, muscle pain, depression, anxiety attacks etc. In this study, we present a collection of predictive models to identify patients at risk of opioid abuse and mortality by using their prescription histories. Also, we discover particularly threatening drug-drug interactions in the context of opioid usage. Using a publicly available dataset from MIMIC-III, two models were trained, Logistic Regression with L2 regularization (baseline) and Extreme Gradient Boosting (enhanced model), to classify the patients of interest into two categories based on their susceptibility to opioid abuse. We've also used K-Means clustering, an unsupervised algorithm, to explore drug-drug interactions that might be of concern. The baseline model for classifying patients susceptible to opioid abuse has an F1 score of 76.64% (accuracy 77.16%) while the enhanced model has an F1 score of 94.45% (accuracy 94.35%). These models can be used as a preliminary step towards inferring the causal effect of opioid usage and can help monitor the prescription practices to minimize the opioid abuse. Results suggest that the enhanced model provides a promising approach in preemptive identification of patients at risk for opioid abuse. By discovering and correlating the patterns contributing to opioid overdose or abuse among a variety of patients, machine learning models can be used as an efficient tool to help uncover the existing gaps and/or fraudulent practices in prescription writing. To quote an example of one such incidental finding, our study discovered that insulin might possibly be interacting with opioids in an unfavourable way leading to complications in diabetic patients. This indicates that diabetic patients under long term opioid usage might need to take increased amounts of insulin to make it more effective. This observation backs up prior research studies done on a similar aspect. To increase the translational value of our work, the predictive models and the associated software code are made available under the MIT License.

Irritable bowel syndrome (IBS) is a chronic gastrointestinal disorder characterized by abdominal pain or discomfort. Previous studies have illustrated that the gut microbiota might play a critical role in IBS, but the conclusions of these studies, based on various methods, were almost impossible to compare, and reproducible microorganism signatures were still in question. To cope with this problem, previously published 16S rRNA gene sequencing data from 439 fecal samples, including 253 IBS samples and 186 control samples, were collected and processed with a uniform bioinformatic pipeline. Although we found no significant differences in community structures between IBS and healthy controls at the amplicon sequence variants (ASV) level, machine learning (ML) approaches enabled us to discriminate IBS from healthy controls at genus level. Linear discriminant analysis effect size (LEfSe) analysis was subsequently used to seek out 97 biomarkers across all studies. Then, we quantified the standardized mean difference (SMDs) for all significant genera identified by LEfSe and ML approaches. Pooled results showed that the SMDs of nine genera had statistical significance, in which the abundance of , and in IBS were higher, while the dominant abundance genera of healthy controls were Ruminococcaceae , , , and . In summary, based on six published studies, this study identified nine new microbiome biomarkers of IBS, which might be a basis for understanding the key gut microbes associated with IBS, and could be used as potential targets for microbiome-based diagnostics and therapeutics.