Hand-rim biomechanics during geared manual wheelchair propulsion over different ground conditions in individuals with spinal cord injury.

Geared manual wheelchair wheels, a recently developed alternative propulsion mechanism, have the potential to alleviate the high upper extremity demands required for wheelchair propulsion and help decrease the risk of secondary injuries in manual wheelchair users. The objective of this study was to investigate the effects of using geared manual wheelchairs on hand-rim biomechanics of wheelchair propulsion in individuals with spinal cord injury (SCI). Seven manual wheelchair users with SCI propelled their wheelchairs equipped with geared wheels over tile, carpet, and up a ramp in low gear (gear ratio 1.5:1) and standard gear (gear ratio 1:1) conditions. Hand-rim kinetics and stroke cycle characteristics were measured using a custom instrumented geared wheel. Using the geared wheels in the low gear condition, propulsion speed (P = 0.013), peak resultant force (P = 0.005), peak propulsive moment (P < 0.006), and peak rate of rise of the resultant force (P = 0.035) decreased significantly in comparison with the standard gear condition. The significant increase in the number of stroke cycles when normalized to distance (P = 0.004) and decrease in the normalized integrated moment (P = 0.030) indicated that although a higher number of stroke cycles are required for travelling a given distance in the low gear than the standard gear condition, the low gear condition might be less demanding for the upper extremity. These results suggest that geared wheels could be a useful technology for manual wheelchair users to independently accomplish strenuous propulsion tasks including mobility on carpeted floors and ramp ascension, while reducing the risk factors contributing to the incidence of secondary upper extremity injuries.

Machine Learning Helps Identify New Drug Mechanisms in Triple-Negative Breast Cancer.

This paper demonstrates the ability of mach- ine learning approaches to identify a few genes among the 23,398 genes of the human genome to experiment on in the laboratory to establish new drug mechanisms. As a case study, this paper uses MDA-MB-231 breast cancer single-cells treated with the antidiabetic drug metformin. We show that mixture-model-based unsupervised methods with validation from hierarchical clustering can identify single-cell subpopulations (clusters). These clusters are characterized by a small set of genes (1% of the genome) that have significant differential expression across the clusters and are also highly correlated with pathways with anticancer effects driven by metformin. Among the identified small set of genes associated with reduced breast cancer incidence, laboratory experiments on one of the genes, CDC42, showed that its downregulation by metformin inhibited cancer cell migration and proliferation, thus validating the ability of machine learning approaches to identify biologically relevant candidates for laboratory experiments. Given the large size of the human genome and limitations in cost and skilled resources, the broader impact of this work in identifying a small set of differentially expressed genes after drug treatment lies in augmenting the drug-disease knowledge of pharmacogenomics experts in laboratory investigations, which could help establish novel biological mechanisms associated with drug response in diseases beyond breast cancer.

Model-based unsupervised learning informs metformin-induced cell-migration inhibition through an AMPK-independent mechanism in breast cancer.

We demonstrate that model-based unsupervised learning can uniquely discriminate single-cell subpopulations by their gene expression distributions, which in turn allow us to identify specific genes for focused functional studies. This method was applied to MDA-MB-231 breast cancer cells treated with the antidiabetic drug metformin, which is being repurposed for treatment of triple-negative breast cancer. Unsupervised learning identified a cluster of metformin-treated cells characterized by a significant suppression of 230 genes (p-value < 2E-16). This analysis corroborates known studies of metformin action: a) pathway analysis indicated known mechanisms related to metformin action, including the citric acid (TCA) cycle, oxidative phosphorylation, and mitochondrial dysfunction (p-value < 1E-9); b) 70% of these 230 genes were functionally implicated in metformin response; c) among remaining lesser functionally-studied genes for metformin-response was CDC42, down-regulated in breast cancer treated with metformin. However, CDC42's mechanisms in metformin response remained unclear. Our functional studies showed that CDC42 was involved in metformin-induced inhibition of cell proliferation and cell migration mediated through an AMPK-independent mechanism. Our results points to 230 genes that might serve as metformin response signatures, which needs to be tested in patients treated with metformin and, further investigation of CDC42 and AMPK-independence's role in metformin's anticancer mechanisms.