Background: Among the ambulant population of children with spastic cerebral palsy (CP), dynamic equinus is one of the most common form of gait deviation that is encountered.Objective: To investigate the combined effects of Functional Electrical Stimulation (FES) and Botulinum Toxin A (BTXA) therapy in children with spastic CP, and to demonstrate the feasibility of this combination therapy.Methods: A single-subject design with repeated measures was adopted. Eight children (six males, two females; mean age 7 y 9 mo, SD 1 y 5 mo; range 7 y to 11 y) diagnosed with hemiplegic (n = 6) or diplegic (n = 2) spastic CP completed the study. Each subject participated in the study for twenty weeks. This period consisted of baseline (one week), BTXA phase (three weeks), first FES phase (four weeks), first control phase (four weeks), second FES phase (four weeks) and second control phase (four weeks). Subjects were assessed at the end of each phase. The ankle angle at the end of swing phase was selected as the primary outcome measure. The secondary outcome measure recorded was the foot contact pattern.Results: There was an increase in ankle dorsiflexion at the end of the combined intervention in most subjects (n = 6), accompanied by an improvement in foot contact pattern.Conclusions: This pilot study demonstrated that it is feasible to combine BTXA therapy with FES in ambulant children with spastic CP.

With phenotypic heterogeneity in whole cell populations widely recognised, the demand for quantitative and temporal analysis approaches to characterise single cell morphology and dynamics has increased. We present CellPhe, a pattern recognition toolkit for the characterisation of cellular phenotypes within time-lapse videos. To maximise data quality for downstream analysis, our toolkit includes automated recognition and removal of erroneous cell boundaries induced by inaccurate tracking and segmentation. We provide an extensive list of features extracted from individual cell time series, with custom feature selection to identify variables that provide greatest discrimination for the analysis in question. We demonstrate the use of ensemble classification for accurate prediction of cellular phenotype and clustering algorithms for the characterisation of heterogeneous subsets. We validate and prove adaptability using different cell types and experimental conditions. Our methods could be extended to other imaging modalities, such as fluorescence, and would be suitable for all time-lapse studies including clinical applications.