Biology subjects

Summers, H.

Publications and source records attributed to Summers, H..

2 recordsLinked to original sources

bioRxiv2024

Cell Targeting and Adjuvant Activity of Dietary Titanium Dioxide

Food-grade titanium dioxide (fgTiO2) is a bio-persistent particle under intense regulatory scrutiny. Paradoxically, meaningful in vivo cellular accumulation has never been demonstrated: the only known cell reservoirs for fgTiO2 are graveyard intestinal pigment cells which are metabolically and immunologically quiescent. Here we identify major new immunocompetent cell targets of fgTiO2 in humans, most notably in the subepithelial dome region of intestinal Peyers patches. Using multimodal microscopy techniques with single-particle detection and per-cell / vesicle image analysis we achieved correlative dosimetry, quantitatively recapitulating human cellular exposures in the terminal ileum of mice fed a fgTiO2-containing diet. Epithelial microfold cells selectively funneled fgTiO2 into LysoMac and LysoDC cells with ensuing accumulation. Notwithstanding, proximity extension analyses for 92 protein targets revealed no measureable perturbation of cell signalling pathways. When chased with oral {Delta}aroA-Salmonella, pro-inflammatory signalling was confirmed, but no augmentation by fgTiO2 was revealed despite marked same-cell loading. Interestingly, Salmonella caused the fgTiO2-recipient cells to migrate basolaterally in the patch and, sporadically, to the lamina propria, thereby fully recreating the intestinal tissue distribution of fgTiO2 in humans. Immunocompetent cells that accumulate fgTiO2 in vivo are now identified and we demonstrate a mouse model that finally enables human-relevant risk assessments of ingested, bio-persistent (nano)particles.

pharmacology and toxicology↗

bioRxiv2021

Determining jumping performance from a single body-worn accelerometer using machine learning

External peak power in the countermovement jump is frequently used to monitor athlete training. The gold standard method uses force platforms, but they are unsuitable for field-based testing. However, alternatives based on jump flight time or Newtonian methods applied to inertial sensor data have not been sufficiently accurate for athlete monitoring. Instead, we developed a machine learning model based on characteristic features (functional principal components) extracted from a single body-worn accelerometer. Data were collected from 69 male and female athletes at recreational, club or national levels, who performed 696 jumps in total. We considered vertical countermovement jumps (with and without arm swing), sensor anatomical locations, machine learning models and whether to use resultant or triaxial signals. Using a novel surrogate model optimisation procedure, we obtained the lowest errors with a support vector machine when using the resultant signal from a lower back sensor in jumps without arm swing. This model had a peak power RMSE of 2.3 W{middle dot}kg-1 (5.1% of the mean), estimated using nested cross validation and supported by an independent holdout test (2.0 W{middle dot}kg-1). This error is lower than in previous studies, although it is not yet sufficiently accurate for a field-based method. Our results demonstrate that functional data representations work well in machine learning by reducing model complexity in applications where signals are aligned in time. Our optimisation procedure also was shown to be robust can be used in wider applications with low-cost, noisy objective functions.

bioinformatics↗