Investigation of the limits of nanoscale filopodial interactions

Mesenchymal stem cells are sensitive to changes in feature height, order and spacing. We had previously noted that there was an inverse relationship between osteoinductive potential and feature height on 15-, 55- and 90 nm-high titania nanopillars, with 15 nm-high pillars being the most effective substrate at inducing osteogenesis of human mesenchymal stem cells. The osteoinductive effect was somewhat diminished by decreasing the feature height to 8 nm, however, which suggested that there was a cut-off point, potentially associated with a change in cell–nanofeature interactions. To investigate this further, in this study, a scanning electron microscopy/three-dimensional scanning electron microscopy approach was used to examine the interactions between mesenchymal stem cells and the 8 and 15 nm nanopillared surfaces. As expected, the cells adopted a predominantly filopodial mode of interaction with the 15 nm-high pillars. Interestingly, fine nanoscale membrane projections, which we have termed ‘nanopodia,’ were also employed by the cells on the 8 nm pillars, and it seems that this is analogous to the cells ‘clinging on with their fingertips’ to this scale of features

Increased efficiency of direct nanoimprinting on planar and curved bulk titanium through surface modification

In this work the direct transfer of nanopatterns into titanium is demonstrated. The nanofeatures are imprinted at room temperature using diamond stamps in a single step. We also show that the imprint properties of the titanium surface can be altered by anodisation yielding a significant reduction in the required imprint force for pattern transfer. The anodisation process is also utilised for curved titanium surfaces where a reduced imprint force is preferable to avoid sample deformation and damage. We finally demonstrate that our process can be applied directly to titanium rods

Lesion detection in epilepsy surgery: Lessons from a prospective evaluation of a machine learning algorithm

AIM: To evaluate a lesion detection algorithm designed to detect focal cortical dysplasia (FCD) in children undergoing stereoelectroencephalography (SEEG) as part of their presurgical evaluation for drug-resistant epilepsy. METHOD: This was a prospective, single-arm, interventional study (Idea, Development, Exploration, Assessment, and Long-Term Follow-Up phase 1/2a). After routine SEEG planning, structural magnetic resonance imaging sequences were run through an FCD lesion detection algorithm to identify putative clusters. If the top three clusters were not already sampled, up to three additional SEEG electrodes were added. The primary outcome measure was the proportion of patients who had additional electrode contacts in the SEEG-defined seizure-onset zone (SOZ). RESULTS: Twenty patients (median age 12 years, range 4-18 years) were enrolled, one of whom did not undergo SEEG. Additional electrode contacts were part of the SOZ in 1 out of 19 patients while 3 out of 19 patients had clusters that were part of the SOZ but they were already implanted. A total of 16 additional electrodes were implanted in nine patients and there were no adverse events from the additional electrodes. INTERPRETATION: We demonstrate early-stage prospective clinical validation of a machine learning lesion detection algorithm used to aid the identification of the SOZ in children undergoing SEEG. We share key lessons learnt from this evaluation and emphasize the importance of robust prospective evaluation before routine clinical adoption of such algorithms

Light power resource availability for energy harvesting photovoltaics for self-powered IoT

As the Internet of Things (IoT) expands, the need for energy-efficient, self-powered devices increases and so a better understanding of the available energy resource is necessary. We examine the light power resource availability for energy harvesting photovoltaics (PV) in various environments and its potential for self-powered IoT applications. We analyse light sources, considering spectral distribution, intensity, and temporal variations, and evaluate the impact of location, seasonal variation, and time of day on light power availability. Additionally, we discuss human and building design factors, such as occupancy, room aspect, sensor placement, and décor, which influence light energy availability and therefore power for IoT electronics. We propose a best-case and non-ideal scenario in terms of light resource for energy-harvesting, and using a commercially available organic PV cell, show that the energy yield generated and available to the IoT electronics, can be anywhere between 0.7 mWh and 75 mWh per day, depending on the lighting conditions