A device for characterising the mechanical properties of the plantar soft tissue of the foot

Identification of potential plantar ulceration among diabetes patients using plantar soft tissue stiffness

This study investigates the relationship between plantar tissue stiffness and selected parameters, including age, diabetes mellitus (DM) duration, body mass index (BMI), and HbA_1c level. 70 diabetes patients with no foot problems were recruited. The plantar soft tissue at the 2nd sub-metatarsal head (MTH) pad was examined using the novel indentation system developed. The stiffness constant, K, was used to describe the tissue stiffness. The four factors (age, DM duration, BMI, and HbA_1c level) were plotted against the plantar tissue stiffness. The scatter plots revealed that a higher plantar tissue stiffness was usually associated with (1) BMI>25  kgm^-2, (2) HbA_1c score >10% (86 mmol/mol), and (3) DM duration >10 years. The three risk criteria were further evaluated using the binary classification test. The predictions were reported to be fairly accurate and reliable in detecting stiffened tissues. The study has successfully identified the strong association of BMI, HbA_1c, and DM duration with the plantar tissue properties. Special attention should be given to the high risk group with BMI>25 kgm^-2, HbA_1c score >10% (86 mmol/mol), and DM duration >10 years. The high diagnostic odds ratio attained suggests its potential usefulness in helping clinicians to diagnose diabetic foot more efficiently.

Micropipette force sensors for in vivo force measurements on single cells and multicellular microorganisms

Measuring forces from the piconewton to millinewton range is of great importance for the study of living systems from a biophysical perspective. The use of flexible micropipettes as highly sensitive force probes has become established in the biophysical community, advancing our understanding of cellular processes and microbial behavior. The micropipette force sensor (MFS) technique relies on measurement of the forces acting on a force-calibrated, hollow glass micropipette by optically detecting its deflections. The MFS technique covers a wide micro- and mesoscopic regime of detectable forces (tens of piconewtons to millinewtons) and sample sizes (micrometers to millimeters), does not require gluing of the sample to the cantilever, and allows simultaneous optical imaging of the sample throughout the experiment. Here, we provide a detailed protocol describing how to manufacture and calibrate the micropipettes, as well as how to successfully design, perform, and troubleshoot MFS experiments. We exemplify our approach using the model nematode Caenorhabditis elegans, but by following this protocol, a wide variety of living samples, ranging from single cells to multicellular aggregates and millimeter-sized organisms, can be studied in vivo, with a force resolution as low as 10 pN. A skilled (under)graduate student can master the technique in ~1-2 months. The whole protocol takes ~1-2 d to finish.

Automated Spatial Pattern Analysis for Identification of Foot Arch Height From 2D Foot Prints

Arch height is an important determinant for the risk of foot pathology, especially in an aging population. Current methods for analyzing footprints require substantial manual processing time. The current research investigated automated determination of foot type based on features derived from the Gabor wavelet utilizing digitized footprints to allow timely assessment of foot type and focused intervention. Two hundred and eighty footprints were collected, and area, perimeter, curvature, circularity, 2nd wavelet moment, mean bending energy (MBE), and entropy were determined using in house developed MATLAB codes. The results were compared to the gold standard using Spearman's Correlation coefficient and multiple linear regression models with significance set at 0.05. The proposed approach found MBE combined with foot perimeter to give the best results as shown by ANOVA (F_(2,211) = 10.18, p < 0.0001) with the mean ±SD of low, normal, and high arch being, respectively, 0.26 ± 0.025,.24 ± 0.021, and 0.23 ± 0.024. A clinical review of the new cut off values, as set by the first and the third quartiles of our sample, lead to reliability up to 87%. Our results suggest that automated wavelet-based foot type classification of 2D binary images of the plantar surface of the foot is comparable to current state-of-the-art methods providing a cost and time effective tool suitable for clinical diagnostics.