Fox, M. C. | bioRxiv Science

bioRxiv2022

Effects of Body Mass on Leg and Vertical Stiffness in Running Humans

Numerous cross-species comparisons have examined the scaling of gait parameters with respect to body mass (i.e., allometry), but few have done so within humans. This study examined how leg and vertical stiffness, force, displacement, and leg spring angle scaled in 64 healthy adults of varying body masses during slow and fast leg-length-adjusted running speeds. We calculated scaling patterns for stiffness and its components via kinematic and kinetic data using log-log regressions with 95% confidence/highest density intervals. To determine if the chosen statistical method influenced conclusions about scaling patterns, we compared regression results across three statistical methods, ordinary least squares (OLS) regression, linear mixed models (LMM), and Bayesian linear mixed models (BLMM). We also performed sex-specific analyses to determine if each sex revealed similar scaling patterns as the pooled sample. In the pooled sample, all variables scaled according to the isometric expectations, suggesting that different-sized humans move in a similar manner. Sex-specific analyses revealed similar patterns of isometry in all variables, except for vertical stiffness, which displayed slight negative allometry (i.e., lower than expected stiffness) in both sexes at the slow speed and negative allometry in females during fast running. Model choice did not significantly affect results, and scaling patterns were the same regardless of the statistical method employed.

bioengineering↗

bioRxiv2022

Deep learning on reflectance confocal microscopy improves Raman spectral diagnosis of basal cell carcinoma

SignificanceRaman spectroscopy may be useful to assist Mohs micrographic surgery for skin cancer diagnosis; however, the specificity of Raman spectroscopy is limited by the high spectral similarity between tumors and normal tissues structures such as epidermis and hair follicles. Reflectance confocal microscopy (RCM) can provide imaging guidance with morphological and cytological details similar to histology. Combining Raman spectroscopy with deep-learning-aided RCM has the potential to improve the diagnostic accuracy of Raman without requiring additional input from the clinician. AimWe seek to improve the specificity of Raman for basal cell carcinoma (BCC) by integrating information from RCM images using an Artificial Neural Network. ApproachA Raman biophysical model was used in prior work to classify BCC tumors from surrounding normal tissue structures. 191 RCM images were collected from the same site as the Raman data and served as inputs to train two ResNet50 networks. The networks selected the hair structure images and epidermis images respectively within all the images corresponding to the positive predictions of the Raman Biophysical Model. ResultsDeep learning on RCM images removes 54% of false positive predictions from the Raman Biophysical Model result and keeps the sensitivity as 100%. The specificity was improved from 84.8% by using Raman spectra alone to 93.0% by integrating Raman spectra with RCM images ConclusionsCombining Raman spectroscopy with deep-learning-aided RCM imaging is a promising tool to guide tumor resection surgery.

bioengineering↗