Biology subjects

Sebastian, F.

Publications and source records attributed to Sebastian, F..

3 recordsLinked to original sources

bioRxiv2026

Nanoscale rheological heterogeneity revealed by Single Particle orientation Tracking (SPoT) of ultrashort carbon nanotubes in brain tissue

Transport in complex biological tissues is governed by local rheological heterogeneity at the nanoscale, yet probing such environments deep inside living systems remains challenging. Here, we introduce an orientation-sensitive single-particle tracking (SPoT) approach that simultaneously resolves translational and rotational dynamics of individual carbon nanotubes deep within biological tissue. By exploiting the intrinsic dipole-like emission and short-wave infrared luminescence of carbon nanotubes enhanced through the incorporation of quantum color-centers our method enables long-duration tracking with high signal-to-noise ratio in optically dense environments. Crucially, the length of these nanotubes can be precisely shortened down to a few tens of nanometers to adapt to diffusion environmental dimensions, further optimizing the tracking applicability. SPoT of single carbon nanotubes provides access to relative changes in local viscosity, steric constraints, and environmental anisotropy. When applied to the brain extracellular space, SPoT demonstrates that local variations in the translational and rotational diffusion of tracers are heterogeneous and not systematically correlated. By combining trajectory-based signatures of confinement with rotational dynamics, this analysis allows the local contributions of effective viscosity and spatial tortuosity to be disentangled. By enabling combined translational and rotational tracking of nano-emitters over unprecedented depths and timescales, this work establishes a new framework for probing nanoscale transport and rheological heterogeneity in intact biological tissues and more generally in complex diffusive environments.

biophysics↗

bioRxiv2024

Capturing sclera anisotropy using direct collagen fiber models. Linking microstructure to macroscopic mechanical properties.

Because of the crucial role of collagen fibers on soft tissue mechanics, there is great interest in techniques to incorporate them in computational models. Recently we introduced a direct fiber modeling approach for sclera based on representing the long-interwoven fibers. Our method differs from the conventional continuum approach to modeling sclera that homogenizes the fibers and describes them as statistical distributions for each element. At large scale our method captured gross collagen fiber bundle architecture from histology and experimental intraocular pressure-induced deformations. At small scale, a direct fiber model of a sclera sample reproduced equi-biaxial experimental behavior from the literature. In this study our goal was a much more challenging task for the direct fiber modeling: to capture specimen-specific 3D fiber architecture and anisotropic mechanics of four sclera samples tested under equibiaxial and four non-equibiaxial loadings. Samples of sclera from three eyes were isolated and tested in five biaxial loadings following an approach previously reported. Using microstructural architecture from polarized light microscopy we then created specimen-specific direct fiber models. Model fiber orientations agreed well with the histological information (adjusted R2s>0.89). Through an inverse-fitting process we determined model characteristics, including specimen-specific fiber mechanical properties to match equibiaxial loading. Interestingly, the equibiaxial properties also reproduced all the non-equibiaxial behaviors. These results indicate that the direct fiber modeling method naturally accounted for tissue anisotropy within its fiber structure. Direct fiber modeling is therefore a promising approach to understand how macroscopic behavior arises from microstructure.

bioengineering↗

bioRxiv2021

Computer-Assisted Mitotic Count Using a Deep Learning-based Algorithm Improves Inter-Observer Reproducibility and Accuracy in canine cutaneous mast cell tumors

The mitotic count (MC) is an important histological parameter for prognostication of malignant neoplasms. However, it has inter- and intra-observer discrepancies due to difficulties in selecting the region of interest (MC-ROI) and in identifying/classifying mitotic figures (MFs). Recent progress in the field of artificial intelligence has allowed the development of high-performance algorithms that may improve standardization of the MC. As algorithmic predictions are not flawless, the computer-assisted review by pathologists may ensure reliability. In the present study we have compared partial (MC-ROI preselection) and full (additional visualization of MF candidate proposal and display of algorithmic confidence values) computer-assisted MC analysis to the routine (unaided) MC analysis by 23 pathologists for whole slide images of 50 canine cutaneous mast cell tumors (ccMCTs). Algorithmic predictions aimed to assist pathologists in detecting mitotic hotspot locations, reducing omission of MF and improving classification against imposters. The inter-observer consistency for the MC significantly increased with computer assistance (interobserver correlation coefficient, ICC = 0.92) compared to the unaided approach (ICC = 0.70). Classification into prognostic stratifications had a higher accuracy with computer assistance. The algorithmically preselected MC-ROIs had a consistently higher MCs than the manually selected MC-ROIs. Compared to a ground truth (developed with immunohistochemistry for phosphohistone H3), pathologist performance in detecting individual MF was augmented when using computer assistance (F1-score of 0.68 increased to 0.79) with a reduction in false negatives by 38%. The results of this study prove that computer assistance may lead to a more reproducible and accurate MCs in ccMCTs.

pathology↗