Determination of the Maturation Status of Dendritic Cells by Applying Pattern Recognition to High-Resolution Images

Deep learning strategy for small dataset from atomic force microscopy mechano-imaging on macrophages phenotypes

The cytoskeleton is involved during movement, shaping, resilience, and functionality in immune system cells. Biomarkers such as elasticity and adhesion can be promising alternatives to detect the status of cells upon phenotype activation in correlation with functionality. For instance, professional immune cells such as macrophages undergo phenotype functional polarization, and their biomechanical behaviors can be used as indicators for early diagnostics. For this purpose, combining the biomechanical sensitivity of atomic force microscopy (AFM) with the automation and performance of a deep neural network (DNN) is a promising strategy to distinguish and classify different activation states. To resolve the issue of small datasets in AFM-typical experiments, nanomechanical maps were divided into pixels with additional localization data. On such an enlarged dataset, a DNN was trained by multimodal fusion, and the prediction was obtained by voting classification. Without using conventional biomarkers, our algorithm demonstrated high performance in predicting the phenotype of macrophages. Moreover, permutation feature importance was employed to interpret the results and unveil the importance of different biophysical properties and, in turn, correlated this with the local density of the cytoskeleton. While our results were demonstrated on the RAW264.7 model cell line, we expect that our methodology could be opportunely customized and applied to distinguish different cell systems and correlate feature importance with biophysical properties to unveil innovative markers for diagnostics.

Segmentation and Morphology Computation of a Spiky Nanoparticle Using the Hourglass Neural Network

Morphological measurements of nanoparticles in electron microscopy images are tedious, laborious, and often succumb to human errors. Deep learning methods in artificial intelligence (AI) paved the way for automated image understanding. This work proposes a deep neural network (DNN) for the automated segmentation of a Au spiky nanoparticle (SNP) in electron microscopic images, and the network is trained with a spike-focused loss function. The segmented images are used for the growth measurement of the Au SNP. The auxiliary loss function captures the spikes of the nanoparticle, which prioritizes the detection of spikes in the border regions. The growth of the particles measured by the proposed DNN is as good as the measurement in manually segmented images of the particles. The proposed DNN composition with the training methodology meticulously segments the particle and consequently provides accurate morphological analysis. Furthermore, the proposed network is tested on an embedded system for integration with the microscope hardware for real-time morphological analysis.

Corneal epithelial dendritic cells, tear neuropeptides and corneal nerves continue to be affected more than 12 months after LASIK

PURPOSE

LASIK causes corneal nerve damage and may affect the neuro-immune crosstalk. This study examined the effects of LASIK on corneal epithelial dendritic cells (CEDC) density and morphology and explored their relationships with corneal nerves and tear neuropeptides. A grading system was developed to assess CEDC morphology.

METHODS

Intra- and inter-observer repeatability of the CEDC morphology grading system was established using kappa (κ). In vivo confocal microscope images of the central cornea were captured from 20 participants who had undergone LASIK 12-16 months earlier and 20 controls (age 18-32 years, 55%F). CEDC density was counted manually, and CEDC morphology was assessed using a new grading system. CEDC sub-types (contacting nerves [CEDCc] and not contacting nerves [CEDCnc]) were also assessed. Differences in CEDC density and morphology were examined using mixed models and chi-squared test. Relationships between CEDC and corneal nerve parameters and tear substance P were explored using Spearman's correlation.

RESULTS

Excellent intra- and inter-observer repeatability was demonstrated for the grading system (κ = 0.82-0.97). In post-LASIK participants, CEDC density was lower compared with controls (5 [0-34] vs. 21 [7-77] cells/mm² ; p = 0.01), and the proportion of CEDC with thick dendrites was higher (55%-73% vs. 11%-21%, p < 0.003). Higher tear substance P levels were associated with higher CEDC density (rho = 0.48, p = 0.003). Fewer nerve interconnections were observed in participants in whom CEDC had dendrites (p = 0.03). CEDC sub-types followed a similar pattern to CEDC.

CONCLUSIONS

The findings suggest that CEDC may remain altered more than 12 months post-LASIK. The association with substance P suggests a role for CEDC in corneal neurogenic inflammation.