Latent diffusion augmentation enhances deep learning analysis of neuro-morphology in limbal stem cell deficiency.

Introduction: Limbal Stem Cell Deficiency (LSCD) is a blinding corneal disease characterized by the loss of function or deficiency in adult stem cells located at the junction between the cornea and the sclera (i.e., the limbus), namely the limbal stem cells (LSCs). Recent advances in in vivo imaging technology have improved disease diagnosis and staging to quantify several biomarkers of in vivo LSC function including epithelial thickness measured by anterior segment optical coherence tomography, and basal epithelial cell density and subbasal nerve plexus by in vivo confocal microscopy. A decrease in central corneal sub-basal nerve density and nerve fiber and branching number has been shown to correlate with the severity of the disease in parallel with increased nerve tortuosity. Yet, image acquisition and manual quantification require a high level of expertise and are time-consuming. Manual quantification presents inevitable interobserver variability. Methods: The current study employs a novel deep learning approach to classify neuron morphology in various LSCD stages and healthy controls, by integrating images created through latent diffusion augmentation. The proposed model, a residual U-Net, is based in part on the InceptionResNetV2 transfer learning model. Results: Deep learning was able to determine fiber number, branching, and fiber length with high accuracy (R2 of 0.63, 0.63, and 0.80, respectively). The model trained on images generated through latent diffusion on average outperformed the same model when trained on solely original images. The model was also able to detect LSCD with an AUC of 0.867, which showed slightly higher performance compared to classification using manually assessed metrics. Discussion: The results suggest that utilizing latent diffusion to supplement training data may be effective in bolstering model performance. The results of the model emphasize the ability as well as the shortcomings of this novel deep learning approach to predict various nerve morphology metrics as well as LSCD disease severity.

Manipulating Relative Permittivity for High-Performance Wearable Triboelectric Nanogenerators.

As the world marches into the era of the Internet of Things (IoT), the practice of human health care is on the cusp of a revolution, driven by an unprecedented level of personalization enabled by a variety of wearable bioelectronics. A sustainable and wearable energy solution is highly desired , but challenges still remain in its development. Here, we report a high-performance wearable electricity generation approach by manipulating the relative permittivity of a triboelectric nanogenerator (TENG). A compatible active carbon (AC)-doped polyvinylidene fluoride (AC@PVDF) composite film was invented with high relative permittivity and a specific surface area for wearable biomechanical energy harvesting. Compared with the pure PVDF, the 0.8% AC@PVDF film-based TENG obtained an enhancement in voltage, current, and power by 2.5, 3.5, and 9.8 times, respectively. This work reports a stable, cost-effective, and scalable approach to improve the performance of the triboelectric nanogenerator for wearable biomechanical energy harvesting, thus rendering a sustainable and pervasive energy solution for on-body electronics.