Optical coherence elastography to evaluate depth-resolved elasticity of tissue

Combined skin injury model from airblast overpressure and seawater immersion in rats: establishment, characterization, and mechanistic insights

In maritime operations, individuals often face the threat of combined injury caused by airblast overpressure and seawater immersion. Airblast overpressure, induced by explosions, leads to significant internal damage despite the absence of visible open wounds. Seawater immersion exacerbates injuries due to its high osmolarity, microbial content, and thermal conductivity. Given the critical role of the skin as the body's largest organ, understanding its specific injuries in this scenario is imperative but currently underexplored. To bridge this gap, the study developed a novel rat skin combined injury model (RSCIM) in which rats were exposed to calibrated airblast overpressure followed by immediate seawater immersion. Physical simulations, histopathological examinations, and immunological assessments were used to confirm the model's accuracy. Specifically, finite element analysis reveals that the epidermal layer could effectively disperse and resist the immediate effects of overpressure. Histologically, the epidermal layer after combined injury maintained a continuous and complete structure. The collagen fibers of dermis were dispersed and broken. There were scattered capillaries, red blood cells and no skin appendages within the adipose layer. The muscle layer was manifested by deformation and breakage of muscle fibers. The fluorescence intensity of iNOS tended to decrease as the distance from the explosion source increased, which demonstrated significant inflammatory effects in the skin with combined injury. Furthermore, the transcriptome sequencing data revealed major physiological changes caused by combined injury, including inflammatory response, ion transport, biomechanical response, apoptosis, etc. Notably, S100A9 serves as a critical marker for combined injuries in RSCIM, but its expression characteristics and localization during tissue injury still need to be further explored. The model provides a robust foundation for exploring the combined injury mechanisms of airblast overpressure and seawater immersion and developing targeted therapeutic approaches.

VP-net: an end-to-end deep learning network for elastic wave velocity prediction in human skin in vivo using optical coherence elastography

Introduction

Acne vulgaris, one of the most common skin conditions, affects up to 85% of late adolescents, currently no universally accepted assessment system. The biomechanical properties of skin provide valuable information for the assessment and management of skin conditions. Wave-based optical coherence elastography (OCE) quantitatively assesses these properties of tissues by analyzing induced elastic wave velocities. However, velocity estimation methods require significant expertise and lengthy image processing times, limiting the clinical translation of OCE technology. Recent advances in machine learning offer promising solutions to simplify velocity estimation process.

Methods

In this study, we proposed a novel end-to-end deep-learning model, named velocity prediction network (VP-Net), aiming to accurately predict elastic wave velocity from raw OCE data of in vivo healthy and abnormal human skin. A total of 16,424 raw phase slices from 1% to 5% agar-based tissue-mimicking phantoms, 28,270 slices from in vivo human skin sites including the palm, forearm, back of the hand from 16 participants, and 580 slices of facial closed comedones were acquired to train, validate, and test VP-Net.

Results

VP-Net demonstrated highly accurate velocity prediction performance compared to other deep-learning-based methods, as evidenced by small evaluation metrics. Furthermore, VP-Net exhibited low model complexity and parameter requirements, enabling end-to-end velocity prediction from a single raw phase slice in 1.32 ms, enhancing processing speed by a factor of ∼100 compared to a conventional wave velocity estimation method. Additionally, we employed gradient-weighted class activation maps to showcase VP-Net's proficiency in discerning wave propagation patterns from raw phase slices. VP-Net predicted wave velocities that were consistent with the ground truth velocities in agar phantom, two age groups (20s and 30s) of multiple human skin sites and closed comedones datasets.

Discussion

This study indicates that VP-Net could rapidly and accurately predict elastic wave velocities related to biomechanical properties of in vivo healthy and abnormal skin, offering potential clinical applications in characterizing skin aging, as well as assessing and managing the treatment of acne vulgaris.

Correlation analysis between texture features and elasticity of skin hyperspectral images in the near-infrared band

BACKGROUND/PURPOSE

Skin elasticity was used to evaluate healthy and diseased skin. Correlation analysis between image texture characteristics and skin elasticity was performed to study the feasibility of assessing skin elasticity using a non-contact method.

MATERIALS AND METHODS

Skin images in the near-infrared band were acquired using a hyperspectral camera, and skin elasticity was obtained using a skin elastimeter. Texture features of the mean, standard deviation, entropy, contrast, correlation, homogeneity, and energy were extracted from the acquired skin images, and a correlation analysis with skin elasticity was performed.

RESULTS

The texture features, and skin elasticity of skin images in the near-infrared band had the highest correlation on the side of eye and under of arm, and the mean and correlation were features of texture suitable for distinguishing skin elasticity according to the body part.

CONCLUSION

In this study, we performed elasticity and correlation analyses for various body parts using the texture characteristics of skin hyperspectral images in the near-infrared band, confirming a significant correlation in some body parts. It is expected that this will be used as a cornerstone of skin elasticity evaluation research using non-contact methods.

Bioinspired Young's Modulus-Hierarchical E-Skin with Decoupling Multimodality and Neuromorphic Encoding Outputs to Biosystems

As key interfaces for the disabled, optimal prosthetics should elicit natural sensations of skin touch or proprioception, by unambiguously delivering the multimodal signals acquired by the prosthetics to the nervous system, which still remains challenging. Here, a bioinspired temperature-pressure electronic skin with decoupling capability (TPD e-skin), inspired by the high-low modulus hierarchical structure of human skin, is developed to restore such functionality. Due to the bionic dual-state amplifying microstructure and contact resistance modulation, the MXene TPD e-skin exhibits high sensitivity over a wide pressure range and excellent temperature insensitivity (91.2% reduction). Additionally, the high-low modulus structural configuration enables the pressure insensitivity of the thermistor. Furthermore, a neural model is proposed to neutrally code the temperature-pressure signals into three types of nerve-acceptable frequency signals, corresponding to thermoreceptors, slow-adapting receptors, and fast-adapting receptors. Four operational states in the time domain are also distinguished after the neural coding in the frequency domain. Besides, a brain-like machine learning-based fusion process for frequency signals is also constructed to analyze the frequency pattern and achieve object recognition with a high accuracy of 98.7%. The TPD neural system offers promising potential to enable advanced prosthetic devices with the capability of multimodality-decoupling sensing and deep neural integration.

Optical coherence elastography based on inverse compositional Gauss-Newton digital volume correlation with second-order shape function

A digital volume correlation (DVC)-based optical coherence elastography (OCE) method with inverse compositional Gauss-Newton (IC-GN) algorithm and second-order shape function is presented in this study. The systematic measurement errors of displacement and strain from our OCE method were less than 0.2 voxel and 4 × 10^-4, respectively. Second-order shape function could better match complex deformation and decrease speckle rigidity-induced error. Compared to conventional methods, our OCE method could track a larger strain range up to 0.095 and reduce relative error by 30-50%. This OCE method has the potential to become an effective tool in characterising mechanical properties of biological tissue.