Quantifying innervation facilitated by deep learning in wound healing

Learning-based inference of longitudinal image changes: Applications in embryo development, wound healing, and aging brain

Longitudinal imaging data are routinely acquired for health studies and patient monitoring. A central goal in longitudinal studies is tracking relevant change over time. Traditional methods remove nuisance variation with custom pipelines to focus on significant changes. In this work, we present a machine learning-based method that automatically ignores irrelevant changes and extracts the time-varying signal of interest. Our method, called Learning-based Inference of Longitudinal imAge Changes (LILAC), performs a pairwise comparison of longitudinal images in order to make a temporal difference prediction. LILAC employs a convolutional Siamese architecture to extract feature pairs, followed by subtraction and a bias-free fully connected layer to learn meaningful temporal image differences. We first showcase LILAC's ability to capture key longitudinal changes by simply training it to predict the temporal ordering of images. In our experiments, temporal ordering accuracy exceeded 0.98, and predicted time differences were strongly correlated with actual changes in relevant variables (Pearson Correlation Coefficient r = 0.911 with embryo phase change, and r = 0.875 with time interval in wound healing). Next, we trained LILAC to explicitly predict specific targets, such as the change in clinical scores in patients with mild cognitive impairment. LILAC models achieved over a 40% reduction in root mean square error compared to baseline methods. Our empirical results demonstrate that LILAC effectively localizes and quantifies relevant individual-level changes in longitudinal imaging data, offering valuable insights for studying temporal mechanisms or guiding clinical decisions.

Current status, challenges, and prospects of artificial intelligence applications in wound repair theranostics

Skin injuries caused by physical, pathological, and chemical factors not only compromise appearance and barrier function but can also lead to life-threatening microbial infections, posing significant challenges for patients and healthcare systems. Artificial intelligence (AI) technology has demonstrated substantial advantages in processing and analyzing image information. Recently, AI-based methods and algorithms, including machine learning, deep learning, and neural networks, have been extensively explored in wound care and research, providing effective clinical decision support for wound diagnosis, treatment, prognosis, and rehabilitation. However, challenges remain in achieving a closed-loop care system for the comprehensive application of AI in wound management, encompassing wound diagnosis, monitoring, and treatment. This review comprehensively summarizes recent advancements in AI applications in wound repair. Specifically, it discusses AI's role in injury type classification, wound measurement (including area and depth), wound tissue type classification, wound monitoring and prediction, and personalized treatment. Additionally, the review addresses the challenges and limitations AI faces in wound management. Finally, recommendations for the application of AI in wound repair are proposed, along with an outlook on future research directions, aiming to provide scientific evidence and technological support for further advancements in AI-driven wound repair theranostics.

Decellularized Biohybrid Nerve Promotes Motor Axon Projections

Developing nerve grafts with intact mesostructures, superior conductivity, minimal immunogenicity, and improved tissue integration is essential for the treatment and restoration of neurological dysfunctions. A key factor is promoting directed axon growth into the grafts. To achieve this, biohybrid nerves are developed using decellularized rat sciatic nerve modified by in situ polymerization of poly(3,4-ethylenedioxythiophene) (PEDOT). Nine biohybrid nerves are compared with varying polymerization conditions and cycles, selecting the best candidate through material characterization. These results show that a 1:1 ratio of FeCl3 oxidant to ethylenedioxythiophene (EDOT) monomer, cycled twice, provides superior conductivity (>0.2 mS cm-1), mechanical alignment, intact mesostructures, and high compatibility with cells and blood. To test the biohybrid nerve's effectiveness in promoting motor axon growth, human Spinal Cord Spheroids (hSCSs) derived from HUES 3 Hb9:GFP cells are used, with motor axons labeled with green fluorescent protein (GFP). Seeding hSCS onto one end of the conduit allows motor axon outgrowth into the biohybrid nerve. The construct effectively promotes directed motor axon growth, which improves significantly after seeding the grafts with Schwann cells. This study presents a promising approach for reconstructing axonal tracts in humans.

Cutaneous-immuno-neuro-endocrine (CINE) system: A complex enterprise transforming skin into a super organ

Skin is now emerging as a complex realm of three chief systems viz. immune system, nervous system, and endocrine system. The cells involved in their intricate crosstalk, namely native skin cells, intra-cutaneous immune cells and cutaneous sensory neurons have diverse origin and distinct functions. However, recent studies have explored their role beyond their pre-defined functional boundaries, such that the cells shun their traditional functions and adopt unconventional roles. For example, the native skin cells, apart from providing for basic structural framework of skin, also perform special immune functions and participate in extensive neuro-endocrine circuitry, which were traditionally designated as functions of cutaneous resident immune cells and sensory neurons respectively. At the cellular level, this unique collaboration is brought out by special molecules called neuromediators including neurotransmitters, neuropeptides, neurotrophins, neurohormones and cytokines/chemokines. While this intricate crosstalk is essential for maintaining cutaneous homeostasis, its disruption is seen in various cutaneous diseases. Recent study models have led to a paradigm shift in our understanding of pathophysiology of many such disorders. In this review, we have described in detail the interaction of immune cells with neurons and native skin cells, role of neuromediators, the endocrine aspect in skin and current understanding of cutaneous neuro-immuno-endocrine loop in one of the commonest skin diseases, psoriasis. An accurate knowledge of this unique crosstalk can prove crucial in understanding the pathophysiology of different skin diseases and allow for generation of targeted therapeutic modalities.