Ultrasound elastography reliably identifies altered mechanical properties of burned soft tissues

Lipid bilayer-based biological nanoplatforms for sonodynamic cancer therapy

Sonodynamic therapy (SDT) has been developed as a promising alternative therapeutic modality for cancer treatment, involving the synergetic application of sonosensitizers and low-intensity ultrasound. However, the antitumor efficacy of SDT is significantly limited due to the poor performance of conventional sonosensitizers in vivo and the constrained tumor microenvironment (TME). Recent breakthroughs in lipid bilayer-based nanovesicles (LBBNs), including multifunctional liposomes, exosomes, and isolated cellular membranes, have brought new insights into the advancement of SDT. Despite their distinct sources and preparation methods, the lipid bilayer structure in common allows them to be functionalized in many comparable ways to serve as ideal nanocarriers against challenges arising from the tumor-specific sonosensitizer delivery and the complicated TME. In this review, we provide a comprehensive summary of the recent advances in LBBN-based SDT, with particular attention on how LBBNs can be engineered to improve the delivery efficiency of sonosensitizers and overcome physical, biological, and immune barriers within the TME for enhanced sonodynamic cancer therapy. We anticipate that this review will offer valuable guidance in the construction of LBBN-based nanosonosensitizers and contribute to the development of advanced strategies for next-generation sonodynamic cancer therapy.

A statistical machine learning approach linking molecular conformational changes to altered mechanical characteristics of skin due to thermal injury

This article develops statistical machine learning models to predict the mechanical properties of skin tissue subjected to thermal injury based on the Raman spectra associated with conformational changes of the molecules in the burned tissue. Ex vivo porcine skin tissue samples were exposed to controlled burn conditions at 200 °F for five different durations: (i) 10s, (ii) 20s, (iii) 30s, (iv) 40s, and (v) 50s. For each burn condition, Raman spectra of wavenumbers 500-2000 cm^-1 were measured from the tissue samples, and tensile testing on the same samples yielded their material properties, including, ultimate tensile strain, ultimate tensile stress, and toughness. Partial least squares regression models were established such that the Raman spectra, describing conformational changes in the tissue, could accurately predict ultimate tensile stress, toughness, and ultimate tensile strain of the burned skin tissues with R² values of 0.8, 0.8, and 0.7, respectively, using leave-two-out cross validation scheme. An independent assessment of the resultant models showed that amino acids, proteins & lipids, and amide III components of skin tissue significantly influence the prediction of the properties of the burned skin tissue. In contrast, amide I has a lesser but still noticeable effect. These results are consistent with similar observations found in the literature on the mechanical characterization of burned skin tissue.

Synthetic tissues lack the fidelity for the use in burn care simulators

This work compares the mechanical response of synthetic tissues used in burn care simulators from ten different manufacturers with that of ex vivo full thickness burned porcine skin as a surrogate for human skin tissues. This is of high practical importance since incorrect mechanical properties of synthetic tissues may introduce a negative bias during training due to the inaccurate haptic feedback from burn care simulator. A negative training may result in inadequately performed procedures, such as in escharotomy, which may lead to muscle necrosis endangering life and limb. Accurate haptic feedback in physical simulators is necessary to improve the practical training of non-expert providers for pre-deployment/pre-hospital burn care. With the U.S. Army's emerging doctrine of prolonged field care, non-expert providers must be trained to perform even invasive burn care surgical procedures when indicated. The comparison reported in this article is based on the ultimate tensile stress, ultimate tensile strain, and toughness that are measured at strain rates relevant to skin surgery. A multivariate analysis using logistic regression reveals significant differences in the mechanical properties of the synthetic and the porcine skin tissues. The synthetic and porcine skin tissues show a similar rate dependent behavior. The findings of this study are expected to guide the development of high-fidelity burn care simulators for the pre-deployment/pre-hospital burn care provider education.

Thermally damaged porcine skin is not a surrogate mechanical model of human skin

Porcine skin is considered a de facto surrogate for human skin. However, this study shows that the mechanical characteristics of full thickness burned human skin are different from those of porcine skin. The study relies on five mechanical properties obtained from uniaxial tensile tests at loading rates relevant to surgery: two parameters of the Veronda-Westmann hyperelastic material model, ultimate tensile stress, ultimate tensile strain, and toughness of the skin samples. Univariate statistical analyses show that human and porcine skin properties are dissimilar (p < 0.01) for each loading rate. Multivariate classification involving the five mechanical properties using logistic regression can successfully separate the two skin types with a classification accuracy exceeding 95% for each loading rate individually as well as combined. The findings of this study are expected to guide the development of effective training protocols and high-fidelity simulators to train burn care providers.

A deep learning model for burn depth classification using ultrasound imaging

Identification of burn depth with sufficient accuracy is a challenging problem. This paper presents a deep convolutional neural network to classify burn depth based on altered tissue morphology of burned skin manifested as texture patterns in the ultrasound images. The network first learns a low-dimensional manifold of the unburned skin images using an encoder-decoder architecture that reconstructs it from ultrasound images of burned skin. The encoder is then re-trained to classify burn depths. The encoder-decoder network is trained using a dataset comprised of B-mode ultrasound images of unburned and burned ex vivo porcine skin samples. The classifier is developed using B-mode images of burned in situ skin samples obtained from freshly euthanized postmortem pigs. The performance metrics obtained from 20-fold cross-validation show that the model can identify deep-partial thickness burns, which is the most difficult to diagnose clinically, with 99% accuracy, 98% sensitivity, and 100% specificity. The diagnostic accuracy of the classifier is further illustrated by the high area under the curve values of 0.99 and 0.95, respectively, for the receiver operating characteristic and precision-recall curves. A post hoc explanation indicates that the classifier activates the discriminative textural features in the B-mode images for burn classification. The proposed model has the potential for clinical utility in assisting the clinical assessment of burn depths using a widely available clinical imaging device.

Organic Sonosensitizers for Sonodynamic Therapy: From Small Molecules and Nanoparticles toward Clinical Development

Sonodynamic therapy (SDT) is a novel noninvasive therapeutic modality that combines low-intensity ultrasound and sonosensitizers. Versus photo-mediated therapy, SDT has the advantages of deeper tissue penetration, high accuracy, and less side effects. Sonosensitizers are critical for therapeutic efficacy during SDT and organic sonosensitizers are important because of their clear structure, easy monitoring, evaluation of drug metabolism, and clinical transformation. Notably, nanotechnology can be used in the field of sonosensitizers and SDT to overcome the inherent obstacles and achieve sustainable innovation. This review introduces organic small molecule sonosensitizers, nano organic sonosensitizers, and their clinical translation by providing ideas and references for the design of sonosensitizers and SDT so as to promote its transformation to clinical applications in the future.

Self-assembly of differentiated progenitor cells facilitates spheroid human skin organoid formation and planar skin regeneration

Self-assembly of solid organs from single cells would greatly expand applicability of regenerative medicine. Stem/progenitor cells can self-organize into micro-sized organ units, termed organoids, partially modelling tissue function and regeneration. Here we demonstrated 3D self-assembly of adult and induced pluripotent stem cell (iPSC)-derived fibroblasts, keratinocytes and endothelial progenitors into both, planar human skin in vivo and a novel type of spheroid-shaped skin organoids in vitro, under the aegis of human platelet lysate. Methods: Primary endothelial colony forming cells (ECFCs), skin fibroblasts (FBs) and keratinocytes (KCs) were isolated from human tissues and polyclonally propagated under 2D xeno-free conditions. Human tissue-derived iPSCs were differentiated into endothelial cells (hiPSC-ECs), fibroblasts (hiPSC-FBs) and keratinocytes (hiPSC-KCs) according to efficiency-optimized protocols. Cell identity and purity were confirmed by flow cytometry and clonogenicity indicated their stem/progenitor potential. Triple cell type floating spheroids formation was promoted by human platelet-derived growth factors containing culture conditions, using nanoparticle cell labelling for monitoring the organization process. Planar human skin regeneration was assessed in full-thickness wounds of immune-deficient mice upon transplantation of hiPSC-derived single cell suspensions. Results: Organoids displayed a distinct architecture with surface-anchored keratinocytes surrounding a stromal core, and specific signaling patterns in response to inflammatory stimuli. FGF-7 mRNA transfection was required to accelerate keratinocyte long-term fitness. Stratified human skin also self-assembled within two weeks after either adult- or iPSC-derived skin cell-suspension liquid-transplantation, healing deep wounds of mice. Transplant vascularization significantly accelerated in the presence of co-transplanted endothelial progenitors. Mechanistically, extracellular vesicles mediated the multifactorial platelet-derived trophic effects. No tumorigenesis occurred upon xenografting. Conclusion: This illustrates the superordinate progenitor self-organization principle and permits novel rapid 3D skin-related pharmaceutical high-content testing opportunities with floating spheroid skin organoids. Multi-cell transplant self-organization facilitates development of iPSC-based organ regeneration strategies using cell suspension transplantation supported by human platelet factors.

Real-time Burn Classification using Ultrasound Imaging

This article presents a real-time approach for classification of burn depth based on B-mode ultrasound imaging. A grey-level co-occurrence matrix (GLCM) computed from the ultrasound images of the tissue is employed to construct the textural feature set and the classification is performed using nonlinear support vector machine and kernel Fisher discriminant analysis. A leave-one-out cross-validation is used for the independent assessment of the classifiers. The model is tested for pair-wise binary classification of four burn conditions in ex vivo porcine skin tissue: (i) 200 °F for 10 s, (ii) 200 °F for 30 s, (iii) 450 °F for 10 s, and (iv) 450 °F for 30 s. The average classification accuracy for pairwise separation is 99% with just over 30 samples in each burn group and the average multiclass classification accuracy is 93%. The results highlight that the ultrasound imaging-based burn classification approach in conjunction with the GLCM texture features provide an accurate assessment of altered tissue characteristics with relatively moderate sample sizes, which is often the case with experimental and clinical datasets. The proposed method is shown to have the potential to assist with the real-time clinical assessment of burn degrees, particularly for discriminating between superficial and deep second degree burns, which is challenging in clinical practice.

Burn-related Collagen Conformational Changes in ex vivo Porcine Skin using Raman Spectroscopy

This study utilizes Raman spectroscopy to analyze the burn-induced collagen conformational changes in ex vivo porcine skin tissue. Raman spectra of wavenumbers 500-2000 cm^-1 were measured for unburnt skin as well as four different burn conditions: (i) 200 °F for 10 s, (ii) 200 °F for the 30 s, (iii) 450 °F for 10 s and (iv) 450 °F for 30 s. The overall spectra reveal that protein and amino acids-related bands have manifested structural changes including the destruction of protein-related functional groups, and transformation from α-helical to disordered structures which are correlated with increasing burn severity. The deconvolution of the amide I region (1580-1720 cm^-1) and the analysis of the sub-bands reveal a change of the secondary structure of the collagen from the α-like helix dominated to the β-aggregate dominated one. Such conformational changes may explain the softening of mechanical response in burnt tissues reported in the literature.