Osteocytes locally remodel their surrounding tissue through perilacunar canalicular remodeling (PLR). During lactation, osteocytes remove minerals to satisfy the metabolic demand, resulting in increased lacunar volume, quantifiable with synchrotron X-ray radiation micro-tomography (SRµCT). Although the effects of lactation on PLR are well-studied, it remains unclear whether PLR occurs uniformly throughout the bone and what mechanisms prevent PLR from undermining bone quality. We used SRµCT imaging to conduct an in-depth spatial analysis of the impact of lactation and osteocyte-intrinsic MMP13 deletion on PLR in murine bone. We found larger lacunae undergoing PLR are located near canals in the mid-cortex or endosteum. We show lactation-induced hypomineralization occurs 14 µm away from lacunar edges, past a hypermineralized barrier. Our findings reveal that osteocyte-intrinsic MMP13 is crucial for lactation-induced PLR near lacunae in the mid-cortex but not for whole-bone resorption. This research highlights the spatial control of PLR on mineral distribution during lactation.

Acute and chronic wounds involving deeper layers of the skin are often not adequately healed by dressings alone and require therapies such as skin grafting, skin substitutes, or growth factors. Here we report the development of an autologous heterogeneous skin construct (AHSC) that aids wound closure. AHSC is manufactured from a piece of healthy full-thickness skin. The manufacturing process creates multicellular segments, which contain endogenous skin cell populations present within hair follicles. These segments are physically optimized for engraftment within the wound bed. The ability of AHSC to facilitate closure of full thickness wounds of the skin was evaluated in a swine model and clinically in 4 patients with wounds of different etiologies. Transcriptional analysis demonstrated high concordance of gene expression between AHSC and native tissues for extracellular matrix and stem cell gene expression panels. Swine wounds demonstrated complete wound epithelialization and mature stable skin by 4 months, with hair follicle development in AHSC-treated wounds evident by 15 weeks. Biomechanical, histomorphological, and compositional analysis of the resultant swine and human skin wound biopsies demonstrated the presence of epidermal and dermal architecture with follicular and glandular structures that are similar to native skin. These data suggest that treatment with AHSC can facilitate wound closure.

Acute and chronic wounds involving deeper layers of the skin are often not adequately healed by dressings alone and require therapies such as skin grafting, skin substitutes, or growth factors. Here we report the development of an autologous heterogeneous skin construct (AHSC) that aids wound closure. AHSC is manufactured from a piece of healthy full-thickness skin. The manufacturing process creates multicellular segments, which contain endogenous skin cell populations present within hair follicles. These segments are physically optimized for engraftment within the wound bed. The ability of AHSC to facilitate closure of full thickness wounds of the skin was evaluated in a swine model and clinically in 4 patients with wounds of different etiologies. Transcriptional analysis demonstrated high concordance of gene expression between AHSC and native tissues for extracellular matrix and stem cell gene expression panels. Swine wounds demonstrated complete wound epithelialization and mature stable skin by 4 months, with hair follicle development in AHSC-treated wounds evident by 15 weeks. Biomechanical, histomorphological, and compositional analysis of the resultant swine and human skin wound biopsies demonstrated the presence of epidermal and dermal architecture with follicular and glandular structures that are similar to native skin. These data suggest that treatment with AHSC can facilitate wound closure.

When studying bone fragility diseases, it is difficult to identify which factors reduce bone’s resistance to fracture because these diseases alter bone at many length scales. Here, we investigate the contribution of nanoscale collagen behavior on macroscale toughness and microscale toughening mechanisms using a bovine heat-treatment fragility model. This model is assessed by developing an in situ toughness testing technique for synchrotron radiation micro-computed tomography to study the evolution of microscale crack growth in 3D. Low-dose imaging is employed with deep learning to denoise images while maintaining bone’s innate mechanical properties. We show that collagen damage significantly reduces macroscale toughness and post-yield properties. We also find that bone samples with a compromised collagen network have reduced amounts of crack deflection, the main microscale mechanism of fracture resistance. This research demonstrates that collagen damage at the nanoscale adversely affects bone’s toughening mechanisms at the microscale and reduces the overall toughness of bone.

When studying metabolic disease, it is essential to investigate the disease's effect on multiple tissues and identify any communication, or cross-talk, between organs, tissues, and cells. In bone marrow cancer, adipose tissue triggers inflammation and growth of malignant plasma cells within the bone marrow and results in localized bone loss. Synchrotron radiation microtomography imaging enables 3D quantitative analysis of bone and adipose tissues and provides high resolution to observe local changes in tissue microstructure. However, optimal imaging techniques differ for hard bone tissues (absorption imaging) and soft adipose tissues (phase-contrast imaging). Here we introduce a new technique that leverages image reconstruction and deep learning in combination with the high-resolution imaging capabilities of synchrotron radiation microtomography to gain insight into the marrow microenvironment of human bone samples. This approach allowed for successful tissue segmentation and analysis of human core samples. Using high-resolution images such as these could allow for a better understanding of early bone-related changes that may predict disease progression or bone fractures.

A growing remote sensing network comprised of consumer dashcams presents Departments of Transportation (DOTs) worldwide with opportunities to dramatically reduce the costs and effort associated with monitoring and maintaining hundreds of thousands of sign assets on public roadways. However, many technical challenges confront the applications and technologies that will enable this transformation of roadway maintenance. This paper highlights an efficient approach to the problem of detection and classification of more than 600 classes of traffic signs in the United States, as defined in the Manual on Uniform Traffic Control Devices (MUTCD). Given the variability of specifications and the quality of images and metadata collected from consumer dashcams, a deep learning approach offers an efficient development tool to small organizations that want to leverage this data type for detection and classification. This paper presents a two-step process, a detection network that locates signs in dashcam images and a classification network that first extracts the bounding box from the previous detection to assign a specific sign class from over 600 classes of signs. The detection network is trained using labeled data from dashcams in Nashville, Tennessee, and a combination of real and synthetic data is used to train the classification network. The architecture presented here was applied to real-world image data provided by the Utah Department of Transportation and Blyncsy, Inc., and achieved modest results (test accuracy of 0.47) with a relatively low development time.

BACKGROUND:Swine dorsum is commonly utilized as a model for studying skin wounds and assessment of dermatological and cosmetic medicaments. The human abdomen is a common location for dermatological intervention.OBJECTIVE:This study provides a correlation between spectral, mechanical, and structural characterization techniques, utilized for evaluating human abdominal skin and swine dorsum.METHODS:Raman spectroscopy (RS), tensile testing, ballistometry, AFM, SEM, and MPM were utilized to characterize and compare full-thickness skin properties in swine and human model.RESULTS:RS of both species' skin types revealed a similar assignment of vibrations in the fingerprint and the high wavenumber spectral regions. Structural imaging and mechanical characterization using ballistometry and tensile testing displayed differences in the inherent functional properties of human and swine skin. These differences correlated with variations in the Raman peak ratios, collagen intensity measured using SEM and MPM and collagen density measured using AFM.CONCLUSION:A comprehensive evaluation of swine skin as a suitable substitute for human skin for mechanical and structural comparisons was performed. This data should be considered for better understanding the swine skin model for cutaneous drug delivery and wound applications. Additionally, correlation between RS, tensile testing, AFM, SEM, and MPM was performed as skin characterization tools.

Raman spectroscopy permits label-free molecular quantitation of biological samples in situ in a non-destructive manner. Combining machine learning with Raman spectroscopy has increased its potential for use in molecular imaging and discrimination of living cells and tissues in biological research fields. In this work, Raman spectroscopy was paired with machine learning techniques to classify specimen of similar tissues. Raman spectra of rat long bone, rabbit long bone, and rabbit crania were collected and classified into their respective categories. The spectra were truncated to the range of 400 to 1800 wavenumbers. To train and validate the machine learning algorithms, the data were randomly split such that 80% (n = 499) of the data were used for training, and 20% (n = 125) were used for validation. Three approaches were taken to prepare the data for classification. The first approach utilized all Raman intensities between 400 and 1800 wavenumbers to perform the classification. The second approach reduced the dimensions of the dataset using Principal Component Analysis (PCA) prior to performing classification. The third approach also reduced the dimensions of the dataset by extracting intensities of peaks that are of interest for bone analysis and using these peaks for classification. The peaks chosen were Amide I, Amide III, Proline, CH2 wag, and Carbonate. Raman spectra were classified using supervised learning techniques for each data preparation approach. The supervised methods include Support Vector Machine (SVM), Decision Tree, Random Forrest, and Naïve Bayes. The three groups were successfully sorted into their respective classes by the applied classification algorithms. The most successful classification models were achieved by reducing the dataset to peaks of interest, and performing classification utilizing Support Vector Machine achieving a validation accuracy up to 98.40%. This proof of concept has potential to be applied to numerous research applications that require sensitive discrimination between similar tissues.

Autologous bone grafts are commonly used to treat large bone defects. Though autologous bone grafts have high rates of success, they are limited by availability of donor tissue and may not be suitable for all treatment pathologies. Allografts are an attractive alternative as they do not utilize tissue from the patient but have a higher risk of infection and graft failure. In this study, an autologous homologous bone construct (AHBC) derived from viable bone, was compared to autologous bone grafts and demineralized bone matrix in rabbit models of critical-sized cranial defects and spinal fusion. AHBC is made from a small bone harvest obtained from an uninjured area of the patient. Without any exogenous supplementation or culturing, the AHBC is expeditiously deployed to the treatment site, where it initiates osteogenesis and osteoinduction and closes the defect from the inside out with cortico-cancellous bone. Treated defects were assessed using imaging modalities (micro CT, confocal, SEM, multiphoton, Raman spectroscopy), molecular and proteomics analysis, as well as mechanical testing. AHBC performed as well as autograft in all modalities and exceeded autograft in several. Both AHBC and autograft were observed to have more positive outcomes than DBM+BMP2 in both cranioplasty and arthrodesis models. Clinical significance: AHBC was able to regenerate cortical and cancellous bone in cranioplasty and spinal arthrodesis translational models and is a viable alternative to autografts and allografts.

Raman spectroscopy has been used for decades to detect and identify biological substances as it provides specific molecular information. Spectra collected from biological samples are often complex, requiring the aid of data truncation techniques such as principal component analysis (PCA) and multivariate classification methods. Classification results depend on the proper selection of principal components (PCs) and how PCA is performed (scaling and/or centering). There are also guidelines for choosing the optimal number of PCs such as a scree plot, Kaiser criterion, or cumulative percent variance. The goal of this research is to evaluate these methods for best implementation of PCA and PC selection to classify Raman spectra of bacteria. Raman spectra of three different isolates of mycobacteria ( Mycobacterium sp. JLS, Mycobacterium sp. KMS, Mycobacterium sp. MCS) were collected and then passed through PCA and linear discriminant analysis for classification. Principal component analysis implementation as well as PC selection was evaluated by comparing the highest possible classification accuracies against accuracies determined by PC selection methods for each centering and scaling option. Centered and unscaled data provided the best results when selecting PCs based on cumulative percent variance.

Traditional bacterial identification methods take one to two days to complete, relying on large bacteria colonies for visual identification. In order to decrease this analysis time in a cost-effective manner, a method to sort and concentrate bacteria based on the bacteria's characteristics itself is needed. One example of such a method is dielectrophoresis, which has been used by researchers to separate bacteria from sample debris and sort bacteria according to species. This work presents variations in which dielectrophoresis can be performed and their associated drawbacks and benefits specifically to bacterial identification. In addition, a potential microfluidic design will be discussed.

Immunoassays are used to detect proteins based on the presence of associated antibodies. Because of their extensive use in research and clinical settings, a large infrastructure of immunoassay instruments and materials can be found. For example, 96- and 384-well polystyrene plates are available commercially and have a standard design to accommodate ultraviolet-visible (UV-Vis) spectroscopy machines from various manufacturers. In addition, a wide variety of immunoglobulins, detection tags, and blocking agents for customized immunoassay designs such as enzyme-linked immunosorbent assays (ELISA) are available. Despite the existing infrastructure, standard ELISA kits do not meet all research needs, requiring individualized immunoassay development, which can be expensive and time-consuming. For example, ELISA kits have low multiplexing (detection of more than one analyte at a time) capabilities as they usually depend on fluorescence or colorimetric methods for detection. Colorimetric and fluorescent-based analyses have limited multiplexing capabilities due to broad spectral peaks. In contrast, Raman spectroscopy-based methods have a much greater capability for multiplexing due to narrow emission peaks. Another advantage of Raman spectroscopy is that Raman reporters experience significantly less photobleaching than fluorescent tags1. Despite the advantages that Raman reporters have over fluorescent and colorimetric tags, protocols to fabricate Raman-based immunoassays are limited. The purpose of this paper is to provide a protocol to prepare functionalized probes to use in conjunction with polystyrene plates for direct detection of analytes by UV-Vis analysis and Raman spectroscopy. This protocol will allow researchers to take a do-it-yourself approach for future multi-analyte detection while capitalizing on pre-established infrastructure.