Biology subjects

Vodovotz, Y.

Publications and source records attributed to Vodovotz, Y..

6 recordsLinked to original sources

bioRxiv2024

The Wound Environment Agent-based Model (WEABM): a digital twin platform for characterization and complex therapeutic discovery for volumetric muscle loss

Volumetric Muscle Loss (VML) injuries are characterized by significant loss of muscle mass, usually due to trauma or surgical resection, often with a residual open wound in clinical settings and subsequent loss of limb function due to the replacement of the lost muscle mass with non-functional scar. Being able to regrow functional muscle in VML injuries is a complex control problem that needs to override robust, evolutionarily conserved healing processes aimed at rapidly closing the defect in lieu of restoration of function. We propose that discovering and implementing this complex control can be accomplished by the development of a Medical Digital Twin of VML. Digital Twins (DTs) are the subject of a recent report from the National Academies of Science, Engineering and Medicine (NASEM), which provides guidance as to the definition, capabilities and research challenges associated with the development and implementation of DTs. Specifically, DTs are defined as dynamic computational models that can be personalized to an individual real world "twin" and are connected to that twin via an ongoing data link. DTs can be used to provide control on the real-world twin that is, by the ongoing data connection, adaptive. We have developed an anatomic scale cell-level agent-based model of VML termed the Wound Environment Agent Based Model (WEABM) that can serve as the computational specification for a DT of VML. Simulations of the WEABM provided fundamental insights into the biology of VML, and we used the WEABM in our previously developed pipeline for simulation-based Deep Reinforcement Learning (DRL) to train an artificial intelligence (AI) to implement a robust generalizable control policy aimed at increasing the healing of VML with functional muscle. The insights into VML obtained include: 1) a competition between fibrosis and myogenesis due to spatial constraints on available edges of intact myofibrils to initiate the myoblast differentiation process, 2) the need to biologically "close" the wound from atmospheric/environmental exposure, which represents an ongoing inflammatory stimulus that promotes fibrosis and 3) that selective, multimodal and adaptive local mediator-level control can shift the trajectory of healing away from a highly evolutionarily beneficial imperative to close the wound via fibrosis. Control discovery with the WEABM identified the following design principles: 1) multimodal adaptive tissue-level mediator control to mitigate pro-inflammation as well as the pro-fibrotic aspects of compensatory anti-inflammation, 2) tissue-level mediator manipulation to promote myogenesis, 3) the use of an engineered extracellular matrix (ECM) to functionally close the wound and 4) the administration of an anti-fibrotic agent focused on the collagen-producing function of fibroblasts and myofibroblasts. The WEABM-trained DRL AI integrates these control modalities and provides design specifications for a potential device that can implement the required wound sensing and intervention delivery capabilities needed. The proposed cyber-physical system integrates the control AI with a physical sense-and-actuate device that meets the tenets of DTs put forth in the NASEM report and can serve as an example schema for the future development of Medical DTs.

bioengineering↗

bioRxiv2023

Simian Immunodeficiency Virus and Storage Buffer: Field-friendly preservation methods for RNA viral detection in primate feces

Wild non-human primates carry many types of RNA viruses, including simian immunodeficiency virus (SIV), simian foamy virus, simian T-cell leukemia virus, and hepatitis C virus. These viruses can also infect humans via zoonotic transmission through handling and consumption of primate bushmeat. Characterizing viral prevalence and shedding in natural hosts is critical to understand infection and transmission risks within and between primate species. Here, we sought to identify a robust "field-friendly" method (i.e., without freezing or refrigeration) for preserving viral RNA, specifically SIV, in primate fecal samples. Fecal samples were collected from a mantled guereza colobus (Colobus guereza) housed at the Columbus Zoo and Aquarium. Samples were homogenized and inoculated with three concentrations (low, medium, high) of inactivated SIV virus and preserved in four different storage buffers (DNA/RNA Shield, RNAlater, 95% Ethanol, and Viral Transport Medium). SIV viral RNA was then extracted from samples at four time points (1 week, 4 weeks, 8 weeks, and 12 weeks) to determine the efficacy of each buffer for preserving SIV RNA. Quantitative RT-PCR was used for detection and quantification of viral RNA. At all concentrations, DNA/RNA Shield yielded the highest average SIV virion concentrations. We then successfully validated this approach using fecal samples from known SIV-positive and SIV-negative sooty mangabeys (Cercocebus atys) housed at Emory National Primate Research Center. Our results indicate that DNA/RNA shield is an optimal "field-friendly" buffer for preserving SIV RNA in fecal samples over time, and may also be effective for preserving other RNA viruses in feces. ImportanceHuman immunodeficiency virus (HIV) was introduced into human populations through zoonotic transmission of SIV from African primates, leading to a global epidemic and ongoing worldwide public health issue. SIV occurs naturally in over 40 primate species in sub-Saharan Africa and these viruses have crossed species barriers on multiple occasions, leading to the spread of HIV-1 and HIV-2. Quantifying RNA viruses in wild primate populations can be challenging as invasive sampling is often not feasible, and many field stations lack ready access to a freezer for storing biological samples. This study compares SIV RNA preservation and recovery across multiple storage buffers to identify a robust field-friendly option for RNA viral detection in noninvasively collected feces. Our results will inform future fieldwork and facilitate improved approaches to characterizing prevalence, shedding, and transmission of RNA viruses like SIV in natural hosts including wild-living nonhuman primates.

microbiology↗

bioRxiv2022

Generating synthetic data with a mechanism-based Critical Illness Digital Twin: Demonstration for Post Traumatic Acute Respiratory Distress Syndrome

Machine learning (ML) and Artificial Intelligence (AI) approaches are increasingly applied to predicting the development of sepsis and multiple organ failure. While there has been success in demonstrating the clinical utility of such systems in terms of affecting various outcomes, there are fundamental challenges to the ML/AI approach in terms of improving the actual predictive performance and future robustness of such systems. Given that one of the primary proposed avenues for improving algorithmic performance is the addition of molecular/biomarker/genetic features to the data used to train these systems, the overall sparsity of such available data suggests the need to generate synthetic data to aid in training, as has been the case in numerous other ML/AI tasks, such as image recognition/generation and text analysis/generation. We propose the need to generate synthetic molecular/mediator time series data coincides with the advent of the concept of medical digital twins, specifically related to interpretations of medical digital twins that hew closely to the original description and use of industrial digital twins, which involve simulating multiple individual twins from a common computational model specification. Herein we present an example of generating synthetic time series data of a panel of pro- and anti-inflammatory cytokines using the Critical Illness Digital Twin (CIDT) regarding the development of post-traumatic acute respiratory distress syndrome.

systems biology↗

bioRxiv2022

Optical Biopsy using a neural network to predict functional state from photos of wounds

BackgroundThe clinical characterization of the functional status of active wounds remains a considerable challenge that at present, requires excision of a tissue biopsy. In this pilot study, we use a convolutional Siamese neural network architecture to predict the functional state of a wound using digital photographs of wounds in a canine model of volumetric muscle loss (VML). Materials and MethodsImages of volumetric muscle loss injuries and tissue biopsies were obtained in a canine model of VML. Gene expression profiles for each image were obtained using RNAseq. These profiles were then converted to functional profiles using a manual review of validated gene ontology databases. A Siamese neural network was trained to regress functional profile expression values as a function of the data contained in an extracted image segment showing the surface of a small tissue biopsy. Network performance was assessed in a test set of images using Mean Absolute Percentage Error (MAPE). ResultsThe network was able to predict the functional expression of a range of functions based with a MAPE ranging from [~]5% to [~]50%, with functions that are most closely associated with the early-state of wound healing to be those best-predicted. ConclusionsThese initial results suggest promise for further research regarding this novel use of ML regression on medical images. The regression of functional profiles, as opposed to specific genes, both addresses the challenge of genetic redundancy and gives a deeper insight into the mechanistic configuration of a region of tissue in wounds. As this preliminary study focuses on the first 14 days of wound healing, future work will focus on extending the training data to include longer time periods which would result in additional functions, such as tissue remodeling, having a larger presence in the training data.

bioinformatics↗

bioRxiv2021

Gut microbiota and age shape susceptibility to clostridial enteritis in lorikeets under human care

BackgroundEnteritis is a common cause of morbidity and mortality in lorikeets that can be challenging to diagnose and treat. In this study, we examine gut microbiota in two lorikeet flocks with enteritis (Columbus Zoo and Aquarium - CZA; Denver Zoo - DZ). Since 2012, the CZA flock has experienced repeated outbreaks of enteritis despite extensive diet, husbandry, and clinical modifications. In 2018, both CZA and DZ observed a spike in enteritis. Recent research has revealed that the gut microbiota can influence susceptibility to enteropathogens. We hypothesized that a dysbiosis, or alteration in the gut microbial community, was making some lorikeets more susceptible to enteritis, and our goal was to characterize this dysbiosis and determine the features that predicted susceptibility. ResultsWe employed 16S rRNA sequencing to characterize the cloacal microbiota in lorikeets (CZA n = 67, DZ n = 24) over time. We compared the microbiota of healthy lorikeets, to lorikeets with enteritis, and lorikeets susceptible to enteritis, with "susceptible" being defined as healthy birds that subsequently developed enteritis. Based on sequencing data, culture, and toxin gene detection in intestinal contents, we identified Clostridium perfringens type A (CZA and DZ) and C. colinum (CZA only) at increased relative abundances in birds with enteritis. Histopathology and immunohistochemistry further identified the presence of gram-positive bacilli and C. perfringens, respectively, in the necrotizing intestinal lesions. Finally, using Random Forests and LASSO models, we identified several features (young age and the presence of Rhodococcus fascians and Pseudomonas umsongensis) associated with susceptibility to clostridial enteritis. ConclusionsWe identified C. perfringens type A and C. colinum associated with lorikeet necrohemorrhagic enteritis at CZA and DZ. Susceptibility testing of isolates lead to an updated clinical treatment plan which ultimately resolved the outbreaks at both institutions. This work provides a foundation for understanding gut microbiota features that are permissive to clostridial colonization and host factors (e.g. age, prior infection) that shape responses to infection.

microbiology↗

bioRxiv2021

Quantifying the size and healing of volumetric muscle wounds using 3D Slicer on CT scans in a canine model

Volumetric soft tissue and muscle wounds can arise from trauma or necrotizing soft tissue infection. Quantifying the size of these wounds can be challenging, as they often have irregular borders and contours and invariably involve skin loss. 3-dimensional Computed Tomography (3dCT) has been used to characterize the volume of numerous tissue structures, but these use cases invariably involve structures for which clear anatomic borders exist. This is not the case for volumetric soft tissue or muscle wounds, where the volume of the wound being assessed, which is actually a void representing the absence of tissue, does not contain an explicit border at the superficial surface. We present a method that allows quantification of the void size of volumetric muscle wounds using CT scans processed with the software package 3D Slicer. This quantification allows us to chart the progression of healing in such wounds with sequential scans. The development of a means to quantify wound size and healing rate is a necessary capability in order to assess the efficacy of potential therapeutic interventions aimed at enhancing healing of such wounds.

physiology↗