Alireza Asadbeygi, Yasutaka Tobe, Naoki Yoshimura, Sean D. Stocker, Simon Watkins, Paul Watton, Anne M. Robertson
The smooth muscle bundles (SMBs) in the bladder act as contractile elements which enable the bladder to void effectively. In contrast to skeletal muscles, these bundles are not highly aligned, rather they are oriented more heterogeneously throughout the bladder wall. In this work, for the first time, this regional orientation of the SMBs is quantified across the whole bladder, without the need for optical clearing or cryosectioning. Immunohistochemistry staining was utilized to visualize smooth muscle cell actin in multiphoton microscopy (MPM) images of bladder smooth muscle bundles (SMBs). Feature vectors for each pixel were generated using a range of filters, including Gaussian blur, Gaussian gradient magnitude, Laplacian of Gaussian, Hessian eigenvalues, structure tensor eigenvalues, Gabor, and Sobel gradients. A Random Forest classifier was subsequently trained to automate the segmentation of SMBs in the MPM images. Finally, the orientation of SMBs in each bladder region was quantified using the CT-FIRE package. This information is essential for biomechanical models of the bladder that include contractile elements.
{"title":"Quantifying Smooth Muscles Regional Organization in the Rat Bladder Using Immunohistochemistry, Multiphoton Microscopy and Machine Learning","authors":"Alireza Asadbeygi, Yasutaka Tobe, Naoki Yoshimura, Sean D. Stocker, Simon Watkins, Paul Watton, Anne M. Robertson","doi":"arxiv-2405.04790","DOIUrl":"https://doi.org/arxiv-2405.04790","url":null,"abstract":"The smooth muscle bundles (SMBs) in the bladder act as contractile elements\u0000which enable the bladder to void effectively. In contrast to skeletal muscles,\u0000these bundles are not highly aligned, rather they are oriented more\u0000heterogeneously throughout the bladder wall. In this work, for the first time,\u0000this regional orientation of the SMBs is quantified across the whole bladder,\u0000without the need for optical clearing or cryosectioning. Immunohistochemistry\u0000staining was utilized to visualize smooth muscle cell actin in multiphoton\u0000microscopy (MPM) images of bladder smooth muscle bundles (SMBs). Feature\u0000vectors for each pixel were generated using a range of filters, including\u0000Gaussian blur, Gaussian gradient magnitude, Laplacian of Gaussian, Hessian\u0000eigenvalues, structure tensor eigenvalues, Gabor, and Sobel gradients. A Random\u0000Forest classifier was subsequently trained to automate the segmentation of SMBs\u0000in the MPM images. Finally, the orientation of SMBs in each bladder region was\u0000quantified using the CT-FIRE package. This information is essential for\u0000biomechanical models of the bladder that include contractile elements.","PeriodicalId":501572,"journal":{"name":"arXiv - QuanBio - Tissues and Organs","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140935764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Eric HermandURePSSS, H&P, Léo LesaintH&P, Laura DenisH&P, Jean-Paul RichaletINSEP, François LhuissierH&P
A laboratory-based hypoxic exercise test, performed on a cycle ergometer, can be used to predict susceptibility to severe high-altitude illness (SHAI) through the calculation of a clinicophysiological SHAI score. Our objective was to design a field-condition test and compare its derived SHAI score and various physiological parameters, such as peripheral oxygen saturation (SpO2), and cardiac and ventilatory responses to hypoxia during exercise (HCRe and HVRe, respectively), to the laboratory test. A group of 43 healthy subjects (15 females and 28 males), with no prior experience at high altitude, performed a hypoxic cycle ergometer test (simulated altitude of 4,800 m) and step tests (20 cm high step) at 3,000, 4,000, and 4,800 m simulated altitudes. According to tested altitudes, differences were observed in O2 desaturation, heart rate, and minute ventilation (p < 0.001), whereas the computed HCRe and HVRe were not different (p = 0.075 and p = 0.203, respectively). From the linear relationships between the step test and SHAI scores, we defined a risk zone, allowing us to evaluate the risk of developing SHAI and take adequate preventive measures in field conditions, from the calculated step test score for the given altitude. The predictive value of this new field test remains to be validated in real high-altitude conditions.
{"title":"A Step Test to Evaluate the Susceptibility to Severe High-Altitude Illness in Field Conditions","authors":"Eric HermandURePSSS, H&P, Léo LesaintH&P, Laura DenisH&P, Jean-Paul RichaletINSEP, François LhuissierH&P","doi":"arxiv-2405.01896","DOIUrl":"https://doi.org/arxiv-2405.01896","url":null,"abstract":"A laboratory-based hypoxic exercise test, performed on a cycle ergometer, can\u0000be used to predict susceptibility to severe high-altitude illness (SHAI)\u0000through the calculation of a clinicophysiological SHAI score. Our objective was\u0000to design a field-condition test and compare its derived SHAI score and various\u0000physiological parameters, such as peripheral oxygen saturation (SpO2), and\u0000cardiac and ventilatory responses to hypoxia during exercise (HCRe and HVRe,\u0000respectively), to the laboratory test. A group of 43 healthy subjects (15\u0000females and 28 males), with no prior experience at high altitude, performed a\u0000hypoxic cycle ergometer test (simulated altitude of 4,800 m) and step tests (20\u0000cm high step) at 3,000, 4,000, and 4,800 m simulated altitudes. According to\u0000tested altitudes, differences were observed in O2 desaturation, heart rate, and\u0000minute ventilation (p < 0.001), whereas the computed HCRe and HVRe were not\u0000different (p = 0.075 and p = 0.203, respectively). From the linear\u0000relationships between the step test and SHAI scores, we defined a risk zone,\u0000allowing us to evaluate the risk of developing SHAI and take adequate\u0000preventive measures in field conditions, from the calculated step test score\u0000for the given altitude. The predictive value of this new field test remains to\u0000be validated in real high-altitude conditions.","PeriodicalId":501572,"journal":{"name":"arXiv - QuanBio - Tissues and Organs","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140883984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Multiplexed imaging data are revolutionizing our understanding of the composition and organization of tissues and tumors. A critical aspect of such tissue profiling is quantifying the spatial relationship relationships among cells at different scales from the interaction of neighboring cells to recurrent communities of cells of multiple types. This often involves statistical analysis of 10^7 or more cells in which up to 100 biomolecules (commonly proteins) have been measured. While software tools currently cater to the analysis of spatial transcriptomics data, there remains a need for toolkits explicitly tailored to the complexities of multiplexed imaging data including the need to seamlessly integrate image visualization with data analysis and exploration. We introduce SCIMAP, a Python package specifically crafted to address these challenges. With SCIMAP, users can efficiently preprocess, analyze, and visualize large datasets, facilitating the exploration of spatial relationships and their statistical significance. SCIMAP's modular design enables the integration of new algorithms, enhancing its capabilities for spatial analysis.
{"title":"SCIMAP: A Python Toolkit for Integrated Spatial Analysis of Multiplexed Imaging Data","authors":"Ajit J. Nirmal, Peter K. Sorger","doi":"arxiv-2405.02076","DOIUrl":"https://doi.org/arxiv-2405.02076","url":null,"abstract":"Multiplexed imaging data are revolutionizing our understanding of the\u0000composition and organization of tissues and tumors. A critical aspect of such\u0000tissue profiling is quantifying the spatial relationship relationships among\u0000cells at different scales from the interaction of neighboring cells to\u0000recurrent communities of cells of multiple types. This often involves\u0000statistical analysis of 10^7 or more cells in which up to 100 biomolecules\u0000(commonly proteins) have been measured. While software tools currently cater to\u0000the analysis of spatial transcriptomics data, there remains a need for toolkits\u0000explicitly tailored to the complexities of multiplexed imaging data including\u0000the need to seamlessly integrate image visualization with data analysis and\u0000exploration. We introduce SCIMAP, a Python package specifically crafted to\u0000address these challenges. With SCIMAP, users can efficiently preprocess,\u0000analyze, and visualize large datasets, facilitating the exploration of spatial\u0000relationships and their statistical significance. SCIMAP's modular design\u0000enables the integration of new algorithms, enhancing its capabilities for\u0000spatial analysis.","PeriodicalId":501572,"journal":{"name":"arXiv - QuanBio - Tissues and Organs","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140884420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Charles Puelz, Craig G. Rusin, Dan Lior, Shagun Sachdeva, Tam T. Doan, Lindsay F. Eilers, Dana Reaves-O'Neal, Silvana Molossi
Computer simulations of blood flow in patients with anomalous aortic origin of a coronary artery (AAOCA) have the promise to provide insight into this complex disease. They provide an in-silico experimental platform to explore possible mechanisms of myocardial ischemia, a potentially deadly complication for patients with this defect. This paper focuses on the question of model calibration for fluid-structure interaction models of pediatric AAOCA patients. Imaging and cardiac catheterization data provide partial information for model construction and calibration. However, parameters for downstream boundary conditions needed for these models are difficult to estimate. Further, important model predictions, like fractional flow reserve (FFR), are sensitive to these parameters. We describe an approach to calibrate downstream boundary condition parameters to clinical measurements of resting FFR. The calibrated models are then used to predict FFR at stress, an invasively measured quantity that can be used in the clinical evaluation of these patients. We find reasonable agreement between the model predicted and clinically measured FFR at stress, indicating the credibility of this modeling framework for predicting hemodynamics of pediatric AAOCA patients. This approach could lead to important clinical applications since it may serve as a tool for risk stratifying children with AAOCA.
{"title":"Fluid-structure interaction simulations for the prediction of fractional flow reserve in pediatric patients with anomalous aortic origin of a coronary artery","authors":"Charles Puelz, Craig G. Rusin, Dan Lior, Shagun Sachdeva, Tam T. Doan, Lindsay F. Eilers, Dana Reaves-O'Neal, Silvana Molossi","doi":"arxiv-2405.01703","DOIUrl":"https://doi.org/arxiv-2405.01703","url":null,"abstract":"Computer simulations of blood flow in patients with anomalous aortic origin\u0000of a coronary artery (AAOCA) have the promise to provide insight into this\u0000complex disease. They provide an in-silico experimental platform to explore\u0000possible mechanisms of myocardial ischemia, a potentially deadly complication\u0000for patients with this defect. This paper focuses on the question of model\u0000calibration for fluid-structure interaction models of pediatric AAOCA patients.\u0000Imaging and cardiac catheterization data provide partial information for model\u0000construction and calibration. However, parameters for downstream boundary\u0000conditions needed for these models are difficult to estimate. Further,\u0000important model predictions, like fractional flow reserve (FFR), are sensitive\u0000to these parameters. We describe an approach to calibrate downstream boundary\u0000condition parameters to clinical measurements of resting FFR. The calibrated\u0000models are then used to predict FFR at stress, an invasively measured quantity\u0000that can be used in the clinical evaluation of these patients. We find\u0000reasonable agreement between the model predicted and clinically measured FFR at\u0000stress, indicating the credibility of this modeling framework for predicting\u0000hemodynamics of pediatric AAOCA patients. This approach could lead to important\u0000clinical applications since it may serve as a tool for risk stratifying\u0000children with AAOCA.","PeriodicalId":501572,"journal":{"name":"arXiv - QuanBio - Tissues and Organs","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140883797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Multicellular tumor spheroids (MCTS) are advanced cell culture systems for assessing the impact of combinatorial radio(chemo)therapy. They exhibit therapeutically relevant in-vivo-like characteristics from 3D cell-cell and cell-matrix interactions to radial pathophysiological gradients related to proliferative activity and nutrient/oxygen supply, altering cellular radioresponse. State-of-the-art assays quantify long-term curative endpoints based on collected brightfield image time series from large treated spheroid populations per irradiation dose and treatment arm. Here, spheroid control probabilities are documented analogous to in-vivo tumor control probabilities based on Kaplan-Meier curves. This analyses require laborious spheroid segmentation of up to 100.000 images per treatment arm to extract relevant structural information from the images, e.g., diameter, area, volume and circularity. While several image analysis algorithms are available for spheroid segmentation, they all focus on compact MCTS with clearly distinguishable outer rim throughout growth. However, treated MCTS may partly be detached and destroyed and are usually obscured by dead cell debris. We successfully train two Fully Convolutional Networks, UNet and HRNet, and optimize their hyperparameters to develop an automatic segmentation for both untreated and treated MCTS. We systematically validate the automatic segmentation on larger, independent data sets of spheroids derived from two human head-and-neck cancer cell lines. We find an excellent overlap between manual and automatic segmentation for most images, quantified by Jaccard indices at around 90%. For images with smaller overlap of the segmentations, we demonstrate that this error is comparable to the variations across segmentations from different biological experts, suggesting that these images represent biologically unclear or ambiguous cases.
{"title":"Image segmentation of treated and untreated tumor spheroids by Fully Convolutional Networks","authors":"Matthias Streller, Soňa Michlíková, Willy Ciecior, Katharina Lönnecke, Leoni A. Kunz-Schughart, Steffen Lange, Anja Voss-Böhme","doi":"arxiv-2405.01105","DOIUrl":"https://doi.org/arxiv-2405.01105","url":null,"abstract":"Multicellular tumor spheroids (MCTS) are advanced cell culture systems for\u0000assessing the impact of combinatorial radio(chemo)therapy. They exhibit\u0000therapeutically relevant in-vivo-like characteristics from 3D cell-cell and\u0000cell-matrix interactions to radial pathophysiological gradients related to\u0000proliferative activity and nutrient/oxygen supply, altering cellular\u0000radioresponse. State-of-the-art assays quantify long-term curative endpoints\u0000based on collected brightfield image time series from large treated spheroid\u0000populations per irradiation dose and treatment arm. Here, spheroid control\u0000probabilities are documented analogous to in-vivo tumor control probabilities\u0000based on Kaplan-Meier curves. This analyses require laborious spheroid\u0000segmentation of up to 100.000 images per treatment arm to extract relevant\u0000structural information from the images, e.g., diameter, area, volume and\u0000circularity. While several image analysis algorithms are available for spheroid\u0000segmentation, they all focus on compact MCTS with clearly distinguishable outer\u0000rim throughout growth. However, treated MCTS may partly be detached and\u0000destroyed and are usually obscured by dead cell debris. We successfully train\u0000two Fully Convolutional Networks, UNet and HRNet, and optimize their\u0000hyperparameters to develop an automatic segmentation for both untreated and\u0000treated MCTS. We systematically validate the automatic segmentation on larger,\u0000independent data sets of spheroids derived from two human head-and-neck cancer\u0000cell lines. We find an excellent overlap between manual and automatic\u0000segmentation for most images, quantified by Jaccard indices at around 90%. For\u0000images with smaller overlap of the segmentations, we demonstrate that this\u0000error is comparable to the variations across segmentations from different\u0000biological experts, suggesting that these images represent biologically unclear\u0000or ambiguous cases.","PeriodicalId":501572,"journal":{"name":"arXiv - QuanBio - Tissues and Organs","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140838705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Seyedali Mirmohammadsadeghi, Davis Juhas, Mikhail Parker, Kristina Peranidze, Dwight Austin Van Horn, Aayushi Sharma, Dhruvi Patel, Tatyana A. Sysoeva, Vladislav Klepov, Vladimir Reukov
Hospital-acquired infections are considered a priority for public health systems, which poses a significant burden for society. High-touch surfaces of healthcare centers, including textiles, provide a suitable environment for pathogenic bacteria to grow, necessitating incorporating effective antibacterial agents into textiles. This paper introduces a highly durable antibacterial gel-like solution, Silver Shell finish, which contains chitosan-bound silver chloride microparticles. The study investigates the coating's environmental impact, health risks, and durability during repeated washing. The structure of the Silver Shell finish was studied using Transmission Electron Microscopy (TEM) and Energy-Dispersive X-ray Spectroscopy (EDX). TEM images showed a core-shell structure, with chitosan forming a protective shell around groupings of silver micro-particles. Field Emission Scanning Electron Microscopy (FESEM) demonstrated the uniform deposition of Silver Shell on the surface of fabrics. AATCC Test Method 100 was employed to quantitatively analyze the antibacterial properties of fabrics coated with silver microparticles. Two types of bacteria, Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli) were used in this study. The antibacterial results showed that after 75 wash cycles, a 100% reduction for both S. aureus and E. coli in the coated samples using crosslinking agents was observed. The coated samples without a crosslinking agent exhibited a 99.88% and 99.81% reduction for S. aureus and E. coli after 50 washing cycles. AATCC-147 was performed to investigate the coated samples' leaching properties and the crosslinking agent's effect against S. aureus and E. coli. All coated samples demonstrated remarkable antibacterial efficacy even after 75 wash cycles.
{"title":"The Highly Durable Antibacterial Gel-like Coatings for Textiles","authors":"Seyedali Mirmohammadsadeghi, Davis Juhas, Mikhail Parker, Kristina Peranidze, Dwight Austin Van Horn, Aayushi Sharma, Dhruvi Patel, Tatyana A. Sysoeva, Vladislav Klepov, Vladimir Reukov","doi":"arxiv-2405.00530","DOIUrl":"https://doi.org/arxiv-2405.00530","url":null,"abstract":"Hospital-acquired infections are considered a priority for public health\u0000systems, which poses a significant burden for society. High-touch surfaces of\u0000healthcare centers, including textiles, provide a suitable environment for\u0000pathogenic bacteria to grow, necessitating incorporating effective\u0000antibacterial agents into textiles. This paper introduces a highly durable\u0000antibacterial gel-like solution, Silver Shell finish, which contains\u0000chitosan-bound silver chloride microparticles. The study investigates the\u0000coating's environmental impact, health risks, and durability during repeated\u0000washing. The structure of the Silver Shell finish was studied using\u0000Transmission Electron Microscopy (TEM) and Energy-Dispersive X-ray Spectroscopy\u0000(EDX). TEM images showed a core-shell structure, with chitosan forming a\u0000protective shell around groupings of silver micro-particles. Field Emission\u0000Scanning Electron Microscopy (FESEM) demonstrated the uniform deposition of\u0000Silver Shell on the surface of fabrics. AATCC Test Method 100 was employed to\u0000quantitatively analyze the antibacterial properties of fabrics coated with\u0000silver microparticles. Two types of bacteria, Staphylococcus aureus (S. aureus)\u0000and Escherichia coli (E. coli) were used in this study. The antibacterial\u0000results showed that after 75 wash cycles, a 100% reduction for both S. aureus\u0000and E. coli in the coated samples using crosslinking agents was observed. The\u0000coated samples without a crosslinking agent exhibited a 99.88% and 99.81%\u0000reduction for S. aureus and E. coli after 50 washing cycles. AATCC-147 was\u0000performed to investigate the coated samples' leaching properties and the\u0000crosslinking agent's effect against S. aureus and E. coli. All coated samples\u0000demonstrated remarkable antibacterial efficacy even after 75 wash cycles.","PeriodicalId":501572,"journal":{"name":"arXiv - QuanBio - Tissues and Organs","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140838703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recent clinical xenotransplantation and human decedent studies demonstrate that clinical hyperacute rejection of genetically engineered porcine organs can be reliably avoided but that antibody mediated rejection continues to limit graft survival. We previously identified porcine glycans and proteins which are immunogenic after cardiac xenotransplantation in nonhuman primates, but the clinical immune response to antigens present in glycan depleted triple knockout (TKO) donor pigs is poorly understood. In this study we use fluorescence barcoded HEK cells and HEK cell lines expressing porcine glycans (Gal and SDa) or proteins (CD9, CD46, CD59, PROCR and ANXA2) to screen antibody reactivity in human serum from 160 swine veterinarians, a serum source with potential occupational immune challenge from porcine tissues and pathogens. High levels of anti-Gal IgM were present in all samples and lower levels of anti-SDa IgM were present in 41% of samples. IgM binding to porcine proteins, primarily CD9 and CD46, previously identified as immunogenic in pig to non-human primate cardiac xenograft recipients, was detected in 28 of the 160 swine veterinarian samples. These results suggest that barcoded HEK cell lines expressing porcine protein antigens can be useful for screening human patient serum. A comprehensive analysis of sera from clinical xenotransplant recipients to define a panel of commonly immunogenic porcine antigens will likely be necessary to establish an array of porcine non-Gal antigens for effective monitoring of patient immune responses and allow earlier therapies to reverse antibody mediated rejection.
{"title":"Anti-pig Antibodies in Swine Veterinarian Serum: Implications for Clinical Xenotransplantation","authors":"Guerard Byrne, Christopher McGregor","doi":"arxiv-2404.14658","DOIUrl":"https://doi.org/arxiv-2404.14658","url":null,"abstract":"Recent clinical xenotransplantation and human decedent studies demonstrate\u0000that clinical hyperacute rejection of genetically engineered porcine organs can\u0000be reliably avoided but that antibody mediated rejection continues to limit\u0000graft survival. We previously identified porcine glycans and proteins which are\u0000immunogenic after cardiac xenotransplantation in nonhuman primates, but the\u0000clinical immune response to antigens present in glycan depleted triple knockout\u0000(TKO) donor pigs is poorly understood. In this study we use fluorescence\u0000barcoded HEK cells and HEK cell lines expressing porcine glycans (Gal and SDa)\u0000or proteins (CD9, CD46, CD59, PROCR and ANXA2) to screen antibody reactivity in\u0000human serum from 160 swine veterinarians, a serum source with potential\u0000occupational immune challenge from porcine tissues and pathogens. High levels\u0000of anti-Gal IgM were present in all samples and lower levels of anti-SDa IgM\u0000were present in 41% of samples. IgM binding to porcine proteins, primarily CD9\u0000and CD46, previously identified as immunogenic in pig to non-human primate\u0000cardiac xenograft recipients, was detected in 28 of the 160 swine veterinarian\u0000samples. These results suggest that barcoded HEK cell lines expressing porcine\u0000protein antigens can be useful for screening human patient serum. A\u0000comprehensive analysis of sera from clinical xenotransplant recipients to\u0000define a panel of commonly immunogenic porcine antigens will likely be\u0000necessary to establish an array of porcine non-Gal antigens for effective\u0000monitoring of patient immune responses and allow earlier therapies to reverse\u0000antibody mediated rejection.","PeriodicalId":501572,"journal":{"name":"arXiv - QuanBio - Tissues and Organs","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140804156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Amadeus M. Gebauer, Martin R. Pfaller, Jason M. Szafron, Wolfgang A. Wall
In the last decades, many computational models have been developed to predict soft tissue growth and remodeling (G&R). The constrained mixture theory describes fundamental mechanobiological processes in soft tissue G&R and has been widely adopted in cardiovascular models of G&R. However, even after two decades of work, large organ-scale models are rare, mainly due to high computational costs (model evaluation and memory consumption), especially in long-range simulations. We propose two strategies to adaptively integrate history variables in constrained mixture models to enable large organ-scale simulations of G&R. Both strategies exploit that the influence of deposited tissue on the current mixture decreases over time through degradation. One strategy is independent of external loading, allowing the estimation of the computational resources ahead of the simulation. The other adapts the history snapshots based on the local mechanobiological environment so that the additional integration errors can be controlled and kept negligibly small, even in G&R scenarios with severe perturbations. We analyze the adaptively integrated constrained mixture model on a tissue patch for a parameter study and show the performance under different G&R scenarios. To confirm that adaptive strategies enable large organ-scale examples, we show simulations of different hypertension conditions with a real-world example of a biventricular heart discretized with a finite element mesh. In our example, adaptive integrations sped up simulations by a factor of three and reduced memory requirements to one-sixth. The reduction of the computational costs gets even more pronounced for simulations over longer periods. Adaptive integration of the history variables allows studying more finely resolved models and longer G&R periods while computational costs are drastically reduced and largely constant in time.
{"title":"Adaptive integration of history variables in constrained mixture models for organ-scale growth and remodeling","authors":"Amadeus M. Gebauer, Martin R. Pfaller, Jason M. Szafron, Wolfgang A. Wall","doi":"arxiv-2404.09706","DOIUrl":"https://doi.org/arxiv-2404.09706","url":null,"abstract":"In the last decades, many computational models have been developed to predict\u0000soft tissue growth and remodeling (G&R). The constrained mixture theory\u0000describes fundamental mechanobiological processes in soft tissue G&R and has\u0000been widely adopted in cardiovascular models of G&R. However, even after two\u0000decades of work, large organ-scale models are rare, mainly due to high\u0000computational costs (model evaluation and memory consumption), especially in\u0000long-range simulations. We propose two strategies to adaptively integrate\u0000history variables in constrained mixture models to enable large organ-scale\u0000simulations of G&R. Both strategies exploit that the influence of deposited\u0000tissue on the current mixture decreases over time through degradation. One\u0000strategy is independent of external loading, allowing the estimation of the\u0000computational resources ahead of the simulation. The other adapts the history\u0000snapshots based on the local mechanobiological environment so that the\u0000additional integration errors can be controlled and kept negligibly small, even\u0000in G&R scenarios with severe perturbations. We analyze the adaptively\u0000integrated constrained mixture model on a tissue patch for a parameter study\u0000and show the performance under different G&R scenarios. To confirm that\u0000adaptive strategies enable large organ-scale examples, we show simulations of\u0000different hypertension conditions with a real-world example of a biventricular\u0000heart discretized with a finite element mesh. In our example, adaptive\u0000integrations sped up simulations by a factor of three and reduced memory\u0000requirements to one-sixth. The reduction of the computational costs gets even\u0000more pronounced for simulations over longer periods. Adaptive integration of\u0000the history variables allows studying more finely resolved models and longer\u0000G&R periods while computational costs are drastically reduced and largely\u0000constant in time.","PeriodicalId":501572,"journal":{"name":"arXiv - QuanBio - Tissues and Organs","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140584713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aurélie Beaufrère, Nora Ouzir, Paul Emile Zafar, Astrid Laurent-Bellue, Miguel Albuquerque, Gwladys Lubuela, Jules Grégory, Catherine Guettier, Kévin Mondet, Jean-Christophe Pesquet, Valérie Paradis
The diagnosis of primary liver cancers (PLCs) can be challenging, especially on biopsies and for combined hepatocellular-cholangiocarcinoma (cHCC-CCA). We automatically classified PLCs on routine-stained biopsies using a weakly supervised learning method. Weak tumour/non-tumour annotations served as labels for training a Resnet18 neural network, and the network's last convolutional layer was used to extract new tumour tile features. Without knowledge of the precise labels of the malignancies, we then applied an unsupervised clustering algorithm. Our model identified specific features of hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (iCCA). Despite no specific features of cHCC-CCA being recognized, the identification of HCC and iCCA tiles within a slide could facilitate the diagnosis of primary liver cancers, particularly cHCC-CCA. Method and results: 166 PLC biopsies were divided into training, internal and external validation sets: 90, 29 and 47 samples. Two liver pathologists reviewed each whole-slide hematein eosin saffron (HES)-stained image (WSI). After annotating the tumour/non-tumour areas, 256x256 pixel tiles were extracted from the WSIs and used to train a ResNet18. The network was used to extract new tile features. An unsupervised clustering algorithm was then applied to the new tile features. In a two-cluster model, Clusters 0 and 1 contained mainly HCC and iCCA histological features. The diagnostic agreement between the pathological diagnosis and the model predictions in the internal and external validation sets was 100% (11/11) and 96% (25/26) for HCC and 78% (7/9) and 87% (13/15) for iCCA, respectively. For cHCC-CCA, we observed a highly variable proportion of tiles from each cluster (Cluster 0: 5-97%; Cluster 1: 2-94%).
{"title":"Primary liver cancer classification from routine tumour biopsy using weakly supervised deep learning","authors":"Aurélie Beaufrère, Nora Ouzir, Paul Emile Zafar, Astrid Laurent-Bellue, Miguel Albuquerque, Gwladys Lubuela, Jules Grégory, Catherine Guettier, Kévin Mondet, Jean-Christophe Pesquet, Valérie Paradis","doi":"arxiv-2404.04983","DOIUrl":"https://doi.org/arxiv-2404.04983","url":null,"abstract":"The diagnosis of primary liver cancers (PLCs) can be challenging, especially\u0000on biopsies and for combined hepatocellular-cholangiocarcinoma (cHCC-CCA). We\u0000automatically classified PLCs on routine-stained biopsies using a weakly\u0000supervised learning method. Weak tumour/non-tumour annotations served as labels\u0000for training a Resnet18 neural network, and the network's last convolutional\u0000layer was used to extract new tumour tile features. Without knowledge of the\u0000precise labels of the malignancies, we then applied an unsupervised clustering\u0000algorithm. Our model identified specific features of hepatocellular carcinoma\u0000(HCC) and intrahepatic cholangiocarcinoma (iCCA). Despite no specific features\u0000of cHCC-CCA being recognized, the identification of HCC and iCCA tiles within a\u0000slide could facilitate the diagnosis of primary liver cancers, particularly\u0000cHCC-CCA. Method and results: 166 PLC biopsies were divided into training, internal and\u0000external validation sets: 90, 29 and 47 samples. Two liver pathologists\u0000reviewed each whole-slide hematein eosin saffron (HES)-stained image (WSI).\u0000After annotating the tumour/non-tumour areas, 256x256 pixel tiles were\u0000extracted from the WSIs and used to train a ResNet18. The network was used to\u0000extract new tile features. An unsupervised clustering algorithm was then\u0000applied to the new tile features. In a two-cluster model, Clusters 0 and 1\u0000contained mainly HCC and iCCA histological features. The diagnostic agreement\u0000between the pathological diagnosis and the model predictions in the internal\u0000and external validation sets was 100% (11/11) and 96% (25/26) for HCC and 78%\u0000(7/9) and 87% (13/15) for iCCA, respectively. For cHCC-CCA, we observed a\u0000highly variable proportion of tiles from each cluster (Cluster 0: 5-97%;\u0000Cluster 1: 2-94%).","PeriodicalId":501572,"journal":{"name":"arXiv - QuanBio - Tissues and Organs","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140584712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dyke Ferber, Omar S. M. El Nahhas, Georg Wölflein, Isabella C. Wiest, Jan Clusmann, Marie-Elisabeth Leßman, Sebastian Foersch, Jacqueline Lammert, Maximilian Tschochohei, Dirk Jäger, Manuel Salto-Tellez, Nikolaus Schultz, Daniel Truhn, Jakob Nikolas Kather
Multimodal artificial intelligence (AI) systems have the potential to enhance clinical decision-making by interpreting various types of medical data. However, the effectiveness of these models across all medical fields is uncertain. Each discipline presents unique challenges that need to be addressed for optimal performance. This complexity is further increased when attempting to integrate different fields into a single model. Here, we introduce an alternative approach to multimodal medical AI that utilizes the generalist capabilities of a large language model (LLM) as a central reasoning engine. This engine autonomously coordinates and deploys a set of specialized medical AI tools. These tools include text, radiology and histopathology image interpretation, genomic data processing, web searches, and document retrieval from medical guidelines. We validate our system across a series of clinical oncology scenarios that closely resemble typical patient care workflows. We show that the system has a high capability in employing appropriate tools (97%), drawing correct conclusions (93.6%), and providing complete (94%), and helpful (89.2%) recommendations for individual patient cases while consistently referencing relevant literature (82.5%) upon instruction. This work provides evidence that LLMs can effectively plan and execute domain-specific models to retrieve or synthesize new information when used as autonomous agents. This enables them to function as specialist, patient-tailored clinical assistants. It also simplifies regulatory compliance by allowing each component tool to be individually validated and approved. We believe, that our work can serve as a proof-of-concept for more advanced LLM-agents in the medical domain.
{"title":"Autonomous Artificial Intelligence Agents for Clinical Decision Making in Oncology","authors":"Dyke Ferber, Omar S. M. El Nahhas, Georg Wölflein, Isabella C. Wiest, Jan Clusmann, Marie-Elisabeth Leßman, Sebastian Foersch, Jacqueline Lammert, Maximilian Tschochohei, Dirk Jäger, Manuel Salto-Tellez, Nikolaus Schultz, Daniel Truhn, Jakob Nikolas Kather","doi":"arxiv-2404.04667","DOIUrl":"https://doi.org/arxiv-2404.04667","url":null,"abstract":"Multimodal artificial intelligence (AI) systems have the potential to enhance\u0000clinical decision-making by interpreting various types of medical data.\u0000However, the effectiveness of these models across all medical fields is\u0000uncertain. Each discipline presents unique challenges that need to be addressed\u0000for optimal performance. This complexity is further increased when attempting\u0000to integrate different fields into a single model. Here, we introduce an\u0000alternative approach to multimodal medical AI that utilizes the generalist\u0000capabilities of a large language model (LLM) as a central reasoning engine.\u0000This engine autonomously coordinates and deploys a set of specialized medical\u0000AI tools. These tools include text, radiology and histopathology image\u0000interpretation, genomic data processing, web searches, and document retrieval\u0000from medical guidelines. We validate our system across a series of clinical\u0000oncology scenarios that closely resemble typical patient care workflows. We\u0000show that the system has a high capability in employing appropriate tools\u0000(97%), drawing correct conclusions (93.6%), and providing complete (94%), and\u0000helpful (89.2%) recommendations for individual patient cases while consistently\u0000referencing relevant literature (82.5%) upon instruction. This work provides\u0000evidence that LLMs can effectively plan and execute domain-specific models to\u0000retrieve or synthesize new information when used as autonomous agents. This\u0000enables them to function as specialist, patient-tailored clinical assistants.\u0000It also simplifies regulatory compliance by allowing each component tool to be\u0000individually validated and approved. We believe, that our work can serve as a\u0000proof-of-concept for more advanced LLM-agents in the medical domain.","PeriodicalId":501572,"journal":{"name":"arXiv - QuanBio - Tissues and Organs","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140585047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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