Biology subjects

Teodoro, G.

Publications and source records attributed to Teodoro, G..

6 recordsLinked to original sources

bioRxiv2022

Artificial Intelligence based Liver Portal Tract Region Identification and Quantification with Transplant Biopsy Whole-Slide Images

Liver fibrosis staging is clinically important for liver disease progression prediction. As the portal tract fibrotic quantity and size in a liver biopsy correlate with the fibrosis stage, an accurate analysis of portal tract regions is clinically critical. Manual annotations of portal tract regions, however, are time-consuming and subject to large inter- and intra-observer variability. To address such a challenge, we develop a Multiple Up-sampling and Spatial Attention guided UNet model (MUSA-UNet) to segment liver portal tract regions in whole-slide images of liver tissue slides. To enhance the segmentation performance, we propose to use depth-wise separable convolution, the spatial attention mechanism, the residual connection, and multiple up-sampling paths in the developed model. This study includes 53 histopathology whole slide images from patients who received liver transplantation. In total, 6,012 patches derived from 30 images are used for our deep learning model training and validation. The remaining 23 whole slide images are utilized for the model testing. The average liver portal tract segmentation performance of the developed MUSA-UNet is 0.94 (Precision), 0.85 (Recall), 0.89 (F1 Score), 0.89 (Accuracy), 0.80 (Jaccard Index), and 0.91 (Fowlkes-Mallows Index), respectively. The clinical Scheuer fibrosis stage presents a strong correlation with the resulting average portal tract fibrotic area (R=0.681, p<0.001) and portal tract percentage (R=0.335, p=0.02) computed from the MUSA-UNet segmentation results. In conclusion, our developed deep learning model MUSA-UNet can accurately segment portal tract regions from whole-slide images of liver tissue biopsies, presenting its promising potential to assist liver disease diagnosis in a computational manner.

bioinformatics↗

bioRxiv2022

An integrative web-based software tool for multi-dimensional pathology whole-slide image analytics

In the era of precision medicine, human tumor atlas oriented studies have been significantly facilitated by high-resolution, multi-modal tissue based microscopic pathology image analytics. To better support such tissue-based investigations, we develop Digital Pathology Laboratory (DPLab), a publicly available web-based platform, to assist biomedical research groups, non-technical end users, and clinicians for pathology Whole-Slide Image (WSI) visualization, annotation, analysis, and sharing via web browsers. A major advance of this work is the easy-to-follow methods to reconstruct three-dimension (3D) tissue image volumes by registering two-dimension (2D) whole-slide pathology images of serial tissue sections stained by hematoxylin and eosin (H&E), and immunohistochemistry (IHC). The integration of these serial slides stained by different methods provides cellular phenotype and pathophysiologic states in the context of a 3D tissue micro-environment. DPLab is hosted on a publicly accessible server and connected to a backend computational cluster for intensive image analysis computations, with results visualized, downloaded, and shared via a web interface. Equipped with an analysis toolbox of numerous image processing algorithms, DPLab supports continued integration of community-contributed algorithms and presents an effective solution to improve the accessibility and dissemination of image analysis algorithms by research communities. It represents the first step in making next generation tissue investigation tools widely available to the research community, enabling and facilitating discovery of clinically relevant disease mechanisms in a digital 3D tissue space.

bioinformatics↗

bioRxiv2022

Deep Learning Based Registration of Serial Whole-slide Histopathology Images in Different Stains

For routine pathology diagnosis and imaging based biomedical research, Whole-Slide Image (WSI) analyses have been largely limited to a 2D tissue image space. For a more definitive tissue representation to support fine-resolution spatial and integrative analyses, it is critical to extend such tissue based investigations to a 3D tissue space with spatially aligned serial tissue WSIs in different stains, such as Hematoxylin and Eosin (H&E) and Immunohistochemistry (IHC) biomarkers. However, such WSI registration is technically challenged by the overwhelming image scale, the complex histology structure change, and the significant difference in tissue appearances in different stains. We propose a novel translation based deep learning registration network CycGANRegNet that spatially aligns serial WSIs stained in H&E and by IHC biomarkers without prior deformation information for the model training. First, synthetic IHC images are produced from H&E slides through a robust image synthesis algorithm. Next, the synthetic and the real IHC images are registered through a Fully Convolutional Network with multi-scaled deformable vector fields and a joint loss optimization. We perform the registration at the full image resolution, retaining the tissue details in the results. Evaluated with a dataset of 76 breast cancer patients with one H&E and two IHC serial WSIs for each patient, CycGANRegNet outperforms multiple state-of-the-art deep learning based and conventional pathology image registration methods. Our results suggest that CycGAN-RegNet can produce promising registration results with serial WSIs in different stains, enabling integrative 3D tissue-based biomedical investigations.

bioinformatics↗

bioRxiv2022

A Spatial Attention Guided Deep Learning System for Prediction of Pathological Complete Response Using Breast Cancer Histopathology Images

Predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in triple-negative breast cancer (TNBC) patients accurately is direly needed for clinical decision making. pCR is also regarded as a strong predictor of overall survival. In this work, we propose a deep learning system to predict pCR to NAC based on serial pathology images stained with hematoxylin and eosin (H&E) and two immunohistochemical biomarkers (Ki67 and PHH3). To support human prior domain knowledge based guidance and enhance interpretability of the deep learning system, we introduce a human knowledge derived spatial attention mechanism to inform deep learning models of informative tissue areas of interest. For each patient, three serial breast tumor tissue sections from biopsy blocks were sectioned, stained in three different stains, and integrated. The resulting comprehensive attention information from the image triplets is used to guide our prediction system for prognostic tissue regions. The experimental dataset consists of 26,419 pathology image patches of 1,000 x 1,000 pixels from 73 TNBC patients treated with NAC. Image patches from randomly selected 43 patients are used as a training dataset and images patches from the rest 30 are used as a testing dataset. By the maximum voting from patch-level results, our proposed model achieves a 93% patient-level accuracy, outperforming baselines and other state-of-the-art systems, suggesting its high potential for clinical decision making. The codes, the documentation, and example data are available on an open source at: https://github.com/jkonglab/PCR_Prediction_Serial_WSIs_biomarkers

bioinformatics↗

bioRxiv2022

Mapping the landscape of anti-phage defense mechanisms in the E. coli pangenome

The ancient, ongoing coevolutionary battle between bacteria and their viral predators, bacteriophages, has given rise to sophisticated immune systems including restriction-modification and CRISPR-Cas. Dozens of additional anti-phage systems have been identified based on their co-location within so-called defense islands, but whether these computational screens are exhaustive is unclear. Here, we developed an experimental selection scheme agnostic to genomic context to identify defense systems encoded in 71 diverse E. coli strains. Our results unveil 21 new and conserved defense systems, none of which were previously detected as enriched in defense islands. Additionally, our work indicates that intact prophages and mobile genetic elements are primary reservoirs and distributors of defense systems in E. coli, with defense systems typically carried in specific locations, or hotspots. These hotspots in homologous prophages and mobile genetic elements encode dozens of additional, as-yet uncharacterized defense system candidates. Collectively, our findings reveal an extended landscape of antiviral immunity in E. coli and provide a generalizable approach for mapping defense systems in other species.

microbiology↗

bioRxiv2021

The DarTG toxin-antitoxin system provides phage defense by ADP-ribosylating viral DNA

Toxin-antitoxin (TA) systems are broadly distributed, yet poorly conserved, genetic elements whose biological functions are unclear and controversial. Some TA systems may provide bacteria with immunity to infection by their ubiquitous viral predators, the bacteriophage. To identify TA systems that protect E. coli MG1655 against phage, we searched for those frequently encoded near known phage defense genes in bacterial genomes. Two of the systems tested provide strong protection against phage infection and are homologs of DarTG, a recently discovered family of TA systems whose biological functions and natural activating conditions were unclear. We demonstrate that phage infection triggers the release of DarT toxin, a DNA ADP-ribosyltransferase, to modify viral DNA and prevent replication, thereby blocking the production of mature virions. Phages can evolve to overcome DarTG defense either through mutations to their DNA polymerase or to an anti-DarT factor, gp61.2, encoded by many T-even phages. Collectively, our results indicate that phage defense may be a common function for TA systems and reveal the mechanism by which DarTG systems inhibit phage infection.

microbiology↗