Finding a noninvasive radiomic surrogate of tumor immune features could help identify patients more likely to respond to novel immune checkpoint inhibitors. Particularly, CD73 is an ectonucleotidase ...that catalyzes the breakdown of extracellular AMP into immunosuppressive adenosine, which can be blocked by therapeutic antibodies. High CD73 expression in colorectal cancer liver metastasis (CRLM) resected with curative intent is associated with early recurrence and shorter patient survival. The aim of this study was hence to evaluate whether machine learning analysis of preoperative liver CT-scan could estimate high vs low CD73 expression in CRLM and whether such radiomic score would have a prognostic significance.
We trained an Attentive Interpretable Tabular Learning (TabNet) model to predict, from preoperative CT images, stratified expression levels of CD73 (CD73
vs. CD73
) assessed by immunofluorescence (IF) on tissue microarrays. Radiomic features were extracted from 160 segmented CRLM of 122 patients with matched IF data, preprocessed and used to train the predictive model. We applied a five-fold cross-validation and validated the performance on a hold-out test set.
TabNet provided areas under the receiver operating characteristic curve of 0.95 (95% CI 0.87 to 1.0) and 0.79 (0.65 to 0.92) on the training and hold-out test sets respectively, and outperformed other machine learning models. The TabNet-derived score, termed rad-CD73, was positively correlated with CD73 histological expression in matched CRLM (Spearman's ρ = 0.6004; P < 0.0001). The median time to recurrence (TTR) and disease-specific survival (DSS) after CRLM resection in rad-CD73
vs rad-CD73
patients was 13.0 vs 23.6 months (P = 0.0098) and 53.4 vs 126.0 months (P = 0.0222), respectively. The prognostic value of rad-CD73 was independent of the standard clinical risk score, for both TTR (HR = 2.11, 95% CI 1.30 to 3.45, P < 0.005) and DSS (HR = 1.88, 95% CI 1.11 to 3.18, P = 0.020).
Our findings reveal promising results for non-invasive CT-scan-based prediction of CD73 expression in CRLM and warrant further validation as to whether rad-CD73 could assist oncologists as a biomarker of prognosis and response to immunotherapies targeting the adenosine pathway.
Colorectal cancer liver metastases (CLM) are the most common type of distant metastases originating from the abdomen and are characterized by a high recurrence rate after curative resection. It has ...been previously reported that CLM presenting a low cluster of differentiation 3 (CD3) positive T-cell infiltration density concurrent with a high major histocompatibility complex class I (MHC-I) expression were associated with poor clinical outcomes. In this study, we attempt to noninvasively predict whether a CLM exhibits the CD3 Low MHC High immunological profile using preoperative CT images. To this end, we propose an ensemble network combining multiple Attentive Interpre table Tabular learning (TabNet) models, trained using CT-derived radiomic features. A total of 160 CLM were included in this study and randomly divided between a training set (n=130) and a hold-out test set (n=30). The proposed model yielded good prediction performance on the test set with an accuracy of 70.0% 95% confidence interval 53.6%-86.4% and an area under the curve of 69.4% 52.9%-85.9%. It also outperformed other off-the-shelf machine learning models. We finally demonstrated that the predicted immune profile was associated with a shorter disease-specific survival (p = .023) and time-to-recurrence (p = .020), showing the value of assessing the immune response.
Non small cell lung cancer (NSCLC) is the most common type of lung cancer and is classified into two main histological subtypes: adenocarcinoma and squamous cell carcinoma. The identification of the ...histological subtype is a crucial step in the diagnosis of NSCLC. RNA sequencing data hold valuable biological information but may contain missing gene expression counts, which limit their potential exploitation in practice. In this work, we address the issue of missing gene expression data in NSCLC histological subtype prediction from RNA sequencing. To this end, we propose a pipeline based on the generative adversarial imputation network (GAIN) for the generation of plausible imputations of missing data and tree-based ensemble models for NSCLC histological subtype prediction. We adopted a nested cross validation scheme for the evaluation of the classification models. The proposed pipeline exhibited an outstanding performance with an area under the receiver operating characteristic curve of 0.98 ± 0.03 and an accuracy of 0.96 ± 0.05 obtained with the Light Gradient Boosting Machine. Experimental results showed that GAIN-derived imputations are useful to boost classification performance. Finally, we used the Shapley Additive Explanations technique and found a set of genes that were the most relevant for NSCLC subtyping across different models.
Colorectal cancer liver metastases (CLM) are the most common type of distant metastases originating from the abdomen and are characterized by a high recurrence rate after curative resection. It has ...been previously reported that CLM presenting a low cluster of differentiation 3 (CD3) positive T-cell infiltration density concurrent with a high major histocompatibility complex class I (MHC-I) expression were associated with poor clinical outcomes. In this study, we attempt to noninvasively predict whether a CLM exhibit the CD3LowMHCHigh immunological profile using preoperative CT images. To this end, we propose an ensemble network combining multiple Attentive Interpretable Tabular learning (TabNet) models, trained using CT-derived radiomic features. A total of 160 CLM were included in this study and randomly divided between a training set (n=130) and a hold-out test set (n=30). The proposed model yielded good prediction performance on the test set with an accuracy of 70.0% 95% confidence interval 53.6%-86.4% and an area under the curve of 69.4% 52.9%-85.9%. It also outperformed other off-the-shelf machine learning models. We finally demonstrated that the predicted immune profile was associated with a shorter disease-specific survival (p = .023) and time-to-recurrence (p = .020), showing the value of assessing the immune response.
Drug discovery is a long and costly procedure that requires the prediction of many candidate molecules' attributes, including ADMET (Absorption, Distribution, Metabolism, Elimination and Toxicity) ...characteristics. Pharmaceutical companies complementarily use in silico and in vitro models at different stages for this purpose. The permeability across the blood-brain barrier (BBB) is a very important ADMET property since it inhibits the delivery of multiple drugs to the brain. In this context, this paper presents two BBB models designed and implemented for early stage drug discovery: an in silico and an in vitro one. The highest overall accuracy obtained with the former model was 96.23% with both Quadratic Discriminant Analysis and Support Vector Machine classifiers, after applying Genetic Algorithm for feature selection. In the latter case, we have proposed the novel approach of applying cellulose filter papers of 2 μm porosity with PLA 3D printed inserts in order to build a valid in vitro model. Different coatings were tested to increase the adhesion of the endothelial cells to the substrate. The highest degree of confluency was obtained with the collagen type I coating. Moreover, the highest trans-endothelial electric resistance (TEER) value obtained was 45.6 ± 12.07 Ω.cm 2 which is comparable to the values reported using the same cell line. This shows that paper-based cell culture can be a promising tool for the implementation of low-cost BBB models that could validate and refine computational models.
When solar radiation hits a roof surface, a part of solar energy is reflected and part is absorbed. The absorbed part of solar energy results in an increase of the surface temperature of the roof. ...Cool reflective (white) roofs use bright surfaces to reflect a significant portion of the incident short-wave solar radiation, which lowers the surface temperature compared to conventional (black) roofs with bituminous membrane. As such, white roofs help reduce the urban heat island effect during the summer. The question is "do white roofs lead to moisture-related problems in northern and southern climates?" To help answer this question, numerical simulations were conducted to compare the hygrothermal performance of a single kind of white and black roofs under different outdoor and indoor conditions. The outdoor conditions are obtained from the weather database of the National Research Council of Canada, Institute for Research in Construction (NRC-IRC). The indoor conditions are taken based on the European standard (EN 15026) and ASHRAE recommendations for conditioned space. The type of roofs considered in this study is Modified-Bitumen (MOD-BIT) roofing systems. The numerical simulations were conducted for the outdoor climate of Toronto (ON), Montreal (QC), St John's (NL), Saskatoon (SK), Seattle (WA), Wilmington (NC) and Phoenix (AZ). Results showed that for the outdoor climates of St John's and Saskatoon, the white roofs could lead to longer-term moisture-related problems. However, for the outdoor climates of Toronto, Montreal, Seattle, Wilmington and Phoenix, buildings with white roofs were shown to have a low risk of experiencing moisture damage. Also, buildings with white roofs in these locations were predicted to show a net yearly energy savings compared to buildings with black roofs.
Background
Correctional settings are hotspots for SARS-CoV-2 transmission. Social and biological risk factors contribute to higher rates of COVID-19 morbidity and mortality among justice-involved ...individuals. Rapidly identifying new cases in congregate settings is essential to promote proper isolation and quarantine. We sought perspectives of individuals incarcerated during COVID-19 on how to improve carceral infection control and their perspectives on acceptability of wastewater-based surveillance (WBS) accompanying individual testing.
Methods
We conducted semi-structured interviews with 20 adults who self-reported being incarcerated throughout the United States between March 2020 and May 2021. We asked participants about facility enforcement of the Centers for Disease Control and Prevention (CDC) COVID-19 guidelines, and acceptability of integrating WBS into SARS-CoV-2 monitoring strategies at their most recent facility. We used descriptive statistics to characterize the study sample and report on acceptability of WBS. We analyzed qualitative data thematically using an iterative process.
Results
Participants were predominantly Black or multiple races (50%) and men (75%); 46 years old on average. Most received a mask during their most recent incarceration (90%), although only 40% received counseling on proper mask wearing. A quarter of participants were tested for SARS-CoV-2 at intake. Most (70%) believed they were exposed to the virus while incarcerated. Reoccurring themes included (1) Correctional facility environment leading to a sense of insecurity, (2) Perceptions that punitive conditions in correctional settings were exacerbated by the pandemic; (3) Importance of peers as a source of information about mitigation measures; (4) Perceptions that the safety of correctional environments differed from that of the community during the pandemic; and (5) WBS as a logical strategy, with most (68%) believing WBS would work in the last correctional facility they were in, and 79% preferred monitoring SARS-CoV-2 levels through WBS rather than relying on just individual testing.
Conclusion
Participants supported routine WBS to monitor for SARS-CoV-2. Integrating WBS into existing surveillance strategies at correctional facilities may minimize the impact of future COVID-19 outbreaks while conserving already constrained resources. To enhance the perception and reality that correctional systems are maximizing mitigation, future measures might include focusing on closer adherence to CDC recommendations and clarity about disease pathogenesis with residents.
Chemical patents represent a valuable source of information about new chemical compounds, which is critical to the drug discovery process. Automated information extraction over chemical patents is, ...however, a challenging task due to the large volume of existing patents and the complex linguistic properties of chemical patents. The Cheminformatics Elsevier Melbourne University (ChEMU) evaluation lab 2020, part of the Conference and Labs of the Evaluation Forum 2020 (CLEF2020), was introduced to support the development of advanced text mining techniques for chemical patents. The ChEMU 2020 lab proposed two fundamental information extraction tasks focusing on chemical reaction processes described in chemical patents: (1)
, requiring identification of essential chemical entities and their roles in chemical reactions, as well as reaction conditions; and (2)
, which aims at identification of event steps relating the entities involved in chemical reactions. The ChEMU 2020 lab received 37 team registrations and 46 runs. Overall, the performance of submissions for these tasks exceeded our expectations, with the top systems outperforming strong baselines. We further show the methods to be robust to variations in sampling of the test data. We provide a detailed overview of the ChEMU 2020 corpus and its annotation, showing that inter-annotator agreement is very strong. We also present the methods adopted by participants, provide a detailed analysis of their performance, and carefully consider the potential impact of data leakage on interpretation of the results. The ChEMU 2020 Lab has shown the viability of automated methods to support information extraction of key information in chemical patents.
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