Extracting structured information from unstructured histopathology reports using generative pre-trained transformer 4 (GPT-4).

Deep learning applied to whole-slide histopathology images (WSIs) has the potential to enhance precision oncology and alleviate the workload of experts. However, developing these models necessitates large amounts of data with ground truth labels, which can be both time-consuming and expensive to obtain. Pathology reports are typically unstructured or poorly structured texts, and efforts to implement structured reporting templates have been unsuccessful, as these efforts lead to perceived extra workload. In this study, we hypothesised that large language models (LLMs), such as the generative pre-trained transformer 4 (GPT-4), can extract structured data from unstructured plain language reports using a zero-shot approach without requiring any re-training. We tested this hypothesis by utilising GPT-4 to extract information from histopathological reports, focusing on two extensive sets of pathology reports for colorectal cancer and glioblastoma. We found a high concordance between LLM-generated structured data and human-generated structured data. Consequently, LLMs could potentially be employed routinely to extract ground truth data for machine learning from unstructured pathology reports in the future. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Automated curation of large-scale cancer histopathology image datasets using deep learning.

BACKGROUND: Artificial intelligence (AI) has numerous applications in pathology, supporting diagnosis and prognostication in cancer. However, most AI models are trained on highly selected data, typically one tissue slide per patient. In reality, especially for large surgical resection specimens, dozens of slides can be available for each patient. Manually sorting and labelling whole-slide images (WSIs) is a very time-consuming process, hindering the direct application of AI on the collected tissue samples from large cohorts. In this study we addressed this issue by developing a deep-learning (DL)-based method for automatic curation of large pathology datasets with several slides per patient. METHODS: We collected multiple large multicentric datasets of colorectal cancer histopathological slides from the United Kingdom (FOXTROT, N = 21,384 slides; CR07, N = 7985 slides) and Germany (DACHS, N = 3606 slides). These datasets contained multiple types of tissue slides, including bowel resection specimens, endoscopic biopsies, lymph node resections, immunohistochemistry-stained slides, and tissue microarrays. We developed, trained, and tested a deep convolutional neural network model to predict the type of slide from the slide overview (thumbnail) image. The primary statistical endpoint was the macro-averaged area under the receiver operating curve (AUROCs) for detection of the type of slide. RESULTS: In the primary dataset (FOXTROT), with an AUROC of 0.995 [95% confidence interval [CI]: 0.994-0.996] the algorithm achieved a high classification performance and was able to accurately predict the type of slide from the thumbnail image alone. In the two external test cohorts (CR07, DACHS) AUROCs of 0.982 [95% CI: 0.979-0.985] and 0.875 [95% CI: 0.864-0.887] were observed, which indicates the generalizability of the trained model on unseen datasets. With a confidence threshold of 0.95, the model reached an accuracy of 94.6% (7331 classified cases) in CR07 and 85.1% (2752 classified cases) for the DACHS cohort. CONCLUSION: Our findings show that using the low-resolution thumbnail image is sufficient to accurately classify the type of slide in digital pathology. This can support researchers to make the vast resource of existing pathology archives accessible to modern AI models with only minimal manual annotations.

Screening of normal endoscopic large bowel biopsies with interpretable graph learning: a retrospective study.

OBJECTIVE: To develop an interpretable artificial intelligence algorithm to rule out normal large bowel endoscopic biopsies, saving pathologist resources and helping with early diagnosis. DESIGN: A graph neural network was developed incorporating pathologist domain knowledge to classify 6591 whole-slides images (WSIs) of endoscopic large bowel biopsies from 3291 patients (approximately 54% female, 46% male) as normal or abnormal (non-neoplastic and neoplastic) using clinically driven interpretable features. One UK National Health Service (NHS) site was used for model training and internal validation. External validation was conducted on data from two other NHS sites and one Portuguese site. RESULTS: Model training and internal validation were performed on 5054 WSIs of 2080 patients resulting in an area under the curve-receiver operating characteristic (AUC-ROC) of 0.98 (SD=0.004) and AUC-precision-recall (PR) of 0.98 (SD=0.003). The performance of the model, named Interpretable Gland-Graphs using a Neural Aggregator (IGUANA), was consistent in testing over 1537 WSIs of 1211 patients from three independent external datasets with mean AUC-ROC=0.97 (SD=0.007) and AUC-PR=0.97 (SD=0.005). At a high sensitivity threshold of 99%, the proposed model can reduce the number of normal slides to be reviewed by a pathologist by approximately 55%. IGUANA also provides an explainable output highlighting potential abnormalities in a WSI in the form of a heatmap as well as numerical values associating the model prediction with various histological features. CONCLUSION: The model achieved consistently high accuracy showing its potential in optimising increasingly scarce pathologist resources. Explainable predictions can guide pathologists in their diagnostic decision-making and help boost their confidence in the algorithm, paving the way for its future clinical adoption.

Direct prediction of genetic aberrations from pathology images in gastric cancer with swarm learning.

BACKGROUND: Computational pathology uses deep learning (DL) to extract biomarkers from routine pathology slides. Large multicentric datasets improve performance, but such datasets are scarce for gastric cancer. This limitation could be overcome by Swarm Learning (SL). METHODS: Here, we report the results of a multicentric retrospective study of SL for prediction of molecular biomarkers in gastric cancer. We collected tissue samples with known microsatellite instability (MSI) and Epstein-Barr Virus (EBV) status from four patient cohorts from Switzerland, Germany, the UK and the USA, storing each dataset on a physically separate computer. RESULTS: On an external validation cohort, the SL-based classifier reached an area under the receiver operating curve (AUROC) of 0.8092 (± 0.0132) for MSI prediction and 0.8372 (± 0.0179) for EBV prediction. The centralized model, which was trained on all datasets on a single computer, reached a similar performance. CONCLUSIONS: Our findings demonstrate the feasibility of SL-based molecular biomarkers in gastric cancer. In the future, SL could be used for collaborative training and, thus, improve the performance of these biomarkers. This may ultimately result in clinical-grade performance and generalizability.

IAA-producing plant growth promoting rhizobacteria from Ceanothus velutinus enhance cutting propagation efficiency and Arabidopsis biomass.

Climate-induced drought impacts plant growth and development. Recurring droughts increase the demand for water for food production and landscaping. Native plants in the Intermountain West region of the US are of keen interest in low water use landscaping as they are acclimatized to dry and cold environments. These native plants do very well at their native locations but are difficult to propagate in landscape. One of the possible reasons is the lack of associated microbiome in the landscaping. Microbiome in the soil contributes to soil health and impacts plant growth and development. Here, we used the bulk soil from the native plant Ceanothus velutinus (snowbrush ceanothus) as inoculant to enhance its propagation. Snowbrush ceanothus is an ornamental plant for low-water landscaping that is hard to propagate asexually. Using 50% native bulk soil as inoculant in the potting mix significantly improved the survival rate of the cuttings compared to no-treated cuttings. Twenty-four plant growth-promoting rhizobacteria (PGPR) producing indole acetic acid (IAA) were isolated from the rhizosphere and roots of the survived snowbrush. Seventeen isolates had more than 10µg/mL of IAA were shortlisted and tested for seven different plant growth-promoting (PGP) traits; 76% showed nitrogen-fixing ability on Norris Glucose Nitrogen free media,70% showed phosphate solubilization activity, 76% showed siderophore production, 36% showed protease activity, 94% showed ACC deaminase activity on DF-ACC media, 76% produced catalase and all of isolates produced ammonia. Eight of seventeen isolates, CK-6, CK-22, CK-41, CK-44, CK-47, CK-50, CK-53, and CK-55, showed an increase in shoot biomass in Arabidopsis thaliana. Seven out of eight isolates were identified as Pseudomonas, except CK-55, identified as Sphingobium based on 16S rRNA gene sequencing. The shortlisted isolates are being tested on different grain and vegetable crops to mitigate drought stress and promote plant growth.

Using histopathology latent diffusion models as privacy-preserving dataset augmenters improves downstream classification performance.

Latent diffusion models (LDMs) have emerged as a state-of-the-art image generation method, outperforming previous Generative Adversarial Networks (GANs) in terms of training stability and image quality. In computational pathology, generative models are valuable for data sharing and data augmentation. However, the impact of LDM-generated images on histopathology tasks compared to traditional GANs has not been systematically studied. We trained three LDMs and a styleGAN2 model on histology tiles from nine colorectal cancer (CRC) tissue classes. The LDMs include 1) a fine-tuned version of stable diffusion v1.4, 2) a Kullback-Leibler (KL)-autoencoder (KLF8-DM), and 3) a vector quantized (VQ)-autoencoder deploying LDM (VQF8-DM). We assessed image quality through expert ratings, dimensional reduction methods, distribution similarity measures, and their impact on training a multiclass tissue classifier. Additionally, we investigated image memorization in the KLF8-DM and styleGAN2 models. All models provided a high image quality, with the KLF8-DM achieving the best Frechet Inception Distance (FID) and expert rating scores for complex tissue classes. For simpler classes, the VQF8-DM and styleGAN2 models performed better. Image memorization was negligible for both styleGAN2 and KLF8-DM models. Classifiers trained on a mix of KLF8-DM generated and real images achieved a 4% improvement in overall classification accuracy, highlighting the usefulness of these images for dataset augmentation. Our systematic study of generative methods showed that KLF8-DM produces the highest quality images with negligible image memorization. The higher classifier performance in the generatively augmented dataset suggests that this augmentation technique can be employed to enhance histopathology classifiers for various tasks.

Regression-based Deep-Learning predicts molecular biomarkers from pathology slides.

Deep Learning (DL) can predict biomarkers from cancer histopathology. Several clinically approved applications use this technology. Most approaches, however, predict categorical labels, whereas biomarkers are often continuous measurements. We hypothesize that regression-based DL outperforms classification-based DL. Therefore, we develop and evaluate a self-supervised attention-based weakly supervised regression method that predicts continuous biomarkers directly from 11,671 images of patients across nine cancer types. We test our method for multiple clinically and biologically relevant biomarkers: homologous recombination deficiency score, a clinically used pan-cancer biomarker, as well as markers of key biological processes in the tumor microenvironment. Using regression significantly enhances the accuracy of biomarker prediction, while also improving the predictions' correspondence to regions of known clinical relevance over classification. In a large cohort of colorectal cancer patients, regression-based prediction scores provide a higher prognostic value than classification-based scores. Our open-source regression approach offers a promising alternative for continuous biomarker analysis in computational pathology.

Self-supervised attention-based deep learning for pan-cancer mutation prediction from histopathology.

The histopathological phenotype of tumors reflects the underlying genetic makeup. Deep learning can predict genetic alterations from pathology slides, but it is unclear how well these predictions generalize to external datasets. We performed a systematic study on Deep-Learning-based prediction of genetic alterations from histology, using two large datasets of multiple tumor types. We show that an analysis pipeline that integrates self-supervised feature extraction and attention-based multiple instance learning achieves a robust predictability and generalizability.

Development and validation of artificial intelligence-based prescreening of large-bowel biopsies taken in the UK and Portugal: a retrospective cohort study.

BACKGROUND: Histopathological examination is a crucial step in the diagnosis and treatment of many major diseases. Aiming to facilitate diagnostic decision making and improve the workload of pathologists, we developed an artificial intelligence (AI)-based prescreening tool that analyses whole-slide images (WSIs) of large-bowel biopsies to identify typical, non-neoplastic, and neoplastic biopsies. METHODS: This retrospective cohort study was conducted with an internal development cohort of slides acquired from a hospital in the UK and three external validation cohorts of WSIs acquired from two hospitals in the UK and one clinical laboratory in Portugal. To learn the differential histological patterns from digitised WSIs of large-bowel biopsy slides, our proposed weakly supervised deep-learning model (Colorectal AI Model for Abnormality Detection [CAIMAN]) used slide-level diagnostic labels and no detailed cell or region-level annotations. The method was developed with an internal development cohort of 5054 biopsy slides from 2080 patients that were labelled with corresponding diagnostic categories assigned by pathologists. The three external validation cohorts, with a total of 1536 slides, were used for independent validation of CAIMAN. Each WSI was classified into one of three classes (ie, typical, atypical non-neoplastic, and atypical neoplastic). Prediction scores of image tiles were aggregated into three prediction scores for the whole slide, one for its likelihood of being typical, one for its likelihood of being non-neoplastic, and one for its likelihood of being neoplastic. The assessment of the external validation cohorts was conducted by the trained and frozen CAIMAN model. To evaluate model performance, we calculated area under the convex hull of the receiver operating characteristic curve (AUROC), area under the precision-recall curve, and specificity compared with our previously published iterative draw and rank sampling (IDaRS) algorithm. We also generated heat maps and saliency maps to analyse and visualise the relationship between the WSI diagnostic labels and spatial features of the tissue microenvironment. The main outcome of this study was the ability of CAIMAN to accurately identify typical and atypical WSIs of colon biopsies, which could potentially facilitate automatic removing of typical biopsies from the diagnostic workload in clinics. FINDINGS: A randomly selected subset of all large bowel biopsies was obtained between Jan 1, 2012, and Dec 31, 2017. The AI training, validation, and assessments were done between Jan 1, 2021, and Sept 30, 2022. WSIs with diagnostic labels were collected between Jan 1 and Sept 30, 2022. Our analysis showed no statistically significant differences across prediction scores from CAIMAN for typical and atypical classes based on anatomical sites of the biopsy. At 0·99 sensitivity, CAIMAN (specificity 0·5592) was more accurate than an IDaRS-based weakly supervised WSI-classification pipeline (0·4629) in identifying typical and atypical biopsies on cross-validation in the internal development cohort (p<0·0001). At 0·99 sensitivity, CAIMAN was also more accurate than IDaRS for two external validation cohorts (p<0·0001), but not for a third external validation cohort (p=0·10). CAIMAN provided higher specificity than IDaRS at some high-sensitivity thresholds (0·7763 vs 0·6222 for 0·95 sensitivity, 0·7126 vs 0·5407 for 0·97 sensitivity, and 0·5615 vs 0·3970 for 0·99 sensitivity on one of the external validation cohorts) and showed high classification performance in distinguishing between neoplastic biopsies (AUROC 0·9928, 95% CI 0·9927-0·9929), inflammatory biopsies (0·9658, 0·9655-0·9661), and atypical biopsies (0·9789, 0·9786-0·9792). On the three external validation cohorts, CAIMAN had AUROC values of 0·9431 (95% CI 0·9165-0·9697), 0·9576 (0·9568-0·9584), and 0·9636 (0·9615-0·9657) for the detection of atypical biopsies. Saliency maps supported the representation of disease heterogeneity in model predictions and its association with relevant histological features. INTERPRETATION: CAIMAN, with its high sensitivity in detecting atypical large-bowel biopsies, might be a promising improvement in clinical workflow efficiency and diagnostic decision making in prescreening of typical colorectal biopsies. FUNDING: The Pathology Image Data Lake for Analytics, Knowledge and Education Centre of Excellence; the UK Government's Industrial Strategy Challenge Fund; and Innovate UK on behalf of UK Research and Innovation.

Direct image to subtype prediction for brain tumors using deep learning.

Background: Deep Learning (DL) can predict molecular alterations of solid tumors directly from routine histopathology slides. Since the 2021 update of the World Health Organization (WHO) diagnostic criteria, the classification of brain tumors integrates both histopathological and molecular information. We hypothesize that DL can predict molecular alterations as well as WHO subtyping of brain tumors from hematoxylin and eosin-stained histopathology slides. Methods: We used weakly supervised DL and applied it to three large cohorts of brain tumor samples, comprising N = 2845 patients. Results: We found that the key molecular alterations for subtyping, IDH and ATRX, as well as 1p19q codeletion, were predictable from histology with an area under the receiver operating characteristic curve (AUROC) of 0.95, 0.90, and 0.80 in the training cohort, respectively. These findings were upheld in external validation cohorts with AUROCs of 0.90, 0.79, and 0.87 for prediction of IDH, ATRX, and 1p19q codeletion, respectively. Conclusions: In the future, such DL-based implementations could ease diagnostic workflows, particularly for situations in which advanced molecular testing is not readily available.

Exploration of the rhizosphere microbiome of native plant Ceanothus velutinus - an excellent resource of plant growth-promoting bacteria.

Continuous demand for an increase in food production due to climate change and a steady rise in world population requires stress-resilient, sustainable agriculture. Overuse of chemical fertilizers and monoculture farming to achieve this goal deteriorated soil health and negatively affected its microbiome. The rhizosphere microbiome of a plant plays a significant role in its growth and development and promotes the plant's overall health through nutrient uptake/availability, stress tolerance, and biocontrol activity. The Intermountain West (IW) region of the US is rich in native plants recommended for low water use landscaping because of their drought tolerance. The rhizosphere microbiome of these native plants is an excellent resource for plant growth-promoting rhizobacteria (PGPR) to use these microbes as biofertilizers and biostimulants to enhance food production, mitigate environmental stresses and an alternative for chemical fertilizer, and improve soil health. Here, we isolated, purified, identified, and characterized 64 bacterial isolates from a native plant, Ceanothus velutinus, commonly known as snowbrush ceanothus, from the natural habitat and the greenhouse-grown native soil-treated snowbrush ceanothus plants. We also conducted a microbial diversity analysis of the rhizosphere of greenhouse-grown native soil-treated and untreated plants (control). Twenty-seven of the 64 isolates were from the rhizosphere of the native region, and 36 were from the greenhouse-grown native soil-treated plants. These isolates were also tested for plant growth-promoting (PGP) traits such as their ability to produce catalase, siderophore, and indole acetic acid, fix atmospheric nitrogen and solubilize phosphate. Thirteen bacterial isolates tested positive for all five plant growth-promoting abilities and belonged to the genera Pantoea, Pseudomonas, Bacillus, and Ancylobacter. Besides, there are isolates belonging to the genus Streptomyces, Bacillus, Peribacillus, Variovorax, Xenophilus, Brevundimonas, and Priestia, which exhibit at least one of the plant growth-promoting activities. This initial screen provided a list of potential PGPR to test for plant health improvement on model and crop plants. Most of the bacterial isolates in this study have a great potential to become biofertilizers and bio-stimulants.

Psychotropic Polypharmacy Among Children and Youth with Autism: A Systematic Review.

Objectives: Majority of youth with autism are taking two or more medications (psychotropic or nonpsychotropic) simultaneously, also known as polypharmacy. Yet the efficacy and the potential outcomes of polypharmacy in this population are widely unknown. This systematic literature review described the trends of polypharmacy among autistic youth, and identified factors associated with polypharmacy. Methods: Sixteen studies were included, encompassing over 300,000 youth with autism. Results: Rates of polypharmacy varied quite substantially across studies, ranging from 6.8% to 87% of autistic youth. Having psychiatric comorbidities, self-injurious behaviors, and physical aggression, as well as being male and older, were associated with higher rates of polypharmacy. Conclusion: Findings emphasize the importance of further research to determine appropriate practices related to the monitoring of adverse side effects, and the long-term impact of polypharmacy among autistic youth.

Diagnostic concordance and discordance in digital pathology: a systematic review and meta-analysis.

BACKGROUND: Digital pathology (DP) has the potential to fundamentally change the way that histopathology is practised, by streamlining the workflow, increasing efficiency, improving diagnostic accuracy and facilitating the platform for implementation of artificial intelligence-based computer-assisted diagnostics. Although the barriers to wider adoption of DP have been multifactorial, limited evidence of reliability has been a significant contributor. A meta-analysis to demonstrate the combined accuracy and reliability of DP is still lacking in the literature. OBJECTIVES: We aimed to review the published literature on the diagnostic use of DP and to synthesise a statistically pooled evidence on safety and reliability of DP for routine diagnosis (primary and secondary) in the context of validation process. METHODS: A comprehensive literature search was conducted through PubMed, Medline, EMBASE, Cochrane Library and Google Scholar for studies published between 2013 and August 2019. The search protocol identified all studies comparing DP with light microscopy (LM) reporting for diagnostic purposes, predominantly including H&E-stained slides. Random-effects meta-analysis was used to pool evidence from the studies. RESULTS: Twenty-five studies were deemed eligible to be included in the review which examined a total of 10 410 histology samples (average sample size 176). For overall concordance (clinical concordance), the agreement percentage was 98.3% (95% CI 97.4 to 98.9) across 24 studies. A total of 546 major discordances were reported across 25 studies. Over half (57%) of these were related to assessment of nuclear atypia, grading of dysplasia and malignancy. These were followed by challenging diagnoses (26%) and identification of small objects (16%). CONCLUSION: The results of this meta-analysis indicate equivalent performance of DP in comparison with LM for routine diagnosis. Furthermore, the results provide valuable information concerning the areas of diagnostic discrepancy which may warrant particular attention in the transition to DP.

Evaluation of a high-throughput in vitro endothelial cell migration assay for the assessment of nicotine and tobacco delivery products.

Endothelial cell migration is a critical process in the maintenance of healthy blood vessels. Impaired endothelial migration is reportedly associated with the development of cardiovascular diseases. Here, we report on the development of a 96-well in vitro endothelial migration assay for the purpose of comparative toxicological assessment of a novel THP relative to cigarette smoke, to be able to rapidly inform regulatory decision making. Uniform scratches were induced in confluent human umbilical vein endothelial cells using the 96-pin wound maker and exposed to 3R4F cigarette or THP aqueous extracts (AqE). Endothelial migration was recorded over 24 h, and the rate of wound closure calculated using mean relative wound density rather than migration rate as previously reported. This self-normalising parameter accounts for starting wound size, by comparing the density of the scratch to the outer region at each time-point. Furthermore, wound width acceptance criteria was defined to further increase the sensitivity of the assay. 3R4F and THP AqE samples were tested at comparable nicotine concentrations. 3R4F showed significant cytotoxicity and inhibition of wound healing whereas THP AqE did not show any response in either endpoint. This 96-well endothelial migration assay was suitably sensitive to distinguish combustible cigarette and THP test articles.

Cellular community detection for tissue phenotyping in colorectal cancer histology images.

Classification of various types of tissue in cancer histology images based on the cellular compositions is an important step towards the development of computational pathology tools for systematic digital profiling of the spatial tumor microenvironment. Most existing methods for tissue phenotyping are limited to the classification of tumor and stroma and require large amount of annotated histology images which are often not available. In the current work, we pose the problem of identifying distinct tissue phenotypes as finding communities in cellular graphs or networks. First, we train a deep neural network for cell detection and classification into five distinct cellular components. Considering the detected nuclei as nodes, potential cell-cell connections are assigned using Delaunay triangulation resulting in a cell-level graph. Based on this cell graph, a feature vector capturing potential cell-cell connection of different types of cells is computed. These feature vectors are used to construct a patch-level graph based on chi-square distance. We map patch-level nodes to the geometric space by representing each node as a vector of geodesic distances from other nodes in the network and iteratively drifting the patch nodes in the direction of positive density gradients towards maximum density regions. The proposed algorithm is evaluated on a publicly available dataset and another new large-scale dataset consisting of 280K patches of seven tissue phenotypes. The estimated communities have significant biological meanings as verified by the expert pathologists. A comparison with current state-of-the-art methods reveals significant performance improvement in tissue phenotyping.

Deep Learning With Sampling in Colon Cancer Histology.

This study applied a deep-learning cell identification algorithm to diagnostic images from the colon cancer repository at The Cancer Genome Atlas (TCGA). Within-image sampling improved performance without loss of accuracy. The features thus derived were associated with various clinical variables including metastasis, residual tumor, venous invasion, and lymphatic invasion. The deep-learning algorithm was trained using images from a locally available data set, then applied to the TCGA images by tiling them, and identifying cells in each patch defined by the tiling. In this application the average number of patches containing tissue in an image was ~900. Processing a random sample of patches greatly reduced computation costs. The cell identification algorithm was applied directly to each sampled patch, resulting in a list of cells. Each cell was labeled with its location and classification ("epithelial," "inflammatory," "fibroblast," or "other"). The number of cells of a given type in the patch was calculated, resulting in a patch profile containing four features. A morphological profile that applied to the entire image was obtained by averaging profiles over all patches. Two sampling policies were examined. The first policy was random sampling which samples patches with uniform weighting. The second policy was systematic random sampling which takes spatial dependencies into account. Compared with the processing of complete whole slide images there was a seven-fold improvement in performance when systematic random spatial sampling was used to select 100 tiles from the whole-slide image for processing, with very little loss of accuracy (~4% on average). We found links between the predicted features and clinical variables in the TCGA colon cancer data set. Several significant associations were found: increased fibroblast numbers were associated with the presence of metastasis, venous invasion, lymphatic invasion and residual tumor while decreased numbers of inflammatory cells were associated with mucinous carcinomas. Regarding the four different types of cell, deep learning has generated morphological features that are indicators of cell density. The features are related to cellularity, the numbers, degree, or quality of cells present in a tumor. Cellularity has been reported to be related to patient survival and other diagnostic and prognostic indicators, indicating that the features calculated here may be of general usefulness.

A comparative assessment of e-cigarette aerosols and cigarette smoke on in vitro endothelial cell migration.

Cigarette smoking is a risk factor for several diseases. There has been a steep increase in the use of e-cigarettes that may offer a safer alternative to cigarette smoking. In vitro models of smoking-related diseases may provide valuable insights into disease mechanisms associated with tobacco use and could be used to assess e-cigarettes. We previously reported the application of a 'scratch wound' assay, measuring endothelial cell migration rate following artificial wounding, in the presence or absence of cigarette smoke extracts. This study reports the comparative effects of two commercial e-cigarette products (Vype ePen and Vype eStick) and a scientific reference cigarette (3R4F) on endothelial migration in vitro. Puff-matched extracts were generated using the Health Canada Intense (HCI) regime for cigarettes and a modified HCI for e-cigarettes. Exposure to 3R4F extract (20h) induced concentration-dependent inhibition of endothelial cell migration, with complete inhibition at concentrations >20%. E-cigarette extracts did not inhibit migration, even at double the 3R4F extract nicotine concentration, allowing cells to migrate into the wounded area. Our data demonstrate that e-cigarettes do not induce the inhibition of endothelial cell migration in vitro when compared to 3R4F. The scratch wound assay enables the comparative assessment between tobacco and nicotine products in vitro.

Cigarette smoke extract counteracts atheroprotective effects of high laminar flow on endothelial function.

Tobacco smoking and hemodynamic forces are key stimuli in the development of endothelial dysfunction and atherosclerosis. High laminar flow has an atheroprotective effect on the endothelium and leads to a reduced response of endothelial cells to cardiovascular risk factors compared to regions with disturbed or low laminar flow. We hypothesize that the atheroprotective effect of high laminar flow could delay the development of endothelial dysfunction caused by cigarette smoking. Primary human endothelial cells were stimulated with increasing dosages of aqueous cigarette smoke extract (CSEaq). CSEaq reduced cell viability in a dose-dependent manner. The main mediator of cellular adaption to oxidative stress, nuclear factor erythroid 2-related factor 2 (NRF2) and its target genes heme oxygenase (decycling) 1 (HMOX1) or NAD(P)H quinone dehydrogenase 1 (NQO1) were strongly increased by CSEaq in a dose-dependent manner. High laminar flow induced elongation of endothelial cells in the direction of flow, activated the AKT/eNOS pathway, increased eNOS expression, phosphorylation and NO release. These increases were inhibited by CSEaq. Pro-inflammatory adhesion molecules intercellular adhesion molecule-1 (ICAM1), vascular cell adhesion molecule-1 (VCAM1), selectin E (SELE) and chemokine (C-C motif) ligand 2 (CCL2/MCP-1) were increased by CSEaq. Low laminar flow induced VCAM1 and SELE compared to high laminar flow. High laminar flow improved endothelial wound healing. This protective effect was inhibited by CSEaq in a dose-dependent manner through the AKT/eNOS pathway. Low as well as high laminar flow decreased adhesion of monocytes to endothelial cells. Whereas, monocyte adhesion was increased by CSEaq under low laminar flow, this was not evident under high laminar flow. This study shows the activation of major atherosclerotic key parameters by CSEaq. Within this process, high laminar flow is likely to reduce the harmful effects of CSEaq to a certain degree. The identified molecular mechanisms might be useful for development of alternative therapy concepts.

A novel hybrid tobacco product that delivers a tobacco flavour note with vapour aerosol (Part 2): In vitro biological assessment and comparison with different tobacco-heating products.

This study assessed the toxicological and biological responses of aerosols from a novel hybrid tobacco product. Toxicological responses from the hybrid tobacco product were compared to those from a commercially available Tobacco Heating Product (c-THP), a prototype THP (p-THP) and a 3R4F reference cigarette, using in vitro test methods which were outlined as part of a framework to substantiate the risk reduction potential of novel tobacco and nicotine products. Exposure matrices used included total particulate matter (TPM), whole aerosol (WA), and aqueous aerosol extracts (AqE) obtained after machine-puffing the test products under the Health Canada Intense smoking regime. Levels of carbonyls and nicotine in these matrices were measured to understand the aerosol dosimetry of the products. The hybrid tobacco product tested negative across the in vitro assays including mutagenicity, genotoxicity, cytotoxicity, tumour promotion, oxidative stress and endothelial dysfunction. All the THPs tested demonstrated significantly reduced responses in these in vitro assays when compared to 3R4F. The findings suggest these products have the potential for reduced health risks. Further pre-clinical and clinical assessments are required to substantiate the risk reduction of these novel products at individual and population levels.