MesoGraph: Automatic profiling of mesothelioma subtypes from histological images.

Mesothelioma is classified into three histological subtypes, epithelioid, sarcomatoid, and biphasic, according to the relative proportions of epithelioid and sarcomatoid tumor cells present. Current guidelines recommend that the sarcomatoid component of each mesothelioma is quantified, as a higher percentage of sarcomatoid pattern in biphasic mesothelioma shows poorer prognosis. In this work, we develop a dual-task graph neural network (GNN) architecture with ranking loss to learn a model capable of scoring regions of tissue down to cellular resolution. This allows quantitative profiling of a tumor sample according to the aggregate sarcomatoid association score. Tissue is represented by a cell graph with both cell-level morphological and regional features. We use an external multicentric test set from Mesobank, on which we demonstrate the predictive performance of our model. We additionally validate our model predictions through an analysis of the typical morphological features of cells according to their predicted score

Malignant Mesothelioma subtyping via sampling driven multiple instance prediction on tissue image and cell morphology data

Malignant Mesothelioma is a difficult to diagnose and highly lethal cancer usually associated with asbestos exposure. It can be broadly classified into three subtypes: Epithelioid, Sarcomatoid, and a hybrid Biphasic subtype in which significant components of both of the previous subtypes are present. Early diagnosis and identification of the subtype informs treatment and can help improve patient outcome. However, the subtyping of malignant mesothelioma, and specifically the recognition of transitional features from routine histology slides has a high level of inter-observer variability. In this work, we propose an end-to-end multiple instance learning (MIL) approach for malignant mesothelioma subtyping. This uses an adaptive instance-based sampling scheme for training deep convolutional neural networks on bags of image patches that allows learning on a wider range of relevant instances compared to max or top-N based MIL approaches. We also investigate augmenting the instance representation to include aggregate cellular morphology features from cell segmentation. The proposed MIL approach enables identification of malignant mesothelial subtypes of specific tissue regions. From this a continuous characterisation of a sample according to predominance of sarcomatoid vs epithelioid regions is possible, thus avoiding the arbitrary and highly subjective categorisation by currently used subtypes. Instance scoring also enables studying tumor heterogeneity and identifying patterns associated with different subtypes. We have evaluated the proposed method on a dataset of 234 tissue micro-array cores with an AUROC of 0.89 ± 0.05 for this task. The dataset and developed methodology is available for the community at: https://github.com/measty/PINS

Access Impediments to Health Care and Social Services Between Anglophone and Francophone African Immigrants Living in Philadelphia with Respect to HIV/AIDS

Objectives To describe the social and cultural differences between Anglophone and Francophone African immigrants which define the impediments that Francophone African immigrants face trying to access health and human services in Philadelphia, Pennsylvania. Methods Surveys and personal interviews were administered to participants in social events, community meetings, and health centers. A Chi-squared analysis was used to contrast the communities. Results Francophone Africans demonstrated less acculturation, education, English fluency, and more legal documentation problems, and thus face greater challenges accessing health care. Anglophone Africans had a higher level of acculturation, fewer language problems, and perceived fewer barriers in accessing health care than Francophone Africans. Conclusions Educating new immigrants, through a more culturally sensitive infectious disease treatment and prevention program, is integral to achieving a higher access and utilization rates of available services; especially in recent Francophone immigrants. A larger study is needed to extend the findings to other cities where immigrants with similar backgrounds or acculturation issues reside

MesoGraph: Automatic profiling of mesothelioma subtypes from histological images

Data and code availability: • Tissue Micro-array cores and labels for the primary cohort are linked in the github repository at: https://github.com/measty/MesoGraph The Mesobank data is available from Mesobank (https://www.mesobank.com/) on request. This would require the completion of mesobank’s standard application form. It would then be reviewed to make sure that the proposed use of the data is covered by mesobank’s generic ethical approval, and a suitable Data Sharing Agreement would need to be in place before any data is released. • All original code is publicly available at: https://github.com/measty/MesoGraph. • Any additional data is available from the lead contact on request.Supplemental information is available online at: https://www.sciencedirect.com/science/article/pii/S2666379123004032#appsec2 .Copyright © 2023 The Authors. Mesothelioma is classified into three histological subtypes, epithelioid, sarcomatoid, and biphasic, according to the relative proportions of epithelioid and sarcomatoid tumor cells present. Current guidelines recommend that the sarcomatoid component of each mesothelioma is quantified, as a higher percentage of sarcomatoid pattern in biphasic mesothelioma shows poorer prognosis. In this work, we develop a dual-task graph neural network (GNN) architecture with ranking loss to learn a model capable of scoring regions of tissue down to cellular resolution. This allows quantitative profiling of a tumor sample according to the aggregate sarcomatoid association score. Tissue is represented by a cell graph with both cell-level morphological and regional features. We use an external multicentric test set from Mesobank, on which we demonstrate the predictive performance of our model. We additionally validate our model predictions through an analysis of the typical morphological features of cells according to their predicted score.CRUK-STFC Early Detection Innovation Award. F.M. and M.E. also acknowledge funding support from EPSRC grant EP/W02909X/1

Malignant Mesothelioma subtyping via sampling driven multiple instance prediction on tissue image and cell morphology data

Copyright © 2023 The Authors. Malignant Mesothelioma is a difficult to diagnose and highly lethal cancer usually associated with asbestos exposure. It can be broadly classified into three subtypes: Epithelioid, Sarcomatoid, and a hybrid Biphasic subtype in which significant components of both of the previous subtypes are present. Early diagnosis and identification of the subtype informs treatment and can help improve patient outcome. However, the subtyping of malignant mesothelioma, and specifically the recognition of transitional features from routine histology slides has a high level of inter-observer variability. In this work, we propose an end-to-end multiple instance learning (MIL) approach for malignant mesothelioma subtyping. This uses an adaptive instance-based sampling scheme for training deep convolutional neural networks on bags of image patches that allows learning on a wider range of relevant instances compared to max or top-N based MIL approaches. We also investigate augmenting the instance representation to include aggregate cellular morphology features from cell segmentation. The proposed MIL approach enables identification of malignant mesothelial subtypes of specific tissue regions. From this a continuous characterisation of a sample according to predominance of sarcomatoid vs epithelioid regions is possible, thus avoiding the arbitrary and highly subjective categorisation by currently used subtypes. Instance scoring also enables studying tumor heterogeneity and identifying patterns associated with different subtypes. We have evaluated the proposed method on a dataset of 234 tissue micro-array cores with an AUROC of for this task. The dataset and developed methodology is available for the community at: https://github.com/measty/PINSPRISM project, kindly funded by Cancer Research UK through the CRUK-STFC Early Detection Innovation Award

Long-term effects of flooding on mortality in England and Wales, 1994-2005: controlled interrupted time-series analysis

BACKGROUND: Limited evidence suggests that being flooded may increase mortality and morbidity among affected householders not just at the time of the flood but for months afterwards. The objective of this study is to explore the methods for quantifying such long-term health effects of flooding by analysis of routine mortality registrations in England and Wales. METHODS: Mortality data, geo-referenced by postcode of residence, were linked to a national database of flood events for 1994 to 2005. The ratio of mortality in the post-flood year to that in the pre-flood year within flooded postcodes was compared with that in non-flooded boundary areas (within 5 km of a flood). Further analyses compared the observed number of flood-area deaths in the year after flooding with the number expected from analysis of mortality trends stratified by region, age-group, sex, deprivation group and urban-rural status. RESULTS: Among the 319 recorded floods, there were 771 deaths in the year before flooding and 693 deaths in the year after (post-/pre-flood ratio of 0.90, 95% CI 0.82, 1.00). This ratio did not vary substantially by age, sex, population density or deprivation. A similar post-flood 'deficit' of deaths was suggested by the analyses based on observed/expected deaths. CONCLUSIONS: The observed post-flood 'deficit' of deaths is counter-intuitive and difficult to interpret because of the possible influence of population displacement caused by flooding. The bias that might arise from such displacement remains unquantified but has important implications for future studies that use place of residence as a marker of exposure

ADAM33, a New Candidate for Psoriasis Susceptibility

Psoriasis is a chronic skin disorder with multifactorial etiology. In a recent study, we reported results of a genome-wide scan on 46 French extended families presenting with plaque psoriasis. In addition to unambiguous linkage to the major susceptibility locus PSORS1 on Chromosome 6p21, we provided evidence for a susceptibility locus on Chromosome 20p13. To follow up this novel psoriasis susceptibility locus we used a family-based association test (FBAT) for an association scan over the 17 Mb candidate region. A total of 85 uncorrelated SNP markers located in 65 genes of the region were initially investigated in the same set of large families used for the genome wide search, which consisted of 295 nuclear families. When positive association was obtained for a SNP, candidate genes nearby were explored more in detail using a denser set of SNPs. Thus, the gene ADAM33 was found to be significantly associated with psoriasis in this family set (The best association was on a 3-SNP haplotype P = 0.00004, based on 1,000,000 permutations). This association was independent of PSORS1. ADAM33 has been previously associated with asthma, which demonstrates that immune system diseases may be controlled by common susceptibility genes with general effects on dermal inflammation and immunity. The identification of ADAM33 as a psoriasis susceptibility gene identified by positional cloning in an outbred population should provide insights into the pathogenesis and natural history of this common disease

Evolving health information technology and the timely availability of visit diagnoses from ambulatory visits: A natural experiment in an integrated delivery system

<p>Abstract</p> <p>Background</p> <p>Health information technology (HIT) may improve health care quality and outcomes, in part by making information available in a timelier manner. However, there are few studies documenting the changes in timely availability of data with the use of a sophisticated electronic medical record (EMR), nor a description of how the timely availability of data might differ with different types of EMRs. We hypothesized that timely availability of data would improve with use of increasingly sophisticated forms of HIT.</p> <p>Methods</p> <p>We used an historical observation design (2004–2006) using electronic data from office visits in an integrated delivery system with three types of HIT: Basic, Intermediate, and Advanced. We calculated the monthly percentage of visits using the various types of HIT for entry of visit diagnoses into the delivery system's electronic database, and the time between the visit and the availability of the visit diagnoses in the database.</p> <p>Results</p> <p>In January 2004, when only Basic HIT was available, 10% of office visits had diagnoses entered on the same day as the visit and 90% within a week; 85% of office visits used paper forms for recording visit diagnoses, 16% used Basic at that time. By December 2006, 95% of all office visits had diagnoses available on the same day as the visit, when 98% of office visits used some form of HIT for entry of visit diagnoses (Advanced HIT for 67% of visits).</p> <p>Conclusion</p> <p>Use of HIT systems is associated with dramatic increases in the timely availability of diagnostic information, though the effects may vary by sophistication of HIT system. Timely clinical data are critical for real-time population surveillance, and valuable for routine clinical care.</p

Mimicking microbial 'education' of the immune system: a strategy to revert the epidemic trend of atopy and allergic asthma?

Deficient microbial stimulation of the immune system, caused by hygiene, may underly the atopy and allergic asthma epidemic we are currently experiencing. Consistent with this 'hygiene hypothesis', research on immunotherapy of allergic diseases also centres on bacteria-derived molecules (eg DNA immunostimulatory sequences) as adjuvants for allergen-specific type 1 immune responses. If we understood how certain microbes physiologically 'educate' our immune system to interact safely with environmental nonmicrobial antigens, we might be able to learn to mimic their beneficial actions. Programmed 'immunoeducation' would consist of safe administration, by the correct route, dose and timing, of those microbial stimuli that are necessary to 'train' the developing mucosal immune system and to maintain an appropriate homeostatic equilibrium between its components. Overall, this would result in a prevention of atopy that is not limited to certain specific allergens. Although such a strategy is far beyond our present potential, it may in principle revert the epidemic trend of atopy and allergic asthma without jeopardizing the fight against infectious diseases

Changes in cortical cytoskeletal and extracellular matrix gene expression in prostate cancer are related to oncogenic ERG deregulation

Abstract Background The cortical cytoskeleton network connects the actin cytoskeleton to various membrane proteins, influencing cell adhesion, polarity, migration and response to extracellular signals. Previous studies have suggested changes in the expression of specific components in prostate cancer, especially of 4.1 proteins (encoded by EPB41 genes) which form nodes in this network. Methods Expression of EPB41L1, EPB41L2, EPB41L3 (protein: 4.1B), EPB41L4B (EHM2), EPB41L5, EPB49 (dematin), VIL2 (ezrin), and DLG1 (summarized as „cortical cytoskeleton" genes) as well as ERG was measured by quantitative RT-PCR in a well-characterized set of 45 M0 prostate adenocarcinoma and 13 benign tissues. Hypermethylation of EPB41L3 and GSTP1 was compared in 93 cancer tissues by methylation-specific PCR. Expression of 4.1B was further studied by immunohistochemistry. Results EPB41L1 and EPB41L3 were significantly downregulated and EPB41L4B was upregulated in cancer tissues. Low EPB41L1 or high EPB41L4B expression were associated with earlier biochemical recurrence. None of the other cortical cytoskeleton genes displayed expression changes, in particular EPB49 and VIL2, despite hints from previous studies. EPB41L3 downregulation was significantly associated with hypermethylation of its promoter and strongly correlated with GSTP1 hypermethylation. Protein 4.1B was detected most strongly in the basal cells of normal prostate epithelia. Its expression in carcinoma cells was similar to the weaker one in normal luminal cells. EPB41L3 downregulation and EPB41L4B upregulation were essentially restricted to the 22 cases with ERG overexpression. Expression changes in EPB41L3 and EPB41L4B closely paralleled those previously observed for the extracellular matrix genes FBLN1 and SPOCK1, respectively. Conclusions Specific changes in the cortical cytoskeleton were observed during prostate cancer progression. They parallel changes in the expression of extracellular matrix components and all together appear to be associated with oncogenic ERG overexpression. We hypothesize that these alterations may contribute to the increased invasivity conferred to prostate cancer cells by ERG deregulation.</p