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

A mitochondrial membrane-bridging machinery mediates signal transduction of intramitochondrial oxidation

Mitochondria are the main site for generating reactive oxygen species, which are key players in diverse biological processes. However, the molecular pathways of redox signal transduction from the matrix to the cytosol are poorly defined. Here we report an inside-out redox signal of mitochondria. Cysteine oxidation of MIC60, an inner mitochondrial membrane protein, triggers the formation of disulfide bonds and the physical association of MIC60 with Miro, an outer mitochondrial membrane protein. The oxidative structural change of this membrane-crossing complex ultimately elicits cellular responses that delay mitophagy, impair cellular respiration and cause oxidative stress. Blocking the MIC60–Miro interaction or reducing either protein, genetically or pharmacologically, extends lifespan and health-span of healthy fruit flies, and benefits multiple models of Parkinson’s disease and Friedreich’s ataxia. Our discovery provides a molecular basis for common treatment strategies against oxidative stress

Chondroblastoma of the cuboid with an associated aneurysmal bone cyst: a case report

We report the case of a young adult who presented with a painful foot due to chondroblastoma associated with an aneurismal bone cyst

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

Impact of Antioxidant Therapy on Natural Pregnancy Outcomes and Semen Parameters in Infertile Men: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

Purpose: Seminal oxidative stress (OS) is a recognized factor potentially associated with male infertility, but the efficacy of antioxidant (AOX) therapy is controversial and there is no consensus on its utility. Primary outcomes of this study were to investigate the effect of AOX on spontaneous clinical pregnancy, live birth and miscarriage rates in male infertile patients. Secondary outcomes were conventional semen parameters, sperm DNA fragmentation (SDF) and seminal OS. Materials and methods: Literature search was performed using Scopus, PubMed, Ovid, Embase, and Cochrane databases. Only randomized controlled trials (RCTs) were included and the meta-analysis was conducted according to PRISMA guidelines. Results: We assessed for eligibility 1,307 abstracts, and 45 RCTs were finally included, for a total of 4,332 infertile patients. We found a significantly higher pregnancy rate in patients treated with AOX compared to placebo-treated or untreated controls, without significant inter-study heterogeneity. No effects on live-birth or miscarriage rates were observed in four studies. A significantly higher sperm concentration, sperm progressive motility, sperm total motility, and normal sperm morphology was found in patients compared to controls. We found no effect on SDF in analysis of three eligible studies. Seminal levels of total antioxidant capacity were significantly higher, while seminal malondialdehyde acid was significantly lower in patients than controls. These results did not change after exclusion of studies performed following varicocele repair. Conclusions: The present analysis upgrades the level of evidence favoring a recommendation for using AOX in male infertility to improve the spontaneous pregnancy rate and the conventional sperm parameters. The failure to demonstrate an increase in live-birth rate, despite an increase in pregnancy rates, is due to the very few RCTs specifically assessing the impact of AOX on live-birth rate. Therefore, further RCTs assessing the impact of AOX on live-birth rate and miscarriage rate, and SDF will be helpful

Associations of Early Systolic Blood Pressure Control and Outcome after Thrombolysis-Eligible Acute Ischemic Stroke: Results from the ENCHANTED Study

Background and Purpose: In thrombolysis-eligible patients with acute ischemic stroke, there is uncertainty over the most appropriate systolic blood pressure (SBP) lowering profile that provides an optimal balance of potential benefit (functional recovery) and harm (intracranial hemorrhage). We aimed to determine relationships of SBP parameters and outcomes in thrombolyzed acute ischemic stroke patients. Methods: Post hoc analyzes of the ENCHANTED (Enhanced Control of Hypertension and Thrombolysis Stroke Study), a partial-factorial trial of thrombolysis-eligible and treated acute ischemic stroke patients with high SBP (150-180 mm Hg) assigned to low-dose (0.6 mg/kg) or standard-dose (0.9 mg/kg) alteplase and intensive (target SBP, 130-140 mm Hg) or guideline-recommended (target SBP <180 mm Hg) treatment. All patients were followed up for functional status and serious adverse events to 90 days. Logistic regression models were used to analyze 3 SBP summary measures postrandomization: attained (mean), variability (SD) in 1-24 hours, and magnitude of reduction in 1 hour. The primary outcome was a favorable shift on the modified Rankin Scale. The key safety outcome was any intracranial hemorrhage. Results: Among 4511 included participants (mean age 67 years, 38% female, 65% Asian) lower attained SBP and smaller SBP variability were associated with favorable shift on the modified Rankin Scale (per 10 mm Hg increase: odds ratio, 0.76 [95% CI, 0.71-0.82]; P<0.001 and 0.86 [95% CI, 0.76-0.98]; P=0.025) respectively, but not for magnitude of SBP reduction (0.98, [0.93-1.04]; P=0.564). Odds of intracranial hemorrhage was associated with higher attained SBP and greater SBP variability (1.18 [1.06-1.31]; P=0.002 and 1.34 [1.11-1.62]; P=0.002) but not with magnitude of SBP reduction (1.05 [0.98-1.14]; P=0.184). Conclusions: Attaining early and consistent low levels in SBP <140 mm Hg, even as low as 110 to 120 mm Hg, over 24 hours is associated with better outcomes in thrombolyzed acute ischemic stroke patients. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT01422616

Parasite infection is associated with Kaposi's sarcoma associated herpesvirus (KSHV) in Ugandan women

Background: Immune modulation by parasites may influence susceptibility to bacteria and viruses. We examined the association between current parasite infections, HIV and syphilis (measured in blood or stool samples using standard methods) and antibodies against Kaposi's sarcoma herpesvirus (KSHV), measured by ELISA, in 1915 stored plasma samples from pregnant women in Entebbe, Uganda.&lt;p&gt;&lt;/p&gt; Results: Seroprevalence of KSHV was higher in women with malaria parasitaemia (73% vs 60% p = 0.01), hookworm (67% vs 56% p = 0.001) and Mansonella perstans (69% vs 59% p = 0.05); seroprevalence increased with increasing intensity of hookworm infection (p &lt; 0.001[trend]). No associations were found for HIV, five other parasites or active syphilis. These effects were not explained by socioeconomic status or education.&lt;p&gt;&lt;/p&gt; Conclusions: Specific parasite infections are associated with presence of antibodies against KSHV, perhaps mediated via their effect on immune function.&lt;p&gt;&lt;/p&gt

Global, regional, and national prevalence and mortality burden of sickle cell disease, 2000-2021: a systematic analysis from the Global Burden of Disease Study 2021

BACKGROUND: Previous global analyses, with known underdiagnosis and single cause per death attribution systems, provide only a small insight into the suspected high population health effect of sickle cell disease. Completed as part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021, this study delivers a comprehensive global assessment of prevalence of sickle cell disease and mortality burden by age and sex for 204 countries and territories from 2000 to 2021. METHODS: We estimated cause-specific sickle cell disease mortality using standardised GBD approaches, in which each death is assigned to a single underlying cause, to estimate mortality rates from the International Classification of Diseases (ICD)-coded vital registration, surveillance, and verbal autopsy data. In parallel, our goal was to estimate a more accurate account of sickle cell disease health burden using four types of epidemiological data on sickle cell disease: birth incidence, age-specific prevalence, with-condition mortality (total deaths), and excess mortality (excess deaths). Systematic reviews, supplemented with ICD-coded hospital discharge and insurance claims data, informed this modelling approach. We employed DisMod-MR 2.1 to triangulate between these measures-borrowing strength from predictive covariates and across age, time, and geography-and generated internally consistent estimates of incidence, prevalence, and mortality for three distinct genotypes of sickle cell disease: homozygous sickle cell disease and severe sickle cell β-thalassaemia, sickle-haemoglobin C disease, and mild sickle cell β-thalassaemia. Summing the three models yielded final estimates of incidence at birth, prevalence by age and sex, and total sickle cell disease mortality, the latter of which was compared directly against cause-specific mortality estimates to evaluate differences in mortality burden assessment and implications for the Sustainable Development Goals (SDGs). FINDINGS: Between 2000 and 2021, national incidence rates of sickle cell disease were relatively stable, but total births of babies with sickle cell disease increased globally by 13·7% (95% uncertainty interval 11·1-16·5), to 515 000 (425 000-614 000), primarily due to population growth in the Caribbean and western and central sub-Saharan Africa. The number of people living with sickle cell disease globally increased by 41·4% (38·3-44·9), from 5·46 million (4·62-6·45) in 2000 to 7·74 million (6·51-9·2) in 2021. We estimated 34 400 (25 000-45 200) cause-specific all-age deaths globally in 2021, but total sickle cell disease mortality burden was nearly 11-times higher at 376 000 (303 000-467 000). In children younger than 5 years, there were 81 100 (58 800-108 000) deaths, ranking total sickle cell disease mortality as 12th (compared to 40th for cause-specific sickle cell disease mortality) across all causes estimated by the GBD in 2021. INTERPRETATION: Our findings show a strikingly high contribution of sickle cell disease to all-cause mortality that is not apparent when each death is assigned to only a single cause. Sickle cell disease mortality burden is highest in children, especially in countries with the greatest under-5 mortality rates. Without comprehensive strategies to address morbidity and mortality associated with sickle cell disease, attainment of SDG 3.1, 3.2, and 3.4 is uncertain. Widespread data gaps and correspondingly high uncertainty in the estimates highlight the urgent need for routine and sustained surveillance efforts, further research to assess the contribution of conditions associated with sickle cell disease, and widespread deployment of evidence-based prevention and treatment for those with sickle cell disease. FUNDING: Bill & Melinda Gates Foundation