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The Gleason grading system remains the most powerful prognostic predictor for patients with prostate cancer since the 1960s. Its application requires highly-trained pathologists, is tedious and yet suffers from limited inter-pathologist reproducibility, especially for the intermediate Gleason score 7. Automated annotation procedures constitute a viable solution to remedy these limitations. In this study, we present a deep learning approach for automated Gleason grading of prostate cancer tissue microarrays with Hematoxylin and Eosin (H&E) staining. Our system was trained using detailed Gleason annotations on a discovery cohort of 641 patients and was then evaluated on an independent test cohort of 245 patients annotated by two pathologists. On the test cohort, the inter-annotator agreements between the model and each pathologist, quantified via Cohen’s quadratic kappa statistic, were 0.75 and 0.71 respectively, comparable with the inter-pathologist agreement (kappa = 0.71). Furthermore, the model’s Gleason score assignments achieved pathology expert-level stratification of patients into prognostically distinct groups, on the basis of disease-specific survival data available for the test cohort. Overall, our study shows promising results regarding the applicability of deep learning-based solutions towards more objective and reproducible prostate cancer grading, especially for cases with heterogeneous Gleason patterns.
The translation eukaryotic elongation factor 1alpha (eEF1A) is a monomeric GTPase involved in protein synthesis. In addition, this protein is thought to participate in other cellular functions such as actin bundling, cell cycle regulation, and apoptosis. Here we show that eEF1A is associated with the alpha2 subunit of the inhibitory glycine receptor in pulldown experiments with rat brain extracts. Moreover, additional proteins involved in translation like ribosomal S6 protein and p70 ribosomal S6 protein kinase as well as ERK1/2 and calcineurin were identified in the same pulldown approaches. Glycine receptor activation in spinal cord neurons cultured for 1 week resulted in an increased phosphorylation of ribosomal S6 protein. Immunocytochemistry showed that eEF1A and ribosomal S6 protein are localized in the soma, dendrites, and at synapses of cultured hippocampal and spinal cord neurons. Consistent with our biochemical data, immunoreactivities of both proteins were partially overlapping with glycine receptor immunoreactivity in cultured spinal cord and hippocampal neurons. After 5 weeks in culture, eEF1A immunoreactivity was redistributed to the cytoskeleton in about 45% of neurons. Interestingly, the degree of redistribution could be increased at earlier stages of in vitro differentiation by inhibition of either the ERK1/2 pathway or glycine receptors and simultaneous N-methyl-D-aspartate receptor activation. Our findings suggest a functional coupling of eEF1A with both inhibitory and excitatory receptors, possibly involving the ERK-signaling pathway.
Background: The COVID-19 pandemic has spurred large-scale, inter-institutional research efforts. To enable these efforts, the German Corona Consensus (GECCO) dataset has been developed previously as a harmonized, interoperable collection of the most relevant data elements for COVID-19-related patient research. As GECCO has been developed as a compact core dataset across all medical fields, the focused research within particular medical domains demanded the definition of extension modules that include those data elements that are most relevant to the research performed in these individual medical specialties.
Main body: We created GECCO extension modules for the immunization, pediatrics, and cardiology domains with respect to the pandemic requests. The data elements included in each of these modules were selected in a consensus-based process by working groups of medical experts from the respective specialty to ensure that the contents are aligned with the research needs of the specialty. The selected data elements were mapped to international standardized vocabularies and data exchange specifications were created using HL7 FHIR profiles on the appropriate resources. All steps were performed in close interdisciplinary collaboration between medical domain experts, medical information scientists and FHIR developers. The profiles and vocabulary mappings were syntactically and semantically validated in a two-stage process. In that way, we defined dataset specifications for a total number of 23 (immunization), 59 (pediatrics), and 50 (cardiology) data elements that augment the GECCO core dataset. We created and published implementation guides and example implementations as well as dataset annotations for each extension module.
Conclusions: We here present extension modules for the GECCO core dataset that contain data elements most relevant to COVID-19-related patient research in immunization, pediatrics and cardiology. These extension modules were defined in an interdisciplinary, iterative, consensus-based approach that may serve as a blueprint for the development of further dataset definitions and GECCO extension modules. The here developed GECCO extension modules provide a standardized and harmonized definition of specialty-related datasets that can help to enable inter-institutional and cross-country COVID-19 research in these specialties.
R-flurbiprofen is the non-COX-inhibiting enantiomer of flurbiprofen and is not converted to S-flurbiprofen in human cells. Nevertheless, it reduces extracellular prostaglandin E2 (PGE2) in cancer or immune cell cultures and human extracellular fluid. Here, we show that R-flurbiprofen acts through a dual mechanism: (i) it inhibits the translocation of cPLA2α to the plasma membrane and thereby curtails the availability of arachidonic acid and (ii) R-flurbiprofen traps PGE2 inside of the cells by inhibiting multidrug resistance–associated protein 4 (MRP4, ABCC4), which acts as an outward transporter for prostaglandins. Consequently, the effects of R-flurbiprofen were mimicked by RNAi-mediated knockdown of MRP4. Our data show a novel mechanism by which R-flurbiprofen reduces extracellular PGs at physiological concentrations, particularly in cancers with high levels of MRP4, but the mechanism may also contribute to its anti-inflammatory and immune-modulating properties and suggests that it reduces PGs in a site- and context-dependent manner.
Background: The introduction of modern troponin assays has facilitated diagnosis of acute myocardial infarction due to improved sensitivity with corresponding loss of specificity. Atrial fibrillation (AF) is associated with elevated levels of troponin. The aim of the present study was to evaluate the diagnostic performance of troponin I in patients with suspected acute coronary syndrome and chronic AF.
Methods: Contemporary sensitive troponin I was assayed in a derivation cohort of 90 patients with suspected acute coronary syndrome and chronic AF to establish diagnostic cut-offs. These thresholds were validated in an independent cohort of 314 patients with suspected myocardial infarction and AF upon presentation. Additionally, changes in troponin I concentration within 3 hours were used.
Results: In the derivation cohort, optimized thresholds with respect to a rule-out strategy with high sensitivity and a rule-in strategy with high specificity were established. In the validation cohort, application of the rule-out cut-off led to a negative predictive value of 97 %. The rule-in cut-off was associated with a positive predictive value of 88 % compared with 71 % if using the 99th percentile cut-off. In patients with troponin I levels above the specificity-optimized threshold, additional use of the 3-hour change in absolute/relative concentration resulted in a further improved positive predictive value of 96 %/100 %.
Conclusions: Troponin I concentration and the 3-hour change in its concentration provide valid diagnostic information in patients with suspected myocardial infarction and chronic AF. With regard to AF-associated elevation of troponin levels, application of diagnostic cut-offs other than the 99th percentile might be beneficial.
Patients with acute myeloid leukemia (AML) are often exposed to broad-spectrum antibiotics and thus at high risk of Clostridioides difficile infections (CDI). As bacterial infections are a common cause for treatment-related mortality in these patients, we conducted a retrospective study to analyze the incidence of CDI and to evaluate risk factors for CDI in a large uniformly treated AML cohort. A total of 415 AML patients undergoing intensive induction chemotherapy between 2007 and 2019 were included in this retrospective analysis. Patients presenting with diarrhea and positive stool testing for toxin-producing Clostridioides difficile were defined to have CDI. CDI was diagnosed in 37 (8.9%) of 415 AML patients with decreasing CDI rates between 2013 and 2019 versus 2007 to 2012. Days with fever, exposition to carbapenems, and glycopeptides were significantly associated with CDI in AML patients. Clinical endpoints such as length of hospital stay, admission to ICU, response rates, and survival were not adversely affected. We identified febrile episodes and exposition to carbapenems and glycopeptides as risk factors for CDI in AML patients undergoing induction chemotherapy, thereby highlighting the importance of interdisciplinary antibiotic stewardship programs guiding treatment strategies in AML patients with infectious complications to carefully balance risks and benefits of anti-infective agents.
Purpose: Acute-on-chronic subdural hematoma (acSDH) describes acute bleeding into a chronic subdural hematoma (SDH), after surgery or second trauma. Because seizures are a well-known complication of SDH, associated with substantial morbidity and mortality, we aimed to analyze the incidence of acute symptomatic seizures (ASz), including status epilepticus, and determine the functional outcomes in this specific cohort of patients.
Methods: A retrospective analysis was performed, including patients with acSDH who were admitted to our department between 2010 and 2019. The incidence and timely onset of ASz and status epilepticus were evaluated. Functional outcomes at discharge and at 3–6 month follow-up were analyzed based on the modified Rankin scale.
Results: Of 506 patients with chronic SDH, 29 patients (5.7%) were diagnosed with acSDH. The overall incidence of ASz and status epilepticus were 72.4% and 10.3%, respectively. Favorable outcomes were identified in 11 patients (52.4%) in the ASz group compared with 6 patients (75%) in the non-ASz group. The mortality rate was higher in the ASz group compared with that in the control group (29% vs 0%). At follow-up, favorable outcomes were similar to those observed at discharge (52.4% in the ASz group and 71.4% in the control group). The mortality rate was still higher in the ASz group, at 32% compared with 14% for the control group.
Conclusion: AcSDH has a high risk for ASz, including status epilepticus, and is associated with unfavorable outcomes and high mortality. Thus, prophylactic treatment with antiepileptic drugs should be considered among this specific cohort of patients.
Aim: Pharmacoresistance is a major burden in epilepsy treatment. We aimed to identify genetic biomarkers in response to specific antiepileptic drugs (AEDs) in genetic generalized epilepsies (GGE). Materials & methods: We conducted a genome-wide association study (GWAS) of 3.3 million autosomal SNPs in 893 European subjects with GGE – responsive or nonresponsive to lamotrigine, levetiracetam and valproic acid. Results: Our GWAS of AED response revealed suggestive evidence for association at 29 genomic loci (p <10-5) but no significant association reflecting its limited power. The suggestive associations highlight candidate genes that are implicated in epileptogenesis and neurodevelopment. Conclusion: This first GWAS of AED response in GGE provides a comprehensive reference of SNP associations for hypothesis-driven candidate gene analyses in upcoming pharmacogenetic studies.