A new clinico-pathological classification system for mesial temporal sclerosis

We propose a histopathological classification system for hippocampal cell loss in patients suffering from mesial temporal lobe epilepsies (MTLE). One hundred and seventy-eight surgically resected specimens were microscopically examined with respect to neuronal cell loss in hippocampal subfields CA1–CA4 and dentate gyrus. Five distinct patterns were recognized within a consecutive cohort of anatomically well-preserved surgical specimens. The first group comprised hippocampi with neuronal cell densities not significantly different from age matched autopsy controls [no mesial temporal sclerosis (no MTS); n = 34, 19%]. A classical pattern with severe cell loss in CA1 and moderate neuronal loss in all other subfields excluding CA2 was observed in 33 cases (19%), whereas the vast majority of cases showed extensive neuronal cell loss in all hippocampal subfields (n = 94, 53%). Due to considerable similarities of neuronal cell loss patterns and clinical histories, we designated these two groups as MTS type 1a and 1b, respectively. We further distinguished two atypical variants characterized either by severe neuronal loss restricted to sector CA1 (MTS type 2; n = 10, 6%) or to the hilar region (MTS type 3, n = 7, 4%). Correlation with clinical data pointed to an early age of initial precipitating injury (IPI < 3 years) as important predictor of hippocampal pathology, i.e. MTS type 1a and 1b. In MTS type 2, IPIs were documented at a later age (mean 6 years), whereas in MTS type 3 and normal appearing hippocampus (no MTS) the first event appeared beyond the age of 13 and 16 years, respectively. In addition, postsurgical outcome was significantly worse in atypical MTS, especially MTS type 3 with only 28% of patients having seizure relief after 1-year follow-up period, compared to successful seizure control in MTS types 1a and 1b (72 and 73%). Our classification system appears suitable for stratifying the clinically heterogeneous group of MTLE patients also with respect to postsurgical outcome studies

DNA methylation-based classification of sinonasal tumors

The diagnosis of sinonasal tumors is challenging due to a heterogeneous spectrum of various differential diagnoses as well as poorly defined, disputed entities such as sinonasal undifferentiated carcinomas (SNUCs). In this study, we apply a machine learning algorithm based on DNA methylation patterns to classify sinonasal tumors with clinical-grade reliability. We further show that sinonasal tumors with SNUC morphology are not as undifferentiated as their current terminology suggests but rather reassigned to four distinct molecular classes defined by epigenetic, mutational and proteomic profiles. This includes two classes with neuroendocrine differentiation, characterized by IDH2 or SMARCA4/ARID1A mutations with an overall favorable clinical course, one class composed of highly aggressive SMARCB1-deficient carcinomas and another class with tumors that represent potentially previously misclassified adenoid cystic carcinomas. Our findings can aid in improving the diagnostic classification of sinonasal tumors and could help to change the current perception of SNUCs

DNA methylation-based classification of sinonasal tumors

The diagnosis of sinonasal tumors is challenging due to a heterogeneous spectrum of various differential diagnoses as well as poorly defined, disputed entities such as sinonasal undifferentiated carcinomas (SNUCs). In this study, we apply a machine learning algorithm based on DNA methylation patterns to classify sinonasal tumors with clinical-grade reliability. We further show that sinonasal tumors with SNUC morphology are not as undifferentiated as their current terminology suggests but rather reassigned to four distinct molecular classes defined by epigenetic, mutational and proteomic profiles. This includes two classes with neuroendocrine differentiation, characterized by IDH2 or SMARCA4/ARID1A mutations with an overall favorable clinical course, one class composed of highly aggressive SMARCB1-deficient carcinomas and another class with tumors that represent potentially previously misclassified adenoid cystic carcinomas. Our findings can aid in improving the diagnostic classification of sinonasal tumors and could help to change the current perception of SNUCs

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements