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Hintergrund: Ab Frühjahr 2020 kam es zur weltweiten Verbreitung von SARS-CoV‑2 mit der heute als erste Welle der Pandemie bezeichneten Phase ab März 2020. Diese resultierte an vielen Kliniken in Umstrukturierungen und Ressourcenverschiebungen. Ziel unserer Arbeit war die Erfassung der Auswirkungen der Pandemie auf die universitäre Hals-Nasen-Ohren(HNO)-Heilkunde für die Forschung, Lehre und Weiterbildung. Material und Methoden: Die Direktorinnen und Direktoren der 39 Universitäts-HNO-Kliniken in Deutschland wurden mithilfe einer strukturierten Online-Befragung zu den Auswirkungen der Pandemie im Zeitraum von März bis April 2020 auf die Forschung, Lehre und die Weiterbildung befragt. Ergebnisse: Alle 39 Direktorinnen und Direktoren beteiligten sich an der Umfrage. Hiervon gaben 74,4 % (29/39) an, dass es zu einer Verschlechterung ihrer Forschungstätigkeit infolge der Pandemie gekommen sei. Von 61,5 % (24/39) wurde berichtet, dass pandemiebezogene Forschungsaspekte aufgegriffen wurden. Von allen Kliniken wurde eine Einschränkung der Präsenzlehre berichtet und 97,5 % (38/39) führten neue digitale Lehrformate ein. Im Beobachtungszeitraum sahen 74,4 % der Klinikdirektoren die Weiterbildung der Assistenten nicht gefährdet. Schlussfolgerung: Die Ergebnisse geben einen Einblick in die heterogenen Auswirkungen der Pandemie. Die kurzfristige Bearbeitung pandemiebezogener Forschungsthemen und die Einführung innovativer digitaler Konzepte für die studentische Lehre belegt eindrücklich das große innovative Potenzial und die schnelle Reaktionsfähigkeit der HNO-Universitätskliniken, um auch während der Pandemie ihre Aufgaben in der Forschung, Lehre und Weiterbildung bestmöglich zu erfüllen.
Non-standard errors
(2021)
In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants.
Simple Summary: The incidence of brain metastases from breast cancer is increasing and the treatment is still a major challenge. Several scores have been developed in order to estimate the prognosis of patients with brain metastases by objective criteria. Here, we validated all three published graded-prognostic-assessment (GPA)-scores in a subcohort of 882 breast cancer patients with brain metastases in the Brain Metastases in the German Breast Cancer (BMBC) registry. Although all three available GPA-scores were associated with OS, they all show limitations mainly in predicting short-term (below 3 months) survival but also in long-term (above 12 months) survival. We discuss the test performances of all scores in our work and provide evidence how physicians should use them as a tool to select patients for different treatment options.
Abstract: Several scores have been developed in order to estimate the prognosis of patients with brain metastases (BM) by objective criteria. The aim of this analysis was to validate all three published graded-prognostic-assessment (GPA)-scores in a subcohort of 882 breast cancer (BC) patients with BM in the Brain Metastases in the German Breast Cancer (BMBC) registry. The median age at diagnosis of BM was 57 years. All in all, 22.3% of patients (n = 197) had triple-negative, 33.4% (n = 295) luminal A like, 25.1% (n = 221) luminal B/HER2-enriched like and 19.2% (n = 169) HER2 positive like BC. Age ≥60 years, evidence of extracranial metastases (ECM), higher number of BM, triple-negative subtype and low Karnofsky-Performance-Status (KPS) were all associated with worse overall survival (OS) in univariate analysis (p < 0.001 each). All three GPA-scores were associated with OS. The breast-GPA showed the highest probability of classifying patients with survival above 12 months in the best prognostic group (specificity 68.7% compared with 48.1% for the updated breast-GPA and 21.8% for the original GPA). Sensitivities for predicting 3 months survival were very low for all scores. In this analysis, all GPA-scores showed only moderate diagnostic accuracy in predicting the OS of BC patients with BM.
Characteristics and clinical outcome of breast cancer patients with asymptomatic brain metastases
(2020)
Simple Summary: The prognosis for patients with breast cancer that has spread to the brain is poor, and survival for these women hasn’t improved over the last few decades. We do not currently test for asymptomatic brain metastases in breast cancer patients, although this does happen in some other types of cancer. In this study we wanted to find out more about breast cancer that has spread to the brain and in particular to see whether there might be any advantage to spotting brain metastases before the development of neurological symptoms. Overall, our results suggest that women could be better off if their brain metastases are diagnosed before they begin to cause symptoms. We now need to carry out a clinical trial to see what happens if we screen high-risk breast cancer patients for brain metastases. This will verify whether doing so could increase survival, symptom control or quality of life.
Abstract: Background: Brain metastases (BM) have become a major challenge in patients with metastatic breast cancer. Methods: The aim of this analysis was to characterize patients with asymptomatic BM (n = 580) in the overall cohort of 2589 patients with BM from our Brain Metastases in Breast Cancer Network Germany (BMBC) registry. Results: Compared to symptomatic patients, asymptomatic patients were slightly younger at diagnosis (median age: 55.5 vs. 57.0 years, p = 0.01), had a better performance status at diagnosis (Karnofsky index 80–100%: 68.4% vs. 57%, p < 0.001), a lower number of BM (>1 BM: 56% vs. 70%, p = 0.027), and a slightly smaller diameter of BM (median: 1.5 vs. 2.2 cm, p < 0.001). Asymptomatic patients were more likely to have extracranial metastases (86.7% vs. 81.5%, p = 0.003) but were less likely to have leptomeningeal metastasis (6.3% vs. 10.9%, p < 0.001). Asymptomatic patients underwent less intensive BM therapy but had a longer median overall survival (statistically significant for a cohort of HER2-positive patients) compared to symptomatic patients (10.4 vs. 6.9 months, p < 0.001). Conclusions: These analyses show a trend that asymptomatic patients have less severe metastatic brain disease and despite less intensive local BM therapy still have a better outcome (statistically significant for a cohort of HER2-positive patients) than patients who present with symptomatic BM, although a lead time bias of the earlier diagnosis cannot be ruled out. Our analysis is of clinical relevance in the context of potential trials examining the benefit of early detection and treatment of BM.
Background: Identification of families at risk for ovarian cancer offers the opportunity to consider prophylactic surgery thus reducing ovarian cancer mortality. So far, identification of potentially affected families in Germany was solely performed via family history and numbers of affected family members with breast or ovarian cancer. However, neither the prevalence of deleterious variants in BRCA1/2 in ovarian cancer in Germany nor the reliability of family history as trigger for genetic counselling has ever been evaluated.
Methods: Prospective counseling and germline testing of consecutive patients with primary diagnosis or with platinum-sensitive relapse of an invasive epithelial ovarian cancer. Testing included 25 candidate and established risk genes. Among these 25 genes, 16 genes (ATM, BRCA1, BRCA2, CDH1, CHEK2, MLH1, MSH2, MSH6, NBN, PMS2, PTEN, PALB2, RAD51C, RAD51D, STK11, TP53) were defined as established cancer risk genes. A positive family history was defined as at least one relative with breast cancer or ovarian cancer or breast cancer in personal history.
Results: In total, we analyzed 523 patients: 281 patients with primary diagnosis of ovarian cancer and 242 patients with relapsed disease. Median age at primary diagnosis was 58 years (range 16–93) and 406 patients (77.6%) had a high-grade serous ovarian cancer. In total, 27.9% of the patients showed at least one deleterious variant in all 25 investigated genes and 26.4% in the defined 16 risk genes. Deleterious variants were most prevalent in the BRCA1 (15.5%), BRCA2 (5.5%), RAD51C (2.5%) and PALB2 (1.1%) genes. The prevalence of deleterious variants did not differ significantly between patients at primary diagnosis and relapse. The prevalence of deleterious variants in BRCA1/2 (and in all 16 risk genes) in patients <60 years was 30.2% (33.2%) versus 10.6% (18.9%) in patients ≥60 years. Family history was positive in 43% of all patients. Patients with a positive family history had a prevalence of deleterious variants of 31.6% (36.0%) versus 11.4% (17.6%) and histologic subtype of high grade serous ovarian cancer versus other showed a prevalence of deleterious variants of 23.2% (29.1%) and 10.2% (14.8%), respectively. Testing only for BRCA1/2 would miss in our series more than 5% of the patients with a deleterious variant in established risk genes.
Conclusions: 26.4% of all patients harbor at least one deleterious variant in established risk genes. The threshold of 10% mutation rate which is accepted for reimbursement by health care providers in Germany was observed in all subgroups analyzed and neither age at primary diagnosis nor histo-type or family history sufficiently enough could identify a subgroup not eligible for genetic counselling and testing. Genetic testing should therefore be offered to every patient with invasive epithelial ovarian cancer and limiting testing to BRCA1/2 seems to be not sufficient.
Coarse-grained modeling has become an important tool to supplement experimental measurements, allowing access to spatio-temporal scales beyond all-atom based approaches. The GōMartini model combines structure- and physics-based coarse-grained approaches, balancing computational efficiency and accurate representation of protein dynamics with the capabilities of studying proteins in different biological environments. This paper introduces an enhanced GōMartini model, which combines a virtual-site implementation of Gō models with Martini 3. The implementation has been extensively tested by the community since the release of the new version of Martini. This work demonstrates the capabilities of the model in diverse case studies, ranging from protein-membrane binding to protein-ligand interactions and AFM force profile calculations. The model is also versatile, as it can address recent inaccuracies reported in the Martini protein model. Lastly, the paper discusses the advantages, limitations, and future perspectives of the Martini 3 protein model and its combination with Gō models.
Emissions of the potent greenhouse gas perfluorocyclobutane (c-C4F8, PFC-318, octafluorocyclobutane) into the global atmosphere inferred from atmospheric measurements have been increasing sharply since the early 2000s. We find that these inferred emissions are highly correlated with the production of hydrochlorofluorocarbon-22 (HCFC-22, CHClF2) for feedstock (FS) uses, because almost all HCFC-22 FS is pyrolyzed to produce (poly)tetrafluoroethylene ((P)TFE) and hexafluoropropylene (HFP), a process in which c-C4F8 is a known by-product, causing a significant fraction of global c-C4F8 emissions. We find a global emission factor of ∼0.003 kg c-C4F8 per kilogram of HCFC-22 FS pyrolyzed. Mitigation of these c-C4F8 emissions, e.g., through process optimization, abatement, or different manufacturing processes, such as refined methods of electrochemical fluorination and waste recycling, could reduce the climate impact of this industry. While it has been shown that c-C4F8 emissions from developing countries dominate global emissions, more atmospheric measurements and/or detailed process statistics are needed to quantify c-C4F8 emissions at country to facility levels.