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Dryopteris lacunosa – eine neue Art des Dryopteris-affinis-Aggregats (Dryopteridaceae, Pteridophyta)
(2011)
Eine weitere Sippe des Dryopteris-affinis-Aggregats wird als Art, D. lacunosa S. JESS., ZENNER, CH. STARK & BUJNOCH beschrieben. Sie lässt sich morphologisch von den anderen bisher beschriebenen Taxa des Aggregats unterscheiden, ist triploid und zeigt spezifische Phloroglucid- und RAPD- Bandenmuster. Bisher sind Funde aus Norditalien, Österreich, der Schweiz, Frankreich, Deutschland, Großbritannien und Irland belegt.
Background: The progression of mild cognitive impairment (MCI) to Alzheimer’s disease (AD) dementia can be predicted by cognitive, neuroimaging, and cerebrospinal fluid (CSF) markers. Since most biomarkers reveal complementary information, a combination of biomarkers may increase the predictive power. We investigated which combination of the Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR)-sum-of-boxes, the word list delayed free recall from the Consortium to Establish a Registry of Dementia (CERAD) test battery, hippocampal volume (HCV), amyloid-beta1–42 (Aβ42), amyloid-beta1–40 (Aβ40) levels, the ratio of Aβ42/Aβ40, phosphorylated tau, and total tau (t-Tau) levels in the CSF best predicted a short-term conversion from MCI to AD dementia.
Methods: We used 115 complete datasets from MCI patients of the "Dementia Competence Network", a German multicenter cohort study with annual follow-up up to 3 years. MCI was broadly defined to include amnestic and nonamnestic syndromes. Variables known to predict progression in MCI patients were selected a priori. Nine individual predictors were compared by receiver operating characteristic (ROC) curve analysis. ROC curves of the five best two-, three-, and four-parameter combinations were analyzed for significant superiority by a bootstrapping wrapper around a support vector machine with linear kernel. The incremental value of combinations was tested for statistical significance by comparing the specificities of the different classifiers at a given sensitivity of 85%.
Results: Out of 115 subjects, 28 (24.3%) with MCI progressed to AD dementia within a mean follow-up period of 25.5 months. At baseline, MCI-AD patients were no different from stable MCI in age and gender distribution, but had lower educational attainment. All single biomarkers were significantly different between the two groups at baseline. ROC curves of the individual predictors gave areas under the curve (AUC) between 0.66 and 0.77, and all single predictors were statistically superior to Aβ40. The AUC of the two-parameter combinations ranged from 0.77 to 0.81. The three-parameter combinations ranged from AUC 0.80–0.83, and the four-parameter combination from AUC 0.81–0.82. None of the predictor combinations was significantly superior to the two best single predictors (HCV and t-Tau). When maximizing the AUC differences by fixing sensitivity at 85%, the two- to four-parameter combinations were superior to HCV alone.
Conclusion: A combination of two biomarkers of neurodegeneration (e.g., HCV and t-Tau) is not superior over the single parameters in identifying patients with MCI who are most likely to progress to AD dementia, although there is a gradual increase in the statistical measures across increasing biomarker combinations. This may have implications for clinical diagnosis and for selecting subjects for participation in clinical trials.
Eine Hybride zwischen Asplenium septentrionale (L.) Hoffm. subsp. septentrionale und A. viride (L.) HUDS. wird beschrieben. Der Bastard wurde in einem Exemplar bei Bosco-Gurin im Schweizer Kanton Tessin gefunden. Die morphologische Mittelstellung sowie Sporen-, Cytologische und Molekulargenetische Untersuchungen bestätigten die vermutete Abstammung.
Mitte des 19. Jahrhunderts wurde auf der Ostseeinsel Rügen ein Schachtelhalm entdeckt, dessen Identität unter den zeitgenössischen Botanikern umstritten war. ZABEL (1863) interpretierte die Pflanzen zwar korrekt als Bastard zwischen Equisetum palustre und E. telmateia, MILDE (1864) hielt jedoch eine Beteiligung von E. telmateia für ausgeschlossen und beschrieb sie als eine neue Varietät, var. fallax, von E. palustre. Belege von var. fallax aus dem Herbarium des Botanischen Instituts Greifswald (GFW), die 1852 und 1853 von Münter, Marsson und Zabel auf Rügen gesammelt wurden, konnten von uns überprüft und eindeutig als Equisetum xfont-queri ROTHM. (= Equisetum palustre x E. telmateia) identifiziert werden. Damit ist der Name Equisetum palustre var. fallax ein älteres Synonym von Equisetum xfont-queri, welches wegen der abweichenden Rangstufe jedoch keine Priorität besitzt. Equisetum xfont-queri kommt auf Rügen also seit mehr als 150 Jahren vor; die Pflanzen im Greifswalder Herbar sind die ältesten uns bekannten Belege von dieser Hybride.
Comparative proteomics reveals a diagnostic signature for pulmonary head‐and‐neck cancer metastasis
(2018)
Patients with head‐and‐neck cancer can develop both lung metastasis and primary lung cancer during the course of their disease. Despite the clinical importance of discrimination, reliable diagnostic biomarkers are still lacking. Here, we have characterised a cohort of squamous cell lung (SQCLC) and head‐and‐neck (HNSCC) carcinomas by quantitative proteomics. In a training cohort, we quantified 4,957 proteins in 44 SQCLC and 30 HNSCC tumours. A total of 518 proteins were found to be differentially expressed between SQCLC and HNSCC, and some of these were identified as genetic dependencies in either of the two tumour types. Using supervised machine learning, we inferred a proteomic signature for the classification of squamous cell carcinomas as either SQCLC or HNSCC, with diagnostic accuracies of 90.5% and 86.8% in cross‐ and independent validations, respectively. Furthermore, application of this signature to a cohort of pulmonary squamous cell carcinomas of unknown origin leads to a significant prognostic separation. This study not only provides a diagnostic proteomic signature for classification of secondary lung tumours in HNSCC patients, but also represents a proteomic resource for HNSCC and SQCLC.