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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.
Antigenic and 3D structural characterization of soluble X4 and hybrid X4-R5 HIV-1 Env trimers
(2014)
Background: HIV-1 is decorated with trimeric glycoprotein spikes that enable infection by engaging CD4 and a chemokine coreceptor, either CCR5 or CXCR4. The variable loop 3 (V3) of the HIV-1 envelope protein (Env) is the main determinant for coreceptor usage. The predominant CCR5 using (R5) HIV-1 Env has been intensively studied in function and structure, whereas the trimeric architecture of the less frequent, but more cytopathic CXCR4 using (X4) HIV-1 Env is largely unknown, as are the consequences of sequence changes in and near V3 on antigenicity and trimeric Env structure.
Results: Soluble trimeric gp140 Env constructs were used as immunogenic mimics of the native spikes to analyze their antigenic properties in the context of their overall 3D structure. We generated soluble, uncleaved, gp140 trimers from a prototypic T-cell line-adapted (TCLA) X4 HIV-1 strain (NL4-3) and a hybrid (NL4-3/ADA), in which the V3 spanning region was substituted with that from the primary R5 isolate ADA. Compared to an ADA (R5) gp140, the NL4-3 (X4) construct revealed an overall higher antibody accessibility, which was most pronounced for the CD4 binding site (CD4bs), but also observed for mAbs against CD4 induced (CD4i) epitopes and gp41 mAbs. V3 mAbs showed significant binding differences to the three constructs, which were refined by SPR analysis. Of interest, the NL4-3/ADA construct with the hybrid NL4-3/ADA CD4bs showed impaired CD4 and CD4bs mAb reactivity despite the presence of the essential elements of the CD4bs epitope. We obtained 3D reconstructions of the NL4-3 and the NL4-3/ADA gp140 trimers via electron microscopy and single particle analysis, which indicates that both constructs inherit a propeller-like architecture. The first 3D reconstruction of an Env construct from an X4 TCLA HIV-1 strain reveals an open conformation, in contrast to recently published more closed structures from R5 Env. Exchanging the X4 V3 spanning region for that of R5 ADA did not alter the open Env architecture as deduced from its very similar 3D reconstruction.
Conclusions: 3D EM analysis showed an apparent open trimer configuration of X4 NL4-3 gp140 that is not modified by exchanging the V3 spanning region for R5 ADA.
Formalin‐fixed, paraffin‐embedded (FFPE ), biobanked tissue samples offer an invaluable resource for clinical and biomarker research. Here, we developed a pressure cycling technology (PCT )‐SWATH mass spectrometry workflow to analyze FFPE tissue proteomes and applied it to the stratification of prostate cancer (PC a) and diffuse large B‐cell lymphoma (DLBCL ) samples. We show that the proteome patterns of FFPE PC a tissue samples and their analogous fresh‐frozen (FF ) counterparts have a high degree of similarity and we confirmed multiple proteins consistently regulated in PC a tissues in an independent sample cohort. We further demonstrate temporal stability of proteome patterns from FFPE samples that were stored between 1 and 15 years in a biobank and show a high degree of the proteome pattern similarity between two types of histological regions in small FFPE samples, that is, punched tissue biopsies and thin tissue sections of micrometer thickness, despite the existence of a certain degree of biological variations. Applying the method to two independent DLBCL cohorts, we identified myeloperoxidase, a peroxidase enzyme, as a novel prognostic marker. In summary, this study presents a robust proteomic method to analyze bulk and biopsy FFPE tissues and reports the first systematic comparison of proteome maps generated from FFPE and FF samples. Our data demonstrate the practicality and superiority of FFPE over FF samples for proteome in biomarker discovery. Promising biomarker candidates for PC a and DLBCL have been discovered.
Background: Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes. Methods: A Germany-wide electronic registry was established to pseudonymously collect admission, therapeutic and discharge information of SARS-CoV-2 ICU patients retrospectively and prospectively. Machine learning approaches were evaluated for the accuracy and interpretability of predictions. The Explainable Boosting Machine approach was selected as the most suitable method. Individual, non-linear shape functions for predictive parameters and parameter interactions are reported. Results: 1039 patients were included in the Explainable Boosting Machine model, 596 patients retrospectively collected, and 443 patients prospectively collected. The model for prediction of general ICU outcome was shown to be more reliable to predict “survival”. Age, inflammatory and thrombotic activity, and severity of ARDS at ICU admission were shown to be predictive of ICU survival. Patients’ age, pulmonary dysfunction and transfer from an external institution were predictors for ECMO therapy. The interaction of patient age with D-dimer levels on admission and creatinine levels with SOFA score without GCS were predictors for renal replacement therapy. Conclusions: Using Explainable Boosting Machine analysis, we confirmed and weighed previously reported and identified novel predictors for outcome in critically ill COVID-19 patients. Using this strategy, predictive modeling of COVID-19 ICU patient outcomes can be performed overcoming the limitations of linear regression models. Trial registration “ClinicalTrials” (clinicaltrials.gov) under NCT04455451.
Epigenetic neural glioblastoma enhances synaptic integration and predicts therapeutic vulnerability
(2023)
Neural-tumor interactions drive glioma growth as evidenced in preclinical models, but clinical validation is nascent. We present an epigenetically defined neural signature of glioblastoma that independently affects patients survival. We use reference signatures of neural cells to deconvolve tumor DNA and classify samples into low- or high-neural tumors. High-neural glioblastomas exhibit hypomethylated CpG sites and upregulation of genes associated with synaptic integration. Single-cell transcriptomic analysis reveals high abundance of stem cell-like malignant cells classified as oligodendrocyte precursor and neural precursor cell-like in high-neural glioblastoma. High-neural glioblastoma cells engender neuron-to-glioma synapse formation in vitro and in vivo and show an unfavorable survival after xenografting. In patients, a high-neural signature associates with decreased survival as well as increased functional connectivity and can be detected via DNA analytes and brain-derived neurotrophic factor in plasma. Our study presents an epigenetically defined malignant neural signature in high-grade gliomas that is prognostically relevant.