Refine
Year of publication
Document Type
- Article (30)
- Preprint (2)
- Conference Proceeding (1)
Has Fulltext
- yes (33)
Is part of the Bibliography
- no (33)
Keywords
- Antimicrobial treatment (2)
- Biofilm (2)
- Complications (2)
- Deep brain stimulation (2)
- Low-virulent infection (2)
- Sonication (2)
- ARDS (1)
- Affective disorders (1)
- Biomarker (1)
- Bipolar disorder (1)
The C. elegans nervous system is particularly well suited for optogenetic analyses of circuit function: Essentially all connections have been mapped, and light can be directed at the neuron of interest in the freely moving, transparent animals, while behavior is observed. Thus, different nodes of a neuronal network can be probed for their role in controlling a particular behavior, using different optogenetic tools for photo-activation or –inhibition, which respond to different colors of light. As neurons may act in concert or in opposing ways to affect a behavior, one would further like to excite these neurons concomitantly, yet independent of each other. In addition to the blue-light activated Channelrhodopsin-2 (ChR2), spectrally red-shifted ChR variants have been explored recently. Here, we establish the green-light activated ChR chimera C1V1 (from Chlamydomonas and Volvox ChR1′s) for use in C. elegans. We surveyed a number of red-shifted ChRs, and found that C1V1-ET/ET (E122T; E162T) works most reliable in C. elegans, with 540–580 nm excitation, which leaves ChR2 silent. However, as C1V1-ET/ET is very light sensitive, it still becomes activated when ChR2 is stimulated, even at 400 nm. Thus, we generated a highly efficient blue ChR2, the H134R; T159C double mutant (ChR2-HR/TC). Both proteins can be used in the same animal, in different neurons, to independently control each cell type with light, enabling a further level of complexity in circuit analyses.
Die vielgestaltigen Habitate der Südharzer Karstlandschaft bieten einer artenreichen Tierwelt Lebensraum. Zwar sind aus dem Südharz eine Vielzahl von Angaben zu den verschiedensten Tierarten bekannt, systematische Untersuchungen begannen aber erst in der jüngsten Zeit. Fast alle Daten wurden von Einzelpersonen zusammengetragen. Eine Zusammenstellung für den gesamten Harz, der auch alle verfügbaren Meldungen zum Südharz enthält, wurde 1997 mit dem Arten- und Biotopschutzprogramm "Landschaftsraum Harz" vom Landesamt für Umweltschutz veröffentlicht.
Poster presentation In pharmaceutical research and drug development, machine learning methods play an important role in virtual screening and ADME/Tox prediction. For the application of such methods, a formal measure of similarity between molecules is essential. Such a measure, in turn, depends on the underlying molecular representation. Input samples have traditionally been modeled as vectors. Consequently, molecules are represented to machine learning algorithms in a vectorized form using molecular descriptors. While this approach is straightforward, it has its shortcomings. Amongst others, the interpretation of the learned model can be difficult, e.g. when using fingerprints or hashing. Structured representations of the input constitute an alternative to vector based representations, a trend in machine learning over the last years. For molecules, there is a rich choice of such representations. Popular examples include the molecular graph, molecular shape and the electrostatic field. We have developed a molecular similarity measure defined directly on the (annotated) molecular graph, a long-standing established topological model for molecules. It is based on the concepts of optimal atom assignments and iterative graph similarity. In the latter, two atoms are considered similar if their neighbors are similar. This recursive definition leads to a non-linear system of equations. We show how to iteratively solve these equations and give bounds on the computational complexity of the procedure. Advantages of our similarity measure include interpretability (atoms of two molecules are assigned to each other, each pair with a score expressing local similarity; this can be visualized to show similar regions of two molecules and the degree of their similarity) and the possibility to introduce knowledge about the target where available. We retrospectively tested our similarity measure using support vector machines for virtual screening on several pharmaceutical and toxicological datasets, with encouraging results. Prospective studies are under way.
We present a computational method for the reaction-based de novo design of drug-like molecules. The software DOGS (Design of Genuine Structures) features a ligand-based strategy for automated ‘in silico’ assembly of potentially novel bioactive compounds. The quality of the designed compounds is assessed by a graph kernel method measuring their similarity to known bioactive reference ligands in terms of structural and pharmacophoric features. We implemented a deterministic compound construction procedure that explicitly considers compound synthesizability, based on a compilation of 25'144 readily available synthetic building blocks and 58 established reaction principles. This enables the software to suggest a synthesis route for each designed compound. Two prospective case studies are presented together with details on the algorithm and its implementation. De novo designed ligand candidates for the human histamine H4 receptor and γ-secretase were synthesized as suggested by the software. The computational approach proved to be suitable for scaffold-hopping from known ligands to novel chemotypes, and for generating bioactive molecules with drug-like properties.
Background: Lithium has proven suicide preventing effects in the long-term treatment of patients with affective disorders. Clinical evidence from case reports indicate that this effect may occur early on at the beginning of lithium treatment. The impact of lithium treatment on acute suicidal thoughts and/or behavior has not been systematically studied in a controlled trial. The primary objective of this confirmatory study is to determine the association between lithium therapy and acute suicidal ideation and/or suicidal behavior in inpatients with a major depressive episode (MDE, unipolar and bipolar disorder according to DSM IV criteria). The specific aim is to test the hypothesis that lithium plus treatment as usual (TAU), compared to placebo plus TAU, results in a significantly greater decrease in suicidal ideation and/or behavior over 5 weeks in inpatients with MDE.
Methods/Design: We initiated a randomized, placebo-controlled multicenter trial. Patients with the diagnosis of a moderate to severe depressive episode and suicidal thoughts and/or suicidal behavior measured with the Sheehan-Suicidality-Tracking Scale (S-STS) will be randomly allocated to add lithium or placebo to their treatment as usual. Change in the clinician administered S-STS from the initial to the final visit will be the primary outcome.
Discussion: There is an urgent need to identify treatments that will acutely decrease suicidal ideation and/or suicidal behavior. The results of this study will demonstrate whether lithium reduces suicidal ideation and behavior within the first 5 weeks of treatment.
Poster presentation at 5th German Conference on Cheminformatics: 23. CIC-Workshop Goslar, Germany. 8-10 November 2009 We demonstrate the theoretical and practical application of modern kernel-based machine learning methods to ligand-based virtual screening by successful prospective screening for novel agonists of the peroxisome proliferator-activated receptor gamma (PPARgamma) [1]. PPARgamma is a nuclear receptor involved in lipid and glucose metabolism, and related to type-2 diabetes and dyslipidemia. Applied methods included a graph kernel designed for molecular similarity analysis [2], kernel principle component analysis [3], multiple kernel learning [4], and, Gaussian process regression [5]. In the machine learning approach to ligand-based virtual screening, one uses the similarity principle [6] to identify potentially active compounds based on their similarity to known reference ligands. Kernel-based machine learning [7] uses the "kernel trick", a systematic approach to the derivation of non-linear versions of linear algorithms like separating hyperplanes and regression. Prerequisites for kernel learning are similarity measures with the mathematical property of positive semidefiniteness (kernels). The iterative similarity optimal assignment graph kernel (ISOAK) [2] is defined directly on the annotated structure graph, and was designed specifically for the comparison of small molecules. In our virtual screening study, its use improved results, e.g., in principle component analysis-based visualization and Gaussian process regression. Following a thorough retrospective validation using a data set of 176 published PPARgamma agonists [8], we screened a vendor library for novel agonists. Subsequent testing of 15 compounds in a cell-based transactivation assay [9] yielded four active compounds. The most interesting hit, a natural product derivative with cyclobutane scaffold, is a full selective PPARgamma agonist (EC50 = 10 ± 0.2 microM, inactive on PPARalpha and PPARbeta/delta at 10 microM). We demonstrate how the interplay of several modern kernel-based machine learning approaches can successfully improve ligand-based virtual screening results.
Single nucleotide polymorphisms (SNPs) in the ADGRL3 gene have been significantly associated with the development of ADHD, the aetiology of which remains poorly understood. The rs1397547 SNP has additionally been associated with significantly altered ADGRL3 transcription. We therefore generated iPSCs from two wild type ADHD patients, and two ADHD patients heterozygous for the risk SNP. With this resource we aim to facilitate further investigation into the complex and heterogenous pathology of ADHD. Furthermore, we demonstrate the feasibility of using magnetic activated cell sorting to allow the unbiased selection of fully reprogrammed iPSCs.
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.
Importance: The entry of artificial intelligence into medicine is pending. Several methods have been used for the predictions of structured neuroimaging data, yet nobody compared them in this context.
Objective: Multi-class prediction is key for building computational aid systems for differential diagnosis. We compared support vector machine, random forest, gradient boosting, and deep feed-forward neural networks for the classification of different neurodegenerative syndromes based on structural magnetic resonance imaging.
Design, setting, and participants: Atlas-based volumetry was performed on multi-centric T1-weighted MRI data from 940 subjects, i.e., 124 healthy controls and 816 patients with ten different neurodegenerative diseases, leading to a multi-diagnostic multi-class classification task with eleven different classes.
Interventions: N.A.
Main outcomes and measures: Cohen’s kappa, accuracy, and F1-score to assess model performance.
Results: Overall, the neural network produced both the best performance measures and the most robust results. The smaller classes however were better classified by either the ensemble learning methods or the support vector machine, while performance measures for small classes were comparatively low, as expected. Diseases with regionally specific and pronounced atrophy patterns were generally better classified than diseases with widespread and rather weak atrophy.
Conclusions and relevance: Our study furthermore underlines the necessity of larger data sets but also calls for a careful consideration of different machine learning methods that can handle the type of data and the classification task best.
Highlights
• A panel of 20 biomarkers was identified capable of differentiating BD patients from controls.
• Excellent discrimination between established BD patients and controls.
• Good to excellent discrimination between misdiagnosed BD patients and first onset MDD patients.
• Fair to good discrimination between pre-diagnostic BD patients and controls.
• Study demonstrates the potential utility of a protein biomarker panel as a diagnostic test for BD.
Abstract
Background: Bipolar disorder (BD) is a costly, devastating and life shortening mental disorder that is often misdiagnosed, especially on initial presentation. Misdiagnosis frequently results in ineffective treatment. We investigated the utility of a biomarker panel as a diagnostic test for BD.
Methods and findings: We performed a meta-analysis of eight case-control studies to define a diagnostic biomarker panel for BD. After validating the panel on established BD patients, we applied it to undiagnosed BD patients. We analysed 249 BD, 122 pre-diagnostic BD, 75 pre-diagnostic schizophrenia and 90 first onset major depression disorder (MDD) patients and 371 controls. The biomarker panel was identified using ten-fold cross-validation with lasso regression applied to the 87 analytes available across the meta-analysis studies.
We identified 20 protein analytes with excellent predictive performance [area under the curve (AUC) ⩾ 0.90]. Importantly, the panel had a good predictive performance (AUC 0.84) to differentiate 12 misdiagnosed BD patients from 90 first onset MDD patients, and a fair to good predictive performance (AUC 0.79) to differentiate between 110 pre-diagnostic BD patients and 184 controls. We also demonstrated the disease specificity of the panel.
Conclusions: An early and accurate diagnosis has the potential to delay or even prevent the onset of BD. This study demonstrates the potential utility of a biomarker panel as a diagnostic test for BD.
Purpose: The role of obesity in glioblastoma remains unclear, as previous analyses have reported contradicting results. Here, we evaluate the prognostic impact of obesity in two trial populations; CeTeG/NOA-09 (n = 129) for MGMT methylated glioblastoma patients comparing temozolomide (TMZ) to lomustine/TMZ, and GLARIUS (n = 170) for MGMT unmethylated glioblastoma patients comparing TMZ to bevacizumab/irinotecan, both in addition to surgery and radiotherapy.
Methods: The impact of obesity (BMI ≥ 30 kg/m2) on overall survival (OS) and progression-free survival (PFS) was investigated with Kaplan–Meier analysis and log-rank tests. A multivariable Cox regression analysis was performed including known prognostic factors as covariables.
Results: Overall, 22.6% of patients (67 of 297) were obese. Obesity was associated with shorter survival in patients with MGMT methylated glioblastoma (median OS 22.9 (95% CI 17.7–30.8) vs. 43.2 (32.5–54.4) months for obese and non-obese patients respectively, p = 0.001), but not in MGMT unmethylated glioblastoma (median OS 17.1 (15.8–18.9) vs 17.6 (14.7–20.8) months, p = 0.26). The prognostic impact of obesity in MGMT methylated glioblastoma was confirmed in a multivariable Cox regression (adjusted odds ratio: 2.57 (95% CI 1.53–4.31), p < 0.001) adjusted for age, sex, extent of resection, baseline steroids, Karnofsky performance score, and treatment arm.
Conclusion: Obesity was associated with shorter survival in MGMT methylated, but not in MGMT unmethylated glioblastoma patients.