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Introduction: Survival analysis based on cancer registry data is of paramount importance for monitoring the effectiveness of health care. As new methods arise, the compendium of statistical tools applicable to cancer registry data grows. In recent years, machine learning approaches for survival analysis were developed. The aim of this study is to compare the model performance of the well established Cox regression and novel machine learning approaches on a previously unused dataset., Material and Methods: The study is based on lung cancer data from the Schleswig-Holstein Cancer Registry. Four survival analysis models are compared: Cox Proportional Hazard Regression (CoxPH) as the most commonly used statistical model, as well as Random Survival Forests (RSF) and two neural network architectures based on the DeepSurv and TabNet approaches. The models are evaluated using the concordance index (C-I), the Brier score and the AUC-ROC score. In addition, to gain more insight in the decision process of the models, we identified the features that have an higher impact on patient survival using permutation feature importance scores and SHAP values., Results: Using a dataset including the cancer stage established by the Union for International Cancer Control (UICC), the best performing model is the CoxPH (C-I: 0.698±0.005), while using a dataset which includes the tumor size, lymph node and metastasis status (TNM) leads to the RSF as best performing model (C-I: 0.703±0.004). The explainability metrics show that the models rely on the combined UICC stage and the metastasis status in the first place, which corresponds to other studies., Discussion: The studied methods are highly relevant for epidemiological researchers to create more accurate survival models, which can help physicians make informed decisions about appropriate therapies and management of patients with lung cancer, ultimately improving survival and quality of life., Competing Interests: Declaration of Competing Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024. Published by Elsevier B.V.)

Determining the label to target ratio, also known as the degree of labeling (DOL), is crucial for quantitative fluorescence microscopy and a high DOL with minimal unspecific labeling is beneficial for fluorescence microscopy in general. Yet robust, versatile and easy-to-use tools for measuring cell-specific labeling efficiencies are not available. Here we present a DOL determination technique named protein-tag DOL (ProDOL), which enables fast quantification and optimization of protein-tag labeling. With ProDOL various factors affecting labeling efficiency, including substrate type, incubation time and concentration, as well as sample fixation and cell type can be easily assessed. We applied ProDOL to investigate how human immunodeficiency virus-1 pathogenesis factor Nef modulates CD4 T cell activation measuring total and activated copy numbers of the adapter protein SLP-76 in signaling microclusters. ProDOL proved to be a versatile and robust tool for labeling calibration, enabling determination of labeling efficiencies, optimization of strategies and quantification of protein stoichiometry., (© 2024. The Author(s).)

Polycyclic aromatic hydrocarbons and their nitrogen-substituted analogues are of great interest for various applications in organic electronics. The performance of such devices is determined not only by the properties of the single molecules, but also by the structure of their aggregates, which often form via self-aggregation. Gaining insight into such aggregation processes is a challenging task, but crucial for a fine-tuning of the materials properties. In this work, an efficient approach for the generation and characterisation of aggregates is described, based on matrix-isolation experiments and quantum-chemical calculations. This approach is exemplified for aggregation of acridine. The acridine dimer and trimer are thoroughly analysed on the basis of experimental and calculated UV and IR absorption spectra, which agree well with each other. Thereby a novel structure of the acridine dimer is found, which disagrees with a previously reported one. The calculations also show the changes from excitonic coupling towards orbital interactions between two molecules with decreasing distance to each other. In addition, a structure of the trimer is determined. Finally, an outlook is given on how even higher aggregates can be made accessible through experiment., (© 2024 The Authors. Chemistry - A European Journal published by Wiley-VCH GmbH.)

Pharmacologic activity of proanthocyanidins (PACs) in Ginkgo biloba leaf extract has recently been reported. The objective of the present study was to screen the PACs content in herbal medicinal products containing Ginkgo extracts. A recently published HPLC method for quantification of PACs in Ginkgo biloba leaf extract EGb 761 was adopted to be also suitable for finished herbal medicinal products. The method was applied to 14 products from the German market. For each product a set of 3 individual batches was purchased and analyzed. Substantial differences in PACs content were found among distinct products ranging from 0.30 % to 5.86 %. The batch-to-batch variability within each product was low. The highest concentrations are in a similar range as for example the amount of Ginkgo terpene trilactones specified in the monograph for Ginkgo biloba leaf extract in the Ph. Eur.. Although it has not yet been established whether and to which extent PACs contribute to the overall pharmacological or clinical efficacy of Ginkgo extracts, a potential impact on the purported benefits of different contents in PACs cannot be ruled out. Quality assessment of different Ginkgo extracts in future may include the PACs., Competing Interests: The authors Stefan Germer and Thomas Ritter are employees of Dr. Willmar Schwabe GmbH & Co. KG. The author Mario Wurglics has received speaker’s honoraria and research grants from Dr. Willmar Schwabe GmbH & Co. KG., (The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).)