Molecular causality in the advent of foundation models

Correlation is not causation. As simple as this widely agreed-upon statement may seem, scientifically defining causality and using it to drive our modern biomedical research is immensely challenging. In this perspective, we attempt to synergise the partly disparate fields of systems biology, causal reasoning, and machine learning, to inform future approaches in the field of systems biology and molecular networks.Comment: 22 pages, 0 figures, 87 references; submitted to MS

Physikalische Parameter extrakorporaler Stoßwellen

Prerequisites for the successful investigation of the mechanism of action of ESWT (extracorporeal shockwave therapy) and the establishment of treatment standards, are the ability to measure, and a knowledge of, the physical parameters involved. The most accurate measurements are obtained with laser hydrophones. Various parameters (amplitude, rise time, pulse width, pressure pulse decay, rarification phase) of a typical shock wave can thus be determined. These can then be used to calculate energy flux density, focal extent, focal volume and as well as focal energy, effective energy in a defined area, and effective biological energy. These parameters can be utilized to work out a theoretical treatment protocol

Energy relaxation of an excited electron gas in quantum wires: many-body electron LO-phonon coupling

We theoretically study energy relaxation via LO-phonon emission in an excited one-dimensional electron gas confined in a GaAs quantum wire structure. We find that the inclusion of phonon renormalization effects in the theory extends the LO-phonon dominated loss regime down to substantially lower temperatures. We show that a simple plasmon-pole approximation works well for this problem, and discuss implications of our results for low temperature electron heating experiments in quantum wires.Comment: 10 pages, RevTex, 4 figures included. Also available at http://www-cmg.physics.umd.edu/~lzheng

Drugst.One -- A plug-and-play solution for online systems medicine and network-based drug repurposing

In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood. In response, computational systems and network medicine tools have emerged to identify potential drug repurposing candidates. However, these tools often require complex installation and lack intuitive visual network mining capabilities. To tackle these challenges, we introduce Drugst.One, a platform that assists specialized computational medicine tools in becoming user-friendly, web-based utilities for drug repurposing. With just three lines of code, Drugst.One turns any systems biology software into an interactive web tool for modeling and analyzing complex protein-drug-disease networks. Demonstrating its broad adaptability, Drugst.One has been successfully integrated with 21 computational systems medicine tools. Available at https://drugst.one, Drugst.One has significant potential for streamlining the drug discovery process, allowing researchers to focus on essential aspects of pharmaceutical treatment research.Comment: 45 pages, 6 figures, 7 table

Scavenging of Interleukin 6 Receptor by Bioidentical Recombinant gp130 as Intervention in Covid-19 Exacerbation

Acute exacerbations in the form of cytokine storm are the main cause of organ failure caused by SARS-related coronaviruses and mediated by pro-inflammatory transcriptional programs in immune cells. While the effectors of these transcriptional changes have been sufficiently studied, there is a noticeable lack of interventional treatments for acute exacerbations. An important interface of innate immune signalling are the components of the JAK/STAT pathway, activated by interferons as well as pro-inflammatory cytokines such as interleukin (IL)-6. An intervention based on the IL-6 receptor antibody, tocilizumab, has already been proposed and is currently in clinical trial; however, the antibody does not differentiate between soluble and membrane-bound IL-6 receptors und thus may inhibit regenerative processes. Here, I propose utilising recombinant soluble protein gp130, also known as IL6ST, as a bioidentical scavenger molecule for activated soluble IL6R, while retaining the desirable functionality of membrane-bound IL6R. In light of SARS patient expression profiles, and of IL6R and gp130 tissue distribution, the clinical testing of soluble gp130 is a warranted alternative to monoclonal antibody therapy

Small RNA dynamics in cholinergic systems

Natural science is only just beginning to understand the complex processes surrounding transcription. Epitranscriptional regulation is in large parts conveyed by transcription factors (TFs) and two recently discovered small RNA (smRNA) species: microRNAs (miRNAs) and transfer RNA fragments (tRFs). As opposed to the fairly well-characterised function of TFs in shaping the phenotype of the cell, the effects and mechanism of action of smRNA species is less well understood. In particular, the multi-levelled combinatorial interaction (many-to-many) of smRNAs presents new challenges to molecular biology. This dissertation contributes to the study of smRNA dynamics in mammalian cells in several ways, which are presented in three main chapters. I) The exhaustive analysis of the many-to-many network of smRNA regulation is reliant on bioinformatic support. Here, I describe the development of an integrative database capable of fast and efficient computation of complex multi-levelled transcriptional interactions, named miRNeo. This infrastructure is then applied to two use cases. II) To elucidate smRNA dynamics of cholinergic systems and their relevance to psychiatric disease, an integrative transcriptomics analysis is performed on patient brain sample data, single-cell sequencing data, and two closely related in vitro human cholinergic cellular models reflecting male and female phenotypes. III) The dynamics between small and large RNA transcripts in the blood of stroke victims are analysed via a combination of sequencing, analysis of sorted blood cell populations, and bioinformatic methods based on the miRNeo infrastructure. Particularly, importance and practicality of smRNA:TF:gene feedforward loops are assessed. In both analytic scenarios, I identify the most pertinent regulators of disease-relevant processes and biological pathways implicated in either pathogenesis or responses to the disease. While the examples described in chapters three and four of this dissertation are disease-specific applications of miRNeo, the database and methods described have been developed to be applicable to the whole genome and all known smRNAs

Why most pre-published research findings are false.

Caused by the recent surge in preprint volume, particularly in the light of the immense rapidity of Covid-19 research, the question arises, “How reliable are the findings that are reported via preprint?” This question poses serious challenges in estimation and validation of the extent of false or even fraudulent science on preprint servers, and has far-reaching implications for editorial policies. As preprint volume continuously grows, but the interval between preprint and publication does not, the limit of peer-review is fast approaching. The scientific merit or validity of preprints is not assessed by preprint service providers, and hence it is feasible to assume that, comparatively, preprints will be less reproducible than peer-reviewed articles. Publication metadata predict a saturation of the peer-review process in the coming decade, and necessitate an open discussion about editorial policies and publication infrastructure in the biomedical field

Der Sichere weg zum prufungsaufsatz

Band III253 p.; 21 cm

A Platform for the Biomedical Application of Large Language Models

The wealth of knowledge we have amassed in the context of biomedical science has grown exponentially in the last decades. Consequently, understanding and contextualising scientific results has become increasingly difficult for any single individual. In contrast, current Large Language Models (LLMs) can remember an enormous amount of information, but have notable shortcomings, such as a lack of generalised awareness, logical deficits, and a propensity to hallucinate. To improve biomedical analyses, we propose to combine human ingenuity and machine memory by means of an open and modular conversational platform, ChatGSE (https://chatgse.streamlit.app). We safeguard against common LLM shortcomings using general and biomedicine-specific measures and allow automated integration of popular bioinformatics methods. Ultimately, we aim to improve the AI-readiness of biomedicine and make LLMs more useful and trustworthy in research applications.Comment: 12 pages, 1 figur