Prognostic value of metabolic tumor volume on [F]FDG PET/CT in addition to the TNM classification system of locally advanced non-small cell lung cancer.

Purpose: Staging of non-small cell lung cancer (NSCLC) is commonly based on [F]FDG PET/CT, in particular to exclude distant metastases and guide local therapy approaches like resection and radiotherapy. Although it is hoped that PET/CT will increase the value of primary staging compared to conventional imaging, it is generally limited to the characterization of TNM. The first aim of this study was to evaluate the PET parameter metabolic tumor volume (MTV) above liver background uptake as a prognostic marker in lung cancer.

Post-coronavirus disease 2019-associated symptoms among children and adolescents in the SARS-CoV-2 Omicron era.

Purpose: Lack of a control group(s) and selection bias were the main criticisms of previous studies investigating the prevalence of post-coronavirus disease 2019 (COVID-19) syndrome (PCS). There are insufficient data regarding paediatric PCS, particularly in the SARS-CoV-2 Omicron era. As such, our study investigated PCS-associated symptoms in a representative control-matched cohort.

A Current Perspective of Medical Informatics Developments for a Clinical Translation of (Non-coding)RNAs and Single-Cell Technologies.

The journey from laboratory research to clinical practice is marked by significant advancements in the fields of single-cell technologies and non-coding RNA (ncRNA) research. This convergence may reshape our approach to personalized medicine, offering groundbreaking insights and treatments in various clinical settings. This chapter discusses advancements in (nc)RNAs in the clinics, innovations in single-cell technologies and algorithms, and the impact on actual precision medicine, showing the integration of single-cell and ncRNA research can have a tangible impact on precision medicine.

Advances in Data Pre-Processing Methods for Distributed Fiber Optic Strain Sensing.

Because of their high spatial resolution over extended lengths, distributed fiber optic sensors (DFOS) enable us to monitor a wide range of structural effects and offer great potential for diverse structural health monitoring (SHM) applications. However, even under controlled conditions, the useful signal in distributed strain sensing (DSS) data can be concealed by different types of measurement principle-related disturbances: strain reading anomalies (SRAs), dropouts, and noise. These disturbances can render the extraction of information for SHM difficult or even impossible.

3D Interlocking Metallo-COFs as a Visible Light Responsive Photocatalyst.

Woven covalent organic frameworks (COFs) offer immense potential as photoactive materials because Cu(I) complexes are periodically integrated into the COF structure via a single synthetic step. A photoactive interlocking (woven) COF featuring Cu(I) photosensitizers that are spatially isolated and periodically arranged in three dimensions has been successfully synthesized and characterized. The optoelectronic properties of this COF, such as light absorption and photocatalytic performance toward the degradation of sulfamethoxazole (SMX) under visible light, are investigated.

Endovascular therapy for acute vertebrobasilar occlusion (VERITAS): a systematic review and individual patient data meta-analysis.

Background: Trials of endovascular therapy for basilar artery occlusion, including vertebral occlusion extending into the basilar artery, have shown inconsistent results. We aimed to pool data to estimate safety and efficacy and to explore the benefit across pre-specified subgroups through individual patient data meta-analysis.

Methods: VERITAS was a systematic review and meta-analysis that pooled patient-level data from trials that recruited patients with vertebrobasilar ischaemic stroke who were randomly assigned to treatment with either endovascular therapy or standard medical treatment alone.

Electroporation of Sliced Human Cortical Organoids for Studies of Gene Function.

Human cortical organoids have become important tools for studying human brain development, neurodevelopmental disorders, and human brain evolution. Studies analyzing gene function by overexpression or knockout have been instrumental in animal models to provide mechanistic insights into the regulation of neocortex development. Here, we present a detailed protocol for CRISPR/Cas9-mediated acute gene knockout by electroporation of sliced human cortical organoids.

6G networks for the operating room of the future.

Technical setups in today's operating rooms (ORs) are becoming increasingly complex, especially with the integration of applications which rely on the fusion of multiple information sources. While manufacturers have already started to make use of such approaches, the quest for fully integrated ORs becoming standard is still ongoing. We describe a variety of state-of-the-art projects that envision an OR of the future in order to identify missing building blocks.

Heavy Atom Cluster Chains with Strong Spin-Orbit Coupling and Magnetic Cations in Mn[PtBiI].

In the search for new magnetic topological insulators with strong spin-orbit coupling, by following conceptual considerations that have already proven to be suitable, the bismuth-rich subiodide Mn[PtBiI] was discovered. Single crystals were grown from mixtures of the elements and BiI using a temperature program developed on the basis of thermal analyses. Single-crystal X-ray diffraction revealed a rhombohedral structure of the cuboctahedral cluster anions [PtBiI], which are linked into chains via octahedrally coordinated Mn cations.

Ensemble representation of animacy could be based on mid-level visual features.

Studies suggest that mid-level features could underlie object animacy perception. In the current research, we tested whether ensemble animacy perception is based on high- or mid-level features. We used five types of images of animals and inanimate objects: color, grayscale, silhouettes, texforms - unrecognizable images that preserve mid-level texture and shape information - and scrambled images.

The path forward for large language models in medicine is open.

Large language models (LLMs) are increasingly applied in medical documentation and have been proposed for clinical decision support. We argue that the future for LLMs in medicine must be based on transparent and controllable open-source models. Openness enables medical tool developers to control the safety and quality of underlying AI models, while also allowing healthcare professionals to hold these models accountable.

Computational Design of the Electronic Response for Volatile Organic Compounds Interacting with Doped Graphene Substrates.

Changes in the work function provide a fingerprint to characterize analyte binding in charge transfer-based sensor devices. Hence, a rational sensor design requires a fundamental understanding of the microscopic factors controlling the modification of the work function. In the current investigation, we address the mechanisms behind the work function change (WFC) for the adsorption of four common volatile organic compounds (toluene, ethanol, 2-Furfurylthiol, and guaiacol) on different nitrogen-doped graphene-based 2D materials using density functional theory.

Artificial Intelligence awarded two Nobel Prizes for innovations that will shape the future of medicine.

John J. Hopfield and Geoffrey E. Hinton were awarded the 2024 Nobel Prize in Physics for developing machine learning technology using artificial neural networks.

Use of artificial intelligence for liver diseases: A survey from the EASL congress 2024.

Artificial intelligence (AI) methods enable humans to analyse large amounts of data, which would otherwise not be feasibly quantifiable. This is especially true for unstructured visual and textual data, which can contain invaluable insights into disease. The hepatology research landscape is complex and has generated large amounts of data to be mined.

Counterfactual Diffusion Models for Mechanistic Explainability of Artificial Intelligence Models in Pathology.

Deep learning can extract predictive and prognostic biomarkers from histopathology whole slide images, but its interpretability remains elusive. We develop and validate MoPaDi (Morphing histoPathology Diffusion), which generates counterfactual mechanistic explanations. MoPaDi uses diffusion autoencoders to manipulate pathology image patches and flip their biomarker status by changing the morphology.

Effects of climate and forest development on habitat specialization and biodiversity in Central European mountain forests.

Mountain forests are biodiversity hotspots with competing hypotheses proposed to explain elevational trends in habitat specialization and species richness. The altitudinal-niche-breadth hypothesis suggests decreasing specialization with elevation, which could lead to decreasing species richness and weaker differences in species richness and beta diversity among habitat types with increasing elevation. Testing these predictions for bacteria, fungi, plants, arthropods, and vertebrates, we found decreasing habitat specialization (represented by forest developmental stages) with elevation in mountain forests of the Northern Alps - supporting the altitudinal-niche-breadth hypothesis.

Characteristic of mental health app usage: a cross-sectional survey in the general population.

Background: Mental health apps (MHA) have gained popularity in recent years. Most freely available apps are of low quality and lack evidence for effectiveness. Yet, download rates indicate high usage.