Browse Articles

Influence of soil-rock composite stratum on mechanical response and failure modes of underground utility tunnel

Bro̷nsted Acid-Catalyzed Reduction of Furans

Rethinking model prototyping through the MedMNIST+ dataset collection

Abstract The integration of deep learning based systems in clinical practice is often impeded by challenges rooted in limited and heterogeneous medical datasets. In addition, the field has increasingly prioritized marginal performance gains on a few, narrowly scoped benchmarks over clinical applicability, slowing down meaningful algorithmic progress. This trend often results in excessive fine-tuning of existing methods on selected datasets rather than fostering clinically relevant innovations. In response, this work introduces a comprehensive benchmark for the MedMNIST+ dataset collection, designed to diversify the evaluation landscape across several imaging modalities, anatomical regions, classification tasks and sample sizes. We systematically reassess commonly used Convolutional Neural Networks (CNNs) and Vision Transformer (ViT) architectures across distinct medical datasets, training methodologies, and input resolutions to validate and refine existing assumptions about model effectiveness and development. Our findings suggest that computationally efficient training schemes and modern foundation models offer viable alternatives to costly end-to-end training. Additionally, we observe that higher image resolutions do not consistently improve performance beyond a certain threshold. This highlights the potential benefits of using lower resolutions, particularly in prototyping stages, to reduce computational demands without sacrificing accuracy. Notably, our analysis reaffirms the competitiveness of CNNs compared to ViTs, emphasizing the importance of comprehending the intrinsic capabilities of different architectures. Finally, by establishing a standardized evaluation framework, we aim to enhance transparency, reproducibility, and comparability within the MedMNIST+ dataset collection as well as future research. Code is available at (https://github.com/sdoerrich97/rethinking-model-prototyping-MedMNISTPlus).

Intermolecular N–N Coupling of a Dinitrosyl Iron Complex Induced by Hydrogen Bond Donors in the Secondary Coordination Sphere

Spontaneous evolution of density peaking factor in TEM turbulence-dominated H-mode plasma on the EAST Tokamak

Differential Packing of Cs₂Mo₆Br₁₄ Cluster-Based Halide in Variable Diameter Carbon Nanotubes with Elimination and Polymerization to 1D [Mo₂Br₆]_<i>x</i> Ising Model Structures by Steric Confinement

Family-based genetics identifies association of CUBN, IL1RL1 and PRKN variants with leprosy in Bangladesh

Multimodal Precise Control Over Multiselective Carbonylation of 1,3-Enynes

Uncovering hidden insights in the chair rise performance of older adults using Dynamic Time Warping and K-means clustering

Abstract The five time chair rise test (5CRT) is commonly used in geriatric medicine and research to assess functional capacity and lower extremity strength to detect early age-related changes in older adults. Traditional stopwatch-based analyses may mask temporal variations in 5CRT transitions due to averaging. Temporal variations and dynamic characteristics are better assessed by motion variability analysis. This work employs k-means clustering using Dynamic Time Warping (DTW) as a metric for 5CRT to examine compensation mechanisms of older adults. The observational study included 172 healthy, community-dwelling adults aged 70+, yielding 860 chair rises recorded on a force plate and clustered using k-means. Descriptive statistics summarized performance distribution across clusters. Optimal clustering revealed two movement patterns, differing significantly (p $$&lt;0.01$$ ) in 5CRT duration and forces during the stabilization phase. These patterns did not correlate directly with shorter or longer 5CRT durations, indicating overlap and highlighting the limitations of traditional stopwatch methods. This study demonstrates the potential of DTW and k-means clustering in geriatric medicine and research, enabling analysis of 5CRT performance independent of temporal variations, identifying potential health issues undetectable by conventional methods. The k-means model can be further trained to automate analysis, enhancing insights from 5CRT.

A Route toward the On-Surface Synthesis of Organic Ferromagnetic Quantum Spin Chains

Deep learning for hepatocellular carcinoma recurrence before and after liver transplantation: a multicenter cohort study

A Copper–Zinc Cyanamide Solid-Solution Catalyst with Tailored Surface Electrostatic Potentials Promotes Asymmetric N-Intermediate Adsorption in Nitrite Electroreduction

Research on forest fire information propagation model based on IoV

From Magic Size to Atomic Precision: Facile Synthesis of a CdTe Semiconductor Nanocluster

Prevalence of dementia risk factors in the Oxford Brain Health Clinic

Abstract With promising disease-modifying therapies (DMTs) emerging and good evidence to support risk reduction in the delay of dementia onset and progression, it is important to understand the profile of patients attending memory assessment services to estimate what proportion of patients might benefit from different types of interventions. The Oxford Brain Health Clinic (OBHC) is a psychiatry-led, clinical-research service that offers memory clinic patients detailed clinical assessments and equal access to research opportunities as part of their secondary care pathway. In this work, we describe the characteristics of OBHC patients in terms of demographics, diagnoses and prevalence of potentially modifiable risk factors compared with a cohort of healthy volunteers and the average memory clinic population. Our results suggest that high research consent rates (91.5%) in the OBHC resulted in a highly representative cohort of the clinical population. Based on Lecanemab trial inclusion criteria, 24.6% of the OBHC population may be suitable for further investigation into DMTs. Furthermore, 67.4% of OBHC patients have at least one potentially modifiable risk factor that may benefit from lifestyle interventions, particularly those focused on depression, sleep and physical activity.

Arylsulfonothioates: Thiol-Activated Donors of Hydropersulfides which are Excreted to Maintain Cellular Redox Homeostasis or Retained to Counter Oxidative Stress

Association of optic neuritis with incident depressive disorder risk in a Korean nationwide cohort

Tailored Lattice-Matched Carbazole Self-Assembled Molecule for Efficient and Stable Perovskite Solar Cells

Oxidative stress in a cellular model of alcohol-related liver disease: protection using curcumin nanoformulations

Abstract Alcohol-related liver disease (ARLD) is a global health issue causing significant morbidity and mortality, due to lack of suitable therapeutic options. ARLD induces a spectrum of biochemical and cellular alterations, including chronic oxidative stress, mitochondrial dysfunction, and cell death, resulting in hepatic injury. Natural antioxidant compounds such as curcumin have generated interest in ARLD due to their ability to scavenge reactive oxygen species (ROS), however, therapy using these compounds is limited due to poor bioavailability and stability. Therefore, the aim of this study was to assess the antioxidant potential of free antioxidants and curcumin entrapped formulations against oxidative damage in an ARLD cell model. HepG2 (VL-17A) cells were treated with varying concentrations of alcohol (from 200 to 350 mM) and parameters of oxidative stress and mitochondrial function were assessed over 72 h. Data indicated 350 mM of ethanol led to a significant decrease in cell viability at 72 h, and a significant increase in ROS at 30 min. A substantial number of cells were in late apoptosis at 72 h, and a reduction in the mitochondrial membrane potential was also found. Pre-treatment with curcumin nanoformulations increased viability, as well as, reducing ROS at 2 h, 48 h and 72 h. In summary, antioxidants and entrapped nanoformulations of curcumin were able to ameliorate reduced cell viability and increased ROS caused by ethanol treatment. This demonstrates their potential at mitigating oxidative damage and warrants further investigation to evaluate their efficacy for ARLD therapy.

Tuning the Crystallinity of a Metal–Organic Coordination Network at the Liquid–Solid Interface