Low molecular weight ϵ-caprolactone-pcoumaric acid copolymers as potential biomaterials for skin regeneration applications

ϵ-caprolactone-p-coumaric acid copolymers at different mole ratios (ϵ-caprolactone:p-coumaric acid 1:0, 10:1, 8:1, 6:1, 4:1, and 2:1) were synthesized by melt-polycondensation and using 4-dodecylbenzene sulfonic acid as catalyst. Chemical analysis by NMR and GPC showed that copolyesters were formed with decreasing molecular weight as p-coumaric acid content was increased. Physical characteristics, such as thermal and mechanical properties, as well as water uptake and water permeability, depended on the mole fraction of pcoumaric acid. The p-coumarate repetitive units increased the antioxidant capacity of the copolymers, showing antibacterial activity against the common pathogen Escherichia coli. In addition, all the synthesized copolyesters, except the one with the highest concentration of the phenolic acid, were cytocompatible and hemocompatible, thus becoming potentially useful for skin regeneration applications

Deep Learning Framework for Spleen Volume Estimation from 2D Cross-sectional Views

Abnormal spleen enlargement (splenomegaly) is regarded as a clinical indicator for a range of conditions, including liver disease, cancer and blood diseases. While spleen length measured from ultrasound images is a commonly used surrogate for spleen size, spleen volume remains the gold standard metric for assessing splenomegaly and the severity of related clinical conditions. Computed tomography is the main imaging modality for measuring spleen volume, but it is less accessible in areas where there is a high prevalence of splenomegaly (e.g., the Global South). Our objective was to enable automated spleen volume measurement from 2D cross-sectional segmentations, which can be obtained from ultrasound imaging. In this study, we describe a variational autoencoder-based framework to measure spleen volume from single- or dual-view 2D spleen segmentations. We propose and evaluate three volume estimation methods within this framework. We also demonstrate how 95% confidence intervals of volume estimates can be produced to make our method more clinically useful. Our best model achieved mean relative volume accuracies of 86.62% and 92.58% for single- and dual-view segmentations, respectively, surpassing the performance of the clinical standard approach of linear regression using manual measurements and a comparative deep learning-based 2D-3D reconstruction-based approach. The proposed spleen volume estimation framework can be integrated into standard clinical workflows which currently use 2D ultrasound images to measure spleen length. To the best of our knowledge, this is the first work to achieve direct 3D spleen volume estimation from 2D spleen segmentations.Comment: 22 pages, 7 figure

Microbial Biosurfactants: Antimicrobial Activity and Potential Biomedical and Therapeutic Exploits

The rapid emergence of multidrug-resistant pathogens worldwide has raised concerns regarding the effectiveness of conventional antibiotics. This can be observed in ESKAPE pathogens, among others, whose multiple resistance mechanisms have led to a reduction in effective treatment options. Innovative strategies aimed at mitigating the incidence of antibiotic-resistant pathogens encompass the potential use of biosurfactants. These surface-active agents comprise a group of unique amphiphilic molecules of microbial origin that are capable of interacting with the lipidic components of microorganisms. Biosurfactant interactions with different surfaces can affect their hydrophobic properties and as a result, their ability to alter microorganisms’ adhesion abilities and consequent biofilm formation. Unlike synthetic surfactants, biosurfactants present low toxicity and high biodegradability and remain stable under temperature and pH extremes, making them potentially suitable for targeted use in medical and pharmaceutical applications. This review discusses the development of biosurfactants in biomedical and therapeutic uses as antimicrobial and antibiofilm agents, in addition to considering the potential synergistic effect of biosurfactants in combination with antibiotics. Furthermore, the anti-cancer and anti-viral potential of biosurfactants in relation to COVID-19 is also discussed

Uncertainty Aware Training to Improve Deep Learning Model Calibration for Classification of Cardiac MR Images

Quantifying uncertainty of predictions has been identified as one way to develop more trustworthy artificial intelligence (AI) models beyond conventional reporting of performance metrics. When considering their role in a clinical decision support setting, AI classification models should ideally avoid confident wrong predictions and maximise the confidence of correct predictions. Models that do this are said to be well-calibrated with regard to confidence. However, relatively little attention has been paid to how to improve calibration when training these models, i.e., to make the training strategy uncertainty-aware. In this work we evaluate three novel uncertainty-aware training strategies comparing against two state-of-the-art approaches. We analyse performance on two different clinical applications: cardiac resynchronisation therapy (CRT) response prediction and coronary artery disease (CAD) diagnosis from cardiac magnetic resonance (CMR) images. The best-performing model in terms of both classification accuracy and the most common calibration measure, expected calibration error (ECE) was the Confidence Weight method, a novel approach that weights the loss of samples to explicitly penalise confident incorrect predictions. The method reduced the ECE by 17% for CRT response prediction and by 22% for CAD diagnosis when compared to a baseline classifier in which no uncertainty-aware strategy was included. In both applications, as well as reducing the ECE there was a slight increase in accuracy from 69% to 70% and 70% to 72% for CRT response prediction and CAD diagnosis respectively. However, our analysis showed a lack of consistency in terms of optimal models when using different calibration measures. This indicates the need for careful consideration of performance metrics when training and selecting models for complex high-risk applications in healthcare