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Lung-related diseases continue to be a leading cause of global mortality. Timely and precise diagnosis is crucial to save lives, but the availability of testing equipment remains a challenge, often coupled with issues of reliability. Recent research has highlighted the potential of Chest X-Ray (CXR) images in identifying various lung diseases, including COVID-19, fibrosis, pneumonia, and more. In this comprehensive study, four publicly accessible datasets have been combined to create a robust dataset comprising 6650 CXR images, categorized into seven distinct disease groups. To effectively distinguish between normal and six different lung-related diseases (namely, bacterial pneumonia, COVID-19, fibrosis, lung opacity, tuberculosis, and viral pneumonia), a Deep Learning (DL) architecture called a Multi-Scale Convolutional Neural Network (MS-CNN) is introduced. The model is adapted to classify multiple numbers of lung disease classes, which is considered to be a persistent challenge in the field. While prior studies have demonstrated high accuracy in binary and limited-class scenarios, the proposed framework maintains this accuracy across a diverse range of lung conditions. The innovative model harnesses the power of combining predictions from multiple feature maps at different resolution scales, significantly enhancing disease classification accuracy. The approach aims to shorten testing duration compared to the state-of-the-art models, offering a potential solution toward expediting medical interventions for patients with lung-related diseases and integrating explainable AI (XAI) for enhancing prediction capability. The results demonstrated an impressive accuracy of 96.05%, with average values for precision, recall, F1-score, and AUC at 0.97, 0.95, 0.95, and 0.94, respectively, for the seven-class classification. The model exhibited exceptional performance across multi-class classifications, achieving accuracy rates of 100%, 99.65%, 99.21%, 98.67%, and 97.47% for tw

Alzheimer’s disease (AD) is the most common form of dementia and apart from the individual suffering AD also causes a large economic burden for society. AD is associated with progressive neurodegeneration and atrophy of the brain. Extracellular fibrillar assemblies of the amyloid-β peptide (Aβ) in the brain represent a clinical hallmark of AD and these are today considered to be the initial cause of the disease. The tissue-damaging properties of Aβ assemblies are, however, linked to their structures. Aβ represents a spectrum of peptides between 38-43 residues that can adopt several structures that differ both concerning their morphology and pathological properties. The mechanisms by which Aβ self-assembles, the binding strength of these structures to Aβ monomers, as well as the cross-interaction between different Aβ variants are today not fully understood. Aβ amyloid formation follows a nucleation-dependent mechanism which implies that a kinetically unfavorable nucleus must form before the formation of an amyloid fibril. The elongation of the fibril then proceeds via a template-dependent mechanism where monomeric peptides are incorporated in a highly ordered manner. Using SPR the template-dependent mode of elongation can be selectively monitored. Here, we have used the technique to probe the binding strength of Aβ fibrils and in paper 1 the role of pH and the intrinsic histidines in the Aβ sequence were investigated. The result shows that the histidines do not contribute to the previously observed increase in fibrillar strength at low pH. In paper 2 we analyzed the cross-templation between the in vivo most common variants of Aβ, represented by Aβ1-40 and Aβ1-42. Within this work, we revealed two intrinsic mechanisms preventing Aβ to adopt the structure of the significantly more pathogenic Aβ1-42 variant. In paper 3 we characterized the effect of apolipoprotein E (ApoE) on Aβ amyloid formation. ApoE is today the strongest genetic linker to the development of AD and

Plant priming is an induced physiological state where plants are protected from biotic and abiotic stresses. Whether seaweed extracts promote priming is largely unknown as is the mechanism by which priming may occur. In this study, we examined the effect of a seaweed extract (SWE) on two distinct stages of plant priming (priming phase and post-challenge primed state) by characterising (i) plant gene expression responses using qRT-PCR and (ii) signal transduction responses by evaluating reactive oxygen species (ROS) production. The SWE is made from the brown algae Ascophyllum nodosum and Durvillaea potatorum. The priming phase was examined using both Arabidopsis thaliana and Solanum lycopersicum. At this stage, the SWE up-regulated key priming-related genes, such as those related to systemic acquired resistance (SAR) and activated the production of ROS. These responses were found to be temporal (lasting 3 days). The post-challenge primed state was examined using A. thaliana challenged with a root pathogen. Similarly, defence response-related genes, such as PR1 and NPR1, were up-regulated and ROS production was activated (lasting 5 days). This study found that SWE induces plant priming-like responses by (i) up-regulating genes associated with plant defence responses and (ii) increasing production of ROS associated with signalling responses.

Pathogens and plants are in a constant battle with one another, the result of which is either the restriction of pathogen growth via constitutive or induced plant defense responses or the pathogen colonization of plant cells and tissues that cause disease. Elicitins are a group of highly conserved proteins produced by certain oomycete species, and their sterol binding ability is recognized as an important feature in sterol–auxotrophic oomycetes. Elicitins also orchestrate other aspects of the interactions of oomycetes with their plant hosts. The function of elicitins as avirulence or virulence factors is controversial and is dependent on the host species, and despite several decades of research, the function of these proteins remains elusive. We summarize here our current understanding of elicitins as either defense-promoting or defense-suppressing agents and propose that more recent approaches such as the use of ‘omics’ and gene editing can be used to unravel the role of elicitins in host–pathogen interactions. A better understanding of the role of elicitins is required and deciphering their role in host–pathogen interactions will expand the strategies that can be adopted to improve disease resistance and reduce crop losses.

Phytophthora cinnamomi causes plant disease globally and resistance is rare. Using biochemical, molecular and transcriptomic approaches and analyses, phytohormones were found to regulate disease. The pathogen was also found to produce specific proteins that facilitated host colonisation. The study thus provided a detailed understanding of how some plants survive infection.