Alzheimer’s disease (AD) is now classified as a silent pandemic due to concerning current statistics and future predictions. Despite this, no effective treatment or accurate diagnosis currently exists. The negative impacts of invasive techniques and the failure of clinical trials have prompted a shift in research towards non-invasive treatments. In light of this, there is a growing need for early detection of AD through non-invasive approaches. The abundance of data generated by non-invasive techniques such as blood component monitoring, imaging, wearable sensors, and bio-sensors not only offers a platform for more accurate and reliable bio-marker developments but also significantly reduces patient pain, psychological impact, risk of complications, and cost. Nevertheless, there are challenges concerning the computational analysis of the large quantities of data generated, which can provide crucial information for the early diagnosis of AD. Hence, the integration of artificial intelligence and deep learning is critical to addressing these challenges. This work attempts to examine some of the facts and the current situation of these approaches to AD diagnosis by leveraging the potential of these tools and utilizing the vast amount of non-invasive data in order to revolutionize the early detection of AD according to the principles of a new non-invasive medicine era.

AlphaFold3, the latest release of AlphaFold developed by Google DeepMind and Isomorphic Labs, was designed to predict protein structures with remarkable accuracy. AlphaFold3 enhances our ability to model not only single protein structures but also complex biomolecular interactions, including protein–protein interactions, protein–ligand docking, and protein-nucleic acid complexes. Herein, we provide a detailed examination of AlphaFold3’s capabilities, emphasizing its applications across diverse biological fields and its effectiveness in complex biological systems. The strengths of the new AI model are also highlighted, including its ability to predict protein structures in dynamic systems, multi-chain assemblies, and complicated biomolecular complexes that were previously challenging to depict. We explore its role in advancing drug discovery, epitope prediction, and the study of disease-related mutations. Despite its significant improvements, the present review also addresses ongoing obstacles, particularly in modeling disordered regions, alternative protein folds, and multi-state conformations. The limitations and future directions of AlphaFold3 are discussed as well, with an emphasis on its potential integration with experimental techniques to further refine predictions. Lastly, the work underscores the transformative contribution of the new model to computational biology, providing new insights into molecular interactions and revolutionizing the fields of accelerated drug design and genomic research.

The 70S ribosome is a major target for antibacterial drugs. Two of the classical antibiotics, chloramphenicol (CHL) and erythromycin (ERY), competitively bind to adjacent but separate sites on the bacterial ribosome: the catalytic peptidyl transferase center (PTC) and the nascent polypeptide exit tunnel (NPET), respectively. The previously reported competitive binding of CHL and ERY might be due either to a direct collision of the two drugs on the ribosome or due to a drug-induced allosteric effect. Because of the resolution limitations, the available structures of these antibiotics in complex with bacterial ribosomes do not allow us to discriminate between these two possible mechanisms. In this work, we have obtained two crystal structures of CHL and ERY in complex with the Thermus thermophilus 70S ribosome at a higher resolution (2.65 and 2.89 Å, respectively) allowing unambiguous placement of the drugs in the electron density maps. Our structures provide evidence of the direct collision of CHL and ERY on the ribosome, which rationalizes the observed competition between the two drugs.

Purine 5′,8-cyclo-2′-deoxynucleosides (cPu) are tandem-type lesions observed among the DNA purine modifications and identified in mammalian cellular DNA in vivo. These lesions can be present in two diasteroisomeric forms, 5′R and 5′S, for each 2′-deoxyadenosine and 2′-deoxyguanosine moiety. They are generated exclusively by hydroxyl radical attack to 2′-deoxyribose units generating C5′ radicals, followed by cyclization with the C8 position of the purine base. This review describes the main recent achievements in the preparation of the cPu molecular library for analytical and DNA synthesis applications for the studies of the enzymatic recognition and repair mechanisms, their impact on transcription and genetic instability, quantitative determination of the levels of lesions in various types of cells and animal model systems, and relationships between the levels of lesions and human health, disease, and aging, as well as the defining of the detection limits and quantification protocols.

Invasive diagnostic techniques, while offering critical insights into disease pathophysiology, are often limited by high costs, procedural risks, and patient discomfort. Non-invasive biomarkers represent a transformative alternative, providing diagnostic precision through accessible biological samples or physiological data, including blood, saliva, breath, and wearable health metrics. They encompass molecular and imaging approaches, revealing genetic, epigenetic, and metabolic alterations associated with disease states. Furthermore, advances in breathomics and gut microbiome profiling further expand their diagnostic scope. Even with their strengths in terms of safety, cost-effectiveness, and accessibility, non-invasive biomarkers face challenges in achieving monitoring sensitivity and specificity comparable to traditional clinical approaches. Computational advancements, particularly in artificial intelligence and machine learning, are addressing these limitations by uncovering complex patterns in multi-modal datasets, enhancing diagnostic accuracy and facilitating personalized medicine. The present review integrates recent innovations, examines their clinical applications, highlights their limitations and provides a concise overview of the evolving role of non-invasive biomarkers in precision diagnostics, positioning them as a compelling choice for large-scale healthcare applications.

Analysis of blood-based indicators of brain health could provide an understanding of early disease mechanisms and pinpoint possible intervention strategies. By examining lipid profiles in extracellular vesicles (EVs), secreted particles from all cells, including astrocytes and neurons, and circulating in clinical samples, important insights regarding the brain’s composition can be gained. Herein, a targeted lipidomic analysis was carried out in EVs derived from plasma samples after removal of lipoproteins from individuals with Alzheimer’s disease (AD) and healthy controls. Differences were observed for selected lipid species of glycerolipids (GLs), glycerophospholipids (GPLs), lysophospholipids (LPLs) and sphingolipids (SLs) across three distinct EV subpopulations (all-cell origin, derived by immunocapture of CD9, CD81 and CD63; neuronal origin, derived by immunocapture of L1CAM; and astrocytic origin, derived by immunocapture of GLAST). The findings provide new insights into the lipid composition of EVs isolated from plasma samples regarding specific lipid families (MG, DG, Cer, PA, PC, PE, PI, LPI, LPE, LPC), as well as differences between AD and control individuals. This study emphasizes the crucial role of plasma EV lipidomics analysis as a comprehensive approach for identifying biomarkers and biological targets in AD and related disorders, facilitating early diagnosis and potentially informing novel interventions.