Extracellular vesicles (EVs) are small membrane-surrounded structures containing transmembrane proteins and enclosing cytosolic proteins and nucleic acids. They are released in the extracellular space by both normal and neoplastic cells and play an important role in cell-cell communication in numerous physiological processes and pathological conditions, through the transfer of their functional cargo to recipient cells. EVs are highly abundant in biological fluids, and even more represented in cancer patients’ biofluids, therefore many studies suggested that they can be instrumental in liquid biopsies as prognostic markers or for early detection of tumors. Moreover, being secreted by potentially all the cells, they can serve in oncology to represent the tumor heterogeneity, which is underestimated by the current diagnostic tools. Given their small size, EVs are difficult to isolate in a high-throughput way and, therefore, one of the main obstacles to their clinical application, is that the existing isolation methods are impractical. During these years, I worked at the development and optimization of a novel technique that allows purification of heterogeneous EVs from biological fluids in an efficient, fast and reproducible way. This technique, named Nickel-Based Isolation (NBI), is a biochemical assay that allows obtaining polydisperse EVs in a physiological pH solution, therefore, preserving their morphology, heterogeneity, and stability. We tested and optimized this assay in protein-enriched systems and comparing it to the techniques currently used to characterize and measure EVs, such as flow cytometry and Tunable Resistive Pulse Sensing. We challenged the reproducibility of this method by isolating EVs from different biological fluids. Interestingly, the EVs purified with NBI result more intact and stable compared to the ones obtained with other methods, and can be studied in a clinical setting and used as an innovative tool for detection of molecules associated with diseases. We demonstrated the specificity of the procedure by using individual isolated vesicles in biochemical and molecular assay, optimized to characterize the biological content of EVs. We were able to detect picomolar concentration of PSMA on 105 EVs isolated from plasma of prostate cancer patients and BRAF-V600E transcript in just 103 EVs from the plasma of colon cancer patients, reaching unprecedented matching with tissue biopsy results. We also investigated the transcriptome of EVs isolated from glioblastoma cancer stem cells, in order to exploit the potential of EVs as diagnostic markers.

Frontotemporal Dementia with parkinsonism linked to chromosome 17 (FTDP-17) (OMIM # 600274) is a tauopathy caused by mutations in the MAPT gene. This gene encodes for Tau protein and its alternative splicing normally produces 6 different isoforms with three (3R) or four (4R) repeats of microtubule-binding domains, originated from the alternative splicing of exon 10 in the MAPT transcript. In normal adult brain, neurons and glia cells contain both 3R and 4R splicing isoforms in a 1:1 ratio. Several mutations in the MAPT gene impair exon 10 splicing, causing unbalance between 4R and 3R Tau isoforms (4R > 3R), leading to Tau 4R protein accumulation as insoluble neuronal deposits. Therapeutical correction of MAPT splicing isoforms balance is, in principle, possible using either exon-specific siRNAs, which degrade exon-10-containing mRNA in the cytoplasm, or splice-switching antisense RNAs, that induce skipping of exon 10 during the splicing of MAPT pre-mRNA in the nucleus. Both approaches have been explored in the Laboratory of RNA Biology and Biotechnology at CIBIO (University of Trento) using splicing reporters. Subsequently, several siRNAs and antisense RNAs have been demonstrated to efficiently engage their target (pre-)mRNA and restore 4R:3R balance in neuroblastoma cell lines in culture. Aim of the present work is to obtain pre-clinical evidence of the efficiency of the two approaches, in order to move towards clinical studies. To this purpose, I set up a disease model consisting in hiPSCs-derived neurons carrying a mutation in intron 10, where a C is substituted with a T in position 16 (MAPT IVS10+16; EBiSC, depositor Sigma-Aldrich SIGi001-A-12) and compared them with the appropriate isogenic healthy control (EBiSC; depositor Sigma-Aldrich SIGi001-A-1). 3R and 4R Tau mRNA and protein levels were evaluated at various days of differentiation and neuronal maturation. In my hands, IVS10+16 neurons showed increase 4R Tau mRNA expression at 120 days of differentiation, resembling the patient’s phenotype. The unbalance 4R:3R is reflected in the Tau protein, as assessed by Western blotting . I am presently evaluating other outcome measures of disease in this cellular model, such as synaptic impairment and electrophysiology . The Laboratory of RNA Biology and Biotechnology has reported that microRNAs (miRNAs) can be used as biomarkers of Frontotemporal Dementia (FTD). In particular, we recently reported that miR-92a-3p, miR-320a and miR-320b are misregulated in the plasma of FTD patients in comparison to healthy individuals (manuscript under review). I set out to measure these miRNAs in d120 IVS10+16 and isogenic neurons and in their conditioned medium. I found that all three miRNAs of interest were significantly increased in IVS10+16 samples compared to WT neurons, therefore representing a useful measure of therapeutical efficacy in our protocols. With the use of fluorescently labelled siRNAs, I then tackled the problem of delivering siRNA molecules to mature neurons and set up a protocol for their efficient delivery. Consequently, day120 IVS10+16 and WT neurons were transfected with different concentration of scramble and therapeutic siRNAs and the restoration of the 4R:3R Tau balance was assessed. My results suggest a promising potential for the use of isoform-specific siRNAs in FTDP-17 and possibly in other tauopathies. At the same time, I intended to validate in the same hiPSC-derived neuronal disease model, U1 and U7 chimeric splice-switching antisense RNAs that had been previously tested by plasmid transfection in neuroblastoma cell lines. To overcome the limitation represented by poor plasmid transfection efficiency in mature neurons, I encapsulated them into recombinant adeno-associated viruses (rAAVs). After having optimized the production of rAAVs and set the transduction conditions, IVS10+16 mature neurons were transduced with AAV expressing chimeric splice-switching antisense RNAs. Although neurons successfully got transduced and the cassette transcribed, there was no therapeutic effect when viruses were tested in d130 IVS10+16 neurons. I am presently exploring different experimental paradigms, to test the hypothesis that the 4R:3R unbalance can be prevented in mature neurons.

Gene expression regulation encompasses a wide range of mechanisms that govern cellular processes. Among these, post-transcriptional regulation, including translation control, plays a pivotal role in ensuring precise protein synthesis, timing, and quantity. Perturbations of mechanisms such as RNA modifications, and interactions between RNA-binding proteins (RBPs) and specific RNA motifs, can lead to dysregulation of essential cellular processes. These alterations contribute to the development of various disorders, including cancer, neurodegenerative diseases, and metabolic disorders. Many publicly available datasets and studies offer opportunities to investigate the link between alterations in these mechanisms and disease manifestations. However, the limited availability of datasets for certain conditions or notable inconsistencies among reported associations prevent complete understanding of the underlying processes. Therefore, extending the investigations to encompass a diverse range of genes and/or diseases will enhance our comprehension of these intricate regulatory and disease mechanisms, aiding in the identification of potential therapeutic targets and innovative interventions to mitigate pathological conditions. In particular, we focused on three separate aspects involved in gene expression regulation: RNA modifications, RBPs interactions with RNA secondary structures, and the Kozak consensus sequence as a translational modulator. Each part uncovers essential mechanisms that govern post-transcriptional control of gene expression, shedding light on their roles in cellular processes and disease contexts. At first, we performed a comprehensive exploration of 15 RBPs involved in the regulation of the N6-methyladenosine (m6A) methylation. Leveraging data from The Cancer Genome Atlas (TCGA), we conducted a pan-cancer analysis across 31 tumor types to uncover the distribution of alterations of these factors, and we developed a user-friendly web application to enable users to conduct similar analyses. Additionally, we performed a parallel analysis focused on neuroblastoma, using data from publicly available and in-house datasets. These investigations unveil the potential impact of a subset of m6A factors on cancer development and progression. While in the first case, VIRMA and YTHDF reader proteins, emerged as the most frequently altered genes with significant pan-cancer prognostic implications, in the context of neuroblastoma, the writer METTL14 and the reader ALKBH5, showed the most prominent roles. Subsequently, our focus shifted to a distinct subset of RBPs capable of interacting with RNA secondary structures, particularly with RNA G-quadruplexes (RG4s). We established a comprehensive database cataloging RBPs with potential RG4-binding capabilities. This resource represents a valuable tool for researchers aiming to explore the intricate interplays between RBPs and RG4s, and their putative implications in diverse biological processes and diseases. Finally, attention was directed to the Kozak sequence, a pivotal determinant of the regulation of translation initiation. Exploiting the power of base editors, we developed a method to optimize translation initiation by modifying the Kozak sequence. This strategy offers promise in addressing haploinsufficiency-related disorders, where enhancing the functional protein is essential. Overall, these findings present opportunities for the identification of potential therapeutic targets and precision medicine strategies to alleviate a spectrum of pathological conditions, thus fostering advancements in the field of molecular biology and disease management.

During the past three years, we assisted to the rise of a new pathogen that afflicted the world with a global pandemic. Working in an era of rapid change has posed important challenges and the focus of research has shifted more and more toward topics of greater social utility. However, this period has also brought a new role for wastewater highlighting how it can provide insight into the health of a community. This is the approach of Wastewater-based Epidemiology (WBE). The work presented here aimed to deepen this approach not only at the theoretical level but also contributing with an ongoing monitoring of about 30 months. The main objectives were (i) to collect information on the recently discovered SARS-CoV-2 virus, its biology, transmission mechanism, and role in wastewater treatment plants (WWTP); (ii) set up a surveillance system that would allow to monitor SARS-CoV-2 infections over time, obtaining early information on its spread among the population to support the Health Authority. Starting from a detailed study of the shedding mechanisms of SARS-CoV-2 in the feces of infected patients, we moved on to the evaluation of the viral concentrations in the sewage system and the wastewater entering the WWTP. The possibility of a faecal-oral transmission route of the virus was investigated by evaluating the data about viability and infectivity in wastewater. The natural processes of decay of the virus in wastewater and the reduction of its concentration in the different treatment stages of WWTPs were explored in literature and with experimental data. At the same time, we developed a SARS-CoV-2 surveillance system in wastewater by applying different detection methods. Some practical and scientific aspects of the analysis protocol have been studied in depth such as the choice of the type of sample, the storage temperatures, and the pre-heat treatments aimed at making the analysis safer for the operator. The choice of the concentration method was evaluated to comply with the low concentration of the viral titer and therefore the crucial importance of this phase of the protocol. During the monitoring campaign, we further investigated aspects related to data processing and developed normalization approaches. Samples from WWTPs in the province of Trento were analysed weekly and sampling frequencies and curve smoothing methods deriving from those data were evaluated. The trend curves thus obtained were compared with those deriving from clinical data provided by the local Health Authority and signals of early warnings of virus diffusion trends in the population were highlighted. With the alternation of the different variants of the virus and the evidence of their importance in the development of new waves of infection, a PCR based genotyping method has been devised to rapidly identify the already known variants. In conclusion, this research project addressed a broad spectrum of aspects related to the WBE approach in contrasting the COVID-19 emergency and confirmed that wastewater could be a valuable source of information and management support for this and other emerging pathogens or micropollutants.

Nowadays, both gene mutations detection and function investigation are expected to assume a key role in diseases understanding and in many other biotechnological fields. In fact, gene mutations are often cause of genetic diseases and gene function analysis itself can help to have a broader vision on cells health status. Traditionally, gene mutations detection is carried out at pre-translational/sequence level (transcriptomic approach). On the other hand, the function of innumerable sequenced genes can be investigated by delivering them into cells through transfection methods and observing their expression result at post-translational level (proteomic approach). In this context, Micro-ElectroMechanical Systems (MEMSs) offer the intrinsic advantages of miniaturization: low sample and reagent consumption, reduction of costs, shorter analysis time and higher sensitivity. Their applications range from the whole cell assays to molecular biology investigations. On this subject, the thesis deals with two different tools for gene analysis: a Lab-on-Cell and cantilever-based sensors for in-vitro cell transfection and label-free Single Nucleotide Poly-morphisms (SNPs) detection, respectively. Regarding the first topic, an enhanced platform for single-site electroporation and controlled transfectants delivery has been presented. The device consists of a gold MicroElectrode Array (MEA) with multiple cell compartments, integrated microfluidics based on independent channels and nanostructured titanium dioxide (ns-TiO2) functionalized electrodes. Different activities have been reported, from the study of the microfabrication substrates bioaffinity and device development to the electroporation results. The functional characterization of the system has been carried out by electroporating HeLa cells with a small fluorescent dye and then, in order to validate the approach for gene delivery, with plasmid for the enhanced expression of the Green Fluorescent Protein (pEGFP-N1). The second research activity has been focused on a detection module aimed at the integration in a Lab-on-Chip (LOC) for the early screening of autoimmune diseases. The proposed approach consists of piezoresistive SOI-MEMS cantilever arrays operating in static mode. Their gold surface (aimed at the binding of specific thiolated DNA probes) has been deeply analyzed by means of Atomic Force Microscopy (AFM) and X-ray Photoelectron Spectroscopy (XPS) revealing an evident gold non-uniformity and low content together with oxygen and carbon contaminations. Different technological and cleaning solutions have been chosen in order to optimize the system. However, other improvements will be required. Moreover, the feasibility of the spotting technique has been demonstrated by verifying microcantilever mechanical resistance and good surface coverage without cross-contaminations. Finally, as future perspective, possible biological protocols and procedures have been also proposed and discussed starting from literature.

In the current era of the Anthropocene, human activities are powerful forces that affect the geosphere, atmosphere, and biosphere – globally, fundamentally, and in most cases irreversibly. In freshwaters, land use change, chemical pollution, decline in biodiversity, spread of invasive species, climate change, and shifts in the hydrological regime are among the key drivers of changes. In the 21st century, major water engineering projects such as large dams and water diversion schemes will fundamentally alter the natural hydrological regime of entire landscapes and even continents. At the same time, the hydrological regime is the governing variable for biodiversity, ecosystem functions and services in river networks. Indeed, there will be an increasing conflict between managing water as a resource for human use and waters as highly valuable ecosystems. Therefore, research needs to unravel the challenges that the freshwaters are facing, understand their potential drivers and impacts, and develop sustainable management practices – for the benefit of humans and ecosystems alike. The present thesis focuses on three currently understudied alterations in flow and material dynamics within river networks, namely (i) on the dynamics of floating organic matter (FOM) and its modification in dammed rivers, (ii) on river intermittency and its effects on nutrient and organic matter (OM) dynamics, and (iii) on major future water transfer schemes. Massive construction and operation of dams cause modification of water flow and material fluxes in rivers, such as of FOM. FOM serves as an essential component of river integrity, but a comprehensive understanding of its dynamics is still lacking. River damming, climate change and water extraction for human needs lead to a rapid expansion in number and extent of intermittent rivers worldwide, with major biogeochemical consequences on both regional and global scales. Increased intermittency of river networks also forces people to implement engineering solutions, such as water transfer schemes, which help to supply water to places of demand. Water transfer projects introduce artificial links among freshwater bodies modifying the hydrological balance. Impacts of abovementioned activities on freshwaters have been assessed in single case studies. However, the current knowledge does not allow a generalization of their globally applicable meaning for ecosystems. Furthermore, mostly neglected aspects of these alterations, such as the potential consequences of FOM extraction from rivers, the biogeochemical role of intermittent rivers upon rewetting, and the current scale of water transfers require better understanding before bold conclusions could be made. By combining research methods such as extensive literature reviews, laboratory experiments and quantitative analyses including spatial analyses with Geographic Information Systems, I investigated (1) the natural cycle, functions, and amounts of FOM in rivers fragmented by dams, (2) effects of rewetting events on the pulsed release of nutrients and OM in intermittent rivers and ephemeral streams (IRES), and (3) the potential extent of water transfer megaprojects (WTMP) that are currently under construction or in the planning phase and their role in modifying the global freshwater landscape. In all three cases, I provide a global perspective. The role of FOM in rivers as a geomorphological agent, a resource, a dispersal vector and a biogeochemical component was investigated based on an extensive literature review. Collected information allowed for conceptualizing its natural cycle and dynamics, applicable to a wide range of rivers. Data on FOM accumulations at 31 dams located within catchments of 13 rivers showed that damming leads to FOM entrapment (partly or completely) and modifies its natural cycling. The results of a spatial analysis considering environmental properties revealed that catchment characteristics can explain around 57% in the variation of amounts of trapped FOM. Effects of rewetting events on the release of nutrients and OM from bed sediments and course particulate organic materials (CPOM) accumulated in IRES was studied in laboratory experiments. Using a large set of samples collected from 205 rivers, located in 27 countries and distributed across five major climate zones, I determined the concentrations and qualitative characteristics of nutrients and OM released from sediments and CPOM. I also assessed how these characteristics can be predicted based on environmental variables within sampled IRES. In addition, I calculated area-specific fluxes of nutrients and OM from dry river beds. I found that the characteristics of released substances are climate specific. In the Continental zone I found the highest concentrations of released nutrients, but the lowest quality of OM in terms of its potential bioavailability. In contrast, in the Arid zone the concentrations of released nutrients were the lowest, but the quality of OM the highest. The effect of environmental variables on the concentrations of nutrients and the quality of OM was better predicted for sediments than for other substrates with the highest share of explained variance in the Continental and Tropical zones. On the global scale, dissolved organic carbon, phenolics, and nitrate dominate fluxes released during rewetting events. Overall, this study emphasized that on the global scale rewetting events in IRES represent biogeochemical “hot moments†, but characteristics of released nutrients and OM differ greatly among climate zones. The present thesis fills also a major knowledge gap on the global distribution of large water transfer schemes (referred to as “megaprojects†) that are actually planned or under construction. To provide an inventory of WTMP, I collected data from various literature sources, ranging from published academic studies, the official web-sites of water transfer projects, environmental impact assessments, reports of non-governmental organizations, and information available in on-line newspapers. In total, 60 WTMP were identified. Information on spatial location, distances and volumes of water transfer, costs, and purposes of WTMP was collected and compared with those of existing schemes. The results showed that North America, Asia and Africa will be the most affected by future WTMP having the highest densities of projects and the largest water transfer distances and volumes. If all projects were completed by 2050, the total water transfer distances would reach 77,063 km transferring more than 1,249 km3 per year, which corresponds to about 20 times the annual flow of the river Rhine. The outcomes of the thesis provide major implications for environmental management. Natural FOM is an important component for sustaining the ecological and geomorphic integrity of rivers and, therefore, should be managed appropriately. Intermittent rivers must be considered in models quantifying nutrient and OM fluxes in river networks. First flush events in particular release huge amounts of nutrients and OM, which may cause dramatic metabolic effects on downstream receiving waters. Finally, the future WTMP alter the hydrological balance of entire river basins and continents. They require multiple assessments before construction and careful management practices for sustainable operation in order to consider both freshwater as a resource as well as freshwaters as pivotal ecosystems.

This thesis develops an Artificial Intelligence (AI) approach intended for accurate patient stratification and precise diagnostics/prognostics in clinical and preclinical applications. The rapid advance in high throughput technologies and bioinformatics tools is still far from linking precisely the genome-phenotype interactions with the biological mechanisms that underlie pathophysiological conditions. In practice, the incomplete knowledge on individual heterogeneity in complex diseases keeps forcing clinicians to settle for surrogate endpoints and therapies based on a generic one-size-fits-all approach. The working hypothesis is that AI can add new tools to elaborate and integrate together in new features or structures the rich information now available from high-throughput omics and bioimaging data, and that such re- structured information can be applied through predictive models for the precision medicine paradigm, thus favoring the creation of safer tailored treatments for specific patient subgroups. The computational techniques in this thesis are based on the combination of dimensionality reduction methods with Deep Learning (DL) architectures to learn meaningful transformations between the input and the predictive endpoint space. The rationale is that such transformations can introduce intermediate spaces offering more succinct representations, where data from different sources are summarized. The research goal was attacked at increasing levels of complexity, starting from single input modalities (omics and bioimaging of different types and scales), to their multimodal integration. The approach also deals with the key challenges for machine learning (ML) on biomedical data, i.e. reproducibility, stability, and interpretability of the models. Along this path, the thesis contribution is thus the development of a set of specialized AI models and a core framework of three tools of general applicability: i. A Data Analysis Plan (DAP) for model selection and evaluation of classifiers on omics and imaging data to avoid selection bias. ii. The histolab Python package that standardizes the reproducible pre-processing of Whole Slide Images (WSIs), supported by automated testing and easily integrable in DL pipelines for Digital Pathology. iii. Unsupervised and dimensionality reduction techniques based on the UMAP and TDA frameworks for patient subtyping. The framework has been successfully applied on public as well as original data in precision oncology and predictive toxicology. In the clinical setting, this thesis has developed1: 1. (DAPPER) A deep learning framework for evaluation of predictive models in Digital Pathology that controls for selection bias through properly designed data partitioning schemes. 2. (RADLER) A unified deep learning framework that combines radiomics fea- tures and imaging on PET-CT images for prognostic biomarker development in head and neck squamous cell carcinoma. The mixed deep learning/radiomics approach is more accurate than using only one feature type. 3. An ML framework for automated quantification tumor infiltrating lymphocytes (TILs) in onco-immunology, validated on original pathology Neuroblastoma data of the Bambino Gesu’ Children’s Hospital, with high agreement with trained pathologists. The network-based INF pipeline, which applies machine learning models over the combination of multiple omics layers, also providing compact biomarker signatures. INF was validated on three TCGA oncogenomic datasets. In the preclinical setting the framework has been applied for: 1. Deep and machine learning algorithms to predict DILI status from gene expression (GE) data derived from cancer cell lines on the CMap Drug Safety dataset. 2. (ML4TOX) Deep Learning and Support Vector Machine models to predict potential endocrine disruption of environmental chemicals on the CERAPP dataset. 3. (PathologAI) A deep learning pipeline combining generative and convolutional models for preclinical digital pathology. Developed as an internal project within the FDA/NCTR AIRForce initiative and applied to predict necrosis on images from the TG-GATEs project, PathologAI aims to improve accuracy and reduce labor in the identification of lesions in predictive toxicology. Furthermore, GE microarray data were integrated with histology features in a unified multi-modal scheme combining imaging and omics data. The solutions were developed in collaboration with domain experts and considered promising for application.

Intracellular chloride and pH are fundamental regulators of neuronal excitability and they are often co-modulated during excitation-inhibition activity. The study of their homeostasis requires simultaneous measurements in vivo in multiple neurons. Combining random mutagenesis screening, protein engineering and two-photon-imaging this thesis work led to the discovery of new chloride-sensitive GFP mutants and to the establishment of ratiometric imaging procedures for the quantitative combined imaging of intraneuronal pH and chloride. These achievements have been demonstrated in vivo in the mouse cortex, in real-time monitoring the dynamic changes of ions concentrations during epileptic-like discharges, and in glioblastoma primary cells, measuring osmotic swelling responses to various drugs treatment.

MicroRNAs are a class of small non-coding RNAs involved in post-transcriptional regulation. Their role in almost all processes of the cell, make microRNAs ubiquitary players of cell development, growth, differentiation, cell to cell communication and cell death. Thus, cells’ physiological or pathological conditions are reflected by variations in the levels of expression of microRNAs, enabling them to be used as biomarkers of such states. In the past decade, there has been an exponential increase of studies using microRNAs as potential biomarkers for cancer, neurodegenerative diseases, inflammation and cardiac diseases, from tissues and liquid biopsies. However, none of them has reached the clinics yet, due to inconsistency of results through the literature and lack of assay standardization and reproducibility. Technological limitations of microRNAs detection have been, to date, the biggest challenge for using these molecules in clinical settings. In fact, although microarrays, RT-qPCR and RNA-seq are well-established technologies, they all require complex procedures and trained personnel, for performing RNA extraction, labelling of the target and PCR amplification. All these steps introduce variability and, in addition, since no universally standardized protocol – from sample extraction to analyte detection - has been produced yet, methodological procedures are difficult to reproduce. For this reason, we developed a new platform for the rapid detection of microRNAs in biofluids composed of an innovative silicon-photomultiplier (SiPM) based detector and a new chemistry for nucleic acid testing (Chem-NAT). Chem-NAT exploits a dynamic labelling chemistry which allows the sensitive detection of nucleic acids till single base level. On the other hand, SiPM-based device, compared to normal vacuum photomultipliers, grants miniaturization and higher capacity of fitting in a bench-top solution for clinical settings, among other advantages. The new platform – ODG – has been validated for the direct detection – neither RNA extraction nor PCR amplification needed - of microRNA-21 in plasma of lung cancer patients. In this work, we also explored the use of microRNAs as biomarkers in metastatic castration resistant prostate cancer (mCRPC). We collected plasma samples from mCRPC patients before and after abiraterone acetate treatment – androgen deprivation type of drug – and performed a miRnome analysis for discovering microRNAs predicting the efficacy of the drug. We chose miR-103a-3p and miR-378a-5p and we validated them via TaqMan RT-qPCR. We discovered that the ratio between the two microRNAs is able to predict the efficacy of abiraterone acetate and follow the responsiveness in time. In liquid biopsies, extracellular vesicles are getting increasing importance for diagnostic and prognostic purposes. Therefore, in this work we also explored the expression of some microRNAs in extracellular vesicles from plasma, isolated via nickel-based method. We discovered that microRNA-21 and microRNA-223 are not enriched in vesicles from healthy individuals.