https://www.um.edu.mt/library/oar/handle/123456789/16097 2026-10-27T22:20:33Z https://www.um.edu.mt/library/oar/handle/123456789/149885 Title: Glioblastoma multiforme : molecular biology and updated systematic review on natural extract and phytochemical efficacy in-vitro Authors: Cuschieri, Andrea; Baron, Byron Abstract: Glioblastoma multiforme (GBM) is the most prevalent primary malignant central nervous system tumours (CNST). Current treatment consists of surgical resection and adjuvant chemoradiotherapy, the gold-standard agent being Temozolomide (TMZ). Despite optimal treatment, GBM has an abysmal associated prognosis of approximately 15 months. The disproportionately high morbidity and mortality necessitate studies which strive to increase knowledge on putative therapeutic agents to confirm or deny their possible use against GBM Therefore, there is an urgent need to develop a deep understanding of GBM molecular biology upon which more effective therapeutic strategies may be developed.; Natural extracts and phytochemicals have shown promise as potential therapeutic agents against GBM. In this review, the molecular biology of GBM and molecular factors which contribute to therapeutic resistance are discussed in depth. The mechanisms by which phytochemicals and natural extracts exhibit can inhibit GBM growth and proliferation are discussed. Moreover, statistical analysis demonstrated that natural extracts and phytochemicals may be superior to TMZ in-vitro. This review demonstrates that phytochemicals have great potential as novel therapeutic agents against GBM. Further preclinical and clinical studies are needed to determine the optimal doses, routes of administration, and potential toxicities of these agents, but their unique molecular targets and low toxicity profiles make them attractive candidates for further investigation. 2023-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/148117 Title: Advancing microRNA target site identification via bias-corrected chimeric datasets for machine learning approaches Abstract: microRNAs (miRNAs) are small, non-coding RNA molecules that regulate gene expression post-transcriptionally. These ∼22-nucleotide long RNA molecules are loaded onto a protein of the Argonaute (AGO) family guiding it to specific RNA target transcripts, which are consequently inhibited from translation to protein. Despite years of research, the precise mechanisms that determine miRNA target recognition remain unclear. Given that a single miRNA can potentially target any RNA transcript, experimental validation of all possible interactions is impractical. To this end, with the recent availability of volumes of data from high-throughput CLASH experiments that capture interacting RNA molecules mediated by a protein, many miRNA target site prediction methods employing data-driven approaches have been developed. However, despite substantial efforts to produce more accurate models, currently, no standardised framework for benchmarking miRNA target site prediction methods exists, and various strategies have been adopted, hindering fair and reliable comparison. Consequently, this undermines the validity of performance claims. Recently, another high-throughput experimental technique, called chimeric eCLIP, was developed, leading to a 70-fold increase in the recovery of miRNA– target site interactions. Here, we identify a unique opportunity to leverage this immense resource of publicly available raw data, to curate benchmark datasets for miRNA target site prediction. Throughout this work, we additionally uncover a miRNA frequency class bias that arises as a result of the method used to generate negative examples in silico. Such methods are required when modeling miRNA target site prediction as a supervised binary classification problem, due to the absence of experimentally confirmed non-binding miRNA–target pairs. To this end, we develop a novel method for generating negatives that mitigates the identified bias. Leveraging data from these high-throughput experiments and applying this new method for generating negatives, we curate three collections of datasets: one novel dataset containing almost three million examples, and bias-corrected versions of two smaller, published datasets. We contribute these datasets to the publicly available and easy-to-use miRBench Python package, providing a framework for benchmarking miRNA target site prediction methods. We benchmark six state-of-the-art deep learning models on these datasets and train simple models to establish a baseline. Retraining a convolutional neural network, that was originally trained on a biased dataset (Average Precision Score (APS): 0.71), on a larger, bias-corrected dataset improves its performance (APS: 0.81), surpassing the previous state of the art (TargetScanCnn, APS: 0.76). This highlights the advantages of well-curated, unbiased benchmarks, facilitating the development of more accurate miRNA target site prediction models thus enabling more reliable downstream analyses. Description: M.Sc.(Melit.) 2025-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/146213 Title: A practical checklist for return of results from genomic research in the European context Authors: Vears, Danya F.; Hallowell, Nina; Bentzen, Heidi Beate; Ellul, Bridget; Nøst, Therese Haugdahl; Kerasidou, Angeliki; Kerr, Shona M.; Mayrhofer, Michaela Th.; Mežinska, Signe; Ormondroyd, Elizabeth; Solberg, Berge; Sand, Birgitte Wirum; Budin-Ljøsne, Isabelle Abstract: An increasing number of European research projects return, or plan to return, individual genomic research results (IRR) to participants. While data access is a data subject’s right under the General Data Protection Regulation (GDPR), and many legal and ethical guidelines allow or require participants to receive personal data generated in research, the practice of returning results is not straightforward and raises several practical and ethical issues. Existing guidelines focusing on return of IRR are mostly projectspecific, only discuss which results to return, or were developed outside Europe. To address this gap, we analysed existing normative documents identified online using inductive content analysis. We used this analysis to develop a checklist of steps to assist European researchers considering whether to return IRR to participants. We then sought feedback on the checklist from an interdisciplinary panel of European experts (clinicians, clinical researchers, population-based researchers, biobank managers, ethicists, lawyers and policy makers) to refine the checklist. The checklist outlines seven major components researchers should consider when determining whether, and how, to return results to adult research participants: 1) Decide which results to return; 2) Develop a plan for return of results; 3) Obtain participant informed consent; 4) Collect and analyse data; 5) Confirm results; 6) Disclose research results; 7) Follow-up and monitor. Our checklist provides a clear outline of the steps European researchers can follow to develop ethical and sustainable result return pathways within their own research projects. Further legal analysis is required to ensure this checklist complies with relevant domestic laws. 2023-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/144349 Title: Determining the genotype frequency of the Diego blood group system in Malta Abstract: This study investigated the prevalence of key Diego (Di) blood group antigens (Dia , Dib , Wra , and Wrb ) in Maltese blood donors using a molecular genotyping approach, with the aim of improving rare blood group detection and enhancing transfusion compatibility. A polymerase chain reaction followed by restriction fragment length polymorphism (PCR-RFLP) was developed to genotype four alleles: DI*A (Dia ), DI*B (Dib ), DI02.03* (Wra ), and DI02.04* (Wrb ). High-quality genomic DNA was extracted from donor blood samples, and allele-specific primers were used to amplify target regions of the solute carrier family 4 member 1 SLC4A1 gene. Selected samples were validated through direct DNA sequencing to confirm assay specificity. Additionally, a standardised questionnaire was administered to record donor parental ancestry. The PCR-RFLP method yielded consistent and reproducible results. All individuals tested negative for the rare DI*A and DI02.03* alleles, indicating an absence of the Dia and Wra antigens. The common DI*B and DI02.04* alleles were detected in all samples, suggesting a Di(a-b+) and Wr(a-b+) phenotype across the cohort. Ancestry data showed that the majority of participants reported full Maltese lineage, and allele frequencies were consistent with those expected in European populations. In conclusion, this study provides the first molecular characterisation of the Di Blood Group System in the Maltese population. Although no rare alleles were identified, the developed PCR-RFLP platform proved reliable for Di genotyping and is suitable for integration into routine blood donor screening. These findings establish a reference point for future studies and support the implementation of DNA-based methods to enhance transfusion safety in increasingly diverse populations. Description: M.Sc.(Melit.) 2025-01-01T00:00:00Z