University of Tunis El Manar

Treatment resistance and leukemic stem cell survival are significant challenges in managing chronic myeloid leukemia (CML). While Breakpoint Cluster Region-Abelson (BCR::ABL1) fusion drives disease initiation, emerging evidence suggests that non-coding RNAs, particularly long non-coding RNAs (lncRNAs), regulate critical cellular processes that promote leukemic transformation and treatment failure. We employed a comprehensive in silico approach integrating multiple omics platforms to investigate CML-associated lncRNAs. Candidate lncRNAs were identified by cross-referencing LncRNA Disease and LncTarD repositories, followed by validation expression analysis of data in leukemia transcriptomic cohorts. StarBase and miRWalk algorithms predicted the interaction networks between lncRNAs and microRNAs, while transcription factor (TF) binding was determined through ChIP-seq analysis and TF databases to map the regulatory networks governing lncRNA and miRNA expression. Four critical long non-coding RNAs (lncRNAs), H19, HOTAIR, MEG3, and UCA1, were differentially expressed in CML and were involved in regulating proliferation, cell death, and therapeutic response pathways. Network analysis revealed extensive regulatory interactions with microRNAs, especially hsa-miR-18a-5p and hsa-miR-106b-5p, which influence major oncogenic pathways. TFs mapping identified pivotal hubs, including MYC and STAT5A, as key regulators of lncRNA and miRNA networks, which contribute to disrupted normal growth control, and apoptotic and survival pathways in leukemic cells. This study identified a non-coding RNA interactome in CML involving lncRNAs and microRNAs that may cooperatively contribute to CML progression. These interconnected circuits offer novel insights into treatment resistance and stem cell persistence, identifying promising therapeutic targets. Future validation of these interactions may guide the development of non-coding RNA-targeted therapies for resistant CML.

Detecting psychoactive substances in biological samples presents significant challenges in clinical diagnostics, forensic analysis, and public health monitoring. This study introduces a highly sensitive electrochemical biosensing platform for the detection of cocaine, addressing the critical need for rapid, field-deployable testing methods. By integrating functionalized magnetic beads (MBs) with screen-printed carbon electrodes (SPCEs), we developed a competitive immunoassay system that leverages the superior molecular recognition capabilities of antibodies while maintaining operational simplicity. The biosensor operates via a competitive binding mechanism, in which cocaine present in the sample competes with cocaine-bovine serum albumin (BSA) conjugates immobilized on MBs for binding sites on horseradish peroxidase-labeled anti-cocaine antibodies (HRP-DAb). Electrochemical detection is achieved through amperometric measurement of enzyme activity using a redox system consisting of hydrogen peroxide/hydroquinone (H₂O₂/HQ). The optimized biosensor demonstrates excellent analytical performance with a linear response range from 0.3 to 300 ng mL^-1 and a detection limit of 0.1 ng mL^-1. Notably, the biosensor maintains its performance when analyzing cocaine in complex biological matrices, including human saliva and urine, successfully quantifying concentrations with minimal matrix interference. The platform offers significant advantages, including single-use disposable electrodes, rapid analysis time (< 30 min), minimal sample preparation requirements, and the potential for miniaturization into portable devices. These characteristics combined with high selectivity, a simple fabrication process, and cost-effectiveness, position this biosensor as a promising tool for point-of-care testing and field applications in clinical, forensic, and roadside testing scenarios.

Coastal areas are increasingly threatened by marine sediment contamination resulting from industrial discharge, agricultural runoff, and urban expansion, posing serious risks to marine ecosystems and human health. This study aims to predict sediment contamination risks in the Bizerte Lagoon, Tunisia, by applying an Optimized Long Short-Term Memory (OP-LSTM) deep learning model, supported by comprehensive geochemical and mineralogical analyses. The methodology involved characterizing sediment samples using X-ray diffraction (XRD) to identify mineral species and quantify the clay fraction, while atomic absorption spectroscopy (AAS) was used to determine major and trace element concentrations, with major elements expressed as oxides. The OP-LSTM model was trained and validated using these datasets alongside contamination scoring systems to enhance predictive accuracy. Mineralogical findings revealed eleven dominant minerals: quartz, feldspar, calcite, dolomite, gypsum, hematite, goethite, aragonite, muscovite, kaolinite (75.47 %), illite (27.87 %), and smectites (4.05 %), indicating combined terrigenous and anthropogenic inputs. Major element oxides varied spatially, with concentrations ranging from 0.04 to 1.20 % for K₂O, 0.13 to 1.31 % for Na₂O, 3.86 to 19.47 % for CaO, and 0.18 to 0.48 % for MgO. Trace metals were also detected at concerning levels, with Pb up to 3.974 ppm, Cr up to 1.800 ppm, and Cd up to 817 ppm-exceeding Tunisian environmental standards. The OP-LSTM model outperformed the standard LSTM, exhibiting lower RMSE (0.142-0.164), MSE (0.020-0.027), and training loss (from 0.413 to 0.200), alongside higher R² scores (up to 0.413), confirming its robustness and suitability for effective sediment contamination risk prediction and management in coastal lagoon environments.