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Drug Repositioning via Entity Transformation in Biomedical Knowledge Systems
No full textThe drug discovery process for known diseases is crucial in bioinformatics, given the extensive clinical trials, regulatory approvals, and high costs. Computational in silico methods are essential to mitigate these challenges, as they help identify promising drug candidates, thereby reducing the time and cost associated with drug discovery. An effective strategy in this domain is drug repositioning, where existing drugs, already approved for one disease, are repurposed for treating another. This approach is advantageous as it leverages the established safety profiles of existing drugs, avoiding toxic effects on human metabolism. In this effort, we employed a translational entity embedding-based neural network model to advance drug repositioning efforts. We utilize the Semantic Medline Database (SemMedDB) as the primary source of biomedical entity relationships for model training. The model is validated using repoDB, a gold standard dataset for drug repositioning. Technically, the model will learn to minimize the vector distance between related entities. This distance will serve as the basis for predicting potential drug-disease pairs in drug repositioning, offering a novel computational method to expedite the drug discovery process. © 2025 Elsevier B.V., All rights reserved
Design and Analysis of a Solar-Assisted Combined Cooling, Heating, and Power System for Smart Cities: Case Study From Doha
No full textThe rising demand for sustainable and energy-efficient solutions in urban areas has driven interest in renewable systems for smart cities. This chapter presents a solar-assisted combined cooling, heating, and power (SA-CCHP) system designed for Doha, Qatar, where high solar radiation and cooling needs prevail. Powered solely by a parabolic trough collector (PTC) field, the system delivers net power from 1200 kW in winter to 195 kW in summer, with cooling loads of ~2100-3400 kW and heating loads of ~90)00-14500 kW. Increasing the superheating degree at the ORC turbine inlet enhances power and heating but reduces cooling, while raising the pressure ratio (A) from 0.5 to 0.8 boosts net output and efficiency, cutting CO2 emissions from 0.22 to 0.13 kg/kWh. Overall energy efficiency rises from 85% to 90% and exergy efficiency from 76% to 78.5%, while costs decline from 36/hr, confirming both environmental and economic viability. The study demonstrates the feasibility of solar-powered CCHP systems as scalable models for achieving clean energy goals in smart cities. © 2026, IGI Global Scientific Publishing. All rights reserved
Achieving Extreme Solubility and Green Solvent-Processed Organic Field-Effect Transistors: A Viable Asymmetric Functionalization of [1]Benzothieno[3,2-B][1]Benzothiophenes
No full textNovel structural engineering strategies for solubilizing high-mobility semiconductors are critical, which enables green solvent processing for eco-friendly, sustainable device fabrication, and unique molecular properties. Here, we introduce a viable asymmetric functionalization approach, synthesizing monocarbonyl [1]benzothieno[3,2-b][1]benzothiophene molecules on a gram scale in two transition-metal-free steps. An unprecedented solubility of up to 176.0 mg·mL–1(at room temperature) is achieved, which is the highest reported to date for a high-performance organic semiconductor. The single-crystal structural analysis reveals a herringbone motif with multiple edge-to-face interactions and nonclassical hydrogen bonds involving the carbonyl unit. The asymmetric backbones adopt an antiparallel arrangement, enabling face-to-face π-π interactions. The mono(alkyl-aryl)carbonyl-BTBT compound, m-C6PhCO-BTBT enables formulations in varied green solvents, including acetone and ethanol, all achieving p-channel top-contact/bottom-gate OFETs in ambient conditions. Charge carrier mobilities of up to 1.87 cm2/V·s (μeff≈ 0.4 cm2/V·s; Ion/Ioff≈ 107–108) were achieved. To the best of our knowledge, this is one of the highest OFET performances achieved using a green solvent. Hansen solubility parameters (HSP) analysis, combined with Scatchard–Hildebrand regular solution theory and single-crystal packing analysis, elucidates this exceptional solubility and reveals unique relationships between molecular structure, interaction energy densities, cohesive energetics, and solute–solvent distances (Ra). An optimal solute–green solvent interaction distance in HSP space proves critical for green solvent-processed thin-film properties. This asymmetric functionalization approach, with demonstrated unique solubility insights, provides a foundation for designing green solvent-processable π-conjugated systems, potentially advancing innovation in sustainable (opto)electronics and bioelectronics. © 2025 Elsevier B.V., All rights reserved
Functional Combination of Resveratrol and Midostaurin Induces Cytotoxicity to Overcome Acquired Midostaurin Resistance in FLT3-ITD Expressing Acute Myeloid Leukemia Cells
No full textThe most important challenge in treating FLT3-ITD AML is the development of resistance to FLT3 inhibitors, such as midostaurin, via both FLT3-dependent and FLT3-independent mechanisms. The study explored the potential cytotoxic effects of combining resveratrol and midostaurin on the sensitization of midostaurin-resistant cells. MTT assay revealed resveratrol's chemo-sensitizing influence on midostaurin-resistant cells, and combination indexes (CI) were calculated using Chou-Talalay's method. Apoptosis induction and cell cycle progression was analyzed by flow cytometry. The apoptotic molecular markers caspase 3, PARP, Bcl-2, and Bax were analyzed using a western blot. Sphingosine kinase-1 (SK-1) expression, total and phosphorylated FLT3, and STAT5A levels were measured using western blotting. Resveratrol enhanced the cytotoxic effects of midostaurin additively in resistant MV4-11MR and MOLM-13MR cells. It effectively reversed midostaurin resistance by inhibiting the activating phosphorylation of FLT3, STAT5A, and modulating the expression of SK-1 while concurrently increasing the levels of cleaved caspase-3 and PARP without noticeable alterations in Bax/Bcl-2 ratios except MV4-11MR cells. Additionally, there was an arrest at the S or G0/G1 phase of the cell cycle, depending on the resistant cells, compared to midostaurin alone, but not to the control group. In conclusion, the FLT3/STAT5A axis and SK-1 might play an important role in the reversal of midostaurin resistance by resveratrol. Therefore, the concurrent administration of resveratrol plus midostaurin could potentially serve as a therapeutic approach to address midostaurin resistance and enhance the overall therapy efficacy for FLT3-ITD AML patients after being validated with future in vivo and ex vivo studies
Machine Learning-Based Prediction of Autism Spectrum Disorder and Discovery of Related Metagenomic Biomarkers With Explainable AI
No full textBackground: Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized by social communication deficits and repetitive behaviors. Recent studies have suggested that gut microbiota may play a role in the pathophysiology of ASD. This study aims to develop a classification model for ASD diagnosis and to identify ASD-associated biomarkers by analyzing metagenomic data at the taxonomic level. Methods: The performances of five different methods were tested in this study. These methods are (i) SVM-RCE, (ii) RCE-IFE, (iii) microBiomeGSM, (iv) different feature selection methods, and (v) a union method. The last method is based on creating a union feature set consisting of the features with importance scores greater than 0.5, identified using the best-performing feature selection methods. Results: In our 10-fold Monte Carlo cross-validation experiments on ASD-associated metagenomic data, the most effective performance metric (an AUC of 0.99) was obtained using the union feature set (17 features) and the AdaBoost classifier. In other words, we achieve superior machine learning performance with a few features. Additionally, the SHAP method, which is an explainable artificial intelligence method, is applied to the union feature set, and Prevotella sp. 109 is identified as the most important microorganism for ASD development. Conclusions: These findings suggest that the proposed method may be a promising approach for uncovering microbial patterns associated with ASD and may inform future research in this area. This study should be regarded as exploratory, based on preliminary findings and hypothesis generation.Abdullah Gul University Support Foundation (AGUV); Zefat Academic College; TUBITAK 2211-A BIDEB programThe work of B.B.G. has also been supported by the Abdullah Gul University Support Foundation (AGUV). B.B.G. would like to express her gratitude to the L'Oreal-UNESCO Young Women Scientist Program. The work of M.Y. has been supported by Zefat Academic College. The work of N.S.E. is supported by the TUBITAK 2211-A BIDEB program
Measurement of Autophagic Activity in Cancer Cells With Flow Cytometric Analysis Using Cyto-Id Staining
No full textAutophagy is an evolutionarily conserved process providing the energy that cells need to survive, especially in stress situations, through catabolic processes. Considering the dual role of autophagy in cancer cells depending on the cellular context, it is crucial to comprehend the effect of drug candidates put forward to prevent cancer through the autophagy pathway. The CYTO-ID® Autophagy Detection Kit allows a rapid, specific and quantitative measurement of autophagic activity at the cellular level using a 488 nm-excitable green fluorescent detection reagent via flow cytometer. In this chapter, we present the CYTO-ID® Autophagy Detection method with a stepwise protocol to monitor the autophagy flux after the application of any compound to suspension cancer cell lines with flow cytometric analysis. © 2025 Elsevier B.V., All rights reserved
Recent Advances in CsPbX3 (X = Cl, Br, I) Perovskite NCs@Glass: Structures, Characterizations, and Applications
No full textSoheyli, Ehsan/0000-0002-1403-7934Encapsulation of perovskite nanocrystals (PeNCs) within metal oxide glasses and fabrication of PeNCs@glass composites has emerged as a transformative approach to enhance the stability and functionality of these promising luminescent materials. This review comprehensively examines the current state of research on encapsulation techniques, highlighting their effectiveness in preserving the structural integrity, and optical properties of PeNCs. The advantages and mechanisms by which metal oxide glasses mitigate the degradation of PeNCs are discussed and the tunable properties of metal oxide glass structures for optimizing the photoluminescence and quantum efficiency of encapsulated PeNCs are explored. The review further explores the various experimental techniques for characterizing composites made by nanoscale extreme crystalline species within the short-range ordered (amorphous) microstructures. As the ultimate aim of any advanced material for commercialization, diverse optoelectronic devices of these encapsulated systems, emphasize the potential for improved device performance and longevity. Finally, key challenges and future directions in the field are identified, including the need for scalable fabrication methods and the exploration of novel glass compositions to enhance the encapsulation efficacy. This review aims to provide a comprehensive overview of the advancements in the encapsulation of PeNCs with metal oxide glasses, underscoring their significance in developing next-generation optoelectronic devices.Ilam University; National Natural Science Foundation of China [52272141]; Natural Science Foundation of Fujian Province [2024J02014]S.S. and J.L. contributed equally in the work. E.S. acknowledges the funding support from Ilam University. D.C. acknowledges the funding support from National Natural Science Foundation of China (52272141), and Natural Science Foundation of Fujian Province (2024J02014)
Building Composite Indicators for the Territorial Quality of Life Assessment in European Regions: Combining Data Reduction and Alternative Weighting Techniques
No full textUstaoglu, Eda/0000-0001-6874-5162Development of composite indicators is a challenging task given that sustainability indices are strongly dependent on how the sub-indicators are weighted. This is because relative indicator weights may significantly differ based on the chosen weighting methods used in the analysis. There is hardly any study that has paid attention to this issue so far. Therefore, this paper aims to fill this gap in the literature by searching the robustness of selected weighting methods, i.e. entropy-weight (EW), principal component analysis (PCA), machine learning approaches (random forest-RF), regression analysis (RA) and benefit-of-the-doubt (BOD) when constructing a composite indicator. To research the current sustainability performance of European regions, the present study focuses on the Territorial Quality of Life Index-initially proposed by the ESPON Programme-that are aligned with the specific targets of the Sustainable Development Goals of the 2030 Agenda. The methods to construct composite indicators include stages of data preparation (including the estimation of missing values with random forest method), normalization, statistical transformation of raw data, reduction of indicators in order to ease public communication (using the PCA method) and data interpretation, weighting of the sub-indicators using EW, PCA, RF, RA and BOD methods and their linear weighted aggregation, and checking for robustness and sensitivity. The results suggest that there are significant differences in the rank and spatial distribution of composite indicators based on the use of different weighting methods considered in the analysis. The results from sensitivity analysis support the robustness of entropy-weight method among others. The methodology used in the current analysis can be adapted to other study areas and regions internationally. The findings showed that Eastern European countries and some Mediterranean countries have relatively lower index values compared to other European regions; therefore, policy and planning actions are needed covering these regions specifically
NLP-Driven Fake News Detection: A Machine Learning Perspective
No full textThe rapid spread of fake news poses a significant challenge, impacting public opinion, decision-making, and societal trust. This study explores the application of Natural Language Processing (NLP) and Machine Learning (ML) techniques for robust fake news detection. Using datasets such as ISOT Fake News, WELFake, and Football Fake News, the project employs advanced preprocessing methods and feature extraction techniques, including TF-IDF, Word2Vec, and GloVe. A comprehensive evaluation of machine learning models-Random Forest, Support Vector Machines (SVM), and Neural Networks-was conducted to identify the optimal configuration. Results demonstrate that Random Forest with TF-IDF excels in in-domain detection, achieving an F1-score of 99.70%, while Neural Networks paired with Word2Vec and GloVe embeddings outperform in cross-dataset scenarios. The study highlights the importance of dataset size, domain relevance, and feature representation in achieving high generalizability. These findings provide a scalable framework for combating misinformation on digital platforms
Burg-Aided 2D MIMO Array Extrapolation for Improved Spatial Resolution
No full textIn this paper, the extrapolation of a 2D multiple-input multiple-output (MIMO) array is proposed using the Burg algorithm to achieve higher angular resolution beyond that of the corresponding 2D MIMO virtual array. The main advantage of such an approach is that it allows us to dramatically decrease both the physical size and the number of antenna elements of the MIMO array. The performance and limitations of the Burg algorithm are examined through both simulation and experimentation at 77 GHz. The experimental methodology used to acquire 3D data of range, azimuth and elevation information with the 1D MIMO off-the-shelf radar is described. Using this method, the performance of the proposed array can be tested experimentally, especially at frequencies where it is desired to assess the antenna response prior to fabricating the antenna.STREAM Project [EP/S033238/1]; Engineering and Physical Sciences Research Council (EPSRC); SBISAR Project [EP/Y022092/1]This research was funded by the Engineering and Physical Sciences Research Council (EPSRC) U.K. through the STREAM Project under Grant EP/S033238/1 and SBISAR Project under Grant EP/Y022092/1