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17 pages, 2654 KB

Open AccessTechnical Note

Development and Validation of Nanoedw 1.0: An Integrated Computational Tool for Drug Delivery Research and Nanotechnology Applications

by Edwar D. Montenegro, Marcia S. Rizzo, Heurison de Sousa e Silva and Marcília Pinheiro da Costa

J 2025, 8(4), 47; https://doi.org/10.3390/j8040047 - 11 Dec 2025

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Abstract

Quantitative analyses in drug-delivery research are frequently distributed across multiple tools, which increases manual handling and the risk of transcription errors. NanoEDW 1.0 is an open source Python application that integrates calibration-curve generation, encapsulation-efficiency (EE%) calculation, and release kinetics modeling in a single, [...] Read more.

Quantitative analyses in drug-delivery research are frequently distributed across multiple tools, which increases manual handling and the risk of transcription errors. NanoEDW 1.0 is an open source Python application that integrates calibration-curve generation, encapsulation-efficiency (EE%) calculation, and release kinetics modeling in a single, streamlined workflow. This study aims to validate the performance of NanoEDW 1.0 by benchmarking it against spreadsheet/OriginLab^® OriginPro 2025 analyses on experimental datasets from polymeric nanocarrier systems commonly used in drug encapsulation. The software performs linear regression to convert absorbance into concentration, computes EE% from raw experimental values, and fits drug-release profiles to classical models (including zero/first-order, Higuchi, Korsmeyer–Peppas, Weibull, and Modified Gompertz) using non-linear least squares with standard goodness-of-fit metrics (R², RMSE). Results show close agreement with reference workflows for calibration parameters and EE%, as well as statistically comparable release-model fits, while reducing manual steps and analysis time. In conclusion, the validation confirms that NanoEDW 1.0 can streamline routine analyses and enhance reproducibility and accessibility in nanopharmaceutical research; source code and example datasets are provided to foster adoption. Full article

(This article belongs to the Special Issue Feature Papers of J—Multidisciplinary Scientific Journal in 2025)

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13 pages, 2658 KB

Open AccessArticle

Development of Biodegradable Bioplastic from Banana Pseudostem Cellulose

by David A. Servellón, Fabrizzio R. Pérez, Enrique Posada-Granados, Marlon Enrique López and Marvin J. Núñez

J 2025, 8(4), 46; https://doi.org/10.3390/j8040046 - 2 Dec 2025

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Abstract

Banana pseudostem is an abundant lignocellulosic residue with potential for value-added applications. This study evaluated five banana varieties to determine their suitability for bioplastic production, with Williams showing the highest cellulose yield (26.99% ± 0.23). Cellulose extracted from this variety was combined with [...] Read more.

Banana pseudostem is an abundant lignocellulosic residue with potential for value-added applications. This study evaluated five banana varieties to determine their suitability for bioplastic production, with Williams showing the highest cellulose yield (26.99% ± 0.23). Cellulose extracted from this variety was combined with corn-starch (1:1 w/w) to synthesize a bioplastic through gelatinization and lyophilization. FTIR confirmed effective removal of lignin and hemicellulose from the pseudostem and evidenced new hydrogen-bond interactions between cellulose and starch through O–H band shifts (3335 → 3282 cm⁻¹). SEM revealed a porous laminar morphology with cellulose particles (40–52 µm) embedded within the starch matrix. DSC analysis showed that the bioplastic exhibits an intermediate thermal profile between its components, while mechanical compression increased the endothermic transition temperature (from 69 °C to 85 °C) and reduced molecular mobility. Tensile testing demonstrated that compression markedly improved mechanical performance, increasing tensile strength from 0.094 MPa to 0.69 MPa and density from 110 to 638.7 kg/m³. These findings indicate that cellulose–starch bioplastics derived from banana pseudostem possess favorable structural, thermal, and mechanical characteristics for short-use applications. The approach also contributes to the valorization of agricultural waste through biodegradable material development. Full article

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14 pages, 592 KB

Open AccessArticle

Large-Scale Analysis of the Medical Discourse on Rheumatoid Arthritis: Complementing with AI a Socio-Anthropologic Analysis

by Mario Santoro and Christine Nardini

J 2025, 8(4), 45; https://doi.org/10.3390/j8040045 - 23 Nov 2025

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Abstract

The medical discourse entails the analysis of the modalities, which are far from unbiased, by which hypotheses and results are laid out in the dissemination of findings in scientific publications. This gives different emphases on the background, relevance, robustness, and assumptions that the [...] Read more.

The medical discourse entails the analysis of the modalities, which are far from unbiased, by which hypotheses and results are laid out in the dissemination of findings in scientific publications. This gives different emphases on the background, relevance, robustness, and assumptions that the audience takes for granted. This concept is extensively studied in socio-anthropology. However, it remains generally overlooked within the scientific community conducting the research. Yet, analyzing the discourse is crucial for several reasons: to frame policies that take into account an appropriately large screen of medical opportunities; to avoid overseeing promising but less walked paths; to grasp different types of representations of diseases, therapies, patients, and other stakeholders; to understand how these terms are conditioned by time and culture. While socio-anthropologists traditionally use manual curation methods–limited by the lengthy process–machine learning and AI may offer complementary tools to explore the vastness of an ever-growing body of medical literature. In this work, we propose a pipeline for the analysis of the medical discourse on the therapeutic approaches to rheumatoid arthritis using topic modeling and transformer-based emotion and sentiment analysis, overall offering complementary insights to previous curation. Full article

(This article belongs to the Section Computer Science & Mathematics)

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14 pages, 5377 KB

Open AccessArticle

The Impact of Penalty Function and Equivalence Factor on the Performance of ECMS Controller in Range Extended Electric Vehicles

by Malika Keldiyarova, Umidjon Usmanov, Sanjarbek Ruzimov and Akmal Mukhitdinov

J 2025, 8(4), 44; https://doi.org/10.3390/j8040044 - 19 Nov 2025

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Abstract

This study discusses the role of the equivalence factor and penalty function in improving the performance of energy consumption minimization strategies in Range Extended Electric Vehicles (REEVs). In conventional ECMS, equivalence factors are typically derived from constant efficiency assumptions for simplicity or adaptively [...] Read more.

This study discusses the role of the equivalence factor and penalty function in improving the performance of energy consumption minimization strategies in Range Extended Electric Vehicles (REEVs). In conventional ECMS, equivalence factors are typically derived from constant efficiency assumptions for simplicity or adaptively adjusted according to driving conditions in adaptive ECMS. In REEVs, however, the battery efficiency exhibits nonlinear behavior in the low SOC range, which directly leads to variability in the equivalence factor within conventional ECMS. This study investigates the influence of the variable equivalence factor on the overall fuel economy. The equivalence factors are usually considered constant or vary adaptively depending on driving cycles. However, the variation in battery efficiency is often neglected. The present study compares the results obtained for both constant and variable battery efficiencies in deriving the equivalence factors. The simulation results show that an improvement of approximately 3% in fuel economy was obtained for UDDS, NEDC, and WLTC driving cycles as a result of applying the variable equivalence factor. Additionally, through an analysis of various penalty function designs, the study highlights their crucial role in optimizing fuel consumption across different driving cycles. Full article

(This article belongs to the Section Engineering)

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11 pages, 2133 KB

Open AccessArticle

Evaluation of Photodynamic Therapy Using AuNPs@Ce6 in 3D Cultures of Triple-Negative Breast Cancer

by Aveline Ventura, Giulia Capizzani Gonçalves, Cristina Pacheco Soares, Luciana Barros Sant’anna, Vitor Luca Moura Marmo, Sônia Khouri Sibelino and Leandro Raniero

J 2025, 8(4), 43; https://doi.org/10.3390/j8040043 - 17 Nov 2025

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Abstract

Conventional cancer treatments have limited efficacy for aggressive subtypes such as triple-negative breast cancer (TNBC), which points to the importance of new therapeutic strategies. Functionalized nanoparticles in conjunction with photodynamic therapy (PDT) represent a promising alternative. Additionally, 3D cell culture emerges as a [...] Read more.

Conventional cancer treatments have limited efficacy for aggressive subtypes such as triple-negative breast cancer (TNBC), which points to the importance of new therapeutic strategies. Functionalized nanoparticles in conjunction with photodynamic therapy (PDT) represent a promising alternative. Additionally, 3D cell culture emerges as a more effective model, as it better replicates the structural and functional characteristics of the tumor microenvironment. In this study, 3D microtumors of TNBC were cultivated and treated with PDT using gold nanoparticles functionalized with Chlorin e6 (AuNPs@Ce6). Cell viability was assessed using the MTT colorimetric assay, combined with histological analysis using hematoxylin-eosin staining. The MTT assay and histological evaluation of the 3D spheroids demonstrated that PDT with AuNPs@Ce6 effectively reduced cell viability and induced necrotic morphological changes, while maintaining biocompatibility with the non-irradiated control group. These findings reinforce the potential of this approach for further investigation in TNBC models and underscore the value of 3D cultures as physiologically relevant and ethical alternatives to animal testing. Full article

(This article belongs to the Special Issue Feature Papers of J—Multidisciplinary Scientific Journal in 2025)

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15 pages, 2791 KB

Open AccessArticle

Tagging Fluorescent Reporter to Epinecidin-1 Antimicrobial Peptide

by Sivakumar Jeyarajan, Harini Priya Ramesh, Atchyasri Anbarasu, Jayasudha Jayachandran and Anbarasu Kumarasamy

J 2025, 8(4), 42; https://doi.org/10.3390/j8040042 - 2 Nov 2025

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Abstract

In this study, we successfully cloned the fluorescent proteins eGFP and DsRed in-frame with the antimicrobial peptide epinecidin-1 (FIFHIIKGLFHAGKMIHGLV) at the N-terminal. The cloning strategy involved inserting the fluorescent reporters into the expression vector, followed by screening for positive clones through visual fluorescence [...] Read more.

In this study, we successfully cloned the fluorescent proteins eGFP and DsRed in-frame with the antimicrobial peptide epinecidin-1 (FIFHIIKGLFHAGKMIHGLV) at the N-terminal. The cloning strategy involved inserting the fluorescent reporters into the expression vector, followed by screening for positive clones through visual fluorescence detection and molecular validation. The visually identified fluorescent colonies were confirmed as positive by PCR and plasmid migration assays, indicating successful cloning. This fusion of fluorescent reporters with a short antimicrobial peptide enables real-time visualization and monitoring of the peptide’s mechanism of action on membranes and within cells, both in vivo and in vitro. The fusion of eGFP and DsRed to epinecidin-1 did not impair the expression or fluorescence of the reporter protein. Full article

(This article belongs to the Special Issue Feature Papers of J—Multidisciplinary Scientific Journal in 2025)

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22 pages, 1460 KB

Open AccessReview

Insights into Molecular Interplay in Tuberculosis–COVID-19 Co-Infection via Integrated Multi-Omics Strategies

by Megha Chaudhari, Sunita Verma and Sushanta Deb

J 2025, 8(4), 41; https://doi.org/10.3390/j8040041 - 22 Oct 2025

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Abstract

The simultaneous occurrence of tuberculosis (TB) and COVID-19 posed a major public health challenge, particularly in regions heavily impacted by both diseases, due to their shared effects on the lungs, immune system dysfunction, and the possibility of more severe clinical outcomes. The role [...] Read more.

The simultaneous occurrence of tuberculosis (TB) and COVID-19 posed a major public health challenge, particularly in regions heavily impacted by both diseases, due to their shared effects on the lungs, immune system dysfunction, and the possibility of more severe clinical outcomes. The role of immunopathogenesis is crucial in influencing the progression of co-infection, which is marked by heightened inflammation, immune exhaustion, weakened T-cell responses, and unregulated cytokine production. To better understand the intricate interactions between host and pathogen and the immune disruptions associated with this dual epidemic, multi-omics approaches such as genomics, transcriptomics, proteomics, metabolomics, epigenomics, and microbiomics have proven to be effective methods. These comprehensive strategies provide detailed insights into the mechanisms of disease, help identify potential biomarkers, and aid in the identification of therapeutic targets. This review emphasizes the importance of immune responses and systems biology in comprehending the TB-COVID-19 syndemic and highlights the promise of multi-omics in advancing precision medicine and enhancing disease management. Full article

(This article belongs to the Special Issue Feature Papers of J—Multidisciplinary Scientific Journal in 2025)

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26 pages, 4555 KB

Open AccessArticle

Modeling the Mutual Dynamic Correlations of Words in Written Texts Using Multivariate Hawkes Processes

by Hiroshi Ogura, Yasutaka Hanada, Keitaro Osakabe and Masato Kondo

J 2025, 8(4), 40; https://doi.org/10.3390/j8040040 - 14 Oct 2025

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Abstract

The occurrence patterns of important words found in six texts (one historical pamphlet and five renowned academic books) are analyzed using both univariate and multivariate Hawkes processes. By treating the occurrence patterns as binary time-series data along the texts, we investigate how effectively [...] Read more.

The occurrence patterns of important words found in six texts (one historical pamphlet and five renowned academic books) are analyzed using both univariate and multivariate Hawkes processes. By treating the occurrence patterns as binary time-series data along the texts, we investigate how effectively univariate and multivariate Hawkes processes capture the characteristics of these word occurrence signals. Through maximum likelihood estimation and subsequent simulations, we found that the multivariate Hawkes process clearly outperforms the univariate Hawkes process in modeling word occurrence signals. Moreover, we found that the multivariate Hawkes process can provide a Hawkes graph, which serves as an intuitive representation of the relationships between concepts appearing in the analyzed text. Furthermore, our study demonstrates that the importance of concepts within a given text can be quantitatively estimated based on the optimized parameter values of the multivariate Hawkes process. Full article

(This article belongs to the Special Issue Feature Papers of J—Multidisciplinary Scientific Journal in 2025)

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37 pages, 2156 KB

Open AccessReview

Experimental Fish Models in the Post-Genomic Era: Tools for Multidisciplinary Science

by Camila Carlino-Costa and Marco Antonio de Andrade Belo

J 2025, 8(4), 39; https://doi.org/10.3390/j8040039 - 2 Oct 2025

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Abstract

Fish have become increasingly prominent as experimental models due to their unique capacity to bridge basic biological research with translational applications across diverse scientific disciplines. Their biological traits, such as external fertilization, high fecundity, rapid embryonic development, and optical transparency, facilitate in vivo [...] Read more.

Fish have become increasingly prominent as experimental models due to their unique capacity to bridge basic biological research with translational applications across diverse scientific disciplines. Their biological traits, such as external fertilization, high fecundity, rapid embryonic development, and optical transparency, facilitate in vivo experimentation and real-time observation, making them ideal for integrative research. Species like zebrafish (Danio rerio) and medaka (Oryzias latipes) have been extensively validated in genetics, toxicology, neuroscience, immunology, and pharmacology, offering robust platforms for modeling human diseases, screening therapeutic compounds, and evaluating environmental risks. This review explores the multidisciplinary utility of fish models, emphasizing their role in connecting molecular mechanisms to clinical and environmental outcomes. We address the main species used, highlight their methodological advantages, and discuss the regulatory and ethical frameworks guiding their use. Additionally, we examine current limitations and future directions, particularly the incorporation of high-throughput omics approaches and real-time imaging technologies. The growing scientific relevance of fish models reinforces their strategic value in advancing cross-disciplinary knowledge and fostering innovation in translational science. Full article

(This article belongs to the Special Issue Feature Papers of J—Multidisciplinary Scientific Journal in 2025)

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18 pages, 2133 KB

Open AccessArticle

A Simulation Game in Mineral Exploration: A Mineral Adventure from Exploration to Exploitation

by George Valakas, Daphne Sideri and Konstantinos Modis

J 2025, 8(4), 38; https://doi.org/10.3390/j8040038 - 1 Oct 2025

Viewed by 770

Abstract

In recent decades, simulation has emerged as a pivotal educational tool, bolstering scientific knowledge and honing decision-making skills across diverse disciplines. Surgery and flight simulators are well-known tools used to practice and train safely in surgeries and piloting. Meanwhile, the development of simulation [...] Read more.

In recent decades, simulation has emerged as a pivotal educational tool, bolstering scientific knowledge and honing decision-making skills across diverse disciplines. Surgery and flight simulators are well-known tools used to practice and train safely in surgeries and piloting. Meanwhile, the development of simulation games advances in other scientific fields, such as economics, management, engineering, and mathematics. These simulations offer learners a risk-free virtual platform to apply and refine their knowledge, leveraging animations, graphics, and interactive environments to enrich the learning experience. In engineering, while simulation is widely utilized as a powerful training tool for heavy equipment and process handling, the creation of strategy games for educational purposes is less frequent. This gap primarily stems from the challenge of converting complex engineering concepts and theories into a user-friendly yet comprehensive setup that preserves the more difficult aspects. This study adopts a design-based research approach to develop and evaluate an educational simulation game aimed at enhancing probabilistic and spatial reasoning in mineral exploration. The application generates random scenarios, within which users deploy strategies based on their knowledge, while accommodating the randomness of physical phenomena. The simulation game is adopted as an educational tool in the course “Introduction to Mineral Exploration” in the School of Mining and Metallurgical Engineering of the National Technical University of Athens. Additionally, we present the outcomes of game analytics and a qualitative evaluation derived from three workshops at higher education institutions in Greece. Full article

(This article belongs to the Special Issue Feature Papers of J—Multidisciplinary Scientific Journal in 2025)

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