MDPI - Publisher of Open Access Journals

19 pages, 796 KiB

Open AccessReview

Relevance of Glucagon-Like Peptide 1 (GLP-1) in Inflammatory Bowel Diseases: A Narrative Review

by Antonietta Gerarda Gravina, Raffaele Pellegrino, Michele Izzo, Ilaria De Costanzo, Giuseppe Imperio, Fabio Landa, Assunta Tambaro and Alessandro Federico

Curr. Issues Mol. Biol. 2025, 47(5), 383; https://doi.org/10.3390/cimb47050383 - 21 May 2025

Abstract

Inflammatory bowel diseases (IBDs) are complex immune-mediated disorders characterised by an unpredictable direction and commonly associated metabolic comorbidities along with obesity and type 2 diabetes mellitus (T2DM). Recent evidence has highlighted the therapeutic capacity of glucagon-like peptide 1 receptor agonists (GLP-1 RAs), already [...] Read more.

Inflammatory bowel diseases (IBDs) are complex immune-mediated disorders characterised by an unpredictable direction and commonly associated metabolic comorbidities along with obesity and type 2 diabetes mellitus (T2DM). Recent evidence has highlighted the therapeutic capacity of glucagon-like peptide 1 receptor agonists (GLP-1 RAs), already employed in treating T2DM and obesity, in modulating systemic and intestinal inflammatory responses. This narrative review examines the general organic traits of GLP-1, with a specific awareness of its primary gastrointestinal actions and the efficacy of GLP-1 RAs in promoting weight loss and dealing with glycaemic control, mainly in sufferers with IBD. Furthermore, the effects of those agonists on the progression of IBD, their protection profile, their impact on bowel preparation for endoscopic procedures, and their therapeutic capacity, supported through preclinical and early clinical studies, are discussed. GLP-1 RAs appear to lessen the intestinal inflammatory burden by enhancing intestinal epithelial barrier features and modulating the gut microbiota. However, further clinical research will be necessary to verify whether GLP-1 RAs could play a position in IBD treatment. Full article

(This article belongs to the Special Issue Intestinal Inflammation: From Molecular Mechanisms to Therapeutic Strategies)

31 pages, 863 KiB

Open AccessReview

Unveiling the Hidden Allies in the Fight Against Antimicrobial Resistance—Medicinal Plant Endophytes

by Adeoye J. Kayode, Aboi Igwaran, Folasade Banji-Onisile, Nneka A. Akwu, John O. Unuofin, Ayodeji C. Osunla, Samson O. Egbewale and Hery Purnobasuki

Bacteria 2025, 4(2), 26; https://doi.org/10.3390/bacteria4020026 - 21 May 2025

Abstract

Medicinal plants have long been a vital source of various natural products in the form of pure compounds or standardized extracts. The World Health Organization estimated that 80% of populations in Africa, Asia, and Latin America rely on traditional medicine for primary health [...] Read more.

Medicinal plants have long been a vital source of various natural products in the form of pure compounds or standardized extracts. The World Health Organization estimated that 80% of populations in Africa, Asia, and Latin America rely on traditional medicine for primary health care. In recent decades, endophytic microorganisms living within plants have gained attention for their ability to produce bioactive compounds with significant therapeutic potential. This review explores the diversity of medicinal plant endophytes, focusing on their pharmacological significance, including antimicrobial, anticancer, antidiabetic, and antioxidant properties. Additionally, we discuss the application of nanotechnology and computational tools in enhancing the potency and screening of endophyte-derived metabolites. Despite the promising potential, challenges such as scalability, safety, and commercial viability remain. Future research should prioritize optimizing production, elucidating biosynthetic pathways, and integrating advanced technologies to effectively harness these bioactive compounds for novel drug development. Full article

19 pages, 438 KiB

Open AccessArticle

Nonregular Physical Activity and Handgrip Strength as Indicators of Fatigue and Psychological Distress in Cancer Survivors

by Ilaria Pepe, Alessandro Petrelli, Francesco Fischetti, Carla Minoia, Stefania Morsanuto, Livica Talaba, Stefania Cataldi and Gianpiero Greco

Curr. Oncol. 2025, 32(5), 289; https://doi.org/10.3390/curroncol32050289 - 21 May 2025

Abstract

Background: Cancer survivors who do not engage in regular physical activity often experience persistent psychological distress and fatigue, which can significantly impact their quality of life. While handgrip strength (HGS) is recognized as an indicator of overall health and physical resilience, the combined [...] Read more.

Background: Cancer survivors who do not engage in regular physical activity often experience persistent psychological distress and fatigue, which can significantly impact their quality of life. While handgrip strength (HGS) is recognized as an indicator of overall health and physical resilience, the combined role of HGS and physical inactivity in predicting psychological distress and fatigue in this population remains unclear. This study aimed to examine the relationships between self-reported physical inactivity, HGS, and psychological distress, specifically depressive symptoms, anxiety, and cancer-related fatigue (CRF), in physically inactive cancer survivors. Methods: This cross-sectional study included 42 physically inactive cancer survivors (mean age = 63.2 years, SD = 8.96) recruited from the Cancer Institute (IRCCS) in Bari, Italy. Physical inactivity was quantified based on self-reported weekly physical activity minutes, with all participants engaging in less than 150 min per week. The participants underwent HGS assessment and completed validated psychological measures, including the Beck Depression Inventory (BDI), the State-Trait Anxiety Inventory (STAI-Y1 and STAI-Y2), and the Fatigue Severity Scale (FSS). Results: Bivariate correlations were examined via Spearman's rank correlation coefficients, and multiple linear regression analyses were performed to identify independent predictors of psychological distress and fatigue, adjusting for covariates such as age, sex, cancer type, and time since treatment completion. Both lower HGS and greater physical inactivity were significantly correlated with greater depressive symptoms (HGS: ρ = −0.524, p < 0.001; physical inactivity: ρ = −0.662, p < 0.001), greater fatigue severity (HGS: ρ = −0.599, p < 0.001; physical inactivity: ρ = −0.662, p < 0.001), and increased trait anxiety (HGS: ρ = −0.532, p < 0.001; physical inactivity: ρ = −0.701, p < 0.001). No significant associations were found between physical inactivity or HGS and state anxiety (p > 0.05). Multiple regression analyses confirmed that both HGS and physical inactivity independently predicted depressive symptoms (HGS: β = −0.435, p = 0.009; physical inactivity: β = −0.518, p = 0.002), trait anxiety (HGS: β = −0.313, p = 0.038; physical inactivity: β = −0.549, p < 0.001), and fatigue (HGS: β = −0.324, p = 0.033; physical inactivity: β = −0.565, p < 0.001), even after adjusting for covariates. Low physical activity and reduced muscle strength independently predict psychological distress and fatigue in cancer survivors. Conclusions: These findings highlight the potential exacerbating role of physical inactivity in both physical and psychological vulnerability, underscoring the need for interventions promoting regular exercise. Integrating strength assessments and structured physical activity programs may be key strategies in survivorship care to improve mental well-being and overall quality of life. Full article

(This article belongs to the Section Psychosocial Oncology)

41 pages, 4178 KiB

Open AccessArticle

Impact of Influencer Marketing on Consumer Behavior and Online Shopping Preferences

by Stavros P. Migkos, Nikolaos T. Giannakopoulos and Damianos P. Sakas

J. Theor. Appl. Electron. Commer. Res. 2025, 20(2), 111; https://doi.org/10.3390/jtaer20020111 - 21 May 2025

Abstract

Influencer marketing has emerged as a crucial element in digital marketing, significantly shaping consumer behavior and online shopping preferences. This study examined the multidimensional impact of influencer marketing by analyzing engagement metrics, marketing effectiveness, and consumer decision-making processes, based on consumers in the [...] Read more.

Influencer marketing has emerged as a crucial element in digital marketing, significantly shaping consumer behavior and online shopping preferences. This study examined the multidimensional impact of influencer marketing by analyzing engagement metrics, marketing effectiveness, and consumer decision-making processes, based on consumers in the Greek sector. Through a structured methodological framework, the research employed a questionnaire-based survey, statistical modeling, and Fuzzy Cognitive Mapping (FCM) scenarios to assess consumer interactions with influencer-driven content. Findings highlight that while influencer marketing enhances brand engagement and sales, its effectiveness varies based on content authenticity, transparency, and user trust. Additionally, consumer purchasing decisions are influenced by social media visibility, personalized marketing strategies, and digital platform usability. This study underscores the need for strategic influencer selection and information-driven marketing optimization to sustain long-term consumer engagement. These insights provide practical implications for businesses aiming to enhance digital marketing strategies and contribute to the ongoing discourse on social commerce and consumer-centric marketing models. Full article

(This article belongs to the Special Issue Electronic Commerce and Information Management Towards the Digital Era)

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27 pages, 1166 KiB

Open AccessArticle

Uncertainty Analysis and Quantification of Rainfall-Induced Slope in Fine-Grained Clayey Soils

by Samuel A. Espinosa F. and M. Hesham El Naggar

Geotechnics 2025, 5(2), 31; https://doi.org/10.3390/geotechnics5020031 - 21 May 2025

Abstract

This study investigates rainfall-induced slope instability in fine-grained clayey soils through a probabilistic and sensitivity analysis framework that integrates spatial variability. Moving beyond traditional deterministic methods, Monte Carlo simulations were employed to quantify uncertainty in geotechnical parameters—unit weight, cohesion, and friction angle—modeled as [...] Read more.

This study investigates rainfall-induced slope instability in fine-grained clayey soils through a probabilistic and sensitivity analysis framework that integrates spatial variability. Moving beyond traditional deterministic methods, Monte Carlo simulations were employed to quantify uncertainty in geotechnical parameters—unit weight, cohesion, and friction angle—modeled as random fields with a 1 m spatial resolution. This approach realistically captures natural soil heterogeneity and its influence on slope behavior during rainfall events. Transient seepage and slope stability analyses were performed using SEEP/W and SLOPE/W, respectively, with the Spencer method ensuring full equilibrium. This study examined how slope height, inclination, rainfall intensity and duration, and soil properties affect the factor of safety (FS). The results showed that higher rainfall intensity and longer durations significantly increase failure risk. For example, under 9 mm/h rainfall for 48 h, slopes taller than 10 m at 45° inclination exhibited failure probabilities over 30%. At 20 m, FS dropped to 0.68 with a 100% probability of failure. Sensitivity analysis confirmed cohesion and friction angle as key stabilizing factors, though their impact diminishes with infiltration. A dataset of 9984 slope scenarios was generated, supporting future machine learning applications for risk assessment and climate-resilient slope design. Full article

(This article belongs to the Special Issue Recent Advances in Geotechnical Engineering (2nd Edition))

12 pages, 498 KiB

Open AccessArticle

Influence of Magnetic Field on Atrazine Adsorption and Degradation by Ferroxite and Hematite

by Marcos Antônio Sousa, Mateus Aquino Gonçalves, Thais Aparecida Sales, Jessica Boreli dos Reis Lino, Stéfany Gonçalves de Moura, Joaquim Paulo da Silva and Teodorico Castro Ramalho

Magnetism 2025, 5(2), 11; https://doi.org/10.3390/magnetism5020011 - 21 May 2025

Abstract

This study approaches the characterization of Ferroxite and Hematite and the test of their magnetic properties on the degradation and adsorption of Atrazine, an herbicide of the triazine class. This herbicide was compared with a sample of Ferroxite in the absence of a [...] Read more.

This study approaches the characterization of Ferroxite and Hematite and the test of their magnetic properties on the degradation and adsorption of Atrazine, an herbicide of the triazine class. This herbicide was compared with a sample of Ferroxite in the absence of a magnetic field and with Hematite, a non-magnetic material which should not be attracted by the magnet. In the sample, the Atrazine determination was carried out by Fenton analysis. Preliminary results were satisfactory, gathering a reduction rate up to 85% for Ferroxite in the presence of a magnetic field and 53% for Hematite. The Fenton reaction, however, showed an 87% reduction rate for Ferroxite in the presence of a magnetic field, and 56% for Hematite. These findings have shown that there is a relation between the magnetic field intensity and the adsorption capacity for these materials. Full article

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20 pages, 6818 KiB

Open AccessArticle

Two New Troglobitic Species of the Genus Spelaeogammarus da Silva Brum, 1975 (Amphipoda, Artesiidae) from Brazil

by Júlia Barbosa Galo, Giovanna Monticelli Cardoso and Rodrigo Lopes Ferreira

Taxonomy 2025, 5(2), 28; https://doi.org/10.3390/taxonomy5020028 - 21 May 2025

Abstract

Two new subterranean species of the genus Spelaeogammarus da Silva Brum, 1975 in Serra do Ramalho municipality, Bahia state, and Montes Claros municipality, Minas Gerais state, both in Brazil, are described herein. With these additions, the genus now comprises ten known species. This [...] Read more.

Two new subterranean species of the genus Spelaeogammarus da Silva Brum, 1975 in Serra do Ramalho municipality, Bahia state, and Montes Claros municipality, Minas Gerais state, both in Brazil, are described herein. With these additions, the genus now comprises ten known species. This study includes a comparative table detailing the diagnostic characteristics of all Spelaeogammarus species and an updated genus diagnosis. Additionally, it provides insights into the species’ habitats and the threats they face. Some of the type localities mentioned in this study are not within protected areas, making these species particularly vulnerable to environmental risks. Threats primarily arise from surrounding land use, which can impact the water table and disrupt food resource availability. The discovery of these new troglobitic species underscores the urgent need for their inclusion in future threatened species assessments and highlights the importance of conservation measures to protect both the species and their cave habitats, along with the surrounding landscape. Finally, the discovery of these new species highlights the remarkable diversity of Spelaeogammarus in subterranean environments, emphasizing the need for further research and conservation efforts. Full article

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14 pages, 2154 KiB

Open AccessArticle

In Situ Encapsulated RhB@Er-MOF with Dual-Emitting Rationmetric Fluorescence for Rapid and Selective Detection of Fe(III) by Dual-Signal Output

by Xiaoyan Yao, Xueyi Lv, Dongmei Zhang, Xiangyu Zhao, Kaixuan Zhong, Hanlei Sun, Hongzhi Wang, Licheng Liu, Wentai Wang and Shuo Yao

Chemistry 2025, 7(3), 83; https://doi.org/10.3390/chemistry7030083 - 21 May 2025

Abstract

A novel polyhedron-based anionic Er-MOF with three types of cages and abundant open metal sites (OMSs) and Lewis base sites (LBSs) has been successfully synthesized. The inorganic secondary unit possesses a rarely reported six-connected three-nucleated rare-earth cluster, and the overall framework shows a [...] Read more.

A novel polyhedron-based anionic Er-MOF with three types of cages and abundant open metal sites (OMSs) and Lewis base sites (LBSs) has been successfully synthesized. The inorganic secondary unit possesses a rarely reported six-connected three-nucleated rare-earth cluster, and the overall framework shows a new (3,3,6)-connected topology. The Er-MOF has good fluorescence selectivity and anti-interference performance with Fe³⁺ and Cu²⁺. In addition, benefiting from the anionic framework, nanoscale cavity and small window size of the Er-MOF, the composite RhB@Er-MOF has been synthesized by in situ encapsulation of the cationic dye Rhodamine B (RhB). It can provide dual-emitting fluorescence that facilitates self-calibration in sensing. The RhB@Er-MOF has higher accuracy than the Er-MOF with regard to the fluorescence-selective and anti-interference performance of Fe³⁺ and quenching coefficient K_sv values of 1.97 × 10⁴ M⁻¹, which are attributed to its self-calibration function that can eliminate environmental interference. The fluorescence quenching mechanism was explained by our experiments and density functional theory (DFT) calculations. Furthermore, RhB@Er-MOF can achieve the visual and rapid selective detection of Fe³⁺ by a smartphone RGB color analysis application, resulting in the dual-signal output performance of the material. Full article

30 pages, 19867 KiB

Open AccessArticle

Geomorphological Analysis and Heritage Value of Dobreștilor–Brusturet Cave: A Significant Geomorphosite in the Bran–Dragoslavele Corridor, Romania

by Septimius Trif, Ștefan Bilașco, Roșca Sanda, Fodorean Ioan, Iuliu Vescan, András-István Barta and Raboșapca Irina

Heritage 2025, 8(5), 183; https://doi.org/10.3390/heritage8050183 - 21 May 2025

Abstract

This study examines the morphology and development of Dobreștilor–Brusturet Cave, located in the Brusturet gorge at the western edge of the Bran–Dragoslavele Corridor, an important tourist route in the Romanian Carpathians. The research aims to analyze the geomorphological characteristics and establish the heritage [...] Read more.

This study examines the morphology and development of Dobreștilor–Brusturet Cave, located in the Brusturet gorge at the western edge of the Bran–Dragoslavele Corridor, an important tourist route in the Romanian Carpathians. The research aims to analyze the geomorphological characteristics and establish the heritage value of the Dobreştilor Cave geomorphosite, supporting protection efforts for invertebrate species that led to the cave’s designation as a natural monument. The inventory of physical features prompted the Piatra Craiului National Park Scientific Council to consider including this speleological site in a thematic geotourism circuit called “The Road of Gorges and Caves in the Upper Basin of the Dâmbovițean”, integrated within protected areas. This represents the first geomorphological study of the cave. Given its ecological significance within the national park’s strict protection zone, recreational tourism is prohibited. The cave should only be used as a geotourism resource for scientific research and education. Morphogenetic analysis reveals that the cave has evolved in a vadose hydrological regime since the Pleistocene, with cavity expansion influenced by free-flowing water alternating with that under pressure during torrential episodes, concomitant with the precipitation of calcium carbonate that formed various speleothems. This research supports documentation for promotional materials and could assist local authorities in the Dâmbovicioara commune with geotourism development decisions, potentially integrating the site into a proposed “Moieciu–Fundata–Dâmbovicioara–Rucăr Geological and Geomorphological Complex”. Full article

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22 pages, 1362 KiB

Open AccessArticle

Comparison of Optimisation Techniques for the Electric Vehicle Scheduling Problem

by Jacques Wüst, Marthinus Johannes Booysen and James Bekker

Smart Cities 2025, 8(3), 85; https://doi.org/10.3390/smartcities8030085 - 21 May 2025

Abstract

The Electric Vehicle Scheduling Problem (E-VSP) addresses the challenge of efficiently assigning predetermined trips to an electric vehicle fleet while accounting for charging infrastructure and battery range constraints. Despite numerous optimisation approaches proposed in the literature, comparative analyses of these methods remain scarce, [...] Read more.

The Electric Vehicle Scheduling Problem (E-VSP) addresses the challenge of efficiently assigning predetermined trips to an electric vehicle fleet while accounting for charging infrastructure and battery range constraints. Despite numerous optimisation approaches proposed in the literature, comparative analyses of these methods remain scarce, with researchers typically focusing on developing novel algorithms rather than evaluating existing algorithms. Moreover, studies often employ convenient assumptions tailored to improve the performance of their optimisation technique. This study presents a comprehensive comparison of several optimisation techniques (mixed integer linear programming (MILP) using the branch-and-cut algorithm, metaheuristics, and heuristics) applied to the E-VSP under identical assumptions and constraints. The techniques are evaluated across multiple metrics, including solution quality, computational efficiency, and implementation complexity. Findings reveal that the branch-and-cut algorithm cannot solve instances with more than 10 trips in a reasonable time. Among metaheuristics, only genetic algorithms and simulated annealing demonstrate competitive performance, but both struggle with instances exceeding 100 trips. Our recently developed heuristic algorithm consistently found better solutions in significantly shorter computation times than the metaheuristics due to its ability to efficiently navigate the solution space while respecting the unique constraints of the E-VSP. Full article

22 pages, 1701 KiB

Open AccessArticle

Influence of Fuel Types and Equivalence Ratios on NO_x Emissions in Combustion: A Comparative Analysis of Methane, Methanol, Propane, and Hydrogen Blends

by Amr Abbass

Clean Technol. 2025, 7(2), 42; https://doi.org/10.3390/cleantechnol7020042 - 21 May 2025

Abstract

This study utilizes a zero-dimensional, constant-pressure, perfectly stirred reactor (PSR) model within the Cantera framework to examine the combustion characteristics of hydrogen, methane, methanol, and propane, both singly and in hydrogen-enriched mixtures. The impact of the equivalence ratio (ϕ = 0.75, 1.0, 1.5), [...] Read more.

This study utilizes a zero-dimensional, constant-pressure, perfectly stirred reactor (PSR) model within the Cantera framework to examine the combustion characteristics of hydrogen, methane, methanol, and propane, both singly and in hydrogen-enriched mixtures. The impact of the equivalence ratio (ϕ = 0.75, 1.0, 1.5), fuel composition, and residence duration on temperature increase, heat release, ignition delay, and emissions (NO_x and CO₂) is methodically assessed. The simulations are performed under steady-state settings to emulate the ignition and flame propagation processes within pre-chambers and primary combustion zones of internal combustion engines. The results demonstrate that hydrogen significantly improves combustion reactivity, decreasing ignition delay and increasing peak flame temperature, especially at short residence times. The incorporation of hydrogen into hydrocarbon fuels, such as methane and methanol, enhances ignition speed, improves thermal efficiency, and stabilizes lean combustion. Nevertheless, elevated hydrogen concentrations result in increased NO_x emissions, particularly at stoichiometric equivalence ratios, due to higher flame temperatures. The examination of fuel mixtures at varying hydrogen concentrations (10–50% by mole) indicates that thermal performance is optimal under stoichiometric settings and diminishes in both fuel-lean and fuel-rich environments. A thermodynamic model was created utilizing classical combustion theory to validate the heat release estimates based on Cantera. The model computes the heat release per unit volume (MJ/m³) by utilizing stoichiometric oxygen demand, nitrogen dilution, fuel mole fraction, and higher heating values (HHVs). The thermodynamic estimates—3.61 MJ/m³ for H₂–CH₃OH, 3.43 MJ/m³ for H₂–CH₄, and 3.35 MJ/m³ for H₂–C₃H₈—exhibit strong concordance with the Cantera results (2.82–3.02 MJ), thereby validating the physical consistency of the numerical methodology. This comparison substantiates the Cantera model for the precise simulation of hydrogen-blended combustion, endorsing its use in the design and development of advanced low-emission engines. Full article

14 pages, 4240 KiB

Open AccessArticle

Machine Learning Classification of Fossilized Pectinodon bakkeri Teeth Images: Insights into Troodontid Theropod Dinosaur Morphology

by Jacob Bahn, Germán H. Alférez and Keith Snyder

Mach. Learn. Knowl. Extr. 2025, 7(2), 45; https://doi.org/10.3390/make7020045 - 21 May 2025

Abstract

Although the manual classification of microfossils is possible, it can become burdensome. Machine learning offers an alternative that allows for automatic classification. Our contribution is to use machine learning to develop an automated approach for classifying images of Pectinodon bakkeri teeth. This can [...] Read more.

Although the manual classification of microfossils is possible, it can become burdensome. Machine learning offers an alternative that allows for automatic classification. Our contribution is to use machine learning to develop an automated approach for classifying images of Pectinodon bakkeri teeth. This can be expanded for use with many other species. Our approach is composed of two steps. First, PCA and K-means were applied to a numerical dataset with 459 samples collected at the Hanson Ranch Bonebed in eastern Wyoming, containing the following features: crown height, fore-aft basal length, basal width, anterior denticles, and posterior denticles per millimeter. The results obtained in this step were used to automatically organize the P. bakkeri images from two out of three clusters generated. Finally, the tooth images were used to train a convolutional neural network with two classes. The model has an accuracy of 71%, a precision of 71%, a recall of 70.5%, and an F1-score of 70.5%. Full article

(This article belongs to the Special Issue Deep Learning in Image Analysis and Pattern Recognition, 2nd Edition)

18 pages, 2484 KiB

Open AccessArticle

Empowering Smallholder Farmers with UAV-Based Early Cotton Disease Detection Using AI

by Halimjon Khujamatov, Shakhnoza Muksimova, Mirjamol Abdullaev, Jinsoo Cho, Cheolwon Lee and Heung-Seok Jeon

Drones 2025, 9(5), 385; https://doi.org/10.3390/drones9050385 - 21 May 2025

Abstract

Early detection of cotton diseases is critical for safeguarding crop yield and minimizing agrochemical usage. However, most state-of-the-art systems rely on multispectral or hyperspectral sensors, which are costly and inaccessible to smallholder farmers. This paper introduces CottoNet, a lightweight and efficient deep learning [...] Read more.

Early detection of cotton diseases is critical for safeguarding crop yield and minimizing agrochemical usage. However, most state-of-the-art systems rely on multispectral or hyperspectral sensors, which are costly and inaccessible to smallholder farmers. This paper introduces CottoNet, a lightweight and efficient deep learning framework for detecting early-stage cotton diseases using only RGB images captured by unmanned aerial vehicles (UAVs). The proposed model integrates an EfficientNetV2-S backbone with a Dual-Attention Feature Pyramid Network (DA-FPN) and a novel Early Symptom Emphasis Module (ESEM) to enhance sensitivity to subtle visual cues such as chlorosis, minor lesions, and texture irregularities. A custom-labeled dataset was collected from cotton fields in Uzbekistan to evaluate the model under realistic agricultural conditions. CottoNet achieved a mean average precision (mAP@50) of 89.7%, an F1 score of 88.2%, and an early detection accuracy (EDA) of 91.5%, outperforming existing lightweight models while maintaining real-time inference speed on embedded devices. The results demonstrate that CottoNet offers a scalable, accurate, and field-ready solution for precision agriculture in resource-limited settings. Full article

(This article belongs to the Special Issue Advances of UAV in Precision Agriculture—2nd Edition)

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10 pages, 1280 KiB

Open AccessArticle

Analysis of GmERF5 Response to the Rhizobial Type III Effector NopAA Underlying the Nodule in Soybeans

by Lianheng Xia, Yunshan Song, Tong Yu, Ying Pei, Hongwei Jiang, Qingshan Chen and Dawei Xin

Nitrogen 2025, 6(2), 38; https://doi.org/10.3390/nitrogen6020038 - 21 May 2025

Abstract

Soybean, an important leguminous crop valued for its high protein and oil content, obtains most of its nitrogen through symbiotic fixation processes. The symbiosis between soybeans and rhizobium can provide sufficient nitrogen for soybean growth. However, the signaling pathways underlying the establishment of [...] Read more.

Soybean, an important leguminous crop valued for its high protein and oil content, obtains most of its nitrogen through symbiotic fixation processes. The symbiosis between soybeans and rhizobium can provide sufficient nitrogen for soybean growth. However, the signaling pathways underlying the establishment of the symbiosis are not so clear, especially the rhizobial type III effector-induced host response. In this study, we found that the single mutant HH103 nopAA::kan significantly affected the nodule number in soybeans. To further demonstrate the NopAA-triggered response in soybeans. Initial quantitative real-time PCR (qRT-PCR) tests showed that NopAA affects the expression of the soybean gene GmERF5, which was significantly upregulated upon inoculation with HH103 nopAA::kan, acting as a positive regulator of nodulation. The direct interaction between NopAA and GmERF5 was confirmed through yeast-two hybrid analysis. Furthermore, overexpression of GmERF5 in hairy roots indicated that GmERF5 may underlie the nodule phenotype of soybeans in response to NopAA. These findings provide new insights into the mechanisms by which soybean genes respond to rhizobial type III effectors to regulate symbiosis. Full article

(This article belongs to the Special Issue Microbial Interactions with Plants: Advancing Nitrogen Fixation, Uptake, and Utilization)

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13 pages, 5120 KiB

Open AccessArticle

Hepcidin Deficiency Disrupts Iron Homeostasis and Induces Ferroptosis in Zebrafish Liver

by Mingli Liu, Mingjian Peng, Jingwen Ma, Ruiqin Hu, Qianghua Xu, Peng Hu and Liangbiao Chen

Fishes 2025, 10(5), 243; https://doi.org/10.3390/fishes10050243 - 21 May 2025

Abstract

Hepcidin is a key regulator of systemic iron homeostasis, which is essential for maintaining iron balance and cellular health. To investigate its role in zebrafish, we empolyed a hepcidin knockout model. Morphological and histological analyses revealed pale livers and significant iron accumulation in [...] Read more.

Hepcidin is a key regulator of systemic iron homeostasis, which is essential for maintaining iron balance and cellular health. To investigate its role in zebrafish, we empolyed a hepcidin knockout model. Morphological and histological analyses revealed pale livers and significant iron accumulation in hep^−/− zebrafish, particularly in liver, skin, and egg tissues. RNA sequencing identified 1,424 differentially expressed genes (DEGs) between wild-type (WT) and hep^−/− zebrafish, with significant enrichment in pathways related to ferroptosis, fatty acid degradation, and heme binding. Western blot analysis showed reduced levels of key iron-related proteins, including GPX4, Fth1, and ferroportin (FPN), indicating impaired iron transport and increased oxidative stress. Gene Ontology (GO) and KEGG analyses highlighted disruptions in iron metabolism and lipid oxidation, linking iron overload to ferroptosis in the absence of hepcidin. These findings demonstrate that hepcidin deficiency leads to profound dysregulation of iron homeostasis, driving lipid peroxidation and ferroptosis in the zebrafish liver. Our study provides mechanistic insights into the molecular consequences of hepcidin loss, advancing our understanding of iron-related oxidative damage and its physiological impacts. Full article

(This article belongs to the Special Issue Genomics Applied to Fish Health)

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27 pages, 295 KiB

Open AccessArticle

A Practical Performance Benchmark of Post-Quantum Cryptography Across Heterogeneous Computing Environments

by Maryam Abbasi, Filipe Cardoso, Paulo Váz, José Silva and Pedro Martins

Cryptography 2025, 9(2), 32; https://doi.org/10.3390/cryptography9020032 - 21 May 2025

Abstract

The emergence of large-scale quantum computing presents an imminent threat to contemporary public-key cryptosystems, with quantum algorithms such as Shor’s algorithm capable of efficiently breaking RSA and elliptic curve cryptography (ECC). This vulnerability has catalyzed accelerated standardization efforts for post-quantum cryptography (PQC) by [...] Read more.

The emergence of large-scale quantum computing presents an imminent threat to contemporary public-key cryptosystems, with quantum algorithms such as Shor’s algorithm capable of efficiently breaking RSA and elliptic curve cryptography (ECC). This vulnerability has catalyzed accelerated standardization efforts for post-quantum cryptography (PQC) by the U.S. National Institute of Standards and Technology (NIST) and global security stakeholders. While theoretical security analysis of these quantum-resistant algorithms has advanced considerably, comprehensive real-world performance benchmarks spanning diverse computing environments—from high-performance cloud infrastructure to severely resource-constrained IoT devices—remain insufficient for informed deployment planning. This paper presents the most extensive cross-platform empirical evaluation to date of NIST-selected PQC algorithms, including CRYSTALS-Kyber and NTRU for key encapsulation mechanisms (KEMs), alongside BIKE as a code-based alternative, and CRYSTALS-Dilithium and Falcon for digital signatures. Our systematic benchmarking framework measures computational latency, memory utilization, key sizes, and protocol overhead across multiple security levels (NIST Levels 1, 3, and 5) in three distinct hardware environments and various network conditions. Results demonstrate that contemporary server architectures can implement these algorithms with negligible performance impact (<5% additional latency), making immediate adoption feasible for cloud services. In contrast, resource-constrained devices experience more significant overhead, with computational demands varying by up to 12× between algorithms at equivalent security levels, highlighting the importance of algorithm selection for edge deployments. Beyond standalone algorithm performance, we analyze integration challenges within existing security protocols, revealing that naive implementation of PQC in TLS 1.3 can increase handshake size by up to 7× compared to classical approaches. To address this, we propose and evaluate three optimization strategies that reduce bandwidth requirements by 40–60% without compromising security guarantees. Our investigation further encompasses memory-constrained implementation techniques, side-channel resistance measures, and hybrid classical-quantum approaches for transitional deployments. Based on these comprehensive findings, we present a risk-based migration framework and algorithm selection guidelines tailored to specific use cases, including financial transactions, secure firmware updates, vehicle-to-infrastructure communications, and IoT fleet management. This practical roadmap enables organizations to strategically prioritize systems for quantum-resistant upgrades based on data sensitivity, resource constraints, and technical feasibility. Our results conclusively demonstrate that PQC is deployment-ready for most applications, provided that implementations are carefully optimized for the specific performance characteristics and security requirements of target environments. We also identify several remaining research challenges for the community, including further optimization for ultra-constrained devices, standardization of hybrid schemes, and hardware acceleration opportunities. Full article

23 pages, 3453 KiB

Open AccessArticle

Effects of Social Enrichment Induced by Different-Sized Groups and Live Bait on Growth, Aggressive Behavior, Physiology, and Neurogenesis in Juvenile Sebastes schlegelii

by Zhen Zhang, Xiaoming Yu, Zhongxin Wu and Tao Tian

Fishes 2025, 10(5), 242; https://doi.org/10.3390/fishes10050242 - 21 May 2025

Abstract

This study examined the effects of stress and social enrichment on fish neuroplasticity and antioxidant capacity, addressing growing concerns about fish welfare in aquaculture. A 2 × 2 × 2 factorial design comprising eight treatment groups was implemented to investigate how bait type, [...] Read more.

This study examined the effects of stress and social enrichment on fish neuroplasticity and antioxidant capacity, addressing growing concerns about fish welfare in aquaculture. A 2 × 2 × 2 factorial design comprising eight treatment groups was implemented to investigate how bait type, group size (two distinct sizes tested), and stress level affected the expression of neurogenesis-related genes (PCNA, DCX, and NeuroD) and antioxidant parameters (MDA levels, CAT, GSH-Px, and SOD activity) in the fish. The findings demonstrated that social enrichment significantly reduced aggressive behavior and basal cortisol levels and enhanced the expression of neurogenesis-related gene. However, the optimal group-size augmentation (between the two group sizes tested) considerably increased the activity of antioxidant enzymes and decreased MDA levels. Acute stress further upregulated cortisol levels and the expression of genes related to neurogenesis. A scientific foundation for enhancing fish welfare in aquaculture is provided by the study’s confirmation that social enrichment reduces stress and fosters neuroplasticity. Full article

21 pages, 6179 KiB

Open AccessArticle

Utilizing Environmental DNA for Early Monitoring of Non-Indigenous Fish Species in Maritime Ballast Water

by Hanglei Li, Hui Jia and Hui Zhang

Fishes 2025, 10(5), 241; https://doi.org/10.3390/fishes10050241 - 21 May 2025

Abstract

Ballast water has become a significant vector for the global spread of non-indigenous aquatic species. These species may cause severe ecological disruption and economic losses when introduced into new environments. Traditional monitoring techniques often lack the sensitivity and efficiency required for early monitoring, [...] Read more.

Ballast water has become a significant vector for the global spread of non-indigenous aquatic species. These species may cause severe ecological disruption and economic losses when introduced into new environments. Traditional monitoring techniques often lack the sensitivity and efficiency required for early monitoring, hindering timely and effective management. In this study, we used environmental DNA (eDNA) technology to assess fish diversity and identify non-indigenous fish species in ballast water samples collected from 14 international vessels entering Dongjiakou Port, China. Genetic evidence of five non-indigenous fish species was monitored, including two recognized invasive species (Lates calcarifer and Anguilla anguilla). Among all groups, samples from Group B (V2, V3, V6, V8) exhibited the highest diversity of non-indigenous species, suggesting regional differences in species composition that may reflect source port biodiversity. These findings highlight the utility of eDNA-based monitoring not only for early detection of potentially non-indigenous taxa but also for capturing biogeographic patterns associated with global maritime traffic. By demonstrating the effectiveness of this approach at an international port, this study contributes a scientific foundation for both local biodiversity conservation and broader ecological surveillance, offering valuable insights for the ongoing development of ballast water management strategies worldwide. Full article

(This article belongs to the Section Fishery Economics, Policy, and Management)

64 pages, 16633 KiB

Open AccessArticle

Multi-Strategy-Assisted Hybrid Crayfish-Inspired Optimization Algorithm for Solving Real-World Problems

by Wenzhou Lin, Yinghao He, Gang Hu and Chunqiang Zhang

Biomimetics 2025, 10(5), 343; https://doi.org/10.3390/biomimetics10050343 - 21 May 2025

Abstract

In order to solve problems with the original crayfish optimization algorithm (COA), such as reduced diversity, local optimization, and insufficient convergence accuracy, a multi-strategy optimization algorithm for crayfish based on differential evolution, named the ICOA, is proposed. First, the elite chaotic difference strategy [...] Read more.

In order to solve problems with the original crayfish optimization algorithm (COA), such as reduced diversity, local optimization, and insufficient convergence accuracy, a multi-strategy optimization algorithm for crayfish based on differential evolution, named the ICOA, is proposed. First, the elite chaotic difference strategy is used for population initialization to generate a more uniform crayfish population and increase the quality and diversity of the population. Secondly, the differential evolution strategy and the dimensional variation strategy are introduced to improve the quality of the crayfish population before its iteration and to improve the accuracy of the optimal solution and the local search ability for crayfish at the same time. To enhance the updating approach to crayfish exploration, the Levy flight strategy is adopted. This strategy aims to improve the algorithm’s search range and local search capability, prevent premature convergence, and enhance population stability. Finally, the adaptive parameter strategy is introduced to improve the development stage of crayfish, so as to better balance the global search and local mining ability of the algorithm, and to further enhance the optimization ability of the algorithm, and the ability to jump out of the local optimal. In addition, a comparison with the original COA and two sets of optimization algorithms on the CEC2019, CEC2020, and CEC2022 test sets was verified by Wilcoxon rank sum test. The results show that the proposed ICOA has strong competition. At the same time, the performance of ICOA is tested against different high-performance algorithms on 6 engineering optimization examples, 30 high–low-dimension constraint problems and 2 large-scale NP problems. Numerical experiments results show that ICOA has superior performance on a range of engineering problems and exhibits excellent performance in solving complex optimization problems. Full article

(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications: 3rd Edition)

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24 pages, 8298 KiB

Open AccessArticle

Native Grasses Enhance Topsoil Organic Carbon and Nitrogen by Improving Soil Aggregates and Microbial Communities in Navel Orange Orchards in China

by Wenqian Wang, Zhaoyan Ren, Jianjun Wang, Ying Dai, Jingwen Huang, Yang Yang, Xia Zhuang, Mujun Ye, Zhonglan Yang, Fengxian Yao and Chen Cheng

Horticulturae 2025, 11(5), 560; https://doi.org/10.3390/horticulturae11050560 - 21 May 2025

Abstract

In Gannan navel orange (Citrus sinensis) orchards—a typical sloped farmland ecosystem—selected native grasses outperform conventional green manure due to their stronger ecological adaptability and lower management requirements. However, few studies have investigated how native grasses enhance soil organic carbon and nitrogen [...] Read more.

In Gannan navel orange (Citrus sinensis) orchards—a typical sloped farmland ecosystem—selected native grasses outperform conventional green manure due to their stronger ecological adaptability and lower management requirements. However, few studies have investigated how native grasses enhance soil organic carbon and nitrogen contents at the soil aggregate level. A 5-year field study was carried out to analyze the impacts of the native grasses practice on the accumulation of soil organic carbon and nitrogen and the physicochemical properties and microbial communities of soil aggregates in navel orange orchards. Three treatments were tested: (i) clean tillage (CK); (ii) intercropping Centella asiatica (L.) Urban (CA); (iii) intercropping Stellaria media (L.) Cvr. (SM). Our work found that, compared to CK, the soil physical properties improved under the long-term management of native grasses, and the content of nutrients in the soil increased. The contents of SOC (+118.3–184.2%) and total nitrogen (TN) (+73.3–81.5%) changed significantly. The proportion of soil macro-aggregates and the stability of soil aggregates increased, and the contents of SOC and TN in the soil aggregates increased. In addition, under the long-term management of native grasses, the community diversity of beneficial microbes and the abundance of functional genes related to nitrogen cycling increased significantly in the soil aggregates. Native grasses increased the content of nutrients in the soil aggregates by increasing aggregate stability and the abundance of related microorganisms, altering the microbial community structure, and increasing the abundance of related genes for nutrient cycling, thereby enhancing the sequestration of SOC and TN in topsoil. Our results will provide a theoretical basis for the carbon enhancement and fertilization of native grasses as green manure in navel orange orchards and their popularization and application. Full article

(This article belongs to the Section Plant Nutrition)

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43 pages, 7583 KiB

Open AccessArticle

A Particle Swarm Optimization-Guided Ivy Algorithm for Global Optimization Problems

by Kaifan Zhang, Fujiang Yuan, Yang Jiang, Zebing Mao, Zihao Zuo and Yanhong Peng

Biomimetics 2025, 10(5), 342; https://doi.org/10.3390/biomimetics10050342 - 21 May 2025

Abstract

In recent years, metaheuristic algorithms have garnered significant attention for their efficiency in solving complex optimization problems. However, their performance critically depends on maintaining a balance between global exploration and local exploitation; a deficiency in either can result in premature convergence to local [...] Read more.

In recent years, metaheuristic algorithms have garnered significant attention for their efficiency in solving complex optimization problems. However, their performance critically depends on maintaining a balance between global exploration and local exploitation; a deficiency in either can result in premature convergence to local optima or low convergence efficiency. To address this challenge, this paper proposes an enhanced ivy algorithm guided by a particle swarm optimization (PSO) mechanism, referred to as IVYPSO. This hybrid approach integrates PSO’s velocity update strategy for global searches with the ivy algorithm’s growth strategy for local exploitation and introduces an ivy-inspired variable to intensify random perturbations. These enhancements collectively improve the algorithm’s ability to escape local optima and enhance the search stability. Furthermore, IVYPSO adaptively selects between local growth and global diffusion strategies based on the fitness difference between the current solution and the global best, thereby improving the solution diversity and convergence accuracy. To assess the effectiveness of IVYPSO, comprehensive experiments were conducted on 26 standard benchmark functions and three real-world engineering optimization problems, with the performance compared against 11 state-of-the-art intelligent optimization algorithms. The results demonstrate that IVYPSO outperformed most competing algorithms on the majority of benchmark functions, exhibiting superior search capability and robustness. In the stability analysis, IVYPSO consistently achieved the global optimum across multiple runs on the three engineering cases with reduced computational time, attaining a 100% success rate (SR), which highlights its strong global optimization ability and excellent repeatability. Full article

21 pages, 4100 KiB

Open AccessArticle

De Novo Assembly and Comparative Analysis of the Mitochondrial Genomes for Six Rubus Species

by Yujie Shi, Zhen Chen, Jingyong Jiang, Qianfan Li and Wei Zeng

Horticulturae 2025, 11(5), 559; https://doi.org/10.3390/horticulturae11050559 - 21 May 2025

Abstract

Rubus is a genus of small berry-producing shrubs, valued for their medicinal properties and as a food source. This genus is a large, globally distributed group that includes over 700 species. Despite numerous plastid and nuclear genomes having been reported for Rubus, [...] Read more.

Rubus is a genus of small berry-producing shrubs, valued for their medicinal properties and as a food source. This genus is a large, globally distributed group that includes over 700 species. Despite numerous plastid and nuclear genomes having been reported for Rubus, there is a notable lack of research on its mitogenomes. We utilized PMAT to assemble the mitogenomes of six Rubus species according to long-read HiFi reads and annotated them through homologous alignment. Subsequently, we compared their characteristic differences within Rubus mitogenomes. The complete mitogenomes of R. parviflorus, R. spectabilis, R. idaeus, R. armeniacus, and R. caesius all exhibit master circle structures, with lengths ranging from 360,869 bp to 447,754 bp. However, R. chamaemorus displays a double-circle structure composed of two small circular molecules, spanning 392,134 bp. These mitogenomes encode a total of 54–61 genes, including 33–34 PCGs, 17–24 tRNAs, and 3 rRNA genes. Compared to the other five Rubus species, R. chamaemorus has fewer sequence repeats. These six species exhibit similar codon usage patterns. A large number of gene transfers were detected between organellar genomes of six Rubus species. Additionally, two phylogenetic trees were constructed using 31 mitogenomes and 94 chloroplast genomes, revealing a minor conflict within Rubus. Overall, this study clarifies the mitogenome characteristics of Rubus and provides valuable insights into the evolution of the genus. Full article

(This article belongs to the Special Issue Fruit Tree Physiology and Molecular Biology)

21 pages, 422 KiB

Open AccessArticle

Fine Mapping Identifies Candidate Genes Associated with Swine Inflammation and Necrosis Syndrome

by Katharina Gerhards, Sabrina Becker, Josef Kühling, Joel Mickan, Mirjam Lechner, Hermann Willems and Gerald Reiner

Vet. Sci. 2025, 12(5), 508; https://doi.org/10.3390/vetsci12050508 - 21 May 2025

Abstract

Swine inflammation and necrosis syndrome (SINS) is a widespread disease in pigs, causing pain, suffering, and damage. Inflammation is documented at different levels based on clinical signs, histopathology, clinical chemistry, metabolomics and transcriptomics. The influence of sow and boar, as well as a [...] Read more.

Swine inflammation and necrosis syndrome (SINS) is a widespread disease in pigs, causing pain, suffering, and damage. Inflammation is documented at different levels based on clinical signs, histopathology, clinical chemistry, metabolomics and transcriptomics. The influence of sow and boar, as well as a heritability of around 0.3, suggest a genetic component to the disease. The aim of the present study was to identify functional single nucleotide polymorphisms (SNPs) in the vicinity of gene markers previously mapped using GWAS. DNA samples were available from 234 already phenotyped piglets. These animals were re-sequenced with additional prior enrichment. The nine selected chromosomal regions cover a total length of 22 Mbp. The genome-wide association study (GWAS) revealed two series with a total of 15 significant missense polymorphisms on chromosomes 11, 14, and 15. The homozygous genotypes of the most discriminating SNPs in series 1 resulted in SINS scores of 3.5 and 17.9, respectively. Despite the partial linkage of the SNPs, interesting candidate genes were defined. The results allow a significant narrowing of the possible candidate genes for understanding the pathogenesis of SINS and for future use in selection breeding to overcome the syndrome. Further studies should be carried out on larger animal populations. Full article

13 pages, 802 KiB

Open AccessArticle

Predictive Value of Optical Coherence Tomography Biomarkers in Patients with Persistent Diabetic Macular Edema Undergoing Cataract Surgery Combined with a Dexamethasone Intravitreal Implant

by Giuseppe Fasolino, Maryam Lazaar, Domenico Giovanni Della Rocca, Silke Oellerich and Sorcha Ní Dhubhghaill

Bioengineering 2025, 12(5), 556; https://doi.org/10.3390/bioengineering12050556 - 21 May 2025

Abstract

Background: Diabetic macular edema (DME) is the most common cause of vision loss among diabetic patients. The first-line treatments for DME are anti-vascular endothelial growth factor (VEGF)-drugs, while intravitreal steroids are generally reserved for second-line treatment. Limited data exist on the role of [...] Read more.

Background: Diabetic macular edema (DME) is the most common cause of vision loss among diabetic patients. The first-line treatments for DME are anti-vascular endothelial growth factor (VEGF)-drugs, while intravitreal steroids are generally reserved for second-line treatment. Limited data exist on the role of optical coherence tomography (OCT) biomarkers as predictors of success in non-responders to anti-VEGF treatment undergoing simultaneous cataract surgery and dexamethasone intravitreal implant (DEX-I). Methods: This study was designed as a retrospective analysis of patients with DME who were refractory to anti-VEGF treatment but underwent cataract surgery and received a DEX-I at the time of surgery. All procedures were performed between May 2021 and February 2024. The best-corrected visual acuity (BCVA) and central subfoveal thickness (CST) were recorded at baseline and at 1 week, 1 month, and 3 months. The following OCT-based biomarkers were also collected: ellipsoid zone (EZ) integrity, disorganization of the retinal inner layers (DRIL), CST, and hyperreflective foci (HRF). Correlations between the baseline biomarkers and the anatomical outcome were analyzed using linear mixed models (LMMs). Results: Eleven patients (eighteen eyes) met the inclusion criteria. The mean CST decreased significantly from 469.4 ± 53.8 µm at baseline, to 373.1 ± 34.7 µm at 1 week (p = 0.002) and 354.4 ± 24.1 µm at 1 month (p = 0.011). The mean BCVA improved significantly from 0.47 LogMAR to 0.33 LogMAR at 1 week (p = 0.001), 0.23 LogMAR at 1 month (p < 0.001), and 0.25 LogMAR at 3 months (p < 0.001). Baseline predictors significantly influencing CST included the presence of DRIL, a disrupted/absent EZ, and a higher CST. Conclusions: The administration of DEX-I for DME refractory to anti-VEGF treatment in patients undergoing cataract surgery promoted functional improvements persisting longer than the anatomical ones. Patients presenting with DRIL, disrupted EZ, and higher CST at baseline may be better candidates for the combination of DEX-I and cataract surgery. Full article

(This article belongs to the Special Issue Biomedical Applications of Optical Coherence Tomography, Second Edition)

14 pages, 358 KiB

Open AccessArticle

Does Metamizole Cause Less Acute Kidney Injury than Non-Steroidal Anti-Inflammatory Drugs When Combined with Diuretics and Antihypertensives?

by Dulce Maria Calvo, Luis Carlos Saiz, Leire Leache, Maria C. Celaya, Marta Gutiérrez-Valencia, Alvaro Alonso and Juan Erviti

Toxics 2025, 13(5), 417; https://doi.org/10.3390/toxics13050417 - 21 May 2025

Abstract

The concurrent use of (a) diuretics, (b) renin–angiotensin–aldosterone system inhibitors (RAASIs), and (c) non-steroidal anti-inflammatory drugs (NSAIDs) or metamizole, known as the triple whammy (TW) combination, increases the risk of acute kidney injury (AKI). This study compared TWs including metamizole versus NSAIDs regarding [...] Read more.

The concurrent use of (a) diuretics, (b) renin–angiotensin–aldosterone system inhibitors (RAASIs), and (c) non-steroidal anti-inflammatory drugs (NSAIDs) or metamizole, known as the triple whammy (TW) combination, increases the risk of acute kidney injury (AKI). This study compared TWs including metamizole versus NSAIDs regarding hospitalisation for AKI, need for renal replacement therapy (RRT), and all-cause mortality during hospitalisation. Serum creatinine (sCr) and estimated glomerular filtration rate (eGFR) changes in the first year after TW initiation were also assessed. A nested case–control study was conducted within a cohort of adults receiving TW therapy (2009–2018). Logistic regression models analysed the associations between TW type and outcomes. Among 65,077 individuals (mean age 79.7 years; 26.3% male), TW including an NSAID was associated with a lower risk of AKI-related hospitalisation [adjusted odds ratio (aOR) 0.81, 95%CI 0.74–0.87] and all-cause mortality (aOR 0.64, 95%CI 0.49–0.82) compared to TW including metamizole. No significant differences were found in other variables. These findings suggest that TW including an NSAID may reduce the risk of AKI-related hospitalisation and mortality compared to TW including metamizole, although kidney function parameters remained unaffected. Further research is needed to confirm these results. Full article

(This article belongs to the Special Issue Nephrotoxicity Induced by Drugs and Chemicals in the Environment)

20 pages, 690 KiB

Open AccessArticle

Safeguarding Patients, Relatives, and Nurses: A Screening Approach for Detecting 5-FU Residues on Elastomeric Infusion Pumps Using HPLC-DAD

by Andreia Cardoso, Ângelo Jesus, Luísa Barreiros, Daniel Carvalho, Maria dos Anjos Sá, Susana Carvalho, Patrícia Correia and Fernando Moreira

Toxics 2025, 13(5), 416; https://doi.org/10.3390/toxics13050416 - 21 May 2025

Abstract

Background/Objectives: The leakage of 5-fluorouracil (5-FU) from elastomeric infusion pumps used in cancer therapy poses a potential risk of unintentional exposure to multiple individuals, including patients’ relatives and healthcare professionals, and may also compromise the accurate administration of 5-FU dosages to patients. [...] Read more.

Background/Objectives: The leakage of 5-fluorouracil (5-FU) from elastomeric infusion pumps used in cancer therapy poses a potential risk of unintentional exposure to multiple individuals, including patients’ relatives and healthcare professionals, and may also compromise the accurate administration of 5-FU dosages to patients. This study aimed to develop, validate, and apply an analytical method to detect and quantify 5-FU residues on the external surfaces of infusion pumps. Methods: A high-performance liquid chromatography with diode-array detection (HPLC-DAD) method was optimized for the quantification of 5-FU contamination across different components of the infusion pump, including the hard casing, infusion tubing, and catheter connection port. A mobile phase containing 5 % acetic acid was used to obtain more efficient separation of 5-FU and the detection was performed at 260 nm. The method was evaluated for linearity, sensitivity, precision, accuracy, selectivity, robustness, and stability. Results: The method demonstrated linearity within the range of 0.150 to 3.000 µg/cm², with limits of detection and quantification of 0.05 µg/cm² and 0.14 µg/cm², respectively. Relative standard deviations ranged from 1.8% to 12.7%, and accuracy exceeded 85%. In real sample analysis, detectable residues were found around the catheter connection port. Conclusions: This screening-oriented method addresses an existing gap, as previous contamination reports were based solely on self-reported user observations. The detection of 5-FU residues highlights the critical need for safe handling practices and the consistent use of personal protective equipment (PPE) to protect healthcare workers, especially nursing staff involved in the removal of the infusion pumps, after treatment. Full article

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