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23 pages, 9287 KiB

Open AccessArticle

Emulsifying Stability, Digestive Sustained Release, and Cellular Uptake of Alcohol-Soluble Artemisia argyi Flavonoids Were Improved by Glycosylation of Casein Micelles with Oat Glucan

by Ye Zhang, Dongliang Wang, Mengling Peng, Min Yang, Ya Yu, Mengting Yuan, Yanan Liu, Bingyu Zhu, Xiuheng Xue and Juhua Wang

Foods 2025, 14(14), 2435; https://doi.org/10.3390/foods14142435 (registering DOI) - 10 Jul 2025

Abstract

Flavonoids, widely present in Artemisia argyi (AA), offer potential health benefits but are limited in food applications because of their bitter taste, inadequate absorption, and stability. Casein micelles encapsulation can enhance the flavonoid absorption, stability, and bioactivity. In this study, Artemisia argyi flavonoids [...] Read more.

Flavonoids, widely present in Artemisia argyi (AA), offer potential health benefits but are limited in food applications because of their bitter taste, inadequate absorption, and stability. Casein micelles encapsulation can enhance the flavonoid absorption, stability, and bioactivity. In this study, Artemisia argyi flavonoids (AAFs) were extracted using ultrasound-assisted extraction (UAE) to optimize the process. The glycosylation reaction between casein (CN) micelles and oat β-glucan (OBG) was employed to improve AAF’s emulsifying stability, sustained release during digestion, and cellular uptake. The maximum glycosylation degree of 32.33% was achieved at a CN-to-OBG ratio of 1:2, 120 min browning time, and 95 °C temperature. This glycosylated delivery system enhanced the emulsifying properties of the AAFs, digestive sustained release, and cellular uptake, showing potential as a cross-linking material for fat-soluble substances and medicines. Full article

(This article belongs to the Section Food Physics and (Bio)Chemistry)

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19 pages, 2155 KiB

Open AccessArticle

Effects of Degossypolized Cottonseed Protein on the Laying Performance, Egg Quality, Blood Indexes, Gossypol Residue, Liver and Uterine Histopathological Changes, and Intestinal Health of Laying Hens

by Ru Li, Xingyuan Luo, Shiping Bai, Xuemei Ding, Jianping Wang, Qiufeng Zeng, Yue Xuan, Shanshan Li, Sharina Qi, Xiaojuan Bi, Chao He, Xuanming Chen and Keying Zhang

Agriculture 2025, 15(14), 1482; https://doi.org/10.3390/agriculture15141482 (registering DOI) - 10 Jul 2025

Abstract

This experiment aimed to investigate the appropriate level of degossypolized cottonseed protein (DGCP) in the diet of laying hens. A total of 600 49-week-old Lohmann pink laying hens were allocated to five treatments, with six replicates per treatment and 20 birds per replicate. [...] Read more.

This experiment aimed to investigate the appropriate level of degossypolized cottonseed protein (DGCP) in the diet of laying hens. A total of 600 49-week-old Lohmann pink laying hens were allocated to five treatments, with six replicates per treatment and 20 birds per replicate. The control group was fed a corn-soybean meal basal diet. Four experimental diets were formulated by replacing 25%, 50%, 75%, and 100% of the soybean meal protein-equivalent capacity with DGCP, where 100% replacement corresponded to the maximum safe inclusion of DGCP. The study period lasted for 8 weeks. The results showed that the feed intake, average egg weight, egg mass, laying rate, and the albumen percentage were significantly reduced in the 100% DGCP group (p < 0.05). Plasma uric acid (UA), total cholesterol (TC), triglyceride (TG), and potassium (K) levels were significantly lower (p < 0.05), and depth of crypt (CD) was significantly higher (p < 0.05) in the 100% DGCP group. The DGCP diet linearly increased the relative abundance of Bacteroidota and Bacteroide and significantly increased the relative abundance of Desulfobacterotas in the cecum contents compared to the control group (p < 0.05). The ACE and Chao1 indices in both the control group and the 100% DGCP group were significantly decreased (p < 0.05). In conclusion, the dietary addition of DGCP can reach up to 114.6 g/kg. Full article

(This article belongs to the Special Issue Alternative and Novel Feeds for Poultry: Nutritive Value, Product Quality and Environmental Aspects)

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12 pages, 782 KiB

Open AccessArticle

Bariatric Conversion Surgery Impact on LDL Cholesterol in Patients Previously Treated with Sleeve Gastrectomy

by David Benaiges, Max Calzada, Anna Casajoana, Belen Deza, Manuel Pera, Elisenda Climent, Juana A. Flores Le Roux, Marc Beisani, Miguel Olano, Karla A. Pérez-Vega, Juan Pedro-Botet and Albert Goday

J. Clin. Med. 2025, 14(14), 4901; https://doi.org/10.3390/jcm14144901 (registering DOI) - 10 Jul 2025

Abstract

Background/Objectives: Many patients with obesity require conversion bariatric surgery (CBS) after sleeve gastrectomy (SG). The objective of this study was to assess the evolution of LDL cholesterol and other cardiometabolic parameters in patients who have undergone an SG and require a CBS, [...] Read more.

Background/Objectives: Many patients with obesity require conversion bariatric surgery (CBS) after sleeve gastrectomy (SG). The objective of this study was to assess the evolution of LDL cholesterol and other cardiometabolic parameters in patients who have undergone an SG and require a CBS, as the metabolic effects of such conversion procedures remain insufficiently understood. Methods: A retrospective analysis was conducted in a non-randomized prospective cohort of patients with severe obesity who were previously treated with SG and undergoing CBS. Changes in LDL cholesterol levels after SG were compared to those following CBS using repeated-measures ANOVA. Results: Twenty-eight patients were included (mean age 44.5 ± 7.2 years; 68% female; mean BMI 47.3 ± 7.2 kg/m²). Of these, 57% underwent Roux-en-Y gastric bypass (RYGB), and 43% underwent single-anastomosis duodeno–ileal bypass with sleeve gastrectomy (SADI-S) as conversion procedures. The mean time between SG and CBS was 93.5 ± 45.3 months for RYGB and 31.0 ± 45.2 months for SADI-S. The change in LDL cholesterol pre- vs. post-SG was 3.3 mg/dL (95% CI: −13.6 to 20.1), whereas the change pre- vs. post-CBS was −25.7 mg/dL (95% CI: −37.5 to −13.9) (p < 0.001). Remission of high LDL-C was 18.8% after SG and 73.3% after CBS (p = 0.023). The cardiometabolic profile showed a marked improvement profile during the SG period, followed by maintenance of these improvements during the CBS period. Conclusions: CBS (with either RYGB or SADI-S) results in a reduction in LDL-C, in contrast to the initial surgery with SG. However, CBS does not appear to provide additional benefits over SG in terms of other cardiometabolic parameters. Full article

(This article belongs to the Special Issue Obesity Surgery—State of the Art)

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16 pages, 3999 KiB

Open AccessArticle

Towards a Concept for a Multifunctional Mobility Hub: Combining Multimodal Services, Urban Logistics, and Energy

by Jonas Fahlbusch, Felix Fischer, Martin Gegner, Alexander Grahle and Lars Tasche

Logistics 2025, 9(3), 92; https://doi.org/10.3390/logistics9030092 (registering DOI) - 10 Jul 2025

Abstract

Background: This paper proposes a conceptual framework for a multifunctional mobility hub (MMH) that co-locates shared e-mobility services, urban logistics, and charging/storage infrastructure within a single site. Aimed at high-density European cities, the MMH model addresses current gaps in both research and practice, [...] Read more.

Background: This paper proposes a conceptual framework for a multifunctional mobility hub (MMH) that co-locates shared e-mobility services, urban logistics, and charging/storage infrastructure within a single site. Aimed at high-density European cities, the MMH model addresses current gaps in both research and practice, where multimodal mobility services, logistics, and energy are rarely planned in an integrated manner. Methods:A mixed-methods approach was applied, including a systematic literature review (PRISMA), expert interviews, case studies, and a stakeholder workshop, to identify synergies across fleet types and operational domains. Results: The analysis reveals key design principles for MMHs, such as interoperable charging, the functional separation of passenger and freight flows, and modular, scalable infrastructure adapted to urban constraints. Conclusions: The MMH serves as a preliminary concept for planning next-generation mobility stations. It offers qualitative insights for urban planners, operators, and policymakers into how multifunctional hubs may support lower emissions, more efficient operations, and shared infrastructure use. Full article

(This article belongs to the Section Sustainable Supply Chains and Logistics)

13 pages, 2865 KiB

Open AccessArticle

Fine Mapping of BrTCP1 as a Key Regulator of Branching in Flowering Chinese Cabbage (Brassica rapa subsp. chinensis)

by Chuanhong Liu, Xinghua Qi, Shuo Fu, Chao Zheng, Chao Wu, Xiaoyu Li, Yun Zhang and Xueling Ye

Horticulturae 2025, 11(7), 824; https://doi.org/10.3390/horticulturae11070824 (registering DOI) - 10 Jul 2025

Abstract

Branching is a critical agronomic trait in flowering Chinese cabbage (Brassica rapa subsp. chinensis), influencing plant architecture and yield. In this study, there was a highly significant difference between CX010 (single primary rosette branches) and BCT18 (multiple primary rosette branches). Phenotypic [...] Read more.

Branching is a critical agronomic trait in flowering Chinese cabbage (Brassica rapa subsp. chinensis), influencing plant architecture and yield. In this study, there was a highly significant difference between CX010 (single primary rosette branches) and BCT18 (multiple primary rosette branches). Phenotypic analysis revealed significant differences in primary rosette branch numbers, with BCT18 showing up to 15 branches and CX010 displaying only one main stem branch. Genetic analysis indicated that branching was controlled by quantitative trait loci (QTL) with a normal distribution of branch numbers. Using bulked segregant analysis coupled with sequencing (BSA-seq), we identified a candidate interval of approximately 2.96 Mb on chromosome A07 linked to branching. Fine mapping narrowed this to a 172 kb region containing 29 genes, with BraA07g032600.3C (BrTCP1) as the most likely candidate. cDNA cloning of the BrTCP1 gene revealed several variations in BCT18 compared to CX010, including a 6 bp insertion, 10 SNPs, and two single-nucleotide deletions. Expression analysis indicated that BrTCP1 was highly expressed in the rosette stems of CX010 compared to BCT18, consistent with its role as a branching suppressor. The heterologous mutants in Arabidopsis confirmed the conserved role of BrTCP1 in branch inhibition. These findings reveal that BrTCP1 might be a key regulator of branching in flowering Chinese cabbage, providing insights into the molecular mechanisms underlying this trait and offering a framework for genetic improvement in Brassica crops. Full article

(This article belongs to the Special Issue Genetics and Molecular Breeding of Brassica Crops)

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31 pages, 5478 KiB

Open AccessArticle

WHU-RS19 ABZSL: An Attribute-Based Dataset for Remote Sensing Image Understanding

by Mattia Balestra, Marina Paolanti and Roberto Pierdicca

Remote Sens. 2025, 17(14), 2384; https://doi.org/10.3390/rs17142384 (registering DOI) - 10 Jul 2025

Abstract

The advancement of artificial intelligence (AI) in remote sensing (RS) increasingly depends on datasets that offer rich and structured supervision beyond traditional scene-level labels. Although existing benchmarks for aerial scene classification have facilitated progress in this area, their reliance on single-class annotations restricts [...] Read more.

The advancement of artificial intelligence (AI) in remote sensing (RS) increasingly depends on datasets that offer rich and structured supervision beyond traditional scene-level labels. Although existing benchmarks for aerial scene classification have facilitated progress in this area, their reliance on single-class annotations restricts their application to more flexible, interpretable and generalisable learning frameworks. In this study, we introduce WHU-RS19 ABZSL: an attribute-based extension of the widely adopted WHU-RS19 dataset. This new version comprises 1005 high-resolution aerial images across 19 scene categories, each annotated with a vector of 38 features. These cover objects (e.g., roads and trees), geometric patterns (e.g., lines and curves) and dominant colours (e.g., green and blue), and are defined through expert-guided annotation protocols. To demonstrate the value of the dataset, we conduct baseline experiments using deep learning models that had been adapted for multi-label classification—ResNet18, VGG16, InceptionV3, EfficientNet and ViT-B/16—designed to capture the semantic complexity characteristic of real-world aerial scenes. The results, which are measured in terms of macro F1-score, range from 0.7385 for ResNet18 to 0.7608 for EfficientNet-B0. In particular, EfficientNet-B0 and ViT-B/16 are the top performers in terms of the overall macro F1-score and consistency across attributes, while all models show a consistent decline in performance for infrequent or visually ambiguous categories. This confirms that it is feasible to accurately predict semantic attributes in complex scenes. By enriching a standard benchmark with detailed, image-level semantic supervision, WHU-RS19 ABZSL supports a variety of downstream applications, including multi-label classification, explainable AI, semantic retrieval, and attribute-based ZSL. It thus provides a reusable, compact resource for advancing the semantic understanding of remote sensing and multimodal AI. Full article

(This article belongs to the Special Issue Remote Sensing Datasets and 3D Visualization of Geospatial Big Data)

19 pages, 3566 KiB

Open AccessArticle

Surface Ice Detection Using Hyperspectral Imaging and Machine Learning

by Steve Vanlanduit, Arnaud De Vooght and Thomas De Kerf

Sensors 2025, 25(14), 4322; https://doi.org/10.3390/s25144322 (registering DOI) - 10 Jul 2025

Abstract

Ice formation on critical infrastructure such as wind turbine blades can lead to severe performance degradation and safety hazards. This study investigates the use of hyperspectral imaging (HSI) combined with machine learning to detect and classify ice on various coated and uncoated surfaces. [...] Read more.

Ice formation on critical infrastructure such as wind turbine blades can lead to severe performance degradation and safety hazards. This study investigates the use of hyperspectral imaging (HSI) combined with machine learning to detect and classify ice on various coated and uncoated surfaces. Hyperspectral reflectance data were acquired using a push-broom HSI system under controlled laboratory conditions, with ice and rime ice generated using a thermoelectric cooling setup. Support Vector Machine (SVM) and Random Forest (RF) classifiers were trained on uncoated aluminum samples and evaluated on surfaces with different coatings to assess model generalization. Both models achieved high classification accuracy, though performance declined on black-coated surfaces due to increased absorbance by the coating. The study further examined the impact of spectral band reduction to simulate different sensor types (e.g., NIR vs. SWIR), revealing that model performance is sensitive to wavelength range, with SVM performing optimally in a reduced band set and RF benefiting from the full spectral range. A multiclass classification approach using RF successfully distinguished between glaze and rime ice, offering insights into more targeted mitigation strategies. The results confirm the potential of HSI and machine learning as robust tools for surface ice monitoring in safety-critical environments. Full article

(This article belongs to the Section Optical Sensors)

18 pages, 1900 KiB

Open AccessArticle

Recovery of Optical Transport Coefficients Using Diffusion Approximation in Bilayered Tissues: A Theoretical Analysis

by Suraj Rajasekhar and Karthik Vishwanath

Photonics 2025, 12(7), 698; https://doi.org/10.3390/photonics12070698 (registering DOI) - 10 Jul 2025

Abstract

Time-domain (TD) diffuse reflectance can be modeled using diffusion theory (DT) to non-invasively estimate optical transport coefficients of biological media, which serve as markers of tissue physiology. We employ an optimized N-layer DT solver in cylindrical geometry to reconstruct optical coefficients of bilayered [...] Read more.

Time-domain (TD) diffuse reflectance can be modeled using diffusion theory (DT) to non-invasively estimate optical transport coefficients of biological media, which serve as markers of tissue physiology. We employ an optimized N-layer DT solver in cylindrical geometry to reconstruct optical coefficients of bilayered media from TD reflectance generated via Monte Carlo (MC) simulations. Optical properties for 384 bilayered tissue models representing human head or limb tissues were obtained from the literature at three near-infrared wavelengths. MC data were fit using the layered DT model to simultaneously recover transport coefficients in both layers. Bottom-layer absorption was recovered with errors under 0.02 cm⁻¹, and top-layer scattering was retrieved within 3 cm⁻¹ of input values. In contrast, recovered bottom-layer scattering had mean errors exceeding 50%. Total hemoglobin concentration and oxygen saturation were reconstructed for the bottom layer to within 10 μM and 5%, respectively. Extracted transport coefficients were significantly more accurate when obtained using layered DT compared to the conventional, semi-infinite DT model. Our results suggest using improved theoretical modeling to analyze TD reflectance analysis significantly improves recovery of deep-layer absorption. Full article

(This article belongs to the Special Issue Optical Technologies for Biomedical Science)

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23 pages, 504 KiB

Open AccessArticle

Non-Performing Loans and Their Impact on Investor Confidence: A Signaling Theory Perspective—Evidence from U.S. Banks

by Richard Arhinful, Bright Akwasi Gyamfi, Leviticus Mensah and Hayford Asare Obeng

J. Risk Financial Manag. 2025, 18(7), 383; https://doi.org/10.3390/jrfm18070383 (registering DOI) - 10 Jul 2025

Abstract

Bank operations are contingent upon investor confidence, particularly during periods of economic distress. If investor confidence drops, a bank faces difficulties obtaining money, higher borrowing costs, and lower stock values. Non-performing loans (NPLs) potentially jeopardize a bank’s long-term viability and short-term profitability, and [...] Read more.

Bank operations are contingent upon investor confidence, particularly during periods of economic distress. If investor confidence drops, a bank faces difficulties obtaining money, higher borrowing costs, and lower stock values. Non-performing loans (NPLs) potentially jeopardize a bank’s long-term viability and short-term profitability, and investors are naturally wary of institutions that pose a high credit risk. The purpose of the study was to explore how non-performing loans influence investor confidence in banks. A purposive sampling technique was used to identify 253 New York Stock Exchange banks in the Thomson Reuters Eikon DataStream that satisfied all the inclusion and exclusion selection criteria. The Common Correlated Effects Mean Group (CCEMG) and Generalized Method of Moments (GMM) models were used to analyze the data, providing insight into the relationship between the variables. The study discovered that NPLs had a negative and significant influence on price–earnings (P/E) and price-to-book value (P/B) ratios. Furthermore, the bank’s age was found to have a positive and significant relationship with the P/E and P/B ratio. The moderating relationship between NPLs and bank age was found to have a negative and significant influence on price–earnings (P/E) and price-to-book value (P/B) ratios. The findings underscore the importance of asset quality and institutional reputation in influencing market perceptions. Bank managers should focus on managing non-performing loans effectively and leveraging institutional credibility to sustain investor confidence, particularly during financial distress. Full article

(This article belongs to the Special Issue Financial Markets and Institutions and Financial Crises)

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17 pages, 5285 KiB

Open AccessArticle

Fabrication and Performance Study of 3D-Printed Ceramic-in-Gel Polymer Electrolytes

by Xiubing Yao, Wendong Qin, Qiankun Hun, Naiyao Mao, Junming Li, Xinghua Liang, Ying Long and Yifeng Guo

Gels 2025, 11(7), 534; https://doi.org/10.3390/gels11070534 (registering DOI) - 10 Jul 2025

Abstract

Solid-state electrolytes (SSEs) have emerged as a promising solution for next-generation lithium-ion batteries due to their excellent safety and high energy density. However, their practical application is still hindered by critical challenges such as their low ionic conductivity and high interfacial resistance at [...] Read more.

Solid-state electrolytes (SSEs) have emerged as a promising solution for next-generation lithium-ion batteries due to their excellent safety and high energy density. However, their practical application is still hindered by critical challenges such as their low ionic conductivity and high interfacial resistance at room temperature. The innovative application of 3D printing in the field of electrochemistry, particularly in solid-state electrolytes, endows energy storage devices with attractive characteristics. In this study, ceramic-in-gel polymer electrolytes (GPEs) based on PVDF-HFP/PAN@LLZTO were fabricated using a direct ink writing (DIW) 3D printing technique. Under the optimal printing conditions (printing speed of 40 mm/s and fill density of 70%), the printed electrolyte exhibited a uniform and dense sponge-like porous structure, achieving a high ionic conductivity of 5.77 × 10⁻⁴ S·cm⁻¹, which effectively facilitated lithium-ion transport. A structural analysis indicated that the LLZTO fillers were uniformly dispersed within the polymer matrix, significantly enhancing the electrochemical stability of the electrolyte. When applied in a LiFePO₄|GPEs|Li cell configuration, the electrolyte delivered excellent electrochemical performance, with high initial discharge capacities of 168 mAh·g⁻¹ at 0.1 C and 166 mAh·g⁻¹ at 0.2 C, and retained 92.8% of its capacity after 100 cycles at 0.2 C. This work demonstrates the great potential of 3D printing technology in fabricating high-performance GPEs. It provides a novel strategy for the structural design and industrial scalability of lithium-ion batteries. Full article

(This article belongs to the Special Issue Research Progress and Application Prospects of Gel Electrolytes)

32 pages, 9426 KiB

Open AccessArticle

Multi-Output Prediction and Optimization of CO₂ Laser Cutting Quality in FFF-Printed ASA Thermoplastics Using Machine Learning Approaches

by Oguzhan Der

Polymers 2025, 17(14), 1910; https://doi.org/10.3390/polym17141910 (registering DOI) - 10 Jul 2025

Abstract

This research article examines the CO₂ laser cutting performance of Fused Filament Fabricated Acrylonitrile Styrene Acrylate (ASA) thermoplastics by analyzing the influence of plate thickness, laser power, and cutting speed on four quality characteristics: surface roughness (Ra), top kerf width (Top KW), [...] Read more.

This research article examines the CO₂ laser cutting performance of Fused Filament Fabricated Acrylonitrile Styrene Acrylate (ASA) thermoplastics by analyzing the influence of plate thickness, laser power, and cutting speed on four quality characteristics: surface roughness (Ra), top kerf width (Top KW), bottom kerf width (Bottom KW), and bottom heat-affected zone (Bottom HAZ). Forty-five experiments were conducted using five thickness levels, three power levels, and three cutting speeds. To model and predict these outputs, seven machine learning approaches were employed: Autoencoder, Autoencoder–Gated Recurrent Unit, Autoencoder–Long Short-Term Memory, Random Forest, Extreme Gradient Boosting (XGBoost), Support Vector Regression, and Linear Regression. Among them, XGBoost yielded the highest accuracy across all performance metrics. Analysis of Variance results revealed that Ra is mainly affected by plate thickness, Bottom KW by cutting speed, and Bottom HAZ by power, while Top KW is influenced by all three parameters. The study proposes an effective prediction framework using multi-output modeling and hybrid deep learning, offering a data-driven foundation for process optimization. The findings are expected to support intelligent manufacturing systems for real-time quality prediction and adaptive laser post-processing of engineering-grade thermoplastics such as ASA. This integrative approach also enables a deeper understanding of nonlinear dependencies in laser–material interactions. Full article

(This article belongs to the Section Polymer Composites and Nanocomposites)

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24 pages, 1920 KiB

Open AccessReview

Advances in Doxorubicin Chemotherapy: Emerging Polymeric Nanocarriers for Drug Loading and Delivery

by Abhi Bhadran, Himanshu Polara, Godwin K. Babanyinah, Sruthy Baburaj and Mihaela C. Stefan

Cancers 2025, 17(14), 2303; https://doi.org/10.3390/cancers17142303 (registering DOI) - 10 Jul 2025

Abstract

Background/Objectives: Effective and targeted delivery of doxorubicin (DOX) remains a significant challenge due to its dose-limiting cardiotoxicity and systemic side effects. Liposomal formulations like Doxil^® have improved tumor targeting and reduced toxicity, but issues such as limited stability, poor release control, and [...] Read more.

Background/Objectives: Effective and targeted delivery of doxorubicin (DOX) remains a significant challenge due to its dose-limiting cardiotoxicity and systemic side effects. Liposomal formulations like Doxil^® have improved tumor targeting and reduced toxicity, but issues such as limited stability, poor release control, and insufficient site-specific delivery persist. As a result, there is a growing interest in advanced drug delivery systems, particularly polymeric nanocarriers, which offer biocompatibility, tunable properties, and ease of fabrication. Methods: This review is organized into two key sections. The first section provides a comprehensive overview of DOX, including its mechanism of action, clinical challenges, and the limitations of current chemotherapy approaches. The second section highlights recent advances in polymeric nanocarriers for DOX delivery, focusing on polymeric micelles as well as other promising systems like hydrogels, dendrimers, polymersomes, and polymer–drug conjugates. Results: Initial discussions explore current strategies enhancing DOX’s clinical translation, including methods to address cardiotoxicity and multidrug resistance. The latter part presents recent studies that report improved drug loading efficiency in polymeric nanocarriers through techniques such as core/shell modifications, enhanced hydrophobic interactions, and polymer–drug conjugation. Conclusions: Despite notable progress in polymeric nanocarrier-based DOX delivery, challenges like limited circulation time, immunogenicity, and manufacturing scalability continue to hinder clinical application. Continued innovation in this field is crucial for the development of safe, effective, and clinically translatable polymeric nanocarriers for cancer therapy. Full article

(This article belongs to the Section Cancer Drug Development)

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32 pages, 10309 KiB

Open AccessArticle

Demographic Change and Commons Governance: Examining the Impacts of Rural Out-Migration on Public Open Spaces in China Through a Social–Ecological Systems Framework

by Xuerui Shi, Gabriel Hoh Teck Ling and Pau Chung Leng

Land 2025, 14(7), 1444; https://doi.org/10.3390/land14071444 (registering DOI) - 10 Jul 2025

Abstract

Rapid urbanization in China has driven substantial rural population out-migration, raising concerns about its implications for the governance of land commons in villages. While existing studies have acknowledged the effects of migration on rural resource management, little attention has been paid to its [...] Read more.

Rapid urbanization in China has driven substantial rural population out-migration, raising concerns about its implications for the governance of land commons in villages. While existing studies have acknowledged the effects of migration on rural resource management, little attention has been paid to its influence on the self-governance of rural public open spaces (POSs). This study adopts the social–ecological systems (SES) framework to examine how rural out-migration shapes POS self-governance mechanisms. Based on survey data from 594 villagers across 198 villages in Taigu District, partial least squares structural equation modeling (PLS-SEM) and a mediation model grounded in the SES framework were employed for analysis. The results indicate that rural out-migration does not exert a direct impact on POS self-governance. Instead, it negatively influences governance outcomes through full mediation by villager organizations, the left-behind population, collective investment in POSs, and self-organizing activities. Notably, the mediating roles of the left-behind population and self-organizing activities account for 67.38% of the total effect, underscoring their critical importance. Drawing on these insights, the study proposes four policy recommendations to strengthen rural POS self-governance under conditions of demographic transition. This research contributes to the literature by being the first to incorporate an external social factor—rural out-migration—within the SES framework in the context of POS governance, thereby advancing both theoretical and practical understandings of rural commons management. Full article

37 pages, 100738 KiB

Open AccessArticle

Hybrid GIS-Transformer Approach for Forecasting Sentinel-1 Displacement Time Series

by Lama Moualla, Alessio Rucci, Giampiero Naletto, Nantheera Anantrasirichai and Vania Da Deppo

Remote Sens. 2025, 17(14), 2382; https://doi.org/10.3390/rs17142382 (registering DOI) - 10 Jul 2025

Abstract

This study presents a deep learning-based approach for forecasting Sentinel-1 displacement time series, with particular attention to irregular temporal patterns—an aspect often overlooked in previous works. Displacement data were generated using the Parallel Small BAseline Subset (P-SBAS) technique via the Geohazard Thematic Exploitation [...] Read more.

This study presents a deep learning-based approach for forecasting Sentinel-1 displacement time series, with particular attention to irregular temporal patterns—an aspect often overlooked in previous works. Displacement data were generated using the Parallel Small BAseline Subset (P-SBAS) technique via the Geohazard Thematic Exploitation Platform (G-TEP). Initial experiments on a regular dataset from Lombardy employed Long Short-Term Memory (LSTM) models to forecast multiple future time steps. Empirical analysis determined that optimal forecasting is achieved with a 50-time-step input sequence, and that predicting 10% of the input sequence length strikes a balance between temporal coverage and accuracy. The investigation then extended to irregular datasets from Lisbon and Washington, comparing two preprocessing strategies: imputation and the inclusion of time intervals as a second feature. While imputation improved one-step predictions, it was inadequate for multi-step forecasting. To address this, a Time-Gated LSTM (TG-LSTM) was implemented. TG-LSTM outperformed standard LSTM for irregular data in one-step prediction but faced limitations in handling heteroscedasticity and computational cost during multi-step forecasting. These issues were effectively resolved using Temporal Fusion Transformers (TFT), which achieved the best performance, with RMSE values of 1.71 mm/year (Lisbon) and 1.26 mm/year (Washington). A key contribution of this work is the development of a GIS-integrated forecasting toolbox that incorporates LSTM models for regular sequences and TG-LSTM/TFT models for irregular ones. The toolbox enables both single- and multi-step displacement predictions, offering a scalable solution for geohazard monitoring and early warning applications. Full article

(This article belongs to the Special Issue Synthetic Aperture Radar (SAR) Remote Sensing for Civil and Environmental Applications)

21 pages, 10356 KiB

Open AccessArticle

Autonomous Greenhouse Cultivation of Dwarf Tomato: Performance Evaluation of Intelligent Algorithms for Multiple-Sensor Feedback

by Stef C. Maree, Pinglin Zhang, Bart M. van Marrewijk, Feije de Zwart, Monique Bijlaard and Silke Hemming

Sensors 2025, 25(14), 4321; https://doi.org/10.3390/s25144321 (registering DOI) - 10 Jul 2025

Abstract

Greenhouse horticulture plays an important role globally by producing nutritious fruits and vegetables with high resource use efficiency. Modern greenhouses are large-scale high-tech production factories that are increasingly data-driven, and where climate and irrigation control are gradually becoming more autonomous. This is enabled [...] Read more.

Greenhouse horticulture plays an important role globally by producing nutritious fruits and vegetables with high resource use efficiency. Modern greenhouses are large-scale high-tech production factories that are increasingly data-driven, and where climate and irrigation control are gradually becoming more autonomous. This is enabled by technological developments and driven by shortages in skilled labor and the demand for improved resource use efficiency. In the Autonomous Greenhouse Challenge, it has been shown that controlling greenhouse cultivation can be done efficiently with intelligent algorithms. For an optimal strategy, however, it is essential that control algorithms properly account for crop responses, which requires appropriate sensors, reliable data, and accurate models. This paper presents the results of the 4th Autonomous Greenhouse Challenge, in which international teams developed six intelligent algorithms that fully controlled a dwarf tomato cultivation, a crop that is well-suited for robotic harvesting, but for which little prior cultivation data exists. Nevertheless, the analysis of the experiment showed that all teams managed to obtain a profitable strategy, and the best algorithm resulted a production equivalent to 45 kg/m²/year, higher than in the commercial practice of high-wire cherry tomato growing. The predominant factor was found to be the much higher plant density that can be achieved in the applied growing system. More difficult challenges were found to be related to measuring crop status to determine the harvest moment. Finally, this experiment shows the potential for novel greenhouse cultivation systems that are inherently well-suited for autonomous control, and results in a unique and rich dataset to support future research. Full article

(This article belongs to the Special Issue AI, IoT and Smart Sensors for Precision Agriculture: 2nd Edition)

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15 pages, 2997 KiB

Open AccessArticle

Contribution to Distribution and Toxicity Prediction of Organic Pollutants in Receiving Waters from Wastewater Plant Tailwater: A Case Study of the Yitong River, China

by Xiaoyu Zhang, Mingxuan Bai, Ang Dong, Xinrong Du, Yuzhu Ding and Ke Zhao

Water 2025, 17(14), 2061; https://doi.org/10.3390/w17142061 (registering DOI) - 10 Jul 2025

Abstract

Urban river ecosystems are increasingly threatened by anthropogenic activities, with wastewater discharge being a significant contributor. The complex nature and diverse sources of wastewater pose challenges in assessing its impact on water quality and ecological health. This study investigated the distribution, toxicity, and [...] Read more.

Urban river ecosystems are increasingly threatened by anthropogenic activities, with wastewater discharge being a significant contributor. The complex nature and diverse sources of wastewater pose challenges in assessing its impact on water quality and ecological health. This study investigated the distribution, toxicity, and ecological effects of organic pollutants in an urban river system during the dry season. A comprehensive analysis was conducted of 16 phthalate esters (PAEs), 16 polycyclic aromatic hydrocarbons (PAHs), and 8 antibiotics, with a focus on several key pollutants. The results revealed distinct pollutant profiles: Dibutyl phthalate (DBP), Dimethyl phthalate (DEHP), and Diisobutyl phthalate (DIBP) were the predominant PAEs, while Chrysene was the most abundant PAH. Among antibiotics, Oxytetracycline and Norfloxacin were the dominant compounds. Wastewater treatment plant (WWTP) effluents significantly altered the composition of organic pollutants in receiving waters. Although dilution reduced the concentrations of some pollutants, certain organic compounds were detected for the first time downstream of the WWTP, and some specific compounds exhibited increased concentrations. Toxicity prediction using the Concentration Addition (CA) model identified DBP as the primary contributor to overall toxicity, accounting for the highest toxic load among all detected pollutants. Furthermore, WWTP effluents induced significant shifts in microbial community structure downstream, with incomplete recovery to upstream conditions. Integrated analysis of 16S rRNA gene sequencing, water quality assessment, and toxicity prediction elucidated the multifaceted impacts of pollution sources on aquatic ecosystems. This study provides critical insights into the composition, spatial distribution, and toxicity characteristics of organic pollutants in urban rivers, as well as their effects on bacterial community structure. The findings offer a scientific foundation for urban river water quality management and ecological protection strategies. Full article

(This article belongs to the Section Wastewater Treatment and Reuse)

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21 pages, 1847 KiB

Open AccessArticle

Fusion of Recurrence Plots and Gramian Angular Fields with Bayesian Optimization for Enhanced Time-Series Classification

by Maria Mariani, Prince Appiah and Osei Tweneboah

Axioms 2025, 14(7), 528; https://doi.org/10.3390/axioms14070528 (registering DOI) - 10 Jul 2025

Abstract

Time-series classification remains a critical task across various domains, demanding models that effectively capture both local recurrence structures and global temporal dependencies. We introduce a novel framework that transforms time series into image representations by fusing recurrence plots (RPs) with both Gramian Angular [...] Read more.

Time-series classification remains a critical task across various domains, demanding models that effectively capture both local recurrence structures and global temporal dependencies. We introduce a novel framework that transforms time series into image representations by fusing recurrence plots (RPs) with both Gramian Angular Summation Fields (GASFs) and Gramian Angular Difference Fields (GADFs). This fusion enriches the structural encoding of temporal dynamics. To ensure optimal performance, Bayesian Optimization is employed to automatically select the ideal image resolution, eliminating the need for manual tuning. Unlike prior methods that rely on individual transformations, our approach concatenates RP, GASF, and GADF into a unified representation and generalizes to multivariate data by stacking transformation channels across sensor dimensions. Experiments on seven univariate datasets show that our method significantly outperforms traditional classifiers such as one-nearest neighbor with Dynamic Time Warping, Shapelet Transform, and RP-based convolutional neural networks. For multivariate tasks, the proposed fusion model achieves macro F1 scores of 91.55% on the UCI Human Activity Recognition dataset and 98.95% on the UCI Room Occupancy Estimation dataset, outperforming standard deep learning baselines. These results demonstrate the robustness and generalizability of our framework, establishing a new benchmark for image-based time-series classification through principled fusion and adaptive optimization. Full article

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24 pages, 2913 KiB

Open AccessArticle

Generative and Contrastive Self-Supervised Learning for Virulence Factor Identification Based on Protein–Protein Interaction Networks

by Yalin Yao, Hao Chen, Jianxin Wang and Yeru Wang

Microorganisms 2025, 13(7), 1635; https://doi.org/10.3390/microorganisms13071635 (registering DOI) - 10 Jul 2025

Abstract

Virulence factors (VFs), produced by pathogens, facilitate pathogenic microorganisms to invade, colonize, and damage the host cells. Accurate VF identification advances pathogenic mechanism understanding and provides novel anti-virulence targets. Existing models primarily utilize protein sequence features while overlooking the systematic protein–protein interaction (PPI) [...] Read more.

Virulence factors (VFs), produced by pathogens, facilitate pathogenic microorganisms to invade, colonize, and damage the host cells. Accurate VF identification advances pathogenic mechanism understanding and provides novel anti-virulence targets. Existing models primarily utilize protein sequence features while overlooking the systematic protein–protein interaction (PPI) information, despite pathogenesis typically resulting from coordinated protein–protein actions. Moreover, a severe imbalance exists between virulence and non-virulence proteins, which causes existing models trained on balanced datasets by sampling to fail in incorporating proteins’ inherent distributional characteristics, thus restricting generalization to real-world imbalanced data. To address these challenges, we propose a novel Generative and Contrastive self-supervised learning framework for Virulence Factor identification (GC-VF) that transforms VF identification into an imbalanced node classification task on graphs generated from PPI networks. The framework encompasses two core modules: the generative attribute reconstruction module learns attribute space representations via feature reconstruction, capturing intrinsic data patterns and reducing noise; the local contrastive learning module employs node-level contrastive learning to precisely capture local features and contextual information, avoiding global aggregation losses while ensuring node representations truly reflect inherent characteristics. Comprehensive benchmark experiments demonstrate that GC-VF outperforms baseline methods on naturally imbalanced datasets, exhibiting higher accuracy and stability, as well as providing a potential solution for accurate VF identification. Full article

(This article belongs to the Section Molecular Microbiology and Immunology)

17 pages, 1856 KiB

Open AccessArticle

Chemical Stability and Leaching Behavior of ECO EPDM in Acidic Fuel Cell-like Conditions

by Daniel Foltuț, Georgiana-Iulia Șoșoi and Viorel-Aurel Șerban

Materials 2025, 18(14), 3260; https://doi.org/10.3390/ma18143260 (registering DOI) - 10 Jul 2025

Abstract

This study investigates the chemical stability and leaching behavior of two environmentally sustainable EPDM elastomers filled with circular carbon black (CCB) and recycled carbon black (RCB) when exposed to acidic, fuel cell-like environments. Accelerated aging tests were conducted in sulfuric acid solutions of [...] Read more.

This study investigates the chemical stability and leaching behavior of two environmentally sustainable EPDM elastomers filled with circular carbon black (CCB) and recycled carbon black (RCB) when exposed to acidic, fuel cell-like environments. Accelerated aging tests were conducted in sulfuric acid solutions of varying concentrations (1 M, 0.1 M, and 0.001 M) at 90 °C for 1000 h to simulate long-term degradation in proton exchange membrane fuel cell (PEMFC) sealing applications. Complementary hot water extraction tests (HWET) were performed at 80 °C for up to 168 h to evaluate ionic leaching via conductivity measurements. HPLC-DAD analysis was used to assess organic leachates, while surface changes were examined by SEM and thermal transitions by DSC. Results revealed lower leaching and improved surface preservation in the CCB-filled EPDM, which remained below the critical 5 µS/cm ionic conductivity threshold for longer durations than its RCB counterpart. HPLC results showed filler-dependent trends in organic compound release, with CCB EPDM exhibiting higher leaching only under strong acid exposure. SEM confirmed greater surface damage and porosity in RCB EPDM. Overall, both materials demonstrated adequate chemical resistance, but the CCB formulation exhibited superior long-term stability, supporting its use in sustainable PEMFC sealing applications. Full article

(This article belongs to the Collection Materials and Technologies for Hydrogen and Fuel Cells)

19 pages, 3243 KiB

Open AccessArticle

Jujube–Cotton Intercropping Enhances Yield and Economic Benefits via Photosynthetic Regulation in Oasis Agroecosystems of Southern Xinjiang

by Shuting Zhang, Jinbin Wang, Zhengjun Cui, Tiantian Li, Zhenlin Dong, Hang Qiao, Ling Li, Sumei Wan, Xiaofei Li, Wei Zhang, Qiang Hu and Guodong Chen

Agronomy 2025, 15(7), 1676; https://doi.org/10.3390/agronomy15071676 (registering DOI) - 10 Jul 2025

Abstract

This study aimed to clarify the effects of jujube–cotton intercropping on cotton yield and photosynthetic characteristics, providing a theoretical basis for its application in the oasis irrigation areas of southern Xinjiang and offering practical recommendations to local farmers for increasing economic benefits. The [...] Read more.

This study aimed to clarify the effects of jujube–cotton intercropping on cotton yield and photosynthetic characteristics, providing a theoretical basis for its application in the oasis irrigation areas of southern Xinjiang and offering practical recommendations to local farmers for increasing economic benefits. The effects were investigated from 2020 to 2023 using Zhongmian 619 cotton and juvenile jujube trees. Changes in leaf area index (LAI), transpiration rate (Tr), stomatal conductance (Gs), net photosynthetic rate (Pn), intercellular CO₂ concentration (Ci), yield, and economic benefits were evaluated over the years. The results showed that (1) a positive correlation was observed between LAI and the photosynthetic characteristics of cotton. Compared to monoculture cotton, intercropped cotton exhibited lower Pn, Gs, and Tr, and at the peak boll stage, monoculture cotton had significantly higher photosynthetic characteristics, indicating that intercropping affected cotton photosynthesis. (2) From 2020 to 2023, the land equivalent ratio (LER) of jujube–cotton intercropping remained above 1, with overall yield and economic benefit surpassing those of monoculture cotton and jujube, particularly in 2023 when the yield increased by 55.35%. (3) A significant positive correlation was found between cotton yield and LAI. In conclusion, jujube–cotton intercropping enhances photosynthesis, improving yield, economic benefits, and land use efficiency. Full article

(This article belongs to the Special Issue Innovations in Green and Efficient Cotton Cultivation)

22 pages, 995 KiB

Open AccessArticle

Designing a Generalist Education AI Framework for Multimodal Learning and Ethical Data Governance

by Yuyang Yan, Hui Liu, Helen Zhang, Toby Chau and Jiahui Li

Appl. Sci. 2025, 15(14), 7758; https://doi.org/10.3390/app15147758 (registering DOI) - 10 Jul 2025

Abstract

The integration of artificial intelligence (AI) into education requires frameworks that are not only technically robust but also ethically and pedagogically grounded. This paper proposes the Generalist Education Artificial Intelligence (GEAI) framework—a conceptual blueprint designed to enable privacy-preserving, personalized, and multimodal AI-supported learning [...] Read more.

The integration of artificial intelligence (AI) into education requires frameworks that are not only technically robust but also ethically and pedagogically grounded. This paper proposes the Generalist Education Artificial Intelligence (GEAI) framework—a conceptual blueprint designed to enable privacy-preserving, personalized, and multimodal AI-supported learning in educational contexts. GEAI features a Trusted Domain architecture that supports secure, voluntary multimodal data collection via multimedia registration devices (MM Devices), edge-based AI inference, and institutional data sovereignty. Drawing on principles from constructivist pedagogy and regulatory standards such as GDPR and FERPA, GEAI supports adaptive feedback, engagement monitoring, and learner-centered interaction while addressing key challenges in ethical data governance, transparency, and accountability. To bridge theory and application, we outline a staged validation roadmap informed by technical feasibility assessments and stakeholder input. This roadmap lays the foundation for future prototyping and responsible deployment in real-world educational settings, positioning GEAI as a forward-looking contribution to both AI system design and education policy alignment. Full article

(This article belongs to the Special Issue ICT in Education, 2nd Edition)

19 pages, 984 KiB

Open AccessArticle

Effects of Time-Restricted Eating (Early and Late) Combined with Energy Restriction vs. Energy Restriction Alone on the Gut Microbiome in Adults with Obesity

by Bernarda Habe, Tanja Črešnovar, Matjaž Hladnik, Jure Pražnikar, Saša Kenig, Dunja Bandelj, Nina Mohorko, Ana Petelin and Zala Jenko Pražnikar

Nutrients 2025, 17(14), 2284; https://doi.org/10.3390/nu17142284 (registering DOI) - 10 Jul 2025

Abstract

Background: Early time-restricted eating combined with energy restriction (eTRE + ER) has been shown to reduce fat mass, diastolic blood pressure (DBP) and fasting glucose more effectively than late TRE with energy restriction (lTRE + ER) or energy restriction (ER) alone. Given the [...] Read more.

Background: Early time-restricted eating combined with energy restriction (eTRE + ER) has been shown to reduce fat mass, diastolic blood pressure (DBP) and fasting glucose more effectively than late TRE with energy restriction (lTRE + ER) or energy restriction (ER) alone. Given the gut microbiome’s sensitivity to circadian rhythms, we examined whether adding TRE, particularly eTRE, to ER alters gut microbiota composition beyond ER alone, and whether such effects persist during follow-up. Methods: We analysed anthropometric, biochemical and gut microbiome data from 76 participants at baseline and after a 3-month intervention (eTRE + ER: n = 33; lTRE + ER: n = 23; ER: n = 20). Follow-up microbiome data 6-months after the end of intervention were available for 43 participants. Gut microbiota composition was assessed via 16S rRNA gene sequencing of stool samples. Results: No significant between-group differences in beta diversity were observed over time. However, changes in alpha diversity differed significantly across groups at the end of the intervention (Shannon: F = 5.72, p < 0.001; Simpson: F = 6.72, p < 0.001; Richness: F = 3.99, p = 0.01) and at follow-up (Richness: F = 3.77, p = 0.02). lTRE + ER led to the greatest reductions in diversity post intervention, while ER was least favourable during follow-up. Although no significant between-group differences were observed at the phylum level either at the end of the intervention or during follow-up, only the eTRE + ER group exhibited a significant decrease in Bacillota and an increase in Bacteroidota during follow-up. At the genus level, differential abundance analysis revealed significant shifts in taxa such as Faecalibacterium, Subdoligranulum, and other genera within the Ruminococcaceae and Oscillospiraceae families. In the eTRE + ER, Faecalibacterium and Subdoligranulum increased, while in other groups decreased. Notably, the changes in Faecalibacterium were negatively correlated with fasting glucose, while the increase in Subdoligranulum was inversely associated with DBP; however, both associations were weak in strength. Conclusions: eTRE + ER may promote beneficial, lasting shifts in the gut microbiome associated with improved metabolic outcomes. These results support further research into personalized TRE strategies for treatment of obesity. Full article

(This article belongs to the Section Nutrition and Obesity)

22 pages, 1885 KiB

Open AccessArticle

Breast Cancer Classification with Various Optimized Deep Learning Methods

by Mustafa Güler, Gamze Sart, Ömer Algorabi, Ayse Nur Adıguzel Tuylu and Yusuf Sait Türkan

Diagnostics 2025, 15(14), 1751; https://doi.org/10.3390/diagnostics15141751 (registering DOI) - 10 Jul 2025

Abstract

Background/Objectives: In recent years, there has been a significant increase in the number of women with breast cancer. Breast cancer prediction is defined as a medical data analysis and image processing problem. Experts may need artificial intelligence technologies to distinguish between benign [...] Read more.

Background/Objectives: In recent years, there has been a significant increase in the number of women with breast cancer. Breast cancer prediction is defined as a medical data analysis and image processing problem. Experts may need artificial intelligence technologies to distinguish between benign and malignant tumors in order to make decisions. When the studies in the literature are examined, it can be seen that applications of deep learning algorithms in the field of medicine have achieved very successful results. Methods: In this study, 11 different deep learning algorithms (Vanilla, ResNet50, ResNet152, VGG16, DenseNet152, MobileNetv2, EfficientB1, NasNet, DenseNet201, ensemble, and Tuned Model) were used. Images of pathological specimens from breast biopsies consisting of two classes, benign and malignant, were used for classification analysis. To limit the computational time and speed up the analysis process, 10,000 images, 6172 IDC-negative and 3828 IDC-positive, were selected. Of the images, 80% were used for training, 10% were used for validation, and 10% were used for testing the trained model. Results: The results demonstrate that DenseNet201 achieved the highest classification accuracy of 89.4%, with a precision of 88.2%, a recall of 84.1%, an F1 score of 86.1%, and an AUC score of 95.8%. Conclusions: In conclusion, this study highlights the potential of deep learning algorithms in breast cancer classification. Future research should focus on integrating multi-modal imaging data, refining ensemble learning methodologies, and expanding dataset diversity to further improve the classification accuracy and real-world clinical applicability. Full article

(This article belongs to the Topic Machine Learning and Deep Learning in Medical Imaging)

22 pages, 2406 KiB

Open AccessReview

Sirtuins Contribute to the Migraine–Stroke Connection

by Jan Krekora, Michal Fila, Maria Mitus-Kenig, Elzbieta Pawlowska, Justyna Ciupinska and Janusz Blasiak

Int. J. Mol. Sci. 2025, 26(14), 6634; https://doi.org/10.3390/ijms26146634 (registering DOI) - 10 Jul 2025

Abstract

The prevalence of stroke in patients with migraine is higher than in the general population, suggesting certain shared mechanisms of pathogenesis. Migrainous infarction is a pronounced example of the migraine–stroke connection. Some cases of migraine with aura may be misdiagnosed as stroke, with [...] Read more.

The prevalence of stroke in patients with migraine is higher than in the general population, suggesting certain shared mechanisms of pathogenesis. Migrainous infarction is a pronounced example of the migraine–stroke connection. Some cases of migraine with aura may be misdiagnosed as stroke, with subsequent mistreatment. Therefore, it is important to identify these shared mechanisms of pathogenesis contributing to the migraine–stroke connection to improve diagnosis and treatment. Sirtuins (SIRTs) are a seven-member family of NAD⁺-dependent histone deacetylases that can epigenetically regulate gene expression. Sirtuins possess antioxidant properties, making them a first-line defense against oxidative stress, which is important in the pathogenesis of migraine and stroke. Mitochondrial localization of SIRT2, SIRT3, and SIRT4 supports this function, as most reactive oxygen and nitrogen species are produced in mitochondria. In this narrative review, we present arguments that sirtuins may link migraine with stroke through their involvement in antioxidant defense, mitochondrial quality control, neuroinflammation, and autophagy. We also indicate mediators of this involvement that can be, along with sirtuins, therapeutic targets to ameliorate migraine and prevent stroke. Full article

(This article belongs to the Special Issue Molecular and Clinical Aspects of Migraine and Its Comorbidities: Epilepsy, Stroke, and Restless Leg Syndrome)

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32 pages, 6617 KiB

Open AccessArticle

Hyaluronan-Containing Injectable Magnesium–Calcium Phosphate Cements Demonstrated Improved Performance, Cytocompatibility, and Ability to Support Osteogenic Differentiation In Vitro

by Natalia S. Sergeeva, Polina A. Krokhicheva, Irina K. Sviridova, Margarita A. Goldberg, Dinara R. Khayrutdinova, Suraya A. Akhmedova, Valentina A. Kirsanova, Olga S. Antonova, Alexander S. Fomin, Ivan V. Mikheev, Aleksander V. Leonov, Pavel A. Karalkin, Sergey A. Rodionov, Sergey M. Barinov, Vladimir S. Komlev and Andrey D. Kaprin

Int. J. Mol. Sci. 2025, 26(14), 6624; https://doi.org/10.3390/ijms26146624 (registering DOI) - 10 Jul 2025

Abstract

Due to their biocompatibility, biodegradability, injectability, and self-setting properties, calcium–magnesium phosphate cements (MCPCs) have proven to be effective biomaterials for bone defect filling. Two types of MCPC powders based on the magnesium whitlockite or stanfieldite phases with MgO with different magnesium contents (20 [...] Read more.

Due to their biocompatibility, biodegradability, injectability, and self-setting properties, calcium–magnesium phosphate cements (MCPCs) have proven to be effective biomaterials for bone defect filling. Two types of MCPC powders based on the magnesium whitlockite or stanfieldite phases with MgO with different magnesium contents (20 and 60%) were synthesised. The effects of magnesium ions (Mg²⁺) on functional properties such as setting time, temperature, mechanical strength, injectability, cohesion, and in vitro degradation kinetics, as well as cytocompatibility in the MG-63 cell line and the osteogenic differentiation of BM hMSCs in vitro, were analysed. The introduction of NaHA into the cement liquid results in an increase in injectability of up to 83%, provides a compressive strength of up to 22 MPa, and shows a reasonable setting time of about 20 min without an exothermic reaction. These cements had the ability to support MG-63 cell adhesion, proliferation, and spread and the osteogenic differentiation of BM hMSCs in vitro, stimulating ALPL, SP7, and RUNX2 gene expression and ALPL production. The combination of the studied physicochemical and biological properties of the developed cement compositions characterises them as bioactive, cytocompatible, and promising biomaterials for bone defect reconstruction. Full article

(This article belongs to the Special Issue Fundamental and Practical Perspectives in Regenerative Medicine: Proceedings of the VI National Congress of Regenerative Medicine (2024))

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26 pages, 1261 KiB

Open AccessReview

Recent Advances in Biocatalytic Dearomative Spirocyclization Reactions

by Xiaorui Chen, Changtong Zhu, Luyun Ji, Changmei Liu, Yan Zhang, Yijian Rao and Zhenbo Yuan

Catalysts 2025, 15(7), 673; https://doi.org/10.3390/catal15070673 (registering DOI) - 10 Jul 2025

Abstract

Spirocyclic architectures, which feature two rings sharing a single atom, are common in natural products and exhibit beneficial biological and material properties. Due to the significance of these architectures, biocatalytic dearomative spirocyclization has recently emerged as a powerful approach for constructing three-dimensional spirocyclic [...] Read more.

Spirocyclic architectures, which feature two rings sharing a single atom, are common in natural products and exhibit beneficial biological and material properties. Due to the significance of these architectures, biocatalytic dearomative spirocyclization has recently emerged as a powerful approach for constructing three-dimensional spirocyclic frameworks under mild, sustainable conditions and with exquisite stereocontrol. This review surveys the latest advances in biocatalyzed spirocyclization of all-carbon arenes (phenols and benzenes), aza-aromatics (indoles and pyrroles), and oxa-aromatics (furans). We highlight cytochrome P450s, flavin-dependent monooxygenases, multicopper oxidases, and novel metalloenzyme platforms that effect regio- and stereoselective oxidative coupling, epoxidation/semi-pinacol rearrangement, and radical-mediated cyclization to produce diverse spirocycles. Mechanistic insights gleaned from structural, computational, and isotope-labeling studies are discussed where necessary to help the readers further understand the reported reactions. Collectively, these examples demonstrate the transformative potential of biocatalysis to streamline access to spirocyclic scaffolds that are challenging to prepare through traditional methods, underscoring biocatalysis as a transformative tool for synthesizing pharmaceutically relevant spiroscaffolds while adhering to green chemistry paradigms to ultimately contribute to a cleaner and more sustainable future. Full article

(This article belongs to the Section Biocatalysis)

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