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Urban heat islands (UHIs) are a growing sustainability challenge impacting public health, energy use, and climate resilience, especially in hot, arid cities like San Antonio, Texas, where land surface temperatures reach up to 47.63 °C. This study advances a data-driven, interdisciplinary approach to UHI mitigation by integrating Machine Learning (ML) with physical and socio-demographic data for sustainable urban planning. Using high-resolution spatial data across five functional zones (residential, commercial, industrial, official, and downtown), we apply three ML models, Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting Machine (GBM), to predict land surface temperature (LST). The models incorporate both environmental variables, such as imperviousness, Normalized Difference Vegetation Index (NDVI), building area, and solar influx, and social determinants, such as population density, income, education, and age distribution. SVM achieved the highest R² (0.870), while RF yielded the lowest RMSE (0.488 °C), confirming robust predictive performance. Key predictors of elevated LST included imperviousness, building area, solar influx, and NDVI. Our results underscore the need for zone-specific strategies like more greenery, less impervious cover, and improved building design. These findings offer actionable insights for urban planners and policymakers seeking to develop equitable and sustainable UHI mitigation strategies aligned with climate adaptation and environmental justice goals. Full article

(This article belongs to the Special Issue Emerging Topics in the Sustainable Built Environment: Climate Change Adaptation, Energy Poverty and Well-Being)

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22 pages, 13770 KiB

Open AccessArticle

Prediction Model of Powdery Mildew Disease Index in Rubber Trees Based on Machine Learning

by Jiazheng Zhu, Xize Huang, Xiaoyu Liang, Meng Wang and Yu Zhang

Plants 2025, 14(15), 2402; https://doi.org/10.3390/plants14152402 (registering DOI) - 3 Aug 2025

Abstract

Powdery mildew, caused by Erysiphe quercicola, is one of the primary diseases responsible for the reduction in natural rubber production in China. This disease is a typical airborne pathogen, characterized by its ability to spread via air currents and rapidly escalate into an epidemic under favorable environmental conditions. Accurate prediction and determination of the prevention and control period represent both a critical challenge and key focus area in managing rubber-tree powdery mildew. This study investigates the effects of spore concentration, environmental factors, and infection time on the progression of powdery mildew in rubber trees. By employing six distinct machine learning model construction methods, with the disease index of powdery mildew in rubber trees as the response variable and spore concentration, temperature, humidity, and infection time as predictive variables, a preliminary predictive model for the disease index of rubber-tree powdery mildew was developed. Results from indoor inoculation experiments indicate that spore concentration directly influences disease progression and severity. Higher spore concentrations lead to faster disease development and increased severity. The optimal relative humidity for powdery mildew development in rubber trees is 80% RH. At varying temperatures, the influence of humidity on the disease index differs across spore concentration, exhibiting distinct trends. Each model effectively simulates the progression of powdery mildew in rubber trees, with predicted values closely aligning with observed data. Among the models, the Kernel Ridge Regression (KRR) model demonstrates the highest accuracy, the R² values for the training set and test set were 0.978 and 0.964, respectively, while the RMSE values were 4.037 and 4.926, respectively. This research provides a robust technical foundation for reducing the labor intensity of traditional prediction methods and offers valuable insights for forecasting airborne forest diseases. Full article

(This article belongs to the Section Plant Modeling)

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27 pages, 2930 KiB

Open AccessArticle

Comparative Performance Analysis of Gene Expression Programming and Linear Regression Models for IRI-Based Pavement Condition Index Prediction

by Mostafa M. Radwan, Majid Faissal Jassim, Samir A. B. Al-Jassim, Mahmoud M. Elnahla and Yasser A. S. Gamal

Eng 2025, 6(8), 183; https://doi.org/10.3390/eng6080183 (registering DOI) - 3 Aug 2025

Abstract

Traditional Pavement Condition Index (PCI) assessments are highly resource-intensive, demanding substantial time and labor while generating significant carbon emissions through extensive field operations. To address these sustainability challenges, this research presents an innovative methodology utilizing Gene Expression Programming (GEP) to determine PCI values based on International Roughness Index (IRI) measurements from Iraqi road networks, offering an environmentally conscious and resource-efficient approach to pavement management. The study incorporated 401 samples of IRI and PCI data through comprehensive visual inspection procedures. The developed GEP model exhibited exceptional predictive performance, with coefficient of determination (R²) values achieving 0.821 for training, 0.858 for validation, and 0.8233 overall, successfully accounting for approximately 82–85% of PCI variance. Prediction accuracy remained robust with Mean Absolute Error (MAE) values of 12–13 units and Root Mean Square Error (RMSE) values of 11.209 and 11.00 for training and validation sets, respectively. The lower validation RMSE suggests effective generalization without overfitting. Strong correlations between predicted and measured values exceeded 0.90, with acceptable relative absolute error values ranging from 0.403 to 0.387, confirming model effectiveness. Comparative analysis reveals GEP outperforms alternative regression methods in generalization capacity, particularly in real-world applications. This sustainable methodology represents a cost-effective alternative to conventional PCI evaluation, significantly reducing environmental impact through decreased field operations, lower fuel consumption, and minimized traffic disruption. By streamlining pavement management while maintaining assessment reliability and accuracy, this approach supports environmentally responsible transportation systems and aligns contemporary sustainability goals in infrastructure management. Full article

(This article belongs to the Section Chemical, Civil and Environmental Engineering)

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22 pages, 3304 KiB

Open AccessArticle

The Mechanism by Which Colour Patch Characteristics Influence the Visual Landscape Quality of Rhododendron simsii Landscape Recreational Forests

by Yan Liu, Juyang Liao, Yaqi Huang, Qiaoyun Li, Linshi Wu, Xinyu Yi, Ling Wang and Chan Chen

Horticulturae 2025, 11(8), 898; https://doi.org/10.3390/horticulturae11080898 (registering DOI) - 3 Aug 2025

Abstract

Landscape quality and the productivity of

Rhododendron simsii

Landscape quality and the productivity of

Rhododendron simsii

are directly related to the maintenance of ecological functions in the alpine region. The specific relationship between the spatial pattern of colour patches and the visual quality of R. simsii landscape recreational forests has been insufficiently explored. In this study, we constructed a model of the relationship between landscape colour patches and the aesthetic value of such a forest, analysing the key factors driving changes in its landscape quality. A total of 1549 participants were asked to assess 16 groups of landscape photographs. The results showed that variations in perceived aesthetic quality were stimulated by colour patch dynamics and spatial heterogeneity. Utilising structural equation modelling (SEM), we identified key indicators synergistically influencing aesthetic quality, including the area percentage, shape, and distribution of colour patches, which demonstrated strong explanatory power (

R^{2}

= 0.83). The SEM also revealed that the red patch area, mean perimeter area ratio, and separation index are critical latent variables with standardised coefficients of 0.54, 0.65, and 0.62, respectively. These findings provide actionable design strategies: (1) optimising chromatic contrast through high-saturation patches, (2) controlling geometric complexity, and (3) improving spatial coherence. These results advance the theoretical framework for landscape aesthetic evaluation and offer practical guidance for landscape recreational forest management. Full article

(This article belongs to the Section Floriculture, Nursery and Landscape, and Turf)

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25 pages, 5704 KiB

Open AccessArticle

A Robust Framework for Bamboo Forest AGB Estimation by Integrating Geostatistical Prediction and Ensemble Learning

by Lianjin Fu, Qingtai Shu, Cuifen Xia, Zeyu Li, Hailing He, Zhengying Li, Shaoyang Ma, Chaoguan Qin, Rong Wei, Qin Xiang, Xiao Zhang, Yiran Zhang and Huashi Cai

Remote Sens. 2025, 17(15), 2682; https://doi.org/10.3390/rs17152682 (registering DOI) - 3 Aug 2025

Abstract

Accurate above-ground biomass (AGB) quantification is confounded by signal saturation and data fusion challenges, particularly in structurally complex ecosystems like bamboo forests. To address these gaps, this study developed a two-stage framework to map the AGB of Dendrocalamus giganteus in a subtropical mountain environment. This study first employed Empirical Bayesian Kriging Regression Prediction (EBKRP) to spatialize sparse GEDI and ICESat-2 LiDAR metrics using Sentinel-2 and topographic covariates. Subsequently, a stacked ensemble model, integrating four machine learning algorithms, predicted AGB from the full suite of continuous variables. The stacking model achieved high predictive accuracy (R² = 0.84, RMSE = 11.07 Mg ha⁻¹) and substantially mitigated the common bias of underestimating high AGB, improving the predicted observed regression slope from a base model average of 0.63 to 0.81. Furthermore, SHAP analysis provided mechanistic insights, identifying the canopy photon rate as the dominant predictor and quantifying the ecological thresholds governing AGB distribution. The mean AGB density was 71.8 ± 21.9 Mg ha⁻¹, with its spatial pattern influenced by elevation and human settlements. This research provides a robust framework for synergizing multi-source remote sensing data to improve AGB estimation, offering a refined methodological pathway for large-scale carbon stock assessments. Full article

(This article belongs to the Special Issue Land Use Monitoring Based on Remote Sensing and Artificial Intelligence)

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16 pages, 1961 KiB

Open AccessArticle

A Novel Glycosylated Ferulic Acid Conjugate: Synthesis, Antioxidative Neuroprotection Activities In Vitro, and Alleviation of Cerebral Ischemia–Reperfusion Injury (CIRI) In Vivo

by Jian Chen, Yongjun Yuan, Litao Tong, Manyou Yu, Yongqing Zhu, Qingqing Liu, Junling Deng, Fengzhang Wang, Zhuoya Xiang and Chen Xia

Antioxidants 2025, 14(8), 953; https://doi.org/10.3390/antiox14080953 (registering DOI) - 3 Aug 2025

Abstract

Antioxidative neuroprotection is effective at preventing ischemic stroke (IS). Ferulic acid (FA) offers benefits in the treatment of many diseases, mostly due to its antioxidant activities. In this study, a glycosylated ferulic acid conjugate (FA-Glu), with 1,2,3-triazole as a linker and bioisostere between glucose at the C6 position and FA at the C4 position, was designed and synthesized. The hydrophilicity and chemical stability of FA-Glu were tested. FA-Glu’s protection against DNA oxidative cleavage was tested using pBR322 plasmid DNA under the Fenton reaction. The cytotoxicity of FA-Glu was examined via the PC12 cell and bEnd.3 cell tests. Antioxidative neuroprotection was evaluated, in vitro, via a H₂O₂-induced PC12 cell test, measuring cell viability and ROS levels. Antioxidative alleviation of cerebral ischemia–reperfusion injury (CIRI), in vivo, was evaluated using a rat middle cerebral artery occlusion (MCAO) model. The results indicated that FA-Glu was water-soluble (LogP −1.16 ± 0.01) and chemically stable. FA-Glu prevented pBR322 plasmid DNA cleavage induced via •OH radicals (SC% 88.00%). It was a non-toxic agent based on PC12 cell and bEnd.3 cell tests results. FA-Glu significantly protected against H₂O₂-induced oxidative damage in the PC12 cell (cell viability 88.12%, 100 μM) and inhibited excessive cell ROS generation (45.67% at 100 μM). FA-Glu significantly reduced the infarcted brain areas measured using TTC stain observation, quantification (FA-Glu 21.79%, FA 28.49%, I/R model 43.42%), and H&E stain histological observation. It sharply reduced the MDA level (3.26 nmol/mg protein) and significantly increased the GSH level (139.6 nmol/mg protein) and SOD level (265.19 U/mg protein). With superior performance to FA, FA-Glu is a safe agent with effective antioxidative DNA and neuronal protective actions and an ability to alleviate CIRI, which should help in the prevention of IS. Full article

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14 pages, 1728 KiB

Open AccessArticle

Accelerating High-Frequency Circuit Optimization Using Machine Learning-Generated Inverse Maps for Enhanced Space Mapping

by Jorge Davalos-Guzman, Jose L. Chavez-Hurtado and Zabdiel Brito-Brito

Electronics 2025, 14(15), 3097; https://doi.org/10.3390/electronics14153097 (registering DOI) - 3 Aug 2025

Abstract

The optimization of high-frequency circuits remains a computationally intensive task due to the need for repeated high-fidelity electromagnetic (EM) simulations. To address this challenge, we propose a novel integration of machine learning-generated inverse maps within the space mapping (SM) optimization framework to significantly accelerate circuit optimization while maintaining high accuracy. The proposed approach leverages Bayesian Neural Networks (BNNs) and surrogate modeling techniques to construct an inverse mapping function that directly predicts design parameters from target performance metrics, bypassing iterative forward simulations. The methodology was validated using a low-pass filter optimization scenario, where the inverse surrogate model was trained using electromagnetic simulations from COMSOL Multiphysics 2024 r6.3 and optimized using MATLAB R2024b r24.2 trust region algorithm. Experimental results demonstrate that our approach reduces the number of high-fidelity simulations by over 80% compared to conventional SM techniques while achieving high accuracy with a mean absolute error (MAE) of 0.0262 (0.47%). Additionally, convergence efficiency was significantly improved, with the inverse surrogate model requiring only 31 coarse model simulations, compared to 580 in traditional SM. These findings demonstrate that machine learning-driven inverse surrogate modeling significantly reduces computational overhead, accelerates optimization, and enhances the accuracy of high-frequency circuit design. This approach offers a promising alternative to traditional SM methods, paving the way for more efficient RF and microwave circuit design workflows. Full article

(This article belongs to the Special Issue Advances in Algorithm Optimization and Computational Intelligence)

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23 pages, 3283 KiB

Open AccessArticle

Light-Driven Optimization of Exopolysaccharide and Indole-3-Acetic Acid Production in Thermotolerant Cyanobacteria

by Antonio Zuorro, Roberto Lavecchia, Karen A. Moncada-Jacome, Janet B. García-Martínez and Andrés F. Barajas-Solano

Sci 2025, 7(3), 108; https://doi.org/10.3390/sci7030108 (registering DOI) - 3 Aug 2025

Abstract

Cyanobacteria are a prolific source of bioactive metabolites with expanding applications in sustainable agriculture and biotechnology. This work explores, for the first time in thermotolerant Colombian isolates, the impact of light spectrum, photoperiod, and irradiance on the co-production of exopolysaccharides (EPS) and indole-3-acetic acid (IAA). Six strains from hot-spring environments were screened under varying blue:red (B:R) LED ratios and full-spectrum illumination. Hapalosiphon sp. UFPS_002 outperformed all others, reaching ~290 mg L⁻¹ EPS and 28 µg mL⁻¹ IAA in the initial screen. Response-surface methodology was then used to optimize light intensity and photoperiod. EPS peaked at 281.4 mg L⁻¹ under a B:R ratio of 1:5 LED, 85 µmol m⁻² s⁻¹, and a 14.5 h light cycle, whereas IAA was maximized at 34.4 µg mL⁻¹ under cool-white LEDs at a similar irradiance. The quadratic models exhibited excellent predictive power (R² > 0.98) and a non-significant lack of fit, confirming the light regime as the dominant driver of metabolite yield. These results demonstrate that precise photonic tuning can selectively steer carbon flux toward either EPS or IAA, providing an energy-efficient strategy to upscale thermotolerant cyanobacteria for climate-resilient biofertilizers, bioplastics precursors, and other high-value bioproducts. Full article

(This article belongs to the Section Biology Research and Life Sciences)

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21 pages, 4252 KiB

Open AccessArticle

AnimalAI: An Open-Source Web Platform for Automated Animal Activity Index Calculation Using Interactive Deep Learning Segmentation

by Mahtab Saeidifar, Guoming Li, Lakshmish Macheeri Ramaswamy, Chongxiao Chen and Ehsan Asali

Animals 2025, 15(15), 2269; https://doi.org/10.3390/ani15152269 (registering DOI) - 3 Aug 2025

Abstract

Monitoring the activity index of animals is crucial for assessing their welfare and behavior patterns. However, traditional methods for calculating the activity index, such as pixel intensity differencing of entire frames, are found to suffer from significant interference and noise, leading to inaccurate results. These classical approaches also do not support group or individual tracking in a user-friendly way, and no open-access platform exists for non-technical researchers. This study introduces an open-source web-based platform that allows researchers to calculate the activity index from top-view videos by selecting individual or group animals. It integrates Segment Anything Model2 (SAM2), a promptable deep learning segmentation model, to track animals without additional training or annotation. The platform accurately tracked Cobb 500 male broilers from weeks 1 to 7 with a 100% success rate, IoU of 92.21% ± 0.012, precision of 93.87% ± 0.019, recall of 98.15% ± 0.011, and F1 score of 95.94% ± 0.006, based on 1157 chickens. Statistical analysis showed that tracking 80% of birds in week 1, 60% in week 4, and 40% in week 7 was sufficient (r ≥ 0.90; p ≤ 0.048) to represent the group activity in respective ages. This platform offers a practical, accessible solution for activity tracking, supporting animal behavior analytics with minimal effort. Full article

(This article belongs to the Section Animal Welfare)

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11 pages, 782 KiB

Open AccessArticle

Exploring the Association Between Platelet Count, the Systemic Immune Inflammation Index, and Fracture Risk in Postmenopausal Women with Osteoporosis: A Cross-Sectional Study

by Cecilia Oliveri, Anastasia Xourafa, Rita Maria Agostino, Valentina Corigliano, Antonino Botindari, Agostino Gaudio, Nunziata Morabito, Alessandro Allegra and Antonino Catalano

J. Clin. Med. 2025, 14(15), 5453; https://doi.org/10.3390/jcm14155453 (registering DOI) - 2 Aug 2025

Abstract

Background/Objectives: Platelets play a role in bone metabolism and fracture healing. This study aimed to investigate the association between platelet indices and the derived systemic immune inflammation index (SII) with fracture risk in postmenopausal women. Methods: Platelet count, mean platelet volume, platelet distribution width (PDW), platelet crit, percentage of large platelets (P-LCR), platelet–lymphocyte ratio, and the SII, calculated as (NxP)/L, where N, P, and L represented neutrophils, platelets and lymphocytes counts, respectively, were evaluated. Bone mineral density (BMD) was measured using dual-energy X-ray absorptiometry. Results: A total of 124 women (mean age 68.4 ± 9 years) were stratified into two groups based on the median platelet count; the “lower platelet count group” (n = 58) had a count of 200,000 (174,000 to 226,000), while the “higher platelet count group” (n = 66) had a count of 281,500 (256,500 to 308,500). The higher platelet count group showed a higher hip fracture risk (7.4 vs. 4.5%, p = 0.08) and lower lumbar spine BMD (0.773 vs. 0.83 gr/cm², p = 0.03). By dividing the participants into two groups with higher SSI (950,848.6 ± 746,097.99) (n = 61) and lower SII (355,751.2 ± 88,662.6) (n = 63), the group with the higher SII showed the higher hip fracture risk (7.4 vs. 3.6%, p = 0.01). Univariate regression analysis revealed correlations between chronological age and PDW (r = 0.188, p = 0.047), and P-LCR (r = 0.208, p = 0.03), as well as associations between vitamin D status and P-LCR (r = −0.301, p = 0.034), and between SII and hip fracture risk (r = 0.12, p = 0.007). Conclusions: Platelet count and SII were associated with fracture risk in postmenopausal women undergoing osteoporosis assessment. Given their reproducibility and cost-effectiveness, these markers warrant further investigation in future prospective studies focused on bone fragility. Full article

(This article belongs to the Special Issue Diagnosis, Treatment, Prevention and Rehabilitation in Osteoporosis)

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16 pages, 8522 KiB

Open AccessArticle

Plant Extracts as Modulators of the Wound Healing Process—Preliminary Study

by Anna Herman, Aleksandra Leska, Patrycja Wińska and Andrzej Przemysław Herman

Int. J. Mol. Sci. 2025, 26(15), 7490; https://doi.org/10.3390/ijms26157490 (registering DOI) - 2 Aug 2025

Abstract

The treatment of chronic wounds is one of the most complex therapeutic problems of modern medicine. It leads to patients’ protracted recovery, generating high treatment costs. Herbal products may be useful in the treatment of chronic wounds via a wide range of pharmacological properties and multidirectional effects on the wound healing phases. The study aims to determine the ability of selected plant extracts to modulate the processes involved in wound healing. The antimicrobial (MIC, MBC, MFC) and antioxidant (ABTS, DPPH) activities, cytotoxicity (MTT test), scratch wound test, and collagen assay were tested. R. canina (MBC 0.39 mg/mL) and V. venifera (MBC 3.13 mg/mL) extracts had bactericidal activities against P. aeruginosa and S. aureus, respectively. The V. vinifera extract showed the highest antioxidant activity in both ABTS (EC50 0.078 mg/mL) and DPPH (EC50 0.005 mg/mL) methods. The percentage of wound closure observed for C. cardunculus, R. rosea, and R. canina extracts with HaCaT, and V. vinifera extract with Hs27 cells was set as 100%. V. vinifera extract (50 μg/mL) stimulated collagen synthesis 5.16 times more strongly than ascorbic acid. Our preliminary study showed that some plant extracts may be promising modulators of the wound healing process, although further in-depth studies are necessary to determine their effectiveness in the in vivo model. Full article

(This article belongs to the Special Issue Novel Natural Compound for Wound and Tissue Repair and Regeneration: 3rd Edition)

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24 pages, 1376 KiB

Open AccessArticle

Smart Agriculture in Ecuador: Adoption of IoT Technologies by Farmers in Guayas to Improve Agricultural Yields

by Ruth Rubí Peña-Holguín, Carlos Andrés Vaca-Coronel, Ruth María Farías-Lema, Sonnia Valeria Zapatier-Castro and Juan Diego Valenzuela-Cobos

Agriculture 2025, 15(15), 1679; https://doi.org/10.3390/agriculture15151679 (registering DOI) - 2 Aug 2025

Abstract

The adoption of digital technologies, such as the Internet of Things (IoT), has emerged as a key strategy to improve efficiency, sustainability, and productivity in the agricultural sector, especially in contexts of modernization and digital transformation in developing regions. This study analyzes the key factors influencing the adoption of IoT technologies by farmers in the province of Guayas, Ecuador, and their impact on agricultural yields. The research is grounded in innovation diffusion theory and technology acceptance models, which emphasize the role of perception, usability, training, and economic viability in digital adoption. A total of 250 surveys were administered, with 232 valid responses (92.8% response rate), reflecting strong interest from the agricultural sector in digital transformation and precision agriculture. Using structural equation modeling (SEM), the results confirm that general perception of IoT (β = 0.514), practical functionality (β = 0.488), and technical training (β = 0.523) positively influence adoption, while high implementation costs negatively affect it (β = −0.651), all of which are statistically significant (p < 0.001). Furthermore, adoption has a strong positive effect on agricultural yield (β = 0.795). The model explained a high percentage of variance in both adoption (R² = 0.771) and performance (R² = 0.706), supporting its predictive capacity. These findings underscore the need for public and private institutions to implement targeted training and financing strategies to overcome economic barriers and foster the sustainable integration of IoT technologies in Ecuadorian agriculture. Full article

(This article belongs to the Special Issue Internet of Things and Wireless Sensor Networks for Smart Vegetable Growing)

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12 pages, 319 KiB

Open AccessCommunication

Raw Sheep Milk as a Reservoir of Multidrug-Resistant Staphylococcus aureus: Evidence from Traditional Farming Systems in Romania

by Răzvan-Dragoș Roșu, Adriana Morar, Alexandra Ban-Cucerzan, Mirela Imre, Sebastian Alexandru Popa, Răzvan-Tudor Pătrînjan, Alexandra Pocinoc and Kálmán Imre

Antibiotics 2025, 14(8), 787; https://doi.org/10.3390/antibiotics14080787 (registering DOI) - 2 Aug 2025

Abstract

Background/Objectives: Staphylococcus aureus is a major pathogen of concern in raw milk due to its potential to cause foodborne illness and its increasing antimicrobial resistance (AMR). In Romania, data on the occurrence and resistance patterns of S. aureus in raw sheep milk from traditional farming systems remain limited. This study investigated the presence and antimicrobial resistance of S. aureus in 106 raw sheep milk samples collected from traditional farms in the Banat region of western Romania. Methods: Coagulase-positive staphylococci (CPS) were enumerated using ISO 6888-1:2021 protocols. Isolates were identified at the species level using the Vitek 2 system and molecularly confirmed via PCR targeting the 16S rDNA and nuc genes. Methicillin resistance was assessed by detecting the mecA gene. Antimicrobial susceptibility was determined using the Vitek 2 AST-GP79 card. Results: CPS were detected in 69 samples, with S. aureus confirmed in 34.9%. The mecA gene was identified in 13.5% of S. aureus isolates, indicating the presence of methicillin-resistant S. aureus (MRSA). Resistance to at least two antimicrobials was observed in 97.3% of isolates, and 33 strains (89.2%) met the criteria for multidrug resistance (MDR). The most frequent MDR phenotype involved resistance to lincomycin, macrolides, β-lactams, tetracyclines, and aminoglycosides. Conclusions: The high prevalence of S. aureus, including MRSA and MDR strains, in raw sheep milk from traditional farms represents a potential public health risk, particularly in regions where unpasteurized dairy consumption persists. These findings underscore the need for enhanced hygiene practices, prudent antimicrobial use, and AMR monitoring in small-scale dairy systems. Full article

(This article belongs to the Special Issue Virulence, Antimicrobial Resistance and Biofilm Production in Veterinary, Zoonotic and Food-Related Pathogens)

9 pages, 206 KiB

Open AccessArticle

Examining the Relationship Between Balance and Functional Status in the Geriatric Population

by Eleni Vermisso, Effrosyni Stamou, Garyfallia Tsichli, Ioanna Foteinou and Anna Christakou

Med. Sci. 2025, 13(3), 110; https://doi.org/10.3390/medsci13030110 (registering DOI) - 2 Aug 2025

Abstract

Background/Objectives: Aging is associated with a gradual decline in physical capabilities, often leading to impaired balance and reduced functional status, which are major contributors to falls in older adults. Although many studies have assessed these variables independently, a limited amount of research has explored the direct relationship between balance and functional status in a healthy geriatric population. The aim of this study was to investigate the relationship between balance and functional capacity and to assess the influence of demographic factors such as age, comorbidities, smoking status, and history of falls. Methods: A sample of community-dwelling older adults (19 women, 16 men) (n = 35), aged 60 years and above (M = 78 years; SD = 9.23) from Sparta, Greece, took part in the present study. Participants were assessed using three validated tools: (a) the Five Times Sit-to-Stand test, (b) the Timed Up-and-Go test, and (c) the Berg Balance Scale. Spearman’s rank correlation coefficient was used for statistical analysis (α = 0.05). Results: Age was positively correlated with poorer performance in the Five Times Sit-to-Stand (r = 0.40; p < 0.01) and the Timed Up-and-Go test (r = 0.47; p < 0.01) and negatively correlated with Berg Balance Scale scores (r = −0.51; p < 0.01). Comorbidities and smoking were also associated with the Berg Balance Scale. A strong negative correlation was observed between balance and the other two functional tests (Five Times Sit-to-Stand: r = −0.51; Timed Up-and-Go: r = −0.66; both p < 0.01). Conclusions: The findings highlight the importance of evaluating both balance and functional capacity in older adults as interrelated factors that can significantly influence quality of life and fall risk. Future research with larger and more diverse populations is recommended to confirm the present findings and to use exercise programs to prevent falls in the geriatric population. Full article

28 pages, 2816 KiB

Open AccessArticle

Influence of the Origin, Feeding Status, and Trypanosoma cruzi Infection in the Microbial Composition of the Digestive Tract of Triatoma pallidipennis

by Everardo Gutiérrez-Millán, Alba N. Lecona-Valera, Mario H. Rodriguez and Ana E. Gutiérrez-Cabrera

Biology 2025, 14(8), 984; https://doi.org/10.3390/biology14080984 (registering DOI) - 2 Aug 2025

Abstract

Triatoma pallidipennis, the main vector of Chagas disease in central Mexico, hosts a diverse and complex gut bacterial community shaped by environmental and physiological factors. To gain insight into these microbes’ dynamics, we characterised the gut bacterial communities of wild and insectary insects under different feeding and Trypanosoma cruzi infection conditions, using 16S rRNA gene sequencing. We identified 91 bacterial genera across 8 phyla, with Proteobacteria dominating most samples. Wild insects showed greater bacterial diversity, led by Acinetobacter and Pseudomonas, while insectary insects exhibited lower diversity and were dominated by Arsenophonus. The origin of the insects, whether they were reared in the insectary (laboratory) or collected from wild populations, was the principal factor structuring the gut microbiota, followed by feeding and T. cruzi infection. A stable core microbiota of 12 bacterial genera was present across all conditions, suggesting key functional roles in host physiology. Co-occurrence and functional enrichment analyses revealed that feeding and infection induced condition-specific microbial interactions and metabolic pathways. Our findings highlight the ecological plasticity of the triatomine gut microbiota and its potential role in modulating vector competence, providing a foundation for future microbiota-based control strategies. Full article

(This article belongs to the Special Issue Metabolic Interactions between the Gut Microbiome and Host)

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