Search Results (55,409)

Background: Tularemia is a rarely identified disease in Slovenia. In summer 2024, we detected a tularemia outbreak in the Kranjsko-Sorško polje, located in North-Western part of Slovenia. Aim: To describe the epidemiological investigations and preventive measures to contain the outbreak. Methods: The patients with confirmed tularemia were interviewed. Serology and PCR was used for microbiological confirmation of tularemia and in some patients by isolation from blood or by RT-PCR. Results: The majority of confirmed tularemia cases in 2024 were infected in the geographically limited area in North-Western part of Slovenia (38/46). Tularemia was confirmed in two patients by isolation Francisella tularensis subsp. holarctica from blood or wound, in one by blood PCR, and in the others by serology. Most cases were associated with mowing or harvesting hay with intensive dusting. Twenty-eight (75.7%) out of 37 cases developed pulmonary tularemia. Sixteen cases were hospitalized. After confirming the outbreak, we alerted medical professionals in the region and the general public using the regional and national media and website of National Institute of Public Health. Conclusions: Endemic tularemia in Slovenia is associated with handling wild life and presents in ulceroglandular form. In the localized outbreak in year 2024 there was an extraordinary upsurge of pulmonary tularemia, with many of the cases initially investigated for lung cancer based on the radiology reports. Due to dry weather condition in summer 2024, excessive dusting associated with mowing the grass and handling hay resulted in inhalation of infective aerosols leading to the infection with F. tularensis. Full article

Unmanned Aerial Vehicles (UAVs) integrated with lightweight deep learning models represent an effective solution for image-based rapid post-earthquake damage assessment. UAVs, equipped with cameras, capture high-resolution aerial imagery of disaster-stricken areas, providing essential data for evaluating structural damage. When paired with light eight Convolutional Neural Network (CNN) models, these UAVs can process the captured images onboard, enabling real-time, accurate damage level predictions that might with potential interest to orient efficiently the efforts of the Search and Rescue (SAR) teams. This study investigates the use of the MobileNetV3-Small lightweight CNN model for real-time post-earthquake damage level prediction using UAV-captured imagery. The model is trained to classify three levels of post-earthquake damage, ranging from no damage to severe damage. Experimental results show that the adapted MobileNetV3-Small model achieves the lowest FLOPs, with a significant reduction of 58.8% compared to the ShuffleNetv2 model. Fine-tuning the last five layers resulted in a slight increase of approximately 0.2% in FLOPs, but significantly improved accuracy and robustness, yielding a 4.5% performance boost over the baseline. The model achieved a weighted average F-score of 0.93 on a merged dataset composed of three post-earthquake damage level datasets. It was successfully deployed and tested on a Raspberry Pi 5, demonstrating its feasibility for edge-device applications. This deployment highlighted the model’s efficiency and real-time performance in resource-constrained environments. Full article

Progressive pulmonary fibrosis (PPF) is a clinical syndrome associated with worsening quality of life and increased mortality among patients with various interstitial lung diseases. This review aims to update the concepts and criteria that adequately define PPF, aiming to facilitate earlier recognition and optimize clinical management. Fibrosing interstitial lung disease (ILD-f) can progress over time despite optimal management of the underlying conditions. Current criteria for defining PPF include worsening respiratory symptoms, decline in pulmonary function tests (particularly forced vital capacity and diffusing capacity), and radiographic progression over a 1-year follow-up period. However, implementation of these criteria in clinical practice poses challenges. This review discusses the limitations of current evaluation methods and proposes future directions, including the need for validated symptom assessment tools, standardization of pulmonary function testing, and improvements in quantitative radiological evaluation methods. Full article

Fusarium verticillioides, an important global pathogenic fungus, compromises crop quality and yield by infecting maize, sugarcane, and some Solanaceae, endangering food security through contaminated grains and cereals with the fumonisin B1 (FB1) toxin. While Con7 has been reported as a transcription factor involved in the sporulation and pathogenicity of some pathogenic fungi, the function of FvCon7 and its regulatory genes in F.verticillioides remains uncharacterized. Gene deletion mutants of ΔFvcon7 were constructed through homologous recombination, which exhibited defects in vegetative growth, survival, sporophore development, conidiation, conidial germination, and carbon metabolism. Carbon metabolism defects led to a significant accumulation of glycogen granules in hypha and lipid bodies in conidia. Additionally, ΔFvcon7 displayed impaired cell wall structure and integrity, along with an altered expression of genes encoding cell wall-degrading enzymes (such as chitinase), as detected by qRT-PCR. Moreover, Fvcon7 also plays a role in the pathogenicity of maize and sugarcane through different splicing, defective conidia, reduced survival viability, differential expression of secreted proteins, and deficiencies in antioxidant stress capacity. Furthermore, using yeast one-hybrid (Y1H) assays, FvCon7 was found for the first time to directly regulate the expression of FvFUMs by binding to the CCAAT box within the promoters of six key FvFUMs, thereby affecting FB1 production. Overall, FvCon7 functions as a global transcription factor regulating multiple phenotypes. This study provides a theoretical basis for elucidating the mechanism of transcription factor FvCon7 regulating toxin production and pathogenesis in F. verticillioides. Full article

Recently we studied a collocation–quadrature method in weighted ${\mathbf{L}}^2$ spaces as well as in the space of continuous functions for a Volterra-like integral equation of the form $u\left(x\right)-{\int }_{\alpha x}^{1}h(x-\alpha y)u\left(y\right)dy=f\left(x\right),0<x<1,$ where $h\left(x\right)$ (with a possible singularity at $x=0$) and $f\left(x\right)$ are given (in general complex-valued) functions, and $\alpha \in (0,1)$ is a fixed parameter. Here, we want to investigate the same method for the case when $\alpha =1.$ More precisely, we consider (in general weakly singular) Volterra integral equations of the form $u\left(x\right)-{\int }_0^{x}h(x,y){(x-y)}^{\kappa }u\left(y\right)dy=f\left(x\right),0<x<1,$ where $\kappa >-1$, and $h:D⟶\mathbb{C}$ is a continuous function, $D=\left\{(x,y)\in {\mathbb{R}}^2:0<y<x<1\right\}.$ The passage from $0<\alpha <1$ to $\alpha =1$ and the consideration of more general kernel functions $h(x,y)$ make the studies more involved. Moreover, we enhance the family of interpolation operators defining the approximating operators, and, finally, we ask if, in comparison to collocation–quadrature methods, the application of the Nyström method together with the theory of collectively compact operator sequences is possible. Full article

Background/Objectives: This study aims to build an interpretable and accurate predictive model for myocardial infarction (MI) using Explainable Boosting Machines (EBM), a state-of-the-art Explainable Artificial Intelligence (XAI) technique. The objective is to identify and rank clinically relevant biomarkers that contribute to MI diagnosis while maintaining transparency to support clinical decision making. Methods: The dataset comprises 1319 patient records collected in 2018 from a cardiology center in the Erbil region of Iraq. Each record includes eight routinely measured clinical and biochemical features, such as troponin, CK-MB, and glucose levels, and a binary outcome variable indicating the presence or absence of MI. After preprocessing (e.g., one-hot encoding, normalization), the EBM model was trained using 80% of the data and tested on the remaining 20%. Model performance was evaluated using standard metrics including AUC, accuracy, sensitivity, specificity, F1 score, and Matthews correlation coefficient. Feature importance was assessed to identify key predictors. Partial dependence analyses provided insights into how each variable affected model predictions. Results: The EBM model demonstrated excellent diagnostic performance, achieving an AUC of 0.980, an accuracy of 96.6%, sensitivity of 96.8%, and specificity of 96.2%. Troponin and CK-MB were identified as the top predictors, confirming their established clinical relevance in MI diagnosis. In contrast, demographic and hemodynamic variables such as age and blood pressure contributed minimally. Partial dependence plots revealed non-linear effects of key biomarkers. Local explanation plots demonstrated the model’s ability to make confident, interpretable predictions for both positive and negative cases. Conclusions: The findings highlight the potential of EBM as a clinically useful and ethical AI approach for MI diagnosis. By combining high predictive accuracy with transparency, EBM supports biomarker prioritization and clinical risk stratification, thus aligning with precision medicine and responsible AI principles. Future research should validate the model on multi-center datasets and explore additional features for broader clinical use. Full article

Background/Objectives: To analyze how the different phases of the menstrual cycle affect agility in female football players. Methods: A total of 11 female football players were selected from the third tier of the Spanish Football Federation (Third RFEF) and an agility test (t-test) was conducted to measure agility during the three phases of the menstrual cycle: the menstrual, late follicular, and mid-luteal phases. These phases were determined through self-reporting and the use of ovulation test strips for luteinizing hormone detection. Perceptual variables, such as sleep quality, stress, muscle pain, and fatigue, as well as the rating of perceived exertion, were measured. Results: There were no statistically significant differences in agility performance across menstrual cycle phases (F_(2,20) = 1.86; p = 0.18). However, performance in the mid-luteal phase was slightly better compared to other phases. Similarly, no significant differences were found in perceptual variables such as fatigue, sleep quality, stress, and muscle soreness (p > 0.05), although slightly better perceptual responses were observed in the late follicular phase. Conclusions: No significant differences were found when analyzing the influence of menstrual cycle phases on agility, although performance appeared slightly better in the mid-luteal phase. No significant differences were observed in the perceptual variables. Both objective and perceptual variables should be considered in future studies or training programs based on the menstrual cycle. Full article

Background/Objectives: The intranasal (IN) route of administration is a promising non-invasive approach for brain targeting, bypassing the blood–brain barrier and enhancing bioavailability. Citalopram hydrobromide (CT), a widely prescribed sparingly water-soluble selective serotonin reuptake inhibitor (SSRI), faces challenges with oral and intravenous administration, including delayed onset, adverse effects, and patient compliance issues. Methods: This study aimed to develop a novel thermoresponsive polymeric micelle (PM) system based on Pluronic^® copolymers (Pluronic F127 and Poloxamer 188) improving CT’s solubility, stability, and nasal permeability for enhanced antidepressant efficacy. A preliminary study was conducted to select the optimized formulation. The preparation process involved using the thin-film hydration method, followed by freeze-drying. Comprehensive evaluations of optimized formulation characteristics included Z-average, polydispersity index (PdI), thermal behavior (lower critical solution temperature, LCST), encapsulation efficiency, X-ray powder diffraction (XRPD), thermodynamic solubility, and biological stability. Additionally, in vitro CT release and CT permeability in nasal conditions were studied. Stability under storage was also evaluated. Results: The optimized CT-PM formulation showed nanoscale micelle size (Z-average of 31.41 ± 0.99 nm), narrow size distribution (polydispersity index = 0.241), and a suitable thermal behavior for intranasal delivery (lower critical solution temperature (LCST) ~31 °C). Encapsulation efficiency reached approximately 90%, with an amorphous structure confirmed via XRPD, leading to a 95-fold increase in CT solubility. The formulation demonstrated appropriate biological and physical stability. In vitro studies showed a 25-fold faster CT release from optimized formulation compared to the initial CT, while CT-PM permeability in nasal conditions increased four-fold. Conclusions: This novel nanoscale thermosensitive formulation is a value-added strategy for nasal drug delivery systems, offering enhanced drug solubility, rapid drug release, stability, and improved permeability. This smart nanosystem represents a promising platform to overcome the limitations of conventional CT administration, improving therapeutic outcomes and patient compliance in depression management. Full article

Maxwell’s equations epitomize our knowledge of standard electromagnetic theory in vacuums and matter. Here, we report the clearcut results of an extensive, ongoing investigation aiming to mathematically digest Maxwell’s equations in virtually all problems based on the three standard building units, dielectric and magnetic, found in practice (i.e., spheres, cylinders and plates). Specifically, we address the static/quasi-static case of a linear, homogeneous and isotropic dielectric and magnetic sphere subjected to a DC/low-frequency AC external scalar potential, Uext (vector field, Fext), of any form, produced by a primary/free source residing outside the sphere. To this end, we introduce an expansion-based mathematical strategy that enables us to obtain immediate access to the response of the dielectric and magnetic sphere, i.e., to the internal scalar potential, Uint (vector field, Fint), produced by the induced secondary/bound source. Accordingly, the total scalar potential, U = Uext + Uint (vector field, F = Fext + Fint), is immediately accessible as well. Our approach provides ready-to-use expressions for Uint and U (Fint and F) in all space, i.e., both inside and outside the dielectric and magnetic sphere, applicable for any form of Uext (Fext). Using these universal expressions, we can obtain Uint and U (Fint and F) in essentially one step, without the need to solve each particular problem of different Uext (Fext) every time from scratch. The obtained universal relation between Uint and Uext (Fint and Fext) provides a means to tailor the responses of dielectric and magnetic spheres at all instances, thus facilitating applications. Our approach surpasses conventional mathematical procedures that are employed to solve analytically addressable problems of electromagnetism. Full article

Since 2007, the Yellow Sea has experienced the world’s largest green tides, with Ulva prolifera O.F. Müller as the dominant species. Those blooms severely impacted the local tourism and aquaculture, resulting in significant economic losses, as well as negative social and ecological consequences. Unlike other global green tides, those in the Yellow Sea are characterized by long-distance drifting and an astonishing scale. These destructive events display significant temporal and spatial variability, which is largely driven by dynamic environmental conditions and human activities. In this review, we summarize recent advancements in understanding the spatiotemporal patterns of long-distance transport, the interannual variability in bloom size, and the underlying mechanisms driving these fluctuations. Additionally, we highlight important knowledge gaps that need further investigation to support the development of effective management strategies for mitigating the impacts of green tides in the Yellow Sea. Full article

The integrity of subsea oil and gas pipelines is essential for offshore safety and environmental protection. Conventional leak detection approaches, such as manual inspection and indirect sensing, are often costly, time-consuming, and prone to subjectivity, motivating the development of automated methods. In this study, we present a deep learning-based framework for detecting underwater leaks using images acquired in controlled experiments designed to reproduce representative conditions of subsea monitoring. The dataset was generated by simulating both gas and liquid leaks in a water tank environment, under scenarios that mimic challenges observed during Remotely Operated Vehicle (ROV) inspections along the Brazilian coast. It was further complemented with artificially generated synthetic images (Stable Diffusion) and publicly available subsea imagery. Multiple Convolutional Neural Network (CNN) architectures, including VGG16, ResNet50, InceptionV3, DenseNet121, InceptionResNetV2, EfficientNetB0, and a lightweight custom CNN, were trained with transfer learning and evaluated on validation and blind test sets. The best-performing models achieved stable performance during training and validation, with macro F1-scores above 0.80, and demonstrated improved generalization compared to traditional baselines such as VGG16. In blind testing, InceptionV3 achieved the most balanced performance across the three classes when trained with synthetic data and augmentation. The study demonstrates the feasibility of applying CNNs for vision-based leak detection in complex underwater environments. A key contribution is the release of a novel experimentally generated dataset, which supports reproducibility and establishes a benchmark for advancing automated subsea inspection methods. Full article

Helicobacter pylori (Hp), a Class I carcinogen infecting over 50% of the global population, is increasingly resistant to conventional antibiotics. This study presents an AI-engineered probiotic strategy targeting urease, a key Hp virulence factor. A humanized single-domain antibody (UreBAb), previously identified and selected in our laboratory, was synthesized commercially and modeled using AlphaFold2, with structural validation conducted via SAVES 6.0. Molecular docking (PyMOL/ClusPro2) and binding energy analysis (InterProSurf) identified critical urease-active residues: K40, P41, K43, E82, F84, T86, K104, I107, K108, and R109. Machine learning-guided optimization using mCSA-AB, I-Mutant, and FoldX prioritized four mutational hotspots (K43, E82, I107, R109), leading to the generation of nine antibody variants. Among them, the I107W mutant exhibited the highest activity, achieving 65.6% urease inhibition—a 24.95% improvement over the wild-type antibody (p < 0.001). Engineered Escherichia coli Nissle 1917 (EcN) expressing the I107W antibody significantly reduced gastric HP colonization by 4.42 log10 CFU in the treatment group and 3.30 log10 CFU in the prevention group (p < 0.001 and p < 0.05, respectively), while also suppressing pro-inflammatory cytokine levels. Histopathological (H&E) analysis confirmed that the I107W antibody group showed significantly enhanced mucosal repair compared to wild-type probiotic-treated mice. Notably, 16S rRNA sequencing revealed that intestinal microbiota diversity and the abundance of core microbial species remained stable across different ethnic backgrounds. By integrating AI-guided antibody engineering with targeted probiotic delivery, this platform provides a transformative and microbiota-friendly strategy to combat antibiotic-resistant Hp infections. Full article

Newcastle disease (ND) is a highly contagious and economically significant viral infection that affects poultry globally, with recurrent outbreaks occurring even among vaccinated flocks in Egypt. Caused by the Newcastle disease virus (NDV), the disease results in substantial losses due to high mortality rates, decreased productivity, and the imposition of trade restrictions. This study aimed to develop a rapid, sensitive, and field-deployable diagnostic assay based on real-time reverse transcription recombinase-aided amplification (RT-RAA) for the detection of all NDV genotypes in clinical avian specimens. Primers and an exo-probe were designed based on the most conserved region of the NDV matrix gene. After testing ten primer combinations, the pair NDV RAA-F1 and RAA-R5 demonstrated the highest sensitivity, detecting as low as 6.89 EID₅₀/mL (95% CI). The RT-RAA assay showed excellent clinical sensitivity and specificity, with no cross-reactivity to other common respiratory pathogens such as avian influenza virus, infectious bronchitis virus, Mycoplasma gallisepticum or infectious laryngotracheitis virus. All 25 field samples that were tested positive by real-time RT-PCR, including those with high CT values (~35), were detected by RT-RAA in 2–11 min, indicating superior sensitivity and speed. The assay requires only basic equipment and can be performed under isothermal conditions, making it highly suitable for on-site detection in resource-limited or rural settings. The successful implementation of RT-RAA can improve NDV outbreak response, support timely vaccination strategies, and enhance disease control efforts. Overall, the assay presents a promising alternative to conventional diagnostic methods, contributing to the sustainability and productivity of the poultry sector in endemic regions. Full article

Directed cell migration is crucial for numerous biological processes, including tissue regeneration and cancer metastasis. However, conventional symmetrical micropatterns typically result in bidirectional cell migration guidance instead of unidirectional guidance. In this study, polydimethylsiloxane (PDMS)-based platforms with asymmetrical arrowhead micropatterns, nanopillars, and selective fibronectin coating were developed to enhance unidirectional cell migration. The platforms were fabricated using nanoimprint lithography and PDMS replication techniques, allowing for precise control over surface topography and biochemical modification. The MC3T3 osteoblastic cells cultured on these platforms demonstrated significantly enhanced directional migration, characterized by increased displacement, and directional alignment with micropattern orientation compared to symmetrical patterns. Quantitative analyses revealed that asymmetrical arrowheads combined with nanopillars induced more focal adhesions and F-actin polarization at cell front regions, supporting the observed unidirectional cell migration enhancement. These results confirm that integrating micropattern asymmetry, nanoscale features, and biochemical functionalization synergistically promotes unidirectional cell migration. The developed platforms offer valuable insights and practical strategies for designing advanced biomaterials capable of precise spatial cell guidance that can be applied to the designs of organ-on-a-chip systems. Full article

In this study, we aimed to evaluate physiological and agronomic traits in 120 sugarcane genotypes under early drought stress conditions in a field trial across various soil types. The experiment used a split-plot arrangement, with a randomized complete block design and two replications. Two different water regimes were assigned to the main plot: (1) non-water stress (CT) and (2) drought (DT) at the early growth stage, during which sugarcane was subjected to drought stress by withholding water for 4 months. The subplot consisted of 120 sugarcane genotypes. The stalk height, stalk diameter, number of stalks, photosynthetic traits including SPAD chlorophyll meter reading (SCMR) and maximum quantum efficiency of photosystem II photochemistry (Fv/Fm), and normalized difference vegetation index (NDVI) were measured at 3, 6, and 9 months after planting (MAP). Yield and yield component parameters were measured at 12 MAP. Drought treatments lead to significant changes in various physiological traits in the sugarcane. Clustering analysis classified 36 sugarcane varieties grown in sandy loam soil and 15 genotypes in loam soil into two main clusters. In sandy loam soils, Biotec4 and CO1287 exhibited outstanding performance in drought conditions, delivering high cane yields. Meanwhile, in loam soil, MPT13-118, MPT07-1, Q47, F174, MPT14-1-902, and UT1 exhibited the best drought tolerance. Under drought conditions, cluster 1 showed higher values for SCMR, NDVI, height growth rate (HGR), cane yield, and drought tolerance index compared to cluster 2. These findings suggest that breeders can utilize these genotypes to enhance drought resistance, and the identified physiological traits can assist in selecting stronger candidates for drought tolerance. Full article