Search Results (2,458)

Rickettsia rickettsii is the most virulent agent of the genus Rickettsia that causes one of the most relevant vector-borne diseases in the Americas (RRSF). RRSF manifests with many non-specific acute clinical symptoms complicating its diagnosis and can lead to death if not treated appropriately. RRSF has been reported in Canada, the United States of America, Mexico, Costa Rica, Panama, Colombia, Brazil, and Argentina. In addition to R. rickettsii, mild and severe spotted fever group rickettsioses (SFGR) have been reported in the Americas; however, the true prevalence of these diseases is unknown. In Panama, RRSF have been reported in four of 14 provinces during two outbreak periods: five cases including two fatalities were identified in 1950–1951, and 23 cases including 17 fatalities between 2004 and 2025. This paper presents the clinical characterization of a fatal case of RRSF in Coclé province and a severe case of SFGR in a mountainous area of the Gnäbe Buglé Indigenous Comarca (GBIC). Laboratory confirmation was performed by molecular analysis of tissues obtained from necropsies in the case of RRSF and by immunofluorescence assay (IFA) in the case of SFGR. Furthermore, this paper identifies existing gaps in the initial clinical suspicion and pertinent to SFGR in Panama, which may be applicable to other countries in the region. In the last 21 years, cases have occurred upon contact with ticks in rural areas (13), urban and suburban locations (7), rural woodlands (2), and forests (1). Provinces with more cases are Panamá (7 of 23, 6 died), Coclé (5 of 23, 5 died), Colón (3 of 23, 1 died), Panamá Oeste (1 of 23, 1 died), and GBIC (7 of 23, 4 died), including a cluster of seven cases in 2019. Therefore, Coclé province is considered one of the endemic areas for RRSF in Panama, while the latest cases from the GBIC since 2019 indicate that mountainous areas are an eco-epidemiological scenario to include in the transmission of these diseases. Although this disease has a low prevalence, patients who present symptoms commonly associated with more common diseases such as dengue, other arboviruses, malaria, and leptospirosis, among others, should be included in the diagnostic suspicion. Without diagnostic suspicion and adequate treatment, the patient can die. Full article

Background: In elite football, understanding how age impacts players’ physical performance is essential for optimizing training, career longevity, and team management. Objectives: This study aimed to compare variations in physical capabilities of professional football players by chronological age and identify peak performance ages. Methods: Data from 5203 match performances across 351 official games were analyzed, involving 98 male players aged 18–39 years. Physical capacities (speed, explosive actions, and endurance) were assessed using the Catapult VECTOR7 system. Results: showed that players over 32 years experienced declines in high-intensity and explosive actions, while endurance remained relatively stable with age. Peak performance occurred around 25.7 years for speed, 24.8 years for endurance, and 26 years for explosiveness. Conclusions: Overall, players aged 17–26 years demonstrated the highest physical performance, with notable declines observed in older age groups. Full article

This retrospective review traces personal encounters along the complex path of plant yellowing diseases—graft-transmissible disorders historically attributed to elusive viruses, but later linked to phloem-invading, wall-less bacteria known as Mollicutes. These include two plant-infecting genera: the cultivable Spiroplasma and the non-cultivable ‘Candidatus Phytoplasma’. A third group—the walled, psyllid-transmitted Candidatus Liberibacter—was later implicated in closely similar syndromes. This shift in understanding marked a major turning point in plant pathology, offering new insights into yellowing diseases characterized by stunting, decline, and poor or deformed growth. The review focuses on key syndromes: citrus little leaf disease (LLD), or citrus stubborn disease (CSD), caused by Spiroplasma citri; and several Mollicute -related disorders, including safflower phyllody, Bermuda grass yellowing, and papaya dieback (PDD) (Nivun Haamir), the latter linked to ‘Candidatus Phytoplasma australiense’. Despite differing causes and vectors, citrus LLD-CSD and PPD share an erratic, unpredictable pattern of natural outbreaks—sometimes a decade apart—hindering grower engagement and sustained control efforts. While scientific understanding has deepened, practical management remains limited. The recent global spread of Huanglongbing (HLB), caused by Candidatus Liberibacter species, underscores the urgent need for improved strategies to manage this resilient group of phloem-limited bacterial pathogens. Full article

Crop diseases cause significant losses in agricultural production; early capture and identification of disease spores enable disease monitoring and prevention. This study experimentally optimized the preparation of Au@Ag NRS (Gold core@Silver shell Nanorods) sol as a Surface-Enhanced Raman Scattering (SERS) enhancement reagent via a modified seed-mediated growth method. Using an existing microfluidic chip developed by the research group, disease spores were separated and enriched, followed by combining Au@Ag NRS with Crop Disease Spores through electrostatic adsorption. Raman spectroscopy was employed to collect SERS fingerprint spectra of Crop Disease Spores. The spectra underwent baseline correction using Adaptive Least Squares (ALS) and standardization via Standard Normal Variate (SNV). Dimensionality reduction preprocessing was performed using Principal Component Analysis (PCA) and Successive Projections Algorithm combined with Competitive Adaptive Reweighted Sampling (SCARS). Classification was then executed using Support Vector Machine (SVM) and Multilayer Perceptron (MLP). The SCARS-MLP model achieved the highest accuracy at 97.92% on the test set, while SCARS-SVM, PCA-SVM, and SCARS-MLP models attained test set accuracy of 95.83%, 95.24%, and 96.55%, respectively. Thus, the proposed Au@Ag NRS-based SERS technology can be applied to detect airborne disease spores, establishing an early and precise method for Crop Disease detection. Full article

Background and Objectives: Sarcopenia is a progressive loss of skeletal muscle mass and function in elderly adults, posing a significant risk of frailty, falls, and morbidity. The current study designs and evaluates SarcoNet, a novel artificial neural network (ANN)-based classification framework developed in order to classify Sarcopenic from non-Sarcopenic subjects using a comprehensive real-time dataset. Methods: This pilot study involved 30 subjects, who were divided into Sarcopenic and non-Sarcopenic groups based on physician assessment. The collected dataset consists of thirty-one clinical parameters like skeletal muscle mass, which is collected using various equipment such as Body Composition Analyser, along with ten kinetic features which are derived from video-based gait analysis of joint angles obtained during walking on three terrain types such as slope, steps, and parallel path. The performance of the designed ANN-based SarcoNet was benchmarked against the traditional machine learning classifiers utilised including Support Vector Machine (SVM), k-Nearest Neighbours (k-NN), and Random Forest (RF), as well as hard and soft voting ensemble classifiers. Results: SarcoNet achieved the highest overall classification accuracy of about 94%, with a specificity and precision of about 100%, an F1-score of about 92.4%, and an AUC of 0.94, outperforming all other models. The incorporation of lower-limb joint kinetics such as knee flexion, extension, ankle plantarflexion and dorsiflexion significantly enhanced predictive capability of the model and thus reflecting the functional deterioration characteristic of muscles in Sarcopenia. Conclusions: SarcoNet provides a promising AI-driven solution in Sarcopenia diagnosis, especially in low-resource healthcare settings. Future work will focus on improving the dataset, validating the model across diverse populations, and incorporating explainable AI to improve clinical adoption. Full article

This comprehensive review explores the distribution, diversity, and epidemiology of tick-borne borrelioses across Eurasia, focusing on Lyme borreliosis (LB) and other Borrelia-related infections. The genus Borrelia is categorized into three major groups, the Lyme Group (LG), the Relapsing Fever Group (RFG), and the Echidna–Reptile Group (REPG), each with distinct vectors, reservoirs, and pathogenic profiles. LB, caused by Borrelia burgdorferi sensu lato, is highly endemic in Europe and is increasingly reported in Asia, although it is underdiagnosed in Southeast Asia due to limited surveillance. This review details the ecological dynamics of tick vectors—primarily Ixodes spp.—and their vertebrate hosts, emphasizing the role of migratory birds and climate change in disease spread. It also highlights the presence of relapsing fever Borrelia species transmitted by soft ticks (Ornithodoros spp.) and the emergence of novel species such as Borrelia miyamotoi (RFG) and Borrelia turcica (REPG). This study underscores the need for harmonized surveillance systems, improved diagnostic tools, and integrated public health strategies to address the growing threat of borreliosis in Eurasia. Full article

Fractional differential equations have emerged as a prominent focus of modern scientific research due to their advantages in describing the complexity and nonlinear behavior of many physical phenomena. In particular, when considering problems with initial-boundary value conditions, the solution of nonlinear fractional differential equations becomes particularly important. This paper aims to explore the fractional soliton solutions for the three-component fractional nonlinear Schrödinger (TFNLS) equation under the zero background. According to the Lax pair and fractional recursion operator, we obtain fractional nonlinear equations with Riesz fractional derivatives, which ensure the integrability of these equations. In particular, by the completeness relation of squared eigenfunctions, we derive the explicit form of the TFNLS equation. Subsequently, in the reflectionless case, we construct the fractional N-soliton solutions via the Riemann–Hilbert (RH) method. The analysis results indicate that as the order of the Riesz fractional derivative increases, the widths of both one-soliton and two-soliton solutions gradually decrease. However, the absolute values of wave velocity, phase velocity, and group velocity of one component of the vector soliton exhibit an increasing trend, and show power-law relationships with the amplitude. Full article

Early and accurate diagnosis of dementia is essential to improving patient outcomes and reducing societal burden. The Mini-Mental State Examination (MMSE) is widely used to assess cognitive function, yet traditional statistical and machine learning approaches often face limitations in capturing nonlinear interactions and subtle decline patterns. This study developed a novel deep learning-based dementia prediction model using MMSE data collected from domestic clinical settings and compared its performance with traditional machine learning models. A notable strength of this work lies in its use of item-level MMSE features combined with explainable AI (SHAP analysis), enabling both high predictive accuracy and clinical interpretability—an advancement over prior approaches that primarily relied on total scores or linear modeling. Data from 164 participants, classified into cognitively normal, mild cognitive impairment (MCI), and dementia groups, were analyzed. Individual MMSE items and total scores were used as input features, and the dataset was divided into training and validation sets (8:2 split). A fully connected neural network with regularization techniques was constructed and evaluated alongside Random Forest and support vector machine (SVM) classifiers. Model performance was assessed using accuracy, F1-score, confusion matrices, and receiver operating characteristic (ROC) curves. The deep learning model achieved the highest performance (accuracy 0.90, F1-score 0.90), surpassing Random Forest (0.86) and SVM (0.82). SHAP analysis identified Q11 (immediate memory), Q12 (calculation), and Q17 (drawing shapes) as the most influential variables, aligning with clinical diagnostic practices. These findings suggest that deep learning not only enhances predictive accuracy but also offers interpretable insights aligned with clinical reasoning, underscoring its potential utility as a reliable tool for early dementia diagnosis. However, the study is limited by the use of data from a single clinical site with a relatively small sample size, which may restrict generalizability. Future research should validate the model using larger, multi-institutional, and multimodal datasets to strengthen clinical applicability and robustness. Full article

Microplastic (MPL) and nanoplastic (NPL) contamination in soils is widespread, impacting soil invertebrates, microbial communities, and soil–plant systems. Here, we compiled the information from 100 research articles from 2018 onwards to enhance and synthesize the status quo of MPLs’ and NPLs’ impacts on such groups. The effects of these pollutants depend on multiple factors, including polymer composition, size, shape, concentration, and aging processes. Research on soil invertebrates has focused on earthworms and some studies on nematodes and collembolans, but studies are still limited to other groups, such as mites, millipedes, and insect larvae. Beyond soil invertebrates, plastics are also altering microbial communities at the soil–plastic interface, fostering the development of specialized microbial assemblages and shifting microbial functions in ways that remain poorly understood. Research has largely centered on bacterial interactions with MPLs, leaving understudied fungi, protists, and other soil microorganisms. Furthermore, MPLs and NPLs also interact with terrestrial plants, and their harmful effects, such as adsorption, uptake, translocation, and pathogen vectors, raise public awareness. Given the complexity of these interactions, well-replicated experiments and community- and ecosystem-level studies employing objective-driven technologies can provide insights into how MPLs and NPLs influence microbial and faunal diversity, functional traits, and soil ecosystem stability. Full article

Background: Prostate cancer is the most commonly occurring cancer amongst men. The detection and treatment of this cancer is therefore of great importance. The severity level of this cancer, which is established as a score in the Gleason Grading Group (GGC), guides the treatment of the cancer. Methods: In this paper, traditional machine learning (ML) classification methods such as Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and XGBoost (XGB), which have recently been shown to accurately identifying biomarkers for computational biology, are leveraged to find potential biomarkers for the different GGC scores. A ML framework that maps the Gleason Grading Group (GGG) into five severity levels—low, intermediate-low, intermediate, intermediate-high, and high—has been developed using the above methods. The microarray data for this ML method have been derived from immunohistochemical tests. The study includes severity level-wise biomarker identification, incorporating missing value imputation, class imbalance handling using the SMOTE-Tomek link method, and stratified k-fold validation to ensure robust biomarker selection. Results: The framework is evaluated on prostate cancer tissue microarray gene expression data from 1119 samples. A combination of high-aggressive and low-aggressive signatures are used in four experimental setups. The results demonstrate the effectiveness of the approach in distinguishing between critical biomarkers with highly accurate models, obtaining 96.85% accuracy using the XGBoost method. Conclusions: Leveraging ML gives a potential ground to involve the domain experts and the satisfactory results have approved that. For the future physician-in-the-loop approach can be tested to ensure further diagnosis impact. Full article

Heavy minerals, such as Rutile, Ilmenite and Zircon, and other essential trace elements are important in modern technology development. The integration of hyperspectral imaging and artificial intelligence presents a promising approach for the accurate identification of heavy minerals, especially Rare Earth Element (REE)–bearing phases such as Monazite. This study evaluates three AI classifiers, Support Vector Machine (SVM), Neural Networks (NNs) and Convolutional Neural Networks (CNNs), for their performance in classifying ten different minerals distributed across six grain size groups ranging from 125 μm to over 300 μm. The analysis focuses on how grain size affects spectral reflectance and classification accuracy. Among the tested models, SVM consistently outperformed NN and CNN, achieving the highest precision, recall and spectral similarity, particularly within the 150–300 μm grain size range. CNN showed the lowest performance and frequently misclassified spectrally similar minerals, such as Zircon and Rutile, likely due to its 1D architecture and limited spatial representation. Monazite, notable for its strong Nd³⁺ absorption features, was accurately identified across applicable grain sizes, highlighting its reliability for REE detection. Spectral Angle Mapper (SAM) analysis confirmed that SVM and NN maintained better spectral similarity than CNN. In general, the results highlight the significant influence of grain size, spectral similarity and dataset size on classification accuracy and the overall effectiveness of AI models in hyperspectral mineral analysis. Full article

Just as Gauss’s interpretation of complex numbers as points in a number plane in the form of a suitably formulated axiom found its way into the vector representation of Fourier transforms, this is the case with Euler’s formula and Riemann’s Zeta function considered here. The description of the connection between variables through complex numbers as it is given in Euler’s formula and emphasized by Riemann is reflected here with great flexibility in the introduction of non-classically generalized complex numbers and the vector representation of the generalized Zeta function based on them. For describing such dependencies of two variables with the help of generalized complex numbers, we introduce manifolds underlying certain Lie groups as level sets of norms, antinorms or semi-antinorms. No undefined or “imaginary” quantities are used for this. In contrast to the approach of Hamilton and his numerous successors, the vector-valued vector product of non-classically generalized complex numbers is commutative, and the whole number system satisfies a weak distributivity property as considered by Hankel, but not the strong one. Full article

This paper studies the problem of locating wireless sensor networks (WSNs) based on time-of-arrival (TOA) measurements in mixed line of sight/non-line-of-sight (LOS/NLOS) environments. To mitigate the impacts of NLOS and improve performance both in positioning accuracy and computation time, we hereby propose an exhaustive method (i.e., EM). The EM method mainly consists of two processes. In the first process, all BSs are arranged into various combinations. For each combination, a solution and its corresponding residual vector can be obtained. For each combination, all BSs can be divided into two categories: BSs that participate in positioning and BSs that do not. Therefore, the above residual vector can also be divided into two categories in each group. In the second process, combining the comparison results of two residual vectors and the characteristics of NLOS errors, we propose a new criterion to find out solutions with only LOS-BSs. Then the final solution can be obtained by further processing these solutions. This method does not require any prior information regarding NLOS status, NLOS amplitude, or noise variance, and only needs three LOS-BSs. Numerical simulation results shows that our method greatly improves the accuracy and reduces the computation time compared to state-of-art methods. Full article

Background: The cerebral compliance (or compensatory reserve) index, RAP, is a critical yet underutilized physiological marker in the management of moderate-to-severe traumatic brain injury (TBI). While RAP offers promise as a continuous bedside metric, its broader cerebral physiological context remains partly understood. This study aims to characterize the burden of impaired RAP in relation to other key components of cerebral physiology. Methods: Archived data from 379 moderate-to-severe TBI patients were analyzed using descriptive and threshold-based methods across three RAP states (impaired, intact/transitional, and exhausted). Agglomerative hierarchical clustering, principal component analysis, and kernel-based clustering were applied to explore multivariate covariance structures. Then, high-frequency temporal analyses, including vector autoregressive integrated moving average impulse response functions (VARIMA IRF), cross-correlation, and Granger causality, were performed to assess dynamic coupling between RAP and other physiological signals. Results: Impaired and exhausted RAP states were associated with elevated intracranial pressure (p = 0.021). Regarding AMP, impaired RAP was associated with elevated levels, while exhausted RAP was associated with reduced pulse amplitude (p = 3.94 × 10⁻⁹). These two RAP states were also associated with compromised autoregulation and diminished perfusion. Clustering analyses consistently grouped RAP with its constituent signals (ICP and AMP), followed by brain oxygenation parameters (brain tissue oxygenation (PbtO₂) and regional cerebral oxygen saturation (rSO₂)). Cerebral autoregulation (CA) indices clustered more closely with RAP under impaired autoregulatory states. Temporal analyses revealed that RAP exhibited comparatively stronger responses to ICP and arterial blood pressure (ABP) at 1-min resolution. Moreover, when comparing ICP-derived and near-infrared spectroscopy (NIRS)-derived CA indices, they clustered more closely to RAP, and RAP demonstrated greater sensitivity to changes in these ICP-derived CA indices in high-frequency temporal analyses. These trends remained consistent at lower temporal resolutions as well. Conclusion: RAP relationships with other parameters remain consistent and differ meaningfully across compliance states. Integrating RAP into patient trajectory modelling and developing predictive frameworks based on these findings across different RAP states can map the evolution of cerebral physiology over time. This approach may improve prognostication and guide individualized interventions in TBI management. Therefore, these findings support RAP’s potential as a valuable metric for bedside monitoring and its prospective role in guiding patient trajectory modeling and interventional studies in TBI. Full article

This study aims to address the challenges junior high school students often encounter when learning abstract spatial vector concepts. By developing and implementing a virtual robotic system, this research intends to improve students’ spatial reasoning, deepen their conceptual understanding, and increase engagement through an interactive, visual, and experiential learning environment that remedies the shortcomings of traditional teaching methods. The system was developed with the Unity Game Engine to deliver 3D visualization, interactive manipulation, and real-time feedback, thereby enhancing conceptual learning. In addition, the instructional design employed the ADDIE model (Analysis, Design, Development, Implementation, Evaluation) to enhance students’ understanding of spatial vector concepts. A quasi-experimental design was conducted involving 60 eighth-grade students divided evenly into experimental and control groups. Pre- and post-tests—including achievement assessments, learning attitude questionnaires, and cognitive load scales—were administered to evaluate learning outcomes. The main findings are as follows: (1) The experimental group demonstrated significantly higher learning achievement compared to the control group. (2) Both groups showed improvements in mathematics learning attitudes, with the experimental group exhibiting greater gains in practicality and confidence. (3) Although the experimental group experienced a slightly higher cognitive load, this difference was not statistically significant. (4) The experimental group reported high satisfaction with the system, especially in perceived usefulness. This study demonstrates that integrating virtual reality with the ADDIE model can substantially enhance learners’ conceptual understanding and motivation. Full article