Search Results (11,481)

The forest fly (Hippobosca equina) is an obligate haematophagous dipteran insect (order Diptera) that primarily infests horses and may contribute to the circulation of vector-borne pathogens. This study aimed to investigate the presence of Anaplasma phagocytophilum, Borrelia burgdorferi s.l., Babesia caballi, and Theileria equi, important vector-borne pathogens of equids, in forest flies collected from horses in endemic areas of Spain. A total of 170 forest flies were collected from 39 equids across four geographical regions in Spain (Segovia, Madrid, Toledo, and Menorca) and blood samples were collected from 27 of these horses. All flies were morphologically and molecularly identified as H. equina, and DNA extracted from flies and equine blood was screened using multiplex real-time and nested PCR, followed by sequencing and phylogenetic analysis. Neither flies nor horses tested positive for A. phagocytophilum, whereas one fly was positive for B. burgdorferi s.l. (0.6%). In contrast, T. equi and B. caballi DNA were detected in 11.2% and 1.2% of flies, respectively, and all positive flies were collected from horses positive for equine piroplasmosis (T. equi/B. caballi infection), with identical 18S rRNA sequences between hosts and flies. Nested PCR showed a higher detection rate than real-time PCR for the detection of these piroplasms in flies and blood samples. These findings provide the first molecular evidence of EP pathogens in H. equina and support further investigation into the epidemiological importance of forest flies in equine pathogen surveillance. Full article

Background: The interaction between helminth and viral infections has important implications for understanding viral disease outcomes and vaccine efficacy in helminth-endemic regions. We previously demonstrated that helminth seropositivity is associated with reduced Th1/Th17 cytokine levels and reduced COVID-19 severity; however, the underlying immunological mechanisms remain unclear. This study further investigated these mechanisms by assessing how helminth antigens influence SARS-CoV-2-induced T-cell responses in individuals infected with filarial parasites in vitro. Methods: Peripheral blood mononuclear cells (PBMCs) from 43 participants, including Onchocerca volvulus-infected individuals, filarial lymphedema patients, and non-endemic controls, were stimulated in vitro with SARS-CoV-2 peptides and Ascaris lumbricoides antigens. Results: Fluorescence-activated cell sorting analysis showed a significant reduction in SARS-CoV-2-induced CD154 expression on CD4⁺ T cells but an increase on CD8⁺ T cells in O. volvulus-infected participants (p < 0.0001). A. lumbricoides antigens alone did not induce significant T-cell activation in O. volvulus-infected individuals. However, SARS-CoV-2 peptides strongly activated CD4⁺CD154⁺ T cells response (p = 0.0074), but co-stimulation with A. lumbricoides antigens markedly reduced CD3⁺ and CD4⁺CD154⁺ T-cell expression frequencies (p = 0.0329 and p = 0.0452). A. lumbricoides-specific IgG correlated inversely with SARS-CoV-2-induced CD4⁺CD154⁺ expression (r = −0.6025, p = 0.0049), whereas SARS-CoV-2-specific IgG was positively associated with CD4⁺CD154⁺ and CD8⁺CD154⁺ T-cell responses (β = 0.532, p = 0.016 and β = 0.509, p = 0.022). Conclusion: These findings demonstrate that helminth antigens modulate functional SARS-CoV-2-induced T-cell responses, offering a potential mechanism through which helminth co-infections shape antiviral immunity, vaccine efficacy, and clinical disease outcomes. Full article

Background: Systemic inflammation plays a central role in the pathogenesis and progression of type 2 diabetes mellitus (T2DM). Hematologic inflammatory indices-such as the Systemic Immune-Inflammation Index (SII), Neutrophil-to-Lymphocyte Ratio (NLR), Platelet-to-Lymphocyte Ratio (PLR), and Monocyte-to-Lymphocyte Ratio (MLR)-have emerged as accessible markers of chronic inflammation, yet longitudinal comparisons across oral antidiabetic therapies remain limited. This study uniquely integrates longitudinal correlation and network analyses in a large real-world T2DM cohort, allowing assessment of the temporal stability and class-specific inflammatory patterns across four oral antidiabetic therapies. Methods: This retrospective, longitudinal study analyzed 13,425 patients with T2DM treated with Biguanidines, Dipeptidyl Peptidase-4 (DPP-4) inhibitors, Sodium–Glucose Cotransporter-2 (SGLT-2) inhibitors or Thiazolidinediones (TZDs) between 2020 and 2024. Data were retrieved from the Probel^® Hospital Information System and included baseline, early (30–180 days), and late (180–360 days) follow-up laboratory results. Systemic inflammatory indices were computed from hematologic parameters, and correlations among inflammatory and biochemical markers were assessed using Spearman’s coefficients. Results: At baseline, all hematologic indices were strongly intercorrelated (SII–NLR r = 0.83, p < 0.001; SII–PLR r = 0.73, p < 0.001), with moderate associations to C-reactive protein (CRP; r ≈ 0.3–0.4) and weak or no correlations with Ferritin (r ≈ −0.1). These relationships remained stable throughout follow-up, confirming reproducibility of systemic inflammatory coupling. Longitudinally, SII and NLR showed modest early increases followed by significant declines at one year (p < 0.05), while PLR and MLR remained stable. Class-specific differences were observed: SGLT-2 inhibitors and TZDs demonstrated stronger and more integrated anti-inflammatory networks, whereas Biguanidines and DPP-4 inhibitors exhibited moderate coherence. Principal Component Analysis (PCA) explained 62.4% of total variance and revealed distinct clustering for TZD and SGLT-2 groups, reflecting class-specific inflammatory modulation. Conclusions: Systemic inflammatory indices (SII, NLR, PLR) provide reproducible and accessible measures of low-grade inflammation in T2DM. Despite overall inflammation reduction with treatment, drug-specific patterns emerged-SGLT-2 inhibitors and TZDs showed greater anti-inflammatory coherence, while Biguanidines and DPP-4 inhibitors maintained moderate effects. Full article

This quantitative observational study with pre–post design aimed to examine joint-specific kinematic adaptations and the relationship between trunk stability and spatiotemporal gait parameters following intensive robotic rehabilitation. A total of 12 neurological patients completed 16 sessions of gait training using the Tecnobody Smart Gravity Walker. Pre- and post-training kinematic data were collected for bilateral hip and knee flexion–extension, trunk flexion–extension, trunk lateral flexion, and center-of-gravity displacement. Waveforms were normalized to 100% stride. Paired t-tests assessed pre–post differences, and correlations examined associations between trunk stability and gait performance. Significant increases were found in right hip flexion–extension (t = 3.44, p < 0.001), trunk flexion–extension (t = 9.49, p < 0.001), and center-of-gravity displacement (t = 15.15, p < 0.001), with reduced trunk lateral flexion (t = –8.64, p < 0.001). Trunk flexion–extension correlated with gait speed (r = 0.74), step length (r = 0.68), and stride length (r = 0.71); trunk lateral flexion correlated with cadence (r = 0.66) and stride length (r = 0.70). Intensive robotic rehabilitation improved trunk and hip kinematics, supporting trunk stability as an important biomechanical correlate of gait recovery. Sensor-derived metrics revealed strong neuromechanical coupling between postural control and locomotion in neurological patients. Full article

Background: Sclerostin, an osteocyte-derived glycoprotein, plays a key role in bone metabolism by inhibiting the Wnt/β-catenin signaling pathway. While it is a recognized therapeutic target in osteoporosis, its relationship with sarcopenia remains unclear. This study aimed to investigate the associations between serum sclerostin levels, sarcopenia, and osteoporosis in older women. Methods: We conducted a cross-sectional study of 79 postmenopausal women aged ≥65 years. Sarcopenia was defined based on grip strength and appendicular skeletal muscle mass (ASM), osteoporosis was diagnosed according to femoral T-scores, and serum sclerostin levels were measured using ELISA. Associations with clinical variables and bone mineral density (BMD) were evaluated using correlation and logistic regression analyses. Results: Sclerostin levels were significantly higher in women with sarcopenia (p = 0.036) and exhibited a negative correlation with grip strength (r = −0.298, p = 0.008) but not with ASM. Positive correlations were found between sclerostin and multiple femoral BMD parameters. In a logistic regression analysis, sclerostin was modestly associated with sarcopenia (p = 0.045); however, no significant association was observed with osteoporosis (p = 0.257). Conclusions: Elevated sclerostin levels are associated with reduced muscle strength and sarcopenia in older women, independent of muscle mass, indicating that sclerostin may reflect a functional decline in musculoskeletal health. Muscle strength should therefore be considered when interpreting sclerostin’s clinical implications in aging populations. Full article

Background: Anorexia nervosa (AN) may require nasogastric tube (NGT) feeding when oral intake is insufficient. Evidence on the psychological impact and prognostic correlates of NGT use in adolescents affected with AN is limited. Methods: Fifty-seven adolescent inpatients (96.5% female; age range 12–18 years; and mean age 15.0 ± 1.51 years) affected with AN admitted in a child psychiatry ward and treated with NGT re-feeding in addition to oral nutrition were included in the study. A 21-item VAS questionnaire was administered at intake (T0), after NGT introduction (T1), after one week of NGT use (T2), and after NGT dismissal (T3) to assess the physical and psychological effects. Participants were also assessed with psychometric measures including personality (TCI), eating psychopathology (EDI-2), general psychopathology (BDI-II, SCL-90-R, and TAS), and family functioning (FAD). The measures were compared between each timepoint with paired t-tests and ANOVA for repeated measures. Pearson correlations were performed between the VAS scores and psychometric measures. Results: From admission to discharge, weight increased by +3.2 kg and BMI by +1.2 kg/m². Items 1, 3, 4, 6, 15, 18, and 20 of the VAS questionnaire items showed significant improvement over time. TCI personality traits, EDI-2 eating and BDI, SCL-90 and TAS general psychopathology, and FAD family functioning were related to NGT perception by the AN adolescents. Conclusions: NGT was helpful in weight progression during inpatient treatment. It was generally well tolerated, with progressive improvement in psychological and physical discomfort during treatment. The meaningful associations with specific psychometric features suggest the possibility to tailor the NGT use based on adolescent characteristics. Multidisciplinary care and tailored psychoeducation may enhance NGT acceptance. Full article

Analysis of pulmonary vascular dysfunction in various lung pathologies remains challenging due to the lack of functional ex vivo models. Paracrine signaling in the lung plays a critical role in regulating endothelial maturation and vascular homeostasis. Previously, we employed single-cell RNA-sequencing (scRNAseq) to systematically map ligand–receptor (L/R) interactions within the lung vascular niche. However, the functional impact of these ligands on endothelial biology remained unknown. Here, we systematically evaluated selected ligands in vitro to assess their effects on endothelial barrier integrity, anti-inflammatory responses, and phenotypic maturation. Among the top soluble ligands, we found that adrenomedulin (ADM) exhibited superior barrier enhancing effect on human pulmonary endothelial cell monolayers, as evidenced by electrical cell impedance sensing (ECIS) and XperT assays. ADM also exhibited anti-inflammatory properties, decreasing ICAM1 and increasing IkBa expression in a dose-dependent manner. Perfusion is commonly used in bioengineered vascular model systems. Shear stress (15 dynes/cm²) alone increased endothelial characteristics, including homeostatic markers such as CDH5, NOS3, TEK, and S1PR1. ADM treatment maintained the enhanced level of these markers under shear stress and further improved anti-coagulation by increasing THBD and decreasing F3 expression and synergistically enhanced the expression of the native lung aerocyte capillary endothelial marker EDNRB. This effect was completely attenuated by a blockade of ADM receptor, RAMP2. Together, these findings identify ADM/RAMP2 signaling as a key paracrine pathway that enhances vascular barrier integrity, anti-inflammatory phenotype, and endothelial homeostasis, providing a framework for improving the physiological relevance of engineered vascular models. Full article

This study investigated the impact of post-activation performance enhancement (PAPE) on the parameters of the 3 min all-out test (3MT) in non-motorized tethered running, applying the concept of complex networks for integrative analysis. Ten recreational runners underwent anthropometric assessments, a one-repetition maximum test (1RM), a running ramp test, and 3MT trials under both PAPE and CONTROL conditions across five separate sessions. The conditioning activity consisted of two sets of six back squats at 60% 1RM. For each scenario, complex network graphs were constructed and analyzed using Degree, Eigenvector, PageRank, and Betweenness centrality metrics. In the PAPE condition, anthropometric parameters and parameters related to aerobic efficiency exhibited greater centrality, ranking among the top five nodes. Paired Student’s t-tests (p ≤ 0.05) revealed significant differences between conditions for end power (EP-W) (CONTROL: 407.83 ± 119.30 vs. PAPE: 539.33 ± 177.10 (effect size d = −0.84)) and end power relativized by body mass (rEP-W·kg⁻¹) (CONTROL: 5.38 ± 1.70 vs. PAPE: 6.91 ± 2.00 (effect size d = −0.76)), as well as for the absolute and relative values of peak output power, mean output power, peak force, and mean force. These findings suggest that PAPE alters the configuration of complex networks, increasing network density, and may enhance neuromuscular function and running economy. Moreover, PAPE appears to modulate both aerobic and anaerobic contributions to performance. These results highlight the importance of network-based approaches for advancing exercise science and providing individualized strategies for training and performance optimization. Full article

Pseudouridine (Ψ), the most abundant RNA modification, plays essential roles in shaping RNA structure, stability, and translational output. Beyond cancer, Ψ is dynamically regulated across numerous physiological and pathological contexts—including immune activation, metabolic disorders, stress responses, and pregnancy-related conditions such as preeclampsia—where elevated Ψ levels reflect intensified RNA turnover and modification activity. These broad functional roles highlight pseudouridylation as a central regulator of cellular homeostasis. Emerging evidence demonstrates that Ψ dysregulation contributes directly to the development and progression of several women’s cancers, including breast, ovarian, endometrial, and cervical malignancies. Elevated Ψ levels in tissues, blood, and urine correlate with tumor burden, metastatic potential, and therapeutic responsiveness. Aberrant activity of Ψ synthases such as PUS1, PUS7, and the H/ACA ribonucleoprotein component dyskerin alters pseudouridylation patterns across multiple RNA substrates, including rRNA, tRNA, mRNA, snoRNAs, and ncRNAs. These widespread modifications reshape ribosome function, modify transcript stability and translational efficiency, reprogram RNA–protein interactions, and activate oncogenic signaling programs. Advances in high-resolution, site-specific Ψ mapping technologies have further revealed mechanistic links between pseudouridylation and malignant transformation, highlighting how modification of distinct RNA classes contributes to altered cellular identity and tumor progression. Collectively, Ψ and its modifying enzymes represent promising biomarkers and therapeutic targets across women’s cancers, while also serving as sensitive indicators of diverse non-cancer physiological and disease states. Full article

The abundant satellite data have enabled the study of the dynamics of forest logging and its corresponding carbon balance with remote sensing. Change detection techniques with moderate-resolution imagery have been widely developed. Yet the signal processing or machine learning methods are sample-dependent, lacking an understanding of spectral signals of forest growth and logging cycles, which is necessary to distinguish logging from other types of disturbance, and mechanism models addressing post-logging tree changes are too complex for parameter inversion. We therefore proposed an efficient physical-based model for spectral simulation of annual forest logging by coupling forest dynamic model ZELIG and the stochastic radiative transfer (SRT) model. The forest logging simulation was conducted and validated by Abies forest field data before and after logging in Wangqing County, Northeastern China (R² = 0.85, RMSE = 10.82 t/ha). The spectral changes in Abies forest stands with annual growth and varying logging intensities were simulated by the novel model. The annual Landsat-8 and Gaofen-1 fusion multispectral imagery of the study area from 2013 to 2016 was furtherly used to extract annual sequence spectral data of 350 forest plots and perform inversion of the annual difference in above-ground biomass (dAGB). With the inversion method combining the look-up table of the ZELIG-SRT model and the random forest regression, the retrieved dAGB of the 350 plots indicated consistency with the measured data on the whole (R² = 0.71, RMSE = 13.32 t/ha). The novel physical-based approach for AGB monitoring is more efficient than previous 3D computer models and less dependent on field samples than data-driven models. This study provides a theoretical basis for understanding the remote sensing response mechanism of forest logging and a methodological basis for improving forest logging monitoring algorithms. Full article

Aging is accompanied by a chronic, low-grade inflammatory state known as “inflammaging,” largely driven by dysregulated signaling of pro-inflammatory cytokines like IL-1 and TNF-α. The biological impact of these cytokines is modulated by the expression of their cellular receptors, which is influenced by genetic polymorphisms. However, the interplay between age, genetic variation, and cell-type-specific receptor expression remains incompletely characterized. This study aimed to determine the relative and absolute expression levels of IL-1 and TNF receptors on major immunocompetent cell populations in healthy donors of different age groups and to assess the influence of receptor gene polymorphisms on this expression. A cohort of 144 healthy donors was stratified into two age clusters using unsupervised clustering: a “young” group (18–31 years, n = 71) and an “older” group (32–59 years, n = 73). Membrane expression of TNFR1, TNFR2, IL-1R1, and IL-1R2 on T-lymphocytes, B-lymphocytes, and monocytes was analyzed by flow cytometry. The analysis included both the percentage of receptor-positive cells and the number of receptors per cell using absolute quantification with calibration beads. Genotyping for eight SNPs in the TNF1, TNFR2, IL1R1, and IL1R2 genes was performed via PCR-RFLP. The most pronounced age-related differences were observed in monocytes, in which the young cohort exhibited a significantly higher percentage of TNFR1- and TNFR2-positive monocytes, as well as a higher number of IL-1R1 receptors. In contrast, T-lymphocytes from the older cluster showed a higher percentage of TNFR2-positive cells. Genetic polymorphisms significantly modulated receptor expression in an age-dependent manner. For example, in the young cluster, polymorphisms primarily affected receptor levels on B-lymphocytes, whereas in the older cluster, the most significant associations were observed in monocytes. This study reveals significant, cell-specific alterations in the IL-1 and TNF receptor landscapes with age, with monocytes being particularly affected. The observed receptor downregulation in older adults is likely to reflect an active process of ligand-induced desensitization driven by chronic inflammation. Furthermore, genetic polymorphisms exert age-dependent effects on receptor expression, highlighting the dynamic interplay between genetics and immunosenescence. These findings provide a foundation for personalized strategies to mitigate inflammaging. Full article

This study successfully developed an oral vaccine for Type 1 Diabetes utilizing recombinant Lactococcus lactis expressing the GAD65 autoantigen. We conducted an in-depth investigation into its protective mechanisms in NOD mice, with a particular focus on its effects on the gut microbiota and metabolome. The administration of the GAD65-L. lactis vaccine resulted in a significant delay in diabetes onset and the preservation of pancreatic function. Our analyses revealed notable alterations in the gut microbial ecosystem, enhancing its diversity and the abundance of beneficial bacteria. Metabolomic profiling indicated time-dependent changes in metabolic pathways, with a marked enrichment of pyrimidine metabolism at 16 weeks and arachidonic acid metabolism at 24 weeks after vaccination by both GAD65-L. lactis and NZ9000-L. lactis. Integrated correlation analysis identified specific microbiota–metabolite interactions, including associations between Ruminiclostridium and lipid species in the GAD65-L. lactis group. These modifications in the microbial community and metabolic landscape were accompanied by enhanced immunoregulatory responses in intestinal LPLs, including expanded Treg populations and suppressed CD8⁺ T cells, a rising trend in IL-10-producing naive dendritic cells, and increased concentrations of TGF-β. Full article

Unmanned Aerial Vehicles (UAVs) have become essential tools across critical domains, including infrastructure inspection, public safety monitoring, traffic surveillance, environmental sensing, and target tracking, owing to their ability to collect high-resolution spatial data rapidly. However, maintaining stable and accurate flight trajectories remains a significant challenge, particularly during autonomous missions in dynamic or uncertain environments. This study presents a novel flight path prediction framework based on Gated Recurrent Units (GRUs), designed for both single-step and multi-step-ahead forecasting of four-dimensional UAV coordinates, Easting (X), Northing (Y), Altitude (Z), and Time (T), using historical sensor flight data. Model performance was systematically validated against traditional Recurrent Neural Network architectures. On unseen test data, the GRU model demonstrated enhanced predictive accuracy in single-step prediction, achieving a MAE of 0.0036, Root Mean Square Error (RMSE) of 0.0054, and a (R²) of 0.9923. Crucially, in multi-step-ahead forecasting designed to simulate real-world challenges such as GPS outages, the GRU model maintained exceptional stability and low error, confirming its resilience to error accumulation. The findings establish that the GRU-based model is a highly accurate, computationally efficient, and reliable solution for UAV trajectory forecasting. This framework enhances autonomous navigation and directly supports the data integrity required for high-fidelity photogrammetric mapping, ensuring reliable site assessment in complex and dynamic environments. Full article

Rice yield prediction at high spatial resolution is essential to support precision management and sustainable intensification in irrigated systems. While many remote sensing studies provide yield estimates at the field scale, pixel-level predictions are required to characterize within-field variability. This study assesses the potential of multitemporal Sentinel-2 imagery and machine learning to estimate rice yield at pixel level in the Albufera rice area (Valencia, Spain). Yield data from combine harvester maps were collected for ‘JSendra’ and ‘Bomba’ Japonica varieties over five growing seasons (2020–2024) and linked to 10 m Sentinel-2 bands in the visible, near-infrared (NIR) and short-wave infrared (SWIR) regions. Random Forest (RF) and XGBoost (XGB) models were trained with 2020–2023 data and independently validated in 2024. XGB systematically outperformed RF, achieving at 110 and 130 DAS (days after showing), R² values of 0.74 and 0.85 and RMSE values of 0.63 and 0.28 t·ha⁻¹ for ‘JSendra’ and ‘Bomba’. Prediction accuracy increased as the season progressed, and models using all spectral bands clearly outperformed configurations based only on spectral indices, confirming the dominant contribution of NIR reflectance. Spatial error analysis revealed errors at field edges and headlands, while central pixels were more accurately predicted. Overall, the proposed approach provides accurate, spatially explicit rice yield maps that capture within-field variability and support both end-of-season yield estimation and early season forecasting, enabling the identification of potentially low-yield zones to support targeted management decisions. Full article

Environmental time series, such as near-surface air temperature, exhibit strong multi-scale structure and persistent autocorrelation. Accurate forecasting therefore requires careful consideration of both temporal scale separation and serial dependence. In this study, we evaluate a unified framework that integrates Kolmogorov–Zurbenko (KZ) filtering with two classes of models: (i) classical regression with Cochrane–Orcutt autocorrelation correction, and (ii) an autocorrelation-adjusted Long Short-Term Memory (LSTM) network that learns an embedded correlation coefficient ($\rho$). All models are assessed using standardized meteorological predictors of T2M under walk-forward validation. The LSTM trained on raw predictors shows moderate performance (RMSE = 0.73, $R^2=0.46$, DW = 0.79), which improves after KZ filtering (RMSE = 0.59, $R^2=0.63$, DW = 1.84). Classical regression applied to KZ-decomposed predictors and corrected using the Cochrane–Orcutt procedure achieves substantially higher accuracy (RMSE = 0.41, $R^2=0.89$, DW $\approx 2.0$), outperforming the LSTM in both predictive precision and residual behavior. Visual diagnostics further confirm tighter predicted–actual alignment and near-white residuals in the classical models, whereas the LSTM retains small systematic deviations even after filtering. Overall, the results demonstrate that addressing multi-scale structures and autocorrelation had a greater impact than increasing model complexity. Integrating spectral decomposition with autocorrelation correction thus produces more reliable, statistically valid forecasts, demonstrating that classical regression with KZ filtering can surpass LSTM models in both accuracy and interpretability. These findings emphasize the value of combining time series–aware pre-processing with both traditional and neural network approaches for environmental prediction. Full article