Search Results (194)

Columnar cacti do not provide direct economic benefits; they are vital for maintaining the ecological balance of flora and fauna in desert ecosystems. Over the past 20 years, necrotic disease has been observed in the southern part of the peninsula, leading to a decline in populations. The infectious nature of the disease associated with cactus necrosis syndrome (CNS) was determined by estimating the incidence, severity, and dispersion index. The pathogenic fungi were studied based on symptoms, dispersion patterns, morphological features, and molecular analysis. To determine the pathogenicity and severity of the isolated fungi, experimental inoculations were performed on small cacti. Results showed that at least five fungi (Phoma spp., Nigrospora sphaerica, Chaetomium spp., Coniochaeta spp., and Alternaria alternata) may be associated with CNS, with responses varying depending on whether infections are mixed or straightforward. Common symptoms included canker and pustules, with prevalence rates of 78.87% and 85.3%, respectively. An increase in incidence of 9.89% was noted compared to 2019 (74.83%) and 2020 (84.72%). The average severity rate for all observed symptoms was 55.21%. These findings identify the causal agents involved in necrotic diseases, which could influence the severity and progression of CNS in B.C.S. Full article

Digital health observatories require predictive frameworks that transform administrative health data into reliable territorial risk indicators. We developed a biomedical informatics framework for machine-learning-based epidemiological risk stratification in the Colombian Orinoquía, with explicit attention to data-leakage prevention, validated population denominators, and unit-of-analysis discipline. From 354,088 morbidity records (2018–2023; Arauca, Casanare, Meta, Vichada) we derived 20,212 independent strata (municipality × year × diagnostic group × sex × age category × health component) and computed morbidity rates using official population projections from the Colombian National Administrative Department of Statistics (DANE), correcting a denominator instability present in the original extract. After excluding leakage-generating variables, Gradient Boosting, Random Forest, and a one-hot Logistic Regression baseline were evaluated through temporal validation, group-aware cross-validation, leave-one-department-out validation, and a geographic ablation experiment. Under leakage-controlled, stratum-level conditions, Gradient Boosting achieved an area under the receiver operating characteristic curve (AUC-ROC) of 0.913 [95% confidence interval (CI): 0.903–0.923] (Brier: 0.106). SHapley Additive exPlanations (SHAP) analysis identified diagnostic group and age category as the dominant predictors, supported by broadly stable performance without geographic identifiers (AUC 0.871) and consistent cross-department transferability (0.807–0.889); a residual municipality-level clustering effect is reported transparently. Validated denominators reversed the apparent territorial gradient, with the most remote department exhibiting the lowest documented morbidity, consistent with under-registration. Full article

Dispersal mechanisms of phytopathogenic fungi and oomycetes are critical components of forest disease dynamics. While wind and water are well-studied pathways, the role of large forest herbivores as passive vectors remains significantly overlooked. This study quantifies and compares the pathogen loads carried on the hooves and hair of wild red deer (Cervus elaphus) to evaluate their epidemiological potential. Swab samples were collected from the hooves and hair of harvested deer in the Czerwony Bór Forest District, Poland. Quantitative PCR (qPCR) assays targeting the ITS1 region were deployed to detect total fungal DNA, Alternaria alternata, Fusarium avenaceum/F. tricinctum, and several Phytophthora species. A linear mixed-effects model was implemented to statistically evaluate variations in pathogen loads across anatomical sampling locations while controlling for individual animal variability. Fungal DNA was detected in 87.5% of hoof samples, showing significantly lower Ct values (13.85–18.54) compared to fur samples (17.02–29.56), which exhibited a more patchy distribution (p = 0.016). Similarly, A. alternata transfer was highly favored by hooves (p < 0.001). Conversely, F. avenaceum was more frequently detected on hair. Among oomycetes, Phytophthora pseudosyringae was detected in all sampled animals, whereas Phytophthora cactorum occurred rarely, and other tested Phytophthora species were not detected. Wild deer carry DNA of multiple fungal and oomycete pathogens and may act as potential passive carriers within forest ecosystems. Hooves constitute the primary vector for soil-borne pathogens due to sustained contact with topsoil, whereas hair facilitates the movement of specific canopy or airborne taxa. These findings suggest that wildlife movements should be considered in future forest biosecurity assessments for comprehensive forest health management and for understanding pathogen exchange between forest and agricultural ecosystems. Full article

A number of studies have examined the acute effects of shinrin-yoku (forest bathing), or forest walking; however, only a limited number of epidemiological studies have examined the association between habitual forest walking and daily health. The aim of this study was to examine the association of daily happiness, the Japanese concept of ikigai, and enjoyment of daily life with the frequency of forest walking. We used data from the secondary survey of the Japan Multi-Institutional Collaborative Cohort (J-MICC) Daiko study for this cross-sectional study. A total of 3472 participants (2518 women) were included in the analysis. Logistic regression analysis showed that the aORs for frequency of feeling happy, degree of happiness, ikigai, and daily life enjoyment were 1.72 (95% confidence interval [CI]: 1.29–2.28), 1.54 (95% CI: 1.14–2.09), 1.65 (95% CI: 1.25–2.20), and 2.38 (95% CI: 1.60–3.35), respectively, for those who reported going for forest walks at least once per month when those who reported rarely engaging in this practice served as the reference. This study found that the frequency of forest walking was associated with daily happiness, ikigai, and enjoyment of daily life, suggesting that occasional forest bathing may lead to improved well-being and quality of life. Full article

Background: Assessing overall diet quality has become increasingly important in nutritional epidemiology. The Healthy Eating Index (HEI) is one of the most widely used measures of diet quality; however, comparable data on Hungarian children are scarce. The current study aimed to assess the diet quality of Hungarian children using the Healthy Eating Index and to examine demographic factors associated with diet quality. Methods: This cross-sectional study included 666 children aged 4–10 years. Dietary intake was measured using three-day dietary records, and sociodemographic factors were collected via parental questionnaires. Diet quality was assessed using the Healthy Eating Index-2020 (HEI-2020). For the statistical analysis, linear regression, random forest models and one-way ANOVA with Tukey’s post hoc test were used. Results: The mean HEI score was 48.2 (SD 8.02), indicating low diet quality. Settlement type was significantly associated with the HEI score (p = 0.009). The multiplicity-corrected p-values for pairwise comparisons showed that children living in towns had significantly lower HEI scores (44.9, SD 7.97) than those living in county capitals (3.9, 95% CI:0.8–7.0, p = 0.006) and villages (−3.6, 95% CI: −0.61–−6.6, p = 0.011), but not significantly lower than those living in the capital (3.09, 95% CI: −0.25–6.4, p = 0.08). These differences were primarily related to whole-fruit and whole-grain component scores. Sex, age, and maternal education were not significantly associated with HEI score. The random forest model showed weak predictive performance (RMSE = 7.66). Conclusions: Diet quality among Hungarian children was generally suboptimal. The examined sociodemographic characteristics accounted for only a small proportion of the variability in the HEI score. This highlights the importance of ongoing research to understand dietary patterns and to uncover additional social, environmental, and behavioral aspects of dietary habits across cultures. Furthermore, the results indicate that interventions should also consider local food environments. Full article

Background: Retinoblastoma (RB) is a rare pediatric malignancy characterized by small sample sizes and low event rates, where conventional association-driven feature selection may lead to unstable models, overadjustment, and limited generalizability. However, existing survival prediction studies lack a careful treatment of feature selection that accounts for underlying causal structure. Objectives: To develop and validate a causality-guided machine learning model for RB survival prediction by jointly incorporating survival time and survival status as outcome variables. Methods: We analyzed 1015 RB patients from the SEER database (1975–2020). A causality-informed feature selection framework was developed to address the challenges of rare-disease data. Specifically, candidate variables were evaluated through a three-step evidence-integration process: (1) univariate Cox proportional hazards (CPH) analysis for initial statistical screening; (2) causal structure learning using the PC algorithm on the variables retained from Step 1 to construct a directed acyclic graph (DAG) and exclude structurally inappropriate variables (colliders or descendants of the outcome); and (3) LASSO-based feature screening performed independently on the full set of candidate variables. The final features were obtained by taking the intersection of the variables retained from Step 2 and Step 3. Survival models were then trained using the selected features, with model comparison performed as a secondary step. Results: The proposed framework consistently identified four structurally and prognostically robust predictors—laterality, “SEER historic stage A”, “RX Summ”, and sequence number—through this evidence-integration process. Compared with conventional approaches, the causality-informed framework reduced the feature set while improving model stability and interpretability. Notably, compared with LASSO-only selection, which retained a larger set of variables, the causality-informed approach yielded a more parsimonious feature set with improved predictive performance, suggesting reduced overfitting in a low-event setting. Survival models trained on this refined feature set demonstrated reliable predictive performance, with the random survival forest achieving the highest discrimination (C-index = 0.739). Importantly, the selected predictors aligned with clinically plausible pathways in the learned DAG, supporting their causal relevance. Conclusions: This study demonstrates that incorporating causal structure into feature selection provides a more reliable and interpretable foundation for survival modeling in retinoblastoma. Rather than focusing on algorithmic comparison alone, our findings highlight that careful, causality-informed feature selection is critical for improving robustness in rare-disease prediction tasks. This framework may serve as a generalizable methodological template for other rare clinical settings prone to spurious associations. Full article

The prevalence of vitamin D deficiency has been noted to be of great concern to the population due to its association with multiple chronic ailments, such as those that affect the cardiovascular system, diabetes, bone diseases, and the immune system, and even some cancerous forms. The underlying mechanics are not well understood, but epidemiological and clinical research demonstrates notable correlations, correlated to different degrees by population, based on genetic, environmental, and lifestyle differences. The use of advanced machine learning methods in this study aimed to examine the association between serum vitamin D levels and chronic disease risk using the de-identified electronic health records from Intensive Care Unit (ICU) patients in the MIMIC-III database. A final cohort of 38,712 unique patients was selected after applying predefined inclusion and exclusion criteria, the data being used for machine learning model development and evaluation. To predict the status of chronic diseases based on vitamin D levels, supervised machine learning models were used: Logistic Regression (LR), Support Vector Machines (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN). Data preprocessing involved strategies such as missing data imputation, normalization, and feature engineering to improve model training. The accuracy, precision, recall, F1-score, and receiver operating characteristic curve (ROC-AUC) were used to assess model performance. LR performed the best with the ROC-AUC of 92.5% and accuracy of 85.2%, and SVM was close behind the ROC-AUC: 90.9 percent. The chronic disease was associated with 2.1 times higher risk of chronic disease among people with severe vitamin D deficiency (p < 0.001), which was in agreement with earlier observational data. RF did not work as well (ROC-AUC: 87.7%) because it required higher dimensionality data, e.g., genetic and lifestyle parameters. The study shows the possible use of AI-based diagnostics in preventive medicine. As an example, presented vitamin D testing of high-risk population (e.g., increasing tests in old age, obesity, or dark-skinned population) adapted to ML-based risk assessment as well as supplementation plans could enhance this prevention. Full article

Ixodes ricinus is the principal vector of numerous tick-borne pathogens (TBPs) in Europe and is typically associated with forest sites that provide favorable microclimatic conditions. However, this species may also occur in meadow ecosystems, which are generally regarded as suboptimal environments and remain insufficiently studied from an epidemiological perspective. The aim of this study was to determine and compare the prevalence of Borrelia spp. and Rickettsia spp. in adult I. ricinus occurring in urban and rural meadow sites in eastern Poland. Ticks collected between June 2023 and May 2024 were screened for Borrelia spp. and Rickettsia spp. using high-throughput microfluidic real-time PCR targeting the 23S rRNA and ITS regions, respectively. The DNA of Borrelia spp. was detected in 14.8% of ticks from the urban site and 10.7% from the rural site, whereas Rickettsia spp. were detected in 5.6% and 8.9% of specimens, respectively. No significant differences in pathogen prevalence were observed between sites. The results confirmed the presence of Borrelia spp. and Rickettsia spp. in adult I. ricinus collected in urban and rural meadow sites. Full article

Equids, primarily horses, are mostly used for recreational purposes, although in some rural areas they also serve as working animals, maintaining close and frequent contact with humans. Their risk of exposure to vector-borne and zoonotic pathogens can be affected by host-related factors, management practices and environmental conditions. This study aimed to investigate the seroprevalence and associated risk factors for infections by Babesia caballi, Toxoplasma gondii, Coxiella burnetii, and Borrelia burgdorferi sensu lato in equids from Southwestern Greece. A total of 159 equids were tested using commercial serological assays. Weighted prevalence estimates were applied to account for unequal sampling. Associations were assessed using chi-square tests and logistic regression. Ecological niche modelling was employed to evaluate geographic patterns and environmental correlates. Seroprevalence was highest for B. caballi (8.81%), followed by T. gondii (7.55%) and C. burnetii (1.26%). No seropositive animals were detected for B. burgdorferi sensu lato. Ecological niche modelling showed acceptable predictive performance for B. caballi, with BIO14 and BIO6 emerging as the main environmental predictors. In contrast, the T. gondii model exhibited unacceptable predictive performance, and its environmental associations should therefore be interpreted cautiously. Complementary Random Forest analyses yielded comparable environmental rankings but showed higher classification performance for T. gondii than for B. caballi. Overall, the findings contribute to understanding pathogen exposure patterns in equids and underscore the importance of integrating epidemiological and environmental data in surveillance efforts. Full article

Background/Objectives: The interplay between oral and systemic diseases is highlighted by the shared inflammatory mechanisms and epidemiological associations between periodontitis and rheumatoid arthritis (RA). Building on previous syntheses of the effect of periodontal therapy on RA disease activity, we sought to refine the evidence base through strict restriction to randomized controlled trials (RCTs), separate analysis of the two non-interchangeable formulations of the Disease Activity Score on 28 joints (DAS28-CRP and DAS28-ESR, based on either C-reactive protein or erythrocyte sedimentation rate, respectively), and inclusion of recent randomized trials. We aimed to determine whether the first two steps of periodontal therapy (steps 1 and 2 of the 2020 EFP S3-level clinical practice guideline), delivered through supragingival professional mechanical plaque removal and subgingival instrumentation, reduce DAS28 in adults with concurrent RA and periodontitis. Methods: The review protocol was registered in PROSPERO (CRD420261400735). Five databases were searched in accordance with PRISMA 2020. Only RCTs were eligible. Risk of bias was assessed with RoB-2. DAS28-CRP and DAS28-ESR were analyzed in separate random-effects forest plots. Sensitivity analyses addressed adjunctive antibiotics and high baseline disease activity. Results: Ten trials (n = 430 randomized patients) were included. At 3 months, DAS28-CRP was significantly reduced (between-group MD = −0.84, 95% CI −1.38 to −0.29; change-from-baseline MD = −0.55, −0.92 to −0.19). On DAS28-ESR at 3 months, the change-from-baseline estimate was significant (MD = −1.27, −2.22 to −0.31) and the follow-up estimate concordant in direction but not significant (MD = −0.89, −1.85 to 0.07), with substantial heterogeneity. Conclusions: Periodontal therapy may be associated with short-term reductions in RA disease activity, particularly DAS28-CRP at 3 months, with directionally concordant but less certain effects on DAS28-ESR. The evidence remains limited by small sample sizes, risk of bias, substantial heterogeneity of the DAS28-ESR estimates, and sparse follow-up beyond 3 months. As no trial reported individual responder categories, these group-level findings support periodontal therapy as a possible adjunctive measure in RA rather than a predictable, clinically meaningful benefit at the individual patient level. Full article

Aspartame is a widely used artificial sweetener, but its possible relationship with rheumatoid arthritis (RA) remains insufficiently understood. This study aimed to explore, rather than prove, potential molecular links between aspartame-related targets and RA-associated gene networks. Three public RA transcriptomic datasets (GSE55235, GSE55457, and GSE77298) from the Gene Expression Omnibus (GEO) database were integrated as discovery/training data. Because these datasets included different tissue origins, batch correction was used to reduce dataset-level technical variation, whereas tissue-origin-related biological variation was not assumed to be fully removable. After differential expression analysis, RA-associated differentially expressed genes (DEGs) were identified. The single-cell dataset GSE200815 was used for cell annotation and cellular expression visualization; because its comparator group consists of psoriatic arthritis (PsA) samples rather than healthy controls, single-cell results were interpreted as RA-vs-PsA observations and were not treated as disease-versus-healthy-control evidence. Potential targets of aspartame were retrieved from ChEMBL, SwissTargetPrediction, and the Similarity Ensemble Approach (SEA), and were intersected with RA-related DEGs to construct an aspartame-gene-RA regulatory network. Diagnostic models were developed using 113 machine-learning algorithm combinations to determine an optimal multigene model and its core genes. HLA-DRB1 was selected for exploratory scTenifoldKnk-based virtual knockout mainly because it was included in the optimal model and has a well-established role in RA immunogenetics; the single-cell analysis was used only to describe cellular distribution in the RA/PsA dataset. Molecular docking was then used to evaluate the possible interaction between aspartame and HLA-DRB1. Forty-four intersected genes linked the predicted aspartame targets with RA DEGs. The random forest plus partial least-squares generalized linear model (RF + plsRglm) identified 16 core genes. Network-level interpretation indicated that these genes were distributed across immune/antigen-processing, inflammatory-signaling, protease/extracellular-matrix-remodeling, adhesion, metabolic, and proliferation-related modules; therefore, HLA-DRB1 was treated as a prioritized immune-module candidate rather than as the sole driver of the network. Following virtual knockout of HLA-DRB1, affected genes were enriched in extracellular matrix organization, extracellular structure organization, extracellular matrix, collagen trimer, extracellular matrix structural constituent, and collagen binding. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways included integrin signaling, focal adhesion, proteoglycans in cancer, cytoskeleton in muscle, and phosphoinositide 3-kinase/protein kinase B (PI3K/AKT) signaling. Molecular docking showed a minimum binding energy of −6.7 kcal/mol, which was more negative than the preset stability criterion of −5.0 kcal/mol, and the docking pose suggested contacts around ARG-146. This integrative analysis suggests a hypothesis-generating association between aspartame-related predicted targets and RA-relevant molecular networks involving HLA-DRB1 and other core genes. The findings do not establish causality and require experimental, epidemiological, biophysical, and tissue-stratified validation before any causal or clinical inference can be made. Full article

Dengue virus infection remains a significant public health challenge in endemic regions, with growing evidence of autochthonous transmission in Europe. Assessing serological exposure in high-risk populations such as military personnel deployed to endemic areas is essential to quantify exposure risk and support operational decision-making, particularly regarding pre-deployment counselling and risks associated with secondary infection. We conducted a cross-sectional study involving 1355 members of the Italian Armed Forces, measuring anti-dengue IgG antibodies by ELISA and collecting data on deployment history and exposure risk. Overall, IgG seropositivity was 8.12%, with significantly higher prevalence among individuals reporting travel or deployment to endemic regions (24.71%) compared with non-exposed personnel (4.27%). Seropositivity increased with age and correlated with a CDC-derived cumulative dengue risk score (Spearman’s ρ = 0.299, p < 0.0001). A multivariable logistic regression model including age and exposure risk achieved an AUC of 0.75, while machine-learning models provided complementary predictive assessment, with random forest reaching an AUC of 0.79. These findings indicate substantial anti-dengue IgG seropositivity compatible with previous dengue exposure among Italian military personnel, particularly those deployed to endemic settings. The study highlights the need for targeted surveillance and risk-based preventive strategies, and supports the use of exposure-based models to improve epidemiological assessment and inform vaccination strategies in mobile populations. Full article

This study presents a reproducible high-resolution framework for assessing urban chikungunya environmental suitability and outbreak-related spatial heterogeneity during the 2025 outbreak in the western Guangdong–Hong Kong–Macao Greater Bay Area. Using Sentinel-2–derived environmental indicators together with a random forest–based residual correction of Landsat surface temperature, we developed a 10 m weighted additive Mosquito Habitat Suitability Index (MHSI). Index weights were empirically derived by comparing reported case locations at the street and town level with randomly sampled background points. The optimized weighting scheme indicated that humidity- and water-related conditions contributed more strongly to habitat suitability than vegetation and temperature. Reported case locations generally corresponded to higher MHSI values than background locations, suggesting that the index captures broad spatial patterns of environmental suitability. Comparison with a coarser, model-derived global chikungunya risk map was used as an external comparative consistency assessment rather than predictive validation, showing moderate agreement at the macro-spatial scale (Pearson r = 0.3421) after correction for spatial autocorrelation. Residual-difference analysis, combined with multiple points-of-interest (POI) categories, ordinary least squares (OLS), and geographically weighted regression (GWR), further suggested that human activity, transport connectivity, and healthcare accessibility may account for part of the remaining spatial mismatch not explained by environmental suitability alone. Sensitivity analyses indicated that the broad LST downscaling pattern and the exploratory GWR interpretation were reasonably stable under alternative sampling, smoothing, grid-size, and bandwidth settings. Taken together, this framework provides preliminary spatial evidence for high-resolution environmental suitability assessment and exploratory interpretation of outbreak-related spatial heterogeneity, while underscoring the need for finer-scale epidemiological data and more explicit representation of human-driven processes. Full article

Pine gall rust has emerged as a serious threat to Pinus taiwanensis (Huangshan pine) in Huangshan Mountain, a UNESCO World Heritage site in China. This study systematically investigated the etiology, host range, and epidemiological characteristics of the disease through field surveys, morphological observations, molecular phylogenetic analyses, and inoculation tests. The pathogen was identified as Cronartium orientale based on multi-locus sequencing (SSU, LSU, and ITS) and distinct basidiospore morphology. Quercus stewardii and Castanea seguinii were confirmed as alternate hosts, with Q. stewardii showing higher susceptibility. Microscopic examination revealed detailed spore morphology, and germination assays demonstrated that aeciospores and urediniospores germinate optimally at 12 °C and under near-saturated humidity. Aeciospore dispersal peaked from late April to early May, with spores detected up to 8 m from infected trees, under nearly windless conditions. The life cycle of C. orientale in this region involves annual production of pycnia and aecia on pine, followed by uredinia and telia on alternate hosts, enabling repeated infections. These findings clarify the etiology and epidemiology of pine gall rust on Huangshan pine, providing a scientific basis for disease monitoring and management strategies to protect the ecologically and culturally valuable Huangshan pine forests. Full article

African swine fever (ASF) poses a persistent and escalating threat to global swine production. To comprehensively characterize its global spatiotemporal dynamics from 1996 to 2025, we developed an integrated framework combining multi-distance spatial analysis and advanced time series forecasting, utilizing a dataset of 57,253 outbreak records. Our findings reveal a clear divergence in transmission patterns: wild boar accounted for approximately 70% of outbreaks and predominantly sustained transmission in Eastern Europe, whereas domestic pig outbreaks were largely concentrated in Southeast Asia. A pronounced epidemiological shift occurred between 2017 and 2020, during which ASF spread transitioned from a predominantly north–south axis linking Africa and the Caucasus to a broad east–west expansion across Eurasia, coinciding with rapid dissemination throughout Asia. In the Northern Hemisphere, ASF outbreaks exhibited a bimodal seasonal pattern, with peaks observed in January–March and July–August. Comparative forecasting analyses demonstrated that machine learning approaches consistently outperformed both traditional statistical and deep learning models. Among these, the random forest algorithm achieved the highest predictive accuracy, surpassing SARIMA, Prophet, XGBoost, and GRU. Collectively, these findings underscore the pivotal role of wild boar in maintaining global ASF transmission and highlight the necessity of integrated surveillance at wildlife–livestock interfaces. Furthermore, they support the application of machine learning-based approaches for improving early warning systems and enhancing the effectiveness of global ASF control strategies. Full article