Search Results (135)

Introduction: Yellow fever (YF) is a viral hemorrhagic fever transmitted by mosquitoes, characterized by a high mortality due to kidney and liver failure, massive coagulation disorders, and hemorrhages. With no specific treatment, prevention through vaccination and vector control is essential. This study investigates the epidemiology of YF in Brazil from 2011 to 2020, focusing on its trends and distribution across the territory. Methods: This ecological time-series study analyzed confirmed YF cases in Brazil’s 27 federative units between 2011 and 2020. Data were sourced from DATASUS, IBGE, and IPEA. Incidence rates per 100,000 inhabitants were calculated, and various sociodemographic and health indicators were analyzed. Prais–Winsten autoregressive models assessed the trends, while a spatial analysis identified the risk areas using global and local Moran’s I statistics. The data were processed using Stata and GeoDa^® software, version 1.12. Results: YF cases were concentrated in the Amazon and Atlantic Forest biomes. The majority of the cases occurred in males (83.3%), non-white individuals (94.3%), and rural workers. Pará showed an increasing trend in incidence. A higher vaccination coverage correlated with a lower YF incidence, though endemic areas with good vaccination coverage still exhibited high rates. Health and socioeconomic indicators were inversely related to incidence, highlighting disparities in regional development. Conclusion: Effective YF control requires multidisciplinary strategies, including expanded vaccination coverage, intensified vector control, and active surveillance. Research should focus on developing better vaccines, monitoring immunity, and improving the global response coordination. Full article

Occupational health risk prediction of miners is a core issue to ensure the safety of high-risk operations. Current risk assessment methodologies face critical limitations, as conventional unimodal prediction systems frequently demonstrate limited efficacy in capturing the multifactorial nature of occupational health deterioration. This study presents a novel stacked ensemble architecture employing dual-phase algorithmic optimization to address these muti-parametric interactions. The proposed framework implements a hierarchical modeling paradigm: (1) a primary predictive layer employing heterogeneous base learners (Random Forest and Logistic Regression classifiers) to establish foundational decision boundaries, and (2) a meta-modeling stratum utilizing regularized logistic regression with hyperparameter optimization via grid search-assisted k-fold cross-validation. Empirical validation through comparative analysis reveals the enhanced ensemble achieves a mean accuracy of 90%. Receiver operating characteristic analysis confirms superior discriminative capacity (AUC = 0.89), surpassing conventional ensemble methods by 23.3 percentile points. The model’s capacity to quantify nonlinear exposure–response relationships while maintaining computational tractability suggests significant utility in occupational health surveillance systems. These findings substantiate that the proposed dual-layer optimization framework substantially advances predictive capabilities in occupational health epidemiology, particularly in addressing the complex synergies between environmental hazards and physiological responses in confined industrial environments. Full article

Cannabis abuse has been linked to acute psychotic symptoms as well as to the development of schizophrenia. Although the association has been well described, causation has not yet been investigated. Therefore, we investigated whether cannabis or cannabinoid use is causal for the development of schizophrenia, conducting a systematic literature review according to the PRISM guidelines. Epidemiological studies and randomized clinical trials investigating the links between cannabis and psychosis-like events (PLE) and schizophrenia were identified (according to PRISM guidelines), and relevant studies were included in a Forest plot analysis. Confounder analysis was performed using a funnel plot, and the Hill causality criteria were used to estimate causation. A total of 18 studies fulfilled the search criteria; 10 studies were included in a forest plot. All studies reported an increased risk for PLE or schizophrenia, and nine of the ten studies, a significant increase; the overall OR was calculated to be 2.88 (CI 2.24 to 3.70), with a twofold-higher risk calculated for cannabis use during adolescence. Confounder effects were indicated by a funnel plot. The Hill criteria indicated a high likelihood for the contribution of cannabis to schizophrenia development. Cannabinoids likely contribute to chronic psychotic events and schizophrenia, especially if taken during adolescence. This effect likely increases with a high cannabis THC concentration and increased frequency of cannabis use, and is stronger in males than in females. This points to the possibility of a selective cannabis toxicity on synaptic plasticity in adolescence, as compared to adult cannabis use. Cannabis use should be regulated and discouraged, and prevention efforts should be strengthened, especially with reference to adolescence. Full article

The COVID-19 pandemic has accelerated advances in molecular biology and virology, enabling the identification of key biomarkers to differentiate between severe and mild cases. Furthermore, the use of artificial intelligence (AI) and machine learning (ML) to analyze large datasets has been crucial for rapidly identifying relevant biomarkers for disease prognosis, including COVID-19. This approach enhances diagnostics in emergency settings, allowing for more accurate and efficient patient management. This study demonstrates how machine learning algorithms in emergency departments can rapidly identify key biomarkers for the vital prognosis in an emerging pandemic using COVID-19 as an example by analyzing clinical, epidemiological, analytical, and radiological data. All consecutively admitted patients were included, and more than 89 variables were processed using the Random Forest (RF) algorithm. The RF model achieved the highest balanced accuracy at 92.61%. The biomarkers most predictive of mortality included procalcitonin (PCT), lactate dehydrogenase (LDH), and C-reactive protein (CRP). Additionally, the system highlighted the significance of interstitial infiltrates in chest X-rays and D-dimer levels. Our results demonstrate that RF is crucial in identifying critical biomarkers in emerging diseases, accelerating data analysis, and optimizing prognosis and personalized treatment, emphasizing the importance of PCT and LDH in high-risk patients. Full article

Background: Previous epidemiological studies have shown that diabetes is associated with an increased risk of several cancers, including bladder cancer. However, prediction models for bladder cancer among diabetes patients remain scarce. This study aims to develop a scoring system for bladder cancer risk prediction among diabetes patients who receive routine care in general outpatient clinics using a machine learning-guided approach. Methods: A territory-wide retrospective cohort study was conducted using electronic health records of Hong Kong. Patients who received diabetes care in public general outpatient clinics between 2010 and 2019 without a history of malignancy were identified and followed up until December 2019. To develop a scoring system for bladder cancer risk prediction, random survival forest was employed to guide variable selection, and Cox regression was subsequently applied for weight assignment. Results: Of the 382,770 patients identified, 644 patients developed bladder cancer during follow-up (median: 6.2 years). The incidence rate was 0.29 per 1000 person-years. In the final time-to-event scoring system, age, serum creatinine, sex, and smoking were included as predictors. Serum creatinine ≥94 µmol/L appeared to be associated with an increased risk of developing bladder cancer. The 2-year and 5-year AUCs on test set were 0.88 (95%CI: 0.84–0.92) and 0.86 (95%CI: 0.80–0.92) respectively. Conclusions: Renal dysfunction could be a potential predictor of bladder cancer among diabetes patients. The proposed scoring system could be potentially useful for providing individualized risk prediction among diabetes patients. Full article

Poor sanitation, improper food handling, limited access to safe drinking water sources, and limited access to healthcare services contribute to a high prevalence of infections caused by soil-transmitted helminths (STHs) among the BaAka Pygmies, an indigenous community living in Central Africa. The aim of this study was to determine the rates of STH infections in the BaAka people inhabiting the rain forests of the south-western parts of the Central African Republic (CAR) as well as to examine the validity of biannual deworming with a single dose of albendazole 400 mg in high-risk communities exposed to extreme environmental conditions. The study was conducted in August 2021 and involved a sample of 49 BaAka Pygmies inhabiting the rain forest of the Sangha-Mbaéré Prefecture, CAR. The study consisted of collecting single stool samples from each participant and examining the samples for intestinal parasites by light microscopy methods. The collected samples were fixed in SAF fixative and next transported from Africa to Europe, where they were analyzed by light microscopy using three different diagnostic methods (direct smear in Lugol’s solution, the Fülleborn’s flotation, the Kato–Katz thick smear) at the Department of Epidemiology and Tropical Medicine in Gdynia, Poland. Microscopic examination found that 61.2% of the study group were infected with at least one helminthic species. The parasitological screening found invasions with four different species of nematodes, of which hookworm invasions were the most prevalent. The study results demonstrated that although the WHO-recommended mass deworming, which is provided to the BaAka Pygmies in healthcare centers set up on the premises of catholic missions, can effectively reduce the number of infections with soil-transmitted helminths, the prevalence of STH infections remains high in the region. The study findings suggest that in order to contain the spread of STHs in the local community, it will be necessary to implement additional preventive measures, apart from only conducting mass deworming programs. Full article

Robust high spatiotemporal resolution daily PM_2.5 exposure estimates are limited in Australia. Estimates of daily PM_2.5 and the PM_2.5 component from extreme pollution events (e.g., bushfires and dust storms) are needed for epidemiological studies and health burden assessments attributable to these events. We sought to: (1) estimate daily PM_2.5 at a 5 km × 5 km spatial resolution across the Australian continent between 1 January 2001 and 30 June 2020 using a Random Forest (RF) algorithm, and (2) implement a seasonal-trend decomposition using loess (STL) methodology combined with selected statistical flags to identify extreme events and estimate the extreme pollution PM_2.5 component. We developed an RF model that achieved an out-of-bag R-squared of 71.5% and a root-mean-square error (RMSE) of 4.5 µg/m³. We predicted daily PM_2.5 across Australia, adequately capturing spatial and temporal variations. We showed how the STL method in combination with statistical flags can identify and quantify PM_2.5 attributable to extreme pollution events in different locations across the country. Full article

Advancements in genomics and machine learning have significantly enhanced the study of Salmonella epidemiology. Whole-genome sequencing has revolutionized bacterial genomics, allowing for detailed analysis of genetic variation and aiding in outbreak investigations and source tracking. Short-read sequencing technologies, such as those provided by Illumina, have been instrumental in generating draft genomes that facilitate serotyping and the detection of antimicrobial resistance. Long-read sequencing technologies, including those from Pacific Biosciences and Oxford Nanopore Technologies, offer the potential for more complete genome assemblies and better insights into genetic diversity. In addition to these sequencing approaches, machine learning techniques like decision trees and random forests provide powerful tools for pattern recognition and predictive modeling. Importantly, the study of bacteriophages, which interact with Salmonella, offers additional layers of understanding. Phages can impact Salmonella population dynamics and evolution, and their integration into Salmonella genomics research holds promise for novel insights into pathogen control and epidemiology. This review revisits the history of Salmonella and its pathogenesis and highlights the integration of these modern methodologies in advancing our understanding of Salmonella. Full article

Begomoviruses are a major threat to cassava production in Africa. Indeed, during the 1990s, the emergence of a recombinant begomovirus (East African cassava mosaic virus-Uganda, EACMV-Ug) resulted in crop devastation and severe famine in Uganda. In 2023, during a pre-survey of cassava farms at Forécariah, South-West Guinea, 22 samples showing peculiar cassava mosaic disease (CMD) symptoms were collected, and subsequent laboratory analysis confirmed the presence of EACMV-Ug in the samples. Deep analysis of DNA-A and DNA-B of the EACMV-Ug isolates from Guinea indicated that they are similar to those associated with the severe CMD epidemic in Uganda in the 1990s. Therefore, a country-wide survey was conducted throughout Guinea in April 2024 to evaluate the extent of the spread of EACMV-Ug in the country and to collect critical CMD epidemiological data. Findings showed a high whitefly population in Lower Guinea averaging 17 per plant; however, the data suggest a spread of EACMV-Ug via infected cuttings. High CMD incidence was found in Lower Guinea and Forest Guinea, whereas the highest CMD severity was observed in Forest Guinea (2.70 ± 0.06) and the lowest CMD severity was found in Middle Guinea (2.20 ± 0.05). Several cases of double and triple infections involving African cassava mosaic virus, East African cassava mosaic virus, East African cassava mosaic Cameroon virus, and EACMV-Ug were observed. EACMV-Ug was detected throughout Guinea, as well as from samples collected in 2022 in Kambia (Sierra Leone). The 63 accessions cultivated in Guinea that were assessed in this study were found susceptible to at least one of the viruses cited above. This study alerts us to an alarming situation in development in West Africa and provides scientific evidence to guide the rapid response needed to contain and stop the progression of EACMV-Ug in West Africa. Full article

Wild felids are vital to maintaining the ecological balance in natural environments as they regulate prey populations at different levels of the food chain. Changes in the dynamics of predator populations can impact the entire biodiversity of an ecosystem. There are few reports of Adenovirus infections in these animals, and little is known about their epidemiology. Therefore, a deeper understanding of these viruses within a One Health framework is essential, given their importance to animal, human, and environmental health. This study aimed to detect Adenovirus DNA in fecal samples of wild felids from a remnant of the Atlantic Forest in southeastern Brazil, renowned for its high biodiversity. A total of 43 fecal samples, 11 from jaguar (Panthera onca) and 32 from ocelot (Leopardus pardalis), were collected. The samples were subjected to viral nucleic acid extraction and genetic material amplification through PCR, followed by nucleotide sequencing. All phylogenetic analyses were based on the amino acid sequences of the DNA polymerase and IV2a genes. Adenovirus DNA was detected in the feces of both species, with two samples of each feline testing positive. This study reports, for the first time, the occurrence of Adenovirus associated with feces of Panthera onca and Leopardus pardalis. All detected sequences were grouped within the Mastadenovirus genus. Based solely on phylogenetic distance criteria, the identified sequences could be classified as Mastadenovirus bosprimum and Mastadenovirus from the vampire bat Desmodus rotundus. We hypothesize that Adenoviruses were associated with the prey consumed, which may allow the felines to act as eventual viral dispersing agents in the environment, in addition to the risk of being infected. This study provides new information on the association of Adenoviruses with wild felids and their prey, and offers important insights into the ecological dynamics of these viruses in natural environments. It suggests that wild felines may play a crucial role in viral surveillance programs. Full article

Objective: Understanding the epidemiological features of malaria is a key step to monitoring and quantifying the impact of the current control efforts to inform future ones. This study establishes the prevalence and frequency of malaria in a forest–savannah ecozone for 5 consecutive years in Cameroon. Methods: A retrospective study was conducted in 3 health centers of Makenene from 2016 to 2020, a period covering the second long-lasting insecticide net mass distribution campaign. Malaria infectious records were reviewed from laboratory registers. The difference in exposure to malaria was estimated using a regression logistic model. Results: A total of 13525 patients underwent malaria diagnostic tests, with a general malaria prevalence of 65.3%. A greater prevalence of malaria was observed in males (68.39%) compared to females (63.14%). The frequency of consultations in health centers was dominated by females, with a gender ratio (M/F) of 0.66. Annual trends in malaria prevalence slightly varied from 2016 to 2020, exceeding 60%: 65.2% in 2016; 66.7% in 2017, 68.1% in 2018, 63.2% in 2019, and 65.3% in 2020, with a significant seasonal variation (p < 0.0001). The highest malaria prevalence was observed during the short rainy season, no matter the year. Among positive cases, the most represented age groups were 6–15 (p < 0.0001), followed by those under 5, while the age group >25 years was the least represented. Conclusion: Close monitoring and additional intervention measures for malaria control are needed, as are more studies on vector bionomics and transmission patterns. Full article

Hepatitis B virus (HBV) and Hepatitis E virus (HEV) are zoonotic pathogens posing significant health concerns in rural Amazonia, a region marked by high endemicity, poverty, and limited healthcare access. However, the epidemiology of HBV and HEV in this ecosystem remains underexplored. This study examines the circulation of HBV and HEV at the human–wildlife interface and identifies risk factors within an isolated Amazonian indigenous community reliant on hunting for subsistence. Antibodies against HBV core antigens (HBcAbs) were found in three wildlife species: Cuniculus paca (0.8%), Tayassu pecari (1.6%), and Mazama americana (4.1%), marking the first record of HBV antibodies in free-ranging wildlife in the Amazon. However, further research is necessary to identify circulating strains and their relation to human HBV. HBcAbs were also detected in 9.1% of human samples, confirming exposure to HBV in the region. HEV IgG antibodies were present in 17.1% of humans and were associated with higher age. All wildlife and domestic animal samples tested negative for HEV, but transmission through consumption of wild animals and contaminated water needs further investigation. The identified risk factors highlight the urgent need for measures to promote safer food handling, improved sanitation, hygiene, and practices related to contact with wild animals. Full article

Citrus trees and their fruits have significant nutritional value and contain antioxidants that are important components of the Mediterranean diet. However, pathogenic diseases pose a threat to citriculture by reducing crop yield and quality. Therefore, there is a need for novel technologies to maintain healthy citrus crops and enable early and accurate detection of the related pathogens, such as the citrus tristeza virus (CTV). Remote sensing offers a non-destructive, cost effective and efficient method for assessing plant health dynamics. It can provide insights into chlorophyll content, water stress and disease presence. This study provides new insights by integrating a combination of remote sensing approaches (FCCs, NDVI, PCA), optical and proximal techniques with in situ field data collection as well as various serological/molecular technologies to detect CTV effectively and evaluate its temporal epidemiology pattern. In addition, the integration of the adopted techniques in case studies of known fields being infected by CTV provides the basis for remote sensing procedures, such as random forest machine learning algorithm, to become powerful in verifying and identifying new CTV-infected fields in a broader extent coverage area, reaching 89.7% accuracy assessment. Thus, it offers decision-makers a robust approach that contributes to CTV epidemiology monitoring and can aid in the development of effective and sustainable disease management strategies. Full article

Current medical limitations in predicting cancer survival status and time necessitate advancements beyond traditional methods and physical indicators. This research introduces a novel two-stage prognostic framework for renal cell carcinoma, addressing the inadequacies of existing diagnostic approaches. In the first stage, the framework accurately predicts the survival status (alive or deceased) with metrics Accuracy, Precision, Recall, and F1 score to evaluate the effects of the classification results, while the second stage focuses on forecasting the future survival time of deceased patients with Root Mean Square Error and Mean Absolute Error to evaluate the regression results. Leveraging popular machine learning models, such as Adaptive Boosting, Extra Trees, Gradient Boosting, Random Forest, and Extreme Gradient Boosting, along with fusion models like Voting, Stacking, and Blending, our approach significantly improves prognostic accuracy as shown in our experiments. The novelty of our research lies in the integration of a logistic regression meta-model for interpreting the blending model’s predictions, enhancing transparency. By the SHapley Additive exPlanations’ interpretability, we provide insights into variable contributions, aiding understanding at both global and local levels. Through modal segmentation and multimodal fusion applied to raw data from the Surveillance, Epidemiology, and End Results program, we enhance the precision of renal cell carcinoma prognosis. Our proposed model provides an interpretable analysis of model predictions, highlighting key variables influencing classification and regression decisions in the two-stage renal cell carcinoma prognosis framework. By addressing the black-box problem inherent in machine learning, our proposed model helps healthcare practitioners with a more reliable and transparent basis for applying machine learning in cancer prognostication. Full article

The adequacy and efficacy of simple and hybrid machine learning and Computational Intelligence algorithms were evaluated for the classification of potential prostate cancer patients in two distinct categories, the high- and the low-risk group for PCa. The evaluation is based on randomly generated surrogate data for the biomarker PSA, considering that reported epidemiological data indicated that PSA values follow a lognormal distribution. In addition, four more biomarkers were considered, namely, PSAD (PSA density), PSAV (PSA velocity), PSA ratio, and Digital Rectal Exam evaluation (DRE), as well as patient age. Seven simple classification algorithms, namely, Decision Trees, Random Forests, Support Vector Machines, K-Nearest Neighbors, Logistic Regression, Naïve Bayes, and Artificial Neural Networks, were evaluated in terms of classification accuracy. In addition, three hybrid algorithms were developed and introduced in the present work, where Genetic Algorithms were utilized as a metaheuristic searching technique in order to optimize the training set, in terms of minimizing its size, to give optimal classification accuracy for the simple algorithms including K-Nearest Neighbors, a K-means clustering algorithm, and a genetic clustering algorithm. Results indicated that prostate cancer cases can be classified with high accuracy, even by the use of small training sets, with sizes that could be even smaller than 30% of the dataset. Numerous computer experiments indicated that the proposed training set minimization does not cause overfitting of the hybrid algorithms. Finally, an easy-to-use Graphical User Interface (GUI) was implemented, incorporating all the evaluated algorithms and the decision-making procedure. Full article