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Background: Aedes aegypti is the dominant vector of several arboviruses that threaten urban populations in tropical and subtropical countries. Because of the climate changes and the spread of the disease worldwide, the population at risk of acquiring the disease is increasing. Methods: This study investigated the impact of the larval habitats control (CC), nebulization (NEB), and both methods (CCâ¯+â¯NEB) using the distribution of Ae. aegypti eggs collected in urban area of Santa Bárbara d'Oeste, São Paulo State, Brazil. A total of 142,469 eggs were collected from 2014 to 2017. To verify the effects of control interventions, a spatial trend, and a predictive machine learning modeling analytical approaches were adopted. Results: The spatial analysis revealed sites with the highest probability of Ae. aegypti occurrence and the machine learning generated an asymmetric histogram for predicting the presence of the mosquito. Results of analyses showed that CC, NEB, and CCâ¯+â¯NEB control methods had a negative impact on the number of eggs collected in ovitraps, with effects on the distribution of eggs in the three weeks following the treatments, according to the predictive machine learning modeling. Conclusions: The vector control interventions are essential to decrease both occurrence of the mosquito vectors and urban arboviruses. The inference processes proposed in this study revealed the relative causal impact of distinct mosquito control interventions. The spatio-temporal and the machine learning analysis are relevant and Powered by Editorial Manager® and ProduXion Manager® from Aries Systems Corporation robust analytical approach to be employed in surveillance and monitoring the results of public health programs focused on combating urban arboviruses.
RESUMO
OBJECTIVE: It is increasingly common to find patients affected by a combination of type 2 diabetes mellitus (T2DM), dyslipidemia (DLP) and periodontitis (PD), which are chronic inflammatory diseases. More studies able to capture unknown relationships among these diseases will contribute to raise biological and clinical evidence. The aim of this study was to apply association rule mining (ARM) to discover whether there are consistent patterns of clinical features (CFs) and differentially expressed genes (DEGs) relevant to these diseases. We intend to reinforce the evidence of the T2DM-DLP-PD-interplay and demonstrate the ARM ability to provide new insights into multivariate pattern discovery. METHODS: We utilized 29 clinical glycemic, lipid and periodontal parameters from 143 patients divided into five groups based upon diabetic, dyslipidemic and periodontal conditions (including a healthy-control group). At least 5 patients from each group were selected to assess the transcriptome by microarray. ARM was utilized to assess relevant association rules considering: (i) only CFs; and (ii) CFs+DEGs, such that the identified DEGs, specific to each group of patients, were submitted to gene expression validation by quantitative polymerase chain reaction (qPCR). RESULTS: We obtained 78 CF-rules and 161 CF+DEG-rules. Based on their clinical significance, Periodontists and Geneticist experts selected 11 CF-rules, and 5 CF+DEG-rules. From the five DEGs prospected by the rules, four of them were validated by qPCR as significantly different from the control group; and two of them validated the previous microarray findings. CONCLUSIONS: ARM was a powerful data analysis technique to identify multivariate patterns involving clinical and molecular profiles of patients affected by specific pathological panels. ARM proved to be an effective mining approach to analyze gene expression with the advantage of including patient's CFs. A combination of CFs and DEGs might be employed in modeling the patient's chance to develop complex diseases, such as those studied here.
