Chemical Composition and larvicidal activity against Aedes aegypti of essential oils from Croton jacobinenesis Baill

The chemical composition of essential oils from leaves, stalks and inflorescences of Croton jacobinensis obtained by hydrodistillation were analyzed by GC-MS. E-caryophyllene, 1,8-cineole, α-pinene, viridiflorene, -cadinene were the main components in essential oils from plant parts. Essential oils of leaves, stalks, and inflorescences were tested at different concentrations against instar III larvae of Aedes aegypti and showed LC50 of 79.3, 117.2, 65.8 μg/ml, respectively

Chemical composition, cytotoxicity and larvicidal activity against Aedes aegypti of essential oils from Vitex gardineriana Schauer

Vitex gardneriana Schauer (Lamiaceae) popularly known as “jaramataia”, is a shrub commonly found in caatinga biome located in Northeast Brazil. In folk medicine, its leaves have been used as analgesic and anti-inflammatory agents. The chemical composition of the essential oil from leaves obtained by hydrodistillation was analyzed and identified by GC-MS and GC-FID and showing a total of 26 constituents (95.9%) being 2 monoterpenes (0.4%) and 24 sesquiterpenes (95.4%). The main constituents identified were cis-calamenene (29.7%), 6,9-guaiadiene (14.5%) and caryophyllene oxide (14.0%). The essential oil has been demonstrated high larvicidal activity againstAedes aegypti (LC50 = 28.0 μg/mL). In the evaluation of the bioassay with Artemia salina the essential oil showed LC50 = 98.11 μg/mL. Inaddition, the essential oil did not show cytotoxicity (IC50 > 2.50 mg/mL) by the hemolysis assay

Sudden cardiac death multiparametric classification system for Chagas heart disease's patients based on clinical data and 24-hours ECG monitoring

About 6.5 million people are infected with Chagas disease (CD) globally, and WHO estimates that $ > million people worldwide suffer from ChHD. Sudden cardiac death (SCD) represents one of the leading causes of death worldwide and affects approximately 65% of ChHD patients at a rate of 24 per 1000 patient-years, much greater than the SCD rate in the general population. Its occurrence in the specific context of ChHD needs to be better exploited. This paper provides the first evidence supporting the use of machine learning (ML) methods within non-invasive tests: patients' clinical data and cardiac restitution metrics (CRM) features extracted from ECG-Holter recordings as an adjunct in the SCD risk assessment in ChHD. The feature selection (FS) flows evaluated 5 different groups of attributes formed from patients' clinical and physiological data to identify relevant attributes among 57 features reported by 315 patients at HUCFF-UFRJ. The FS flow with FS techniques (variance, ANOVA, and recursive feature elimination) and Naive Bayes (NB) model achieved the best classification performance with 90.63% recall (sensitivity) and 80.55% AUC. The initial feature set is reduced to a subset of 13 features (4 Classification; 1 Treatment; 1 CRM; and 7 Heart Tests). The proposed method represents an intelligent diagnostic support system that predicts the high risk of SCD in ChHD patients and highlights the clinical and CRM data that most strongly impact the final outcome