The chemical characterization of Nigerian propolis samples and their activity against Trypanosoma brucei.

Profiling of extracts from twelve propolis samples collected from eight regions in Nigeria was carried out using high performance liquid chromatography (LC) coupled with evaporative light scattering (ELSD), ultraviolet detection (UV) and mass spectrometry (MS), gas chromatography mass spectrometry (GC-MS) and nuclear magnetic resonance spectroscopy (NMR). Principal component analysis (PCA) of the processed LC-MS data demonstrated the varying chemical composition of the samples. Most of the samples were active against Trypanosoma b.brucei with the highest activity being in the samples from Southern Nigeria. The more active samples were fractionated in order to isolate the component(s) responsible for their activity using medium pressure liquid chromatography (MPLC). Three xanthones, 1,3,7-trihydroxy-2,8-di-(3-methylbut-2-enyl)xanthone, 1,3,7-trihydroxy-4,8-di-(3-methylbut-2-enyl)xanthone a previously undescribed xanthone and three triterpenes: ambonic acid, mangiferonic acid and a mixture of α-amyrin with mangiferonic acid (1:3) were isolated and characterised by NMR and LC-MS. These compounds all displayed strong inhibitory activity against T.b.brucei but none of them had higher activity than the crude extracts. Partial least squares (PLS) modelling of the anti-trypanosomal activity of the sample extracts using the LC-MS data indicated that high activity in the extracts, as judged from LCMS 2data, could be correlated to denticulatain isomers in the extracts

Portuguese propolis disturbs glycolytic metabolism of human colorectal cancer in vitro

Propolis is a resin collected by bees from plant buds and exudates, which is further processed through the activity of bee enzymes. Propolis has been shown to possess many biological and pharmacological properties, such as antimicrobial, antioxidant, immunostimulant and antitumor activities. Due to this bioactivity profile, this resin can become an alternative, economic and safe source of natural bioactive compounds.Antitumor action has been reported in vitro and in vivo for propolis extracts or its isolated compounds; however, Portuguese propolis has been little explored. The aim of this work was to evaluate the in vitro antitumor activity of Portuguese propolis on the human colon carcinoma cell line HCT-15, assessing the effect of different fractions (hexane, chloroform and ethanol residual) of a propolis ethanol extract on cell viability, proliferation, metabolism and death. METHODS: Propolis from Angra do Heroísmo (Azores) was extracted with ethanol and sequentially fractionated in solvents with increasing polarity, n-hexane and chloroform. To assess cell viability, cell proliferation and cell death, Sulforhodamine B, BrDU incorporation assay and Anexin V/Propidium iodide were used, respectively. Glycolytic metabolism was estimated using specific kits. RESULTS: All propolis samples exhibited a cytotoxic effect against tumor cells, in a dose- and time-dependent way. Chloroform fraction, the most enriched in phenolic compounds, appears to be the most active, both in terms of inhibition of viability and cell death. Data also show that this cytotoxicity involves disturbance in tumor cell glycolytic metabolism, seen by a decrease in glucose consumption and lactate production. CONCLUSION: Our results show that Portuguese propolis from Angra do Heroísmo (Azores) can be a potential therapeutic agent against human colorectal cancer.We thank the Portuguese Science and Technology Foundation (FCT) for VMG fellowship (ref. SFRH/BI/33503/2008). The authors thank Mr. Antonio Marques from Frutercoop - Azores, who kindly collected and provided the propolis sample for the study

Antimicrobial activity against oral pathogens and immunomodulatory effects and toxicity of geopropolis produced by the stingless bee Melipona fasciculata Smith

<p>Abstract</p> <p>Background</p> <p>Native bees of the tribe Meliponini produce a distinct kind of propolis called geopropolis. Although many pharmacological activities of propolis have already been demonstrated, little is known about geopropolis, particularly regarding its antimicrobial activity against oral pathogens. The present study aimed at investigating the antimicrobial activity of <it>M. fasciculata </it>geopropolis against oral pathogens, its effects on <it>S. mutans </it>biofilms, and the chemical contents of the extracts. A gel prepared with a geopropolis extract was also analyzed for its activity on <it>S. mutans </it>and its immunotoxicological potential.</p> <p>Methods</p> <p>Antimicrobial activities of three hydroalcoholic extracts (HAEs) of geopropolis, and hexane and chloroform fractions of one extract, were evaluated using the agar diffusion method and the broth dilution technique. Ethanol (70%, v/v) and chlorhexidine (0.12%, w/w) were used as negative and positive controls, respectively. Total phenol and flavonoid concentrations were assayed by spectrophotometry. Immunotoxicity was evaluated in mice by topical application in the oral cavity followed by quantification of biochemical and immunological parameters, and macro-microscopic analysis of animal organs.</p> <p>Results</p> <p>Two extracts, HAE-2 and HAE-3, showed inhibition zones ranging from 9 to 13 mm in diameter for <it>S. mutans </it>and <it>C. albicans</it>, but presented no activity against <it>L</it>. <it>acidophilus</it>. The MBCs for HAE-2 and HAE-3 against <it>S. mutans </it>were 6.25 mg/mL and 12.5 mg/mL, respectively. HAE-2 was fractionated, and its chloroform fraction had an MBC of 14.57 mg/mL. HAE-2 also exhibited bactericidal effects on <it>S. mutans </it>biofilms after 3 h of treatment. Significant differences (p < 0.05) in total phenol and flavonoid concentrations were observed among the samples. Signs toxic effects were not observed after application of the geopropolis-based gel, but an increase in the production of IL-4 and IL-10, anti-inflammatory cytokines, was detected.</p> <p>Conclusions</p> <p>In summary, geopropolis produced by <it>M. fasciculata </it>can exert antimicrobial action against <it>S. mutans </it>and <it>C. albicans</it>, with significant inhibitory activity against <it>S. mutans </it>biofilms. The extract with the highest flavonoid concentration, HAE-2, presented the highest antimicrobial activity. In addition, a geopropolis-based gel is not toxic in an animal model and displays anti-inflammatory effect.</p

Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods

An unprecedented spectroscopic data stream will soon become available with forthcoming Earth-observing satellite missions equipped with imaging spectroradiometers. This data stream will open up a vast array of opportunities to quantify a diversity of biochemical and structural vegetation properties. The processing requirements for such large data streams require reliable retrieval techniques enabling the spatiotemporally explicit quantification of biophysical variables. With the aim of preparing for this new era of Earth observation, this review summarizes the state-of-the-art retrieval methods that have been applied in experimental imaging spectroscopy studies inferring all kinds of vegetation biophysical variables. Identified retrieval methods are categorized into: (1) parametric regression, including vegetation indices, shape indices and spectral transformations; (2) nonparametric regression, including linear and nonlinear machine learning regression algorithms; (3) physically based, including inversion of radiative transfer models (RTMs) using numerical optimization and look-up table approaches; and (4) hybrid regression methods, which combine RTM simulations with machine learning regression methods. For each of these categories, an overview of widely applied methods with application to mapping vegetation properties is given. In view of processing imaging spectroscopy data, a critical aspect involves the challenge of dealing with spectral multicollinearity. The ability to provide robust estimates, retrieval uncertainties and acceptable retrieval processing speed are other important aspects in view of operational processing. Recommendations towards new-generation spectroscopy-based processing chains for operational production of biophysical variables are given

Production Performance and Nutrient Composition of Fodder Triticale (X Triticosecale W.)

A study was undertaken to compare the productivity and nutrient compositions of different varieties of fodder triticale (xTriticosecale W.) from 2019 to 2021. The experiments were laid-out in a Randomized Complete Block Design with four treatments consisting three varieties of triticale (Winter Max, Crack Jack, and Bolt) and one local wheat variety (as a check), with three replications. The fodder dry matter (DM) yields of evaluated varieties significantly varied (p&lt;0.05) in 2020 and in 2021, although it was non-significant in pooled data analysis of three years. The interaction effects of the varieties and locations on fodder dry matter yield were non-significant in 2019, 2020 and pooled data analysis of three years but was significantly different in 2021. The seed yield was statistically different for the varieties in different years and also in pooled data analysis. Similarly, the interaction effects of varieties and locations were significantly different in seed yields in all three years. The seed yields were significantly different for the fodder triticale varieties in both the locations and pooled data analysis. The interaction effects of varieties and years were significant for seed yields. The average protein percentage was ranged from 8.88 to 10.39%. Bolt performed well in terms of dry matter and Winter Max did well in terms of seed production in different years while Crack Jack was found to be best for the protein percentage. The temporal and spatial effects on varieties indicate the need of the further niche or region-specific studies

A novel enhanced convolution neural network with extreme learning machine : facial emotional recognition in psychology practices

Facial emotional recognition is one of the essential tools used by recognition psychology to diagnose patients. Face and facial emotional recognition are areas where machine learning is excelling. Facial Emotion Recognition in an unconstrained environment is an open challenge for digital image processing due to different environments, such as lighting conditions, pose variation, yaw motion, and occlusions. Deep learning approaches have shown significant improvements in image recognition. However, accuracy and time still need improvements. This research aims to improve facial emotion recognition accuracy during the training session and reduce processing time using a modified Convolution Neural Network Enhanced with Extreme Learning Machine (CNNEELM). The proposed system consists of an optical flow estimation technique that detects the motion of change in facial expression and extracts peak images from video frames for image pre-processing. The system entails (CNNEELM) improving the accuracy in image registration during the training session. Furthermore, the system recognizes six facial emotions – happy, sad, disgust, fear, surprise, and neutral with the proposed CNNEELM model. The study shows that the overall facial emotion recognition accuracy is improved by 2% than the state of art solutions with a modified Stochastic Gradient Descent (SGD) technique. With the Extreme Learning Machine (ELM) classifier, the processing time is brought down to 65 ms from 113 ms, which can smoothly classify each frame from a video clip at 20fps. With the pre-trained InceptionV3 model, the proposed CNNEELM model is trained with JAFFE, CK+, and FER2013 expression datasets. The simulation results show significant improvements in accuracy and processing time, making the model suitable for the video analysis process. Besides, the study solves the issue of the large processing time required to process the facial images