Photoprotective activity of geopropolis produced by Melipona subnitida (Apidae, Meliponinae) in the semiarid of the Brazilian Northeast

Although geopropolis has been the subject of many chemical and pharmacological studies, there are few studies investigating the photoprotective activity of formulations containing propolis. Thus, we investigated in vivo the photoprotective efficacy of the cream containing geopropolis extract by macroscopic and histological evaluation of the skin of Wistar rats subjected to ultraviolet radiation (UVB). We also evaluated the chemical composition of hydroethanolic extract using the HPLC-DADESI-MS/MS technique, as well as antioxidant activity by the photocolorimetric method of free radical DPPH (2.2-diphenyl-1-picrylhydrazine) and cytotoxic activity by the in vitro MTT quantitative method [brometo de 3- (4.5dimetiltiazol-2-il)-2.5-difeniltetrazolio]. The extract had a varied chemical composition, 29 different phenolic compounds being detected, distributed between phenols and flavonoids, the latter being represented by chalcones, flavones and flavonols. The highest percentages of DPPH inhibition e o baixo valor de IC50 indicaram que o extrato apresentou alta atividade antioxidante. The hydroethanolic extract did not exert cytotoxic effects since high percentages of viability of L929 fibroblasts were observed after incubation for 72 hours at different concentrations of the extract. On skin submitted to cream application containing of geopropolis extract and the irradiation with UVB did not occur macroscopic and histological lesions. Thus, we concluded that the cream containing of geopropolis extract produced by Melipona subnitida was able to protect the skin from lesions induced by UVB irradiation, thus demonstrating photoprotective effect.

Network machine learning maps phytochemically rich “Hyperfoods” to fight COVID-19

In this paper, we introduce a network machine learning method to identify potential bioactive anti-COVID-19 molecules in foods based on their capacity to target the SARS-CoV-2-host gene-gene (protein-protein) interactome. Our analyses were performed using a supercomputing DreamLab App platform, harnessing the idle computational power of thousands of smartphones. Machine learning models were initially calibrated by demonstrating that the proposed method can predict anti-COVID-19 candidates among experimental and clinically approved drugs (5658 in total) targeting COVID-19 interactomics with the balanced classification accuracy of 80–85% in 5-fold cross-validated settings. This identified the most promising drug candidates that can be potentially “repurposed” against COVID-19 including common drugs used to combat cardiovascular and metabolic disorders, such as simvastatin, atorvastatin and metformin. A database of 7694 bioactive food-based molecules was run through the calibrated machine learning algorithm, which identified 52 biologically active molecules, from varied chemical classes, including flavonoids, terpenoids, coumarins and indoles predicted to target SARS-CoV-2-host interactome networks. This in turn was used to construct a “food map” with the theoretical anti-COVID-19 potential of each ingredient estimated based on the diversity and relative levels of candidate compounds with antiviral properties. We expect this in silico predicted food map to play an important role in future clinical studies of precision nutrition interventions against COVID-19 and other viral diseases.

Coloring ultrasensitive MRI with tunable metal–organic frameworks

Metal organic frameworks with tunable pore structures are able to provide varied chemical environments for hyperpolarized 129Xe atom hosting, which results in distinguishing magnetic resonance signals, and stains ultra-sensitive magnetic resonance imaging (MRI) with diverse colors.

Experimental Study of Post Installed Rebar Anchor Systems for Concrete Structure

This study investigated the effectiveness of several types of adhesives used in post-installed rebar connections as a bonding agent between steel reinforcement bars and old concrete under pull out test. The experimental samples were; cylindrical samples of (150 mm dia. × 300 mm high) with anchors rebar of varying diameter (12 and 16 mm), different embedded length (100 and 150) mm with different holes’ diameters. The strategy of control were cast-in-place rebar concrete specimens while other samples are post-installed rebar concrete specimens of varied chemical adhesives as bonding agents, namely KUT EPOXY ANCHOR ‘NS’ and SIKAFLOOR169. The output showed that the different adhesives yielded closed pull-out load values. It is found that the pull-out capacity (bond strength) is increased by increasing the embedded length, the diameter of the rebar and slightly with the diameter of the hole. In addition, the failure mode of post-installed rebar concrete was governed by the embedded length and the area of contact with the adhesives. On the other hand, the larger diameter of rebar favors splitting or failure of concrete due to higher strength in binder-rebar interface compare to the binder-concrete interface. The results showed that the pull-out load was increased by (26 % and 32 %) as the rebar diameter increased from 12 mm to 16 mm for KUT “NS” and SIKAFLOOR respectively. The hole diameter had slightly effect of the pull out load where the average of increment was only 6 %. Finally, the bonding strength is considerably depended on the embedded length and less affected by the type of epoxy.

Network machine learning maps phytochemically-rich “Hyperfoods” to fight COVID-19

In this paper, we introduce a network machine learning method to identify potential bioactive anti-COVID-19 molecules in foods based on their capacity to target the SARS-CoV-2-host gene-gene (protein-protein) interactome. Our experiments were run using a supercomputing DreamLab App platform, harnessing the idle computational power of thousands of smartphones. Machine learning models were initially calibrated by demonstrating that the proposed method can predict anti-COVID-19 candidates among experimental and clinically approved drugs (5658 in total) targeting COVID-19 interactomics with the balanced classification accuracy of 80-85% in 5-fold cross-validated settings. This identified the most promising drug candidates that can be potentially “repurposed” against COVID-19 including common drugs used to combat cardiovascular and metabolic disorders, such as simvastatin, atorvastatin and metformin. A database of 7694 bioactive food-based molecules was run through the calibrated machine-learning algorithm, which identified 52 biologically active molecules, from varied chemical classes, including flavonoids, terpenoids, coumarins and indoles predicted to target SARS-CoV-2-host interactome networks. This in turn was used to construct a “food map” with the theoretical anti-COVID-19 potential of each ingredient estimated based on the diversity and relative levels of candidate compounds with antiviral properties. We expect this in-silico predicted food map to play an important role in future clinical studies of precision nutrition interventions against COVID-19 and other viral diseases.

Deployment and Comparison of a Low-Cost High-Sensitivity Concentration Meters Using Micro-Optical Resonators

Micro-optical resonators have been introduced as sensors in many applications for a wide number of variable types of stimuli due to their very high resolution, high sensitivity, and high-quality factor. In this paper, a novel micro-optical sensor was designed and tested as a concentration meter for chemical composition of a solution. The micro-optical resonator used is based on whispering gallery mode (WGM). This phenomenon appears when a tapered, single-mode laser carrying micro-optical fiber is evanescently coupled with a polymeric or silica micro-optical resonator. The presented sensor shows the change in concentration by experiencing a change in its morphology due to the varied viscosity of its environment. The variation of concentrations or fluid contents results in a change between the radii of the micro-optical resonator. With varied chemical composition and concentration in the tested sample varied infinitesimally small morphological changes are detected. The change in the resonators shape is read as a WGM shift in the resonance transmission spectrum, which is interpreted using a technique called cross-correlation, which compares the output across time to display the shift, which is later translated into distinct concentration levels. The proposed, exceptionally low-cost sensors were able to detect change at very high resolutions allowing better sensitivity along with wider range of variation. Experimental work for detection of ranges of concentrations of variable type of contaminants is presented.

Dyeing Hair is Dire or Desire

Background: Hair coloring, or hair coloring, is the follow of fixing the hair color. The most reasons for this area unit cosmetic: to hide gray or white hair, to alter to a color thought to be additional modern or fascinating, or to revive the initial hair color once, it's been stained by hairdressing process or sun bleaching. Hair coloring is often done professionally by a stylist or severally reception. Celtic folks colored their hair blonde; they bleach it by laundry them in lime and brushing it back from their foreheads. The coloring of hair is associate ancient art that involves treatment of the hair with varied chemical compounds. In history, the dyes were obtained from plants. The event of artificial dyes for hair is derived from the legendary discovery of the reactivity of para-phenylenediamine (PPD) with air. Results: Hair dyes are cosmetic compounds that make contact with the skin throughout application. As a result of this skin contact, there exists some health risk related to use of hair dyes. People allergic to protein as an example, can have to be compelled to take care once buying hair color since bound dye includes protein. Protein doesn't have to be compelled to be eaten for it to cause associate hypersensitivity reaction. Skin contact with protein might cause a reaction; thus, resulting in associate hypersensitivity reaction. Symptoms of those reactions will embody redness, sores, itching, burning sensation, and discomfort. Symptoms can typically not be apparent instantly following the appliance and process of the tint, however may arise once hours or maybe daily later. Conclusion: Pigments of the hair got colored by the tactic of removing, replacing, or covering up. Employment of those chemicals may cause varied adverse effects, at the facet of temporary skin irritation and hypersensitivity, hair breakage, skin discoloration, and explosive hair color results. The ultimate color of every strand of hair can depend upon its original color and body. As a result of hair's color and body across the pinnacle and on the length of a hair strand, there'll be delicate variations in shade across the complete head. This provides an additional natural-looking result than the solid, everywhere color of a permanent color. Thus, hair dyes area unit regulated within the industrial marketplace and, as new toxicity knowledge is generated for a few hair dye, and health risks area unit discovered, in a controlled people, employment of hair coloring may end up in aversions and/or skin irritation.