Evaluation of antimalarial, free-radical-scavenging and insecticidal activities of Artemisia scoparia and A. Spicigera, Asteraceae

Artemisia species (Asteraceae), widespread throughout the world, are a group of important medicinal plants. The extracts of two medicinal plants of this genus, Artemisia scoparia Waldst. & Kit. and A. spicigera C. Koch, were evaluated for potential antimalarial, free-radical-scavenging and insecticidal properties, using the heme biocrystallisation and inhibition assay, the DPPH assay and the contact toxicity bioassay using the pest Tribolium castaneum, respectively. The methanol extracts of both species showed strong free-radical-scavenging activity and the RC50 values were 0.0317 and 0.0458 mg/mL, respectively, for A. scoparia and A. spicigera. The dichloromethane extracts of both species displayed a moderate level of potential antimalarial activity providing IC50 at 0.778 and 0.999 mg/mL for A. scoparia and A. spicigera, respectively. Both species of Artemisia showed insecticidal properties. However, A. spicigera was more effective than A. scoparia

Attenuation of doxorubicin-induced cardiotoxicity by mdivi-1: a mitochondrial division/mitophagy inhibitor

Doxorubicin is one of the most effective anti-cancer agents. However, its use is associated with adverse cardiac effects, including cardiomyopathy and progressive heart failure. Given the multiple beneficial effects of the mitochondrial division inhibitor (mdivi-1) in a variety of pathological conditions including heart failure and ischaemia and reperfusion injury, we investigated the effects of mdivi-1 on doxorubicin-induced cardiac dysfunction in naïve and stressed conditions using Langendorff perfused heart models and a model of oxidative stress was used to assess the effects of drug treatments on the mitochondrial depolarisation and hypercontracture of cardiac myocytes. Western blot analysis was used to measure the levels of p-Akt and p-Erk 1/2 and flow cytometry analysis was used to measure the levels p-Drp1 and p-p53 upon drug treatment. The HL60 leukaemia cell line was used to evaluate the effects of pharmacological inhibition of mitochondrial division on the cytotoxicity of doxorubicin in a cancer cell line. Doxorubicin caused a significant impairment of cardiac function and increased the infarct size to risk ratio in both naïve conditions and during ischaemia/reperfusion injury. Interestingly, co-treatment of doxorubicin with mdivi-1 attenuated these detrimental effects of doxorubicin. Doxorubicin also caused a reduction in the time taken to depolarisation and hypercontracture of cardiac myocytes, which were reversed with mdivi-1. Finally, doxorubicin caused a significant elevation in the levels of signalling proteins p-Akt, p-Erk 1/2, p-Drp1 and p-p53. Co-incubation of mdivi-1 with doxorubicin did not reduce the cytotoxicity of doxorubicin against HL-60 cells. These data suggest that the inhibition of mitochondrial fission protects the heart against doxorubicin-induced cardiac injury and identify mitochondrial fission as a new therapeutic target in ameliorating doxorubicin-induced cardiotoxicity without affecting its anti-cancer properties

The Role of Transporters in the Pharmacokinetics of Orally Administered Drugs

Drug transporters are recognized as key players in the processes of drug absorption, distribution, metabolism, and elimination. The localization of uptake and efflux transporters in organs responsible for drug biotransformation and excretion gives transporter proteins a unique gatekeeper function in controlling drug access to metabolizing enzymes and excretory pathways. This review seeks to discuss the influence intestinal and hepatic drug transporters have on pharmacokinetic parameters, including bioavailability, exposure, clearance, volume of distribution, and half-life, for orally dosed drugs. This review also describes in detail the Biopharmaceutics Drug Disposition Classification System (BDDCS) and explains how many of the effects drug transporters exert on oral drug pharmacokinetic parameters can be predicted by this classification scheme

Deep learning-based hybrid feature selection for the semantic segmentation of crops and weeds

Deep convolution neural networks are the recent algorithms used for robotic vision. However, the complex crop–weed vegetation and the background interferences required a robust feature representation. Therefore, we proposed a Dual-branch Deep neural network for the semantic segmentation of crops and weeds. The branches utilized distinct feature extraction algorithms that extract essential semantic cues, and a decoder combined these features to improve the global contextual information. Finally, the hybrid feature selection module(HSFM) utilized the decoder features to complement one another. Experimental results show the proposed method obtained mean intersection of union scores of 0.8613 and 0.9099 on CWFID and BoniRob datasets, respectively

Multi-level feature re-weighted fusion for the semantic segmentation of crops and weeds

Intelligent farm robots empowered by proper vision algorithms are the new agricultural machinery that eases weed control with speed and accuracy. Based on the farmland substantial similarity between the crops, and weeds, or other background interference objects, an improved deep convolutional neural network (DCNN) algorithms is proposed for the pixel semantic segmentation of crop and weed. First, a lightweight backbone is proposed to balance the features map textual and shape signals, which are essential cues for better crop and weed prediction. Second, a multi-level feature re-weighted fusion (MFRWF) module is suggested to combine only the relevant information from every backbone layer output to improve the contextual maps of crops and weeds. Finally, a decoder is designed based on convolutional weighted fusion (CWF) to preserve the relevant crop and weed context information by reducing the possible feature context distortion. Experimental results show that our improved neural network obtained the mean intersection of union (MIOU) scores of 0.8646, 0.9164, and 0.8459 on the carrot/weed field image (CWFID), sugar beet (BoniRob), and Rice seedling datasets, respectively. Therefore, the results have not only outperformed the commonly used architectures but can precisely identify crops/weeds and substantially improve the robot inference speed with minimal memory overhead. The code is available at:https://github.com/jannehlamin/MFRWF