Dispersed differential hunger games search for high dimensional gene data feature selection.

The realms of modern medicine and biology have provided substantial data sets of genetic roots that exhibit a high dimensionality. Clinical practice and associated processes are primarily dependent on data-driven decision-making. However, the high dimensionality of the data in these domains increases the complexity and size of processing. It can be challenging to determine representative genes while reducing the data's dimensionality. A successful gene selection will serve to mitigate the computing costs and refine the accuracy of the classification by eliminating superfluous or duplicative features. To address this concern, this research suggests a wrapper gene selection approach based on the HGS, combined with a dispersed foraging strategy and a differential evolution strategy, to form a new algorithm named DDHGS. Introducing the DDHGS algorithm to the global optimization field and its binary derivative bDDHGS to the feature selection problem is anticipated to refine the existing search balance between explorative and exploitative cores. We assess and confirm the efficacy of our proposed method, DDHGS, by comparing it with DE and HGS combined with a single strategy, seven classic algorithms, and ten advanced algorithms on the IEEE CEC 2017 test suite. Furthermore, to further evaluate DDHGS' performance, we compare it with several CEC winners and DE-based techniques of great efficiency on 23 popular optimization functions and the IEEE CEC 2014 benchmark test suite. The experimentation asserted that the bDDHGS approach was able to surpass bHGS and a variety of existing methods when applied to fourteen feature selection datasets from the UCI repository. The metrics measured--classification accuracy, the number of selected features, fitness scores, and execution time--all showed marked improvements with the use of bDDHGS. Considering all results, it can be concluded that bDDHGS is an optimal optimizer and an effective feature selection tool in the wrapper mode.

Multi-threshold image segmentation using an enhanced fruit fly optimization for COVID-19 X-ray images.

Since the outbreak of COVID-19, it has seriously endangered the health of human beings. Computer automatic segmentation of COVID-19 X-ray images is an important means to assist doctors in rapid and accurate diagnosis. Therefore, this paper proposes a modified FOA (EEFOA) with two optimization strategies added to the original FOA, including elite natural evolution (ENE) and elite random mutation (ERM). To be specific, ENE and ERM can effectively speed up the convergence and deal with the problem of local optima, respectively. The outstanding performance of EEFOA was confirmed by experimental results comparing EEFOA with the original FOA, other FOA variants, and advanced algorithms at CEC2014. After that, EEFOA is implemented for multi-threshold image segmentation (MIS) of COVID-19 X-ray images, where a 2D histogram consisting of the original greyscale image and the non-local means image is used to represent the image information, and Rényi's entropy is used as the objective function to find the maximum value. The evaluation results of the MIS segmentation experiments show that, whether high or low threshold, EEFOA can achieve higher quality segmentation results and greater robustness than other advanced segmentation methods.

Metabolism of liver CYP450 and ultrastructural changes after long-term administration of aspirin and ibuprofen.

Worldwide, aspirin and ibuprofen are the most commonly used non-steroidal anti-inflammatory drugs (NSAIDs). Some adverse reactions, including gastrointestinal reactions, have been concerned extensively. Nevertheless, the mechanism of liver injury remains unclear. In the present study, we focused on the metabolism of liver cytochrome P450 (CYP450) and ultrastructural morphology of liver cells. A total of thirty rats were divided into three groups of 10. Rats in the aspirin and ibuprofen groups were given enteric-coated aspirin (15 mg/kg) and ibuprofen (15 mg/kg), respectively by gavage for four weeks. The body weights were recorded every two days. Liver function and metabolic capacity of CYP450 were studied on days 14 and 28. We then conducted ultrastructural examinations. Body weights in the Ibuprofen group were lower than those of the Control group, and ALT and AST levels were significantly higher (P < 0.05). There were no significant differences in terms of body weight, ALT or AST between the Aspirin and Control groups. The metabolic capacity of CYP450 was evaluated using five probe drugs, phenacetin, tolbutamide, metoprolol, midazolam, and bupropion. We found that ibuprofen and aspirin induced metabolism of the probe drugs. Moreover, according to the pharmacokinetic data, the Control, Aspirin and Ibuprofen groups could be discriminated accurately. Ultrastructural examination showed that the number of mitochondria was increased in both the Ibuprofen and Aspirin groups. Long-term administration of enteric-coated aspirin and ibuprofen induced the metabolic activity of the CYP450 enzyme. Aspirin had better tolerability than did ibuprofen, as reflected by pharmacokinetic data of probe drug metabolism.

Pharmacokinetics and tissue distribution model of cabozantinib in rat determined by UPLC-MS/MS.

Cabozantinib (XL184) is a novel small molecule inhibitor of receptor tyrosine kinases (RTKs) targeted at mesenchymal-epithelial transition factor (MET). In order to study the pharmacokinetics and tissue distribution in rat, a specific ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method was developed with midazolam as internal standard. The calibration curves in plasma and tissues were linear in the range of 5-5000ng/mL (r(2)>0.99). The recoveries were better than 80.4% and matrix effects ranged from 96.9% to 105.1%. Then, the developed UPLC-MS/MS method was applied to determine the concentration of XL184 in blood and tissues. The pharmacokinetics of four different dosages (iv 5, 10mg/kg and ig 15, 30mg/kg) revealed that XL184 was eliminated slowly, the t1/2 was longer than 10h and the absolute bioavailability was 25.6±8.3%. The concentration distribution of XL184 in tissues was liver>lung>kidney>spleen>heart. Based on the concentration-time of XL184 in tissues, a BP-ANN distribution model was developed with good performance, and can be used to predict the concentration of XL184 in tissues.

Application of back-propagation artificial neural network and curve estimation in pharmacokinetics of losartan in rabbit.

In order to develop pharmacokinetic model, a well-known multilayer feed-forward algorithm back-propagation artificial neural networks (BP-ANN) was applied to the pharmacokinetics of losartan in rabbit. The plasma concentrations of losartan in twelve rabbits, which were divided into two groups and given losartan 2 mg/kg by intravenous (Iv) and intragastrical (Ig) administration, were determined by LC-MS. The BP-ANN model included one input layer, hidden layers, and one output layer was constructed and compared with curve estimation based on the time-concentration data of losartan. The results showed the BP-ANN model had high goodness of fit index and good coherence (R > 0.99) between forecasted concentration and measured concentration both in Iv and Ig administration. The residuals of each concentrations generated by BP-ANN model were all smaller than Curve estimation. The pharmacokinetic result showed there was no significant difference between measured and simulated pharmacokinetic parameters including AUC(0-t), AUC(0-∞), MRT(0-t), MRT(0-∞), T1/2 V and Cmax (P > 0.05). In conclusion, the BP-ANN model has remarkably accurate predictions ability, which better than Curve estimation, and can be used as a utility tool in pharmacokinetic experiment.

Development of LC-MS determination method and back-propagation ANN pharmacokinetic model of corynoxeine in rat.

Corynoxeine(CX), isolated from the extract of Uncaria rhynchophylla, is a useful and prospective compound in the prevention and treatment for vascular diseases. A simple and selective liquid chromatography mass spectrometry (LC-MS) method was developed to determine the concentration of CX in rat plasma. The chromatographic separation was achieved on a Zorbax SB-C18 (2.1 mm × 150 mm, 5 µm) column with acetonitrile-0.1% formic acid in water as mobile phase. Selective ion monitoring (SIM) mode was used for quantification using target ions m/z 383 for CX and m/z 237 for the carbamazepine (IS). After the LC-MS method was validated, it was applied to a back-propagation artificial neural network (BP-ANN) pharmacokinetic model study of CX in rats. The results showed that after intravenous administration of CX, it was mainly distributed in blood and eliminated quickly, t1/2 was less than 1h. The predicted concentrations generated by BP-ANN model had a high correlation coefficient (R>0.99) with experimental values. The developed BP-ANN pharmacokinetic model can be used to predict the concentration of CX in rats.