Role of serotonin in the paradoxical calming effect of psychostimulants on hyperactivity

The mechanism by which psychostimulants act as calming agents in humans with attention-deficit hyperactivity disorder (ADHD) or hyperkinetic disorder is currently unknown. Mice lacking the gene encoding the plasma membrane dopamine transporter (DAT) have elevated dopaminergic tone and are hyperactive. This activity was exacerbated by exposure to a novel environment. Additionally, these mice were impaired in spatial cognitive function, and they showed a decrease in locomotion in response to psychostimulants. This paradoxical calming effect of psychostimulants depended on serotonergic neurotransmission. The parallels between the DAT knockout mice and individuals with ADHD suggest that common mechanisms may underlie some of their behaviors and responses to psychostimulants.     

siRNA nanotherapeutics: a promising strategy for anti‐HBV therapy

Chronic hepatitis B (CHB) is the most common cause of hepatocellular carcinoma (HCC) and liver cirrhosis worldwide. In spite of the numerous advances in the treatment of CHB, drugs and vaccines have failed because of many factors like complexity, resistance, toxicity, and heavy cost. New RNA interference (RNAi)‐based technologies have developed innovative strategies to target Achilles'' heel of the several hazardous diseases involving cancer, some genetic disease, autoimmune illnesses, and viral disorders particularly hepatitis B virus (HBV) infections. Naked siRNA delivery has serious challenges including failure to cross the cell membrane, susceptibility to the enzymatic digestion, and excretion by renal filtration, which ideally can be addressed by nanoparticle‐mediated delivery systems. cccDNA formation is a significant problem in obtaining HBV infections complete cure because of strength, durability, and lack of proper immune response. Nano‐siRNA drugs have a great potential to address this problem by silencing specific genes which are involved in cccDNA formation. In this article, the authors describe siRNA nanocarrier‐mediated delivery systems as a promising new strategy for HBV infections therapy. Simultaneously, the authors completely represent the clinical trials which use these strategies for treatment of the HBV infections.Inspec keywords: tumours, drugs, genetics, cellular biophysics, RNA, nanomedicine, diseases, molecular biophysics, microorganisms, cancer, liver, nanoparticles, patient treatmentOther keywords: siRNA nanotherapeutics, anti‐HBV therapy, chronic hepatitis B, CHB, HCC, hazardous diseases, cancer, genetic disease, autoimmune illnesses, viral disorders, hepatitis B virus infections, naked siRNA delivery, cell membrane, enzymatic digestion, renal filtration, nanoparticle‐mediated delivery systems, cccDNA formation, HBV infections complete cure, nanosiRNA drugs, siRNA nanocarrier‐mediated delivery systems, HBV infections therapy, liver cirrhosis, RNA interference, immune response, hepatocellular carcinoma     

Cuckoo Optimized Convolution Support Vector Machine for Big Health Data Processing

Big health data collection and storing for further analysis is a challenging task because this knowledge is big and has many features. Several cloud-based IoT health providers have been described in the literature previously. Furthermore, there are a number of issues related to time consumed and overall network performance when it comes to big data information. In the existing method, less performed optimization algorithms were used for optimizing the data. In the proposed method, the Chaotic Cuckoo Optimization algorithm was used for feature selection, and Convolutional Support Vector Machine (CSVM) was used. The research presents a method for analyzing healthcare information that uses in future prediction. The major goal is to take a variety of data while improving efficiency and minimizing process time. The suggested method employs a hybrid method that is divided into two stages. In the first stage, it reduces the features by using the Chaotic Cuckoo Optimization algorithm with Levy flight, opposition-based learning, and distributor operator. In the second stage, CSVM is used which combines the benefits of convolutional neural network (CNN) and SVM. The CSVM modifies CNN’s convolution product to learn hidden deep inside data sources. For improved economic flexibility, greater protection, greater analytics with confidentiality, and lower operating cost, the suggested approach is built on fog computing. Overall results of the experiments show that the suggested method can minimize the number of features in the datasets, enhances the accuracy by 82%, and decrease the time of the process.     

Hemodialysis for treatment of levofloxacin‐induced neurotoxicity

Levofloxacin, a third‐generation fluoroquinolone antibiotic, is rarely associated with neurotoxicity. Patients with advanced kidney disease are particularly vulnerable to this adverse effect. We present two elderly patients with kidney failure who developed levofloxacin‐induced neurotoxicity, which was successfully treated with frequent hemodialysis, resulting in the full resolution of their symptoms. Neurotoxicity is a well‐known side effect of fluoroquinolone antibiotics. Postulated mechanisms include inhibition of the gamma‐aminobutyric acid A receptors and activation of the excitatory N‐methyl‐D‐aspartate receptors. Risk factors include older age, kidney disease, pre‐existing neurological disorders, and drug–drug interactions. While management of levofloxacin‐induced neurotoxicity includes discontinuation of the drug and supportive care, hemodialysis is not recommended, despite available pharmacokinetic data in support of its dialyzability. The successful use of hemodialysis for the treatment of levofloxacin‐induced neurotoxicity observed in our two patients with kidney failure should be further considered for rapid resolution of this rare fluoroquinolone‐related adverse effect in patients with impaired kidney function.     

A novel mixed polymeric micelle for co-delivery of paclitaxel and retinoic acid and overcoming multidrug resistance: synthesis,characterization, cytotoxicity,and pharmacokinetic evaluation

In the current study, retinoic acid (RA) was conjugated to Pluronic F127 (PF127) through an esterification process. Mixed micelles were formed with tocopheryl polyethylene glycol 1000 (TPGS) for co-delivery of paclitaxel (PTX) and RA to the cancer cells. Mixed micelles of RA-PF127 and TPGS in different weight ratios (10:0, 7:3, 5:5, 3:7, 0:10 w/w) were prepared and physicochemical properties including, particle size, zeta potential, critical micelle concentration (CMC), drug loading content, entrapment efficiency, drug release, cellular uptake and in vitro cytotoxicity, were investigated in details. Furthermore, the pharmacokinetics of PTX-loaded optimized mixed micelles were evaluated in Sprague-Dawley rats and compared with Stragen^® (PTX in Cremophor EL^®). Particle sizes and zeta potentials of the drug-loaded micelles were in the range of 102.6–223.5?nm and ?5.3 to ?9.6?mV, respectively. The 7:3 and 5:5 micellar combinations had lower CMC values (0.034–0.042?mg/mL) than 0:10 (0.124?mg/mL). The entrapment efficiencies of 10:0, 7:3, and 5:5 were 53.4?±?9.3%, 61.3?±?0.5%, and 78.7?±?1.66%, respectively. The release rates of PTX from 7:3 and 5:5 mixed micelles were significantly slower than other formulations. Cytotoxicity assay demonstrated increased cytotoxic activity of PTX-loaded mixed micelles compared to free PTX. The V_d and t_1/2ß of PTX-loaded RA-PF127/TPGS (7:3) were increased by 2.61- and 1.27-fold, respectively, while the plasma area under the curve (AUC) of the micelles was 2.03-fold lower than those of Stragen^®. Therefore, these novel mixed micelles could be effectively used for delivery of PTX and RA to the cancer cells. Moreover, TPGS as part of micelle composition could enhance the therapeutic effect of PTX and reduce side effects.     

A multi-stage production-inventory model with learning and forgetting effects,rework and scrap

This paper studies a serial production line where a proportion of defective items is produced at each stage. Defective units enter a rework process, which is imperfect as well. Twice defective items are scrapped. This paper also considers learning and forgetting in production and rework processes and studies how the number of shipments of a lot from a production stage to the next influences the overall performance of the system. A model for a multi-stage production-inventory system is developed and optimized against an aggregate performance measure of four partial measures that are based on production time, process yield, in-process inventory and shipment frequency. Each of these partial performance measures is weighed by the system’s decision maker in accordance to importance. The numerical results show how the values of learning rates, weights assigned to the partial performance measures and the number of production stages influence the overall performance of the system.     

Search and Rescue Optimization with Machine Learning Enabled Cybersecurity Model

Presently, smart cities play a vital role to enhance the quality of living among human beings in several ways such as online shopping, e-learning, e-healthcare, etc. Despite the benefits of advanced technologies, issues are also existed from the transformation of the physical word into digital word, particularly in online social networks (OSN). Cyberbullying (CB) is a major problem in OSN which needs to be addressed by the use of automated natural language processing (NLP) and machine learning (ML) approaches. This article devises a novel search and rescue optimization with machine learning enabled cybersecurity model for online social networks, named SRO-MLCOSN model. The presented SRO-MLCOSN model focuses on the identification of CB that occurred in social networking sites. The SRO-MLCOSN model initially employs Glove technique for word embedding process. Besides, a multiclass-weighted kernel extreme learning machine (M-WKELM) model is utilized for effectual identification and categorization of CB. Finally, Search and Rescue Optimization (SRO) algorithm is exploited to fine tune the parameters involved in the M-WKELM model. The experimental validation of the SRO-MLCOSN model on the benchmark dataset reported significant outcomes over the other approaches with precision, recall, and F1-score of 96.24%, 98.71%, and 97.46% respectively.     

Improved Chameleon Swarm Optimization-Based Load Scheduling for IoT-Enabled Cloud Environment

Internet of things (IoT) and cloud computing (CC) becomes widespread in different application domains such as business, e-commerce, healthcare, etc. The recent developments of IoT technology have led to an increase in large amounts of data from various sources. In IoT enabled cloud environment, load scheduling remains a challenging process which is applied for ensuring network stability with maximum resource utilization. The load scheduling problem was regarded as an optimization problem that is solved by metaheuristics. In this view, this study develops a new Circle Chaotic Chameleon Swarm Optimization based Load Scheduling (C3SOA-LS) technique for IoT enabled cloud environment. The proposed C3SOA-LS technique intends to effectually schedule the tasks and balance the load uniformly in such a way that maximum resource utilization can be accomplished. Besides, the presented C3SOA-LS model involves the design of circle chaotic mapping (CCM) with the traditional chameleon swarm optimization (CSO) algorithm for improving the exploration process, shows the novelty of the work. The proposed C3SOA-LS model computes an objective with the minimization of energy consumption and makespan. The experimental outcome implied that the C3SOA-LS model has showcased improved performance and uniformly balances the load over other approaches.     

The EOQ repair and waste disposal model with switching costs

This paper addresses the problem of determining the optimal batch sizes for production and recovery in an EOQ (economic order/production quantity) repair and waste disposal model context. This paper assumes that a first shop is manufacturing new products as well as repairing products used by a second shop. The used products can either be stored in the second shop and then be brought back to the first shop in an approach used to reduce inventory costs, or be disposed outside the system. The works available in the literature assumed a general time interval and ignored the very first time interval where no repair runs are performed. This assumption resulted in an over estimation of the average inventory level and subsequently the holding cost. These works also have not accounted for switching costs when alternating between production and recovery runs, which are common when switching among products or jobs in a manufacturing facility. This paper addresses these two limitations. Mathematical models are developed with numerical examples presented and results discussed.     

Estimating spatial variations in soil organic carbon using satellite hyperspectral data and map algebra

This study evaluated the effectiveness of using Hyperion hyperspectral data in improving existing remote-sensing methodologies for estimating soil organic carbon (SOC) content on farmland. The study area is Big Creek Watershed in Southern Illinois, USA. Several data-mining techniques were tested to calibrate and validate models that could be used for predicting SOC content using Hyperion bands as predictors. A combined model of stepwise regression followed by a five hidden nodes artificial neural network was selected as the best model, with a calibration coefficient of determination (R ²) of 78.9% and a root mean square error (RMSE) of 3.3 tonnes per hectare (t ha^?1). The validation RMSE, however, was found to be 11.3 t ha^?1. Map algebra was implemented to extrapolate this model and produce a SOC map for the watershed. Hyperspectral data improved marginally the predictability of SOC compared to multispectral data under natural field conditions. They could not capture small annual variations in SOC, but could measure decadal variations with moderate error. Satellite-based hyperspectral data combined with map algebra can measure total SOC pools in various ecosystem or soil types to within a few per cent error.