C-phycocyanin shows neuroprotective effect against rotenone-induced Parkinson’s disease in mice

Objective:To investigate the neuroprotective effect of C-phycocyanin in a mouse model of rotenone-induced Parkinson’s disease.Methods:C-phycocyanin(50 mg/kg,i.p.,daily)was administered to rotenone(30 mg/kg,p.o.,daily)treated mice for 28 days.Behavioral studies(Y-maze,rotarod,round beam walk,and wire-hang tests)were carried out to assess neurobehavioral deficits.Glutathione and malondialdehyde were determined in both serum and striatal tissue.Molecular proteins(AKT,AMPK,NF-κB,BDNF,and alpha-synuclein)in the striatum were estimated using ELISA.Histopathological analyses(hematoxylin and eosin stainning as well as Nissl staining)were carried out to assess structural abnormalities in the striatum.Results:C-phycocyanin significantly increased BDNF levels and decreased alpha-synuclein levels.It also slightly upregulated AMPK and AKT levels without significant difference compared with the rotenone group.Additionally,rotenone-induced elevated oxidative stress and structural abnormalities in the striatum were markedly mitigated by C-phycocyanin.Conclusions:C-phycocyanin might have potential neuroprotective effects against Parkinson’s disease.Further studies are warranted to verify its efficacy and to understand the molecular mechanisms behind the neuroprotective effects of C-phycocyanin in Parkinson’s disease.

FedRec:Trusted rank-based recommender scheme for service provisioning in federated cloud environment

The emergence of on-demand service provisioning by Federated Cloud Providers(FCPs)to Cloud Users(CU)has fuelled significant innovations in cloud provisioning models.Owing to the massive traffic,massive CU resource requests are sent to FCPs,and appropriate service recommendations are sent by FCPs.Currently,the FourthGeneration(4G)-Long Term Evolution(LTE)network faces bottlenecks that affect end-user throughput and latency.Moreover,the data is exchanged among heterogeneous stakeholders,and thus trust is a prime concern.To address these limitations,the paper proposes a Blockchain(BC)-leveraged rank-based recommender scheme,FedRec,to expedite secure and trusted Cloud Service Provisioning(CSP)to the CU through the FCP at the backdrop of base 5G communication service.The scheme operates in three phases.In the first phase,a BCintegrated request-response broker model is formulated between the CU,Cloud Brokers(BR),and the FCP,where a CU service request is forwarded through the BR to different FCPs.For service requests,Anything-as-aService(XaaS)is supported by 5G-enhanced Mobile Broadband(eMBB)service.In the next phase,a weighted matching recommender model is proposed at the FCP sites based on a novel Ranking-Based Recommender(RBR)model based on the CU requests.In the final phase,based on the matching recommendations between the CU and the FCP,Smart Contracts(SC)are executed,and resource provisioning data is stored in the Interplanetary File Systems(IPFS)that expedite the block validations.The proposed scheme FedRec is compared in terms of SC evaluation and formal verification.In simulation,FedRec achieves a reduction of 27.55%in chain storage and a transaction throughput of 43.5074 Mbps at 150 blocks.For the IPFS,we have achieved a bandwidth improvement of 17.91%.In the RBR models,the maximum obtained hit ratio is 0.9314 at 200 million CU requests,showing an improvement of 1.2%in average servicing latency over non-RBR models and a maximization trade-off of QoE index of 2.7688 at the flow request 1.088 and at granted service price of USD 1.559 million to FCP for provided services.The obtained results indicate the viability of the proposed scheme against traditional approaches.

Solar photocatalytic pathogenic disinfection:Fundamentals to state-of-the-art

It is necessary to treat pathogen-infected water before its utilisation.Of conventionally used treatment methods,solar photocatalysis has gained considerable momentum owing to its operational simplicity and capacity to use freely and abundantly available solar energy.This article systematically reviewed the disinfection of water with photocatalysis.It addressed the concerns of microbial infection of water and the fundamentals behind its treatment with photocatalysis.It presented an in-depth description of pathogenic deactivation with powerful reactive oxygen species.Special emphasis was given to process intensification as it is an attractive technique that provides multifunctionality and/or equipment miniaturisation.Solar reactor design regarding mobilised/immobilised photocatalysts and compound parabolic concentrators were elucidated.Finally,key parameters governing photoperformance,corresponding trade-offs,and the need for their optimisation were discussed.Overall,this article is a single point of reference for researchers,environmentalists,and industrialists who address the ever-severing challenge of providing clean water whilst also maintaining energy sustainability.

Insights on drug and gene delivery systems in liver fibrosis

Complications of the liver are amongst the world’s worst diseases.Liver fibrosis is the first stage of liver problems,while cirrhosis is the last stage,which can lead to death.The creation of effective anti-fibrotic drug delivery methods appears critical due to the liver’s metabolic capacity for drugs and the presence of insurmountable physiological impediments in the way of targeting.Recent breakthroughs in anti-fibrotic agents have substantially assisted in fibrosis;nevertheless,the working mechanism of anti-fibrotic medications is not fully understood,and there is a need to design delivery systems that are well-understood and can aid in cirrhosis.Nanotechnology-based delivery systems are regarded to be effective but they have not been adequately researched for liver delivery.As a result,the capability of nanoparticles in hepatic delivery was explored.Another approach is targeted drug delivery,which can considerably improve efficacy if delivery systems are designed to target hepatic stellate cells(HSCs).We have addressed numerous delivery strategies that target HSCs,which can eventually aid in fibrosis.Recently genetics have proved to be useful,and methods for delivering genetic material to the target place have also been investigated where different techniques are depicted.To summarize,this review paper sheds light on themost recent breakthroughs in drug and gene-based nano and targeted delivery systems that have lately shown useful for the treatment of liver fibrosis and cirrhosis.

Waste fly ash-ZnO as a novel sunlight-responsive photocatalyst for dye discoloration

Treating waste with a waste material using freely available solar energy is the most effective way towards sustainable future.In this study,a novel photocatalyst,partly derived from waste material from the coal industry,was developed.Fly ash hybridized with ZnO(FAeZn)was synthesized as a potential photocatalyst for dye discoloration.The synthesized photocatalyst was characterized by X-ray diffraction,scanning electron microscopy,transmission electron microscopy,and ultravioletevisible/near infra-red spectroscopy.The photocatalytic activity was examined with the discoloration of methylene blue used as synthetic dye wastewater.All the experiments were performed in direct sunlight.The photocatalytic performance of FAeZn was found to be better than that of ZnO and the conventionally popular TiO2.The LangmuireHinshelwood model rate constant values of ZnO,TiO2,and FAeZn were found to be 0.016 min1,0.017 min1,and 0.020 min1,respectively.There were two reasons for this:(1)FAeZn was able to utilize both ultraviolet and visible parts of the solar spectrum,and(2)its BrunauereEmmetteTeller surface area and porosity were significantly enhanced.This led to increased photon absorption and dye adsorption,thus exhibiting an energy-efficient performance.Therefore,FAeZn,partly derived from waste,can serve as a suitable material for environmental remediation and practical solar energy applications.

Analytical method for cell displacement defect quantum-dot cellular automata primitive

Quantum-dot cellular automata(QCA)is an emerging computational paradigm which can overcome scaling limitations of the existing complementary metal oxide semiconductor(CMOS)technology.The existence of defects cannot be ignored,considering the fabrication of QCA devices at the molecular level where it could alter the functionality.Therefore,defects in QCA devices need to be analyzed.So far,the simulation-based displacement defect analysis has been presented in the literature,which results in an increased demand in the corresponding mathematical model.In this paper,the displacement defect analysis of the QCA main primitive,majority voter(MV),is presented and carried out both in simulation and mathematics,where the kink energy based mathematical model is applied.The results demonstrate that this model is valid for the displacement defect in QCA MV.

GRADE: Deep learning and garlic routing-based secure data sharing framework for IIoT beyond 5G

The rise of automation with Machine-Type Communication(MTC)holds great potential in developing Industrial Internet of Things(IIoT)-based applications such as smart cities,Intelligent Transportation Systems(ITS),supply chains,and smart industries without any human intervention.However,MTC has to cope with significant security challenges due to heterogeneous data,public network connectivity,and inadequate security mechanism.To overcome the aforementioned issues,we have proposed a blockchain and garlic-routing-based secure data exchange framework,i.e.,GRADE,which alleviates the security constraints and maintains the stable connection in MTC.First,the Long-Short-Term Memory(LSTM)-based Nadam optimizer efficiently predicts the class label,i.e.,malicious and non-malicious,and forwards the non-malicious data requests of MTC to the Garlic Routing(GR)network.The GR network assigns a unique ElGamal encrypted session tag to each machine partaking in MTC.Then,an Advanced Encryption Standard(AES)is applied to encrypt the MTC data requests.Further,the InterPlanetary File System(IPFS)-based blockchain is employed to store the machine's session tags,which increases the scalability of the proposed GRADE framework.Additionally,the proposed framework has utilized the indispensable benefits of the 6G network to enhance the network performance of MTC.Lastly,the proposed GRADE framework is evaluated against different performance metrics such as scalability,packet loss,accuracy,and compromised rate of the MTC data request.The results show that the GRADE framework outperforms the baseline methods in terms of accuracy,i.e.,98.9%,compromised rate,i.e.,18.5%,scalability,i.e.,47.2%,and packet loss ratio,i.e.,24.3%.

Deep Learning-Based Robust Morphed Face Authentication Framework for Online Systems

The amalgamation of artificial intelligence(AI)with various areas has been in the picture for the past few years.AI has enhanced the functioning of several services,such as accomplishing better budgets,automating multiple tasks,and data-driven decision-making.Conducting hassle-free polling has been one of them.However,at the onset of the coronavirus in 2020,almost all worldly affairs occurred online,and many sectors switched to digital mode.This allows attackers to find security loopholes in digital systems and exploit them for their lucrative business.This paper proposes a three-layered deep learning(DL)-based authentication framework to develop a secure online polling system.It provides a novel way to overcome security breaches during the face identity(ID)recognition and verification process for online polling systems.This verification is done by training a pixel-2-pixel Pix2pix generative adversarial network(GAN)for face image reconstruction to remove facial objects present(if any).Furthermore,image-to-image matching is done by implementing the Siamese network and comparing the result of various metrics executed on feature embeddings to obtain the outcome,thus checking the electorate credentials.

Deep Learning-Based Model for Detection of Brinjal Weed in the Era of Precision Agriculture

The overgrowth of weeds growing along with the primary crop in the fields reduces crop production.Conventional solutions like hand weeding are labor-intensive,costly,and time-consuming;farmers have used herbicides.The application of herbicide is effective but causes environmental and health concerns.Hence,Precision Agriculture(PA)suggests the variable spraying of herbicides so that herbicide chemicals do not affect the primary plants.Motivated by the gap above,we proposed a Deep Learning(DL)based model for detecting Eggplant(Brinjal)weed in this paper.The key objective of this study is to detect plant and non-plant(weed)parts from crop images.With the help of object detection,the precise location of weeds from images can be achieved.The dataset is collected manually from a private farm in Gandhinagar,Gujarat,India.The combined approach of classification and object detection is applied in the proposed model.The Convolutional Neural Network(CNN)model is used to classify weed and non-weed images;further DL models are applied for object detection.We have compared DL models based on accuracy,memory usage,and Intersection over Union(IoU).ResNet-18,YOLOv3,CenterNet,and Faster RCNN are used in the proposed work.CenterNet outperforms all other models in terms of accuracy,i.e.,88%.Compared to other models,YOLOv3 is the least memory-intensive,utilizing 4.78 GB to evaluate the data.

Therapeutic potential of snake venom in cancer therapy:current perspectives

Many active secretions produced by animals have been employed in the development of new drugs to treat diseases such as hypertension and cancer.Snake venom toxins coutributed significantly to the treatment of many medical conditions.There are many published studies describing and elucidating the anti-cancer potential of snake venom.Cancer therapy is one of the main areas for the use of protein peptides and enzymes originating from animals of different species.Some of these proteins or peptides and enzymes from snake venom when isolated and evaluated may bind specifically to cancer cell membranes,affecting the migration and proliferation of these cells.Some of substances found in the snake venom present a great potential as anti-tumor agent.In this review,we presented the main results of recent years of research involving the active compounds of snake venom that have anticancer activity.

Recent research advances on Chromobacterium violaceum

Chromobacterium violaceum is a gram-negative bacterium, which has been used widely in microbiology labs involved in quorum sensing(QS) research. Among the QS-regulated traits of this bacterium, violacein production has received the maximum attention. Violacein production in this organism, however is not under sole control of QS machinery, and other QSregulated traits of this bacterium also need to be investigated in better detail. Though not often involved in human infections, this bacterium is being viewed as an emerging pathogen. This review attempts to highlight the recent research advances on Chromobacterium violaceum, with respect to violacein biosynthesis, development of various applications of this bacterium and its bioactive metabolite violacein, and its pathogenicity.

Phytopharmacological evaluation and anti-asthmatic activity of Ficus religiosa leaves

Objective:To discuss phytopharmacological potential and anti-asthmatic activity of Ficus religiosa(F.religiosa)(L.).Methods:Fresh leaves of F.religiosa were obtained from Vastrapur Lake,Ahmedabad,and dried to obtain powder.Histamine and acetylcholine were used to guinea pigs to establish bronchospasm model.In in vivo study,the aqueous extract of F.religiosa leaves (AEFR) at doses of 150 and 300 mg/ kg was administrated to guinea pigs,and the broncho-protective activity of AEFR was compared with aminophylline at 25 mg/kg.While in in vitro study,and 10 g/mL,20 g/mL,30 g/mL of AEFRL was administrated to guinea pigs,respectively, and mast cell stabilizing activity of AEFR was compared with ketotifen at 10 g/mL.Results: In the in-vivo model,pre-treatment with aminophylline(25 mg/kg,ip.) could significantly delay the onset of histamine induced pre-convulsive dyspnea,compared with vehicle control. Administration of AEFRL(150 and 300 mg/kg,ip.) also produced significant effect on latency to develop histamine & acetylcholine induced pre-convulsive dyspnea.In the mast cell stabilizing model,AEFRL at 10,20 and 30μg/mL could significantly increase the number of intact cells. Conclusions:It can be concluded that AEFRL is effective on histamine & acetylcholine induced bronchospasm in guinea pigs.In addition,AEFRL can potentiate the number of intact cells in the mast cell stabilizing model._____________________________________________________

Euphorbia neriifolia L.:Review on botany,ethnomedicinal uses,phytochemistry and biological activities

The present review is intended to provide information on botany,ethnomedicinal uses,phytochemistry and biological activities of various parts of Euphorbia neriifolia(E. neriifolia). E. neriifolia has several ethnomedicinal uses. The latex of E. neriifolia is used as laxative,purgative,rubefacient,carminative and expectorant as well as in treatment of whooping cough,gonorrhea,leprosy,asthma,dyspepsia,jaundice,enlargement of the spleen,tumors,stone in the bladder,abdominal troubles and leucoderma. Leaves are brittle,heating,carminative,and good for improving the appetite and treatment of tumors,pains,inflammations,abdominal swellings and bronchial infections. Roots are used as symptomatic treatment of snake bite,scorpion sting and antispasmodic. Various plant parts or whole E. neriifolia extract and its isolates have been reported scientifically using various in-vivo and in-vitro experimental methods for anaesthetic,analgesic,anti-anxiety,anti-convulsant,anti-psychotic,anti-arthritis,anti-carcinogenic,antidiabetic,anti-diarrhoeal,anti-inflammatory,anti-thrombotic,antimicrobial,antioxidant,antiulcer,cytotoxic,death-receptor expression enhancing,dermal irritation,diuretic,hemolytic,immunomodulatory,radioprotective,scorpion venom and wound healing properties. It is reported to have chemical constituents like,neriifolin-S,neriifolin,neriifoliene,euphol,neriifolione,cycloartenol,nerifoliol,lectin,euphonerins A–G,3-O-acetyl-8-O-tigloylingol,taraxerol,antiquorin,etc. Identified chemical constituents are still required to be explored for their advanced isolation techniques and biological activities.

Capillary electrophoresis methods for impurity profiling of drugs:A review of the past decade

Capillary electrophoresis(CE)is widely used for the impurity profiling of drugs that contain stereochemical centers in their structures,analysis of biomolecules,and characterization of biopharmaceuticals.Currently,CE is the method of choice for the analysis of foodstuffs and the determination of adulterants.This article discusses the general theory and instrumentation of CE as well as the classification of various CE techniques.It also presents an overview of research on the applications of different CE techniques in the impurity profiling of drugs in the past decade.The review briefly presents a comparison between CE and liquid chromatography methods and highlights the strengths of CE using drug compounds as examples.This review will help scientists,fellow researchers,and students to understand the applications of CE techniques in the impurity profiling of drugs.

Comprehensive review of text‑mining applications in finance

Text-mining technologies have substantially affected financial industries.As the data in every sector of finance have grown immensely,text mining has emerged as an important field of research in the domain of finance.Therefore,reviewing the recent literature on text-mining applications in finance can be useful for identifying areas for further research.This paper focuses on the text-mining literature related to financial forecasting,banking,and corporate finance.It also analyses the existing literature on text mining in financial applications and provides a summary of some recent studies.Finally,the paper briefly discusses various text-mining methods being applied in the financial domain,the challenges faced in these applications,and the future scope of text mining in finance.

Celastrus paniculatus oil ameliorates synaptic plasticity in a rat model of attention deficit hyperactivity disorder

Objective:To explore the effect and mechanism of action of Celastrus paniculatus oil on the treatment of perinatal rats with attention deficit hyperactivity disorder.Methods:In the perinatal stage,the rats were either isolated or administered with lead acetate to establish an animal model of attention deficit hyperactivity disorder.Atomoxetine served as the reference standard.Animals’behaviours were assessed through Y-maze,novel object preference,fear conditioning and residentintruder aggression tests.Oxidative stress parameters,bioamine concentration(dopamine,noradrenaline and 5-hydroxytryptamine),nerve growth factor,interleukin-6,nuclear factor-κB,and tumour necrosis factor(TNF)-αwere estimated.Synaptophysin immunohistochemical assay was performed.Results:Celastrus paniculatus oil significantly improved behavioural parameters in Y maze,novel object preference,discrimination index,fear conditioning and resident intruder aggressive tests.The treatment groups showed a decrease in malondialdehyde level.Changes in the levels of dopamine,noradrenaline,and serotonin were restored by Celastrus paniculatus oil.Celastrus paniculatus oil increased nerve growth factor and decreased interleukin-6,nuclear factor-κB,and TNF-α.Synaptophysin immunoreactivity was also improved by Celastrus paniculatus oil with alleviated reactive gliosis,degeneration,and vascular proliferation.Conclusions:This research shows the therapeutic potential of Celastrus paniculatus oil for the treatment of attention deficit hyperactivity disorder.

Does the EVA valuation model explain the market value of equity better under changing required return than constant required return?

Through the Economic-Value-Added(EVA)valuation model,the expected market value of equity can be determined by adding the book value of equity with the present value of expected EVAs under the assumption of constant required return and constant return on equity.The equation of EVA valuation model has taken its shape under the assumption of constant required return and constant return on equity.However,a large body of empirical evidence indicates that required rate of return never remain constant.The EVA-valuation model formulated under constant required return cannot be implemented under the scenario of changing required return.In this study,we explored whether the EVA valuation model could be implemented under changing required return by making any changes in the model and found that it could be implemented under the scenario of changing required return by replacing the book value of the equity of the existing model with the present value of required earnings or normal market earnings.We further examined whether the explanatory ability of the EVA valuation model under the assumption of changing required return is better than that of the valuation model under the assumption of constant required return.Relative information content analyses were conducted by considering sample of the intrinsic value of equities determined by valuation models and the market value of equities of 69 large-cap,88 mid-cap,and 79 small-cap companies.The results showed that the EVA-based valuation model with changing normal market return outperformed the EVA-based valuation model with constant required return.

Enhancement in antinociceptive and anti-inflammatory effects of tramadol by transdermal proniosome gel

Oral therapy of tramadol,an opiate analgesic,undergoes extensive hepatic metabolism and requires frequent administration.Transdermal therapy by virtue can overcome these issues and can improve the efficacy and reduce abuse liability of tramadol.The aim of this research was to investigate the possibility of transdermal delivery of tramadol by formulating proniosome gel and evaluate its therapeutic potential in vivo.The effect of formulation composition as well as amount of drug on physicochemical characteristics of prepared proniosomes were examined.Best proniosome gel(F4)was selected and evaluated for drug release,stability and transdermal efficacy by ex vivo and in vivo experiments.The vesicles demonstrated optimal properties including spherical shape,nanosize with good entrapment efficiency,adequate zeta potential,higher stability and greater transdermal flux.The amorphization and dispersion of tramadol in the aqueous core of proniosome vesicles was confirmed by differential scanning calorimeter.Release profile of F4 was distinct(P<0.001)from control and displayed steady and prolonged tramadol release by Fickian diffusion.Transdermal therapy of F4 showed prominent reduction of induced twitches(P<0.005)in mice and edema(P<0.05)in rats,as compared to oral tramadol.The improvement in clinical efficacy of tramadol in transdermal therapy is correlated with the pharmacokinetic data observed.In conclusion,the observed improvement in antinociceptive and anti-inflammatory effects from proniosome carriers signifies its potential to be a suitable alternative to oral therapy of tramadol with greater efficacy.

Montmorillonite/Poly(L-Lactide)microcomposite spheres as reservoirs of antidepressant drugs and their controlled release property

This work evaluates intercalation of Nortriptyline(NT)and Venlafaxine(VFX)in an interlayer gallery of Na^(+)-MMT(Montmorillonite),which was further compounded with Poly(LLactide)(PLLA)to form microcomposite spheres(MPs)for oral controlled drug delivery.The XRD patterns,thermal and spectroscopic analyses indicated intercalation of drugs into the MMT interlayer that was stabilized by electrostatic interaction.No significant changes in structural and functional properties of drugs were found in the MMT layers.In vitro drug release studies showed controlled release pattern.

A comprehensive review of machine learning techniques on diabetes detection

Diabetes mellitus has been an increasing concern owing to its high morbidity,and the average age of individual affected by of individual affected by this disease has now decreased to mid-twenties.Given the high prevalence,it is necessary to address with this problem effectively.Many researchers and doctors have now developed detection techniques based on artificial intelligence to better approach problems that are missed due to human errors.Data mining techniques with algorithms such as-density-based spatial clustering of applications with noise and ordering points to identify the cluster structure,the use of machine vision systems to learn data on facial images,gain better features for model training,and diagnosis via presentation of iridocyclitis for detection of the disease through iris patterns have been deployed by various practitioners.Machine learning classifiers such as support vector machines,logistic regression,and decision trees,have been comparative discussed various authors.Deep learning models such as artificial neural networks and recurrent neural networks have been considered,with primary focus on long short-term memory and convolutional neural network architectures in comparison with other machine learning models.Various parameters such as the root-mean-square error,mean absolute errors,area under curves,and graphs with varying criteria are commonly used.In this study,challenges pertaining to data inadequacy and model deployment are discussed.The future scope of such methods has also been discussed,and new methods are expected to enhance the performance of existing models,allowing them to attain greater insight into the conditions on which the prevalence of the disease depends.