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Pulp-Dentin regeneration is a key aspect of maintain tooth vitality and enabling good oral-systemic health. This study aimed to investigate a nanofibrous scaffold loaded with a small molecule i.e. Tideglusib to promote odontogenic differentiation. Tideglusib (GSK-3β inhibitor) interaction with GSK-3β was determined using molecular docking and stabilization of β-catenin was examined by confocal microscopy. 3D nanofibrous scaffolds were fabricated through electrospinning and their physicochemical characterizations were performed. Scaffolds were seeded with mesenchymal stem cells or pre-odontoblast cells to determine the cells proliferation and odontogenic differentiation. Our results showed that Tideglusib (TG) binds with GSK-3β at Cys199 residue. Stabilization and nuclear translocation of β-catenin was increased in the odontoblast cells treated with TG. SEM analysis revealed that nanofibers exhibited controlled architectural features that effectively mimicked the natural ECM. UV-Vis spectroscopy demonstrated that TG was incorporated successfully and released in a controlled manner. Both kinds of biomimetic nanofibrous matrices (PCLF-TG100, PCLF-TG1000) significantly stimulated cells proliferation. Furthermore, these scaffolds significantly induced dentinogenic markers (ALP, and DSPP) expression and biomineralization. In contrast to current pulp capping material driving dentin repair, the sophisticated, polymeric scaffold systems with soluble and insoluble spatiotemporal cues described here can direct stem cell differentiation and dentin regeneration. Hence, bioactive small molecule-incorporated nanofibrous scaffold suggests an innovative clinical tool for dentin tissue engineering. Graphical Abstract [ABSTRACT FROM AUTHOR]

The query optimizer uses cost-based optimization to create an execution plan with the least cost, which also consumes the least amount of resources. The challenge of query optimization for relational database systems is a combinatorial optimization problem, which renders exhaustive search impossible as query sizes rise. Increases in CPU performance have surpassed main memory, and disk access speeds in recent decades, allowing data compression to be used--strategies for improving database performance systems. For performance enhancement, compression and query optimization are the two most factors. Compression reduces the volume of data, whereas query optimization minimizes execution time. Compressing the database reduces memory requirement, data takes less time to load into memory, fewer buffer missing occur, and the size of intermediate results is more diminutive. This paper performed query optimization on the graph database in a cloud dew environment by considering, which requires less time to execute a query. The factors compression and query optimization improve the performance of the databases. This research compares the performance of MySQL and Neo4j databases in terms of memory usage and execution time running on cloud dew servers. [ABSTRACT FROM AUTHOR]

In software development, the main problem is recognizing the security-oriented issues within the reported bugs due to their unacceptable failure rate to provide satisfactory reliability on customer and software datasets. The misclassification of bug reports has a direct impact on the effectiveness of the bug prediction model. The misclassification issue surely compromises the accuracy of the system. Manually reviewing bug reports is necessary to solve this problem, but doing so takes a lot of time and is tiresome for developers and testers. This paper proposes a novel hybrid approach based on natural language processing (NLP) and machine learning. To address these issues, the intended outcomes are multi-class supervised classification and bug prioritization using supervised classifiers. After being collected, the dataset was prepared for vectorization, subjected to exploratory data analysis, and preprocessed. The feature extraction and selection methods used for a bag of words are TF-IDF and word2vec. Machine learning models are created after the dataset has undergone a full transformation. This study proposes, develops, and assesses four classifiers: multinomial Naive Bayes, decision tree, logistic regression, and random forest. The hyper-parameters of the models are tuned, and it is concluded that random forest outperformed with a 91.73% test and 100% training accuracy. The SMOTE technique was used to balance the highly imbalanced dataset, which was initially created for the justified classification. The comparison between balanced and imbalanced dataset models clearly showed the importance of the balanced dataset in classification as it outperformed in all experiments. [ABSTRACT FROM AUTHOR]

Kubernetes, a container orchestrator for cloud-deployed applications, allows the application provider to scale automatically to match the fluctuating intensity of processing demand. Container cluster technology is used to encapsulate, isolate, and deploy applications, addressing the issue of low system reliability due to interlocking failures. Cloud-based platforms usually entail users define application resource supplies for eco container virtualization. There is a constant problem of over-service in data centers for cloud service providers. Higher operating costs and incompetent resource utilization can occur in a waste of resources. Kubernetes revolutionized the orchestration of the container in the cloud-native age. It can adaptively manage resources and schedule containers, which provide real-time status of the cluster at runtime without the user’s contribution. Kubernetes clusters face unpredictable traffic, and the cluster performs manual expansion configuration by the controller. Due to operational delays, the system will become unstable, and the service will be unavailable. This work proposed an RBACS that vigorously amended the distribution of containers operating in the entire Kubernetes cluster. RBACS allocation pattern is analyzed with the Kubernetes VPA. To estimate the overall cost of RBACS, we use several scientific benchmarks comparing the accomplishment of container to remote node migration and on-site relocation. The experiments ran on the simulations to show the method’s effectiveness yielded high precision in the real-time deployment of resources in eco containers. Compared to the default baseline, Kubernetes results in much fewer dropped requests with only slightly more supplied resources. [ABSTRACT FROM AUTHOR]

There are an increasing number of Internet of Things (IoT) devices connected to the network these days, and due to the advancement in technology, the security threads and cyberattacks, such as botnets, are emerging and evolving rapidly with high-risk attacks. These attacks disrupt IoT transition by disrupting networks and services for IoT devices. Many recent studies have proposed ML and DL techniques for detecting and classifying botnet attacks in the IoT environment. This study proposes machine learning methods for classifying binary classes. This purpose is served by using the publicly available dataset UNSW-NB15. This dataset resolved a class imbalance problem using the SMOTE-OverSampling technique. A complete machine learning pipeline was proposed, including exploratory data analysis, which provides detailed insights into the data, followed by preprocessing. During this process, the data passes through six fundamental steps. A decision tree, an XgBoost model, and a logistic regression model are proposed, trained, tested, and evaluated on the dataset. In addition to model accuracy, F1-score, recall, and precision are also considered. Based on all experiments, it is concluded that the decision tree outperformed with 94% test accuracy. [ABSTRACT FROM AUTHOR]

This rapidly changing digital world is always sensitive to improving security and resilience to protect the inhabitants of this ecosystem in terms of data, processes, repositories, communication, and functions. The transformation of this digital ecosystem is heavily dependent on cloud computing, as it is becoming the global platform for individuals, corporates, and even governments. Therefore, the concerns related to security are now linked closely with cloud computing. In this paper, a multi-cloud security framework takes a view on the development of security mechanisms to provide a diversion to the attacker. The purpose is to gain more time to analyze the attack and mitigate the intrusion without compromises. This mechanism is designed using the honeypot technology that has been around for some time but has not been used in cloud computing and other technologies. The proposed framework provides modules related to managing the multi-cloud platform, the intrusion detection and prevention system, and honeypots. The results show significant improvement in the accuracy of detecting attacks. These results are generated in a two-phase scenario, and the first phase has been analyzed without the engagement of the honeypot module presented in the framework. The second phase has been executed with same parameters and conditions by engaging the honeypot module. It includes a comparison taxonomy of both results and an in-depth study of existing honeypots, as well as critical design elements for current honeypot research and outstanding concerns for future honeypots in IoT, multi-cloud contexts. [ABSTRACT FROM AUTHOR]

The digital transformation disrupts the various professional domains in different ways, though one aspect is common: the unified platform known as cloud computing. Corporate solutions, IoT systems, analytics, business intelligence, and numerous tools, solutions and systems use cloud computing as a global platform. The migrations to the cloud are increasing, causing it to face new challenges and complexities. One of the essential segments is related to data storage. Data storage on the cloud is neither simplistic nor conventional; rather, it is becoming more and more complex due to the versatility and volume of data. The inspiration of this research is based on the development of a framework that can provide a comprehensive solution for cloud computing storage in terms of replication, and instead of using formal recovery channels, erasure coding has been proposed for this framework, which in the past proved itself as a trustworthy mechanism for the job. The proposed framework provides a hybrid approach to combine the benefits of replication and erasure coding to attain the optimal solution for storage, specifically focused on reliability and recovery. Learning and training mechanisms were developed to provide dynamic structure building in the future and test the data model. RAID architecture is used to formulate different configurations for the experiments. RAID-1 to RAID-6 are divided into two groups, with RAID-1 to 4 in the first group while RAID-5 and 6 are in the second group, further categorized based on FTT, parity, failure range and capacity. Reliability and recovery are evaluated on the rest of the data on the server side, and for the data in transit at the virtual level. The overall results show the significant impact of the proposed hybrid framework on cloud storage performance. RAID-6c at the server side came out as the best configuration for optimal performance. The mirroring for replication using RAID-6 and erasure coding for recovery work in complete coherence provide good results for the current framework while highlighting the interesting and challenging paths for future research [ABSTRACT FROM AUTHOR]

One of the most well-known methods for solving real-world and complex optimization problems is the gravitational search algorithm (GSA). The gravitational search technique suffers from a sluggish convergence rate and weak local search capabilities while solving complicated optimization problems. A unique hybrid population-based strategy is designed to tackle the problem by combining dynamic multiswarm particle swarm optimization with gravitational search algorithm (GSADMSPSO). In this manuscript, GSADMSPSO is used as novel training techniques for Feedforward Neural Networks (FNNs) in order to test the algorithm's efficiency in decreasing the issues of local minima trapping and existing evolutionary learning methods' poor convergence rate. A novel method GSADMSPSO distributes the primary population of masses into smaller subswarms, according to the proposed algorithm, and also stabilizes them by offering a new neighborhood plan. At this time, each agent (particle) increases its position and velocity by using the suggested algorithm's global search capability. The fundamental concept is to combine GSA's ability with DMSPSO's to improve the performance of a given algorithm's exploration and exploitation. The suggested algorithm's performance on a range of well-known benchmark test functions, GSA, and its variations is compared. The results of the experiments suggest that the proposed method outperforms the other variants in terms of convergence speed and avoiding local minima; FNNs are being trained. [ABSTRACT FROM AUTHOR]

There are many thyroid diseases affecting people all over the world. Many diseases affect the thyroid gland, like hypothyroidism, hyperthyroidism, and thyroid cancer. Thyroid inefficiency can cause severe symptoms in patients. Effective classification and machine learning play a significant role in the timely detection of thyroid diseases. This timely classification will indeed affect the timely treatment of the patients. Automatic and precise thyroid nodule detection in ultrasound pictures is critical for reducing effort and radiologists' mistake rate. Medical images have evolved into one of the most valuable and consistent data sources for machine learning generation. In this paper, various machine learning algorithms like decision tree, random forest algorithm, KNN, and artificial neural networks on the dataset create a comparative analysis to better predict the disease based on parameters established from the dataset. Also, the dataset has been manipulated for accurate prediction for the classification. The classification was performed on both the sampled and unsampled datasets for better comparison of the dataset. After dataset manipulation, we obtained the highest accuracy for the random forest algorithm, equal to 94.8% accuracy and 91% specificity. [ABSTRACT FROM AUTHOR]

Efforts were exerted to enhance the live virtual machines (VMs) migration, including performance improvements of the live migration of services to the cloud. The VMs empower the cloud users to store relevant data and resources. However, the utilization of servers has increased significantly because of the virtualization of computer systems, leading to a rise in power consumption and storage requirements by data centers, and thereby the running costs. Data center migration technologies are used to reduce risk, minimize downtime, and streamline and accelerate the data center move process. Indeed, several parameters, such as non-network overheads and downtime adjustment, may impact the live migration time and server downtime to a large extent. By virtualizing the network resources, the infrastructure as a service (IaaS) can be used dynamically to allocate the bandwidth to services and monitor the network flow routing. Due to the large amount of filthy retransmission, existing live migration systems still suffer from extensive downtime and significant performance degradation in cross-data-center situations. This study aims to minimize the energy consumption by restricting the VMs migration and switching off the guests depending on a threshold, thereby boosting the residual network bandwidth in the data center with a minimal breach of the service level agreement (SLA). In this research, we analyzed and evaluated the findings observed through simulating different parameters, like availability, downtime, and outage of VMs in data center processes. This new paradigm is composed of two forms of detection strategies in the live migration approach from the source host to the destination source machine. [ABSTRACT FROM AUTHOR]

Determining factors influencing the success of software projects has been the emphasis of extensive research for more than 40 years. However, the majority of research in this domain has focused on developed countries, with little attention paid to underdeveloped and developing countries. The primary objective of this article was to assess the effect of critical elements on the success of software projects in underdeveloped countries (like Pakistan), because enterprise environmental factors and staff working habits, as well as their experience and expertise level, all have an effect on a project's success. For this purpose, data were collected from 339 senior developers and project managers working in Pakistan Software Export Board (PSEB) registered software companies. Structural Equation Modelling (SEM) was used to analyze the constructs and to assess the relationship between factors affecting software success. The empirical results showed that improper planning, inadequate human resources, wrong estimation of time and cost significantly negatively impacted the success of software projects. This research has opened new doors to extend our work in the software community to ultimately succeed in software projects. [ABSTRACT FROM AUTHOR]

With the ongoing revolution in the Internet of Things (IoT) and cloud computing has made the potential of every stack holder that is connected through the Internet, to exchange and transfer data. Various users perceive this connection and interaction with devices as very helpful and serviceable in their daily life. However, an improperly configured network system is a soft target to security threats, therefore there is a dire need for a security embedded framework for IoT and cloud communication models is the latest research area. In this paper, different IoT and cloud computing frameworks are discussed in detail and describes the importance of the daily life of people. The main focus is to design the Cloud IoT integration that is used to implement IoT and Cloud Framework for M2M communication, also building a relationship between different devices to connect through a cloud and also find different security methods to secure those devices. Extensive papers finding and results showed different ways they have been introduced to manipulate M2M in the digital field of health care and the virtual world. While focusing on the methodology used in M2M it is also imperative to concentrate on security levels from different inside and outside attacks on IoT and cloud ecosystem. There is a need to create a strong and secure connection between all of our IoT devices with a cloud so that there should be a fixed and safe connection between cloud environments concerning M2M connection between all wired and wireless devices. Meanwhile in contemplation of security mode also conducive to maintain M2M connection between IoT devices and Cloud and in which areas these methodologies have been implemented. [ABSTRACT FROM AUTHOR]

Diabetes is one of the ever-increasing menace crippling millions of people worldwide. It is an independent risk factor for many cardiovascular diseases including medium and small vessels and results in heart attack, stroke, kidney failure, blindness, and lower-limb amputations. According to a World Health Organization (WHO) report estimated 1.6 million deaths were the direct result of diabetes. Nutrition plays a vital role in diabetes management alongside physical activity, drugs, and insulin. Weight management can help to avert or delay at pre-diabetic stages. This research work explains the features of the Nutrition Diet Expert System (NDES), which will preferably be used by the health care professionals (HCPs) for calculating per day calorie requirements of diabetic patients and recommend the best diet plan to control diabetes. The primary objective of this proposed model for diet plan is to help individuals attain healthy body weight and optimum check on diabetes by gaining control over blood pressure and lipid count. The ultimate focus of this research result is prevention of diabetes related complications using nutrition diet expert system. In this paper, proposed recommender system has come in handy in figuring out the diet plan by determining the individual dietary requirements at the level of micro and macro nutrients for expert system using fuzzy logic. The results are very promising indicating testing and assessment of the expert system worked well for the individual diet plan. [ABSTRACT FROM AUTHOR]

Development in the field of opinion mining and sentiment analysis has been rapid and aims to explore views or texts on various social media sites through machine-learning techniques with the sentiment, subjectivity analysis and calculations of polarity. Sentiment analysis is a natural language processing strategy used to decide if the information is positive, negative, or neutral and it is frequently performed on literature information to help organizations screen brand, item sentiment in client input, and comprehend client needs. In this paper, two strategies for sentiment analysis is proposed for word embedding and a bag of words on Urdu and English tweets. Word embedding is a notable arrangement of procedures that can remember words linguistics dependent on the spread theory which expresses that word is utilized and happens within the same settings tend to indicate comparable implications. Bag of words is an approach used in natural language processing to retrieve information and features from written documents. For the bag of words, machine learning techniques like naive bayes, decision tree, k-nearest neighbor, and support vector machine is used to enhance the accuracy. For word embedding the neural network technique is proposed by the combination of recurrent neural network (RNN) with long-short term memory (LSTM) for sentimental analysis of tweets. Datasets of Urdu and English tweets are used for negative and positive classification tweets with machine learning techniques. The contribution of this paper involves the implementation of a hybrid approach that focused on a sentiment analyzer to overcome social network challenges and also provided the comparative analysis of different machine learning algorithms. The results indicate improvement while using the combination of RNN with the help of LSTM showed accuracy 87% on the Urdu dataset and 92% on the English dataset. [ABSTRACT FROM AUTHOR]

The past two decades witnessed a broad-increase in web technology and on-line gaming. Enhancing the broadband confinements is viewed as one of the most significant variables that prompted new gaming technology. The immense utilization of web applications and games additionally prompted growth in the handled devices and moving the limited gaming experience from user devices to online cloud servers. As internet capabilities are enhanced new ways of gaming are being used to improve the gaming experience. In cloud-based video gaming, game engines are hosted in cloud gaming data centers, and compressed gaming scenes are rendered to the players over the internet with updated controls. In such systems, the task of transferring games and video compression imposes huge computational complexity is required on cloud servers. The basic problems in cloud gaming in particular are high encoding time, latency, and low frame rates which require a new methodology for a better solution. To improve the bandwidth issue in cloud games, the compression of video sequences requires an alternative mechanism to improve gaming adaption without input delay. In this paper, the proposed improved methodology is used for automatic unnecessary scene detection, scene removing and bit rate reduction using an adaptive algorithm for object detection in a game scene. As a result, simulations showed without much impact on the players' quality experience, the selective object encoding method and object adaption technique decrease the network latency issue, reduce the game streaming bitrate at a remarkable scale on different games. The proposed algorithm was evaluated for three video game scenes. In this paper, achieved 14.6% decrease in encoding and 45.6% decrease in bit rate for the first video game scene. [ABSTRACT FROM AUTHOR]

Cloud computing is becoming popular technology due to its functional properties and variety of customer-oriented services over the Internet. The design of reliable and high-quality cloud applications requires a strong Quality of Service QoS parameter metric. In a hyperconverged cloud ecosystem environment, building high-reliability cloud applications is a challenging job. The selection of cloud services is based on the QoS parameters that play essential roles in optimizing and improving cloud rankings. The emergence of cloud computing is significantly reshaping the digital ecosystem, and the numerous services offered by cloud service providers are playing a vital role in this transformation. Hyperconverged software-based unified utilities combine storage virtualization, compute virtualization, and network virtualization. The availability of the latter has also raised the demand for QoS. Due to the diversity of services, the respective quality parameters are also in abundance and need a carefully designed mechanism to compare and identify the critical, common, and impactful parameters. It is also necessary to reconsider the market needs in terms of service requirements and the QoS provided by various CSPs. This research provides a machine learning-based mechanism to monitor the QoS in a hyperconverged environment with three core service parameters: service quality, downtime of servers, and outage of cloud services. [ABSTRACT FROM AUTHOR]

Security is a significant issue for everyone due to new and creative ways to commit cybercrime. The Closed-Circuit Television (CCTV) systems are being installed in offices, houses, shopping malls, and on streets to protect lives. Operators monitor CCTV; however, it is difficult for a single person to monitor the actions of multiple people at one time. Consequently, there is a dire need for an automated monitoring system that detects a person with ammunition or any other harmful material Based on our research and findings of this study, we have designed a new Intelligent Ammunition Detection and Classification (IADC) system using Convolutional Neural Network (CNN). The proposed system is designed to identify persons carrying weapons and ammunition using CCTV cameras. When weapons are identified, the cameras sound an alarm. In the proposed IADC system, CNN was used to detect firearms and ammunition. The CNN model which is a Deep Learning technique consists of neural networks, most commonly applied to analyzing visual imagery has gained popularity for unstructured (images, videos) data classification. Additionally, this system generates an early warning through detection of ammunition before conditions become critical. Hence the faster and earlier the prediction, the lower the response time, loses and potential victims. The proposed IADC system provides better results than earlier published models like VGGNet, OverFeat-1, OverFeat-2, and OverFeat-3. [ABSTRACT FROM AUTHOR]

Bioinformatics education has been a hot topic in South Asia, and the interest in this education peaks with the start of the 21st century. The governments of South Asian countries had a systematic effort for bioinformatics. They developed the infrastructures to provide maximum facility to the scientific community to gain maximum output in this field. This article renders bioinformatics, measures, and its importance of implementation in South Asia with proper ways of improving bioinformatics education flaws. It also addresses the problems faced in South Asia and proposes some recommendations regarding bioinformatics education. The information regarding bioinformatics education and institutes was collected from different existing research papers, databases, and surveys. The information was then confirmed by visiting each institution's website, while problems and solutions displayed in the article are mostly in line with South Asian bioinformatics conferences and institutions' objectives. Among South Asian countries, India and Pakistan have developed infrastructure and education regarding bioinformatics rapidly as compared to other countries, whereas Bangladesh, Sri Lanka, and Nepal are still in a progressing phase in this field. To advance in a different sector, the bioinformatics industry has to be revolutionized, and it will contribute to strengthening the pharmaceutical, agricultural, and molecular sectors in South Asia. To advance in bioinformatics, universities' infrastructure needs to be on a par with the current international standards, which will produce well-trained professionals with skills in multiple fields like biotechnology, mathematics, statistics, and computer science. The bioinformatics industry has revolutionized and strengthened the pharmaceutical, agricultural, and molecular sectors in South Asia, and it will serve as the standard of education increases in the South Asian countries. A framework for developing a centralized database is suggested after the literature review to collect and store the information on the current status of South Asian bioinformatics education. This will be named as the South Asian Bioinformatics Education Database (SABE). This will provide comprehensive information regarding the bioinformatics in South Asian countries by the country name, the experts of this field, and the university name to explore the top-ranked outputs relevant to queries. [ABSTRACT FROM AUTHOR]

Hyper-convergence is a new innovation in data center technology, it changes the way clouds manage and maintain enterprise IT infrastructure. Hyper-convergence is more efficient and basically agile technology environment. Cloud computing is a latest technology due to provision of latest cloud services over the internet. The cloud service providers cannot promise accurate reliability of their services i.e. problem in provisioning of software or hardware failure etc. Reliability of cloud computing services depends on the ability of fault tolerance during the execution of services. There are so many factors can cause faults, such as network failure, browser crash, request time out or hacker attacks. When users are facing these types of faults, they usually resubmit their requests. However, if there is any key element involved in faults or errors, additional action may be needed to deal with system logs. If there is anomaly behavior occurred in faulted virtual machine, these VMs may need extra attention from cloud system protection and security point of view. In this paper, provision of reliability management in hyper-convergence cloud infrastructure is proposed and self-healing techniques in software as a service on the basis of failure in cloud services. Intelligent cloud service reliability framework will increase the reliability during execution of cloud service. [ABSTRACT FROM AUTHOR]

Abnormalities in skin pigmentation can produce disorders such as albinism or melasma. There is a research need to discover novel compounds that safely and effectively regulate pigmentation. To identify novel modulators of pigmentation, we attempted to purify compounds from a bioactive fraction of the Korean medicinal plant Artemisia capillaris Thunberg. The novel compound isofraxidin 7-O-(6′-O-p-coumaroyl)-β-glucopyranoside (compound 1) was isolated and its pigmentation activity was characterized in mammalian melanocytes. Compound 1 stimulated melanin accumulation and increased tyrosinase activity, which regulates melanin synthesis. Moreover, compound 1 increased the expression of tyrosinase and the key melanogenesis regulator microphthalmia-associated transcription factor (MITF) in melanocytes. Compared to the parent compound, isofraxidin, compound 1 produced greater effects on these pigmentation parameters. To validate compound 1 as a novel hyperpigmentation agent in vivo, we utilized the zebrafish vertebrate model. Zebrafish treated with compound 1 showed higher melanogenesis and increased tyrosinase activity. Compound 1 treated embryos had no developmental defects and displayed normal cardiac function, indicating that this compound enhanced pigmentation without producing toxicity. In summary, our results describe the characterization of novel natural product compound 1 and its bioactivity as a pigmentation enhancer, demonstrating its potential as a therapeutic to treat hypopigmentation disorders. [ABSTRACT FROM AUTHOR]

There is a continual need to develop novel and effective melanogenesis inhibitors for the prevention of hyperpigmentation disorders. The plant Artemisia capillaris Thunberg (Oriental Wormwood) was screened for antipigmentation activity using murine cultured cells (B16-F10 malignant melanocytes). Activity-based fractionation using HPLC and NMR analyses identified the compound 4,5-O-dicaffeoylquinic acid as an active component in this plant. 4,5-O-Dicaffeoylquinic acid significantly reduced melanin synthesis and tyrosinase activity in a dose-dependent manner in the melanocytes. In addition, 4,5-O-dicaffeoylquinic acid treatment reduced the expression of tyrosinase-related protein-1. Significantly, we could validate the antipigmentation activity of this compound in vivo, using a zebrafish model. Moreover, 4,5-O-dicaffeoylquinic acid did not show toxicity in this animal model. Our discovery of 4,5-O-dicaffeoylquinic acid as an inhibitor of pigmentation that is active in vivo shows that this compound can be developed as an active component for formulations to treat pigmentation disorders. [ABSTRACT FROM AUTHOR]

Diabetes mellitus affects millions of people worldwide and significantly impacts their quality of life. Moreover, life threatening diseases, such as myocardial infarction, blindness, and renal disorders, increase the morbidity rate associated with diabetes. Various natural products from medicinal plants have shown potential as antidiabetes agents in cell-based screening systems. However, many of these potential “hits” fail in mammalian tests, due to issues such as poor pharmacokinetics and/or toxic side effects. To address this problem, the zebrafish (Danio rerio) model has been developed as a “bridge” to provide an experimentally convenient animal-based screening system to identify drug candidates that are active in vivo. In this review, we discuss the application of zebrafish to drug screening technologies for diabetes research. Specifically, the discovery of natural product-based antidiabetes compounds using zebrafish will be described. For example, it has recently been demonstrated that antidiabetic natural compounds can be identified in zebrafish using activity guided fractionation of crude plant extracts. Moreover, the development of fluorescent-tagged glucose bioprobes has allowed the screening of natural product-based modulators of glucose homeostasis in zebrafish. We hope that the discussion of these advances will illustrate the value and simplicity of establishing zebrafish-based assays for antidiabetic compounds in natural products-based laboratories. [ABSTRACT FROM AUTHOR]