In the modern era of digitization, the analysis in the Internet of Things (IoT) environment demands a brisk amalgamation of domains such as high-dimension (images) data sensing technologies, robust internet connection (4 G or 5 G) and dynamic (adaptive) deep learning approaches. This is required for a broad range of indispensable intelligent applications, like intelligent healthcare systems. Dynamic image classification is one of the major areas of concern for researchers, which may take place during analysis under the IoT environment. Dynamic image classification is associated with several temporal data perturbations (such as novel class arrival and class evolution issue) which cause a massive classification deterioration in the deployed classification models and make them in-effective. Therefore, this study addresses such temporal inconsistencies (novel class arrival and class evolution issue) and proposes an adapted deep learning framework (ameliorated adaptive convolutional neural network (CNN) ensemble framework), which handles novel class arrival and class evaluation issue during dynamic image classification. The proposed framework is an improved version of previous adaptive CNN ensemble with an additional online training (OT) and online classifier update (OCU) modules. An OT module is a clustering-based approach which uses the Euclidean distance and silhouette method to determine the potential new classes, whereas, the OCU updates the weights of the existing instances of the ensemble with newly arrived samples. The proposed framework showed the desirable classification improvement under non-stationary scenarios for the benchmark (CIFAR10) and real (ISIC 2019: Skin disease) data streams. Also, the proposed framework outperformed against state-of-art shallow learning and deep learning models. The results have shown the effectiveness and proven the diversity of the proposed framework to adapt the new concept changes during dynamic image classification. In future work, the authors of this study aim to develop an IoT-enabled adaptive intelligent dermoscopy device (for dermatologists). Therefore, further improvements in classification accuracy (for real dataset) is the future concern of this study.
Oleic acid is a mono-unsaturated fatty acid that can be found abundantly in various vegetable oils and potentially attractive to be used as raw material for epoxide chemical. In-situ epoxidation of oleic acid was conducted in batch reactor using peroxy-formic at 30-60°C. Pseudo-steady-state-hypothesis (PSSH) was applied to develop the kinetic model. Heterogeneous liquid-liquid system was chosen and four models which emphasized on the ring opening agent (ROA) and reversibility of the epoxidation reaction were proposed. It has been suggested that reversible model is well suited to represent the experimental data. Activation energy obtained from Arrhenius equation is in the range of 40-195 kJ/mol.
In 2020, there were 2.21 million new instances of lung cancer, making it the top cause of mortality globally, responsible for close to 10 million deaths. The physicochemical problems of chemotherapy drugs are the primary challenge that now causes a drug's low effectiveness. Solubility is a physicochemical factor that has a significant impact on a drug's biopharmaceutical properties, starting with the rate at which it dissolves and extending through how well it is absorbed and bioavailable. One of the most well-known methods for addressing a drug's solubility is mesoporous silica, which has undergone excellent development due to the conjugation of polymers and ligands that increase its effectiveness. However, there are still very few papers addressing the success of this discovery, particularly those addressing its molecular pharmaceutics and mechanism. Our study's objectives were to explore and summarize the effects of targeting mediator on drug development using mesoporous silica with and without functionalized polymer. We specifically focused on highlighting the molecular pharmaceutics and mechanism in this study's innovative findings. Journals from the Scopus, PubMed, and Google Scholar databases that were released during the last ten years were used to compile this review. According to inclusion and exclusion standards adjusted. This improved approach produced very impressive results, a very significant change in the characteristics of mesoporous silica that can affect effectiveness. Mesoporous silica approaches have the capacity to greatly enhance a drug's physicochemical issues, boost therapeutic efficacy, and acquire superb features.
Since the beginning of the coronavirus pandemic in late 2019, viral infections have become one of the top three causes of mortality worldwide. Immunization and the use of immunomodulatory drugs are effective ways to prevent and treat viral infections. However, the primary therapy for managing viral infections remains antiviral and antiretroviral medication. Unfortunately, these drugs are often limited by physicochemical constraints such as low target selectivity and poor aqueous solubility. Although several modifications have been made to enhance the physicochemical characteristics and efficacy of these drugs, there are few published studies that summarize and compare these modifications. Our review systematically synthesized and discussed antiviral drug modification reports from publications indexed in Scopus, PubMed, and Google Scholar databases. We examined various approaches that were investigated to address physicochemical issues and increase activity, including liposomes, cocrystals, solid dispersions, salt modifications, and nanoparticle drug delivery systems. We were impressed by how well each strategy addressed physicochemical issues and improved antiviral activity. In conclusion, these modifications represent a promising way to improve the physicochemical characteristics, functionality, and effectiveness of antivirals in clinical therapy.
The prevalence of active pharmaceutical ingredients (APIs) with low water solubility has experienced a significant increase in recent years. These APIs present challenges in formulation, particularly for oral dosage forms, despite their considerable therapeutic potential. Therefore, the improvement of solubility has become a major concern for pharmaceutical enterprises to increase the bioavailability of APIs. A promising formulation approach that can effectively improve the dissolution profile and the bioavailability of poorly water-soluble drugs is the utilization of amorphous systems. Numerous formulation methods have been developed to enhance poorly water-soluble drugs through amorphization systems, including co-amorphous formulations, amorphous solid dispersions (ASDs), and the use of mesoporous silica as a carrier. Furthermore, the successful enhancement of certain drugs with poor aqueous solubility through amorphization has led to their incorporation into various commercially available preparations, such as ASDs, where the crystalline structure of APIs is transformed into an amorphous state within a hydrophilic matrix. A novel approach, known as ternary solid dispersions (TSDs), has emerged to address the solubility and bioavailability challenges associated with amorphous drugs. Meanwhile, the introduction of a third component in the ASD and co-amorphous systems has demonstrated the potential to improve performance in terms of solubility, physical stability, and processability. This comprehensive review discusses the preparation and characterization of poorly water-soluble drugs in ternary solid dispersions and their mechanisms of drug release and physical stability.
The cocoa pod borer (CPB) Conopomorpha cramerella (Snellen) (Lepidoptera: Gracillaridae) is one of the major constraints for cocoa production in South East Asia. In addition to cultural and chemical control methods, autocidal control tactics such as the Sterile Insect Technique (SIT) could be an efficient addition to the currently control strategy, however SIT implementation will depend on the population genetics of the targeted pest. The aim of the present work was to search for suitable microsatellite loci in the genome of CPB that is partially sequenced. Twelve microsatellites were initially selected and used to analyze moths collected from Indonesia, Malaysia, and the Philippines. A quality control verification process was carried out and seven microsatellites found to be suitable and efficient to distinguish differences between CPB populations from different locations. The selected microsatellites were also tested against a closely related species, i.e. the lychee fruit borer Conopomorpha sinensis (LFB) from Vietnam and eight loci were found to be suitable. The availability of these novel microsatellite loci will provide useful tools for the analysis of the population genetics and gene flow of these pests, to select suitable CPB strains to implement the SIT.