Pharmacological advances in anti-retroviral therapy for human immunodeficiency virus-1 infection: A comprehensive review.

The discovery of anti-retroviral (ARV) drugs over the past 36 years has introduced various classes, including nucleoside/nucleotide reverse transcriptase inhibitors, non-nucleoside reverse transcriptase inhibitors, protease inhibitor, fusion, and integrase strand transfer inhibitors inhibitors. The introduction of combined highly active anti-retroviral therapies in 1996 was later proven to combat further ARV drug resistance along with enhancing human immunodeficiency virus (HIV) suppression. As though the development of ARV therapies was continuously expanding, the variation of action caused by ARV drugs, along with its current updates, was not comprehensively discussed, particularly for HIV-1 infection. Thus, a range of HIV-1 ARV medications is covered in this review, including new developments in ARV therapy based on the drug's mechanism of action, the challenges related to HIV-1, and the need for combination therapy. Optimistically, this article will consolidate the overall updates of HIV-1 ARV treatments and conclude the significance of HIV-1-related pharmacotherapy research to combat the global threat of HIV infection.

Correction: Clinical outcomes in a primary-level non-communicable disease programme for Syrian refugees and the host population in Jordan: A cohort analysis using routine data.

[This corrects the article DOI: 10.1371/journal.pmed.1003279.].

Green-fabricated silver nanoparticles from Quercus incana leaf extract to control the early blight of tomatoes caused by Alternaria solani.

BACKGROUND: Early blight (EB) of Tomatoes, caused by Alternaria solani, is a serious fungal disease that adversely affects tomato production. Infection is characterized by dark lesions on leaves, stems, and fruits. Several agrochemicals can be used to control infection, these chemicals may disrupt environmental equilibrium. An alternative technology is needed to address this significant fungal threat. This study was designed to control the growth of EB in tomatoes caused by A. solani, using green-fabricated silver nanoparticles (Ag-NPs). RESULTS: Ag-NPs were synthesized through an environmentally friendly and cost-effective approach using leaf extract of Quercus incana Roxb. (Fagaceae). The physico-chemical characterization of the Ag-NPs was conducted through UV-visible spectroscopy, scanning electron microscopy, X-ray diffraction analysis, and Fourier transform infrared spectrometry. The Ag-NPs produced were round with a mean diameter of 27 nm. The antifungal activity of these Ag-NPs was assessed through in vitro Petri plate and in vitro leaflet assays against A. solani. The green fabricated Ag-NPs exhibited excellent antifungal activity in vitro at a concentration of 100 mg/l against A. solani, inhibiting growth by 98.27 ± 1.58% and 92.79 ± 1.33% during Petri plate and leaflet assays, respectively. CONCLUSION: In conclusion, this study suggests the practical application of green-fabricated Ag-NPs from Q. incana leaf extract against A. solani to effectively control EB disease in tomatoes.

The role of PKC in X-ray-induced megakaryocyte apoptosis and thrombocytopenia.

Thrombocytopenia is a critical complication after radiation therapy and exposure. Dysfunction of megakaryocyte development and platelet production are key pathophysiological stages in ionizing radiation (IR)-induced thrombocytopenia. Protein kinase C (PKC) plays an important role in regulating megakaryocyte development and platelet production. However, it remains unclear how PKC regulates IR-induced megakaryocyte apoptosis. In this study, we found that pretreatment of PKC pan-inhibitor Go6983 delayed IR-induced megakaryocyte apoptosis, and inhibited IR-induced mitochondrial membrane potential and ROS production in CMK cells. Moreover, suppressing PKC activation inhibited cleaved caspase3 expression and reduced p38 phosphorylation levels, and IR-induced PKC activation might be regulated by p53. In vivo experiments confirmed that Go6983 promoted platelet count recovery after 21 days of 3 Gy total body irradiation. Furthermore, Go6983 reduced megakaryocyte apoptosis, increased the number of megakaryocyte and polyploid formation in bone marrow, and improved the survival rate of 6 Gy total body irradiation. In conclusion, our results provided a potential therapeutic target for IR-induced thrombocytopenia.

Prediction of leukemia peptides using convolutional neural network and protein compositions.

Leukemia is a type of blood cell cancer that is in the bone marrow's blood-forming cells. Two types of Leukemia are acute and chronic; acute enhances fast and chronic growth gradually which are further classified into lymphocytic and myeloid leukemias. This work evaluates a unique deep convolutional neural network (CNN) classifier that improves identification precision by carefully examining concatenated peptide patterns. The study uses leukemia protein expression for experiments supporting two different techniques including independence and applied cross-validation. In addition to CNN, multilayer perceptron (MLP), gated recurrent unit (GRU), and recurrent neural network (RNN) are applied. The experimental results show that the CNN model surpasses competitors with its outstanding predictability in independent and cross-validation testing applied on different features extracted from protein expressions such as amino acid composition (AAC) with a group of AAC (GAAC), tripeptide composition (TPC) with a group of TPC (GTPC), and dipeptide composition (DPC) for calculating its accuracies with their receiver operating characteristic (ROC) curve. In independence testing, a feature expression of AAC and a group of GAAC are applied using MLP and CNN modules, and ROC curves are achieved with overall 100% accuracy for the detection of protein patterns. In cross-validation testing, a feature expression on a group of AAC and GAAC patterns achieved 98.33% accuracy which is the highest for the CNN module. Furthermore, ROC curves show a 0.965% extraordinary result for the GRU module. The findings show that the CNN model is excellent at figuring out leukemia illnesses from protein expressions with higher accuracy.

Molecular insights into the functional analysis of P450 CYP321A7 gene in the involvement of detoxification of lambda-cyhalothrin in Spodoptera frugiperda.

Fall armyworm, Spodoptera frugiperda (J. E. Smith), is a widely recognized global agricultural pest that has significantly reduced crop yields all over the world. S. frugiperda has developed resistance to various insecticides. Insect cytochrome P450 monooxygenases (CYPs or P450s) play an important role in detoxifying insecticides, leading to increased resistance in insect populations. However, the function of the specific P450 gene for lambda-cyhalothrin resistance in S. frugiperda was unclear. Herein, the expression patterns of 40 P450 genes in the susceptible and lambda-cyhalothrin-resistant populations were analyzed. Among them, CYP321A7 was found to be overexpressed in the resistant population, specifically LRS (resistance ratio = 25.38-fold) derived from a lambda-cyhalothrin-susceptible (SS) population and FLRS (a population caught from a field, resistance ratio = 63.80-fold). Elevated enzyme activity of cytochrome P450 monooxygenases (P450s) was observed for LRS (2.76-fold) and the FLRS (4.88-fold) as compared to SS, while no significant differences were observed in the activities of glutathione S-transferases and esterases. Furthermore, the knockdown of CYP321A7 gene by RNA interference significantly increased the susceptibility to lambda-cyhalothrin. Remarkably, the knockdown of CYP321A7 reduced the enzymatic activity of P450 by 43.7%, 31.9%, and 22.5% in SS, LRS, and FLRS populations, respectively. Interestingly, fourth-instar larvae treated with lambda-cyhalothrin at the LC30 dosage had a greater mortality rate due to RNA interference-induced suppression of CYP321A7 (with increases of 61.1%, 50.0%, and 45.6% for SS, LRS, and FLRS populations, respectively). These findings suggest a link between lambda-cyhalothrin resistance and continual overexpression of CYP321A7 in S. frugiperda larvae, emphasizing the possible importance of CYP321A7 in lambda-cyhalothrin detoxification in S. frugiperda.

Genus Berberis: A comprehensive and updated review on ethnobotanical uses, phytochemistry and pharmacological activities.

Genus Berberis is an excellent choice for research due to its history in traditional medicine, diverse pharmacological properties, and it has potential for drug discovery. This review presents information on the ethnobotany, pharmacological activities, and many phytochemicals identified from Berberis species. It examines the existing literature on the genus Berberis, drawn from online databases, including PubMed, Web of Science, Science Direct, Elsevier, and Google Scholar, etc encompassing the data from 1960 to 2023. This review focuses on the structural details of reported phytochemicals of Berberis species and pharmacological actions. Different extraction techniques were evaluated for extracts preparation. According to literature review, phytochemical analysis exhibited the presence of alkaloids, flavonoids, and phenolic compounds. A major bioactive alkaloid, berberine exhibits its main role in treatment of many gastric, infectious, and chronic disorders. This literature indicates that Berberis genus exhibits a variety of biological activities, i.e anti-inflammatory, cytotoxic, hepatoprotective, antimicrobial, antidiabetic and antioxidant activities and utilization of these effects in the treatment and management of various diseases, like diabetes, microbial infections, inflammation, liver disorders, and cancer. However, conventional medicines, validation of traditional uses, and in-depth phytochemical analysis are areas of research in genus Berberis.

Cooperative Communication Based Protocols for Underwater Wireless Sensors Networks: A Review.

Underwater wireless sensor networks are gaining popularity since supporting a broad range of applications, both military and civilian. Wireless acoustics is the most widespread technology adopted in underwater networks, the realization of which must face several challenges induced by channel propagation like signal attenuation, multipath and latency. In order to address such issues, the attention of researchers has recently focused on the concept of cooperative communication and networking, borrowed from terrestrial systems and to be conveniently recast in the underwater scenario. In this paper, we present a comprehensive literature review about cooperative underwater wireless sensor networks, investigating how nodes cooperation can be exploited at the different levels of the network protocol stack. Specifically, we review the diversity techniques employable at the physical layer, error and medium access control link layer protocols, and routing strategies defined at the network layer. We also provide numerical results and performance comparisons among the most widespread approaches. Finally, we present the current and future trends in cooperative underwater networks, considering the use of machine learning algorithms to efficiently manage the different aspects of nodes cooperation.

Correction: Sarwer et al. Green Synthesis and Characterization of Silver Nanoparticles Using Myrsine africana Leaf Extract for Their Antibacterial, Antioxidant and Phytotoxic Activities. Molecules 2022, 27, 7612.

The authors wish to make the following corrections to this paper [...].

X-rays Stimulate Granular Secretions and Activate Protein Kinase C Signaling in Human Platelets.

X-rays can induce morphological as well as functional changes in cells. Platelets are anuclear cellular fragments originating from megakaryocytes and are the major regulators in hemostasis and thrombosis. Platelet products are irradiated to avoid medical complications associated with platelet transfusion. So far, gamma, UV, and laser radiation have been used for this purpose. However, scientists are divided about the effects of radiation on platelet quality. The present study was designed to explore the possible effects of X-rays in washed human platelets and understand the molecular mechanism behind them. In the present study, we exposed washed human platelets to 10 or 30 Gy X-rays at 0.25 Gy/min. Flow cytometry, aggregometry, and western blot were performed to investigate the effect of X-rays on platelet degranulation, integrin activation, platelet aggregation, and apoptosis. It was found that X-rays immediately induced granular secretions with no effect on GP IIb/IIIa activation. Not surprisingly, due to granule secretions in irradiated platelets, platelet aggregation was significantly reduced. In contrast to granular secretions and platelet aggregation, X-rays induced mitochondrial transmembrane potential depolarization in a time-dependent manner to induce apoptosis and activated protein kinase C (PKC) signaling. This study revealed and explained the molecular mechanism activated by X-rays in washed human platelets. Here we also introduced Gö 6983, a PKC inhibitor, as an agent that counteracts X-ray-induced changes and maintains the integrity of platelets.

Insights into phytonutrient profile and postharvest quality management of jackfruit: A review.

Jackfruit (Artocarpus heterophyllus Lam.), also known as 'vegetarian's meat', is an excellent source of carbohydrates, protein, fiber, vitamins, minerals, and several phytochemicals. It is a climacteric fruit that exhibits an increase in ethylene biosynthesis and respiration rate during fruit ripening. The market value of jackfruit is reduced due to the deterioration of fruit quality during storage and transportation. There is a lack of standardized harvest maturity index in jackfruit, where consequently, fruit harvested at immature or overmature stages result in poor quality ripe fruit with short storage life. Other factors responsible for its short postharvest life relate to its highly perishable nature, chilling sensitivity and susceptibility to fruit rot which result in significant qualitative and quantitative losses. Various postharvest management techniques have been adopted to extend the storage life, including cold storage, controlled atmosphere storage, modified atmosphere packaging, edible coatings, chemical treatment, and non-chemical alternatives. Diversified products have been prepared from jackfruit to mitigate such losses. This comprehensive review highlights the nutritional profile, fruit ripening physiology, pre and postharvest quality management, and value addition of jackfruit as well as the way forward to reduce postharvest losses in the supply chain.

Nimbolide attenuates complete Freund's adjuvant induced arthritis through expression regulation of toll-like receptors signaling pathway.

Nimbolide is an active constituent of Azadirachta indica and is known for its anti-inflammatory, anti-oxidant, immune-modulatory, and anti-cancer effects. Few studies suggest that nimbolide treatment influences the responses to rheumatoid arthritis, but the underlying molecular mechanisms involved are not yet well established. Therefore, the present study was designed to determine the effect of nimbolide on expression regulation of toll-like receptors to attenuate rheumatoid arthritis. The rheumatoid arthritis model was established by injecting complete Freund's adjuvant (CFA) intra-dermally into the sub-plantar region of the left hind paw of rats. Nimbolide (20 mg/kg) and piroxicam (10 mg/kg) were given to arthritic rats. Rats treated with nimbolide showed a significant reduction in inflammatory cells, rheumatoid factor, ESR, and improved the body weight. The results indicated that nimbolide possesses the capacity to attenuate rheumatoid arthritis by downregulating toll-like receptors, IL-17, IL-23, HSP70, and IFN-γ expression levels. Nimbolide treatment showed significant reduction in the severity of inflammation and destruction of joints and showed comparable effects to piroxicam, which is a standard non-steroidal anti-inflammatory drug used for the treatment of rheumatoid arthritis. It can be concluded that nimbolide can be considered as a potential candidate for therapeutic targeting of the toll-like receptors pathway in rheumatoid arthritis.

Evaluation of Machine Learning Algorithms for Malware Detection.

This research study mainly focused on the dynamic malware detection. Malware progressively changes, leading to the use of dynamic malware detection techniques in this research study. Each day brings a new influx of malicious software programmes that pose a threat to online safety by exploiting vulnerabilities in the Internet. The proliferation of harmful software has rendered manual heuristic examination of malware analysis ineffective. Automatic behaviour-based malware detection using machine learning algorithms is thus considered a game-changing innovation. Threats are automatically evaluated based on their behaviours in a simulated environment, and reports are created. These records are converted into sparse vector models for use in further machine learning efforts. Classifiers used to synthesise the results of this study included kNN, DT, RF, AdaBoost, SGD, extra trees and the Gaussian NB classifier. After reviewing the test and experimental data for all five classifiers, we found that the RF, SGD, extra trees and Gaussian NB Classifier all achieved a 100% accuracy in the test, as well as a perfect precision (1.00), a good recall (1.00), and a good f1-score (1.00). Therefore, it is reasonable to assume that the proof-of-concept employing autonomous behaviour-based malware analysis and machine learning methodologies might identify malware effectively and rapidly.

Weed Detection Using Deep Learning: A Systematic Literature Review.

Weeds are one of the most harmful agricultural pests that have a significant impact on crops. Weeds are responsible for higher production costs due to crop waste and have a significant impact on the global agricultural economy. The importance of this problem has promoted the research community in exploring the use of technology to support farmers in the early detection of weeds. Artificial intelligence (AI) driven image analysis for weed detection and, in particular, machine learning (ML) and deep learning (DL) using images from crop fields have been widely used in the literature for detecting various types of weeds that grow alongside crops. In this paper, we present a systematic literature review (SLR) on current state-of-the-art DL techniques for weed detection. Our SLR identified a rapid growth in research related to weed detection using DL since 2015 and filtered 52 application papers and 8 survey papers for further analysis. The pooled results from these papers yielded 34 unique weed types detection, 16 image processing techniques, and 11 DL algorithms with 19 different variants of CNNs. Moreover, we include a literature survey on popular vanilla ML techniques (e.g., SVM, random forest) that have been widely used prior to the dominance of DL. Our study presents a detailed thematic analysis of ML/DL algorithms used for detecting the weed/crop and provides a unique contribution to the analysis and assessment of the performance of these ML/DL techniques. Our study also details the use of crops associated with weeds, such as sugar beet, which was one of the most commonly used crops in most papers for detecting various types of weeds. It also discusses the modality where RGB was most frequently used. Crop images were frequently captured using robots, drones, and cell phones. It also discusses algorithm accuracy, such as how SVM outperformed all machine learning algorithms in many cases, with the highest accuracy of 99 percent, and how CNN with its variants also performed well with the highest accuracy of 99 percent, with only VGGNet providing the lowest accuracy of 84 percent. Finally, the study will serve as a starting point for researchers who wish to undertake further research in this area.

A Survey on the Role of Industrial IoT in Manufacturing for Implementation of Smart Industry.

The Internet of Things (IoT) is an innovative technology that presents effective and attractive solutions to revolutionize various domains. Numerous solutions based on the IoT have been designed to automate industries, manufacturing units, and production houses to mitigate human involvement in hazardous operations. Owing to the large number of publications in the IoT paradigm, in particular those focusing on industrial IoT (IIoT), a comprehensive survey is significantly important to provide insights into recent developments. This survey presents the workings of the IoT-based smart industry and its major components and proposes the state-of-the-art network infrastructure, including structured layers of IIoT architecture, IIoT network topologies, protocols, and devices. Furthermore, the relationship between IoT-based industries and key technologies is analyzed, including big data storage, cloud computing, and data analytics. A detailed discussion of IIoT-based application domains, smartphone application solutions, and sensor- and device-based IIoT applications developed for the management of the smart industry is also presented. Consequently, IIoT-based security attacks and their relevant countermeasures are highlighted. By analyzing the essential components, their security risks, and available solutions, future research directions regarding the implementation of IIoT are outlined. Finally, a comprehensive discussion of open research challenges and issues related to the smart industry is also presented.

Trends in maintaining postharvest freshness and quality of Rubus berries.

Blackberries and raspberries, commonly known as Rubus berries, are commercially grown worldwide across different climates. Rubus berries contain wide array of phytochemicals, vitamins, dietary fibers, minerals, and unsaturated fatty acids. Nevertheless, these berries have short storage life which is the major constraint in their supply chains leading to higher postharvest losses. Inappropriate harvest handling, physical bruising, insect pests, and postharvest diseases lower the acceptability of fruit among consumers and other supply chain stakeholders. Additionally, the susceptibility to microbial decay, fruit softening, higher ethylene production, respiratory activity, and increased oxidation of anthocyanins, phenolics, and flavonoids considerably affects the marketability of Rubus berries at domestic and international markets. To date, several postharvest strategies such as cold storage, precooling, modified and controlled atmospheres, anti-ripening chemicals, edible coatings, biological agents, and nonchemical alternatives (heat treatment, ultrasound, irradiations, ozone) have been reported to prolong storage life, ensure food safety, and maintain the nutritional quality of Rubus berries. This review briefly encompasses multiple aspects including harvest maturity indices, regulation of fruit ripening, pre and postharvest factors affecting fruit quality, and an update on postharvest quality preservation by employing postharvest technologies to extend the storage life and maintaining the bioactive compounds in Rubus berries which are lacking in the literature. Accordingly, this review provides valuable information to the industry stakeholders and scientists offering relevant solutions, limitations in the application of certain technologies at commercial scale, highlighting research gaps, and paving the way forward for future investigations.

Unilateral developmental absence of internal carotid artery with contralateral middle cerebral artery occlusion.

PURPOSE: Developmental absence of the internal carotid artery (ICA) is a rare congenital anomaly that results from an insult to the third aortic arch or dorsal aorta during early embryogenesis. Patients are often asymptomatic and are diagnosed incidentally during imaging to investigate neurological complaints. METHODS: We report a rare finding of an absent ICA during a workup of stroke in a middle-aged patient. RESULTS: CT brain perfusion (CTP) and CT angiography (CTA) revealed the right middle cerebral artery (MCA) thrombotic stroke with the demonstration of contralateral left ICA absence. The patient showed spontaneous recovery, and no thrombolysis or neurointervention was considered. CONCLUSIONS: This article highlights the importance of screening the head CT on bone window settings in case of the non-visualization of ICA to differentiate congenital absence from a steno-occlusive disease. It also illustrates the role of Magnetic resonance imaging (MRI) and MR Angiography (MRA) in demonstrating further possible vascular anomalies, structural brain malformations, and collateral circulation.

A Conceptual Multi-Layer Framework for the Detection of Nighttime Pedestrian in Autonomous Vehicles Using Deep Reinforcement Learning.

The major challenge faced by autonomous vehicles today is driving through busy roads without getting into an accident, especially with a pedestrian. To avoid collision with pedestrians, the vehicle requires the ability to communicate with a pedestrian to understand their actions. The most challenging task in research on computer vision is to detect pedestrian activities, especially at nighttime. The Advanced Driver-Assistance Systems (ADAS) has been developed for driving and parking support for vehicles to visualize sense, send and receive information from the environment but it lacks to detect nighttime pedestrian actions. This article proposes a framework based on Deep Reinforcement Learning (DRL) using Scale Invariant Faster Region-based Convolutional Neural Networks (SIFRCNN) technologies to efficiently detect pedestrian operations through which the vehicle, as agents train themselves from the environment and are forced to maximize the reward. The SIFRCNN has reduced the running time of detecting pedestrian operations from road images by incorporating Region Proposal Network (RPN) computation. Furthermore, we have used Reinforcement Learning (RL) for optimizing the Q-values and training itself to maximize the reward after getting the state from the SIFRCNN. In addition, the latest incarnation of SIFRCNN achieves near-real-time object detection from road images. The proposed SIFRCNN has been tested on KAIST, City Person, and Caltech datasets. The experimental results show an average improvement of 2.3% miss rate of pedestrian detection at nighttime compared to the other CNN-based pedestrian detectors.

Advances in database systems education: Methods, tools, curricula, and way forward.

Fundamentals of Database Systems is a core course in computing disciplines as almost all small, medium, large, or enterprise systems essentially require data storage component. Database System Education (DSE) provides the foundation as well as advanced concepts in the area of data modeling and its implementation. The first course in DSE holds a pivotal role in developing students' interest in this area. Over the years, the researchers have devised several different tools and methods to teach this course effectively, and have also been revisiting the curricula for database systems education. In this study a Systematic Literature Review (SLR) is presented that distills the existing literature pertaining to the DSE to discuss these three perspectives for the first course in database systems. Whereby, this SLR also discusses how the developed teaching and learning assistant tools, teaching and assessment methods and database curricula have evolved over the years due to rapid change in database technology. To this end, more than 65 articles related to DSE published between 1995 and 2022 have been shortlisted through a structured mechanism and have been reviewed to find the answers of the aforementioned objectives. The article also provides useful guidelines to the instructors, and discusses ideas to extend this research from several perspectives. To the best of our knowledge, this is the first research work that presents a broader review about the research conducted in the area of DSE.

Human immunodeficiency virus: The potential of medicinal plants as antiretroviral therapy.

Human immunodeficiency virus (HIV) infection is fast becoming widespread in the world with 37.7 million people living with HIV in 2020. Antiretroviral therapy involving chemical drugs has declined acquired immunodeficiency syndrome (AIDS)-related mortality and improved the life quality of AIDS/HIV sufferers. However, the emergence of drug resistance and side effects are the main obstacles for the long-term use of these chemicals as antiretroviral therapy. Recently, a lot of emphasis is being put on finding naturally occurring drug candidates that show activity against HIV and can be potentially used as antiretroviral therapy. In this study, different medicinal plants, Pistacia khinjuk, Teucrium stocksianum, Uncaria tomentosa, Pistacia integerrima, Trigonella gharuensis, and Artocarpus lakoocha, were explored for their anti-HIV potential. Syncytium and p24 assays were performed to determine antiviral activity, while the MTT assay was used to determine cytotoxicity. Results showed that extracts from all six plants inhibited HIV replication in vitro. Also, extracts from Pistacia khinjuk, Teucrium stocksianum, Uncaria tomentosa, and Pistacia integerrima showed low cytotoxicity with a 50% cytotoxicity concentration value of >200 µM. Results of this study indicate that there is potential in these natural extracts to become candidate drugs to be used as complementary and alternative medicine for HIV infection.