Automated deep bottleneck residual 82-layered architecture with Bayesian optimization for the classification of brain and common maternal fetal ultrasound planes

Despite a worldwide decline in maternal mortality over the past two decades, a significant gap persists between low- and high-income countries, with 94% of maternal mortality concentrated in low and middle-income nations. Ultrasound serves as a prevalent diagnostic tool in prenatal care for monitoring fetal growth and development. Nevertheless, acquiring standard fetal ultrasound planes with accurate anatomical structures proves challenging and time-intensive, even for skilled sonographers. Therefore, for determining common maternal fetuses from ultrasound images, an automated computer-aided diagnostic (CAD) system is required. A new residual bottleneck mechanism-based deep learning architecture has been proposed that includes 82 layers deep. The proposed architecture has added three residual blocks, each including two highway paths and one skip connection. In addition, a convolutional layer has been added of size 3 × 3 before each residual block. In the training process, several hyper parameters have been initialized using Bayesian optimization (BO) rather than manual initialization. Deep features are extracted from the average pooling layer and performed the classification. In the classification process, an increase occurred in the computational time; therefore, we proposed an improved search-based moth flame optimization algorithm for optimal feature selection. The data is then classified using neural network classifiers based on the selected features. The experimental phase involved the analysis of ultrasound images, specifically focusing on fetal brain and common maternal fetal images. The proposed method achieved 78.5% and 79.4% accuracy for brain fetal planes and common maternal fetal planes. Comparison with several pre-trained neural nets and state-of-the-art (SOTA) optimization algorithms shows improved accuracy.

Novel approach for quantitative and qualitative authors research profiling using feature fusion and tree-based learning approach

Article citation creates a link between the cited and citing articles and is used as a basis for several parameters like author and journal impact factor, H-index, i10 index, etc., for scientific achievements. Citations also include self-citation which refers to article citation by the author himself. Self-citation is important to evaluate an author's research profile and has gained popularity recently. Although different criteria are found in the literature regarding appropriate self-citation, self-citation does have a huge impact on a researcher's scientific profile. This study carries out two cases in this regard. In case 1, the qualitative aspect of the author's profile is analyzed using hand-crafted feature engineering techniques. The sentiments conveyed through citations are integral in assessing research quality, as they can signify appreciation, critique, or serve as a foundation for further research. Analyzing sentiments within in-text citations remains a formidable challenge, even with the utilization of automated sentiment annotations. For this purpose, this study employs machine learning models using term frequency (TF) and term frequency-inverse document frequency (TF-IDF). Random forest using TF with Synthetic Minority Oversampling Technique (SMOTE) achieved a 0.9727 score of accuracy. Case 2 deals with quantitative analysis and investigates direct and indirect self-citation. In this study, the top 2% of researchers in 2020 is considered as a baseline. For this purpose, the data of the top 25 Pakistani researchers are manually retrieved from this dataset, in addition to the citation information from the Web of Science (WoS). The self-citation is estimated using the proposed model and results are compared with those obtained from WoS. Experimental results show a substantial difference between the two, as the ratio of self-citation from the proposed approach is higher than WoS. It is observed that the citations from the WoS for authors are overstated. For a comprehensive evaluation of the researcher's profile, both direct and indirect self-citation must be included.

Heart failure patients monitoring using IoT-based remote monitoring system

Intelligent health monitoring systems are becoming more important and popular as technology advances. Nowadays, online services are replacing physical infrastructure in several domains including medical services as well. The COVID-19 pandemic has also changed the way medical services are delivered. Intelligent appliances, smart homes, and smart medical systems are some of the emerging concepts. The Internet of Things (IoT) has changed the way communication occurs alongside data collection sources aided by smart sensors. It also has deployed artificial intelligence (AI) methods for better decision-making provided by efficient data collection, storage, retrieval, and data management. This research employs health monitoring systems for heart patients using IoT and AI-based solutions. Activities of heart patients are monitored and reported using the IoT system. For heart disease prediction, an ensemble model ET-CNN is presented which provides an accuracy score of 0.9524. The investigative data related to this system is very encouraging in real-time reporting and classifying heart patients with great accuracy.

Low-Latency Federated Learning via Dynamic Model Partitioning for Healthcare IoT

Federated learning (FL) is receiving much attention in the Healthcare Internet of Things (H-IoT) to support various instantaneous E-health services. Today, the deployment of FL suffers from several challenges, such as high training latency and data privacy leakage risks, especially for resource-constrained medical devices. In this article, we develop a three-layer FL architecture to decrease training latency by introducing split learning into FL. We formulate a long-term optimization problem to minimize the local model training latency while preserving the privacy of the original medical data in H-IoT. Specially, a Privacy-ware Model Partitioning Algorithm (PMPA) is proposed to solve the formulated problem based on the Lyapunov optimization theory. In PMPA, the local model is partitioned properly between a resource-constrained medical end device and an edge server, which meets privacy requirements and energy consumption constraints. The proposed PMPA is separated into two phases. In the first phase, a partition point set is obtained using Kullback-Leibler (KL) divergence to meet the privacy requirement. In the second phase, we employ the model partitioning function, derived through Lyapunov optimization, to select the partition point from the partition point set that that satisfies the energy consumption constraints. Simulation results show that compared with traditional FL, the proposed algorithm can significantly reduce the local training latency. Moreover, the proposed algorithm improves the efficiency of medical image classification while ensuring medical data security.

Detection of Android Malware in the Internet of Things through the K-Nearest Neighbor Algorithm

Predicting attacks in Android malware devices using machine learning for recommender systems-based IoT can be a challenging task. However, it is possible to use various machine-learning techniques to achieve this goal. An internet-based framework is used to predict and recommend Android malware on IoT devices. As the prevalence of Android devices grows, the malware creates new viruses on a regular basis, posing a threat to the central system's security and the privacy of the users. The suggested system uses static analysis to predict the malware in Android apps used by consumer devices. The training of the presented system is used to predict and recommend malicious devices to block them from transmitting the data to the cloud server. By taking into account various machine-learning methods, feature selection is performed and the K-Nearest Neighbor (KNN) machine-learning model is proposed. Testing was carried out on more than 10,000 Android applications to check malicious nodes and recommend that the cloud server block them. The developed model contemplated all four machine-learning algorithms in parallel, i.e., naive Bayes, decision tree, support vector machine, and the K-Nearest Neighbor approach and static analysis as a feature subset selection algorithm, and it achieved the highest prediction rate of 93% to predict the malware in real-world applications of consumer devices to minimize the utilization of energy. The experimental results show that KNN achieves 93%, 95%, 90%, and 92% accuracy, precision, recall and f1 measures, respectively.

Millimeter-wave channel modeling in a VANETs using coding techniques

The Vehicular ad-Hoc Network (VANET) is envisioned to ensure wireless transmission with ultra-high reliability. In the presence of fading and mobility of vehicles, error-free information between Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) requires extensive investigation. The current literature lacks in designing an ultra-reliable comprehensive tractable model for VANET using millimeter wave. Ultra-reliable communication is needed to support autonomous vehicular communication. This article aims to provide a comprehensive tractable model for VANET over millimeter waves using Space-Time-Block-Coding (STBC) concatenated with Reed Solomon (RS) coding. The designed model provides the fastest way of designing and analyzing VANET networks on 60 GHz. By using the derived BER expressions and Reed Solomon coded doppler expression ultra-reliable vehicular networks can be build meeting the demands of massive growing volume of traffic. The performance of the model is compared with previous BER computational techniques and existing VANET communication systems, i.e., IEEE 802.11bd and 3rd generation partnership project vehicle to everything (3GPP V2X). The findings show that our proposed approach outperforms IEEE 802.11bd and the results are comparable with V2X NR. Packet Error Rate (PER), Packet Reception Ratio (PRR) and throughput are used as performance metrics. We have also evaluated the model on higher velocities of vehicles. Further, the simulation and numerical findings show that the proposed system surpass the existing BER results comprising of various modulation and coding techniques. The simulation results are verified by the numerical results there-by, showing the accuracy of our derived expressions.

A Critical Cybersecurity Analysis and Future Research Directions for the Internet of Things: A Comprehensive Review

The emergence of the Internet of Things (IoT) technology has brought about tremendous possibilities, but at the same time, it has opened up new vulnerabilities and attack vectors that could compromise the confidentiality, integrity, and availability of connected systems. Developing a secure IoT ecosystem is a daunting challenge that requires a systematic and holistic approach to identify and mitigate potential security threats. Cybersecurity research considerations play a critical role in this regard, as they provide the foundation for designing and implementing security measures that can address emerging risks. To achieve a secure IoT ecosystem, scientists and engineers must first define rigorous security specifications that serve as the foundation for developing secure devices, chipsets, and networks. Developing such specifications requires an interdisciplinary approach that involves multiple stakeholders, including cybersecurity experts, network architects, system designers, and domain experts. The primary challenge in IoT security is ensuring the system can defend against both known and unknown attacks. To date, the IoT research community has identified several key security concerns related to the architecture of IoT systems. These concerns include issues related to connectivity, communication, and management protocols. This research paper provides an all-inclusive and lucid review of the current state of anomalies and security concepts related to the IoT. We classify and analyze prevalent security distresses regarding IoT's layered architecture, including connectivity, communication, and management protocols. We establish the foundation of IoT security by examining the current attacks, threats, and cutting-edge solutions. Furthermore, we set security goals that will serve as the benchmark for assessing whether a solution satisfies the specific IoT use cases.

Chest X ray and cough sample based deep learning framework for accurate diagnosis of COVID-19

All witnessed the terrible effects of the COVID-19 pandemic on the health and work lives of the population across the world. It is hard to diagnose all infected people in real time since the conventional medical diagnosis of COVID-19 patients takes a couple of days for accurate diagnosis results. In this paper, a novel learning framework is proposed for the early diagnosis of COVID-19 patients using hybrid deep fusion learning models. The proposed framework performs early classification of patients based on collected samples of chest X-ray images and Coswara cough (sound) samples of possibly infected people. The captured cough samples are pre-processed using speech signal processing techniques and Mel frequency cepstral coefficient features are extracted using deep convolutional neural networks. Finally, the proposed system fuses extracted features to provide 98.70% and 82.7% based on Chest-X ray images and cough (audio) samples for early diagnosis using the weighted sum-rule fusion method.

Reusable Component Retrieval: A Semantic Search Approach for Low Resource Languages

Abstract: A common practice among programmers is to reuse existing code, accomplished by performing natural language queries through search engines. The main aim of code retrieval is to search for the most relevant snippet from a corpus of code snippets but unfortunately, code retrieval frameworks for low resource languages are insufficient. Retrieving the most relevant code snippet efficiently can only be accomplished by eliminating the semantic gap between the code snippets residing in the repository and the user’s query (natural language description). The primary objective of the research is to contribute to this field by providing a code search framework that can be extended for low resource languages. Secondly, to give a code retrieval mechanism that is semantically relevant to the user query and provide programmers with the ability to locate source code that they want to use when developing new applications. The proposed approach is implemented using a web platform to search for source code. As code retrieval is a sophisticated task, the proposed approach incorporates a semantic search mechanism. This research uses a semantic model for code retrieval, which generates meanings or synonyms of words. The proposed model integrates ontologies and Natural Language Processing. System performance measures and classification accuracy are computed using precision, recall, and F1-score. We also compare the proposed approach with state-of-the-art baseline models. The retrieved results are ranked, showing that our approach significantly outperforms robust code matching. Our evaluation shows that semantic matching leads to improved source code retrieval. This study marks a substantial advancement in integrating programming expertise with code retrieval techniques. Moreover, our system lets users know when and how it is used for successful semantic searching.

Conditional Anonymous Remote Healthcare Data Sharing Over Blockchain

As an important carrier of healthcare data, Electronic Medical Records (EMRs) generated from various sensors, i.e., wearable, implantable, are extremely valuable research materials for artificial intelligence and machine learning. The efficient circulation of EMRs can improve remote medical services and promote the development of the related healthcare industry. However, in traditional centralized data sharing architectures, the balance between privacy and traceability still cannot be well handled. To address the issue that malicious users cannot be locked in the fully anonymous sharing schemes, we propose a trackable anonymous remote healthcare data storing and sharing scheme over decentralized consortium blockchain. Through an "on-chain & off-chain" model, it relieves the massive data storage pressure of medical blockchain. By introducing an improved proxy re-encryption mechanism, the proposed scheme realizes the fine-gained access control of the outsourced data, and can also prevent the collusion between semi-trusted cloud servers and data requestors who try to reveal EMRs without authorization. Compared with the existing schemes, our solution can provide a lower computational overhead in repeated EMRs sharing, resulting in a more efficient overall performance.

Distance Based Pattern Driven Mining for Outlier Detection in High Dimensional Big Dataset

Detection of outliers or anomalies is one of the vital issues in pattern-driven data mining. Outlier detection detects the inconsistent behavior of individual objects. It is an important sector in the data mining field with several different applications such as detecting credit card fraud, hacking discovery and discovering criminal activities. It is necessary to develop tools used to uncover the critical information established in the extensive data. This paper investigated a novel method for detecting cluster outliers in a multidimensional dataset, capable of identifying the clusters and outliers for datasets containing noise. The proposed method can detect the groups and outliers left by the clustering process, like instant irregular sets of clusters (C) and outliers (O), to boost the results. The results obtained after applying the algorithm to the dataset improved in terms of several parameters. For the comparative analysis, the accurate average value and the recall value parameters are computed. The accurate average value is 74.05% of the existing COID algorithm, and our proposed algorithm has 77.21%. The average recall value is 81.19% and 89.51% of the existing and proposed algorithm, which shows that the proposed work efficiency is better than the existing COID algorithm.

AI-Driven Synthetic Biology for Non-Small Cell Lung Cancer Drug Effectiveness-Cost Analysis in Intelligent Assisted Medical Systems

According to statistics, in the 185 countries' 36 types of cancer, the morbidity and mortality of lung cancer take the first place, and non-small cell lung cancer (NSCLC) accounts for 85% of lung cancer [1-2]. Significantly in many developing countries, limited medical resources and excess population seriously affect the diagnosis and treatment of lung cancer patients. The 21st century is an era of life medicine, big data, and information technology. Synthetic biology is known as the driving force of natural product innovation and research in this era. Based on the research of NSCLC targeted drugs, through the cross-fusion of synthetic biology and artificial intelligence, using the idea of bioengineering, we construct an artificial intelligence assisted medical system and propose a drug selection framework for the personalized selection of NSCLC patients. Under the premise of ensuring the efficacy, considering the economic cost of targeted drugs as an auxiliary decision-making factor, the system predicts the drug effectiveness-cost then. The experiment shows that our method can rely on the provided clinical data to screen drug treatment programs suitable for the patient's conditions and assist doctors in making an efficient diagnosis.

Analyzing fibrous tissue pattern in fibrous dysplasia bone images using deep R-CNN networks for segmentation

Predictive health monitoring systems help to detect human health threats in the early stage. Evolving deep learning techniques in medical image analysis results in efficient feedback in quick time. Fibrous dysplasia (FD) is a genetic disorder, triggered by the mutation in Guanine Nucleotide binding protein with alpha stimulatory activities in the human bone genesis. It slowly occupies the bone marrow and converts the bone cell into fibrous tissues. It weakens the bone structure and leads to permanent disability. This paper proposes the study of FD bone image analyzing techniques with deep networks. Also, the linear regression model is annotated for predicting the bone abnormality levels with observed coefficients. Modern image processing begins with various image filters. It describes the edges, shades, texture values of the receptive field. Different types of segmentation and edge detection mechanisms are applied to locate the tumor, lesion, and fibrous tissues in the bone image. Extract the fibrous region in the bone image using the region-based convolutional neural network algorithm. The segmented results are compared with their accuracy metrics. The segmentation loss is reduced by each iteration. The overall loss is 0.24% and the accuracy is 99%, segmenting the masked region produces 98% of accuracy, and building the bounding boxes is 99% of accuracy.

ResMem-Net: memory based deep CNN for image memorability estimation

Image memorability is a very hard problem in image processing due to its subjective nature. But due to the introduction of Deep Learning and the large availability of data and GPUs, great strides have been made in predicting the memorability of an image. In this paper, we propose a novel deep learning architecture called ResMem-Net that is a hybrid of LSTM and CNN that uses information from the hidden layers of the CNN to compute the memorability score of an image. The intermediate layers are important for predicting the output because they contain information about the intrinsic properties of the image. The proposed architecture automatically learns visual emotions and saliency, shown by the heatmaps generated using the GradRAM technique. We have also used the heatmaps and results to analyze and answer one of the most important questions in image memorability: "What makes an image memorable?". The model is trained and evaluated using the publicly available Large-scale Image Memorability dataset (LaMem) from MIT. The results show that the model achieves a rank correlation of 0.679 and a mean squared error of 0.011, which is better than the current state-of-the-art models and is close to human consistency (p = 0.68). The proposed architecture also has a significantly low number of parameters compared to the state-of-the-art architecture, making it memory efficient and suitable for production.

Toward real-time and efficient cardiovascular monitoring for COVID-19 patients by 5G-enabled wearable medical devices: a deep learning approach

Patients with deaths from COVID-19 often have co-morbid cardiovascular disease. Real-time cardiovascular disease monitoring based on wearable medical devices may effectively reduce COVID-19 mortality rates. However, due to technical limitations, there are three main issues. First, the traditional wireless communication technology for wearable medical devices is difficult to satisfy the real-time requirements fully. Second, current monitoring platforms lack efficient streaming data processing mechanisms to cope with the large amount of cardiovascular data generated in real time. Third, the diagnosis of the monitoring platform is usually manual, which is challenging to ensure that enough doctors online to provide a timely, efficient, and accurate diagnosis. To address these issues, this paper proposes a 5G-enabled real-time cardiovascular monitoring system for COVID-19 patients using deep learning. Firstly, we employ 5G to send and receive data from wearable medical devices. Secondly, Flink streaming data processing framework is applied to access electrocardiogram data. Finally, we use convolutional neural networks and long short-term memory networks model to obtain automatically predict the COVID-19 patient's cardiovascular health. Theoretical analysis and experimental results show that our proposal can well solve the above issues and improve the prediction accuracy of cardiovascular disease to 99.29%.

Edge Computing to Secure IoT Data Ownership and Trade with the Ethereum Blockchain

With an increasing penetration of ubiquitous connectivity, the amount of data describing the actions of end-users has been increasing dramatically, both within the domain of the Internet of Things (IoT) and other smart devices. This has led to more awareness of users in terms of protecting personal data. Within the IoT, there is a growing number of peer-to-peer (P2P) transactions, increasing the exposure to security vulnerabilities, and the risk of cyberattacks. Blockchain technology has been explored as middleware in P2P transactions, but existing solutions have mainly focused on providing a safe environment for data trade without considering potential changes in interaction topologies. we present EdgeBoT, a proof-of-concept smart contracts based platform for the IoT built on top of the ethereum blockchain. With the Blockchain of Things (BoT) at the edge of the network, EdgeBoT enables a wider variety of interaction topologies between nodes in the network and external services while guaranteeing ownership of data and end users’ privacy. in EdgeBoT, edge devices trade their data directly with third parties and without the need of intermediaries. This opens the door to new interaction modalities, in which data producers at the edge grant access to batches of their data to different third parties. Leveraging the immutability properties of blockchains, together with the distributed nature of smart contracts, data owners can audit and are aware of all transactions that have occurred with their data. we report initial results demonstrating the potential of EdgeBoT within the IoT. we show that integrating our solutions on top of existing IoT systems has a relatively small footprint in terms of computational resource usage, but a significant impact on the protection of data ownership and management of data trade.

COVID-19 Patient Health Prediction Using Boosted Random Forest Algorithm

Integration of artificial intelligence (AI) techniques in wireless infrastructure, real-time collection, and processing of end-user devices is now in high demand. It is now superlative to use AI to detect and predict pandemics of a colossal nature. The Coronavirus disease 2019 (COVID-19) pandemic, which originated in Wuhan China, has had disastrous effects on the global community and has overburdened advanced healthcare systems throughout the world. Globally; over 4,063,525 confirmed cases and 282,244 deaths have been recorded as of 11th May 2020, according to the European Centre for Disease Prevention and Control agency. However, the current rapid and exponential rise in the number of patients has necessitated efficient and quick prediction of the possible outcome of an infected patient for appropriate treatment using AI techniques. This paper proposes a fine-tuned Random Forest model boosted by the AdaBoost algorithm. The model uses the COVID-19 patient's geographical, travel, health, and demographic data to predict the severity of the case and the possible outcome, recovery, or death. The model has an accuracy of 94% and a F1 Score of 0.86 on the dataset used. The data analysis reveals a positive correlation between patients' gender and deaths, and also indicates that the majority of patients are aged between 20 and 70 years.

The effect of a strongly basic alkaloidal fraction of Rhazya stricta, a traditional medicinal plant, on cytochrome P450-mediated metabolism of theophylline in mice

The strongly basic alkaloidal fraction of the traditional medicinal plant Rhazya stricta (RS) was given orally to mice, in a single dose of 10 mg/kg (group 1) or, twice daily for 3 days at the same dose (group 2). A third group (control) received normal saline. Liver homogenates from all animals were used to assess the microsomal activity of cytochrome P450 and its isoforms as well as its catalytic activity (using theophylline as a substrate). RS alkaloidal fraction had no significant effect on the total amount of microsomal cytochrome P450, but it caused a significant increase in the cytochrome P450 isoforms CYPs 1A1 and 1A2. It also significantly increased the concentrations of some metabolites of theophylline. These results suggest that RS has the potential to interact with other drugs that are biotransformed by cytochrome P450, when given concomitantly with it.

Central nervous system activity of Leucas inflata Benth. in mice

The analgesic activity of the methanol and acetone extracts of Leucas inflata L. (family Labiatae) was evaluated in mice using different experimental models. The effect of the two extracts on pentobarbitone-sleeping time, motor activity, sensorimotor coordination, carrageen induced inflammation, and brewer's yeast-induced pyrexia has also been investigated. The two crude extracts have been phytochemically analyzed and some constituents isolated and characterized. These included stigmasterols, a chromone and coumarins. Extracts of L. inflata L., given at single oral doses of 0.25, 0.5, 1.0 or 2.0 g/kg, significantly and dose-dependently, reduced formalin-induced pain, acetic acid induced abdominal constrictions and increased the reaction time in the hot-plate test. Both extracts caused significant and dose-related impairment in the sensorimotor control and ambulatory and total motor activity of treated mice. Both extracts exhibited anti-inflammatory action by reducing paw edema of treated mice. The extracts did not significantly affect the rectal temperature of normothermic mice. However, they were effective in preventing Brewers yeast induced pyrexia. It is concluded that the crude methanol and acetone extract of L. inflata has CNS depressant properties, manifested as antinociception and sedation. Both extracts have anti-inflammatory and antipyretic actions.

Some effects of Salvia aegyptiaca L. on the central nervous system in mice

Salvia aegyptiaca L. is used for treating various unrelated conditions that include nervous disorders, dizziness, trembling, diarrhoea and piles. This work examines some effects of the crude acetone and methanol extracts of the plant given at single oral doses of 0.25, 0.5, 1 or 2 g/kg, on the central nervous system (CNS) in mice. The extracts were also tested for anti-inflammatory and antipyretic actions. Several models of nociception have been used to examine the analgesic effect of the extract. In treated mice, the extracts caused dose-related inhibition of acetic acid-induced abdominal constriction, and significantly reduced formalin-induced pain. Treatment with the extracts at doses of 0.5 and 1 g/kg significantly increased the reaction time in the hot-plate test. In treated mice both extracts caused significant and dose-related impairment of the sensorimotor control and motor activity. Treatment with both extracts did not significantly affect the rectal temperature of normothermic mice. The methanol extract (0.5 and 1.0 g/kg) did not affect the rectal temperature of hyperthermic mice, but the acetone extract was effective in significantly reducing the rectal temperature of hyperthermic mice, 0.5 and 1 h after administration of the extract at doses of 0.25-2 g/kg. It is concluded that the crude methanol and acetone extracts of S. aegyptiaca have CNS depressant properties, manifested as antinociception and sedation. Both extracts have some anti-inflammatory and antipyretic actions. On the whole, the acetone extract appeared to be slightly more effective than the methanol extract in this regard.

Comparative pharmacokinetics of theophylline in camels (Camelus dromedarius) and goats (Caprus hircus)

A comparative randomized crossover study was conducted to determine the pharmacokinetics of theophylline in male and female camels (Camelus dromedarius) and goats (Caprus hircus). Theophylline is an established 'probe drug' to evaluate the drug metabolizing enzyme activity of animals. It was administered by the intravenous (i.v.) route and then intramuscularly (i.m.) at a dose of 2 mg/kg. The concentration of the drug in plasma was measured using a high-performance liquid chromatography (HPLC) technique on samples collected at frequent intervals after administration. Following i.v. injection, the overall elimination rate constant (lambda z,) in goats was 0.006 +/- 0.00076/min and in camels was 0.0046 +/- 0.0008/min (P < 0.01). The elimination half-life (t 1/2 lambda z) in goats (112 .7 min) was lower than in camels (154.7 min) (P < 0.01). The apparent volume of distribution (Vz) and the total body clearance (Cl) in goats were 1440.1 +/- 166.6 ml/kg and 8.9 +/- 1.4 ml/min/kg, respectively. The corresponding values in camels were 1720.3 +/- 345.3 ml/kg and 6.1 +/- 1.0 ml/min/kg, respectively. After i.m. administration, theophylline reached a peak plasma concentration (Cmax) of 1.8 +/- 0.1 and 1.7 +/- 0.2 microg/ml at a post-injection time (Tmax) of 67.5 +/- 8.6 and 122.3 +/- 6.7 min in goats and camels, respectively. The mean bioavailability (T) in both goats and camels was 0.9 +/- 0.2. The above data suggest that camels eliminate theophylline at a slower rate than goats.

Effect of the traditional medicinal plants Rhazya stricta, Balanitis aegyptiaca and Haplophylum tuberculatum on paracetamol-induced hepatotoxicity in mice

This work examines the effects of lyophilized extracts of the medicinal plants Rhazya stricta, Balanites aegyptiaca and Haplophylum tuberculatum on liver damage induced by paracetamol in mice. Rapid HPLC finger prints for some of these extracts were made. The hepatoprotective effects of the plant extracts were compared with that of the standard hepatoprotective agent silymarin. The extracts (1 g/kg) and silymarin (0.1 g/kg) were given orally for 5 consecutive days. On the last day of treatment a hepatotoxic oral dose of paracetamol (0.6 g/kg) was given, and 3 h later, the hepatic function of mice was evaluated using pentobarbitone -induced sleeping time, the concentration of reduced glutathione (GSH) in liver, and the activities of aspartate aminotransferase (AST), alanine aminotransferase (ALT), gamma-glutamyl transferase (GGT) and cholesterol concentration in plasma. The livers were weighed and examined for macro- and microscopic changes. Pretreatment with R. stricta or with silymarin protected the livers of treated mice against paracetamol hepatotoxicity as evidenced by a significant improvement of the above liver function tests. B. Aegyptiaca had a relatively modest hepatoprotective activity, while H. tuberculatum was almost ineffective. Oral pretreatment of mice for 5 consecutive days with an extract of R. stricta or silymarin protected about 57% and 92% of the treated mice, respectively, against the lethal effect of paracetamol (1 g/kg). B. aegyptiaca and H. tuberculatum protected only 27% and 16% of the animals, respectively.

Effect of Rhazya stricta Decne on monoamine oxidase and cholinesterase activity and brain biogenic amine levels in rats

The effect of treatment with the medicinal plant Rhayva stricta Decne, on monoamine oxidase (MAO) and cholinesterase activity, and on the concentration of brain biogenic amines was studied in rats. R. stricta extract, at doses of 0.2 and 0.5 g kg(-1), significantly (P < 0.05-0.01) increased the hepatic and cerebral activity of MAO by 36-127%. The higher doses used (2.0 and 8.0 g kg(-1)) produced smaller (10-26%) and statistically insignificant increases in MAO activity in liver and brain. Cholinesterase activity in blood, liver and brain was not significantly influenced by treatment with R. stricta. The concentrations of the measured biogenic amines (noradrenaline, adrenaline, 5-hydroxytryptamine and dopamine) were significantly lowered in rats treated with R. stricta. The observed increase in MAO activity may be responsible for the lowered biogenic amines levels and may, in part, be responsible for the pharmacological effects of R. stricta extract in rats.

Antioxidant action of extract of the traditional medicinal plant Rhazya stricta Decne. in rats

The effects of a leaf extract of the traditional medicinal plant Rhazya stricta (0.25, 1.0 and 4.0 g/kg/day for 3 days) on reduced glutathione (GSH), lipid peroxidation (LP) and ascorbic acid (AA) concentrations in the liver and kidneys were studied in rats 24 h after the last dose. The plant extract, at a dose of 0.25 g/kg, did not significantly affect the concentrations of GSH, LP or AA in the liver or kidneys. At a dose of 1.0 g/kg, the plant extract significantly increased the GSH concentration in the liver, but did not affect the GSH concentration in the kidneys, or LP or AA in the liver or kidneys. The plant extract (4.0 g/kg) significantly increased the GSH and decreased LP peroxidation, but did not affect the AA concentrations in the liver and kidneys. It may be concluded that the R. stricta extract, at some of the doses used, has antioxidant actions in the rat.

Erratum. H.M. Shaheen, B.H. Ali, A.A. Alqarawi and A.K. Bashir. 'Effect Of psidium guajava leaves on some aspects of the central nervous system in mice'. Phytotherapy research 14(2) 2000, 107-111

The original article to which this Erratum refers was published in Phytotherapy Research 14(2) 2000, 107-111. Following publication of this paper in the March 2000 issue of Phytotherapy Research (14(2), 107-111), it has come to our attention that Figure 3 was printed incorrectly. The correct Figure 3 appears below. The publishers would like to apologise for any confusion caused.

Phytochemistry, pharmacology and toxicity of Rhazya stricta decne: a review

Phytochemical, pharmacological and toxicological properties of the medicinal plant Rhazya stricta Decne. are reviewed. Several types of alkaloids and a few flavonoids have been isolated and their structures and stereochemistry characterized. However, in most cases the biological activity of these compounds has not been studied. Most of the pharmacological activity of the plant resides in its alkaloidal fractions which cause depression of the central nervous system and hypotension. Extracts of R. stricta appear to have low toxicity, although its use in pregnant women may be inadvisable.

Mechanism of the hypotensive action of Rhazya stricta leaf extract in rats

The hypotensive action of Rhazya stricta lyophilized leaf extract was found to be partly caused by the electrolyte content of the extract, and partly caused by a strongly basic alkaloidal fraction (AF). AF (0.05-1.6 mg animal(-1)) caused a dose-dependent reduction in mean arterial blood pressure (MAP) of urethane-anaesthetized rat preparations. In naiuml;ve pithed rats, AF administration (0.5-2.0 mg animal(-1)) significantly increased MAP. In pithed or spinalized rats made normotensive by noradrenaline infusion, AF (0.25 mg animal(-1)) did not cause any significant changes. Direct intracerebroventricular injection of AF (0.1-0.4 mg) markedly and significantly reduced MAP. It is suggested that the hypotensive action of AF to be mediated by a central mechanism.

Effect of Psidium guajava leaves on some aspects of the central nervous system in mice

The present work examines the effects of hexane, ethyl acetate and methanol extracts of Psidium guajava leaves (20,100,500 and 1250 mg/kg) on the central nervous system in mice. The three extracts exhibited mostly dose-dependent antinociceptive effects in chemical and thermal tests of analgesia. The extracts also produced dose-dependent prolongation of pentobarbitone-induced sleeping time. However, they had variable and mostly non-significant effects on locomotor coordination, locomotor activity or exploration. In the pharmacological tests used, the ethyl acetate extract seemed to be the most active, followed by the hexane and then the methanol extracts.

The effect of Rhazya stricta Decne, a traditional medicinal plant, on spontaneous and drug-induced alterations in activity of rats

The effect of acute and chronic treatment of rats with a lyophilized extract of the leaves of the medicinal plant Rhazya stricta on total and ambulatory activity was studied. Given acutely at single oral doses of 1, 2, 4, and 8 g/kg, the extract produced dose-dependent decreases in total activity and ambulatory activity. Diazepam (20 mg/kg, orally) produced a decrease in rat activity comparable to that produced by a dose of 1 g/kg of the extract. When given daily at an oral dose of 2 g/kg for 21 consecutive days, the extract produced, on the last day of treatment, significant decrease in activity amounting to about 30% of control activity levels. Subcutaneous (SC) treatment of rats with caffeine (7.5, 15, and 30 mg/kg), dose-dependently and significantly increased total activity and ambulatory activity. These effects were dose-dependently attenuated when the extract was given concomitantly with caffeine at oral doses of 1, 2, and 4 mg/kg. Treatment of rats with zoxazolamine alone (10, 20, or 40 mg/kg, SC) or R. Stricta (1 and 4 g/kg orally) alone significantly decreased total and ambulatory activities. Concomitant treatment with zoxazolamine and R. Stricta decreased the rats activity to a greater degree than with either treatment given alone.

The effect of gentamicin on acetylsalicylic acid-induced platelet antiaggregatory action in mouse pial arterioles

Gentamicin (G) treatment (5, 20, 40 and 80 mg kg[-1] day[-1] given intramuscularly for 6 days) was shown to cause a dose-related platelet proaggregatory effect in mouse pial microcirculation. This was associated with a reduction in mouse renal function, indicated by high plasma creatinine and urea concentrations. When G was given at the same doses but as a single injection, it caused no change in renal function or platelet aggregation. Gentamicin (20 and 80 mg kg/day, given intramuscularly for 6 days) significantly (P < 0.05) impeded the platelet antiaggregatory effect of acetylsalicylic acid (100 mg kg[-1], intraperitoneally).

Effect of extract of Rhazya stricta, a traditional medicinal plant, on rat brain tribulin

Rhazya stricta leaves, which have both antidepressant and sedative properties in animal models, are widely used in folk medicine in the Arabian peninsula. In this study, the effects of oral administration of leaf extracts on rat brain tribulin levels [endogenous monoamine oxidase (MAO) A and B inhibitory activity], were determined. In an acute study, low doses brought about an increase in MAO A inhibitory activity, while intermediate doses caused a significant reduction. The highest doses had no significant effects on activity. There were no significant effects on MAO B inhibitory activity at any dose. Subchronic administration (21 days) caused a significant decrease in MAO A inhibitory activity, most prominent at low dosage, and an increase in MAO B inhibitory activity. Acute intramuscular administration also resulted in a similar pattern. Such paradoxical effects were at least partially explained when different extracts of the leaves were used; a weakly basic chloroform fraction caused an increase in MAO A inhibitory activity, whereas butanol extracts brought about a decrease. These fractions had no significant effects on MAO B inhibitory activity. The findings show that Rhazya stricta leaves contain at least two different components that affect MAO inhibitory activity in opposite directions. It may be that the antidepressant and sedative actions of the plant are explicable in terms of these different components.

Toxicological studies on the leaves of Avicennia marina (mangrove) in rats

Haematological, biochemical and pathological effects in rats produced by the salt-tolerant plant Avicennia marina given at oral doses of 1 or 4 g kg(-1) for three consecutive days or 0.5 g kg(-1) day(-1) for 28 consecutive days are reported. No overt behavioral changes, moribundity or mortality were seen in either of the two experiments. A dose of 1 g kg(-1) did not affect significantly either body or liver weights. However, at a dose of 4 g kg(-1) the extract reduced both body and liver weights. The extract at both doses significantly increased leucocyte (mainly neutrophil) counts but did not affect significantly erythrocyte counts, haemoglobin concentration or the haematocrit. Except for a slight, but statistically significant, decrease in plasma glucose concentration and an increase in Na, Ca, Cu, Mg and cholesterol concentrations and aspartate aminotransferase (AST) and alanine aminotransferase (ALT) activities, the extract exerted no significant effects on plasma biochemistry. The treatment produced dose-related mild cellular degeneration in the liver and congestion in the central veins. There were also prominent Kupffer's cells and monocellular infiltrations. In the kidneys there was shrinkage and cellular degeneration of glomeruli and patches of medullary haemorrhage. In the spleen a slight activation of the germinal centre in the white pulp was noted. Subchronic treatment with the extract did not affect significantly the body and liver weights, the water intake, faecal and urinary output, leucocyte and erythrocyte counts, haemoglobin or haematocrit. There was a significant decrease in the number of platelets and an increase in the number of neutrophils. No significant changes in plasma biochemistry were observed, except for a 15% increase in AST activity. Subchronic treatment produced a significant reduction in glutathione concentration, amounting to about 20%. Histopathological findings after the subchronic treatment were similar in nature but milder than those seen after the acute treatment.

The effect of Rhazya stricta Decne, a traditional medicinal plant, on the forced swimming test in rats

Immobility induced by forced swimming is well known as an animal model of depression. Using this paradigm, we have, in the present work, tested the possibility that the medicinal plant Rhazya stricta, which has previously been found to affect the monoamine oxidase inhibitory activity in rat brain, may have an antidepressant-like action. Rats were pretreated with various doses (0.025-6.4 g/kg) of the lyophilized extract of the plant leaves, or with desipramine (10, 20, and 40 mg/kg) and were subjected to the forced swimming test. The results indicated that the plant extract produced a biphasic (bell-shaped) effect on the immobility time. The lower doses (0.1, 0.2, and 0.4 g/kg) elicited a highly significant and inversely dose-dependent decrease in immobility time, and the higher doses (0.8, 1.6, and 6.4 g/kg) showed a dose-dependent decrease in immobility time. Under the same experimental conditions desipramine (20 and 40 mg/kg) produced dose-dependent significant decreases in immobility time. Following administration of R. stricta (6.4 g/kg) the immobility time recovered progressively with time, and 4 h after its administration the immobility time was about 70% of the control level (statistically insignificant). It is concluded that R. stricta extract [or component(s) thereof] may possess an antidepressant-like effect.

Plasma pharmacokinetics of intravenous and intramuscular furosemide in the camel (Camelus dromedarius)

Single bolus intravenous (i.v.) and intramuscular (i.m.) doses of furosemide (1.5 mg kg(-1)) were administered in a crossover design to three female and three male adult, dromedary camels. Plasma furosemide concentrations were assayed by HPLC and sodium, potassium and chloride concentrations were measured using ion-selective electrodes. Plasma furosemide concentration-time plots indicated multi-compartment disposition, and there was considerable intersubject variability in the pharmacokinetic parameters. The mean (SD) i.v. terminal elimination half-life was 118 (67) minutes. The systemic clearance was 5.4 (1.2) ml min(-1) kg(-1). and the steady-state volume of distribution was 0.43 (0.14) litre kg(-1). The mean absorption time after i.m. dosing was 33 (62) minutes, while the absolute bioavailability was 71 (20) per cent. The glucuronide metabolite of furosemide was not detected in plasma. Clearance was lower, volume of distribution was larger, and half-life was longer compared with published data for dogs, horses, rats and humans. Plasma potassium and sodium concentrations were significantly diminished (P<0.05) by an average of 35.6 per cent (two to six hours post-dose) and 11.3 per cent (eight hours post-dose), respectively. Plasma chloride concentrations were not significantly affected by furosemide administration.

Furosemide-induced changes in plasma and blood volume of camels (Camelus dromedarius)

This work examines the effect of treatment of camels with furosemide (1 or 2 mg/kg, intravenously) on blood volume (BV), plasma volume (PV) and the plasma concentrations of total solids (PTS), plasma total protein (PTP) haemoglobin (Hb) and haematocrit (PCV). The cumulative urine produced during the 4 h following furosemide administration (2 mg/kg) averaged 22.2 mL/kg, compared to 2.3 mL/kg in controls. None of the above parameters were significantly changed by furosemide treatment at either dose. In another experiment, the effect of the two doses of furosemide on the sequential changes on some of the above parameters was investigated at 5, 10, 15, 30, 45, 60, 75, 90, 105, 120 and 240 min after the drug administration. On the whole, there were no significant changes in any of the measured parameters, except for small but statistically significant increases in the PCV and Hb at 30 and 45 min post treatment. It is concluded that, despite the marked diuresis following furosemide administration, the camel appears to be able to maintain its bodily fluid haemostasis.

Some effects of Cassia italica on the central nervous system in mice

This work examines some effects of the crude ethanolic extract of the medicinal plant Cassia italica, given at single oral doses of 0.25, 0.5 or 1 g kg-1, on the central nervous system in mice. Several models of nociception have been used to examine the analgesic effect of the extract. HPLC fingerprinting of the extract was performed to ensure uniformity of the extract material used. In treated mice, the extract caused dose-related inhibition of acetic acid-induced abdominal constriction, and in the formalin test of antinociception the extract reduced formalin-induced pain in the second (late) but not in the first (early) phase of the pain. Treatment with the extract at doses of 0.5 and 1 g kg-1 significantly increased the reaction time in the hot-plate and warm-water tail-flick tests. Naloxone was ineffective in antagonizing the analgesic effect of C. italica on tail-flick and abdominal constriction tests, possibly indicating that the effect occurs via non-opiate pathways. The C. italica extract caused slight dose-related impairment of motor control which was significant only at a dose of 1 g kg-1. Treatment at the three doses used did not affect the rectal temperature of normothermic mice, but was effective in significantly reducing the rectal temperature of hyperthermic rats, 0.5 and 1 h (but not 6 h) after administration of the extract at doses of 0.5 and 1 g kg-1. The extract also produced progressive diminution in the ambulatory and total activity of treated mice for up to 2 h after administration. It is concluded that the crude ethanolic extract of C. italica has CNS depressant properties, manifested as antinociception and sedation.

Neuromuscular and microvascular changes associated with chronic administration of an extract of Teucrium stocksianum in mice

This work examines the effect on the weights of vital body organs, on blood biochemical variables, on neuromuscular coordination and on cerebral microcirculation of aqueous extracts of Teucrium stocksianum, given to mice in drinking water at concentrations of 2 and 4% for 56 days. The treatment caused progressive impairment of neuromuscular coordination, as evidenced by the time spent on the rota-rod. After photochemical challenge, the time for first observable platelet aggregation in arterioles was shorter than for the control group by 22 and 45% in the 2 and 4% T. stocksianum-treated groups, respectively. Platelet aggregation on the venular side was not affected by the treatment nor were microvascular diameters. Treatment with the plant extract produced no statistically significant effect on the plasma biochemical variables that are considered indices of liver and kidney function. Histologically, brains obtained from mice treated with T. stocksianum showed loss of cerebellar Purkinje cells. Although it is likely that the accelerated platelet aggregation might have contributed to an ischaemic effect which could, at least in part, have caused the cytotoxicological changes, this does not exclude the possibility of a direct cytotoxicological effect of the plant extract. Further pharmacological and toxicological investigations on Teucrium species seem warranted.