Integrated approach of federated learning with transfer learning for classification and diagnosis of brain tumor

Brain tumor classification using MRI images is a crucial yet challenging task in medical imaging. Accurate diagnosis is vital for effective treatment planning but is often hindered by the complex nature of tumor morphology and variations in imaging. Traditional methodologies primarily rely on manual interpretation of MRI images, supplemented by conventional machine learning techniques. These approaches often lack the robustness and scalability needed for precise and automated tumor classification. The major limitations include a high degree of manual intervention, potential for human error, limited ability to handle large datasets, and lack of generalizability to diverse tumor types and imaging conditions.To address these challenges, we propose a federated learning-based deep learning model that leverages the power of Convolutional Neural Networks (CNN) for automated and accurate brain tumor classification. This innovative approach not only emphasizes the use of a modified VGG16 architecture optimized for brain MRI images but also highlights the significance of federated learning and transfer learning in the medical imaging domain. Federated learning enables decentralized model training across multiple clients without compromising data privacy, addressing the critical need for confidentiality in medical data handling. This model architecture benefits from the transfer learning technique by utilizing a pre-trained CNN, which significantly enhances its ability to classify brain tumors accurately by leveraging knowledge gained from vast and diverse datasets.Our model is trained on a diverse dataset combining figshare, SARTAJ, and Br35H datasets, employing a federated learning approach for decentralized, privacy-preserving model training. The adoption of transfer learning further bolsters the model's performance, making it adept at handling the intricate variations in MRI images associated with different types of brain tumors. The model demonstrates high precision (0.99 for glioma, 0.95 for meningioma, 1.00 for no tumor, and 0.98 for pituitary), recall, and F1-scores in classification, outperforming existing methods. The overall accuracy stands at 98%, showcasing the model's efficacy in classifying various tumor types accurately, thus highlighting the transformative potential of federated learning and transfer learning in enhancing brain tumor classification using MRI images.

Investigating attention mechanisms for plant disease identification in challenging environments

There is an increasing demand for efficient and precise plant disease detection methods that can quickly identify disease outbreaks. For this, researchers have developed various machine learning and image processing techniques. However, real-field images present challenges due to complex backgrounds, similarities between different disease symptoms, and the need to detect multiple diseases simultaneously. These obstacles hinder the development of a reliable classification model. The attention mechanisms emerge as a critical factor in enhancing the robustness of classification models by selectively focusing on relevant regions or features within infected regions in an image. This paper provides details about various types of attention mechanisms and explores the utilization of these techniques for the machine learning solutions created by researchers for image segmentation, feature extraction, object detection, and classification for efficient plant disease identification. Experiments are conducted on three models: MobileNetV2, EfficientNetV2, and ShuffleNetV2, to assess the effectiveness of attention modules. For this, Squeeze and Excitation layers, the Convolutional Block Attention Module, and transformer modules have been integrated into these models, and their performance has been evaluated using different metrics. The outcomes show that adding attention modules enhances the original models' functionality.

Towards blockchain based federated learning in categorizing healthcare monitoring devices on artificial intelligence of medical things investigative framework

Categorizing Artificial Intelligence of Medical Things (AIoMT) devices within the realm of standard Internet of Things (IoT) and Internet of Medical Things (IoMT) devices, particularly at the server and computational layers, poses a formidable challenge. In this paper, we present a novel methodology for categorizing AIoMT devices through the application of decentralized processing, referred to as "Federated Learning" (FL). Our approach involves deploying a system on standard IoT devices and labeled IoMT devices for training purposes and attribute extraction. Through this process, we extract and map the interconnected attributes from a global federated cum aggression server. The aim of this terminology is to extract interdependent devices via federated learning, ensuring data privacy and adherence to operational policies. Consequently, a global training dataset repository is coordinated to establish a centralized indexing and synchronization knowledge repository. The categorization process employs generic labels for devices transmitting medical data through regular communication channels. We evaluate our proposed methodology across a variety of IoT, IoMT, and AIoMT devices, demonstrating effective classification and labeling. Our technique yields a reliable categorization index for facilitating efficient access and optimization of medical devices within global servers.

Advances in real time smart monitoring of environmental parameters using IoT and sensors

People who work in dangerous environments include farmers, sailors, travelers, and mining workers. Due to the fact that they must evaluate the changes taking place in their immediate surroundings, they must gather information and data from the real world. It becomes crucial to regularly monitor meteorological parameters such air quality, rainfall, water level, pH value, wind direction and speed, temperature, atmospheric pressure, humidity, soil moisture, light intensity, and turbidity in order to avoid risks or calamities. Enhancing environmental standards is largely influenced by IoT. It greatly advances sustainable living with its innovative and cutting-edge techniques for monitoring air quality and treating water. With the aid of various sensors, microcontroller (Arduino Uno), GSM, Wi-Fi, and HTTP protocols, the suggested system is a real-time smart monitoring system based on the Internet of Things. Also, the proposed system has HTTP-based webpage enabled by Wi-Fi to transfer the data to remote locations. This technology makes it feasible to track changes in the weather from any location at any distance. The proposed system is a sophisticated, efficient, accurate, cost-effective, and dependable weather station that will be valuable to anyone who wants to monitor environmental changes on a regular basis.

Hybrid optimization algorithm for enhanced performance and security of counter-flow shell and tube heat exchangers

A shell and tube heat exchanger (STHE) for heat recovery applications was studied to discover the intricacies of its optimization. To optimize performance, a hybrid optimization methodology was developed by combining the Neural Fitting Tool (NFTool), Particle Swarm Optimization (PSO), and Grey Relational Analysis (GRE). STHE heat exchangers were analyzed systematically using the Taguchi method to analyze the critical elements related to a particular response. To clarify the complex relationship between the heat exchanger efficiency and operational parameters, grey relational grades (GRGs) are first computed. A forecast of the grey relation coefficients was then conducted using NFTool to provide more insight into the complex dynamics. An optimized parameter with a grey coefficient was created after applying PSO analysis, resulting in a higher grey coefficient and improved performance of the heat exchanger. A major and far-reaching application of this study was based on heat recovery. A detailed comparison was conducted between the estimated values and the experimental results as a result of the hybrid optimization algorithm. In the current study, the results demonstrate that the proposed counter-flow shell and tube strategy is effective for optimizing performance.

Enhancing accessibility for improved diagnosis with modified EfficientNetV2-S and cyclic learning rate strategy in women with disabilities and breast cancer

Breast cancer, a prevalent cancer among women worldwide, necessitates precise and prompt detection for successful treatment. While conventional histopathological examination is the benchmark, it is a lengthy process and prone to variations among different observers. Employing machine learning to automate the diagnosis of breast cancer presents a viable option, striving to improve both precision and speed. Previous studies have primarily focused on applying various machine learning and deep learning models for the classification of breast cancer images. These methodologies leverage convolutional neural networks (CNNs) and other advanced algorithms to differentiate between benign and malignant tumors from histopathological images. Current models, despite their potential, encounter obstacles related to generalizability, computational performance, and managing datasets with imbalances. Additionally, a significant number of these models do not possess the requisite transparency and interpretability, which are vital for medical diagnostic purposes. To address these limitations, our study introduces an advanced machine learning model based on EfficientNetV2. This model incorporates state-of-the-art techniques in image processing and neural network architecture, aiming to improve accuracy, efficiency, and robustness in classification. We employed the EfficientNetV2 model, fine-tuned for the specific task of breast cancer image classification. Our model underwent rigorous training and validation using the BreakHis dataset, which includes diverse histopathological images. Advanced data preprocessing, augmentation techniques, and a cyclical learning rate strategy were implemented to enhance model performance. The introduced model exhibited remarkable efficacy, attaining an accuracy rate of 99.68%, balanced precision and recall as indicated by a significant F1 score, and a considerable Cohen's Kappa value. These indicators highlight the model's proficiency in correctly categorizing histopathological images, surpassing current techniques in reliability and effectiveness. The research emphasizes improved accessibility, catering to individuals with disabilities and the elderly. By enhancing visual representation and interpretability, the proposed approach aims to make strides in inclusive medical image interpretation, ensuring equitable access to diagnostic information.

Oral squamous cell carcinoma detection using EfficientNet on histopathological images

Introduction

Oral Squamous Cell Carcinoma (OSCC) poses a significant challenge in oncology due to the absence of precise diagnostic tools, leading to delays in identifying the condition. Current diagnostic methods for OSCC have limitations in accuracy and efficiency, highlighting the need for more reliable approaches. This study aims to explore the discriminative potential of histopathological images of oral epithelium and OSCC. By utilizing a database containing 1224 images from 230 patients, captured at varying magnifications and publicly available, a customized deep learning model based on EfficientNetB3 was developed. The model's objective was to differentiate between normal epithelium and OSCC tissues by employing advanced techniques such as data augmentation, regularization, and optimization.

Methods

The research utilized a histopathological imaging database for Oral Cancer analysis, incorporating 1224 images from 230 patients. These images, taken at various magnifications, formed the basis for training a specialized deep learning model built upon the EfficientNetB3 architecture. The model underwent training to distinguish between normal epithelium and OSCC tissues, employing sophisticated methodologies including data augmentation, regularization techniques, and optimization strategies.

Results

The customized deep learning model achieved significant success, showcasing a remarkable 99% accuracy when tested on the dataset. This high accuracy underscores the model's efficacy in effectively discerning between normal epithelium and OSCC tissues. Furthermore, the model exhibited impressive precision, recall, and F1-score metrics, reinforcing its potential as a robust diagnostic tool for OSCC.

Discussion

This research demonstrates the promising potential of employing deep learning models to address the diagnostic challenges associated with OSCC. The model's ability to achieve a 99% accuracy rate on the test dataset signifies a considerable leap forward in earlier and more accurate detection of OSCC. Leveraging advanced techniques in machine learning, such as data augmentation and optimization, has shown promising results in improving patient outcomes through timely and precise identification of OSCC.

Early Detection of Lung Nodules Using a Revolutionized Deep Learning Model

According to the WHO (World Health Organization), lung cancer is the leading cause of cancer deaths globally. In the future, more than 2.2 million people will be diagnosed with lung cancer worldwide, making up 11.4% of every primary cause of cancer. Furthermore, lung cancer is expected to be the biggest driver of cancer-related mortality worldwide in 2020, with an estimated 1.8 million fatalities. Statistics on lung cancer rates are not uniform among geographic areas, demographic subgroups, or age groups. The chance of an effective treatment outcome and the likelihood of patient survival can be greatly improved with the early identification of lung cancer. Lung cancer identification in medical pictures like CT scans and MRIs is an area where deep learning (DL) algorithms have shown a lot of potential. This study uses the Hybridized Faster R-CNN (HFRCNN) to identify lung cancer at an early stage. Among the numerous uses for which faster R-CNN has been put to good use is identifying critical entities in medical imagery, such as MRIs and CT scans. Many research investigations in recent years have examined the use of various techniques to detect lung nodules (possible indicators of lung cancer) in scanned images, which may help in the early identification of lung cancer. One such model is HFRCNN, a two-stage, region-based entity detector. It begins by generating a collection of proposed regions, which are subsequently classified and refined with the aid of a convolutional neural network (CNN). A distinct dataset is used in the model's training process, producing valuable outcomes. More than a 97% detection accuracy was achieved with the suggested model, making it far more accurate than several previously announced methods.

A Deep Learning Framework with an Intermediate Layer Using the Swarm Intelligence Optimizer for Diagnosing Oral Squamous Cell Carcinoma

One of the most prevalent cancers is oral squamous cell carcinoma, and preventing mortality from this disease primarily depends on early detection. Clinicians will greatly benefit from automated diagnostic techniques that analyze a patient's histopathology images to identify abnormal oral lesions. A deep learning framework was designed with an intermediate layer between feature extraction layers and classification layers for classifying the histopathological images into two categories, namely, normal and oral squamous cell carcinoma. The intermediate layer is constructed using the proposed swarm intelligence technique called the Modified Gorilla Troops Optimizer. While there are many optimization algorithms used in the literature for feature selection, weight updating, and optimal parameter identification in deep learning models, this work focuses on using optimization algorithms as an intermediate layer to convert extracted features into features that are better suited for classification. Three datasets comprising 2784 normal and 3632 oral squamous cell carcinoma subjects are considered in this work. Three popular CNN architectures, namely, InceptionV2, MobileNetV3, and EfficientNetB3, are investigated as feature extraction layers. Two fully connected Neural Network layers, batch normalization, and dropout are used as classification layers. With the best accuracy of 0.89 among the examined feature extraction models, MobileNetV3 exhibits good performance. This accuracy is increased to 0.95 when the suggested Modified Gorilla Troops Optimizer is used as an intermediary layer.

Cancer Diagnosis through Contour Visualization of Gene Expression Leveraging Deep Learning Techniques

Prompt diagnostics and appropriate cancer therapy necessitate the use of gene expression databases. The integration of analytical methods can enhance detection precision by capturing intricate patterns and subtle connections in the data. This study proposes a diagnostic-integrated approach combining Empirical Bayes Harmonization (EBS), Jensen-Shannon Divergence (JSD), deep learning, and contour mathematics for cancer detection using gene expression data. EBS preprocesses the gene expression data, while JSD measures the distributional differences between cancerous and non-cancerous samples, providing invaluable insights into gene expression patterns. Deep learning (DL) models are employed for automatic deep feature extraction and to discern complex patterns from the data. Contour mathematics is applied to visualize decision boundaries and regions in the high-dimensional feature space. JSD imparts significant information to the deep learning model, directing it to concentrate on pertinent features associated with cancerous samples. Contour visualization elucidates the model's decision-making process, bolstering interpretability. The amalgamation of JSD, deep learning, and contour mathematics in gene expression dataset analysis diagnostics presents a promising pathway for precise cancer detection. This method taps into the prowess of deep learning for feature extraction while employing JSD to pinpoint distributional differences and contour mathematics for visual elucidation. The outcomes underscore its potential as a formidable instrument for cancer detection, furnishing crucial insights for timely diagnostics and tailor-made treatment strategies.

Cloud-Based Quad Deep Ensemble Framework for the Detection of COVID-19 Omicron and Delta Variants

The mortality rates of patients contracting the Omicron and Delta variants of COVID-19 are very high, and COVID-19 is the worst variant of COVID. Hence, our objective is to detect COVID-19 Omicron and Delta variants from lung CT-scan images. We designed a unique ensemble model that combines the CNN architecture of a deep neural network-Capsule Network (CapsNet)-and pre-trained architectures, i.e., VGG-16, DenseNet-121, and Inception-v3, to produce a reliable and robust model for diagnosing Omicron and Delta variant data. Despite the solo model's remarkable accuracy, it can often be difficult to accept its results. The ensemble model, on the other hand, operates according to the scientific tenet of combining the majority votes of various models. The adoption of the transfer learning model in our work is to benefit from previously learned parameters and lower data-hunger architecture. Likewise, CapsNet performs consistently regardless of positional changes, size changes, and changes in the orientation of the input image. The proposed ensemble model produced an accuracy of 99.93%, an AUC of 0.999 and a precision of 99.9%. Finally, the framework is deployed in a local cloud web application so that the diagnosis of these particular variants can be accomplished remotely.

Optimizing Inference Distribution for Efficient Kidney Tumor Segmentation Using a UNet-PWP Deep-Learning Model with XAI on CT Scan Images

Kidney tumors represent a significant medical challenge, characterized by their often-asymptomatic nature and the need for early detection to facilitate timely and effective intervention. Although neural networks have shown great promise in disease prediction, their computational demands have limited their practicality in clinical settings. This study introduces a novel methodology, the UNet-PWP architecture, tailored explicitly for kidney tumor segmentation, designed to optimize resource utilization and overcome computational complexity constraints. A key novelty in our approach is the application of adaptive partitioning, which deconstructs the intricate UNet architecture into smaller submodels. This partitioning strategy reduces computational requirements and enhances the model's efficiency in processing kidney tumor images. Additionally, we augment the UNet's depth by incorporating pre-trained weights, therefore significantly boosting its capacity to handle intricate and detailed segmentation tasks. Furthermore, we employ weight-pruning techniques to eliminate redundant zero-weighted parameters, further streamlining the UNet-PWP model without compromising its performance. To rigorously assess the effectiveness of our proposed UNet-PWP model, we conducted a comparative evaluation alongside the DeepLab V3+ model, both trained on the "KiTs 19, 21, and 23" kidney tumor dataset. Our results are optimistic, with the UNet-PWP model achieving an exceptional accuracy rate of 97.01% on both the training and test datasets, surpassing the DeepLab V3+ model in performance. Furthermore, to ensure our model's results are easily understandable and explainable. We included a fusion of the attention and Grad-CAM XAI methods. This approach provides valuable insights into the decision-making process of our model and the regions of interest that affect its predictions. In the medical field, this interpretability aspect is crucial for healthcare professionals to trust and comprehend the model's reasoning.

IoT-blockchain empowered Trinet: optimized fall detection system for elderly safety

Numerous elderly folks reside alone in their homes. Seniors may find it difficult to ask for assistance if they fall. As the elderly population keeps growing, elderly fall incidents are becoming a critical public health concern. Creating a fall detection system for the elderly using IoT and blockchain is the aim of this study. Data collection, pre-processing, feature extraction, feature selection, fall detection, and emergency response and assistance are the six fundamental aspects of the proposed model. The sensor data is collected from wearable devices using elderly such as accelerometers and gyroscopes. The collected data is pre-processed using missing value removal, null value handling. The features are extracted after pre-processed data using statistical features, autocorrelation, and Principal Component Analysis The proposed approach utilizes a novel hybrid HSSTL combines Teaching-Learning-Based Optimization and Spring Search Algorithm to select the optimal features. The proposed approach employs TriNet, including Long Short-Term Memory, optimized Convolutional Neural Network (CNN), and Recurrent Neural Network for accurate fall detection. To enhance fall detection accuracy, use the optimized Convolutional Neural Network obtained through the hybrid optimization model HSSTL. Securely store fall detection information in the Blockchain network when a fall occurs. Alert neighbours, family members, or those providing immediate assistance about the fall occurrence using Blockchain network. The proposed model is implemented in Python. The effectiveness of the suggested model is evaluated using metrics for accuracy, precision, recall, sensitivity, specificity, f-measure, NPV, FPR, FNR, and MCC. The proposed model outperformed with the maximum accuracy of 0.974015 at an 80% learning rate, whereas the suggested model had the best accuracy score of 0.955679 at a 70% learning rate.

Detection of Alzheimer's Disease Based on Cloud-Based Deep Learning Paradigm

Deep learning is playing a major role in identifying complicated structure, and it outperforms in term of training and classification tasks in comparison to traditional algorithms. In this work, a local cloud-based solution is developed for classification of Alzheimer's disease (AD) as MRI scans as input modality. The multi-classification is used for AD variety and is classified into four stages. In order to leverage the capabilities of the pre-trained GoogLeNet model, transfer learning is employed. The GoogLeNet model, which is pre-trained for image classification tasks, is fine-tuned for the specific purpose of multi-class AD classification. Through this process, a better accuracy of 98% is achieved. As a result, a local cloud web application for Alzheimer's prediction is developed using the proposed architectures of GoogLeNet. This application enables doctors to remotely check for the presence of AD in patients.

An Approach to Binary Classification of Alzheimer's Disease Using LSTM

In this study, we use LSTM (Long-Short-Term-Memory) networks to evaluate Magnetic Resonance Imaging (MRI) data to overcome the shortcomings of conventional Alzheimer's disease (AD) detection techniques. Our method offers greater reliability and accuracy in predicting the possibility of AD, in contrast to cognitive testing and brain structure analyses. We used an MRI dataset that we downloaded from the Kaggle source to train our LSTM network. Utilizing the temporal memory characteristics of LSTMs, the network was created to efficiently capture and evaluate the sequential patterns inherent in MRI scans. Our model scored a remarkable AUC of 0.97 and an accuracy of 98.62%. During the training process, we used Stratified Shuffle-Split Cross Validation to make sure that our findings were reliable and generalizable. Our study adds significantly to the body of knowledge by demonstrating the potential of LSTM networks in the specific field of AD prediction and extending the variety of methods investigated for image classification in AD research. We have also designed a user-friendly Web-based application to help with the accessibility of our developed model, bridging the gap between research and actual deployment.

A Cluster-Based Energy-Efficient Secure Optimal Path-Routing Protocol for Wireless Body-Area Sensor Networks

Recently, research into Wireless Body-Area Sensor Networks (WBASN) or Wireless Body-Area Networks (WBAN) has gained much importance in medical applications, and now plays a significant role in patient monitoring. Among the various operations, routing is still recognized as a resource-intensive activity. As a result, designing an energy-efficient routing system for WBAN is critical. The existing routing algorithms focus more on energy efficiency than security. However, security attacks will lead to more energy consumption, which will reduce overall network performance. To handle the issues of reliability, energy efficiency, and security in WBAN, a new cluster-based secure routing protocol called the Secure Optimal Path-Routing (SOPR) protocol has been proposed in this paper. This proposed algorithm provides security by identifying and avoiding black-hole attacks on one side, and by sending data packets in encrypted form on the other side to strengthen communication security in WBANs. The main advantages of implementing the proposed protocol include improved overall network performance by increasing the packet-delivery ratio and reducing attack-detection overheads, detection time, energy consumption, and delay.

Load Balancing Using Artificial Intelligence for Cloud-Enabled Internet of Everything in Healthcare Domain

The emergence of the Internet of Things (IoT) and its subsequent evolution into the Internet of Everything (IoE) is a result of the rapid growth of information and communication technologies (ICT). However, implementing these technologies comes with certain obstacles, such as the limited availability of energy resources and processing power. Consequently, there is a need for energy-efficient and intelligent load-balancing models, particularly in healthcare, where real-time applications generate large volumes of data. This paper proposes a novel, energy-aware artificial intelligence (AI)-based load balancing model that employs the Chaotic Horse Ride Optimization Algorithm (CHROA) and big data analytics (BDA) for cloud-enabled IoT environments. The CHROA technique enhances the optimization capacity of the Horse Ride Optimization Algorithm (HROA) using chaotic principles. The proposed CHROA model balances the load, optimizes available energy resources using AI techniques, and is evaluated using various metrics. Experimental results show that the CHROA model outperforms existing models. For instance, while the Artificial Bee Colony (ABC), Gravitational Search Algorithm (GSA), and Whale Defense Algorithm with Firefly Algorithm (WD-FA) techniques attain average throughputs of 58.247 Kbps, 59.957 Kbps, and 60.819 Kbps, respectively, the CHROA model achieves an average throughput of 70.122 Kbps. The proposed CHROA-based model presents an innovative approach to intelligent load balancing and energy optimization in cloud-enabled IoT environments. The results highlight its potential to address critical challenges and contribute to developing efficient and sustainable IoT/IoE solutions.

Synthesis and evaluation of coastal and marine biodiversity spatial information in the United Arab Emirates for ecosystem-based management

The United Arab Emirates (UAE) host valuable coastal and marine biodiversity that is subjected to multiple pressures under extreme conditions. To mitigate impacts on marine ecosystems, the UAE protects almost 12% of its Exclusive Economic Zone. This study mapped and validated the distribution of key coastal and marine habitats, species and critical areas for their life cycle in the Gulf area of the UAE. We identified gaps in the current protection of these ecological features and assessed the quality of the data used. The overall dataset showed good data quality, but deficiencies in information for the coastline of the north-western emirates. The existing protected areas are inadequate to safeguard key ecological features such as mangroves and coastal lagoons. This study offers a solid basis to understand the spatial distribution and protection of marine biodiversity in the UAE. This information should be considered for implementing effective conservation planning and ecosystem-based management.

Domination of Filamentous Anoxygenic Phototrophic Bacteria and Prediction of Metabolic Pathways in Microbial Mats from the Hot Springs of Al Aridhah

Microbial mats in hot springs form a dynamic ecosystem and support the growth of diverse communities with broad-ranging metabolic capacity. In this study, we used 16S rRNA gene amplicon sequencing to analyse microbial communities in mat samples from two hot springs in Al Aridhah, Saudi Arabia. Putative metabolic pathways of the microbial communities were identified using phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt). Filamentous anoxygenic phototrophic bacteria associated with phylum Chloroflexi were abundant (> 50 %) in both hot springs at 48 °C. Chloroflexi were mainly represented by taxa Chloroflexus followed by Roseiflexus. Cyanobacteria of genus Arthrospira constituted 3.4 % of microbial mats. Heterotrophic microorganisms were mainly represented by Proteobacteria, Actinobacteria, Bacteroidetes, and Firmicutes. Archaea were detected at a lower relative abundance (< 1 %). Metabolic pathways associated with membrane transport, carbon fixation, methane metabolism, amino acid biosynthesis, and degradation of aromatic compounds were commonly found in microbial mats of both hot springs. In addition, pathways for production of secondary metabolites and antimicrobial compounds were predicted to be present in microbial mats. In conclusion, microbial communities in the hot springs of Al Aridhah were composed of diverse bacteria, with taxa of Chloroflexus being dominant.

Featuring dental education research: applying the principles of action research to improve teaching of dental prosthetics

This article focuses on educational research conducted at the newly merged UWC faculty of dentistry. The research emphasises the change in teaching methods employed to address the concerns experienced in teaching the new large classes as observed in the prosthetic techniques module. These educational interventions were conducted over 5 years and the study design included the principles of action research. Students were assisted in learning the theory of the practical procedures and the subsequent completion of these procedures with the accurate application of the theoretical concepts. Changes in the teaching methods enhanced students learning and successful translation of the theory into practical work. The active learning exercises incorporated into the teaching further motivated and assisted students with deep learning. The debates indicated that students know and accept the value of the module as part of their training.

Determining the dimensional stability, fracture toughness and flexural strength of light-cured acrylic resin custom tray material

Light-cured acrylic resin custom tray material is used in commercial dental laboratories but little evidence-based scientific information on its physical properties is available.

OBJECTIVES

This study investigates the dimensional stability of light-cured acrylic resin custom tray material and compares its fracture toughness and flexural strength to a chemically-cured acrylic material.

METHOD

For dimensional stability, 20 light-cured specimens were fabricated and measured 3 times at regular time intervals over 48 hours. Mean shrinkage was calculated for each time interval and the mean values were compared to the standard using the Wilcoxon Rank Sum test. A p-value of <0.05 was considered significant. For fracture toughness, 2 groups of 20 light-cured and chemically-cured acrylic materials with a single-edge notch were subjected to a compressive load using the 3-point bending technique. For flexural strength, 1 group (n=20) of each material was subjected to a compressive load using 3-point bending. The highest load before failure was used to calculate the fracture toughness and flexural strength. Differences in fracture toughness and flexural strength values between the 2 groups were compared using ANOVA testing. A p-value of <0.05 was considered significant. The chemically-cured group was accepted as the control group.

RESULTS

Compared to the standard, shrinkage was significant for all time intervals (p<0.05). The difference in shrinkage among time intervals was not significant (p>0.05). The fracture toughness and flexural strength were significantly higher for the light-cured material.

CONCLUSIONS

Trays made from light-cured acrylic resin can be used immediately following polymerization. The light-cured material is more resistant to bending and crack propagation than the chemically-cured type.

The use of light-cured acrylic resin for custom trays by undergraduate dental students: a survey

INTRODUCTION

It is unknown how the use of a light-cured acrylic resin is appreciated over the traditional chemically cured resins for the construction of custom trays in a teaching environment.

OBJECTIVE

To evaluate the acceptance of light-cured acrylic resin for custom trays by dental students.

METHOD

A questionnaire addressing the use and handling properties of both light-cured (Megatray, Megadent, Germany) and chemically-cured (Excel, Wright Health Group, UK) custom tray materials was distributed amongst undergraduate dental students of the University of the Western Cape.

RESULTS

Of a total of 196 dental students, 38 were absent on the day of the survey. Of the 158 questionnaires that were distributed and returned, 18 did not meet the inclusion criteria and 1 person chose not to participate. Of the 139 participating students, 98 were in 4th year, 41 in 5th year. With regards to the light-cured acrylic custom tray material, 77% used it most often, 64% said it saved time and 62 % said that it was easier to handle. Fifty two percent indicated that both types of materials should be taught in undergraduate training, 26% preferred the light-cured acrylic resin custom tray material, 20% suggested that only the light-cured resin be used and no one suggested the chemically-cured resin exclusively.

CONCLUSIONS

Most undergraduate students positively accepted the light-cured resin, but training in the use of both materials was recommended.

Tyrosinase inhibitory lignans from the methanol extract of the roots of Vitex negundo Linn. and their structure-activity relationship

Phytochemical investigation of the methanol extract of Vitex negundo afforded eight lignans; negundin A 1, negundin B 2, 6-hydroxy-4-(4-hydroxy-3-methoxy)-3-hydroxymethyl-7-methoxy-3,4-dihydro-2-naphthaledehyde 3, vitrofolal E 4, (+)-lyoniresinol 5, (+)-lyoniresinol-3alpha-O-beta-d-glucoside 6, (+)-(-)-pinoresinol 7, and (+)-diasyringaresinol 8. The structures of these compounds were elucidated unambiguously by spectroscopic methods including 1D and 2D NMR analysis and also by comparing experimental data with literature data. The tyrosinase inhibitory potency of these compounds has been evaluated and attempts to justify their structure-activity relationships have been made in the present work. The compound 5 was found to be the most potent (IC(50)=3.21 microM) while other compounds demonstrated moderate to potent inhibitions. It was found that the substitution of functional group(s) at C-2 and C-3 positions and the presence of the -CH(2)OH group plays a vital role in the potency of the compounds. The compound 5 can act as a potential lead molecule to develop new drugs for the treatment of hyperpigmentation associated with the high production of melanocytes.

Aesthetic clasp design for removable partial dentures: a literature review

Removable partial dentures (RPD) are an effective and affordable treatment option for partial edentulism. If the main reason for seeking treatment is the need for improved aesthetics, treatment should be geared towards achieving this goal. This article is the result of a literature study on aesthetic clasp design for the conventional RPD. In this context, the position of the clasp on the tooth, clasp types, clasp material and alternative methods of retention are reviewed. Although published in reputable journals, the authors report that many articles published on this subject are of a descriptive nature and lack scientific evidence. Therefore, clinicians are encouraged to be critical in their interpretation of literature and the application of published information in their clinical practices.

Analysis of histone deacetylase inhibitor, depsipeptide (FR901228), effect on multiple myeloma

Multiple myeloma (MM) is a neoplastic proliferation of plasma cells and remains an incurable disease because of the development of drug resistance. Histone deacytylase (HDAC) inhibitors are a new class of chemotherapeutic reagents that cause growth arrest and apoptosis of neoplastic cells. Depsipeptide, a new member of the HDAC inhibitors, was found to be safe in humans and has been shown to induce apoptosis in various cancers. In order to evaluate the effects of depsipeptide, a MM cell line, U266 [interleukin (IL)-6 dependent], was analysed for viability and apoptosis. The combined effect of depsipeptide with melphalan and changes in BCL-2 family proteins (BCL-2, BCL-XL, BAX and MCL-1) were also investigated. In addition, the RPMI 8226 cell line (IL-6 independent), and primary patient myeloma cells were also analysed for apoptosis after depsipeptide treatment. Depsipeptide induced apoptosis in both U266 and RPMI 8226 cell lines in a time- and dose-dependent fashion, and in primary patient myeloma cells. We also demonstrated that depsipeptide had an additive effect with melphalan (10 micromol/l). BCL-2, BCL-XL and MCL-1 showed decreased expression in depsipeptide-treated samples. Based on recent clinical trials demonstrating minimal clinical toxicity, our study supports the future clinical utilization of depsipeptide in the management of MM.

Prevalence of hypertension and sociodemographic characteristics of adult hypertensives in Riyadh City, Saudi Arabia

OBJECTIVES

To estimate the prevalence of hypertension in adults residing in Riyadh city and to study the sociodemographic characteristics of adult hypertensives.

DESIGN

Cross-sectional survey.

SETTING

Primary Health Care Centres (PHCCs) in Riyadh city selected by stratified random sampling, the subjects resident in each PHCC catchment area were selected by systematic sampling from their records in the PHCCs.

SUBJECTS AND METHODS

A total of 1394 adults aged 15 years and over were interviewed and examined during March 1993 to March 1994. The average of three measurements of blood pressure (BP) was taken to represent their current pressures. A subject is considered hypertensive if the average BP reading is 160/95 mm Hg or more, or is currently under treatment.

RESULTS

The total hypertensive subjects were 214 giving an overall prevalence of hypertension of 15.4%. Of these 157 (11.3%) subjects were known hypertensives and were under some form of treatment. On the other hand 57 (4.1%) other subjects were newly detected by the study. Hypertension (BP = 160/95 mm Hg or more) was significantly related to age, marriage, education, occupation and employment status and consanguinity. Male subjects had a higher prevalence of hypertension but the differences were not significant. Nationality and income were not related to high BP.

CONCLUSION

Hypertension is a problem among adults in Riyadh city. It is significantly related to some sociodemographic and family factors. About 27% of all hypertensives are not aware of their disease and more than 31% of known hypertensives are apparently not well controlled. There is a need for a programme to prevent and control hypertension in Riyadh city. Similar studies need to be done in other areas of the country to estimate the prevalence of hypertension and associated factors as prerequisites for any programme to control the disease.

Smoking behaviour and attitudes among adult Saudi nationals in Riyadh City, Saudi Arabia

OBJECTIVE

To measure the smoking behaviour and attitudes among Saudi adults residing in Riyadh City, the capital of the Kingdom of Saudi Arabia.

DESIGN

Cross-sectional survey.

SETTING AND SUBJECTS

Primary health care centres (PHCCs) in Riyadh City were selected by stratified random sampling. Subjects resident in each PHCC catchment area were selected by systematic sampling from their records in the PHCCs; 1534 adults aged 15 years and older were interviewed during January to April 1994.

MAIN OUTCOME MEASURES

Self-reported smoking prevalence; age of smoking initiation; daily cigarette consumption; duration of smoking; reasons for smoking, not smoking, and quitting smoking; intentions to smoke in the future; and attitudes toward various tobacco control measures.

RESULTS

25.3% of respondents were current smokers, 10.2% were ex-smokers, and 64.5% had never smoked. About 79% of all smokers started smoking between the ages of 15 and 30 years, and 19.5% before age 15. Significantly higher smoking prevalence and daily cigarette consumption were associated with being male, single, and being more highly educated. Relief of psychological tension, boredom, and imitating others were the most important reasons for smoking, whereas health and religious considerations were the most important reasons for not smoking among never-smokers, for quitting among ex-smokers, and for attempting to quit or thinking about quitting among current smokers. About 90% of all subjects thought that they would not smoke in the future. Physicians and religious men were identified as the most effective anti-smoking advocates by a much higher proportion of respondents (44%) than nurses, health educators, and teachers (each less than 5%). Health and religious education were generally cited as more effective in deterring smoking than tobacco control laws and policies.

CONCLUSIONS

Cigarette smoking is prevalent among Saudi adults in Riyadh, particularly males, most of whom begin to smoke rather early in life and continue for many years. Health and religious education should be the cornerstone for any organised tobacco control activities, which are urgently needed to combat the expected future epidemic of smoking-related health problems.

An internal criterion of test validity

It has been suggested that the procedures of construct validation include at least two distinct processes: one is the validation of the test for measuring a hypothesized construct and the other is the validation of the construct, i.e., its usefulness and significance as a part of a theoretical system. Factor analysis has been re-evaluated as a method for studying construct validity of new instruments in face of both theoretical and computational advances in the field of factor analysis. A numerical index of construct validity has been suggested to be the multiple correlation of the observed variables with the factor (hypothesized construct). The notion of universe of content, the limiting value of the multiple correlation when n → ∞, etc. have been shown to have important implications for the concept of construct validity. The usefulness of such a procedure for the study of content and predictive validities, item analysis, interpretation of scores and validation of psychological theories has been outlined.