Addressing differences in cancer: a framework for synergistic programming in cancer prevention and control

Background

Cancer remains a leading cause of death worldwide and continues to disproportionately impact certain populations. Several frameworks have been developed that illustrate the multiple determinants of cancer. Expanding upon the work of others, we present an applied framework for cancer prevention and control designed to help clinicians, as well as public health practitioners and researchers, better address differences in cancer outcomes.

Methods

The framework was developed by the Cancer Prevention and Control Research Network's Health Behaviors Workgroup. An initial framework draft was developed based on workgroup discussion, public health theory, and rapid literature review on the determinants of cancer. The framework was refined through interviews and focus groups with Federally Qualified Health Center providers (n=2) and cancer patients (n=2); participants were asked to provide feedback on the framework's causal pathways, completeness, and applicability to their work and personal life.

Results

The framework provides an overview of the relationships between sociodemographic inequalities, social and structural determinants, and key risk factors associated with cancer diagnosis, survivorship, and cancer morbidity and mortality across the lifespan. The framework emphasizes how health-risk behaviors like cigarette smoking interact with psychological, psychosocial, biological, and psychosocial risk factors, as well as healthcare-related behavior and other chronic diseases. Importantly, the framework emphasizes addressing social and structural determinants that influence health behaviors to reduce the burden of cancer and improve health equity. Aligned with previous theory, our framework underscores the importance of addressing co-occurring risk factors and disease states, understanding the complex relationships between factors that influence cancer, and assessing how multiple forms of inequality or disadvantage intersect to increase cancer risk across the lifespan.

Conclusions

This paper presents an applied framework for cancer prevention and control to address cancer differences. Because the framework highlights determinants and factors that influence cancer risk at multiple levels, it can be used to inform the development, implementation, and evaluation of interventions to address cancer morbidity and mortality.

A Smartphone-Based Detection System for Tomato Leaf Disease Using EfficientNetV2B2 and Its Explainability with Artificial Intelligence (AI)

The occurrence of tomato diseases has substantially reduced agricultural output and financial losses. The timely detection of diseases is crucial to effectively manage and mitigate the impact of episodes. Early illness detection can improve output, reduce chemical use, and boost a nation's economy. A complete system for plant disease detection using EfficientNetV2B2 and deep learning (DL) is presented in this paper. This research aims to develop a precise and effective automated system for identifying several illnesses that impact tomato plants. This will be achieved by analyzing tomato leaf photos. A dataset of high-resolution photographs of healthy and diseased tomato leaves was created to achieve this goal. The EfficientNetV2B2 model is the foundation of the deep learning system and excels at picture categorization. Transfer learning (TF) trains the model on a tomato leaf disease dataset using EfficientNetV2B2's pre-existing weights and a 256-layer dense layer. Tomato leaf diseases can be identified using the EfficientNetV2B2 model and a dense layer of 256 nodes. An ideal loss function and algorithm train and tune the model. Next, the concept is deployed in smartphones and online apps. The user can accurately diagnose tomato leaf diseases with this application. Utilizing an automated system facilitates the rapid identification of diseases, assisting in making informed decisions on disease management and promoting sustainable tomato cultivation practices. The 5-fold cross-validation method achieved 99.02% average weighted training accuracy, 99.22% average weighted validation accuracy, and 98.96% average weighted test accuracy. The split method achieved 99.93% training accuracy and 100% validation accuracy. Using the DL approach, tomato leaf disease identification achieves nearly 100% accuracy on a test dataset.

Association of Chest Pain with Rate Pressure Product and ST-segment Changes after Sub Arachnoid Block in Caesarean Section

When healthy women undergo caesarean section (CS) under sub arachnoid anaesthesia, transient electrocardiographic changes, such as ST-segment depression and T-wave abnormalities, are observed. During an elective caesarean section under sub arachnoid anaesthesia, about one-third of healthy parturient experience chest pain and ECG changes suggestive of myocardial ischemia. To assess the ST-segment and Rate Pressure Product changes with chest pain in patients with elective caesarean section under subarachnoid block. The Department of Anesthesia, Analgesia and Intensive Care Medicine at Bangabandhu Sheikh Mujib Medical University (BSMMU), Bangladesh was the site of this prospective observational study. The study included 86 healthy women between the ages of 20 and 35 who needed an elective caesarean section under a single shot sub arachnoid block and who visited the Department of Anesthesia, Analgesia, and Intensive Care Medicine at BSMMU in Shahbagh, Dhaka from January 2019 to June 2019. In comparison to the no chest pain group, ST-segment changes among the chest pain group at delivery, 5 minute, 10 minute after delivery and at the end of the surgery were highly significant (p=0.001). Comparatively, Rate Pressure Product changes were found to be significantly higher in the group with chest pain than in the group without chest pain (p=0.001). It is concluded that there is a substantial association of chest pain with rate pressure product and ST-segment changes after subarachroid block in caesarean section.

Dexmedetomidine as an Adjuvant to Bupivacaine in Caudal Analgesia in Children Undergoing Infra-Umbilical Surgery

When performing infra-umbilical procedures, caudal epidural analgesia with bupivacaine is frequently used to provide both intra- and post-operative analgesia. Dexmedetomidine, an alpha 2 agonistsare extensively used in neuraxial blocks and peripheral nerve blocks to prolong the action of bupivacaine. To find out the effects of dexmedetomidine as an adjuvant to bupivacaine for caudal analgesia in children undergoing infra-umbilical surgery. This was a randomized, controlled double-blinded prospective observational study and was performed from July 2019 to December 2019. A total of 60 (Sixty) patients with different infra-umbilical surgical problems underwent different procedure under caudal anaesthesia in different operation theatre in Bangabandhu Sheikh Mujib Medical University, Dhaka were enrolled in this study. Elaborate personal history, meticulous clinical examinations and relevant laboratory investigations was done. Post-operative adverse effects also were monitored. All information from history of illness, clinical, laboratory findings, duration of analgesia and post-operative adverse effects were recorded in a preformed data sheet (Appendix-I) and statistical analysis was done by SPSS 22.0. Mean age of the children in Group A (dexmedetomidine + bupivacaine) was 5.50±2.61 years and in Group B (bupivacaine) was 5.66±2.75. Mean weight of the children in Group A was 19.22±8.58 kg and in Group B was 19.70±8.94 kg in this study. Mean duration of anaesthesia was 27.5±6.5 minute in Group A and 28.5±5.5 minute in Group B. The mean duration of analgesia was 4.32±0.54 hours for Group A and 2.12±0.32 hours in Group B. In Group A, 46.7% patients required 1 and 3.3% required 2 rescue analgesic but in Group B, 43.3% patients required single rescue analgesic and 33.3% required two rescue analgesics (p<0.05). In Group A, 6.7% patients had nausea/ vomiting and in Group B, 16.7% patients had nausea/ vomiting (p>0.05). It can be concluded that dexmedetomidine with bupivacaine for caudal analgesia in infra-umbilical surgery significantly prolongs the duration of postoperative analgesia when compared to bupivacaine alone without any side-effects.

Anonymity Assurance Using Efficient Pseudonym Consumption in Internet of Vehicles

The Internet of vehicles (IoVs) is an innovative paradigm which ensures a safe journey by communicating with other vehicles. It involves a basic safety message (BSM) that contains sensitive information in a plain text that can be subverted by an adversary. To reduce such attacks, a pool of pseudonyms is allotted which are changed regularly in different zones or contexts. In base schemes, the BSM is sent to neighbors just by considering their speed. However, this parameter is not enough because network topology is very dynamic and vehicles can change their route at any time. This problem increases pseudonym consumption which ultimately increases communication overhead, increases traceability and has high BSM loss. This paper presents an efficient pseudonym consumption protocol (EPCP) which considers the vehicles in the same direction, and similar estimated location. The BSM is shared only to these relevant vehicles. The performance of the purposed scheme in contrast to base schemes is validated via extensive simulations. The results prove that the proposed EPCP technique outperformed compared to its counterparts in terms of pseudonym consumption, BSM loss rate and achieved traceability.

Predicting Difficult Intubation by using Modified Mallampati (MMT) with or without Thyromental Height Test (TMHT)

Failed Tracheal Intubation with Subsequent inability to maintain an open airway and adequate oxygenation is the most frequent cause of brain damage or death during anesthesia. Recognizing before anesthesia the potential for difficult intubation allows time for optimal preparation. Proper Selection of equipment and techniques is needed to avoid unwanted situation. To find out difficulties associated with endotracheal intubation using Modified Mallampati Test (MMT) combined with Thyromental Height Test (TMHT) and MMT without TMHT. This prospective observational study was conducted at the Department of Anesthesia in Bangabandhu Sheikh Mujib Medical University (BSMMU), Dhaka, Bangladesh from April 2018 to September 2018. Two hundred two patients with different surgical procedures under general anaesthesia in different operation theaters of BSMMU, Dhaka were selected as study population. After taking written consents from each patient or his/her attendant elaborate history of illness, meticulous clinical examinations were performed and relevant laboratory investigations were done. All information was recorded in a preformed data sheet and statistical analysis was done by SPSS-22.0. Mean age ±SD of the study subjects was 42.49±14.29 years in MMT with TMHT group and 43.40±15.39 years in MMT without TMHT group. Females were enrolled more than males in both the groups. BMI was 28.75±3.59kg/m² in MMT with TMHT group and 29.44±8.64kg/m² in MMT without TMHT group. There were no significant differences in age, gender and BMI between the groups. Sensitivity, specificity, PPV, NPV and accuracy were 100.0%, 96.0%, 96.2%, 100.0% and 98.0% respectively of MMT with TMHT in predicting intubation difficulty. Sensitivity, specificity, PPV, NPV and accuracy were 100.0%, 96.0%, 96.2%, 100.0% and 98.0% respectively of MMT only in predicting intubation difficulty. MMT combined with TMHT is a better predictor of intubation difficulty than MMT alone.

Secured and Privacy-Preserving Multi-Authority Access Control System for Cloud-Based Healthcare Data Sharing

With continuous advancements in Internet technology and the increased use of cryptographic techniques, the cloud has become the obvious choice for data sharing. Generally, the data are outsourced to cloud storage servers in encrypted form. Access control methods can be used on encrypted outsourced data to facilitate and regulate access. Multi-authority attribute-based encryption is a propitious technique to control who can access encrypted data in inter-domain applications such as sharing data between organizations, sharing data in healthcare, etc. The data owner may require the flexibility to share the data with known and unknown users. The known or closed-domain users may be internal employees of the organization, and unknown or open-domain users may be outside agencies, third-party users, etc. In the case of closed-domain users, the data owner becomes the key issuing authority, and in the case of open-domain users, various established attribute authorities perform the task of key issuance. Privacy preservation is also a crucial requirement in cloud-based data-sharing systems. This work proposes the SP-MAACS scheme, a secure and privacy-preserving multi-authority access control system for cloud-based healthcare data sharing. Both open and closed domain users are considered, and policy privacy is ensured by only disclosing the names of policy attributes. The values of the attributes are kept hidden. Characteristic comparison with similar existing schemes shows that our scheme simultaneously provides features such as multi-authority setting, expressive and flexible access policy structure, privacy preservation, and scalability. The performance analysis carried out by us shows that the decryption cost is reasonable enough. Furthermore, the scheme is demonstrated to be adaptively secure under the standard model.

Early Prediction of Diabetes Using an Ensemble of Machine Learning Models

Diabetes is one of the most rapidly spreading diseases in the world, resulting in an array of significant complications, including cardiovascular disease, kidney failure, diabetic retinopathy, and neuropathy, among others, which contribute to an increase in morbidity and mortality rate. If diabetes is diagnosed at an early stage, its severity and underlying risk factors can be significantly reduced. However, there is a shortage of labeled data and the occurrence of outliers or data missingness in clinical datasets that are reliable and effective for diabetes prediction, making it a challenging endeavor. Therefore, we introduce a newly labeled diabetes dataset from a South Asian nation (Bangladesh). In addition, we suggest an automated classification pipeline that includes a weighted ensemble of machine learning (ML) classifiers: Naive Bayes (NB), Random Forest (RF), Decision Tree (DT), XGBoost (XGB), and LightGBM (LGB). Grid search hyperparameter optimization is employed to tune the critical hyperparameters of these ML models. Furthermore, missing value imputation, feature selection, and K-fold cross-validation are included in the framework design. A statistical analysis of variance (ANOVA) test reveals that the performance of diabetes prediction significantly improves when the proposed weighted ensemble (DT + RF + XGB + LGB) is executed with the introduced preprocessing, with the highest accuracy of 0.735 and an area under the ROC curve (AUC) of 0.832. In conjunction with the suggested ensemble model, our statistical imputation and RF-based feature selection techniques produced the best results for early diabetes prediction. Moreover, the presented new dataset will contribute to developing and implementing robust ML models for diabetes prediction utilizing population-level data.

Rank and Wormhole Attack Detection Model for RPL-Based Internet of Things Using Machine Learning

The proliferation of the internet of things (IoT) technology has led to numerous challenges in various life domains, such as healthcare, smart systems, and mission-critical applications. The most critical issue is the security of IoT nodes, networks, and infrastructures. IoT uses the routing protocol for low-power and lossy networks (RPL) for data communication among the devices. RPL comprises a lightweight core and thus does not support high computation and resource-consuming methods for security implementation. Therefore, both IoT and RPL are vulnerable to security attacks, which are broadly categorized into RPL-specific and sensor-network-inherited attacks. Among the most concerning protocol-specific attacks are rank attacks and wormhole attacks in sensor-network-inherited attack types. They target the RPL resources and components including control messages, repair mechanisms, routing topologies, and sensor network resources by consuming. This leads to the collapse of IoT infrastructure. In this paper, a lightweight multiclass classification-based RPL-specific and sensor-network-inherited attack detection model called MC-MLGBM is proposed. A novel dataset was generated through the construction of various network models to address the unavailability of the required dataset, optimal feature selection to improve model performance, and a light gradient boosting machine-based algorithm optimized for a multiclass classification-based attack detection. The results of extensive experiments are demonstrated through several metrics including confusion matrix, accuracy, precision, and recall. For further performance evaluation and to remove any bias, the multiclass-specific metrics were also used to evaluate the model, including cross-entropy, Cohn's kappa, and Matthews correlation coefficient, and then compared with benchmark research.

LLDNet: A Lightweight Lane Detection Approach for Autonomous Cars Using Deep Learning

Lane detection plays a vital role in making the idea of the autonomous car a reality. Traditional lane detection methods need extensive hand-crafted features and post-processing techniques, which make the models specific feature-oriented, and susceptible to instability for the variations on road scenes. In recent years, Deep Learning (DL) models, especially Convolutional Neural Network (CNN) models have been proposed and utilized to perform pixel-level lane segmentation. However, most of the methods focus on achieving high accuracy while considering structured roads and good weather conditions and do not put emphasis on testing their models on defected roads, especially ones with blurry lane lines, no lane lines, and cracked pavements, which are predominant in the real world. Moreover, many of these CNN-based models have complex structures and require high-end systems to operate, which makes them quite unsuitable for being implemented in embedded devices. Considering these shortcomings, in this paper, we have introduced a novel CNN model named LLDNet based on an encoder-decoder architecture that is lightweight and has been tested in adverse weather as well as road conditions. A channel attention and spatial attention module are integrated into the designed architecture to refine the feature maps for achieving outstanding results with a lower number of parameters. We have used a hybrid dataset to train our model, which was created by combining two separate datasets, and have compared the model with a few state-of-the-art encoder-decoder architectures. Numerical results on the utilized dataset show that our model surpasses the compared methods in terms of dice coefficient, IoU, and the size of the models. Moreover, we carried out extensive experiments on the videos of different roads in Bangladesh. The visualization results exhibit that our model can detect the lanes accurately in both structured and defected roads and adverse weather conditions. Experimental results elicit that our designed method is capable of detecting lanes accurately and is ready for practical implementation.

Robust sliding-Backstepping mode control of a wind system based on the DFIG generator

This paper presents a new contribution in the field of the optimization of the techniques of control of the wind systems and the improvement of the quality of energy produced in the grid. The Sliding Mode control technique gives quite interesting results, but its major drawback lies in the phenomenon of chattering (oscillations), which reduces the system's precision. We propose in this work a solution to cancel this chattering phenomenon by the implication of the adaptive Backstepping technique to control the powers of the double-fed asynchronous generator (DFIG) connected to the electrical network by two converters (network side and side machine) in the nominal part of the sliding mode model. This hybrid technique will correct errors of precision and stability and the performance of the wind system obtained in terms of efficiency, active and reactive power is significant. First, a review of the wind system was presented. Then, an exhaustive explanation of the Backstepping technique based on the Lyapunov stability and optimization method has been reported. Subsequently, a validation on the Matlab & Simulink environment was carried out to test the performance and robustness of the proposed model. The results obtained from this work, either by follow-up or robustness tests, show a significant performance improvement compared to other control techniques.

Earlier Detection of Brain Tumor by Pre-Processing Based on Histogram Equalization with Neural Network

MRI is an influential diagnostic imaging technology specifically worn to detect pathological changes in tissues with organs early. It is also a non-invasive imaging method. Medical image segmentation is a complex and challenging process due to the intrinsic nature of images. The most consequential imaging analytical approach is MRI, which has been in use to detect abnormalities in tissues and human organs. The portrait was actualized for CAD (computer-assisted diagnosis) utilizing image processing techniques with deep learning, initially to perceive a brain tumor in a person with early signs of brain tumor. Using AHCN-LNQ (adaptive histogram contrast normalization with learning-based neural quantization), the first image is preprocessed. When compared to extant techniques, the simulation outcome shows that this proposed method achieves an accuracy of 93%, precision of 92%, and 94% of specificity.

Improve correlation matrix of Discrete Fourier Transformation technique for finding the missing values of MRI images

Missing values in the k-NN algorithm are a significant research concern, especially in low-grade tumours and CSF fluid, which are commonly identified in MRI scans. Missing values are usually ignored, but when data is mined, they can lead to bias and errors. In addition, the data is not missing at random. This study improves image accuracy, boosts the efficiency of missing k-NN hybrid values, and develops a research technique for detecting CSF fluid deposits in brain areas separated from non-tumor areas. We also offer a new method for detecting low-grade tumours or cerebrospinal fluid (CSF) formation in its early stages. In this study, we combine the hybrid K-Nearest Neighbor algorithm with the Discrete Fourier transform (DFT), as well as Time-Lagged analysis of four-dimensional (4D) MRI images. These dependencies exist in both space and time, but present techniques do not account for both sequential linkages and numerous types of missingness. To address this, we propose the DFLk-NN imputation method, which combines two imputation approaches based on a hybrid k-NN extension and the DFT to capture time-lag correlations both within and across variables. There are several types of missingness are enables the imputation of missing values across the variable even when all the data for a given time point is missing. The proposed method gives high accuracies of MRI datasets and retrieves the missing data in the images.

Transfer Learning for Sentiment Analysis Using BERT Based Supervised Fine-Tuning

The growth of the Internet has expanded the amount of data expressed by users across multiple platforms. The availability of these different worldviews and individuals' emotions empowers sentiment analysis. However, sentiment analysis becomes even more challenging due to a scarcity of standardized labeled data in the Bangla NLP domain. The majority of the existing Bangla research has relied on models of deep learning that significantly focus on context-independent word embeddings, such as Word2Vec, GloVe, and fastText, in which each word has a fixed representation irrespective of its context. Meanwhile, context-based pre-trained language models such as BERT have recently revolutionized the state of natural language processing. In this work, we utilized BERT's transfer learning ability to a deep integrated model CNN-BiLSTM for enhanced performance of decision-making in sentiment analysis. In addition, we also introduced the ability of transfer learning to classical machine learning algorithms for the performance comparison of CNN-BiLSTM. Additionally, we explore various word embedding techniques, such as Word2Vec, GloVe, and fastText, and compare their performance to the BERT transfer learning strategy. As a result, we have shown a state-of-the-art binary classification performance for Bangla sentiment analysis that significantly outperforms all embedding and algorithms.

Explainable AI in Diagnosing and Anticipating Leukemia Using Transfer Learning Method

White blood cells (WBCs) are blood cells that fight infections and diseases as a part of the immune system. They are also known as "defender cells." But the imbalance in the number of WBCs in the blood can be hazardous. Leukemia is the most common blood cancer caused by an overabundance of WBCs in the immune system. Acute lymphocytic leukemia (ALL) usually occurs when the bone marrow creates many immature WBCs that destroy healthy cells. People of all ages, including children and adolescents, can be affected by ALL. The rapid proliferation of atypical lymphocyte cells can cause a reduction in new blood cells and increase the chances of death in patients. Therefore, early and precise cancer detection can help with better therapy and a higher survival probability in the case of leukemia. However, diagnosing ALL is time-consuming and complicated, and manual analysis is expensive, with subjective and error-prone outcomes. Thus, detecting normal and malignant cells reliably and accurately is crucial. For this reason, automatic detection using computer-aided diagnostic models can help doctors effectively detect early leukemia. The entire approach may be automated using image processing techniques, reducing physicians' workload and increasing diagnosis accuracy. The impact of deep learning (DL) on medical research has recently proven quite beneficial, offering new avenues and possibilities in the healthcare domain for diagnostic techniques. However, to make that happen soon in DL, the entire community must overcome the explainability limit. Because of the black box operation's shortcomings in artificial intelligence (AI) models' decisions, there is a lack of liability and trust in the outcomes. But explainable artificial intelligence (XAI) can solve this problem by interpreting the predictions of AI systems. This study emphasizes leukemia, specifically ALL. The proposed strategy recognizes acute lymphoblastic leukemia as an automated procedure that applies different transfer learning models to classify ALL. Hence, using local interpretable model-agnostic explanations (LIME) to assure validity and reliability, this method also explains the cause of a specific classification. The proposed method achieved 98.38% accuracy with the InceptionV3 model. Experimental results were found between different transfer learning methods, including ResNet101V2, VGG19, and InceptionResNetV2, later verified with the LIME algorithm for XAI, where the proposed method performed the best. The obtained results and their reliability demonstrate that it can be preferred in identifying ALL, which will assist medical examiners.

A light-weight convolutional Neural Network Architecture for classification of COVID-19 chest X-Ray images

The COVID-19 pandemic has opened numerous challenges for scientists to use massive data to develop an automatic diagnostic tool for COVID-19. Since the outbreak in January 2020, COVID-19 has caused a substantial destructive impact on society and human life. Numerous studies have been conducted in search of a suitable solution to test COVID-19. Artificial intelligence (AI) based research is not behind in this race, and many AI-based models have been proposed. This paper proposes a lightweight convolutional neural network (CNN) model to classify COVID and Non_COVID patients by analyzing the hidden features in the X-Ray images. The model has been evaluated with different standard metrics to prove the reliability of the model. The model obtained 98.78%, 93.22%, and 92.7% accuracy in the training, validation, and testing phases. In addition, the model achieved 0.964 scores in the Area Under Curve (AUC) metric. We compared the model with four state-of-art pre-trained models (VGG16, InceptionV3, DenseNet121, and EfficientNetB6). The evaluation results demonstrate that the proposed CNN model is a candidate for an automatic diagnostic tool for the classification of COVID-19 patients using chest X-ray images. This research proposes a technique to classify COVID-19 patients and does not claim any medical diagnosis accuracy.

Cataract Disease Detection by Using Transfer Learning-Based Intelligent Methods

One of the most common visual disorders is cataracts, which people suffer from as they get older. The creation of a cloud on the lens of our eyes is known as a cataract. Blurred vision, faded colors, and difficulty seeing in strong light are the main symptoms of this condition. These symptoms frequently result in difficulty doing a variety of tasks. As a result, preliminary cataract detection and prevention may help to minimize the rate of blindness. This paper is aimed at classifying cataract disease using convolutional neural networks based on a publicly available image dataset. In this observation, four different convolutional neural network (CNN) meta-architectures, including InceptionV3, InceptionResnetV2, Xception, and DenseNet121, were applied by using the TensorFlow object detection framework. By using InceptionResnetV2, we were able to attain the avant-garde in cataract disease detection. This model predicted cataract disease with a training loss of 1.09%, a training accuracy of 99.54%, a validation loss of 6.22%, and a validation accuracy of 98.17% on the dataset. This model also has a sensitivity of 96.55% and a specificity of 100%. In addition, the model greatly minimizes training loss while boosting accuracy.

Deep Learning-Based Sentiment Analysis of COVID-19 Vaccination Responses from Twitter Data

The COVID-19 pandemic has had a devastating effect on many people, creating severe anxiety, fear, and complicated feelings or emotions. After the initiation of vaccinations against coronavirus, people's feelings have become more diverse and complex. Our aim is to understand and unravel their sentiments in this research using deep learning techniques. Social media is currently the best way to express feelings and emotions, and with the help of Twitter, one can have a better idea of what is trending and going on in people's minds. Our motivation for this research was to understand the diverse sentiments of people regarding the vaccination process. In this research, the timeline of the collected tweets was from December 21 to July21. The tweets contained information about the most common vaccines available recently from across the world. The sentiments of people regarding vaccines of all sorts were assessed using the natural language processing (NLP) tool, Valence Aware Dictionary for sEntiment Reasoner (VADER). Initializing the polarities of the obtained sentiments into three groups (positive, negative, and neutral) helped us visualize the overall scenario; our findings included 33.96% positive, 17.55% negative, and 48.49% neutral responses. In addition, we included our analysis of the timeline of the tweets in this research, as sentiments fluctuated over time. A recurrent neural network- (RNN-) oriented architecture, including long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM), was used to assess the performance of the predictive models, with LSTM achieving an accuracy of 90.59% and Bi-LSTM achieving 90.83%. Other performance metrics such as precision,, F1-score, and a confusion matrix were also used to validate our models and findings more effectively. This study improves understanding of the public's opinion on COVID-19 vaccines and supports the aim of eradicating coronavirus from the world.

A Lightweight and Robust Secure Key Establishment Protocol for Internet of Medical Things in COVID-19 Patients Care

Due to the outbreak of COVID-19, the Internet of Medical Things (IoMT) has enabled the doctors to remotely diagnose the patients, control the medical equipment, and monitor the quarantined patients through their digital devices. Security is a major concern in IoMT because the Internet of Things (IoT) nodes exchange sensitive information between virtual medical facilities over the vulnerable wireless medium. Hence, the virtual facilities must be protected from adversarial threats through secure sessions. This article proposes a lightweight and physically secure mutual authentication and secret key establishment protocol that uses physical unclonable functions (PUFs) to enable the network devices to verify the doctor's legitimacy (user) and sensor node before establishing a session key. PUF also protects the sensor nodes deployed in an unattended and hostile environment from tampering, cloning, and side-channel attacks. The proposed protocol exhibits all the necessary security properties required to protect the IoMT networks, like authentication, confidentiality, integrity, and anonymity. The formal AVISPA and informal security analysis demonstrate its robustness against attacks like impersonation, replay, a man in the middle, etc. The proposed protocol also consumes fewer resources to operate and is safe from physical attacks, making it more suitable for IoT-enabled medical network applications.

A novel air quality monitoring and improvement system based on wireless sensor and actuator networks using LoRa communication

In this paper, we study the air quality monitoring and improvement system based on wireless sensor and actuator network using LoRa communication. The proposed system is divided into two parts, indoor cluster and outdoor cluster, managed by a Dragino LoRa gateway. Each indoor sensor node can receive information about the temperature, humidity, air quality, dust concentration in the air and transmit them to the gateway. The outdoor sensor nodes have the same functionality, add the ability to use solar power, and are waterproof. The full-duplex relay LoRa modules which are embedded FreeRTOS are arranged to forward information from the nodes they manage to the gateway via uplink LoRa. The gateway collects and processes all of the system information and makes decisions to control the actuator to improve the air quality through the downlink LoRa. We build data management and analysis online software based on The Things Network and TagoIO platform. The system can operate with a coverage of 8.5 km, where optimal distances are established between sensor nodes and relay nodes and between relay nodes and gateways at 4.5 km and 4 km, respectively. Experimental results observed that the packet loss rate in real-time is less than 0.1% prove the effectiveness of the proposed system.

Predicting the Risk of Depression Based on ECG Using RNN

This paper presents a model to predict the risk of depression based on electrocardiogram (ECG). This proposed model uses a Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) autoencoder to predict normal, abnormal, and PVC heartbeats. The RNN model is a deep learning-based model to classify normal, abnormal, and PVC heartbeats. We used the model as a classifier. The model uses a heart rates dataset to predict abnormal and PVC heartbeats. As for the dataset, we have used 5000 ECG samples. The model was trained on a training dataset and validation dataset. After that, it was tested on a test dataset. The model is trained on normal heartbeat rates, so the model can predict any heartbeat rates other than normal. Our contribution here is to build a model that can differentiate between "normal," "abnormal," and "risky" heartbeats. Our model predicts "normal" heartbeats with 97.24% accuracy and can predict "PVC" heartbeats with 100% accuracy. Other than the accuracy, we evaluated our model on the training loss graphs. These two types of training loss graphs were evaluated as "normal" versus "risky" and "abnormal" versus "risky." We have seen great results there as well. The best losses for "normal," "abnormal," and "risky" are 5.71, 33.36, and 34.78. However, these results may improve if a larger dataset is used. In studies, it was found that patients suffering from depression may have a different kind of heartbeat than the normal ones. In most cases, it is PVC (Premature Ventricular Contraction) heartbeats. Therefore, the target is to predict abnormal heartbeats and PVC heartbeats.

Artificial Neural Network-Based Deep Learning Model for COVID-19 Patient Detection Using X-Ray Chest Images

The world is experiencing an unprecedented crisis due to the coronavirus disease (COVID-19) outbreak that has affected nearly 216 countries and territories across the globe. Since the pandemic outbreak, there is a growing interest in computational model-based diagnostic technologies to support the screening and diagnosis of COVID-19 cases using medical imaging such as chest X-ray (CXR) scans. It is discovered in initial studies that patients infected with COVID-19 show abnormalities in their CXR images that represent specific radiological patterns. Still, detection of these patterns is challenging and time-consuming even for skilled radiologists. In this study, we propose a novel convolutional neural network- (CNN-) based deep learning fusion framework using the transfer learning concept where parameters (weights) from different models are combined into a single model to extract features from images which are then fed to a custom classifier for prediction. We use gradient-weighted class activation mapping to visualize the infected areas of CXR images. Furthermore, we provide feature representation through visualization to gain a deeper understanding of the class separability of the studied models with respect to COVID-19 detection. Cross-validation studies are used to assess the performance of the proposed models using open-access datasets containing healthy and both COVID-19 and other pneumonia infected CXR images. Evaluation results show that the best performing fusion model can attain a classification accuracy of 95.49% with a high level of sensitivity and specificity.

A robust and lightweight secure access scheme for cloud based E-healthcare services

Traditional healthcare services have transitioned into modern healthcare services where doctors remotely diagnose the patients. Cloud computing plays a significant role in this change by providing easy access to patients' medical records to all stakeholders, such as doctors, nurses, patients, life insurance agents, etc. Cloud services are scalable, cost-effective, and offer a broad range of mobile access to patients' electronic health record (EHR). Despite the cloud's enormous benefits like real-time data access, patients' EHR security and privacy are major concerns. Since the information about patients' health is highly sensitive and crucial, sharing it over the unsecured wireless medium brings many security challenges such as eavesdropping, modifications, etc. Considering the security needs of remote healthcare, this paper proposes a robust and lightweight, secure access scheme for cloud-based E-healthcare services. The proposed scheme addresses the potential threats to E-healthcare by providing a secure interface to stakeholders and prohibiting unauthorized users from accessing information stored in the cloud. The scheme makes use of multiple keys formed through the key derivation function (KDF) to ensure end-to-end ciphering of information for preventing misuse. The rights to access the cloud services are provided based on the identity and the association between stakeholders, thus ensuring privacy. Due to its simplicity and robustness, the proposed scheme is the best fit for protecting data security and privacy in cloud-based E-healthcare services.

Medical image-based detection of COVID-19 using Deep Convolution Neural Networks

The demand for automatic detection of Novel Coronavirus or COVID-19 is increasing across the globe. The exponential rise in cases burdens healthcare facilities, and a vast amount of multimedia healthcare data is being explored to find a solution. This study presents a practical solution to detect COVID-19 from chest X-rays while distinguishing those from normal and impacted by Viral Pneumonia via Deep Convolution Neural Networks (CNN). In this study, three pre-trained CNN models (EfficientNetB0, VGG16, and InceptionV3) are evaluated through transfer learning. The rationale for selecting these specific models is their balance of accuracy and efficiency with fewer parameters suitable for mobile applications. The dataset used for the study is publicly available and compiled from different sources. This study uses deep learning techniques and performance metrics (accuracy, recall, specificity, precision, and F1 scores). The results show that the proposed approach produced a high-quality model, with an overall accuracy of 92.93%, COVID-19, a sensitivity of 94.79%. The work indicates a definite possibility to implement computer vision design to enable effective detection and screening measures.

Prospects for beyond the Standard Model physics searches at the Deep Underground Neutrino Experiment: DUNE Collaboration

The Deep Underground Neutrino Experiment (DUNE) will be a powerful tool for a variety of physics topics. The high-intensity proton beams provide a large neutrino flux, sampled by a near detector system consisting of a combination of capable precision detectors, and by the massive far detector system located deep underground. This configuration sets up DUNE as a machine for discovery, as it enables opportunities not only to perform precision neutrino measurements that may uncover deviations from the present three-flavor mixing paradigm, but also to discover new particles and unveil new interactions and symmetries beyond those predicted in the Standard Model (SM). Of the many potential beyond the Standard Model (BSM) topics DUNE will probe, this paper presents a selection of studies quantifying DUNE's sensitivities to sterile neutrino mixing, heavy neutral leptons, non-standard interactions, CPT symmetry violation, Lorentz invariance violation, neutrino trident production, dark matter from both beam induced and cosmogenic sources, baryon number violation, and other new physics topics that complement those at high-energy colliders and significantly extend the present reach.

An empirical study on vulnerability assessment and penetration detection for highly sensitive networks

With the advancement of internet and the emergence of network globalization, security has always been a major concern. During the trial operation, the management control platform discussed in this article included more than 600 network security vulnerabilities in the industry, with dozens of incidents, which were promptly dealt with and rectified, effectively improving the level of network security management and protection in the industry. As networks are very much vulnerable to denial of service attacks, much more emphasis has been given to security. By improving their network security, network administrators have often tried their best. To attempt penetration testing, it is the best way of ensuring the system security. With the development of information technology, the security requirement of information system is increasing day by day. The use of penetration testing technology is conducive to the realization of accurate positioning, accurate detection, and active alarm of security vulnerabilities, and the optimization of monitoring and rectification of the combination of network security management control system. Taking penetration testing technology as one of the core elements of management and control, the risk index model is optimized to make network security management controllable and efficient, and effectively achieve management and control objectives.

A Novel Hybrid Precoding-Companding Technique for Peak-to-Average Power Ratio Reduction in 5G and beyond

Several high-speed wireless systems use Orthogonal Frequency Division Multiplexing (OFDM) due to its advantages. 5G has adopted OFDM and is expected to be considered beyond 5G (B5G). Meanwhile, OFDM has a high Peak-to-Average Power Ratio (PAPR) problem. Hybridization between two PAPR reduction techniques gains the two techniques’ advantages. Hybrid precoding-companding techniques are attractive as they require small computational complexity to achieve high PAPR reduction gain. Many precoding-companding techniques were introduced to increasing the PAPR reduction gain. However, reducing Bit Error Rate (BER) and out-of-band (OOB) radiation are more significant than increasing PAPR reduction gain. This paper proposes a new precoding-companding technique to better reduce the BER and OOB radiation than previous precoding-companding techniques. Results showed that the proposed technique outperforms all previous precoding-companding techniques in BER enhancement and OOB radiation reduction. The proposed technique reduces the Error Vector Magnitude (EVM) by 15 dB compared with 10 dB for the best previous technique. Additionally, the proposed technique increases high power amplifier efficiency (HPA) by 11.4%, while the best previous technique increased HPA efficiency by 9.8%. Moreover, our proposal achieves PAPR reduction gain better than the most known powerful PAPR reduction technique with a 99% reduction in required computational complexity.

A Novel Bayesian Optimization-Based Machine Learning Framework for COVID-19 Detection From Inpatient Facility Data

The whole world faces a pandemic situation due to the deadly virus, namely COVID-19. It takes considerable time to get the virus well-matured to be traced, and during this time, it may be transmitted among other people. To get rid of this unexpected situation, quick identification of COVID-19 patients is required. We have designed and optimized a machine learning-based framework using inpatient's facility data that will give a user-friendly, cost-effective, and time-efficient solution to this pandemic. The proposed framework uses Bayesian optimization to optimize the hyperparameters of the classifier and ADAptive SYNthetic (ADASYN) algorithm to balance the COVID and non-COVID classes of the dataset. Although the proposed technique has been applied to nine state-of-the-art classifiers to show the efficacy, it can be used to many classifiers and classification problems. It is evident from this study that eXtreme Gradient Boosting (XGB) provides the highest Kappa index of 97.00%. Compared to without ADASYN, our proposed approach yields an improvement in the kappa index of 96.94%. Besides, Bayesian optimization has been compared to grid search, random search to show efficiency. Furthermore, the most dominating features have been identified using SHapely Adaptive exPlanations (SHAP) analysis. A comparison has also been made among other related works. The proposed method is capable enough of tracing COVID patients spending less time than that of the conventional techniques. Finally, two potential applications, namely, clinically operable decision tree and decision support system, have been demonstrated to support clinical staff and build a recommender system.

Controlling the Outbreak of COVID-19: A Noncooperative Game Perspective

COVID-19 is a global epidemic. Till now, there is no remedy for this epidemic. However, isolation and social distancing are seemed to be effective preventive measures to control this pandemic. Therefore, in this article, an optimization problem is formulated that accommodates both isolation and social distancing features of the individuals. To promote social distancing, we solve the formulated problem by applying a noncooperative game that can provide an incentive for maintaining social distancing to prevent the spread of COVID-19. Furthermore, the sustainability of the lockdown policy is interpreted with the help of our proposed game-theoretic incentive model for maintaining social distancing where there exists a Nash equilibrium. Finally, we perform an extensive numerical analysis that shows the effectiveness of the proposed approach in terms of achieving the desired social-distancing to prevent the outbreak of the COVID-19 in a noncooperative environment. Numerical results show that the individual incentive increases more than 85% with an increasing percentage of home isolation from 25% to 100% for all considered scenarios. The numerical results also demonstrate that in a particular percentage of home isolation, the individual incentive decreases with an increasing number of individuals.

Convolutional neural network-based models for diagnosis of breast cancer

Breast cancer is the most prevailing cancer in the world and each year affecting millions of women. It is also the cause of largest number of deaths in women dying in cancers. During the last few years, researchers are proposing different convolutional neural network models in order to facilitate diagnostic process of breast cancer. Convolutional neural networks are showing promising results to classify cancers using image datasets. There is still a lack of standard models which can claim the best model because of unavailability of large datasets that can be used for models’ training and validation. Hence, researchers are now focusing on leveraging the transfer learning approach using pre-trained models as feature extractors that are trained over millions of different images. With this motivation, this paper considers eight different fine-tuned pre-trained models to observe how these models classify breast cancers applying on ultrasound images. We also propose a shallow custom convolutional neural network that outperforms the pre-trained models with respect to different performance metrics. The proposed model shows 100% accuracy and achieves 1.0 AUC score, whereas the best pre-trained model shows 92% accuracy and 0.972 AUC score. In order to avoid biasness, the model is trained using the fivefold cross validation technique. Moreover, the model is faster in training than the pre-trained models and requires a small number of trainable parameters. The Grad-CAM heat map visualization technique also shows how perfectly the proposed model extracts important features to classify breast cancers.

A Novel Method to Identify Pneumonia through Analyzing Chest Radiographs Employing a Multichannel Convolutional Neural Network

Pneumonia is a virulent disease that causes the death of millions of people around the world. Every year it kills more children than malaria, AIDS, and measles combined and it accounts for approximately one in five child-deaths worldwide. The invention of antibiotics and vaccines in the past century has notably increased the survival rate of Pneumonia patients. Currently, the primary challenge is to detect the disease at an early stage and determine its type to initiate the appropriate treatment. Usually, a trained physician or a radiologist undertakes the task of diagnosing Pneumonia by examining the patient's chest X-ray. However, the number of such trained individuals is nominal when compared to the 450 million people who get affected by Pneumonia every year. Fortunately, this challenge can be met by introducing modern computers and improved Machine Learning techniques in Pneumonia diagnosis. Researchers have been trying to develop a method to automatically detect Pneumonia using machines by analyzing and the symptoms of the disease and chest radiographic images of the patients for the past two decades. However, with the development of cogent Deep Learning algorithms, the formation of such an automatic system is very much within the realms of possibility. In this paper, a novel diagnostic method has been proposed while using Image Processing and Deep Learning techniques that are based on chest X-ray images to detect Pneumonia. The method has been tested on a widely used chest radiography dataset, and the obtained results indicate that the model is very much potent to be employed in an automatic Pneumonia diagnosis scheme.

Mitigation of Distributed Denial of Service Attacks in the Cloud

Cybersecurity attacks resulting in loss of availability of cloud services can have significantly higher impact than those in the traditional stand-alone enterprise setups. Therefore, availability attacks, such as Denial of Service attacks (DoS); Distributed DoS attacks (DDoS) and Economical Denial of Sustainability (EDoS) attacks receive increasingly more attention. This paper surveys existing DDoS attacks analyzing the principles, ways of launching and their variants. Then, current mitigation systems are critically discussed. Based on the identification of the weak points, the paper proposes a new mitigation system named as DDoS-Mitigation System (DDoS-MS) that attempts to overcome the identified gap. The proposed framework is evaluated, and an enhanced version of the proposed system called Enhanced DDoS-MS is presented. In the end, the paper presents some future directions of the proposed framework.

Role of Oral Clonidine Premedication on Intra-operative Haemodynamics and PONV in Laparoscopic Cholecystectomy

Laparoscopic cholecystectomy is a gold standard surgical procedure for gallbladder operation. It causes altered haemodynamic responses due to pneumoperitoneum and surgical procedure also causes high incidence of postoperative nausea &vomiting. Clonidine has been shown to reduce intraoperative haemodynamic instability. This randomized prospective single blind clinical study was designed to evaluate the efficacy of oral clonidine premedication for attenuation of haemodynamic responses associated with pneumoperitoneum & also reduce the incidence of postoperative nausea & vomiting and carried out in the department of Anaesthesia Analgesia and Intensive care Medicine, Bangabandhu Sheikh Mujib Medical University, Dhaka, Bangladesh from January 2010 to June 2010. Sixty adult patients of ASA physical status I & II scheduled for elective laparoscopic cholecystecomy were recruited for a prospective randomized single blind study. They were selected randomized into two equal groups, thirty in each group. Group A received no premedication & Group B (Clonidine) received clonidine (100μgm) orally 60 minutes before induction of anaesthesia. Pulse rate, mean arterial pressure were recorded prior to induction, 2 minutes after endotracheal intubation, before pneumoperitoneum, 10 minutes & 20 minutes after pneumoperitoneum, 10 minutes after release of carbon dioxide & 10 minutes after extubation. Patients in Group B (Clonidine) maintained greater haemodynamic stability intraoperatively compare to Group A after intubation, during pneumoperitoneum and also extubation. Pulse rate & mean arterial pressure significantly varies in Group A compare with Group B (Clonidine) at different times of intraoperative period (p<0.05). Postoperative nausea & vomiting was significantly less in Group B (Clonidine). Premedication with oral clonidine attenuates the haemodynamic responses produced by pneumoperitoneum during laparoscopic cholecystectomy and also significantly nausea and vomiting, results better patient satisfaction and cost effective.

Cerebral metabolic changes in men after chiropractic spinal manipulation for neck pain

BACKGROUND

Chiropractic spinal manipulation (CSM) is an alternative treatment for back pain. The autonomic nervous system is often involved in spinal dysfunction. Although studies on the effects of CSM have been performed, no chiropractic study has examined regional cerebral metabolism using positron emission tomography (PET).

OBJECTIVE

The aim of the present study was to investigate the effects of CSM on brain responses in terms of cerebral glucose metabolic changes measured by [18F]fluorodeoxyglucose positron emission tomography (FDG-PET).

METHODS

Twelve male volunteers were recruited. Brain PET scanning was performed twice on each participant, at resting and after CSM. Questionnaires were used for subjective evaluations. A visual analogue scale (VAS) was rated by participants before and after chiropractic treatment, and muscle tone and salivary amylase were measured.

RESULTS

Increased glucose metabolism was observed in the inferior prefrontal cortex, anterior cingulated cortex, and middle temporal gyrus, and decreased glucose metabolism was found in the cerebellar vermis and visual association cortex, in the treatment condition (P < .001). Comparisons of questionnaires indicated a lower stress level and better quality of life in the treatment condition. A significantly lower VAS was noted after CSM. Cervical muscle tone and salivary amylase were decreased after CSM. Conclusion The results of this study suggest that CSM affects regional cerebral glucose metabolism related to sympathetic relaxation and pain reduction.

Application of positron emission tomography in physical medicine

Positron emission tomography (PET) is widely used in the fields of clinical and basic medicine. The PET device utilizes coincidence logic to detect annihilation photons emitted from positrons and estimates physiological functions of human organs in vivo. Radiopharmaceutical 18F- fluorodeoxyglucose (FDG), an analogue of glucose, is trapped metabolically in cells after being administered into the body, and can be substantially used for evaluating physiological and biochemical functions in vivo. Here, we attempted to describe the basics of PET as well as to apply the technique together with 18F-FDG as a tracer for evaluating organ glucose metabolism induced by exercise. Three-dimensional (3D) FDG-PET was applied to normal volunteers who performed exercise to evaluate whole-body glucose metabolism. Regions of interest analysis were drawn on visually defined regions (i.e., lower limbs, thigh, liver, intestine, brain, heart, etc.) to determine radioactivity distribution. FDG-PET clearly showed the recruitment of energy resources from abdominal organs to lower limb skeletal muscles to balance energy expenditures. The results suggested that 3D FDG-PET can be applied as an imaging tool to physical medicine.

Effects of aerobic exercise training on brain structure and psychological well-being in young adults

AIM

There is convergent evidence that exercise increases psychological well-being; however, the mechanism of this psychological effect of exercise is not yet completely understood. The purpose of this study was to examine the effects of aerobic exercise training on brain structure and psychological well-being in young adults.

METHODS

University students who had not regularly exercised were divided into training group (N.=15) and control group (N.=15). The training group performed a total 30 periods of aerobic exercise training, while the control group never performed. Whole-brain magnetic resonance imaging scans and mental health questionnaire examinations were performed before and after the exercise training period for all of the participants. A voxel-based morphometry (VBM) analysis was used to compare the changes in gray-matter volumes in the two groups. VBM is an objective whole-brain technique for characterization of regional cerebral volume and tissue concentration differences in structural magnetic resonance images.

RESULTS

The results of VBM analysis revealed no change in gray-matter volume in the training group, although the gray-matter volume of the left insula was significantly decreased in the control group after the exercise training period. The training group exhibited significant improvement in some scores on the mental health questionnaire after the exercise training period, compared with the control group.

CONCLUSIONS

These findings suggest that aerobic exercise training may inhibit gray-matter volume loss in the insula, and that a relationship may exist between preservation of insula gray-matter and improvement of psychological well-being by aerobic exercise training.

Regional cerebral glucose metabolism of patients with malignant diseases in different clinical phases

BACKGROUND

Psychological and psychiatric aspects of cancer patients have not been studied well in terms of functional neuroimaging. The aim of this pilot study was to investigate the relationship between regional cerebral metabolism and different clinical phases.

MATERIAL AND METHODS

Relative cerebral glucose metabolism of 77 Japanese patients with various types of malignant diseases was studied by positron emission tomography with 18F-fluorodeoxyglucose. They were subgrouped into the 1) pre-treatment, 2) post-treatment, 3) recurrence and 4) terminal patient groups. These subgroups were compared to the control group of 17 in-patients with benign diseases, using voxel-based Statistic Parametrical Mapping software (SPM).

RESULTS

Relative reduction in the regional cerebral metabolism was detected in the prefrontal and basolateral (inferolateral) prefrontal cortex, orbitofrontal cortex, anterior and posterior cingulate gyrus, insula, basal ganglia, hippocampus and thalamus in the pre-treatment group. The anterior and posterior cingulate gyri were hypometabolic only in the pre-treatment group. Hypometabolic areas were detected only in the basolateral prefrontal cortex, orbitofrontal cortex, ventral part of cingulate gyrus and insula in the post-treatment group. In the terminal group, the hypometabolic pattern seemed very close to that in the pre-treatment group. The results seemed to suggest that hypometabolic findings before treatments could reverse after treatments but became prominent again in terminal stages. These results do not contradict with previous epidemiological findings that high incidence of adjustment disorders was seen in earlier stages and more brain organic syndromes were seen in terminal stages.

CONCLUSION

It could be speculated that decreased metabolism in early stages reflects state-dependent changes and that decreased metabolism in the terminal stages reflects organic brain damages. brain of cancer patients show a fluctuation in the regional metabolism. This finding might give a suggestion that functional imaging could be used as a supplementary method for psychological evaluation of patients with severe diseases.

Evaluation of resting brain conditions measured by two different methods (i.v. and oral administration) with18F-FDG-PET

Our aim was to evaluate regional differences between brain activity in two resting control conditions measured by 3D PET after administration of FDG through either the intravenous (i.v.) or the oral route. Ten healthy male volunteers engaged in the study as the i.v. group (mean age, 26 +/- 9.3 years, +/- S.D.) who received FDG intravenously and another 10 volunteers as the oral group (mean age, 27.9 +/- 11.3 years, +/- S.D.) who received FDG per os. A set of 3D-PET scans (emission and transmission scans) were performed in both groups. To explore possible functional differences between the brains of the two groups, the SPM-96 software was used for statistical analysis. The results revealed that glucose metabolism was significantly higher in the superior frontal gyrus, superior parietal lobule, lingual gyrus and left cerebellar hemisphere in the i.v. group than in the oral group. Metabolically active areas were found in the superior, middle and inferior temporal gyrus, parahippocampal gyrus, amygdaloid nucleus, pons and cerebellum in the oral group when compared with the i.v. group. These differences were presumably induced by differences between FDG kinetics and/or time-weighted behavioral effects in the two studies. This study suggests the need for extreme caution when selecting a pooled control population for designated activation studies.