World Academy of Science, Engineering and Technology

Authors: Shaikah Alsubayae, Gianluigi Casse, Carlos Chavez, Jon Taylor, Alan Taylor, Mohammad Alsulimane

Abstract:

Ensuring accurate radiotherapy with carbon therapy requires precise monitoring of radiation dose distribution within the patient's body. This monitoring is essential for targeted tumor treatment, minimizing harm to healthy tissues, and improving treatment effectiveness while lowering side effects. In our investigation, we employed a methodological approach to monitor secondary proton doses in carbon therapy using Monte Carlo simulations. Initially, Geant4 simulations were utilized to extract the initial positions of secondary particles formed during interactions between carbon ions and water. These particles included protons, gamma rays, alpha particles, neutrons, and tritons. Subsequently, we studied the relationship between the carbon ion beam and these secondary particles. Interaction Vertex Imaging (IVI) is valuable for monitoring dose distribution in carbon therapy. It provides details about the positions and amounts of secondary particles, particularly protons. The IVI method depends on charged particles produced during ion fragmentation to gather information about the range by reconstructing particle trajectories back to their point of origin, referred to as the vertex. In our simulations regarding carbon ion therapy, we observed a strong correlation between some secondary particles and the range of carbon ions. However, challenges arose due to the target's unique elongated geometry, which hindered the straightforward transmission of forward-generated protons. Consequently, the limited protons that emerged mostly originated from points close to the target entrance. The trajectories of fragments (protons) were approximated as straight lines, and a beam back-projection algorithm, using recorded interaction positions in Si detectors, was developed to reconstruct vertices. The analysis revealed a correlation between the reconstructed and actual positions.

Keywords: radiotherapy, carbon therapy, monitoring of radiation dose, interaction vertex imaging

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Authors: Shaikah Alsubayae, Gianluigi Casse, Carlos Chavez, Jon Taylor, Alan Taylor, Mohammad Alsulimane

Abstract:

Achieving precise radiotherapy through carbon therapy necessitates the accurate monitoring of radiation dose distribution within the patient's body. This process is pivotal for targeted tumor treatment, minimizing harm to healthy tissues, and enhancing overall treatment effectiveness while reducing the risk of side effects. In our investigation, we adopted a methodological approach to monitor secondary proton doses in carbon therapy using Monte Carlo (MC) simulations. Initially, Geant4 simulations were employed to extract the initial positions of secondary particles generated during interactions between carbon ions and water, including protons, gamma rays, alpha particles, neutrons, and tritons. Subsequently, we explored the relationship between the carbon ion beam and these secondary particles. Interaction vertex imaging (IVI) proves valuable for monitoring dose distribution during carbon therapy, providing information about secondary particle locations and abundances, particularly protons. The IVI method relies on charged particles produced during ion fragmentation to gather range information by reconstructing particle trajectories back to their point of origin, known as the vertex. In the context of carbon ion therapy, our simulation results indicated a strong correlation between some secondary particles and the range of carbon ions. However, challenges arose due to the unique elongated geometry of the target, hindering the straightforward transmission of forward-generated protons. Consequently, the limited protons that did emerge predominantly originated from points close to the target entrance. Fragment (protons) trajectories were approximated as straight lines, and a beam back-projection algorithm, utilizing interaction positions recorded in Si detectors, was developed to reconstruct vertices. The analysis revealed a correlation between the reconstructed and actual positions.

Keywords: radiotherapy, carbon therapy, monitor secondary proton doses, interaction vertex imaging

Procedia PDF Downloads 46

Authors: Malik Muhammad Arslan, Muneeb Ullah, Dai Shihan, Daniyal Haider, Xiaodong Yang

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Chest X-rays are instrumental in the detection and monitoring of a wide array of diseases, including viral infections such as COVID-19, tuberculosis, pneumonia, lung cancer, and various cardiac and pulmonary conditions. To enhance the accuracy of diagnosis, artificial intelligence (AI) algorithms, particularly deep learning models like Convolutional Neural Networks (CNNs), are employed. However, these deep learning models demand a substantial and varied dataset to attain optimal precision. Generative Adversarial Networks (GANs) can be employed to create new data, thereby supplementing the existing dataset and enhancing the accuracy of deep learning models. Nevertheless, GANs have their limitations, such as issues related to stability, convergence, and the ability to distinguish between authentic and fabricated data. In order to overcome these challenges and advance the detection and classification of CXR normal and abnormal images, this study introduces a distinctive technique known as DCWGAN (Diverse Conditional Wasserstein GAN) for generating synthetic chest X-ray (CXR) images. The study evaluates the effectiveness of this Idiosyncratic DCWGAN technique using the ResNet50 model and compares its results with those obtained using the traditional GAN approach. The findings reveal that the ResNet50 model trained on the DCWGAN-generated dataset outperformed the model trained on the classic GAN-generated dataset. Specifically, the ResNet50 model utilizing DCWGAN synthetic images achieved impressive performance metrics with an accuracy of 0.961, precision of 0.955, recall of 0.970, and F1-Measure of 0.963. These results indicate the promising potential for the early detection of diseases in CXR images using this Inimitable approach.

Keywords: CNN, classification, deep learning, GAN, Resnet50

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Authors: Muneeb Ullah, Daishihan, Xiadong Young

Abstract:

Deep learning plays a crucial role in identifying COVID-19 and preventing its spread. To improve the accuracy of COVID-19 diagnoses, it is important to have access to a sufficient number of training images of CXRs (chest X-rays) depicting the disease. However, there is currently a shortage of such images. To address this issue, this paper introduces COVID-19 GAN, a model that uses generative adversarial networks (GANs) to generate realistic CXR images of COVID-19, which can be used to train identification models. Initially, a generator model is created that uses digressive channels to generate images of CXR scans for COVID-19. To differentiate between real and fake disease images, an efficient discriminator is developed by combining the dense connectivity strategy and instance normalization. This approach makes use of their feature extraction capabilities on CXR hazy areas. Lastly, the deep regret gradient penalty technique is utilized to ensure stable training of the model. With the use of 4,062 grape leaf disease images, the Leaf GAN model successfully produces 8,124 COVID-19 CXR images. The COVID-19 GAN model produces COVID-19 CXR images that outperform DCGAN and WGAN in terms of the Fréchet inception distance. Experimental findings suggest that the COVID-19 GAN-generated CXR images possess noticeable haziness, offering a promising approach to address the limited training data available for COVID-19 model training. When the dataset was expanded, CNN-based classification models outperformed other models, yielding higher accuracy rates than those of the initial dataset and other augmentation techniques. Among these models, ImagNet exhibited the best recognition accuracy of 99.70% on the testing set. These findings suggest that the proposed augmentation method is a solution to address overfitting issues in disease identification and can enhance identification accuracy effectively.

Keywords: classification, deep learning, medical images, CXR, GAN.

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Authors: Kathrina Aseanne Acapulco-Gomez, Shayne Julieane Morales, Tzar Francis Verame

Abstract:

Mean platelet volume (MPV) is the most accurate measure of the size of platelets and is routinely measured by most automated hematological analyzers. Several studies have shown associations between MPV and cardiovascular risks and outcomes. Although its measurement may provide useful data, MPV remains to be a diagnostic tool that is yet to be included in routine clinical decision making. The aim of this systematic review and meta-analysis is to determine summary estimates of the diagnostic accuracy of mean platelet volume for the diagnosis of myocardial infarction among adult patients with angina and/or its equivalents in terms of sensitivity, specificity, diagnostic odds ratio, and likelihood ratios, and to determine the difference of the mean MPV values between those with MI and those in the non-MI controls. The primary search was done through search in electronic databases PubMed, Cochrane Review CENTRAL, HERDIN (Health Research and Development Information Network), Google Scholar, Philippine Journal of Pathology, and Philippine College of Physicians Philippine Journal of Internal Medicine. The reference list of original reports was also searched. Cross-sectional, cohort, and case-control articles studying the diagnostic performance of mean platelet volume in the diagnosis of acute myocardial infarction in adult patients were included in the study. Studies were included if: (1) CBC was taken upon presentation to the ER or upon admission (within 24 hours of symptom onset); (2) myocardial infarction was diagnosed with serum markers, ECG, or according to accepted guidelines by the Cardiology societies (American Heart Association (AHA), American College of Cardiology (ACC), European Society of Cardiology (ESC); and, (3) if outcomes were measured as significant difference AND/OR sensitivity and specificity. The authors independently screened for inclusion of all the identified potential studies as a result of the search. Eligible studies were appraised using well-defined criteria. Any disagreement between the reviewers was resolved through discussion and consensus. The overall mean MPV value of those with MI (9.702 fl; 95% CI 9.07 – 10.33) was higher than in those of the non-MI control group (8.85 fl; 95% CI 8.23 – 9.46). Interpretation of the calculated t-value of 2.0827 showed that there was a significant difference in the mean MPV values of those with MI and those of the non-MI controls. The summary sensitivity (Se) and specificity (Sp) for MPV were 0.66 (95% CI; 0.59 - 0.73) and 0.60 (95% CI; 0.43 – 0.75), respectively. The pooled diagnostic odds ratio (DOR) was 2.92 (95% CI; 1.90 – 4.50). The positive likelihood ratio of MPV in the diagnosis of myocardial infarction was 1.65 (95% CI; 1.20 – 22.27), and the negative likelihood ratio was 0.56 (95% CI; 0.50 – 0.64). The intended role for MPV in the diagnostic pathway of myocardial infarction would perhaps be best as a triage tool. With a DOR of 2.92, MPV values can discriminate between those who have MI and those without. For a patient with angina presenting with elevated MPV values, it is 1.65 times more likely that he has MI. Thus, it is implied that the decision to treat a patient with angina or its equivalents as a case of MI could be supported by an elevated MPV value.

Keywords: mean platelet volume, MPV, myocardial infarction, angina, chest pain

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Authors: Nihar Bheda

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Protecting digital assets such as networks, systems, and data from advanced cyber threats is the aim of Digital Immunity Systems (DIS), which are a subset of cybersecurity. With features like continuous monitoring, coordinated reactions, and long-term adaptation, DIS seeks to mimic biological immunity. This minimizes downtime by automatically identifying and eliminating threats. Traditional security measures, such as firewalls and antivirus software, are insufficient for enterprises, such as healthcare providers, given the rapid evolution of cyber threats. The number of medical record breaches that have occurred in recent years is proof that attackers are finding healthcare data to be an increasingly valuable target. However, obstacles to enhancing security include outdated systems, financial limitations, and a lack of knowledge. DIS is an advancement in cyber defenses designed specifically for healthcare settings. Protection akin to an "immune system" is produced by core capabilities such as anomaly detection, access controls, and policy enforcement. Coordination of responses across IT infrastructure to contain attacks is made possible by automation and orchestration. Massive amounts of data are analyzed by AI and machine learning to find new threats. After an incident, self-healing enables services to resume quickly. The implementation of DIS is consistent with the healthcare industry's urgent requirement for resilient data security in light of evolving risks and strict guidelines. With resilient systems, it can help organizations lower business risk, minimize the effects of breaches, and preserve patient care continuity. DIS will be essential for protecting a variety of environments, including cloud computing and the Internet of medical devices, as healthcare providers quickly adopt new technologies. DIS lowers traditional security overhead for IT departments and offers automated protection, even though it requires an initial investment. In the near future, DIS may prove to be essential for small clinics, blood banks, imaging centers, large hospitals, and other healthcare organizations. Cyber resilience can become attainable for the whole healthcare ecosystem with customized DIS implementations.

Keywords: digital immunity system, cybersecurity, healthcare data, emerging technology

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Authors: Irene Alevizos, Jessica Lewis, Marina Harper, John Boyce

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Acinetobacter baumannii causes a range of severe nosocomial-acquired infections, and many strains are multi-drug resistant. A. baumannii possesses survival mechanisms allowing it to thrive in competitive polymicrobial environments, including a Type VI Secretion System (T6SS) that injects effector proteins into other bacteria to give a competitive advantage. The effects of T6SS firing are broad and depend entirely on the effector that is delivered. Effects can include toxicity against prokaryotic or eukaryotic cells and the acquisition of essential nutrients. The T6SS of some species can deliver ‘specialised effectors’ that are fused directly to T6SS components, such as PAAR proteins. PAAR proteins are predicted to form the piercing tip of the T6SS and are essential for T6SS function. Although no specialised effectors have been identified in A. baumannii, many strains encode multiple PAAR proteins. Analysis of PAAR proteins across the species identified 12 families of PAAR proteins with distinct C-terminal extensions. A. baumannii AB307-0294 encodes two PAAR proteins, one of which has a C-terminal extension. Mutation of one or both of the PAAR-encoding genes in this strain showed that expression of either PAAR protein was sufficient for T6SS function. We employed a heterologous expression approach and determined that PAAR proteins from different A. baumannii strains, as well as the closely related A. baylyi species, could complement the A. baumannii ∆paar mutant and restore T6SS function. Furthermore, we showed that PAAR fusions could be used to deliver artificially cloned protein fragments by generating Histidine- and Streptavidin- tagged PAAR specialised effectors, which restored T6SS activity. This provides evidence that the fusion of protein fragments onto PAAR proteins in A. baumannii is compatible with a functional T6SS. Successful delivery by this mechanism extends the scope of what the T6SS can deliver, including user designed proteins.

Keywords: A. baumannii, effectors, PAAR, T6SS

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Authors: Bhavishya Ramchandani

Abstract:

A little electronic device that is worn on the finger is called a smart ring. It incorporates mobile technology and has features that make it simple to use the device. These gadgets, which resemble conventional rings and are usually made to fit on the finger, are outfitted with features including access management, gesture control, mobile payment processing, and activity tracking. A poor sleep pattern, an irregular schedule, and bad eating habits are all part of the problems with health that a lot of people today are facing. Diets lacking fruits, vegetables, legumes, nuts, and whole grains are common. Individuals in India also experience metabolic issues. In the medical field, smart rings will help patients with problems relating to stomach illnesses and the incapacity to consume meals that are tailored to their bodies' needs. The smart ring tracks all bodily functions, including blood sugar and glucose levels, and presents the information instantly. Based on this data, the ring generates what the body will find to be perfect insights and a workable site layout. In addition, we conducted focus groups and individual interviews as part of our core approach and discussed the difficulties they're having maintaining the right diet, as well as whether or not the smart ring will be beneficial to them. However, everyone was very enthusiastic about and supportive of the concept of using smart rings in healthcare, and they believed that these rings may assist them in maintaining their health and having a well-balanced diet plan. This response came from the primary data, and also working on the Emerging Technology Canvas Analysis of smart rings in healthcare has led to a significant improvement in our understanding of the technology's application in the medical field. It is believed that there will be a growing demand for smart health care as people become more conscious of their health. The majority of individuals will finally utilize this ring after three to four years when demand for it will have increased. Their daily lives will be significantly impacted by it.

Keywords: smart ring, healthcare, electronic wearable, emerging technology

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Authors: Sahraoui Halima, Dahani Ameur, Tigrine Abedelkader

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DigiMesh technology represents a pioneering advancement in wireless networking, offering cost-effective and energy-efficient capabilities. Its inherent simplicity and adaptability facilitate the seamless transfer of data between network nodes, extending the range and ensuring robust connectivity through autonomous self-healing mechanisms. In light of these advantages, this study introduces a medical platform harnessed with DigiMesh wireless network technology characterized by low power consumption, immunity to interference, and user-friendly operation. The primary application of this platform is the real-time, long-distance monitoring of Electrocardiogram (ECG) signals, with the added capacity for simultaneous monitoring of ECG signals from multiple patients. The experimental setup comprises key components such as Raspberry Pi, E-Health Sensor Shield, and Xbee DigiMesh modules. The platform is composed of multiple ECG acquisition devices labeled as Sensor Node 1 and Sensor Node 2, with a Raspberry Pi serving as the central hub (Sink Node). Two communication approaches are proposed: Single-hop and multi-hop. In the Single-hop approach, ECG signals are directly transmitted from a sensor node to the sink node through the XBee3 DigiMesh RF Module, establishing peer-to-peer connections. This approach was tested in the first experiment to assess the feasibility of deploying wireless sensor networks (WSN). In the multi-hop approach, two sensor nodes communicate with the server (Sink Node) in a star configuration. This setup was tested in the second experiment. The primary objective of this research is to evaluate the performance of both Single-hop and multi-hop approaches in diverse scenarios, including open areas and obstructed environments. Experimental results indicate the DigiMesh network's effectiveness in Single-hop mode, with reliable communication over distances of approximately 300 meters in open areas. In the multi-hop configuration, the network demonstrated robust performance across approximately three floors, even in the presence of obstacles, without the need for additional router devices. This study offers valuable insights into the capabilities of DigiMesh wireless technology for real-time ECG monitoring in healthcare applications, demonstrating its potential for use in diverse medical scenarios.

Keywords: DigiMesh protocol, ECG signal, real-time monitoring, medical platform

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Authors: Samir Hessas

Abstract:

Modern medicine is witnessing a profound transformation as advanced technology reshapes surgical environments. Hybrid operating rooms, where state-of-the-art medical equipment, advanced imaging solutions, and Artificial Intelligence (AI) converge, are at the forefront of this revolution. In this comprehensive exploration, we scrutinize the multifaceted facets of AI and delve into an array of groundbreaking technologies. We also discuss visionary concepts that hold the potential to revolutionize hybrid operating rooms, making them more efficient and patient-centered. These innovations encompass real-time imaging, surgical simulation, IoT and remote monitoring, 3D printing, telemedicine, quantum computing, and nanotechnology. The outcome of this fusion of technology and imagination is a promising future of surgical precision, individualized patient care, and unprecedented medical advances in hybrid operating rooms.

Keywords: artificial intelligence, hybrid operating rooms, telemedicine, monitoring

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Authors: Maryam Fallahpoor, Biswajeet Pradhan

Abstract:

Background: Artificial intelligence (AI) serves as a valuable tool in mitigating the scarcity of human resources required for the evaluation and categorization of vast quantities of medical imaging data. When AI operates with optimal precision, it minimizes the demand for human interpretations and, thereby, reduces the burden on radiologists. Among various AI approaches, deep learning (DL) stands out as it obviates the need for feature extraction, a process that can impede classification, especially with intricate datasets. The advent of DL models has ushered in a new era in medical imaging, particularly in the context of COVID-19 detection. Traditional 2D imaging techniques exhibit limitations when applied to volumetric data, such as Computed Tomography (CT) scans. Medical images predominantly exist in one of two formats: neuroimaging informatics technology initiative (NIfTI) and digital imaging and communications in medicine (DICOM). Purpose: This study aims to employ DL for the classification of COVID-19-infected pulmonary patients and normal cases based on 3D CT scans while investigating the impact of image format. Material and Methods: The dataset used for model training and testing consisted of 1245 patients from IranMehr Hospital. All scans shared a matrix size of 512 × 512, although they exhibited varying slice numbers. Consequently, after loading the DICOM CT scans, image resampling and interpolation were performed to standardize the slice count. All images underwent cropping and resampling, resulting in uniform dimensions of 128 × 128 × 60. Resolution uniformity was achieved through resampling to 1 mm × 1 mm × 1 mm, and image intensities were confined to the range of (−1000, 400) Hounsfield units (HU). For classification purposes, positive pulmonary COVID-19 involvement was designated as 1, while normal images were assigned a value of 0. Subsequently, a U-net-based lung segmentation module was applied to obtain 3D segmented lung regions. The pre-processing stage included normalization, zero-centering, and shuffling. Four distinct 3D CNN models (ResNet152, ResNet50, DensNet169, and DensNet201) were employed in this study. Results: The findings revealed that the segmentation technique yielded superior results for DICOM images, which could be attributed to the potential loss of information during the conversion of original DICOM images to NIFTI format. Notably, ResNet152 and ResNet50 exhibited the highest accuracy at 90.0%, and the same models achieved the best F1 score at 87%. ResNet152 also secured the highest Area under the Curve (AUC) at 0.932. Regarding sensitivity and specificity, DensNet201 achieved the highest values at 93% and 96%, respectively. Conclusion: This study underscores the capacity of deep learning to classify COVID-19 pulmonary involvement using real 3D hospital data. The results underscore the significance of employing DICOM format 3D CT images alongside appropriate pre-processing techniques when training DL models for COVID-19 detection. This approach enhances the accuracy and reliability of diagnostic systems for COVID-19 detection.

Keywords: deep learning, COVID-19 detection, NIFTI format, DICOM format

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Authors: Zahra Khodabandeh, Leila Rezaeian, Mohammad Amin Edalatmanesh, Asghar Mogheiseh, Nader Tanideh, Mehdi Dianatpour, Shahrokh Zare, Hossein Bordbar, Neda Baghban, Amin Tamadon

Abstract:

Background: In-utero xenotransplantation of stem cells in abnormal fetuses effectively treats several genetic illnesses. Objective: The current research aimed to evaluate structural and morphological alterations in the liver of rabbit fetuses following xenotransplantation of human Wharton’s jelly-derived mesenchymal stromal cells (hWJ-MSCs) using a stereological technique. Methods: hWJ-MSCs were isolated from the human umbilical cord, and their authenticity was established by flow cytometry and differentiation. At gestational day 14, the rabbits were anesthetized, and hWJ-MSCs were injected into the uteri of 24 fetuses. Twenty-two fetuses were born successfully. Ten rabbit liver specimens were prepared from injected fetuses, including eight rabbits on day three following birth and two rabbits on the 21st post-natal day. The non-injected fetuses were considered positive controls. The livers of the control and hWJ-MSCs-treated rabbits were fixed, processed, stained, and examined through stereological approaches. Results: In the hWJ-MSCs-treated group, the mean liver weight and volume increased by 42% and 78% compared to the control group. The total volume of the hepatocytes increased by 63% and that of sinusoids by threefold in the treated rabbits. The total volume of the central veins increased by 70%. The total number corresponding to hepatocytes in the experimental group increased by 112% compared to the rabbits in the control. The total volume of the hepatocyte nuclei in the experimental group increased by 117% compared to the rabbits in the control. Conclusion: After xenotransplantation of human MSCs, host tissue microenvironments (here, the rabbit liver) were altered, and these included quantitative factors corresponding to the liver tissue and hepatocyte morphometric indices.

Keywords: xenotransplantation, mesenchymal stromal, stem cell, Wharton ‘s jelly, liver

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Authors: Ahmed Torad

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Background: Knee osteoarthritis (OA) is a prevalent musculoskeletal condition characterized by pain and functional impairment. While various rehabilitation approaches have been employed, the effectiveness of high-intensity interval training (HIIT) compared to traditional rehabilitation exercises remains unclear. Objective: This randomized controlled trial aimed to compare the effects of HIIT and traditional rehabilitation exercises on pain reduction, functional improvement, and quality of life in individuals with knee OA. Methods: A total of 120 participants diagnosed with knee OA were randomly allocated into two groups: the HIIT group (n=60) and the traditional rehabilitation group (n=60). The HIIT group participated in a 12-week supervised program consisting of high-intensity interval exercises, while the traditional rehabilitation group followed a conventional physiotherapy regimen. Outcome measures included visual analog scale (VAS) pain scores, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and the Short Form-36 Health Survey (SF-36) at baseline and after the intervention period. Results: Both groups showed significant improvements in pain scores, functional outcomes (WOMAC), and quality of life (SF-36) after 12 weeks of intervention. However, the HIIT group demonstrated superior pain reduction (p<0.001), functional improvement (p<0.001), and physical health-related quality of life (p=0.002) compared to the traditional rehabilitation group. No significant differences were observed in mental health-related quality of life between the two groups. Conclusion: High-intensity interval training appears to be a more effective rehabilitation approach than traditional exercises for individuals with knee osteoarthritis, resulting in greater pain reduction, improved function, and enhanced physical health-related quality of life. These findings suggest that HIIT may represent a promising intervention strategy for managing knee OA and enhancing the overall well-being of affected individuals.

Keywords: knee osteoarthritis, high-intensity interval training, traditional rehabilitation exercises, randomized controlled trial, pain reduction, functional improvement, quality of life

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Authors: Zeinab Jafari A., Ali Sharifnezhad B., Mohammad Razi C., Mohammad Haghpanahi D., Arash Maghsoudi

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Background and Objective: Sports-related rupture of the anterior cruciate ligament (ACL) and following injuries have been associated with various disorders, such as long-lasting changes in muscle activation patterns in athletes, which might last after ACL reconstruction (ACLR). The rupture of the ACL might result in abnormal patterns of movement execution, extending the treatment period and delaying athletes’ return to sports (RTS). As ACL injury is especially prevalent among athletes, the lengthy treatment process and athletes’ absence from sports are of great concern to athletes and coaches. Thus, estimating safe time of RTS is of crucial importance. Therefore, using a deep neural network (DNN) to classify the health levels of ACL in injured athletes, this study aimed to estimate the safe time for athletes to return to competitions. Methods: Ten athletes with ACLR and fourteen healthy controls participated in this study. Three health levels of ACL were defined: healthy, six-month post-ACLR surgery and nine-month post-ACLR surgery. Athletes with ACLR were tested six and nine months after the ACLR surgery. During the course of this study, surface electromyography (sEMG) signals were recorded from five knee muscles, namely Rectus Femoris (RF), Vastus Lateralis (VL), Vastus Medialis (VM), Biceps Femoris (BF), Semitendinosus (ST), during single-leg drop landing (SLDL) and forward hopping (SLFH) tasks. The Pseudo-Wigner-Ville distribution (PWVD) was used to produce three-dimensional (3-D) images of the energy distribution patterns of sEMG signals. Then, these 3-D images were converted to two-dimensional (2-D) images implementing the heat mapping technique, which were then fed to a deep convolutional neural network (DCNN). Results: In this study, we estimated the safe time of RTS by designing a DCNN classifier with an accuracy of 90 %, which could classify ACL into three health levels. Discussion: The findings of this study demonstrate the potential of the DCNN classification technique using sEMG signals in estimating RTS time, which will assist in evaluating the recovery process of ACLR in athletes.

Keywords: anterior cruciate ligament reconstruction, return to sports, surface electromyography, deep convolutional neural network

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Authors: Abdulrahman Sumayli, Saad M. AlShahrani

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For the hydrogenation process, knowing the solubility of hydrogen (H2) in hydrocarbons is critical to improve the efficiency of the process. We investigated the H2 solubility computation in four heavy crude oil feedstocks using machine learning techniques. Temperature, pressure, and feedstock type were considered as the inputs to the models, while the hydrogen solubility was the sole response. Specifically, we employed three different models: Support Vector Regression (SVR), Gaussian process regression (GPR), and Bayesian ridge regression (BRR). To achieve the best performance, the hyper-parameters of these models are optimized using the whale optimization algorithm (WOA). We evaluated the models using a dataset of solubility measurements in various feedstocks, and we compared their performance based on several metrics. Our results show that the WOA-SVR model tuned with WOA achieves the best performance overall, with an RMSE of 1.38 × 10− 2 and an R-squared of 0.991. These findings suggest that machine learning techniques can provide accurate predictions of hydrogen solubility in different feedstocks, which could be useful in the development of hydrogen-related technologies. Besides, the solubility of hydrogen in the four heavy oil fractions is estimated in different ranges of temperatures and pressures of 150 ◦C–350 ◦C and 1.2 MPa–10.8 MPa, respectively

Keywords: temperature, pressure variations, machine learning, oil treatment

Procedia PDF Downloads 38

Authors: Komal Vig

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Cardiovascular disease (CVD)-related deaths occur in 17.3 million people globally each year, accounting for 30% of all deaths worldwide, with a predicted annual incidence of deaths to reach 23.3 million globally by 2030. Autologous bypass grafts remain an important therapeutic option for the treatment of CVD, but the poor quality of the donor patient’s blood vessels, the invasiveness of the resection surgery, and postoperative movement restrictions create issues. The present study is aimed to improve the endothelialization of intimal surface of graft by using low temperature plasma (LTP) to increase the cell attachment and proliferation. Polytetrafluoroethylene (PTFE) was treated with LTP. Air was used as the feed-gas, and the pressure in the plasma chamber was kept at 800 mTorr. Scaffolds were also modified with gelatin and collagen by dipping method. Human umbilical vein endothelial cells (HUVEC) were plated on the developed scaffolds, and cell proliferation was determined by the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assay and by microscopy. mRNA expressions levels of different cell markers were investigated using quantitative real-time PCR (qPCR). XPS confirmed the introduction of oxygenated functionalities from LTP. HUVEC cells showed 80% seeding efficiency on the scaffold. Microscopic and MTT assays indicated increase in cell viability in LTP treated scaffolds, especially when treated with gelatin or collagen, compared to untreated scaffolds. Gene expression studies shows enhanced expression of cell adhesion marker Integrin- α 5 gene after LTP treatment. LTP treated scaffolds exhibited better cell proliferation and viability compared to untreated scaffolds. Protein treatment of scaffold increased cell proliferation. Based on our initial results, more scaffolds alternatives will be developed and investigated for cell growth and vascularization studies. Acknowledgments: This work is supported by the NSF EPSCoR RII-Track-1 Cooperative Agreement OIA-2148653.

Keywords: LTP, HUVEC cells, vascular graft, endothelialization

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Authors: Abrar Noorain, Zeinab Amara, Sulaf Abdelaziz

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Background: Diabetic foot disease imposes a financial burden on diabetic patients and healthcare services. In Sudan, diabetic foot ulcer prevalence reached 18.1%. This study aims to assess the knowledge, attitudes, and practices and the correlation between the level of foot care knowledge and self-care practices among diabetic patients in Sudan. Methodology: In a cross-sectional study involving 262 patients with type 1 and type 2 diabetes attending diabetic clinics in three primary care centers in Khartoum, Sudan, during September to November 2022, information regarding participants sociodemographic status, foot care knowledge, attitudes, and practices was gathered using a validated, structured questionnaire in a face-to-face interview method. These data were analyzed using the statistical package for the social sciences (SPSS) 22. Results: The patients’ mean age was 54.9 years, with a female predominance (56%). Of the participants, 37% had diabetes mellitus for over ten years. On the topic of foot care, 35.5% of patients showed good knowledge, and 76% were aware of the risk of reduced foot sensation. In relation to nail care, only 19% knew how to cut nails correctly. Conclusion: Knowledge, attitudes, and practices about diabetic foot care are substandard. There is a positive correlation between foot care knowledge and self-care practices. Hence, educating diabetic patients with foot care knowledge through an awareness program and the characteristics of diabetic shoes may improve self-care practices.

Keywords: DM, DFD, DFU, PHC, SPSS

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Authors: Luisa Mesquita Oliveira Ribeiro, Alexei Manso Correa Machado

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Handwritten signature is a unique form for recognizing an individual, used to discern documents, carry out investigations in the criminal, legal, banking areas and other applications. Signature verification is based on large amounts of biometric data, as they are simple and easy to acquire, among other characteristics. Given this scenario, signature forgery is a worldwide recurring problem and fast and precise techniques are needed to prevent crimes of this nature from occurring. This article carried out a study on the efficiency of the Capsule Network in analyzing and recognizing signatures. The chosen architecture achieved an accuracy of 98.11% and 80.15% for the CEDAR and GPDS databases, respectively.

Keywords: biometrics, deep learning, handwriting, signature forgery

Procedia PDF Downloads 42

Authors: Kaiyi Zhang, Wenlong Li, Haicheng Li, Yifei Luo, Zheng Li, Xiaoshi Wang, Xiaodong Chen

Abstract:

Plant wearable sensors facilitate the real-time monitoring of plant physiological status. In situ monitoring of the plant chlorophyll content over days could provide valuable information on the photosynthetic capacity, nitrogen content, and general plant health. However, it cannot be achieved by current chlorophyll measuring methods. Here, a miniaturized and plant-wearable chlorophyll meter was developed for rapid, non-destructive, in situ, and long-term chlorophyll monitoring. This reflectance-based chlorophyll sensor with 1.5 mm thickness and 0.2 g weight (1000 times lighter than the commercial chlorophyll meter), includes a light emitting diode (LED) and two symmetric photodetectors (PDs) on a flexible substrate and is patched onto the leaf upper epidermis with a conformal light guiding layer. A chlorophyll content index (CCI) calculated based on this sensor shows a better linear relationship with the leaf chlorophyll content (r² > 0.9) than the traditional chlorophyll meter. This meter can wirelessly communicate with a smartphone to monitor the leaf chlorophyll change under various stresses and indicate the unhealthy status of plants for long-term application of plants under various stresses earlier than chlorophyll meter and naked-eye observation. This wearable chlorophyll sensing patch is promising in smart and precision agriculture.

Keywords: plant wearable sensors, reflectance-based measurements, chlorophyll content monitoring, smart agriculture

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Authors: Breanna Macumber, Victor A. Huayamave, Emir A. Vela, Wangdo Kim, Tamara T. Chamber, Esteban Centeno

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Spina bifida is a type of neural tube defect that affects the nervous system and can lead to problems such as total leg paralysis. Treatment requires physical therapy and rehabilitation. Robotic exoskeletons have been used for rehabilitation to train muscle movement and assist in injury recovery; however, current models focus on the adult populations and not on the infant population. The proposed framework aims to couple a musculoskeletal infant model with a robotic exoskeleton using vacuum-powered artificial muscles to provide rehabilitation to infants affected by spina bifida. The study that drove the input values for the robotic exoskeleton used motion capture technology to collect data from the spontaneous kicking movement of a 2.4-month-old infant lying supine. OpenSim was used to develop the musculoskeletal model, and Inverse kinematics was used to estimate hip joint angles. A total of 4 kicks (A, B, C, D) were selected, and the selection was based on range, transient response, and stable response. Kicks had at least 5° of range of motion with a smooth transient response and a stable period. The robotic exoskeleton used a Vacuum-Powered Artificial Muscle (VPAM) the structure comprised of cells that were clipped in a collapsed state and unclipped when desired to simulate infant’s age. The artificial muscle works with vacuum pressure. When air is removed, the muscle contracts and when air is added, the muscle relaxes. Bench testing was performed using a 6-month-old infant mannequin. The previously developed exoskeleton worked really well with controlled ranges of motion and frequencies, which are typical of rehabilitation protocols for infants suffering with spina bifida. However, the random kicking motion in this study contained high frequency kicks and was not able to accurately replicate all the investigated kicks. Kick 'A' had a greater error when compared to the other kicks. This study has the potential to advance the infant rehabilitation field.

Keywords: musculoskeletal modeling, soft robotics, rehabilitation, pediatrics

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Authors: Sunny Deo, Eve Barnes, Peter Arnold-Smith

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Introduction: Healthcare technology is a rapidly expanding field in healthcare, with enthusiasts suggesting a revolution in the quality and efficiency of healthcare delivery based on the utilisation of better e-healthcare, including the move to paperless healthcare. The role and use of computers and programmes for healthcare have been increasing over the past 50 years. Despite this, there is no standardised method of assessing the quality of hardware and software utilised by frontline healthcare workers. Methods and subjects: Based on standard Patient Related Outcome Measures, a questionnaire was devised with the aim of providing quantitative and qualitative data on clinicians’ perspectives of their hospital’s Information Technology (IT). The survey was distributed via the Institution’s Intranet to all contracted doctors, and the survey's qualitative results were analysed. Qualitative opinions were grouped as positive, neutral, or negative and further sub-grouped into speed/usability, software/hardware, integration, IT staffing, clinical risk, and wellbeing. Analysis was undertaken on the basis of doctor seniority and by specialty. Results: There were 196 responses, with 51% from senior doctors (consultant grades) and the rest from junior grades, with the largest group of respondents 52% coming from medicine specialties. Differences in the proportion of principle and sub-groups were noted by seniority and specialty. Negative themes were by far the commonest stated opinion type, occurring in almost 2/3’s of responses (63%), while positive comments occurred less than 1 in 10 (8%). Conclusions: This survey confirms strongly negative attitudes to the current state of electronic documentation and IT in a large single-centre cohort of hospital-based frontline physicians after two decades of so-called progress to a paperless healthcare system. Greater use would provide further insights and potentially optimise the focus of development and delivery to improve the quality and effectiveness of IT for clinicians and their patients.

Keywords: information technology, electronic patient records, digitisation, paperless healthcare

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Authors: Rolivhuwa Bishop Ramagoma1*, Lynn Cairncross1, , Saartjie Roux1

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According to the world health organisation, colon cancer is among the most common cancers diagnosed in both men and women. Specifically, it is the second leading cause of cancer related deaths accounting for over 860 000 deaths worldwide in 2018. Currently, chemotherapy has become an essential component of most cancer treatments. Despite progress in cancer drug development over the previous years, traditional chemotherapeutic drugs still have low selectivity for targeting tumour tissues and are frequently constrained by dose-limiting toxicity. The creation of nanoscale delivery vehicles capable of directly directing treatment into cancer cells has recently caught the interest of researchers. Herein, the development of peptide-functionalized polyethylene glycol gold nanoparticles (Peptide-PEG-AuNPs) as a cellular probe and delivery agent is described, with the higher aim to develop a specific diagnostic prototype and assess their specificity not only against cell lines but primary human cells as well. Gold nanoparticles (AuNPs) were synthesized and stabilized through chemical conjugation. The synthesized AuNPs were characterized, stability in physiological solutions was assessed, their cytotoxicity against colon carcinoma and non-carcinoma skin fibroblasts was also studied. Furthermore, genetic effect through real-time polymerase chain reaction (RT-PCR), localization and uptake, peptide specificity were also determined. In this study, different peptide-AuNPs were found to have preferential toxicity at higher concentrations, as revealed by cell viability assays, however, all AuNPs presented immaculate stability for over 3 months following the method of synthesis. The final obtained peptide-PEG-AuNP conjugates showed good biocompatibility in the presence of high ionic solutions and biological media and good cellular uptake. Formulation of colon cancer specific targeting peptide was successful, additionally, the genes/pathways affected by the treatments were determined through RT-PCR. Primary cells study is still on going with promising results thus far.

Keywords: nanotechnology, cancer, diagnosis, therapeutics, gold nanoparticles.

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Authors: Ali Hamza

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Accurate segmentation of breast ultrasound images is of paramount importance in enhancing the diagnostic capabilities of breast cancer detection. This study presents an approach utilizing the U-Net architecture for segmenting breast ultrasound images aimed at improving the accuracy and reliability of mass identification within the breast tissue. The proposed method encompasses a multi-stage process. Initially, preprocessing techniques are employed to refine image quality and diminish noise interference. Subsequently, the U-Net architecture, a deep learning convolutional neural network (CNN), is employed for pixel-wise segmentation of regions of interest corresponding to potential breast masses. The U-Net's distinctive architecture, characterized by a contracting and expansive pathway, enables accurate boundary delineation and detailed feature extraction. To evaluate the effectiveness of the proposed approach, an extensive dataset of breast ultrasound images is employed, encompassing diverse cases. Quantitative performance metrics such as the Dice coefficient, Jaccard index, sensitivity, specificity, and Hausdorff distance are employed to comprehensively assess the segmentation accuracy. Comparative analyses against traditional segmentation methods showcase the superiority of the U-Net architecture in capturing intricate details and accurately segmenting breast masses. The outcomes of this study emphasize the potential of the U-Net-based segmentation approach in bolstering breast ultrasound image analysis. The method's ability to reliably pinpoint mass boundaries holds promise for aiding radiologists in precise diagnosis and treatment planning. However, further validation and integration within clinical workflows are necessary to ascertain their practical clinical utility and facilitate seamless adoption by healthcare professionals. In conclusion, leveraging the U-Net architecture for breast ultrasound image segmentation showcases a robust framework that can significantly enhance diagnostic accuracy and advance the field of breast cancer detection. This approach represents a pivotal step towards empowering medical professionals with a more potent tool for early and accurate breast cancer diagnosis.

Keywords: mage segmentation, U-Net, deep learning, breast cancer detection, diagnostic accuracy, mass identification, convolutional neural network

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Authors: Leonnel Mhuka

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Background: The field of photonics has witnessed substantial growth, with an increasing demand for miniaturized and high-performance optical components. Microstructured optical waveguides have gained significant attention due to their ability to confine and manipulate light at the subwavelength scale. Conventional fabrication methods, however, face limitations in achieving intricate and customizable waveguide structures. Two-photon polymerization (TPP) emerges as a promising additive manufacturing technique, enabling the fabrication of complex 3D microstructures with submicron resolution. Objectives: This experiment aimed to utilize two-photon polymerization to fabricate microstructured optical waveguides with precise control over geometry and dimensions. The objective was to demonstrate the feasibility of TPP as an additive manufacturing method for producing functional waveguide devices with enhanced performance. Methods: A femtosecond laser system operating at a wavelength of 800 nm was employed for two-photon polymerization. A custom-designed CAD model of the microstructured waveguide was converted into G-code, which guided the laser focus through a photosensitive polymer material. The waveguide structures were fabricated using a layer-by-layer approach, with each layer formed by localized polymerization induced by non-linear absorption of the laser light. Characterization of the fabricated waveguides included optical microscopy, scanning electron microscopy, and optical transmission measurements. The optical properties, such as mode confinement and propagation losses, were evaluated to assess the performance of the additive manufactured waveguides. Conclusion: The experiment successfully demonstrated the additive manufacturing of microstructured optical waveguides using two-photon polymerization. Optical microscopy and scanning electron microscopy revealed the intricate 3D structures with submicron resolution. The measured optical transmission indicated efficient light propagation through the fabricated waveguides. The waveguides exhibited well-defined mode confinement and relatively low propagation losses, showcasing the potential of TPP-based additive manufacturing for photonics applications. The experiment highlighted the advantages of TPP in achieving high-resolution, customized, and functional microstructured optical waveguides. Conclusion: his experiment substantiates the viability of two-photon polymerization as an innovative additive manufacturing technique for producing complex microstructured optical waveguides. The successful fabrication and characterization of these waveguides open doors to further advancements in the field of photonics, enabling the development of high-performance integrated optical devices for various applications

Keywords: Additive Manufacturing, Microstructured Optical Waveguides, Two-Photon Polymerization, Photonics Applications

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Authors: A. Fomujong, P. Mobit, A. Ndlovu, R. Teboh

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Purpose: Film’s unique qualities, such as tissue equivalence, high spatial resolution, near energy independence and comparatively less expensive dosimeter, ought to make it the preferred and widely used in radiotherapy centers in low and middle income countries (LMICs). This, however, is not always the case, as other factors that are often maybe taken for granted in advanced radiotherapy centers remain a challenge in LMICs. We explored the unique qualities of film dosimetry that can make it possible for one Institution to benefit from another’s protocols via collaboration. Methods: For simplicity, two Institutions were considered in this work. We used a single batch of films (EBT-XD) and established a calibration protocol, including scan protocols and calibration curves, using the radiotherapy delivery system at Institution A. We then proceeded and performed patient-specific QA for patients treated on system A (PSQA-A-A). Films from the same batch were then sent to a remote center for PSQA on radiotherapy delivery system B. Irradiations were done at Institution B and then returned to Institution A for processing and analysis (PSQA-B-A). The following points were taken into consideration throughout the process (a) A reference film was irradiated to a known dose on the same system irradiating the PSQA film. (b) For calibration, we utilized the one-scan protocol and maintained the same scan orientation of the calibration, PSQA and reference films. Results: Gamma index analysis using a dose threshold of 10% and 3%/2mm criteria showed a gamma passing rate of 99.8% and 100% for the PSQA-A-A and PSQA-B-A, respectively. Conclusion: This work demonstrates that one could use established film dosimetry protocols in one Institution, e.g., an advanced radiotherapy center and apply similar accuracies to irradiations performed at another institution, e.g., a center located in LMIC, which thus encourages collaboration between the two for worldwide patient benefits.

Keywords: collaboration, film dosimetry, LMIC, radiotherapy, calibration

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Authors: Wei-Chia Huang, Jane Wang

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Damage to nerves is considered one of the most irreversible injuries. The regeneration of nerves has always been an important topic in regenerative medicine. In general, damage to human tissue will naturally repair overtime. However, when the nerves are damaged, healed flesh wound cannot guarantee full restoration to its original function, as truncated nerves are often irreversible. Therefore, the development of treatment methods to successfully guide and accelerate the regeneration of nerves has been highly sought after. In order to induce nerve tissue growth, nerve conduits are commonly used to help reconnect broken nerve bundles to provide protection to the location of the fracture while guiding the growth of the nerve bundles. To prevent the protected tissue from becoming necrotic and to ensure the growth rate, the conduits used are often modified with microstructures or blended with neuron growth factors that may facilitate nerve regeneration. Electrical stimulation is another attempted treatment for medical rehabilitation. With appropriate range of voltages and stimulation frequencies, it has been demonstrated to promote cell proliferation and migration. Biodegradability are critical for medical devices like nerve conduits, while conductive polymers pose great potential toward the differentiation and growth of nerve cells. In this work, biodegradability and conductivity were combined into a novel biodegradable, photocurable, conductive polymer composite materials by embedding conductive nanoparticles in poly(glycerol sebacate) acrylate (PGSA) and 3D-printed into nerve conduits. Rat pheochromocytoma cells and rat neuronal Schwann cells were chosen for the in vitro tests of the conduits and had demonstrate selective growth upon culture in the conductive conduits with built-in microchannels and electrical stimulation.

Keywords: biodegradable polymer, 3d printing, neural regeneration, electrical stimulation

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Authors: Nyathi Mpumelelo; Moeng Thabiso Maria

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BACKGROUND AND OBJECTIVES: Radiopharmaceuticals are used for diagnosis, treatment, staging and follow up of various diseases. However, there is concern that the ionizing radiation (gamma rays, α and ß particles) emitted by radiopharmaceuticals may result in exposure of radiographers and nurses with limited knowledge of the principles of radiation protection and safety, raising the risk of cancer induction. This study aimed at investigation radiation safety awareness levels among radiographers and nurses at a small tertiary hospital in South Africa. METHODS: An analytical cross-sectional study. A validated two-part questionnaire was implemented to consenting radiographers and nurses working in a Nuclear Medicine Department. Part 1 gathered demographic information (age, gender, work experience, attendance to/or passing ionizing radiation protection courses). Part 2 covered questions related to knowledge and awareness of radiation protection principles. RESULTS: Six radiographers and five nurses participated (27% males and 73% females). The mean age was 45 years (age range 20-60 years). The study revealed that neither professional development courses nor radiation protection courses are offered at the Nuclear Medicine Department understudy. However, 6/6 (100%) radiographers exhibited a high level of awareness of radiation safety principles on handling and working with radiopharmaceuticals which correlated to their years of experience. As for nurses, 4/5 (80%) showed limited knowledge and awareness of radiation protection principles irrespective of the number of years in the profession. CONCLUSION: Despite their major role of caring for patients undergoing diagnostic and therapeutic treatments, the nurses showed limited knowledge of ionizing radiation and associated side effects. This was not surprising since they never received any formal basic radiation safety course. These findings were not unique to this Centre. A study conducted in a Kuwaiti Radiology Department also established that the vast majority of nurses did not understand the risks of working with ionizing radiation. Similarly, nurses in an Australian hospital exhibited knowledge limitations. However, nursing managers did provide the necessary radiation safety training when requested. In Guatemala and Saudi Arabia, where there was shortage of professional radiographers, nurses underwent radiography training, a course that equipped them with basic radiation safety principles. The radiographers in the Centre understudy unlike others in various parts of the world demonstrated substantial knowledge and awareness on radiation protection. Radiations safety courses attended when an opportunity arose played a critical role in their awareness. The knowledge and awareness levels of these radiographers were comparable to their counterparts in Sudan. However, it was much more above that of their counterparts in Jordan, Nigeria, Nepal and Iran who were found to have limited awareness and inadequate knowledge on radiation dose. Formal radiation safety and awareness courses and workshops can play a crucial role in raising the awareness of nurses and radiographers on radiation safety for their personal benefit and that of their patients.

Keywords: radiation safety, radiation awareness, training, nuclear medicine

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Authors: Matheus Madureira Matos, Alexandre Rodrigues Farias

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Magnetic Resonance Imaging (MRI) is a vital imaging technique used in both clinical and pre-clinical areas to obtain detailed anatomical and functional information. However, MRI scans can be expensive, time-consuming, and often require the use of anesthetics to keep animals still during the imaging process. Anesthetics are commonly administered to animals undergoing MRI scans to ensure they remain still during the imaging process. However, prolonged or repeated exposure to anesthetics can have adverse effects on animals, including physiological alterations and potential toxicity. Minimizing the duration and frequency of anesthesia is, therefore, crucial for the well-being of research animals. In recent years, various sampling trajectories have been investigated to reduce the number of MRI measurements leading to shorter scanning time and minimizing the duration of animal exposure to the effects of anesthetics. Compressed sensing (CS) and sampling trajectories, such as cartesian, spiral, and radial, have emerged as powerful tools to reduce MRI data while preserving diagnostic quality. This work aims to apply CS and cartesian, spiral, and radial sampling trajectories for the reconstruction of MRI of the abdomen of mice sub-sampled at levels below that defined by the Nyquist theorem. The methodology of this work consists of using a fully sampled reference MRI of a female model C57B1/6 mouse acquired experimentally in a 4.7 Tesla MRI scanner for small animals using Spin Echo pulse sequences. The image is down-sampled by cartesian, radial, and spiral sampling paths and then reconstructed by CS. The quality of the reconstructed images is objectively assessed by three quality assessment techniques RMSE (Root mean square error), PSNR (Peak to Signal Noise Ratio), and SSIM (Structural similarity index measure). The utilization of optimized sampling trajectories and CS technique has demonstrated the potential for a significant reduction of up to 70% of image data acquisition. This result translates into shorter scan times, minimizing the duration and frequency of anesthesia administration and reducing the potential risks associated with it.

Keywords: compressed sensing, magnetic resonance, sampling trajectories, small animals

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Authors: Kadir Gök

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In this study, a vacuum system was developed for orthopedic drilling processes, and the most efficient processing parameters were determined using statistical analysis of temperature rise. A reverse engineering technique was used to obtain a 3D model of the chip vacuum system, and the obtained point cloud data was transferred to Solidworks software in STL format. An experimental design method was performed by selecting different parameters and their levels, such as RPM, feed rate, and drill bit diameter, to determine the most efficient processing parameters in temperature rise using ANOVA. Additionally, the bone chip-vacuum device was developed and performed successfully to collect the whole chips and fragments in the bone drilling experimental tests, and the chip-collecting device was found to be useful in removing overheating from the drilling zone. The effects of processing parameters on the temperature levels during the chip-vacuuming were determined, and it was found that bone chips and fractures can be used as autograft and allograft for tissue engineering. Overall, this study provides significant insights into the development of a vacuum system for orthopedic drilling processes and the use of bone chips and fractures in tissue engineering applications.

Keywords: vacuum system, orthopedic drilling, temperature rise, bone chips

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Authors: Samaneh Farokhirad, Fatemeh Ahmadpoor

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Biological membranes are constantly in contact with various filamentous soft nanostructures that either reside on their surface or are being transported between the cell and its environment. In particular, viral infections are determined by the interaction of viruses (such as filovirus) with cell membranes, membrane protein organization (such as cytoskeletal proteins and actin filament bundles) has been proposed to influence the mechanical properties of lipid membranes, and the adhesion of filamentous nanoparticles influence their delivery yield into target cells or tissues. The goal of this research is to integrate the rapidly increasing but still fragmented experimental observations on the adhesion and self-assembly of nanofilaments (including filoviruses, actin filaments, as well as natural and synthetic nanofilaments) on cell membranes into a general, rigorous, and unified knowledge framework. The global outbreak of the coronavirus disease in 2020, which has persisted for over three years, highlights the crucial role that nanofilamentbased delivery systems play in human health. This work will unravel the role of a unique property of all cell membranes, namely flexoelectricity, and the significance of nanofilaments’ flexibility in the adhesion and self-assembly of nanofilaments on cell membranes. This will be achieved utilizing a set of continuum mechanics, statistical mechanics, and molecular dynamics and Monte Carlo simulations. The findings will help address the societal needs to understand biophysical principles that govern the attachment of filoviruses and flexible nanofilaments onto the living cells and provide guidance on the development of nanofilament-based vaccines for a range of diseases, including infectious diseases and cancer.

Keywords: virus nanofilaments, cell mechanics, computational biophysics, statistical mechanics

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