Frontiers in Biomedical Technologies
Volume:7 Issue: 4, Autumn 2020

Low energy radiation can be produced by all types of high energy radiation. Studies of low energy particle radiation help us to understand the chemistry induced by high energy radiations. Low energy electrons are capable of chemical selectivity in contrast to high energy electrons due to the large number of open dissociative channels in the former case and their resonant nature. Among different types of radiation, low energy electrons have a higher cross-section to DNA damage and they have an important role in the synergistic effect between radiation and chemotherapy anticancer agents in cancer treatment. Analysis of these combined records helps assign function of cells, identify metabolic and regulatory pathways and suggest targets for diagnostics and therapeutics identify animal models to develop new drugs, among other goals of biomedical interest.

Cell experiments are vitally dependent on CO2 incubators. The heating system of usual incubators result in undesirable induction of Electromagnetic (EM) fields on cells that result in decreased accuracy in bio-electromagnetic tests. EM shields can cause a considerable decrease in the stray fields and eliminate the undesirable induction.

CST-2019 is used for simulations. five different shielding systems have been examined in this paper. We try to modify shape and material used for shielding to achieve better result. (Iron, Mu-Metal, steel).

We introduce a simple practical design, together with variations of previously reported ones, and numerical evaluation of their magnetic field attenuation.

The targeted design decreases the field within the shield to about 0.03 times of the incident magnetic field, while having holes for air and CO2 exchange.

Thermoplastic immobilization devices are used to position the patient on the table in order to correctly reposition the patient during treatment courses.

The Purpose of this work is investigating the degradation of surface dose and the dose distribution in the build-up region for photon beams associated with immobilization devices using Gafchromic films.

After heating, these masks are stretched and fitted over the considered location of body before treatment simulation for insuring the reproducibility of patient position during treatment fractions. In this research, dosimetry was carried out using Gafchromic EBT3 film and three kinds of thermoplastic masks (Orfit with thickness 2.2mm, holes diameter 2.5mm, Orfit with thickness 2mm, holes diameter 1mm, and Klarity mask, thickness 2mm, holes diameter 3mm). Measurements were made with and without the mask materials on the surface of the Perspex phantom for 6 and 15 MV X-ray beams of a LINAC machine.

The results showed that surface dose increases 2.1 to 6.7 times and 2 to 3.9 times than the surface dose in the open field for 15 MV and 6 MV photons, respectively. According to the obtained results from the Analyses of Variances (ANOVA) test , it is defined that there is a significant difference in surface dose among three kind of thermoplastic masks (χ2 = 49.78 and df = 3 and P<0.0001). The surface dose in Klarity has a  significant difference in comparison of other masks according to PostHoc exams and there is no significant difference among two other masks (P>0.05).

According to the results, Klarity mask is more acceptable immobilization device when compared with other masks in the test.

The artificial aperture imaging method owns a good contrast in the data recording and imaging process. However, this method is very time consuming that prevents its practical implementation.

In this paper, the separated waveforms are sent by two elements together, instead of a single element, and the combination of the methods of independent component analysis and adaptive filtering both are used to extract different components in the received echoes. The obtained result illustrates that the imaging is performed in less time, and the computational complexity of this method is declined.

The proposed algorithm has been evaluated on two sets of simulated data and experimental data. The results indicate that the proposed method in the point phantom mode is only 1.5% worse in the resolution than the conventional artificial aperture method. Also, from the contrasting viewpoint, the proposed method has made the CR parameter worse by about 1.34dB than the conventional artificial aperture method. These adverse points of resolution and contrast in the proposed method are neglected than the conventional artificial aperture method because of a slight decrease in image quality than the artificial aperture method. However, the proposed method improves the computational complexity by 45% than the conventional artificial aperture method. As a result, it has brought the researchers closer to the practical implementation of artificial aperture imaging.

Pandemic COVID-19 has created an emergency for the medical community. Researchers require extensive study of scientific literature in order to discover drugs and vaccines. In this situation where every minute is valuable to save the lives of hundreds of people, a quick understanding of scientific articles will help the medical community. Automatic text summarization makes this possible.

In this study, a recurrent neural network-based extractive summarization is proposed. The extractive method identifies the informative parts of the text. Recurrent neural network is very powerful for analyzing sequences such as text. The proposed method has three phases: sentence encoding, sentence ranking, and summary generation. To improve the performance of the summarization system, a coreference resolution procedure is used. Coreference resolution identifies the mentions in the text that refer to the same entity in the real world. This procedure helps to summarization process by discovering the central subject of the text.

The proposed method is evaluated on the COVID-19 research articles extracted from the CORD-19 dataset. The results show that the combination of using recurrent neural network and coreference resolution embedding vectors improves the performance of the summarization system. The Proposed method by achieving the value of ROUGE1-recall 0.53 demonstrates the improvement of summarization performance by using coreference resolution embedding vectors in the RNN-based summarization system.

In this study, coreference information is stored in the form of coreference embedding vectors. Jointly use of recurrent neural network and coreference resolution results in an efficient summarization system.

The purpose of this study is estimating and comparing the three different dimensions of the EEG and studying the trials variability for two auditory and visually oddball tasks in the healthy subjects. They include regional as the region of the brain, longitudinal as the repetition of the stimuli, and functional as whole curve of Evoked Related Potential (ERP), dimensions.

The sample size is seventeen, with six females, in this three-trial study with standard and target stimuli per task. The dataset was downloaded from the internet and preprocessed. The Hybrid Principal Component Analysis (HPCA) decomposed the ERPs and estimated eigen components of three dimensions. The 95% Bayesian credible sets and trial effects as random effects of the first eigen component of each dimensions studied with the Generalized Additive Mixed Model (GAMM).

The p-values of the interaction effects between time and stimuli, repeats and stimuli and regions and stimuli are <0.05 for three dimensions, except in auditory task of longitudinal dimension and in visual task of regional dimension that are >0.05. The p-value of trial effects are <0.05 and for auditory task in the longitudinal dimension is borderline.  

The HPCA methodology decompose the time-domain ERPs to the functional-longitudinal and regional dimensions. The first eigencompments capture the most variations of every dimensions and we study the behavior of three-dimensions with them. We conclude that the repeating of the stimuli has a positive effect on the visual tasks. We also study the variability between trials with GAMM that are statistically significant.

Brain Computer Interface (BCI) has provided a novel way of communication that can significantly revolutionize life of people suffering from disabilities. Motor Imagery (MI) EEG BCI is one of the most promising solutions to address. The main phases of such systems include signal acquisition, pre-processing, feature extraction, classification and the intended interface. The challenging obstacles in such systems are to detect and extract efficient features that present reliability and robustness alongside promising classification accuracy. In this paper it is endeavored to present a robust method for a two-class MI BCI that results in high accuracy.

For this purpose, the dataset 2b from BCI competition 2008, consisting of three channels (C3, C and Cz), was utilized. Firstly, the signals were bandpass filtered. Secondly, Common Spatial Pattern (CSP) was employed and then a number of features, including non-linear chaotic features were extracted from channels C3 and C4. After feature selection phase the number of features were reduced to 38 and 47. Finally, these features were fed into two classifiers, namely Support Vector Machine classifier (SVM) and Bagging to evaluate the performance of the system.

Classification accuracy and Cohen’s Kappa coefficient of the proposed method for two MI EEG channels are 96.40% and 0.92, respectively.

These results indicate the high accuracy and stability of our method in comparison with similar studies. Therefore, it can be a promising approach in two-class MI BCI systems.

Thyroid cancer is common worldwide with a rapid increase in prevalence across North America in recent years. While most patients present with palpable nodules through physical examination, a large number of small and medium-sized nodules are detected by ultrasound examination. Suspicious nodules are then sent for biopsy through fine needle aspiration to determine whether the nodule is malignant. Since biopsies are invasive and sometimes inconclusive, various research groups have tried to develop computer-aided diagnosis systems aimed at characterizing thyroid nodules based on ultrasound scans. Earlier approaches along these lines relied on clinically relevant features that were manually identified by radiologists. With the recent success of Artificial Intelligence (AI), various new methods using deep learning are being developed to identify these features in thyroid ultrasound automatically. In this paper, we present a systematic review of state-of-the-art on Artificial Intelligence (AI) application in sonographic diagnosis of thyroid cancer. This review follows a methodology-based classification of the different techniques available for thyroid cancer diagnosis, from methods using feature-based models to the most recent deep learning-based approaches. In this review, we reflect on the trends and challenges of the field of sonographic diagnosis of thyroid malignancies and potential of computer-aided diagnosis to increase the impact of ultrasound applications on the future of thyroid cancer diagnosis. Machine learning will continue to play a fundamental role in the development of future thyroid cancer diagnosis frameworks.

In this work, we have focused on fabrication and validation of a respiratory belt that produces respiratory signals from the patient’s abdominal and thorax.

A load cell transducer was attached to the belt to create an electrical signal from respiratory movement. It converts a force or load into an equivalent electrical signal or digitized load value. The accuracy of the signals from our respiratory control belt was evaluated according to a comparison with the signals from commercial SOMNO medical device.

The pattern of the signals from our respiratory belt is  in good agreement (4%/3mm) with the pattern of commercial SOMNO medical device.

The manufactured respiratory control belt can be used during imaging and radiotherapy procedures for breath control in a patient's abdominal and thorax region.