Journal of Medical Signals & Sensors最新文献

Introduction: Organ dose distribution calculation in radiotherapy and knowledge about its side effects in cancer etiology is the most concern for medical physicists. Calculation of organ dose distribution for breast cancer treatment plans with Monte Carlo (MC) simulation is the main goal of this study.

Materials and methods: Elekta Precise linear accelerator (LINAC) photon mode was simulated and verified using the GEANT4 application for tomographic emission. Eight different radiotherapy treatment plans on RANDO's phantom left breast were produced with the ISOgray treatment planning system (TPS). The simulated plans verified photon dose distribution in clinical tumor volume (CTV) with TPS dose volume histogram (DVH) and gamma index tools. To verify photon dose distribution in out-of-field organs, the point dose measurement results were compared with the same point doses in the MC simulation. Eventually, the DVHs for out-of-field organs that were extracted from the TPS and MC simulation were compared.

Results: Based on the implementation of gamma index tools with 2%/2 mm criteria, the simulated LINAC output demonstrated high agreement with the experimental measurements. Plan simulation for in-field and out-of-field organs had an acceptable agreement with TPS and experimental measurement, respectively. There was a difference between DVHs extracted from the TPS and MC simulation for out-of-field organs in low-dose parts. This difference is due to the inability of the TPS to calculate dose distribution in out-of-field organs.

Conclusion and discussion: Based on the results, it was concluded that the treatment plans with the MC simulation have a high accuracy for the calculation of out-of-field dose distribution and could play a significant role in evaluating the important role of dose distribution for second primary cancer estimation.

Background: Radiotherapy is one of the routine treatment strategies for breast cancer (BC) patients. Different responses of the patient to radiation due to different intrinsic radiosensitivity (RS) were induced to the researcher try to introduce a standard assay for the prediction of RS. Clonogenic assay is recognized as a gold standard method in this subject but because of some of its disadvantages, it is needed for alternative assays. In this study, two assays were evaluated for this reason in ten BC patients with different RSs.

Methods: The peripheral blood of 10 volunteers with BC was obtained, and the peripheral blood mononuclear cells were extracted. After exposed with 2 Gy, survival fraction at 2 Gy (SF2) was calculated by clonogenic assay. γ-H2AX assay was performed for all patients, and apoptosis assay was evaluated for three represented categorized patients.

Results: RS of patients showed SF2 and categorized in three groups (high, medium, and low RS). Double-strand breaks (DSBs) were decreased in high radiosensitive patients, but the residual DSBs were clearly higher than other two groups. It is shown that the repair system in these patients is lower active than others. Apoptosis frequency in patient 4 is highly active which could induce the enhancement of her RS.

Conclusion: γ-H2AX and apoptosis assays could predict the intrinsic RS, but evaluation of them separately is not sufficient for this aim. It is necessary to consider all the parameters together and consideration of the combination of assays could fit a better prediction of intrinsic RS.

In the past decade, tensors have become increasingly attractive in different aspects of signal and image processing areas. The main reason is the inefficiency of matrices in representing and analyzing multimodal and multidimensional datasets. Matrices cannot preserve the multidimensional correlation of elements in higher-order datasets and this highly reduces the effectiveness of matrix-based approaches in analyzing multidimensional datasets. Besides this, tensor-based approaches have demonstrated promising performances. These together, encouraged researchers to move from matrices to tensors. Among different signal and image processing applications, analyzing biomedical signals and images is of particular importance. This is due to the need for extracting accurate information from biomedical datasets which directly affects patient's health. In addition, in many cases, several datasets have been recorded simultaneously from a patient. A common example is recording electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) of a patient with schizophrenia. In such a situation, tensors seem to be among the most effective methods for the simultaneous exploitation of two (or more) datasets. Therefore, several tensor-based methods have been developed for analyzing biomedical datasets. Considering this reality, in this paper, we aim to have a comprehensive review on tensor-based methods in biomedical image analysis. The presented study and classification between different methods and applications can show the importance of tensors in biomedical image enhancement and open new ways for future studies.

The employees who work in infection prevention and control (IPC) are very important in the field of health-care because they are committed to protecting patients, staff, and visitors from the risk of acquiring infections while in the hospital. The complexity of infection control in hospitals is alwaysgrowing in tandem with the rapid developments that are being made in medical technology andpractices. IPC personnel are required to maintain vigilance and continually improve their monitoringof the entire health-care process due to the ongoing development of IPC guidelines and regulations, the fluctuating infection risks, and the emergence of new infectious diseases. As a result, individuals involved in the prevention and control of infections in health-care settings absolutely need to participate in continual training and professional development. This reviewemphasizes the need of relevant professionals to engage in ongoing training and professional development to maintain their skills in the area of healthcare-associated infection control and prevention. Personnel working in IPC may more effectively react to newly discovered health risks andmake certain that hospital infection (HI) management gets the appropriate attention if they have atimely and in-depth awareness of best practices. They are better able to maintain their composure, react correctly, and deliver the most effective infection control and prevention techniques for the health-care system, all while increasing awareness about the significance of effective HI management.

Background: Devices that mimic the functions of human skin are known as "electronic skin," and they must have characteristics like high sensitivity, a wide dynamic range, high spatial homogeneity, cheap cost, wide area easy processing, and the ability to distinguish between diverse external inputs.

Methods: This study introduces a novel approach, termed microfluidic droplet-based emulsion self-assembly (DMESA), for fabricating 3D microstructured elastomer layers using polydimethylsiloxane (PDMS). The method aims to produce accurate capacitive pressure sensors suitable for electronic skin (e-skin) applications. The DMESA method facilitates the creation of uniform-sized spherical micropores dispersed across a significant area without requiring a template, ensuring excellent spatial homogeneity.

Results: Micropore size adjustment, ranging from 100 to 600 μm, allows for customization of pressure sensor sensitivity. The active layer of the capacitive pressure sensor is formed by the three-dimensional elastomer itself. Experimental results demonstrate the outstanding performance of the DMESA approach. It offers simplicity in processing, the ability to adjust performance parameters, excellent spatial homogeneity, and the capability to differentiate varied inputs. Capacitive pressure sensors fabricated using this method exhibit high sensitivity and dynamic amplitude, making them promising candidates for various e-skin applications.

Conclusion: The DMESA method presents a highly promising solution for fabricating 3D microstructured elastomer layers for capacitive pressure sensors in e-skin technology. Its simplicity, performance adjustability, spatial homogeneity, and sensitivity to different inputs make it suitable for a wide range of electronic skin applications.

Background: A significant number of frames captured by the wireless capsule endoscopy are involved with varying amounts of bubbles. Whereas different studies have considered bubbles as nonuseful agents due to the fact that they reduce the visualization quality of the small intestine mucosa, this research aims to develop a practical way of assessing the rheological capability of the circular bubbles as a suggestion for future clinical diagnostic purposes.

Methods: From the Kvasir-capsule endoscopy dataset, frames with varying levels of bubble engagements were chosen in two categories based on bubble size. Border reflections are present on the edges of round-shaped bubbles in their boundaries, and in the frequency domain, high-frequency bands correspond to these edges in the spatial domain. The first step is about high-pass filtering of border reflections using wavelet transform (WT) and Differential of Gaussian, and the second step is related to applying the Fast Circlet Transform (FCT) and the Hough transform as circle detection tools on extracted borders and evaluating the distribution and abundance of all bubbles with the variety of radii.

Results: Border's extraction using WT as a preprocessing approach makes it easier for circle detection tool for better concentration on high-frequency circular patterns. Consequently, applying FCT with predefined parameters can specify the variety and range of radius and the abundance for all bubbles in an image. The overall discrimination factor (ODF) of 15.01, and 7.1 showing distinct bubble distributions in the gastrointestinal (GI) tract. The discrimination in ODF from datasets 1-2 suggests a relationship between the rheological properties of bubbles and their coverage area plus their abundance, highlighting the WT and FCT performance in determining bubbles' distributions for diagnostic objectives.

Conclusion: The implementation of an object-oriented attitude in gastrointestinal analysis makes it intelligible for gastroenterologists to approximate the constituent features of intra-intestinal fluids. this can't be evaluated until the bubbles are considered as non-useful agents. The obtained results from the datasets proved that the difference between the calculated ODF can be used as an indicator for the quality estimation of intraintestinal fluids' rheological features like viscosity, which helps gastroenterologists evaluate the quality of patient digestion.

Background: Computed tomography (CT) imaging has a large portion in the dose of patients from radiological procedures; therefore, accurate calculation of radiation risk estimation in this modality is inevitable. In this study, a method for determining the patient-specific effective dose using the dose-length product (DLP) index in lung CT scan using Monte Carlo (MC) simulation is introduced.

Methods: EGSnrc/BEAMnrc MC code was used to simulate a CT scanner. The DOSxyznrc simulation code was used to simulate a specific voxelized phantom from the patient's lungs and irradiate it according to X-ray parameter of routing lung CT scan, and dose delivered to thorax organs was calculated. Three types of phantoms were simulated according to three different body habits (slim, standard, and fat patients) in two groups of men and women. A factor was used to convert the relative dose per particle in MC code to the absolute dose. The dose was calculated in all lung organs, and the effective dose was calculated for all three groups of patient body habits. DLP index and volume CT dose index (CTDIvol) were extracted from the patient's dose report in the CT scanner. The DLP to effective dose conversion factor (k-factor) for patients with different body habitus was calculated.

Results: Lung radiation dose in slim, standard, and fat patients in men was 0.164, 0.103, and 0.078 mGy/mAs and in women was 0.164, 0.105, and 0.079 mGy/mAs, respectively. The k-factor in the group of slim patients, especially in women, was higher than in other groups.

Conclusions: CT scan dose indexes for slim patients are reported to be underestimated in studies. The dose report in CT scan systems should be modified in proportion to the patient's body habitus, to accurately estimate the radiation risk.

Fabricating three-dimensional (3D) scaffolds is attractive due to various advantages for tissue engineering, such as cell migration, proliferation, and adhesion. Since cell growth depends on transmitting nutrients and cell residues, naturally vascularized scaffolds are superior for tissue engineering. Vascular passages help the inflow and outflow of liquids, nutrients, and waste disposal from the scaffold and cell growth. Porous scaffolds can be prepared by plant tissue decellularization which allows for the cultivation of various cell lines depending on the intended application. To this end, researchers decellularize plant tissues by specific chemical and physical methods. Researchers use plant parts depending on their needs, for example, decellularizing the leaves, stems, and fruits. Plant tissue scaffolds are advantageous for regenerative medicine, wound healing, and bioprinting. Studies have examined various plants such as vegetables and fruits such as orchid, parsley, spinach, celery, carrot, and apple using various materials and techniques such as sodium dodecyl sulfate, Triton X-100, peracetic acid, deoxyribonuclease, and ribonuclease with varying percentages, as well as mechanical and physical techniques like freeze-thaw cycles. The process of data selection, retrieval, and extraction in this review relied on scholarly journal publications and other relevant papers related to the subject of decellularization, with a specific emphasis on plant-based research. The obtained results indicate that, owing to the cellulosic structure and vascular nature of the decellularized plants and their favorable hydrophilic and biological properties, they have the potential to serve as biological materials and natural scaffolds for the development of 3D-printing inks and scaffolds for tissue engineering.

Background: Cognitive flexibility, a vital component of executive function, entails the utilization of extended brain networks. Olfactory stimulation has been shown to influence various brain functions, particularly cognitive performance.

Method: To investigate aroma inhalation's effects on brain activity dynamics associated with cognitive flexibility, 20 healthy adults were recruited to complete a set-shifting task during two experimental conditions: no aroma stimuli vs. lavender essential oil inhalation. Using Thomson's multitaper approach, the normalized power spectral density (NPSD) was assessed for five frequency bands.

Results: Findings confirm that aroma inhalation significantly affects behavioral indices (i.e., reaction time (RT) and response accuracy) and electroencephalogram (EEG) signatures, especially in the frontal lobe. Participants showed a tremendous increase in theta and alpha NPSD, associated with relaxation, along with beta NPSD, associated with clear and fast thinking after inhaling the aroma. NPSD of the delta band, an indicator of the unconscious mind, significantly decreased when stimulated with lavender essential oil. Further, participants exhibited shorter RT and more accurate responses following aroma inhalation.

Conclusion: Our findings revealed significant changes in oscillatory power and behavioral performance after aroma inhalation, providing neural evidence that olfactory stimulation with lavender essential oil may facilitate cognitive flexibility.

Background: Brain tumor segmentation is highly contributive in diagnosing and treatment planning. Manual brain tumor delineation is a time-consuming and tedious task and varies depending on the radiologist's skill. Automated brain tumor segmentation is of high importance and does not depend on either inter- or intra-observation. The objective of this study is to automate the delineation of brain tumors from the Fluid-attenuated inversion recovery (FLAIR), T1-weighted (T1W), T2-weighted (T2W), and T1W contrast-enhanced (T1ce) magnetic resonance (MR) sequences through a deep learning approach, with a focus on determining which MR sequence alone or which combination thereof would lead to the highest accuracy therein.

Methods: The BraTS-2020 challenge dataset, containing 370 subjects with four MR sequences and manually delineated tumor masks, is applied to train a residual neural network. This network is trained and assessed separately for each one of the MR sequences (single-channel input) and any combination thereof (dual- or multi-channel input).

Results: The quantitative assessment of the single-channel models reveals that the FLAIR sequence would yield higher segmentation accuracy compared to its counterparts with a 0.77 ± 0.10 Dice index. As to considering the dual-channel models, the model with FLAIR and T2W inputs yields a 0.80 ± 0.10 Dice index, exhibiting higher performance. The joint tumor segmentation on the entire four MR sequences yields the highest overall segmentation accuracy with a 0.82 ± 0.09 Dice index.

Conclusion: The FLAIR MR sequence is considered the best choice for tumor segmentation on a single MR sequence, while the joint segmentation on the entire four MR sequences would yield higher tumor delineation accuracy.