Frontiers in Biomedical Technologies
Volume:11 Issue: 1, Winter 2024

Hybrid nanoparticles have emerged as promising tools in cancer diagnosis and treatment, offering the potential for early detection and precise eradication of malignant cells by integrating diverse materials. However, navigating the intricacies, limitations, and hurdles within this domain underscores the importance of thoughtful decision-making. This editorial provides a comprehensive exploration of the merits and challenges of nanotechnology in the context of cancer diagnosis, therapy, and theranostics. It sheds light on the current applications and delves into the promising future prospects in this field. By doing so, this editorial aims to foster a deeper understanding of the intricacies involved in designing efficient protocols for hybrid nanoparticle production, contributing to advancing cancer management strategies.

The correlation of different samples can be described by analytical models such as random matrix theory. In this study, we tried to describe the correlation of different types of ultraviolet values in different months, weeks, and hours to get a significant relationship of special times, which one needs to get enough intensity of the sun or avoid getting sunburn.

To this aim, we focused on the hourly and daily mean amounts of ultraviolet A, B, and C intensities of solar radiation in Tabriz urban area were measured during a full year of 2017-2018. We used such ultraviolet values which are measured at the same hour of the day to satisfy the same symmetry criteria which are necessary in random matrix theory. These data are unfolded and classified in different sequences to analyze in the nearest neighbor spacing distribution framework via the maximum likelihood estimation technique.

Strong correlation is yielded for daily values of UVA in comparison with the other types of ultraviolet radiations. Also, we considered the dependence of correlation degrees of these three types of ultraviolet to average temperature and humidity at different months.

The results propose more correlation of UVA indices in August while such correlation of UVC radiations are yielded in December.

Attention-Deficit-Hyperactivity-Disorder (ADHD) is a neurodevelopmental disorder that begins in early childhood and often persists into adulthood, causing personality issues and social behavior problems. Thus, detecting ADHD in its early stages and developing an effective therapy is of tremendous interest. This study presents a deep learning-based model for ADHD diagnosis in children.

The 'First-National-EEG-Data-Analysis-Competition-with-Clinical-Application' dataset is used for this purpose. Following preprocessing, data is segmented into 3-second epochs, and frequency features are extracted from these epochs. The Fourier transform is applied to each channel separately, and the resulting two-dimensional matrix (channel×frequency) for each epoch is used as the Convolutional Neural Network's (CNN) input. The CNN is made up of two convolutional layers, two max pooling layers and two fully connected layers as well as the output layer (a total of 9 layers) for classification. To improve the method's performance, the output of the classification of each input variable is analyzed. In other words, the role of each channel/frequency in the final classification is being investigated using the Layer-wise Relevance Propagation (LRP) algorithm.

According to the results of the LRP algorithm, only efficient channels are employed as Convolutional Neural Network (CNN) inputs in the following stage. This method yields a final accuracy of 94.52% for validation data. In this study, the feature space is visualized, useful channels are selected, and deep structure capabilities are exploited to diagnose ADHD disorder.

The findings suggest that the proposed technique can be used to effectively diagnose ADHD in children.

Radiation shielding requires deep knowledge about the shielding materials properties. Additionally, the interaction between the radiation and materials should be well understood.

Monte Carlo (MC) simulation, NXCOM, and WinXCOM computational programs were utilized for the concrete shielding properties against 1.5 MeV neutron beam and 137Cs emitted γ-ray. In a simulated “good geometry” using MCNP5 MC code, NXCOM and WinXCOM, radiation attenuation factor (µ), microscopic neutron removal cross-section (  Half and Tenth Value Layers (HVL and TVL) of the studied concretes were derived. Obtained results by the methods were compared and discussed.

For 137Cs emitted γ-ray, mass attenuation factor (µ/ρ) obtained as 0.026 cm2/g, 0.025 cm2/g, and 0.025 cm2/g for the Serpentine concrete as the minimum factors by MCNP5 code, WinXCOM and NXCOM software, respectively. Good agreement was seen in the results derived by the use of the applied calculation methods. Maximum values for the µ/ρ were calculated as 0.03 cm2/g, 0.029 cm2/g and 0.029 cm2/g by MCNP5 code, WinXCOM and NXCOM, respectively. For the neutron attenuation factor, calculations were conducted for the concretes and the highest and lowest ΣR/ρ were derived for Serpentine and Ordinary concretes. MCNP5 MC code was calculated ΣR/ρ for the Serpentine and Ordinary concretes as 0.039 cm2/g and 0.030 cm2/g, respectively. ΣR/ρ for the Serpentine and Ordinary concretes as 0.039 cm2/g and 0.030 cm2/g, respectively.

It was concluded that the calculated results showed N-XCOM program can be applied for the shielding calculations for the conventional concretes studied in this work.

Obsessive-Compulsive Disorder (OCD) is a mental and behavioral disorder in which an individual has intrusive thoughts and rituals which decrease the distress. It seems that there are many treatments such as EX/RP, repetitive Transcranial Magnetic Stimulation (rTMS), medications or mindfulness for OCD, but there is no single effective treatment yet. In this study, we investigated a multi-element daily program on symptom reduction of OCD people.

In a quasi-experimental design 13 patients were included in the study and received daily rTMS with EX/RP and biofeedback. We utilized BDI, BAI, and YBOCS tools to collect data before and after the treatment and in the subsequent one-month follow-up.

According to the BDI, BAI, and YBOCS results, the decrease in score was observed at the p<0.001 level and the changes were significant after a one-month follow-up period.

The results indicated that the combination of sequenced treatments simultaneously such as rTMS and biofeedback with exposure therapy can facilitate the engagement and enhancement of self-control and distress tolerance.

The osteological characteristics of fish, especially the head structure, are important in understanding the biological characteristics. Periophthalmus waltoni and Boleophthalmus dussumieri are among the mudskippers and are distributed along the coasts of the Oman Sea and the Persian Gulf.

After catching and fixing the samples in 96% ethanol, the samples were then sent to the preclinical laboratory (Lotus-InVivo) for micro-CT scanning (TPCF, in Tehran university of medical sciences) for imaging.

In B. dussumieri, the skull is rudimentary and a high percentage of the bones is still cartilaginous. In this species, despite the larger head size, the braincase is small. In P. waltoni, the braincase is larger, but the skull tissue is completely bony and has very little cartilaginous. The jaws have also undergone drastic changes, corresponding to the change from a nearly fixed biting mouth to a flexible sucking mouth. In both species, the teeth are sharp and in two parts in the jaws. In P. waltoni, there are three pairs of sharp teeth for hunting in the upper jaw, the number of these teeth in B. dussumieri is four passes and it is less curved.

In this report, for the first time, the skull structure of the Persian Gulf was investigated. Micro CT technique has also been used for the first time. Mudskippers have developed special adaptations to live in mud in terrestrial and aquatic conditions. These adaptations are greater in P. waltoni, which shows greater degrees of terrestrialization, and requires detailed studies in this field.

In the recent decade, proton therapy facilities are increasing worldwide. This study aimed to analyze the influence of volumetric changes in bladder and rectum filling on the dose received by normal surrounding tissues at prostate cancer proton therapy. In this work, an anthropomorphic phantom dedicated to the prostate organs and nearby tissues has been developed using the FLUKA simulation code.

The geometry of the prostate and normal nearby tissues, bladder volumetric changes, and rectum filling/emptying status were simulated according to a database of real patients to mimic actual treatment, assuming the prostate as the target receives the prescribed dose uniformly with no over- and under dosage at each treatment session. Furthermore, the dosimetric effect of air- and water-filled balloons as prostate fixation tools was considered on the rectum, during our simulation process.

Final analyzed results showed that the overall dose received by normal nearby organs with be decreased at proton therapy of prostate cancer if the bladder is full, although this dose reduction is not remarkable. Moreover, rectum filling/emptying and also implementation of balloons with different matters have no significant effect on the amount of dose received by this organ.

The dosimetric impact of bladder volumetric variations onto normal nearby organs will not be a crucial issue in proton therapy of prostate cancer if the prescribed high dose is delivered on the target with proper uniformity laterally and in-depth. Based on the obtained results, a full bladder is recommended while target bombarding by a proton beam.

Listening to music has a great impact on people's emotions and would change brain activity. In other words, music-induced emotions are trackable in electrical brain activities. Therefore, Electroencephalography can be a suitable tool to detect these induced emotions. The present study attempted to use electroencephalography in order to recognize four types of emotions (happy, relaxing, stressful, and sad) induced in response to listening to music excerpts, using three classifiers

In this empirical study, electroencephalography signals were collected from 20 participants, as they were listening to pieces of selected music... The collected data was then pre-processed, and 28 linear and nonlinear features for recognizing the aforementioned emotions were extracted. Feature-space components were then reduced through a principal components analysis. Finally, the first ten components of feature-space were used as input for classifiers to identify the induced emotions.

The outputs showed that the suggested method was well capable of emotion recognition.  Evaluating the music excerpts, on the self-assessment manikin scale, demonstrated that the labelling of the music tracks was accurate. The highest accuracy found among neural network, K-nearest neighbors, and support vector machine algorithms was respectively %84, %84, and %89 for happy emotions.

Reduction of features via principal components analysis, led to an acceptable accuracy in classification. Happiness was the most recognizable emotion and the support vector machine had the highest performance among the classifiers. In the end, the outcomes of the proposed method demonstrate that this system is better than the several research in EEG-based emotion recognition.

Evaluating organ radiation doses and also lifetime risk (LAR) of breast cancer from lung CT scans of 735 female patients from the age of 20-50 with Covid19 surveyed by four Corona center hospitals in Tehran, Iran. Patients’ data and exposure information were extracted from dose report pages in picture archiving and communication systems.

Patients were divided into six age groups 20-25; 25-30; 30-35; 35–40; 40-45 and 45–50 years. Breast, thyroid, lung and heart doses were calculated by NCICT, and LAR of breast cancer incidence has been evaluated by BEIR VII report.

The average dose of breast, thyroid, lung and heart were 3.97, 4.75, 4.10 and 3.37 mGy and also the average of the effective dose was 2.56 mSv. Also, the average LAR of breast cancer in female patients was 7.45 per 100,000 exposures and it decreased with age.

Although CT scan is a useful instrument in the diagnosis and treatment of Corona disease, but it should be recommended with caution due to the increased risk of breast cancer, especially in younger women.

Developing an efficient and reliable method for the identification of depression has high importance. The aim of this paper is to propose an approach for depression diagnosis using an interhemispheric asymmetry matrix and machine learning algorithms.

First, EEG signal was acquired from 24 depressed patients and 24 healthy subjects. The EEG signal was acquired from participants for 5 minutes in eyes-closed (EC) and 5 minutes in eyes-open (EO) condition. After preprocessing data, interhemispheric asymmetry for absolute and relative powers of theta and beta frequency bands, theta-to-alpha power ratio, and IAF features were computed. Then, the proposed asymmetry matrix is used as a feature for statistical and classification analysis. In this paper, classification was performed using a support vector machine (SVM), logistic regression, and multi-layer perceptron (MLP). 

The results demonstrated that central and temporal theta absolute power, central and temporal individual alpha frequency (IAF) asymmetries in EC condition and occipital beta absolute power, temporal theta relative power, temporal theta-to-alpha power ratio, and temporal IAF asymmetries in EO condition have significant differences between depressed and healthy groups. Findings show that beta absolute power asymmetry in the occipital region and EO condition is a good biomarker for depression identification with 77.1% accuracy using Gaussian SVM classifier.

The results of this study show performance of proposed asymmetry matrix features in depression detection. Findings show that beta absolute power asymmetry in the occipital region and EO condition is a good biomarker for depression identification.

This study aimed to design an improved form of a composite shield with different materials and shapes and simultaneously reduce the radiation dose to both the patient and operator.

A female phantom study was performed with and without bismuth belt-shaped composite shields on the breast region at different beam projections used in coronary angiography. Dose measurements were conducted using GR-200 thermo-luminescence dosimeters, dose area product (DAP), and air kerma (AK) over regular and large breast locations, with and without using bismuth shields. An electronic personal dosimeter was used for operator dose assessment. Patients received doses between 2.27 mSv and 3.38 mSv, depending on the size and strength of beam projections.

The use of the developed shields caused a dose reduction of 18%–25% of sensitive breast tissue due to breast size and shield type. During coronary angiography, the mean values of DAP and AK were 2.02 (1.24-2.80) mGy.m2 and 314.1 (202.8-500) mGy, respectively. The highest recorded dose was at the LAO/CRA and LAO/CAU beam projections for both the patient and operator. After applying a belt shield, the operator's radiation dose was decreased by approximately 32%. We found a statistically significant correlation between the radiation dose received by the operator and the patient's breast radiation exposure dose (p<0.001, r2=0.93).

The designed belt shield can be a potentially promising protective device for decreasing the radiation risk to the patient's breast and the operator during coronary angiography. However, further studies will be considered before the application of this shield in standard clinical practice.

The main goal of this study was to determine the optimal collimator in the absence of medium energy collimators along with the impact of Attenuation Correction (AC) and different iterative reconstruction protocols on the quantitative evaluation of Gallium-67 (67Ga) SPECT/CT imaging.

A GE Discovery 670 dual-head SPECT/CT scanner and a NEMA phantom filled with 67Ga solution were used to scan the patients. The projections were acquired with both Low Energy High Resolution (LEHR) and High Energy General Purpose (HEGP) collimators, and CT images were acquired to evaluate the effect of attenuation correction. SPECT data were reconstructed using the ordered subset expectation maximization (OSEM) method with various combinations of iterations and subsets. The performance was quantified, and a clinical study validated the phantom study.

Acquired images by the HEGP collimator yielded higher Contrast Recovery (CR) and Contrast to Noise Ratio (CNR) in images with AC than those without non-AC (41.6% and 74.2%, respectively). The CNR in all spheres after AC was increased by 80.4% (82.1%) for the HEGP collimator against the LEHR collimator. Also, an increase in iterations × subsets from 16 to 48 led to the Coefficient of Variation (COV) increasing by 17.2%, 16.67%, 15.50%, 14.4%, 14.2%, and 14.1% for 10 mm to 37 mm sphere diameter, respectively.

CT-based AC and HEGP collimators can yield improved 67Ga SPECT quantification compared to Non-AC and LEHR collimators. The choice of the optimal collimator with the reconstruction protocol led to changes in the image quality and quantitative accuracy, emphasizing the need to carefully select the appropriate combination of data acquisition factors.

High-resolution ultrasound imaging is a non-invasive and objective appraisal. Ultrasound imaging accomplishes the target assessment and follow-up of radiation-induced skin injury. The study aimed to investigate the complete anatomical and structural alternations of acute wound healing in skin tissue radiation injury after cell therapy with high-frequency ultrasound imaging techniques.

Female guinea pigs (250 g) were divided into 3 groups: (a) controls, consisting of non-treated guinea pigs; (b) radiation-treated; (c) radiation-treated receiving adipose-derived mesenchymal stem cells. Acute radiation-induced skin injury was induced by a single fraction of X-ray irradiation of 60Gy to a 3.0×3.0-cm area with a 1.3-cm bolus on 100-cm SSD in the abdominal skin tissue. Ultrasonic imaging of the depth and quality of healing in the skin tissue was performed by processing ultrasound images at 40-MHz and 75-MHz frequencies.

Skin thickness indicated a significant difference between the treatment and control groups on Day 10 after 60 Gy irradiation (P<0.05). The highest skin thickness was observed in the irradiated group, and the lowest skin thickness was found in the stem cell treatment group.

Evaluation of skin thickness, wound depth, and scar formation is important for the proper assessment and management of wound healing in stem cell therapy of radiation-induced skin damage. High-resolution ultrasound at 40- and 75-MHz frequencies is a major non-invasive method providing unprecedented insight into determining the characterization of the skin, particularly in the context of wound healing.

Magnetic Resonance Imaging (MRI) applications offer superior soft tissue contrast compared with computed tomography (CT) for accurate radiotherapy planning. Although, MRI images suffer from poor image quality and lack electron density for radiation dose calculation. The present study aims to use the deep learning (DL) approach to 1) enhance the quality of MRI images and 2) generate synthetic CT images using MRI images for more accurate radiotherapy planning.

In this paper, the pix2pix Generative Adversarial Network was utilized to synthesize CT images from noisy MRI images of 20 arbitrarily patients with brain disease. The standard statistical measurements investigated the accuracy comparison of the modeled Hounsfield unit (HU) value from MRI images and referenced CT of each patient. The famous quality metrics that were used to compare synthetic CTs and referenced CTs were the mean absolute error (MAE), the structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR(.

The higher quality measurements between the synthetic pseudo-CT and the referenced CT images as PSNR, and SSIM, should correlate to the lower MAE value. For the overall brain among blind test data, the measured peak signal-to-noise ratio, mean absolute error, and structural similarity index values were about 16.5, 28.13, and 93.46, respectively.  

The proposed method provides an acceptable level of statistical measurements computed on the Pseudo-CT and referenced CT, and it could be concluded that the p-CT can be implemented in radiotherapy treatment planning with acceptable accuracy.

We synthesized folic acid-conjugated Fe3O4/Au-pralidoxime chloride Nanoparticles (Fe2O3/Au@PAM NPs) for use as dual-modal contrast agents for Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) in the diagnosis of breast cancer.

Fe2O3/Au@PAM NPs labeled or not to folic acid were synthesized and analyzed by dynamic light scattering, transmission electron microscopy, and vibrating sample magnetometry. The ability of these NPs to create image contrast was also investigated in silico and in vitro (in MCF-7 breast cancer cells and A549 lung cancer cells) with CT and MRI.

Dynamic light scattering and transmission electron microscopy revealed that the Fe2O3/Au@PAM NPs were nearly spherical. The average diameter of Fe2O3/Au NPs increased from 11.6 nm to 98 nm after folic acid conjugation. The saturation magnetization values of Fe2O3/Au@PAM NPs with and without folic acid conjugation were 25.56 and 32.6 emu/g, respectively. Conjugation of folic acid to NPs greatly improved their uptake by cancer cells. The additional coating of NPs with FA reduced the T2 relaxation time and signal intensity for MRI. Folic acid-labeled MCF-7 cells had a radiodensity measurement of 208 Hunsfield Units (HU) compared to 95 HU for A549 cells. For breast cancer cells, NPs labeled with folic acid significantly improved the X-ray absorption coefficient as a sign of active cellular uptake compared to NPs without labeling.

Folic acid-labeled Fe2O3/Au@PAM NPs can serve as dual CT/MRI contrast agents and improve the sensitivities of both modalities for the detection of cancer cells.

In today’s era, the lifestyle of people has become much more sophisticated due to the involvement of stress, anxiety, and depression in the daily routine of human beings. In such a scenario, cardiac diseases are growing rapidly in youngsters and senior citizens. It is also observed that cardiac diseases are crucial and sensitive, including life-threatening chances. So, it is essential to detect and prevent such cardiac disorders within the required time for recovery. Since there has been a lot of research in the prediction and prevention of cardiac disorders, cardiac arrhythmia is also one of the majorly occurring diseases in the bulk of the population. The electrocardiogram is the cheap and best way to diagnose the problem of cardiac arrhythmia, and a huge amount of data is collected daily in hospitals and pathological centers. Previously, various automated models were developed for detecting cardiac arrhythmia using deep learning approaches and machine learning. In this work, we have reviewed recently developed automated models and evaluated their performance based on specific parameters like deployed datasets, variation of input data, applied application, methodology, and results obtained by the developed model. The limitations of reviewed papers are also mentioned in addition to their future scope for improvement.

The collimator design and optimization are essential in small animal molecular imaging for preclinical studies. In this study, a mathematical model was derived and used to optimize the slit collimator for small animal imaging applications. 

The geometric efficiency was formulated as a source-to-detector distance for a certain amount of the collimator resolution (). The first-order derivative of the derived formula gives the optimized parameters. The detector performance was modeled in terms of intrinsic resolution . Furthermore, the edge penetration effect was considered using the validated model.

Optimum source-to-detector distance  was found as . For an ideal detector, optimal, geometric efficiency  and slit aperture width  were found as ,  and , respectively. Where   and  are the source-to-collimator distance and detector length, respectively. For the fixed resolution of 1.0 mm, the sensitivity for different source-to-collimator distances of 50.0, 100.0, and 150.0 mm was calculated as, , and , respectively. In addition, for a sub-millimeter resolution of 0.5 mm at 15.0, 30.0, and 50.0 mm, the geometric efficiency was calculated as, , , and . For a typical source-to-collimator distance (15.0 mm), the optimal geometric efficiencies are, , , , and   for the resolutions of 0.25, 0.50, 1.0, 1.5, and 2.0 mm, respectively.

Based on the analytic model predictions, the performance characteristics of the slit collimator in terms of geometric efficiency and resolution were extracted. The importance of the proposed model lies both in its speed and ease of application.