Systematic Review Between Resting-State fMRI and Task fMRI in Planning for Brain Tumour Surgery.

As an alternative to task-based functional magnetic resonance imaging (T-fMRI), resting-state functional magnetic resonance imaging (Rs-fMRI) is suggested for preoperative mapping of patients with brain tumours, with an emphasis on treatment guidance and neurodegeneration prediction. A systematic review was conducted of 18 recent studies involving 1035 patients with brain tumours and Rs-fMRI protocols. This was accomplished by searching the electronic databases PubMed, Scopus, and Web of Science. For clinical benefit, we compared Rs-fMRI to standard T-fMRI and intraoperative direct cortical stimulation (DCS). The results of Rs-fMRI and T-fMRI were compared and their correlation with intraoperative DCS results was examined through a systematic review. Our exhaustive investigation demonstrated that Rs-fMRI is a dependable and sensitive preoperative mapping technique that detects neural networks in the brain with precision and identifies crucial functional regions in agreement with intraoperative DCS. Rs-fMRI comes in handy, especially in situations where T-fMRI proves to be difficult because of patient-specific factors. Additionally, our exhaustive investigation demonstrated that Rs-fMRI is a valuable tool in the preoperative screening and evaluation of brain tumours. Furthermore, its capability to assess brain function, forecast surgical results, and enhance decision-making may render it applicable in the clinical management of brain tumours.

Promoting the suitability of graphitic carbon nitride and metal oxide nanoparticles: A review of sulfonamides photocatalytic degradation.

The widespread consumption of pharmaceutical drugs and their incomplete breakdown in organisms has led to their extensive presence in aquatic environments. The indiscriminate use of antibiotics, such as sulfonamides, has contributed to the development of drug-resistant bacteria and the persistent pollution of water bodies, posing a threat to human health and the safety of the environment. Thus, it is paramount to explore remediation technologies aimed at decomposing and complete elimination of the toxic contaminants from pharmaceutical wastewater. The review aims to explore the utilization of metal-oxide nanoparticles (MONPs) and graphitic carbon nitrides (g-C3N4) in photocatalytic degradation of sulfonamides from wastewater. Recent advances in oxidation techniques such as photocatalytic degradation are being exploited in the elimination of the sulfonamides from wastewater. MONP and g-C3N4 are commonly evolved nano substances with intrinsic properties. They possessed nano-scale structure, considerable porosity semi-conducting properties, responsible for decomposing wide range of water pollutants. They are widely applied for photocatalytic degradation of organic and inorganic substances which continue to evolve due to the low-cost, efficiency, less toxicity, and more environmentally friendliness of the materials. The review focuses on the current advances in the application of these materials, their efficiencies, degradation mechanisms, and recyclability in the context of sulfonamides photocatalytic degradation.

Geant4 Simulation of Photon- and Neutron-Shielding Capabilities of Biopolymer Blends of Poly(lactic acid) and Poly(hydroxybutyrate).

Simulation is used by scientists to imitate a real-life experimental setup in order to save time, costs and effort. Geant4, a toolkit based on the Monte Carlo method, has been widely used in investigating the radiation-shielding properties of different materials. In many recent studies, researchers have focused on polymers and their shielding capabilities. Poly(lactic acid) (PLA) is a widely used biopolymer in many applications due to its excellent mechanical properties. However, it has limitations related to its degree of crystallinity and molecular characteristics, which could be improved through blending with other biodegradable polymers such as poly(hydroxybutyrate) (PHB). Previous published studies have shown that the mechanical properties of such blends can be improved further. In this work, the effect of blending PHB with PLA on the photon- and neutron-shielding capabilities will be investigated using Geant4 over a wide energy range, as well as the effect of doping those blends with metal oxides. The results show that the shielding properties of the polymers are affected by blending with other polymers and by doping the polymer blends with different metal oxides, and they confirm that Geant4 is a very reliable tool that can simulate any material's shielding properties against photons and neutrons.

Geant4 Simulation of the Effect of Different Composites on Polyimide Photon and Neutron Shielding Properties.

Polymers are widely used materials that have many medical and industrial applications. Some polymers have even been introduced as radiation-shielding materials; therefore, many studies are focusing on new polymers and their interactions with photons and neutrons. Research has recently focused on the theoretical estimation of the shielding effectiveness of Polyimide doped with different composites. It is well known that theoretical studies on the shielding properties of different materials through modeling and simulation have many benefits, as they help scientists to choose the right shielding material for a specific application, and they are also much more cost-effective and take much less time compared to experimental studies. In this study, Polyimide (C35H28N2O7) was investigated. It is a high-performance polymer, well known for its outstanding chemical and thermal stability, as well as for its high mechanical resistance. Because of its exceptional properties, it is used in high-end applications. The performance of Polyimide and Polyimide doped with different weight fractions of composites (5, 10, 15, 20 and 25 wt.%) as a shielding material against photons and neutrons were investigated using a Monte Carlo-based simulation toolkit Geant4 within a wide range of energies of both photons and neutrons from 10 to 2000 KeVs. Polyimide can be considered a good neutron shielding material, and its photon shielding abilities could be further enhanced when adding different high atomic number composites to it. The results showed that Au and Ag gave the best results in terms of the photon shielding properties, while ZnO and TiO2 had the least negative effect on the neutron shielding properties. The results also indicate that Geant4 is a very reliable tool when it comes to evaluating the shielding properties against photons and neutrons of any material.

Particle Swarm Optimized Fuzzy CNN With Quantitative Feature Fusion for Ultrasound Image Quality Identification.

Inherently ultrasound images are susceptible to noise which leads to several image quality issues. Hence, rating of an image's quality is crucial since diagnosing diseases requires accurate and high-quality ultrasound images. This research presents an intelligent architecture to rate the quality of ultrasound images. The formulated image quality recognition approach fuses feature from a Fuzzy convolutional neural network (fuzzy CNN) and a handcrafted feature extraction method. We implement the fuzzy layer in between the last max pooling and the fully connected layer of the multiple state-of-the-art CNN models to handle the uncertainty of information. Moreover, the fuzzy CNN uses Particle swarm optimization (PSO) as an optimizer. In addition, a novel Quantitative feature extraction machine (QFEM) extracts hand-crafted features from ultrasound images. Next, the proposed method uses different classifiers to predict the image quality. The classifiers categories ultrasound images into four types (normal, noisy, blurry, and distorted) instead of binary classification into good or poor-quality images. The results of the proposed method exhibit a significant performance in accuracy (99.62%), precision (99.62%), recall (99.61%), and f1-score (99.61%). This method will assist a physician in automatically rating informative ultrasound images with steadfast operation in real-time medical diagnosis.

Theoretical Investigation of Fast Neutron and Gamma Radiation Properties of Polycarbonate-Bismuth Oxide Composites Using Geant4.

The gamma mass (µm) and linear (µ) attenuation coefficients of polycarbonate-bismuth oxide composites (PC-Bi2O3) with different bismuth oxide weight factors were investigated theoretically using EpiXS and a Monte Carlo simulation-based toolkit and Geant4 within an energy range between 0.1 and 2 MeV. The wide energy ranges of gamma rays and neutrons were chosen to cover as many applications as possible. The attenuation coefficients were then used to compute the half-value layers. The effective atomic numbers and effective electron densities of the studied samples obtained by EpiXS were compared as well. In order to further evaluate the shielding effectiveness of the studied samples, the thicknesses of all the investigated samples equivalent to 0.5 mm lead at a gamma energy of 511 keV were compared using a Geant4 code simulating a female numerical phantom with a gamma source placed facing the chest and a cylinder-shaped shield wrapped around the trunk area. The fast neutron removal cross sections of the investigated samples were studied to evaluate the effect of the weight factor of nanocomposites on the neutron shielding capabilities of the polymer as well.

Secondary Electrons in Gold Nanoparticle Clusters and Their Role in Therapeutic Ratio: The Outcome of a Monte Carlo Simulation Study.

Gold nanoparticles (GNPs) are used in proton therapy radio-sensitizers to help increase the dose of radiation to targeted tumors by the emission of secondary electrons. Thus, this study aimed to investigate the link between secondary electron yields produced from a nanoshell of GNPs and dose absorption according to the distance from the center of the nanoparticles by using a Monte Carlo model. Microscopic evaluation was performed by modeling the interactions of secondary electrons in a phase-space file (PSF), where the number of emitted electrons was calculated within a spherical GNP of 15 nm along with the absorbed dose near it. Then, the Geant4-DNA physics list was used to facilitate the tracking of low-energy electrons down to an energy below 50 eV in water. The results show a remarkable change in the number of secondary electrons, which can be compared at concentrations less than and greater than 5 mg/mL, with increased secondary electron production exhibited around NPs within a distance of 10-100 nm from the surface of all nanospheres. It was found that there was a steep dose enhancement drop-off up to a factor of dose enhancement factor (DFE) ≤ 1 within a short distance of 100 nm from the surface of the GNPs, which revealed that the dose enhancement existed locally at nanometer distances from the GNPs. Overall, our results indicate that the physical interactions of protons with GNP clusters should not be considered as being directly responsible for the radio-sensitization effect, but should be regarded as playing a major role in NP properties and concentrations, which has a subsequent impact on local dose enhancement.

Theoretical Investigation of Gamma- and Neutron-Shielding Properties of Polysulfone (PSU) Polymer Material Using Geant4.

Polymers are widely used materials that have many medical and industrial applications. Some polymers have even been introduced as radiation-shielding materials; therefore, many studies are focusing on new polymers and their interactions with photons and neutrons. Research has focused on theoretical estimation of the shielding effectiveness of different materials. It is well known that theoretical studies on the shielding properties of different materials through modeling and simulation have many benefits, as they help scientists to choose the right shielding material for a specific application, and they are also much more cost-effective and take much less time compared to experimental studies. In this study, polysulfone (PSU) was investigated. PSU is a high-temperature, amber-colored, semi-transparent plastic material with good mechanical properties. It is resistant to degradation from hot water and steam and is often used in medical and food preparation applications, where repeated sterilization is required. The interactions of photons and neutrons with PSU were investigated using a Monte Carlo-based simulation toolkit, Geant4, within a wide range of energies of both photons and neutrons. The mass attenuation coefficients (µm), the half-value layers (HVL), the effective atomic numbers (Zeff), and the effective electron densities (Neff) of gammas were investigated. In addition, the effective removal cross-sections (ΣR) and the mean free paths (λ) of neutrons were also studied. The results were then compared to other commonly used polymer materials.

Deep CNN-LSTM With Self-Attention Model for Human Activity Recognition Using Wearable Sensor.

Human Activity Recognition (HAR) systems are devised for continuously observing human behavior - primarily in the fields of environmental compatibility, sports injury detection, senior care, rehabilitation, entertainment, and the surveillance in intelligent home settings. Inertial sensors, e.g., accelerometers, linear acceleration, and gyroscopes are frequently employed for this purpose, which are now compacted into smart devices, e.g., smartphones. Since the use of smartphones is so widespread now-a-days, activity data acquisition for the HAR systems is a pressing need. In this article, we have conducted the smartphone sensor-based raw data collection, namely H-Activity, using an Android-OS-based application for accelerometer, gyroscope, and linear acceleration. Furthermore, a hybrid deep learning model is proposed, coupling convolutional neural network and long-short term memory network (CNN-LSTM), empowered by the self-attention algorithm to enhance the predictive capabilities of the system. In addition to our collected dataset (H-Activity), the model has been evaluated with some benchmark datasets, e.g., MHEALTH, and UCI-HAR to demonstrate the comparative performance of our model. When compared to other models, the proposed model has an accuracy of 99.93% using our collected H-Activity data, and 98.76% and 93.11% using data from MHEALTH and UCI-HAR databases respectively, indicating its efficacy in recognizing human activity recognition. We hope that our developed model could be applicable in the clinical settings and collected data could be useful for further research.

Early-Stage Detection of Ovarian Cancer Based on Clinical Data Using Machine Learning Approaches.

One of the common types of cancer for women is ovarian cancer. Still, at present, there are no drug therapies that can properly cure this deadly disease. However, early-stage detection could boost the life expectancy of the patients. The main aim of this work is to apply machine learning models along with statistical methods to the clinical data obtained from 349 patient individuals to conduct predictive analytics for early diagnosis. In statistical analysis, Student's t-test as well as log fold changes of two groups are used to find the significant blood biomarkers. Furthermore, a set of machine learning models including Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), Extreme Gradient Boosting Machine (XGBoost), Logistic Regression (LR), Gradient Boosting Machine (GBM) and Light Gradient Boosting Machine (LGBM) are used to build classification models to stratify benign-vs.-malignant ovarian cancer patients. Both of the analysis techniques recognized that the serumsamples carbohydrate antigen 125, carbohydrate antigen 19-9, carcinoembryonic antigen and human epididymis protein 4 are the top-most significant biomarkers as well as neutrophil ratio, thrombocytocrit, hematocrit blood samples, alanine aminotransferase, calcium, indirect bilirubin, uric acid, natriumas as general chemistry tests. Moreover, the results from predictive analysis suggest that the machine learning models can classify malignant patients from benign patients with accuracy as good as 91%. Since generally, early-stage detection is not available, machine learning detection could play a significant role in cancer diagnosis.

Expired EBT3 Films' Sensitivity for the Measurement of X-ray and UV Radiation: An Optical Analysis.

The aim of this study is to compare the optical responses of external beam therapy 3 (EBT3) films exposed to X-rays and solar ultraviolet rays (SUV-rays), as a dose control technique in the clinical sector for various radiation types, energies, and absorbed doses up to 4 Gy. In this study, EBT3 films with three different expiry dates were prepared and cut into pieces of size 2 by 2 cm2. The first group was exposed to 90 kVp X-rays, while the second group was exposed to the SUV-rays at noon. The analysis was performed using a visible Jaz spectrometer and an EPSON Perfection V370 Photo scanner to obtain the absorbance, the net reflective optical density (ROD) and the red-green-blue (RGB) values of the samples. The results have shown that spectroscopic measurements of the exposed expired EBT3 films with these radiation sources are able to produce primary peaks and secondary peaks at λ = 641.74 nm and λ = 585.98 nm for X-rays, and at λ = 637.93 nm and λ = 584.45 nm for SUV-rays, respectively. According to these findings, compared to 2021 films that expired shortly before the trial start date; 2018 films responded better to the absorbed dose than 2016 films when exposed to both X-ray and SUV-rays. In terms of energy dependence, the expired EBT3 2018 had the largest net ROD value. Using L*a*b* indices extracted from the RGB data, and despite that EBT3 films have expiry dates according to the manufacturer; all the films exhibited a substantial colour change, indicating that these films are still usable for clinical and research purposes.

Ionization Radiation Shielding Effectiveness of Lead Acetate, Lead Nitrate, and Bismuth Nitrate-Doped Zinc Oxide Nanorods Thin Films: A Comparative Evaluation.

The fabrication of Nano-based shielding materials is an advancing research area in material sciences and nanotechnology. Although bulky lead-based products remain the primary choice for radiation protection, environmental disadvantages and high toxicity limit their potentials, necessitating less costly, compatible, eco-friendly, and light-weight alternatives. The theme of the presented investigation is to compare the ionization radiation shielding potentialities of the lead acetate (LA), lead nitrate (LN), and bismuth nitrate (BN)-doped zinc oxide nanorods-based thin films (ZONRs-TFs) produced via the chemical bath deposition (CBD) technique. The impact of the selected materials' doping content on morphological and structural properties of ZONRs-TF was investigated. The X-ray diffractometer (XRD) analyses of both undoped and doped TFs revealed the existence of hexagonal quartzite crystal structures. The composition analysis by energy dispersive (EDX) detected the corrected elemental compositions of the deposited films. Field emission scanning electronic microscope (FESEM) images of the TFs showed highly porous and irregular surface morphologies of the randomly aligned NRs with cracks and voids. The undoped and 2 wt.% BN-doped TFs showed the smallest and largest grain size of 10.44 nm and 38.98 nm, respectively. The linear attenuation coefficient (µ) values of all the optimally doped ZONRs-TFs measured against the X-ray photon irradiation disclosed their excrement shielding potency. The measured µ values of the ZONRs-TFs displayed the trend of 1 wt.% LA-doped TF > 1 wt.% LN-doped TF > 3 wt.% BN-doped TF > undoped TFs). The values of µ of the ZONRs-TFs can be customized by adjusting the doping contents, which in turn controls the thickness and morphology of the TFs. In short, the proposed new types of the LA-, LN- and BN-doped ZONRs-TFs may contribute towards the development of the prospective ionization radiation shielding materials.