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.

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.