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Meningiomas are the most common primary intracranial tumors and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on brain MRI for diagnosis, treatment planning, and longitudinal treatment monitoring. However, automated, objective, and quantitative tools for non-invasive assessment of meningiomas on multi-sequence MR images are not available. Here we present the BraTS Pre-operative Meningioma Dataset, as the largest multi-institutional expert annotated multilabel meningioma multi-sequence MR image dataset to date. This dataset includes 1,141 multi-sequence MR images from six sites, each with four structural MRI sequences (T2-, T2/FLAIR-, pre-contrast T1-, and post-contrast T1-weighted) accompanied by expert manually refined segmentations of three distinct meningioma sub-compartments: enhancing tumor, non-enhancing tumor, and surrounding non-enhancing T2/FLAIR hyperintensity. Basic demographic data are provided including age at time of initial imaging, sex, and CNS WHO grade. The goal of releasing this dataset is to facilitate the development of automated computational methods for meningioma segmentation and expedite their incorporation into clinical practice, ultimately targeting improvement in the care of meningioma patients.

Abstract Triple-negative breast cancer (TNBC) lacks estrogen, progesterone, and human epidermal growth factor receptors and has a poor prognosis as it is resistant to chemotherapy. A new treatment option for this type of cancer may be by putting these malignant cells into dormancy. The oocyte’s embryonic milieu presents a unique tumor reversion microenvironment by inducing growth arrest and changing cells’ phenotypes. We conducted an in-silico study to determine the most likely oocyte extract (OE) proteins involved in inducing dormancy using HDock, CluPro, and molecular dynamic (MD) simulation. Results showed low energy scores for complexes between OE proteins and four surface markers: K1C14, CLD3, CLD4, and ITA6. Apolipoprotein A1 (APOA1) and Apolipoprotein C3 (APOC3) showed the highest stability and affinity with these four surface markers: K1C14, CLD3, CLD4, and ITA6. These proteins are involved in key tumor-related pathways such as angiogenesis, proliferation, apoptosis, and migration. This will pave the way for exploring novel therapeutic options to induce dormancy in TNBC cells.

Background: Maternal serum ferritin is being explored as a potential predictor for Intrauterine Growth Restriction (IUGR). Early detection of IUGR is crucial for improving perinatal outcomes and reducing associated mortality and morbidity. Objective: To evaluate the role of elevated maternal serum ferritin in predicting the risk of developing IUGR. Patients and Methods: A prospective case-control study was conducted at Al-Batool Teaching Hospital, Diyala, involving 51 pregnant women divided into two groups based on pregnancy status. Demographic data, obstetric history, and risk factors for IUGR were collected through a questionnaire, and blood samples were taken to measure hemoglobin and serum ferritin levels. Results: The study found that the mean serum ferritin level was significantly higher in the patient group (209.89±50.95 ng/ml) compared to the control group (66.91±37.49 ng/ml), with a p-value of

Three frequent factors such as low classification accuracy, computational complexity, and resource consumption have an impact on malware evaluation methods. These challenges are exacerbated by elements such as unbalanced data environments and specific feature generation. To address these challenges, we aim to identify optimal texture features and classifiers for effective malware detection. The article outlines a method that consists of four stages: malware conversion to grayscale, feature extraction using (segmentation-based fractal texture analysis (SFTA), Local Binary Pattern (LBP), Haralick, Gabor, and Tamura), classification using (Gaussian Discriminant Analysis (GDA), k-Nearest Neighbor (KNN), Logistic, Support Vector Machines (SVM), Random Forest (RF), Extreme Learning Machine (Ensemble)), and finally the evaluation. Using the Malimg imbalanced and MaleVis balanced datasets, we assess classifier performance and feature effectiveness. Comparative analysis indicates that KNN outperforms other classifiers in terms of Accuracy, Error, F1, and Precision, while SVM and RF as runners-up. Gabor performs better in MaleVis, whereas the SFTA feature performs better under the Malimg dataset. The proposed SFTA-KNN and Gabor-KNN methods achieve 96.29% and 98.02% accuracy, respectively, surpassing current state-of-the-art approaches. Additionally, higher computing performance is achieved by using fewer dimensions when employing our feature extraction method.

Abstract Background Hypertrophic cardiomyopathy (HCM) is a hereditary myocardial disorder, often due to sarcomere gene mutations, characterized by the left ventricular hypertrophy. Current treatments offer symptomatic relief but lack specificity. Mavacamten, an allosteric inhibitor, has shown significant improvements in HCM patients in trials, reducing the requirement for invasive treatments. This meta-analysis assesses Mavacamten’s efficacy and safety as a targeted HCM intervention. Methods This study examined four randomized controlled trials comparing Mavacamten to placebo in HCM patients. Each trial had a unique primary endpoint, and secondary outcomes included improvements in NYHA-FC, eligibility for septal reduction therapy (SRT) or undergoing it, adverse events (serious and treatment-related), atrial fibrillation, and non-sustained ventricular tachycardia. Statistical analysis involved calculating risk ratios (RRs) and assessing heterogeneity. Results The four included studies showed minimal risk of bias and involved 503 patients with HCM (273 Mavacamten and 230 placebo). Mavacamten significantly increased the primary endpoint (RR 2.15, 95% CI 1.20–3.86, P = 0.01) and ≥ 1 NYHA-FC class (RR 2.21, 95% CI 1.48–3.3, P = 0.0001). Mavacamten group had lower rates of SRT compared to those receiving placebo (RR, 0.30, 95% CI 0.22–0.40; P

The majority of applications use automatic image recognition technologies to carry out a range of tasks. Therefore, it is crucial to identify and classify image distortions to improve image quality. Despite efforts in this area, there are still many challenges in accurately and reliably classifying distorted images. In this paper, we offer a comprehensive analysis of models of both non-lightweight and lightweight deep convolutional neural networks (CNNs) for the classification of distorted images. Subsequently, an effective method is proposed to enhance the overall performance of distortion image classification. This method involves selecting features from the pretrained models’ capabilities and using a strong classifier. The experiments utilized the kadid10k dataset to assess the effectiveness of the results. The K-nearest neighbor (KNN) classifier showed better performance than the naïve classifier in terms of accuracy, precision, error rate, recall and F1 score. Additionally, SqueezeNet outperformed other deep CNN models, both lightweight and non-lightweight, across every evaluation metric. The experimental results demonstrate that combining SqueezeNet with KNN can effectively and accurately classify distorted images into the correct categories. The proposed SqueezeNet-KNN method achieved an accuracy rate of 89%. As detailed in the results section, the proposed method outperforms state-of-the-art methods in accuracy, precision, error, recall, and F1 score measures.