Optimizing meningioma grading with radiomics and deep features integration, attention mechanisms, and reproducibility analysis.

Objective: This study aims to develop a robust and clinically applicable framework for preoperative grading of meningiomas using T1-contrast-enhanced and T2-weighted MRI images. The approach integrates radiomic feature extraction, attention-guided deep learning models, and reproducibility assessment to achieve high diagnostic accuracy, model interpretability, and clinical reliability.

Materials And Methods: We analyzed MRI scans from 2546 patients with histopathologically confirmed meningiomas (1560 low-grade, 986 high-grade).

Machine learning-based prediction of speed of sound in fatty acid ethyl esters.

Unlabelled: This research explores the application of fatty acid ethyl esters (FAEEs) in the pharmaceutical industry due to their biodegradable, renewable nature and versatility as excipients or drug delivery agents. The research seeks to create predictive models utilizing various methods in machine learning to calculate the speed of sound in FAEEs under different temperature, pressure, molar mass, and elemental composition conditions. Laboratory data figures from earlier research were used to train the models.

Reproducible meningioma grading across multi-center MRI protocols via hybrid radiomic and deep learning features.

Objective: This study aimed to create a reliable method for preoperative grading of meningiomas by combining radiomic features and deep learning-based features extracted using a 3D autoencoder. The goal was to utilize the strengths of both handcrafted radiomic features and deep learning features to improve accuracy and reproducibility across different MRI protocols.

Materials And Methods: The study included 3,523 patients with histologically confirmed meningiomas, consisting of 1,900 low-grade (Grade I) and 1,623 high-grade (Grades II and III) cases.

A Comprehensive Framework for Parkinson's Disease Detection Using Spiral Drawings and Advanced Machine Learning Techniques.

Objective: This study aims to create a reliable and scalable framework for detecting Parkinson's disease (PD) using spiral drawings. It integrates advanced machine learning techniques to improve diagnostic accuracy and practical application in clinical settings.

Materials And Methods: Spiral drawing data were collected from a comprehensive dataset, including samples from both Parkinson's patients and healthy individuals.

Prediction of speed of sound of deep eutectic solvents using artificial neural network coupled with group contribution approach.

Predicting the physiochemical properties of deep eutectic solvents (DESs) is crucial for designing new solvents. Heat capacity and speed of sound are important thermodynamic properties in chemical processes. However, experimental data on the speed of sound in DESs is limited.

Prediction of thermodynamic properties of aqueous carbohydrates solution using the PHSC and ANN models.

In this work the Artificial Neural Network (ANN) and the Perturbed Hard Sphere Chain (PHSC) equation of state (EoS) have been utilized to estimate the osmotic coefficient, activity coefficient, and water activity of aqueous sugar solutions containing glucose, fructose, fucose, xylose, maltose, mannitol, mannose, sorbitol, xylitol, galactose, lactose, ribose, arabinose, and sucrose. The PHSC model parameters have been adjusted using the osmotic coefficient experimental data. Then, the water activity and sugar activity coefficient were predicted.

Implementing partial least squares and machine learning regressive models for prediction of drug release in targeted drug delivery application.

A combined methodology was performed based on chemometrics and machine learning regressive models in estimation of polysaccharide-coated colonic drug delivery. The release of medication was measured using Raman spectroscopy and the data was used for estimation of drug delivery using machine learning models. Raman data was used along with some inputs including coating type, medium, and release time to estimate the drug release as the sole target.

Computational fluid dynamic and machine learning modeling of nanofluid flow for determination of temperature distribution and models comparison.

This paper introduces an approach to temperature prediction by employing three distinct machine learning models: K-Nearest Neighbors (KNN), Gaussian Process Regression (GPR), and Multi-layer Perceptron (MLP) which are integrated into Computational Fluid Dynamics (CFD). The dataset consists of inputs, represented by the features x and y, and the corresponding output, which is the temperature. The case study is fluid flow of nanofluid through a pipe and the nanofluid contains CuO particles.

Vanari Gutika supplementation enhances testicular function by modulating spermatogenesis, oxidative balance, and blood-testis barrier integrity.

Objectives: Vanari Gutika (VG) is an ayurvedic formulation that has been traditionally utilized for the treatment of various male sexual problems. The primary components of VG include , honey, and clarified butter, which are recognized for their aphrodisiac properties. However, currently, there is no scientific evidence supporting the use of this formulation as a drug for enhancing male fertility or elucidating its mechanism for improving testicular physiology.

A novel framework for esophageal cancer grading: combining CT imaging, radiomics, reproducibility, and deep learning insights.

Objective: This study aims to create a reliable framework for grading esophageal cancer. The framework combines feature extraction, deep learning with attention mechanisms, and radiomics to ensure accuracy, interpretability, and practical use in tumor analysis.

Materials And Methods: This retrospective study used data from 2,560 esophageal cancer patients across multiple clinical centers, collected from 2018 to 2023.

TabNet and TabTransformer: Novel Deep Learning Models for Chemical Toxicity Prediction in Comparison With Machine Learning.

The prediction of chemical toxicity is crucial for applications in drug discovery, environmental safety, and regulatory assessments. This study aims to evaluate the performance of advanced deep learning architectures, TabNet and TabTransformer, in comparison to traditional machine learning methods, for predicting the toxicity of chemical compounds across 12 toxicological endpoints. The dataset consisted of 12,228 training and 3057 test samples, each characterized by 801 molecular descriptors representing chemical and structural features.

Carbon dioxide solubility in polyethylene glycol polymer: an accurate intelligent estimation framework.

Polyethylene glycol (PEG), a synthetic polymer made up of repeating ethylene oxide units, is widely recognized for its broad utility and adaptable properties. Precise estimation of CO solubility in PEG plays a vital role in enhancing processes such as supercritical fluid extraction, carbon capture, and polymer modification, where CO serves as a solvent or transport medium. This study focuses on building advanced predictive models using machine-learning approaches, such as random forest (RF), decision tree (DT), adaptive boosting (AdaBoost), k-nearest neighbors (KNN), and ensemble learning (EL) to forecast CO solubility in PEG across a wide range of conditions.

Multi-objective deep learning for lung cancer detection in CT images: enhancements in tumor classification, localization, and diagnostic efficiency.

Objective: This study aims to develop and evaluate an advanced deep learning framework for the detection, classification, and localization of lung tumors in computed tomography (CT) scan images.

Materials And Methods: The research utilized a dataset of 1608 CT scan images, including 623 cancerous and 985 non-cancerous cases, all carefully labeled for accurate tumor detection, classification (benign or malignant), and localization. The preprocessing involved optimizing window settings, adjusting slice thickness, and applying advanced data augmentation techniques to enhance the model's robustness and generalizability.

Prediction of methane hydrate equilibrium in saline water solutions based on support vector machine and decision tree techniques.

The formation of clathrate hydrates offers a powerful approach for separating gaseous substances, desalinating seawater, and energy storage at low temperatures. On the other hand, this phenomenon may lead to practical challenges, including the blockage of pipelines, in some industries. Consequently, accurately predicting the equilibrium conditions for clathrate hydrate formation is crucial.

Enhancing convolutional neural networks in electroencephalogram driver drowsiness detection using human inspired optimizers.

Driver drowsiness is a significant safety concern, contributing to numerous traffic accidents. To address this issue, researchers have explored electroencephalogram (EEG)-based detection systems. Due to the high-dimensional nature of EEG signals and the subtle temporal patterns of drowsiness, there is increasing recognition of the need for deep neural networks (DNNs) to capture the dynamics of drowsy driving better.

Advanced machine learning framework for enhancing breast cancer diagnostics through transcriptomic profiling.

Purpose: This study proposes an advanced machine learning (ML) framework for breast cancer diagnostics by integrating transcriptomic profiling with optimized feature selection and classification techniques.

Materials And Methods: A dataset of 1759 samples (987 breast cancer patients, 772 healthy controls) was analyzed using Recursive Feature Elimination, Boruta, and ElasticNet for feature selection. Dimensionality reduction techniques, including Non-Negative Matrix Factorization (NMF), Autoencoders, and transformer-based embeddings (BioBERT, DNABERT), were applied to enhance model interpretability.

Enhanced lung cancer subtype classification using attention-integrated DeepCNN and radiomic features from CT images: a focus on feature reproducibility.

Objective: This study aims to assess a hybrid framework that combines radiomic features with deep learning and attention mechanisms to improve the accuracy of classifying lung cancer subtypes using CT images.

Materials And Methods: A dataset of 2725 lung cancer images was used, covering various subtypes: adenocarcinoma (552 images), SCC (380 images), small cell lung cancer (SCLC) (307 images), large cell carcinoma (215 images), and pulmonary carcinoid tumors (180 images). The images were extracted as 2D slices from 3D CT scans, with tumor-containing slices selected from scans obtained across five healthcare centers.

Chronic unpredictable stress exposure disrupts testicular function by modulating germ cell-junctional dynamics and Nrf2/HO-1/IKKβ/NF-κB pathway.

The unpredictable nature of stress complicates understanding its relationship with male infertility. In this study, we investigated testicular germ cell and junctional dynamics in male mice following exposure to chronic unpredictable stress (CUS). Adult Parkes male mice were exposed to CUS for 35 days (one complete spermatogenic cycle), with a random stressor (restraint stress, water deprivation, food deprivation, light flashing, wet bedding, cage shaking, or cage tilting) applied once per day in an intermittent and unpredictable manner to avoid repeating the same stimulus on consecutive days.

Shilajit mitigates chemotherapeutic drug-induced testicular toxicity: Study on testicular germ cell dynamics, steroidogenesis modulation, and Nrf-2/Keap-1 signaling.

Background: Medications, including chemotherapeutic drugs, contribute to male infertility as external factors by inducing oxidative stress in testicular cells. Shilajit is a naturally occurring bioactive antioxidant used in Ayurvedic medicine to treat a variety of ailments.

Objective: This study examines the potential of Shilajit to counteract the negative effects of the chemotherapeutic drug cyclophosphamide (CPA) on testicular germ cell dynamics.

Phographene as a new carbon allotrope for adsorption and detection of SO, AsH, NO, CFH, and CO air pollutant gaseous species.

Context: Phographene and its family member structures are of the newly proposed semiconductors for detection of chemicals. That is, in this project, the potential of using α-phographene (α-POG) both for adsorption and detection of five types of the most important air pollutant gases containing SO, AsH, CFH, NO, and CO species were investigated.  The results of the time dependent density functional theory (TD-DFT) calculations indicate that during the adsorption of NO, and SO by the sorbent, big redshifts occur (up to 866.

Removal of heavy metal ions from wastewater using two-dimensional transition metal carbides.

Water is an indispensable material for human life. Unfortunately, the development of industrial activities has reduced the quality of water resources in the world. Meantime, heavy metals are an important factor in water pollution due to their toxicity.

Desvenlafaxine-Triggered Acneiform Eruptions on the Hand: A Compelling Case Report.

A 45-year-old male developed a skin eruption after starting Desvenlafaxine for depressive symptoms associated with schizophreniform disorder. The patient developed a rash on the hand, hyperpigmentation, and itching, which resolved after discontinuing the medication. The Naranjo score suggested a probable link between desvenlafaxine and the skin reaction.

Navigating Adherence: Unraveling Factors Shaping Opioid Substitution Therapy Compliance.

Background and objective In drug-deaddiction programs, dropout is a major problem in any drug de-addiction program, as dependence is a chronic illness known to relapse frequently. Understanding factors that predict dropout can help design targeted interventions to promote follow-up. This study aimed to assess the various sociodemographic characteristics of opioid-dependent subjects on buprenorphine maintenance treatment and dropping out at or before the three-month follow-up period.