An adaptive enhanced human memory algorithm for multi-level image segmentation for pathological lung cancer images.

Lung cancer is a critical health issue that demands swift and accurate diagnosis for effective treatment. In medical imaging, segmentation is crucial for identifying and isolating regions of interest, which is essential for precise diagnosis and treatment planning. Traditional metaheuristic-based segmentation methods often struggle with slow convergence speed, poor optimized thresholds results, balancing exploration and exploitation, leading to suboptimal performance in the multi-thresholding segmenting of lung cancer images.

Efficient bladder cancer diagnosis using an improved RIME algorithm with Orthogonal Learning.

Bladder cancer (BC) diagnosis presents a critical challenge in biomedical research, necessitating accurate tumor classification from diverse datasets for effective treatment planning. This paper introduces a novel wrapper feature selection (FS) method that leverages a hybrid optimization algorithm combining Orthogonal Learning (OL) with a rime optimization algorithm (RIME), termed mRIME. The mRIME algorithm is designed to avoid local optima, streamline the search process, and select the most relevant features without compromising classifier performance.

An efficient improved parrot optimizer for bladder cancer classification.

Bladder Cancer (BC) is a common disease that comes with a high risk of morbidity, death, and expense. Primary risk factors for BC include exposure to carcinogens in the workplace or the environment, particularly tobacco. There are several difficulties, such as the requirement for a qualified expert in BC classification.

Optimizing cancer diagnosis: A hybrid approach of genetic operators and Sinh Cosh Optimizer for tumor identification and feature gene selection.

The identification of tumors through gene analysis in microarray data is a pivotal area of research in artificial intelligence and bioinformatics. This task is challenging due to the large number of genes relative to the limited number of observations, making feature selection a critical step. This paper introduces a novel wrapper feature selection method that leverages a hybrid optimization algorithm combining a genetic operator with a Sinh Cosh Optimizer (SCHO), termed SCHO-GO.

An enhanced Coati Optimization Algorithm for global optimization and feature selection in EEG emotion recognition.

Emotion recognition based on Electroencephalography (EEG) signals has garnered significant attention across diverse domains including healthcare, education, information sharing, and gaming, among others. Despite its potential, the absence of a standardized feature set poses a challenge in efficiently classifying various emotions. Addressing the issue of high dimensionality, this paper introduces an advanced variant of the Coati Optimization Algorithm (COA), called eCOA for global optimization and selecting the best subset of EEG features for emotion recognition.

Liver Cancer Algorithm: A novel bio-inspired optimizer.

This paper introduces a new bio-inspired optimization algorithm named the Liver Cancer Algorithm (LCA), which mimics the liver tumor growth and takeover process. It uses an evolutionary search approach that simulates the behavior of liver tumors when taking over the liver organ. The tumor's ability to replicate and spread to other organs inspires the algorithm.

Effect of coping materials zirconia or polyetheretherketone with different techniques of fabrication on vertical marginal gap and fracture resistance of posterior crowns with composite veneering.

Background: Insufficient research has been conducted in the literature assessing the performance of zirconia and polyetheretherketone (PEEK) crowns in relation to the essential requirements of successful restorations, such as fracture resistance or margin adaptation. The purpose of this study was to evaluate the effect of the coping materials zirconia or PEEK with different fabrication techniques on the vertical marginal gap and fracture resistance of posterior crowns with composite veneering.

Methods: Ceramic copings (n = 18) restoring mandibular first molar were fabricated from zirconia (Zircon.

Self-adaptive moth flame optimizer combined with crossover operator and Fibonacci search strategy for COVID-19 CT image segmentation.

The COVID-19 is one of the most significant obstacles that humanity is now facing. The use of computed tomography (CT) images is one method that can be utilized to recognize COVID-19 in early stage. In this study, an upgraded variant of Moth flame optimization algorithm (Es-MFO) is presented by considering a nonlinear self-adaptive parameter and a mathematical principle based on the Fibonacci approach method to achieve a higher level of accuracy in the classification of COVID-19 CT images.

Stress distribution and fracture resistance of green reprocessed polyetheretherketone (PEEK) single implant crown restorations compared to unreprocessed PEEK and Zirconia: an in-vitro study.

Background: It is unclear which crown materials are optimum to disperse the generated stresses around dental implants. The objective of this study is to assess stress distribution and fracture resistance of green reprocessed Polyetheretherketone (PEEK) in comparison to un-reprocessed PEEK and zirconia single implant crown restorations.

Methods: Twenty crowns (n = 20) were obtained, five from zirconia and fifteen from pressed PEEK that were subdivided into 3 groups of five specimens each (n = 5) according to weight% of reprocessed material used.

Optimized deep learning architecture for brain tumor classification using improved Hunger Games Search Algorithm.

One of the worst diseases is a brain tumor, which is defined by abnormal development of synapses in the brain. Early detection of brain tumors is essential for improving prognosis, and classifying tumors is a vital step in the disease's treatment. Different classification strategies using deep learning have been presented for the diagnosis of brain tumors.

Effect of different intraoral scanners and post-space depths on the trueness of digital impressions.

Background: The trueness of intraoral scanners (IOSs) has been evaluated in many clinical situations. However, the tests of their performance when scanning post-space preparations are still lacking.

Objectives: The aim of the present study was to compare the trueness of the digital impressions of post spaces with different depths, captured by means of different IOSs.

A modified reptile search algorithm for global optimization and image segmentation: Case study brain MRI images.

In this paper, we proposed an enhanced reptile search algorithm (RSA) for global optimization and selected optimal thresholding values for multilevel image segmentation. RSA is a recent metaheuristic optimization algorithm depending on the hunting behavior of crocodiles. RSA is inclined to inadequate diversity, local optima, and unbalanced exploitation abilities as other metaheuristic algorithms.

Adrenomedullin Mitigates Doxorubicin-Induced Nephrotoxicity in Rats: Role of Oxidative Stress, Inflammation, Apoptosis, and Pyroptosis.

Doxorubicin (DOX) is an anticancer antibiotic which has various effects in human cancers. It is one of the commonly known causes of drug-induced nephrotoxicity, which results in acute renal injury. Adrenomedullin (ADM), a vasodilator peptide, is widely distributed in many tissues and has potent protective effects.

An efficient image segmentation method for skin cancer imaging using improved golden jackal optimization algorithm.

Skin cancer is one of the worst cancers nowadays that poses a severe threat to the health and safety of individuals. Therefore, skin cancer classification and early diagnosis are recommended to preserve human life. Multilevel thresholding image segmentation is well-known and influential technique for extracting regions of interest from skin cancer images to improve the classification process.

Irisin improves adiposity and exercise tolerance in a rat model of postmenopausal obesity through enhancing adipo-myocyte thermogenesis.

The prevalence of obesity and its associated metabolic disorders, along with their healthcare costs, is rising exponentially. Irisin, an adipomyokine, may serve as a critical cross-organ messenger, linking skeletal muscle with adipose tissue and the liver to integrate the energy homeostasis under diet-induced obesity. We aimed to explore the putative role of irisin in the protection against obesity in a postmenopausal rat model by modulating energy expenditure (EE).

Hesperidin hexosomal loaded nanodispersion: insights of its antimycobacterial, cytotoxic and anti-HCoV effects.

Fruits of L. Osbeck var. Valencia contain hesperidin as a major flavanone glycoside.

Fracture Resistance of Custom Lithium Disilicate Implant Restorations with Two Fabrication Techniques and Two Designs.

Purpose: To compare the fracture resistance of a press-on ceramic custom implant restoration with pressed and cemented restorations.

Materials And Methods: Thirty-two (32) lithium disilicate (IPS e.max Press) custom hybrid abutment restorations were fabricated.

An optimized deep learning architecture for breast cancer diagnosis based on improved marine predators algorithm.

Breast cancer is the second leading cause of death in women; therefore, effective early detection of this cancer can reduce its mortality rate. Breast cancer detection and classification in the early phases of development may allow for optimal therapy. Convolutional neural networks (CNNs) have enhanced tumor detection and classification efficiency in medical imaging compared to traditional approaches.

Mechanistic Insights into Ameliorating Effect of Geraniol on D-Galactose Induced Memory Impairment in Rats.

Geraniol (GE), an important ingredient in several essential oils, displayed pleiotropic biological activities through targeting multiple signaling cascades. In the current study, we aimed to examine the protective effect of GE on D-galactose (D-gal) induced cognitive impairment and explore the underlying mechanisms. Forty male Wistar rats (8 weeks old) were randomly categorized into 4 groups; Group I (saline + vehicle [edible oil]), group II (saline + geraniol) (100 mg/kg/day orally), group III (D-galactose) (100 mg/kg/day subcutaneously injected), and group IV (D-galactose + geraniol).

UPLC-MS/MS and GC-MS based metabolites profiling of seed with its anti- and anti-inflammatory activities.

Compared to its leaf, few studies have been reported on the seeds of Lam. Metabolites profiling analysis of seed methanolic extract (ME) and its fixed oil (MO) was attempted LC/MS and GC/MS. LC/MS analysis of seeds annotated 84 peaks of which glucosinolates and their corresponding acetyl isomers were abundant.

Melatonin epigenetic potential on testicular functions and fertility profile in varicocele rat model is mediated by silent information regulator 1.

Background And Purpose: Varicocele is a leading cause of male infertility. Melatonin is a highly pleiotropic neurohormone. We aimed to characterize the melatonin epigenetic potential in varicocele and the involved molecular mechanisms.

Impact of Berberine on Some Epigenetic, Transcription Regulation and Inflammatory Biomarkers in a Mice Model of Epilepsy.

Background: Epilepsy is one of the most widespread neurological disease worldwide. Status epilepticus (SE) is a life-threatening neurologic disorder. Neuroprotective approaches are increasingly to discover a promising therapy to manage epileptic disorders.