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. The Parrot Optimizer (PO), is an optimization method inspired by key behaviors observed in trained Pyrrhura Molinae parrots, but the PO algorithm becomes stuck in sub-regions, has less accuracy, and a high error rate. So, an Improved variant of the PO (IPO) algorithm was developed using a combination of two strategies: (1) Mirror Reflection Learning (MRL) and (2) Bernoulli Maps (BMs). Both strategies improve optimization performance by avoiding local optimums and striking a compromise between convergence speed and solution diversity. The performance of the proposed IPO is evaluated against eight other competitor algorithms in terms of statistical convergence and other metrics according to Friedman's test and Bonferroni-Dunn test on the IEEE Congress on Evolutionary Computation conducted in 2022 (CEC 2022) test suite functions and nine BC datasets from official repositories. The IPO algorithm ranked number one in best fitness and is more optimal than the other eight MH algorithms for CEC 2022 functions. The proposed IPO algorithm was integrated with the Support Vector Machine (SVM) classifier termed (IPO-SVM) approach for bladder cancer classification purposes. Nine BC datasets were then used to confirm the effectiveness of the proposed IPO algorithm. The experiments show that the IPO-SVM approach outperforms eight recently proposed MH algorithms. Using the nine BC datasets, IPO-SVM achieved an Accuracy (ACC) of 84.11%, Sensitivity (SE) of 98.10%, Precision (PPV) of 95.59%, Specificity (SP) of 95.98%, and F-score (F1) of 94.15%. This demonstrates how the proposed IPO approach can help to classify BCs effectively. The open-source codes are available at https://www.mathworks.com/matlabcentral/fileexchange/169846-an-efficient-improved-parrot-optimizer.

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. The SCHO-GO algorithm is designed to avoid local optima, streamline the search process, and select the most relevant features without compromising classifier performance. Traditional methods often falter with extensive search spaces, necessitating hybrid approaches. Our method aims to reduce the dimensionality and improve the classification accuracy, which is essential in pattern recognition and data analysis. The SCHO-GO algorithm, integrated with a support vector machine (SVM) classifier, significantly enhances cancer classification accuracy. We evaluated the performance of SCHO-GO using the CEC'2022 benchmark function and compared it with seven well-known metaheuristic algorithms. Statistical analyses indicate that SCHO-GO consistently outperforms these algorithms. Experimental tests on eight microarray gene expression datasets, particularly the Gene Expression Cancer RNA-Seq dataset, demonstrate an impressive accuracy of 99.01% with the SCHO-GO-SVM model, highlighting its robustness and precision in handling complex datasets. Furthermore, the SCHO-GO algorithm excels in feature selection and solving mathematical benchmark problems, presenting a promising approach for tumor identification and classification in microarray data analysis.

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. Specifically, COA suffers from local optima and imbalanced exploitation abilities as other metaheuristic methods. The proposed eCOA incorporates the COA and RUNge Kutta Optimizer (RUN) algorithms. The Scale Factor (SF) and Enhanced Solution Quality (ESQ) mechanism from RUN are applied to resolve the raised shortcomings of COA. The proposed eCOA algorithm has been extensively evaluated using the CEC'22 test suite and two EEG emotion recognition datasets, DEAP and DREAMER. Furthermore, the eCOA is applied for binary and multi-class classification of emotions in the dimensions of valence, arousal, and dominance using a multi-layer perceptron neural network (MLPNN). The experimental results revealed that the eCOA algorithm has more powerful search capabilities than the original COA and seven well-known counterpart methods related to statistical, convergence, and diversity measures. Furthermore, eCOA can efficiently support feature selection to find the best EEG features to maximize performance on four quadratic emotion classification problems compared to the methods of its counterparts. The suggested method obtains a classification accuracy of 85.17% and 95.21% in the binary classification of low and high arousal emotions in two public datasets: DEAP and DREAMER, respectively, which are 5.58% and 8.98% superior to existing approaches working on the same datasets for different subjects, respectively.

Application of modified artificial hummingbird algorithm in optimal power flow and generation capacity in power networks considering renewable energy sources.

Today's electrical power system is a complicated network that is expanding rapidly. The power transmission lines are more heavily loaded than ever before, which causes a host of problems like increased power losses, unstable voltage, and line overloads. Real and reactive power can be optimized by placing energy resources at appropriate locations. Congested networks benefit from this to reduce losses and enhance voltage profiles. Hence, the optimal power flow problem (OPF) is crucial for power system planning. As a result, electricity system operators can meet electricity demands efficiently and ensure the reliability of the power systems. The classical OPF problem ignores network emissions when dealing with thermal generators with limited fuel. Renewable energy sources are becoming more popular due to their sustainability, abundance, and environmental benefits. This paper examines modified IEEE-30 bus and IEEE-118 bus systems as case studies. Integrating renewable energy sources into the grid can negatively affect its performance without adequate planning. In this study, control variables were optimized to minimize fuel cost, real power losses, emission cost, and voltage deviation. It also met operating constraints, with and without renewable energy. This solution can be further enhanced by the placement of distributed generators (DGs). A modified Artificial Hummingbird Algorithm (mAHA) is presented here as an innovative and improved optimizer. In mAHA, local escape operator (LEO) and opposition-based learning (OBL) are integrated into the basic Artificial Hummingbird Algorithm (AHA). An improved version of AHA, mAHA, seeks to improve search efficiency and overcome limitations. With the CEC'2020 test suite, the mAHA has been compared to several other meta-heuristics for addressing global optimization challenges. To test the algorithm's feasibility, standard and modified test systems were used to solve the OPF problem. To assess the effectiveness of mAHA, the results were compared to those of seven other global optimization algorithms. According to simulation results, the proposed algorithm minimized the cost function and provided convergent solutions.

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. LCA algorithm is developed using genetic operators and a Random Opposition-Based Learning (ROBL) strategy to efficiently balance local and global searches and explore the search space. The algorithm's efficiency is tested on the IEEE Congress of Evolutionary Computation in 2020 (CEC'2020) benchmark functions and compared to seven widely used metaheuristic algorithms, including Genetic Algorithm (GA), particle swarm optimization (PSO), Differential Evolution (DE), Adaptive Guided Differential Evolution Algorithm (AGDE), Improved Multi-Operator Differential Evolution (IMODE), Harris Hawks Optimization (HHO), Runge-Kutta Optimization Algorithm (RUN), weIghted meaN oF vectOrs (INFO), and Coronavirus Herd Immunity Optimizer (CHIO). The statistical results of the convergence curve, boxplot, parameter space, and qualitative metrics show that the LCA algorithm performs competitively compared to well-known algorithms. Moreover, the versatility of the LCA algorithm extends beyond mathematical benchmark problems. It was also successfully applied to tackle the feature selection problem and optimize the support vector machine for various biomedical data classifications, resulting in the creation of the LCA-SVM model. The LCA-SVM model was evaluated in a total of twelve datasets, among which the MonoAmine Oxidase (MAO) dataset stood out, showing the highest performance compared to the other datasets. In particular, the LCA-SVM model achieved an impressive accuracy of 98.704% on the MAO dataset. This outstanding result demonstrates the efficacy and potential of the LCA-SVM approach in handling complex datasets and producing highly accurate predictions. The experimental results indicate that the LCA algorithm surpasses other methods to solve mathematical benchmark problems and feature selection.

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.x, Presidentdental, Germany), milled PEEK (PEEK CAD) (breCAM.BioHPP, Bredent, Germany) and pressed PEEK (PEEK Press) (BioHPP Granules, Bredent, Germany) six specimens each (n = 6). The copings were veneered with high impact polymer composite (HIPC) material (breCAM.HIPC, Bredent, Germany). The vertical marginal gap was captured under a magnification of 40X. Five equidistant marks on each surface of the die distinguished the points of measurement for a total of 20 readings per sample. The analysis was completed using an image analysis system (ImageJ 1.53t, National Institute of Health, USA). The specimens were loaded to failure at a crosshead speed of 1 mm/min and the load at failure was recorded to measure the fracture resistance. RESULTS: The marginal gap was analyzed using one-way ANOVA followed by Tukey's post hoc test. Fracture resistance was analyzed using Welch one-way ANOVA followed by the Games-Howell post hoc test. Marginal gap values showed a significant difference between the tested groups, with zirconia having significantly lower gap values (48.67 ± 11.98 µm) than both the PEEK CAD (108.00 ± 20.08 µm) and Press groups (108.00 ± 25.10 µm) (p < 0.001). However, the results of fracture resistance showed no significant difference (p = 0.06) with 1687.47 ± 253.29 N, 2156.82 ± 407.64 N, 2436.72 ± 725.93 N for zirconia, PEEK CAD, and Press, respectively. The significance level was p < 0.05. CONCLUSIONS: Zirconia framework crowns have a smaller vertical marginal gap than milled and pressed PEEK crowns. Crowns fabricated from zirconia, PEEK CAD, or PEEK Press frameworks and veneered with composite resin have comparable fracture resistance lower than the maximum biting force in the posterior region. CLINICAL RELEVANCE: Posterior crowns with zirconia frameworks are preferred over milled and pressed PEEK frameworks regarding margin adaptation, although all can safely survive the maximum occlusal forces without fracture.

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. The proposed Es-MFO algorithm is evaluated using nineteen different basic benchmark functions, thirty and fifty dimensional IEEE CEC'2017 test functions, and compared the proficiency with a variety of other fundamental optimization techniques as well as MFO variants. Moreover, the suggested Es-MFO algorithm's robustness and durability has been evaluated with tests including the Friedman rank test and the Wilcoxon rank test, as well as a convergence analysis and a diversity analysis. Furthermore, the proposed Es-MFO algorithm resolves three CEC2020 engineering design problems to examine the problem-solving ability of the proposed method. The proposed Es-MFO algorithm is then used to solve the COVID-19 CT image segmentation problem using multi-level thresholding with the help of Otsu's method. Comparison results of the suggested Es-MFO with basic and MFO variants proved the superiority of the newly developed algorithm.

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. A 100% new PEEK was used for the first group, 50% new and 50% reprocessed PEEK were used for the second group, and a 100% reprocessed PEEK was used for the third group. Epoxy resin model with dental implant in the second mandibular premolar was constructed with strain gauges located mesially and distally to the implant to record strain while a load of 100 N was applied with 0.5 mm/min then specimens of all groups were vertically loaded till failure in a universal testing machine at cross head speed 1 mm/min. Data was statistically analyzed by using One-way Analysis of Variance (ANOVA) followed by Post-hoc test when ANOVA test is significant. RESULTS: No significant difference between strain values of tested groups (p = 0.174) was noticed. However, a significant difference between fracture resistance values was noticed where the zirconia group recorded a significantly higher value (p < 0.001). CONCLUSIONS: Implant restorative materials with different moduli of elasticity have similar effects regarding stresses distributed through dental implant and their surrounding bone. Reprocessed PEEK implant restorations transmit similar stresses to dental implant and surrounding bone as non-reprocessed PEEK and zirconia restorations. Zirconia failed at higher load values than all tested PEEK restorations but all can be safely used in the posterior area as crown restorations for single implants. CLINICAL RELEVANCE: Applying "green dentistry" principles may extend to include reprocessing of pressed PEEK restorative materials without affecting the material's shock absorption properties.

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. However, several challenges exist, such as the need for a competent specialist in classifying brain cancers by deep learning models and the problem of building the most precise deep learning model for categorizing brain tumors. We propose an evolved and highly efficient model based on deep learning and improved metaheuristic algorithms to address these challenges. Specifically, we develop an optimized residual learning architecture for classifying multiple brain tumors and propose an improved variant of the Hunger Games Search algorithm (I-HGS) based on combining two enhancing strategies: Local Escaping Operator (LEO) and Brownian motion. These two strategies balance solution diversity and convergence speed, boosting the optimization performance and staying away from the local optima. First, we have evaluated the I-HGS algorithm on the IEEE Congress on Evolutionary Computation held in 2020 (CEC'2020) test functions, demonstrating that I-HGS outperformed the basic HGS and other popular algorithms regarding statistical convergence, and various measures. The suggested model is then applied to the optimization of the hyperparameters of the Residual Network 50 (ResNet50) model (I-HGS-ResNet50) for brain cancer identification, proving its overall efficacy. We utilize several publicly available, gold-standard datasets of brain MRI images. The proposed I-HGS-ResNet50 model is compared with other existing studies as well as with other deep learning architectures, including Visual Geometry Group 16-layer (VGG16), MobileNet, and Densely Connected Convolutional Network 201 (DenseNet201). The experiments demonstrated that the proposed I-HGS-ResNet50 model surpasses the previous studies and other well-known deep learning models. I-HGS-ResNet50 acquired an accuracy of 99.89%, 99.72%, and 99.88% for the three datasets. These results efficiently prove the potential of the proposed I-HGS-ResNet50 model for accurate brain tumor classification.

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. MATERIAL AND METHODS: Digital impressions of teeth (N = 16) with post spaces of depths of 8 mm and 10 mm were captured. Three IOSs were used, including Primescan AC, Medit i500 and CS 3600. The STL files were compared to the files obtained from the traditional impression scanning performed with an InEos X5 desktop scanner. Then, reverse engineering software measured the trueness values, which were analyzed using the two-way analysis of variance (ANOVA), followed by Tukey's post-hoc test. The significance level was set at p < 0.05. RESULTS: Significant differences were found between the scanners in terms of root mean square (RMS) values (p < 0.001). The highest RMS value was found for CS 3600 (0.30 ±0.11 mm), followed by Primescan AC (0.26 ±0.09 mm), while the lowest value was found for Medit i500 (0.18 ±0.05 mm). The 8-millimeter-deep post spaces had a significantly higher RMS value than the 10-millimeter-deep ones (0.28 ±0.10 mm and 0.21 ±0.09 mm, respectively) (p = 0.009). CONCLUSIONS: The Medit i500 scanner showed the highest post-space digital impression trueness as compared to Primescan AC and CS 3600. In the digital impressions captured with CS 3600, the 10 mm postspace depth had higher trueness than the 8 mm depth. Moreover, CS 3600 was less able to capture the full length of both the 8 mm and 10 mm post-space depths than Primescan AC and Medit i500.

Seasonal metabolic profiling of Valencia orange leaf essential oil using GC coupled with chemometrics, nano-formulation, and insecticidal evaluation: in vivo and in silico.

Mosquitoes and mosquito-borne infectious diseases are a global challenge, especially with increased resistance to synthetic insecticides. The foregoing study aimed to utilize the essential oil of leaves of Citrus sinensis var. Valencia as a cheap, safe, eco-friendly (green), and effective alternative to chemical insecticides. Essential oil samples were collected from fresh and dried leaves across different seasons. They are subjected to hydrodistillation and then GC analysis to be compared. Seventy-seven compounds were detected in all samples where monoterpene hydrocarbons represented the most abundant class of hydrocarbons in fresh leaves (52.6-74.4%) and dried leaves (58.6-66.9%). Sabinene (8.26-29.2%), delta-3-carene (8.23-16.4%), d-limonene (2.50-11.2%), and ß-myrcene (2.40-4.93%) were the major monoterpene hydrocarbons in all seasons. Oxygenated monoterpenes comprising ß-linalool, citronellal, terpinen-4-ol, ß-citral, and α-citral exhibited also appreciable percentages in fresh (21.2-43.4%) and dried leaves (23.4-33.0%). Hierarchical cluster analysis (HCA) and principal component analysis (PCA) effectively segregated all samples into three discriminate clusters where, ß-linalool, terpinen-4-ol, ß-elemene enantiomer, sabinene, and ß-phellandrene constitute the main discriminatory biomarkers. Essential oil of fresh spring leaves (FS) was chosen for nano-formulation adopting the hot emulsification method. Both FS sample and the prepared nano-hexosomal formula were screened against the 3rd instar larvae Culex pipiens L. (common house mosquito). LC50 and LC95 values of FS and oil loaded nano-formula were (48 and 30 552 mg L-1) and (30 and 1830 mg L-1) respectively. α-Citral followed by citronellal showed the best fitting within the binding sites of acetylcholine esterase enzyme utilizing molecular docking. Thus, it can be concluded that Valencia orange leaf as a nano-formulation could serve as an effective and sustainable insecticidal agent.

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. The RUNge Kutta optimizer (RUN) is a novel metaheuristic algorithm that has demonstrated effectiveness in solving real-world optimization problems. The enhanced solution quality (ESQ) in RUN utilizes the thus-far best solution to promote the quality of solutions, improve the convergence speed, and effectively balance the exploration and exploitation steps. Also, the Scale factor (SF) has a randomized adaptation nature, which helps RUN in further improving the exploration and exploitation steps. This parameter ensures a smooth transition from exploration to exploitation. In order to mitigate the drawbacks of the RSA algorithm, this paper proposed a modified RSA (mRSA), which combines the RSA algorithm with the RUN. The ESQ mechanism and the scale factor boost the original RSA's performance, enhance convergence speed, bypass local optimum, and enhance the balance between exploitation and exploration. The validity of mRSA was verified using two experimental sequences. First, we applied mRSA to CEC'2020 benchmark functions of various types and dimensions, showing that mRSA has more robust search capabilities than the original RSA and popular counterpart algorithms concerning statistical, convergence, and diversity measurements. The second experiment evaluated mRSA for a real-world application to solve magnetic resonance imaging (MRI) brain image segmentation. Overall experimental results confirm that the mRSA has a strong optimization ability. Also, mRSA method is a more successful multilevel thresholding segmentation and outperforms comparison methods according to different performance measures.

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

Fruits of Citrus sinensis L. Osbeck var. Valencia contain hesperidin as a major flavanone glycoside. Hesperidin (H) was isolated from the peels of Valencia orange and formulated as hexosomal nanodispersions (F1) adopting the hot emulsification method. The antimycobacterial activity(anti-TB) was evaluated through a microplate Alamar blue (MABA) assay where F1 showed significant activity with MIC = 0.19 µM. To unravel the potential mechanism of the anti-TB, a molecular docking study of H using the Mycobacterial Dihydrofolate reductase (Mtb. DHFR) enzyme was performed. Hesperidin exhibited significant interactions with Mtb. DHFR active site. Sulforhodamine B assay was applied to evaluate cytotoxic activity against the lung cancer cell line (A-549). F1 showed a cytotoxic effect at IC50= 33 µM. It also has potent antiviral activity against Human Coronavirus 229E with IC50= 258.8 µM utilising crystal violet assay. Peels of Valencia orange could be a source of bioactive metabolites to control significant diseases.

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. Therefore, the current study aimed to examine the protective potential mechanisms of ADM against DOX-induced nephrotoxicity. A total of 28 male Wistar rats were randomized into four groups: control group, doxorubicin group (15 mg/kg single intraperitoneal injection of DOX), adrenomedullin + doxorubicin group (12 µg/kg/day intraperitoneal injection of ADM) 3 days prior to DOX injection and continuing for 14 days after the model was established, and adrenomedullin group. Kidney function biomarkers, oxidative stress markers, and inflammatory mediators (TNF-α, NLRP3, IL-1ß, and IL-18) were assessed. The expressions of gasdermin D and ASC were assessed by real-time PCR. Furthermore, the abundances of caspase-1 (p20), Bcl-2, and Bax immunoreactivity were evaluated. ADM administration improved the biochemical parameters of DOX-induced nephrotoxicity, significantly reduced oxidative damage markers and inflammatory mediators, and suppressed both apoptosis and pyroptosis. These results were confirmed by the histopathological findings and revealed that ADM's antioxidant, anti-inflammatory, anti-apoptotic, and anti-pyroptotic properties may have prospective applications in the amelioration of DOX-induced nephrotoxicity.

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. Therefore, this paper proposes an efficient version of the recently developed golden jackal optimization (GJO) algorithm, the opposition-based golden jackal optimizer (IGJO). The IGJO algorithm is used to solve the multilevel thresholding problem using Otsu's method as an objective function. The proposed algorithm is compared with seven other meta-heuristic algorithms: whale optimization algorithm, seagull optimization algorithm, salp swarm algorithm, Harris hawks optimization, artificial gorilla troops optimizer, marine predators' algorithms, and original GJO algorithm. The performance of the proposed algorithm is evaluated using four popular performance measures: peak signal-to-noise ratio, structure similarity index, feature similarity index, and mean square error. Experimental results show that the proposed algorithm outperforms other alternative algorithms in terms of PSNR, SSIM, FSIM, and MSE segmentation metrics and effectively resolves the segmentation problem.

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). Bilateral ovariectomy (OVX) was performed. After 3 weeks of recovery, the OVX rats were classified according to their dietary protocol into rats maintained on normal diets (ND) (OVX) or high-fat diet (HFD) groups. The HFD-fed animals were equally divided into OVX/HFD, or irisin-treated OVX/HFD groups. Sham rats, maintained on ND, were selected as the control group. We evaluated anthropometric, EE, and molecular biomarkers of browning and thermogenesis in inguinal white adipose tissue and skeletal muscle, and the activity of the proteins related to mitochondrial long chain fatty acid transport, oxidation, and glycolysis. HFD of OVX further deteriorated the disturbed glucose homeostasis, lipid profile, and the reduced irisin, thermogenic parameters in adipose tissue and skeletal muscle, and EE. Irisin treatment improved the lipid profile and insulin resistance. That was associated with reduced hepatic gluconeogenic enzyme activities and restored hepatic glycogen content. Irisin reduced ectopic lipid infiltration. Irisin augmented EE by activating non-shivering thermogenesis in muscle and adipose tissues and decreasing metabolic efficiency. Our experimental evidence suggests irisin's use as a potential thermogenic agent, therapeutically targeting obesity in postmenopausal patients. Irisin modulates the non-shivering thermogenesis in skeletal muscle and adipose tissue in postmenopausal model.

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. The restorations were divided into two groups (n = 16) according to the construction technique: the commercial control group (C) and the press-on group (P). For the control group, lithium disilicate restorations were pressed and cemented on titanium bases. For the press-on group, lithium disilicate pressable ceramic (IPS e.max Press) was pressed on the titanium bases with injection molding. Each group was further divided according to the restoration design, either screw- or cement-retained, into two subgroups of eight specimens each. Specimens of C group were divided into screw-retained (cemented hybrid abutment crown, CHAC) or cement-retained (cemented hybrid abutment, CHA). Specimens of the P group were also divided into screw-retained (pressed hybrid abutment crown, PHAC) and cement-retained (pressed hybrid abutment, PHA). The specimens were subjected to static loading until failure with a universal testing machine. Two-way analysis of variance (ANOVA) was used to assess the effect of different techniques and designs on the fracture resistance of the samples (P < .05), followed by one-way ANOVA and Tukey honest significant difference (HSD) test (α = .05). RESULTS: C group showed higher mean fracture resistance (812.443 ± 129.14 N) than P group (596.71 ± 108.83 N), and the difference was statistically significant (P < .05). Regarding restoration design, HA groups showed higher mean fracture resistance (742.621 ± 153.82 N) than HAC (666.53 ± 163.07 N) groups with no statistically significant difference. CHA showed the highest mean fracture resistance (817.65 ± 161.76 N), while PHAC showed the lowest mean fracture resistance values (525.83 ± 47.29 N). CONCLUSION: The commercial cemented lithium disilicate restorations showed higher fracture resistance than the press-on restorations, although both showed a maximum load capacity that was greater than physiologic incisal force in the anterior region, and both hybrid abutments and hybrid abutment crowns were equally efficient in withstanding occlusal loading forces.

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. This paper proposes a novel classification model for breast cancer diagnosis based on a hybridized CNN and an improved optimization algorithm, along with transfer learning, to help radiologists detect abnormalities efficiently. The marine predators algorithm (MPA) is the optimization algorithm we used, and we improve it using the opposition-based learning strategy to cope with the implied weaknesses of the original MPA. The improved marine predators algorithm (IMPA) is used to find the best values for the hyperparameters of the CNN architecture. The proposed method uses a pretrained CNN model called ResNet50 (residual network). This model is hybridized with the IMPA algorithm, resulting in an architecture called IMPA-ResNet50. Our evaluation is performed on two mammographic datasets, the mammographic image analysis society (MIAS) and curated breast imaging subset of DDSM (CBIS-DDSM) datasets. The proposed model was compared with other state-of-the-art approaches. The obtained results showed that the proposed model outperforms the compared state-of-the-art approaches, which are beneficial to classification performance, achieving 98.32% accuracy, 98.56% sensitivity, and 98.68% specificity on the CBIS-DDSM dataset and 98.88% accuracy, 97.61% sensitivity, and 98.40% specificity on the MIAS dataset. To evaluate the performance of IMPA in finding the optimal values for the hyperparameters of ResNet50 architecture, it compared to four other optimization algorithms including gravitational search algorithm (GSA), Harris hawks optimization (HHO), whale optimization algorithm (WOA), and the original MPA algorithm. The counterparts algorithms are also hybrid with the ResNet50 architecture produce models named GSA-ResNet50, HHO-ResNet50, WOA-ResNet50, and MPA-ResNet50, respectively. The results indicated that the proposed IMPA-ResNet50 is achieved a better performance than other counterparts.