Bone scintigraphy based on deep learning model and modified growth optimizer.

Bone scintigraphy is recognized as an efficient diagnostic method for whole-body screening for bone metastases. At the moment, whole-body bone scan image analysis is primarily dependent on manual reading by nuclear medicine doctors. However, manual analysis needs substantial experience and is both stressful and time-consuming.

Preparation and optimization of grafted hydroxyethyl cellulose, polypyrrole, and nitrogen-doped carbon quantum dots bionanocomposites for electrical, optical, and photoluminescence multicoloring applications.

The major objective of this research revolves around the integration of polypyrrole (PPy) and various concentrations of nitrogen-doped carbon quantum dots (N-CQDs) into a polyacrylamide (PAm)-grafted hydroxyethyl cellulose (gHEC) to produce gHEC@PPy@N-CQDs bionanocomposites that possess environmentally sustainable properties. The intercalation and uniform distribution of N-CQDs inside the gHEC@PPy matrix have been demonstrated through the analysis of data obtained from X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FTIR). The samples underwent analysis using thermogravimetric analysis (TGA and DTG) as well as scanning and transmission electron microscopy.

Emerging Role of Circular RNAs in Hepatocellular Carcinoma Immunotherapy.

Hepatocellular carcinoma (HCC) is a highly fatal malignancy with limited therapeutic options and high recurrence rates. Recently, immunotherapeutic agents such as immune checkpoint inhibitors (ICIs) have emerged as a new paradigm shift in oncology. ICIs, such as programmed cell death protein 1 (PD-1) inhibitors, have provided a new source of hope for patients with advanced HCC.

Synthesis and characterization of bio-nanocomposite based on chitosan and CaCO nanoparticles for heavy metals removal.

Water is a vital component of life; therefore, it is critical to have access to pure water for various life-sustaining activities including agriculture and human consumption. An eco-friendly nanocomposite based on chitosan (Cs) and nanomaterials (CaCO-NPs) were combined to amalgamate the advantages of biopolymers and nanomaterials to overcome the problems of instability, poor mechanical properties, and low removal percentage of biopolymers. The as-prepared samples were characterized and were used for the removal of heavy metal from wastewater.

High- and low-dose cyclophosphamide in Egyptian lupus nephritis patients: a multicenter retrospective analysis.

Background: Lupus nephritis (LN) is a common serious presentation of systemic lupus erythematosus. Cyclophosphamide (CYC) and mycophenolate mofetil (MMF) are listed as the first-line drugs in induction therapy for LN.

Objective: This study aimed to compare high- and low-dose CYC in a cohort of Egyptian LN patients.

Decoding hepatocarcinogenesis from a noncoding RNAs perspective.

Being a leading lethal malignancy worldwide, the pathophysiology of hepatocellular carcinoma (HCC) has gained a lot of interest. Yet, underlying mechanistic basis of the liver tumorigenesis is poorly understood. The role of some coding genes and their respective translated proteins, then later on, some noncoding RNAs (ncRNAs) such as microRNAs have been extensively studied in context of HCC pathophysiology; however, the implication of long noncoding RNAs (lncRNAs) and circular RNAs (circRNAs) in HCC is indeed less investigated.

Pharmacological updates of nifuroxazide: Promising preclinical effects and the underlying molecular mechanisms.

Nifuroxazide (NFX) is a safe nitrofuran antibacterial drug used clinically to treat acute diarrhea and infectious traveler diarrhea or colitis. Recent studies revealed that NFX displays multiple pharmacological effects, including anticancer, antioxidant, and anti-inflammatory effects. NFX has potential roles in inhibiting thyroid, breast, lung, bladder, liver, and colon cancers and osteosarcoma, melanoma, and others mediated by suppressing STAT3 as well as ALDH1, MMP2, MMP9, Bcl2 and upregulating Bax.

Development of an in vitro regeneration system from immature inflorescences and CRISPR/Cas9-mediated gene editing in sudangrass.

Background: Sudangrass (Sorghum sudanense) is a major biomass producer for livestock feed and biofuel in many countries. It has a wide range of adaptations for growing on marginal lands under biotic and abiotic stresses. The immature inflorescence is an explant with high embryogenic competence and is frequently used to regenerate different sorghum cultivars.

Optimal Skin Cancer Detection Model Using Transfer Learning and Dynamic-Opposite Hunger Games Search.

Recently, pre-trained deep learning (DL) models have been employed to tackle and enhance the performance on many tasks such as skin cancer detection instead of training models from scratch. However, the existing systems are unable to attain substantial levels of accuracy. Therefore, we propose, in this paper, a robust skin cancer detection framework for to improve the accuracy by extracting and learning relevant image representations using a MobileNetV3 architecture.

Medical Image Classifications for 6G IoT-Enabled Smart Health Systems.

As day-to-day-generated data become massive in the 6G-enabled Internet of medical things (IoMT), the process of medical diagnosis becomes critical in the healthcare system. This paper presents a framework incorporated into the 6G-enabled IoMT to improve prediction accuracy and provide a real-time medical diagnosis. The proposed framework integrates deep learning and optimization techniques to render accurate and precise results.

Preparation and performance of bionanocomposites based on grafted chitosan, GO and TiO-NPs for removal of lead ions and basic-red 46.

Wastewater rich in heavy metals and organic compounds represents one of the essential environmental pollutants. Therefore, a practical approach is to fabricate eco-friendly polymer-based systems with a high ability to absorb pollutants. Herein, bionanocomposites consisting of chitosan (Cs) grafted by various monomers, such as acrylamide (Am), acrylic acid (AA), and 4-styrene sulfonic acid (SSA), and hybrid nanoparticles of graphene oxide/titanium dioxide nanoparticles (GO@TiO-NPs) were fabricated.

Pectoral nerve blocks for transvenous subpectoral pacemaker insertion in children: a randomized controlled study.

Background: Postoperative pain management after pacemaker insertion routinely requires opioid agents, nonsteroidal anti-inflammatory drugs, or paracetamol. However, interest in opioid-sparing multimodal pain management to minimize postoperative narcotic use has increased recently. This study aimed to assess the pectoral nerve (PECS) block versus standard treatment on postoperative pain control and opioid consumption in pediatric patients after transvenous subpectoral pacemaker insertion.

MMKE: Multi-trial vector-based monkey king evolution algorithm and its applications for engineering optimization problems.

Monkey king evolution (MKE) is a population-based differential evolutionary algorithm in which the single evolution strategy and the control parameter affect the convergence and the balance between exploration and exploitation. Since evolution strategies have a considerable impact on the performance of algorithms, collaborating multiple strategies can significantly enhance the abilities of algorithms. This is our motivation to propose a multi-trial vector-based monkey king evolution algorithm named MMKE.

Predicting CO2 trapping in deep saline aquifers using optimized long short-term memory.

A sustainable environment by decreasing fossil fuel utilization and anthropogenic greenhouse gases is a globally main goal due to climate change and serious air pollution. Carbon dioxide (CO2) is a heat-trapping (greenhouse) that is released into the earth's atmosphere from natural processes, such as volcanic respiration and eruptions, as well as human activities, such as burning fossil fuels and deforestation. Due to this fact, underground carbon storage (UCS) is a promising technology to cut carbon emissions.

The Applications of Metaheuristics for Human Activity Recognition and Fall Detection Using Wearable Sensors: A Comprehensive Analysis.

In this paper, we study the applications of metaheuristics (MH) optimization algorithms in human activity recognition (HAR) and fall detection based on sensor data. It is known that MH algorithms have been utilized in complex engineering and optimization problems, including feature selection (FS). Thus, in this regard, this paper used nine MH algorithms as FS methods to boost the classification accuracy of the HAR and fall detection applications.

Enhanced feature selection technique using slime mould algorithm: a case study on chemical data.

Feature selection techniques are considered one of the most important preprocessing steps, which has the most significant influence on the performance of data analysis and decision making. These FS techniques aim to achieve several objectives (such as reducing classification error and minimizing the number of features) at the same time to increase the classification rate. FS based on Metaheuristic (MH) is considered one of the most promising techniques to improve the classification process.

Multithreshold Segmentation and Machine Learning Based Approach to Differentiate COVID-19 from Viral Pneumonia.

Coronavirus disease (COVID-19) has created an unprecedented devastation and the loss of millions of lives globally. Contagious nature and fatalities invariably pose challenges to physicians and healthcare support systems. Clinical diagnostic evaluation using reverse transcription-polymerase chain reaction and other approaches are currently in use.

Medical Image Classification Using Transfer Learning and Chaos Game Optimization on the Internet of Medical Things.

The Internet of Medical Things (IoMT) has dramatically benefited medical professionals that patients and physicians can access from all regions. Although the automatic detection and prediction of diseases such as melanoma and leukemia is still being investigated and studied in IoMT, existing approaches are not able to achieve a high degree of efficiency. Thus, with a new approach that provides better results, patients would access the adequate treatments earlier and the death rate would be reduced.

Adaptive Machine Learning Based Distributed Denial-of-Services Attacks Detection and Mitigation System for SDN-Enabled IoT.

The development of smart network infrastructure of the Internet of Things (IoT) faces the immense threat of sophisticated Distributed Denial-of-Services (DDoS) security attacks. The existing network security solutions of enterprise networks are significantly expensive and unscalable for IoT. The integration of recently developed Software Defined Networking (SDN) reduces a significant amount of computational overhead for IoT network devices and enables additional security measurements.

Boosting Marine Predators Algorithm by Salp Swarm Algorithm for Multilevel Thresholding Image Segmentation.

Pixel rating is considered one of the commonly used critical factors in digital image processing that depends on intensity. It is used to determine the optimal image segmentation threshold. In recent years, the optimum threshold has been selected with great interest due to its many applications.

Binary Horse herd optimization algorithm with crossover operators for feature selection.

This paper proposes a binary version of Horse herd Optimization Algorithm (HOA) to tackle Feature Selection (FS) problems. This algorithm mimics the conduct of a pack of horses when they are trying to survive. To build a Binary version of HOA, or referred to as BHOA, twofold of adjustments were made: i) Three transfer functions, namely S-shape, V-shape and U-shape, are utilized to transform the continues domain into a binary one.

Lightning search algorithm: a comprehensive survey.

The lightning search algorithm (LSA) is a novel meta-heuristic optimization method, which is proposed in 2015 to solve constraint optimization problems. This paper presents a comprehensive survey of the applications, variants, and results of the so-called LSA. In LSA, the best-obtained solution is defined to improve the effectiveness of the fitness function through the optimization process by finding the minimum or maximum costs to solve a specific problem.

A Novel Hybrid Gradient-Based Optimizer and Grey Wolf Optimizer Feature Selection Method for Human Activity Recognition Using Smartphone Sensors.

Human activity recognition (HAR) plays a vital role in different real-world applications such as in tracking elderly activities for elderly care services, in assisted living environments, smart home interactions, healthcare monitoring applications, electronic games, and various human-computer interaction (HCI) applications, and is an essential part of the Internet of Healthcare Things (IoHT) services. However, the high dimensionality of the collected data from these applications has the largest influence on the quality of the HAR model. Therefore, in this paper, we propose an efficient HAR system using a lightweight feature selection (FS) method to enhance the HAR classification process.

Utilization of random vector functional link integrated with manta ray foraging optimization for effluent prediction of wastewater treatment plant.

An innovative predictive model was employed to predict the key performance indicators of a full-scale wastewater treatment plant (WWTP) operated with an activated sludge treatment process. The data-driven model was obtained using data gathered from Cairo, Egypt. The proposed model consists of Random Vector Functional Link (RVFL) Networks incorporated with Manta Ray Foraging Optimizer (MRFO).