Search

Abstract Breast cancer is among the highest morbidity and mortality rates in women around the world. In the present investigation we aimed to synthesis novel nanosystem combining two naturally important anticancer agents with different mechanism of action namely Moringa oleifera and caffeine. Firstly, chemical analysis of Moringa oleifera extract and caffeine was done by gas chromatography-mass spectroscopy (GC–MS) in order to assess the main chemical compounds present and correlate between them and the possible anticancer effect. The novel nanosystem was characterized through dynamic light scattering techniques which revealed the stability and homogeneity of the prepared M. oleifera leaves extract/Caffeine loaded chitosan nanoparticles, while FTIR and transmission electron microscope (TEM) proved the shape and the successful incorporation of M. oleifera leaves extract/Caffeine onto the nanochitosan carrier. Our initial step was to assess the anticancer effect in vitro in cancer cell line MCF-7 which proved the significant enhanced effect of M. oleifera leaves extract/Caffeine nanosystem compared to M. oleifera leaves extract or caffeine loaded nanoparticles. Further studies were conducted in vivo namely tumor biomarkers, tumor volume, bioluminescence imaging, molecular and histopathological investigations. The present study proved the potent anticancer effect of the synthesized M. oleifera leaves extract/Caffeine loaded chitosan nanoparticles. Mo/Caf/CsNPs exhibited a large number of apoptotic cells within the tumor mass while the adipose tissue regeneration was higher compared to the positive control. The prepared nanoparticles downregulated the expression of Her2, BRCA1 and BRCA2 while mTOR expression was upregulated. The aforementioned data demonstrated the successful synergistic impact of Moringa and caffeine in decreasing the carcinoma grade.

Abstract This study investigated the prevalence, morphology, molecular identification, and histopathological effects of larval tapeworms (plerocercoids) infecting the skeletal muscles of the Indian halibut (Psettodes erumei) collected from the coastal waters of the Arabian Gulf. Numerous oval or round blastocysts, measuring 13–26 mm, were found embedded within the muscular tissues of the Indian halibut, rendering the fish unsuitable for human consumption. Morphological and molecular analyses identified the plerocercoids as Dasyrhynchus giganteus (family Dasyrhynchidae), with an overall prevalence of 15.4%. The seasonal prevalence was the highest in summer (14.6%), followed by spring (10.6%), winter (4.4%), and autumn (3.5%). Infection rates increased with fish size. Histopathological examination revealed fibrous connective tissue capsules surrounding the larvae, causing muscular atrophy and degenerative changes, with few inflammatory eosinophilic cells. Molecular and phylogenetic analysis of the 28S rDNA gene sequences confirmed the specimens as D. giganteus, clustered closely with other sequences of D. giganteus with 100% bootstrap values. This study provided valuable insights into the parasitic infection dynamics, seasonal variation, molecular identification, and histopathological effects, highlighting the importance of monitoring fish for food safety and public health implications.

Abstract One of the major problems of using nanofluids in heat exchange applications is the forming and deposition of nanoparticles on the inner surface of the heat exchanger. In this paper, Water-Cmc fluid is used as a surfactant for nanoparticles to prevent deposition and congregation. The pressure drops and heat transfer in U-bend double pipe heat exchanger based on water-MgO-Cmc fluid, are examined. Nanoparticles of Magnesium Oxide (MgO) and Carboxymethyl Cellulose (Cmc) are used with pure water as a base fluid. The experimental rig and procedures are designed to facilitate various operational conditions such as flow rate, volume concentration of MgO particles and weight concentration of Cmc particles. Furthermore, convective heat transfer coefficient, heat exchanger effectiveness, pressure drop, friction factor, under different conditions, are measured. The results demonstrate convective heat transfer coefficient of U-bend double pipe heat exchangers is enhanced by 35% for 1 MgO vol.% and 0.2 Cmc wt.% compared to base fluid (Water-Cmc). It is concluded that pressure drops are directly proportion to the increase of MgO nanoparticles at same Cmc concentration by 23% at 0.2 wt.%. Whilst, friction factor of the system is inversely proportion to the increase of volumetric flow rate of water-MgO-Cmc fluid. An increase in MgO nanoparticle concentration increases the friction factor, hence maximum friction factor enhancement by 38% for MgO concentration of 1 vol.%. The effectiveness of heat exchanger is slightly increased by 8% with increase of MgO concentration and flow rate. Finally, thermo-physical characteristics of water-MgO-Cmc fluid at various temperatures, are measured

Aims: The study aimed to assess the midfacial soft and hard tissue changes of males and females following maxillary skeletal expansion in young adults. Materials and methods: 24 patients, 11 males, and 13 females (mean age, 19.54 years; between 17 to 27) with maxillary transverse deficiency treated with MSE. CBCT scans taken before and after expansion were used to measure the changes in soft and hard tissue landmarks. Paired t-tests were used for statistical analyses. Results: Hard tissue lower interzygomatic distance increased by (3.41mm in males, and 2.97mm in females), intermolar distance increased by (5.84mm in males, and 5.51mm in females), the palatal separation anteriorly was (4.14mm in males, and 3.76mm in females), posteriorly was (3.24 mm in males, and 3.25 mm in females), however nasal width increased by (3.49mm in males, and 3.29mm in females). Soft tissue average lateral movement of the alar curvature points was (2.1mm in males, and 1.98mm in females). the nasolabial fold width was (3.54mm in males, and 2.55mm in females). and interzygomatic width was (2.27mm in males, and 1.85mm in females). Conclusions: Maxillary expansion using MSE resulted in an almost parallel expansion with significant lateral movements of the soft tissues of the cheek and alar curvature points on both sides in young adults and it was correlated with the hard tissue changes.

An accurate prediction of fetal birth weight is crucial in ensuring safe delivery without health complications for the mother and baby. The uncertainty surrounding the fetus’s birth situation, including its weight range, can lead to significant risks for both mother and baby. As there is a standard birth weight range, if the fetus exceeds or falls below this range, it can result in considerable health problems. Although ultrasound imaging is commonly used to predict fetal weight, it does not always provide accurate readings, which may lead to unnecessary decisions such as early delivery and cesarian section. Besides that, no supporting system is available to predict the weight range in Saudi Arabia. Therefore, leveraging the available technologies to build a system that can serve as a second opinion for doctors and health professionals is essential. Machine learning (ML) offers significant advantages to numerous fields and can address various issues. As such, this study aims to utilize ML techniques to build a predictive model to predict the birthweight range of infants into low, normal, or high. For this purpose, two datasets were used: one from King Fahd University Hospital (KFHU), Saudi Arabia, and another publicly available dataset from the Institute of Electrical and Electronics Engineers (IEEE) data port. KFUH’s best result was obtained with the Extra Trees model, achieving an accuracy, precision, recall, and F1-score of 98%, with a specificity of 99%. On the other hand, using the Random Forest model, the IEEE dataset attained an accuracy, precision, recall, and F1-score of 96%, respectively, with a specificity of 98%. These results suggest that the proposed ML system can provide reliable predictions, which could be of significant value for doctors and health professionals in Saudi Arabia.

Salinity is a significant factor restricting plant growth and production. The effect of salinity stress on different growth parameters of 111 fenugreek genotypes was examined in an experiment with three salinity levels (0, 3000, 6000 mgL−1). A completely randomized block design with two replicated pots per treatment was used. Non-significant treatment effects were observed on fresh weight (FW); however, all traits showed significant genotype-by-treatment (GxT) interactions. This GxT was reflected in substantial SNP x environment interactions. Of 492 significant SNPs associated with the measured traits, 212 SNPs were linked to the correlated traits using an arbitrary threshold of three. Several SNPs were associated with FW and dry weight, measured under the same salinity treatment. The correlation between both traits was 0.98 under the three salinity treatments. In addition, 280 SNPs with conditional neutrality effects were mapped. The identified SNPs can be used in future marker-assisted breeding programs to select salt-tolerant genotypes. The results of this research shed light on the salt-tolerant properties of fenugreek.

The objective of this research was to explore the link between Helicobacter pylori (H. pylori) infection and alterations in ischemia modified albumin (IMA), thiol, and disulfide levels, with a focus on their potential clinical implications.

Spinal cord injury (SCI) results in severe atrophy of skeletal muscle in paralyzed regions, and a decrease in the force generated by muscle per unit of cross-sectional area. Oxidation of skeletal muscle ryanodine 1 receptors (RyR1) reduces contractile force as a result of reduced binding of calstabin 1 to RyR1. One cause of RyR1 oxidation is nicotinamide adenine dinucleotide phosphate (NADPH) oxidase 4 (Nox4). We have previously shown that, in rats, RyR1 was oxidized and bound less to calstabin 1 at 56 days after SCI by spinal cord transection. Here, we used a conditional knockout (KO) mouse model of Nox4 in skeletal muscle to investigate the role of Nox4 in reduced muscle specific force after SCI. Peak twitch force of extensor digitorum longus muscles in control mice after SCI was reduced by 42% compared with sham-operated controls, but was increased by ?43% in SCI Nox4 conditional KO mice compared with SCI controls, although it remained less than that for sham-operated controls. Unlike what was previously observed in rats after SCI, the expression of Nox4 was not increased in gastrocnemius muscle, and binding of calstabin 1 to RyR1 was not reduced in this muscle. The results suggest that Nox4 is directly involved in reduction in muscle twitch force after SCI, although further studies are needed to understand the mechanistic basis for this linkage.

Aims: Traditionally, crustacean wastes have been managed by using acid and alkali which leads to major environmental issue. However, over the recent years microbial fermentation has gained its way whereby producing similar effects as chemical treatment and a higher quality product can be obtained. Extracellular protease from Bacillus subtilis was used further by optimizing its culture medium to enhance protease production. Methodology and Results: The culture media was optimized with 4 various sources; Shrimp Crab Shell Powder (SCSP), nitrogen sources, inorganic salts, and carbon sources. It was found that culture media supplemented with 9% SCSP, 3% yeast extract, 1% sodium chloride and 9% glucose augmented protease activity up to 565.80 ± 19.41 U/mL compared to the un-optimized media (170.57 ± 6.75 U/mL). By using this optimized media, the ability and efficiency of B. subtilis in a period of 6 days was investigated whereby acid treated shrimp shells (ATSS) and raw shrimp shell powder (RSSP) were used in substitution of SCSP. In a period of 6 days, the protein content in both ATSS and RSSP was found to have been removed up to 60% and 42% respectively. However deproteinization was found to be more efficient in RSSP with the ratio of tyrosine to protein remained constantly high throughout the 6 days period. Conclusion, significance and impact of study: A better, more efficient and environmental friendly method iscontinuously being improvised to manage shrimp wastes with the use of microbes.

Cardiac allografts suffer diastolic dysfunction early post-heart transplantation (HTx) due to ischemic injury, however the natural course of diastology recovery post HTx remains unknown (Tallaj et al., 2007 [1]). We retrospectively reviewed 60 adult HTx patients between 2015 and 2021 at a single site. Invasive hemodynamics and echocardiograms were obtained at 2 weeks and 1, 3, 6, and 12 months post-HTx. RA strain by 2D feature tracking was compared to intracardiac pressure measurements. In all patients, we observed normalization of RV and RA filling pressures by post-operative week 12 and recovery of diastolic dysfunction by month 6. There was an inverse correlation between RV end-diastolic pressure and RA contractile (r = −0.192, p

Background: Left ventricular assist devices (LVADs) may induce electromagnetic interference (EMI) affecting implanted cardiac devices, including more novel subcutaneous implantable cardiac defibrillators (S-ICDs). Methods: In this case series, the authors retrospectively reviewed courses of 6 patients with S-ICDs who underwent LVAD implantation at a single center. Results: Of the 6 patients reviewed, 4 experienced inappropriate ICD shocks, of which 3 resulted from EMI. Five of the 6 patients ultimately had S-ICD therapies disabled. Conclusions: Due to EMI resulting in inappropriate shocks and improved tolerability of malignant arrhythmias, deactivation or removal of S-ICDs should be considered in patients undergoing LVAD implantation.

Introduction: Diuretics are the mainstay of maintaining and restoring euvolemia in the management of heart failure. Loop diuretics are often preferred, however, combination diuretic therapy (CDT) with a thiazide diuretic is often used to overcome diuretic resistance and increase diuretic effect. We performed an analysis of the GUIDE-IT study to assess all-cause mortality and time to first hospitalizations in patients necessitating CDT. Methods: Patients from the GUIDE-IT dataset were stratified by their requirement for CDT with a thiazide to achieve euvolemia. A total of 894 patients were analyzed, 733 of which were treated with loop diuretics alone vs 161 used either chlorothiazide or metolazone in addition to loop diuretics. Kaplan-Meir curves were derived with log-rank p-values to evaluate for differences between the groups. Results: There was no significant difference in all-cause mortality regardless of CDT utilization status (mean survival of 612.704 days vs 603.326 days, p = 0.083). On subgroup analysis, there was no significant difference in all-cause mortality amongst those using loop diuretics compared to CDT in the BNP-guided therapy group, (mean survival time 576.385 days vs 620.585 days, p = 0.0523), nor the control group (614.1 days vs 588.9 days; p = 0.5728). Time to first hospitalization was reduced in all using CDT compared to loop diuretics alone (280.5 days vs 407.2 days, p

The use of recycled concrete aggregate (RAC) in creating new concrete has gained significant attention for environmental and financial reasons. However, the compressive strength of the product concrete is hard to predict due to many variables. In this study, the compressive strength of recycled aggregate concrete was predicted using eight well-known machine learning algorithms, including support vector machine (SVM), artificial neural network (ANN), XGboost, Tree, Random Forest, Gradient Boosting, CatBoost, and AdaBoost. Every machine learning algorithm's general methodology entails gathering and analyzing input data, training the algorithm, testing the algorithm, and producing an output. A total of 419 data samples (experimental tests) were used in training and testing all the machine learning models. The results show that the best models for estimating RAC compressive strength are Neural Network, AdaBoost, and XGBoost. The other algorithms, random forest, gradient boosting, and Catboost, performed well in predicting the compressive strength of RAC, however, tree decision and SVM performed badly. The primary evaluation metrics used in this study were Mean Squared Error (MSE) and R-squared (R²), which helped determine the accuracy and reliability of the predictive models.

In the current study, the incline edge crack growth behavior in an aluminum alloy 7075-T6 specimens (which were coated using nickel-carbon nanotubes electroless coating) has been investigated undergoes uniaxial tensile fatigue loading. High purity multi walled carbon nanotubes have been used in nano-coating solutions with various concentrations (1 g/l, 2 g/l and 3 g/l) to examine the improvement extent of fatigue crack growth resistance and other mechanical properties. Water jet technique was used to cutting aluminum plates to the required dimensions before the nano-coating procedure. Multi-purpose fatigue apparatus has been adopted to apply tensile fatigue loading with zero stress ratios. High magnification camera with image j software has been utilized to measure the propagated crack lengths with the number of fatigue cycles. To inspection the quality, thickness uniformity and homogeneity of the nano-coating layer over specimen surfaces, EDXRF and FESEM techniques have been adopted in this study. Numerical simulation using ABAQUS 2021 programming was performed for the purpose of the results validation. The measured results offered a 25.9 %, 44.5 % and 47.9 % increasing in the hardness of the coated specimens using 1 g/l, 2 g/l and 3 g/l MWCNTs respectively. The modulus of elasticity produced an increasing of 26.7 %, 44.4 % and 46.3 % for the coated specimens using 1 g/l, 2 g/l and 3 g/l MWCNTs respectively. The nano-coating with 2 g/l MWCNTs produced more homogeneous, uniform thickness coating layer and fatigue crack growth resistance than that of the coated specimens using other concentrations of MWCNTs. The similarity in the crack growth behavior was the major outcome of the results validation from the fatigue tests and numerical simulation.

A wireless sensor network (WSN) consists of several sensor nodes; all these nodes can sense physical events, including light, heat, and pressure. These networks are essential in smart homes, smart agriculture, and smart water management, swelling with the concept of the Internet of Things. However, WSN needs to address the challenges of energy issues; thus, energy-conserving techniques have been pursued for communication. Optimization of energy is normally solved using the Particle Swarm Optimization (PSO) algorithm since it offers high accuracy but is prone to local optima, thus resulting in early convergence. To tackle this challenge, this paper proposes the development of an enhanced particle swarm optimization for the node power estimation (EPSO-NPE) model. EPSO-NPE calculates distinct transmission powers for each node, preventing the formation of isolated areas within a sensor cluster. Unlike the original PSO, the EPSO algorithm enhances exploration capabilities by avoiding stagnation on search space boundaries. A comparative analysis with the original PSO-based model (PSO-NPE), where nodes adopt maximum power for connectivity, reveals superior performance by EPSO-NPE. The enhanced model exhibits heightened energy-saving capabilities, ultimately extending the network’s lifetime.