Search

For the time being, there is a growing endeavor towards supporting energy-saving technologies, most significantly heat exchanger systems, by improving the performance of the heat transfer process. The study model employed is a double-pipe heat exchanger (DPHEX), which is served in an actual project at the Low-Density Polyethylene (LDPE) unit of the State Company for Petrochemical Industries in Basra, Iraq. The prominent goals of this study are to gain a deeper comprehension of the exchanger's performance under advanced operating conditions and to maximize the efficiency of heat transfer between the two fluids in the DPHEX system. The current work applies a distinctive connection of SOLIDWORKS with ANSYS FLUENT software to conduct a simulation design and Computational Fluid Dynamics (CFD) analysis of a heat transfer system in DPHEX. Thermal analysis of exchangers is challenging since many parameters, such as the geometry of the heat exchangers and the varying flow regimes influence the overall heat transfer coefficient. As a result, a Genetic Algorithm (GA) was employed to investigate the optimal design and operating conditions of DPHEX, using a COM server to provide flexible communication between the MATLAB GA toolbox and Aspen HYSYS® software. The preliminary findings from ANSYS FLUENT simulations and GA optimizations demonstrated significant improvements. Specifically, the heat transfer rate rose by 24% and 28%, respectively, also there made up an elevate in the overall heat transfer coefficient to 675 W/m²·K and 751 W/m²·K, correspondingly, from 440 W/m²·K in the as-built heat exchanger. Notably, these results were observed while the outgoing temperature of the chilled ethylene gas was at 49℃, and the efficiency accounted for 87.7%. The study exhibited the feasibility of reducing the cooling water quantity from the traditional mass flow rate of 73,860 kg/hr to 50,400 kg/hr while maintaining a reasonable efficiency of 83% at the LDPE unit. These results were achieved with the leaving temperature of ethylene gas and cooling water set at 50℃ and 34℃, respectively. Hence, minimizing water consumption in heat exchangers brings about environmental, economic, and operational advantages. Generally, the study results have reinforced the importance of simulation tools and their direct contribution to achieving efficient and eco-friendly energy systems.

Autism is a neurodevelopmental disorder distinguished by impaired social interaction and repetitive behaviors. Global estimates indicate that autism affects approximately 1.6% of children, with the condition progressively becoming more prevalent over time. Despite noteworthy progress in autism research, the condition remains untreatable. This serves as a driving force for scientists to explore new approaches to disease management. Autism is linked to elevated levels of oxidative stress and disturbances in the DNA repair mechanism, which may potentially play a role in its comorbidities development. The current investigation aimed to evaluate the beneficial effect of the naturally occurring flavonoid proanthocyanidins on the behavioral characteristics and repair efficacy of autistic BTBR mice. Moreover, the mechanisms responsible for these effects were clarified. The present findings indicate that repeated administration of proanthocyanidins effectively reduces altered behavior in BTBR animals without altering motor function. Proanthocyanidins decreased oxidative DNA strand breaks and accelerated the rate of DNA repair in autistic animals, as evaluated by the modified comet test. In addition, proanthocyanidins reduced the elevated oxidative stress and recovered the disrupted DNA repair mechanism in the autistic animals by decreasing the expressions of Gadd45a and Parp1 levels and enhancing the expressions of Ogg1, P53, and Xrcc1 genes. This indicates that proanthocyanidins have significant potential as a new therapeutic strategy for alleviating autistic features.

Background: Coronavirus disease 2019 (COVID-19) is a respiratory illness that leads to severe acute respiratory syndrome and various cardiorespiratory complications, contributing to morbidity and mortality. Entropy analysis has demonstrated its ability to monitor physiological states and system dynamics during health and disease. The main objective of the study is to extract information about cardiorespiratory control by conducting a complexity analysis of OSV signals using scale-based entropy measures following a two-month timeframe after recovery. Methods: This prospective study collected data from subjects meeting specific criteria, using a Beurer PO-80 pulse oximeter to measure oxygen saturation (SpO2) and pulse rate. Excluding individuals with a history of pulmonary/cardiovascular issues, the study analyzed 88 recordings from 44 subjects (26 men, 18 women, mean age 45.34 ± 14.40) during COVID-19 and two months post-recovery. Data preprocessing and scale-based entropy analysis were applied to assess OSV signals. Results: The study found a significant difference in mean OSV during illness (95.08 ± 0.15) compared to post-recovery (95.59 ± 1.03), indicating reduced cardiorespiratory dynamism during COVID-19. Multiscale entropy analyses (MSE, MPE, MFE) confirmed lower entropy values during illness across all time scales, particularly at higher scales. Notably, the maximum distinction between illness and recovery phases was seen at specific time scales and similarity criteria for each entropy measure, showing statistically significant differences. Conclusions: The study demonstrates that the loss of complexity in OSV signals, quantified using scale-based entropy measures, has the potential to detect malfunctioning of cardiorespiratory control in COVID-19 patients. This finding suggests that OSV signals could serve as a valuable indicator for assessing the cardiorespiratory status of COVID-19 patients and monitoring their recovery progress.

Lung cancer metastasis remains a significant challenge in cancer therapy, necessitating the exploration of novel treatment modalities. Silymarin, a natural compound derived from milk thistle, has demonstrated promising anticancer properties. This work explored the inhibitory effects of silymarin on lung cancer metastasis and revealed the underlying processes, focusing on matrix metalloproteinase (MMP) 2 and MMP-9 activities. Using a combination of in vitro and molecular docking analyses, we found that silymarin effectively reducing the lung cancer cells' motility and invasion by modulation of expression of MMP-2 and MMP-9. Furthermore, MTT assays revealed a dose-dependent inhibition of cell proliferation upon silymarin treatment and found the IC50 value at 58 μM. We observe that apoptotic morphology characteristic in silymarin treated groups. Cell cycle analysis exhibit the cell cycle arrest at G1 phase, 25.8 % increased apoptosis in silymarin treated groups, as evidenced by Annexin V staining. Moreover, silymarin treatment shows the lipid peroxidation in elevated level and reduced in enzymatic antioxidant level, indicating its potential role in mitigating oxidative stress induce cell death. Gelatin zymography assay indicates the silymarin has ability to inhibit the MMP-2 and MMP-9 expression in lung cancer. Additionally, cell migration assays and colony formation assays demonstrated impaired migratory and colony-forming abilities of lung cancer cells when treated with silymarin. Molecular docking studies further supported the binding affinity of silymarin with MMP-2 and MMP-9, demonstrate the −10.26 and −6.69 kcal/mol of binding energy. Collectively, our findings highlight the multifaceted anticancer properties of silymarin against lung cancer metastasis, providing insights into its therapeutic potential as an adjuvant treatment strategy.

The research targeting the prevention of complications through natural constituents, instigated by the cancer has recently drawn much more attention over the globe. The research in this direction also revealed that the use of natural constituents would considered a promising strategy for diminishing the aforementioned disease and its consequences. Because of the easy availability and safe nature, the recent years, natural resources as strong anticancer agents. In this regard, here we introduced the possibility of using the methanolic extract of Physalis angulata root as a strong candidate and implemented the applicability of LC-MS to unveil the presence of various phytocompounds. The anticancer potential exhibited by Physalis angulata root followed by its ability to induce toxicity against the microbial population enhanced the interest in unveiling the phytochemical compounds including Absintholide, Curcumin dimer 1, Mytilin A, Ginsenoside F1, Encecalin , Ganoderic acid TQ, Alnustone, Rhamnetin 3-sophoroside, Gibberellin A14 aldehyde, Thiolutin, Euglobal III and Epomusenin B. The presence of various macro and micronutrients suggested that Physalis angulata is a prominent resource for future research targeting pharmacological research, especially anticancer research.

The power system incorporates renewable energy resources into the main utility grid, which possesses low or no inertia, and these systems generate harmonics due to the utilization of power electronic equipment. The precise and effective assessment of harmonic characteristics is necessary for maintaining power quality in distributed power systems. In this paper, the Marine Predator Algorithm (MPA) that mimics the hunting behavior of predators is exploited for harmonics estimation. The MPA utilizes the concepts of Levy and Brownian motions to replicate the movement of predators as they search for prey. The identification model for parameter estimation of harmonics is presented, and an objective function is developed that minimizes the difference between the real and predicted harmonic signals. The efficacy of the MPA is assessed for different levels of noise, population sizes, and iterations. Further, the comparison of the MPA is conducted with a recent metaheuristic of the Reptile Search Algorithm (RSA). The statistical analyses through sufficient autonomous executions established the accurate, stable, reliable and robust behavior of MPA for all variations. The substantial enhancement in estimation accuracy indicates that MPA holds great potential as a strategy for estimating harmonic parameters in distributed power systems.

Computer-aided decision support systems (DSSs) are becoming popular in a variety of professions. Notably, medical DSSs assist healthcare professionals (decision makers) choose the optimal course of action (decisions) while treating patients. Such systems help decision-makers in situations when there is uncertainty in manual decisions due to lack of information or expertise. Choosing a suitable learning algorithm in a DSS is essential and affects its performance. Among machine learning (ML) algorithms, artificial neural networks (ANNs) are considered the most suitable framework for many classification tasks. In healthcare, an ML-based prediction system/DSS employs data (genetic profile or clinical characteristics) and learning algorithms to forecast target values, which may give promising results. However, improving prediction accuracy is a crucial step in making informed decisions. One can apply various preprocessing methods (cross validation, feature selection, bagging, boosting, etc.) to achieve this. For complex classification tasks like cancer, decision-makers can utilize the hybridization of classifiers to increase prediction accuracy. The presented study investigates the possibilities of improvements in the design of hybridized systems for DSSs to assist healthcare professionals in robust decision-making before, during, and after cancer diagnosis. Since the network weights and the activation functions are the two crucial elements in the learning process of an ANN, this study is organized to investigates the improvement in the hybrid system by selecting suitable features from gene expression microarray data and using these features to compute the more realistic initial weights instead of using random guesses as initial weights for ANN. The use of the proposed framework gives promising results (upto 6.67% gain in accuracy when compared to previous study (Table-5) while 10.43% increase in accuracy when compared to conventional ML classifiers (Table-4).

Background Simulation is increasingly being adopted by healthcare educators throughout the developed world as it offers a safe environment to practice skills. While there is literature on learning via simulation in healthcare in the developed world, more studies are required to investigate the factors influencing this approach in the developing world. Objective This scoping review highlights the key factors that act as deterrents as well as encouragement to the uptake of simulation as a teaching methodology in healthcare education in developing countries. Design The MEDLINE (via OVID, using keywords and MeSH in OVID), and PubMed (via NCBI using MeSH), and CINAHL databases were searched between January 2000 and January 2024 for research articles published in peer reviewed English language journals using a combination of keywords. Results A total of 48 articles were included in the final analysis. Challenges and opportunities were divided into professional, academic, and resource-based factors, and their individual sub-themes. The main challenges reported were the lack of a contextual curriculum, content heavy curricula, dearth of trained simulationists and cost of simulators. Performance anxiety was an important challenge reported by both trainers and trainees. Main opportunities were an interest in adopting simulation-based education from both trainers and trainees, and the opportunity to improve patient safety and quality of education. Other findings were that academic leadership and faculty show interest and urgency to adopt simulation in curricula and allocate funds for this. Facilitators need to be provided with protected time to become simulationists. Local manufacturers need to be sourced for simulators, and transfer of technology and expertise needs to be negotiated. Conclusion Simulation needs to be looked at from the lens of not only education, but more importantly of patient safety in developing countries to allow simulation-based education to be mainstreamed in health professions education in low- and middle-income contexts.

Background Exosomes, microvesicles, carry and release several vital molecules across cells, tissues, and organs. Epicardial adipose tissue exosomes are critical in the development and progression of coronary artery disease (CAD). It is hypothesized that exosomes may transport causative molecules from inflamed tissue and deliver to the target tissue and progress CAD. Thus, identifying and inhibiting the CAD-associated proteins that are being transported to other cells via exosomes will help slow the progression of CAD. Methods This study uses a systems biological approach that integrates differential gene expression in the CAD, exosomal cargo assessment, protein network construction, and functional enrichment to identify the crucial exosomal cargo protein target. Meanwhile, absorption, distribution, metabolism, and excretion (ADME) screening of Panax ginseng-derived compounds was conducted and then docked against the protein target to identify potential inhibitors and then subjected to molecular dynamics simulation (MDS) to understand the behavior of the protein-ligand complex till 100 nanoseconds. Finally, density functional theory (DFT) calculation was performed on the ligand with the highest affinity with the target. Results Through the systems biological approach, Mothers against decapentaplegic homolog 2 protein (SMAD2) was determined as a potential target that linked with PI3K-Akt signaling, Ubiquitin mediated proteolysis, and the focal adhesion pathway. Further, screening of 190 Panax ginseng compounds, 27 showed drug-likeness properties. Inermin, a phytochemical showed good docking with −5.02 kcal/mol and achieved stability confirmation with SMAD2 based on MDS when compared to the known CAD drugs. Additionally, DFT analysis of inermin showed high chemical activity that significantly contributes to effective target binding. Overall, our computational study suggests that inermin could act against SMAD2 and may aid in the management of CAD.

Purpose: The clinical study of fulminant hepatic failure is challenging due to its high mortality and relative rarity, necessitating reliance on pre-clinical models to gain insight into its pathophysiology and develop potential therapies. Methods and Results: In our study, the combination of the commonly used solvent dimethyl sulfoxide to the current-day model of lipopolysaccharide/d-galactosamine-caused fulminant hepatic failure was found to cause significantly greater hepatic damage, as indicated by alanine aminotransferase level. The effect was dose-dependent, with the maximum increase in alanine aminotransferase observed following 200 μl/kg dimethyl sulfoxide co-administration. Co-administration of 200 μl/kg dimethyl sulfoxide also remarkably increased histopathological changes induced by lipopolysaccharide/d-galactosamine. Importantly, alanine aminotransferase levels and survival rate in the 200 μl/kg dimethyl sulfoxide co-administration groups were both greater than those in the classical lipopolysaccharide/d-galactosamine model. We found that dimethyl sulfoxide co-administration aggravated lipopolysaccharide/d-galactosamine-caused liver damage by stimulating inflammatory signaling, as indicated by tumor necrosis factor alpha (TNF-α), interferon gamma (IFN-γ), inducible nitric oxide synthase (iNOS), and cyclooxygenase-2 (COX-2) levels. Further, nuclear factor kappa B (NF-kB) and transcription factor activator 1 (STAT1) were upregulated, as was neutrophil recruitment, indicated by myeloperoxidase activity. Hepatocyte apoptosis was also increased, and greater nitro‐oxidative stress was noted, as determined based on nitric oxide, malondialdehyde, and glutathione levels. Conclusion: Co-treatment with low doses of dimethyl sulfoxide enhanced the lipopolysaccharide/d-galactosamine-caused hepatic failure in animals, with higher toxicity and greater survival rates. The current findings also highlight the potential danger of using dimethyl sulfoxide as a solvent in experiments involving the hepatic immune system, suggesting that the new lipopolysaccharide/d-galactosamine/dimethyl sulfoxide model described herein could be used for pharmacological screening with the goal to better understand hepatic failure and evaluate treatment approaches.