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Abstract Background Newer generation ultrathin strut stents are associated with less incidence of target lesion failure (TLF) in patients undergoing percutaneous coronary intervention (PCI) in the short term. However, its long-term effect on different cardiovascular outcomes remains unknown. Objectives We aim to identify the effects of newer-generation ultrathin-strut stents vs. standard thickness second-generation drug-eluting stents (DES) on long-term outcomes of revascularization in coronary artery disease. Methods We searched PubMed, Web of Science, Cochrane Library databases, and Scopus for randomized controlled trials (RCTs) and registries that compare newer-generation ultrathin-strut ( 70 mm) DES to evaluate cardioprotective effects over a period of up to 5 years. Primary outcome was TLF, a composite of cardiac death, target vessel myocardial infarction (TVMI) or target lesion revascularization (TLR). Secondary outcomes included the components of TLF, stent thrombosis (ST), and all-cause death were pooled as the standardized mean difference between the two groups from baseline to endpoint. Results We included 19 RCTs and two prospective registries (103,101 patients) in this analysis. The overall effect on the primary outcome was in favor of second-generation ultrathin struts stents in terms of TLF at ≥ 1 year, ≥ 2 years, and ≥ 3 years (P value = 0.01, 95% CI [0.75, 0.96]), P value = 0.003, 95% CI [0.77, 0.95]), P value = 0.007, 95% CI [0.76, 0.96]), respectively. However, there was no reported benefit in terms of TLF when we compared the two groups at ≥ 5 years (P value = 0.21), 95% CI [0.85, 1.04]). Some of the reported components of the primary and secondary outcomes, such as TLR, target vessel revascularization (TVR), and TVMI, showed the same pattern as the TLF outcome. Conclusion Ultrathin-strut DES showed a beneficial effect over thicker strut stents for up to 3 years. However, at the 5-year follow-up, the ultrathin strut did not differ in terms of TLF, TLR, TVR, and TVMI compared with standard-thickness DES, with similar risks of patient-oriented composite endpoint (POCE), MI, ST, cardiac death, and all-cause mortality.

Abstract Naringenin (NAR) has various biological activities but low bioavailability. The current study examines the effect of Naringenin-loaded hybridized nanoparticles (NAR-HNPs) and NAR on depression induced by streptozotocin (STZ) in rats. NAR-HNPs formula with the highest in vitro NAR released profile, lowest polydispersity index value (0.21 ± 0.02), highest entrapment efficiency (98.7 ± 2.01%), as well as an acceptable particle size and zeta potential of 415.2 ± 9.54 nm and 52.8 ± 1.04 mV, respectively, was considered the optimum formulation. It was characterized by differential scanning calorimetry, examined using a transmission electron microscope, and a stability study was conducted at different temperatures to monitor its stability efficiency showing that NAR-HNP formulation maintains stability at 4 °C. The selected formulation was subjected to an acute toxicological test, a pharmacokinetic analysis, and a Diabetes mellitus (DM) experimental model. STZ (50 mg/kg) given as a single i.p. rendered rats diabetic. Diabetic rat groups were allocated into 4 groups: one group received no treatment, while the remaining three received oral doses of unloaded HNPs, NAR (50 mg/kg), NAR-HNPs (50 mg/kg) and NAR (50 mg/kg) + peroxisome proliferator-activated receptor-γ (PPAR-γ) antagonist, GW9662 (1mg/kg, i.p.) for three weeks. Additional four non-diabetic rat groups received: distilled water (normal), free NAR, and NAR-HNPs, respectively for three weeks. NAR and NAR-HNPs reduced immobility time in forced swimming test and serum blood glucose while increasing serum insulin level. They also reduced cortical and hippocampal 5-hydroxyindoeacetic acid, 3,4-Dihydroxy-phenylacetic acid, malondialdehyde, NLR family pyrin domain containing-3 (NLRP3) and interleukin-1beta content while raised serotonin, nor-epinephrine, dopamine and glutathione level. PPAR-γ gene expression was elevated too. So, NAR and NAR-HNPs reduced DM-induced depression by influencing brain neurotransmitters and exhibiting anti-oxidant and anti-inflammatory effects through the activation PPAR-γ/ NLRP3 pathway. NAR-HNPs showed the best pharmacokinetic and therapeutic results.

Abstract Centrifugal water pumps account for more than 20% of energy use all over the world. Even a little increase in centrifugal pump operating efficiency may result in billions of dollars in savings each year and help reduce carbon dioxide emissions. This study creates a design tool that integrates one-dimensional equations and computational fluid dynamics (CFD) simulations to design centrifugal pump impellers. This examines different combinations of design factors by utilizing both the powerful numerical tools that allow the modeling of numerous scenarios and the large number of empirical equations and coefficients that represent the significant knowledge accumulated in this field. The design tool runs through a series of equations to select the beginning candidates for the different impeller design variables. After that, a design space is established by generating upper and lower bounds for each design variable. The CFD tool executes numerous scenarios inside the design space in order to find the best candidates with the maximum operating efficiency and meet the design objectives. A low specific speed centrifugal pump with a semi-open impeller is used as a case study. The design tool is successful in designing it, fulfilling the design requirement, and increasing operational efficiency when compared to a simple single-arc comparable impeller.

Abstract Horizontal drilling and multistage hydraulic fracturing have seen widespread application in shale formations during the past decade, leading to significant economic productivity gains through the creation of extensive fracture surfaces. The determination of the ideal cluster spacing in shale gas wells is contingent upon the unique geological and formation characteristics. Generally, reducing the spacing between clusters has the potential to augment gas recovery, albeit at the expense of higher drilling and completion costs, as well as the influence of stress shadows on fracture propagation. This study introduces an integrated methodology designed to explore the impact of cluster interference on well performance. Commencing with a fracture propagation model accommodating stress shadow effects for an equivalent slurry volume injection, analytical rate transient analysis (RTA) was amalgamated with reservoir numerical simulation to compute the effective fracture surface area (Aca.) for hydrocarbon production. The correlation between the effective fracture surface area determined by RTA and the actual stimulated fracture area (Aca.) derived from numerical simulations was established in relation to cluster spacing. The findings of this research reveal that wells featuring a greater number of stages and tighter cluster spacing tend to exhibit elevated cluster interference, resulting in a lower effective-to-actual fracture surface area ratio and heightened stress shadow effects impeding fracture propagation. A cluster spacing of 33 feet with six clusters per stage emerges as the optimal choice at formation permeability of 0.00005 md that decreased to 18 ft at formation permeability of 0.00001 md. ACe either stabilizes or decreases above the optimal value, suggesting that more clusters would not have a major impact on increasing the effective stimulated area. Allowing 20% interference, regardless of the permeability of the formation, maximized cumulative production while preventing thief zones and excessive cluster interference. The insights gained from this study will serve as a valuable resource for completion and reservoir engineers, enabling them to fine-tune cluster spacing to maximize well revenue in the dynamic landscape of shale gas extraction.

Abstract The present study provides an evaluation for the wound healing activity of the ethanolic extract of Thespesia populnea L. bark (EBE) and its successive fractions in two doses level (1&2%), designed for determining the most bioactive fraction and the suitable dose. Furthermore, development of the most convenient formulation for these bioactive fractions through either their direct incorporation into hydrogel formulations or incorporation of chitosan-loaded nanoparticles with these bioactive fractions into hydrogel formulations. The highest excision wound healing activity was observed in petroleum ether (Pet-B) followed by ethyl acetate (Etac-B) fractions at the high dose (2%). The most suitable formulation designed for the Etac-B fraction was found to be the chitosan-loaded nanoparticles incorporated in the hydrogel formulation, while the conventional hydrogel formulation was observed to be the highly acceptable formulation for Pet-B fraction. Further phytochemical studies of the bioactive fractions led to the isolation of many compounds of different chemical classes viz; beta-sitosterol and lupeol acetate isolated from the Pet-B, in addition to cyanidin and delphinidin from the Etac-B. Our results revealed that EBE and its bioactive fractions (Pet-B & Etac-B) could be considered as strong wound healers through their anti-oxidant and anti-inflammatory activities, in addition to stimulating collagen synthesis.

Abstract Reservoir characterization, essential for understanding subsurface heterogeneity, often faces challenges due to scale-dependent variations. This study addresses this issue by utilizing hydraulic flow unit (HFU) zonation to group rocks with similar petrophysical and flow characteristics. Flow Zone Indicator (FZI), a crucial measure derived from pore throat size, permeability, and porosity, serves as a key parameter, but its determination is time-consuming and expensive. The objective is to employ supervised and unsupervised machine learning to predict FZI and classify the reservoir into distinct HFUs. Unsupervised learning using K-means clustering and supervised algorithms including Random Forest (RF), Extreme Gradient Boosting (XGB), Support Vector Machines (SVM), and Artificial Neural Networks (ANN) were employed. FZI values from RCAL data formed the basis for model training and testing, then the developed models were used to predict FZI in unsampled locations. A methodical approach involves 3 k-fold cross-validation and hyper-parameter tuning, utilizing the random search cross-validation technique over 50 iterations was applied to optimize each model. The four applied algorithms indicate high performance with coefficients determination (R2) of 0.89 and 0.91 in training and testing datasets, respectively. RF showed the heist performance with training and testing R2 values of 0.957 and 0.908, respectively. Elbow analysis guided the successful clustering of 212 data points into 10 HFUs using k-means clustering and Gaussian mixture techniques. The high-quality reservoir zone was successfully unlocked using the unsupervised technique. It has been discovered that the areas between 2370–2380 feet and 2463–2466 feet are predicted to be high-quality reservoir potential areas, with average FZI values of 500 and 800, consecutively. The application of machine learning in reservoir characterization is deemed highly valuable, offering rapid, cost-effective, and precise results, revolutionizing decision-making in field development compared to conventional methods.

Abstract This study aimed to investigate the effects of eugenol treatment on reproductive parameters in acrylamide (ACR)-intoxicated rats. The study evaluated alterations in relative testes and epididymides weights, sperm quality, serum hormonal status, seminal plasma amino acids, testicular cell energy and phospholipids content, oxidative and nitrosative stress parameters, adenosine monophosphate-activated protein kinase/ phosphoinositide 3-kinase/phosphor-protein kinase B/mammalian target of rapamycin (AMPK/PI3K/p-AKT/mTOR) signaling pathway, blood–testis barrier (BTB) remodeling markers, testicular autophagy and apoptotic markers, as well as histopathological alterations in testicular tissues. The results revealed that eugenol treatment demonstrated a significant improvement in sperm quality parameters, with increased sperm cell concentration, progressive motility live sperm, and a reduction in abnormal sperm, compared to the ACR-intoxicated group. Furthermore, eugenol administration increased the levels of seminal plasma amino acids in a dose-dependent manner. In addition, eugenol treatment dose-dependently improved testicular oxidative/nitrosative stress biomarkers by increasing oxidized and reduced glutathione levels and reducing malondialdehyde and nitric oxide contents as compared to ACRgroup. However, eugenol treatment at a high dose restored the expression of AMPK, PI3K, and mTOR genes, to levels comparable to the control group, while significantly increasing p-AKT content compared to the ACRgroup. In conclusion, the obtained findings suggest the potential of eugenol as a therapeutic agent in mitigating ACR-induced detrimental effects on the male reproductive system via amelioration of ROS-mediated autophagy, apoptosis, AMPK/p-AKT/mTOR signaling pathways and BTB remodeling.

Obesity stands as a pervasive and significant global health issue, with a lack of definitive and curative therapeutic solutions currently available. Here we studied the synergetic ameliorating effect of coupled administration of GABA agonist; baclofen with histidine against the high fat (HFD) induced lipotoxicity in experimentally obese rats. Animals were divided into six groups. The control group (group 1) was fed a basal diet. Obesity was induced in groups 2 to 6 by HFD for 60 days and concomitantly treated with distilled water or Fluoxetine or Baclofen or L-Histidine or a half-dose combination of Bac plus His, respectively. HFD leads to aberrations in neuronal function, affecting neurotransmitters such as monoamines, turnover rates, and levels of excitatory and inhibitory amino acids when compared to control (P < 0.05). Additionally, it induces notable cellular oxidative stress and energy homeostasis distortion. Simultaneous dysregulation of genes associated with glucose sensing and availability was observed, characterized by a notable decrease in the expression of the glycogen phosphorylase gene and a marked increase in the expression of glucokinase and proglucagon genes (P < 0.05). Both Bac and His single dosing rectified the HDF-induced alterations in 5HT, DA and NE neurotransmitters with their metabolites 5HIAA, DOPAC and HVA, together with Nrf1 and NF-KB1 inflammatory markers and partially quenched the HDF-induced increase in the level of MDA and 8OHdG oxidative stress byproducts, and restored the activity of GSH and SOD antioxidant enzymes. Remarkably, the combined administration of Bac and His at half-dose exhibited significant behavioral improvement, fully normalizing neurochemical and cellular energy homeostasis, and restoring cellular redox and inflammatory parameters to control values following HFD-induced cellular insult. In conclusion, the combined half-dosing of Bac plus His treatment exhibited substantial improvements in behavior and fully normalized neurochemical and cellular parameters, offering a promising approach for addressing obesity-related complexities.

Anorexia is a neuronal metabolic disorders induced by factors such as malnutrition and brain metabolic abnormalities during crucial developmental stages. Anorexia nervosa is a complex condition that often requires a multidisciplinary approach, involving medical, nutritional, and psychological interventions. The available medications for specific symptoms for increasing feed intake, not applicable for children and pregnant and cancer patients such as Olanzapine, and Cyproheptadine. This study looked at how Artemisia absinthium (ART) affects brain metabolic syndrome brought on zinc deficiency diet (ZDD). The investigation examined the impact of a ZDD on a rat anorexia model. The second goal is to contrast how antihistamines and Artemisia absinthe extract affect the brain functions connected to appetite. For the studies, 40 mature male rats were divided into four groups. Four groups of forty rats each were used in the experiment. Each group consists of ten rats. The following groups are included in the study: the 1st group control group, the 2nd group treated with ZDD, the 3rd group treated with ZDD concurrent with Cyproheptadine (CH), the 4th group treated with ZDD concurrent with ART. CH and ART were taken orally for 30 days. Tests on biochemical markers were conducted after the examination. The findings showed that ZDD led to reduced levels of neurotransmitters in the brain. Lower concentrations of antioxidants, monoamine, and neuropeptide Y were also found. On the other hand, groups treated with CH and ART improved in comparison to ZDD and nearly recovered in comparison to the control group. Histopathological analysis was used to confirm biochemical data. Obtained data concluded that CH and ART reduce the effects of anorexia and oxidative stress, which function as neuromodulators and promote the release of monoamines.

In the current study, the effects of enhancing semen quality, brain neurotransmitters, and antioxidant status in rabbits under heat stress with acacia gum, vitamin C, and lycopene as dietary supplements were estimated in vitro and in silico. A total of 40 males from New Zealand White rabbits aged 3 months were equally divided into four groups (n = 10). The first group was a control. The other three groups were the heat stress group (oral acacia gum, 100 mg/kg body weight), the ascorbic acid group (30 mg/kg body weight), and the lycopene group (50 mg/kg body weight). All semen showed physical characteristics in terms of increasing sperm motility, motility index, sperm normality, and live sperm as compared to the control group. On the other hand, sperm abnormalities, and dead sperm significantly (p

Abstract The uniaxial compressive strength (UCS) and tensile strength (T0) are crucial parameters in field development and excavation projects. Traditional lab-based methods for directly measuring these properties face practical challenges. Therefore, non-destructive techniques like machine learning have gained traction as innovative tools for predicting these parameters. This study leverages machine learning methods, specifically random forest (RF) and decision tree (DT), to forecast UCS and T0 using real well-logging data sourced from a Middle East reservoir. The dataset comprises 2600 data points for model development and over 600 points for validation. Sensitivity analysis identified gamma-ray, compressional time (DTC), and bulk density (ROHB) as key factors influencing the prediction. Model accuracy was assessed using the correlation coefficient (R) and the absolute average percentage error (AAPE) against actual parameter profiles. For UCS prediction, both RF and DT achieved R values of 0.97, with AAPE values at 0.65% for RF and 0.78% for DT. In T0 prediction, RF yielded R values of 0.99, outperforming DT's 0.93, while AAPE stood at 0.28% for RF and 1.4% for DT. These outcomes underscore the effectiveness of both models in predicting strength parameters from well-logging data, with RF demonstrating superior performance. These models offer the industry an economical and rapid tool for accurately and reliably estimating strength parameters from well-logging data.