Real-Time Prediction of Petrophysical Properties Using Machine Learning Based on Drilling Parameters.

The prediction of rock porosity and permeability is crucial for assessing reservoir productivity and economic feasibility. However, traditional methods for obtaining these properties are time-consuming and expensive, making them impractical for comprehensive reservoir evaluation. This study introduces a novel approach to efficiently predict rock porosity and permeability for reservoir assessment by leveraging real-time machine learning models. Utilizing readily available drilling parameters, this approach offers a cost-effective alternative to traditional time-consuming methods to predict formation petrophysical parameters in real-time. The data set used in this study was collected from two vertical wells located in the Middle East. It encompasses drilling parameters such as the rate of penetration (ROP), gallons per minute (GPM), revolutions per minute (RPM), strokes per minute (SPP), torque, and weight on bit (WOB), along with the corresponding measurements of porosity (ϕ) and permeability (k) obtained through core analysis. Three machine learning models, namely, decision trees (DTs), random forest (RFs), and support vector machines (SVMs), were employed and evaluated for their effectiveness in predicting porosity and permeability. The results demonstrate promising performance across the different data sets. All three models achieved correlation coefficients (R) higher than 0.91 in predicting porosity. The RF model exhibited accurate predictions of permeability, achieving R values surpassing 0.92 in the various data sets. While the DT model displayed slightly lower performance, with the R-value decreasing to 0.88 in the testing data set, the SVM model suffered from overfitting, with R values dropping to 0.83 in the testing data set. The novelty of this work lies in the successful application of machine learning models to the real-time prediction of reservoir properties, providing a practical and efficient solution for the oil and gas industry. By achieving correlation coefficients exceeding 0.91 and showcasing the models' efficacy in a dynamic testing data set, this study paves the way for improved decision-making processes and enhanced exploration and production activities. The innovative aspect lies in the utilization of drilling parameters for timely and cost-effective estimation, transforming conventional reservoir evaluation methods.

Phosphorus-doped T-graphene nanocapsule toward O3 and SO2 gas sensing: a DFT and QTAIM analysis.

Tetragonal graphene nano-capsule (TGC), a novel stable carbon allotrope of sp2 hybridization is designed and doped with phosphorus (P) to study the O3 and SO2 gas sensitivity via density functional theory calculation. Real frequencies verified the natural existence of both TGC and P-doped TGC (PTGC). Both TGC and PTGC suffer structural deformations due to interaction with O3 and SO2 gases. The amount of charge transfer from the adsorbent to the gas molecule is significantly greater for O3 adsorption than SO2 adsorption. The adsorption energies for TGC + O3 and PTGC + O3 complexes are - 3.46 and - 4.34 eV respectively, whereas for TGC + SO2 and PTGC + SO2 complexes the value decreased to - 0.29 and - 0.30 eV respectively. The dissociation of O3 is observed via interaction with PTGC. A significant variation in electronic energy gap and conductivity results from gas adsorption which can provide efficient electrical responses via gas adsorption. The blue/red shift in the optical response proved to be a way of detecting the types of adsorbed gases. The adsorption of O3 is exothermic and spontaneous whereas the adsorption of SO2 is endothermic and non-spontaneous. The negative change in entropy verifies the thermodynamic stability of all the complexes. QTAIM analysis reveals strong covalent or partial covalent interactions between absorbent and adsorbate. The significant variation in electrical and optical response with optimal adsorbent-gas interaction strength makes both TGC and PTGC promising candidates for O3 and SO2 sensing.

A first principles study of RbSnCl3 perovskite toward NH3, SO2, and NO gas sensing.

The sensitivity of a RbSnCl3 perovskite 2D layer toward NH3, SO2, and NO toxic gases has been studied via DFT analysis. The tri-atomic layer of RbSnCl3 possessed a tetragonal symmetry with a band gap of 1.433 eV. The adsorption energies of RbSnCl3 for NH3, SO2 and NO are -0.09, -0.43, and -0.56 eV respectively with a recovery time ranging from 3.4 × 10-8 to 3.5 ms. RbSnCl3 is highly sensitive toward SO2 and NO compared to NH3. The adsorption of SO2 and NO results in a significant structural deformation and a semiconductor-to-metal transition of RbSnCl3 perovskite. A high absorption coefficient (>103 cm-1), excessive optical conductivity (>1014 s-1), and a very low reflectivity (<3%) make RbSnCl3 a potential candidate for numerous optoelectronic applications. A significant shift in optical responses is observed through SO2 and NO adsorption, which can enable identification of the adsorbed gases. The studied characteristics signify that RbSnCl3 can be a potential candidate for SO2 and NO detection.

The study of wound healing activity of Thespesia populnea L. bark, an approach for accelerating healing through nanoparticles and isolation of main active constituents.

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.

Utilizing machine learning for flow zone indicators prediction and hydraulic flow unit classification.

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.

Impact of Eco-Friendly Drilling Additives on Foaming Properties for Sustainable Underbalanced Foam Drilling Applications.

Underbalanced foam drilling stands out as a drilling technique acclaimed for its capacity to enhance safety and efficiency in operations. Utilizing foams as drilling fluids offers several benefits over traditional methods, including lower density, diminished formation damage, and augmented borehole stability. However, the persistent challenge of sustaining foam stability in demanding conditions, particularly amid elevated water salinity and alkaline environments, remains a critical issue. Current literature lacks comprehensive insights into foam stability under such specific circumstances, raising concerns about the practicality of numerous reported foaming agents in field applications. This study aims to fill this knowledge void to align with industry standards. With a heightened focus on sustainability due to mounting environmental considerations, the research explores the use of an eco-friendly surfactant, ammonium alcohol ether sulfate (AAES). Additionally, the investigation delves into the impact of environmentally friendly drilling additives-polyanionic cellulose (PAC), carboxymethyl cellulose (CMC), and starch-on the stability of bulk foam under mildly alkaline conditions. Employing a dynamic foam analyzer, diverse foam properties of AAES foams were assessed, encompassing stability, foamability, and bubble structure. The results demonstrated that the optimal concentrations of the tested additives, in the order of PAC > CMC > starch, significantly prolonged the half-life of the AAES foam bubbles. The introduction of PAC and CMC additives elevated the viscosity of AAES foaming solutions, enhancing the liquid retention within the foam structure. In contrast, starch addition exerted no influence on the solution viscosity and did not impede liquid drainage, although it did reduce bubble coalescence. Furthermore, the PAC- and CMC-based AAES foams manifested as considerably wetter foams with a rounded bubble structure, while the starch-based AAES foam exhibited a dry foam characterized by a distinct polyhedral bubble structure. These findings offer valuable insights into the potential application of the AAES surfactant in foam drilling, showcasing its efficacy in improving foam stability and contributing to the evolution of eco-friendly drilling practices.

Impact of Pressure and Temperature on Foam Behavior for Enhanced Underbalanced Drilling Operations.

Foam, a versatile underbalanced drilling fluid, shows potential for improving the drilling efficiency and reducing formation damage. However, the existing literature lacks insight into foam behavior under high-pH drilling conditions. This study introduces a novel approach using synthesized seawater, replacing the conventional use of freshwater on-site for the foaming system's liquid base. This approach is in line with sustainability objectives and offers novel perspectives on foam stability under high-pH conditions. Experiments, conducted with a high-pressure, high-temperature (HPHT) foam analyzer, investigate how pressure and temperature affect foam properties. The biodegradable foaming agent ammonium alcohol ether sulfate (AAES) is employed. Results demonstrate that the pressure significantly impacts foam stability. Increasing pressure enhances stability, reducing decay rates and promoting uniform bubble sizes, especially at lower temperatures. This highlights foam's capacity to withstand high-pressure conditions. Conversely, the temperature plays a substantial role in foam decay, particularly at elevated temperatures (75 and 90 °C). Decreased liquid viscosity accelerates the liquid drainage and foam decay. While pressure mainly influences the AAES foam stability at temperatures up to 50 °C, temperature becomes the dominant factor at higher temperatures. Temperature's impact on foamability is minimal under constant pressure, maintaining consistent gas volume for maximum foam height. However, foam stability is sensitive to temperature variations, with increasing temperature leading to a more significant bubble size increase gradient. These findings stress the importance of considering temperature effects in foam drilling, particularly in deep and high-temperature environments. AAES foam exhibits stability at lower temperatures, making it suitable for surface and intermediate drilling. Understanding temperature-induced changes in foam structure and bubble size is essential for optimizing performance in high-temperature and deep drilling scenarios.

Eugenol alleviates acrylamide-induced rat testicular toxicity by modulating AMPK/p-AKT/mTOR signaling pathway and blood-testis barrier remodeling.

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.

A-Site Cation Replacement of Hydrazinium Lead Iodide Perovskites by Borane Ammonium Ions: A DFT Calculation.

Organometallic perovskites have become one of the most common multifunctional materials in optoelectronic research fields. This research studies density functional theory calculation on orthorhombic hydrazinium lead iodide (N2 H5 PbI3 ) perovskite by replacing A-site cation with a borane ammonium (BH2 NH3 + ) ion. The perovskite showed a significant structural deformation and an orthorhombic to triclinic phase transition due to A-site ion replacement. The N2 H5 PbI3 perovskite has a band gap of 1.64 eV, suitable for the solar cell absorber layer. The band gap has increased to 2.12 eV after complete A-site ion replacement. All structures showed a high absorption coefficient over 104  cm-1 in the low wavelength region and an increase in refractive index from 2.5 to 2.75 due to ion replacement. All the structures showed high optical conductivity of 1015  s-1 order in the blue wavelength region. These new perovskite structures hold the potential to provide a revolution in optoelectronic research.

Exploring antiproliferative activities and kinase profile of ortho-substituted N-(4-(2-(benzylamino)-2-oxoethyl)phenyl)benzamides.

Designing kinase inhibitors that bind to the substrate site of oncogenic kinases in a promising, albeit less explored, approach to kinase inhibition as it was sought to avoid the issue of untoward off-target modulations. Our previously identified compound KAC-12 with a meta-chlorophenyl substitution was an example of this approach. While it showed confirmed inhibitory activity against cancer cells, this substitution shifted the profile of affected targets away from Src/tubulin which were seen with the parent KX-01. In this paper, we synthesized compounds with ortho-substitutions, and we investigated the effect of such substitutions on their cellular and subcellular activities. The compound N-(4-(2-(benzylamino)-2-oxoethyl)phenyl)-2-(morpholine-4-carbonyl)benzamide (4) exhibited substantial activities against cell lines such HCT116 (IC50 of 0.97 µM) and IC50 HL60 (2.84 µM). Kinase profiling showed that compound 4 trended consistently with KAC-12 as it did not affect Src, but it had more impact on members of the Src family of kinases (SFK) such as Yes, Hck, Fyn, Lck, and Lyn. Both compounds exhibited profound downregulation effects on Erk1/2 but differed on others such as GSK3α/ß and C-Jun. Collectively, this study further support to the hypothesis that small structural changes might bring higher changes in their kinome profile.

Neurological study on the effect of CeNPs and/or La Cl3 on adult male albino rats.

Lanthanides are a group of 15 elements (8 heavy and 7 light) grouped for their proximity in the chemical and physical properties. Recently, this group of elements has received great attention because of their importance, and their entrance into many industrial technologies making the probability of the living organisms' exposure to it increase. The present study aims to study ability of cerium nanoparticles (CeNPs) or lanthanum (LaCl3) to cross the blood brain barrier also, investigate their neuro effect separately or together on some parameters in six brain areas (cortex, cerebellum, hippocampus, striatum, midbrain, and hypothalamus) of the adult male albino rats. The results showed the ability of both elements to distribute and accumulate in the different brain areas. Also, the results of CeNPs or LaCl3 treatment were in the same line where each element caused a significant decrease in norepinephrine (NE), dopamine (DA), serotonin (5-HT) and GABA accompanied with a significant increase in 5- hydroxyl indoleacetic acid (5-HIAA) glucose level. On the other hand, GSH and MDA showed a significant decrease after CeNPs treatment while, with LaCl3 treatment, MDA showed a significant increase in the different brain areas after 3 weeks of treatment. The coadministration of CeNPs and La Cl3 caused an ameliorating effect in all the tested parameters. In conclusion, from the previous studies the effects of lanthanides in the present study may be in part due to its effect on the release or turnover of neurotransmitters and insulin secretion. Finally, the ameliorative effect of CeNPs may be regarded as its high activity to scavenge the free radicals.

Applications of Different Classification Machine Learning Techniques to Predict Formation Tops and Lithology While Drilling.

Accurate prediction of formation tops and lithology plays a critical role in optimizing drilling processes, cost reduction, and risk mitigation in hydrocarbon operations. Although several techniques like well logging, core sampling, cuttings analysis, seismic surveys, and mud logging are available for identifying formation tops, they have limitations such as high costs, lower accuracy, manpower-intensive processes, and time or depth lags that impede real-time estimation. Consequently, this study aims to leverage machine learning models based on easily accessible drilling parameters to predict formation tops and lithologies, overcoming the limitations associated with traditional methods. Data from two wells (A and B) in the Middle East, encompassing drilling mechanical parameters such as rate of penetration (ROP), drill string rotation (DSR), pumping rate (Q), standpipe pressure (SPP), weight on bit (WOB), and torque, were collected for real-field analysis. Machine learning models including Gaussian naive Bayes (GNB), logistic regression (LR), and linear discriminant analysis (LDA) were trained and tested on the data set from well A, while the data set from well B was utilized for model validation as unseen data. The formations of wells A and B consist of four lithologies, namely, sandstone, anhydrite, carbonate/shale, and carbonates, necessitating the development of multiclass classification models. The drilling parameters, specifically the WOB and ROP, exhibited a strong influence on lithology identification. Among the models, GNB demonstrated exceptional performance in predicting formation lithology from the drilling parameters, achieving accuracy and nearly perfect precision, recall, and F1 score for the different classes. LDA and LR models accurately predicted sandstone and carbonate lithologies, although some misclassifications occurred in approximately 5% of points for anhydrite and around 20% in carbonate/shale formations. During validation, the models demonstrated accuracies of around 0.96, 0.95, and 0.92 for the GNB, LR, and LDA, respectively. The study highlights the efficacy of the developed machine learning models in accurately predicting the formation lithology and tops in real time. This is achieved by utilizing readily available drilling parameters, making the approach highly accurate and cost effective by leveraging existing real-time drilling data.

Fluorescence switch based on NIR-emitting carbon dots revealing high selectivity in the rapid response and bioimaging of oxytetracycline.

The abuse of antibiotics has led to serious environmental pollution and the emergence of drug-resistant bacteria surpassing the replacement rate of antibiotics. Herein, near-infrared fluorescent carbon dots (NIR-CDs) were developed to meet the requirements for oxytetracycline (OTC) detection in food and water samples (milk, honey, and lake water) with a detection limit of 0.112 µM. These NIR-CDs, possessing excellent water-solubility, deep tissue penetration ability, and tunable optical properties, exhibit maximum emission at 790 nm (NIR-I window). Unlike traditional CDs, this novel NIR-CDs nanoprobe provides a dual response in the presence of OTC (quenching and bathochromic shifting), without obvious interference from other existing biomolecules and metal ions. Additionally, these NIR-CDs exhibit excellent photostability and multi-resistance under UV irradiation, exceptional pH stability (pH 6-12), reliable long-time exposure, and durability in ionic (NaCl) environments. Moreover, NIR-CDs and NIR-CDs@OTC are nontoxic and were successfully utilized for cell-imaging applications in normal (NIH3T3) and cancer cells (HeLa).

Fabrication of a three-dimensional bone marrow niche-like acute myeloid Leukemia disease model by an automated and controlled process using a robotic multicellular bioprinting system.

BACKGROUND: Acute myeloid leukemia (AML) is a hematological malignancy that remains a therapeutic challenge due to the high incidence of disease relapse. To better understand resistance mechanisms and identify novel therapies, robust preclinical models mimicking the bone marrow (BM) microenvironment are needed. This study aimed to achieve an automated fabrication process of a three-dimensional (3D) AML disease model that recapitulates the 3D spatial structure of the BM microenvironment and applies to drug screening and investigational studies. METHODS: To build this model, we investigated a unique class of tetramer peptides with an innate ability to self-assemble into stable hydrogel. An automated robotic bioprinting process was established to fabricate a 3D BM (niche-like) multicellular AML disease model comprised of leukemia cells and the BM's stromal and endothelial cellular fractions. In addition, monoculture and dual-culture models were also fabricated. Leukemia cell compatibility, functionalities (in vitro and in vivo), and drug assessment studies using our model were performed. In addition, RNAseq and gene expression analysis using TaqMan arrays were also performed on 3D cultured stromal cells and primary leukemia cells. RESULTS: The selected peptide hydrogel formed a highly porous network of nanofibers with mechanical properties similar to the BM extracellular matrix. The robotic bioprinter and the novel quadruple coaxial nozzle enabled the automated fabrication of a 3D BM niche-like AML disease model with controlled deposition of multiple cell types into the model. This model supported the viability and growth of primary leukemic, endothelial, and stromal cells and recapitulated cell-cell and cell-ECM interactions. In addition, AML cells in our model possessed quiescent characteristics with improved chemoresistance attributes, resembling more the native conditions as indicated by our in vivo results. Moreover, the whole transcriptome data demonstrated the effect of 3D culture on enhancing BM niche cell characteristics. We identified molecular pathways upregulated in AML cells in our 3D model that might contribute to AML drug resistance and disease relapse. CONCLUSIONS: Our results demonstrate the importance of developing 3D biomimicry models that closely recapitulate the in vivo conditions to gain deeper insights into drug resistance mechanisms and novel therapy development. These models can also improve personalized medicine by testing patient-specific treatments.

A first-principles investigation of Cr adsorption on C8 and B4N4 nanocages in aqueous mediums.

Heavy metal removal from polluted environments is one of the vital research areas for better and healthier living. In this research, C8 and B4N4 nanocage-like quantum dots are investigated for heavy metal (Cr) removal applications via density functional theory calculations. The adsorption of up to two Cr atoms has been studied in both air and a water medium. The adsorption of Cr atoms results in significant structural deformation of the adsorbents with a high adsorption energy of -8.74 and -5.77 eV for C8 and B4N4 nanostructures, respectively, which is further increased with an increasing number of Cr atoms. All adsorbents and complex structures showed real vibrational frequencies. Mulliken charge and electrostatic potential analysis reveal a significant charge transfer between adsorbate-adsorbent. The adsorption process causes a decrease in the energy gap of the adsorbents. All the reactions in this study were spontaneous and thermodynamically ordered. QTAIM analysis verifies that the interactions of the adsorbents with Cr atoms are strong partial covalent. The study's findings make C8 and B4N4 nanostructures potential candidates for Cr-detection and removal applications.

A DFT and QTAIM insight into ethylene oxide adsorption on the surfaces of pure and metal-decorated inorganic fullerene-like nanoclusters.

In this industrial era, the use of low-dimensional nanomaterials as gas sensors for environmental monitoring has received enormous interest. To develop an effective sensing method for ethylene oxide (EO), DFT computations are conducted using method ωB97X-D and B3LYP with 6-31G(d,p) basis set to evaluate the adsorption behavior of ethylene oxide gas on the surfaces of pristine, as well as Scandium and Titanium decorated B12N12, Al12N12, and Al12P12 nanocages. Several properties like structural, physical, and electronic are studied methodically to better understand the sensing behavior. Scandium-decorated aluminum phosphate and boron nitride nanocages were shown to perform better in terms of adsorption properties. The short recovery time observed in this study is beneficial for the repetitive use of the gas sensor. The Natural Bond Orbital and molecular electrostatic potential analysis demonstrated a substantial quantity of charge transfer from adsorbate to adsorbents. The bandgap alternation after adsorption shows an influence of adsorption on electronic properties. The interactions of adsorbate and adsorbents are further studied using the ultraviolet-visible predicted spectrum, and quantum theory of atoms in molecules all of which yielded promising findings.

A DFT study to investigate the physical, electrical, optical properties and thermodynamic functions of boron nanoclusters (MxB2n0; x=1,2, n=3,4,5).

First Principle DFT calculations employing the B3LYP/LanL2DZ/SDD level of theory were used to analyze the various characteristics of boron nanoclusters (B6, B8, and B10). These pure structures were further doped with four transition metals (Ta, Ti, Tc, and V) to examine the enhancement of the pure structures' structural, electrical, and optical features. To study structural stability, we have estimated cohesion energy and imaginary frequencies. Cohesion energies were entirely negative, with a range of -3.37 eV to -8.07 eV, and most constructions had no imaginary frequencies, indicating their structural occurrences. The calculated adsorption energy suggests that the order of stability of the pristine boron nanoclusters is B10>B8>B6, and TcB10 and Tc2B10 are the more stable structures. Mulliken charge, DOS, HOMO-LUMO, and the HOMO-LUMO gap have all been examined in-depth to provide insight into electrical characteristics. UV-Vis and CD measurements show the doped boron nanoclusters have excellent optical properties. Aside from calculating thermodynamic functions, we have also calculated the global DFT parameters, which give us a deep quantum mechanical understanding of the optimized structure for further research and applications in the field of science and technology.

Characterization of human umbilical cord blood-derived mast cells using high-throughput expression profiling and next-generation knowledge discovery platforms.

Mast cells (MCs) are tissue-resident innate immune cells that express the high-affinity receptor for immunoglobulin E and are responsible for host defense and an array of diseases related to immune system. We aimed in this study to characterize the pathways and gene signatures of human cord blood-derived MCs (hCBMCs) in comparison to cells originating from CD34- progenitors using next-generation knowledge discovery methods. CD34+ cells were isolated from human umbilical cord blood using magnetic activated cell sorting and differentiated into MCs with rhIL-6 and rhSCF supplementation for 6-8 weeks. The purity of hCBMCs was analyzed by flow cytometry exhibiting the surface markers CD117+CD34-CD45-CD23-FcεR1αdim. Total RNA from hCBMCs and CD34- cells were isolated and hybridized using microarray. Differentially expressed genes were analyzed using iPathway Guide and Pre-Ranked Gene Set Enrichment Analysis. Next-generation knowledge discovery platforms revealed MC-specific gene signatures and molecular pathways enriched in hCBMCs and pertain the immunological response repertoire.

Hordeum vulgare ethanolic extract mitigates high salt-induced cerebellum damage via attenuation of oxidative stress, neuroinflammation, and neurochemical alterations in hypertensive rats.

High salt intake increases inflammatory and oxidative stress responses and causes an imbalance of neurotransmitters involved in the pathogenesis of hypertension that is related to the onset of cerebral injury. Using natural compounds that target oxidative stress and neuroinflammation pathways remains a promising approach for treating neurological diseases. Barley (Hordeum vulgare L.) seeds are rich in protein, fiber, minerals, and phenolic compounds, that exhibit potent neuroprotective effects in various neurodegenerative diseases. Therefore, this work aimed to investigate the efficacy of barley ethanolic extract against a high salt diet (HSD)-induced cerebellum injury in hypertensive rats. Forty-eight Wistar rats were divided into six groups. Group (I) was the control. The second group, the HSD group, was fed a diet containing 8% NaCl. Groups II and III were fed an HSD and simultaneously treated with either amlodipine (1 mg /kg b.wt p.o) or barley extract (1000 mg /kg b.wt p.o) for five weeks. Groups IV and V were fed HSD for five weeks, then administered with either amlodipine or barley extract for another five weeks. The results revealed that barley treatment significantly reduced blood pressure and effectively reduced oxidative stress and inflammation in rat's cerebellum as indicated by higher GSH and nitric oxide levels and lower malondialdehyde, TNF-α, and IL-1ß levels. Additionally, barley restored the balance of neurotransmitters and improved cellular energy performance in the cerebellum of HSD-fed rats. These findings suggest that barley supplementation exerted protective effects against high salt-induced hypertension by an antioxidant, anti-inflammatory, and vasodilating effects and restoring neurochemical alterations.