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Breast milk is the natural source of nutrition for infants, but while it supports their health, it can also be a potential source of toxic inorganic particulate matter, and this applies to both breast milk and industrially produced milk. The aim of the present study was to evaluate the presence of nanoparticles in both breast milk and formula milk samples. We collected and analyzed, via a new electron scanning microscopic procedure, 19 samples of breast milk from Italian women and 19 formula milk samples produced by different companies. Organic–inorganic agglomerates were detected in 58% of formula and in 63% of breast milk samples, respectively. In addition, a significantly (p < 0.05) greater size of nanoparticles was observed in formula milk samples. The results, showing the presence of inorganic nanosized particles in breast and artificial milk, may lead to future studies aimed at investigating possible nanosized contamination of milk and identifying early prevention strategies for women and animals involved in the food chain.

Two forms of flower-like ZnO nanostructures were synthesized using hydrothermal methods at various growth times/temperatures and zinc precursors. The morphology, structure, chemical composition, and optical properties of these ZnO nanoflowers were studied using a scanning electron microscope (SEM), X-ray diffraction spectroscopy (XRD), X-ray photoelectrons spectroscopy (XPS), Raman spectroscopy, and UV–Vis spectroscopy. The SEM images revealed two forms of flower-like nanostructures, namely lotus- and tulip-like flower ZnO nanostructures. The XPS analysis revealed the oxidation state of the Zn and O elements, as well as the presence of OH groups on the surface of the lotus-like flower ZnO nanostructure. The XRD results revealed less crystallinity of the lotus-like ZnO nanoflowers (NFs) compared with the tulip-like ZnO NFs. The XRD results revealed the presence of Zn (OH)2 in the ZnO NFs. The Raman results confirmed less crystallinity of the lotus-like ZnO NFs. The estimated optical bandgap was 2.92 and 3.0 eV for the tulip- and lotus-like ZnO NFs, respectively. The tulip-like ZnO NFs showed superior photocatalytic degradation of methylene blue dye, verified via UV–Vis radiation, compared with the lotus-like ZnO NFs, which show the impact of the structure defects and OH- impurities on the photocatalytic performance of ZnO nanoflowers.

Background and Objectives: Dovitinib (DVB) is a pan-tyrosine kinase inhibitor (TKI) that can be administered orally. In September 2023, the FDA granted Oncoheroes approval to proceed with an Investigational New Drug (IND) application for dovitinib. This application is intended for the treatment of relapsed or advanced juvenile solid tumors, namely, osteosarcoma. Materials and Methods: The target of the present study was to develop a rapid, green, accurate, and sensitive UHPLC-MS/MS method for measuring DVB levels in human liver microsomes (HLMs). The validations of the HLMs were performed via the established UHPLC-MS/MS approach, as stated in the US FDA reported guidelines for the standards of bioanalytical method validation protocol. The StarDrop in silico software package (version 6.6), which involves the DEREK and WhichP450 in silico modules, was used to check the DVB structure for hazardous alerts and metabolic instability. The DVB and encorafenib (EFB), internal standard, and chromatographic peaks were successfully separated using a reversed phase column (an Eclipse Plus Agilent C8 column) and an isocratic mobile phase. The production of DVB parent ions was accomplished by utilizing the positive ionization mode of an ESI source. The identification and measurement of DVB daughter ions were conducted using the MRM mode. Results: The inter-day accuracy and precision exhibited a spectrum of values in the range of −0.56% to 9.33%, while the intra-day accuracy and precision showcased a range of scores between 0.28% and 7.28%. The DVB calibration curve showed a linear relationship that ranged from 1 to 3000 ng/mL. The usefulness of the currently validated UHPLC-MS/MS method was approved by the lower limit of quantification (LLOQ) of 1 ng/mL. The AGREE findings demonstrate that the UHPLC-MS/MS method had a noteworthy degree of ecological greenness. The in vitro half-life (t1/2) and intrinsic clearance (Clint) of DVB were calculated to be 15.48 min and 52.39 mL/min/kg, respectively, which aligned with the findings from the WhichP450 software (version 6.6). Conclusions: Via the usage of in silico software, it has been observed that making small changes to the structure of the aryl piperazine ring and quinolinone moieties, or replacing these groups in the drug design process, shows potential for enhancing the metabolic safety and stability of newly developed derivatives compared to DVB.

The abundance and diversity of antimicrobial-resistant bacteria (ARB) and antimicrobial resistance genes (ARGs) in agricultural landscapes may be important for the spread of antimicrobial resistance (AMR) in the environment. The aim of this study was to apply screening methods for ARB and ARGs to investigate the impact of farming on the prevalence of AMR in a country with low antibiotic usage. We have analyzed samples (n = 644) from soil and wild terrestrial animals and plants (slugs, snails, mice, shrews, earthworms, and red clover) collected over two years in agricultural fields accompanied by nearby control areas with low human activity. All samples were investigated for the occurrence of 35 different ARGs using high-throughput quantitative PCR (HT-qPCR) on a newly developed DNA array. In addition, samples from the first year (n = 415) were investigated with a culture-based approach combined with whole-genome sequencing (WGS) to identify antimicrobial-resistant E. coli (AREC). ARGs were detected in 59.5% of all samples (2019 + 2020). AREC, which was only investigated in the 2019 samples, was identified in 1.9% of these. Samples collected in the autumn showed more ARGs and AREC than spring samples, and this was more pronounced for organic fields than for conventional fields. Control areas with low human activity showed lower levels of ARGs and a lack of AREC. The use of livestock manure was correlated with a higher level of ARG load than other farming practices. None of the soil samples contained antibiotics, and no association was found between AMR and the levels of metals or pesticides. High qualitative similarity between HT-qPCR and WGS, together with the positive controls to the validation of our 35 ARG assays, show that the microfluid DNA array may be an efficient screening tool on environmental samples. In conclusion, even in a country with a very low consumption of antimicrobials by production animals, our results support the hypothesis of these animals being a source of AREC and ARGs in agricultural environments, primarily through the use of manure.

Salvia guaranitica is considered one of the most significant medicinal and aromatic herbs in terms of nutritional and medical benefits due to its wealth of important active components. Among these compounds, terpenoids are the most prominent and abundant, particularly monoterpenes (C10), sesquiterpenes (C15), and diterpenes (C20). They are biologically advantageous to plants and perform a multitude of functions. The current study aimed to clone the S. guaranitica gene that encodes for geranyllinalool synthases (SgGES, EC: 4.2.3.144), with consideration for these features. The open reading frame of the 867-amino-acid protein encoded by SgGES consists of 2.721 base pairs. In addition, the SgGES protein has five domains that belong to the terpene synthase family, which are related to the terpene and terpenoid synthase domains. We manipulated and overexpressed the SgGES gene in Nicotiana tabacum to explore its function. When compared to the GUS control, the transgenic N. tabacum plants displayed an increase in leaf production and diameter when compared with the wild-type plants. Finally, analysis of transgenic plants using gas chromatography/mass spectrometry (GC-MS) showed that SgGES is responsible for producing various terpene species, especially diterpenes.

BIM use has proved to be inevitable in construction project phases, starting from the project initiation phase and ending with the project closeout phase. Through the use of 4D/5D/6D models in processes of the construction project such as scheduling, bills of quantities (BoQs), and environmental analysis, BIM provides massive assistance to all project participants. In addition, BIM has been utilized in facility management through the use of 7D models. These well-established benefits positively impact construction project variables: cost, time, and quality. Despite these invaluable contributions in construction projects, BIM is not extensively applied in the Gulf Cooperation Council (GCC) of Arab countries, especially in small/medium-scale construction projects. BIM use at these project scales is mainly exclusive to the design phases. The present use of BIM in GCC construction projects leads to the missing of numerous opportunities, which negatively affects project delivery methods, project timelines, and, consequently, project budgets. To shed more light on this subject, this research paper attempts to answer emerging questions, such as the following: How do BIM and other software programs support the technical team of construction projects? Do these software programs have a direct impact on construction project management, in terms of project timelines and work changes? Does BIM add unnecessary cost to the project budget? This research paper proposes four technical task groups used in construction project phases to evaluate the use of BIM and the associated software programs, namely technical work, Time and Accuracy, budget, and BoQ. These four task groups, moreover, have been ratified and are the subject of consensus by project managers and senior engineers in GCC construction projects, who provided the research primary data through interviews and field surveys. Qualitative and quantitative analyses were conducted to investigate and analyze the present status of BIM use in construction projects, and subsequently, to establish a basis to propose improvements to support the technical team of construction projects. Two approaches are proposed to overcome the obstacles that hinder BIM adoption in GCC countries. The first one is a top-down approach, in which BIM use and BIM documents are mandatory in construction projects. The second approach is a bottom-up approach, where having more BIM professionals in the GCC market will facilitate the transformation to BIM.

This study aims to explore the skills of Jordanian Arabic-speaking fourth graders with learning difficulties in terms of auditory analysis and dictation tests. It mainly aims to investigate the relationship between students’ abilities to perform auditory analyses and dictation tests. The sample in the study consists of 110 Jordanian fourth graders, who are then divided into 54 students with learning difficulties who are diagnosed as having a satisfactory level of reading and writing and 56 typically developing students. The students are asked to respond to two tests, the auditory analysis and the phonological awareness test, which are prepared by the researcher himself. The results demonstrate a statistically significant positive correlation between the auditory analysis and dictation skills of fourth-grade students with learning difficulties. This means that improving the auditory analysis skills corresponds to an increase in the dictation skills of these students. The results also reveal a statistically significant correlation between auditory analysis and dictation skills in typically developing students.

The characteristic chemical composition of Nigella seeds is directly linked to their beneficial properties. This study aimed to investigate the phytochemical composition of Nigella sativa seeds using a 100% ethanolic extract using HPLC-ESI-MS/MS. Additionally, it explored the potential biological effects of the extract on female rat reproduction. Follicle Stimulating Hormone (FSH), Luteinizing Hormone (LH), Estrogen (E2), and Progesterone (P4) hormone levels were also assessed, along with the morphological and histological effects of the extract on ovarian, oviductal, and uterine tissues. Molecular docking was performed to understand the extract’s activity and its role in regulating female reproduction by assessing its binding affinity to hormonal receptors. Twenty metabolites, including alkaloids, saponins, terpenes, flavonoids, phenolic acids, and fatty acids, were found in the ethanolic extract of N. sativa seeds through the HPLC-ESI-MS/MS study. The N. sativa seed extract exhibited strong estrogenic and LH-like activities (p < 0.05) with weak FSH-like activity. Furthermore, it increased the serum levels of LH (p < 0.05), P4 hormones (p < 0.001), and E2 (p < 0.0001). Molecular docking results displayed a strong interaction with Erβ, LH, GnRH, and P4 receptors, respectively. Based on these findings, N. sativa seeds demonstrated hormone-like activities, suggesting their potential as a treatment for improving female fertility.

This review is concerned with chronic wounds, with an emphasis on biofilm and its complicated management process. The basics of antimicrobial photodynamic therapy (PDT) and its underlying mechanisms for microbial eradication are presented. Intrinsically active nanocarriers (polydopamine NPs, chitosan NPs, and polymeric micelles) that can further potentiate the antimicrobial photodynamic effect are discussed. This review also delves into the role of photoactive electrospun nanofibers, either in their eluting or non-eluting mode of action, in microbial eradication and accelerating the healing of wounds. Synergic strategies to augment the PDT-mediated effect of photoactive nanofibers are reviewed.

Replication is a fundamental aspect of cancer, and replication is about reproducing all the elements and structures that form a cell. Among them are DNA, RNA, enzymes, and coenzymes. All the DNA is doubled during each S (synthesis) cell cycle phase. This means that six billion nucleic acids must be synthesized in each cycle. Tumor growth, proliferation, and mutations all depend on this synthesis. Cancer cells require a constant supply of nucleotides and other macromolecules. For this reason, they must stimulate de novo nucleotide synthesis to support nucleic acid provision. When deregulated, de novo nucleic acid synthesis is controlled by oncogenes and tumor suppressor genes that enable increased synthesis and cell proliferation. Furthermore, cell duplication must be achieved swiftly (in a few hours) and in the midst of a nutrient-depleted and hypoxic environment. This also means that the enzymes participating in nucleic acid synthesis must work efficiently. pH is a critical factor in enzymatic efficiency and speed. This review will show that the enzymatic machinery working in nucleic acid synthesis requires a pH on the alkaline side in most cases. This coincides with many other pro-tumoral factors, such as the glycolytic phenotype, benefiting from an increased intracellular pH. An increased intracellular pH is a perfect milieu for high de novo nucleic acid production through optimal enzymatic performance.

Acute Lung Injury (ALI) is a life-threatening syndrome that has been identified as a potential complication of COVID-19. There is a critical need to shed light on the underlying mechanistic pathways and explore novel therapeutic strategies. This study aimed to examine the potential therapeutic effects of Citrus clementine essential oil (CCEO) in treating potassium dichromate (PDC)-induced ALI. The chemical profile of CCEO was created through GC–MS analysis. An in vivo study in rats was conducted to evaluate the effect of CCEO administrated via two different delivery systems (oral/inhalation) in mitigating acute lung injury (ALI) induced by intranasal instillation of PDC. Eight volatile compounds were identified, with monoterpene hydrocarbons accounting for 97.03% of the identified constituents, including 88.84% of D-limonene. CCEO at doses of 100 and 200 mg/kg bw exhibited antioxidant and anti-inflammatory properties. These significant antioxidant properties were revealed through the reduction of malondialdehyde (MDA) and the restoration of reduced glutathione (GSH). In addition, inflammation reduction was observed by decreasing levels of cytokines tumor necrosis factor-α and tumor growth factor-β (TNF-α and TGF-β), along with an increase in phosphatidylinositide-3-kinase (PI3K) and Akt overexpression in lung tissue homogenate, in both oral and inhalation routes, compared to the PDC-induced group. These results were supported by histopathological studies and immunohistochemical assessment of TGF-β levels in lung tissues. These findings revealed that CCEO plays an integral role in relieving ALI induced by intranasal PDC and suggests it as a promising remedy.

The rising prevalence of paediatric type 2 diabetes (T2D) is concerning, particularly with limited medical intervention despite evidence of accelerated disease progression. This study of a Barts Health NHS Trust cohort from 2008 to 2022 aims to elucidate the incidence, clinical outcomes, and complications associated with paediatric T2D. A retrospective analysis utilising electronic and paper records identified 40 patients with T2D. The incidence doubled from 2.6/year in 2008–2013 to 5.4/year in 2014–2018. Sixty-eight percent exhibited co-morbidities, notably learning disabilities. At diagnosis, the mean BMI was 32.4 ± 6.71 kg/m2, with no gender-based disparity and no significant change over a two-year follow-up. The initial HbA1c was 75.2 ± 21.0 mmol/mol, decreasing to 55.0 ± 17.4 mmol/mol after three months (p = 0.001) and then rising to 63.0 ± 25.5 mmol/mol at one year (p = 0.07). While 22/37 patients achieved HbA1c < 48 mmol/mol, only 9 maintained this for a year. Several metabolic and cardiovascular complications were observed at diagnosis and follow-up, with no significant change in frequency. In 2022, 15 patients transitioned to adult services. HbA1c at transition was 74.7 ± 27.6 mmol/mol, showing no change one year post-transition (71.9 ± 26.9 mmol/mol, p = 0.34). This study highlights substantial therapeutic failure, with current management falling short in achieving a sustained reduction in BMI or HbA1c. Novel treatment approaches are needed to improve clinical outcomes and address the high burden of co-morbidities and complications.

The main purpose of this work is to investigate the phytochemical composition of Marrubium alysson L. non-polar fraction. GC/MS analysis was used to evaluate the plant extract’s saponifiable and unsaponifiable matter. Although M. alysson L. lipoidal matter saponification produced 30.3% of fatty acid methyl esters and 69.7% of unsaponifiable matter. Phytol was the most dominant substance in the unsaponifiable materials. Notably, marrubiin which is one of the most prominent metabolites of Marrubium alysson L. was not detected through our adopted GC/MS technique. Thus, further characterization was proceeded through simple and rapid HPTLC analysis which successfully managed to identify marrubiin. Based on the regression equation, the concentration of marrubiin in M. alysson L. extract was 14.09 mg/g of dry extract. Concerning acetylcholinesterase inhibitory activity, both the crude M. alysson L. total methanolic extract and the non-polar fraction displayed reasonable inhibitory activity against acetylcholinesterase (AChE), whereas the pure compound marrubiin was considered to be the most effective and potent AChE inhibitor, with an IC50 value of 52.66 (µM). According to the molecular docking studies, potential sites of interaction between the pure chemical marrubiin and AChE were examined. The results show that Tyr124 on AChE residue was critical to the activity of the aforementioned drug. Based on the depicted marrubin AChE inhibition activity and reported safety profile, this chemical metabolite is considered as a promising lead compound for further pre-clinical investigation as well as drug development and optimization.

The COVID-19 epidemic poses a worldwide threat that transcends provincial, philosophical, spiritual, radical, social, and educational borders. By using a connected network, a healthcare system with the Internet of Things (IoT) functionality can effectively monitor COVID-19 cases. IoT helps a COVID-19 patient recognize symptoms and receive better therapy more quickly. A critical component in measuring, evaluating, and diagnosing the risk of infection is artificial intelligence (AI). It can be used to anticipate cases and forecast the alternate incidences number, retrieved instances, and injuries. In the context of COVID-19, IoT technologies are employed in specific patient monitoring and diagnosing processes to reduce COVID-19 exposure to others. This work uses an Indian dataset to create an enhanced convolutional neural network with a gated recurrent unit (CNN-GRU) model for COVID-19 death prediction via IoT. The data were also subjected to data normalization and data imputation. The 4692 cases and eight characteristics in the dataset were utilized in this research. The performance of the CNN-GRU model for COVID-19 death prediction was assessed using five evaluation metrics, including median absolute error (MedAE), mean absolute error (MAE), root mean squared error (RMSE), mean square error (MSE), and coefficient of determination (R2). ANOVA and Wilcoxon signed-rank tests were used to determine the statistical significance of the presented model. The experimental findings showed that the CNN-GRU model outperformed other models regarding COVID-19 death prediction.

The paper focuses on the hepatitis C virus (HCV) infection in Egypt, which has one of the highest rates of HCV in the world. The high prevalence is linked to several factors, including the use of injection drugs, poor sterilization practices in medical facilities, and low public awareness. This paper introduces a hyOPTGB model, which employs an optimized gradient boosting (GB) classifier to predict HCV disease in Egypt. The model’s accuracy is enhanced by optimizing hyperparameters with the OPTUNA framework. Min-Max normalization is used as a preprocessing step for scaling the dataset values and using the forward selection (FS) wrapped method to identify essential features. The dataset used in the study contains 1385 instances and 29 features and is available at the UCI machine learning repository. The authors compare the performance of five machine learning models, including decision tree (DT), support vector machine (SVM), dummy classifier (DC), ridge classifier (RC), and bagging classifier (BC), with the hyOPTGB model. The system’s efficacy is assessed using various metrics, including accuracy, recall, precision, and F1-score. The hyOPTGB model outperformed the other machine learning models, achieving a 95.3% accuracy rate. The authors also compared the hyOPTGB model against other models proposed by authors who used the same dataset.

There is substantial experimental and clinical interest in providing effective ways to both prevent and slow the onset of hearing loss. Auditory hair cells, which occur along the basilar membrane of the cochlea, often lose functionality due to age-related biological alterations, as well as from exposure to high decibel sounds affecting a diminished/damaged auditory sensitivity. Hearing loss is also seen to take place due to neuronal degeneration before or following hair cell destruction/loss. A strategy is necessary to protect hair cells and XIII cranial/auditory nerve cells prior to injury and throughout aging. Within this context, it was proposed that cochlea neural stem cells may be protected from such aging and environmental/noise insults via the ingestion of protective dietary supplements. Of particular importance is that these studies typically display a hormetic-like biphasic dose–response pattern that prevents the occurrence of auditory cell damage induced by various model chemical toxins, such as cisplatin. Likewise, the hormetic dose–response also enhances the occurrence of cochlear neural cell viability, proliferation, and differentiation. These findings are particularly important since they confirmed a strong dose dependency of the significant beneficial effects (which is biphasic), whilst having a low-dose beneficial response, whereas extensive exposures may become ineffective and/or potentially harmful. According to hormesis, phytochemicals including polyphenols exhibit biphasic dose–response effects activating low-dose antioxidant signaling pathways, resulting in the upregulation of vitagenes, a group of genes involved in preserving cellular homeostasis during stressful conditions. Modulation of the vitagene network through polyphenols increases cellular resilience mechanisms, thus impacting neurological disorder pathophysiology. Here, we aimed to explore polyphenols targeting the NF-E2-related factor 2 (Nrf2) pathway to neuroprotective and therapeutic strategies that can potentially reduce oxidative stress and inflammation, thus preventing auditory hair cell and XIII cranial/auditory nerve cell degeneration. Furthermore, we explored techniques to enhance their bioavailability and efficacy.

Acalabrutinib, commercially known as Calquence®, is a pharmacological molecule that has robust inhibitory activity against Bruton tyrosine kinase. The medicine in question was carefully developed by the esteemed pharmaceutical company AstraZeneca. The FDA granted authorization on 21 November 2019 for the utilization of acalabrutinib (ACB) in the treatment of small lymphocytic lymphoma (SLL) or chronic lymphocytic leukemia (CLL) in adult patients. The aim of this study was to develop a UPLC–MS/MS method that is effective, accurate, environmentally sustainable, and has a high degree of sensitivity. The methodology was specifically developed with the intention of quantifying ACB in human liver microsomes (HLMs). The methodology described above was subsequently utilized to assess the metabolic stability of ACB in HLMs in an in vitro environment. The validation procedures for the UPLC–MS/MS method in the HLMs were conducted in accordance with the bioanalytical method validation criteria established by the U.S.- DA. The utilization of the StarDrop software (version 6.6), which integrates the P450 metabolic module and DEREK software (KB 2018 1.1), was employed for the purpose of evaluating the metabolic stability and identifying potential hazardous alarms associated with the chemical structure of ACB. The calibration curve, as established by the ACB, demonstrated a linear correlation across the concentration range of 1 to 3000 ng/mL in the matrix of HLMs. The present study conducted an assessment of the accuracy and precision of the UPLC–MS/MS method in quantifying inter-day and intra-day fluctuations. The inter-day accuracy demonstrated a spectrum of values ranging from −1.00% to 8.36%, whilst the intra-day accuracy presented a range of values spanning from −2.87% to 4.11%. The t1/2 and intrinsic clearance (Clint) of ACB were determined through in vitro testing to be 20.45 min and 39.65 mL/min/kg, respectively. The analysis concluded that the extraction ratio of ACB demonstrated a moderate level, thus supporting the recommended dosage of ACB (100 mg) to be administered twice daily for the therapeutic treatment of persons suffering from B-cell malignancies. Several computational tools have suggested that introducing minor structural alterations to the butynoyl group, particularly the alpha, beta-unsaturated amide moiety, or substituting this group during the drug design procedure, could potentially enhance the metabolic stability and safety properties of novel derivatives in comparison to ACB.

Twenty-seven F4 progenies of individual selections and unselected bulks, either irrigated normally or stressed, were evaluated in early and late summer plantings. The objectives of this study were to elucidate the efficiency of direct and indirect individual selection in some Egyptian cotton segregating populations for reliable performance under harsh environmental conditions. The cotton plants of F4 produced higher lint yields under early sowing with either normal or stressed watering regimes (EN and ES) than those that were planted under late sowings or combined across all environments. Direct selection is better than indirect selection to improve the lint yield and boll weight under normal watering regimes and for the seed index and lint index under stressed watering regimes with either early or late sowing. The maximum relative expected correlated response to direct selection gain was obtained for the L% trait under ES.

To determine the effects of organic selenium (0.0–0.6 mg and 0.9 mg Se/Kg diet) and Zn-Cr mixture (100 mg Zn/Kg diet plus 1.5 mg Cr/Kg diet) on broiler chicken performance, carcass traits, blood hematology, and biochemistry under heat stress conditions, this study was conducted. Under temperatures between 30.21 to 31.82 °C, 240 broiler chickens (Ross-308), which were 7-day-old, were randomly assigned to one of six treatments: T1 (control), T2 (100 mg Zn per kg of diet and 1.5 mg Cr per kg of diet), T3 (0.6 mg Se per kg of diet), T4 (0.9 mg Se per kg of diet), T5 (100 mg Zn, 1.5 mg Cr and (LSe), and T6 (100 mg Zn, 1.5 mg Cr and (HSe)). At 35 days old, the chicks fed a diet containing Zn-Cr with low or high organic selenium (organic-Se) outweighed the control group in terms of live body weight, weight gain, and feed conversion ratio (p < 0.05). In comparison to the control treatment, birds fed diets supplemented with Zn-Cr or organic-Se (LSe, HSe) significantly increased their serum levels of total protein and total antioxidant capacity. However, these additives resulted in a decrease (p < 0.01) in their serum levels of triglycerides, total cholesterol, low-density lipoprotein (LDL) cholesterol, creatinine, and uric acid. Together, it was found that trace elements (Zn-Cr and organic-Se) may greatly lessen the impacts of heat stress on broilers by promoting growth performance and boosting metabolic processes.

Hyperglycemia, as a hallmark of the metabolic malady diabetes mellitus, has been an overwhelming healthcare burden owing to its high rates of comorbidity and mortality, as well as prospective complications affecting different body organs. Available therapeutic agents, with α-glucosidase inhibitors as one of their cornerstone arsenal, control stages of broad glycemia while showing definitive characteristics related to their low clinical efficiency and off-target complications. This has propelled the academia and industrial section into discovering novel and safer candidates. Herein, we provided a thorough computational exploration of identifying candidates from the marine-derived Aspergillus terreus isolates. Combined structural- and ligand-based approaches using a chemical library of 275 metabolites were adopted for pinpointing promising α-glucosidase inhibitors, as well as providing guiding insights for further lead optimization and development. Structure-based virtual screening through escalating precision molecular docking protocol at the α-glucosidase canonical pocket identified 11 promising top-docked hits, with several being superior to the market drug reference, acarbose. Comprehensive ligand-based investigations of these hits’ pharmacokinetics ADME profiles, physiochemical characterizations, and obedience to the gold standard Lipinski’s rule of five, as well as toxicity and mutagenicity profiling, proceeded. Under explicit conditions, a molecular dynamics simulation identified the top-stable metabolites: butyrolactone VI (SK-44), aspulvinone E (SK-55), butyrolactone I 4′’’’-sulfate (SK-72), and terrelumamide B (SK-173). They depicted the highest free binding energies and steadiest thermodynamic behavior. Moreover, great structural insights have been revealed, including the advent of an aromatic scaffold-based interaction for ligand–target complex stability. The significance of introducing balanced hydrophobic/polar moieties, like triazole and other bioisosteres of carboxylic acid, has been highlighted across docking, ADME/Tox profiling, and molecular dynamics studies for maximizing binding interactions while assuring safety and optimal pharmacokinetics for targeting the intestinal-localized α-glucosidase enzyme. Overall, this study provided valuable starting points for developing new α-glucosidase inhibitors based on nature-derived unique scaffolds, as well as guidance for prospective lead optimization and development within future pre-clinical and clinical investigations.

Savolitinib (Orpathys®), was developed by (HUTCHMED (Shanghai, China) and, AstraZeneca (Gaithersburg, Maryland, USA), is an inhibitor of the c-Met receptor tyrosine kinase that is orally bioavailable. It was designed for the treatment of pillary and clear-cell renal-cell carcinoma (RCC), colorectal cancer, gastric cancer, and metastatic non-small-cell lung cancer (NSCLC). The current work aimed to develop a rapid, specific, green, and sensitive liquid chromatography tandem mass spectrometry (LC-MS/MS) methodology for estimating savolitinib (SVB) in human liver microsomes (HLMs) with application to an in vitro metabolic stability assessment of SVB in HLMs. The validation steps of the current LC-MS/MS methodology in the HLMs were carried out following US FDA bioanalytical method validation guidelines including sensitivity, selectivity, linearity, accuracy, stability, precision, extraction recovery, and matrix effect. SVB and olmutinib (OLM) were chromatographically separated on an Eclipse Plus C8 column using an isocratic mobile phase. SVB parent ions were generated using the positive mode of an electrospray ionization (ESI) source. SVB daughter ions were detected and quantified using the multiple reaction monitoring (MRM) mode of a triple quadrupole mass analyser. The constructed SVB calibration curve showed linearity over the range from 1 to 3000 ng/mL. The interday and intraday accuracy and precision of the developed LC-MS/MS analytical methodology were −6.67%–4.11% and −0.51%–8.75%, respectively. A lower limit of quantification (LLOQ) of 0.87 ng/mL confirmed the sensitivity of the established method. Furthermore, the eco-scale methodology using the in silico AGREE software was used for the greenness assessment of the current LC-MS/MS method, and the outcomes showed that the established method was very eco-friendly. The intrinsic clearance (Clint) and in vitro half-life (t1/2) of SVB were 33.05 mL/min/kg and 24.54 min, respectively. SVB exhibited a moderate extraction ratio. The current study is the first to establish and validate LC-MS/MS for estimating SVB and assessing the metabolic stability of SVB.

In this work, complex dynamics are found in a fractional-order multi-scroll chaotic system based on the extended Gamma function. Firstly, the extended left and right Caputo fractional differential operators are introduced. Then, the basic features of the extended left Caputo fractional differential operator are outlined. The proposed operator is shown to have a new fractional parameter (higher degree of freedom) that increases the system’s ability to display more varieties of complex dynamics than the corresponding case of the Caputo fractional differential operator. Numerical results are performed to show the effectiveness of the proposed fractional operators. Then, rich complex dynamics are obtained such as coexisting one-scroll chaotic attractors, coexisting two-scroll chaotic attractors, or approximate periodic cycles, which are shown to persist in a shorter range as compared with the corresponding states of the integer-order counterpart of the multi-scroll system. The bifurcation diagrams, basin sets of attractions, and Lyapunov spectra are used to confirm the existence of the various scenarios of complex dynamics in the proposed systems.

Ribociclib (Kisqali®) is a pharmacological agent that has great selectivity as a cyclin-dependent kinase 4/6 inhibitor. It has received regulatory approval for its application in the treatment of breast cancer. The objective of the current study was to develop a rapid, green, highly sensitive, validated, and specific LC–MS/MS approach for the quantification of RCB in human liver microsomes (HLMs) over the linear range of 1–3000 ng/mL (LLOQ: 0.98 ng/mL). The inter- and intraday precision and accuracy exhibited values ranging from −0.31% to 3.16% and −5.67% to 5.46% correspondingly. The eco-scale technique (AGREE program) was employed to examine the environmental impact of the existing LC–MS/MS technology. The in vitro half-life and intrinsic clearance of RCB were determined to be 23.58 min and 34.39 mL/min/kg, respectively, which indicated the intermediate extraction ratio of RCB. The in silico P450 software (version 6.6) was used to confirm and validate the practical results. The metabolism of RBC was previously studied by our research group, indicating that the piperazine ring and N-dimethyl group are responsible for the metabolic instability of RCB. Drug discovery studies can be conducted taking into account this concept, allowing the development of new drugs with an enhanced safety profile and good metabolic stability.

The virus that causes monkeypox has been observed in Africa for several years, and it has been linked to the development of skin lesions. Public panic and anxiety have resulted from the deadly repercussions of virus infections following the COVID-19 pandemic. Rapid detection approaches are crucial since COVID-19 has reached a pandemic level. This study’s overarching goal is to use metaheuristic optimization to boost the performance of feature selection and classification methods to identify skin lesions as indicators of monkeypox in the event of a pandemic. Deep learning and transfer learning approaches are used to extract the necessary features. The GoogLeNet network is the deep learning framework used for feature extraction. In addition, a binary implementation of the dipper throated optimization (DTO) algorithm is used for feature selection. The decision tree classifier is then used to label the selected set of features. The decision tree classifier is optimized using the continuous version of the DTO algorithm to improve the classification accuracy. Various evaluation methods are used to compare and contrast the proposed approach and the other competing methods using the following metrics: accuracy, sensitivity, specificity, p-Value, N-Value, and F1-score. Through feature selection and a decision tree classifier, the following results are achieved using the proposed approach; F1-score of 0.92, sensitivity of 0.95, specificity of 0.61, p-Value of 0.89, and N-Value of 0.79. The overall accuracy of the proposed methodology after optimizing the parameters of the decision tree classifier is 94.35%. Furthermore, the analysis of variation (ANOVA) and Wilcoxon signed rank test have been applied to the results to investigate the statistical distinction between the proposed methodology and the alternatives. This comparison verified the uniqueness and importance of the proposed approach to Monkeypox case detection.

Microalgae have garnered scientific interest for their potential to produce bioactive compounds. However, the large-scale industrial utilization of microalgae faces challenges related to production costs and achieving optimal growth conditions. Thus, this study aimed to investigate the potential role of exogenous indole-3-acetic acid (IAA) application in improving the growth and production of bioactive metabolites in microalgae. To this end, the study employed different concentrations of exogenously administered IAA ranging from 0.36 µM to 5.69 µM to assess its influence on the growth and biochemical composition of Synechocystis and Chlorella. IAA exposure significantly increased IAA levels in both strains. Consequentially, improved biomass accumulation in parallel with increased total pigment content by approximately eleven-fold in both strains was observed. Furthermore, the application of IAA stimulated the accumulation of primary metabolites. Sugar levels were augmented, providing a carbon source that facilitated amino acid and fatty acid biosynthesis. As a result, amino acid levels were enhanced as well, leading to a 1.55-fold increase in total amino acid content in Synechocystis and a 1.42-fold increase in Chlorella. Total fatty acids content increased by 1.92-fold in Synechocystis and by 2.16-fold in Chlorella. Overall, the study demonstrated the effectiveness of exogenously adding IAA as a strategy for enhancing the accumulation of microalgae biomass and biomolecules. These findings contribute to the advancement of microalgae-based technologies, opening new avenues to produce economically important compounds derived from microalgae.

Metamaterials have unique physical properties. They are made of several elements and are structured in repeating patterns at a smaller wavelength than the phenomena they affect. Metamaterials’ exact structure, geometry, size, orientation, and arrangement allow them to manipulate electromagnetic waves by blocking, absorbing, amplifying, or bending them to achieve benefits not possible with ordinary materials. Microwave invisibility cloaks, invisible submarines, revolutionary electronics, microwave components, filters, and antennas with a negative refractive index utilize metamaterials. This paper proposed an improved dipper throated-based ant colony optimization (DTACO) algorithm for forecasting the bandwidth of the metamaterial antenna. The first scenario in the tests covered the feature selection capabilities of the proposed binary DTACO algorithm for the dataset that was being evaluated, and the second scenario illustrated the algorithm’s regression skills. Both scenarios are part of the studies. The state-of-the-art algorithms of DTO, ACO, particle swarm optimization (PSO), grey wolf optimizer (GWO), and whale optimization (WOA) were explored and compared to the DTACO algorithm. The basic multilayer perceptron (MLP) regressor model, the support vector regression (SVR) model, and the random forest (RF) regressor model were contrasted with the optimal ensemble DTACO-based model that was proposed. In order to assess the consistency of the DTACO-based model that was developed, the statistical research made use of Wilcoxon’s rank-sum and ANOVA tests.

Introduction: In public health, machine learning algorithms have been used to predict or diagnose chronic epidemiological disorders such as diabetes mellitus, which has reached epidemic proportions due to its widespread occurrence around the world. Diabetes is just one of several diseases for which machine learning techniques can be used in the diagnosis, prognosis, and assessment procedures. Methodology: In this paper, we propose a new approach for boosting the classification of diabetes based on a new metaheuristic optimization algorithm. The proposed approach proposes a new feature selection algorithm based on a dynamic Al-Biruni earth radius and dipper-throated optimization algorithm (DBERDTO). The selected features are then classified using a random forest classifier with its parameters optimized using the proposed DBERDTO. Results: The proposed methodology is evaluated and compared with recent optimization methods and machine learning models to prove its efficiency and superiority. The overall accuracy of diabetes classification achieved by the proposed approach is 98.6%. On the other hand, statistical tests have been conducted to assess the significance and the statistical difference of the proposed approach based on the analysis of variance (ANOVA) and Wilcoxon signed-rank tests. Conclusions: The results of these tests confirmed the superiority of the proposed approach compared to the other classification and optimization methods.

Breast cancer is one of the most common cancers in women, with an estimated 287,850 new cases identified in 2022. There were 43,250 female deaths attributed to this malignancy. The high death rate associated with this type of cancer can be reduced with early detection. Nonetheless, a skilled professional is always necessary to manually diagnose this malignancy from mammography images. Many researchers have proposed several approaches based on artificial intelligence. However, they still face several obstacles, such as overlapping cancerous and noncancerous regions, extracting irrelevant features, and inadequate training models. In this paper, we developed a novel computationally automated biological mechanism for categorizing breast cancer. Using a new optimization approach based on the Advanced Al-Biruni Earth Radius (ABER) optimization algorithm, a boosting to the classification of breast cancer cases is realized. The stages of the proposed framework include data augmentation, feature extraction using AlexNet based on transfer learning, and optimized classification using a convolutional neural network (CNN). Using transfer learning and optimized CNN for classification improved the accuracy when the results are compared to recent approaches. Two publicly available datasets are utilized to evaluate the proposed framework, and the average classification accuracy is 97.95%. To ensure the statistical significance and difference between the proposed methodology, additional tests are conducted, such as analysis of variance (ANOVA) and Wilcoxon, in addition to evaluating various statistical analysis metrics. The results of these tests emphasized the effectiveness and statistical difference of the proposed methodology compared to current methods.

Plants belonging to the Launaea genus have been extensively utilized ethnopharmacologically to treat a variety of diseases, including kidney disorders. Chromium is a common industrial pollutant that has been linked to kidney disease. The present work was designed for the investigation of the UPLC-QTOF–MS/MS metabolite profile of the L. mucronate ethanolic extract (LME), along with assessing the mechanistic protective actions of LME and its nano-silver formulation (LMNS) against K2Cr2O7-induced nephrotoxicity in rats. LMNE was successfully biosynthesized and confirmed using UV–Visible (UV–Vis) spectroscopy and transmission electron microscopy (TEM). The nephroprotective effects of LME and LMNE was assessed in rats exposed to potassium dichromate (K2Cr2O7, 15 mg/kg BW) to cause nephrotoxicity. LME and LMNS, separately, were administered twice daily for 14 days at doses of 200 and 400 mg/kg BW, respectively. The kidney function, catalase, UGT, Nrf2, PGE2, Cox-2, ERK, and MAPK levels in renal tissue were all assessed, along with histopathological examinations for exploring their ameliorative effects. Forty-five bioactive metabolites were annotated belonging to flavonoids, phenolic and organic acids, coumarins, and fatty acids. Metabolite profiling revealed that chlorogenic acid, apigenin, and luteolin glycosides were the main phenolics, with chlorogenic acid-O-hexoside reported for the first time in LME. The findings revealed that the serum kidney function indicators (urea and creatinine) were markedly elevated in K2Cr2O7-intoxicated rats. Furthermore, inflammatory indicators (COX-2 and PGE2), MAPK, and ERK were all markedly elevated in kidney tissue, whereas catalase, UGT, and Nrf2 levels were downregulated. Histological and immunohistochemical assays confirmed the toxic effects of K2Cr2O7 in the kidneys. In contrast, the administration of LME and LMNS prior to K2Cr2O7 considerably improved the architecture of the renal tissue, while also restoring levels of most biochemical markers. Functioning via the inhibition of the MAPK/ERK pathway, activating Nrf2, and modifying the antioxidant and metabolic enzymes, LME and LMNS exerted their nephroprotective effects against K2Cr2O7-induced toxicity.

Fenebrutinib is an orally available Bruton tyrosine kinase inhibitor. It is currently in multiple phase III clinical trials for the management of B-cell tumors and autoimmune disorders. Elementary in-silico studies were first performed to predict susceptible sites of metabolism and structural alerts for toxicities by StarDrop WhichP450™ module and DEREK software; respectively. Fenebrutinib metabolites and adducts were characterized in-vitro in rat liver microsomes (RLM) using MS3 method in Ion Trap LC-MS/MS. Formation of reactive and unstable intermediates was explored using potassium cyanide (KCN), glutathione (GSH) and methoxylamine as trapping nucleophiles to capture the transient and unstable iminium, 6-iminopyridin-3(6H)-one and aldehyde intermediates, respectively, to generate a stable adducts that can be investigated and analyzed using mass spectrometry. Ten phase I metabolites, four cyanide adducts, five GSH adducts and six methoxylamine adducts of fenebrutinib were identified. The proposed metabolic reactions involved in formation of these metabolites are hydroxylation, oxidation of primary alcohol to aldehyde, n-oxidation, and n-dealkylation. The mechanism of reactive intermediate formation of fenebrutinib can provide a justification of the cause of its adverse effects. Formation of iminium, iminoquinone and aldehyde intermediates of fenebrutinib was characterized. N-dealkylation followed by hydroxylation of the piperazine ring is proposed to cause the bioactivation to iminium intermediates captured by cyanide. Oxidation of the hydroxymethyl group on the pyridine moiety is proposed to cause the generation of reactive aldehyde intermediates captures by methoxylamine. N-dealkylation and hydroxylation of the pyridine ring is proposed to cause formation of iminoquinone reactive intermediates captured by glutathione. FBB and several phase I metabolites are bioactivated to fifteen reactive intermediates which might be the cause of adverse effects. In the future, drug discovery experiments utilizing this information could be performed, permitting the synthesis of new drugs with better safety profile. Overall, in silico software and in vitro metabolic incubation experiments were able to characterize the FBB metabolites and reactive intermediates using the multistep fragmentation capability of ion trap mass spectrometry.

Fenebrutinib (GDC-0853; FNB) is an oral small molecule that was developed by Roche Pharmaceuticals to slow multiple sclerosis progression. FNB is a reversible bruton tyrosine kinase (BTK) inhibitor, which showed the maximum potency of BTK inhibitors in phase III clinical trials for multiple sclerosis. In the current study, a fast, specific, and sensitive UPLC-MS/MS method for FNB quantification in human liver microsomes (HLMs) was established with application to the evaluation of metabolic stability. The UPLC-MS/MS methodology was verified using the stated USFDA validation guidelines for bioanalytical methodologies that involve selectivity, linearity, accuracy and precision, carryover and extraction recovery, stability, and matrix effect. The FNB calibration curve displayed a linearity in the range from 1 ng/mL to 3000 ng/mL (y = 1.731x + 2.013; R2: 0.9954; RSD < 4.37%) in the HLMs matrix. The limit of quantification was 0.88 ng/mL, which verified the UPLC-MS/MS analytical method sensitivity. The intraday and interday precision and accuracy results of the developed UPLC-MS/MS method were −3.99–14.0% and 0.52–3.83%, respectively. FNB and savolitinib (SVB) (internal standard) were chromatographically separated utilizing an isocratic mobile phase system with a ZORBAX Eclipse plus-C18 (50 mm, 2.1 mm, and 1.8 μm) column. The metabolic stability parameters for FNB, involving high intrinsic clearance (58.21 mL/min/kg) and a short in vitro half-life (13.93 min), revealed the high extraction ratio of FNB. Reviewing the literature revealed that the current UPLC-MS/MS method is the first analytical method for FNB quantification in the HLMs matrix with application to the assessment of FNB metabolic stability.

Epithelial cell transforming 2 (ECT2) is a potential oncogene and a number of recent studies have correlated it with the progression of several human cancers. Despite this elevated attention for ECT2 in oncology-related reports, there is no collective study to combine and integrate the expression and oncogenic behavior of ECT2 in a panel of human cancers. The current study started with a differential expression analysis of ECT2 in cancerous versus normal tissue. Following that, the study asked for the correlation between ECT2 upregulation and tumor stage, grade, and metastasis, along with its effect on patient survival. Moreover, the methylation and phosphorylation status of ECT2 in tumor versus normal tissue was assessed, in addition to the investigation of the ECT2 effect on the immune cell infiltration in the tumor microenvironment. The current study revealed that ECT2 was upregulated as mRNA and protein levels in a list of human tumors, a feature that allowed for the increased filtration of myeloid-derived suppressor cells (MDSC) and decreased the level of natural killer T (NKT) cells, which ultimately led to a poor prognosis survival. Lastly, we screened for several drugs that could inhibit ECT2 and act as antitumor agents. Collectively, this study nominated ECT2 as a prognostic and immunological biomarker, with reported inhibitors that represent potential antitumor drugs.

Capmatinib (CMB) is an orally bioavailable mesenchymal–epithelial transition (MET) inhibitor approved by the US-FDA to treat metastatic non-small cell lung cancer (NSCLC) patients, with MET exon 14 skipping mutation. The current study aimed to establish a specific, rapid, and sensitive ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) analytical method for quantifying CMB in human liver microsomes (HLMs), with therapeutic implications for assessing metabolic stability. Validation of the UPLC-MS/MS analytical method in the HLMs was performed using selectivity, sensitivity, linearity, accuracy, precision, extraction recovery, stability, and matrix effects according to the guidelines for bio-analytical method validation of the US-FDA. CMB was ionized by positive electrospray ionization (ESI) as the ionization source and analysed using multiple reaction monitoring (MRM) as the mass analyser mode. CMB and pemigatinib (PMT) were resolved on the C18 column, with an isocratic mobile phase. The CMB calibration curve showed linearity in the concentration range of 1–3000 ng/mL. The intra- and inter-day accuracy and precision were −7.67–4.48% and 0.46–6.99%, respectively. The lower limit of quantification (LLOQ) of 0.94 ng/mL confirmed the sensitivity of the UPLC-MS/MS analytical method. The intrinsic clearance (Clint) and in vitro half-life (t1/2) of CMB were 61.85 mL/min/kg and 13.11 min, respectively. CMB showed a high extraction ratio. The present study is the first to develop, establish, and standardize UPLC-MS/MS for the purpose of quantifying and evaluating the metabolic stability of CMB.

Gilteritinib (Xospata®) is a tyrosine kinase inhibitor (TKI) that works by inhibiting numerous receptor tyrosine kinases, involving AXL and FMS-like tyrosine kinase 3 (FLT3). Gilteritinib (GTB) was approved (28 November 2018) by the USFDA for the treatment of refractory or relapsed (R/R) acute myeloid leukemia (AML) with a FLT3 mutation. In the current study, a fast, highly sensitive, and specific ultra-performance liquid chromatography tandem mass spectrometry (UPLC–MS/MS) analytical methodology was created for GTB determination in human liver microsomes (HLMs) utilizing an electrospray ionization (ESI) source. The developed methodology (UPLC–ESI–MS/MS) was utilized in the assessment of GTB metabolic stability. The UPLC–ESI–MS/MS methodology was validated following the rules of the FDA that include selectivity, linearity, accuracy, precision, matrix effect, stability, and extraction recovery. The generated data of the optimized validation parameters of the current UPLC–ESI–MS/MS methodology were acceptable as reported in the FDA guidelines. GTB parent ions were generated in the ESI source (positive mode) and GTB daughter ions (two) were quantified in the mass analyzer utilizing multiple reaction monitoring (MRM) modes. The plotted GTB calibration curve showed a wide range of linearity from 1 ng/mL to 3000 ng/mL in HLMs matrix (y = 1.7298x + 3.62941 and r2 = 0.9949). The intraday and interday precision and accuracy outcomes of the current UPLC–ESI–MS/MS methodology were 0.35–11.39% and 0.27–4.32%, respectively. GTB and encorafenib (EFB) (internal standard; IS) were resoluted utilizing a reversed stationary phase (ZORBAX Eclipse plus C18 column; 1.8 μm PS, 2.1 mm ID, and 50 mm L) at 22 ± 2 °C. The calculated lower limit of quantification (LLOQ) was 0.94 ng/mL, revealing the UPLC–ESI–MS/MS methodology sensitivity. The two metabolic stability factors including in vitro half-life (t1/2) and intrinsic clearance (Clint) of GTB were 14.32 min and 56.64 mL/min/kg, respectively, predicting the moderate extraction ratio and good bioavailability of GTB. The current UPLC–ESI–MS/MS methodology is fast, sensitive and exhibits a wider range of linearity (1 to 3000 ng/mL) compared to other reported methods and is considered the first validated methodology for the determination of GTB metabolic stability.

Conventional scolicidal agents are still unsatisfactory in combating hydatid disease due to their low efficacy and increased drug side effects. Therefore, novel scolicides are required. This study aimed to evaluate the antihydatic and immunomodulatory effects of eugenol essential oil (Eug) and its nanoemulsion (Eug-NE) in cystic echinococcosis (CE). Eug and Eug-NE were administered orally to CE-infected rats and compared to albendazole (ABZ). Hydatid cyst development was assessed based on organ weight and hypertrophy indicators of the infected organs, along with a histopathological and histochemical evaluation of collagen content. The immunomodulatory effects of treatment on CE were evaluated by serum cytokine levels measurement of interferon-γ (IFN-γ) and interleukin (IL)-4 and immunohistochemical (IHC) analysis of signal transducer and activator of transcription 4 (STAT4) and GATA-binding protein 3 (GATA3) markers. Eug-NE was the most effective in reducing the cyst weights, organ weights, and hypertrophy indicators and improving histopathological lesions with reduced collagen content. Eug and Eug-NE significantly increased the IFN-γ levels and decreased the IL-4 levels, while IHC analysis demonstrated a significant reduction in STAT4 and GATA3 expression in all treated groups. Eug and Eug-NE demonstrated antihydatic and preventative effects, with a substantial decrease in liver fibrosis compared to that of ABZ. Besides their promising immunomodulatory effects, their good treatment response suggests their use as alternatives or complementary scolicidal agents in hydatid cyst treatment.

Bacterial sepsis induces the production of excessive pro-inflammatory cytokines and oxidative stress, resulting in tissue injury and hyperinflammation. Patients recovering from sepsis have increased rates of central nervous system (CNS) morbidities, which are linked to long-term cognitive impairment, such as neurodegenerative pathologies. This paper focuses on the tissue injury and hyperinflammation observed in the acute phase of sepsis and on the development of long-term neuroinflammation associated with septicemia. Here we evaluate the effects of Coriolus versicolor administration as a novel approach to treat polymicrobial sepsis. Rats underwent cecal ligation and perforation (CLP), and Coriolus versicolor (200 mg/kg in saline) was administered daily by gavage. Survival was monitored, and tissues from vital organs that easily succumb to infection were harvested after 72 h to evaluate the histological changes. Twenty-eight days after CLP, behavioral analyses were performed, and serum and brain (hippocampus) samples were harvested at four weeks from surgery. Coriolus versicolor increased survival and reduced acute tissue injury. Indeed, it reduced the release of pro-inflammatory cytokines in the bloodstream, leading to a reduced chronic inflammation. In the hippocampus, Coriolus versicolor administration restored tight junction expressions, reduce cytokines accumulation and glia activation. It also reduced toll-like receptor 4 (TLR4) and neuronal nitric oxide synthase (nNOS) and the NLR family pyrin domain containing 3 (NLRP3) inflammasome components expression. Coriolus versicolor showed antioxidant activities, restoring glutathione (GSH) levels and catalase and superoxide dismutase (SOD) activities and reducing lipid peroxidation, nitrite and reactive oxygen species (ROS) levels. Importantly, Coriolus versicolor reduced amyloid precursor protein (APP), phosphorylated-Tau (p-Tau), pathologically phosphorylated tau (PHF1), phosphorylated tau (Ser202 and Thr205) (AT8), interferon-induced transmembrane protein 3 (IFITM3) expression, and β-amyloid accumulation induced by CLP. Indeed, Coriolus versicolor restored synaptic dysfunction and behavioral alterations. This research shows the effects of Coriolus versicolor administration on the long-term development of neuroinflammation and brain dysfunction induced by sepsis. Overall, our results demonstrated that Coriolus versicolor administration was able to counteract the degenerative process triggered by sepsis.

Macroalgae are significant biological resources in coastal marine ecosystems. Seasonality influences macroalgae biochemical characteristics, which consequentially affect their ecological and economic values. Here, macroalgae were surveyed from summer 2017 to spring 2018 at three sites at 7 km (south) from El Qusier, 52 km (north) from Marsa Alam and 70 km (south) from Safaga along the Red Sea coast, Egypt. Across all the macroalgae collected, Caulerpa prolifera (green macroalgae), Acanthophora spicifera (red macroalgae) and Cystoseira myrica, Cystoseira trinodis and Turbinaria ornata (brown macroalgae) were the most dominant macroalgal species. These macroalgae were identified at morphological and molecular (18s rRNA) levels. Then, the seasonal variations in macroalgal minerals and biochemical composition were quantified to determine the apt period for harvesting based on the nutritional requirements for commercial utilizations. The chemical composition of macroalgae proved the species and seasonal variation. For instance, minerals were more accumulated in macroalgae C. prolifera, A. spicifera and T. ornata in the winter season, but they were accumulated in both C. myrica and C. trinodis in the summer season. Total sugars, amino acids, fatty acids and phenolic contents were higher in the summer season. Accordingly, macroalgae collected during the summer can be used as food and animal feed. Overall, we suggest the harvesting of macroalgae for different nutrients and metabolites in the respective seasons.

New sets of ibuprofen and indomethacin conjugates comprising triazolyl heterocycle were synthesized via click chemistry, adopting an optimized protocol through the molecular hybridization approach affording the targeted agents in good yields. The new non-steroidal anti-inflammatory drug (NSAID) conjugates were designed and synthesized and could be considered as potential drug candidates for the treatment of pain and inflammation. The anti-inflammatory properties were investigated for all the synthesized conjugates. Among 14 synthesized conjugates, four (5a, 5b, 5d, and 5e) were found to have significant anti-inflammatory properties potency 117.6%, 116.5%, 93.8%, and 109.1% in comparison to reference drugs ibuprofen (97.2%) and indomethacin (100%) in the rat paw edema carrageenan test without any ulcerogenic liability. The suppression effect of cytokines IL-6, TNF-α, and iNOS in addition to NO in the LPS-induced RAW264.7 cells supports the promising anti-inflammatory properties observed in the ibuprofen conjugates. In addition, several conjugates showed promising peripheral and central analgesic activity. The selectivity index (SI) of compound 5a (23.096) indicates the significant efficacy and selectivity for COX-2 over COX-1. Molecular modeling (docking and QSAR) studies described the observed biological properties.

Camel milk (CM) has potent antibacterial and antifungal effects and camel milk exosomes (CM-EXO) have been shown to inhibit the proliferation of a large variety of cancer cells including HepaRG, MCF7, Hl60, and PANC1. However, little is known regarding the effects of CM-EXO on bacteria, fungi, HepG2, CaCo2, and Vero cells. Therefore, this study aimed to evaluate the antibacterial, antifungal, and anticancer effects of CM-EXO. EXOs were isolated from CM by ultracentrifugation and characterized by transmission electron microscope and flow cytometry. Unlike CM, CM-EXO (6 mg/mL) had no bactericidal effects on Gram-positive bacteria (Staphylococcus aureus, Micrococcus luteus, and Enterococcus feacalis) but they had bacteriostatic effects, especially against Gram-negative strains (Escherichia coli, Pseudomonas aeruginosa, and Proteus mirabilis), and fungistatic effects on Candida albicans. HepG2, CaCo2, and Vero cells were respectively treated with CM-EXOs at low (6.17, 3.60, 75.35 μg/mL), moderate (12.34, 7.20, 150.70 μg/mL), and high (24.68, 14.40, 301.40 μg/mL) doses and the results revealed that CM-EXOs triggered apoptosis in HepG2 and CaCo2 cells, but not in normal Vero cells, as revealed by high Bax expression and caspase 3 activities and lower expression of Bcl2. Interestingly, CM-EXOs also induced the elevation of intracellular reactive oxygen species and downregulated the expression of antioxidant-related genes (NrF2 and HO-1) in cancer cells but not in normal cells. CM-EXOs have antibacterial and antifungal effects as well as a selective anticancer effect against HepG2 and CaCo2 cells with a higher safety margin on normal cells.

The performance of a hybrid solar desalination system is predicted in this work using an enhanced prediction method based on a supervised machine-learning algorithm. A humidification–dehumidification (HDH) unit and a single-stage flashing evaporation (SSF) unit make up the hybrid solar desalination system. The Al-Biruni Earth Radius (BER) and Particle Swarm Optimization (PSO) algorithms serve as the foundation for the suggested algorithm. Using experimental data, the BER–PSO algorithm is trained and evaluated. The cold fluid and injected air volume flow rates were the algorithms’ inputs, and their outputs were the hot and cold fluids’ outlet temperatures as well as the pressure drop across the heat exchanger. Both the volume mass flow rate of hot fluid and the input temperatures of hot and cold fluids are regarded as constants. The results obtained show the great ability of the proposed BER–PSO method to identify the nonlinear link between operating circumstances and process responses. In addition, compared to the other analyzed models, it offers better statistical performance measures for the prediction of the outlet temperature of hot and cold fluids and pressure drop values.

Introduction: Wind speed and solar radiation are two of the most well-known and widely used renewable energy sources worldwide. Coal, natural gas, and petroleum are examples of fossil fuels that are not replenished and are thus non-renewable energy sources due to their high carbon content and the methods by which they are generated. To predict energy production of renewable sources, researchers use energy forecasting techniques based on the recent advances in machine learning approaches. Numerous prediction methods have significant drawbacks, including high computational complexity and inability to generalize for various types of sources of renewable energy sources. Methodology: In this paper, we proposed a novel approach capable of generalizing the prediction accuracy for both wind speed and solar radiation forecasting data. The proposed approach is based on a new optimization algorithm and a new stacked ensemble model. The new optimization algorithm is a hybrid of Al-Biruni Earth Radius (BER) and genetic algorithm (GA) and it is denoted by the GABER optimization algorithm. This algorithm is used to optimize the parameters of the proposed stacked ensemble model to boost the prediction accuracy and to improve the generalization capability. Results: To evaluate the proposed approach, several experiments are conducted to study its effectiveness and superiority compared to other optimization methods and forecasting models. In addition, statistical tests are conducted to assess the significance and difference of the proposed approach. The recorded results proved the proposed approach’s superiority, effectiveness, generalization, and statistical significance when compared to state-of-the-art methods. Conclusions: The proposed approach is capable of predicting both wind speed and solar radiation with better generalization.

This study investigates the multistability phenomenon and coexisting attractors in the modified Autonomous Van der Pol-Duffing (MAVPD) system and its fractional-order form. The analytical conditions for existence of periodic solutions in the integer-order system via Hopf bifurcation are discussed. In addition, conditions for approximating the solutions of the fractional version to periodic solutions are obtained via the Hopf bifurcation theory in fractional-order systems. Moreover, the technique for hidden attractors localization in the integer-order MAVPD is provided. Therefore, motivated by the previous discussion, the appearances of self-excited and hidden attractors are explained in the integer- and fractional-order MAVPD systems. Phase transition of quasi-periodic hidden attractors between the integer- and fractional-order MAVPD systems is observed. Throughout this study, the existence of complex dynamics is also justified using some effective numerical measures such as Lyapunov exponents, bifurcation diagrams and basin sets of attraction.

Cancer is the leading cause of death globally, with an increasing number of cases being annually reported. Nature-derived metabolites have been widely studied for their potential programmed necrosis, cytotoxicity, and anti-proliferation leading to enrichment for the modern medicine, particularly within the last couple of decades. At a more rapid pace, the concept of multi-target agents has evolved from being an innovative approach into a regular drug development procedure for hampering the multi-fashioned pathophysiology and high-resistance nature of cancer cells. With the advent of the Red Sea Penicillium chrysogenum strain S003-isolated indole-based alkaloids, we thoroughly investigated the molecular aspects for three major metabolites: meleagrin (MEL), roquefortine C (ROC), and isoroquefortine C (ISO) against three cancer-associated biological targets Cdc-25A, PTP-1B, and c-Met kinase. The study presented, for the first time, the detailed molecular insights and near-physiological affinity for these marine indole alkaloids against the assign targets through molecular docking-coupled all-atom dynamic simulation analysis. Findings highlighted the superiority of MEL’s binding affinity/stability being quite in concordance with the in vitro anticancer activity profile conducted via sulforhodamine B bioassay on different cancerous cell lines reaching down to low micromolar or even nanomolar potencies. The advent of lengthy structural topologies via the metabolites’ extended tetracyclic cores and aromatic imidazole arm permitted multi-pocket accommodation addressing the selectivity concerns. Additionally, the presence decorating polar functionalities on the core hydrophobic tetracyclic ring contributed compound’s pharmacodynamic preferentiality. Introducing ionizable functionality with more lipophilic characters was highlighted to improve binding affinities which was also in concordance with the conducted drug-likeness/pharmacokinetic profiling for obtaining a balanced pharmacokinetic/dynamic profile. Our study adds to the knowledge regarding drug development and optimization of marine-isolated indole-based alkaloids for future iterative synthesis and pre-clinical investigations as multi-target anticancer agents.

Techniques such as exhaust gas recirculation (EGR) and water-in-fuel emulsions (WFEs) can significantly decrease NOx emissions in diesel engines. As a disadvantage of adopting EGR, the afterburning period lengthens owing to a shortage of oxygen, lowering thermal efficiency. Meanwhile, WFEs can slightly reduce NOx emissions and reduce the afterburning phase without severely compromising thermal efficiency. Therefore, the EGR–WFE combination was modeled utilizing the KIVA-3V code along with GT power and experimental results. The findings indicated that combining EGR with WFEs is an efficient technique to reduce afterburning and enhance thermal efficiency. Under the EGR state, the NO product was evenly lowered. In the WFE, a considerable NO amount was created near the front edge of the combustion flame. Additionally, squish flow from the piston’s up–down movement improved fuel–air mixing, and NO production was increased as a result, particularly at high injection pressure. Using WFEs with EGR at a low oxygen concentration significantly reduced NO emissions while increasing thermal efficiency. For instance, using 16% of the oxygen concentration and a 40% water emulsion, a 94% drop in NO and a 4% improvement in the Indicated Mean Effective Pressure were obtained concurrently. This research proposes using the EGR–WFE combination to minimize NO emissions while maintaining thermal efficiency.

One of the relevant factors in smart energy management is the ability to predict the consumption of energy in smart households and use the resulting data for planning and operating energy generation. For the utility to save money on energy generation, it must be able to forecast electrical demands and schedule generation resources to meet the demand. In this paper, we propose an optimized deep network model for predicting future consumption of energy in smart households based on the Dipper Throated Optimization (DTO) algorithm and Long Short-Term Memory (LSTM). The proposed deep network consists of three parts, the first part contains a single layer of bidirectional LSTM, the second part contains a set of stacked unidirectional LSTM, and the third part contains a single layer of fully connected neurons. The design of the proposed deep network targets represents the temporal dependencies of energy consumption for boosting prediction accuracy. The parameters of the proposed deep network are optimized using the DTO algorithm. The proposed model is validated using the publicly available UCI household energy dataset. In comparison to the other competing machine learning models, such as Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Multi-Layer Perceptron (MLP), Sequence-to-Sequence (Seq2Seq), and standard LSTM, the performance of the proposed model shows promising effectiveness and superiority when evaluated using eight evaluation criteria including Root Mean Square Error (RMSE) and R2. Experimental results show that the proposed optimized deep model achieved an RMSE of (0.0047) and R2 of (0.998), which outperform those values achieved by the other models. In addition, a sensitivity analysis is performed to study the stability and significance of the proposed approach. The recorded results confirm the effectiveness, superiority, and stability of the proposed approach in predicting the future consumption of energy in smart households.

High penetration of renewable energy sources into isolated microgrids (µGs) is considered a critical challenge, as µGs’ operation at low inertia results in frequency stability problems. To solve this challenge, virtual inertia control based on an energy storage system is applied to enhance the inertia and damping properties of the µG. On the other hand, utilization of a phase-locked loop (PLL) is indispensable for measuring system frequency; however, its dynamics, such as measurement delay and noise generation, cause extra deterioration of frequency stability. In this paper, to improve µG frequency stability and minimize the impact of PLL dynamics, a new optimal frequency control technique is proposed. A whale optimization algorithm is used to enhance the virtual inertia control loop by optimizing the parameters of the virtual inertia controller with consideration of PLL dynamics and the uncertainties of system inertia. The proposed controller has been validated through comparisons with an optimized virtual inertia PI controller which is tuned utilizing MATLAB internal model control methodology and with H∞-based virtual inertia control. The results show the effectiveness of the proposed controller against different operating conditions and system disturbances and uncertainties.