Beni-Suef University Journal of Basic and Applied Sciences最新文献

Background

Laser metal deposition (LMD) is a widely used additive manufacturing technique for producing complex high entropy alloys with special properties for several applications. The AlCoCrFeNiCu HEAs compositional design has six elements with a configurational entropy of 1.79 R and atomic concentrations between 5 and 35%, so the HEA system is thermodynamically favorable according to Boltzmann’s theory, attributed to the core effects. However, the high-entropy alloy has dominant Body-Centered Cubic structures which may be too brittle to be examined in tension experimentally. Preheating the substrate before and during layer deposition could be a potential solution that is currently under development since tensile loading necessitates an understanding of a material's behavior under tension through an analysis of its yield and ultimate tensile strength. A computer-aided design (CAD) solid model was used to generate the near-net dog-bone form of the alloy with moderately complicated geometrical characteristics using laser metal deposition (LMD) technology. This study investigates a straightforward and effective computational model for simulating material properties, using COMSOL Multiphysics 5.4 software for laser-deposited high entropy alloys that are excessively brittle to be tested in tension. The AlCoCrFeNiCu high-entropy alloy "dog bone" test sample was modeled using COMSOL Multiphysics for tensile loading. The first principal stresses and longitudinal strain under axial loading conditions were measured using a three-dimensional (3D) structural mechanics’ model.

Results

The results showed the ultimate tensile strength is 8.47 N/m², attributed to the high entropy effect and the dominant phase structure of the alloy.

Conclusion

Numerical models in this paper demonstrate the effect of stresses on the tensile behavior of the AlCoCrFeNiCu high-entropy alloy. The model optimizes the LMD process by analyzing residual stresses and predicting tensile strength, thus, providing insights that show the potential of high entropy alloys for structural integrity in aerospace applications.

Background

Metabolomics is the study of metabolites in cells, tissues, live organisms, and biological fluids to elucidate their composition and possible roles. Metabolomics and its biomarkers have emerged as a powerful tool for evaluating the efficacy of probiotics in various pathological conditions, including toxicological settings. This review explores the use of metabolomics-based markers to assess the safety and efficacy of probiotics in toxicological settings.

Result

This review aims to determine biomarkers for measuring the effectiveness of probiotic therapies in toxicological contexts based on exposure, effects, susceptibility, prognostic, and therapeutic biomarkers. In this study, 1979 articles were systematically searched in PubMed (PM), Scopus (Sc), Google Scholar (GS), and Web of Science (WOS) between the years 2013 and 2023 inclusive using keywords, inclusion, and exclusion criteria. The result showed that 1439 human samples were used from 2013 to 2023 publications with the UK having the highest number of publications, data, and sample types in 2023. Again, the result showed most of the publications are on susceptibility-based biomarkers.

Conclusion

Metabolomics-based biomarkers may provide insight into metabolism-related alterations related to probiotic therapies while clarifying their biological mechanisms, especially in toxicology enabling specific probiotic therapy.

Background

Methotrexate (MTX), a drug utilized in cancer and rheumatoid arthritis treatment, is associated with acute and chronic neurodegenerative alterations. Spirulina platensis (SP) has several important phytochemical substances that act as free radical scavengers or natural antioxidants. The current study investigated the possible effects of the blue-green alga Spirulina platensis on cerebellar damage in male rats exposed to methotrexate. Forty (40) adult male albino rats were randomly divided into 4 groups (n = 10) and treated for one week: GI, the control group; GII was orally given 1000 mg SP/kg/daily, GIII was given a single intraperitoneal injection of MTX 75 mg/kg at the first day, and continued under the normal condition without other treatment till the end of the experiment, and GIV received both SP and MTX together with the same previous doses and duration. Neurobehavioral, histopathological, histochemical, immunohistochemical, ultrastructural, molecular, and biochemical data were recorded.

Results

MTX caused severe cerebellar degeneration in 3 cortical layers, especially the Purkinje layer. The Purkinje layer displayed a disrupted monolayer arrangement with pyknotic nuclei, a significant decrease in cell number, and shrunken cells surrounded by empty spaces. The molecular and granular layers are degenerated with elevated immunoreactions and gene expression of the glial fibrillary acidic protein (GFAP), ionized calcium-binding adapter molecule 1 (Iba-1), and neurofilament light chain antibody (NFL). Moreover, MTX significantly increased malondialdehyde (MDA) and myeloperoxidase (MPO) while decreasing the levels of reduced glutathione (GSH), serotonin, superoxide dismutase (SOD), acetylcholinesterase (ACHE), norepinephrine, and dopamine. These insults were noticeably mitigated by concomitant treatment with spirulina.

Conclusion

Spirulina improves neurological function by modulating the cerebellar damage elicited by MTX. This improvement may be attributed to the anti-inflammatory and antioxidant properties of spirulina.

Background

Today, several bioinformatics tools are available for analyzing restriction fragment length data. RFLP-kenzy is a new bioinformatic tool for identifying restriction key enzyme that cut at least 1 sequence and a maximum of n-1 sequence.

Results

This bioinformatic tool helps researchers to select appropriate enzymes that yield different RFLP patterns, especially from overly identical sequences with single nucleotide mutation or other small variations. By using RFLP-kenzy, multiple DNA sequences could be analyzed simultaneously and the key enzymes list is provided. The present paper also demonstrates the ability of RFLP-kenzy to identify the key enzymes through the analysis of 16S rRNA sequences and the complete genome of various genera of microorganisms.

Conclusion

From the results, several key enzymes were provided indicating the importance of this new tool in the selection of appropriate restriction enzymes.

Background

The process of differentiating neuroblastoma cells (SH-SY5Y) is crucial for obtaining mature neuronal markers. However, there is variability in the concentrations of foetal bovine serum (FBS) used in the differentiation media, ranging from 1 to 10%. This inconsistency in FBS concentrations may contribute to the inconsistent differentiation of cells. To improve the utility of the SH-SY5Y cell line as a model for neuronal cell culture, we investigated the impact of FBS concentrations in the differentiation media using Dulbecco's modified eagle medium and 10 μM all-trans-retinoic acid (ATRA). The aim of this study was to optimise the concentrations of FBS in the differentiation media of SH-SY5Y cells. Our study focused on assessing the length of dendrites in neuronal cells and the expression of β-III tubulin, a marker indicative of mature neurons. SH-SY5Y cells were differentiated with 10 µM of ATRA for 7 days. Four treatment groups with different FBS concentrations (1%, 3%, 5%, and 10%) were examined to assess the alteration of cellular morphology and dendritic length. The expression of the mature neuron marker β-III tubulin was evaluated using immunocytochemistry technique.

Results

SH-SY5Y cells' dendrite length was significantly longer (p < 0.05) when there was a higher concentration of FBS in the differentiation medium. The result was confirmed with the significant increase of β-III tubulin expression (p < 0.001) of the differentiated SH-SY5Y cells that have been incubated with higher concentrations of FBS in the differentiation medium.

Conclusions

We concluded that optimised concentrations of FBS in the differentiation media display longer length of the dendrites and express higher production of β-III tubulin in the differentiated SH-SY5Y cells. The consistency of serum concentration used in the differentiation media is important to produce a sustainable in vitro neuronal model of SH-SY5Y cells for neurodegenerative studies.

Background

The rapid evolution of nanotechnology has fundamentally transformed both medical and dental fields. By harnessing nanomaterials, researchers have unlocked the ability to replicate natural tissue structures and properties, significantly enhancing integration processes. Notably, nanostructures have emerged as pivotal elements in oral medicine, particularly in combating dental caries and enhancing outcomes in dental implants and maxillofacial surgeries.

Main body of the abstract

Nanostructures play multifaceted roles in oral health, promoting osseointegration and expediting healing processes in dental procedures. The impact of these materials extends to improving the adhesive strength and overall properties of dental composites. This review critically evaluates the influence of nanointerfaces on the longevity of dental restorations, exploring innovative nanotechnological interventions aimed at augmenting restoration durability. Furthermore, recent strides in nanodentistry are discussed, highlighting breakthroughs in oral health diagnostics, preventative strategies, and treatment modalities essential for achieving and sustaining optimal oral health.

Short conclusion

Incorporating nanotechnology into dental practice presents exciting prospects for advancing oral healthcare. From enhancing restoration durability to revolutionizing diagnostics and treatments, nanotechnology offers transformative solutions that hold significant promise for the future of oral health management.

Background

For centuries, plants have been used in the folk medicine of various cultures for their healing properties. It is amazing how nature has provided us with such powerful remedies. Oxalis corniculata (O. corniculata) has always been used traditionally for its medicinal attributes. In Asia, this herbaceous plant is used for treating many gastrointestinal disorders, such as diarrhea and dysentery. In African folk medicine, on the other hand, this herb is used for respiratory diseases, skin diseases, and fever.

Main body

Scientific research has revealed numerous pharmacological potentials of this plant, including antitumor, antimicrobial, antioxidant, anti-inflammatory, and hepatoprotective properties. In addition, studies have indicated that the extract of this plant protects against oxidative stress, inflammation, and various diseases, including cancer and diabetes. Phytochemical analysis of O. corniculata revealed various relevant compounds, including phenolic acids, flavonoids, and alkaloids, which are responsible for its therapeutic properties.

Conclusion

The various constituents of this plant have significant ethnomedicinal potential. The plant is a possible source of extracts and chemical compounds with pharmacological activity. In the future, O. corniculata could have an effective role in the development of modern drugs. The objective of this review is to summarize the current knowledge on the medicinal potential of O. corniculata, including its bioactive compounds, mechanisms of action, and therapeutic applications.

Graphical abstract

Background

Within the manufacturing sector, assembly processes relying on mechanical fasteners such as nuts, washers, and bolts hold critical importance. Presently, these fasteners undergo manual inspection or are identified by human operators, a practice susceptible to errors that can adversely affect product efficiency and safety. Given considerations such as time constraints, escalating facility and labor expenses, and the imperative of seamless integration, the integration of machine vision into assembly operations has become imperative.

Results

This study endeavors to construct a robust system grounded in deep learning algorithms to autonomously identify commonly used fasteners and delineate their attributes (e.g., thread type, head type) with acceptable precision. A dataset comprising 6084 images featuring 150 distinct fasteners across various classes was assembled. The dataset was partitioned into training, validation, and testing sets at a ratio of 7.5:2:0.5, respectively. Two prominent object detection algorithms, Mask-RCNN (regional-based convolutional neural network) and You Look Only Once-v5 (YOLO v5), were evaluated for efficiency and accuracy in fastener identification. The findings revealed that YOLO v5 surpassed Mask-RCNN in processing speed and attained an mean average precision (MAP) of 99%. Additionally, YOLO v5 showcased superior performance conducive to real-time deployment.

Conclusions

The development of a resilient system employing deep learning algorithms for fastener identification within assembly processes signifies a significant stride in manufacturing technology. This study underscores the efficacy of YOLO v5 in achieving exceptional accuracy and efficiency, thereby augmenting the automation and dependability of assembly operations in manufacturing environments. Such advancements hold promise for streamlining production processes, mitigating errors, and enhancing overall productivity in the manufacturing sector.

Background

Acute leukemia may present with extramedullary (EM) tissues. Myeloid sarcoma (MS) and leukemia cutis (LC) are considered extramedullary diseases. This study aims to evaluate the incidence, clinical characteristics, and prognostic factors affecting the outcome of pediatric patients with myeloid sarcoma at the pediatric oncology center from July 2007 to December 2017. Radiological imaging was used to stage the tumor, biopsy was done for pathological diagnosis, and bone marrow aspirate for morphology, flow cytometry, cytogenetics, and molecular analysis. Patients received chemotherapy protocols based on those used by the Children's Cancer Group for acute myeloid leukemia (AML).

Result

The study included 91 (13.2%) of 687 pediatric patients with acute myeloid leukemia, with a male-to-female ratio of 1.9 to 1. Prognostic factors that improved the patient’s 5-year overall survival (OS) were age > 5 years old, molecular and cytogenetic detection of t (8; 21), inv 16, presence of a single and small size lesion < 5 cm, negative CNS lesion, and achieved radiological response in isolated disease or radiological and marrow complete remission in disseminated disease post induction 1 with significant P value. Relapse, particularly early relapse, worsens the OS and EFS by 10% and 7.7%, respectively.

Conclusion

Patients over five with low-risk disease based on cytogenetics, a small, single, negative CNS lesion, and a complete response post induction 1 had better outcomes, with no significant difference between those with isolated extramedullary disease and those with marrow dissemination. Early relapse harms the outcome. The study group’s 5-year OS and EFS rates are 51.4% and 49.4%, respectively.

Background

Distinguishing between middle ear cholesteatoma and chronic suppurative otitis media (CSOM) is an ongoing challenge. While temporal bone computed tomography (CT) scan is highly accurate for diagnosing middle ear conditions, its specificity in discerning between cholesteatoma and CSOM is only moderate. To address this issue, we utilized trained machine learning models to enhance the specificity of temporal bone CT scan in diagnosing middle ear cholesteatoma. Our database consisted of temporal bone CT scan native images from 122 patients diagnosed with middle ear cholesteatoma and a control group of 115 patients diagnosed with CSOM, with both groups labeled based on surgical findings. We preprocessed the native images to isolate the region of interest and then utilized the Inception V3 convolutional neural network for image embedding into data vectors. Classification was performed using machine learning models including support vector machine (SVM), k-nearest neighbors (k-NN), random forest, and neural network. Statistical metrics employed to interpret the results included classification accuracy, precision, recall, F1 score, confusion matrix, area under the receiver operating characteristic curve (AUC), and FreeViz diagram.

Results

Our training dataset comprised 5390 images, and the testing dataset included 125 different images. The neural network, k-NN, and SVM models demonstrated significantly higher relevance in terms of classification accuracy, precision, and recall compared to the random forest model. For instance, the F1 scores were 0.974, 0.987, and 0.897, respectively, for the former three models, in contrast to 0.661 for the random forest model.

Conclusion

The performance metrics of the presented trained machine learning models hold promising prospects as potentially clinically useful aids.