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Fulltext In vitro evaluation of the synergistic antioxidant and anti-inflammatory activities of the combined extracts from Malaysian Ganoderma lucidum and Egyptian Chlorella vulgaris

Abu-Serie MM, Habashy NH, Attia WE

BMC Complement Altern Med, 2018 May 10;18(1):154.
PMID: 29747629 DOI: 10.1186/s12906-018-2218-5

BACKGROUND: Since oxidative stress and inflammation are two linked factors in the pathogenesis of several human diseases. Thus identification of effective treatment is of great importance. Edible mushroom and microalgae are rich in the effective antioxidant phytochemicals. Hence, their beneficial effects on oxidative stress-associated inflammation are extremely required to be investigated.
METHODS: This study evaluated the functional constituents, antioxidant and anti-inflammatory activities of Malaysian Ganoderma lucidum aqueous extract (GLE) and Egyptian Chlorella vulgaris ethanolic extract (CVE). Also, the synergistic, addictive or antagonistic activities of the combination between the two extracts (GLE-CVE) were studied. Expression of inducible nitric oxide synthase, cyclooxygenase-2, and nuclear factor-kappa B, as well as levels of nitric oxide, tumor necrosis factor (TNF)-α, lipid peroxidation, reduced glutathione and antioxidant enzymes were determined using in vitro model of lipopolysaccharide-stimulated white blood cells.
Fulltext A Novel Nano-Hydroxyapatite Agarose-Based Hydrogel for Biomimetic Remineralization of Demineralized Human Enamel: An in-vitro Study

El Moshy S, Radwan IA, Matoug-Elwerfelli M, Abdou A, Abbass MMS

Clin Cosmet Investig Dent, 2024;16:453-465.
PMID: 39507288 DOI: 10.2147/CCIDE.S478045

PURPOSE: This study aims to investigate the biomimetic effect of agarose hydrogel loaded with enamel matrix derivative (EMD-agarose) alone or in combination with nano-hydroxyapatite (n-HA-EMD-agarose) on the remineralization of human demineralized enamel.
METHODS: Extracted human mandibular third molars were sectioned into 54 buccal and lingual halves. Acid-resistant nail varnish was applied to each half, except for two enamel windows. Enamel surface microhardness, energy-dispersive X-ray spectroscopy (EDX), and scanning electron microscopy (SEM) analyses were conducted to evaluate enamel surfaces at baseline, following demineralization with 37% phosphoric acid, and after each hydrogel application and remineralization for two, four, and six days. Remineralization was performed using a phosphate solution at 37°C.
RESULTS: At day 6 following remineralization, a statistically significant higher mean microhardness was recorded in n-HA-EMD-agarose hydrogel (260.87 ± 3.52) as compared to EMD-agarose hydrogel (244.63 ± 2.76) (p = 0.027). Similarly, n-HA-EMD-agarose hydrogel showed a higher mean calcium (46.31 ± 2.78), phosphorous (24.92 ± 0.826), and fluoride (0.909 ± 0.053) weight percentage compared to EMD-agarose hydrogel calcium (19.64 ± 1.092), phosphorous (19.64 ± 1.092), and fluoride (0.7033 ± 0.0624) weight percentage (p < 0.05). Further, SEM analysis revealed a substantial deposition of n-HA following the application of the n-HA-EMD-agarose hydrogel, whereas the EMD-agarose exhibited a relatively smooth enamel surface with less visible enamel rods due to mineral deposition.
CONCLUSION: The combined n-HA-EMD-agarose hydrogel demonstrated improved surface microhardness of the remineralized enamel and enhanced mineral content deposition, indicating its potential as a biomimetic approach for dental enamel repair.
Fulltext The integration of 21st century skills in the curriculum of education

Herlinawati H, Marwa M, Ismail N, Junaidi, Liza LO, Situmorang DDB

Heliyon, 2024 Aug 15;10(15):e35148.
PMID: 39170322 DOI: 10.1016/j.heliyon.2024.e35148

The premise of this study, utilizing content analysis and descriptive qualitative designs, posited that teachers' comprehension of 21st-century/4Cs skills' could define the caliber of educational materials in higher education institutions. The study aimed to ascertain how 21st-century skills were incorporated in teachers' term evaluations and instructional plans, and to explore teachers' understanding of these skills. From 2022 to 2023, this research was carried out at the Faculty of Education in one university in Indonesia. There were 54 documents collected, which included 27 teachers' term evaluations and 27 instructional plans. Four teachers were interviewed to collect information related to their 4Cs competencies' familiarity, opinions, and the challenges of the 4Cs competencies integration. To evaluate the collected documents, this study utilized the Career Technical Education (CTE) Career Ready Practices checklist, a 21st-Century Skills/4Cs rubric encompassing "Communication," "Creativity," "Critical Thinking," and "Collaboration." The research indicated that teachers' term evaluations and instructional plans have incorporated 4C skills in the categories of "Not yet reached competency" and "Approaching competency." The research suggests that teachers' understanding of 4Cs competencies can be initially assessed through their instructional materials, 4Cs competencies, familiarity, positive opinions, and challenges. Teachers must have familiarity with 4Cs competencies in order to provide these skills in their instructional materials/plans and develop teaching with the 4Cs competencies. A multifaceted strategy is needed for the next research, including focused professional development, collaboration among educators, institutional leaders' support, and alignment with larger educational priorities and goals.
Fulltext An Al-Biruni Earth Radius Optimization-Based Deep Convolutional Neural Network for Classifying Monkeypox Disease

Khafaga DS, Ibrahim A, El-Kenawy EM, Abdelhamid AA, Karim FK, Mirjalili S, et al.

Diagnostics (Basel), 2022 Nov 21;12(11).
PMID: 36428952 DOI: 10.3390/diagnostics12112892

Human skin diseases have become increasingly prevalent in recent decades, with millions of individuals in developed countries experiencing monkeypox. Such conditions often carry less obvious but no less devastating risks, including increased vulnerability to monkeypox, cancer, and low self-esteem. Due to the low visual resolution of monkeypox disease images, medical specialists with high-level tools are typically required for a proper diagnosis. The manual diagnosis of monkeypox disease is subjective, time-consuming, and labor-intensive. Therefore, it is necessary to create a computer-aided approach for the automated diagnosis of monkeypox disease. Most research articles on monkeypox disease relied on convolutional neural networks (CNNs) and using classical loss functions, allowing them to pick up discriminative elements in monkeypox images. To enhance this, a novel framework using Al-Biruni Earth radius (BER) optimization-based stochastic fractal search (BERSFS) is proposed to fine-tune the deep CNN layers for classifying monkeypox disease from images. As a first step in the proposed approach, we use deep CNN-based models to learn the embedding of input images in Euclidean space. In the second step, we use an optimized classification model based on the triplet loss function to calculate the distance between pairs of images in Euclidean space and learn features that may be used to distinguish between different cases, including monkeypox cases. The proposed approach uses images of human skin diseases obtained from an African hospital. The experimental results of the study demonstrate the proposed framework's efficacy, as it outperforms numerous examples of prior research on skin disease problems. On the other hand, statistical experiments with Wilcoxon and analysis of variance (ANOVA) tests are conducted to evaluate the proposed approach in terms of effectiveness and stability. The recorded results confirm the superiority of the proposed method when compared with other optimization algorithms and machine learning models.
Fulltext Optimizing HCV Disease Prediction in Egypt: The hyOPTGB Framework

Elshewey AM, Shams MY, Tawfeek SM, Alharbi AH, Ibrahim A, Abdelhamid AA, et al.

Diagnostics (Basel), 2023 Nov 13;13(22).
PMID: 37998575 DOI: 10.3390/diagnostics13223439

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.
Fulltext Epitope-Based Peptide Vaccine against Glycoprotein G of Nipah Henipavirus Using Immunoinformatics Approaches

Mohammed AA, Shantier SW, Mustafa MI, Osman HK, Elmansi HE, Osman IA, et al.

J Immunol Res, 2020;2020:2567957.
PMID: 32377531 DOI: 10.1155/2020/2567957

Background: Nipah belongs to the genus Henipavirus and the Paramyxoviridae family. It is an endemic most commonly found at South Asia and has first emerged in Malaysia in 1998. Bats are found to be the main reservoir for this virus, causing disease in both humans and animals. The last outbreak has occurred in May 2018 in Kerala. It is characterized by high pathogenicity and fatality rates which varies from 40% to 70% depending on the severity of the disease and on the availability of adequate healthcare facilities. Currently, there are no antiviral drugs available for NiV disease and the treatment is just supportive. Clinical presentations for this virus range from asymptomatic infection to fatal encephalitis.
Objective: This study is aimed at predicting an effective epitope-based vaccine against glycoprotein G of Nipah henipavirus, using immunoinformatics approaches.
Methods and Materials: Glycoprotein G of the Nipah virus sequence was retrieved from NCBI. Different prediction tools were used to analyze the epitopes, namely, BepiPred-2.0: Sequential B Cell Epitope Predictor for B cell and T cell MHC classes II and I. Then, the proposed peptides were docked using Autodock 4.0 software program. Results and Conclusions. The two peptides TVYHCSAVY and FLIDRINWI have showed a very strong binding affinity to MHC class I and MHC class II alleles. Furthermore, considering the conservancy, the affinity, and the population coverage, the peptide FLIDRINWIT is highly suitable to be utilized to formulate a new vaccine against glycoprotein G of Nipah henipavirus. An in vivo study for the proposed peptides is also highly recommended.