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The selection of material is important in the development of a paediatric speech perception test. Even though several studies have documented various sources of speech testing materials, using corpus was recommended to develop a Matrix Sentence Test (MST). However, as available Malay corpus was limited to written words only, this research aims to construct a Paediatric Malay corpus from a combination of written and spoken words of children in Malaysia and to develop word-based matrix (WBM) for the Paediatric Malay Matrix Sentence Test (PaedMalayMST) based on the developed corpus. The WBM replicates the Malay grammatical structure in these three categories: number, object, and adjective. Based on these criteria, the words from the developed corpus were then filtered by considering the frequency of occurrences and its phonemic distributions to be included in the WBM. As a result, a matrix of three categories with six words for each category was constructed.

Abstract Background Cosmos caudatus is an annual plant known for its medicinal value in treating several health conditions, such as high blood pressure, arthritis, and diabetes mellitus. The α-glucosidase inhibitory activity and total phenolic content of the leaf aqueous ethanolic extracts of the plant at different growth stages (6, 8. 10, 12 and 14 weeks) were determined in an effort to ascertain the best time to harvest the plant for maximum medicinal quality with respect to its glucose-lowering effects. Methods The aqueous ethanolic leaf extracts of C. caudatus were characterized by NMR and LC-MS/MS. The total phenolic content and α-glucosidase inhibitory activity were evaluated by the Folin-Ciocalteu method and α-glucosidase inhibitory assay, respectively. The statistical significance of the results was evaluated using one-way ANOVA with Duncan’s post hoc test, and correlation among the different activities was performed by Pearson’s correlation test. NMR spectroscopy along with multivariate data analysis was used to identify the metabolites correlated with total phenolic content and α-glucosidase inhibitory activity of the C. caudatus leaf extracts. Results It was found that the α-glucosidase inhibitory activity and total phenolic content of the optimized ethanol:water (80:20) leaf extract of the plant increased significantly as the plant matured, reaching a maximum at the 10th week. The IC50 value for α-glucosidase inhibitory activity (39.18 μg mL− 1) at the 10th week showed greater potency than the positive standard, quercetin (110.50 μg mL− 1). Through an 1H NMR-based metabolomics approach, the 10-week-old samples were shown to be correlated with a high total phenolic content and α-glucosidase inhibitory activity. From the partial least squares biplot, rutin and flavonoid glycosides, consisting of quercetin 3-O-arabinofuranoside, quercetin 3-O-rhamnoside, quercetin 3-O-glucoside, and quercetin 3-O-xyloside, were identified as the major bioactive metabolites. The metabolites were identified by NMR spectroscopy (J-resolve, HSQC and HMBC experiments) and further supported by dereplication via LC-MS/MS. Conclusion For high phytomedicinal quality, the 10th week is recommended as the best time to harvest C. caudatus leaves with respect to its glucose lowering potential.

Using e-Learning among Teacher Graduate Programme (TGP) to students who are newly exposed in Teaching and Learning (T&L) proses. The major problem among student’s TGP is the readiness level in knowledge, attitude and skills using e-Learning during Teaching and Learning (T&L) . This study is to identify any significant differences of the knowledge level between male and female TGP students. The design of this study is to survey research using instruments of questionnaire which are distributed to respondents among TGP students at the Faculty of Technical and Vocational, UTHM. The sample is given to 248 respondents and analysis using version SPSS 21.0. Data presented using descriptive method such as, Mean, Standard Deviation(SD), inferens statistic using Mann-Whitney U to identify the knowledge difference between male and female TGP students. The result showed that the level of application of e-Learning among TGP students in terms of knowledge, attitude and skill at moderate level. The study also showed that there are no significant knowledge difference among male and female TGP students because the result of p= 0.044 value is less

The main objective of this study is to explore the efficiency of utilizing a culturally - enhanced serious game in education particularly in Information Technology course. To attain this objective, the academic achievement of 61 participants was measured by using quantitative Quasi- Experimental research design approach. Sets of pre- and post-test questions were administered to all participants, where descriptive analysis and hypothesis evaluation were performed to the collected data. The finding revealed that there were significant increase on the mean values of participants who played culturally-enhanced serious game as compared to those who played contemporary serious game (M = 3.00). Therefore, the study suggested that culturally-enhanced serious game has potential to support education particularly in Information Technology course.

Following the COVID-19 outbreak, teaching and learning have been forced to move fully to the Internet rather than the conventional offline medium. As a result, the use of M-learning has risen dramatically, which was neither expected or anticipated. The challenges and benefits of such widespread usage are beginning to emerge in front of us. Thus, in this paper, we systematically review the benefits and challenges of leveraging M-learning for Science and Technology courses during the COVID-19 pandemic by educators and learners. Related articles were obtained from various databases, namely, IEEE, ACM Digital Library, ScienceDirect, and Springer. In total, 4210 related articles were initially found. Upon executing careful selection criteria, 22 articles were selected for review. After that, the advantages and threats were identified and discussed. As per our findings, it was determined that M-learning has excellent potential to be an effective platform for education provided that the identified shortcomings are resolved. This review will be helpful for education stakeholders and institutions to gauge the impact of leveraging M-learning as the only means for education to proceed. Moreover, it reveals the strengths and shortcomings that would aid in adjusting the relevant policies administered by the institutions. Furthermore, application developers will be able to comprehend the expected features that should be included in novel M-learning platforms.

Simple Summary: Accurate detection of HER2 status in breast cancer is vital for effective diagnosis and treatment. While traditional fluorescence in situ hybridization (FISH) is the standard for assessing HER2, it has limitations, such as the need for expert handling and potential signal fading. Silver-enhanced in situ hybridization (SISH) offers a more stable alternative for analysis using conventional bright-field microscopy. This study explores an innovative deep-learning approach to automatically classify Normal, Amplified, and Non-Amplified regions in HER2-SISH images. Our two-stage process trains and evaluates models on annotated patches and applies the most effective model to entire slide images, generating region-specific visualizations. This method not only reduces manual workload but also shows strong potential for aiding pathologists in identifying HER2-amplified areas, thereby supporting precise breast cancer treatment. Statistical evaluations, including k-fold cross-validation and confidence intervals, further validate the robustness of our approach. Fluorescence in situ hybridization (FISH) is widely regarded as the gold standard for evaluating human epidermal growth factor receptor 2 (HER2) status in breast cancer; however, it poses challenges such as the need for specialized training and issues related to signal degradation from dye quenching. Silver-enhanced in situ hybridization (SISH) serves as an automated alternative, employing permanent staining suitable for bright-field microscopy. Determining HER2 status involves distinguishing between "Amplified" and "Non-Amplified" regions by assessing HER2 and centromere 17 (CEN17) signals in SISH-stained slides. This study is the first to leverage deep learning for classifying Normal, Amplified, and Non-Amplified regions within HER2-SISH whole slide images (WSIs), which are notably more complex to analyze compared to hematoxylin and eosin (H&E)-stained slides. Our proposed approach consists of a two-stage process: first, we evaluate deep-learning models on annotated image regions, and then we apply the most effective model to WSIs for regional identification and localization. Subsequently, pseudo-color maps representing each class are overlaid, and the WSIs are reconstructed with these mapped regions. Using a private dataset of HER2-SISH breast cancer slides digitized at 40× magnification, we achieved a patch-level classification accuracy of 99.9 % and a generalization accuracy of 78.8 % by applying transfer learning with a Vision Transformer (ViT) model. The robustness of the model was further evaluated through k-fold cross-validation, yielding an average performance accuracy of 98 % , with metrics reported alongside 95 % confidence intervals to ensure statistical reliability. This method shows significant promise for clinical applications, particularly in assessing HER2 expression status in HER2-SISH histopathology images. It provides an automated solution that can aid pathologists in efficiently identifying HER2-amplified regions, thus enhancing diagnostic outcomes for breast cancer treatment. [ABSTRACT FROM AUTHOR]

The human epidermal growth factor receptor 2 (HER2) gene is a critical biomarker for determining amplification status and targeting clinical therapies in breast cancer treatment. This study introduces a computer-aided method that automatically measures and scores HER2 gene status from invasive tissue regions of breast cancer using whole slide images (WSI) through silver in situ hybridization (SISH) staining. Image processing and deep learning techniques are employed to isolate untruncated and non-overlapping single nuclei from cancer regions. The Stardist deep learning model is fine-tuned on our HER2-SISH data to identify nuclei regions, followed by post-processing based on identified HER2 and CEP17 signals. Conventional thresholding techniques are used to segment HER2 and CEP17 signals. HER2 amplification status is determined by calculating the HER2-to-CEP17 signal ratio, in accordance with ASCO/CAP 2018 standards. The proposed method significantly reduces the effort and time required for quantification. Experimental results demonstrate a 0.91% correlation coefficient between pathologists manual enumeration and the proposed automatic SISH quantification approach. A one-sided paired t-test confirmed that the differences between the outcomes of the proposed method and the reference standard are statistically insignificant, with p-values exceeding 0.05. This study illustrates how deep learning can effectively automate HER2 status determination, demonstrating improvements over current manual methods and offering a robust, reproducible alternative for clinical practice. [ABSTRACT FROM AUTHOR]

Recent advancements in medical imaging have greatly enhanced the application of computational techniques in digital pathology, particularly for the classification of breast cancer using in situ hybridization (ISH) imaging. HER2 amplification, a key prognostic marker in 20–25% of breast cancers, can be assessed through alterations in gene copy number or protein expression. However, challenges persist due to the heterogeneity of nuclear regions and complexities in cancer biomarker detection. This review examines semi-automated and fully automated computational methods for analyzing ISH images with a focus on HER2 gene amplification. Literature from 1997 to 2023 is analyzed, emphasizing silver-enhanced in situ hybridization (SISH) and its integration with image processing and machine learning techniques. Both conventional machine learning approaches and recent advances in deep learning are compared. The review reveals that automated ISH analysis in combination with bright-field microscopy provides a cost-effective and scalable solution for routine pathology. The integration of deep learning techniques shows promise in improving accuracy over conventional methods, although there are limitations related to data variability and computational demands. Automated ISH analysis can reduce manual labor and increase diagnostic accuracy. Future research should focus on refining these computational methods, particularly in handling the complex nature of HER2 status evaluation, and integrate best practices to further enhance clinical adoption of these techniques. [ABSTRACT FROM AUTHOR]