Completing a course in Immunology is expected to improve health care knowledge (HCK), which in turn is anticipated to influence a healthy lifestyle (HLS), controlled use of health care services (HCS) and an awareness of emerging health care concerns (HCC). This cross-sectional study was designed to determine whether these interrelationships are empirically supported. Participants involved in this study were government servants from two ministries in Malaysia (n = 356) and university students from a local university (n = 147). Participants were selected using the non-random purposive sampling method. Data were collected using a self-developed questionnaire, which had been validated in a pilot study involving similar subjects. The questionnaire items were analyzed using Rasch analysis, SPSS version 21 and AMOS version 22. Results have shown that participants who followed a course in Immunology (CoI) had a higher primary HCK (Mean = 0.69 logit, SD = 1.29 logits) compared with those who had not (Mean = -0.27logit, SD = 1.26 logits). Overall, there were significant correlations among the HLS, the awareness of emerging HCC, and the controlled use of HCS (p <0.001). However, no significant correlations were observed between primary HCK and the other variables. However, significant positive correlation was observed between primary HCK and controlled use of HCS for the group without CoI. Path analysis showed that the awareness of emerging HCC exerted a positive influence on controlled use of HCS (β = 0.156, p < .001) and on HLS (β = 0.224, p < .001). These findings suggest that having CoI helps increase primary HCK which influences controlled use of HCS but does not necessarily influence HLS. Hence, introducing Immunology at various levels of education and increasing the public awareness of emerging HCC might help to improve population health en masse. In addition, further investigations on the factors affecting HLS is required to provide a better understanding on the relationship between primary HCK and HLS.
The oil yield, measured in oil extraction rate per hectare in the palm oil industry, is directly affected by the ripening levels of the oil palm fresh fruit bunches at the point of harvesting. A rapid, non-invasive and reliable method in assessing the maturity level of oil palm harvests will enable harvesting at an optimum time to increase oil yield. This study shows the potential of using Raman spectroscopy to assess the ripeness level of oil palm fruitlets. By characterizing the carotene components as useful ripeness features, an automated ripeness classification model has been created using machine learning. A total of 46 oil palm fruit spectra consisting of 3 ripeness categories; under ripe, ripe, and over ripe, were analyzed in this work. The extracted features were tested with 19 classification techniques to classify the oil palm fruits into the three ripeness categories. The Raman peak averaging at 1515 cm-1 is shown to be a significant molecular fingerprint for carotene levels, which can serve as a ripeness indicator in oil palm fruits. Further signal analysis on the Raman peak reveals 4 significant sub bands found to be lycopene (ν1a), β-carotene (ν1b), lutein (ν1c) and neoxanthin (ν1d) which originate from the C=C stretching vibration of carotenoid molecules found in the peel of the oil palm fruit. The fine KNN classifier is found to provide the highest overall accuracy of 100%. The classifier employs 6 features: peak intensities of bands ν1a to ν1d and peak positions of bands ν1c and ν1d as predictors. In conclusion, the Raman spectroscopy method has the potential to provide an accurate and effective way in determining the ripeness of oil palm fresh fruits.
Stem cells are primitive and precursor cells with the potential to reproduce into diverse mature and functional cell types in the body throughout the developmental stages of life. Their remarkable potential has led to numerous medical discoveries and breakthroughs in science. As a result, stem cell-based therapy has emerged as a new subspecialty in medicine. One promising stem cell being investigated is the induced pluripotent stem cell (iPSC), which is obtained by genetically reprogramming mature cells to convert them into embryonic-like stem cells. These iPSCs are used to study the onset of disease, drug development, and medical therapies. However, functional studies on iPSCs involve the analysis of iPSC-derived colonies through manual identification, which is time-consuming, error-prone, and training-dependent. Thus, an automated instrument for the analysis of iPSC colonies is needed. Recently, artificial intelligence (AI) has emerged as a novel technology to tackle this challenge. In particular, deep learning, a subfield of AI, offers an automated platform for analyzing iPSC colonies and other colony-forming stem cells. Deep learning rectifies data features using a convolutional neural network (CNN), a type of multi-layered neural network that can play an innovative role in image recognition. CNNs are able to distinguish cells with high accuracy based on morphologic and textural changes. Therefore, CNNs have the potential to create a future field of deep learning tasks aimed at solving various challenges in stem cell studies. This review discusses the progress and future of CNNs in stem cell imaging for therapy and research.