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  1. Nadirah, M., Najiah M., Teng, S. Y.
    MyJurnal
    This study described the antibiotic and heavy metal resistance pattern of 17 isolates of Edwardsiella tarda obtained from Asian seabass (Lates calcarifer). E.tarda isolates were resistant to oleandomycin, lincomycin, novobiocin and spiramycin. In contrast, most of the isolates showed high level of susceptibility to tetracycline, doxycycline, florfenicol, chloramplenicol, nitrofurantoin, fosfomycin, kanamycin, oxolinic acid and flumequine. MAR value was 0.35 which indicated that the cultured Asian seabass have received high exposure to those tested antibiotics. Besides, very high level of heavy metal resistance among these isolates was observed. Genotypic profile of DNA fingerprintings generated by RAPD-PCR using M13 universal primer and M13 wild type phage primer showed high degree of genetic diversity with percentages similarity and genetic distance among the isolates were ranging from 10.5% to 100% and 0 to 0.895, respectively. This result indicates that strains that belong to the same origin were not always closely related genetically.
  2. Mohd Rijal O, Mohd Noor N, Shaban H, Lee Teng S
    Conf Proc IEEE Eng Med Biol Soc, 2007 2 7;2005:6418-21.
    PMID: 17281737
    A common practice in medical diagnosis and patient management is the comparison of two chest radiographs images. The difference between two digital images at two time points is a measure of the effect of treatment on the patient. Two measures of similarity, the ordinary regression coefficients, R(s)(2) and coefficients of determination for the Unreplicated linear functional relationship model (ULFR), R(f) (2), are used to compare images for the particular case of Mycobacterium Tuberculosis (MTB). Our results suggest that a series of R2 values indicates gradual declining trends with values falling within a band. New patients with a series of R2 values falling within this band may be consider as making a good or acceptable recovery.
  3. Teng S, Khong KW, Pahlevan Sharif S, Ahmed A
    JMIR Public Health Surveill, 2020 10 01;6(4):e19618.
    PMID: 33001036 DOI: 10.2196/19618
    BACKGROUND: Poor nutrition and food selection lead to health issues such as obesity, cardiovascular disease, diabetes, and cancer. This study of YouTube comments aims to uncover patterns of food choices and the factors driving them, in addition to exploring the sentiments of healthy eating in networked communities.

    OBJECTIVE: The objectives of the study are to explore the determinants, motives, and barriers to healthy eating behaviors in online communities and provide insight into YouTube video commenters' perceptions and sentiments of healthy eating through text mining techniques.

    METHODS: This paper applied text mining techniques to identify and categorize meaningful healthy eating determinants. These determinants were then incorporated into hypothetically defined constructs that reflect their thematic and sentimental nature in order to test our proposed model using a variance-based structural equation modeling procedure.

    RESULTS: With a dataset of 4654 comments extracted from YouTube videos in the context of Malaysia, we apply a text mining method to analyze the perceptions and behavior of healthy eating. There were 10 clusters identified with regard to food ingredients, food price, food choice, food portion, well-being, cooking, and culture in the concept of healthy eating. The structural equation modeling results show that clusters are positively associated with healthy eating with all P values less than .001, indicating a statistical significance of the study results. People hold complex and multifaceted beliefs about healthy eating in the context of YouTube videos. Fruits and vegetables are the epitome of healthy foods. Despite having a favorable perception of healthy eating, people may not purchase commonly recognized healthy food if it has a premium price. People associate healthy eating with weight concerns. Food taste, variety, and availability are identified as reasons why Malaysians cannot act on eating healthily.

    CONCLUSIONS: This study offers significant value to the existing literature of health-related studies by investigating the rich and diverse social media data gleaned from YouTube. This research integrated text mining analytics with predictive modeling techniques to identify thematic constructs and analyze the sentiments of healthy eating.

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