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  1. Akhtar A, Gupta SM, Dwivedi S, Kumar D, Shaikh MF, Negi A
    ACS Omega, 2022 Dec 27;7(51):47504-47517.
    PMID: 36591205 DOI: 10.1021/acsomega.2c05609
    A robust preclinical disease model is a primary requirement to understand the underlying mechanisms, signaling pathways, and drug screening for human diseases. Although various preclinical models are available for several diseases, clinical models for Alzheimer's disease (AD) remain underdeveloped and inaccurate. The pathophysiology of AD mainly includes the presence of amyloid plaques and neurofibrillary tangles (NFT). Furthermore, neuroinflammation and free radical generation also contribute to AD. Currently, there is a wide gap in scientific approaches to preventing AD progression. Most of the available drugs are limited to symptomatic relief and improve deteriorating cognitive functions. To mimic the pathogenesis of human AD, animal models like 3XTg-AD and 5XFAD are the primarily used mice models in AD therapeutics. Animal models for AD include intracerebroventricular-streptozotocin (ICV-STZ), amyloid beta-induced, colchicine-induced, etc., focusing on parameters such as cognitive decline and dementia. Unfortunately, the translational rate of the potential drug candidates in clinical trials is poor due to limitations in imitating human AD pathology in animal models. Therefore, the available preclinical models possess a gap in AD modeling. This paper presents an outline that critically assesses the applicability and limitations of the current approaches in disease modeling for AD. Also, we attempted to provide key suggestions for the best-fit model to evaluate potential therapies, which might improve therapy translation from preclinical studies to patients with AD.
  2. Chia PL, Earnest A, Lee R, Lim J, Wong CP, Chia YW, et al.
    Ann Acad Med Singap, 2013 Sep;42(9):432-6.
    PMID: 24162317
    INTRODUCTION: In Singapore, the age-standardised event rates of myocardial infarction (MI) are 2- and 3-fold higher for Malays and Indians respectively compared to the Chinese. The objectives of this study were to determine the prevalence and quantity of coronary artery calcification (CAC) and non-calcified plaques across these 3 ethnic groups.

    MATERIALS AND METHODS: This was a retrospective descriptive study. We identified 1041 patients (810 Chinese, 139 Malays, 92 Indians) without previous history of cardiovascular disease who underwent cardiac computed tomography for atypical chest pain evaluation. A cardiologist, who was blinded to the patients' clinical demographics, reviewed all scans. We retrospectively analysed all their case records.

    RESULTS: Overall, Malays were most likely to be active smokers (P = 0.02), Indians had the highest prevalence of diabetes mellitus (P = 0.01) and Chinese had the highest mean age (P <0.0001). The overall prevalence of patients with non-calcified plaques as the only manifestation of sub-clinical coronary artery disease was 2.1%. There was no significant difference in the prevalence of CAC, mean CAC score or prevalence of non-calcified plaques among the 3 ethnic groups. Active smoking, age and hypertension were independent predictors of CAC. Non-calcified plaques were positively associated with male gender, age, dyslipidaemia and diabetes mellitus.

    CONCLUSION: The higher MI rates in Malays and Indians in Singapore cannot be explained by any difference in CAC or non-calcified plaque. More research with prospective follow-up of larger patient populations is necessary to establish if ethnic-specific calibration of CAC measures is needed to adjust for differences among ethnic groups.

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