2022
DOI: 10.14202/vetworld.2022.672-678
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Hematological indices and their correlation with glucose control parameters in a prediabetic rat model

Abstract: Background and Aim: Chronic hyperglycemia in prediabetic individuals would progress to diabetes and lead to several systemic disruptions, including hematological parameters. This study aimed to investigate the correlation between prediabetes and hematological indices in a prediabetic rat model. Materials and Methods: Eighteen male rats were randomly divided into two groups of nine. Prediabetes was induced in nine rats by a 3-week high-fat and high-glucose diet, followed by low-dose streptozotocin (STZ) inject… Show more

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Cited by 4 publications
(4 citation statements)
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“…The association between LYM and FBG in this study is in discordance with the previous finding of Arkew et al [5], who reported no association. However, Krisnamurti et al [27] reported a correlation between these variables. The reduction of MCV in the diabetic animals indicates a possible risk of anemia.…”
Section: Discussionmentioning
confidence: 98%
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“…The association between LYM and FBG in this study is in discordance with the previous finding of Arkew et al [5], who reported no association. However, Krisnamurti et al [27] reported a correlation between these variables. The reduction of MCV in the diabetic animals indicates a possible risk of anemia.…”
Section: Discussionmentioning
confidence: 98%
“…This finding is corroborated by similar studies that show medicinal plants improve hyperglycemia-altered coagulation signaling and thrombocyte indices in addition to BT/CT and PLT inverse relationship [ 23 , 25 , 26 ]. Nonetheless, a contrary finding reported no association between PLT and FBG [ 8 , 27 ].…”
Section: Discussionmentioning
confidence: 99%
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“…Recent studies have confirmed that basic information, daily habits, and blood cells are risk factors for IR and can provide help in predicting the occurrence of IR. [8][9][10][11] In recent years, advanced technologies guided by artificial intelligence and machine learning (ML) have made computers increasingly intelligent and independent. These technologies are now applied effectively in every step of clinical work, including disease diagnosis, differential diagnosis, personalized treatment, and prognosis.…”
Section: Introductionmentioning
confidence: 99%