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Influence of Oral Dipping Tombak Smokeless Tobacco on Coagulation Profile and Platelet Counts.

Ahmed M E ElkhalifaNada Y AliAbdelhakam G TamomhMohammed I TabashEsraa T A MustafaZenieb A K MohammedNedal A S Ahamed
Published in: Hematology reports (2022)
The goal of this paper is to investigate the influence of oral dipping of Tombak Smokeless Tobacco (SLT) on prothrombin time (PT), activated partial thromboplastin time (APTT), international normalized ratio(INR) values, and platelet counts (PLTs), in Sudanese Tombak users. An analytical cross-sectional study was conducted at Kosti health insurance hospital, Sudan, in 2019. According to the inclusion and exclusion criteria, 100 adult users of oral Tombak for three or more years were chosen randomly as a study group. Another 100 matched healthy individuals who never used Tombak were randomly selected as a comparative group. Venous blood specimens were collected in ethylene diamine tetra-acetic acid (EDTA) containers for the PLT counts using the automated haematology analyser (Sysmex, Tokyo, Japan XK-21SYSMEX) and in trisodium citrate anti-coagulant containers for coagulation tests using a co-agulometer machine analyser. Our findings show a significant decrease in PLT count mean values in the Tombak users group (212.1 × 10 3 /mm 3 ± 74.3 × 10 3 /mm 3 ) compared with the non-taking Tombak group mean values (243.2 × 10 3 /mm 3 ± 83.0 × 10 3 /mm 3 ), ( p < 0.006). Both PT and APTT were significantly prolonged in Tombak users (16.03 ± 1.22 s vs. 14.44 ± 0.557 s), p < 0.001 for PT, and (41.62 ± 7.28 s vs. 34.99 ± 4.02 s), ( p < 0.001) for APTT. INR mean values were significantly longer in Tombak users (1.11 ± 0.096) vs. (1.07 ± 0.66; p < 0.001). Multiple linear regression analysis findings show a significant impact of the four investigated variables, including duration of taking Tombak, age, and frequency of taking Tombak per day ( p < 0.001). In conclusion, using Tombak a Smokeless Tobacco (SLT) for a long period significantly affect Platelet counts and coagulation profile.
Keyphrases
  • health insurance
  • peripheral blood
  • deep learning
  • machine learning
  • affordable care act
  • high throughput
  • mass spectrometry
  • young adults
  • single cell
  • liquid chromatography
  • neural network