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Hepatotoxicity and ALAD Activity Profile for Prediction of NOAEL of Metal Welding Fumes in Albino Rats.

Ali SaniIbrahim Lawal AbdullahiAminu Inuwa Darma
Published in: Biological trace element research (2022)
Metal fume pollutants of urban Kano, a city of over 10 million people, and widespread metal works have increased exposure with related health effects. Few data on metal fume toxicity and atmospheric levels have been documented in Nigeria and Kano in particular. Hence, the work was aimed at evaluating the metal fume toxicity to laboratory rat species for setting the permissible limit of exposure in urban Kano. The investigation involved the collection of metal welding fumes and subsequent laboratory analysis. Experimental animals were then exposed intratracheally to varying doses of the fumes which were equivalent to normal metal workers' daily routine of 2, 4, and 8 h for 3, 5, 10, and 20 years. Following euthanization, whole blood samples were collected and functions of liver and delta-aminolevunilic acid dehydratase were evaluated in the serum. Exposure to the fumes has caused significant mortality that was observed to be dose-dependent and statistically different (p < 0.05); moreover, the fumes had synergistically affected the functions of liver. In addition, the fumes had increased (statistically) the activity delta-aminolevinilic acid dehydratase. This has indicated that exposure to metal welding fumes being multi-elemental is toxic and had produced mortality at exposure to higher doses of metal welding fumes. It was therefore established from the study that no-observed-adverse-effect level (NOAEL) for metal welding fumes is 25.73 mg with LD 50 of 270 mg which corresponds to the metal worker's 4-h shifts daily for 5 years under existing working conditions. It was recommended that regular monitoring should be put in place to limit exposure and extent of engagement in metal works beyond NOAEL levels.
Keyphrases
  • emergency department
  • oxidative stress
  • coronary artery disease
  • cardiovascular events
  • machine learning
  • risk assessment
  • air pollution
  • deep learning
  • drug induced
  • genetic diversity
  • oxide nanoparticles