International Journal of Advanced Biological and Biomedical Research

Biochemical Characteristics and Antibiotics Susceptibility of Streptococcus Mutans Isolates from Dental Caries in Baghdad City

Aya Raad Salh; Mohsen Hashim Risan; Hameed Majeed Jasim

Volume 10, Issue 1 , January 2022, , Pages 32-43

https://doi.org/10.22034/ijabbr.2022.534492.1363

Abstract

Background: Genus Streptococcus comprises important pathogens that have a severe impact on human health. According to the classification in Bergey's Manual of Determinative Bacteriology, ... Read More Background: Genus Streptococcus comprises important pathogens that have a severe impact on human health. According to the classification in Bergey's Manual of Determinative Bacteriology, the genus Streptococcus includes the pyogenic, oral and anaerobic groups of streptococci, as well as a group of other streptococci. S. mutans is a Gram-positive bacterium, which plays a key role in the formation of the dental plaque biofilm as an early coloniser and is the most important bacterium in the formation of dental caries. S. mutans do not have flagella, but do have pili and are non-motile facultative anaerobes that grow optimally at 37 °C. Methods: Eighty samples were collected from patients with different dental caries (pit, fissure and dental roots). The patients had referred to Al-dora Health center and Al-Zewiya Health center in Baghdad city. Bacterial isolates obtained from dental caries samples were streaked on selective medium (Mitis Salivaris Agar) for isolation of S. mutans, then the plates were incubated at 37 °C for 48 hours under anaerobic conditions. Bacterial isolates were identified according to their morphological and cultural characteristics and biochemical tests. The disk diffusion method was used to test the antibiotic susceptibility of the selected isolate to different antibiotics. A sterile cotton swab was dipped in fresh culture of S. mutans and streaked on the surface of Muller-Hinton agar plates by rotating the plate approximately 60 between streaking to ensure even distribution. The inoculated plates were incubated at room temperature for 10 minutes to allow absorption of excess moisture, then antibiotic disks were fixed by sterile forceps on the surface of plates and incubated at 37 °C for 18 hrs.Results: Totally, 98 bacterial isolates were obtained. The results indicated that antibiotic susceptibility pattern of the locally isolated S. mutans S2 was resistant to two antibiotics (bacitracin and erythromycin), while it was sensitive to the other eight antibiotics, consisting amoxicillin, ampicillin, chloramphenicol, vancomycin, clindamycin, imipenem, gentamycin and cephalothin.Conclusion: Locally isolated S. mutans S2 was sensitive to different antibiotics, while it resisted only against bacitracin and erythromycin.

Antibiotic susceptibility assessment of Escherichia coli isolated from traditional cheeses in Marand, Iran

abolfazl jafari sales; Parisa Hosein-Nezhad; Ahmadreza Shahniani

Volume 8, Issue 3 , September and October 2020, , Pages 236-241

https://doi.org/10.33945/SAMI/IJABBR.2020.3.2

Abstract

Background and Aim: Dairy products, especially cheese, are a suitable environment for the growth of pathogenic bacteria that cause disease after being transmitted to humans. Escherichia ... Read More

Prediction of Antibiotics Residues in Raw Milk by Using Binary Logistic Regression Model

H. Yarabbi; S.A. Mortazavi; M. Shafafi Zenozian; M. Mehraban Sang atash

Volume 2, Issue 4 (2) , April 2014, , Pages 287-296

Abstract

medical compounds, especially antibiotics, in which remains in milk and dairy products on the one hand causes health problems such as allergic reactions and Development of bacterial ... Read More

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Keywords = Antibiotic

Biochemical Characteristics and Antibiotics Susceptibility of Streptococcus Mutans Isolates from Dental Caries in Baghdad City

Abstract

Antibiotic susceptibility assessment of Escherichia coli isolated from traditional cheeses in Marand, Iran

Abstract

Prediction of Antibiotics Residues in Raw Milk by Using Binary Logistic Regression Model

Abstract