Biochemical and Culture-based Approaches to Identification in the Diagnostic Microbiology Laboratory

Altheide, S. Travis

doi:10.29074/ascls.2019001875

Cited by 5 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The API KIT as a replacement for conventional tubebased evaluation for members of the Enterobacteriaceae provides the advantage of being able to reduce by more than half the total cost per identification of an isolate, which is achieved through reduced labor and media. Furthermore, API 20E KIT is a reliable method and provides earlier results when compared to conventional biochemical (Travis, 2019;Al-Howaidi and Al-Hamad, 2020). The interpretation of API 20E identification results was based on the match between the tested bacterial profile and the database, the relative value of the selection, and the number of tests (Popović et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Validation of Analytical Method for Aeromonas hydrophila Identification using Analytical Profile Index (API) 20E KIT Method

Artati,

Oman,

Supriyanto

et al. 2024

J. Med. Vet.

View full text Add to dashboard Cite

The high demand for fish consumption has an impact on increasing aquaculture productivity and causes the vulnerability of increasing bacterial populations in aquaculture fields, so more rapid handling is needed. The use of phenotypic KIT methods (API 20E) has been applied as a targeted and efficient identification support in targeting better bacterial identification accuracy but often provides unequal results. Method validation is one of the general requirements for the competence of a laboratory evaluation: to provide coherent, interpretable, and accurate results with known uncertainties. The purpose of this study was to validate the API 20E KIT method for the identification of A. hydrophila. The conventional method used as a reference is SNI 7303.1:2015. The validation parameters consisted of determining the limit of detection, sensitivity, and specificity tests, as well as the positive predictive value and negative predictive value. The results showed that the limit of the detection value of the API 20E KIT was at a concentration of 100 CFU/mL with an ID of 99.00%. The sensitivity and specificity values in the positive and negative target samples were 100% with a positive predictive value and a negative predictive value of 0%, respectively. In conclusion, the API 20E KIT method as an alternative test method or rapid test was proven valid for identifying A. hydrophila by the test results using the reference method.

show abstract

Section: Introductionmentioning

confidence: 99%

Validation of Analytical Method for Aeromonas hydrophila Identification using Analytical Profile Index (API) 20E KIT Method

Artati,

Oman,

Supriyanto

et al. 2024

J. Med. Vet.

View full text Add to dashboard Cite

show abstract

Leveraging Broad‐Spectrum Fluorescence Data and Machine Learning for High‐Accuracy Bacterial Species Identification

Mito,

Okihara,

Kurita

et al. 2024

Journal of Biophotonics

View full text Add to dashboard Cite

Rapid and accurate identification of bacterial species is essential for the effective treatment of infectious diseases and suppression of antibiotic‐resistant strains. The unique autofluorescence properties of bacterial cells are exploited for rapid and cost‐effective identification that is suitable for point‐of‐care applications. Fluorescence spectroscopy is combined with machine learning to improve the diagnostic accuracy. Good training data for machine learning can be obtained to achieve the same diagnostic accuracy for bacterial species as when each wavelength is measured in detail over a broad spectral width. Experiments were performed testing 14 bacterial strains. The excitation‐emission matrix was analyzed, and Bayesian optimization was used to identify the most effective combinations of wavelengths. The results showed that fluorescence spectra using three specific excitation light regions or excitation spectra using two broad fluorescence detection regions could be used as supervised data to realize diagnostic accuracy comparable to that obtained with more complex instruments.

show abstract

Isolation of α-Amylase Producing Microorganisms from Soil of Kachchh, Gujarat

Morbia,

Pandey,

Mahla

et al. 2024

J. Pure Appl. Microbiol.

View full text Add to dashboard Cite

The purpose of this study is to explore the soil of the Gujarat, Kachchh region to identify amylase-producing bacteria and characterize them using molecular methods. The unique ecological characteristics of the Kachchh region may facilitate the isolation of these bacteria. Samples were collected from multiple locations within the Kachchh District, including Gandhidham, Rapar, Bhuj, Nakhatrana, Mandvi, and Mundra Talukas. These samples were then screened to isolate amylase-producing bacteria. A total of 27 different types of colonies were identified, out of which 16 exhibited amylase production (M1-M16). Out of 27 colonies identified, 16 showed amylase production. Strains M2, M7, and M13 exhibited high amylase activity, with M2 showing a consistent increase over 72 hours, making it a strong candidate for amylase production. Further identification of M2 stain identified M2 as a Gram-positive, spore-forming, capsulated, and motile bacillus, specifically Bacillus licheniformis. This was confirmed through DNA sequencing and analysis in the NCBI database, which showed a 99.15% similarity with Bacillus licheniformis. The study concludes that Soil in Kachchh is rich with microorganisms that produce amylase, an enzyme with diverse industrial applications. These organisms are valuable for sectors like food, textiles, paper, detergents, pharmaceuticals, and biofuel production.

show abstract

Biochemical and Culture-based Approaches to Identification in the Diagnostic Microbiology Laboratory

Cited by 5 publications

References 0 publications

Validation of Analytical Method for Aeromonas hydrophila Identification using Analytical Profile Index (API) 20E KIT Method

Validation of Analytical Method for Aeromonas hydrophila Identification using Analytical Profile Index (API) 20E KIT Method

Leveraging Broad‐Spectrum Fluorescence Data and Machine Learning for High‐Accuracy Bacterial Species Identification

Isolation of α-Amylase Producing Microorganisms from Soil of Kachchh, Gujarat

Contact Info

Product

Resources

About