Biochemical Tests For Identification Of Medical Bacteria Pdf 17
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In many distinct areas of microbiology, the ability to identify microorganisms has important application. For example, in food microbiology it is important to be able to accurately identify food spoilage contaminants. In microbial ecology, the identification of microorganisms helps us characterize biodiversity. In the field of medical microbiology, a branch of microbiology that investigates pathogenic microorganisms, the primary focus is to isolate, identify, and study microorganisms responsible for infectious disease.
Many microorganisms are permanent residents, or normal flora, of the human body. Bacteria that are normal flora are important symbionts of the human body, most of which cause no ill effects and some, which are actually beneficial to human health. Only a small percentage, less than 10%, of all known bacteria are pathogenic, or able to cause disease in a susceptible host. In order to identify an unknown in the clinical laboratory, a sample must be collected from the patient. This could be a sample of urine, feces, saliva, or a swab of the throat or skin. Because the clinical samples will most likely contain many microorganisms, both normal flora and pathogens, it is important to isolate the pathogen in a pure culture using various types of selective and differential media. Following isolation, one of the first steps in identifying a bacterial isolate is the Gram stain, which allows for the determination of the Gram reaction, morphology, and arrangement of the organism. Although this information provides a few good clues, it does not allow us to determine the species or even genus of the organism with certainty. Thus, microbiologists use characteristic biochemicalactivities to more specifically identify bacterial species. A Few Biochemical/Physiological Properties Used for identification of bacteria include: nutrient utilization (carbohydrate utilization, amino acid degradation, lipid degradation), resistance to inhibitory substances (high salt, antibiotics, etc.), enzyme production (catalase, coagulase, hemolysins, etc.) and motility.
This series of lab exercises will introduce many of the physiological characteristics/biochemical activities of bacteria commonly encountered in a clinical microbiology laboratory. Knowledge of these key characteristics will enable the identification of unknown bacterial isolates. It is important to thoroughly understand the basis for each biochemical test and know the key physiological characteristics of the bacterial genera and species presented in these labs.
Your provider will review your medical history, ask about your symptoms and perform a physical exam that may involve feeling for a mass in your stomach. They may order several tests to diagnose and stage stomach cancer.
Abstract:Medical diagnosis in low-resource settings is confronted by the lack of suitable guidelines, protocols and checklists. Online-accessible procedural documents are difficult to find, might be mistranslated or interpreted and usually do not address the needs of developing countries. Urinalysis, one of the most frequently performed diagnostic examinations worldwide, involves a series of tests aiming to detect particular disorders, such as urinary tract infections, kidney disease and diabetes. In this guideline, we present an alternative approach for clinical laboratories with limited resources to identify common bacterial uropathogens. We propose dividing the identification plan into two levels. The implicated pathogen will first be assigned into a bacterial group, basic identification, against which a suitable panel of antimicrobial agents shall be selected for the antimicrobial susceptibility testing (AST). Characterization of the pathogen to the genus or species level, advanced identification, will then be performed to ensure correct reading of the AST results and determine the epidemiology of clinically significant pathogens. Most of the proposed steps in our guideline are tailored to meet the needs of clinical laboratories in low-resource settings. Such guidelines are needed to strengthen the capacity of regional pathology laboratories and to enhance international initiatives on antimicrobial resistance and health equity.Keywords: urinary tract infection; urinalysis; Gram-negative rods; Escherichia coli
Background: There has been increased interest in the study of anaerobic bacteria that cause human infection during the past decade. Many new genera and species have been described using 16S rRNA gene sequencing of clinical isolates obtained from different infection sites with commercially available special culture media to support the growth of anaerobes. Several systems, such as anaerobic pouches, boxes, jars and chambers provide suitable anaerobic culture conditions to isolate even strict anaerobic bacteria successfully from clinical specimens. Beside the classical, time-consuming identification methods and automated biochemical tests, the use of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry has revolutionized identification of even unusual and slow-growing anaerobes directly from culture plates, providing the possibility of providing timely information about anaerobic infections.
Content: The review involves topics on the anaerobes that are members of the commensal microbiota and their role causing infection, the key requirements for collection and transport of specimens, processing of specimens in the laboratory, incubation techniques, identification and antimicrobial susceptibility testing of anaerobic bacteria. Advantages, drawbacks and specific benefits of the methods are highlighted.
During each visit, individuals also completed a questionnaire recording life style, medication, and dietary habits. Anthropometrical measurements (height, weight, waist circumference, and so on) and biochemical tests (glucose, hsCRP, total cholesterol, HDL-C, LDL-C, triglyceride, and so on) were also conducted (Additional file 1: Table S1). The derived homeostasis model assessment (HOMA) index uses the fasting blood sugar and insulin to predict the insulin resistance of patients  and was calculated as standard (insulin * glucose)/405, both measured after fasting and glucose levels measured in mg/dL .
To observe whether catabolic or anabolic reactions contributed to the inter-omic correlation structure, KEGG pathway representation of imputed OTU metagenomes was correlated with the correlation coefficients from the pair-wise microbe-metabolite comparisons. While roughly half of these comparisons had significant positive correlation coefficients, indicating concordance between observed metabolite abundance and metagenomic abundance, a large proportion of correlations were insignificant or significantly negative (Additional file6). While the mechanisms remain unclear, multiple possibilities exist for the differing directionality of observed correlations. For example, a metabolic end product might have a positive correlation with the OTU (and thus the originating pathway) that produces and exports it, but a negative correlation with the OTU (and thus the originating pathway) that imports and processes it in a downstream pathway. Unfortunately, this also means that some metabolites might have less significant correlation curves due to organisms that encode enzymes producing such metabolites, but are not correlated with the metabolites because they are not exported, which was required for us to measure the association. Given the immature understanding of gut metabolic pathways and the imperfect nature of putative metabolite ID picking, we were not able to resolve this issue. Regardless, these data suggest that our predicted metagenomic and putative metabolite ID data were concordant. This was an important finding because defining metabolite IDs using biochemical methods is subject to numerous limitations that could be simplified with metagenomic data. To try and detect signatures of microbial inhibition or stimulation, we attempted to quantify the significance of community structure between microbes with shared metabolite correlations. While significant community structure was observed between microbes, we were unable to deconvolute the transitive effects of correlation and thus, could not conclude that metabolite-mediated microbial stimulation/inhibition occurred. However, we provided two examples of communities that appeared to have exceptional structure, even compared against other metabolite-associated microbial communities. Additional file10 shows the structure of a microbial community that was defined by common correlation with a cecal metabolite (mass = 230.1845, retention time = 0.4078 minutes). This metabolite negatively correlated with 17 bacteria. When correlated with each other, these bacteria formed two tight clusters with extremely well-defined co-exclusion structure. An appealing explanation for this type of behavior is that the two communities actively compete with each other for consumption of the metabolite. The metabolite-associated community in Additional file11 involved a sigmoid metabolite (mass = 434.1867, retention time = 0.6621 minutes). This metabolite was negatively correlated with one Tenericute and eighteen Proteobacteria OTUs and positively correlated with seven Firmicutes OTUs. When correlated with each other, these bacteria also formed two distinct communities that were extremely co-exclusive; one community contained all the Firmicutes, and the other community contained most of the Proteobacteria. This behavior could be indicative of a Firmicutes-produced metabolite that is inhibitory to Proteobacteria. While it is possible that the observed structure was due to the transitive nature of the correlations, these observations would seem to suggest that metabolites might be driving microbial community structure and may directly or indirectly modulate inter-species competition. 1e1e36bf2d