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Development of a polymer-based point-of-care diagnostic in primary care for targeted detection of bacterial DNA in patient samples

Development of a polymer-based point-of-care diagnostic in primary care for targeted detection of bacterial DNA in patient samples
开发初级保健中基于聚合物的护理点诊断,用于有针对性地检测患者样本中的细菌 DNA
批准号:
2108878
负责人:
金额:
$0.0万
依托单位:
依托单位国家:
英国
项目类别:
Studentship
财政年份:
2018
资助国家:
英国
项目状态:
已结题
起止时间:
2018 至 --

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英文摘要
We aim to meet an unmet need for a point of care diagnostic situated directly in the clinic that can identify bacteria in a sample within two hours and produce a colour change, providing information to the clinician that a bacterial infection is ongoing and treat rapidly and appropriately. This diagnostic device will consist of DNA-responsive colloidal particles (such as copolymer latexes) pre-dispersed in a PCR master mix solution containing all of the reagents necessary for DNA polymerisation. To this the clinician will add a specimen collected from a patient such as blood or urine. If bacterial DNA is present in the sample it will be amplified by PCR to form DNA polymers. The resultant DNA will interact with the responsive particles and induce sedimentation, producing a colour change. Particles that respond to bacterial DNA polymers, amplified using universal primers to rRNA synthase, have been synthesised. This PhD project will focus on optimising the PCR reaction to identify specific bacterial pathogens such as H. pylori, S. aureus, C. jejuni and test them in the device to identify causative agents of, for example, stomach ulcers, sepsis, and food poisoning respectively. The project will then move on to identifying antibiotic resistance genes to target with PCR in order to develop the sensor to give indications of not just whether bacteria are in the sample, but what antibiotics would be ineffective for the specific infection. This would be done by choosing primers that recognise, for example, RND efflux pumps that confer resistance to a range of common antibiotics. By developing the device in this way, we envisage an array of tubes that can identify high bacterial load in a sample, the most common pathogen(s) in the sample and any antibiotic resistance genes carried by the bacteria
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