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Les microARN (miARN), qui sont des ARN de 21 à 25 bases, constituent la dernière classe de régulateurs de l'expression génétique découverte. Environ 1500 ont été identifiés chez l'homme à ce jour. Les miARN sont des biomarqueurs de nombreuses pathologies (cancers et maladies cardiovasculaires, auto-immunes (sclérose en plaque), neurodégénératives (Alzheimer, Parkinson) et infectieuses (SIDA, CHIKV)) et l'ambition du projet doctoral est de réaliser un prototype de biocapteur électrochimique qui permette leur détection directe dans des fluides biologiques d'origine humaine (sérum) à des concentrations très faibles, du picomolaire (10^-12 M) au subattomolaire (10^-16 M), sans amplification par réaction de polymérisation en chaîne (PCR). / MicroRNAs (miRNA), which are 21 to 25 base length RNAs, are the last discovered class of genetic expression regulators. Nowadays, about 1500 human miRNAs have been identified. MiRNA are biosensors of many pathologies (cancers, cardiovascular, auto-immune (multiple sclerosis),neurodegenerative (Alzheimer, Parkinson)and infectious diseases (AIDS, CHIKV)) and the doctoral project ambition is to develop an electrochemical biosensor prototype for miRNA direct detection in biological human fluids (serum) at very low concentrations, from picomolar (10^-12 M) to subattomolar (10^-16 M), without amplification by polymerisation chain reaction (PCR).

Infertility affects approximately 15% of couples. Testing and interventions are costly, in time, money, and emotional energy. This paper will discuss using Markov decision and multi-armed bandit processes to identify a systematic approach of interventions that will lead to the desired baby while minimizing costs.

Selected Ion Flow Tube-Mass spectrometry (SIFT-MS) is an analytical measurement technology for the real-time quantification of volatile organic compounds in gaseous samples. This technology has current and potential applications in a wide variety of industries, although the focus of this research is in medical science. In this field, SIFT-MS has potential as a diagnostic device, capable of determining the presence of a particular disease or condition. In addition, SIFT-MS can be used to monitor the progression of a disease state, or predict deviations from expected behaviour. Lastly, SIFT-MS can be used for the identification of biomarkers of a particular disease state. All these possibilities are available non-invasively and in real-time, by analysing breath samples. SIFT-MS produces an extensive amount of data, requiring specific mathematical methods to identify biomarker masses that differ significantly between populations or time-points. Two classification methods are presented for the analysis of SIFT-MS mass scan data. The first method is a cross-sectional classification model, intended to differentiate between the diseased and non-diseased state. This model was validated in a simple test case. The second method is a longitudinal classification model, intended to identify key biomarkers that change over time, or in response to treatment. Both of these classification models were validated in 2 clinical trials, investigating renal function in humans and rats. The first clinical trial monitored changes in breath ammonia, TMA and acetone concentrations over the course of dialysis treatment. Correlations with the current gold standard plasma creatinine, and blood urea nitrogen were reported. Finally, biomarkers of renal function were identified that change predictably over the course of treatment. The second trial induced acute renal failure in rats, and monitored the change in renal function observed during recovery. For comparison and validation of the result, a 2-compartment model was developed for estimating renal function via a bolus injection of a radio-labelled inulin tracer, and was compared with the current gold standard plasma creatinine measurement, modified using the Cockcroft-Gault equation for rats. These two methods were compared with SIFT-MS monitoring of breath analytes, to examine the potential for non-invasive biomarkers of kidney function. Results show good promise for the non-invasive, real-time monitoring of breath analytes for diagnosis and monitoring of kidney function, and, potentially, other disease states.

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