Ulcerative colitis (UC) is a chronic inflammatory bowel disease caused by genetic and environmental factors. The latter act firstly as neonatal primers: their exposure during the perinatal period disturbs early maturation of gut microbiota and immune system and induces an increased susceptibility to colitis, called pathological imprinting. Environmental factors act secondly as colitis triggers, causing or worsening colitis development in patients with genetic susceptibility or pathological imprinting. Our hypothesis is that gut luminal microplastics (MPs), which are increasingly present because of expanding human oral MP exposure, belong to environmental UC primers and/or triggers. Indeed, pilot studies detected MPs in neonatal and adult feces, and accumulating data show that ingestion of MPs promote gut inflammation and dysbiosis in mice. Our main aims are:1) To identify in human feces the MPs with pro-inflammatory and pro-dysbiotic properties. Nano-and microplastics will be characterized in control and UC patient feces and correlated with inflammatory calprotectin level and dysbiosis parameters. Combined analyzes will identify a cocktail of MPs with pro-inflammatory and pro-dysbiotic properties thereafter called pro-UC MP cocktail. 2) To assess MP effects on UC pathogenesis either as neonatal primer of UC susceptibility and/or as promoting UC trigger. Pregnant mice will be exposed to the pro-UC MP cocktail and the effects on gut immune response, permeability, microbiota, nano-and microplastic presence in feces, and susceptibility to experimental colitis will be assessed in male and female offspring. These parameters as well as fecal metabolome will be also assessed in response to the pro-UC MP cocktail exposure in gnotobiotic mice colonized with humanized microbiota from control or UC patients. This multidisciplinary project will provide new essential knowledge on human exposure to MPs and their impact on gut homeostasis, particularly in the UC pathophysiology.

Kidney transplantation is the best renal replacement therapy for eligible End Stage Renal Disease patients, medically and economically. The literature points to a lesser access to kidney transplantation for women. These disparities have been described regarding waitlisting as well as access to the transplantation once waitlisted. Combining quantitative and qualitative approaches, this project aims at a more comprehensive understanding of these disparities and their causes. A quantitative study will compare determinants of access to the waiting list and to kidney transplantation between women and men in France, at regional and departmental level. A qualitative study will describe women’s views regarding transplantation, compared with men’s, as well as nephrologists practices in regards to the HAS national recommendations on waitlisting. This project is important in today’s societal context in which the reduction of gender-based inequalities is a major social expectation.

Spinal cord injury (SCI) results in multi-organ impairment leading to increased risk of morbidity, mortality and disability. Symptoms resulting from spinal cord injury include pelvic (urological and digestive) disorders, which also have a major impact on the quality of life of SCI persons. To date, there is no effective or sustainable treatment for bowel and bladder disorders. A better understanding of the mechanisms responsible for these disorders is therefore a key step towards developing new therapeutic strategies to restore the function of these organs. Thus, the BBSCI project relies on state-of-the-art tools (RNASeq-lipidomics-organoids-FFOCT imaging) applied to the study of SCI animal model and biocollection of spinal cord injury tissues and controls to : 1) identify and characterise the nature of remodelling of colonic and urothelial epithelial barrier functions as well as the enteric nervous system, a key player in the regulation of digestive functions, and identify the mechanisms and pathways responsible; 2) to characterise the changes in the profile of colon and bladder polyunsaturated fatty acid metabolites; 3) to identify their candidate role as a new therapeutic agent to restore colonic and bladder function in the SCI model; and finally 4) to identify and characterise the alterations in colonic and urothelial barrier functions and candidate mediators in people suffering from spinal cord injury (traumatic or congenital (spina bifida)). This project will provide original insights into the pathophysiology of bowel and bladder disorders in SCI and will provide the scientific rationale for identifying new therapeutic targets to restore pelvic function in these individuals.

Magnetic Resonance Image (MRI) and diffusion MRI in particular have become very useful for assessing brain pathologies and trauma, and for diagnosing neurodegenerative disorders. Although it provides sensitive information pertaining to tissue microstructure and structural connectivity, diffusion MRI has been used in clinical context only for population studies and using either its microstructure information or its connectivity information but not both. In addition, the information is always reduced into a small features vector as statistics are otherwise not available. This results in a lack of specificity and a failure to provide meaningful patient-specific biomarkers for clinical follow-up. We hypothesise that providing statistical methods that account for the whole combined information of tissue microstructure and structural connectivity, and compute patient-specific abnormality detection maps will significantly impact clinical routine. We thus propose to develop and validate novel methods along three major axes: (i) robust estimation of tissue microstructure and structural connectivity from diffusion MRI clinical data acquired in limited amount of time by adding priors, (ii) appropriate mathematical representations of microstructure-augmented structural connectivity embedded in meaningful metric spaces practical for statistics and (iii) extension of two-sample hypothesis testing to the case where one sample is of size 1 to detect patient-specific microstructure and/or connectivity abnormalities. We will apply these new methodologies to neuro-traumatology. This project will allow patient-specific characterisation of brain damage and its influence, providing crucial insights for improving further patient care and treatment adaptation. Methods will be easily applicable to other pathologies. We will facilitate their clinical translation by providing appropriate visualisation software in close collaboration with the clinicians.

Preterm birth is a birth that occurred before 37 weeks of gestation. Because of the immaturity of all their physiological functions, premature babies are exposed to high morbidity, especially in terms of neurodevelopment. Their health status is evaluated through the continuous monitoring of several vital signs (cardiac activity, breathing…). Sleep is important for neonatal brain development. Indeed, sleep alterations or deprivations have been associated with impaired neurocognitive function and increase the risk for cardiometabolic diseases and obesity. Until now the assessment of sleep alteration was only accessible through polysomnography or by observation of the behavioral states (body activity, eye state, cardio-respiration regularity, vocalizations…) by experts. These methods are difficult to apply, performed over a limited period of time and, for polysomnography, in a standardized environment. Moreover, the analyses remain subjective and time consuming. The continuous monitoring of sleep can become accessible non-invasively with signal processing automation and artificial intelligence and new ways to automatically assess neonate sleep and wake states are necessary. This will allow neonatal sleep quality to be taken into account in optimizing the environment and treatment of newborns, with potential short- and long-term benefits. In SLEEPINESS, we propose to develop a bedside sleep monitoring tool giving an accurate, detailed, structured, and systemic assessment of sleep organization. For this purpose, two types of data will be investigated: i) electrophysiological signals (electrocardiogram and respiration) and ii) audio-video modalities, where audio and video data will provide information on baby vocalizations and motion, respectively. It is worthwhile to notice that this technique does not require additional sensors for the newborns since i) ECG and respiration are among the signals already acquired permanently during the monitoring of premature newborns, and ii) video and audio acquisitions are contactless. This strategy, as close as possible to the clinical practice applied during manual annotations, should allow us to obtain higher classification performance than existing methods. Indeed, this four-modality approach has never been implemented in literature. One of the reasons is that it requires a specific database, difficult to acquire, but also a large number of annotations. In SLEEPINESS, we will exploit two previous databases already acquired and partially annotated. A set of features will have to be extracted from motion, vocalizations, ECG and respiration variabilities. For this, the specificity of the environment must be taken into account, as the data were recorded in a clinical context. We already addressed this issue in previous works, and observed on this occasion several limitations that will have to be addressed. For motion processing, specific attention will be paid to non-analyzable periods (e.g., when an adult is present in the field of the camera, or when the newborn is not in the bed). For audio processing, baby cry extraction is also a challenge in a clinical environment, since other types of sounds (alarms, adult voices…) can be captured. In addition, other types of vocalizations different from crying (e.g., cooing), sometimes difficult to identify, will be relevant in this context of sleep analysis. Data fusion will be performed using a self-supervised approach. It will combine a supervised general model with a specific-patient self-learning system for an automated sleep-wake scoring based on massive data and artificial intelligence. Then, estimated sleep states will be exported to a sleep platform to be used for better management of the preterm infant health status. An analysis of sleep maturation in relation to clinical events recorded in the clinical database will be performed to determine their impact. Finally, the sleep platform will be designed with a user-centered design approach.