Publications Internationales
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Item Living donors kidney transplantation and oxidative stress: Nitric oxide as a predictive marker of graft function(Public Library of Science, 2024) Izemrane, Djamila; Benziane, Ali; Makrelouf, Mohamed; Hamdis, Nacim; Rabia, Samia Hadj; Boudjellaba, Sofiane; Baz, Ahsene; Benaziza, DjamilaBackground Glomerular filtration rate is the best indicator of renal function and a predictor of graft and patient survival after kidney transplantation. Methods In a single-centre prospective analysis, we assessed the predictive performances of 4 oxidative stress biomarkers in estimating graft function at 6 months and 1 year after kidney transplantation from living donors. Blood samples were achieved on days (D-1, D1, D2, D3, D6 and D8), months (M1, M3 and M6) and after one year (1Y). For donors, a blood sample was collected on D-1. Malondialdehyde (MDA), nitric oxide (NO), glutathione s-transferase (GST), myeloperoxydase (MPO), and creatinine (Cr) were measured by spectrophotometric essays. The estimated glomerular filtration rate by the modification of diet in renal disease equation (MDRD-eGFR) was used to assess renal function in 32 consecutive donor-recipient pairs. Pearson’s and Spearman’s correlations have been applied to filter out variables and covariables that can be used to build predictive models of graft function at six months and one year. The predictive performances of NO and MPO were tested by multivariable stepwise linear regression to estimate glomerular filtration rate at six months. Results Three models with the highest coefficients of determination stand out, combining the two variables nitric oxide at day 6 and an MDRD-eGFR variable at day 6 or MDRD-eGFR at day 21 or MDRD-eGFR at 3 months, associated for the first two models or not for the third model with donor age as a covariable (P = 0.000, r2 = 0.599, r2adj = 0.549; P = 0.000, r2 = 0.548, r2adj = 0.497; P = 0.000, r2 = 0.553, r2adj = 0.517 respectively). Conclusion Quantification of nitric oxide at day six could be useful in predicting graft function at six months in association with donor age and the estimated glomerular filtration rate in recipient at day 6, day 21 and 3 months after transplantation.Item Generalisability of fetal ultrasound deep learning models to low-resource imaging settings in five African countries(Nature Research, 2023) Carla, Sendra-Balcells; Campello, Víctor M.; Torrents-Barrena, Jordina; Ammar, MohammedMost artificial intelligence (AI) research and innovations have concentrated in high-income countries, where imaging data, IT infrastructures and clinical expertise are plentiful. However, slower progress has been made in limited-resource environments where medical imaging is needed. For example, in Sub-Saharan Africa, the rate of perinatal mortality is very high due to limited access to antenatal screening. In these countries, AI models could be implemented to help clinicians acquire fetal ultrasound planes for the diagnosis of fetal abnormalities. So far, deep learning models have been proposed to identify standard fetal planes, but there is no evidence of their ability to generalise in centres with low resources, i.e. with limited access to high-end ultrasound equipment and ultrasound data. This work investigates for the first time different strategies to reduce the domain-shift effect arising from a fetal plane classification model trained on one clinical centre with high-resource settings and transferred to a new centre with low-resource settings. To that end, a classifier trained with 1792 patients from Spain is first evaluated on a new centre in Denmark in optimal conditions with 1008 patients and is later optimised to reach the same performance in five African centres (Egypt, Algeria, Uganda, Ghana and Malawi) with 25 patients each. The results show that a transfer learning approach for domain adaptation can be a solution to integrate small-size African samples with existing large-scale databases in developed countries. In particular, the model can be re-aligned and optimised to boost the performance on African populations by increasing the recall to 0.92 ± 0.04 and at the same time maintaining a high precision across centres. This framework shows promise for building new AI models generalisable across clinical centres with limited data acquired in challenging and heterogeneous conditions and calls for further research to develop new solutions for the usability of AI in countries with fewer resources and, consequently, in higher need of clinical supportItem Detection of rickettsia felis, rickettsia typhi, bartonella species and yersinia pestis in fleas (siphonaptera) from Africa(Public Library of Science, 2014) Leulmi, Hamza; Socolovschi, Cristina; Laudisoit, Anne; Houemenou, Gualbert; Davoust, Bernard; Bitam, Idir; Raoult, Didier; Parola, PhilippeLittle is known about the presence/absence and prevalence of Rickettsia spp, Bartonella spp. and Yersinia pestis in domestic and urban flea populations in tropical and subtropical African countries. Fleas collected in Benin, the United Republic of Tanzania and the Democratic Republic of the Congo were investigated for the presence and identity of Rickettsia spp., Bartonella spp. and Yersinia pestis using two qPCR systems or qPCR and standard PCR. In Xenopsylla cheopis fleas collected from Cotonou (Benin), Rickettsia typhi was detected in 1% (2/199), and an uncultured Bartonella sp. was detected in 34.7% (69/199). In the Lushoto district (United Republic of Tanzania), R. typhi DNA was detected in 10% (2/20) of Xenopsylla brasiliensis, and Rickettsia felis was detected in 65% (13/20) of Ctenocephalides felis strongylus, 71.4% (5/7) of Ctenocephalides canis and 25% (5/20) of Ctenophthalmus calceatus calceatus. In the Democratic Republic of the Congo, R. felis was detected in 56.5% (13/23) of Ct. f. felis from Kinshasa, in 26.3% (10/38) of Ct. f. felis and 9% (1/11) of Leptopsylla aethiopica aethiopica from Ituri district and in 19.2% (5/26) of Ct. f. strongylus and 4.7% (1/21) of Echidnophaga gallinacea. Bartonella sp. was also detected in 36.3% (4/11) of L. a. aethiopica. Finally, in Ituri, Y. pestis DNA was detected in 3.8% (1/26) of Ct. f. strongylus and 10% (3/30) of Pulex irritans from the villages of Wanyale and Zaa. Most flea-borne infections are neglected diseases which should be monitored systematically in domestic rural and urban human populations to assess their epidemiological and clinical relevance. Finally, the presence of Y. pestis DNA in fleas captured in households was unexpected and raises a series of questions regarding the role of free fleas in the transmission of plague in rural Africa, especially in remote areas where the flea density in houses is high