Machine learning analysis of pregnancy data enables early identification of a subpopulation of newborns with ASD

International audienceTo identify newborns at risk of developing ASD and to detect ASD biomarkers early after birth, we compared retrospectively ultrasound and biological measurements of babies diagnosed later with ASD or neurotypical (NT) that are collected routinely during pregnancy and birth. We used a supervised machine learning algorithm with a cross-validation technique to classify NT and ASD babies and performed various statistical tests. With a minimization of the false positive rate, 96% of NT and 41% of ASD babies were identified with a positive predictive value of 77%. We identified the following biomarkers related to ASD: sex, maternal familial history of auto-immune diseases, maternal immunization to CMV, IgG CMV level, timing of fetal rotation on head, femur length in the 3rd trimester, white blood cell count in the 3rd trimester, fetal heart rate during labor, newborn feeding and temperature difference between birth and one day after. Furthermore, statistical models revealed that a subpopulation of 38% of babies at risk of ASD had significantly larger fetal head circumference than age-matched NT ones, suggesting an in utero origin of the reported bigger brains of toddlers with ASD. Our results suggest that pregnancy follow-up measurements might provide an early prognosis of ASD enabling pre-symptomatic behavioral interventions to attenuate efficiently ASD developmental sequels

Genotypic and Phenotypic Study of Antiviral Resistance Mutations in Refractory Cytomegalovirus Infection

[Background] This study describes the genotypic and phenotypic characterization of novel human cytomegalovirus (HCMV) genetic variants of a cohort of 94 clinically resistant HCMV patients.[Methods and results] Antiviral-resistant mutations were detected in the UL97, UL54, and UL56 target genes of 25 of 94 (26.6%) patients. The genotype-phenotype correlation study resolved the status of 5 uncharacterized UL54 deoxyribonucleic acid polymerase (G441S, A543V, F460S, R512C, A928T) and 2 UL56 terminase (F345L, P800L) mutations found in clinical isolates. A928T conferred high, triple resistance to ganciclovir, foscarnet, and cidofovir, and A543V had 10-fold reduced susceptibility to cidofovir. Viral growth assays showed G441S, A543V, F345L, and P800L impaired viral growth capacities compared with wild-type AD169 HCMV. Three-dimensional modeling predicted A543V and A928T phenotypes but not R512C, reinforcing the need for individual characterization of mutations by recombinant phenotyping.[Conclusions] Extending mutation databases is crucial to optimize treatments and to improve the assessment of patients with resistant/refractory HCMV infection.The research was carried out in the Hospital Clinic of Barcelona, Spain and was funded by “Fondo de Investigación en Salud (FIS) PI 17/0150 of the Instituto de Salud Carlos III, Ministerio de Ciencia e Innovación del Gobierno de España”, the Agency for Health Technology Assessment, and “Ministerio de Economía y Competitividad”, by “Fundació La Marató” 201824 (21/267), by “Centro de Investigación Biomédica en Red” (CIBER) CB21/13/0081 and “Agencia de Gestión de Ayudas Universitarias y de Investigación” (AGAUR).Peer reviewe

Genotypic and phenotypic study of antiviral resistance mutations in refractory cytomegalovirus infection

Background This study describes the genotypic and phenotypic characterization of novel human cytomegalovirus (HCMV) genetic variants of a cohort of 94 clinically resistant HCMV patients. Methods and results Antiviral-resistant mutations were detected in the UL97, UL54, and UL56 target genes of 25 of 94 (26.6%) patients. The genotype-phenotype correlation study resolved the status of 5 uncharacterized UL54 deoxyribonucleic acid polymerase (G441S, A543V, F460S, R512C, A928T) and 2 UL56 terminase (F345L, P800L) mutations found in clinical isolates. A928T conferred high, triple resistance to ganciclovir, foscarnet, and cidofovir, and A543V had 10-fold reduced susceptibility to cidofovir. Viral growth assays showed G441S, A543V, F345L, and P800L impaired viral growth capacities compared with wild-type AD169 HCMV. Three-dimensional modeling predicted A543V and A928T phenotypes but not R512C, reinforcing the need for individual characterization of mutations by recombinant phenotyping. Conclusions Extending mutation databases is crucial to optimize treatments and to improve the assessment of patients with resistant/refractory HCMV infection