Biosensor for Detecting Fetal Growth Restriction in a Low-Resource Setting

One strategy for improving detection of fetal growth restriction (FGR) is developing biosensors identifying placental dysfunction as a leading pathogenesis for FGR. The aim of this pilot study was to investigate the performance of a biosensor specified to detect placental dysfunction by means of maternal arterial turbulence acoustics in a low-resource setting. A cohort of 147 singleton pregnant women were prospectively followed with double-blinded biosensor tests, sonographic estimation of fetal weight (EFW) and Doppler flow at 26–28, 32–34 and 37–39 weeks of pregnancy. Full term live births with recorded birth weights (BWs) and without major congenital malformations were included. Outcomes were defined as (A) a solitary biometric measure (BW < 3rd centile) and as (B) a biometric measure and contributory functional measure (BW < 10th centile and antenatally detected umbilical artery pulsatility index > 95th centile). Data from 118 women and 262 antenatal examinations were included. Mean length of pregnancy was 40 weeks (SD ± 8 days), mean BW was 3008 g (SD ± 410 g). Outcome (A) was identified in seven (6%) pregnancies, whereas outcome (B) was identified in one (0.8%) pregnancy. The biosensor tested positive in five (4%) pregnancies. The predictive performance for outcome (A) was sensitivity = 0.29, specificity = 0.97, p = 0.02, positive predictive value (PPV) was 0.40 and negative predictive value (NPV) was 0.96. The predictive performance was higher for outcome (B) with sensitivity = 1.00, specificity = 0.97, p = 0.04, PPV = 0.20 and NPV = 1.00. Conclusively, these pilot-study results show future potential for biosensors as screening modality for FGR in a low-resource setting

Genetic predisposition to PEG-asparaginase hypersensitivity in children treated according to NOPHO ALL2008

To access publisher's full text version of this article click on the hyperlink belowAsparaginase is essential in childhood acute lymphoblastic leukaemia (ALL) treatment, however hypersensitivity reactions to pegylated asparaginase (PEG-asparaginase) hampers anti-neoplastic efficacy. Patients with PEG-asparaginase hypersensitivity have been shown to possess zero asparaginase enzyme activity. Using this measurement to define the phenotype, we investigated genetic predisposition to PEG-asparaginase hypersensitivity in a genome-wide association study (GWAS). From July 2008 to March 2016, 1494 children were treated on the Nordic Society of Paediatric Haematology and Oncology ALL2008 protocol. Cases were defined by clinical hypersensitivity and no enzyme activity, controls had enzyme activity ≥ 100 iu/l and no hypersensitivity symptoms. PEG-asparaginase hypersensitivity was reported in 13·8% (206/1494) of patients. Fifty-nine cases and 772 controls fulfilled GWAS inclusion criteria. The CNOT3 variant rs73062673 on 19q13.42, was associated with PEG-asparaginase allergy (P = 4·68 × 10Danish Childhood Cancer Foundatio

Can machine learning models predict asparaginase-associated pancreatitis in childhood acute lymphoblastic leukemia

Abstract Asparaginase-associated pancreatitis (AAP) frequently affects children treated for acute lymphoblastic leukemia (ALL) causing severe acute and persisting complications. Known risk factors such as asparaginase dosing, older age and single nucleotide polymorphisms (SNPs) have insufficient odds ratios to allow personalized asparaginase therapy. In this study, we explored machine learning strategies for prediction of individual AAP risk. We integrated information on age, sex, and SNPs based on Illumina Omni2.5exome-8 arrays of patients with childhood ALL (N=1564, 244 with AAP 1.0 to 17.9 yo) from 10 international ALL consortia into machine learning models including regression, random forest, AdaBoost and artificial neural networks. A model with only age and sex had area under the receiver operating characteristic curve (ROC-AUC) of 0.62. Inclusion of 6 pancreatitis candidate gene SNPs or 4 validated pancreatitis SNPs boosted ROC-AUC somewhat (0.67) while 30 SNPs, identified through our AAP genome-wide association study cohort, boosted performance (0.80). Most predictive features included rs10273639 (PRSS1-PRSS2), rs10436957 (CTRC), rs13228878 (PRSS1/PRSS2), rs1505495 (GALNTL6), rs4655107 (EPHB2) and age (1 to 7 y). Second AAP following asparaginase re-exposure was predicted with ROC-AUC: 0.65. The machine learning models assist individual-level risk assessment of AAP for future prevention trials, and may legitimize asparaginase re-exposure when AAP risk is predicted to be low