Metabolomics application in maternal-fetal medicine

Metabolomics in maternal-fetal medicine is still an "embryonic" science. However, there is already an increasing interest in metabolome of normal and complicated pregnancies, and neonatal outcomes. Tissues used for metabolomics interrogations of pregnant women, fetuses and newborns are amniotic fluid, blood, plasma, cord blood, placenta, urine, and vaginal secretions. All published papers highlight the strong correlation between biomarkers found in these tissues and fetal malformations, preterm delivery, premature rupture of membranes, gestational diabetes mellitus, preeclampsia, neonatal asphyxia, and hypoxic-ischemic encephalopathy. The aim of this review is to summarize and comment on original data available in relevant published works in order to emphasize the clinical potential of metabolomics in obstetrics in the immediate future

Predictive value of hematological and phenotypical parameters on postchemotherapy leukocyte recovery

Background: Grade IV chemotherapy toxicity is defined as absolute neutrophil count <500/μL. The nadir is considered as the lowest neutrophil number following chemotherapy, and generally is not expected before the 7th day from the start of chemotherapy. The usual prophylactic dose of rHu-G-CSF (Filgrastim) is 300 μg/day, starting 24-48 h after chemotherapy until hematological recovery. However, individual patient response is largely variable, so that rHu-G-CSF doses can be different. The aim of this study was to verify if peripheral blood automated flow cytochemistry and flow cytometry analysis may be helpful in predicting the individual response and saving rHu-G-CSF. Methods: During Grade IV neutropenia, blood counts from 30 cancer patients were analyzed daily by ADVIA 120 automated flow cytochemistry analyzer and by Facscalibur flow cytometer till the nadir. "Large unstained cells" (LUCs), myeloperoxidase index (MPXI), blasts, and various cell subpopulations in the peripheral blood were studied. At nadir rHu-G-CSF was started and 81 chemotherapy cycles were analyzed. Cycles were stratified according to their number and to two dose-levels of rHuG-CSF needed to recovery (300-600 vs. 900-1200 μg) and analyzed in relation to mean values of MPXI and mean absolute number of LUCs in the nadir phase. The linear regressions of LUCs % over time in relation to two dose-levels of rHu-G-CSF and uni-multivariate analysis of lymphocyte subpopulations, CD34+ cells, MPXI, and blasts were also performed. Results: In the nadir phase, the increase of MPXI above the upper limit of normality (>10; median 27.7), characterized a slow hematological recovery. MPXI levels were directly related to the cycle number and inversely related to the absolute number of LUCs and CD34 +/CD45+ cells. A faster hematological recovery was associated with a higher LUC increase per day (0.56% vs. 0.25%), higher blast (median 36.7/μL vs. 19.5/μL) and CD34+/CD45+ cell (median 2.2/μL vs. 0.82/μL) counts. Conclusions: Our study showed that some biological indicators such as MPXI, LUCs, blasts, and CD34 +/CD45+ cells may be of clinical relevance in predicting individual hematological response to rHu-G-CSF. Special attention should be paid when nadir MPXI exceeds the upper limit of normality because the hematological recovery may be delayed. © 2009 Clinical Cytometry Society

Temporal Registration in In-Utero Volumetric MRI Time Series

We present a robust method to correct for motion and deformations in in-utero volumetric MRI time series. Spatio-temporal analysis of dynamic MRI requires robust alignment across time in the presence of substantial and unpredictable motion. We make a Markov assumption on the nature of deformations to take advantage of the temporal structure in the image data. Forward message passing in the corresponding hidden Markov model (HMM) yields an estimation algorithm that only has to account for relatively small motion between consecutive frames. We demonstrate the utility of the temporal model by showing that its use improves the accuracy of the segmentation propagation through temporal registration. Our results suggest that the proposed model captures accurately the temporal dynamics of deformations in in-utero MRI time series.National Institutes of Health (U.S.) (NIH NIBIB NAC P41EB015902)National Institutes of Health (U.S.) (NIH NICHD U01HD087211)National Institutes of Health (U.S.) (NIH NIBIB R01EB017337)Wistron CorporationMerrill Lynch Wealth Management (Fellowship

Predicting heart failure outcome from cardiac and comorbid conditions: The 3C-HF score

Background: Prognostic stratification in heart failure (HF) is crucial to guide clinical management and treatment decision-making. Currently available models to predict HF outcome have multiple limitations. We developed a simple risk stratification model, based on routinely available clinical information including comorbidities, the Cardiac and Comorbid Conditions HF (3C-HF) Score, to predict all-cause 1-year mortality in HF patients. Methods: We recruited in a cohort study 6274 consecutive HF patients at 24 Cardiology and Internal Medicine Units in Europe. 2016 subjects formed the derivation cohort and 4258 the validation cohort.Weentered information on cardiac and comorbid candidate prognostic predictors in amultivariablemodel to predict 1-year outcome

a case of angioinvasive cutaneous anaplastic large cell lymphoma completely regressed after low dose systemic methotrexate

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Temporal Registration in In-Utero Volumetric MRI Time Series

We present a robust method to correct for motion and deformations in in-utero volumetric MRI time series. Spatio-temporal analysis of dynamic MRI requires robust alignment across time in the presence of substantial and unpredictable motion. We make a Markov assumption on the nature of deformations to take advantage of the temporal structure in the image data. Forward message passing in the corresponding hidden Markov model (HMM) yields an estimation algorithm that only has to account for relatively small motion between consecutive frames. We demonstrate the utility of the temporal model by showing that its use improves the accuracy of the segmentation propagation through temporal registration. Our results suggest that the proposed model captures accurately the temporal dynamics of deformations in in-utero MRI time series.National Institutes of Health (U.S.) (NIH NIBIB NAC P41EB015902)National Institutes of Health (U.S.) (NIH NICHD U01HD087211)National Institutes of Health (U.S.) (NIH NIBIB R01EB017337)Wistron CorporationMerrill Lynch Wealth Management (Fellowship

Predictors of Postpartum Depression among Italian Women: A Longitudinal Study

Introduction: Postpartum depression is commonly experienced by mothers worldwide and is associated with anxiety disorders, parenting stress, and other forms of distress, which may lead to a complex illness condition. Several studies have investigated the risk factors for this disorder, including biological and socio-demographic variables, medical and obstetric factors, and psychological and relational dimensions. The present study aimed to describe the psychological status of mothers up to 12 months postpartum, and to investigate the predictors of depressive symptoms at 12 months postpartum, considering obstetric factors along with psychological and relational variables. Methods: A sample of 137 women completed a questionnaire composed of a sheet on anamnestic and obstetric information and the following scales: Wijma Delivery Experience Questionnaire; State-Trait Anxiety Inventory; Edinburgh Postnatal Depression Scale; Parenting Stress Index (Short Form); Dyadic Adjustment Scale; and Multidimensional Scale of Perceived Social Support. Data were collected at four assessment times: 2–3 days, 3 months, 6 months, and 12 months postpartum. Results: Findings showed that the highest percentage of women with clinically significant symptoms of anxiety (state and trait) and depression was found at 12 months postpartum, which indicated that this was the most critical time. The quality of childbirth experience and trait anxiety at three months postpartum emerged as significant predictors of postpartum depression at 12 months. Conclusion: Our findings highlight the importance of providing stable programs (such as educational programs) to mothers in the first year postpartum. Furthermore, because the quality of the childbirth experience is one of the most important predictors of PPD at 12 months postpartum, effort should be made by healthcare professionals to guarantee a positive experience to all women to reduce possible negative long-term consequences of this experience

Phase-rectified signal averaging method to predict perinatal outcome in infants with very preterm fetal growth restriction- a secondary analysis of TRUFFLE-trial

BACKGROUND: Phase-rectified signal averaging, an innovative signal processing technique, can be used to investigate quasi-periodic oscillations in noisy, nonstationary signals that are obtained from fetal heart rate. Phase-rectified signal averaging is currently the best method to predict survival after myocardial infarction in adult cardiology. Application of this method to fetal medicine has established significantly better identification than with short-term variation by computerized cardiotocography of growth-restricted fetuses. OBJECTIVE: The aim of this study was to determine the longitudinal progression of phase-rectified signal averaging indices in severely growth-restricted human fetuses and the prognostic accuracy of the technique in relation to perinatal and neurologic outcome. STUDY DESIGN: Raw data from cardiotocography monitoring of 279 human fetuses were obtained from 8 centers that took part in the multicenter European “TRUFFLE” trial on optimal timing of delivery in fetal growth restriction. Average acceleration and deceleration capacities were calculated by phase-rectified signal averaging to establish progression from 5 days to 1 day before delivery and were compared with short-term variation progression. The receiver operating characteristic curves of average acceleration and deceleration capacities and short-term variation were calculated and compared between techniques for short- and intermediate-term outcome. RESULTS: Average acceleration and deceleration capacities and short-term variation showed a progressive decrease in their diagnostic indices of fetal health from the first examination 5 days before delivery to 1 day before delivery. However, this decrease was significant 3 days before delivery for average acceleration and deceleration capacities, but 2 days before delivery for short-term variation. Compared with analysis of changes in short-term variation, analysis of (delta) average acceleration and deceleration capacities better predicted values of Apgar scores <7 and antenatal death (area under the curve for prediction of antenatal death: delta average acceleration capacity, 0.62 [confidence interval, 0.19–1.0]; delta short-term variation, 0.54 [confidence interval, 0.13–0.97]; P=.006; area under the curve for prediction Apgar <7: average deceleration capacity <24 hours before delivery, 0.64 [confidence interval, 0.52–0.76]; short-term variation <24 hours before delivery, 0.53 [confidence interval, 0.40–0.65]; P=.015). Neither phase-rectified signal averaging indices nor short-term variation showed predictive power for developmental disability at 2 years of age (Bayley developmental quotient, <95 or <85). CONCLUSIONS: The phase-rectified signal averaging method seems to be at least as good as short-term variation to monitor progressive deterioration of severely growth-restricted fetuses. Our findings suggest that for short-term outcomes such as Apgar score, phase-rectified signal averaging indices could be an even better test than short-term variation. Overall, our findings confirm the possible value of prospective trials based on phase-rectified signal averaging indices of autonomic nervous system of severely growth-restricted fetuses