Spontaneous preterm birth is a serious medical condition responsible for substantial perinatal morbidity and mortality. Its phenotypic characteristics, preterm labor with intact membranes (PTL) and preterm premature rupture of the membranes (PPROM), are associated with significantly increased risks of neurological and behavioral alterations in childhood and later life. Recognizing the inflammatory milieu associated with PTL and PPROM, here we examined expression signatures of placental tryptophan metabolism, an important pathway in prenatal brain development and immunotolerance. The study was performed in a well-characterized clinical cohort of healthy term pregnancies (n = 39) and 167 preterm deliveries (PTL, n = 38 and PPROM, n = 129). Within the preterm group, we then investigated potential mechanistic links between differential placental tryptophan pathway expression, preterm birth and both intra-amniotic markers (such as amniotic fluid interleukin-6) and maternal inflammatory markers (such as maternal serum C-reactive protein and white blood cell count). We show that preterm birth is associated with significant changes in placental tryptophan metabolism. Multifactorial analysis revealed similarities in expression patterns associated with multiple phenotypes of preterm delivery. Subsequent correlation computations and mediation analyses identified links between intra-amniotic and maternal inflammatory markers and placental serotonin and kynurenine pathways of tryptophan catabolism. Collectively the findings suggest that a hostile inflammatory environment associated with preterm delivery underlies the mechanisms affecting placental endocrine/transport functions and may contribute to disruption of developmental programming of the fetal brain.

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Flowability is among the most important properties of powders, especially when fine particle size fractions need to be processed. In this study, our goal was to find a possibly simple but accurate mathematical model for predicting the mass flow rate for different fractions of the pharmaceutical excipient sorbitol for direct compression. Various regression models derived from the Jones–Pilpel equation for the prediction of the mass flow rate were investigated. Using validation with experimental data for various particle and hopper orifice sizes, we focused on the prediction accuracy of the respective models, i.e., on the relative difference between measured and model-predicted values. Classical indicators of regression quality from statistics were addressed as well, but we consider high prediction accuracy to be particularly important for industrial processing in practice. For individual particle size fractions, the best results (an average prediction accuracy of 3.8%) were obtained using simple regression on orifice size. However, for higher accuracy (3.1%) in a unifying model, valid in the broad particle size range 0.100–0.346 mm, a fully quadratic model, incorporating interaction between particle and orifice size, appears to be most appropriate.