The S100B protein was identified in the mid-1960s as a protein fraction which -on the basis of chromatographic and electrophoretic methods available at that time -was detectable in the brain but not in non-neural extracts, and was named S100 because of its solubility in a 100% saturated solution with ammonium sulphate (Moore 1965). At present, owing to the discovery of a series of proteins exhibiting structural similarities, the term S100 is used to embrace a multigenic family of mostly dimeric calcium-binding proteins comprising more than 20 members with different degrees of homology to each other at the amino acid level, and representing the largest subgroup within the EF-hand superfamily. The genes encoding the majority of human S100 proteins are organized in a cluster within the chromosomal region 1q21, while some genes coding individual S100 proteins are located in other chromosomal regions, including 21q22 where, in particular, the gene for the S100B protein is located. Each monomer is approximately 10-12 kDa and is characterized by two calcium-binding regions, each comprising two alpha helices with an intervening calcium-binding loop forming a conserved pentagonal arrangement around the calcium ion (EF-hand motif).The binding of calcium to EF-hand domains triggers conformational changes that allow interactions with other proteins, so Abstract S100B is a calcium-binding protein concentrated in glial cells, although it has also been detected in definite extra-neural cell types. Its biological role is still debated. When secreted, S100B is believed to have paracrine/autocrine trophic effects at physiological concentrations, but toxic effects at higher concentrations. Elevated S100B levels in biological fluids (CSF, blood, urine, saliva, amniotic fluid) are thus regarded as a biomarker of pathological conditions, including perinatal brain distress, acute brain injury, brain tumors, neuroinflammatory/neurodegenerative disorders, psychiatric disorders. In the majority of these conditions, high S100B levels offer an indicator of cell damage when standard diagnostic procedures are still silent. The key question remains as to whether S100B is merely leaked from injured cells or is released in concomitance with both physiological and pathological conditions, participating at high concentrations in the events leading to cell injury. In this respect, S100B levels in biological fluids have been shown to increase in physiological conditions characterized by stressful physical and mental activity, suggesting that it may be physiologically regulated and raised during conditions of stress, with a putatively active role. This possibility makes this protein a candidate not only for a biomarker but also for a potential therapeutic target.

, for the SLI Trial Investigators abstract BACKGROUND: Studies suggest that giving newly born preterm infants sustained lung inflation (SLI) may decrease their need for mechanical ventilation (MV) and improve their respiratory outcomes.

ObjectivesTo evaluate the strength of association between maternal and pregnancy characteristics and the risk of adverse perinatal outcomes in pregnancies with laboratory confirmed COVID-19.MethodsSecondary analysis of a multinational, cohort study on all consecutive pregnant women with laboratory-confirmed COVID-19 from February 1, 2020 to April 30, 2020 from 73 centers from 22 different countries. A confirmed case of COVID-19 was defined as a positive result on real-time reverse-transcriptase-polymerase-chain-reaction (RT-PCR) assay of nasal and pharyngeal swab specimens. The primary outcome was a composite adverse fetal outcome, defined as the presence of either abortion (pregnancy loss before 22 weeks of gestations), stillbirth (intrauterine fetal death after 22 weeks of gestation), neonatal death (death of a live-born infant within the first 28 days of life), and perinatal death (either stillbirth or neonatal death). Logistic regression analysis was performed to evaluate parameters independently associated with the primary outcome. Logistic regression was reported as odds ratio (OR) with 95% confidence interval (CI).ResultsMean gestational age at diagnosis was 30.6±9.5 weeks, with 8.0% of women being diagnosed in the first, 22.2% in the second and 69.8% in the third trimester of pregnancy. There were six miscarriage (2.3%), six intrauterine device (IUD) (2.3) and 5 (2.0%) neonatal deaths, with an overall rate of perinatal death of 4.2% (11/265), thus resulting into 17 cases experiencing and 226 not experiencing composite adverse fetal outcome. Neither stillbirths nor neonatal deaths had congenital anomalies found at antenatal or postnatal evaluation. Furthermore, none of the cases experiencing IUD had signs of impending demise at arterial or venous Doppler. Neonatal deaths were all considered as prematurity-related adverse events. Of the 250 live-born neonates, one (0.4%) was found positive at RT-PCR pharyngeal swabs performed after delivery. The mother was tested positive during the third trimester of pregnancy. The newborn was asymptomatic and had negative RT-PCR test after 14 days of life. At logistic regression analysis, gestational age at diagnosis (OR: 0.85, 95% CI 0.8–0.9 per week increase; p<0.001), birthweight (OR: 1.17, 95% CI 1.09–1.12.7 per 100 g decrease; p=0.012) and maternal ventilatory support, including either need for oxygen or CPAP (OR: 4.12, 95% CI 2.3–7.9; p=0.001) were independently associated with composite adverse fetal outcome.ConclusionsEarly gestational age at infection, maternal ventilatory supports and low birthweight are the main determinants of adverse perinatal outcomes in fetuses with maternal COVID-19 infection. Conversely, the risk of vertical transmission seems negligible.

To date, we have little knowledge on the overall metabolic status of neonates with intrauterine growth retardation (IUGR). In the last few years, the analysis of metabolomics has assumed an important clinical role in identifying "disorders" in the metabolic profile of patients. The aim of this work has been to analyze the urine metabolic profiles of neonates with IUGR and compare them with controls to define the metabolic patterns associated with this pathology. To our knowledge, this is the first study of metabolomics performed on neonates with IUGR. Recruited for the study were 26 neonates with IUGR diagnosed in the neonatal period and with weight at birth below the 10th percentile and 30 neonates of proper gestational weight at birth (controls). In the first 24 hours (prior to feeding) (T1) and about 4 days after birth (T2), a urine sample was taken non-invasively from each neonate. The samples were then frozen at -80°C up to the time of the analysis by proton nuclear magnetic resonance spectroscopy (1H-NMR). The data contained in the NMR spectra obtained from the single samples were statistically analyzed using the Principal Components Analysis and the Partial Least Squares-Discriminate Analysis. By means of a multivariate analysis of the NMR spectra obtained, it was possible to highlight the differences between the two groups (IUGRs and controls) owing to the presence of different metabolic patterns. The discriminants in the urine metabolic profiles derived essentially from significant differences in certain metabolites such as: myo-inositol, sarcosine, creatine and creatinine. The metabolomic analysis showed different urine metabolic profiles between neonates with IUGR and controls and made it possible to identify the molecules responsible for such differences.

The purpose of this article is to study one of the most significant causes of neonatal morbidity and mortality: neonatal sepsis. This pathology is due to a bacterial or fungal infection acquired during the perinatal period. Neonatal sepsis has been categorized into two groups: early onset if it occurs within 3-6 days and late onset after 4-7 days. Due to the not-specific clinical signs, along with the inaccuracy of available biomarkers, the diagnosis is still a major challenge. In this regard, the use of a combined approach based on both nuclear magnetic resonance ( 1 H-NMR) and gas-chromatography-mass spectrometry (GC-MS) techniques, coupled with a multivariate statistical analysis, may help to uncover features of the disease that are still hidden. The objective of our study was to evaluate the capability of the metabolomics approach to identify a potential metabolic profile related to the neonatal septic condition. The study population included 25 neonates (15 males and 10 females): 9 (6 males and 3 females) patients had a diagnosis of sepsis and 16 were healthy controls (9 males and 7 females). This study showed a unique metabolic profile of the patients affected by sepsis compared to non-affected ones with a statistically significant difference between the two groups (p = 0.05).