Search

Introduction: It is a shortcoming of traditional cardiotocography (CTG) classification table formats that CTG traces are frequently classified differently by different users, resulting in poor interobserver agreements. A fast‐and‐frugal tree (FFTree) flow chart may help provide better concordance because it is straightforward and has clearly structured binary questions with understandable "yes" or "no" responses. The initial triage to determine whether a fetus is suitable for labor when utilizing fetal ECG ST analysis (STAN) is very important, since a fetus with restricted capacity to respond to hypoxic stress may not generate STAN events and therefore may become falsely negative. This study aimed to compare physiology‐focused FFTree CTG interpretation with FIGO classification for assessing the suitability for STAN monitoring. Material and methods: A retrospective study of 36 CTG traces with a high proportion of adverse outcomes (17/36) selected from a European multicenter study database. Eight experienced European obstetricians evaluated the initial 40 minutes of the CTG recordings and judged whether STAN was a suitable fetal surveillance method and whether intervention was indicated. The experts rated the CTGs using the FFTree and FIGO classifications at least 6 weeks apart. Interobserver agreements were calculated using proportions of agreement and Fleiss' kappa (κ). Results: The proportions of agreement for "not suitable for STAN" were for FIGO 47% (95% confidence interval [CI] 42%–52%) and for FFTree 60% (95% CI 56–64), ie a significant difference; the corresponding figures for "yes, suitable" were 74% (95% CI 71–77) and 70% (95% CI 67–74). For "intervention needed" the figures were 52% (95% CI 47–56) vs 58% (95% CI 54–62) and for "expectant management" 74% (95% CI 71–77) vs 72% (95% CI 69–75). Fleiss' κ agreement on "suitability for STAN" was 0.50 (95% CI 0.44–0.56) for the FIGO classification and 0.57 (95% CI 0.51–0.63) for the FFTree classification; the corresponding figures for "intervention or expectancy" were 0.53 (95% CI 0.47–0.59) and 0.57 (95% CI 0.51–0.63). Conclusions: The proportion of agreement among expert obstetricians using the FFTree physiological approach was significantly higher compared with the traditional FIGO classification system in rejecting cases not suitable for STAN monitoring. That might be of importance to avoid false negative STAN recordings. Other agreement figures were similar. It remains to be shown whether the FFTree simplicity will benefit less experienced users and how it will work in real‐world clinical scenarios. [ABSTRACT FROM AUTHOR]

Defects in natural killer T (NK T) cell function and of interleukin-4 -production in SJL and NOD mice have been linked to susceptibility to autoimmune disease. As SJL and NOD mice both carry the T-cell receptor (TCR) α-chain locus "c" (Tcrac) haplotype, found in few other strains, we have attempted to determine the influence of Tcra polymorphism on NK T-cell recognition of ligand, selection, and immune responses. The majority of NK T cells use an "invariant" TRAV11J15 (previously called AV14J18 or Vα14 Jα281) α- chain paired with either TRBV13–2, BV29, or BV1 to recognize ligands presented by mCD1 molecules, including the glycolipid α-galactosylceramide (α-GalCer). Sequencing of TRAV11 from the mouse strains B10.A (encoding the Tcrab haplotype), B10.A-Tcrac, and NOD (Tcrac) shows that Tcrac has a single TRAV11 gene (TRAV11*01) and that Tcrab has a single expressed gene (TRAV11*02), plus a closely related pseudogene. There is no apparent difference in α-chain J-region usage or in the CDR3α sequence at the TRAV11-J15 junction between the haplotypes in TRAV11-bearing NK T cells. Using Biacore and tetramer-binding and decay assays, we have determined that the interaction between Tcrac TRAV11*01 NK T TCR and the mCD1/α-GalCer complex is slightly weaker than that of Tcrab (i.e., TRAV11*02) NK T TCR. These differences are minor compared with differences between agonist and antagonist ligands in other TCR systems, suggesting that it is unlikely that TCR polymorphism explains the defect in NK T cells in the autoimmune mouse strains. [ABSTRACT FROM AUTHOR]

At birth the immune system of a neonate is immature and viruses can cause severe infections. Since the signs and symptoms of postnatal viral diseases can be identical to those in bacterial infections, viral diseases are often treated with antibiotics. Any infant with signs of serious infections, in whom no bacteria are found, should be suspected for a viral infection. Prevention of transmission of viruses and cohorting of infected infants should be practiced in NICUs. Since postnatal viral infections can have a long-term impact on the health in a child, all effective preventive and curative interventions should be utilized.

Many newborns are exposed to diagnostic or treatment procedures due to a suspicion of sepsis. Since non-specific signs of neonatal sepsis can quickly proceed to a life-threatening condition, it is essential to have a low threshold to diagnostic procedures and to provide antimicrobial therapy while waiting for the test results. After sepsis has been ruled out, antimicrobial therapy should be discontinued without delay. Good clinical practice includes avoiding unnecessarily broad-spectrum antibiotics. The future challenge is to develop a sensitive and specific marker for early detection of the disease and for avoiding unnecessary antibiotics, hospital care days and mother-infant separation.

Phenotypic allelic exclusion at the TCRalpha locus is developmentally regulated in thymocytes. Many immature thymocytes express two cell surface alpha-chain species. Following positive selection, the vast majority of mature thymocytes and peripheral T cells display a single cell surface alpha-chain. A posttranslational mechanism occurring at the same time as positive selection and TCR up-regulation leads to this phenotypic allelic exclusion. Different models have been proposed to explain the posttranslational regulation of the alpha-chain allelic exclusion. In this study, we report that allelic exclusion is not regulated by competition between distinct alpha-chains for a single beta-chain, as proposed by the dueling alpha-chain model, nor by limiting CD3 zeta-chain in mature TCR(high) thymocytes. Our data instead favor the selective retention model where the positive selection signal through the TCR leads to phenotypic allelic exclusion by specifically maintaining cell surface expression of the selected alpha-chain while the nonselected alpha-chain is internalized. The use of inhibitors specific for Lck and/or other Src kinases indicates a role for these protein tyrosine kinases in the signaling events leading to the down-regulation of the nonselectable alpha-chain. Loss of the ubiquitin ligase/TCR signaling adapter molecule c-Cbl, which is important in TCR down-modulation and is a negative regulator of T cell signaling, leads to increased dual alpha-chain expression on the cell surface of double-positive thymocytes. Thus, not only is there an important role for TCR signaling in causing alpha-chain allelic exclusion, but differential ubiquitination by c-Cbl may be an important factor in causing only the nonselected alpha-chain to be down-modulated.