Infection of neonatal Lewis rats with lymphocytic choriomeningitis virus (LCMV) produces distinct retinal, cerebellar, and hippocampal neuropathology. To understand the neurophysiological consequences of LCMV-induced hippocampal pathology, we studied evoked monosynaptic potentials and electro-encephalographic (EEG) activity in the dentate gyrus and CA1 and CA3 subfields of the hippocampus in vivo. Lewis rats were inoculated intracerebrally with LCMV at postnatal day 4. In rats studied 84-107 d postinfection, virus was cleared from the dentate gyrus and the number of dentate granule cells was decreased by 70%. No viral antigen or cell loss was apparent in CA1 or CA3. The hippocampal EEG of LCMV-infected rats 84-102 d postinfection was dominated by continuous theta. Although evoked potentials elicited in CA1 and CA3 by monosynaptic afferent stimulation revealed no differences between sham- and LCMV-infected rats, there was a site-specific dissociation of synaptic [population excitatory postsynaptic potential (pEPSP)] and cellular (population spike) responses and a suppression of GABA-mediated recurrent inhibition in the dentate gyrus of LCMV-infected rats. These findings indicate that GABA-mediated inhibition was markedly decreased in LCMV-infected rats. In support of this, parvalbumin-immunoreactive cell bodies and neuronal processes were decreased in LCMV-infected rats, suggesting that a subpopulation of GABA interneurons was affected. These findings indicate that abnormalities in synaptic function persist after clearance of infectious virus from the central nervous system and suggest that decreased inhibition subsequent to pathological sequence in a subpopulation of GABA interneurons may be implicated in the hyperexcitability of dentate granule cells.

Objective To analyse whether specific proteins in maternal serum and cervical length, alone or in combination, can predict the likelihood that women with intact membranes with threatened preterm labour will deliver spontaneously within 7 days of sampling.Design Cohort study.Setting Sahlgrenska University Hospital, Gothenburg, Sweden.Population Women at between 22 and 33 weeks of gestation with threatened preterm labour (n = 142) admitted to the Sahlgrenska University Hospital, Gothenburg, Sweden, in 1995.Methods Maternal serum was tested for 27 proteins using multiplex xMAP technology. Individual levels of each protein were compared, and calculations were performed to investigate potential associations between different proteins, cervical length and spontaneous preterm delivery. Receiver operating characteristic curves were used to find the best cut-off values for continuous variables in relation to spontaneous preterm delivery within 7 days of sampling. Prediction models were created based on a stepwise logistic regression using binary variables.Main outcome measure Spontaneous preterm delivery within 7 days.Results In order to determine the best prediction model, we analysed models of serum proteins alone, cervical length alone, and the combination of serum proteins and cervical length. We found one multivariable combined model through the data analysis that more accurately predicted spontaneous preterm delivery within 7 days. This model was based on serum interleukin-10 (IL-10) levels, serum RANTES levels and cervical length (sensitivity 74%, specificity 87%, positive predictive value 76%, negative predictive value 86%, likelihood ratio 5.8 and area under the curve 0.88).Conclusions A combination of maternal serum proteins and cervical length constituted the best prediction model, and would help determine whether women with threatened preterm labour are likely to deliver within 7 days of measurement.

Sir, I was interested to read the paper by Tsiartas and colleagues 1 published in the June 2012 issue of BJOG. The authors reported the prediction of preterm labour by building a model using a cohort data set. As the authors point out in their conclusion, such a model needs to be tested in a new cohort in order to confirm its predictive ability. Why did the authors not divide their cohort into two and develop the model on one half and test it on the other? Or use other methods such as bootstrapping to test the reliability of their model?They have reported a sensitivity of 74%, a specificity of 87%, a positive predictive value (PPV) of 76%, a negative predictive value (NPV) of 86%, a likelihood ratio of 5.8 and an area under the receiver operating characteristic (AUC) curve of 0.88, concluding that serum proteins and cervical length constituted the best prediction model for the mentioned outcome. 1 Sensitivity, specificity, PPV, NPV, positive likelihood ratio (true positive/false negative) and negative likelihood ratio (false positive/true negative), as well as odds ratio (true results/false results, preferably with a value of more than 50), are among the tests to evaluate the validity (i.e. accuracy) of a single test compared with a gold standard. [2][3][4] Considering the range of values for the positive likelihood ratio (LR + = 1-infinity) and negative likelihood ratio (LR ) = 0-1), knowing that both LR + and LR ) equal 1 is the worst situation for the test. An LR + of 5.8 says nothing about the predictive value, because this should be compared with another LR + . Moreover, considering the range of possible LR + values, an LR + of 5.8 seems unimpressive. In such a situation, it is better to at least report the LR ) as well. 2,4 The area under the ROC curve is usually reported for diagnostic rather prognostic values of a model. The ROC for models may be comparable with LR + for a test because both of them actually use sensitivity and 1 -specificity; however, in LR + they are divided, and in the ROC we should plot sensitivity to 1 -specificity. 4 Prediction of spontaneous preterm delivery in women with threatened preterm labour: a prospective cohort study of multiple proteins in maternal serum Authors' ReplySir, First, we would like to thank Dr Sabour 1 for his interest in our paper. 2 He has raised important questions regarding the data analyses, which we appreciate taking the opportunity to clarify. We agree, and have pointed out in our Conclusions, that the predictive model needs to be validated in a new cohort in order to confirm its predictive ability. When we performed the analyses, we decided not to divide our data set into two parts to develop the model in one half and test it in the other. This division would have resulted in a substantial reduction of the predictive power of this study, with its limited number of patients. Our results must thus be replicated in another cohort.