Cholinergic basal forebrain structure influences the reconfiguration of white matter connections to support residual memory in mild cognitive impairment.

The fornix and hippocampus are critical to recollection in the healthy human brain. Fornix degeneration is a feature of aging and Alzheimer's disease. In the presence of fornix damage in mild cognitive impairment (MCI), a recognized prodrome of Alzheimer's disease, recall shows greater dependence on other tracts, notably the parahippocampal cingulum (PHC). The current aims were to determine whether this shift is adaptive and to probe its relationship to cholinergic signaling, which is also compromised in Alzheimer's disease. Twenty-five human participants with MCI and 20 matched healthy volunteers underwent diffusion MRI, behavioral assessment, and volumetric measurement of the basal forebrain. In a regression model for recall, there was a significant group × fornix interaction, indicating that the association between recall and fornix structure was weaker in patients. The opposite trend was present for the left PHC. To further investigate this pattern, two regression models were generated to account for recall performance: one based on fornix microstructure and the other on both fornix and left PHC. The realignment to PHC was positively correlated with free recall but not non-memory measures, implying a reconfiguration that is beneficial to residual memory. There was a positive relationship between realignment to PHC and basal forebrain gray matter volume despite this region demonstrating atrophy at a group level, i.e., the cognitive realignment to left PHC was most apparent when cholinergic areas were relatively spared. Therefore, cholinergic systems appear to enable adaptation to injury even as they degenerate, which has implications for functional restoration.

Reversible inhibition of murine cytomegalovirus replication by gamma interferon (IFN-γ) in primary macrophages involves a primed type I IFN-signaling subnetwork for full establishment of an immediate-early antiviral state.

Activated macrophages play a central role in controlling inflammatory responses to infection and are tightly regulated to rapidly mount responses to infectious challenge. Type I interferon (alpha/beta interferon [IFN-α/ß]) and type II interferon (IFN-γ) play a crucial role in activating macrophages and subsequently restricting viral infections. Both types of IFNs signal through related but distinct signaling pathways, inducing a vast number of interferon-stimulated genes that are overlapping but distinguishable. The exact mechanism by which IFNs, particularly IFN-γ, inhibit DNA viruses such as cytomegalovirus (CMV) is still not fully understood. Here, we investigate the antiviral state developed in macrophages upon reversible inhibition of murine CMV by IFN-γ. On the basis of molecular profiling of the reversible inhibition, we identify a significant contribution of a restricted type I IFN subnetwork linked with IFN-γ activation. Genetic knockout of the type I-signaling pathway, in the context of IFN-γ stimulation, revealed an essential requirement for a primed type I-signaling process in developing a full refractory state in macrophages. A minimal transient induction of IFN-ß upon macrophage activation with IFN-γ is also detectable. In dose and kinetic viral replication inhibition experiments with IFN-γ, the establishment of an antiviral effect is demonstrated to occur within the first hours of infection. We show that the inhibitory mechanisms at these very early times involve a blockade of the viral major immediate-early promoter activity. Altogether our results show that a primed type I IFN subnetwork contributes to an immediate-early antiviral state induced by type II IFN activation of macrophages, with a potential further amplification loop contributed by transient induction of IFN-ß.

Associations of homelessness and residential mobility with length of stay after acute psychiatric admission.

BACKGROUND: A small number of patient-level variables have replicated associations with the length of stay (LOS) of psychiatric inpatients. Although need for housing has often been identified as a cause of delayed discharge, there has been little research into the associations between LOS and homelessness and residential mobility (moving to a new home), or the magnitude of these associations compared to other exposures. METHODS: Cross-sectional study of 4885 acute psychiatric admissions to a mental health NHS Trust serving four South London boroughs. Data were taken from a comprehensive repository of anonymised electronic patient records. Analysis was performed using log-linear regression. RESULTS: Residential mobility was associated with a 99% increase in LOS and homelessness with a 45% increase. Schizophrenia, other psychosis, the longest recent admission, residential mobility, and some items on the Health of the Nation Outcome Scales (HoNOS), especially ADL impairment, were also associated with increased LOS. Informal admission, drug and alcohol or other non-psychotic diagnosis and a high HoNOS self-harm score reduced LOS. Including residential mobility in the regression model produced the same increase in the variance explained as including diagnosis; only legal status was a stronger predictor. CONCLUSIONS: Homelessness and, especially, residential mobility account for a significant part of variation in LOS despite affecting a minority of psychiatric inpatients; for these people, the effect on LOS is marked. Appropriate policy responses may include attempts to avert the loss of housing in association with admission, efforts to increase housing supply and the speed at which it is made available, and reforms of payment systems to encourage this.

Fluorescence lifetime imaging of quantum dot labeled DNA microarrays.

Quantum dot (QD) labeling combined with fluorescence lifetime imaging microscopy is proposed as a powerful transduction technique for the detection of DNA hybridization events. Fluorescence lifetime analysis of DNA microarray spots of hybridized QD labeled target indicated a characteristic lifetime value of 18.8 ns, compared to 13.3 ns obtained for spots of free QD solution, revealing that QD labels are sensitive to the spot microenvironment. Additionally, time gated detection was shown to improve the microarray image contrast ratio by 1.8, achieving femtomolar target sensitivity. Finally, lifetime multiplexing based on Qdot525 and Alexa430 was demonstrated using a single excitation-detection readout channel.

A comparison of parametric and nonparametric methods for normalising cDNA microarray data.

Normalisation is an essential first step in the analysis of most cDNA microarray data, to correct for effects arising from imperfections in the technology. Loess smoothing is commonly used to correct for trends in log-ratio data. However, parametric models, such as the additive plus multiplicative variance model, have been preferred for scale normalisation, though the variance structure of microarray data may be of a more complex nature than can be accommodated by a parametric model. We propose a new nonparametric approach that incorporates location and scale normalisation simultaneously using a Generalised Additive Model for Location, Scale and Shape (GAMLSS, Rigby and Stasinopoulos, 2005, Applied Statistics, 54, 507-554). We compare its performance in inferring differential expression with Huber et al.'s (2002, Bioinformatics, 18, 96-104) arsinh variance stabilising transformation (AVST) using real and simulated data. We show GAMLSS to be as powerful as AVST when the parametric model is correct, and more powerful when the model is wrong.

Blood-Based Biomarker Candidates of Cerebral Amyloid Using PiB PET in Non-Demented Elderly.

Increasingly, clinical trials for Alzheimer's disease (AD) are being conducted earlier in the disease phase and with biomarker confirmation using in vivo amyloid PET imaging or CSF tau and Aß measures to quantify pathology. However, making such a pre-clinical AD diagnosis is relatively costly and the screening failure rate is likely to be high. Having a blood-based marker that would reduce such costs and accelerate clinical trials through identifying potential participants with likely pre-clinical AD would be a substantial advance. In order to seek such a candidate biomarker, discovery phase proteomic analyses using 2DGE and gel-free LC-MS/MS for high and low molecular weight analytes were conducted on longitudinal plasma samples collected over a 12-year period from non-demented older individuals who exhibited a range of 11C-PiB PET measures of amyloid load. We then sought to extend our discovery findings by investigating whether our candidate biomarkers were also associated with brain amyloid burden in disease, in an independent cohort. Seven plasma proteins, including A2M, Apo-A1, and multiple complement proteins, were identified as pre-clinical biomarkers of amyloid burden and were consistent across three time points (p <  0.05). Five of these proteins also correlated with brain amyloid measures at different stages of the disease (q <  0.1). Here we show that it is possible to detect a plasma based biomarker signature indicative of AD pathology at a stage long before the onset of clinical disease manifestation. As in previous studies, acute phase reactants and inflammatory markers dominate this signature.

Identification of a human neonatal immune-metabolic network associated with bacterial infection.

Understanding how human neonates respond to infection remains incomplete. Here, a system-level investigation of neonatal systemic responses to infection shows a surprisingly strong but unbalanced homeostatic immune response; developing an elevated set-point of myeloid regulatory signalling and sugar-lipid metabolism with concomitant inhibition of lymphoid responses. Innate immune-negative feedback opposes innate immune activation while suppression of T-cell co-stimulation is coincident with selective upregulation of CD85 co-inhibitory pathways. By deriving modules of co-expressed RNAs, we identify a limited set of networks associated with bacterial infection that exhibit high levels of inter-patient variability. Whereas, by integrating immune and metabolic pathways, we infer a patient-invariant 52-gene-classifier that predicts bacterial infection with high accuracy using a new independent patient population. This is further shown to have predictive value in identifying infection in suspected cases with blood culture-negative tests. Our results lay the foundation for future translation of host pathways in advancing diagnostic, prognostic and therapeutic strategies for neonatal sepsis.

Multi-factorial analysis of class prediction error: estimating optimal number of biomarkers for various classification rules.

Machine learning and statistical model based classifiers have increasingly been used with more complex and high dimensional biological data obtained from high-throughput technologies. Understanding the impact of various factors associated with large and complex microarray datasets on the predictive performance of classifiers is computationally intensive, under investigated, yet vital in determining the optimal number of biomarkers for various classification purposes aimed towards improved detection, diagnosis, and therapeutic monitoring of diseases. We investigate the impact of microarray based data characteristics on the predictive performance for various classification rules using simulation studies. Our investigation using Random Forest, Support Vector Machines, Linear Discriminant Analysis and k-Nearest Neighbour shows that the predictive performance of classifiers is strongly influenced by training set size, biological and technical variability, replication, fold change and correlation between biomarkers. Optimal number of biomarkers for a classification problem should therefore be estimated taking account of the impact of all these factors. A database of average generalization errors is built for various combinations of these factors. The database of generalization errors can be used for estimating the optimal number of biomarkers for given levels of predictive accuracy as a function of these factors. Examples show that curves from actual biological data resemble that of simulated data with corresponding levels of data characteristics. An R package optBiomarker implementing the method is freely available for academic use from the Comprehensive R Archive Network (http://www.cran.r-project.org/web/packages/optBiomarker/).

Statistical estimation of gene expression using multiple laser scans of microarrays.

UNLABELLED: We propose a statistical model for estimating gene expression using data from multiple laser scans at different settings of hybridized microarrays. A functional regression model is used, based on a non-linear relationship with both additive and multiplicative error terms. The function is derived as the expected value of a pixel, given that values are censored at 65 535, the maximum detectable intensity for double precision scanning software. Maximum likelihood estimation based on a Cauchy distribution is used to fit the model, which is able to estimate gene expressions taking account of outliers and the systematic bias caused by signal censoring of highly expressed genes. We have applied the method to experimental data. Simulation studies suggest that the model can estimate the true gene expression with negligible bias. AVAILABILITY: FORTRAN 90 code for implementing the method can be obtained from the authors.