Antipsychotic dose escalation as a trigger for Neuroleptic Malignant Syndrome (NMS): literature review and case series report

Background: “Neuroleptic malignant syndrome” (NMS) is a potentially fatal idiosyncratic reaction to any medication which affects the central dopaminergic system. Between 0.5% and 1% of patients exposed to antipsychotics develop the condition. Mortality rates may be as high as 55% and many risk factors have been reported. Although rapid escalation of antipsychotic dose is thought to be an important risk factor, to date it has not been the focus of a published case series or scientifically defined. <p/>Aims: To identify cases of NMS and review risk factors for its development with a particular focus on rapid dose escalation in the 30 days prior to onset. <p/>Methodology: A review of the literature on rapid dose escalation was undertaken and a pragmatic definition of “rapid dose escalation” was made. NMS cases were defined using DSM-IV criteria and systematically identified within a secondary care mental health service. A ratio of titration rate was calculated for each NMS patient and “rapid escalators” and “non rapid escalators” were compared. <p/>Results: 13 cases of NMS were identified. A progressive mean dose increase 15 days prior to the confirmed episode of NMS was observed (241.7mg/day during days 1-15 to 346.9mg/day during days 16-30) and the mean ratio of dose escalation for NMS patients was 1.4. Rapid dose escalation was seen in 5/13 cases and non rapid escalators had markedly higher daily cumulative antipsychotic dose compared to rapid escalators. <p/>Conclusions: Rapid dose escalation occurred in less than half of this case series (n=5, 38.5%), although there is currently no consensus on the precise definition of rapid dose escalation. Cumulative antipsychotic dose – alongside other known risk factors - may also be important in the development of NMS

Water Dynamics in Shewanella oneidensis at Ambient and High Pressure using Quasi-Elastic Neutron Scattering

Quasielastic neutron scattering (QENS) is an ideal technique for studying water transport and relaxation dynamics at pico-to nanosecond timescales and at length scales relevant to cellular dimensions. Studies of high pressure dynamic effects in live organisms are needed to understand Earth's deep biosphere and biotechnology applications. Here we applied QENS to study water transport in Shewanella oneidensis at ambient (0.1 MPa) and high (200 MPa) pressure using H/D isotopic contrast experiments for normal and perdeuterated bacteria and buffer solutions to distinguish intracellular and transmembrane processes. The results indicate that intracellular water dynamics are comparable with bulk diffusion rates in aqueous fluids at ambient conditions but a significant reduction occurs in high pressure mobility. We interpret this as due to enhanced interactions with macromolecules in the nanoconfined environment. Overall diffusion rates across the cell envelope also occur at similar rates but unexpected narrowing of the QENS signal appears between momentum transfer values Q = 0.7-1.1 Å-1 corresponding to real space dimensions of 6-9 Å. The relaxation time increase can be explained by correlated dynamics of molecules passing through Aquaporin water transport complexes located within the inner or outer membrane structures

Latent cluster analysis of ALS phenotypes identifies prognostically differing groups

BACKGROUND Amyotrophic lateral sclerosis (ALS) is a degenerative disease predominantly affecting motor neurons and manifesting as several different phenotypes. Whether these phenotypes correspond to different underlying disease processes is unknown. We used latent cluster analysis to identify groupings of clinical variables in an objective and unbiased way to improve phenotyping for clinical and research purposes. METHODS Latent class cluster analysis was applied to a large database consisting of 1467 records of people with ALS, using discrete variables which can be readily determined at the first clinic appointment. The model was tested for clinical relevance by survival analysis of the phenotypic groupings using the Kaplan-Meier method. RESULTS The best model generated five distinct phenotypic classes that strongly predicted survival (p<0.0001). Eight variables were used for the latent class analysis, but a good estimate of the classification could be obtained using just two variables: site of first symptoms (bulbar or limb) and time from symptom onset to diagnosis (p<0.00001). CONCLUSION The five phenotypic classes identified using latent cluster analysis can predict prognosis. They could be used to stratify patients recruited into clinical trials and generating more homogeneous disease groups for genetic, proteomic and risk factor research

Are genetic risk factors for psychosis also associated with dimension-specific psychotic experiences in adolescence?

Psychosis has been hypothesised to be a continuously distributed quantitative phenotype and disorders such as schizophrenia and bipolar disorder represent its extreme manifestations. Evidence suggests that common genetic variants play an important role in liability to both schizophrenia and bipolar disorder. Here we tested the hypothesis that these common variants would also influence psychotic experiences measured dimensionally in adolescents in the general population. Our aim was to test whether schizophrenia and bipolar disorder polygenic risk scores (PRS), as well as specific single nucleotide polymorphisms (SNPs) previously identified as risk variants for schizophrenia, were associated with adolescent dimension-specific psychotic experiences. Self-reported Paranoia, Hallucinations, Cognitive Disorganisation, Grandiosity, Anhedonia, and Parent-rated Negative Symptoms, as measured by the Specific Psychotic Experiences Questionnaire (SPEQ), were assessed in a community sample of 2,152 16-year-olds. Polygenic risk scores were calculated using estimates of the log of odds ratios from the Psychiatric Genomics Consortium GWAS stage-1 mega-analysis of schizophrenia and bipolar disorder. The polygenic risk analyses yielded no significant associations between schizophrenia and bipolar disorder PRS and the SPEQ measures. The analyses on the 28 individual SNPs previously associated with schizophrenia found that two SNPs in TCF4 returned a significant association with the SPEQ Paranoia dimension, rs17512836 (p-value=2.57x10-4) and rs9960767 (p-value=6.23x10-4). Replication in an independent sample of 16-year-olds (N=3,427) assessed using the Psychotic-Like Symptoms Questionnaire (PLIKS-Q), a composite measure of multiple positive psychotic experiences, failed to yield significant results. Future research with PRS derived from larger samples, as well as larger adolescent validation samples, would improve the predictive power to test these hypotheses further. The challenges of relating adult clinical diagnostic constructs such as schizophrenia to adolescent psychotic experiences at a genetic level are discussed

New directions in cellular therapy of cancer: a summary of the summit on cellular therapy for cancer

A summit on cellular therapy for cancer discussed and presented advances related to the use of adoptive cellular therapy for melanoma and other cancers. The summit revealed that this field is advancing rapidly. Conventional cellular therapies, such as tumor infiltrating lymphocytes (TIL), are becoming more effective and more available. Gene therapy is becoming an important tool in adoptive cell therapy. Lymphocytes are being engineered to express high affinity T cell receptors (TCRs), chimeric antibody-T cell receptors (CARs) and cytokines. T cell subsets with more naïve and stem cell-like characteristics have been shown in pre-clinical models to be more effective than unselected populations and it is now possible to reprogram T cells and to produce T cells with stem cell characteristics. In the future, combinations of adoptive transfer of T cells and specific vaccination against the cognate antigen can be envisaged to further enhance the effectiveness of these therapies

Simple, Fast and Accurate Implementation of the Diffusion Approximation Algorithm for Stochastic Ion Channels with Multiple States

The phenomena that emerge from the interaction of the stochastic opening and closing of ion channels (channel noise) with the non-linear neural dynamics are essential to our understanding of the operation of the nervous system. The effects that channel noise can have on neural dynamics are generally studied using numerical simulations of stochastic models. Algorithms based on discrete Markov Chains (MC) seem to be the most reliable and trustworthy, but even optimized algorithms come with a non-negligible computational cost. Diffusion Approximation (DA) methods use Stochastic Differential Equations (SDE) to approximate the behavior of a number of MCs, considerably speeding up simulation times. However, model comparisons have suggested that DA methods did not lead to the same results as in MC modeling in terms of channel noise statistics and effects on excitability. Recently, it was shown that the difference arose because MCs were modeled with coupled activation subunits, while the DA was modeled using uncoupled activation subunits. Implementations of DA with coupled subunits, in the context of a specific kinetic scheme, yielded similar results to MC. However, it remained unclear how to generalize these implementations to different kinetic schemes, or whether they were faster than MC algorithms. Additionally, a steady state approximation was used for the stochastic terms, which, as we show here, can introduce significant inaccuracies. We derived the SDE explicitly for any given ion channel kinetic scheme. The resulting generic equations were surprisingly simple and interpretable - allowing an easy and efficient DA implementation. The algorithm was tested in a voltage clamp simulation and in two different current clamp simulations, yielding the same results as MC modeling. Also, the simulation efficiency of this DA method demonstrated considerable superiority over MC methods.Comment: 32 text pages, 10 figures, 1 supplementary text + figur

Living donor liver transplantation for neonatal hemochromatosis using non-anatomically resected segments II and III: a case report

<p>Abstract</p> <p>Introduction</p> <p>Neonatal hemochromatosis is the most common cause of liver failure and liver transplantation in the newborn. The size of the infant determines the liver volume that can be transplanted safely without incurring complications arising from a large graft. Transplantation of monosegments II or III is a standard method for the newborns with liver failure.</p> <p>Case presentation</p> <p>A three-week old African-American male neonate was diagnosed with acute liver failure secondary to neonatal hemochromatosis. Living-related liver transplantation was considered after the failure of intensive medical therapy. Intra-operatively a non-anatomical resection and transplantation of segments II and III was performed successfully. The boy is growing normally two years after the transplantation.</p> <p>Conclusion</p> <p>Non-anatomical resection and transplantation of liver segments II and III is preferred to the transplantation of anatomically resected monosegements, especially when the left lobe is thin and flat. It allows the use of a reduced-size donor liver with intact hilar structures and outflow veins. In an emergency, living-related liver transplantation should be offered to infants with liver failure secondary to neonatal hemochromatosis who fail to respond to medical treatment.</p

Determining Interacting Objects in Human-Centric Activities via Qualitative Spatio-Temporal Reasoning

Abstract. Understanding the activities taking place in a video is a chal-lenging problem in Artificial Intelligence. Complex video sequences con-tain many activities and involve a multitude of interacting objects. De-termining which objects are relevant to a particular activity is the first step in understanding the activity. Indeed many objects in the scene are irrelevant to the main activity taking place. In this work, we consider human-centric activities and look to identify which objects in the scene are involved in the activity. We take an activity-agnostic approach and rank every moving object in the scene with how likely it is to be involved in the activity. We use a comprehensive spatio-temporal representation that captures the joint movement between humans and each object. We then use supervised machine learning techniques to recognize relevant objects based on these features. Our approach is tested on the challeng-ing Mind’s Eye dataset.

ImageNet Auto-Annotation with Segmentation Propagation

ImageNet is a large-scale hierarchical database of object classes with millions of images.We propose to automatically populate it with pixelwise object-background segmentations, by leveraging existing manual annotations in the form of class labels and bounding-boxes. The key idea is to recursively exploit images segmented so far to guide the segmentation of new images. At each stage this propagation process expands into the images which are easiest to segment at that point in time, e.g. by moving to the semantically most related classes to those segmented so far. The propagation of segmentation occurs both (a) at the image level, by transferring existing segmentations to estimate the probability of a pixel to be foreground, and (b) at the class level, by jointly segmenting images of the same class and by importing the appearance models of classes that are already segmented. Through experiments on 577 classes and 500k images we show that our technique (i) annotates a wide range of classes with accurate segmentations; (ii) effectively exploits the hierarchical structure of ImageNet; (iii) scales efficiently, especially when implemented on superpixels; (iv) outperforms a baseline GrabCut (Rother et al. 2004) initialized on the image center, as well as segmentation transfer from a fixed source pool and run independently on each target image (Kuettel and Ferrari 2012). Moreover, our method also delivers state-of-the-art results on the recent iCoseg dataset for co-segmentation.ISSN:0920-5691ISSN:1573-140

Left gaze bias in humans, rhesus monkeys and domestic dogs

While viewing faces, human adults often demonstrate a natural gaze bias towards the left visual field, that is, the right side of the viewee’s face is often inspected first and for longer periods. Using a preferential looking paradigm, we demonstrate that this bias is neither uniquely human nor limited to primates, and provide evidence to help elucidate its biological function within a broader social cognitive framework. We observed that 6-month-old infants showed a wider tendency for left gaze preference towards objects and faces of different species and orientation, while in adults the bias appears only towards upright human faces. Rhesus monkeys showed a left gaze bias towards upright human and monkey faces, but not towards inverted faces. Domestic dogs, however, only demonstrated a left gaze bias towards human faces, but not towards monkey or dog faces, nor to inanimate object images. Our findings suggest that face- and species-sensitive gaze asymmetry is more widespread in the animal kingdom than previously recognised, is not constrained by attentional or scanning bias, and could be shaped by experience to develop adaptive behavioural significance