Brain herniation in a patient with apparently normal intracranial pressure: a case report

Introduction Intracranial pressure monitoring is commonly implemented in patients with neurologic injury and at high risk of developing intracranial hypertension, to detect changes in intracranial pressure in a timely manner. This enables early and potentially life-saving treatment of intracranial hypertension. Case presentation An intraparenchymal pressure probe was placed in the hemisphere contralateral to a large basal ganglia hemorrhage in a 75-year-old Caucasian man who was mechanically ventilated and sedated because of depressed consciousness. Intracranial pressures were continuously recorded and never exceeded 17 mmHg. After sedation had been stopped, our patient showed clinical signs of transtentorial brain herniation, despite apparently normal intracranial pressures (less than 10 mmHg). Computed tomography revealed that the size of the intracerebral hematoma had increased together with significant unilateral brain edema and transtentorial herniation. The contralateral hemisphere where the intraparenchymal pressure probe was placed appeared normal. Our patient underwent emergency decompressive craniotomy and was tracheotomized early, but did not completely recover. Conclusions Intraparenchymal pressure probes placed in the hemisphere contralateral to an intracerebral hematoma may dramatically underestimate intracranial pressure despite apparently normal values, even in the case of transtentorial brain herniation

Reduced Protein Expression of the Na+/Ca2++K+-Exchanger (SLC24A4) in Apical Plasma Membranes of Maturation Ameloblasts of Fluorotic Mice

Exposure of forming enamel to fluoride results into formation of hypomineralized enamel. We tested whether enamel hypomineralization was caused by lower expression of the NCKX4/SLC24A4 Ca2+-transporter by ameloblasts. Three commercial antibodies against NCKX4 were tested on enamel organs of wild-type and Nckx4-null mice, one of which (a mouse monoclonal) was specific. This antibody gave a prominent staining of the apical plasma membranes of maturation ameloblasts, starting at early maturation. The layer of immuno-positive ameloblasts contained narrow gaps without immunostaining or with reduced staining. In fluorotic mouse incisors, the quantity of NCKX4 protein in ameloblasts as assessed by western blotting was not different from that in non-fluorotic ameloblasts. However, immunostaining of the apical plasma membranes of fluorotic ameloblasts was strongly reduced or absent suggesting that trafficking of NCKX4 to the apical membrane was strongly reduced. Exposure to fluoride may reduce NCKX4-mediated transport of Ca2+ by maturation stage ameloblasts which delays ameloblast modulation and reduces enamel mineralization

The AI Neuropsychologist: Automatic scoring of memory deficits with deep learning

Memory deficits are a hallmark of many different neurological and psychiatric conditions. The Rey-Osterrieth complex figure (ROCF) is the state–of-the-art assessment tool for neuropsychologists across the globe to assess the degree of non-verbal visual memory deterioration. To obtain a score, a trained clinician inspects a patient’s ROCF drawing and quantifies deviations from the original figure. This manual procedure is time-consuming, slow and scores vary depending on the clinician’s experience, motivation and tiredness. Here, we leverage novel deep learning architectures to automatize the rating of memory deficits. For this, a multi-head convolutional neural network was trained on 20225 ROCF drawings. Unbiased ground truth ROCF scores were obtained from crowdsourced human intelligence. The neural network outperforms both online raters and clinicians. Our AI-powered scoring system provides healthcare institutions worldwide with a digital tool to assess objectively, reliably and time-efficiently the performance in the ROCF test from hand-drawn images

Freund's vaccine adjuvant promotes Her2/Neu breast cancer

<p>Abstract</p> <p>Background</p> <p>Inflammation has been linked to the etiology of many organ-specific cancers. Indirect evidence suggests a possible role for inflammation in breast cancer. We investigated whether the systemic inflammation induced by Freund's adjuvant (FA) promotes mammary carcinogenesis in a rat model in which cancer is induced by the <it>neu </it>oncogene.</p> <p>Methods</p> <p>The effects of FA on hyperplastic mammary lesions and mammary carcinomas were determined in a <it>neu</it>-induced rat model. The inflammatory response to FA treatment was gauged by measuring acute phase serum haptoglobin. In addition, changes in cell proliferation and apoptosis following FA treatment were assessed.</p> <p>Results</p> <p>Rats receiving FA developed twice the number of mammary carcinomas as controls. Systemic inflammation following FA treatment is chronic, as shown by a doubling of the levels of the serum biomarker, haptoglobin, 15 days following initial treatment. We also show that this systemic inflammation is associated with the increased growth of hyperplastic mammary lesions. This increased growth results from a higher rate of cellular proliferation in the absence of changes in apoptosis.</p> <p>Conclusion</p> <p>Our data suggests that systemic inflammation induced by Freund's adjuvant (FA) promotes mammary carcinogenesis. It will be important to determine whether adjuvants currently used in human vaccines also promote breast cancer.</p

Predicting dementia from primary care records: a systematic review and meta-analysis

Introduction Possible dementia is usually identified in primary care by general practitioners (GPs) who refer to specialists for diagnosis. Only two-thirds of dementia cases are currently recorded in primary care, so increasing the proportion of cases diagnosed is a strategic priority for the UK and internationally. Clinical entities in the primary care record may indicate risk of developing dementia, and could be combined in a predictive model to help find patients who are missing a diagnosis. We conducted a meta-analysis to identify clinical entities with potential for use in such a predictive model for dementia in primary care. Methods and Findings We conducted a systematic search in PubMed, Web of Science and primary care database bibliographies. We included cohort or case-control studies which used routinely collected primary care data, to measure the association between any clinical entity and dementia. Meta-analyses were performed to pool odds ratios. A sensitivity analysis assessed the impact of non-independence of cases between studies. From a sift of 3836 papers, 20 studies, all European, were eligible for inclusion, comprising >1 million patients. 75 clinical entities were assessed as risk factors for all cause dementia, Alzheimer’s (AD) and Vascular dementia (VaD). Data included were unexpectedly heterogeneous, and assumptions were made about definitions of clinical entities and timing as these were not all well described. Meta-analysis showed that neuropsychiatric symptoms including depression, anxiety, and seizures, cognitive symptoms, and history of stroke, were positively associated with dementia. Cardiovascular risk factors such as hypertension, heart disease, dyslipidaemia and diabetes were positively associated with VaD and negatively with AD. Sensitivity analyses showed similar results. Conclusions These findings are of potential value in guiding feature selection for a risk prediction tool for dementia in primary care. Limitations include findings being UK-focussed. Further predictive entities ascertainable from primary care data, such as changes in consulting patterns, were absent from the literature and should be explored in future studies

Generative Embedding for Model-Based Classification of fMRI Data

Decoding models, such as those underlying multivariate classification algorithms, have been increasingly used to infer cognitive or clinical brain states from measures of brain activity obtained by functional magnetic resonance imaging (fMRI). The practicality of current classifiers, however, is restricted by two major challenges. First, due to the high data dimensionality and low sample size, algorithms struggle to separate informative from uninformative features, resulting in poor generalization performance. Second, popular discriminative methods such as support vector machines (SVMs) rarely afford mechanistic interpretability. In this paper, we address these issues by proposing a novel generative-embedding approach that incorporates neurobiologically interpretable generative models into discriminative classifiers. Our approach extends previous work on trial-by-trial classification for electrophysiological recordings to subject-by-subject classification for fMRI and offers two key advantages over conventional methods: it may provide more accurate predictions by exploiting discriminative information encoded in ‘hidden’ physiological quantities such as synaptic connection strengths; and it affords mechanistic interpretability of clinical classifications. Here, we introduce generative embedding for fMRI using a combination of dynamic causal models (DCMs) and SVMs. We propose a general procedure of DCM-based generative embedding for subject-wise classification, provide a concrete implementation, and suggest good-practice guidelines for unbiased application of generative embedding in the context of fMRI. We illustrate the utility of our approach by a clinical example in which we classify moderately aphasic patients and healthy controls using a DCM of thalamo-temporal regions during speech processing. Generative embedding achieves a near-perfect balanced classification accuracy of 98% and significantly outperforms conventional activation-based and correlation-based methods. This example demonstrates how disease states can be detected with very high accuracy and, at the same time, be interpreted mechanistically in terms of abnormalities in connectivity. We envisage that future applications of generative embedding may provide crucial advances in dissecting spectrum disorders into physiologically more well-defined subgroups

Adaptive Strategy for the Statistical Analysis of Connectomes

We study an adaptive statistical approach to analyze brain networks represented by brain connection matrices of interregional connectivity (connectomes). Our approach is at a middle level between a global analysis and single connections analysis by considering subnetworks of the global brain network. These subnetworks represent either the inter-connectivity between two brain anatomical regions or by the intra-connectivity within the same brain anatomical region. An appropriate summary statistic, that characterizes a meaningful feature of the subnetwork, is evaluated. Based on this summary statistic, a statistical test is performed to derive the corresponding p-value. The reformulation of the problem in this way reduces the number of statistical tests in an orderly fashion based on our understanding of the problem. Considering the global testing problem, the p-values are corrected to control the rate of false discoveries. Finally, the procedure is followed by a local investigation within the significant subnetworks. We contrast this strategy with the one based on the individual measures in terms of power. We show that this strategy has a great potential, in particular in cases where the subnetworks are well defined and the summary statistics are properly chosen. As an application example, we compare structural brain connection matrices of two groups of subjects with a 22q11.2 deletion syndrome, distinguished by their IQ scores

Genome-wide association and HLA fine-mapping studies identify risk loci and genetic pathways underlying allergic rhinitis

Allergic rhinitis is the most common clinical presentation of allergy, affecting 400 million people worldwide, with increasing incidence in westernized countries1,2. To elucidate the genetic architecture and understand the underlying disease mechanisms, we carried out a meta-analysis of allergic rhinitis in 59,762 cases and 152,358 controls of European ancestry and identified a total of 41 risk loci for allergic rhinitis, including 20 loci not previously associated with allergic rhinitis, which were confirmed in a replication phase of 60,720 cases and 618,527 controls. Functional annotation implicated genes involved in various immune pathways, and fine mapping of the HLA region suggested amino acid variants important for antigen binding. We further performed genome-wide association study (GWAS) analyses of allergic sensitization against inhalant allergens and nonallergic rhinitis, which suggested shared genetic mechanisms across rhinitis-related traits. Future studies of the identified loci and genes might identify novel targets for treatment and prevention of allergic rhinitis