Neuropsychiatric lupus and infectious triggers

Infections can act as environmental triggers inducing or promoting systemic lupus erythematosus (SLE) in genetically predisposed individuals. The aim of the present study was to compare the titres of antibodies (Abs) to infectious agents with neuropsychiatric (NPSLE) clinical manifestations. The sera of 260 individuals (120 patients with SLE and 140 geographic controls) were evaluated for the titres of Epstein bar virus (EBV), cytomegalovirus (CMV), toxoplasma, rubella and syphilis Abs using the BioPlex 2200 Multiplexed Immunoassay method (BioRad) and by the ELISA method for Helicobacter pylori and Hepatitis B core Ag. All BioPlex 2200 kits used were in developmental stages. Data analysis was performed using SPSS 9.0 statistical analysis software (SPSS Inc., Chicago, IL, USA, 1999). Correlation analysis indicated that rubella IgM Ab titres were marginally, positively associated with psychosis (P = 0.09). No other associations were detected between the 17 infectious Abs and five NP manifestations. When the positivity cut-off for anti-rubella IgM Abs was set at three standard deviations above normal, three positive subjects were identified: one patient with psychosis and one with depression, for a total NPSLE prevalence of 33.3%. On the contrary, the prevalence of NPSLE in the remaining subjects was 6.5%. Marginally significant correlations between elevated titres of rubella IgM Ab with psychosis and depression were found. Although this nearly 5-fold increase is not statistically significant, it appears that in a larger sample size, significance would be reached. This is the first study reported that examined the correlation of NPSLE manifestations with anti-infectious Abs.     

Hyperferritinemia is Associated with Serologic Antiphospholipid Syndrome in SLE Patients

Ferritin may play a direct role on the immune system. We sought to determine if elevated levels of ferritin in lupus patients correlate with disease activity and organ involvement in a large cohort. Ferritin levels (gender and age adjusted) were assessed in 274 lupus serum samples utilizing the LIASON Ferritin automated immunoassay method. Significant disease activity was determined if European Consensus Lupus Activity Index (ECLAM)?>?2 or Systemic Lupus Erythematosus Disease Activity Index (SLEDAI)?>?4. Utilizing an EXCEL database, we compared elevated ferritin levels to manifestations grouped by organ involvement, serology, and previous therapy. The patients were predominantly female (89%), median age was 37 years old, and disease duration was 10.6?±?7.7 years. Hyperferritinemia was found in 18.6% of SLE patients. Compared to subjects with normal ferritin levels, a significantly greater proportion of patients with hyperferritinemia had thrombocytopenia (15.4% vs. 33.3%, p?=?0.003) and lupus anticoagulant (11.3% vs. 29.0%, p?=?0.01). Additionally, compared to normoferritinemic subjects, hyperferritinemic subjects had significantly higher total aCL (99.7?±?369 vs. 30.9?±?17.3 GPI, p?=?0.02) and aCL IgM antibody levels (75.3?±?357.4 vs. 9.3?±?10.3 GPI, p?=?0.02), and marginally lower aCL IgG antibody levels (9.2?±?4.9 vs. 9.7?±?3.9 GPI, p?=?0.096). While the ECLAM score significantly correlated with hyperferritinemia (p?=?0.04), the SLEDAI score was marginally associated with hyperferritinemia (p?=?0.1). Serositis was marginally associated with hyperferritinemia, but not with other manifestations. An association with serologic APS was encountered. Hyperferritinemia was associated with thrombocytopenia, lupus anticoagulant, and anti-cardiolipin antibodies suggest that it may be an early marker for secondary antiphospholipid syndrome in SLE patients.     

Medical doctors’ knowledge of dental trauma management: A review

Education in dental trauma is extremely important to promote knowledge on the assessment and management of a traumatized tooth. Medical doctors are normally only required to manage the emergency phase of traumatic dental injury (TDI) treatment before referring to a dentist, endodontist or oral and maxillofacial surgeon for continuing care. Medical doctors who possess sufficient theoretical knowledge and are competent enough clinically to handle TDI can provide a higher standard of treatment care and ultimately achieve a better patient outcome. The aim of this literature review was to assess the extent of medical doctors’ knowledge of dental trauma management for injuries in the following four areas: (a) tooth structure; (b) to the supporting bone; (c) to the periodontal tissues; and (d) to the soft tissues. Based on the findings from this literature review, an overall deficiency in knowledge and confidence in managing dental trauma has been identified. Knowledge and understanding to categorize TDI using the same classification of dental injuries commonly used amongst dentists would allow medical doctors to better manage and communicate with dental colleagues concerning referral for further care. If the medical education curriculum provided medical doctors with more information and skills for the management of dental trauma and an understanding of the importance of early management, then more favourable outcomes may prevail for dental trauma patients.     

Ranking and combining multiple predictors without labeled data

In a broad range of classification and decision-making problems, one is given the advice or predictions of several classifiers, of unknown reliability, over multiple questions or queries. This scenario is different from the standard supervised setting, where each classifier’s accuracy can be assessed using available labeled data, and raises two questions: Given only the predictions of several classifiers over a large set of unlabeled test data, is it possible to (i) reliably rank them and (ii) construct a metaclassifier more accurate than most classifiers in the ensemble? Here we present a spectral approach to address these questions. First, assuming conditional independence between classifiers, we show that the off-diagonal entries of their covariance matrix correspond to a rank-one matrix. Moreover, the classifiers can be ranked using the leading eigenvector of this covariance matrix, because its entries are proportional to their balanced accuracies. Second, via a linear approximation to the maximum likelihood estimator, we derive the Spectral Meta-Learner (SML), an unsupervised ensemble classifier whose weights are equal to these eigenvector entries. On both simulated and real data, SML typically achieves a higher accuracy than most classifiers in the ensemble and can provide a better starting point than majority voting for estimating the maximum likelihood solution. Furthermore, SML is robust to the presence of small malicious groups of classifiers designed to veer the ensemble prediction away from the (unknown) ground truth.Every day, multiple decisions are made based on input and suggestions from several sources, either algorithms or advisers, of unknown reliability. Investment companies handle their portfolios by combining reports from several analysts, each providing recommendations on buying, selling, or holding multiple stocks (1, 2). Central banks combine surveys of several professional forecasters to monitor rates of inflation, real gross domestic product growth, and unemployment (3–6). Biologists study the genomic binding locations of proteins by combining or ranking the predictions of several peak detection algorithms applied to large-scale genomics data (7). Physician tumor boards convene a number of experts from different disciplines to discuss patients whose diseases pose diagnostic and therapeutic challenges (8). Peer-review panels discuss multiple grant applications and make recommendations to fund or reject them (9). The examples above describe scenarios in which several human advisers or algorithms provide their predictions or answers to a list of queries or questions. A key challenge is to improve decision making by combining these multiple predictions of unknown reliability. Automating this process of combining multiple predictors is an active field of research in decision science (cci.mit.edu/research), medicine (10), business (refs. 11 and 12 and www.kaggle.com/competitions), and government (www.iarpa.gov/Programs/ia/ACE/ace.html and www.goodjudgmentproject.com), as well as in statistics and machine learning.Such scenarios, whereby advisers of unknown reliability provide potentially conflicting opinions, or propose to take opposite actions, raise several interesting questions. How should the decision maker proceed to identify who, among the advisers, is the most reliable? Moreover, is it possible for the decision maker to cleverly combine the collection of answers from all of the advisers and provide even more accurate answers?In statistical terms, the first question corresponds to the problem of estimating prediction performances of preconstructed classifiers (e.g., the advisers) in the absence of class labels. Namely, each classifier was constructed independently on a potentially different training dataset (e.g., each adviser trained on his/her own using possibly different sources of information), yet they are all being applied to the same new test data (e.g., list of queries) for which labels are not available, either because they are expensive to obtain or because they will only be available in the future, after the decision has been made. In addition, the accuracy of each classifier on its own training data is unknown. This scenario is markedly different from the standard supervised setting in machine learning and statistics. There, classifiers are typically trained on the same labeled data and can be ranked, for example, by comparing their empirical accuracy on a common labeled validation set. In this paper we show that under standard assumptions of independence between classifier errors their unknown performances can still be ranked even in the absence of labeled data.The second question raised above corresponds to the problem of combining predictions of preconstructed classifiers to form a metaclassifier with improved prediction performance. This problem arises in many fields, including combination of forecasts in decision science and crowdsourcing in machine learning, which have each derived different approaches to address it. If we had external knowledge or historical data to assess the reliability of the available classifiers we could use well-established solutions relying on panels of experts or forecast combinations (11–14). In our problem such knowledge is not always available and thus these solutions are in general not applicable. The oldest solution that does not require additional information is majority voting, whereby the predicted class label is determined by a rule of majority, with all advisers assigned the same weight. More recently, iterative likelihood maximization procedures, pioneered by Dawid and Skene (15), have been proposed, in particular in crowdsourcing applications (16–23). Owing to the nonconvexity of the likelihood function, these techniques often converge only to a local, rather than global, maximum and require careful initialization. Furthermore, there are typically no guarantees on the quality of the resulting solution.In this paper we address these questions via a spectral analysis that yields four major insights:

1. Under standard assumptions of independence between classifier errors, in the limit of an infinite test set, the off-diagonal entries of the population covariance matrix of the classifiers correspond to a rank-one matrix.
2. The entries of the leading eigenvector of this rank-one matrix are proportional to the balanced accuracies of the classifiers. Thus, a spectral decomposition of this rank-one matrix provides a computationally efficient approach to rank the performances of an ensemble of classifiers.
3. A linear approximation of the maximum likelihood estimator yields an ensemble learner whose weights are proportional to the entries of this eigenvector. This represents an efficient, easily constructed, unsupervised ensemble learner, which we term Spectral Meta-Learner (SML).
4. An interest group of conspiring classifiers (a cartel) that maliciously attempts to veer the overall ensemble solution away from the (unknown) ground truth leads to a rank-two covariance matrix. Furthermore, in contrast to majority voting, SML is robust to the presence of a small-enough cartel whose members are unknown.

In addition, we demonstrate the advantages of spectral approaches based on these insights, using both simulated and real-world datasets. When the independence assumptions hold approximately, SML is typically better than most classifiers in the ensemble and their majority vote, achieving results comparable to the maximum likelihood estimator (MLE). Empirically, we find SML to be a better starting point for computing the MLE that consistently leads to improved performance. Finally, spectral approaches are also robust to cartels and therefore helpful in analyzing surveys where a biased subgroup of advisers (a cartel) may have corrupted the data.     

Incretin,insulinotropic and glucose-lowering effects of whey protein pre-load in type 2 diabetes: a randomised clinical trial

Aims/hypothesis

Since protein ingestion is known to stimulate the secretion of glucagon-like peptide-1 (GLP-1), we hypothesised that enhancing GLP-1 secretion to harness its insulinotropic/beta cell-stimulating activity with whey protein pre-load may have beneficial glucose-lowering effects in type 2 diabetes.

Methods

In a randomised, open-label crossover clinical trial, we studied 15 individuals with well-controlled type 2 diabetes who were not taking any medications except for sulfonylurea or metformin. These participants consumed, on two separate days, 50 g whey in 250 ml water or placebo (250 ml water) followed by a standardised high-glycaemic-index breakfast in a hospital setting. Participants were randomised using a coin flip. The primary endpoints of the study were plasma concentrations of glucose, intact GLP-1 and insulin during the 30 min following meal ingestion.

Results

In each group, 15 patients were analysed. The results showed that over the whole 180 min post-meal period, glucose levels were reduced by 28% after whey pre-load with a uniform reduction during both early and late phases. Insulin and C-peptide responses were both significantly higher (by 105% and 43%, respectively) with whey pre-load. Notably, the early insulin response was 96% higher after whey. Similarly, both total GLP-1 (tGLP-1) and intact GLP-1 (iGLP-1) levels were significantly higher (by 141% and 298%, respectively) with whey pre-load. Dipeptidyl peptidase 4 plasma activity did not display any significant difference after breakfast between the groups.

Conclusions/interpretation

In summary, consumption of whey protein shortly before a high-glycaemic-index breakfast increased the early prandial and late insulin secretion, augmented tGLP-1 and iGLP-1 responses and reduced postprandial glycaemia in type 2 diabetic patients. Whey protein may therefore represent a novel approach for enhancing glucose-lowering strategies in type 2 diabetes. Trial registration ClinicalTrials.gov NCT01571622 Funding The Israeli Ministry of Health and Milk Council funded the research.     

Autonomic nervous system arousal and cognitive functioning in bipolar disorder

Levy B. Autonomic nervous system arousal and cognitive functioning in bipolar disorder. Bipolar Disord 2012: 00: 000–000. © 2012 John Wiley & Sons A/S.Published by Blackwell Publishing Ltd. Objective: Previous theories about the etiology of cognitive dysfunction in bipolar disorder (BD) emphasized trait factors such as neurological impairment. State factors, other than mood symptoms, that may exacerbate functional deficits have not yet been considered. The purpose of this study was to examine autonomic nervous system (ANS) arousal following cognitive challenge. The study compared patients with BD and healthy controls (HC) in physiological measures and neuropsychological test scores. Methods: Thirty euthymic patients with BD and 22 HC completed the study. Participants completed mood [Beck Depression Inventory‐II (BDI‐II) and Young Mania Rating Scale (YMRS)], anxiety (State–Trait Anxiety Inventory), and substance abuse (Drug Abuse Screening Test–20 item and Alcohol Use Disorders Identification Test) measures. They were connected to an electrogram, a sensitive thermometer for measuring finger temperature, and electrodes that measure galvanic skin response. After a five‐min baseline measurement in a restful state, participants completed a computerized neuropsychological battery (CNS Vital Signs). Results: The group with BD reported significantly more mood symptoms (BDI‐II, t = 3.71, p < 0.001; YMRS, t = 6.73, p < 0.001) and scored higher on a measure of trait‐anxiety (State–Trait Anxiety Inventory, t = 2.91, p < 0.001) than HC. A multivariate analysis of variance revealed higher arousal on all physiological measures in the BD group relative to HC at baseline [F(3,48) = 13.1, p < 0.001] and during cognitive testing [F(3,48) = 11.3, p < 0.001]. The increase in physiological arousal from a restful state to the time of testing was higher for the BD group [F(3,37) = 8.06, p < 0.001]. With respect to cognitive data, HC scored higher than patients with BD across the measures of memory (F = 8.5, p < 0.001), sustained (F = 9.5, p < 0.001) and complex (F = 2.7, p < 0.04) attention, processing speed (F = 10.0, p < 0.001), reaction time (F = 7.8, p < 0.001), cognitive flexibility (F = 19.7, p < 0.001), working memory (F = 10.8, p < 0.001), and social acuity (F = 5.7, p < 0.01), with partial eta‐squared from 0.18 to 0.62. Correlational analysis revealed significant associations between various cognitive test scores and changes in physiological arousal from baseline to testing (?0.59 ≤ r ≤ 0.22). Conclusions: Relative to HC, patients with BD experience larger changes in ANS arousal between a restful baseline and cognitive testing, and achieve lower cognitive test scores. Further research is needed to determine whether acute physiological symptoms of anxiety directly compromise cognitive functioning in BD.