Analysis of physiological signals for recognition of boredom,pain, and surprise emotions

Background

The aim of the study was to examine the differences of boredom, pain, and surprise. In addition to that, it was conducted to propose approaches for emotion recognition based on physiological signals.

Methods

Three emotions, boredom, pain, and surprise, are induced through the presentation of emotional stimuli and electrocardiography (ECG), electrodermal activity (EDA), skin temperature (SKT), and photoplethysmography (PPG) as physiological signals are measured to collect a dataset from 217 participants when experiencing the emotions. Twenty-seven physiological features are extracted from the signals to classify the three emotions. The discriminant function analysis (DFA) as a statistical method, and five machine learning algorithms (linear discriminant analysis (LDA), classification and regression trees (CART), self-organizing map (SOM), Naïve Bayes algorithm, and support vector machine (SVM)) are used for classifying the emotions.

Results

The result shows that the difference of physiological responses among emotions is significant in heart rate (HR), skin conductance level (SCL), skin conductance response (SCR), mean skin temperature (meanSKT), blood volume pulse (BVP), and pulse transit time (PTT), and the highest recognition accuracy of 84.7 % is obtained by using DFA.

Conclusions

This study demonstrates the differences of boredom, pain, and surprise and the best emotion recognizer for the classification of the three emotions by using physiological signals.     

The Prevalence of HIV-1 Drug Resistance among Antiretroviral Treatment Na?ve Individuals in Mainland China: A Meta-Analysis

Background

Surveillance of drug resistance in antiretroviral treatment-naïve patients in China is needed to ensure optimal treatment outcomes and control of the human immunodeficiency virus (HIV) epidemic.

Methods

A systematic literature search was conducted in English and Chinese through PubMed (English), China National Knowledge Infrastructure (Chinese), Chinese Biomedical Literature Database (Chinese), and Wanfang (Chinese). Random effects models were used to calculate the pooled prevalence of transmitted drug resistance and subgroup analyses examined prevalence estimates across time periods, study locations, and study populations.

Results

Analysis of data from 71 studies (47 in Chinese and 24 in English) yielded a pooled prevalence of transmitted HIV drug resistance to any antiretroviral drug class of 3.64% (95% confidence interval [CI]: 3.00%–4.32%). Rates were significantly high at initial stage of free ART program from 2003 to 2005 (5.18%, 95%CI: 3.13%–7.63%), and were much lower among studies conducted in 2006–2008 (3.02%, 95%CI: 2.03%–4.16%). A slight increase was observed again in the most recent study period from 2009 to 2012 (3.68%, 95%CI: 2.78%–4.69%). Subgroup analysis revealed highest prevalence levels of transmitted drug resistance in Beijing city, and Henan and Hubei provinces (above 5%), and although differences in prevalence rates among risk groups were negligible, men who have sex with men were unique in their relatively large portion of protease inhibitor resistance, a second-line drug of limited availability in China.

Conclusions

Overall prevalence of transmitted HIV drug resistance in China is classified as “low” by the World Health Organization. However regional and temporal variability suggest a more complex epidemic for which closer HIV drug resistance surveillance is needed. A nationwide HIV drug resistance surveillance system to monitor both treatment-experienced and treatment-naïve patients will be a cornerstone to ensure the effectiveness of treatment scale-up, particularly as China seeks to expand a national policy of antiretroviral treatment as prevention.     

Exploratory Analysis of Methods for Automated Classification of Laboratory Test Orders into Syndromic Groups in Veterinary Medicine

Background

Recent focus on earlier detection of pathogen introduction in human and animal populations has led to the development of surveillance systems based on automated monitoring of health data. Real- or near real-time monitoring of pre-diagnostic data requires automated classification of records into syndromes–syndromic surveillance–using algorithms that incorporate medical knowledge in a reliable and efficient way, while remaining comprehensible to end users.

Methods

This paper describes the application of two of machine learning (Naïve Bayes and Decision Trees) and rule-based methods to extract syndromic information from laboratory test requests submitted to a veterinary diagnostic laboratory.

Results

High performance (F₁-macro = 0.9995) was achieved through the use of a rule-based syndrome classifier, based on rule induction followed by manual modification during the construction phase, which also resulted in clear interpretability of the resulting classification process. An unmodified rule induction algorithm achieved an F_1-micro score of 0.979 though this fell to 0.677 when performance for individual classes was averaged in an unweighted manner (F_1-macro), due to the fact that the algorithm failed to learn 3 of the 16 classes from the training set. Decision Trees showed equal interpretability to the rule-based approaches, but achieved an F_1-micro score of 0.923 (falling to 0.311 when classes are given equal weight). A Naïve Bayes classifier learned all classes and achieved high performance (F_1-micro = 0.994 and F_1-macro = .955), however the classification process is not transparent to the domain experts.

Conclusion

The use of a manually customised rule set allowed for the development of a system for classification of laboratory tests into syndromic groups with very high performance, and high interpretability by the domain experts. Further research is required to develop internal validation rules in order to establish automated methods to update model rules without user input.     

The Association between the Use of Proton Pump Inhibitors and the Risk of Hypomagnesemia: A Systematic Review and Meta-Analysis

Background

Although many case reports have described patients with proton pump inhibitor (PPI)-induced hypomagnesemia, the impact of PPI use on hypomagnesemia has not been fully clarified through comparative studies. We aimed to evaluate the association between the use of PPI and the risk of developing hypomagnesemia by conducting a systematic review with meta-analysis.

Methods

We conducted a systematic search of MEDLINE, EMBASE, and the Cochrane Library using the primary keywords “proton pump,” “dexlansoprazole,” “esomeprazole,” “ilaprazole,” “lansoprazole,” “omeprazole,” “pantoprazole,” “rabeprazole,” “hypomagnesemia,” “hypomagnesaemia,” and “magnesium.” Studies were included if they evaluated the association between PPI use and hypomagnesemia and reported relative risks or odds ratios or provided data for their estimation. Pooled odds ratios with 95% confidence intervals were calculated using the random effects model. Statistical heterogeneity was assessed with Cochran’s Q test and I ² statistics.

Results

Nine studies including 115,455 patients were analyzed. The median Newcastle-Ottawa quality score for the included studies was seven (range, 6–9). Among patients taking PPIs, the median proportion of patients with hypomagnesemia was 27.1% (range, 11.3–55.2%) across all included studies. Among patients not taking PPIs, the median proportion of patients with hypomagnesemia was 18.4% (range, 4.3–52.7%). On meta-analysis, pooled odds ratio for PPI use was found to be 1.775 (95% confidence interval 1.077–2.924). Significant heterogeneity was identified using Cochran’s Q test (df = 7, P<0.001, I ² = 98.0%).

Conclusions

PPI use may increase the risk of hypomagnesemia. However, significant heterogeneity among the included studies prevented us from reaching a definitive conclusion.     

Genome-wide prediction of discrete traits using bayesian regressions and machine learning

Background

Genomic selection has gained much attention and the main goal is to increase the predictive accuracy and the genetic gain in livestock using dense marker information. Most methods dealing with the large p (number of covariates) small n (number of observations) problem have dealt only with continuous traits, but there are many important traits in livestock that are recorded in a discrete fashion (e.g. pregnancy outcome, disease resistance). It is necessary to evaluate alternatives to analyze discrete traits in a genome-wide prediction context.

Methods

This study shows two threshold versions of Bayesian regressions (Bayes A and Bayesian LASSO) and two machine learning algorithms (boosting and random forest) to analyze discrete traits in a genome-wide prediction context. These methods were evaluated using simulated and field data to predict yet-to-be observed records. Performances were compared based on the models'' predictive ability.

Results

The simulation showed that machine learning had some advantages over Bayesian regressions when a small number of QTL regulated the trait under pure additivity. However, differences were small and disappeared with a large number of QTL. Bayesian threshold LASSO and boosting achieved the highest accuracies, whereas Random Forest presented the highest classification performance. Random Forest was the most consistent method in detecting resistant and susceptible animals, phi correlation was up to 81% greater than Bayesian regressions. Random Forest outperformed other methods in correctly classifying resistant and susceptible animals in the two pure swine lines evaluated. Boosting and Bayes A were more accurate with crossbred data.

Conclusions

The results of this study suggest that the best method for genome-wide prediction may depend on the genetic basis of the population analyzed. All methods were less accurate at correctly classifying intermediate animals than extreme animals. Among the different alternatives proposed to analyze discrete traits, machine-learning showed some advantages over Bayesian regressions. Boosting with a pseudo Huber loss function showed high accuracy, whereas Random Forest produced more consistent results and an interesting predictive ability. Nonetheless, the best method may be case-dependent and a initial evaluation of different methods is recommended to deal with a particular problem.     

Increasing Transfers-Out from an Antiretroviral Treatment Service in South Africa: Patient Characteristics and Rates of Virological Non-Suppression

Objectives

To determine the proportion, characteristics and outcomes of patients who transfer-out from an antiretroviral therapy (ART) service in a South African township.

Methods

This retrospective cohort study included all patients aged ≥15 years who enrolled between September 2002 and December 2009. Follow-up data were censored in December 2010. Kaplan-Meier survival analysis was used to describe time to transfer-out and cox proportional hazard analysis was used to determine associated risk factors.

Results

4511 patients (4003 ART-naïve and 508 non-naïve at baseline) received ART during the study period. Overall, 597 (13.2%) transferred out. The probability of transferring out by one year of ART steadily increased from 1.4% in 2002/2004 cohort to 8.9% for the 2009 cohort. Independent risk factors for transfer-out were more recent calendar year of enrolment, younger age (≤25 years) and being ART non-naïve at baseline (i.e., having previously transferred into this clinic from another facility). The proportions of patients transferred out who had a CD4 cell count <200 cells/µL and/or a viral load ≥1000 copies/mL were 19% and 20%, respectively.

Conclusions

With scale-up of ART over time, an increasing proportion of patients are transferring between ART services and information systems are needed to track patients. Approximately one-fifth of these have viral loads >1000 copies/mL around the time of transfer, suggesting the need for careful adherence counseling and assessment of medication supplies among those planning transfer.     

Discriminating between Glaucoma and Normal Eyes Using Optical Coherence Tomography and the ‘Random Forests’ Classifier

Purpose

To diagnose glaucoma based on spectral domain optical coherence tomography (SD-OCT) measurements using the ‘Random Forests’ method.

Methods

SD-OCT was conducted in 126 eyes of 126 open angle glaucoma (OAG) patients and 84 eyes of 84 normal subjects. The Random Forests method was then applied to discriminate between glaucoma and normal eyes using 151 OCT parameters including thickness measurements of circumpapillary retinal nerve fiber layer (cpRNFL), the macular RNFL (mRNFL) and the ganglion cell layer-inner plexiform layer combined (GCIPL). The area under the receiver operating characteristic curve (AROC) was calculated using the Random Forests method adopting leave-one-out cross validation. For comparison, AROCs were calculated based on each one of the 151 OCT parameters.

Results

The AROC obtained with the Random Forests method was 98.5% [95% Confidence interval (CI): 97.1–99.9%], which was significantly larger than the AROCs derived from any single OCT parameter (maxima were: 92.8 [CI: 89.4–96.2] %, 94.3 [CI: 91.1–97.6] % and 91.8 [CI: 88.2–95.4] % for cpRNFL-, mRNFL- and GCIPL-related parameters, respectively; P<0.05, DeLong’s method with Holm’s correction for multiple comparisons). The partial AROC above specificity of 80%, for the Random Forests method was equal to 18.5 [CI: 16.8–19.6] %, which was also significantly larger than the AROCs of any single OCT parameter (P<0.05, Bootstrap method with Holm’s correction for multiple comparisons).

Conclusions

The Random Forests method, analyzing multiple SD-OCT parameters concurrently, significantly improves the diagnosis of glaucoma compared with using any single SD-OCT measurement.     

Complexity-Based Measures Inform Effects of Tai Chi Training on Standing Postural Control: Cross-Sectional and Randomized Trial Studies

Background

Diminished control of standing balance, traditionally indicated by greater postural sway magnitude and speed, is associated with falls in older adults. Tai Chi (TC) is a multisystem intervention that reduces fall risk, yet its impact on sway measures vary considerably. We hypothesized that TC improves the integrated function of multiple control systems influencing balance, quantifiable by the multi-scale “complexity” of postural sway fluctuations.

Objectives

To evaluate both traditional and complexity-based measures of sway to characterize the short- and potential long-term effects of TC training on postural control and the relationships between sway measures and physical function in healthy older adults.

Methods

A cross-sectional comparison of standing postural sway in healthy TC-naïve and TC-expert (24.5±12 yrs experience) adults. TC-naïve participants then completed a 6-month, two-arm, wait-list randomized clinical trial of TC training. Postural sway was assessed before and after the training during standing on a force-plate with eyes-open (EO) and eyes-closed (EC). Anterior-posterior (AP) and medio-lateral (ML) sway speed, magnitude, and complexity (quantified by multiscale entropy) were calculated. Single-legged standing time and Timed-Up–and-Go tests characterized physical function.

Results

At baseline, compared to TC-naïve adults (n = 60, age 64.5±7.5 yrs), TC-experts (n = 27, age 62.8±7.5 yrs) exhibited greater complexity of sway in the AP EC (P = 0.023), ML EO (P<0.001), and ML EC (P<0.001) conditions. Traditional measures of sway speed and magnitude were not significantly lower among TC-experts. Intention-to-treat analyses indicated no significant effects of short-term TC training; however, increases in AP EC and ML EC complexity amongst those randomized to TC were positively correlated with practice hours (P = 0.044, P = 0.018). Long- and short-term TC training were positively associated with physical function.

Conclusion

Multiscale entropy offers a complementary approach to traditional COP measures for characterizing sway during quiet standing, and may be more sensitive to the effects of TC in healthy adults.

Trial Registration

ClinicalTrials.gov {"type":"clinical-trial","attrs":{"text":"NCT01340365","term_id":"NCT01340365"}}NCT01340365     

Autologous HIV-1 Clade-B Nef Peptides Elicit Increased Frequency,Breadth and Function of CD8+ T-Cells Compared to Consensus Peptides

Objective

To determine the function and phenotype of CD8⁺ T-cells targeting consensus and autologous sequences of entire HIV-1 Nef protein.

Methods

Multiparameter flow cytometry-based analysis was used to evaluate the responses of two treatment naïve HIV-infected individuals, during primary and the chronic phases of infection.

Results

A greater breadth and magnitude of CD8 IFN-γ responses to autologous compared to clade-B consensus peptides was observed in both subjects. Cross recognition between autologous and consensus peptides decreased in both subjects during progression from primary to chronic infection. The frequencies of TEMRA and TEM CD8⁺ T-cells targeting autologous peptides were higher than those targeting consensus peptides and were more polyfunctional (IFN-γ⁺ Gr-B⁺ CD107a⁺).

Conclusions

Our data indicate superior sensitivity and specificity of autologous peptides. The functional and maturational aspects of “real” versus “cross-recognized” responses were also found to differ, highlighting the importance of a sequence-specific approach towards understanding HIV immune response.     

IFN-β Inhibits the Increased Expression of IL-9 during Experimental Autoimmune Uveoretinitis

Purpose

It has been shown that IL-9 plays a proinflammatory role in the pathogenesis of certain autoimmune diseases. This study was designed to investigate the possible role of IL-9 in the development of experimental autoimmune uveoretinitis (EAU) and the effect of IFN-β on its expression.

Methods

EAU was induced in B10RIII mice by immunization with interphotoreceptor retinoid-binding protein peptide 161–180 (IRBP_161–180). IFN-β was administered subcutaneously to IRBP_161–180 immunized mice every other day from day one before immunization to the end of the study. Splenocytes and draining lymph node (DLN) cells from EAU mice or control mice or EAU mice treated with IFN-β or PBS were stimulated with anti-CD3/CD28 or IRBP_161–180 for 3 days. Naïve T cells cultured under Th1 or Th17 polarizing conditions were incubated in the presence or absence of IFN-β for 4 days. Effector/memory T cells were activated by anti-CD3/CD28 in the presence or absence of IFN-β for 3 days. IFN-β-treated monocytes were cocultured with naïve T cells or effector/memory T cells for 3 days. Culture supernatants were collected and IL-9 was detected by ELISA.

Results

IL-9 expression in splenocytes and DLN cells was increased in EAU mice during the inflammatory phase and returned back to lower levels during the recovery phase. IFN-β in vivo treatment significantly inhibited EAU activity in association with a down-regulated expression of IL-9. In vitro polarized Th1 and Th17 cells both secreted IL-9 and the addition of IFN-β suppressed production of IL-9 by both Th subsets. Beside its effect on polarized Th cells, IFN-β also suppressed the secretion of IL-9 by effector/memory T cells. However, IFN-β-treated monocytes had no effect on the production of IL-9 when cocultured with naïve or effector/memory T cells.

Conclusion

IL-9 expression is increased during EAU which could be suppressed by IFN-β.     

Direct Contra Na?ve-Indirect Comparison of Clinical Failure Rates between High-Viscosity GIC and Conventional Amalgam Restorations: An Empirical Study

Background

Naïve-indirect comparisons are comparisons between competing clinical interventions’ evidence from separate (uncontrolled) trials. Direct comparisons are comparisons within randomised control trials (RCTs). The objective of this empirical study is to test the null-hypothesis that trends and performance differences inferred from naïve-indirect comparisons and from direct comparisons/RCTs regarding the failure rates of amalgam and direct high-viscosity glass-ionomer cement (HVGIC) restorations in permanent posterior teeth have similar direction and magnitude.

Methods

A total of 896 citations were identified through systematic literature search. From these, ten and two uncontrolled clinical longitudinal studies for HVGIC and amalgam, respectively, were included for naïve-indirect comparison and could be matched with three out twenty RCTs. Summary effects sizes were computed as Odds ratios (OR; 95% Confidence intervals) and compared with those from RCTs. Trend directions were inferred from 95% Confidence interval overlaps and direction of point estimates; magnitudes of performance differences were inferred from the median point estimates (OR) with 25% and 75% percentile range, for both types of comparison. Mann-Whitney U test was applied to test for statistically significant differences between point estimates of both comparison types.

Results

Trends and performance differences inferred from naïve-indirect comparison based on evidence from uncontrolled clinical longitudinal studies and from direct comparisons based on RCT evidence are not the same. The distributions of the point estimates differed significantly for both comparison types (Mann–Whitney U  =  25, n_indirect  =  26; n_direct  =  8; p  =  0.0013, two-tailed).

Conclusion

The null-hypothesis was rejected. Trends and performance differences inferred from either comparison between HVGIC and amalgam restorations failure rates in permanent posterior teeth are not the same. It is recommended that clinical practice guidance regarding HVGICs should rest on direct comparisons via RCTs and not on naïve-indirect comparisons based on uncontrolled longitudinal studies in order to avoid inflation of effect estimates.     

Comparison of ARIMA and Random Forest time series models for prediction of avian influenza H5N1 outbreaks

Background

Time series models can play an important role in disease prediction. Incidence data can be used to predict the future occurrence of disease events. Developments in modeling approaches provide an opportunity to compare different time series models for predictive power.

Results

We applied ARIMA and Random Forest time series models to incidence data of outbreaks of highly pathogenic avian influenza (H5N1) in Egypt, available through the online EMPRES-I system. We found that the Random Forest model outperformed the ARIMA model in predictive ability. Furthermore, we found that the Random Forest model is effective for predicting outbreaks of H5N1 in Egypt.

Conclusions

Random Forest time series modeling provides enhanced predictive ability over existing time series models for the prediction of infectious disease outbreaks. This result, along with those showing the concordance between bird and human outbreaks (Rabinowitz et al. 2012), provides a new approach to predicting these dangerous outbreaks in bird populations based on existing, freely available data. Our analysis uncovers the time-series structure of outbreak severity for highly pathogenic avain influenza (H5N1) in Egypt.     

The Relative Composition of the Inflammatory Infiltrate as an Additional Tool for Synovial Tissue Classification

Objectives

Traditionally, differences in absolute numbers of cells expressing a certain marker (e.g., positive staining cells per mm²) have been used in immunohistological synovial tissue classification. We have begun to evaluate the relative composition of the inflammatory infiltrates, i.e. percentages of inflammatory cell types in inflammatory infiltrates, as an alternate classification tool that may potentially improve tissue diagnostics, subgrouping in clinical trials, and understanding of pathogenesis of inflammatory and noninflammatory arthropathies.

Methods

Synovial tissue specimens (normal synovium, n=15; orthopedic arthropathies, n=6; osteoarthritis, n=26; early undifferentiated arthritis, n=10; rheumatoid arthritis, n=26; chronic septic arthritis, n=11) were stained for CD15, CD68, CD3, CD20, and CD38. Densities of cells expressing a given marker were determined in the superficial subintima. Binary and multicategory receiver operating characteristic (ROC) analysis and naïve Bayes classifier were used to compare the abilities of (1) the absolute densities of cells expressing a given marker (absolute method) with (2) the percentages of these cells in the inflammatory cell population (relative method) to differentiate among the six tissue classes.

Results

The inflammatory infiltrates in normal synovium and the orthopedic arthropathies consisted almost exclusively of CD68+ and CD3+ cells. Notable fractions of CD20+ and CD38+ cells appeared in a subset of osteoarthritis samples, and increased further in early, rheumatoid and chronic septic arthritis. ROC analyses and naïve Bayes classifier ranked the absolute method above the relative method in terms of overall discriminatory ability. The relative method became slightly superior when the samples were also stratified according to the total number of inflammatory cells/mm².

Conclusions

This exploratory investigation featuring a variety of joint disorders revealed that measuring the relative proportions of inflammatory cell types may aid in synovial tissue classification if the samples are also stratified according to the intensity of inflammation.     

Genome Analysis of Legionella pneumophila Strains Using a Mixed-Genome Microarray

Background

Legionella, the causative agent for Legionnaires’ disease, is ubiquitous in both natural and man-made aquatic environments. The distribution of Legionella genotypes within clinical strains is significantly different from that found in environmental strains. Developing novel genotypic methods that offer the ability to distinguish clinical from environmental strains could help to focus on more relevant (virulent) Legionella species in control efforts. Mixed-genome microarray data can be used to perform a comparative-genome analysis of strain collections, and advanced statistical approaches, such as the Random Forest algorithm are available to process these data.

Methods

Microarray analysis was performed on a collection of 222 Legionella pneumophila strains, which included patient-derived strains from notified cases in the Netherlands in the period 2002–2006 and the environmental strains that were collected during the source investigation for those patients within the Dutch National Legionella Outbreak Detection Programme. The Random Forest algorithm combined with a logistic regression model was used to select predictive markers and to construct a predictive model that could discriminate between strains from different origin: clinical or environmental.

Results

Four genetic markers were selected that correctly predicted 96% of the clinical strains and 66% of the environmental strains collected within the Dutch National Legionella Outbreak Detection Programme.

Conclusions

The Random Forest algorithm is well suited for the development of prediction models that use mixed-genome microarray data to discriminate between Legionella strains from different origin. The identification of these predictive genetic markers could offer the possibility to identify virulence factors within the Legionella genome, which in the future may be implemented in the daily practice of controlling Legionella in the public health environment.     

Peg-Interferon Plus Ribavirin with or without Boceprevir or Telaprevir for HCV Genotype 1: A Meta-Analysis on the Role of Response Predictors

Background & aim

To compare the efficacy of pegylated-interferon (Peg-IFN) α-2a or α-2b and ribavirin given as dual therapy versus triple therapy (Peg-IFN and ribavirin plus boceprevir or telaprevir) in patients with HCV-1 chronic hepatitis naïve for anti-HCV therapy or relapsers to dual therapy in relation to the presence of constitutional, clinical and virological predictors of treatment response.

Methods

Included in the meta-analysis were studies meeting these criteria: original data from randomized trials on the efficacy of dual versus triple therapy in therapy-naïve patients or relapsers; at least one primary outcome clearly defined: sustained virological response in patients with or without rapid virological response (RVR), with genotype 1a or 1b, low or high HCV load, IL28-B CC or non-CC genotype, mild or severe fibrosis; odds ratio estimates of relative risk (RR) and 95% confidence intervals; English language; and published up to the end of June 2013.

Results

Seven original studies met the inclusion criteria, allowing a meta-analysis on 3,652 patients. Triple therapy was more effective than dual, regardless of IL-28B genotype, HCV sub-genotype, liver fibrosis, and baseline HCV load. In 1,045 patients who achieved RVR, SVR was more frequently achieved with dual therapy (RR = 1.11; p = 0.002) than triple. The same results were achieved when only the therapy-naïve patients were considered.

Conclusions

Triple therapy provides a significantly higher SVR rate than dual therapy, but dual therapy obtains a significantly higher SVR rate in patients with RVR. The data stress the clinical importance of a 4-week lead-in phase in direct-acting antiviral-based treatment.     

Duration of Untreated Psychosis Is Associated with Temporal and Occipitotemporal Gray Matter Volume Decrease in Treatment Na?ve Schizophrenia

Background

Long duration of untreated psychosis (DUP) is associated with poor treatment outcome. Whether or not DUP is related to brain gray matter volume abnormalities in antipsychotic medication treatment naïve schizophrenia remains unclear at this time.

Methods

Patients with treatment-naïve schizophrenia and healthy controls went through brain scan using high resolution Magnetic Resonance Imaging. DUP was evaluated using the Nottingham Onset Schedule (NOS), and dichotomized as short DUP (≤ 26 weeks) or long DUP (>26 weeks). Voxel-based methods were used for volumetric measure in the brain.

Results

Fifty-seven patients (27 short DUP and 30 long DUP) and 30 healthy controls were included in the analysis. There were significant gray matter volumetric differences among the 3 groups in bilateral parahippocampus gyri, right superior temporal gyrus, left fusiform gyrus, left middle temporal gyrus, and right superior frontal gyrus (p''s<0.01). Compared with healthy controls, the long DUP group had significantly smaller volume in all these regions (p''s <0.05). Compared with the short-DUP group, the long-DUP group had significantly smaller volume in right superior temporal gyrus, left fusiform gyrus, and left middle temporal gyrus (p''s<0.01).

Conclusion

Our findings suggest that DUP is associated with temporal and occipitotemporal gray matter volume decrease in treatment naïve schizophrenia. The brain structural changes in untreated psychosis might contribute to poor treatment response and long-term prognosis in this patient population.     

Abnormal Resting-State Activities and Functional Connectivities of the Anterior and the Posterior Cortexes in Medication-Na?ve Patients with Obsessive-Compulsive Disorder

Background

Obsessive-compulsive disorder (OCD) is a mental illness characterized by the loss of control. Because the cingulate cortex is believed to be important in executive functions, such as inhibition, we used functional magnetic resonance imaging (fMRI) techniques to examine whether and how activity and functional connectivity (FC) of the cingulate cortex were altered in drug-naïve OCD patients.

Methods

Twenty-three medication-naïve OCD patients and 23 well-matched healthy controls received fMRI scans in a resting state. Functional connectivities of the anterior cingulate (ACC) and the posterior cingulate (PCC) to the whole brain were analyzed using correlation analyses based on regions of interest (ROI) identified by the fractional amplitude of low-frequency fluctuation (fALFF). Independent Component Analysis (ICA) was used to identify the resting-state sub-networks.

Results

fALFF analysis found that regional activity was increased in the ACC and decreased in the PCC in OCD patients when compared to controls. FC of the ACC and the PCC also showed different patterns. The ACC and the PCC were found to belong to different resting-state sub-networks in ICA analysis and showed abnormal FC, as well as contrasting correlations with the severity of OCD symptoms.

Conclusions

Activity of the ACC and the PCC were increased and decreased, respectively, in the medication-naïve OCD patients compared to controls. Different patterns in FC were also found between the ACC and the PCC with respect to these two groups. These findings implied that the cardinal feature of OCD, the loss of control, may be attributed to abnormal activities and FC of the ACC and the PCC.     

Protein functional features are reflected in the patterns of mRNA translation speed

Background

The degeneracy of the genetic code makes it possible for the same amino acid string to be coded by different messenger RNA (mRNA) sequences. These “synonymous mRNAs” may differ largely in a number of aspects related to their overall translational efficiency, such as secondary structure content and availability of the encoded transfer RNAs (tRNAs). Consequently, they may render different yields of the translated polypeptides. These mRNA features related to translation efficiency are also playing a role locally, resulting in a non-uniform translation speed along the mRNA, which has been previously related to some protein structural features and also used to explain some dramatic effects of “silent” single-nucleotide-polymorphisms (SNPs). In this work we perform the first large scale analysis of the relationship between three experimental proxies of mRNA local translation efficiency and the local features of the corresponding encoded proteins.

Results

We found that a number of protein functional and structural features are reflected in the patterns of ribosome occupancy, secondary structure and tRNA availability along the mRNA. One or more of these proxies of translation speed have distinctive patterns around the mRNA regions coding for certain protein local features. In some cases the three patterns follow a similar trend. We also show specific examples where these patterns of translation speed point to the protein’s important structural and functional features.

Conclusions

This support the idea that the genome not only codes the protein functional features as sequences of amino acids, but also as subtle patterns of mRNA properties which, probably through local effects on the translation speed, have some consequence on the final polypeptide. These results open the possibility of predicting a protein’s functional regions based on a single genomic sequence, and have implications for heterologous protein expression and fine-tuning protein function.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1734-7) contains supplementary material, which is available to authorized users.     

Assessment of the Reporting Quality of Randomized Controlled Trials on the Treatment of Diabetes Mellitus with Traditional Chinese Medicine: A Systematic Review

Background

After the publication of the CONSORT 2010 statement, few studies have been conducted to assess the reporting quality of randomized clinical trials (RCTs) on treatment of diabetes mellitus with Traditional Chinese Medicine (TCM) published in Chinese journals.

Objective

To investigate the current situation of the reporting quality of RCTs in leading medical journals in China with the CONSORT 2010 statement as criteria.

Methods

The China National Knowledge Infrastructure (CNKI) electronic database was searched for RCTs on the treatment of diabetes mellitus with TCM published in the Journal of Traditional Chinese Medicine, Chinese Journal of Integrated Traditional & Western Medicine, and the China Journal of Chinese Materia Medica from January to December 2011. We excluded trials reported as “animal studies”, “in vitro studies”, “case studies”, or “systematic reviews”. The CONSORT checklist was applied by two independent raters to evaluate the reporting quality of all eligible trials after discussing and comprehending the items thoroughly. Each item in the checklist was graded as either “yes” or “no” depending on whether it had been reported by the authors.

Results

We identified 27 RCTs. According to the 37 items in the CONSORT checklist, the average reporting percentage was 45.0%, in which the average reporting percentage for the “title and abstract”, the “introduction”, the “methods”, the “results”, the “discussion” and the “other information” was 33.3%, 88.9%, 36.4%, 54.4%, 71.6% and 14.8%, respectively. In the Journal of Traditional Chinese Medicine, Chinese Journal of Integrated Traditional & Western Medicine, and the China Journal of Chinese Materia Medica the average reporting percentage was 42.2%, 56.8%, and 46.0%, respectively.

Conclusions

The reporting quality of RCTs in these three journals was insufficient to allow readers to assess the validity of the trials. We recommend that editors require authors to use the CONSORT statement when reporting their trial results as a condition of publication.