Artificial neural network models for prediction of cardiovascular autonomic dysfunction in general Chinese population.

BACKGROUND: The present study aimed to develop an artificial neural network (ANN) based prediction model for cardiovascular autonomic (CA) dysfunction in the general population. METHODS: We analyzed a previous dataset based on a population sample consisted of 2,092 individuals aged 30-80 years. The prediction models were derived from an exploratory set using ANN analysis. Performances of these prediction models were evaluated in the validation set. RESULTS: Univariate analysis indicated that 14 risk factors showed statistically significant association with CA dysfunction (P < 0.05). The mean area under the receiver-operating curve was 0.762 (95% CI 0.732-0.793) for prediction model developed using ANN analysis. The mean sensitivity, specificity, positive and negative predictive values were similar in the prediction models was 0.751, 0.665, 0.330 and 0.924, respectively. All HL statistics were less than 15.0. CONCLUSION: ANN is an effective tool for developing prediction models with high value for predicting CA dysfunction among the general population.

Polarized Molecule 4-(Aminomethyl) Benzonitrile Hydrochloride for Efficient and Stable Perovskite Solar Cells.

The rapid development of perovskite solar cells (PSCs) makes it one of the most competitive photovoltaic devices in the field of new energy. However, the suboptimal performance and poor stability caused by numerous defects are still the main factors limiting the development of PSCs. Herein, a polarized molecule additive of 4-(aminomethyl) benzonitrile hydrochloride (AMBNCl) is introduced into perovskite. Owing to its special polar electron density distribution, -C≡N group, -NH3+ terminal, and Cl- ions, the modification of AMBNCl can improve the quality of perovskite crystal growth, passivate the defects of Pb2+, adjust the energy level array between the perovskite layer and hole-transport layer, and alleviate the carrier nonradiative recombination. As a result, the AMBNCl-modified device achieves a champion efficiency of 23.52%. The unpacked device still maintained 91.2% of its original efficiency after storing in an air environment (RH ∼40%, 25 °C) for 50 days.

Erratum to: Diagnostic performance analysis for diabetic cardiovascular autonomic neuropathy based on short-term heart rate variability using Bayesian methods: preliminary analysis.

[This corrects the article DOI: 10.1186/s13098-015-0070-z.].

Correction: Comparison of Prediction Model for Cardiovascular Autonomic Dysfunction Using Artificial Neural Network and Logistic Regression Analysis.

[This corrects the article DOI: 10.1371/journal.pone.0070571.].

Randomized, placebo-controlled trial of baclofen and gabapentin for the treatment of methamphetamine dependence.

OBJECTIVE: To conduct a 16-week, randomized, placebo-controlled, double-blind trial of two GABAergic medications, baclofen (20 mg tid) and gabapentin (800 mg tid), for the treatment of methamphetamine dependence. METHODS: Adults with methamphetamine dependence were randomized to one of three conditions for 16 weeks: baclofen (n = 25), gabapentin (n = 26) or placebo (n = 37). All participants attended clinic thrice weekly to receive study medication and psychosocial counseling, complete study assessments, and provide urine samples. RESULTS: No statistically significant main effects for baclofen or gabapentin in reducing methamphetamine use were observed using a generalized estimating equation (GEE). A significant treatment effect was found in post hoc analyses for baclofen, but not gabapentin, relative to placebo among participants who reported taking a higher percentage of study medication (significant treatment group and medication adherence interaction in GEE model of methamphetamine use). CONCLUSIONS: While gabapentin does not appear to be effective in treating methamphetamine dependence, baclofen may have a small treatment effect relative to placebo. Future studies evaluating the effectiveness of baclofen and other GABAergic agents for treatment of methamphetamine may be warranted.

Randomized, placebo-controlled trial of sertraline and contingency management for the treatment of methamphetamine dependence.

BACKGROUND: Methamphetamine dependence and associated medical and psychiatric concerns are significant public health issues. This project evaluated the efficacy of sertraline (50mg bid) and contingency management (CM) for the treatment of methamphetamine dependence. METHOD: In this randomized, placebo-controlled, double-blind trial, participants completed a 2-week non-medication baseline and were randomized to one of four conditions for 12 weeks: sertraline plus CM (n=61), sertraline-only (n=59), matching placebo plus CM (n=54), or matching placebo-only (n=55). All participants attended clinic thrice-weekly for data collection, medication dispensing, and relapse prevention groups. Outcomes included methamphetamine use (urine drug screening and self-reported days of use), retention (length of stay), drug craving (visual analogue scale), and mood symptoms (Beck Depression Inventory). RESULTS: No statistically significant main or interaction effects for sertraline or CM in reducing methamphetamine use were observed using a generalized estimating equation (GEE), although post hoc analyses showed the sertraline-only condition had significantly poorer retention than other conditions (chi(2) (3)=8.40, p<0.05). Sertraline conditions produced significantly more adverse events than placebo conditions. A significantly higher proportion of participants in CM conditions achieved three consecutive weeks of methamphetamine abstinence than those in non-CM conditions. CONCLUSIONS: These data do not demonstrate improved outcomes for sertraline versus placebo for treatment of methamphetamine dependence; indeed, they suggest sertraline is contraindicated for methamphetamine dependence. Findings provide support for the use of contingency management for treatment of methamphetamine dependence.

Diagnostic performance analysis for diabetic cardiovascular autonomic neuropathy based on short-term heart rate variability using Bayesian methods: preliminary analysis.

OBJECTIVES: This study aimed to estimate the performance of diabetic cardiovascular autonomic neuropathy (DCAN) diagnostic tests in the absence of a gold standard. BACKGROUND: The DCAN prevalence is rapidly growing in all populations worldwide. No document has been reported about diagnostic performance for DCAN based on short-term HRV without a gold standard. METHODS: We conducted a cross-sectional study to perform diagnostic test in Chinese diabetic patients. A dataset contained 56 subjects who completed both the short-term HRV test and Ewing's test. Simultaneous inferences about the population prevalence and the performance of each diagnostic test were possible using the Bayesian approach. RESULTS: The HRV test had a high sensitivity (0.837 and 0.821 for independence model) and specificity (0.838 and 0.797 for dependence model) to DCAN. In addition, the non-inferiority test rejected the hypothesis that the performance of the HRV test was inferior to that of Ewing's test (P < 0.05). The estimated DCAN prevalence in our study sample was more than 0.400. CONCLUSION: Our findings provided evidence that short-term HRV were used for the DCAN diagnostic test with a high sensitivity and specificity. ClinicalTrial.org ID: NCT02461381.

Comparison of prediction model for cardiovascular autonomic dysfunction using artificial neural network and logistic regression analysis.

BACKGROUND: This study aimed to develop the artificial neural network (ANN) and multivariable logistic regression (LR) analyses for prediction modeling of cardiovascular autonomic (CA) dysfunction in the general population, and compare the prediction models using the two approaches. METHODS AND MATERIALS: We analyzed a previous dataset based on a Chinese population sample consisting of 2,092 individuals aged 30-80 years. The prediction models were derived from an exploratory set using ANN and LR analysis, and were tested in the validation set. Performances of these prediction models were then compared. RESULTS: Univariate analysis indicated that 14 risk factors showed statistically significant association with the prevalence of CA dysfunction (P<0.05). The mean area under the receiver-operating curve was 0.758 (95% CI 0.724-0.793) for LR and 0.762 (95% CI 0.732-0.793) for ANN analysis, but noninferiority result was found (P<0.001). The similar results were found in comparisons of sensitivity, specificity, and predictive values in the prediction models between the LR and ANN analyses. CONCLUSION: The prediction models for CA dysfunction were developed using ANN and LR. ANN and LR are two effective tools for developing prediction models based on our dataset.