Systematic Review and Meta-analysis of Tongxinluo Capsule Therapy for Acute Myocardial Infarction.

Context: An acute myocardial infarction (AMI) is a serious, life-threatening disease. Practitioners of traditional Chinese medicine (TCM) commonly use the Tongxinluo (TXL) capsule, a Chinese patent medicine, to treat AMIs. The benefits of TXL capsules for AMIs remain unknown. Objective: The systematic review and meta-analysis intended to investigate the effects of TXL capsules for AMI patients. Design: The research team conducted a comprehensive literature search of the PubMed, Embase, Cochrane Library, and Web of Science databases from inception to February 2023. The team used the search terms acute myocardial infarction, myocardial infarction, TXL Capsule Therapy, and TXL Capsule. The team also performed a meta-analysis and evaluated the features of the included studies using the Cochrane Collaboration tool for assessing the risk of bias. Setting: The study took place at the Second Affiliated Hospital at Heilongjiang University of Chinese Medicine in Harbin City, Heilongjiang Province, China. Outcome Measures: The research team: (1) evaluated the studies' quality using the Cochrane Collaboration tool for assessing the risk of bias; (2) analyzed the curative effect of the TXL capsules for AMI; (3) explored the effects of the TXL capsules on left ventricular end-diastolic dimension (LVEDD), left ventricular end systolic diameter (LVESD), and left ventricular ejection fraction (LVEF); and (4) explored the effects of the TXL capsules on creatine kinase isoenzyme (CK-MB) peak time, CK-MB peak value, and cardiac index. Results: The literature search found ten studies. Compared with routine treatment alone, a combination of routine treatment and TXL capsules significantly improved the curative effects (odds ratio = 3.48; 95% CI: 2.34, 5.17; P < .00001) Compared with the control groups, the TXL capsule groups' LVESD and LVEF were significantly lower, with MD=-0.23; 95% CI: -0.37, -0.10; and P = .0007 and MD=-0.43; 95% CI: -0.61, -0.25; and P < .00001, respectively, and its LVEDD was significantly higher, with MD=5.27; 95% CI: 4.33, 6.21; and P < .00001. For myocardial enzymes, the TXL capsule groups' creatine kinase isoenzyme (CK-MB) peak values and cardiac indexes were significantly lower than those of the control groups, with MD=-53.11; 95% CI: -55.26, -50.97; and P < .00001 and MD=-1.87; 95% CI: -2.03, -1.70; and P < .00001, respectively. Conclusions: The meta-analysis showed that the TXL capsule can bring greater therapeutic benefits for AMI patients in combination with routine treatment. The current study was a meta-analysis, and the field needs more well-designed studies.

Enabling identification of component processes in perceptual learning with nonparametric hierarchical Bayesian modeling.

Perceptual learning is a multifaceted process, encompassing general learning, between-session forgetting or consolidation, and within-session fast relearning and deterioration. The learning curve constructed from threshold estimates in blocks or sessions, based on tens or hundreds of trials, may obscure component processes; high temporal resolution is necessary. We developed two nonparametric inference procedures: a Bayesian inference procedure (BIP) to estimate the posterior distribution of contrast threshold in each learning block for each learner independently and a hierarchical Bayesian model (HBM) that computes the joint posterior distribution of contrast threshold across all learning blocks at the population, subject, and test levels via the covariance of contrast thresholds across blocks. We applied the procedures to the data from two studies that investigated the interaction between feedback and training accuracy in Gabor orientation identification over 1920 trials across six sessions and estimated learning curve with block sizes L = 10, 20, 40, 80, 160, and 320 trials. The HBM generated significantly better fits to the data, smaller standard deviations, and more precise estimates, compared to the BIP across all block sizes. In addition, the HBM generated unbiased estimates, whereas the BIP only generated unbiased estimates with large block sizes but exhibited increased bias with small block sizes. With L = 10, 20, and 40, we were able to consistently identify general learning, between-session forgetting, and rapid relearning and adaptation within sessions. The nonparametric HBM provides a general framework for fine-grained assessment of the learning curve and enables identification of component processes in perceptual learning.

Collective endpoint of visual acuity and contrast sensitivity function from hierarchical Bayesian joint modeling.

Clinical trials typically analyze multiple endpoints for signals of efficacy. To improve signal detection for treatment effects using the high-dimensional data collected in trials, we developed a hierarchical Bayesian joint model (HBJM) to compute a five-dimensional collective endpoint (CE5D) of contrast sensitivity function (CSF) and visual acuity (VA). The HBJM analyzes row-by-row CSF and VA data across multiple conditions, and describes visual functions across a hierarchy of population, individuals, and tests. It generates joint posterior distributions of CE5D that combines CSF (peak gain, peak frequency, and bandwidth) and VA (threshold and range) parameters. The HBJM was applied to an existing dataset of 14 eyes, each tested with the quantitative VA and quantitative CSF procedures in four Bangerter foil conditions. The HBJM recovered strong correlations among CE5D components at all levels. With 15 qVA and 25 qCSF rows, it reduced the variance of the estimated components by 72% on average. Combining signals from VA and CSF and reducing noises, CE5D exhibited significantly higher sensitivity and accuracy in discriminating performance differences between foil conditions at both the group and test levels than the original tests. The HBJM extracts valuable information about covariance of CSF and VA parameters, improves precision of the estimated parameters, and increases the statistical power in detecting vision changes. By combining signals and reducing noise from multiple tests for detecting vision changes, the HBJM framework exhibits potential to increase statistical power for combining multi-modality data in ophthalmic trials.

Hierarchical Bayesian modeling of contrast sensitivity functions in a within-subject design.

Recent development of the quick contrast sensitivity function (qCSF) method has made it possible to obtain accurate, precise, and efficient contrast sensitivity function (CSF) assessment. To improve statistical inference on CSF changes in a within-subject design, we developed a hierarchical Bayesian model (HBM) to compute the joint distribution of CSF parameters and hyperparameters at test, subject, and population levels, utilizing information within- and between-subjects and experimental conditions. We evaluated the performance of the HBM relative to a non-hierarchical Bayesian inference procedure (BIP) on an existing CSF dataset of 112 subjects obtained with the qCSF method in three luminance conditions (Hou, Lesmes, Kim, Gu, Pitt, Myung, & Lu, 2016). We found that the average d's of the area under log CSF (AULCSF) and CSF parameters between pairs of luminance conditions at the test-level from the HBM were 33.5% and 103.3% greater than those from the BIP analysis of AULCSF. The increased d' resulted in greater statistical differences between experimental conditions across subjects. In addition, simulations showed that the HBM generated accurate and precise CSF parameter estimates. These results have strong implications for the application of HBM in clinical trials and patient care.

Symmetry of generalized rivalry network models determines patterns of interocular grouping in four-location binocular rivalry.

Previously, symmetry of network models has been proposed to account for interocular grouping during binocular rivalry. Here, we construct and analyze generalized rivalry network models with different types of symmetry (based on different kinds of excitatory coupling) to derive predictions of possible perceptual states in 12 experiments with four retinal locations. Percepts in binocular rivalry involving more than three locations have not been empirically investigated due to the difficulty in reporting simultaneous percepts at multiple locations. Here, we develop a novel reporting procedure in which the stimulus disappears when the subject is cued to report the simultaneously perceived colors in all four retinal locations. This procedure ensures that simultaneous rather than sequential percepts are reported. The procedure was applied in 12 experiments with six binocular rivalry stimulus configurations, all consisting of dichoptic displays of red and green squares at four locations. We call configurations with an even or odd number of red squares even or odd configurations, respectively. In experiments using even stimulus configurations, we found that even percepts were more frequently observed than odd percepts, whereas in experiments using odd stimulus configurations even and odd percepts were observed with equal probability. The generalized rivalry network models in which couplings depend on stimulus features and spatial configurations was in better agreement with the empirical results. We conclude that the excitatory coupling strength in the horizontal and vertical configurations are different and the coupling strengths between the same color and between different colors are different.NEW & NOTEWORTHY Wilson network models of interocular groupings during binocular rivalry are constructed by considering features that indicate equal coupling strengths. Network symmetries, based on equal couplings, predict percepts. For a four-location rivalry experiment with red or green squares at each location, we analyze different possible Wilson networks. In our experiments we develop a novel reporting procedure and show that networks in which stimulus features and spatial configurations are distinguished best agree with experiments.

Evaluating the performance of the staircase and quick Change Detection methods in measuring perceptual learning.

The staircase method has been widely used in measuring perceptual learning. Recently, Zhao, Lesmes, and Lu (2017, 2019) developed the quick Change Detection (qCD) method and applied it to measure the trial-by-trial time course of dark adaptation. In the current study, we conducted two simulations to evaluate the performance of the 3-down/1-up staircase and qCD methods in measuring perceptual learning in a two-alternative forced-choice task. In Study 1, three observers with different time constants (40, 80, and 160 trials) of an exponential learning curve were simulated. Each simulated observer completed staircases with six step sizes (1%, 5%, 10%, 20%, 30%, and 60%) and a qCD procedure, each starting at five levels (+50%, +25%, 0, -25%, and -50% different from the true threshold in the first trial). We found the following results: Staircases with 1% and 5% step sizes failed to generate more than five reversals half of the time; and the bias and standard deviations of thresholds estimated from the post hoc segment-by-segment qCD analysis were much smaller than those from the staircase method with the other four step sizes. In Study 2, we simulated thresholds in the transfer phases with the same time constants and 50% transfer for each observer in Study 1. We found that the estimated transfer indexes from qCD showed smaller biases and standard deviations than those from the staircase method. In addition, rescoring the simulated data from the staircase method using the Bayesian estimation component of the qCD method resulted in much-improved estimates. We conclude that the qCD method characterizes the time course of perceptual learning and transfer more accurately, precisely, and efficiently than the staircase method, even with the optimal 10% step size.

Assessing the detailed time course of perceptual sensitivity change in perceptual learning.

The learning curve in perceptual learning is typically sampled in blocks of trials, which could result in imprecise and possibly biased estimates, especially when learning is rapid. Recently, Zhao, Lesmes, and Lu (2017, 2019) developed a Bayesian adaptive quick Change Detection (qCD) method to accurately, precisely, and efficiently assess the time course of perceptual sensitivity change. In this study, we implemented and tested the qCD method in assessing the learning curve in a four-alternative forced-choice global motion direction identification task in both simulations and a psychophysical experiment. The stimulus intensity in each trial was determined by the qCD, staircase or random stimulus selection (RSS) methods. Simulations showed that the accuracy (bias) and precision (standard deviation or confidence bounds) of the estimated learning curves from the qCD were much better than those obtained by the staircase and RSS method; this is true for both trial-by-trial and post hoc segment-by-segment qCD analyses. In the psychophysical experiment, the average half widths of the 68.2% credible interval of the estimated thresholds from the trial-by-trial and post hoc segment-by-segment qCD analyses were both quite small. Additionally, the overall estimates from the qCD and staircase methods matched extremely well in this task where the behavioral rate of learning is relatively slow. Our results suggest that the qCD method can precisely and accurately assess the trial-by-trial time course of perceptual learning.

Bayesian adaptive assessment of the reading function for vision: The qReading method.

Reading is a fundamental skill that can be significantly affected by visual disabilities. Reading performance, which typically is measured as reading speed with a reading chart, is a key endpoint for quantifying normal or abnormal vision. Despite its importance for clinical vision, existing reading tests for vision are time consuming and difficult to administer. Here, we propose a Bayesian adaptive method, the qReading method, for automated assessment of the reading speed versus print size function. We implemented the qReading method with a word/nonword lexical decision task and validated the method with computer simulations and a psychophysical experiment. Computer simulations showed that both the interrun standard deviation and intrarun half width of the 68.2% credible interval of the estimated reading speeds from the qReading method were less than 0.1 log10 units after 150 trials, with a bias of 0.05 log10 units. In the psychophysical experiment, reading functions measured by the qReading and Psi methods (Kontsevich & Tyler, 1999) in a word/nonword lexical decision task were compared. The estimated reading functions obtained with the qReading and Psi methods were highly correlated (r = 0.966 ± 0.004, p < 0.01). The precision of the qReading method with 225 trials was comparable to that of the Psi method with 450 trials. We conclude that the qReading method can precisely and accurately assess the reading function in much reduced time, with great promise in both basic research and clinical applications.

Schizophrenia susceptibility gene dysbindin regulates glutamatergic and dopaminergic functions via distinctive mechanisms in Drosophila.

The dysfunction of multiple neurotransmitter systems is a striking pathophysiological feature of many mental disorders, schizophrenia in particular, but delineating the underlying mechanisms has been challenging. Here we show that manipulation of a single schizophrenia susceptibility gene, dysbindin, is capable of regulating both glutamatergic and dopaminergic functions through two independent mechanisms, consequently leading to two categories of clinically relevant behavioral phenotypes. Dysbindin has been reported to affect glutamatergic and dopaminergic functions as well as a range of clinically relevant behaviors in vertebrates and invertebrates but has been thought to have a mainly neuronal origin. We find that reduced expression of Drosophila dysbindin (Ddysb) in presynaptic neurons significantly suppresses glutamatergic synaptic transmission and that this glutamatergic defect is responsible for impaired memory. However, only the reduced expression of Ddysb in glial cells is the cause of hyperdopaminergic activities that lead to abnormal locomotion and altered mating orientation. This effect is attributable to the altered expression of a dopamine metabolic enzyme, Ebony, in glial cells. Thus, Ddysb regulates glutamatergic transmission through its neuronal function and regulates dopamine metabolism by regulating Ebony expression in glial cells.

Quantification of Expected Information Gain in Visual Acuity and Contrast Sensitivity Tests.

We introduce expected information gain to quantify measurements and apply it to compare visual acuity (VA) and contrast sensitivity (CS) tests. We simulated observers with parameters covered by the visual acuity and contrast sensitivity tests and observers based on distributions of normal observers tested in three luminance and four Bangerter foil conditions. We first generated the probability distributions of test scores for each individual in each population in the Snellen, ETDRS and qVA visual acuity tests and the Pelli-Robson, CSV-1000 and qCSF contrast sensitivity tests and constructed the probability distributions of all possible test scores of the entire population. We then computed expected information gain by subtracting expected residual entropy from the total entropy of the population. For acuity tests, ETDRS generated more expected information gain than Snellen; scored with VA threshold only or with both VA threshold and VA range, qVA with 15 rows (or 45 optotypes) generated more expected information gain than ETDRS. For contrast sensitivity tests, CSV-1000 generated more expected information gain than Pelli-Robson; scored with AULCSF or with CS at six spatial frequencies, qCSF with 25 trials generated more expected information gain than CSV-1000. The active learning based qVA and qCSF tests can generate more expected information than the traditional paper chart tests. Although we only applied it to compare visual acuity and contrast sensitivity tests, information gain is a general concept that can be used to compare measurements and data analytics in any domain.

Estimating the Trial-by-Trial Learning Curve in Perceptual Learning with Hierarchical Bayesian Modeling.

The learning curve serves as a crucial metric for assessing human performance in perceptual learning. It may encompass various component processes, including general learning, between-session forgetting or consolidation, and within-session rapid relearning and adaptation or deterioration. Typically, empirical learning curves are constructed by aggregating tens or hundreds of trials of data in blocks or sessions. Here, we devised three inference procedures for estimating the trial-by-trial learning curve based on the multi-component functional form identified in Zhao et al. (submitted): general learning, between-session forgetting, and within-session rapid relearning and adaptation. These procedures include a Bayesian inference procedure (BIP) estimating the posterior distribution of parameters for each learner independently, and two hierarchical Bayesian models (HBMv and HBMc) computing the joint posterior distribution of parameters and hyperparameters at the population, subject, and test levels. The HBMv and HBMc incorporate variance and covariance hyperparameters, respectively, between and within subjects. We applied these procedures to data from two studies investigating the interaction between feedback and training accuracy in Gabor orientation identification across about 2000 trials spanning six sessions (Liu et al., 2010, 2012) and estimated the trial-by-trial learning curves at both the subject and population levels. The HBMc generated best fits to the data and the smallest half width of 68.2% credible interval of the learning curves compared to the BIP and HBMv. The parametric HBMc with the multi-component functional form provides a general framework for trial-by-trial analysis of the component processes in perceptual learning and for predicting the learning curve in unmeasured time points.

Quantification of expected information gain in visual acuity and contrast sensitivity tests.

We make use of expected information gain to quantify the amount of knowledge obtained from measurements in a population. In the first application, we compared the expected information gain in the Snellen, ETDRS, and qVA visual acuity (VA) tests, as well as in the Pelli-Robson, CSV-1000, and qCSF contrast sensitivity (CS) tests. For the VA tests, ETDRS generated more expected information gain than Snellen. Additionally, the qVA test with 15 rows (or 45 optotypes) generated more expected information gain than ETDRS, whether scored with VA threshold alone or with both VA threshold and VA range. Regarding the CS tests, CSV-1000 generated more expected information gain than Pelli-Robson, and the qCSF test with 25 trials generated more expected information gain than CSV-1000, whether scored with AULCSF or with CSF at six spatial frequencies. The active learning-based qVA and qCSF tests have the potential to generate more expected information gain than traditional paper chart tests. Although we have specifically applied it to compare VA and CS tests, expected information gain is a general concept that can be used to compare measurements in any domain.

Quantifying Uncertainty of the Estimated Visual Acuity Behavioral Function With Hierarchical Bayesian Modeling.

Purpose: The goal of this study is to develop a hierarchical Bayesian model (HBM) to better quantify uncertainty in visual acuity (VA) tests by incorporating the relationship between VA threshold and range across multiple individuals and tests. Methods: The three-level HBM consisted of multiple two-dimensional Gaussian distributions of hyperparameters and parameters of the VA behavioral function (VABF) at the population, individual, and test levels. The model was applied to a dataset of quantitative VA (qVA) assessments of 14 eyes in 4 Bangerter foil conditions. We quantified uncertainties of the estimated VABF parameters (VA threshold and range) from the HBM and compared them with those from the qVA. Results: The HBM recovered covariances between VABF parameters and provided better fits to the data than the qVA. It reduced the uncertainty of their estimates by 4.2% to 45.8%. The reduction of uncertainty, on average, resulted in 3 fewer rows needed to reach a 95% accuracy in detecting a 0.15 logMAR change of VA threshold or both parameters than the qVA. Conclusions: The HBM utilized knowledge across individuals and tests in a single model and provided better quantification of the uncertainty of the estimated VABF, especially when the number of tested rows was relatively small. Translational Relevance: The HBM can increase the accuracy in detecting VA changes. Further research is necessary to evaluate its potential in clinical populations.

Psychophysical Validation of a Novel Active Learning Approach for Measuring the Visual Acuity Behavioral Function.

Purpose: To evaluate the performance of the quantitative visual acuity (qVA) method in measuring the visual acuity (VA) behavioral function. Methods: We evaluated qVA performance in terms of the accuracy, precision, and efficiency of the estimated VA threshold and range in Monte Carlo simulations and a psychophysical experiment. We also compared the estimated VA threshold from the qVA method with that from the Electronic Early Treatment Diabetic Retinopathy Study (E-ETDRS) and Freiburg Visual Acuity Text (FrACT) methods. Four repeated measures with all three methods were conducted in four Bangerter foil conditions in 14 eyes. Results: In both simulations and psychophysical experiment, the qVA method quantified the full acuity behavioral function with two psychometric parameters (VA threshold and VA range) with virtually no bias and with high precision and efficiency. There was a significant correlation between qVA estimates of VA threshold and range in the psychophysical experiment. In addition, qVA threshold estimates were highly correlated with those from the E-ETDRS and FrACT methods. Conclusions: The qVA method can provide an accurate, precise, and efficient assessment of the full acuity behavioral function with both VA threshold and range. Translational Relevance: The qVA method can accurately, precisely, and efficiently assess the full VA behavioral function. Further research will evaluate the potential value of these rich measures for both clinical research and patient care.

[Heterosis analysis of the main agronomic characters of Platycodon grandiflorum].

OBJECTIVE: Breeding the good varieties characters of Platycodon grandiflorum. METHODS: 20 combination, derived from 2 x 10 incomplete diallel cross with 2 male sterile lines and 10 four-generation inbred lines, analysis the 13 agronomic characters. RESULTS: The agronomic characters of F1 has obvious heterosis. Dry plant weight and fresh root weight have the largest heterosis. Their middle parent heterosis (MPH), high parent heterosis (HPH) and heterosis over CK (CH) were 96.07%, 62.62%, 116.21%, 108.99%, and 111.98%, 113.47%, respectively. CONCLUSION: The F1 hybrids derived from the crossing of male sterile lines and in-bred lines have obvious heterosis. It applied the hybrid heterosis breeding is an effective way for the new variety of Platycodon grandiflorum.

The embedding of fluorescent N-methyl-9-acridone into a topological new layered aluminophosphate SYSU-2 by one-pot synthesis.

A layered aluminophosphate |C14H11NO|2[Al4(HPO4)4F4(H2O)2] (denoted as SYSU-2) with a new topology has been hydrothermally synthesized with N-methyl-9-acridone (NMA) as the organic structure-directing agent. Single-crystal X-ray diffraction analysis reveals that SYSU-2 crystallizes in a triclinic space group P1[combining macron], with the inorganic sheets stacked in an AA sequence. Hydrogen bonds are responsible for the neutral inorganic-organic layer connection. The layer structure of SYSU-2 is constructed by alternating AlO4F2 octahedra and PO4 tetrahedra. The topological analysis of SYSU-2 indicates an independent topology. The NMA layers are self-assembled with π-π interaction. SYSU-2 crystals show interesting dual-band emission fluorescence properties compared with NMA crystals. Under 406 nm UV irradiation, SYSU-2 crystals emit yellow light with two emission bands at 477 and 566 nm, while NMA crystals emit blue light with only one band at 473 nm. The differences may be derived from the difference of stacking orders and distance of NMA molecule layers between the two crystals.

Efficient assessment of the time course of perceptual sensitivity change.

Perceptual sensitivity is usually estimated over trials and time intervals, which results in imprecise and biased estimates when it changes rapidly over time. We develop a novel procedure, the quick Change-Detection (qCD) method, to accurately, precisely, and efficiently assess the trial-by-trial time course of perceptual sensitivity change. Based on Bayesian adaptive testing, qCD selects the optimal stimulus, and updates, trial by trial, a joint probability distribution of the parameters that quantify perceptual sensitivity change over time. We demonstrate the utility of the method in measuring the time course of dark adaptation. Simulations showed that the accuracy and precision of the estimated dark adaptation curve after one qCD run (root mean squared error (RMSE): 0.002; the half width of the 68.2% credible interval (HWCI): 0.016; standard deviation (SD): 0.020; all in log10 units) was higher than those obtained by ten runs of the quick Forced-Choice (qFC) procedure (RMSE: 0.020; HWCI: 0.032; SD: 0.031) and ten runs of a weighted up-down staircase procedure (RMSE: 0.026; SD: 0.031). Further, the dark adaptation curve obtained from one qCD run in a psychophysics experiment was highly consistent with the average of four qFC runs (RMSE = 0.076 log10 units). Overall, qCD provides a procedure to characterize the detailed time course of perceptual sensitivity change in both basic research and clinical applications.