Syndecan-4 is required for early-stage repair responses during zebrafish heart regeneration.

BACKGROUND: The healing process after a myocardial infarction (MI) in humans involves complex events that replace damaged tissue with a fibrotic scar. The affected cardiac tissue may lose its function permanently. In contrast, zebrafish display a remarkable capacity for scar-free heart regeneration. Previous studies have revealed that syndecan-4 (SDC4) regulates inflammatory response and fibroblast activity following cardiac injury in higher vertebrates. However, whether and how Sdc4 regulates heart regeneration in highly regenerative zebrafish remains unknown. METHODS AND RESULTS: This study showed that sdc4 expression was differentially regulated during zebrafish heart regeneration by transcriptional analysis. Specifically, sdc4 expression increased rapidly and transiently in the early regeneration phase upon ventricular cryoinjury. Moreover, the knockdown of sdc4 led to a significant reduction in extracellular matrix protein deposition, immune cell accumulation, and cell proliferation at the lesion site. The expression of tgfb1a and col1a1a, as well as the protein expression of Fibronectin, were all down-regulated under sdc4 knockdown. In addition, we verified that sdc4 expression was required for cardiac repair in zebrafish via in vivo electrocardiogram analysis. Loss of sdc4 expression caused an apparent pathological Q wave and ST elevation, which are signs of human MI patients. CONCLUSIONS: Our findings support that Sdc4 is required to mediate pleiotropic repair responses in the early stage of zebrafish heart regeneration.

A propensity score-integrated approach for leveraging external data in a randomized controlled trial with time-to-event endpoints.

In a randomized controlled trial with time-to-event endpoint, some commonly used statistical tests to test for various aspects of survival differences, such as survival probability at a fixed time point, survival function up to a specific time point, and restricted mean survival time, may not be directly applicable when external data are leveraged to augment an arm (or both arms) of an RCT. In this paper, we propose a propensity score-integrated approach to extend such tests when external data are leveraged. Simulation studies are conducted to evaluate the operating characteristics of three propensity score-integrated statistical tests, and an illustrative example is given to demonstrate how these proposed procedures can be implemented.

The Finkelstein-Schoenfeld Test: A Note on Some Overlooked Issues Concerning Power.

In this note, we express our viewpoint regarding power considerations, via simulation studies, in clinical study design using hierarchical composite endpoint and Finkelstein-Schoenfeld test.

Recovery of Copper and Zinc from Livestock Bio-Sludge with An Environmentally Friendly Organic Acid Extraction.

Pig farmers in Taiwan tend to overdose copper (Cu) and zinc (Zn) in animal feeds to ensure pig health. The application of Cu- or Zn-rich livestock compost to fields can result in high Cu/Zn residues in surface soil and violate limitations for zinc and copper in land applications. This study aims to extract Cu and Zn from sludge using organic acid or H2O2/organic acids. The livestock bio-sludge was dried and treated with different concentrations of acetic acid (1N, 2N, and 4N). The acid-extracted sludge was then treated with or without adding H2O2 during different periods (4, 24, and 48 h) to investigate the efficiency of acid extraction of Cu and Zn. The supernatant of the acid-extracted product was separated from the residues through centrifugation. Experimental results showed that the treatment set of dried bio-sludge with 2% H2O2 significantly promoted the removal efficiency of Cu and Zn from the bio-sludge (p < 0.01). The best removal efficiency of Cu and Zn from the bio-sludge was 40% and 70%, respectively, using 4N acetic acid in the 48 h group. The study shows a green method for extracting Cu and Zn from livestock sludge, enhancing the sustainability of intensive livestock farming.

Propensity score-incorporated adaptive design approaches when incorporating real-world data.

The propensity score-integrated composite likelihood (PSCL) method is one method that can be utilized to design and analyze an application when real-world data (RWD) are leveraged to augment a prospectively designed clinical study. In the PSCL, strata are formed based on propensity scores (PS) such that similar subjects in terms of the baseline covariates from both the current study and RWD sources are placed in the same stratum, and then composite likelihood method is applied to down-weight the information from the RWD. While PSCL was originally proposed for a fixed design, it can be extended to be applied under an adaptive design framework with the purpose to either potentially claim an early success or to re-estimate the sample size. In this paper, a general strategy is proposed due to the feature of PSCL. For the possibility of claiming early success, Fisher's combination test is utilized. When the purpose is to re-estimate the sample size, the proposed procedure is based on the test proposed by Cui, Hung, and Wang. The implementation of these two procedures is demonstrated via an example.

A practical model-based segmentation approach for improved activation detection in single-subject functional magnetic resonance imaging studies.

Functional magnetic resonance imaging (fMRI) maps cerebral activation in response to stimuli but this activation is often difficult to detect, especially in low-signal contexts and single-subject studies. Accurate activation detection can be guided by the fact that very few voxels are, in reality, truly activated and that these voxels are spatially localized, but it is challenging to incorporate both these facts. We address these twin challenges to single-subject and low-signal fMRI by developing a computationally feasible and methodologically sound model-based approach, implemented in the R package MixfMRI, that bounds the a priori expected proportion of activated voxels while also incorporating spatial context. An added benefit of our methodology is the ability to distinguish voxels and regions having different intensities of activation. Our suggested approach is evaluated in realistic two- and three-dimensional simulation experiments as well as on multiple real-world datasets. Finally, the value of our suggested approach in low-signal and single-subject fMRI studies is illustrated on a sports imagination experiment that is often used to detect awareness and improve treatment in patients in persistent vegetative state (PVS). Our ability to reliably distinguish activation in this experiment potentially opens the door to the adoption of fMRI as a clinical tool for the improved treatment and therapy of PVS survivors and other patients.

Principled leveraging of external data in the evaluation of diagnostic devices via the propensity score-integrated composite likelihood approach.

In the area of diagnostics, it is common practice to leverage external data to augment a traditional study of diagnostic accuracy consisting of prospectively enrolled subjects to potentially reduce the time and/or cost needed for the performance evaluation of an investigational diagnostic device. However, the statistical methods currently being used for such leveraging may not clearly separate study design and outcome data analysis, and they may not adequately address possible bias due to differences in clinically relevant characteristics between the subjects constituting the traditional study and those constituting the external data. This paper is intended to draw attention in the field of diagnostics to the recently developed propensity score-integrated composite likelihood approach, which originally focused on therapeutic medical products. This approach applies the outcome-free principle to separate study design and outcome data analysis and can mitigate bias due to imbalance in covariates, thereby increasing the interpretability of study results. While this approach was conceived as a statistical tool for the design and analysis of clinical studies for therapeutic medical products, here, we will show how it can also be applied to the evaluation of sensitivity and specificity of an investigational diagnostic device leveraging external data. We consider two common scenarios for the design of a traditional diagnostic device study consisting of prospectively enrolled subjects, which is to be augmented by external data. The reader will be taken through the process of implementing this approach step-by-step following the outcome-free principle that preserves study integrity.

Synthesis of Optically Active Organofluorides by Ring Opening of Oxazolidinone-Fused Aziridines.

A general method for synthesizing optically active, primary, secondary, and tertiary organofluorides was developed. This chiral pool synthesis utilized the skeleton of arabinose to generate diastereomerically pure 2-oxazolidinone-fused aziridines, which underwent ring opening with a fluoride anion. The adducts, polyoxygenated organofluorides, were useful precursors to various fluorinated compounds, such as fluorinated amino acids.

Propensity score-integrated approach to survival analysis: leveraging external evidence in single-arm studies.

External data, referred to as data external to the traditional clinical study being planned, include but are not limited to real-world data (RWD) and data collected from clinical studies being conducted in the past or in other countries. The external data are sometimes leveraged to augment a single-arm, prospectively designed study when appropriate. In such an application, recently developed propensity score-integrated approaches including PSPP and PSCL can be used for study design and data analysis when the clinical outcomes are binary or continuous. In this paper, the propensity score-integrated Kaplan-Meier (PSKM) method is proposed for a similar situation but the outcome of interest is time-to-event. The propensity score methodology is used to select external subjects that are similar to those in the current study in terms of baseline covariates and to stratify the selected subjects from both data sources into more homogeneous strata. The stratum-specific PSKM estimators are obtained based on all subjects in the stratum with the external data being down-weighted, and then these estimators are combined to obtain an overall PSKM estimator. A simulation study is conducted to assess the performance of the PSKM method, and an illustrative example is presented to demonstrate how to implement the proposed method.

Sudden quadriparesis after non-overdose local anesthesia.

BACKGROUND: A well-known anesthetic, lidocaine is the most widely used local anesthetic. Local anesthetic systemic toxicity (LAST) is a life-threatening event with common and prominent presentations of central nervous system (CNS) toxicity and cardiovascular toxicity. The most frequent and prominent early warning signs and symptoms of LAST are central nervous system symptoms. While rare, cases quadriparesis after the administration of lidocaine has been reported. CASE PRESENTATION: In this paper, we report a very rare case of quadriparesis after local anesthesia administration for vocal cord cyst-removal surgery, which dramatically improved after treatment. LAST can occur during various routes of lidocaine administration, such as local spray. A possible mechanism of our case could be the local diffusion of lidocaine to the spinal cord, which caused the symptoms to mimic anterior cord syndrome. CONCLUSIONS: Our case presented a favorable outcome following the administration of intravenous lipid emulsion (ILE) for non-over dose local anesthetic drug induced spinal cord inhibition symptoms. These findings highlight the need for further research on the use of ILE to reverse LAST and other adverse effects of local anesthetics.

New n-p Junction Floating Gate to Enhance the Operation Performance of a Semiconductor Memory Device.

To lower the charge leakage of a floating gate device and improve the operation performance of memory devices toward a smaller structure size and a higher component capability, two new types of floating gates composed of pn-type polysilicon or np-type polysilicon were developed in this study. Their microstructure and elemental compositions were investigated, and the sheet resistance, threshold voltages and erasing voltages were measured. The experimental results and charge simulation indicated that, by forming an n-p junction in the floating gate, the sheet resistance was increased, and the charge leakage was reduced because of the formation of a carrier depletion zone at the junction interface serving as an intrinsic potential barrier. Additionally, the threshold voltage and erasing voltage of the np-type floating gate were elevated, suggesting that the performance of the floating gate in the operation of memory devices can be effectively improved without the application of new materials or changes to the physical structure.

Estimands in observational studies: Some considerations beyond ICH E9 (R1).

The document ICH E9 (R1) has brought much attention to the concept of estimand in the clinical trials community. ICH stands for International Conference for Harmonization. In this article, we draw attention to one facet of estimand that is not discussed in that document but is crucial in the context of observational studies, namely weighting for covariate balance. How weighting schemes are connected to estimand, or more specifically to one of its five attributes identified in ICH E9 (R1), the attribute of population, is illustrated using the Rubin Causal Model. Three estimands are examined from both theoretical and practical perspectives. Factors that may be considered in choosing among these estimands are discussed.

Exposure to polystyrene microplastics impairs hippocampus-dependent learning and memory in mice.

Microplastics (MPs) pollution has become a serious environmental issue worldwide, but its potential effects on health remain unknown. The administration of polystyrene MPs (PS-MPs) to mice for eight weeks impaired learning and memory behavior. PS-MPs were detected in the brain especially in the hippocampus of these mice. Concurrently, the hippocampus had decreased levels of immediate-early genes, aberrantly enhanced synaptic glutamate AMPA receptors, and elevated neuroinflammation, all of which are critical for synaptic plasticity and memory. Interestingly, ablation of the vagus nerve, a modulator of the gut-brain axis, improved the memory function of PS-MPs mice. These results indicate that exposure to PS-MPs in mice alters the expression of neuronal activity-dependent genes and synaptic proteins, and increases neuroinflammation in the hippocampus, subsequently causing behavioral changes through the vagus nerve-dependent pathway. Our findings shed light on the adverse impacts of PS-MPs on the brain and hippocampal learning and memory.

Leverage multiple real-world data sources in single-arm medical device clinical studies.

The interest in utilizing real-world data (RWD) has been considerably increasing in medical product development and evaluation. With proper usage and analysis of high-quality real-world data, real-world evidence (RWE) can be generated to inform regulatory and healthcare decision-making. This paper proposes a study design and data analysis approach for a prospective, single-arm clinical study that is supplemented with patients from multiple real-world data sources containing patient-level covariate and outcome data. After the amount of information to be borrowed from each real-world data source is determined, the propensity score-integrated composite likelihood method is applied to obtain an estimate of the parameter of interest based on data from the prospective clinical study and this real-world data source. This method is applied to each real-world data source. The final estimate of the parameter of interest is then obtained by taking a weighted average of all these estimates. The performance of the proposed approach is evaluated via a simulation study. A hypothetical example is presented to illustrate how to implement the proposed approach.

Augmenting Both Arms of a Randomized Controlled Trial Using External Data: An Application of the Propensity Score-Integrated Approaches.

Leveraging external data is a topic that have recently received much attention. The propensity score-integrated approaches are a methodological innovation for this purpose. In this paper we adapt these approaches, originally introduced to augment single-arm studies with external data, for the augmentation of both arms of a randomized controlled trial (RCT) with external data. After recapitulating the basic ideas, we provide a step-by-step tutorial of how to implement the propensity score-integrated approaches, from study design to outcome analysis, in the RCT setting in such a way that the study integrity and objectively are maintained. Both the Bayesian (power prior) approach and the frequentist (composite likelihood) approach are included. Some extensions and variations of these approaches are also outlined at the end of this paper.

Propensity score-integrated power prior approach for augmenting the control arm of a randomized controlled trial by incorporating multiple external data sources.

In this paper, a propensity score-integrated power prior approach is developed to augment the control arm of a two-arm randomized controlled trial (RCT) with subjects from multiple external data sources such as real-world data (RWD) and historical clinical studies containing subject-level outcomes and covariates. The propensity scores for the subjects in the external data sources versus the subjects in the RCT are first estimated, and then subjects are placed in different strata based on their estimated propensity scores. Within each propensity score stratum, a power prior is formulated with the information contributed by the external data sources, and Bayesian inference on the treatment effect is obtained. The proposed approach is implemented under the two-stage study design framework utilizing the outcome-free principle to ensure the integrity of a study. An illustrative example is provided to demonstrate the implementation of the proposed approach.

Leveraging real-world evidence for determining performance goals for medical device studies.

Performance goals are numerical target values pertaining to effectiveness or safety endpoints in single-arm medical device clinical studies. Typically, performance goals are determined at the planning stage of the investigational study under consideration based on summarized outcome information from existing relevant clinical trials. In recent years, there is a growing interest in leveraging real-world evidence in medical product development. In this article, we introduce a new method for proposing performance goals by leveraging real-world evidence. The method applies entropy balancing to address possible patient dissimilarities between the study's target patient population and existing real-world patients, and can take into account operation differences between clinical studies and real-world clinical practice. An illustrative example is provided to demonstrate how to implement the proposed method for performance goal determination while leveraging real-world evidence.

The Drude-Smith Equation and Related Equations for the Frequency-Dependent Electrical Conductivity of Materials: Insight from a Memory Function Formalism.

The front cover artwork is provided by Wei-Chen Chen and Rudolph A. Marcus. The image shows an electron undergoing backscattering in a disordered solid, a phenomenon commonly described using the Drude-Smith equation represented by the damping curve. Read the full text of the Article at 10.1002/cphc.202100299.