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.

Assessing emergency physicians' competency gaps in caring for acute psychiatric emergencies: a comparative analysis of self-perceived confidence and performance against training program expectations.

BACKGROUND: The increasing trend of patients with mental health conditions seeking emergency care is a global phenomenon. Numerous studies have emphasized the importance of emergency physicians' ability to recognize and manage psychiatric emergencies, both for patient well-being and societal safety nets. With the growing demand for emergency care related to mental health conditions, it is crucial to assess whether emergency physicians possess sufficient confidence and capabilities to adapt to these changes. Against this backdrop, our study aimed to explore the factors influencing emergency physicians' management of acute psychiatric emergencies and to critically evaluate whether current training programs adequately align with societal expectations for psychiatric emergency care. METHODS: A cross-sectional survey, disseminated online by the Taiwan Society of Emergency Medicine, targeted 936 attending physicians and 449 residents in 43 Resident Review Committee approved training hospitals in winter 2021. The questionnaire covered participant demographics, skill requirements, and confidence in managing acute psychiatric patients independently. Statistical analyses included descriptive statistics, chi-square tests, medians (IQRs), and Wilcoxon rank-sum tests. RESULTS: Out of 229 participants, 63.8% were attending physicians, and 81.7% were affiliated with medical centers. Psychiatrists led 69.9% of emergency psychiatric training curricula, and 66.8% reported that their clinical psychiatric training outcomes were assessed by psychiatrists. Clinical seniority, training hospital level, and the average number of patients with an acute psychiatric presentation seen in a daily shift influenced physicians' self-reported confidence in managing acute psychiatric patients independently. Medical center training improved capability in managing psychiatric emergencies in the elderly, while encountering a higher average number of patients with an acute psychiatric presentation seen in a daily EM shift was associated with better performance, especially in managing specific populations. CONCLUSIONS: Our nationwide study highlights how clinical seniority, training hospital level, and patient volume per shift influence emergency physicians' confidence in managing acute psychiatric patients. However, there remains room for improvement, particularly in addressing the needs of specific patient populations. These findings underscore the importance of refining training curricula to bridge these gaps and enhance the quality of psychiatric emergency patient care.

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 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.

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.

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.

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.

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.

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.

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

The Drude-Smith equation is widely used for treating the frequency-dependent electrical conductivity of materials in the terahertz region. An attractive feature is its sparsity of adjustable parameters. A significant improvement over Drude theory for these materials, the theory includes backscattering of the charge carriers. It has nevertheless been criticized, including by Smith himself, because of the arbitrariness of a step in the derivation. We recall a somewhat similar behavior of back scattering in fluids observed in molecular dynamics computations and discussed in terms of memory functions. We show how theories such as Drude-Smith and Cocker et al. are examples of a broader class of theories by showing how they also arise as particular cases of a memory function formalism that divides the interactions into short and long range.

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.

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.

Magnetotransport in hybrid InSe/monolayer graphene on SiC.

The magnetotransport properties of a hybrid InSe/monolayer graphene in a SiC system are systematically studied. Compared to those of its bare graphene counterpart, in InSe/graphene, we can effectively modify the carrier density, mobility, effective mass, and electron-electron (e-e) interactions enhanced by weak disorder. We show that in bare graphene and hybrid InSe/graphene systems, the logarithmic temperature (lnT) dependence of the Hall slope R H = Î´R xy /δB = Î´ρ xy /δB can be used to probe e-e interaction effects at various temperatures even when the measured resistivity does not show a lnT dependence due to strong electron-phonon scattering. Nevertheless, one needs to be certain that the change of R H is not caused by an increase of the carrier density by checking the magnetic field position of the longitudinal resistivity minimum at different temperatures. Given the current challenges in gating graphene on SiC with a suitable dielectric layer, our results suggest that capping a van der Waals material on graphene is an effective way to modify the electronic properties of monolayer graphene on SiC.

Evaluation of diagnostic tests for low prevalence diseases: a statistical approach for leveraging real-world data to accelerate the study.

The evaluation of diagnostic tests usually involves statistical inference for its sensitivity. As sensitivity is defined as the probability that the test result will be positive when the target condition is present, the key study design consideration of sample size is the determination of the number of subjects with the target condition such that the estimation has adequate precision, or the hypothesis testing has adequate power. Traditionally, one may rely on prospective screening of subjects to obtain the required sample size, which means that if the prevalence of the disease is very low, a large number of subjects would need to be screened, increasing the study duration and cost. In this paper, we consider the possibility of substantially reducing the length and cost of a clinical study by leveraging subjects from a real-world data (RWD) source, focusing specifically on the diagnostic test for the cancer of interest. Using the propensity score methodology, we developed a procedure which ensures that the real-world subjects being leveraged are similar to their prospectively enrolled counterparts, thereby making the leveraging more justified. The procedure allows the down-weighting of the real-world subjects, which can be achieved by either using a Frequentist's method based on the composite likelihood or a Bayesian method based on the power prior. The proposed approach can be applied to the evaluation of any diagnostic test and it is not limited to the current clinical study regarding a cancer diagnostic test. Notably, this paper is in close alignment with a recently released draft framework by the Medical Device Innovation Consortium (MDIC) on real-world clinical evidence and in vitro diagnostics, being a showcase of appropriately leveraging real-world data in diagnostic test evaluation for diseases with low prevalence to support regulatory decision-making.

Propensity score-integrated composite likelihood approach for incorporating real-world evidence in single-arm clinical studies.

In medical product development, there has been an increased interest in utilizing real-world data which have become abundant with recent advances in biomedical science, information technology, and engineering. High-quality real-world data may be analyzed to generate real-world evidence that can be utilized in the regulatory and healthcare decision-making. In this paper, we consider the case in which a single-arm clinical study, viewed as the primary data source, is supplemented with patients from a real-world data source containing both clinical outcome and covariate data at the patient-level. Propensity score methodology is used to identify real-world data patients that are similar to those in the single-arm study in terms of the baseline characteristics, and to stratify these patients into strata based on the proximity of the propensity scores. In each stratum, a composite likelihood function of a parameter of interest is constructed by down-weighting the information from the real-world data source, and an estimate of the stratum-specific parameter is obtained by maximizing the composite likelihood function. These stratum-specific estimates are then combined to obtain an overall population-level estimate of the parameter of interest. The performance of the proposed approach is evaluated via a simulation study. A hypothetical example based on our experience is provided to illustrate the implementation of the proposed approach.

Propensity score-integrated composite likelihood approach for augmenting the control arm of a randomized controlled trial by incorporating real-world data.

In this paper, a propensity score-integrated composite likelihood (PSCL) approach is developed for cases in which the control arm of a two-arm randomized controlled trial (RCT) (treated vs control) is augmented with patients from real-world data (RWD) containing both clinical outcomes and covariates at the patient-level. RWD patients who were treated with the same therapy as the control arm of the RCT are considered for the augmentation. The PSCL approach first estimates the propensity score for every patient as the probability of the patient being in the RCT rather than the RWD, and then stratifies all patients into strata based on the estimated propensity scores. Within each propensity score stratum, a composite likelihood function is specified and utilized to down-weight the information contributed by the RWD source. Estimates of the stratum-specific parameters are obtained by maximizing the composite likelihood function. These stratum-specific estimates are then combined to obtain an overall population-level estimate of the parameter of interest. The performance of the proposed approach is evaluated via a simulation study. A hypothetical two-arm RCT and a hypothetical RWD source are used to illustrate the implementation of the proposed approach.

The Effects of Combining Progressive Tension Sutures, Closed Drain, and Fibrin Sealant in Abdominoplasty Wound After Deep Inferior Epigastric Perforator Flap Harvesting.

BACKGROUND: This study is a retrospective review to compare combining progressive tension sutures, closed drain, and fibrin sealant effects on seroma formation, postoperative drainage volume, and hospital stay for abdominoplasty after deep inferior epigastric artery perforator (DIEP) flap. We ever published a 0% seroma rate in abdominoplasty after DIEP flap using progressive tension sutures and closed drain. Massive abdominal wound drainage caused delayed drain removal and increased hospital stay and medical costs. METHODS: We retrospectively evaluated 54 abdominoplasty patients between December 2013 and September 2017; 43 patients used fibrin glue and 11 used progressive tension sutures and closed drain. RESULTS: Abdominal drainage for the first 3 postoperative days was 84.65 ± 52.95 mL in the fibrin group, with total drainage of 127.70 ± 125.50 mL and 214.45 ± 104.35 mL in the no fibrin group, with total drainage of 350.45 ± 213.58 mL. Drains were removed on postoperative day 6.21 ± 1.44 in the fibrin group and day 9.64 ± 1.96 in the no fibrin group. The association of the first 3 days and total drainage volumes with the drain removal day significantly differed in the fibrin and no fibrin groups. Hospital stay was 9.88 ± 3.55 and 12.45 ± 5.22 days in the fibrin and no fibrin group, respectively, with borderline significant differences. CONCLUSIONS: Donor site abdominoplasty after DIEP flap combining progressive tension sutures, closed drainage, and fibrin glue can prevent seroma occurrence, reduce postoperative abdominal drainage and need for blood transfusion, and achieve early removal of the abdominal drain, shorter hospital stay, and lower medical cost.

The relationship between personality traits, caring characteristics and abuse tendency among professional caregivers of older people with dementia in long-term care facilities.

AIMS AND OBJECTIVES: To explore the relationship between personality traits, caring characteristics and abuse tendency among professional caregivers of older people with dementia in long-term care facilities in China. BACKGROUND: Elder abuse is a serious global health problem and human right violation with high incidence among older people with dementia. There are many investigations about impact factors of risk of abuse among family caregivers of older people with dementia. However, in long-term care facilities, the situation of abuse tendency needs further investigation. DESIGN: Cross-sectional study. METHODS: An observational survey was conducted according to the STROBE checklist. We investigated 156 professional caregivers of older people with dementia in three long-term care facilities in Guangzhou, China. Participants completed a demographic questionnaire, the Caregiver Abuse Screen (CASE) and the Neuroticism Extraversion Openness Five-Factor Inventory (NEO-FFI). Data were analysed using Mann-Whitney U tests, Kruskal-Wallis tests, Spearman's rank correlation and logistic regression analyses. RESULTS: Over half of the participants (51.9%) reported abuse tendency to the older people with dementia. There was a significant negative correlation between the caregivers' agreeableness scores of NEO-FFI and their CASE scores. Multivariate logistic regression analyses highlighted that protective factors of abuse tendency were caregivers' agreeableness, care recipients' source of finances and their duration of dementia while higher care difficulty and presence of older people's behavioural and psychological symptoms of dementia (BPSD) were the risk factors. CONCLUSION: Caregivers' agreeableness personality trait and the caring characteristics of older people with dementia may be relevant to abuse tendency in long-term care facilities. Further study with a larger sample size is needed to validate such a correlation. RELEVANCE TO CLINICAL PRACTICE: Older people with dementia are at high risk for abuse. Prospective caregivers could pay more attention to developing their own agreeableness. The managers might establish monitoring system for reducing the abuse.

Propensity score-integrated power prior approach for incorporating real-world evidence in single-arm clinical studies.

We are now at an amazing time for medical product development in drugs, biological products and medical devices. As a result of dramatic recent advances in biomedical science, information technology and engineering, ``big data'' from health care in the real-world have become available. Although big data may not necessarily be attuned to provide the preponderance of evidence to a clinical study, high-quality real-world data can be transformed into scientific evidence for regulatory and healthcare decision-making using proven analytical methods and techniques, such as propensity score methodology and Bayesian inference. In this paper, we extend the Bayesian power prior approach for a single-arm study (the current study) to leverage external real-world data. We use propensity score methodology to pre-select a subset of real-world data containing patients that are similar to those in the current study in terms of covariates, and to stratify the selected patients together with those in the current study into more homogeneous strata. The power prior approach is then applied in each stratum to obtain stratum-specific posterior distributions, which are combined to complete the Bayesian inference for the parameters of interest. We evaluate the performance of the proposed method as compared to that of the ordinary power prior approach by simulation and illustrate its implementation using a hypothetical example, based on our regulatory review experience.