Heart disease is the leading cause of death worldwide, responsible for 16% of the world's total deaths. Coronary artery disease is the most common type of heart disease and is caused by plaque buildup in the wall of the arteries. X-ray coronary angiography is the gold standard for assessing coronary artery disease via estimation of the percentage of narrowing, also known as stenosis. However, errors in physicians' interpretation of stenosis severity could lead to overuse and underuse of revascularization. Automated techniques can help clinicians in their decision-making. In this paper, we review state-of-the-art techniques developed for coronary artery segmentation, vessel modeling, and stenosis detection. The methods are categorized using criteria such as segmentation methods, evaluation metrics, and validation approaches.
False discovery rate (FDR) is a commonly used criterion in multiple testing and the Benjamini-Hochberg (BH) procedure is arguably the most popular approach with FDR guarantee. To improve power, the adaptive BH procedure has been proposed by incorporating various null proportion estimators, among which Storey's estimator has gained substantial popularity. The performance of Storey's estimator hinges on a critical hyper-parameter, where a pre-fixed configuration lacks power and existing data-driven hyper-parameters compromise the FDR control. In this work, we propose a novel class of adaptive hyper-parameters and establish the FDR control of the associated BH procedure using a martingale argument. Within this class of data-driven hyper-parameters, we present a specific configuration designed to maximize the number of rejections and characterize the convergence of this proposal to the optimal hyper-parameter under a commonly-used mixture model. We evaluate our adaptive Storey's null proportion estimator and the associated BH procedure on extensive simulated data and a motivating protein dataset. Our proposal exhibits significant power gains when dealing with a considerable proportion of weak non-nulls or a conservative null distribution.
Generative AI (GenAI) models have recently achieved remarkable empirical performance in various applications, however, their evaluations yet lack uncertainty quantification. In this paper, we propose a method to compare two generative models based on an unbiased estimator of their relative performance gap. Statistically, our estimator achieves parametric convergence rate and asymptotic normality, which enables valid inference. Computationally, our method is efficient and can be accelerated by parallel computing and leveraging pre-storing intermediate results. On simulated datasets with known ground truth, we show our approach effectively controls type I error and achieves power comparable with commonly used metrics. Furthermore, we demonstrate the performance of our method in evaluating diffusion models on real image datasets with statistical confidence.
This paper introduces a novel method for classifying and predicting cardiac arrhythmia events via a special type of deterministic probabilistic finite-state automata (DPFA). The proposed method constructs the underlying state space and transition probabilities of the DPFA model directly from the input data. The algorithm was employed in the prediction of two types of cardiac events, supraventricular tachycardia (SVT) and atrial high-rate episodes (AHRE), with its performance compared to five other well-established methods. In all experiments, the proposed method achieved over 0.8 AUC for both SVT and AHRE prediction.
Causal estimands can vary significantly depending on the relationship between outcomes in treatment and control groups, potentially leading to wide partial identification (PI) intervals that impede decision making. Incorporating covariates can substantially tighten these bounds, but requires determining the range of PI over probability models consistent with the joint distributions of observed covariates and outcomes in treatment and control groups. This problem is known to be equivalent to a conditional optimal transport (COT) optimization task, which is more challenging than standard optimal transport (OT) due to the additional conditioning constraints. In this work, we study a tight relaxation of COT that effectively reduces it to standard OT, leveraging its well-established computational and theoretical foundations. Our relaxation incorporates covariate information and ensures narrower PI intervals for any value of the penalty parameter, while becoming asymptotically exact as a penalty increases to infinity. This approach preserves the benefits of covariate adjustment in PI and results in a data-driven estimator for the PI set that is easy to implement using existing OT packages. We analyze the convergence rate of our estimator and demonstrate the effectiveness of our approach through extensive simulations, highlighting its practical use and superior performance compared to existing methods.
Abstract Local and general anesthesia are the main techniques used during percutaneous kyphoplasty (PKP); however, both are associated with adverse reactions. Monitored anesthesia with dexmedetomidine may be the appropriate sedative and analgesic technique. Few studies have compared monitored anesthesia with other anesthesia modalities during PKP. Our aim was to determine whether monitored anesthesia is an effective alternative anesthetic approach for PKP. One hundred sixty-five patients undergoing PKP for osteoporotic vertebral compression fractures (OVCFs) were recruited from a single center in this prospective, non-randomized controlled study. PKP was performed under local anesthesia with ropivacaine (n = 55), monitored anesthesia with dexmedetomidine (n = 55), and general anesthesia with sufentanil/propofol/sevoflurane (n = 55). Perioperative pain was assessed using a visual analogue score (VAS). Hemodynamic variables, operative time, adverse effects, and perioperative satisfaction were recorded. The mean arterial pressure (MAP), heart rate, VAS, and operative time during monitored anesthesia were significantly lower than local anesthesia. Compared with general anesthesia, monitored anesthesia led to less adverse anesthetic effects. Monitored anesthesia had the highest perioperative satisfaction and the lowest VAS 2 h postoperatively; however, the monitored anesthesia group had the lowest MAP and heart rate 2 h postoperatively. Based on better sedation and analgesia, monitored anesthesia with dexmedetomidine achieved better patient cooperation, a shorter operative time, and lower adverse events during PKP; however, the MAP and heart rate in the monitored anesthesia group should be closely observed after surgery.
Many kinds of drugs induce pseudo-allergic reactions due to activation of mast cells. We investigated the anti-pseudo-allergic effect of andrographolide (Andro). The effects of Andro on pseudo-allergic reactions were investigated in vivo and in vitro. Andro suppressed compound 48/80 (C48/80) induced pseudo-allergic reactions in mice in a dose-dependent manner. Andro also inhibited C48/80-induced local inflammatory reactions in mice. In vitro studies revealed that Andro reduced C48/80-induced mast cells degranulation. Human phospho-kinase array kit and western blotting showed that Andro could inhibit pseudo-allergic responses via the calcium signaling pathway.
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