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We study the assessment of the accuracy of heterogeneous treatment effect (HTE) estimation, where the HTE is not directly observable so standard computation of prediction errors is not applicable. To tackle the difficulty, we propose an assessment approach by constructing pseudo-observations of the HTE based on matching. Our contributions are three-fold: first, we introduce a novel matching distance derived from proximity scores in random forests; second, we formulate the matching problem as an average minimum-cost flow problem and provide an efficient algorithm; third, we propose a match-then-split principle for the assessment with cross-validation. We demonstrate the efficacy of the assessment approach using simulations and a real dataset.

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.

Background Automated segmentation of coronary arteries is a crucial step for computer-aided coronary artery disease (CAD) diagnosis and treatment planning. Correct delineation of the coronary artery is challenging in X-ray coronary angiography (XCA) due to the low signal-to-noise ratio and confounding background structures. Methods A novel ensemble framework for coronary artery segmentation in XCA images is proposed, which utilizes deep learning and filter-based features to construct models using the gradient boosting decision tree (GBDT) and deep forest classifiers. The proposed method was trained and tested on 130 XCA images. For each pixel of interest in the XCA images, a 37-dimensional feature vector was constructed based on (1) the statistics of multi-scale filtering responses in the morphological, spatial, and frequency domains; and (2) the feature maps obtained from trained deep neural networks. The performance of these models was compared with those of common deep neural networks on metrics including precision, sensitivity, specificity, F1 score, AUROC (the area under the receiver operating characteristic curve), and IoU (intersection over union). Results With hybrid under-sampling methods, the best performing GBDT model achieved a mean F1 score of 0.874, AUROC of 0.947, sensitivity of 0.902, and specificity of 0.992; while the best performing deep forest model obtained a mean F1 score of 0.867, AUROC of 0.95, sensitivity of 0.867, and specificity of 0.993. Compared with the evaluated deep neural networks, both models had better or comparable performance for all evaluated metrics with lower standard deviations over the test images. Conclusions The proposed feature-based ensemble method outperformed common deep convolutional neural networks in most performance metrics while yielding more consistent results. Such a method can be used to facilitate the assessment of stenosis and improve the quality of care in patients with CAD.

Mast cells play an essential role in IgE-FcεR1-mediated allergic diseases. Citrus aurantium is a prolific source of flavonoids with various biological activities, including anti-inflammatory, antioxidant, and anti-tumor efficacies. Neohesperidin is a novel flavonoid isolated from the leaves of C. aurantium. In this study, the anti-allergic and antiinflammatory potentials of neohesperidin were investigated along with its molecular mechanism. The anti-anaphylactic activity of neohesperidin was evaluated through hind paw extravasation study in mice. Calcium imaging was used to assess intracellular Ca 2+ mobilization. The levels of cytokines and chemokines were measured using enzyme immunoassay kits. Western blotting was used to explore the related molecular signaling pathways. Neohesperidin suppressed IgE-induced mast cell activations, including degranulation and secretion of cytokines and eicosanoids through inhibiting phosphorylation of Lyn kinase. Neohesperidin inhibited the release of histamine and other proinflammatory cytokines through a mast cell-dependent passive cutaneous anaphylaxis animal model. Histological studies demonstrated that neohesperidin substantially inhibited IgE-induced cellular infiltration and attenuated mast cell activation in skin tissue. In conclusion, our study revealed that neohesperidin could inhibit allergic responses in vivo and in vitro, and the molecule may be regarded as a novel agent for preventing mast cell-immediate and delayed allergic diseases.

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