Quantifying causal effects from observed data using quasi-intervention.

BACKGROUND: Causal inference is a crucial element within medical decision-making. There have been many methods for investigating potential causal relationships between disease and treatment options developed in recent years, which can be categorized into two main types: observational studies and experimental studies. However, due to the nature of experimental studies, financial resources, human resources, and patients' ethical considerations, researchers cannot fully control the exposure of the research participants. Furthermore, most existing observational research designs are limited to determining causal relationships and cannot handle observational data, let alone determine the dosages needed for medical research. RESULTS: This paper presents a new experimental strategy called quasi-intervention for quantifying the causal effect between disease and treatment options in observed data by using a causal inference method, which converts the potential effect of different treatment options on disease into computing differences in the conditional probability. We evaluated the accuracy of the quasi-intervention by quantifying the impact of adjusting Chinese patients' neutrophil-to-lymphocyte ratio (NLR) on their overall survival (OS) (169 lung cancer patients and 79 controls).The results agree with the literature in this study, consisting of nine papers on cohort studies on the NLR and the prognosis of lung cancer patients, proving that our method is correct. CONCLUSION: Taken together, the results imply that quasi-intervention is a promising method for quantifying the causal effect between disease and treatment options without clinical trials, and it could improve confidence about treatment options' efficacy and safety.

Semi-supervised medical image segmentation via cross-guidance and feature-level consistency dual regularization schemes.

BACKGROUND: Semi-supervised learning is becoming an effective solution for medical image segmentation because of the lack of a large amount of labeled data. PURPOSE: Consistency-based strategy is widely used in semi-supervised learning. However, it is still a challenging problem because of the coupling of CNN-based isomorphic models. In this study, we propose a new semi-supervised medical image segmentation network (DRS-Net) based on a dual-regularization scheme to address this challenge. METHODS: The proposed model consists of a CNN and a multidecoder hybrid Transformer, which adopts two regularization schemes to extract more generalized representations for unlabeled data. Considering the difference in learning paradigm, we introduce the cross-guidance between CNN and hybrid Transformer, which uses the pseudo label output from one model to supervise the other model better to excavate valid representations from unlabeled data. In addition, we use feature-level consistency regularization to effectively improve the feature extraction performance. We apply different perturbations to the feature maps output from the hybrid Transformer encoder and keep an invariance of the predictions to enhance the encoder's representations. RESULTS: We have extensively evaluated our approach on three typical medical image datasets, including CT slices from Spleen, MRI slices from the Heart, and FM Nuclei. We compare DRS-Net with state-of-the-art methods, and experiment results show that DRS-Net performs better on the Spleen dataset, where the dice similarity coefficient increased by about 3.5%. The experimental results on the Heart and Nuclei datasets show that DRS-Net also improves the segmentation effect of the two datasets. CONCLUSIONS: The proposed DRS-Net enhances the segmentation performance of the datasets with three different medical modalities, where the dual-regularization scheme extracts more generalized representations and solves the overfitting problem.

Real-Time NURBS Interpolation under Multiple Constraints.

NURBS interpolation is superior to traditional linear or circular interpolation in terms of code size, surface quality, and machining efficiency. However, with the increasing demands for high-accuracy and efficient machining, NURBS interpolation has faced a growing number of challenges. Many researchers are actively involved in this field with great interest. Due to the special form of NURBS curve, there is a nonlinear relationship between its curve and arc length; feed fluctuations and mechanical shocks which are caused during the interpolation process will seriously affect the surface accuracy and quality of machined parts. To solve these problems, a real-time NURBS interpolation is proposed under multiple constraints (RNIC) in this paper. First, the formulas of the constrained feedrate under geometric errors, kinematic constraints, drive constraints, and contour errors are given. Then, the two stages for the proposed interpolation are established. The former stage is offline preprocessing stage, which aims to quickly find feedrate sensitive areas (FSAs), while the latter online stage is the real-time interpolation, which is responsible for smoothing the velocity. In the preprocessing stage, we utilized FSA scan module and feedrate adjustment module to detect the FSAs and adjust the feedrate at the start/end of each subsegment by a bidirectional scanning algorithm. Each segment contains acceleration and deceleration (some contains uniform speed) stages, which can be well matched with the processing process of acceleration and deceleration. Finally, according to the proposed method and the adaptive speed adjustment method, the simulation of a "butterfly-shaped" NURBS curve using the S-shaped ACC/DEC algorithm is carried out, which verifies the reliability and effectiveness of the proposed algorithm.

Probability of Response as Defined by a Clinical Decision Support Tool Is Associated With Lower Healthcare Resource Utilization in Vedolizumab-Treated Patients With Crohn's Disease.

Background: A previously developed clinical decision support tool (CDST) identified patients with Crohn's disease (CD) most likely to respond to vedolizumab. This study evaluated the ability of the CDST to predict real-world healthcare resource utilization (HRU). Methods: The Optum and Truven healthcare databases were searched for patients with CD treated with vedolizumab (Optum, n = 358; Truven, n = 1445) or an anti-tumor necrosis factor (TNF) agent (Optum, n = 814). Patients were stratified using the 5-variable (prior bowel surgery, prior fistulizing disease, prior anti-TNF exposure, albumin, C-reactive protein) and a new modified 3-variable (without laboratory data) CDST. Annualized expenditures and HRU were compared with both CDSTs across response probability groups for a 12-month period. Results: In the Optum data set, the 5- and 3-variable CDSTs identified lower rates of surgery or hospitalization in CD patients with higher probability of vedolizumab response. Per-patient total costs were 2.5 times lower for CD patients with high versus low probability of vedolizumab response ($12 943 vs $32 931). The 5- and 3-variable CDSTs did not consistently identify anti-TNF-treated CD patients with higher HRU. The 3-variable CDST also identified vedolizumab-treated CD patients with higher probability of response and lower probability for surgery or hospitalization in the Truven data set. Conclusions: The 5-variable CDST identified CD patients treated with vedolizumab, but not an anti-TNF agent, at higher risk for HRU. The 3-variable CDST offers similar performance but more flexibility by removing laboratory data requirements for prediction. These validated CDSTs can be integrated into population health monitoring algorithms using real-world data.

TDD-UNet:Transformer with double decoder UNet for COVID-19 lesions segmentation.

The outbreak of new coronary pneumonia has brought severe health risks to the world. Detection of COVID-19 based on the UNet network has attracted widespread attention in medical image segmentation. However, the traditional UNet model is challenging to capture the long-range dependence of the image due to the limitations of the convolution kernel with a fixed receptive field. The Transformer Encoder overcomes the long-range dependence problem. However, the Transformer-based segmentation approach cannot effectively capture the fine-grained details. We propose a transformer with a double decoder UNet for COVID-19 lesions segmentation to address this challenge, TDD-UNet. We introduce the multi-head self-attention of the Transformer to the UNet encoding layer to extract global context information. The dual decoder structure is used to improve the result of foreground segmentation by predicting the background and applying deep supervision. We performed quantitative analysis and comparison for our proposed method on four public datasets with different modalities, including CT and CXR, to demonstrate its effectiveness and generality in segmenting COVID-19 lesions. We also performed ablation studies on the COVID-19-CT-505 dataset to verify the effectiveness of the key components of our proposed model. The proposed TDD-UNet also achieves higher Dice and Jaccard mean scores and the lowest standard deviation compared to competitors. Our proposed method achieves better segmentation results than other state-of-the-art methods.

Promotion effects of SiO2 or/and Al2O3 doped CeO2/TiO2 catalysts for selective catalytic reduction of NO by NH3.

A series of the CeO2-based catalysts loaded on TiO2, TiO2-SiO2, TiO2-Al2O3, and TiO2-SiO2-Al2O3 supports were prepared by incipient impregnation method for the selective catalytic reduction (SCR) of NO by NH3 in the presence of oxygen. The SCR activities of the catalysts with different supports increases in the order of Ce/TiO2 < Ce/TiO2-20SiO2 ≈ Ce/TiO2-3.5Al2O3 < Ce/TiO2-20SiO2-3.5Al2O3. The Ce/TiO2-20SiO2-3.5Al2O3 catalyst showed 100% NO conversion in the temperature range of 250-425°C and 100% N2 selectivity in the whole temperature range. The catalytic activity of Ce/TiO2-20SiO2-3.5Al2O3 exhibited good stability and strong resistance to SO2 and H2O poisoning. The co-introduction of SiO2 and Al2O3 into TiO2 could increase the amount of chemisorbed oxygen and Lewis acid sites on the surface of catalyst, which should be responsible for the excellent SCR activity.

A randomized, double-blind, one-week study comparing effects of a novel COX-2 inhibitor and naproxen on the gastric mucosa.

CS-706 is a novel cyclooxygenase-2 (COX-2) inhibitor with potent analgesic, anti-inflammatory, and antitumor properties in animal models. This one-week, multicenter study was undertaken to assess the safety and tolerability of CS-706 and to compare the effects of CS-706 versus naproxen on acute gastrointestinal (GI) mucosal injury. Healthy men and women (n=160) without evidence of underlying gastroduodenal lesions were randomized to placebo, 100 mg CS-706 once daily, 200 mg CS-706 once daily, or 500 mg naproxen twice daily, administered for 7 days. On Day 8, subjects underwent a posttreatment upper GI endoscopy to assess development of gastroduodenal petechiae, erosions, and ulcers. Inhibition of COX-1 and COX-2 activity over the 24-hr postdose interval on Day 7 was determined in 48 subjects (12 per treatment group). CS-706 was safe and well tolerated. The extent of upper GI mucosal injury for both CS-706 dose groups was statistically significantly less than that for naproxen (P < 0.001) and was similar to placebo (P=0.615 and P=0.115 for 100 and 200 mg CS-706, respectively). No subject in placebo or either CS-706 treatment group had gastroduodenal ulcers, compared with 11 (28.2%) subjects treated with naproxen (P < 0.001). Both doses of CS-706 inhibited COX-2 activity to a similar extent as naproxen, whereas neither dose of CS-706 showed meaningful inhibition of platelet COX-1. In contrast, naproxen nearly completely inhibited COX-1 over the dosing interval. We conclude that CS-706, dosed up to 200 mg once daily, has an acute, upper GI toxicity profile similar to that of placebo and significantly superior to that of naproxen.