Low-dose sinogram restoration enabled by conditional GAN with cross-domain regularization in SPECT imaging.

In order to generate high-quality single-photon emission computed tomography (SPECT) images under low-dose acquisition mode, a sinogram denoising method was studied for suppressing random oscillation and enhancing contrast in the projection domain. A conditional generative adversarial network with cross-domain regularization (CGAN-CDR) is proposed for low-dose SPECT sinogram restoration. The generator stepwise extracts multiscale sinusoidal features from a low-dose sinogram, which are then rebuilt into a restored sinogram. Long skip connections are introduced into the generator, so that the low-level features can be better shared and reused, and the spatial and angular sinogram information can be better recovered. A patch discriminator is employed to capture detailed sinusoidal features within sinogram patches; thereby, detailed features in local receptive fields can be effectively characterized. Meanwhile, a cross-domain regularization is developed in both the projection and image domains. Projection-domain regularization directly constrains the generator via penalizing the difference between generated and label sinograms. Image-domain regularization imposes a similarity constraint on the reconstructed images, which can ameliorate the issue of ill-posedness and serves as an indirect constraint on the generator. By adversarial learning, the CGAN-CDR model can achieve high-quality sinogram restoration. Finally, the preconditioned alternating projection algorithm with total variation regularization is adopted for image reconstruction. Extensive numerical experiments show that the proposed model exhibits good performance in low-dose sinogram restoration. From visual analysis, CGAN-CDR performs well in terms of noise and artifact suppression, contrast enhancement and structure preservation, particularly in low-contrast regions. From quantitative analysis, CGAN-CDR has obtained superior results in both global and local image quality metrics. From robustness analysis, CGAN-CDR can better recover the detailed bone structure of the reconstructed image for a higher-noise sinogram. This work demonstrates the feasibility and effectiveness of CGAN-CDR in low-dose SPECT sinogram restoration. CGAN-CDR can yield significant quality improvement in both projection and image domains, which enables potential applications of the proposed method in real low-dose study.

Sulforaphane alleviates lung ischemia­reperfusion injury through activating Nrf­2/HO­1 signaling.

Oxidative stress and inflammation are both involved in the pathogenesis of lung ischemia-reperfusion (I/R) injury. Sulforaphane (SFN) is a natural product with cytoprotective, anti-inflammatory, and antioxidant properties. The present study hypothesized that SFN may protect against lung I/R injury via the regulation of antioxidant and anti-inflammatory-related pathways. A rat model of lung I/R injury was established, and rats were randomly divided into 3 groups: Sham group, I/R group, and SFN group. It was shown that SFN protected against a pathological inflammatory response via inhibition of neutrophil accumulation and in the reduction of the serum levels of the pro-inflammatory cytokines, IL-6, IL-1ß, and TNF-α. SFN treatment also significantly inhibited lung reactive oxygen species production, decreased the levels of 8-OH-dG and malondialdehyde, and reversed the decrease in the antioxidant activities of the enzymes catalase, superoxide dismutase, and glutathione peroxidase in the lungs of the I/R treated rats. In addition, SFN ameliorated I/R-induced lung apoptosis in rats by suppressing Bax and cleaved caspase-3 levels and increased Bcl-2 expression. Furthermore, SFN treatment activated an Nrf2-related antioxidant pathway, as indicated by the increased nuclear transfer of Nrf2 and the downstream HO-1 and NADPH quinone oxidoreductase-1. In conclusion, these findings suggested that SFN protected against I/R-induced lung lesions in rats via activation of the Nrf2/HO-1 pathway and the accompanied anti-inflammatory and anti-apoptotic effects.

LU-Net: combining LSTM and U-Net for sinogram synthesis in sparse-view SPECT reconstruction.

Lowering the dose in single-photon emission computed tomography (SPECT) imaging to reduce the radiation damage to patients has become very significant. In SPECT imaging, lower radiation dose can be achieved by reducing the activity of administered radiotracer, which will lead to projection data with either sparse projection views or reduced photon counts per view. Direct reconstruction of sparse-view projection data may lead to severe ray artifacts in the reconstructed image. Many existing works use neural networks to synthesize the projection data of sparse-view to address the issue of ray artifacts. However, these methods rarely consider the sequence feature of projection data along projection view. This work is dedicated to developing a neural network architecture that accounts for the sequence feature of projection data at adjacent view angles. In this study, we propose a network architecture combining Long Short-Term Memory network (LSTM) and U-Net, dubbed LU-Net, to learn the mapping from sparse-view projection data to full-view data. In particular, the LSTM module in the proposed network architecture can learn the sequence feature of projection data at adjacent angles to synthesize the missing views in the sinogram. All projection data used in the numerical experiment are generated by the Monte Carlo simulation software SIMIND. We evenly sample the full-view sinogram and obtain the 1/2-, 1/3- and 1/4-view projection data, respectively, representing three different levels of view sparsity. We explore the performance of the proposed network architecture at the three simulated view levels. Finally, we employ the preconditioned alternating projection algorithm (PAPA) to reconstruct the synthesized projection data. Compared with U-Net and traditional iterative reconstruction method with total variation regularization as well as PAPA solver (TV-PAPA), the proposed network achieves significant improvement in both global and local quality metrics.

Sleep-disordered breathing and risk of lung cancer: a meta-analysis longitudinal follow-up studies.

Consensus remains lack regarding whether sleep-disordered breathing (SDB) is an independent risk factor for lung cancer. We therefore conducted a meta-analysis to clarify the relationship of SDB and lung cancer. Longitudinal follow-up studies investigating the association between SDB and incidence of lung cancer were included by search of electronic databases including PubMed, Embase, and Cochrane's Library. A random-effects model was adopted to combine the results. Seven studies were included. Pooled results showed that presence of SDB was independently associated with higher incidence of lung cancer [adjusted risk ratio (RR): 1.28; 95% confidence interval (CI), 1.11-1.47; P < 0.001; I2 = 37%]. Sensitivity analysis limited to studies with adjustment of smoking showed consistent results (three studies, RR: 1.34; 95% CI, 1.22-1.48; P < 0.001; I2 = 8%). Subgroup analysis suggested that the association between SDB and higher risk of lung cancer was not significantly affected by study characteristics such as study design, source of population, sample size, evaluation methods for SDB, follow-up duration, methods for validation of lung cancer, or score of study quality (P values for subgroup difference all >0.05). No significant publication bias was observed (P for Egger's regression test = 0.258). These results suggested that SDB may be an independent risk factor of lung cancer in adult population. Intensive screening and prevention of lung cancer in subjects with SDB should be considered.

Automatic arrhythmia detection with multi-lead ECG signals based on heterogeneous graph attention networks.

Automatic arrhythmia detection is very important for cardiovascular health. It is generally performed by measuring the electrocardiogram (ECG) signals of standard multiple leads. However, the correlations of multiple leads are often ignored. In addition, an extensive and complex feature extraction process is usually needed in most existing studies. Therefore, these challenges will not only lead to the loss of overall lead information, but also cause the detection performance to depend on the quality of features. To solve these challenges, a novel multi-lead arrhythmia detection model based on a heterogeneous graph attention network is proposed in this paper. We have modeled the multi-lead data as a heterogeneous graph to integrate diverse information and construct intra-lead and inter-lead correlations in multi-lead data, providing a reasonable and effective the data model. A heterogeneous graph network with a dual-level attention strategy has been utilized to capture the interactions among diverse information and information types. At the same time, our model does not require any feature extraction process for the ECG signals, which avoids out complex feature engineering. Extensive experimental results show that multi-lead information and complex correlations can be well captured, thus confirming that the proposed model results in significant improvements in multi-lead arrhythmia detection.

Predictive Role of Tumor-Stroma Ratio for Survival of Patients With Non-Small Cell Lung Cancer: A Meta-Analysis.

Background: Role of tumor-stroma ratio (TSR) as a predictor of survival in patients with non-small cell lung cancer (NSCLC) remains not clear. A systematic review and meta-analysis was conducted to summarize current evidence for the role of TSR in NSCLC. Methods: Relevant cohort studies were retrieved via search of Medline, Embase, and Web of Science databases. The data was combined with a random-effect model by incorporating the between-study heterogeneity. Specifically, subgroup and meta-regression analyses were performed to explore the association between TSR and survival in patients with squamous cell carcinoma (SCC) or adenocarcinoma (AC). Results: Nine cohort studies with 2031 patients with NSCLC were eligible for the meta-analysis. Pooled results showed that compared to those stroma-poor tumor, patients with stroma rich NSCLC were associated with worse recurrence-free survival (RFS, hazard ratio [HR] = 1.52, 95% confidence interval [CI]: 1.07 to 2.16, p = 0.02) and overall survival (OS, HR = 1.48, 95% CI: 1.20 to 1.82, p < 0.001). Subgroup analyses showed that stroma-rich tumor may be associated with a worse survival of SCC (HR = 1.89 and 1.47 for PFS and OS), but a possibly favorable survival of AC (HR = 0.28 and 0.69 for PFS and OS). Results of meta-regression analysis also showed that higher proportion of patients with SCC was correlated with higher HRs for RFS (Coefficient = 0.012, p = 0.03) and OS (Coefficient = 0.014, p = 0.02) in the included patients, while higher proportion of patients with AC was correlated with lower HRs for RFS (Coefficient = -0.012, p = 0.03) and OS (Coefficient = -0.013, p = 0.04), respectively. Conclusion: Tumor TSR could be used as a predictor of survival in patients with NSCLC. The relative proportion of patients with SCC/AC in the included NSCLC patients may be an important determinant for the association between TSR and survival in NSCLC. Stroma richness may be a predictor of poor survival in patients with lung SCC, but a predictor of better survival in patients with lung AC.

Relationship between miR-21 and miR-182 levels in peripheral blood and gastric cancer tissue.

The relationship between miR-21 and miR-182 gene expression in peripheral blood and gastric cancer tissue was investigated, exploring the relationship between the levels of miR-21 and miR-182 and prognosis of gastric cancer patients, and determining the effects of these two genes on the growth and migration of gastric cancer cells. Fifty gastric cancer patients who were treated in the 254th Hospital of PLA, from July 2012 to July 2014 were selected. Peripheral blood samples were drawn from patients, and 50 healthy subjects were studied as controls. The levels of the miR-21 and miR-182 genes were detected by semi-quantitative PCR, and the correlation between miR-21 and miR-182 expression and clinicopathological features was explored. Moreover, the effects of miR-21 and miR-182 expression on the survival time and prognosis of patients were investigated. siRNA was used to downregulate miR-21 and miR-182 gene expression in MGC-803 gastric cancer cells, and MTT and Transwell assays were conducted. As a result, the relative expression levels of miR-21 and miR-182 in peripheral blood of gastric cancer patients were significantly higher than in healthy subjects (p<0.01) and the relative expression of miR-182 was closely related to the clinicopathological features of gastric cancer patients (p<0.05); high expression of miR-21 and miR-182 was associated with reduced survival time of patients (p<0.05); MGC-803 cells with low expression of miR-21 and miR-182 were analyzed, showing that miR-182 promoted cell proliferation and migration (p<0.01). In conclusion, the relative levels of miR-21 and miR-182 in peripheral blood of patients with gastric cancer are significantly increased; low expression of miR-182 can significantly reduce the proliferation and migration of gastric cancer cells. Moreover, miR-182 expression, which is closely related to the clinicopathological features of gastric cancer, can serve as a target for the clinical treatment of gastric cancer.

A novel approach for arrhythmia diagnosis: Self-adaptive and distribution-free mode.

Arrhythmia diagnosis is very significant to ensure human health. In this paper, a new model is developed for arrhythmia diagnosis. A salient feature of the algorithm is a synergistic combination of statistical and fuzzy set-based techniques. It is distribution-free and is realized in an unsupervised mode. Arrhythmia diagnosis is viewed as a certain statistical hypothesis testing. 'Abnormal' is typically a much complex concept, so it can be described with the technology of fuzzy sets which bring a facet of robustness to the overall scheme and play an important role in the successive step of hypothesis testing. Intensive fuzzification is engaged in parameters determination which is self-adaptive and no parameter needs to be specified by the user. The algorithm is validated with a number of experiments, which prove its effectiveness for arrhythmia diagnosis.