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Determining whether nodes can be uniquely localized, called localizability detection, is a concomitant problem in network localization. Localizability detection under the traditional Non-Linear Localization (NLL) schema has been well explored, whereas localizability under the emerging Barycentric coordinate-based Linear Localization (BLL) schema has not been well investigated. Non-awareness of the node localizability in BLL may cause theoretically localizable nodes to converge to wrong locations because their locations are impacted by the wrong locations of the unlocalizable nodes through the iterative location propagation. In this article, the deficiency of existing localizability theories and algorithms in BLL is firstly investigated and then a necessary condition and a sufficient condition for BLL node localizability detection are proposed. Based on these two conditions, an efficient Iterative Maximum Flow (IMF) algorithm is designed to identify BLL localizable nodes, and only localizable nodes are selected to enable a Localizability Aware Barycentric Linear Localization (LABLL) algorithm, which can guarantee the locations of the localizable nodes converging correctly. The proposed IMF and LABLL algorithms are validated by both theoretical analysis and experimental evaluations.

BACKGROUND: To assess the clinical feasibility of using effective atomic number (Zeff) maps derived from non-contrast-enhanced computed tomography (NCECT) scans obtained by dual-layer spectral computed tomography (DLCT) to identify non-calcified atherosclerotic plaques. METHODS: A total of 37 patients with 86 non-calcified atherosclerotic plaques confirmed by contrast-enhanced CT (CECT) were enrolled in this retrospective study. Both spectral-based-images (SBI) and conventional images (CI) were reconstructed from NCECT and CECT scans. The presence of plaques on NCECT Zeff maps and CIs were independently assessed by 2 radiologists. In CECT scans, plaques and regions of interest (ROIs) in vessel lumens were assessed with CT attenuation and Zeff values, and the proportion of plaques was determined as Area (plaque)/Area (vessel). The CT and Zeff values for plaques and blood were recorded from both CECT and NCECT scans. Contrast-to-noise ratios (CNRs) of the plaques were calculated and compared using CT attenuation and Zeff values. Finally, interobserver agreement was evaluated. RESULTS: A total of 47 of the 86 (54.7%) plaques were identified on Zeff map images derived from the NCECT scans while only 7 (8.1%) plaques were identified on the CI. There was no significant difference between the mean vessel ROI area measured on CIs and that measured on Zeff map images (502.19 vs. 498.14 mm(2); P=0.28), while the mean plaque ROI area was larger (81.45 vs. 75.46 mm(2)). The observer consensus of vessel and plaque ROI area measurements using both methods was excellent, with interclass correlation coefficients (ICCs) of 0.99 and 0.94, respectively. For the 7 plaques detected both by NCECT CI and Zeff mapping, the CT attenuation and Zeff blood values were both larger than the plaque values [42.00 vs. 25.67 Hounsfield unit (HU); 7.33 vs. 7.19 HU; both P

Background Separate noncontrast CT to quantify the coronary artery calcium (CAC) score often precedes coronary CT angiography (CTA). Quantifying CAC scores directly at CTA would eliminate the additional radiation produced at CT but remains challenging. Purpose To quantify CAC scores automatically from a single CTA scan. Materials and Methods In this retrospective study, a deep learning method to quantify CAC scores automatically from a single CTA scan was developed on training and validation sets of 292 patients and 73 patients collected from March 2019 to July 2020. Virtual noncontrast scans obtained with a spectral CT scanner were used to develop the algorithm to alleviate tedious manual annotation of calcium regions. The proposed method was validated on an independent test set of 240 CTA scans collected from three different CT scanners from August 2020 to November 2020 using the Pearson correlation coefficient, the coefficient of determination, or r2, and the Bland-Altman plot against the semiautomatic Agatston score at noncontrast CT. The cardiovascular risk categorization performance was evaluated using weighted κ based on the Agatston score (CAC score risk categories: 0-10, 11-100, 101-400, and >400). Results Two hundred forty patients (mean age, 60 years ± 11 [standard deviation]; 146 men) were evaluated. The positive correlation between the automatic deep learning CTA and semiautomatic noncontrast CT CAC score was excellent (Pearson correlation = 0.96; r2 = 0.92). The risk categorization agreement based on deep learning CTA and noncontrast CT CAC scores was excellent (weighted κ = 0.94 [95% CI: 0.91, 0.97]), with 223 of 240 scans (93%) categorized correctly. All patients who were miscategorized were in the direct neighboring risk groups. The proposed method's differences from the noncontrast CT CAC score were not statistically significant with regard to scanner (P = .15), sex (P = .051), and section thickness (P = .67). Conclusion A deep learning automatic calcium scoring method accurately quantified coronary artery calcium from CT angiography images and categorized risk. © RSNA, 2021 See also the editorial by Goldfarb and Cao et al in this issue.

Given that chemotherapy as a stand-alone therapeutic strategy may not be sufficient to effectively treat cancer, there is increasing interest in combination of chemotherapy and alternative therapies. Photodynamic therapy has the advantages of high selectivity and low side effects, so the combination of photodynamic therapy and chemotherapy has become one of the most appealing strategies for tumor treatment. In this work, we constructed a nano drug codelivery system (PPDC) to realize the combined treatment of chemotherapy and photodynamic therapy through encapsulating chemotherapeutic drug dihydroartemisinin and photosensitizer chlorin e6 in PEG-PCL. The potentials, particle size and morphology of nanoparticles were characterized by dynamic light scattering and transmission electron microscopy. We also investigated the reactive oxygen species (ROS) generation and drug release ability. The antitumor effect in vitro was investigated by methylthiazolyldiphenyl-tetrazolium bromide assays and cell apoptosis experiments, and the potential cell death mechanisms were explored by ROS detection and Western blot analysis. The in vivo antitumor effect of PPDC was evaluated under the guidance of fluorescence imaging. Our work provides a potential antitumor treatment approach and expands the application of dihydroartemisinin for breast cancer therapy.

Lijin Lin,1– 4,&ast; Ze Chen,4,5,&ast; Ting Ding,1,2,&ast; Hui Liu,4,6,&ast; Feng Zhou,4,7 Xuewei Huang,3,4 Xingyuan Zhang,4,8 Weifang Liu,4,8 Bing-Hong Zhang,9 Yufeng Yuan,10 Peng Zhang,4,8 Xiao-Jing Zhang,4,8 Zhi-Gang She,3,4 Jingjing Cai,4,11 Wenping Chen,1,2 Hongliang Li2– 4,7 1Department of Endocrinology, Huanggang Central Hospital, Huanggang, People’s Republic of China; 2Huanggang Institute of Translational Medicine, Huanggang, People’s Republic of China; 3Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, People’s Republic of China; 4Institute of Model Animal of Wuhan University, Wuhan, People’s Republic of China; 5Department of Cardiology, Zhongnan Hospital of Wuhan University, Wuhan, People’s Republic of China; 6Neonatology of Gastroenterology, Tongren Hospital of Wuhan University & Wuhan Third Hospital, Wuhan, People’s Republic of China; 7Medical Science Research Center, Zhongnan Hospital of Wuhan University, Wuhan, People’s Republic of China; 8Basic Medical School of Wuhan University, Wuhan, People’s Republic of China; 9Neonatology Department, Renmin Hospital of Wuhan University, Wuhan, People’s Republic of China; 10Department of Hepatobiliary and Pancreatic Surgery, Zhongnan Hospital of Wuhan University, Wuhan, People’s Republic of China; 11Department of Cardiology, The Third Xiangya Hospital, Central South University, Changsha, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Hongliang Li; Wenping Chen Email lihl@whu.edu.cn; chenwenping@hgyy.org.cnPurpose: To analyze the impact of hyperglycemia on the clinical outcome of COVID-19 in patients with newly diagnosed diabetes (NDD).Patients and Methods: We performed a retrospective study of 3114 cases of COVID-19 without pre-existing diabetes, 351 of which had NDD, in Hubei Province, China. The Cox regression model was used to calculate the risk of adverse clinical outcomes comparing the NDD vs non-NDD group before and after propensity score-matched (PSM) analysis. Patients with NDD were further divided into a sustained hyperglycemia group, a fluctuating group, and a remitted group based on their blood glucose levels during hospitalization as well as into hypoglycemic agent users and nonusers.Results: Compared to the non-NDD individuals, individuals with NDD had a significantly increased risk of all-cause mortality (adjusted HR after PSM, 2.65; 95% CI, 1.49– 4.72; P = 0.001) and secondary outcomes involving organ damage during the 28-day follow-up period. Subgroup analyses indicated that among individuals with NDD, the individuals with remitted hyperglycemia had the lowest 28-day mortality, whereas those with sustained hyperglycemia had the highest (IRR 24.27; 95% CI, 3.21– 183.36; P < 0.001). Moreover, individuals treated with hypoglycemic agents had significantly lower all-cause mortality than those not treated with hypoglycemic agents (IRR 0.08; 95% CI, 0.01– 0.56; P < 0.001).Conclusion: Our study reinforces the clinical message that NDD is strongly associated with poor outcomes in COVID-19 patients. Furthermore, resolved hyperglycemia in the later phase of the disease and the use of hypoglycemic agents were associated with improved prognosis in patients with NDD.Keywords: COVID-19, newly diagnosed diabetes, hyperglycemia, prognosis, blood glucose

A single strategy used in the firefly algorithm (FA) cannot effectively solve the complex optimal scheduling problem. Thus, we propose the FA with division of roles (DRFA). Herein, fireflies are divided into leaders, developers, and followers, while a learning strategy is assigned to each role: the leader chooses the greedy Cauchy mutation; the developer chooses two leaders randomly and uses the elite neighborhood search strategy for local development; the follower randomly selects two excellent particles for global exploration. To improve the efficiency of the fixed step size used in FA, a stepped variable step size strategy is proposed to meet different requirements of the algorithm for the step size at different stages. Role division can balance the development and exploration ability of the algorithm. The use of multiple strategies can greatly improve the versatility of the algorithm for complex optimization problems. The optimal performance of the proposed algorithm has been verified by three sets of test functions and a simulation of optimal scheduling of cascade reservoirs.