Search

PurposeThe purpose of this study is to explore the value of combining bpMRI and clinical indicators in the diagnosis of clinically significant prostate cancer (csPCa), and developing a prediction model and Nomogram to guide clinical decision-making.MethodsWe retrospectively analyzed 530 patients who underwent prostate biopsy due to elevated serum prostate specific antigen (PSA) levels and/or suspicious digital rectal examination (DRE). Enrolled patients were randomly assigned to the training group (n = 371, 70%) and validation group (n = 159, 30%). All patients underwent prostate bpMRI examination, and T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) sequences were collected before biopsy and were scored, which were respectively named T2WI score and DWI score according to Prostate Imaging Reporting and Data System version 2 (PI-RADS v.2) scoring protocol, and then PI-RADS scoring was performed. We defined a new bpMRI-based parameter named Total score (Total score = T2WI score + DWI score). PI-RADS score and Total score were separately included in the multivariate analysis of the training group to determine independent predictors for csPCa and establish prediction models. Then, prediction models and clinical indicators were compared by analyzing the area under the curve (AUC) and decision curves. A Nomogram for predicting csPCa was established using data from the training group.ResultsIn the training group, 160 (43.1%) patients had prostate cancer (PCa), including 128 (34.5%) with csPCa. Multivariate regression analysis showed that the PI-RADS score, Total score, f/tPSA, and PSA density (PSAD) were independent predictors of csPCa. The prediction model that was defined by Total score, f/tPSA, and PSAD had the highest discriminatory power of csPCa (AUC = 0.931), and the diagnostic sensitivity and specificity were 85.1% and 87.5%, respectively. Decision curve analysis (DCA) showed that the prediction model achieved an optimal overall net benefit in both the training group and the validation group. In addition, the Nomogram predicted csPCa revealed good estimation when compared with clinical indicators.ConclusionThe prediction model and Nomogram based on bpMRI and clinical indicators exhibit a satisfactory predictive value and improved risk stratification for csPCa, which could be used for clinical biopsy decision-making.

Background Recent studies have focused on initial clinical and epidemiological characteristics of the coronavirus disease 2019 (COVID-19), which is the mainly revealing situation in Wuhan, Hubei. Aim This study aims to reveal more data on the epidemiological and clinical characteristics of COVID-19 patients outside of Wuhan, Zhejiang, China. Design This study was a retrospective case series. Methods Eighty-eight cases of laboratory-confirmed and three cases of clinically confirmed COVID-19 were admitted to five hospitals in Zhejiang province, China. Data were collected from 20 January 2020 to 11 February 2020. Results and discussion Of all 91 patients, 88 (96.70%) were laboratory-confirmed COVID-19 with throat swab samples that tested positive for SARS-Cov-2, three (3.30%) cases were clinically diagnosed. The median age of the patients was 50 (36.5–57) years, and female accounted for 59.34%. In this sample, 40 (43.96%) patients had contracted the disease from local cases, 31 (34.07%) patients had been to Wuhan/Hubei, eight (8.79%) patients had contacted with people from Wuhan, and 11 (12.09%) patients were diagnosed after having flown together in the same flight with no passenger that could later be identified as the source of infection. In particular within the city of Ningbo, 60.52% cases can be traced back to an event held in a temple. The most common symptoms were fever (71.43%), cough (60.44%) and fatigue (43.96%). The median of incubation period was 6 (interquartile range 3–8) days and the median time from the first visit to a doctor to the confirmed diagnosis was 1 (1–2) days. According to the chest computed tomography scans, 67.03% cases had bilateral pneumonia. Conclusions Social activity cluster, family cluster and flying alongside with persons already infected with COVID-19 were how people got infected with COVID-19 in Zhejiang.

Rationale and Objectives Using lower tube voltage can reduce the exposure to radiation and the dose of contrast agent. However, lower tube voltage is often linked to more noise and poor image quality, which create a need for more effective technology to resolve this problem. To explore the feasibility of coronary computed tomographic angiography (CCTA) in patients with obesity at low tube voltage (100 kV) and low contrast agent concentration (270 mg/mL) using iterative reconstruction. Materials and Methods A total of 48 patients with body mass index greater than 30 kg/m 2 were included and randomly divided into two groups. Group A received a traditional protocol (iopromide 370 mg/mL + 120 kV); group B received a protocol with low tube voltage (100 kV), low contrast agent concentration (270 mg/mL), and iterative reconstruction. The effective dose (ED), average attenuation values, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), the figure of merit (FOM), image quality scores, and the total iodine intake were compared. Results No significant differences in average CT attenuations, SNR, CNR, and subjective scores were noticed between the two groups ( P > 0.05), whereas the FOM of group B was significantly higher than that of group A. Effective radiation dose, total iodine, and iodine injection rate in group B were lower than those of group A ( P 0.01). Conclusions In patients with obesity, isotonic contrast agent with low iodine concentration and low-dose CCTA were feasible. Substantial reduction in radiation dose and the iodine intake could be achieved without compromising the image quality.

OBJECTIVE The aim of the study was to investigate the feasibility of low-tube-voltage computed tomography (CT) in combination with 3-dimensional adaptive iterative dose reduction (AIDR-3D) algorithm in lower extremity CT angiography. METHODS The CT data of the 120-kV group (n = 30) were reconstructed with filtered back projection (FBP) algorithm. The CT data of the 100-kV group (n = 30) were reconstructed with FBP as well as AIDR-3D algorithms. RESULTS The 100-kV group showed significantly lower dose-length product than the 120-kV group (P < 0.05). In comparison with the 120-kV and FBP protocol, the 100-kV and FBP protocol showed significantly increased vascular density and noise (P < 0.05). However, in the 100-kV group, images reconstructed with AIDR-3D showed significantly lower noise and significantly higher signal-to-noise ratio and contrast-to-noise ratio than FBP (P < 0.05). CONCLUSIONS Low-tube-voltage (100 kV) 320-row CT in combination with AIDR-3D reconstruction can significantly improve the image quality and reduce radiation dose of lower extremity CT angiography.

Based on green mining idea, this paper developed a kind of filling material with coal gangue as coarse aggregate and designed underground-preparation system with the main processes being breaking, mixing and pumping. As a result of the research, the coal gangue pollution and surface subsidence problem was effectively solved and the load of lift transportation system reduced and the cost of filling technology cut down, which meet the concept of green mining. The industrial experiment verified the safety and stability of the system which can improve enterprises economic benefits and social environmental benefits.

Safety is the most important thing in coal mine production. There are many kinds of hidden dangers. In order to discover and eliminate the hidden dangers, the safety supervision becomes one of the most popular methods in current coal mine safety management, but there is not an unitive and normalized flow of collection, transmission, and processing and so on links of safety supervision information. Through applying the thought of business optimization, this paper formed a set of safety supervision information flow, supplied method and technical support for the coal mine management.

The environment in coal mines is complex, and it is very difficult to study on emergency evacuation in real environment because of its particularity and complexity. A virtual reality platform was designed and developed for emergency evacuation during disasters in coal mines based on Unity 3D. And 21 participants were selected to test and evaluate on the platform. The study has shown that the platform reproduced these scenarios during disasters in coal mines, and participants were stunning with relatively good immersion. The platform proved that it can be used to study the efficiency of emergency evacuation during disasters in coal mines.