CT characteristics of non-small cell lung cancer with epidermal growth factor receptor mutation: a systematic review and meta-analysis

BACKGROUND: To systematically investigate the relationship between CT morphological features and the presence of epidermal growth factor receptor (EGFR) mutations in non-small cell lung cancer (NSCLC). METHODS: All studies about the CT morphological features of NSCLC with EGFR mutations published between January 1, 2000 and March 15, 2015 were searched in the PubMed and EMBASE databases. Qualified studies were selected according to inclusion criteria. The frequency of EGFR mutations and CT features of ground-glass opacity (GGO) content, tumor size, cavitation, air-bronchogram, lobulation, and spiculation were extracted. The relationship between EGFR mutations and each of these CT features was tested based upon the weighted mean difference or inverse variance in the form of an odds ratio at a 95% confidence interval using Forest Plots. The publication bias was examined using Egger’s test. RESULTS: A total of 13 studies, consisting of 2146 NSCLC patients, were included, and 51.12% (1097/2146) of patients had EGFR mutations. The EGFR mutations were present in NSCLC with part-solid GGO in contrast to nonsolid GGO (OR = 0.49, 95% CI = 0.25–0.96, P = 0.04). Other CT features such as tumor size, cavitation, air-bronchogram, lobulation and spiculation did not demonstrate statistically significant correlation with EGFR mutations individually (P = 0.91; 0.67; 0.12; 0.45; and 0.36, respectively). No publication bias among the selected studies was noted in this meta-analysis (Egger’s tests, P > 0.05 for all). CONCLUSION: This meta-analysis demonstrated that NSCLC with CT morphological features of part-solid GGO tended to be EGFR mutated, which might provide an important clue for the correct selection of patients treated with molecular targeted therapies. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12880-016-0175-3) contains supplementary material, which is available to authorized users

Feature Extraction of Flow Sediment Content of Hydropower Unit Based on Voiceprint Signal

The hydropower turbine parts running in the sand-bearing flow will experience surface wear, leading to a decline in the hydropower unit’s stability, mechanical performance, and efficiency. A voiceprint signal-based method is proposed for extracting the flow sediment content feature of the hydropower unit. Firstly, the operating voiceprint information of the hydropower unit is obtained, and the signal is decomposed by the Ensemble Empirical Mode Decomposition (EEMD) algorithm, and a series of intrinsic mode functions (IMFs) are obtained. Combined with correlation analysis, more sensitive IMF components are extracted and input into a convolutional neural network (CNN) for training, and the multi-dimensional output of the fully connected layer of CNN is used as the feature vector. The k-means clustering algorithm is used to calculate the eigenvector clustering center of the hydropower unit with a clean flow state and a high sediment content state, and the characteristic index of the hydropower unit sediment content is constructed based on the Euclidean distance method. We define this characteristic index as SI, and the change in the SI value can reflect the degree of sediment content in the flow of the unit. A higher SI value indicates a lower sediment content, while a lower SI value suggests a higher sediment content. Combined with the sediment voiceprint data of the test bench, when the water flow changed from clear water to high sediment flow (1.492 × 105 mg/L), the SI value decreased from 1 to 0.06, and when the water flow with high sediment content returned to clear water, the SI value returned to 1. The experiment proves the effectiveness of the method. The extracted feature index can be used to detect the flow sediment content of the hydropower unit and give early warning in time, so as to improve the maintenance level of the hydropower unit

CT characteristics of non-small cell lung cancer with epidermal growth factor receptor mutation: a systematic review and meta-analysis

Abstract Background To systematically investigate the relationship between CT morphological features and the presence of epidermal growth factor receptor (EGFR) mutations in non-small cell lung cancer (NSCLC). Methods All studies about the CT morphological features of NSCLC with EGFR mutations published between January 1, 2000 and March 15, 2015 were searched in the PubMed and EMBASE databases. Qualified studies were selected according to inclusion criteria. The frequency of EGFR mutations and CT features of ground-glass opacity (GGO) content, tumor size, cavitation, air-bronchogram, lobulation, and spiculation were extracted. The relationship between EGFR mutations and each of these CT features was tested based upon the weighted mean difference or inverse variance in the form of an odds ratio at a 95% confidence interval using Forest Plots. The publication bias was examined using Egger’s test. Results A total of 13 studies, consisting of 2146 NSCLC patients, were included, and 51.12% (1097/2146) of patients had EGFR mutations. The EGFR mutations were present in NSCLC with part-solid GGO in contrast to nonsolid GGO (OR = 0.49, 95% CI = 0.25–0.96, P = 0.04). Other CT features such as tumor size, cavitation, air-bronchogram, lobulation and spiculation did not demonstrate statistically significant correlation with EGFR mutations individually (P = 0.91; 0.67; 0.12; 0.45; and 0.36, respectively). No publication bias among the selected studies was noted in this meta-analysis (Egger’s tests, P > 0.05 for all). Conclusion This meta-analysis demonstrated that NSCLC with CT morphological features of part-solid GGO tended to be EGFR mutated, which might provide an important clue for the correct selection of patients treated with molecular targeted therapies

Predicted resting metabolic rate and prognosis in patients with ischemic stroke

Abstract Purpose Resting metabolic rate (RMR) could represent metabolic health status. This study aims to examine the association of the predicted RMR with 1‐year poor functional outcome and all‐cause mortality in patients with ischemic stroke as a proxy of metabolic profile. Methods A total of 15,166 patients with ischemic stroke or transient ischemic attack (TIA) from the Third China National Stroke Registry (CNSR‐III) were enrolled in this study. The Harris–Benedict equation based on sex, age, weight, and height was used to predict RMR. The primary endpoints were poor functional outcome defined as ≥3 modified Rankin Scale (mRS) score and all‐cause mortality within 1 year. The association between predicted RMR and prognosis was assessed by multivariable regression analysis. Besides that, subgroup analysis of age, sex, and body mass index (BMI) with predicted RMR was also performed. Results 12.85% (1657) individuals had poor functional outcome and 2.87% (380) died of whatever causes within 1 year. An inverse association was found between predicted RMR with poor functional outcome and all‐cause mortality. Compared to the lowest quartile, the highest quartile was significantly associated with lower risk of poor functional outcome (adjusted odds ratio [OR], 0.43 [95% confidence interval (CI) 0.33–0.56]) and all‐cause mortality (adjusted hazard ratio [HR], 0.44 [95% CI 0.28–0.71]). No significant interaction was between predicted RMR and specified subgroup. Conclusions Predicted RMR by the Harris–Benedict equation seems to be an independent protective predictor of poor functional outcome and all‐cause mortality after ischemic stroke as a metabolic proxy

Urban-Mountain Coupling Characteristics Based on Landscape Form and Its Disaster Effects: A Case Study of the Guangdong-Hong Kong-Macao Greater Bay Area

With the advancement of urbanization in China, hilly and gently sloping mountainous areas have become areas of high disturbance owing to urban construction, with the disturbed areas also having a high incidence of mountain disasters. The large hilly and shallow mountainous areas in the Guangdong-Hong Kong-Macao Greater Bay Area are continually disturbed by rapid urbanization, with frequent geological disasters. This study attempts to reflect the boundary morphology of the interaction zone from the perspective of the landscape morphology of the town-mountain interaction zone by using the landscape pattern index, analyzing the relationship between the morphological index and the intensity of geological disasters, and identifying the key factors. Finally, the functional relationship is fit between the intensity of disasters and the landscape pattern index based on the GAM model to reveal the interaction characteristics of towns and mountains and the disaster effects caused by them. The results of the study show that: 1) the urban-mountain interaction zone in the Bay Area is located in Guangzhou, Shenzhen and Hong Kong, with an area of 131.8, 81.6 and 58.5 km2 respectively, primarily in areas with a high proportion of hilly and shallow mountainous areas and rapid urban development; 2) Shenzhen and Hong Kong had higher landscape pattern indices than other regions, while Jiangmen, Zhaoqing, Foshan, and Huizhou generally had a lower landscape pattern index; 3) Among the nine landscape pattern indices, seven were positively correlated and two were negatively correlated, with CONNECT showing the highest correlation of 0.84 (P<0.001), SPLIT showing a high negative correlation of -0.84 (P<0.001), and AREA and PARA showing a weak correlation; 4) Most of the edge indicators, shape indicators, and agglomerative landscape index had linear relationships with landslide hazard frequency, and the frequency decreased with an increase in LSI and SPLIT and increased with the increase in GYRATE, SHAPE, FRAC, PARA, and CIRCLE; and 5) The larger the area of the urban-mountain interaction zone, the more complex the shape; the longer the boundary length, the more irregular the shape of the interaction zone; and the smaller its closeness to a circle, the more fragmented the interaction zone patches, the higher the ratio of core patches to the total area of the interaction zone, and the higher the probability of landslide disasters. Combined with the results, from the perspective of the construction of a single project in a small area, the larger the scope of excavation to the mountain, the more tortuous and complex the engineering cut, and the higher the probability of triggering landslides and collapses. For large-scale continuous construction projects, the closer the cuts are to each other, the higher the degree of agglomeration, the larger the area occupied by the core project, and the more likely it is to trigger geological disasters. This study is significant for understanding and mastering the heterogeneity law of geological hazards under different land use degrees and configuration measures, which can serve as a guide for land structure adjustment and optimize land use layout more effectively. It is of great practical significance for ecological restoration, sustainable and rational use of land resources, geological hazard prevention and control, and enriches the geological hazard susceptibility evaluation system

Additional file 1: of CT characteristics of non-small cell lung cancer with epidermal growth factor receptor mutation: a systematic review and meta-analysis

PRISMA-checklist. (DOC 67 kb

Ginsenoside compound K induces ferroptosis via the FOXO pathway in liver cancer cells

Abstract Liver cancer is a common malignant tumor worldwide, traditional Chinese medicine is one of the treatment measures for liver cancer because of its good anti-tumor effects and fewer toxic side effects. Ginsenoside CK (CK) is an active component of ginseng. This study explored the mechanism by which CK induced ferroptosis in liver cancer cells. We found that CK inhibited the proliferation of HepG2 and SK-Hep-1 cells, induced ferroptosis of cells. Ferrostatin-1, an ferroptosis inhibitor, was used to verify the role of CK in inducing ferroptosis of liver cancer cells. Network pharmacological analysis identified the FOXO pathway as a potential mechanism of CK, and western blot showed that CK inhibited p-FOXO1. In cells treated with the FOXO1 inhibitor AS1842856, further verify the involvement of the FOXO pathway in regulating CK-induced ferroptosis in HepG2 and SK-Hep-1 cells. A HepG2 cell–transplanted tumor model was established in nude mice, and CK inhibited the growth of transplanted tumors in nude mice, p-FOXO1 was decreased in tumor tissues, and SLC7A11 and GPX4 expressions were also down-regulated after CK treatment. These findings suggested that CK induces ferroptosis in liver cancer cells by inhibiting FOXO1 phosphorylation and activating the FOXO signaling pathway, thus playing an antitumor role

Time Course for Benefit and Risk with Ticagrelor and Aspirin in Individuals with Acute Ischemic Stroke or Transient Ischemic Attack Who Carry CYP2C19 Loss-of-Function Alleles: A Secondary Analysis of the CHANCE-2 Randomized Clinical Trial

Importance: Dual antiplatelet therapy (DAPT) with ticagrelor and aspirin has been found to be effective for secondary prevention after minor ischemic stroke or transient ischemic attack (TIA) in individuals who carry CYP2C19 loss-of-function (LOF) alleles; however, uncertainties remain about the time course of benefit and risk with ticagrelor and aspirin in these patients. Objective: To obtain time-course estimates of efficacy and risk with ticagrelor and aspirin after minor stroke or TIA in individuals with CYP2C19 LOF alleles. Design, Setting, and Participants: The Ticagrelor or Clopidogrel With Aspirin in High-risk Patients With Acute Nondisabling Cerebrovascular Events II (CHANCE-2) randomized clinical trial enrolled patients 40 years and older from 202 hospitals in China with acute minor stroke or TIA who carried CYP2C19 LOF alleles between September 23, 2019, and March 22, 2021, and were followed up for 90 days. All 6412 patients enrolled in the CHANCE-2 trial were included in this secondary analysis. Data were analyzed in October 2021. Interventions: Ticagrelor (180 mg on day 1 followed by 90 mg twice daily on days 2-90) or clopidogrel (300 mg on day 1 followed by 75 mg daily on days 2-90). All patients received aspirin (75-300 mg on day 1 followed by 75 mg daily for 21 days). Main Outcomes and Measures: The efficacy outcome was major ischemic event, defined as the composite of ischemic stroke or nonhemorrhagic death. Safety outcomes included moderate to severe bleeding and any bleeding. Results: A total of 6412 patients were included (3205 in the ticagrelor and aspirin group and 3207 in the clopidogrel and aspirin group). The median (IQR) age was 65 (57-71) years, and 4242 patients (66%) were men. The reduction of major ischemic events with ticagrelor and aspirin predominately occurred in the first week (absolute risk reduction, 1.34%; 95% CI, 0.29 to 2.39) and attenuated but remained in the next 3 weeks (absolute risk reduction in the second week, 0.11%; 95% CI, -0.24 to 0.45; absolute risk reduction in the third week, 0.14%; 95% CI, -0.11 to 0.38; absolute risk reduction in the fourth week, 0.04%; 95% CI, -0.18 to 0.25). The risk of moderate to severe bleeding was consistently low in the ticagrelor and aspirin group. The absolute increase in any bleeding seen in the first week (0.87%; 95% CI, 0.25 to 1.50) remained in the next 3 weeks (absolute increase in the second week, 1.21%; 95% CI, 0.75 to 1.68; absolute increase in the third week, 0.33%; 95% CI, -0.05 to 0.72; absolute increase in the fourth week, 0.23%; 95% CI, -0.03 to 0.49). Conclusion and Relevance: Among patients with minor stroke or TIA who carried CYP2C19 LOF alleles, benefit with ticagrelor and aspirin was present predominately in the first week, with additional small benefit accruing in the next 2 weeks

A Deep Learning System to Predict Recurrence and Disability Outcomes in Patients with Transient Ischemic Attack or Ischemic Stroke

Ischemic strokes (IS) and transient ischemic attacks (TIA) account for approximately 80% of all strokes and are leading causes of death worldwide. Assessing the risk of recurrence or functional impairment in IS and TIA patients is essential to both acute phase treatment and secondary prevention. Current risk prediction systems that rely on clinical parameters alone without leveraging imaging data have only modest performance. Herein, a deep learning‐based risk prediction system (RPS) is developed to predict the probability of stroke recurrence or disability (i.e., deep‐learning stroke recurrence risk score, SRR score). Then, Kaplan–Meier analysis to evaluate the ability of SRR score to stratify patients at stroke recurrence risk is discussed. Using 15 166 Third China National Stroke Registry (CNSR‐III) cases, the RPS's receiver operating characteristic curve (AUC) values of 0.850 for 14 day TIA recurrence prediction and 0.837 for 3 month IS disability prediction are used. Among patients deemed high risk by SRR score, 22.9% and 24.4% of individuals with TIA and IS respectively have stroke recurrence within 1 year, which are significantly higher than the rates in low‐risk individuals. Deep learning‐based RPS can outperform conventional risk scores and has the potential to assist accurate prognostication in stroke patients to optimize management