Preoperative prediction of disease-free survival in pancreatic ductal adenocarcinoma patients after R0 resection using contrast-enhanced CT and CA19-9.

OBJECTIVES: To investigate the efficiency of a combination of preoperative contrast-enhanced computed tomography (CECT) and carbohydrate antigen 19-9 (CA19-9) in predicting disease-free survival (DFS) after R0 resection of pancreatic ductal adenocarcinoma (PDAC). METHODS: A total of 138 PDAC patients who underwent curative R0 resection were retrospectively enrolled and allocated chronologically to training (n = 91, January 2014-July 2019) and validation cohorts (n = 47, August 2019-December 2020). Using univariable and multivariable Cox regression analyses, we constructed a preoperative clinicoradiographic model based on the combination of CECT features and serum CA19-9 concentrations, and validated it in the validation cohort. The prognostic performance was evaluated and compared with that of postoperative clinicopathological and tumor-node-metastasis (TNM) models. Kaplan-Meier analysis was conducted to verify the preoperative prognostic stratification performance of the proposed model. RESULTS: The preoperative clinicoradiographic model included five independent prognostic factors (tumor diameter on CECT > 4 cm, extrapancreatic organ infiltration, CECT-reported lymph node metastasis, peripheral enhancement, and preoperative CA19-9 levels > 180 U/mL). It better predicted DFS than did the postoperative clinicopathological (C-index, 0.802 vs. 0.787; p < 0.05) and TNM (C-index, 0.802 vs. 0.711; p < 0.001) models in the validation cohort. Low-risk patients had significantly better DFS than patients at the high-risk, defined by the model preoperatively (p < 0.001, training cohort; p < 0.01, validation cohort). CONCLUSIONS: The clinicoradiographic model, integrating preoperative CECT features and serum CA19-9 levels, helped preoperatively predict postsurgical DFS for PDAC and could facilitate clinical decision-making. CLINICAL RELEVANCE STATEMENT: We constructed a simple model integrating clinical and radiological features for the prediction of disease-free survival after curative R0 resection in patients with pancreatic ductal adenocarcinoma; this novel model may facilitate preoperative identification of patients at high risk of recurrence and metastasis that may benefit from neoadjuvant treatments. KEY POINTS: • Existing clinicopathological predictors for prognosis in pancreatic ductal adenocarcinoma (PDAC) patients who underwent R0 resection can only be ascertained postoperatively and do not allow preoperative prediction. • We constructed a clinicoradiographic model, using preoperative contrast-enhanced computed tomography (CECT) features and preoperative carbohydrate antigen 19-9 (CA19-9) levels, and presented it as a nomogram. • The presented model can predict disease-free survival (DFS) in patients with PDAC better than can postoperative clinicopathological or tumor-node-metastasis (TNM) models.

Identification of triple-negative breast cancer and androgen receptor expression based on histogram and texture analysis of dynamic contrast-enhanced MRI.

BACKGROUND: Triple-negative breast cancer (TNBC) is highly malignant and has a poor prognosis due to the lack of effective therapeutic targets. Androgen receptor (AR) has been investigated as a possible therapeutic target. This study quantitatively assessed intratumor heterogeneity by histogram analysis of pharmacokinetic parameters and texture analysis on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to discriminate TNBC from non-triple-negative breast cancer (non-TNBC) and to identify AR expression in TNBC. METHODS: This retrospective study included 99 patients with histopathologically proven breast cancer (TNBC: 36, non-TNBC: 63) who underwent breast DCE-MRI before surgery. The pharmacokinetic parameters of DCE-MRI (Ktrans, Kep and Ve) and their corresponding texture parameters were calculated. The independent t-test, or Mann-Whitney U-test was used to compare quantitative parameters between TNBC and non-TNBC groups, and AR-positive (AR+) and AR-negative (AR-) TNBC groups. The parameters with significant difference between two groups were further involved in logistic regression analysis to build a prediction model for TNBC. The ROC analysis was conducted on each independent parameter and the TNBC predicting model for evaluating the discrimination performance. The area under the ROC curve (AUC), sensitivity and specificity were derived. RESULTS: The binary logistic regression analysis revealed that Kep_Range (p = 0.032) and Ve_SumVariance (p = 0.005) were significantly higher in TNBC than in non-TNBC. The AUC of the combined model for identifying TNBC was 0.735 (p < 0.001) with a cut-off value of 0.268, and its sensitivity and specificity were 88.89% and 52.38%, respectively. The value of Kep_Compactness2 (p = 0.049), Kep_SphericalDisproportion (p = 0.049), and Ve_GlcmEntropy (p = 0.008) were higher in AR + TNBC group than in AR-TNBC group. CONCLUSION: Histogram and texture analysis of breast lesions on DCE-MRI showed potential to identify TNBC, and the specific features can be possible predictors of AR expression, enhancing the ability to individualize the treatment of patients with TNBC.

Performance of radiomics models derived from different CT reconstruction parameters for lung cancer risk prediction.

BACKGROUND: This study analysed the performance of radiomics features extracted from computed tomography (CT) images with different reconstruction parameters in differentiating malignant and benign pulmonary nodules. METHODS: We evaluated routine chest CT images acquired from 148 participants with pulmonary nodules, which were pathologically diagnosed during surgery in West China Hospital, including a 5 mm unenhanced lung window, a 5 mm unenhanced mediastinal window, a 5 mm contrast-enhanced mediastinal window and a 1 mm unenhanced lung window. The pulmonary nodules were segmented, and 1409 radiomics features were extracted for each window. Then, we created 15 cohorts consisting of single windows or multiple windows. Univariate correlation analysis and principal component analysis were performed to select the features, and logistic regression analysis was performed to establish models for each cohort. The area under the curve (AUC) was applied to compare model performance. RESULTS: There were 75 benign and 73 malignant pulmonary nodules, with mean diameters of 18.63 and 19.86 mm, respectively. For the single-window setting, the AUCs of the radiomics model from the 5 mm unenhanced lung window, 5 mm unenhanced mediastinal window, 5 mm contrast-enhanced mediastinal window and 1 mm unenhanced lung window were 0.771, 0.808, 0.750, and 0.771 in the training set and 0.711, 0.709, 0.684, and 0.674 in the test set, respectively. Regarding the multiple-window setting, the radiomics model based on all four windows showed an AUC of 0.825 in the training set and 0.743 in the test set. Statistically, the 15 models demonstrated comparable performances (P > 0.05). CONCLUSION: A single chest CT window was acceptable in predicting the malignancy of pulmonary nodules, and additional windows did not statistically improve the performance of the radiomics models. In addition, slice thickness and contrast enhancement did not affect the diagnostic performance.

Effect of expansive optic flow and lateral motion parallax on depth estimation with normal and artificially reduced acuity.

When an observer moves in space, the retinal projection of a stationary object either expands if the motion is toward the object or shifts horizontally if the motion contains a lateral component. This study examined the impact of expansive optic flow and lateral motion parallax on the accuracy of depth perception for observers with normal or artificially reduced acuity and asked whether any benefit is due to the continuous motion or to the discrete object image displacement. Stationary participants viewed a virtual room on a computer screen. They used an on-screen slider to estimate the depth of a target object relative to a reference object after seeing 2-second videos simulating five conditions: static viewing, expansive optic flow, and lateral motion parallax in either continuous motion or image displacement. Ten participants viewed the stimuli with normal acuity in Experiment 1 and 11 with three levels of artificially reduced acuity in Experiment 2. Linear regression models represented the relationship between the depth estimates of participants and the ground truth. Lateral motion parallax produced more accurate depth estimates than expansive optic flow and static viewing. Depth perception with continuous motion was more accurate than that with displacement under mild and moderate, but not severe, acuity reduction. For observers with both normal and artificially reduced acuity, lateral motion parallax was more helpful for object depth estimation than expansive optic flow, and continuous motion parallax was more helpful than object image displacement.

A Multi-Target Localization and Vital Sign Detection Method Using Ultra-Wide Band Radar.

Life detection technology using ultra-wideband (UWB) radar is a non-contact, active detection technology, which can be used to search for survivors in disaster rescues. The existing multi-target detection method based on UWB radar echo signals has low accuracy and has difficulty extracting breathing and heartbeat information at the same time. Therefore, this paper proposes a new multi-target localization and vital sign detection method using ultra-wide band radar. A target recognition and localization method based on permutation entropy (PE) and K means++ clustering is proposed to determine the number and position of targets in the environment. An adaptive denoising method for vital sign extraction based on ensemble empirical mode decomposition (EEMD) and wavelet analysis (WA) is proposed to reconstruct the breathing and heartbeat signals of human targets. A heartbeat frequency extraction method based on particle swarm optimization (PSO) and stochastic resonance (SR) is proposed to detect the heartbeat frequency of human targets. Experimental results show that the PE-K means++ method can successfully recognize and locate multiple human targets in the environment, and its average relative error is 1.83%. Using the EEMD-WA method can effectively filter the clutter signal, and the average relative error of the reconstructed respiratory signal frequency is 4.27%. The average relative error of heartbeat frequency detected by the PSO-SR method was 6.23%. The multi-target localization and vital sign detection method proposed in this paper can effectively recognize all human targets in the multi-target scene and provide their accurate location and vital signs information. This provides a theoretical basis for the technical system of emergency rescue and technical support for post-disaster rescue.

Radiomics analysis of T2 -weighted images for differentiating invasive placentas in women at high risks.

PURPOSE: To develop and validate an MRI-based radiomics model for differentiating invasive placentas in patients with high risks. METHODS: A total of 181 pregnant women suspected of placenta accreta spectrum (PAS) disorders and who underwent MRI for placenta evaluation were retrospectively enrolled. The data set was randomly divided into the training (n = 125; invasive = 63, noninvasive = 62) and test (n = 56; invasive = 28, noninvasive = 28) groups. Radiomics features were extracted from half-Fourier acquisition single-shot turbo spin echo (HASTE) and sagittal true fast imaging in steady-state precession (TRUFISP) sequences independently and mainly selected based on their correlations with invasive placentas to construct two radiomics signatures including HASTE-Radscore and TRUFISP-Radscore. Then, the predictive performance of radiomic signatures, clinical features, radiographic features, and their combination were evaluated. The model with the best predictive performance was validated with its discrimination ability, calibration, and clinical usefulness. RESULTS: Five radiomics features from HASTE and three radiomics features from TRUFISP were retained, respectively, for predicting invasive placentas. The combination of radiomics signatures and clinical features including prior cesarean delivery, placenta previa, and radiographic feature, the placental thickness resulted in the best discrimination ability, with area under the curve of 0.898 (95% confidence interval [CI] 0.844-0.9522) and 0.858 (95% confidence interval 0.7514-0.9655) in the training and test cohort, respectively. The combined model showed a significantly better area under the curve performance and clinical usefulness than independent clinical or radiographic model according to DeLong test (p < .05), net reclassification improvement and integrated discrimination improvement analysis (positive improvement) and decision curve analysis (higher net benefit). CONCLUSIONS: The T2 -weighted imaging MRI radiomics model could serve as a potential prenatal diagnosis tool for identifying invasive placentas in patients with high risks.

Development and Validation of Noninvasive MRI-Based Signature for Preoperative Prediction of Early Recurrence in Perihilar Cholangiocarcinoma.

BACKGROUND: Cholangiocarcinoma is a type of hepatobiliary tumor. For perihilar cholangiocarcinoma (pCCA), patients who experience early recurrence (ER) have a poor prognosis. Preoperative accurate prediction of postoperative ER can avoid unnecessary operation; however, prediction is challenging. PURPOSE: To develop a novel signature based on clinical and/or MRI radiomics features of pCCA to preoperatively predict ER. STUDY TYPE: Retrospective. POPULATION: One hundred eighty-four patients (median age, 61.0 years; interquartile range: 53.0-66.8 years) including 115 men and 69 women. FIELD STRENGTH/SEQUENCE: A 1.5 T; volumetric interpolated breath-hold examination (VIBE) sequence. ASSESSMENT: The models were developed from the training set (128 patients) and validated in a separate testing set (56 patients). The contrast-enhanced arterial and portal vein phase MR images of hepatobiliary system were used for extracting radiomics features. The correlation analysis, least absolute shrinkage and selection operator (LASSO) logistic regression (LR), backward stepwise LR were mainly used for radiomics feature selection and modeling (Modelradiomic ). The univariate and multivariate backward stepwise LR were used for preoperative clinical predictors selection and modeling (Modelclinic ). The radiomics and preoperative clinical predictors were combined by multivariate LR method to construct clinic-radiomics nomogram (Modelcombine ). STATISTICAL TESTS: Chi-squared (χ2 ) test or Fisher's exact test, Mann-Whitney U-test or t-test, Delong test. Two tailed P < 0.05 was considered statistically significant. RESULTS: Based on the comparison of area under the curves (AUC) using Delong test, Modelclinic and Modelcombine had significantly better performance than Modelradiomic and tumor-node-metastasis (TNM) system in training set. In the testing set, both Modelclinic and Modelcombine had significantly better performance than TNM system, whereas only Modelcombine was significantly superior to Modelradiomic . However, the AUC values were not significantly different between Modelclinic and Modelcombine (P = 0.156 for training set and P = 0.439 for testing set). DATA CONCLUSION: A noninvasive model combining the MRI-based radiomics signature and clinical variables is potential to preoperatively predict ER for pCCA. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 4.

The application of apparent diffusion coefficients derived from intratumoral and peritumoral zones for assessing pathologic prognostic factors in rectal cancer.

OBJECTIVE: To investigate the diagnostic performance of the apparent diffusion coefficient (ADC) derived from intratumoral and peritumoral zones for assessing pathologic prognostic factors in rectal cancer. MATERIALS AND METHODS: One hundred forty-six patients with rectal cancer who underwent preoperative MRI were prospectively enrolled. Two radiologists independently placed free-hand regions of interest (ROIs) in the largest tumor cross section and three small ROIs on the peritumoral zone adjacent to the tumor contour. Maximum values of tumor ADC (ADCtmax), minimum values of tumor ADC (ADCtmin), mean values of tumor ADC (ADCtmean), mean values of peritumor ADC (ADCpmean), and ADCpmean/ADCtmean (ADC ratio) were obtained on ADC maps and correlated with prognostic factors using uni- and multivariate logistic regression, and receiver operating characteristic curve (ROC) analysis. RESULTS: Interobserver agreement was excellent for ADCtmax and ADCtmean (intraclass correlation coefficient [ICC], 0.915-0.958), and were good for ADCtmin, ADCpmean, and ADC ratio (ICC, 0.774-0.878). The ADC ratio was significantly higher in the poor differentiation, T3-4 stage, lymph node metastasis (LNM)-positive, extranodal extension (ENE)-positive, tumor deposit (TD)-positive, and lymphovascular invasion (LVI)-positive groups than that in the well-moderate differentiation, T1-2 stage, LNM-negative, ENE-negative, TD-negative, and LVI-negative groups (p = 0.008, < 0.001, < 0.001, 0.001, < 0.001, and < 0.001, respectively). The area under the ROC curve (AUC) of the ADC ratio was the highest for assessing poor differentiation (0.700), T3-4 stage (0.707), LNM-positive (0.776), TD-positive (0.848), and LVI-positive (0.778). Both the ADC ratio (AUC = 0.677) and ADCpmean (AUC = 0.686) showed higher diagnostic performance for assessing ENE. CONCLUSION: The ADC ratio could provide better predictive performance for assessing preoperative prognostic factors in resectable rectal cancer. KEY POINTS: • Both the peritumor/tumor ADC ratio and ADCpmean are correlated with important prognostic factors of resectable rectal cancer. • Both peritumor ADC and peritumor/tumor ADC ratio had higher diagnostic performance than tumor ADC for assessment of prognostic factors in resectable rectal cancer. • Peritumor/tumor ADC ratio showed the most capability for the assessment of prognostic factors in resectable rectal cancer.

A Vital Signs Fast Detection and Extraction Method of UWB Impulse Radar Based on SVD.

The identification of weak vital signs has always been one of the difficulties in the field of life detection. In this paper, a novel vital sign detection and extraction method with high efficiency, high precision, high sensitivity and high signal-to-noise ratio is proposed. Based on the NVA6100 pulse radar system, the radar matrix which contains several radar pulse detection signals is received. According to the characteristics of vital signs and radar matrices, the Singular Value Decomposition (SVD) is adopted to perform signal denoising and decomposition after preprocessing, and the temporal and spatial eigenvectors of each principal component are obtained. Through the energy proportion screening, the Wavelet Transform decomposition and linear trend suppression, relatively pure vital signs in each principal component, are obtained. The human location is detected by the Energy Entropy of spatial eigenvectors, and the respiratory signal and heartbeat signal are restored through a Butterworth Filter and an MTI harmonic canceller. Finally, through an analysis of the performance of the algorithm, it is proved to have the properties of efficiency and accuracy.

Intrahepatic cholangiocarcinoma: MRI texture signature as predictive biomarkers of immunophenotyping and survival.

OBJECTIVE: Clinical evidence suggests that the response to immune checkpoint blockade depends on the immune status in the tumor microenvironment. This study aims to predict the immunophenotyping (IP) and overall survival (OS) of intrahepatic cholangiocarcinoma (ICC) patients using preoperative magnetic resonance imaging (MRI) texture analysis. METHODS: A total of 78 ICC patients were included and divided into inflamed (n = 26) or non-inflamed (n = 52) immunophenotyping based on the density of CD8+ T cells. The enhanced T1-weighted MRI in the arterial phase was employed with texture analysis. The logistic regression analysis was applied to select the significant features related to IP. The OS-related feature was determined by Cox proportional-hazards model and Kaplan-Meier analysis. IP and OS predictive models were developed using the selected features, respectively. RESULTS: Three wavelets and one 3D feature have favorable ability to discriminate IP, a combination of which performed best with an AUC of 0.919. The inflamed immunophenotyping had a better prognosis than the non-inflamed one. The 5-year survival rates of the two groups were 48.5% and 25.3%, respectively (p < 0.05). The only wavelet-HLH_firstorder_Median feature was associated with OS and used to build the OS predictive model with a C-index of 0.70 (95% CI, 0.57, 0.82), which could well stratify ICC patients into high- and low-risk groups. The 1-, 3-, and 5-year survival probabilities of the stratified groups were 62.5%, 30.0%, and 24.2%, and 89.5%, 62.2%, and 42.1%, respectively (p < 0.05). CONCLUSION: The MRI texture signature could serve as a potential predictive biomarker for the IP and OS of ICC patients. KEY POINTS: • The MRI texture signature, including three wavelets and one 3D feature, showed significant associations with immunophenotyping of ICC, and all have favorable ability to discriminate immunophenotyping; a combination of the above features performed best with an AUC of 0.919. • The only wavelet-HLH_firstorder_Median feature was associated with the OS of ICC and used to build the OS predictive model, which could well stratify ICC patients into high- and low-risk groups.

Kernel density estimation in mixture models with known mixture proportions.

In this article, we consider the density estimation for data with a mixture structure, where the component densities are assumed unknown, but for each observation, the probabilities of its membership to the subpopulations are known or estimable from other resources. Data of this kind arise from practice and have wide applications. Motivated from the classical kernel density estimation method for a single population, we propose a weighted kernel density estimation method to estimate the component density functions nonparametrically. Within the framework of the EM algorithm, we derive an algorithm that computes our proposed estimates effectively. Via extensive simulation studies, we demonstrate that our methods outperform the existing methods in most occasions. We further compare our methods with existing methods by real data examples.

CT-based radiomics combined with signs: a valuable tool to help radiologist discriminate COVID-19 and influenza pneumonia.

BACKGROUND: In this COVID-19 pandemic, the differential diagnosis of viral pneumonia is still challenging. We aimed to assess the classification performance of computed tomography (CT)-based CT signs and radiomics features for discriminating COVID-19 and influenza pneumonia. METHODS: A total of 154 patients with confirmed viral pneumonia (COVID-19: 89 cases, influenza pneumonia: 65 cases) were collected retrospectively in this study. Pneumonia signs and radiomics features were extracted from the initial unenhanced chest CT images to build independent and combined models. The predictive performance of the radiomics model, CT sign model, the combined model was constructed based on the whole dataset and internally invalidated by using 1000-times bootstrap. Diagnostic performance of the models was assessed via receiver operating characteristic (ROC) analysis. RESULTS: The combined models consisted of 4 significant CT signs and 7 selected features and demonstrated better discrimination performance between COVID-19 and influenza pneumonia than the single radiomics model. For the radiomics model, the area under the ROC curve (AUC) was 0.888 (sensitivity, 86.5%; specificity, 78.4%; accuracy, 83.1%), and the AUC was 0.906 (sensitivity, 86.5%; specificity, 81.5%; accuracy, 84.4%) in the CT signs model. After combining CT signs and radiomics features, AUC of the combined model was 0.959 (sensitivity, 89.9%; specificity, 90.7%; accuracy, 90.3%). CONCLUSIONS: CT-based radiomics combined with signs might be a potential method for distinguishing COVID-19 and influenza pneumonia with satisfactory performance.

Modelling RNA-Seq data with a zero-inflated mixture Poisson linear model.

RNA sequencing (RNA-Seq) has been frequently used in genomic studies and has generated a vast amount of data. The RNA-Seq data are composed of two parts: (a) a sequence of nucleotides of the genome; and (b) a corresponding sequence of counts, standing for the number of short reads whose mapped positions start at each position of the genome. One common feature of these count data is that they are typically nonuniform; recent studies have revealed that the nonuniformity is partially owing to a systematic bias resulted from the sequencing preference. Existing works in the literature model the nonuniformity with a single component Poisson linear model that incorporates the effects of the sequencing preference. However, we observe consistently that the short reads mapped to a gene may have a mixture structure and can be zero-inflated. A single component model may not suffice to model the complexity of such data. In this paper, we propose a zero-inflated mixture Poisson linear model for the RNA-Seq count data and derive a fast expectation-maximisation-based algorithm for estimating the unknown parameters. Numerical studies are conducted to illustrate the effectiveness of our method.

Survival analysis of patients with stage T2a and T2b perihilar cholangiocarcinoma treated with radical resection.

BACKGROUND: Both the 7th and 8th editions of the American Joint Committee on Cancer (AJCC) staging system for perihilar cholangiocarcinoma (pCCA) had the same definition for T2a and T2b. But the value of this classification as prognostic factor remains unclear. METHODS: 178 patients with stage T2a or T2b who underwent curative intent resection for pCCA between Jan 2010 and Dec 2018 were enrolled. Relationships between survival and clinicopathological factors including patient demographics and tumor characteristics were evaluated using univariate and multivariate Cox regression analysis. The overall survival (OS) were calculated by Kaplan-Meier method. RESULTS: There was no significant difference in OS between T2a and T2b groups, and the median OS duration were 37 and 31 months (P = 0.354). Both the 7th and 8th edition of the AJCC TNM staging demonstrated a poor prognostic predictive performance. High level of preoperative AST (≥85.0 IU/L) and CA19-9 (≥1000 U/mL), vascular resection and lower pathological differentiation of the tumor were the independent predictors for poor survival after resection. CONCLUSION: The newly released 8th edition of AJCC staging system demonstrated a poor ability to discriminate the prognosis of patients with stage T2a and T2b pCCA after resection.

Single-molecule manipulation and detection.

Compared to conventional ensemble methods, studying macromolecules at single-molecule level can reveal extraordinary clear and even surprising views for a biological reaction. In the past 20 years, single-molecule techniques have been undergoing a very rapid development, and these cutting edge technologies have revolutionized the biological research by facilitating single-molecule manipulation and detection. Here we give a brief review about these advanced techniques, including optical tweezers, magnetic tweezers, atomic force microscopy (AFM), hydrodynamic flow-stretching assay, and single-molecule fluorescence resonance energy transfer (smFRET). We are trying to describe their basic principles and provide a few examples of applications for each technique. This review aims to give a rather introductory survey of single-molecule techniques for audiences with biological or biophysical background.

[Relationship between air pollution and the number of pneumonia hospitalization in a children's hospitalin Changsha].

OBJECTIVE: To explore the relationship between air pollution and the number of pneumonia hospitalization in a children's hospital in Changsha.
 Methods: Children who have been in this hospital for the treatment of pneumonia between December 2013 and December 2015 were enrolled in this study. Based on daily meteorological data and air pollution data from December 2013 to December 2015 in Changsha, we constructed a generalized additive model to analyze the relationship between air pollution and the number of pneumonia hospitalization.
 Results: During the research, the average concentration of PM2.5 and PM10 exceeded the Grade II national standards for air quality. The average concentration of SO2 exceeded the Grade I national standards. The change of all the 3 main air pollution indexes showed strong statistical relationship with the change of the number of pneumonia hospitalization (P<0.05), among which, the impact of SO2 ranked number 1, followed by PM2.5 and PM10. Effect of atmospheric pollution on the number of pneumonia boys was basically same as that in the total pneumonia children (P<0.05). The effect on girls showed no statistical relationship in both models (P>0.05).
 Conclusion: The concentrations of SO2, PM2.5 and PM10 are positively correlated with pneumonia hospitalization number of children, and their effect on boys is more obvious than that in the girls.

[Meta analysis for interventional effect on unexpected injury among children and adolescents in China].

OBJECTIVE: To comprehensively evaluate the interventional effect on unexpected injury among children and adolescents in China, and to provide scientific basis for the injury control strategy.
 METHODS: Meta analysis was utilized to analyze the selected literatures. After heterogeneity test of the data, a relevant model was chosen to estimate the combined effect values relative risk (RR) and the corresponding 95% confidence interval (95% CI). Subgroup analysis were performed based on the intervention measures, objects and places. Sensitivity and publication bias were analyzed.
 RESULTS: A total of 18 papers were included in the Meta analysis with a sample size of 32 599. The combined RR value was 0.54 (95% CI 0.44 to 0.68). Subgroup analysis showed that the RR value of health education and comprehensive intervention were 0.59 and 0.50, respectively, with no significant difference between them (P>0.05). The RR values of the interventions in school alone, in school and community or in community alone were 0.51, 0.78 or 0.63. The RR values on children alone, children and parents or parents alone were 0.53, 0.65 or 0.35. The differences were significant when the interventions were performed among different places or objects (P<0.05). Sensitivity analysis revealed that meta-analysis results were relatively stable.
 CONCLUSION: The targeted interventions were significant in the prevention of unexpected injuries among children and adolescents. It is worth further promoting and spreading.

Controlled self-assembly of organic composite microdisks for efficient output coupling of whispering-gallery-mode lasers.

Flexible microdisk whispering-gallery-mode (WGM) resonators with high quality factors were achieved through the controlled assembly of organic materials with an emulsion-solvent-evaporation method. The high material compatibility of the assembled microdisks enabled us to realize low-threshold WGM lasers by doping with organic dyes as gain media. Furthermore, the emulsion-assisted self-assembly provided a strategy for the one-step fabrication of microwire-waveguide-connected microdisk heterostructures, which can be utilized for the efficient output of the isotropic WGM lasers from the coupled waveguides. We hope that these results will pave an avenue for the construction of new types of flexible WGM-based components for photonic integration.

Assessment of the Chinese Resident Health Literacy Scale in a population-based sample in South China.

BACKGROUND: A national health literacy scale was developed in China in 2012, though no studies have validated it. In this investigation, we assessed the reliability, construct validity, and measurement invariance of that scale. METHODS: A population-based sample of 3731 participants in Hunan Province was used to validate the Chinese Resident Health Literacy Scale based on item response theory and classical test theory (including split-half coefficient, Cronbach's alpha, and confirmatory factor analysis). Measurement invariance was examined by differential item functioning. RESULTS: The overall Cronbach's alpha of the scale was 0.95 and Spearman-Brown coefficient 0.94. Confirmatory factor analysis showed that the test measured a unidimensional construct with three highly correlated factors. Highest discrimination was found among participants with limited to moderate health literacy. In all, 64 items were selected from the original scale based on factor loading, Pearson's correlation coefficient, and discrimination and difficulty parameters in item response theory. Measurement invariance was significant but slight. According to the two-level linear model, health literacy was associated with education level, occupation, and income. CONCLUSIONS: The 2012 national health literacy scale was validated, and 64 items were selected based on classical test theory and item response theory. The revised version of the scale has strong psychometric properties with minor measurement invariance.

Giant enhancement of second harmonic generation by engineering double plasmonic resonances at nanoscale.

We have investigated second harmonic generation (SHG) from Ag-coated LiNbO3(LN) core-shell nanocuboids and found that giant SHG can occur via deliberately designed double plasmonic resonances. By controlling the aspect ratio, we can tune fundamental wave (FW) and SHG signal to match the longitudinal and transverse plasmonic modes simultaneously, and achieve giant enhancement of SHG by 3 × 10(5) in comparison to a bare LN nanocuboid and by about one order of magnitude to the case adopting only single plasmonic resonance. The underlying key physics is that the double-resonance nanoparticle enables greatly enhanced trapping and harvesting of incident FW energy, efficient internal transfer of optical energy from FW to the SHG signal, and much improved power to transport the SHG energy from the nanoparticle to the far-field region. The proposed double-resonance nanostructure can serve as an efficient subwavelength coherent light source through SHG and enable flexible engineering of light-matter interaction at nanoscale.