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Background: Dasatinib and imatinib are the recommended tyrosine kinase inhibitors (TKIs) for treating pediatric Philadelphia-positive acute lymphoblastic leukemia (Ph + ALL), and the one which has been approved indication in China is imatinib. Recently, clinical demand for Ph + ALL treatment is becoming unmet gradually with the increasing resistance of imatinib. There are some studies reporting the better efficacy and comparative safety of dasatinib compared with imatinib, but no economic comparison has been published. This study aims to supplement economic evidence by comparing the cost-effectiveness between imatinib and dasatinib in treating pediatric patients with Ph+ ALL in China, and to help clinical rational drug use via multi-dimensional value assessment. Methods: A decision tree model combined with a 10-year Markov model were established based on the disease progression. The parameters were collected from published literatures and our hospital's electronic medical records. From the health system perspective, the incremental cost-effectiveness ratio (ICER) between the two treatment groups was calculated through cost-effectiveness analysis and then compared with the willingness-to-pay (WTP) threshold. The set WTP threshold in this study was 1 times per capita gross domestic product (GDP) of China, as recommended by the World Health Organization. Direct medical costs and quality-adjusted life years (QALYs) were calculated and discounted at 5%. The sensitivity analyses were conducted to assess the uncertainty and robustness of the results. Results: The total costs were CNY 1,020,995.35 and CNY 1,035,788.50 in imatinib group and dasatinib group during the 10-year simulation, and the total QALYs were 2.59 and 4.84. Compared with the imatinib treatment group, the ICER was around CNY 6,575.78/ QALY, which was less than the set threshold CNY 70,892/ QALY. The sensitive analyses indicated the robustness of the results. Conclusions: The cost-effectiveness analysis shows the potential cost-effective advantages of adding dasatinib comparing with adding imatinib for pediatric Ph + ALL patients in China under the set WTP threshold, which indicates that those patients could achieve more QALYs by paying acceptable fee. [ABSTRACT FROM AUTHOR]

According to China's Seventh National Census, 18.70% of a total of 1.41 billion people were 60 or older and 13.50% were 65 or older, so China's population is increasingly aging. In conjunction with China's socioeconomic and scientific and technological development and its promotion of medical insurance-related policies, rational medication management for the elderly is a concern in order to control the risk of polypharmacy. This paper summarizes and discusses the following five key issues inherent in rational medication management: i) an increase in serious polypharmacy and the potential risks of medication; ii) a lack of medication consultation service and medication withdraw without healthcare providers' supervision; iii) poor medication compliance among the elderly; iv) insufficient quantity and incompetence of pharmaceutical staffing; and v) limited awareness of pharmaceutical services and lack of trust in the ability of pharmacists. Based on a discussion of factors influencing these issues, suggestions have been put forward in the hopes that China emphasizes rational medication management in order to reduce the risk of polypharmacy and the disease burden of the elderly in China. [ABSTRACT FROM AUTHOR]

To address the global phenomenon of the salinisation of large land areas, a quantitative inversion model of the salinity of saline soils and soil visible–near-infrared (NIR) spectral data was developed by considering saline soils in Zhenlai County, Jilin Province, China as the research object. The original spectral data were first subjected to Savitzky–Golay (SG) smoothing, multiplicative scattering correction (MSC) pre-processing, and a combined transformation technique. The pre-processed spectral data were then analysed to construct the difference index (DI), ratio index (RI), and normalised difference index (NDI), and the Spearman rank correlation coefficient (r) between these three spectral indices and the salt content in the samples was calculated, while a combined spectral index (r > 0.8) was eventually selected as a sensitive spectral index. Finally, a quantitative inversion model for the salinity of saline soils was developed, and the model's accuracy was evaluated based on partial least squares regression (PLSR), the random forest (RF) algorithm, and the radial basis function (RBF) neural network algorithm. The results indicated that the inversion of soil salt content using the selected combination of spectral indices based on the RBF neural network algorithm was the most effective, with the prediction model yielding an R2 value of 0.950, a root mean square error (RMSE) of 1.014, and a relative percentage deviation (RPD) of 4.479, which suggested a good prediction effect. [ABSTRACT FROM AUTHOR]

• The spectral data are pre-processed using three dimensionality reduction algorithms. • Optimize the parameters of the dimensionality reduction algorithm. • Establishment of a high-precision grade inversion model for Derni copper deposit. At present, chemical analysis is the main method for copper ore grade verification. Because of its long test cycle and lag effect relative to mining and ore blending processes, this approach cannot effectively reduce losses in mineral resource mining. An effective way to solve this problem is to conduct research on in situ grade determination methods for ore bodies based on visible-near-infrared hyperspectral grade modelling techniques. However, the accuracy of the modelling is seriously reduced by the large redundancy of hyperspectral data and the self-limiting nature of some machine learning methods. Therefore, the accurate selection of dimensionality reduction algorithms and machine learning algorithms determines the accuracy of the inverse model, and the setting of parameters such as dimensionality reduction, number of nearest neighbour points and number of hidden layers in neural networks is crucial. In this paper, 190 ore samples were collected from the open pit of the Derni copper deposit in Dawu town, Guoluo Tibetan Autonomous Prefecture, Qinghai Province, China. First, the visible-near-infrared spectral data for the sample set were obtained with an SVC HR-1024 spectrometer. The copper grade of the sample set was verified by chemical analysis. Three algorithms—the Laplacian eigenmap (LE), local tangent space alignment (LTSA) and local linear embedding (LLE)—were used to reduce the dimensionality of the original hyperspectral data. The original data and the three hyperspectral datasets after dimensionality reduction were used as data sources. Several Derni copper grade inversion models based on two machine learning algorithms, the backpropagation neural network and radial basis function (RBF) neural network, were developed. Finally, the coefficient of determination (R2), mean absolute error (MAE) and root mean square error (RMSE) were used to evaluate the accuracy of each model. The results show that the accuracy of the model built with spectral data processed by the dimension reduction algorithm is higher than that of the model constructed with the original spectral data. Among the above combined models, the LLE-RBF model has the highest inversion accuracy. The MAE of the model is 0.117%, the RMSE is 0.136, and R2 is 0.934. [ABSTRACT FROM AUTHOR]

• Pretreatment combined algorithms combining multiple mathematical transformations and dimensionality reduction algorithms to comprehensively process raw spectral data. • The advantage of this method is high accuracy". • A method for selecting the optimal Pretreatment combined algorithm is proposed and can accurately predict the accuracy of skarn-type iron ore. Iron ore grade determination is a key step in mining production, and its efficiency has a significant impact on mining efficiency and profitability. At present, the most commonly used method for ore grade determination is chemical analysis, which not only is expensive but also has a long assay cycle and lag effect relative to the ore allocation process and cannot effectively reduce the loss depletion rate of ore mining. Therefore, in situ iron ore grade data determination technology based on visible and near-infrared hyperspectral data analysis is an effective way to solve the above problems, but the poor quality of raw hyperspectral data and low accuracy of the ore grade inversion model are still one of the bottlenecks in the application of this technology. In this paper, 225 ore samples were collected from Hongling skarn-type iron ore in Chifeng, Inner Mongolia Autonomous Region, China. The visible and near-infrared hyperspectral data of the sample set and the grade data obtained from chemical analysis were used as data sources. The raw spectral data were first smoothed, and then the smoothed spectral data were processed using two algorithms, continuum removal (CR) and standard normal variate transform (SNV). Then, two dimensionality reduction algorithms, principal component analysis (PCA) and genetic algorithm (GA), were used to reduce the dimensionality of the spectral data before and after preprocessing, and the data sources were obtained after the four different preprocessing combination algorithms were processed. Finally, several quantitative inversion models were established based on the random forest algorithm and the backpropagation neural network (BPNN) algorithm. The stability, accuracy and credibility of the models were evaluated by four metrics: the coefficient of determination (R2), root mean square error (RMSE), residual prediction deviation(RPD) and mean relative error (MRE). The results showed that the quantitative inversion model (SNV-PCA-BPNN) established based on the BP neural network algorithm using the data processed by SNV and PCA for dimensionality reduction was the best, with an R2 of 0.99, an RMSE of 0.0048, an MRE of 1.10% and a RPD of 37.70, providing an effective method for the high-precision inversion of iron ore grades. [ABSTRACT FROM AUTHOR]

The safety retaining wall is a critical infrastructure in ensuring the safety of both rock removal vehicles and personnel. However, factors such as precipitation infiltration, tire impact from rock removal vehicles, and rolling rocks can cause local damage to the safety retaining wall of the dump, rendering it ineffective in preventing rock removal vehicles from rolling down and posing a huge safety hazard. To address these issues, this study proposed a safety retaining wall health assessment method based on modeling and analysis of UAV point-cloud data of the safety retaining wall of a dump, which enables hazard warning for the safety retaining wall. The point-cloud data used in this study were obtained from the Qidashan Iron Mine Dump in Anshan City, Liaoning Province, China. Firstly, the point-cloud data of the dump platform and slope were extracted separately using elevation gradient filtering. Then, the point-cloud data of the unloading rock boundary was obtained via the ordered crisscrossed scanning algorithm. Subsequently, the point-cloud data of the safety retaining wall were extracted using the range constraint algorithm, and surface reconstruction was conducted to construct the Mesh model. The safety retaining wall mesh model was isometrically profiled to extract cross-sectional feature information and to compare the standard parameters of the safety retaining wall. Finally, the health assessment of the safety retaining wall was carried out. This innovative method allows for unmanned and rapid inspection of all areas of the safety retaining wall, ensuring the safety of rock removal vehicles and personnel.

Objective: This study aims to survey respiratory infectious disease related health literacy (RIDHL) and health behavior (RIDHB) among residents in Fengtai district, Beijing, analyze impact factors of RIDHL , explore the association between RIDHL and RIDHB., Methods: Multistage sampling was employed and 1100 respondents were surveyed by self-designed questionnaires, which including social-demographic characteristics and evaluation of RIDHL and RIDHB. The survey results were described, the impact factors of RIDHL and the association between RIDHL and RIDHB were analyzed by analysis of variance or covariance., Results: A total of 998 qualified questionnaires were recollected with the effective rate of 90.7%. The respondents aged from 15 to 65, scored (71.3 +/- 19.0) points in RIDHL test. Of those respondents, 25.7% (256/998), 43.2% (432/998) and 31.1% (310/998) were evaluated as low( <60 points), medium (60 - 85 points), and high level ( > 85 points) of RIDHL, respectively. There were significant difference in RIDHL scores between registered and non-registered residents, who scored (74.1 +/- 18.9) and (68.4 +/- 18.8) points, respectively (P < 0.01). RIDHL sections were ranked as audiovisual (77.6%, 4647/5988), internet using (75.2%, 2251/2994), reading (74.6%, 3724/4990), map using (68.3%, 4090/5988) and quantitative (65.5%, 5230/7984) according to the accurate rates from high to low. Analysis of variance or covariance showed that RIDHL scores were significantly different among respondents with different ages, nationalities, educational levels, occupations, and incomes (P < 0.01), yet no significant differences among those with different genders and marital status (P > 0.05). Respondents scored (69.7 +/- 15.5) points in RIDHB test. The RIDHB scores ((64.5 +/- 15.0), (70.4 +/- 15.6), (72.5 +/- 14.9) points, respectively) increased among residents with low, medium and high level of RIDHL (P < 0.01)., Conclusion: Residents in Fengtai district, Beijing possessed medium level of RIDHL. The non-registered residents showed lower RIDHL than registered residents. Ages, nationalities, educational levels, occupations, and incomes were impact factors of RIDHL. People with higher level of RIDHL also showed a higher level of RIDHB.