Colloid synthesis of Ni12P5/N, S-doped graphene as efficient bifunctional catalyst for alkaline hydrogen evolution and triiodide reduction reaction.

Designing high-efficient bifunction catalysts with excellent catalytic activity and enhanced charge-transfer capability in both alkaline hydrogen evolution reaction (HER) and triiodide reduction reaction (IRR) is of utmost significance to advance the development of green hydrogen production and photovoltaics, respectively, yet remains a crucial challenge. Here, highly dispersed and small-sized Ni12P5 nanocrystals with narrow size distribution was well attached on the surface of N, S co-doped graphene (Ni12P5/NSG) by the facile hot-injection method. As expected, the optimized Ni12P5/NSG requires a relatively low overpotential of 132.94 ± 0.08 mV to deliver a current density of 10 mA cm-2 in alkaline condition, accompanied with remarkable long-time durability with negligible attenuation over 50 h. Density functional theory (DFT) calculations revealed that the positively synergic effect between Ni12P5 and NSG are in favor of modulating the rate determining step of the dissociation of H2O to *(OH-H), thereby upgrading its HER activity. When used as the counter electrode catalyst for IRR in DSSCs, the resultant Ni12P5/NSG exhibits extraordinary Pt-like catalytic activity and well electrochemical stability in iodide-based electrolyte, delivering a high photoelectric conversion performance (PCE) comparable to Pt. The improved PCE can be attributed to the accelerated interfacial charge-transfer capability around active sites for facilitating the reaction kinetics of IRR, as demonstrated by DFT calculations. This work provides an effective strategy for synthesizing cost-effective composites with multi-active sites and offering valuable insight into the structure-performance relationship, which is conducive to guide the synthesis of promising catalysts in the energy conversion field.

Near complete response to ceritinib in a pediatric patient with metastatic ALK-rearranged lung adenocarcinoma.

Anaplastic lymphoma kinase (ALK) inhibitors have significant efficacies in ALK-rearranged non-small cell lung cancers (NSCLC). In regard to pediatric NSCLC patients, however, there is a paradox in that on the one hand, they may have a higher probability of ALK-rearrangement positive, but on the other hand, there is no sufficient data for efficacies of ALK inhibitors in pediatric NSCLC patients. Here, we present an 11-year-old boy diagnosed with metastatic ALK-rearranged lung adenocarcinoma. He was treated with ceritinib 450 mg with food once daily and has obtained near complete response in 18th month, with a largely regressed intrathoracic lesion and only localized residual distant metastatic disease. Meanwhile, he showed continued good tolerance after a short period of side effects at the beginning of the dose. This is the first report of the use of ceritinib in pediatric patient with NSCLC.

Generative adversarial network-based super-resolution of diffusion-weighted imaging: Application to tumour radiomics in breast cancer.

Diffusion-weighted imaging (DWI) is increasingly used to guide the clinical management of patients with breast tumours. However, accurate tumour characterization with DWI and the corresponding apparent diffusion coefficient (ADC) maps are challenging due to their limited resolution. This study aimed to produce super-resolution (SR) ADC images and to assess the clinical utility of these SR images by performing a radiomic analysis for predicting the histologic grade and Ki-67 expression status of breast cancer. To this end, 322 samples of dynamic enhanced magnetic resonance imaging (DCE-MRI) and the corresponding DWI data were collected. A SR generative adversarial (SRGAN) and an enhanced deep SR (EDSR) network along with the bicubic interpolation were utilized to generate SR-ADC images from which radiomic features were extracted. The dataset was randomly separated into a development dataset (n = 222) to establish a deep SR model using DCE-MRI and a validation dataset (n = 100) to improve the resolution of ADC images. This random separation of datasets was performed 10 times, and the results were averaged. The EDSR method was significantly better than the SRGAN and bicubic methods in terms of objective quality criteria. Univariate and multivariate predictive models of radiomic features were established to determine the area under the receiver operating characteristic curve (AUC). Individual features from the tumour SR-ADC images showed a higher performance with the EDSR and SRGAN methods than with the bicubic method and the original images. Multivariate analysis of the collective radiomics showed that the EDSR- and SRGAN-based SR-ADC images performed better than the bicubic method and original images in predicting either Ki-67 expression levels (AUCs of 0.818 and 0.801, respectively) or the tumour grade (AUCs of 0.826 and 0.828, respectively). This work demonstrates that in addition to improving the resolution of ADC images, deep SR networks can also improve tumour image-based diagnosis in breast cancer.

UFLC-MS/MS Determination and Population Pharmacokinetic Study of Tanshinol, Ginsenoside Rb1 and Rg1 in Rat Plasma After Oral Administration of Compound Danshen Dripping Pills.

BACKGROUND AND OBJECTIVES: As a traditional Chinese Materia Medica (CMM), the Compound Danshen Dripping Pill (CDDP) is widely used for the treatments of cardiovascular diseases. In view of its undefined applicable population and dosage, a population pharmacokinetic (PPK) study is required. The objective of this study was to explore the feasibility of multi-component CMM PPK in rat plasma after oral administration of CDDP based on sparse sampling. METHODS: In this research, a simple, rapid and highly sensitive UFLC-MS/MS method for the simultaneous determination of tanshinol (TSL), ginsenoside Rb1 (GRb1) and ginsenoside Rg1 (GRg1) has been successfully developed in rat plasma. Moreover, the validated method has been applied to a PPK study of CDDP based on sparse data. We established the PPK models for these three main active constituents using a nonlinear mixed-effects model, taking into account of factors such as gender, age in weeks and weight. RESULTS: The PPK models of TSL and GRb1 were best described by a one-compartment model with linear elimination and first-order absorption. The model of GRg1 was best described by a two-compartment model with first-order absorption. Bootstrap validation and a visual predictive check confirmed the predictive ability, the model stability and the precision of the parameter estimates from these models. CONCLUSION: As a preliminary exploration toward the clinical population pharmacokinetic research, this study provides a reference for the population pharmacokinetic study of traditional CMM.

Integration of dynamic contrast-enhanced magnetic resonance imaging and T2-weighted imaging radiomic features by a canonical correlation analysis-based feature fusion method to predict histological grade in ductal breast carcinoma.

Tumour histological grade has prognostic implications in breast cancer. Tumour features in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and T2-weighted (T2W) imaging can provide related and complementary information in the analysis of breast lesions to improve MRI-based histological status prediction in breast cancer. A dataset of 167 patients with invasive ductal carcinoma (IDC) was assembled, consisting of 72 low/intermediate-grade and 95 high-grade cases with preoperative DCE-MRI and T2W images. The data cohort was separated into development (n = 111) and validation (n = 56) cohorts. Each tumour was segmented in the precontrast and the intermediate and last postcontrast DCE-MR images and was mapped to the tumour in the T2W images. Radiomic features, including texture, morphology, and histogram distribution features in the tumour image, were extracted for those image series. Features from the DCE-MR and T2W images were fused by a canonical correlation analysis (CCA)-based method. The support vector machine (SVM) classifiers were trained and tested on the development and validation cohorts, respectively. SVM-based recursive feature elimination (SVM-RFE) was adopted to identify the optimal features for prediction. The areas under the ROC curves (AUCs) for the T2W images and the DCE-MRI series of precontrast, intermediate and last postcontrast images were 0.750 ± 0.047, 0.749 ± 0.047, and 0.788 ± 0.045, respectively, for the development cohort and 0.715 ± 0.068, 0.704 ± 0.073, and 0.744 ± 0.067, respectively, for the validation cohort. After the CCA-based fusion of features from the DCE-MRI series and T2W images, the AUCs increased to 0.751 ± 0.065, 0.803 ± 0.0600 and 794 ± 0.060 in the validation cohort. Moreover, the method of fusing features between DCE-MRI and T2W images using CCA achieved better performance than the concatenation-based feature fusion or classifier fusion methods. Our results demonstrated that anatomical and functional MR images yield complementary information, and feature fusion of radiomic features by matrix transformation to optimize their correlations produced a classifier with improved performance for predicting the histological grade of IDC.

Simultaneous determination of three saponins in human plasma after oral administration of compound danshen dripping pills by LC-MS/MS and its application in a pharmacokinetic study.

As one of the main constituents of Compound Danshen Dripping Pills (CDDP), Panax notoginseng (PN) plays a pivotal role in the treatment of cardiovascular diseases. Numerous researches have proved that the dammarane type saponins including notoginsenoside R1 (NR1), ginsenoside Rg1 (GRg1) and ginsenoside Rb1 (GRb1) are the main bioactive components of PN in CDDP. An efficient, realiable and sensitive liquid chromatography tandem-mass spectrometry (LC-MS/MS) analysis method for simultaneously detecting NR1, GRg1 and GRb1 in human plasma was established and applied to the pharmacokinetics study of the three PN saponins after oral administration of CDDP. The human plasma samples were processed using acetonitrile and the target materials were separated on an Eclipse plus C18 column (100 × 4.6 mm, 3.5 µm) with a gradient mobile phase consisted of water (containing 0.1% formic acid) and methanol. Within the concentration ranges of 0.25-50 ng/mL, each calibration curve exhibited an excellent linear relationship (r>0.998). The precision deviations of intra-day and inter-day analysis were lower than 9.0%, and accuracy error (RE%) ranged between 1.5% and 10.5%. The average recoveries of analytes were >64.0%. The established method was successfully applied to determine the pharmacokinetics of the three saponins in human plasma. In addition to providing guidance for clinical safe medication, the experimental results also provided a valuable and reliable basis for further pharmacological studies of PN in the human body after oral administration of CDDP.