The fresh or dried stems of many Dendrobium species are well known as one of the most expensive tonics in traditional Chinese medicine. Documented as a “superior grade” herbal medicine in the ancient ...text “Shen Nong’s Herbal Classic”, Dendrobium has been used for thousands of years and is now a popular health food worldwide. The main chemical components of Dendrobium are alkaloids, aromatic compounds, sesquiterpenoids and polysaccharides, with multiple biological activities, including immunomodulatory, neuroprotective and anti-tumor effects, etc. Various qualitative and quantitative methods have been developed for the quality evaluation of Dendrobium. In this review, the research progress since the 1930s relating to the chemistry, bioactivity and quality control of Dendrobium is summarized, existing problems and prospects are also discussed.
Steroid‐refractory (SR) acute graft‐versus‐host disease (aGVHD) is one of the leading causes of early mortality after allogeneic hematopoietic stem cell transplantation (allo‐HSCT). We investigated ...the efficacy, safety, prognostic factors, and optimal therapeutic protocol for SR‐aGVHD patients treated with basiliximab in a real‐world setting. Nine hundred and forty SR‐aGVHD patients were recruited from 36 hospitals in China, and 3683 doses of basiliximab were administered. Basiliximab was used as monotherapy (n = 642) or in combination with other second‐line treatments (n = 298). The cumulative incidence of overall response rate (ORR) at day 28 after basiliximab treatment was 79.4% (95% confidence interval CI 76.5%–82.3%). The probabilities of nonrelapse mortality and overall survival at 3 years after basiliximab treatment were 26.8% (95% CI 24.0%–29.6%) and 64.3% (95% CI 61.2%–67.4%), respectively. A 1:1 propensity score matching was performed to compare the efficacy and safety between the monotherapy and combined therapy groups. Combined therapy did not increase the ORR; conversely, it increased the infection rates compared with monotherapy. The multivariate analysis showed that combined therapy, grade III–IV aGVHD, and high‐risk refined Minnesota aGVHD risk score before basiliximab treatment were independently associated with the therapeutic response. Hence, we created a prognostic scoring system that could predict the risk of having a decreased likelihood of response after basiliximab treatment. Machine learning was used to develop a protocol that maximized the efficacy of basiliximab while maintaining acceptable levels of infection risk. Thus, real‐world data suggest that basiliximab is safe and effective for treating SR‐aGVHD.
Gastric cancer (GC) is one of the most aggressive malignant tumors with low early diagnosis and high metastasis. Despite progress in treatment, to combat this disease, a better understanding of the ...underlying mechanisms and novel therapeutic targets is needed. KIF23, which belongs to the KIF family, plays a vital role in various cell processes, such as cytoplasm separation and axon elongation. Nowadays, KIF23 has been found to be highly expressed in multiple tumor tissues and cells, suggesting a potential link between KIF23 and tumorigenesis. Herein, we reported that KIF23 expression was correlated with poor prognosis of gastric cancer and found an association between KIF23 and pTNM stage. An in vitro assay proved that the proliferation of gastric cancer cells was significantly inhibited, which is caused by KIF23 depletion. Additionally, knockdown of KIF23 resulted in a marked inhibition of cell proliferation of gastric cancer in mice, with significant downregulation of Ki67 and PCNA expression. In conclusion, these data indicate that KIF23 is a potential therapeutic target for gastric cancer treatment.
Accurate identification of protein–DNA binding sites is significant for both understanding protein function and drug design. Machine-learning-based methods have been extensively used for the ...prediction of protein–DNA binding sites. However, the data imbalance problem, in which the number of nonbinding residues (negative-class samples) is far larger than that of binding residues (positive-class samples), seriously restricts the performance improvements of machine-learning-based predictors. In this work, we designed a two-stage imbalanced learning algorithm, called ensembled hyperplane-distance-based support vector machines (E-HDSVM), to improve the prediction performance of protein–DNA binding sites. The first stage of E-HDSVM designs a new iterative sampling algorithm, called hyperplane-distance-based under-sampling (HD-US), to extract multiple subsets from the original imbalanced data set, each of which is used to train a support vector machine (SVM). Unlike traditional sampling algorithms, HD-US selects samples by calculating the distances between the samples and the separating hyperplane of the SVM. The second stage of E-HDSVM proposes an enhanced AdaBoost (EAdaBoost) algorithm to ensemble multiple trained SVMs. As an enhanced version of the original AdaBoost algorithm, EAdaBoost overcomes the overfitting problem. Stringent cross-validation and independent tests on benchmark data sets demonstrated the superiority of E-HDSVM over several popular imbalanced learning algorithms. Based on the proposed E-HDSVM algorithm, we further implemented a sequence-based protein–DNA binding site predictor, called DNAPred, which is freely available at http://csbio.njust.edu.cn/bioinf/dnapred/ for academic use. The computational experimental results showed that our predictor achieved an average overall accuracy of 91.7% and a Mathew’s correlation coefficient of 0.395 on five benchmark data sets and outperformed several state-of-the-art sequence-based protein–DNA binding site predictors.
Acquiring cloudy land surface temperature (LST) is crucial for terrestrial ecosystem monitoring and global climate observation. Although numerous methods have been proposed to retrieve cloudy-sky LST ...using microwave remote sensing, the physical significance of these methods is inadequate due to their oversimplification of the effects of atmospheric components and clouds. To obtain accurate cloudy-sky LST, by simultaneously considering the influences of water vapor and cloud properties on LST, a physics-based LST retrieval algorithm is developed for cloudy skies with adjacent three brightness temperatures (BTs) at 18.7, 23.8, and 36.5 GHz vertically polarization. We develop this algorithm by building a simulated database, which includes a large range of BTs, surface emissivities, air temperatures, and water vapor contents. Meanwhile, various cloudy atmospheric profiles are constructed to reveal multifarious weather conditions. The test results with the simulated database show that the algorithm has good accuracy with an RMSE of 1.75 K and MAE of 1.33 K under cloudy weather. Sensitivity analysis indicates that precipitable water vapor (PWV) and cloud liquid water (CLW) are indispensable for correcting cloudy LST. In particular, LST accuracy shows an evident sensitivity to PWV, and RMSEs are reduced with an increase in PWV. Meanwhile, the proposed algorithm was applied to AMSR-E BTs and ERA5 profile dataset over the China region in 2008 and validated with ground-based air temperatures. Results indicate that RMSEs between retrieved LSTs and true LSTs are 3.47 K for cloudy weather, and there is better performance at high water vapor status, with an RMSE of 2.05 K.
To tackle the problem of semi-supervised learning (SSL), we propose a new autoencoder-based deep model. Ladder networks (LN) is an autoencoder-based method for representation learning which has been ...successfully applied on unsupervised learning and semi-supervised learning. However, It ignores the manifold information of high-dimensional data and usually achieves unmeaning features which are very difficult to use in the subsequent tasks, such as prediction and recognition. To these issues, we proposed Graph Regularized Variational Ladder Networks (GRVLN), which explicitly and implicitly employs the manifold structure of data. Our contributions can be summarized as two folds: (1) Graph regularization is used to build all decoder layers, which explicitly promotes the manifold learning via graph laplacian matrixs; (2) Variational autoencoder is used as the backbone instead of traditional autoencoder in the encoder layers for implicitly learning the manifold structure of data distribution. Compared with ladder networks and other autoencoder-based methods, GRVLN achieves superior performance in semi-supervised classification tasks. Experimental results show that our method also has a comparable performance with state-of-the-art methods on several benchmark data sets.
Nanocarrier drug delivery systems (NDDS) have been paid more attention over conventional drug delivery system for cancer therapy. However, the efficacy is hampered by the fast clearance of activated ...macrophage from the blood circulation system. In this study, glycyrrhizin (GL) was introduced into alginate (ALG) nanogel particles (NGPs) to construct multifunctional delivery vehicle to decrease the fast clearance of activated macrophage and enhance the anticancer efficacy with the combination therapy of GL and doxorubicin (DOX).
: We firstly synthesized the GL-ALG NGPs with intermolecular hydrogen bond and ionic bond as the multifunctional delivery vehicle. The immune response and phagocytosis of macrophage on GL-ALG NGPs were investigated on RAW 264.7 macrophages. The pharmacokinetic study of DOX loaded in GL-ALG NGPs was performed in rats. The active targeting effects of GL-ALG NGPs were further studied on hepatocellular carcinoma cell (HepG2) and H22 tumor-bearing mice. Moreover, the anticancer molecular mechanism of DOX/GL-ALG NGPs was investigated on HepG2 cells
and tumor-bearing mice
.
: GL-ALG NGPs could not only avoid triggering the immuno-inflammatory responses of macrophages but also decreasing the phagocytosis of macrophage. The bioavailability of DOX was increased about 13.2 times by DOX/GL-ALG NGPs than free DOX in blood. The mice with normal immune functions used in constructing the tumor-bearing mice instead of the nude mouse also indicated the good biocompatibility of NGPs. GL-mediated ALG NGPs exhibited excellent hepatocellular carcinoma targeting effect
and
. The results suggested that the anticancer molecular mechanism of the combination therapy of glycyrrhizin and doxorubicin in ALG NGPs was performed via regulating apoptosis pathway of Bax/Bcl-2 ratio and caspase-3 activity, which was also verified in H22 tumor-bearing mice.
: DOX/GL-ALG NGPs could attenuate the activation of macrophage and enhance the therapeutic efficacy for hepatocellular carcinoma. Our results suggest that the combination therapy would provide a new strategy for liver cancer treatment.
Investigating the relations between land surface temperature (LST) and biophysical compositions can help the understanding of the surface biophysical process. However, there are still uncertainties ...in determining the impacts of biophysical compositions on LST due to the atmospheric effects. In this article, four atmospheric correction algorithms were used to correct 12 Landsat 8 images in Xi'an, Beijing, Wuhan, and Guangzhou, China, including the Atmospheric Correction for Flat Terrain (ATCOR2), Quick Atmospheric Correction (QUAC), Fast Line-of-sight Atmospheric Analysis of Spectral Hypercube (FLAASH), and Second Simulation of Satellite Signal in the Solar Spectrum (6S). Then, geodetector was used to investigate the atmospheric correction differences in the spatial heterogeneity relationships between LST and normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and bare soil index (BSI). Results indicate that the selected composition factors were greatly improved after atmospheric correction, and the relations between LST and three factors were characterized by obvious atmospheric correction differences in four study areas. On the whole, the 6S algorithm performed the best in improving the factor values and impacting the spatial heterogeneity relations between LST and biophysical compositions, followed by FLAASH, QUAC, and ATCOR2 algorithms. Except for Wuhan, 6S, FLAASH, and QUAC algorithms significantly enhanced the correlation between LST and NDVI. However, all algorithms weakened the correlations between LST, NDVI, and BSI, except Guangzhou. These findings have been verified using the regression analysis. In addition, with geodetector, combinations of any two composition factors all had strongly enhanced impacts on LST, and a combination between NDVI and NDBI performed the strongest in most cases.
The derivation of microwave land surface emissivity (MLSE) under various weather conditions from the microwave radiometer plays a crucial role in acquiring land surface and atmospheric parameters. ...Nevertheless, currently, most existing studies mainly focus on the clear-sky scenarios due to a lack of cloudy-sky land surface temperature (LST) and uncertainties in simulating the scattering and emission properties of atmospheric hydrometeors. Under this background, with satellite observations and the random forest (RF) model, this study proposes a method to estimate the MLSE under cloudy skies. First, clear-sky MLSEs with satisfactory accuracy are retrieved by using the brightness temperatures (BTs) from the Advanced Microwave Scanning Radiometer-Earth sensor, LSTs from the Moderate Resolution Imaging Spectroradiometer (MODIS), and atmospheric profiles from the ERA5 reanalysis. Then, the relation among the clear-sky MLSE and related impact factors is built with the RF and extended to the cloudy-sky environment for generating all-weather MLSEs with a 0.25°. The results show that the input datasets present a considerable impact on the calculation of instantaneous MLSE, and a 5.73 K bias of ERA5 LST may generate a 0.014-0.021 error in the MLSE from 6.9- to 89-GHz horizontal polarization, while the impacts of BT and profile uncertainties on the MLSE are smaller. The retrieved clear-sky MLSE is coincident with the existing MLSE for the spatiotemporal variations, and there is an average difference range from −0.035 to 0.035 in January 2008. Meanwhile, the constructed RF model can successfully apply to cloudy-sky status and recover the MLSE image gaps affected by cloud contamination.
Metabolite profiling has the potential to comprehensively bridge phenotypes and complex heterogeneous physiological and pathological states. We performed a metabolomics study using parallel liquid ...chromatography–mass spectrometry (LC–MS) combined with multivariate data analysis to screen for biomarkers of primary aldosteronism (PA) from a cohort of 111 PA patients and 218 primary hypertension (PH) patients. Hydrophilic interaction chromatography and reversed‐phase liquid chromatography separations were employed to obtain a global plasma metabolome of endogenous metabolites. The satisfactory classification between PA and PH patients was obtained using the MVDA model. A total of 35 differential metabolites were screened out and identified. A diagnostic biomarker panel was established using the least absolute shrinkage and selection operator (LASSO) binary logistic regression model and receiver operating characteristic analysis. Joint analysis with clinical indicators, including plasma supine aldosterone level, plasma orthostatic aldosterone level, body mass index, and blood potassium, revealed that the combination of metabolite biomarker panel and plasma supine aldosterone has the best clinical diagnostic efficacy.
PDF
