Cytochrome P450 1A (CYP1A), one of the most important phase I drug-metabolizing enzymes in humans, plays a crucial role in the metabolic activation of procarcinogenic compounds to their ultimate ...carcinogens. Herein, we reported the development of a ratiometric two-photon fluorescent probe NCMN that allowed for selective and sensitive detection of CYP1A for the first time. The probe was designed on the basis of substrate preference of CYP1A and its high capacity for O-dealkylation, while 1,8-naphthalimide was selected as fluorophore because of its two-photon absorption properties. To achieve a highly selective probe for CYP1A, a series of 1,8-naphthalimide derivatives were synthesized and used to explore the potential structure–selectivity relationship, by using a panel of human CYP isoforms for selectivity screening. After screening and optimization, NCMN displayed the best combination of selectivity, sensitivity and ratiometric fluorescence response following CYP1A-catalyzed O-demetylation. Furthermore, the probe can be used to real-time monitor the enzyme activity of CYP1A in complex biological systems, and it has the potential for rapid screening of CYP1A modulators using tissue preparation as enzyme sources. NCMN has also been successfully used for two-photon imaging of intracellular CYP1A in living cells and tissues, and showed high ratiometric imaging resolution and deep-tissue imaging depth. In summary, a two-photon excited ratiometric fluorescent probe NCMN has been developed and well-characterized for sensitive and selective detection of CYP1A, which holds great promise for bioimaging of endogenous CYP1A in living cells and for further investigation on CYP1A associated biological functions in complex biological systems.
As a typical example of an Intelligent Transport System (ITS) in a smart city, the bicycle sharing is developing so fast and changing the citizens' travel habits to a large extent in China. However, ...its disorder development brings a heavy burden for city, and it is considered as a junk of city in many citizens' opinions. In our previous work, we proposed the concept of Internet of Shared Bicycle (IoSB) to solve some problems in technique. In this paper, we devote to how to enable bicycle sharing playing a more important role in the construction of smart city, instead of being treated only as a type of traffic mode. We summarize some of the current research status of bicycle sharing in smart cities and put forward some of our own views. Then the Bicycle Sharing System (BSS) is be further studied and explored the possibility of being deployed as a terminal in the city in terms of technology. When a serious disaster comes, it serves as an auxiliary communication system for the city and becomes a measure of the city's emergency network. In particular, we describe a special role of bicycle sharing during public health events. In addition, the feasibility in some interesting cases with great potential in the future is also discussed.
Blend membranes have attracted great attention because they can combine the advantages of different polymers. To investigate the effect of amphiphilic polymer on the separation performance of blend ...membranes, a series of blend membranes were designed and fabricated by blending an amphiphilic polymer of poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) into poly(ether-block-amide) (Pebax) polymer for CO
2
separation. For the as-prepared Pebax/PEDOT:PSS blend membranes, the interconnected CO
2
-philic networks were constructed by hydrophilic anionic chains of PSS
−
for accelerating CO
2
transport. Meanwhile, non-CO
2
-philic networks were constructed by the hydrophobic cationic chains of PEDOT
+
, which distributed around the PSS
−
chains to provide low friction diffusion for CO
2
. Therefore, the amphiphilic polymer of PEDOT:PSS was an excellent material for improving CO
2
separation performance of blend membranes. The results showed that the Pebax/PEDOT:PSS blend membranes were endowed with excellent CO
2
separation performance. Pebax/PEDOT:PSS blend membrane demonstrated the optimal separation performance with a CO
2
permeability of 440.2±3.3 Barrer and a CO
2
/CH
4
separation factor of 28±0.6. This study indicates that introducing the amphiphilic polymer into the blend membranes is an efficient strategy for gas separation.
It is conducive to the application of sEMG signals in helping disabled people through combining wearable devices with deep learning. Therefore, design of sEMG gesture recognition system using deep ...learning based on wearable device is proposed in this paper. The system is mainly consisted of wearable sEMG acquisition device and sEMG gesture recognition method based on deep learning. In the wearable sEMG acquisition device, the sEMG signal sensor is mainly used to convert the human bioelectrical signal into an analog electrical signal. Then it can be acquired using an analog to digital converter. We also use 2.4 GHz wireless communication for data transmission, and use the micro-controller as the core of system control and data processing. In the sEMG gesture recognition method, we designed a model of sEMG signal gesture classification based on convolutional neural network (CNN). It can avoid omission of important feature information and improve accuracy of recognition, effectively. In the experimental part, we collected the sEMG signals of three different gestures using our own wearable sEMG acquisition device. Then, we trained and evaluated on the designed sEMG gesture recognition model using these data. A recognition accuracy of about 79.43% can be achieved in three gestures. Finally, we trained and tested the sEMG gesture recognition model on the Ninapro DB5 dataset and can reach about 74.51% accuracy on 52 gestures. In the case that there are more types of gestures recognized, our accuracy is still 5.02%, 6.61%, and 2.58% higher than Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), and Long Short Term Memory-CNN (LCNN), respectively. Also, the accuracy rate is 5.47% higher than SVM and Random Forests.
Hemorrhagic fever with renal syndrome (HFRS) is still attracting public attention because of its outbreak in various cities in China. Predicting future outbreaks or epidemics disease based on past ...incidence data can help health departments take targeted measures to prevent diseases in advance. In this study, we propose a multistep prediction strategy based on extreme gradient boosting (XGBoost) for HFRS as an extension of the one-step prediction model. Moreover, the fitting and prediction accuracy of the XGBoost model will be compared with the autoregressive integrated moving average (ARIMA) model by different evaluation indicators. We collected HFRS incidence data from 2004 to 2018 of mainland China. The data from 2004 to 2017 were divided into training sets to establish the seasonal ARIMA model and XGBoost model, while the 2018 data were used to test the prediction performance. In the multistep XGBoost forecasting model, one-hot encoding was used to handle seasonal features. Furthermore, a series of evaluation indices were performed to evaluate the accuracy of the multistep forecast XGBoost model. There were 200,237 HFRS cases in China from 2004 to 2018. A long-term downward trend and bimodal seasonality were identified in the original time series. According to the minimum corrected akaike information criterion (CAIC) value, the optimal ARIMA (3, 1, 0) x (1, 1, 0).sub.12 model is selected. The index ME, RMSE, MAE, MPE, MAPE, and MASE indices of the XGBoost model were higher than those of the ARIMA model in the fitting part, whereas the RMSE of the XGBoost model was lower. The prediction performance evaluation indicators (MAE, MPE, MAPE, RMSE and MASE) of the one-step prediction and multistep prediction XGBoost model were all notably lower than those of the ARIMA model. The multistep XGBoost prediction model showed a much better prediction accuracy and model stability than the multistep ARIMA prediction model. The XGBoost model performed better in predicting complicated and nonlinear data like HFRS. Additionally, Multistep prediction models are more practical than one-step prediction models in forecasting infectious diseases.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Uridine-diphosphate glucuronosyltransferase 1A1 (UGT1A1) is an important conjugative enzyme in mammals that is responsible for the conjugation and detoxification of both endogenous and xenobiotic ...compounds. Strong inhibition of UGT1A1 may trigger adverse drug/herb-drug interactions, or result in metabolic disorders of endobiotic metabolism. Therefore, both the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA) have recommended assaying the inhibitory potential of drugs under development on the human UGT1A1 prior to approval. This review focuses on the significance, progress and challenges in discovery and characterization of UGT1A1 inhibitors. Recent advances in the development of UGT1A1 probes and their application for screening UGT1A1 inhibitors are summarized and discussed in this review for the first time. Furthermore, a long list of UGT1A1 inhibitors, including information on their inhibition potency, inhibition mode, and affinity, has been prepared and analyzed. Challenges and future directions in this field are highlighted in the final section. The information and knowledge that are presented in this review provide guidance for rational use of drugs/herbs in order to avoid the occurrence of adverse effects
UGT1A1 inhibition, as well as presenting methods for rapid screening and characterization of UGT1A1 inhibitors and for facilitating investigations on UGT1A1-ligand interactions.
Carbon dots (CDs) synthesized from natural products have drawn numerous attentions due to some unique properties. Here, Prunus cerasifera fruits were used as carbon source to synthesize high ...luminescent CDs by hydrothermal method. The obtained CDs were characterized by TEM, FTIR and XPS methods, founding the CDs were near-spherical and contained abundant nitrogen element. The CDs aqueous solution exhibited bright blue fluorescence under ultraviolet illumination, with the maximum emission at 450 nm. They could be potentially used as invisible fluorescent ink by written on the paper and irradiated by UV light, due to their fluorescent properties. Moreover, the CDs were found being selectively quenched by Fe3+ ion. The quench of CDs was linearly related to the concentration of Fe3+ ion in the range of 0–0.5 mM, meaning they could be developed as fluorescent probe of Fe3+ ion. At last, the CDs were used for cell imaging, founding they were low toxicity to HepG2 cells and exhibited blue and green fluorescence under a fluorescence microscope. In summary, the CDs prepared from Prunus cerasifera fruits exhibited excellent fluorescence properties, and could be potentially applied in the field of fluorescent ink, Fe3+ ion detection and cell imaging.
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•Carbon dots were synthesized using Prunus cerasifera fruits as carbon source.•They exhibited bright blue fluorescence and can be used as invisible fluorescent ink.•They were selectively quenched by Fe3+ ion and can be used as the fluorescent probe.•They were low toxicity to HepG2 cells and can be used to label cells for imaging.
ObjectiveThe COVID-19 outbreak was first reported in Wuhan, China, and has been acknowledged as a pandemic due to its rapid spread worldwide. Predicting the trend of COVID-19 is of great significance ...for its prevention. A comparison between the autoregressive integrated moving average (ARIMA) model and the eXtreme Gradient Boosting (XGBoost) model was conducted to determine which was more accurate for anticipating the occurrence of COVID-19 in the USA.DesignTime-series study.SettingThe USA was the setting for this study.Main outcome measuresThree accuracy metrics, mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE), were applied to evaluate the performance of the two models.ResultsIn our study, for the training set and the validation set, the MAE, RMSE and MAPE of the XGBoost model were less than those of the ARIMA model.ConclusionsThe XGBoost model can help improve prediction of COVID-19 cases in the USA over the ARIMA model.
The extensive utilization of fluorine industrial products has brought abundant fluorine pollution. The fluorine removal is important. In this study, CaO, as a novel coprecipitator, was conducted to ...synthesize Ca‐Fe‐SO4 layered double hydroxide (CF) via co‐precipitation of FeSO4. Ca‐Fe mixed metal oxide adsorbent (CCF) was fabricated by calcinating CF for efficient fluoride adsorption. The results showed that CCF adsorbent had an excellent adsorption performance for fluoride. It was attributed to two reasons as follows: On one hand, Ca had a strong affinity for fluoride, improving the adsorption capacity of adsorbent for fluoride. On the other hand, Fe had a strong magnetism, which could promote the recovery of the adsorbed material. The adsorption capacity of CCF for fluorine was analyzed by batch adsorption experiments, the adsorption capacity was 160.66 mg/g at temperature of 298 K and pH of 7. Moreover, the adsorption process of fluorine and the actual adsorption process were in accordance with Langmuir model and the pseudo‐second‐order kinetic model, respectively. According to the analysis of pHzpc, FITR and XPS, the main adsorption mechanisms of CCF for fluoride in water were complexation and electrostatic interactions. Therefore, this study suggested that CaO can be a novel coprecipitator to prepare a new efficient fluorine adsorbent for the treatment of industrial wastewater.
A novel Ca‐Fe mixed metal oxide adsorbent had an excellent adsorption performance for fluorine, and the adsorption capacity can reach 160.66 mg/g at temperature of 298 K and pH of 7. The adsorption mechanisms of Ca‐Fe mixed metal oxide adsorbent for fluoride in water was mainly depended on the complexation and electrostatic attraction.
The aim of this study was to design and characterize solid lipid nanoparticles (SLNs) modified with stearic acid-octaarginine (SA-R₈) as carriers for oral administration of insulin (SA-R₈-Ins-SLNs). ...The SLNs were prepared by spontaneous emulsion solvent diffusion methods. The mean particle size, zeta potential, drug loading, and encapsulation efficiency of the SA-R₈-Ins-SLNs were 162 nm, 29.87 mV, 3.19%, and 76.54%, respectively. The zeta potential of the SLNs changed dramatically, from -32.13 mV to 29.87 mV, by binding the positively charged SA-R₈. Morphological studies of SA-R₈-Ins-SLNs using transmission electron microscopy showed that they were spherical. In vitro, a degradation experiment by enzymes showed that SLNs and SA-R₈ could partially protect insulin from proteolysis. Compared to the insulin solution, the SA-R₈-Ins-SLNs increased the Caco-2 cell's internalization by up to 18.44 times. In the in vivo studies, a significant hypoglycemic effect in diabetic rats over controls was obtained, with a SA-R₈-Ins-SLN pharmacological availability value of 13.86 ± 0.79. These results demonstrate that SA-R₈-modified SLNs promote the oral absorption of insulin.
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