A novel sponge composite of chitosan-sodium tripolyphosphate-melamine for anionic dye Orange II removal.

Since the potential carcinogenic, toxic and non-degradable dyes trigger serious environmental contamination by improper treatment, developing novel adsorbents remains a major challenge. A novel high efficiency and biopolymer-based environmental-friendly adsorbent, chitosan­sodium tripolyphosphate-melamine sponge (CTS-STPP-MS) composite, was prepared for Orange II removing with chitosan as raw material, sodium tripolyphosphate as cross-linking agent. The composite was carefully characterized by SEM, EDS, FT-IR and XPS. The influence of crosslinking conditions, dosage, pH, initial concentration, contacting time and temperature on adsorption were tested through batch adsorption experiments. CTS-STPP-MS adsorption process was exothermic, spontaneous and agreed with Sips isotherm model accompanying the maximum adsorption capacity as 948 mg∙g-1 (pH = 3). Notably, the adsorption performance was outstanding for high concentration solutions, with a removal rate of 97 % in up to 2000 mg∙L-1 OII solution (100 mg sorbent dosage, 50 mL OII solution, pH = 3, 289.15 K). In addition, the adsorption efficiency yet remained 97.85 % after 5 repeated adsorption-desorption cycles. The driving force of adsorption was attributed to electrostatic attraction and hydrogen bonds which was proved by adsorption results coupled with XPS. Owing to the excellent properties of high-effective, environmental-friendly, easy to separate and regenerable, CTS-STPP-MS composite turned out to be a promising adsorbent in contamination treatment.

TIRPnet: Risk prediction of traditional Chinese medicine ingredients based on a deep neural network.

ETHNOPHARMACOLOGICAL RELEVANCE: Traditional Chinese medicine (TCM) has a history of over 3000 years of medical practice. Due to the complex ingredients and unclear pharmacological mechanism of TCM, it is very difficult to predict its risks. With the increase in the number and severity of spontaneous reports of adverse drug reactions (ADRs) of TCM, its safety has received widespread attention. AIM OF THE STUDY: In this study, we proposed a framework based on deep learning to predict the probability of adverse reactions caused by TCM ingredients and validated the model using real-world data. MATERIALS AND METHODS: The spontaneous reporting data from Jiangsu Province of China was selected as the research data, which included 72,561 ADR reports of TCMs. All the ingredients of these TCMs were collected from the medical website and correlated with the corresponding ADRs. Then, a risk prediction model was constructed based on a deep neural network (DNN), named TIRPnet. Based on one-hot encoded data, our model achieved the optimal performance by fine-tuning some hyperparameters. The ten most commonly used TCM ingredients and their ADRs were collected as the test set to evaluate their performance as objective criteria. RESULTS: TIRPnet was constructed as a 7-layer DNN. The experimental results showed that TIRPnet performs excellently in all indicators, with a sensitivity of 0.950, specificity of 0.995, accuracy of 0.994, precision of 0.708, and F1 of 0.811. CONCLUSIONS: The proposed TIRPnet owns the ability to predict the ADRs of a single TCM ingredient by learning a large number of TCM-related spontaneous reports, which can help doctors design safe prescriptions and provide technical support for the pharmacovigilance of TCM.

Research on named entity recognition of adverse drug reactions based on NLP and deep learning.

Introduction: Adverse drug reactions (ADR) are directly related to public health and become the focus of public and media attention. At present, a large number of ADR events have been reported on the Internet, but the mining and utilization of such information resources is insufficient. Named entity recognition (NER) is the basic work of many natural language processing (NLP) tasks, which aims to identify entities with specific meanings from natural language texts. Methods: In order to identify entities from ADR event data resources more effectively, so as to provide valuable health knowledge for people, this paper introduces ALBERT in the input presentation layer on the basis of the classic BiLSTM-CRF model, and proposes a method of ADR named entity recognition based on the ALBERT-BiLSTM-CRF model. The textual information about ADR on the website "Chinese medical information query platform" (https://www.dayi.org.cn) was collected by the crawler and used as research data, and the BIO method was used to label three types of entities: drug name (DRN), drug component (COM), and adverse drug reactions (ADR) to build a corpus. Then, the words were mapped to the word vector by using the ALBERT module to obtain the character level semantic information, the context coding was performed by the BiLSTM module, and the label decoding was using the CRF module to predict the real label. Results: Based on the constructed corpus, experimental comparisons were made with two classical models, namely, BiLSTM-CRF and BERT-BiLSTM-CRF. The experimental results show that the F 1 of our method is 91.19% on the whole, which is 1.5% and 1.37% higher than the other two models respectively, and the performance of recognition of three types of entities is significantly improved, which proves the superiority of this method. Discussion: The method proposed can be used effectively in NER from ADR information on the Internet, which provides a basis for the extraction of drug-related entity relationships and the construction of knowledge graph, thus playing a role in practical health systems such as intelligent diagnosis, risk reasoning and automatic question answering.

A Faster and More Accurate Iterative Threshold Algorithm for Signal Reconstruction in Compressed Sensing.

Fast iterative soft threshold algorithm (FISTA) is one of the algorithms for the reconstruction part of compressed sensing (CS). However, FISTA cannot meet the increasing demands for accuracy and efficiency in the signal reconstruction. Thus, an improved algorithm (FIPITA, fast iterative parametric improved threshold algorithm) based on mended threshold function, restart adjustment mechanism and parameter adjustment is proposed. The three parameters used to generate the gradient in the FISTA are carefully selected by assessing the impact of them on the performance of the algorithm. The developed threshold function is used to replace the soft threshold function to reduce the reconstruction error and a restart mechanism is added at the end of each iteration to speed up the algorithm. The simulation experiment is carried out on one-dimensional signal and the FISTA, RadaFISTA and RestartFISTA are used as the comparison objects, with the result that in one case, for example, the residual rate of FIPITA is about 6.35% lower than those three and the number of iterations required to achieve the minimum error is also about 102 less than that of FISTA.

Scientometric Analysis of Public Health Emergencies: 1994-2020.

The purpose of this paper is to summarize the research hotspots and frontiers in the field of public health emergencies (PHE) between 1994-2020 through the scientometric analysis method. In total, 2247 literature works retrieved from the Web of Science core database were analyzed by CiteSpace software, and the results were displayed in knowledge mapping. The overall characteristics analysis showed that the number of publications and authors in the field of PHE kept an upward trend during the past decades, and the United States was in the leading position, followed by China and England. Switzerland has the highest central value and plays an important intermediary role in promoting the integration and exchange of international PHE research achievements. The keyword co-occurrence analysis indicated that COVID-19 was the most high-frequency keyword in this field, and there had been no new keywords for a long time until the outbreak of COVID-19 in 2019. The burst detection analysis showed that the top five burst keywords in terms of burst intensity were zika virus, Ebola, United States, emergency preparedness and microcephaly. The results indicated that the research theme of PHE is closely related to the major infectious diseases in a specific period. It will continue to develop with more attention paid to public health. The conclusions can provide help and reference for the PHE potential researchers.

Which Is Safer, Chinese Medicine or Western Medicine? Comparative Analysis Based on Chinese Spontaneous Reporting Database.

OBJECTIVE: To compare the safety differences between Chinese medicine (CM) and Western medicine (WM) based on Chinese Spontaneous Reporting Database (CSRD). METHODS: Reports of adverse events (AEs) caused by CM and WM in the CSRD between 2010 and 2011 were selected. The following assessment indicators were constructed: the proportion of serious AEs (PSE), the average number of AEs (ANA), and the coverage rate of AEs (CRA). Further comparisons were also conducted, including the drugs with the most reported serious AEs, the AEs with the biggest report number, and the 5 serious AEs of interest (including death, anaphylactic shock, coma, dyspnea and abnormal liver function). RESULTS: The PSE, ANA and CRA of WM were 1.09, 8.23 and 2.35 times higher than those of CM, respectively. The top 10 drugs with the most serious AEs were mainly injections for CM and antibiotics for WM. The AEs with the most reports were rash, pruritus, nausea, dizziness and vomiting for both CM and WM. The proportions of CM and WM in anaphylactic shock and coma were similar. For abnormal liver function and death, the proportions of WM were 5.47 and 3.00 times higher than those of CM, respectively. CONCLUSION: Based on CSRD, CM was safer than WM at the average level from the perspective of adverse drug reactions.

[Effects of MICT/HIIT on the ultrastructure of myocardium and soleus in rats with high-fat diet and its mechanisms].

Objective: To investigate the effects of moderate intensity continuous training (MICT) and high intensity intermittent training (HIIT) on the ultrastructure of myocardium and soleus in rats with high fat diet, and to explore the mechanisms. Methods: 5-week-old male SD rats were randomly divided into normal diet quiet group (C), high-fat diet quiet group (F), high-fat MICT group (M) and high-fat HIIT group (H), with 8 rats in each group, and the fat content of the high-fat dietary feed was 45%. The M and H groups were given 12 weeks of treadmill running with an incline of 25°. The M group was given continuous exercise with 70%VO2max intensity, and the H group was given intermittent exercise with 5 min 40%~45%VO2max and 4 min 95%~99%VO2max intensity successively. After the intervention, the contents of free fatty acid (FFA), triglyceride (TG), high density lipoprotein cholesterol (HDL) and low density lipoprotein cholesterol (LDL) in serum were detected. Transmission electron microscopy was used to observe the ultrastructure of myocardium and soleus in rats. Western blot was used to detect the protein expressions of AMPK, malonyl-CoA decarboxylase (MCD) and carnitine palmitoyl transterase 1 (CPT-1) in myocardium and soleus. Results: Compared with C group, the body weight, Lee's index, the contents of LDL, TG and FFA in serum were increased, the content of HDL was decreased (P＜0.05), the protein expressions of AMPK and CPT-1 in myocardium and soleus were increased, the protein expression of MCD was decreased (P＜0.05), and the ultrastructure was damaged in group F. Compared with F group, the body weight and Lee's index were decreased, the contents of LDL and FFA in serum were decreased (P＜0.01), the protein expressions of AMPK, MCD and CPT-1 in myocardium were increased, and the protein expressions of AMPK and MCD in soleus were increased (P＜0.05), and the ultrastructural damage was attenuated in M and H groups. Compared with M group, the content of HDL in serum was increased (P＜0.01), the protein expressions of AMPK and MCD in myocardium were increased, and the ultrastructural damage was mild, the protein expression of AMPK in soleus was decreased, the protein expression of MCD in soleus was increased (P＜0.05), and the ultrastructural damage was severe in group H. Conclusion: MICT and HIIT have different effects on the ultrastructure of myocardium and soleus in high-fat diet rats by intervening the protein expression of AMPK, MCD and CPT-1.

Evaluating Drug Risk Using GAN and SMOTE Based on CFDA's Spontaneous Reporting Data.

Adverse drug reactions (ADRs) pose health threats to humans. Therefore, the risk re-evaluation of post-marketing drugs has become an important part of the pharmacovigilance work of various countries. In China, drugs are mainly divided into three categories, from high-risk to low-risk drugs, namely, prescription drugs (Rx), over-the-counter drugs A (OTC-A), and over-the-counter drugs B (OTC-B). Until now, there has been a lack of automated evaluation methods for the three status switch of drugs. Based on China Food and Drug Administration's (CFDA) spontaneous reporting database (CSRD), we proposed a classification model to predict risk level of drugs by using feature enhancement based on Generative Adversarial Networks (GAN) and Synthetic Minority Over-Sampling Technique (SMOTE). A total of 985,960 spontaneous reports from 2011 to 2018 were selected from CSRD in Jiangsu Province as experimental data. After data preprocessing, a class-imbalance data set was obtained, which contained 887 Rx (accounting for 84.72%), 113 OTC-A (10.79%), and 47 OTC-B (4.49%). Taking drugs as the samples, ADRs as the features, and signal detection results obtained by proportional reporting ratio (PRR) method as the feature values, we constructed the original data matrix, where the last column represents the category label of each drug. Our proposed model expands the ADR data from both the sample space and the feature space. In terms of feature space, we use feature selection (FS) to screen ADR symptoms with higher importance scores. Then, we use GAN to generate artificial data, which are added to the feature space to achieve feature enhancement. In terms of sample space, we use SMOTE technology to expand the minority samples to balance three categories of drugs and minimize the classification deviation caused by the gap in the sample size. Finally, we use random forest (RF) algorithm to classify the feature-enhanced and balanced data set. The experimental results show that the accuracy of the proposed classification model reaches 98%. Our proposed model can well evaluate drug risk levels and provide automated methods for status switch of post-marketing drugs.

A stratification method based on clustering for the minimization of data masking effect in signal detection.

BACKGROUND: Data masking is an inborn defect of measures of disproportionality in adverse drug reactions (ADRs) signal detection. Many previous studies can be roughly classified into three categories: data removal, regression and stratification. However, frequency differences of adverse drug events (ADEs) reports, which would be an important factor of masking, were not considered in these methods. The aim of this study is to explore a novel stratification method for minimizing the impact of frequency differences on real signals masking. METHODS: Reports in the Chinese Spontaneous Reporting Database (CSRD) between 2010 and 2011 were selected. The overall dataset was stratified into some clusters by the frequency of drugs, ADRs, and drug-event combinations (DECs) in sequence. K-means clustering was used to conduct stratification according to data distribution characteristics. The Information Component (IC) was adopted for signal detection in each cluster respectively. By extracting ADRs from drug product labeling, a reference database was introduced for performance evaluation based on Recall, Precision and F-measure. In addition, some DECs from the Adverse Drug Reactions Information Bulletin (ADRIB) issued by CFDA were collected for further reliability evaluation. RESULTS: With stratification, the study dataset was divided into 21 clusters, among which the frequency of DRUGs, ADRs or DECs followed the similar order of magnitude respectively. Recall increased by 34.95% from 29.93 to 40.39%, Precision reduced by 10.52% from 54.56 to 48.82%, while F-measure increased by 14.39% from 38.65 to 44.21%. According to ADRIB after 2011, 5 DECs related to Potassium Magnesium Aspartate, 61 DECs related to Levofloxacin Hydrochloride and 26 DECs related to Cefazolin were highlighted. CONCLUSIONS: The proposed method is effectively and reliably for the minimization of data masking effect in signal detection. Considering the decrease of Precision, it is suggested to be a supplement rather than an alternative to non-stratification method.

Traditional Chinese medicine pharmacovigilance in signal detection: decision tree-based data classification.

BACKGROUND: Traditional Chinese Medicine (TCM) is a style of traditional medicine informed by modern medicine but built on a foundation of more than 2500 years of Chinese medical practice. According to statistics, TCM accounts for approximately 14% of total adverse drug reaction (ADR) spontaneous reporting data in China. Because of the complexity of the components in TCM formula, which makes it essentially different from Western medicine, it is critical to determine whether ADR reports of TCM should be analyzed independently. METHODS: Reports in the Chinese spontaneous reporting database between 2010 and 2011 were selected. The dataset was processed and divided into the total sample (all data) and the subsample (including TCM data only). Four different ADR signal detection methods-PRR, ROR, MHRA and IC- currently widely used in China, were applied for signal detection on the two samples. By comparison of experimental results, three of them-PRR, MHRA and IC-were chosen to do the experiment. We designed several indicators for performance evaluation such as R (recall ratio), P (precision ratio), and D (discrepancy ratio) based on the reference database and then constructed a decision tree for data classification based on such indicators. RESULTS: For PRR: R1-R2 = 0.72%, P1-P2 = 0.16% and D = 0.92%; For MHRA: R1-R2 = 0.97%, P1-P2 = 0.20% and D = 1.18%; For IC: R1-R2 = 1.44%, P2-P1 = 4.06% and D = 4.72%. The threshold of R,Pand Dis set as 2%, 2% and 3% respectively. Based on the decision tree, the results are "separation" for PRR, MHRA and IC. CONCLUSIONS: In order to improve the efficiency and accuracy of signal detection, we suggest that TCM data should be separated from the total sample when conducting analyses.

A novel method for signal detection of adverse drug reactions based on proportional reporting ratios.

OBJECTIVE: The proportional reporting ratio (PRR) is a statistical method for signal detection of adverse drug reactions (ADRs) based on unbalanced proportions. Although effective, this method only takes into account the proportional relation based on target adverse reactions and ignores the relation between a given ADR and the others for the same drug. Therefore, it is necessary to improve the calculation deviation in PRR. In this study, we developed a novel PRR (NPRR) method and compared it with the original PRR method for the purpose of a combined application of these two methods for ADR signal detection. METHODS: NPRR is also based on unbalanced proportions, in which the proportion for a given ADR is linked to a specific drug (or all other drugs), and then divided by the corresponding proportion for all other ADRs. RESULTS: Applying this method to the ADR data of Jiangsu Province, China in 2008 and 2009, we detected 3,021 signals. Compared with the PRR method, the sensitivity of our method is 0.99, the specificity is 0.97, and the Youden index is 0.96. CONCLUSION: NPRR is an excellent method supplementary to PRR. The combination of these two methods can reduce calculation deviation and detect ADRs more effectively.