Importantly, we present a standalone software tool that implements the game-theory algorithm and machine-learning model used in these studies.With the ever-increasing availability of whole-genome sequences, machine-learning approaches can be used as an alternative to traditional alignment-based methods for identifying new antimicrobial-resistance genes. Such approaches are especially helpful when pathogens cannot be cultured in the lab. In previous work, we proposed a game-theory-based feature evaluation algorithm. When using the protein characteristics identified by this algorithm, called ‘features’ in machine learning, our model accurately identified antimicrobial resistance (AMR) genes in Gram-negative bacteria. Here we extend our study to Gram-positive bacteria showing that coupling game-theory-identified features with machine learning achieved classification accuracies between 87% and 90% for genes encoding resistance to the antibiotics  Bacteria can gain AMR by overexpressing or duplicating available genes, undergoing chromosomal mutation, or obtaining resistance genes from other bacteria by means of horizontal gene transfer5. According to a recently released report by the Centers for Disease Control and Prevention (CDC), at least 2.8 million people in the United States are infected every year by antimicrobial-resistant organisms, and these infections result in more than 35,000 deaths6. Also, according to a recently released report by the Organisation for Economic Co-operation and Development (OECD), 2.4 million deaths are predicted in Europe, North America, and Australia in the next 30 years due to antimicrobial-resistant infections, and such infections could cause up to US$3.5 billion in additional health care costs per year8. As AMR becomes a threat worldwide, both economically and to public health13, there is an urgent need to develop a preclinical tool for efficient prediction of AMR.Antimicrobial resistance (AMR) refers to a property of bacteria when they become less susceptible to an antimicrobial agent17 and read-based techniques20 that map sequence data directly to reference databases. Although these methods perform well for known and highly conserved AMR genes, they may produce an unacceptable number of false positives (genes predicted to encode resistance when they do not) for highly dissimilar sequences as was demonstrated previously for Gram-negative bacteria21. Machine-learning techniques can be applied as an alternative solution for predicting putative AMR genes. Rather than using sequence similarity, a machine-learning model detects features, i.e., characteristics of a protein sequence, that are unique to AMR genes. Several machine-learning methods have been proposed to identify novel AMR genes from metagenomic and pan-genome data23, but these methods used a small number of genetic features for predictions. Moreover, these approaches did not use a feature-selection strategy to remove irrelevant and redundant features that might compromise the accuracy of a machine-learning model.One conventional strategy for identifying genetically-encoded mechanisms for AMR involves sequence assembly21. We applied our feature selection approach in Gram-negative bacteria and obtained prediction accuracies ranging from 93% to 99% for prediction of genes that encode resistance to acetyltransferase (aac), bla), and dihydrofolate reductase (dfr). In our current study, we test the GTDWFE algorithm with data from Gram-positive bacteria. We then combine the results for both studies and introduce “Prediction of Antimicrobial Resistance via Game Theory” (PARGT), a software program with a graphical-user interface (GUI) that is designed to identify antimicrobial-resistance genes for both Gram-positive and -negative bacteria.We recently introduced a game-theory-based feature selection approach  predicated on the supposition that a single feature might provide limited predictive value, but that it might contribute to form a strong coalition when used with other featuresbac and van resistance genes in any Gram-positive bacteria and aac, bla, and dfr resistance genes in any Gram-negative bacteria. Users can test a single sequence or an entire genome for these genes. In addition, PARGT allows users to add newly confirmed AMR or non-AMR sequences to the training set as well as to reset the training data back to the original training set downloaded with the tool.A major objective was to develop a software tool with a simple and intuitive GUI that is capable of extracting protein features without the need for manual curation and then use these features to identify putative AMR genes. PARGT integrates all of the tools and scripts required to identify protein features and to automatically generate feature subsets obtained via the GTDWFE algorithm. PARGT can be used with the Windows, Linux, or macOS, operating systems, and it provides options for predicting 21. In our earlier work, we considered the AMR (positive) and non-AMR (negative) amino-acid sequences of aac, bla, and dfr for Acinetobacter, Klebsiella, Campylobacter, Salmonella, and Escherichia as training datasets and tested our trained support vector machine (SVM)25 machine-learning model with sequences from Pseudomonas, Vibrio, and Enterobacter. The combination of GTDWFE and SVM resulted in correct classification rates of 93%, 99%, and 97% for aac, bla, and dfr, respectively. This demonstrated that our approach was promising and that the GTDWFE algorithm is capable of identifying the most relevant, non-redundant, and interdependent features necessary for accurate prediction.We validated the GTDWFE algorithm for feature selection as implemented previouslybac and van from the Gram-positive bacteria Clostridium spp. and Enterococcus spp. as the training datasets for our SVM model. These training datasets are used to generate the best feature subsets by means of the GTDWFE approach. The training datasets contain 25 and 52 AMR (positive) examples for bac and van, respectively. A total of 52 non-AMR examples are considered as negative samples for each of the training datasets. In the GTDWFE approach, we select features based on the relevance, non-redundancy, and interdependency values of all features. For this analysis, we need to set an interdependent group size 26 and indicates the size of each feature group. We selected a value of 21 where we found that an interdependent group size of 3 was sufficient to identify best feature subsets from training datasets. We then test our trained model with known AMR and non-AMR samples from Staphylococcus, Streptococcus, and Listeria. The test datasets contain 6 and 9 AMR (positive) sequences for bac and van, respectively, and 14 non-AMR (negative) sequences are used for each test dataset.In this paper we consider validation of our GTDWFE model for AMR proteins in Gram-positive bacteria. We use the unique AMR and non-AMR sequences available for bac and van AMR sequences from our test datasets, respectively. In each table, we provide the NCBI accession number27 for each protein sequence together with its name, and we note whether an AMR protein was correctly classified as AMR (true positive) or a non-AMR sequence was incorrectly classified as AMR . The GTDWFE algorithm successfully identified all six bac AMR genes (true positives). However, it missclassified 2 of the 14 non-AMR sequences as AMR . Therefore, the number of true positives, true negatives (negatives accurately classified), false positives, and false negatives (positives classified as negatives) for bac are 6, 12, 2, and 0, respectively, and the sensitivity, specificity, and accuracy for bac are 100%, 86%, and 90%, respectively. As shown in Table van, 8 of 9 AMR sequences were correctly classified as AMR (true positives) whereas 2 of 14 non-AMR sequences were classified as AMR . Therefore, the number of true positives, true negatives, false positives, and false negatives for van are 8, 12, 2, and 1, respectively, and the sensitivity, specificity, and accuracy for van are 89%, 86%, and 87%, respectively. Note that the two tables contain one hypothetical protein and one putative uncharacterized protein. We have categorized these two proteins as false positives because they were identified as essential (non-AMR) genes in the Pathosystems Resource Integration Center (PATRIC)29. However, it is quite possible that PARGT correctly identified them as AMR proteins given the number of annotation errors in public databases30. CDC71755 is from a Staphylococcus organism identified from a metagenome sequence, and EUJ19660 is from a Listeria aquatica organism obtained from an environmental water sample.Tables https://blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE=Proteins) and Kalign31  results using default parameter settings. The outcomes shown in Supplementary Table bac AMR and non-AMR samples from Staphylococcus, Streptococcus, and Listeria with respect to the bac AMR samples of Clostridium and Enterococcus. A percent identity for BLASTp and Kalign as low as 38.13% and 46.19%, respectively, are needed to identify all the bac AMR sequences; however, these low percent identities lead to 6 and 3 of 14 false positives for BLASTp and Kalign, respectively, in which non-AMR sequences are miscategorized. Therefore, the low percent identities for BLASTp and Kalign required to identify all AMR sequences increase the number of false positives for a set of diverse AMR sequences. In Supplementary Table van AMR sequences are actually better than that of the GTDWFE algorithm. This is due to the very high similarity (>98.5% identity) between the training AMR and test AMR datasets for van. When training and test sets share high similarity, BLASTp and Kalign are guaranteed to give good results. However, as in the case of bac for which the training and test AMR data similarity ranges between 38.13% and 41.01%, BLASTp does not perform well. For Kalign, the similarity ranges between 46.19% and 49.17% so that it performs better than BLASTp. However, the GTDWFE algorithm will outperform both BLASTp and Kalign because it does not use sequence similarity but rather protein features for prediction. BLASTp and Kalign do not predict; they match sequence similarity.We also compared the performance of our GTDWFE algorithm with BLASTp  as our reference database for relatively fast execution; however, more accurate values for these features can be obtained using large-scale protein databases such as UniRef90 or UniRef100 (http://www.uniprot.org/help/uniref) as target/reference databases. Note, however, that there is a trade-off between accuracy and computational time when using a large-scale reference database to generate features. A parallel version of PARGT would reduce the execution time of the tool for and ameliorate the use of large-scale reference databases.In this work, we implemented a software package PARGT and extended our earlier work of identifying AMR genes in Gram-negative to Gram-positive bacteria. PARGT integrates the required software tools and scripts needed to generate all protein features automatically, and it performs predictions on user-inputted sequences. Moreover, users can update PARGT by including their own known AMR and non-AMR sequences to train the machine-learning model to potentially improve prediction accuracy. As our previous work described the experimental results for Gram-negative bacteria, in this paper we only included prediction results for Gram-positive bacteria. Simulation results showed that PARGT can predict AMR sequences for Gram-positive bacteria with accuracy ranging from 87% to 90%. PARGT gave better results for 32. Briefly, a total of 621D candidate features were collected by means of a thorough literature search, where D stands for dimension . We extracted all 621D features from both our positive (AMR) and negative (non-AMR) datasets and calculated their values. The GTDWFE algorithm was then used to select features for use in our machine-learning model. The GTDWFE selects the best feature at each iteration based on the relevance, non-redundancy, and interdependency values of all features. Initially, the weights of all features are the same i.e., 1. The relevance of a feature to the target class (AMR or non-AMR) and the distance of the feature to other features are calculated using Pearson’s correlation coefficient and the Tanimoto coefficient, respectively. These calculations are performed for all features, and the feature with the highest summation of relevance and distance is chosen as the initial selected feature. The Banzhaf power index26 is then calculated to estimate the interdependency between the selected feature and the remaining features. We measure the contribution of each feature when it forms a group with other features, and the conditional mutual information is calculated to find the Banzhaf power index of the features. The weight of each remaining feature is updated by adding the product of the current weight and the Banzhaf power index to the feature selected previously. In other words, at each step, we readjust the weight of the remaining features dynamically based on the features selected in earlier steps. Thus, the weight of a candidate feature actually corresponds to the interdependence values with the earlier selected features. The feature with the highest summation of relevance and distance values multiplied by the revised weight is chosen as the next selected feature. This process is repeated until the desired number of features has been reached.Feature collection, feature extraction, calculation of feature values, and feature selection using the GTDWFE algorithm are explained in detail in previous workscost value of 4 was identified as the best model for both bac and van training datasets. For the SVM, the RBF is used as a function in the kernel trick to implicitly transform the original space of the data to a high-dimensional space to make the data samples linearly separable, and the cost parameter is used to regulate the classification error.After identifying the best feature subset for use with our classifier by means of the GTDWFE algorithm, we trained an SVM machine-learning model using this feature subset. This binary classifier was then used for prediction. As was true for our previous work, in PARGT we tuned the SVM using the training datasets and chose the best SVM model to predict the AMR proteins in the test sequences. We considered 10-fold cross validation to tune the SVM model. The SVM model with a radial basis function (RBF) kernel and a 39 and scripts necessary to generate the protein features required in our machine-learning model are included in PARGT. PARGT uses the best feature subset identified by our GTDWFE algorithm to make predictions. It allows users to add new AMR and non-AMR sequences to the training datasets, and the software automatically updates the machine-learning model with the additional sequences, potentially resulting in an increase in the accuracy of the model. To minimize execution time, PARGT uses the UniProt database containing 538,585 protein sequences as a reference database, rather than a larger database, for generating PSSM and secondary structure features.PARGT is an open-source software package designed and implemented for predicting antimicrobial resistance genes in bacteria. PARGT is written using both Python 3 and R. R scripts were written to identify physicochemical and secondary structure features and for machine-learning modeling, and Python 3 was used to run the R scripts, to generate position-specific scoring matrix (PSSM) features, and to implement the GUI. PARGT weight the importance of protein features based on their contributions during classification. All the required bioinformatics tools41, 400D feature vector based on the PSSM, and 33D feature vector based on the secondary structure sequence and secondary structure probability matrix are generated from the input protein sequences. Then the best feature subset is constructed using our GTDWFE feature selection algorithm. An SVM is used as the machine-learning model that is trained using the selected feature set. Recall that the SVM model used for PARGT is automatically tuned during the training phase. Finally, the trained SVM model is applied to predict AMR sequences from the test dataset.Figures aac, bla, and dfr resistance genes for Gram-negative bacteria and bac and van resistance genes for Gram-positive bacteria. A user must select the appropriate option for predicting AMR from the GUI menu and also supply the test file for the set of protein sequences in FASTA format that they wish to have classified as AMR or non-AMR. PARGT automatically computes all the required feature values for the test sequences, and it provides an output file containing the set of predicted AMR sequences for the user’s test file. If a user wants to include new known AMR or non-AMR sequences to augment the training datasets, PARGT provides an option to do so for the five above-mentioned resistance classes. In addition, it provides the option of restoring the original training datasets in case a user decides they prefer to use them or or else wants to compare predictions using two different sets of training data.As shown in Fig. 42, and non-AMR sequences were obtained from the PATRIC29. Initially, we gathered 124 bac and 374 van AMR sequences for the Gram-positive bacteria Clostridium spp. and Enterococcus spp., and we randomly chose 52 essential protein sequences to use as non-AMR sequences. As many of the protein sequences were duplicates, CD-HIT44 was applied to find unique sequences. A sequence identity of bac and 52 van AMR sequences; none of the 52 non-AMR sequences were duplicates. We used this dataset to train our machine-learning model. In addition to the training dataset, we also gathered 102 bac and 22 van AMR sequences and 14 non-AMR sequences for the Gram-positive bacteria Staphylococcus spp., Streptococcus spp., and Listeria spp. from the data sources indicated above. We again applied CD-HIT to this dataset, and after the removal of duplicate sequences, 6 bac and 9 van AMR sequences and 14 non-AMR sequences remained. We used these as our test dataset to measure the accuracy of the classifier. The sequence identity of protein sequences could be as low as 10%. After validating our GTDWFE algorithm with the training and test sequences for the bac and van AMR classes, we again trained our classifier, but we used the sequences from all five bacterial genera, i.e., both training and test sequences, to potentially increase the accuracy of PARGT. The same retraining was also performed for our Gram-negative bacteria.We retrieved protein sequences for AMR genes from the Antibiotic Resistance Genes Database (ARDB)Supplementary Tables


Teacher emotions are complex as emotions are unique to individuals, situated within specific contexts, and vary over time. This study contributed in synthesizing theories of the complexity in two characteristics of multi-dimensionality and dynamics. Further, we provided large-scale empirical evidence by employing big data and computational text analysis. The data contained around one million teachers’ online posts from 2007 to 2018. It was scraped from three representative forums of teachers’ workplace events and personal life occasions in a popular American teacher website. By conducting thread-level sentiment analysis in forums, we computed word-frequency-based eight discrete emotions ratios  and the degrees of sentiment polarity . We then used latent Dirichlet allocation for topic classifications. These topics, proxies of contexts, covered a holistic range of teachers’ real-life events. Some topics are in the main interest of scholars, such as teachers’ professional development and students’ behavioral management. This paper is also the first to include the less scholarly studied contexts like professional dressing advice and holiday choices. Then, we examined and visualized variations of emotions and sentiments across 30 topics along with three scales of time . The results showed that teachers tended to have positive sentiments in the online professional community across the past decade, but all eight discrete emotions were presented. The compositions of the specific emotion types varied across topics and time. Regarding the topics of students’ behavior issues, teachers’ negative emotions’ ratios were higher compared when it was presented in other topics. Their negative emotions also peaked during semesters. The forum of teachers’ personal lives had positive emotions pronounced across topics and peaked during the wintertime. This paper summarized the evidenced multi-dimensionality characteristic with the multiple types of emotions as compositions and varying degrees of sentiment polarity of teachers. The dynamics characteristic is that teachers’ emotions vary across contexts from their workplace to their personal lives and over time. These two characteristics of complexity also suggested potential interplay effects among emotions and across contexts over time. The studies on teachers’ emotions have grown increasingly over the past decades. Accumulated scientific evidence showed the critical roles that teachers’ emotions play in many aspects of their professional life, including teaching training , teachinTogether with the expanding research on the many manifestations of teachers’ emotions within educational contexts, scholars have forwarded a critical viewpoint of the complexity of teacher emotions , that isMeanwhile, the emergence of big data provides unprecedented opportunities for social science scholars to study people’s behaviors, emotions, opinions, cognitions, interactions, and experiences . Big datThe contributions of the current paper to the studies of the complexity of teacher emotions are twofold. First, we provided a holistic picture of the complex characteristics of teachers’ emotions. Building on the Affective Events Theory (AET) of This study aims to use big data analytics, including sentiment analysis and topic modeling, to explore the complexity of teachers’ emotions. To fully depict this complexity and its two features of multi-dimensionality and dynamics, this article proposes three research questions:1.What are the compositions of emotions and sentiment polarity in teachers’ online forums?2.How do these compositions and polarities differ across workplace contexts in schools and personal life events?3.How do these compositions and polarities differ across time?Moreover, the multi-dimensionality of teachers’ emotions indicates that emotions may interact with each other. One way is that a type of emotion may eliminate or cancel out the effect of another type of emotion and finally influence the impacts of emotions. For example, a teacher who worries about the low payment and thinks about changing jobs may be compensated by the satisfaction of his/her students’ achievement and thus decides to stay. This phenomenon can be explained by using the broaden-and-build theory developed by Many empirical studies of teachers’ emotions reflected this multi-dimensionality characteristic of emotions by measuring discrete types of emotions within specific educational contexts e.g., . For exaThe previous studies provided essential insights into the multidimensional nature of teachers’ emotions. Other studies called attention to the emotions’ changing nature and the constitution of emotions across persons and situations. Based on the AET, the appraisal process also emphasizes the relationship between work events and emotions. Notably, the AET states that the primary appraisal is influenced by the relevance of the work events to individuals’ overall well-being and, later, the secondary appraisal helps individuals to determine the types of emotions they will experience. For example, students’ learning performance can trigger teachers’ primary appraisal process because it can be viewed as a work event that is relevant to teachers’ emotions. Both It has been long recognized that teachers’ work performances, identity, perceptions, and practices in schools are largely integrated with their personal lives and experiences . In otheEmotions may evolve as time changes, and the context or related environmental factors change. This is consistent with Although the aforementioned research has provided a micro-level evidence in teachers’ emotional variations within mainly daily-based professional tasks, we still need more knowledge on the dynamics of teacher emotions across a more extended time range. The teaching profession has particular work events in certain months or semesters in the academic year cycle, such as students testing each academic year which may happen in April for state-level evaluation and at the end of spring and fall semesters as final tests. These seasonal work events were found to affect teachers’ emotions. During student testing and teacher evaluation, teachers experienced anxiety and pressure, which led to emotional exhaustion and, finally, turnover behaviors . Diary sIt is not uncommon that current teacher emotions literature employed data collected from a few short time points and particular events . This coIn summary, the theoretical framework is built upon AET  and the In online space, such as social media and teachers’ blogs, teachers bonded with trust and emotional support along with their interactions, knowledge sharing, and resource acquisition . ParticuWe here extracted several sentences from an anonymous user’s public post from our sample data as a prelude. It demonstrates the emotion-rich information that teachers’ online communities can carry. This post said: “As an introvert, this really affects my mood and health. I’m scared of ending up in the hospital. I decided to look for other teaching jobs.” In this example, the data provided information of emotions, contexts of time and events, and their impacts on teachers’ decisions.The rich emotion and event measures from online platforms have shown advantages in reliability, validity, and efficiency. Although big data have much potential to help the current need for teacher emotions’ studies, no teacher emotions big data analytic work has been conducted as of now to the authors’ knowledge. This could be due to the many challenges of using big data in the social sciences. One challenge could be the requirement of skills in big data acquisition and analytics. As detailed in later sections, this paper scraped public online data and organized it into a structured longitudinal dataset. Then, we utilized computational text analysis, including sentiment analysis and topic modeling, to compute emotion estimates and further analyzed the results with traditional quantitative methods.The other challenge calls for the development of methodological frameworks of integrating social science research and big data analytics . It relaAside from the many benefits of big data, the veracity or quality of big data is worth discussing as the data were not initially designed and generated based on research . AdditioThis paper utilized online data from a popular American-based teacher website. We then conducted the sentiment analysis to compute emotions’ compositions as ratios and sentiment polarity. The sentiment polarity had two measures: estimated polarity scores  of threads and percentages of positive, negative, and neutral threads. Further, to answer the second question of dynamics across contexts, we firstly employed latent Dirichlet allocation (LDA) to reduce the complex forum text data into interpretable topics. These topics were treated as proxies of contexts that each thread was embedded within. In terms of emotions’ dynamic in time, we developed three scales: calendar years, calendar month, and academic semesters. The academic semesters were defined into fall semesters (September to November), spring semesters (January to May), summer breaks (June to August), and winter breaks (December). Finally, variations of teachers’ emotions across contexts and time were examined through ANOVA tests and presented in visualizations .Used Python language, we scraped 66,515 threads from the three public forums. Two of the forums are workplace events: general teaching activities in classrooms and teachers’ professional development. The school contexts and events in these two forums could cover the most-studied professional events of teachers in teachers’ emotional studies, as discussed in the above contemporary literature. The third forum is teachers’ out-of-school life, in which teachers examine their personal life events, such as holidays and family, and personal health concerns. This forum provides a novel perspective to learn teachers’ emotions outside of school buildings, which could have different emotional manifestos than the ones from professional events.As shown in the top panel of The three forums were considered independent of each other since they are pre-determined with unique themes. Within forums, teachers generate interactive discourses through posts around a common subject within a thread. In the cleaned dataset, each thread and the posts within each thread were assigned with unique thread- and post-level identification numbers by the website. Besides the identification numbers, each thread and the posts within it have corresponding variables of users and created time. Notably, the discourses within a thread were not necessarily formed in a star structure where all the following posts directed to the starting post. The following posts can point to each other. Thus, the structure of a thread is interactive instead of being directed in one way. This study thus further argued that the posts within threads are dependent as they share common interests, while threads are independent if they hold individual interests. Therefore, we aggregated posts to thread level for later analysis. In the bottom panel of Additionally, the peak of thread numbers happened during 2008–2012 at around 4,000 threads that were generated every year in all three forums. The number of threads per year then continuously declined in the following years. Within a year, teachers were most active during the summertime of June and July and the least in December.Psychology and linguistic researchers have examined the emotional information carried in natural language with many computer-aided text analysis tools a long time ago . In thisAdditionally, we considered valence shifters and adversative conjunctions. Valence shifters like “slightly” and “very” influence the degree of sentiment. Adversative conjunctions, such as “but” and “however,” may contrast a statement. Therefore, instead of matching single words, this paper took into account the surrounding words as contextual information to improve the identification and the estimation of sentiments and emotions. The contextual words techniques in the sentiment analysis aligned with the study of Conducted through an R package called “sentimentr” , we specThe chosen lexicon for tagging polarized terms is called NRC. It was initiated from crowdsourcing on Mechanical Turk and annotated about 25,000 English words for emotional labels , 2013. ETopic modeling, such as latent Dirichlet allocation, is commonly used with machine learning or deep learning algorithms to calculate the latent structure  of text . In procText processing in LDA includes lemmatization and the removal of website links, stopwords, and punctuations. Lemmatization shortens each word into its basic forms. For example, “testing” and “test” will be reduced into a common form of “test.” Stopwords such as “a/an” and “you” are words that generally do not carry content information. Finally, observing from the frequency distribution of words, we truncated the top 10% most common words and the top 5% least common ones to improve topic classification .In applications of topic modeling for social science studies, it is critical to make judgments of setting the number of topics and interpret arbitrary topics with meanings for qualified model selections . Nelson Therefore, along with the steps defined in the computational grounded theory framework, we further chose two quality measures of model selection for defining an appropriate topic number and sociologically meaningful topics: the degree of associations of the top words within each topic  and how distinguishable those top words are for each topic  . For exaWith prior knowledge of each forum through in-depth reading, we firstly conducted three LDAs for each forum with reasonable topic numbers of 10, 20, and 30. Then, we listed down the top 25 most frequent words with the highest probability in each topic to gain primary judgments of the topics’ meaning and to what degree a topic can be distinguished from other topics. Meanwhile, we randomly selected 20 threads from the 30 threads which have the highest probabilities belonging to a topic. Two authors then deeply read and discussed the original content of each thread for meaningful interpretations. The other 10 threads in each topic were used as validation evidence for topic interpretations. Lastly, we then found that the models with the topic number of 10 had the most interpretable and efficient results across the three forums. Therefore, this paper used the LDA outputs with 10 topics as the proxies of the workplace or personal life contexts. The 30 topics with interpretations and their prevalence percentages within forums are demonstrated in p < 0.05). Their correlations with negative emotions are generally significant but close to zero. Similar patterns applied to the correlation matrices of each forum, although slight variations exist.The emotion analysis computed the ratios of eight discrete emotions. To present the amount of these emotion types without the bias of language, we further weighted the percentages of the emotion types by the percentages of emotion types from the lexicon NRC. For example, anger’s weighted ratio was calculated through dividing the anger emotion percentage in a forum by the anger percentage in NRC. In Consistently, all three forums have a positive average polarity, which implied that the overall sentiment of teachers’ online discourses tends to be positive. While variations exist across forums, forum 3 has the lowest positive sentiment polarity, and forum 2 has the highest one. At the forum level, positive polarity was dominant, while negative and neutral ones together accounted for only around 10% or less.p < 0.0001).The weighted ratios of positive emotions were higher than the negative ones in all contexts . work schedule and commute had the highest ratio in anticipation and surprise. Its other emotions, the ratios were close to the average forum values. Topic 8  was the most emotional-rich topic in forum 1. It had the highest ratios of positive emotions in joy and trust and also high in anticipation and surprise. In terms of negative emotions, topic 6 of conflicts in classrooms had the highest ratios of multiple negative emotions, including anger, disgust, sadness, and fear. It also had the lowest ratio on joy. However, noticeably, comparing within topic 6, the ratio of joy is higher than the negative emotions. Topic 5 of technologies in schools and teaching tended to be less emotionally extensive. Its ratios of all emotion types were lower than the average numbers.We further identified four representative topics in each forum which had the highest and lowest ratios of each emotion and presented them in three emotion radar charts in behavior management and teaching pressures and health concerns, respectively, had the highest ratios in the negative emotions in general. Moreover, their positive emotion ratios were lower than the forum-level averages, although their positive emotion ratios were still much larger than their negative ones. In contrast, topics 5, 8, and 9 contained the most positive emotions. Teachers shared the most anticipation, joy, and surprise in topic 5 of interview tips and dress code. When teachers are seeking for job market advice, as shown in topic 8, their discourses had the most trust and highest anticipation. Topic 9, job mobility and relocation, had a considerable amount of joy and the least of negative emotions compared with the other topics in this forum.Similar to forum 1, the topics in forum 2 contained much more positive emotions than negative ones. In forum 2, topics 2 and 4, technology products) is the least emotional-rich topic. This is similar to topic 5 of forum 1 which had a similar context. Forum 3’s topic 4  is the most emotion-rich one. Specifically, topic 4 had the highest ratios of positive emotions and the smallest of negative emotions. In this topic, joy is the dominant emotion, which is 10 times larger than its sadness ratio. Topic 2 of health concerns and topic 9 of beliefs of life and family were highest in negative emotions. Topic 2 had ratio peaks in sadness, disgust, and fear, and topic 9 had a ratio peak in anger. In the sentiment boxplot, the median sentiment polarity scores were all positive. Identical to the patterns of emotion ratios, topics 2 and 9 are the lowest in terms of the positive sentiment scores, while topic 4 is the highest.Forum 3, which had the highest percentage of emotion words among forums, also was the most pronounced in showing variations of emotion ratio distributions and sentiment polarities across topics. In forum 3, topic 8 . Forum 3 only had a significant interaction effect of topics and months (p = 0.005). We then dived into prevalent topics that had significant changes in their sentiment scores across time matrices. In forum 2, topic 8 of job market advice-seeking had a noticeable and stable sentiment polarity decreasing trend as the years pass. The decline of the positive sentiment polarity degrees happened along with the decrease in the weighted ratios of positive discrete emotions. The ratio statistics of joy and anticipation in this topic decreased by 20% from 2007 to 2018. The emotion of surprise even decreased by 40% in ratios. The ratios of the other emotions were relatively stable, including trust.The two-way ANOVA tests also detected significant interaction effects of topics and time in forums 1 and 2  had a similar increasing trend in negative emotions during summer to fall. The increase rates in this topic were 10–30%. Although no significant interaction effect of topic and semester was found in forum 3, its topic 4, family and holidays, is a good example to show the increasingly positive sentiment in winter. From fall to winter semesters, its weighted ratios of positive emotions increased by around 10–25%, and anger and disgust decreased by around 40 and 20%, respectively. The other negative emotions remained low.Another exemplar of topic 2, This paper utilized a longitudinal text dataset with emotion-rich information from teachers’ online professional forums to reveal the complexity of teacher emotions. By computational text analysis tools, traditional statistical analysis , and extensive visualizations, we provided a variety of empirical evidence. Although driven by the emergent big data and the computational text analysis tools in a mainly exploratory approach, the research goals and designs were driven by and weighed heavily on the contemporary theories from teacher emotion studies. We discussed the patterns of teachers’ emotions and sentiments in forums and extended to the many specific workplaces and personal life contexts and three time measures. Additionally, the online text dataset with a large sample of American teachers and an extensive time range of 10 years enlarged the external validity of this paper. In the following discussion, we summarize the findings to support the current theoretical argument of the complexity of emotions: multi-dimensionality and dynamic. We also discussed novel perspectives for future studies.Embedded within the many educational contexts  computed from teachers’ online professional forums, teachers exhibit a composite of multiple types of emotions. This empirical evidence extended the multi-dimensionality character of emotions as defined in Our first research question was answered. In general, all eight discrete emotions defined by In certain educational contexts that were negatively valued, only the negative emotions of teachers may have caught the attention of researchers. However, the current paper’s findings suggested that positive emotions exist simultaneously. conflicts in classrooms) of forum 1, which is a commonly negative-valued context. In this topic, additional to the negative emotions, a considerable portion of joyful emotion concurrently exists. Similar patterns showed in the two behavior management topics from forums 1 and 2. They are consistent with the prior research findings that student discipline and teacher–student relationship are significant factors of teachers’ positive (such as joy) and negative (such as anxiety and anger) emotions (An example is topic 6 (emotions . There wemotions  or teachemotions . It is pemotions . TherefoA few studies have shown that there were potential neglected effects of positive or negative emotions in the opposite valence-valued contexts. In Another significant feature of teachers’ emotional multi-dimensionality shown in the results is the pronounced positive emotions and sentiments. Although the eight emotions of positive and negative ones commonly appear simultaneously, teachers tend to express more positive emotions than negative ones across all topics. This is also true even in the contexts that are normally negatively perceived, such as the topic of students’ behavior management. This pattern is consistent with many diary studies of recording teachers’ discrete emotions in their teaching sessions and workdays , as summThis study further explored the dynamics of emotions as the other characteristic of the complexity of teachers’ emotions. It argues the variations and changing status of emotions and degrees of sentiment polarity across contexts and time. Embedded within particular topics, evidence showed certain emotions or emotion combinations dominate. This finding made contributions to the current teachers’ emotions with a more diverse range of contexts. As For example, this paper provided many topics that were relevant to teachers’ corporations outside of local schools, such as topics 1, 5, 8, 9, and 10 in forum 2 about sharing and seeking suggestions for jobs and interviews. In these topics, we observed considerable ratios of trust, along with high ratios of other positive emotions, compared with other topics. This implies to some empirical studies finding that trust among teachers and their communities increase teachers’ knowledge sharing  with theThough as shown in the aforementioned literature that teachers’ personal events could influence teachers’ school activities and professional experience, current literature still knows little about how teachers’ personal life events may affect teachers’ emotions. The current study is the first to show teachers’ emotions in their out-of-school life through two major evidence. Firstly, teachers expressed emotional rich discourses that situated within a wide range of outside-of-school topics, such as family life and holiday, health concerns, and leisure time entertainment. Secondly, the current paper also further found noticeable differences of teachers’ emotional manifestos between school-based activities and personal life events. Specifically, teachers tended to express more negative emotions and sentiments in their personal life related topics than in workspace events. These outside-of-school events are rarely considered in teachers’ emotions studies while are crucial components in individual teachers’ daily life and influence teachers’ school-based activities and emotions. For example, teachers who have conflicts in work and family may less likely to have positive engagements and experience in school activities .The other feature of the dynamics of teacher emotions is time. This feature is less empirically studied mainly due to the limited capacity of survey or interview data with traditional qualitative or quantitative inquires . BenefitLeveraging teachers’ online text data with computational text analysis methods, this paper presented an overall picture of the complexity of teachers’ emotions in a large scale and in an efficient way. However, we did not provide micro-level examinations of the many appraisal processes of particular emotions at the individual teacher level. The appraisal process is complicated as it involves an entire ecosystem of teachers’ own identity and psychological status, students’ behaviors, school dynamics, and the broader educational policy system . This reThis paper also demonstrated the affordances of big data and computational text analysis in teachers’ emotions studies. We provided discrete emotions’ compositions and sentiment polarity estimates, which advanced the current measures of teachers’ emotions. These measures obtained from teachers’ online text captured less subjectively biased emotion measures than the traditional survey or interview approach. However, teachers may hide or fake their genuine emotions . The souhttp://atozteacherstuff.com/.Publicly available datasets were analyzed in this study. This data can be found here: ZC generated the original ideas, led in designing the study, conducted the analyses, wrote the first draft on introduction, methods, results, and discussion. XS developed the original ideas, helped in research design and analyses, wrote the first draft on introduction and literature review, gave feedback, and helped ZC to edit the final draft of the manuscript. LQ and WZ contributed to idea generation, draft finalization, and journal submission.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Zea mays has been carefully studied previously. Classical histology describes its system as having a “closed organization” and a development of xylem that conforms to predictable rules. Among the first cell types to begin differentiation are late-maturing metaxylem (LMX) vessels. As part of a larger study comparing domestic maize root development to a wild subspecies of Z. mays (teosinte), we encountered a metaxylem development abnormality in a small percentage of our specimens that begged further study, as it interrupted normal maturation of LMX. Primary root tips of young seedlings of Zea mays ssp. mexicana were fixed, embedded in appropriate resins, and sectioned for light and transmission electron microscopy. Longitudinal and serial transverse sections were analyzed using computer imaging to determine the position and timing of key xylem developmental events. We observed a severe abnormality of LMX development among 3.5% of the 227 mexicana seedlings we screened. All LMX vessel elements in these abnormal roots collapsed and probably became non-functional shortly after differentiation began. Cytoplasm and nucleoplasm in the abnormal LMX elements became condensed and subdivided into irregularly-shaped “macrovesicles” as their cell walls collapsed inward. We propose that these seedlings possibly suffered from a mutation that affected the timing of the programmed cell death (PCD) that is required to produce functional xylem vessels, such that autolysis of the cytoplasm was prematurely executed, i.e., prior to the development and lignification of secondary walls.Root apical meristem histological organization in  The cellular organization of the apical meristems of primary roots of flowering plants has been described as being “closed” or “open” ,2,3 depeZea mays, we reported that in the two subspecies that were examined, Z. mays “Honey Bantam” (a sweetcorn cultivar) and Z. mays ssp. mexicana (a wild teosinte), the plerome only produced the pericycle, and a fourth biseriate layer basipetal to the plerome produced the rest of the procambium by the proliferation of cells basipetally. We described this as the “vascular initials zone” and suggested the name “vasculogen” for this apparent fourth histogen [Z. mays stele were pericycle cells and vessel elements of late-maturing metaxylem (LMX). LMX vessels could often be traced to initials within the vascular initials zone [Plants in the Poaceae (grasses) family typically have root apical meristems with a closed organization, and have been most often described as having three defined histogen layers. In this scheme for grass root apices, the root cap-producing calyptrogen is usually quite distinct and the protoderm and ground meristem are produced by a single dermatogen/periblem complex. The pericycle and procambial tissues of the stele are produced by the plerome ,5. On thhistogen . We alsoals zone .Z. mays mexicana, we encountered a severe abnormality in the development of the LMX vessels of the primary roots in a small percentage of the seedlings. Root vessels in the abnormal seedlings collapsed early in development, and given that vessel element differentiation concludes with programmed cell death (PCD) [Z. mays ssp. mexicana roots was due to mistimed PCD. The present paper reports the results of our morphological study of this abnormality by comparison to normal vascular development in Z. mays mexicana, and presents a discussion of its implications for understanding the molecular control of xylem development and potential seedling pathogenesis in maize.In the process of examining th (PCD) , we hypomexicana teosinte has the closed anatomical organization typical of most grasses and is similar to that of domestic maize  was mainly limited to and occupied by the 7–12 cells of the dermatogen/periblem complex and the 7–9 cells of the plerome that were centrally located at the end of the root body proper . These hIn the procambium, the pericycle cells could be differentiated earliest, and these proliferated directly from the margin initials of the plerome mainly by tangential divisions anticlinal to the plane of the histogen B. The reNine of the 227 primary roots that were microscopically examined (3.5%) showed evidence of premature death and collapse of LMX and EMX. Roots that had abnormal metaxylem development had normal promeristem organization with a typical QC. The progression of cell proliferation and the establishment of the primary meristem tissues appeared completely normal . No cellIt has been reported that as tracheary elements complete their differentiation, the cytoplasm breaks up into “cytoplasmatic spherules”, some of which contain organelles, that enter the central vacuole and ultimately become digested ,8,9. In Arabidopsis thaliana [Arabidopsis [Zea mays mexicana.Our serendipitous discovery of a significant developmental abnormality limited strictly to metaxylem vessel elements will provide, we think, an unusual opportunity to explore a key aspect of the molecular regulation of metaxylem vessel differentiation, since it is so specific in cell type and timing. The small number of affected individuals 3.5%) among our population of seedlings suggests that the cause might be a recessive single gene mutation carried by a small number of plants in the field population from which the seeds were collected. That it has a synchronized timing among the affected metaxylem vessels suggests that it might be a “control” gene. It is known that programmed cell death (PCD) is involved in the maturation of xylem tracheary elements in plants ,10. Howe.5% amongthaliana . A mutatbidopsis , but theZ. mays mexicana is known to freely hybridize with conventional Z. mays [Z. mays is a wind-pollinated species. Further work is needed to determine if the abnormality is severe enough to prevent reproduction or is ultimately fatal, but if that is the case there is a possibility it could, or already has, spread by hybridization into conventional maize populations and may contribute to reduced viability of some seedlings in cultivation. Admittedly, such a project would be a challenge, given that no specific information on the parental lines was available for us to share with readers. On the other hand, the vendor and seed lot number are known, as is the nation where the seeds were produced, so it is possible that seeds similar to ours can be obtained for further study of this phenomenon. This species produces abundant seminal adventitious roots from the mesocotyl. Therefore, sampling, while it is destructive to the primary root, can be done without killing the germinating seedling. Although, maintaining such plants might have to be done via laboratory culture because of the severity of the vascular abnormality.The abnormality was observed among seedlings grown from commercial seed obtained from plants grown in the open environment. Since it was found in seedlings grown from randomly selected seed from a commercial source under non-stressful conditions, the abnormality appears to be heritable.  Z. mays ,17, and Zea mays L. ssp. mexicana (Poaceae), lot #K0018 raised in Thailand, Snow Brand Seed Co. Ltd, Sapporo, Japan) were surface sterilized in 10% (v/v) household bleach. Seeds were sown in 1 L autoclaved beakers filled with autoclaved, moistened vermiculite (375 mL water/L vermiculite) and covered with sterilized foil under axenic conditions. The beakers were placed in a continuously dark growth chamber at a constant temperature of 25 °C. After 3 d, primary roots were collected.Cultivation methods were modified after Gladish and Niki . TeosintProcedures were modified from Niki et al. . For conRoots of 59 seedlings were embedded and sampled by preliminary partial sectioning. From these, five normal and nine abnormal primary roots were selected for detailed study. Using the strategy pioneered by Heimsch , 320 to Procedures for EM were per Niki et al. . Root tiFigures were prepared using Photoshop™ CS5 and saved in TIFF format.


A recent study has reported that there are >240 million patients infected with chronic hepatitis B (CHB) worldwide. Once patients with CHB start antiviral treatment, they need to take antiviral drugs for a long period, which may lead to a series of side effects, and the resistance to the antiviral drugs may also emerge. We aim to evaluate the efficacy and safety of kushenin (KS) combined with entecavir (ETV) for chronic hepatitis B.Randomized controlled trials (RCTs) of KS combined with ETV for CHB will be identified from PubMed, EMBASE, Web of Science, The Cochrane Library, Chinese Biomedical Database, China National Knowledge Infrastructure, Chongqing VIP, Wangfang Data. Literature screening and data extraction will be independently performed by 2 researchers. The cochrane collaboration tool for assessing risk of bias will be applied to evaluate the risk of bias of the RCTs included. The extracted data will be analyzed by Rev-man 5.3.0 software.A high-quality synthesis of current evidence on the efficacy and safety of KS combined with ETV for CHB will be provided in this study.This systematic review will aim to evaluate the efficacy and safety of KS combined with ETV for CHB.CRD42019124790. The mechanism of nucleos (t)ide analogues in the treatment of CHB is to inhibit viral replication and reduce serum Hepatitis B virus DNA (HBV-DNA) titer. However, its effect on reducing hepatitis B virus surface antigen (HBsAg) transcription is very limited. Entecavir (ETV) is one of the preferred antiviral agents for antiviral therapy recommended in several guidelines.,4 Kushinin (KS) is an active alkaloid extracted from Sophora flavescens, of which the main component is oxymatrine. KS has been proved to have antiviral, immunomodulatory, and anti-inflammatory effects, several studies,7 have shown that KS combined with other antiviral drugs have a better antiviral effect in reducing the level of serum virological indicators, improving drug resistance and delaying the progression of liver fibrosis. This meta-analysis aims to provide a basis for clinical treatment for CHB, and explore whether KS combined with ETV has the advantages of improving efficacy and reducing side effects in antiviral therapy for patients with CHB.More than 240 million people worldwide are suffering from chronic hepatitis B (CHB).22.1The protocol of this systematic review has been registered in PROSPERO (Registration Number: CRD42019124790).2.22.2.1The randomized controlled clinical trials (RCTs) of KS combined with ETV for CHB will be included.2.2.2: positive hepatitis B virus surface antigen and/or positive serum HBV-DNA for more than half a year, elevated serum alanine aminotransferase (ALT) level, there is no limitation on the age, sex. Patients who have coinfection with other types of hepatitis virus, HIV infection, autoimmune hepatitis, drug-induced liver injury, hereditary liver disease, hepatic carcinoma, and so on will be excluded.Patients with the diagnosis of CHB2.2.3The experimental group should be treated with KS combined with ETV, with no restriction on dosage form, including oral administration, intravenous drip or acupoint injection, while the control group should be treated with ETV alone. The course of treatment should be >3 months, and if there were other treatments, the 2 groups should be consistent.2.2.4The main outcomes are the effective rate of serum virological indicators and hepatic function indicator. Additional outcomes are the effective rate of serum hepatic fibrosis indicators, drug resistance rate, side effects rate.2.3Literature searching will cover both English and Chinese electronic databases, including PubMed, EMBASE, Web of Science, The Cochrane Library, Chinese Biomedical Database, China National Knowledge Infrastructure, Chongqing VIP, Wangfang Data. Medical subject headings combine with text word searching will be performed, search terms include Hepatitis B, Chronic, CHB, entecavir, baraclude, kushenin, oxymatrine, matrine. The date ranges from the establishment of databases to February 2019.2.42.4.1Literature screening and data extraction will be carried out independently by 2 researchers  using the unified scale, and cross-checking will be conducted. The inclusion of controversial literature will be determined by a third researcher (QZ). If the required information is incomplete, we will contact the author to obtain the necessary data. The entire process is performed in the flow diagram Fig. .2.4.2The cochrane collaboration tool for assessing risk of bias will be applied to evaluate the risk of bias of the RCTs included. The scale contains 7 parts, including random sequence generation (selection bias), allocation concealment (selection bias), blinding of participants and personnel (performance bias), blinding of outcome assessment (detection bias), incomplete outcome data (attrition bias), and selective outcome reporting (reporting bias). The judgment is categorized as “Low risk” of bias, “High risk” of bias, or “Unclear risk” of bias.2.4.3The extracted data will be analyzed using the Rev-man 5.3.0  software provided by the cochrane collaboration. For continuous variable, mean difference (MD) and 95% confidence intervals (95% CI) will be reported. And for dichotomous variable, risk ratio (RR) and 95% CI will be reported.2.4.4Q-statistic and I2 statistic. If there is no significant heterogeneity , the data will be assessed by fixed-effects model. Otherwise, subgroup analysis and sensitivity analysis will be performed to identify the potential source of heterogeneity, and the data will be assessed by random effects model.Statistical heterogeneity will be assessed by 2.4.5Publication bias will be assessed by a funnel plot when the included studies is >10.2.4.6Subgroup analysis will be conducted if there is a significant heterogeneity  and sensitivity analysis will be performed to identify the potential source of heterogeneity.2.4.7 of which the quality of evidence will be categorized as high quality, moderate quality, low quality, and very low quality.GRADE system will be used for assessing the strength of the body of evidence,3 It was also been suggested that KS can inhibits the expression of heat-stress cognate 70, a host protein required for HBV replication. In a study published in 2017, ETV showed a better efficacy than KS in reducing HBV replication and serum HBV-DNA level, while KS was more effective in reducing serum HBsAg, hepatitis B virus e antigen (HBeAg), and hepatitis B virus c antigen (HBcAg) level, which may explain the improved antiviral effect of KS combined with ETV. This systematic review aims to evaluate the efficacy and safety of KS combined with ETV for CHB. In order to provide more references for the clinical treatment of CHB.The possible antiviral mechanism of KS are interfering with the packaging of HBV paired-guide RNA nucleocapsid, and inhibiting the activity of viral DNA polymerase.None.


Introduction: Sub-Saharan Africa and other low- and middle-income countries (LMICs) have the highest rates of antimicrobial resistance (AMR) driven by high rates of antimicrobial utilization. This is a concern as AMR appreciably increases morbidity, mortality and costs. Pharmacogenetics (PGx) and precision medicine are emerging approaches to combat AMR. Consequently, as a first step there is a need to assess AMR knowledge and attitudes, and knowledge of PGx, among healthcare professionals and use the findings to guide future interventions.Methodology: We conducted a cross-sectional study involving 304 healthcare professionals at tertiary hospitals in Lusaka, Zambia. Structural Equation Modeling (SEM) was used to analyze relationships among latent variables.Results: Overall correctness of answers concerning AMR among healthcare professionals was 60.4% (7/11). Knowledge of pharmacogenetics was low (38%). SEM showed that high AMR knowledge score correlated with a positive attitude toward combating AMR (p < 0.001). Pharmacists had relatively higher AMR knowledge scores , whereas nurses had lower scores . A minority of respondents [31.5% (n = 95)] indicated that poor access to local antibiogram data promoted AMR, with the majority [56.5% (n = 190)] responding that poor adherence to prescribed antimicrobials can lead to AMR. Pharmacists had the highest scores for attitude  whereas nurses had the lowest scores .Conclusion: AMR knowledge and attitudes, as well as knowledge on PGx among healthcare professionals in Zambia, is sub-optimal and has the potential to affect the uptake of precision medicine approaches to reduce AMR rates. Educational and positive behavioral change interventions are required to address this and in future, we will be seeking to introduce these to improve the use of antimicrobials. Antimicrobial resistance (AMR) is an ever-growing global public health problem that needs urgent multi-sectoral approach as AMR appreciably increases morbidity, mortality and costs . AMR impPharmacogenetics also has the potential to identify and reveal new antimicrobial peptides which can be helpful to break antimicrobial resistance. Precise host-directed drug targeting specific peptides can circumvent drug resistance and this allows new intervention angles than traditional prescribing without considering individual genetic differences. Pharmacogenetics promotes precise and individualized medicine, drug options as well as the discovery of novel drug targets and ultimately reduces AMR . HoweverA number of activities can be instigated to reduce inappropriate prescribing and dispensing of antibiotics in ambulatory care for mostly self-limiting conditions, including many upper respiratory tract infections (URTIs). This includes initiatives to reduce self-purchasing of antibiotics where this occurs, including Zambia, as well as educating physicians and patients regarding the appropriate management of patients with URTIs and other self-limiting infections . ActivitDespite the advent of precision medicine approaches in drug development, HCPs’ knowledge, attitudes, and practice of methods of precision medicine such as pharmacogenetics toward AMR have not been adequately determined in LMIC countries where AMR rates are higher. In other settings, low knowledge levels of precision medicine practice such as the implementation of pharmacogenetics have been identified among HCPs , which iA cross-sectional study was conducted at Zambia's highest referral tertiary hospitals. The rationale for starting with these hospitals was based on the fact that these are specialized teaching hospitals and train all health professionals in the country; consequently, would be a good starting point to assess knowledge about AMR, pharmacogenetics and precision medicine among HCPs in Zambia. We used a similar approach in our initial study regarding knowledge of AMS activities in Zambia .A self-administered questionnaire was distributed, and data collected among HCPs  by continuously approaching them and the questionnaire can be found in Regarding the attitude of the HCPs toward interventions for AMR, this item was scored on a 5-point Likert scale in terms of strongly agree, agree, not sure, disagree, and strongly disagree. Responses were again dichotomized such that the first 2 ratings were labeled “good attitude,” whereas the other ratings were labeled “bad attitude.” Regarding knowledge on pharmacogenetics, this item was also scored on a 5-point Likert scale in terms of very important, important, not sure, not very important, and not important. Responses were again dichotomized such that the first 2 ratings were labeled “PGx knowledgeable,” whereas the other ratings were labeled “PGx not knowledgeable.”The percentage AMR knowledge, attitude and knowledge on PGx scores were calculated by dividing the participant's score by the maximum score of each outcome and multiplying by 100. The AMR knowledge, attitude and knowledge on PGx scores were also calculated as continuous variables by adding the participant’s number of appropriate responses. We assumed a prevalence of AMR knowledge of 73% among HCPs  with 5% Complacency: prescribing antibiotics according to patient demands and expectations;Fear: prescribing antibiotics to avoid potential arguments with patients;Ignorance: unaware of antibiotic resistance following over-prescriptions of antibiotics;Responsibility avoidance: a conviction that certain individuals such as health professionals, patients or hospital management and governments are accountable for the problem of antibiotic resistance.We defined attitude based on a previous study . RegardiData were entered in EpiData version 3.1  and analyzed using Stata 15 . The responses from the participants were described using frequencies with percentages and mean scores with standard deviation (SD). The Shapiro–Wilk test was used to assess the normality of continuous data. The ANOVA test was used to evaluate the differences in the overall scores since data was normally distributed among nurses, physicians, pharmacists and biomedical personnel, respectively. ANOVA was followed by the Bonferroni post-hoc test where appropriate to assess pairwise comparison.p-value of <0.05 was considered statistically significant at 95% confidence level.Structural Equation Modeling (SEM) was used as HCPs’ AMR knowledge, attitude and knowledge on PGx cover several facets, and SEM can accomplish the simultaneously modeling of several explanatory variables and multiple outcome variables through using direct variables to estimate the latent variables. This also allows for both direct and indirect effects to be captured . Model fWritten informed consent was obtained from the participants prior to participation in the study. Ethical approval to conduct this study was obtained  from the University of Zambia Health Sciences Research Ethical Committee (Protocol ID: 20190217099) and the National Health Research Authority (NHRA).There were 304 respondents with a mean age of 36.3 years (SD = 8.86), with the majority [187 (61.5%)] being male. Close to half [146 (48.2%)] of the respondents had a bachelor's degree. Approximately three-quarters of the respondents [233 (77.7%)] had no training in microbiology/infectious disease. Approximately one-third of the respondents [99 (32.6%)] had worked for less than one year and the majority of the HCPs participating were nurses .p = 0.01), microbial mutations causing AMR (p < 0.001) as well as patient demands and expectations promoting AMR (p < 0.001). On average, pharmacists had the highest score for AMR knowledge compared to other HCPs (p = 0.01) . The score for nurses was 5.57 ± 1.23, physicians 5.83 ± 1.41, pharmacists 7.67 ± 1.52, and biomedical personnel 6.55 ± 1.71. The majority of respondents reported that patient's poor adherence was the main driver for AMR [168 (56.7%)] with poor infection control in hospitals being the least cause of AMR [94 (31.3%)]. When different questions about AMR knowledge were compared among the participating HCPs, a significant difference was found with questions relating to inappropriate empiric choices promotes antimicrobial resistance ( = 0.01) .On average, the respondents reported a positive attitude towards rational antibiotic prescribing in response to pressures from patient expectations  and the requirements of defensive practice . There was a relatively high level of concern about AMR resulting from over-prescribing of antibiotics . However, a shortage of motivation in changing antibiotic prescribing practices was evident via a negative score . The respondents were inclined to believe that the solution to AMR went beyond their responsibilities  .p = 0.001) in attitudes was found in the scores among the various groups  and generally supported the establishment of national antimicrobial resistance surveillance (53.2%). The overall mean score was 6.02 ± 1.53 (mean ± SD) showing overall a poor attitude to AMR while the scores across the groups showed the highest score among pharmacists [7.23 ± 1.88 (mean ± SD)] followed by biomedical personnel [6.69 ± 1.74 (mean ± SD)], and physicians [6.12 ± 1.32 (mean ± SD)] with the lowest score among nurses [5.04 ± 1.66 (mean ± SD)]. A significant difference (s groups . The minp < 0.001) in knowledge on pharmacogenetics was found in the scores among the various groups. Bonferroni post hoc test showed a significant difference between physicians and pharmacists (p = 0.004) as well as nurses and biomedical scientists (p = 0.003) as shown , showing knowledge on pharmacogenetics was below 50%. The highest score was among pharmacists [3.8.1 ± 1.1 (mean ± SD)] followed by biomedical personnel [3.4 ± 1.2 (mean ± SD)] and physicians [2.7 ± 0.9 (mean ± SD)], with the lowest score among nurses [2.5 ± 0.8 (mean ± SD)]. A significant difference , the highest qualification  and specialization in any field . All eleven questions (p < 0.001) except question 6 (p = 0.04). However, AMR knowledge was correlated with overall attitudes  with PGx . Overall attitudes positively corrected with all questions on Knowledge (p < 0.001). PGx was negatively correlated with attitude . There was a positive and significant correlation between PGx questions 1 and 2 (β = 0.26 and 0.33) with p < 0.001 respectively. However, there was a negative and significant correlation between PGx and questions 3–8 (p < 0.001) as shown in The overall AMR knowledge was correlated with attitude and knowledge on PGx. AMR knowledge correlated with professional qualification  on AMR is being formulated to address the critical elements through the one health approach . TraininSome of the strengths of the study include the use of SEM, which enabled exploration of multiple factors associated with latent variables, and exploratory data is recognized as a key platform to identify gaps and subsequently help with formulating future interventions. In addition, this study is one of the few surveys to date conducted in sub-Saharan Africa on the topic of AMR and introducing the topic of pharmacogenetics.Despite several strengths, limitations should be noted. First, we acknowledge that only nurses, physicians, pharmacists, and biomedical personnel at the specialized tertiary hospitals that constitute UTHs were surveyed in this study and therefore the results may not be generalisable. Second, the topic of pharmacogenetics is relatively novel, and therefore may not currently have relevance to frontline clinicians in resource-poor settings such as Zambia. Third, we believe that overcoming ‘knowledge deficits’ alone will be insufficient for global AMR behavior change because there are important cultural-specific practices around antibiotics and social determinants of health which complicate campaign communication efforts. Fourth, a structured questionnaire gives self-reported answers and is therefore prone to exaggerated responses and participants may have consulted amongst themselves when responding to the questionnaire, which can contribute to bias in the results. We also acknowledge that the objectiveness of some questions on pharmacogenetics knowledge can be further improved.p < 0.001). In the comparison of HCPs, pharmacists reported relatively highest AMR knowledge scores , whereas nurses had the lowest scores . Poor access to local antibiogram data was also reported to contribute toward AMR, whereas the major reason to AMR was cited as poor adherence to prescribed antimicrobials. Regarding pharmacogenetics, knowledge of PGx among HCPs in Zambia is sub-optimal and has the potential to affect the uptake of precision medicine approaches in the future to reduce AMR rates. We will be looking to instigate future programmes to address identified concerns and key issues.In this cross-sectional study, we found that correctness of answers about AMR among HCPs was 60.4% but the knowledge on pharmacogenetics was low (38%). SEM showed that high AMR knowledge score correlated with a positive attitude toward combating AMR . The patients/participants provided their written informed consent to participate in this study.WM, CM, JM, and DM conceptualized the study protocol; CM, JS, and DM managed the project; WM, CM, ACK, MM, DM, and PK collected and analysed data; CN, NS, CM, AK, and BG manuscript writing and internal review of the paper.This project has received support/funding from the German Federal Ministry of Health, Global Health Protection Programme (GHPP).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Aepyceros melampus) and blesbok . Eight animals of each species were immobilised, with 0.09 mg/kg etorphine and 0.09 mg/kg thiafentanil respectively, in a randomised two-way cross-over study. Variables measured and analysed by means of a linear mixed model included time to recumbence, heart rate, respiratory rate, arterial blood pressure, blood gases, lactate and glucose. In blesbok, mean time to recumbence was not significantly different with either drug , but in impala thiafentanil achieved a shorter time to recumbence (2.0 min) than etorphine (3.9 min). Mean heart rates of immobilised impala were within reported physiological limits, but lower in immobilised blesbok when both opioids were used (35 beats/min to 44 beats/min vs. 104 ± 1.4 beats/min resting heart rate). Impala developed severe respiratory compromise and hypoxaemia from both opioids . In contrast, blesbok developed only moderate compromise. Therefore, significantly different species-specific physiological responses to potent opioid drugs exist in blesbok and impala. Given that these different responses are clinically relevant, extrapolation of immobilising drug effects from one species of African ungulate to another is not recommended.Potent opioids are known to cause negative alterations to the physiology of immobilised antelope. How these effects differ between species has not been studied. This study aimed to compare time to recumbence and effects of opioid-based immobilisation on the physiology of impala ( Hippotragus niger), roan antelope (Hippotragus equinus) and oryx (Oryx gazella); and Alcelaphinae, which include blesbok, two wildebeest species (Connochaetes spp.), hartebeest (Alcelaphus spp.) and tsessebe   and eight wild-captured female blesbok weighing 58 kg (SE 1.45) were selected for this experiment and held at the Wildlife Pharmaceuticals Wildlife Research Facility, Mpumalanga, South Africa . The research animals were all adult females of similar size and in good health in order to minimise variability that could confound the results. Horns of the blesbok were piped with plastic pipes to prevent horn injuries commonly encountered in captive blesbok.The wildlife enclosures were constructed according to national guidelines and consisted of several compartments SABS . The aniThe immobilising drugs etorphine  and thiafentanil  were administered intramuscularly via remote injection by darting into the gluteus muscles. Darting equipment consisted of a gas-powered dart projector  combined with 1 mL P-type Pneu-Darts with 1.9 cm barbed needles . Animals were darted on two separate occasions with a wash-out period of two weeks between each occasion. Each antelope received both drug treatments which were allocated to animals at random in a two-way cross-over design. One treatment consisted of 0.09 mg/kg etorphine and the other treatment consisted of 0.09 mg/kg thiafentanil. The immobilisation efficacy of these doses had been determined in a previous study  and defined as time from dart injection to when the animal was recumbent and unable to rise.+ Bluetooth Sensor . Barometric pressure was measured by the epoc portable blood gas analyser . This instrument was also used to analyse arterial blood pH, glucose in mmol/L, lactate in mmol/L, arterial partial pressure of carbon dioxide (PaCO2) in mmHg at 37 °C and arterial partial pressure of oxygen (PaO2) in mmHg at 37 °C. The respiratory rate (breaths/minute) was measured manually by means of visual observation of chest expansions and auscultation with a stethoscope for 1 min . Heart rate (beats/minute) was also measured with a stethoscope in the same manner as respiratory rate. Systolic arterial pressure (SAP) in mmHg, diastolic arterial pressure (DAP) in mmHg and mean arterial pressure (MAP) in mmHg were determined by means of an intra-arterial blood pressure measurement at the auricular (Arteria auricularis) or pedal arteries . A Deltran II pressure transducer  connected to an IntraTorr blood pressure monitor  were used for this purpose. The transducer was placed near the scapulohumeral joint (heart base) of the animal. Arterial blood was drawn anaerobically in a heparinised blood gas syringe  and subjected to blood gas analysis within 5 min using the epoc portable blood gas analyser with epoc BGEM test cards . Samples were collected at the frequencies of 5, 10, 15, 20 and 30 min after recumbence. The alveolar-arterial oxygen (A-a) gradient was calculated according to the publication by Meyer et al. , Pb is the measured barometric pressure (mmHg) and PH2O is the water vapour pressure of saturated air in the alveoli. PH2O (mmHg) was calculated as 4.58 e{(17.27 Tb)/(237.3 + Tb)}, where Tb is the body temperature. The researchers assumed that the partial pressure of CO2 in the alveoli was equal to the arterial partial pressure of CO2. A mean PaO2 < 80 mmHg was defined as moderate hypoxaemia whilst a mean PaO2 of < 60 mmHg was defined as severe hypoxaemia.Monitoring and recording began at 5 min after an animal became recumbent and was continued every 5 min until 40 min post-recumbence. Rectal body temperature was measured in °C by means of a modified handheld digital thermometer . The environmental temperature was measured by the Weatherr et al. . The A-aAfter the 40-min monitoring period was over, the immobilisation of each animal was reversed with intravenous naltrexone , injected at a ratio of 20 mg naltrexone to 1 mg etorphine and 10 mg naltrexone per 1 mg thiafentanil.The partial reversal agent butorphanol  as well as naltrexone were kept on hand to partially reverse or fully reverse the immobilisation in case of adverse anaesthetic events, such as potentially life-threatening apnoea of a duration exceeding 1.5 min. No animals needed this treatment.Ambient temperatures during the blesbok experiments with etorphine were 24.9 ºC (SE 4.99) and during thiafentanil treatment they were 24.3 ºC (SE 1.55). The mean barometric pressure was 698 (SE 1.80) mmHg.For the impala, ambient temperatures measured during the etorphine treatment were 20.7 ºC (SE 4.52) and during thiafentanil treatment they were 21.9 ºC (SE 4.68). The mean barometric pressure was 697 (SE 2.34) mmHg.Analyses were performed with Genstat Version 17  to determine whether or not there was a species-specific difference between the two opioids on the physiological variables of interest. A linear mixed model was fitted to the monitored physiological variables and immobilisation score. Each fixed model included the following factors: species-specific effects, treatment effects, species-specific by treatment interactions, the effects of time after recumbence, and interactions between time after recumbence and species and treatment. The random model included terms for animal, animal by treatment and animal by treatment by minutes after recumbence. The residual variance or covariance model included correlations between measurements made on the same animal for a specific treatment and different variances for each animal.Time to recumbence was analysed using a linear mixed model which included species-specific effects, treatment effects and their interaction in the fixed model, and animal by treatment effects in the random model. The residual variance model included different variances for each species.p-value of less than 0.05 was considered significant. Post hoc comparisons were made for significant species-specific interactions using a 5% least significant difference (5% LSD). In figures, the 5% LSD is used to illustrate whether or not differences between the species means are significant. Any difference larger than the 5% LSD is considered a significant result. Results are presented as mean and standard error (SE).Non-significant random effects and covariances were removed from the model before the significance of fixed effects was assessed. Post-analysis residual plots were examined to confirm that the assumptions required for a linear model were met. When interpreting results, only effects that included a species-specific interaction with treatment were of interest as these indicated differences in the species’ response to treatment. A Ethical approval to conduct the study was obtained from the Murdoch University, ethical clearance number R 3039/18.Mean rectal temperatures of blesbok during the monitoring period varied between 39.0 ºC and 39.2 ºC (SE 0.08) with the etorphine treatment, and between 38.9 ºC and 39.0 ºC (SE 0.08) with the thiafentanil treatment. In impala, mean rectal temperatures during etorphine immobilisation varied from 38.7 ºC to 39.1 ºC (SE 0.12) and with the thiafentanil treatment rectal temperatures varied from 38.8 ºC to 39.2 ºC (SE 0.12).p = 0.009). In blesbok, there were no significant differences between treatments for mean time to recumbence. The mean time to recumbence in blesbok immobilised with etorphine was 2.5 min (SE 0.14) and with thiafentanil, 2.2 min (SE 0.14). This finding was in contrast to impala, where the mean time to recumbence was faster (p = 0.007), with the thiafentanil treatment at 2.0 min (SE 0.17) compared to etorphine treatment at 3.9 min (SE 0.19).There was a significant species by treatment interaction for time to recumbence (p = 0.007) for heart rate. Blesbok, when treated with either opioid, developed much lower overall mean heart rates  compared to impala etorphine mean = 98; thiafentanil mean = 109 beats/min [SE 4.0]). The heart rate of impala, when treated with etorphine, started with a mean of 133 beats/min at 5 min but dropped within 30 min to 82 beats/min (SE 4.5). The range of heart rate actually measured in impala during these experiments varied from 52 beats/min to 190 beats/min. This considerable change over time and variability was not present in blesbok .There was a significant species by treatment by time interaction for mean arterial pressure (MAP) (p < 0.001). In blesbok, the mean MAP was 128 mmHg and 136 mmHg (SE 2.4) with etorphine and thiafentanil respectively. In impala, the mean MAP was 119 mmHg with etorphine and 140 mmHg (SE 2.2) with thiafentanil  as well as a species by time interaction (p < 0.001) for respiratory rate. Blesbok immobilised with thiafentanil over the entire monitoring period showed the highest respiratory rate. Impala, when immobilised with either opioid, showed very low respiratory rates especially in the first 15 min (There was a species by treatment interaction (t 15 min . Twenty 2 (p = 0.027). For the mean PaCO2 a significant species difference could also be observed as PaCO2 values were higher in impala compared to blesbok with both treatments (p = 0.002) (There was a species by treatment by time interaction for PaCO= 0.002) .2 (p = 0.006). Thiafentanil treatment of impala in the beginning elicited an extremely low PaO2 of 38 mmHg, which then improved within the first 10 min and reached 59 mmHg (SE 1.7) after 30 min. Blesbok, in contrast, experienced milder hypoxaemia with thiafentanil throughout the monitoring period with mean PaO2 values ranging from 61 mmHg to 68 mmHg (SE 1.5) (There was a significant species by treatment interaction for PaO(SE 1.5) .p = 0.009) for the A-a gradient. In blesbok, the A-a gradient was significantly lower when animals were treated with thiafentanil (mean = 28 mmHg [SE 1.0]) compared to etorphine (mean = 37 mmHg [SE 1.0]). This difference between the two opioids was not observed in impala in which the mean A-a gradient measured 34 mmHg and 33 mmHg (SE 0.7) for etorphine and thiafentanil respectively (There was a species by treatment interaction (ectively .p < 0.001). With either opioid treatment, blesbok had higher arterial pH values compared to impala  for lactate concentrations. Impala developed higher lactate values than blesbok, irrespective of the treatment. In both species, lactate values decreased over time (There was a significant species by time interaction (ver time .p < 0.001); impala developed higher glucose values than blesbok for both opioid treatments. Glucose decreased over time in impala with both treatments, but glucose values did not decrease in blesbok  than the mean heart rate that was measured in awake blesbok at rest (104 beats/min) (Du Plessis 2008). This could be interpreted as classic reflex bradycardia as a result of opioid-induced vasoconstriction and hypertension. Resting heart rates in impala are not known. However, in sheep and goats, which are similar in size to impala, resting heart rate is 70–110 beats/min  with both opioid treatments. Impala also suffered from severe hypoxaemia (PaO2 means = 53 mmHg [both drugs]).Du Plessis  reported2 values of blesbok (means = 45 mmHg [etorphine] and 41 mmHg [thiafentanil]) were within the upper physiological range of conscious small ruminants  and severe hypoxaemia with the etorphine treatment (mean PaO2 51 mmHg to 54 mmHg) (PaCO54 mmHg) . Possibl54 mmHg) . Therefo54 mmHg) . Opioid-54 mmHg) . Further54 mmHg)  may haveClearly, if the respiratory rate was the only assessment of respiration, then hypoxaemia in blesbok under etorphine immobilisation would have been overlooked. The measurement of blood gases, although expensive and not always practical to use in the field, should therefore not be underestimated as an important monitoring tool.Initially elevated lactate in the impala could have been associated with an initial exaggerated stress response of these animals to the capture procedure. A stress response would lead to an increased metabolism and therefore increase the oxygen demand, further compounding the hypoxaemia (Buss et al. Impala seem to be more fractious in nature compared to blesbok and a stress response, coupled with a greater sensitivity to opioid-induced respiratory compromise, may explain why impala are more at risk and considered to be one of the more challenging species to immobilise. Not only are they a small and fragile target to dart but the severe respiratory disturbances, coupled with stress-related physiological derangements, can also be fatal if not immediately addressed. Furthermore, the time to recumbence induced by thiafentanil and etorphine differed in impala, and this should be taken into consideration when the appropriate opioid is chosen for the capture of this species.In contrast, there was no difference in time to recumbence between the opioids in blesbok. Also, in blesbok, a heart rate lower than the resting heart rate can be expected when animals are immobilised with these opioids. This lower heart rate is likely a baroreceptor reflex to opioid-induced vasoconstriction and hypertension. Heart rate, respiratory rate and blood gas variables did not differ as much over the monitoring time in blesbok as was observed in impala. Furthermore, hypoxaemia and hypercapnia in the immobilised blesbok were not as severe as in impala.Impala, due to their availability and presence in many zoos, have been commonly studied to understand the physiological effects of immobilising drugs. However, as this study indicates, the response to immobilising drugs is not uniform across all antelope species. Therefore, the practice of only using impala for such studies may result in misleading perceptions. Standardised monitoring and data analysis, from multiple antelope species immobilised during routine management procedures, should be undertaken in the future in order to better understand the physiological effects of the drugs used, and enable more clinically relevant, species-specific recommendations for immobilisation.The small sample size is acknowledged as a limitation of this study. However, due to logistics, conservation status and ethical concerns, experiments in wildlife are often carried out with small numbers of 6 to 12 animals (Meyer et al. This experiment confirms the initial hypothesis that significantly different, species-specific physiological responses to opioid immobilising drugs exist between blesbok and impala. The most profound difference was that blesbok seem less affected by respiratory compromise than impala. Impala developed severe hypoxaemia, which is not only due to opioid-induced hypoventilation but also likely due to the impairment of oxygen diffusion as well as hypermetabolism. Some of the changes observed in impala might also be strongly associated with a species-specific stress response to capture, rather than just the actions of the opioids themselves. Extrapolation of immobilisation results from one species of African ungulate to another is not recommended, given the significant differences in the way that these two species responded to the immobilisation.