Differential responses of Hollyhock (Alcea rosea L.) varieties to salt stress in relation to physiological and biochemical parameters.

The response of 14 Hollyhock (Alcea rosea L.) varieties to salinity were evaluated in a field experiment over two growing seasons. Carotenoid, Chl a, Chl b, total Chl, proline and MDA content, CAT, APX and GPX activity and petal and seeds yields were determined in order to investigate the mechanism of salt tolerance exhibited by Hollyhock, and too identify salt tolerant varieties. Overall, the photosynthetic pigment content,petal and seed yields were reduced by salt stress. Whereas the proline and MDA content, and the CAT, APX and GPX activities increased as salt levels increased. However, the values of the measured traits were dependent upon the on the level of salt stress, the Varietie and the interaction between the two variables. Based upon the smallest reduction in petal yield, the Masouleh variety was shown to be the most salt tolerant, when grown under severe salt stress. However, based upon the smallest reduction in seed yield, Khorrmabad was the most tolerant variety to severe salt stress. These data suggest that the selection of more salt tolerant Hollyhock genotypes may be possible based upon the wide variation in tolerance to salinity exhibited by the varieties tested.

Genome-wide identification and expression analysis of the C2H2-zinc finger transcription factor gene family and screening of candidate genes involved in floral development in Coptis teeta Wall. (Ranunculaceae).

Background: C2H2-zinc finger transcription factors comprise one of the largest and most diverse gene superfamilies and are involved in the transcriptional regulation of flowering. Although a large number of C2H2 zinc-finger proteins (C2H2-ZFPs) have been well characterized in a number of model plant species, little is known about their expression and function in Coptis teeta. C. teeta displays two floral phenotypes (herkogamy phenotypes). It has been proposed that the C2H2-zinc finger transcription factor family may play a crucial role in the formation of floral development and herkogamy observed in C. teeta. As such, we performed a genome-wide analysis of the C2H2-ZFP gene family in C. teeta. Results: The complexity and diversity of C. teeta C2H2 zinc finger proteins were established by evaluation of their physicochemical properties, phylogenetic relationships, exon-intron structure, and conserved motifs. Chromosome localization showed that 95 members of the C2H2 zinc-finger genes were unevenly distributed across the nine chromosomes of C. teeta, and that these genes were replicated in tandem and segmentally and had undergone purifying selection. Analysis of cis-acting regulatory elements revealed a possible involvement of C2H2 zinc-finger proteins in the regulation of phytohormones. Transcriptome data was then used to compare the expression levels of these genes during the growth and development of the two floral phenotypes (F-type and M-type). These data demonstrate that in groups A and B, the expression levels of 23 genes were higher in F-type flowers, while 15 genes showed higher expressions in M-type flowers. qRT-PCR analysis further revealed that the relative expression was highly consistent with the transcriptome data. Conclusion: These data provide a solid basis for further in-depth studies of the C2H2 zinc finger transcription factor gene family in this species and provide preliminary information on which to base further research into the role of the C2H2 ZFPs gene family in floral development in C. teeta.

Using natural language processing and patient journey clustering for temporal phenotyping of antimicrobial therapies for cat bite abscesses.

BACKGROUND: Temporal phenotyping of patient journeys, which capture the common sequence patterns of interventions in the treatment of a specific condition, is useful to support understanding of antimicrobial usage in veterinary patients. Identifying and describing these phenotypes can inform antimicrobial stewardship programs designed to fight antimicrobial resistance, a major health crisis affecting both humans and animals, in which veterinarians have an important role to play. OBJECTIVE: This research proposes a framework for extracting temporal phenotypes of patient journeys from clinical practice data through the application of natural language processing (NLP) and unsupervised machine learning (ML) techniques, using cat bite abscesses as a model condition. By constructing temporal phenotypes from key events, the relationship between antimicrobial administration and surgical interventions can be described, and similar treatment patterns can be grouped together to describe outcomes associated with specific antimicrobial selection. METHODS: Cases identified as having a cat bite abscess as a diagnosis were extracted from VetCompass Australia, a database of veterinary clinical records. A classifier was trained and used to label the most clinically relevant event features in each record as chosen by a group of veterinarians. The labeled records were processed into coded character strings, where each letter represents a summary of specific types of treatments performed at a given visit. The sequences of letters representing the cases were clustered based on weighted Levenshtein edit distances with KMeans+ + to identify the main variations of the patient treatment journeys, including the antimicrobials used and their duration of administration. RESULTS: A total of 13,744 records that met the selection criteria was extracted and grouped into 8436 cases. There were 9 clinically distinct event sequence patterns (temporal phenotypes) of patient journeys identified, representing the main sequences in which surgery and antimicrobial interventions are performed. Patients receiving amoxicillin and surgery had the shortest duration of antimicrobial administration (median of 3.4 days) and patients receiving cefovecin with no surgical intervention had the longest antimicrobial treatment duration (median of 27 days). CONCLUSION: Our study demonstrates methods to extract and provide an overview of temporal phenotypes of patient journeys, which can be applied to text-based clinical records for multiple species or clinical conditions. We demonstrate the effectiveness of this approach to derive real-world evidence of treatment impacts using cat bite abscesses as a model condition to describe patterns of antimicrobial therapy prescriptions and their outcomes.

Disease progression modelling of Alzheimer's disease using probabilistic principal components analysis.

The recent biological redefinition of Alzheimer's Disease (AD) has spurred the development of statistical models that relate changes in biomarkers with neurodegeneration and worsening condition linked to AD. The ability to measure such changes may facilitate earlier diagnoses for affected individuals and help in monitoring the evolution of their condition. Amongst such statistical tools, disease progression models (DPMs) are quantitative, data-driven methods that specifically attempt to describe the temporal dynamics of biomarkers relevant to AD. Due to the heterogeneous nature of this disease, with patients of similar age experiencing different AD-related changes, a challenge facing longitudinal mixed-effects-based DPMs is the estimation of patient-realigning time-shifts. These time-shifts are indispensable for meaningful biomarker modelling, but may impact fitting time or vary with missing data in jointly estimated models. In this work, we estimate an individual's progression through Alzheimer's disease by combining multiple biomarkers into a single value using a probabilistic formulation of principal components analysis. Our results show that this variable, which summarises AD through observable biomarkers, is remarkably similar to jointly estimated time-shifts when we compute our scores for the baseline visit, on cross-sectional data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Reproducing the expected properties of clinical datasets, we confirm that estimated scores are robust to missing data or unavailable biomarkers. In addition to cross-sectional insights, we can model the latent variable as an individual progression score by repeating estimations at follow-up examinations and refining long-term estimates as more data is gathered, which would be ideal in a clinical setting. Finally, we verify that our score can be used as a pseudo-temporal scale instead of age to ignore some patient heterogeneity in cohort data and highlight the general trend in expected biomarker evolution in affected individuals.

Evaluation of the Effect of Plectranthus amboinicus L. Leaf Extracts on the Bacterial Antioxidant System and Cell Membrane Integrity of Pseudomonas aeruginosa PA01 and Staphylococcus aureus NCTC8325.

Plectranthus amboinicus (Indian borage) has been extensively studied for its medicinal properties, which can be exploited to develop new antimicrobial therapeutics. The current study investigated the effect of Plectranthus amboinicus leaf extracts on the catalase activity, reactive oxygen species, lipid peroxidation, cytoplasmic membrane permeability, and efflux pump activity in S. aureus NCTC8325 and P. aeruginosa PA01. As the enzyme catalase protects bacteria against oxidative stress, disruption of its activity creates an imbalance in reactive oxygen species (ROS) levels, which subsequently oxidizes lipid chains, leading to lipid peroxidation. In addition, bacterial cell membranes are a potential target for new antibacterial agents, as efflux pump systems play a crucial role in antimicrobial resistance. Upon exposure of the microorganisms to Indian borage leaf extracts, the observed catalase activity decreased by 60% and 20% in P. aeruginosa and S. aureus, respectively. The generation of ROS can cause oxidation reactions to occur within the polyunsaturated fatty acids of the lipid membranes and induce lipid peroxidation. To investigate these phenomena, the increase in ROS activity in P. aeruginosa and S. aureus was studied using H2DCFDA, which is oxidized to 2',7'-dichlorofluorescein (DCF) by ROS. Furthermore, the concentration of lipid peroxidation product (malondialdehyde) was assessed using the Thiobarbituric acid assay and was shown to increase by 42.4% and 42.5% in P. aeruginosa and S. aureus, respectively. The effect of the extracts on the cell membrane permeability was monitored using diSC3-5 dye and it was observed that the cell membrane permeability of P. aeruginosa increased by 58% and of S. aureus by 83%. The effect on efflux pump activity was investigated using Rhodamine-6-uptake assay, which displayed a decrease in efflux activity of 25.5% in P. aeruginosa and 24.2% in S. aureus after treatment with the extracts. This combination of different methods to study various bacterial virulence factors provides a more robust, mechanistic understanding of the effect of P. amboinicus extracts on P. aeruginosa and S. aureus. This study thus represents the first report of the assessment of the effect of Indian borage leaf extracts on bacterial antioxidant systems and bacterial cell membranes, and can facilitate the future development of bacterial resistance modifying agents derived from P. amboinicus.

Analysis of predictive performance and reliability of classifiers for quality assessment of medical evidence revealed important variation by medical area.

OBJECTIVES: A major obstacle in deployment of models for automated quality assessment is their reliability. To analyze their calibration and selective classification performance. STUDY DESIGN AND SETTING: We examine two systems for assessing the quality of medical evidence, EvidenceGRADEr and RobotReviewer, both developed from Cochrane Database of Systematic Reviews (CDSR) to measure strength of bodies of evidence and risk of bias (RoB) of individual studies, respectively. We report their calibration error and Brier scores, present their reliability diagrams, and analyze the risk-coverage trade-off in selective classification. RESULTS: The models are reasonably well calibrated on most quality criteria (expected calibration error [ECE] 0.04-0.09 for EvidenceGRADEr, 0.03-0.10 for RobotReviewer). However, we discover that both calibration and predictive performance vary significantly by medical area. This has ramifications for the application of such models in practice, as average performance is a poor indicator of group-level performance (e.g., health and safety at work, allergy and intolerance, and public health see much worse performance than cancer, pain, and anesthesia, and Neurology). We explore the reasons behind this disparity. CONCLUSION: Practitioners adopting automated quality assessment should expect large fluctuations in system reliability and predictive performance depending on the medical area. Prospective indicators of such behavior should be further researched.

Automating Quality Assessment of Medical Evidence in Systematic Reviews: Model Development and Validation Study.

BACKGROUND: Assessment of the quality of medical evidence available on the web is a critical step in the preparation of systematic reviews. Existing tools that automate parts of this task validate the quality of individual studies but not of entire bodies of evidence and focus on a restricted set of quality criteria. OBJECTIVE: We proposed a quality assessment task that provides an overall quality rating for each body of evidence (BoE), as well as finer-grained justification for different quality criteria according to the Grading of Recommendation, Assessment, Development, and Evaluation formalization framework. For this purpose, we constructed a new data set and developed a machine learning baseline system (EvidenceGRADEr). METHODS: We algorithmically extracted quality-related data from all summaries of findings found in the Cochrane Database of Systematic Reviews. Each BoE was defined by a set of population, intervention, comparison, and outcome criteria and assigned a quality grade (high, moderate, low, or very low) together with quality criteria (justification) that influenced that decision. Different statistical data, metadata about the review, and parts of the review text were extracted as support for grading each BoE. After pruning the resulting data set with various quality checks, we used it to train several neural-model variants. The predictions were compared against the labels originally assigned by the authors of the systematic reviews. RESULTS: Our quality assessment data set, Cochrane Database of Systematic Reviews Quality of Evidence, contains 13,440 instances, or BoEs labeled for quality, originating from 2252 systematic reviews published on the internet from 2002 to 2020. On the basis of a 10-fold cross-validation, the best neural binary classifiers for quality criteria detected risk of bias at 0.78 F1 (P=.68; R=0.92) and imprecision at 0.75 F1 (P=.66; R=0.86), while the performance on inconsistency, indirectness, and publication bias criteria was lower (F1 in the range of 0.3-0.4). The prediction of the overall quality grade into 1 of the 4 levels resulted in 0.5 F1. When casting the task as a binary problem by merging the Grading of Recommendation, Assessment, Development, and Evaluation classes (high+moderate vs low+very low-quality evidence), we attained 0.74 F1. We also found that the results varied depending on the supporting information that is provided as an input to the models. CONCLUSIONS: Different factors affect the quality of evidence in the context of systematic reviews of medical evidence. Some of these (risk of bias and imprecision) can be automated with reasonable accuracy. Other quality dimensions such as indirectness, inconsistency, and publication bias prove more challenging for machine learning, largely because they are much rarer. This technology could substantially reduce reviewer workload in the future and expedite quality assessment as part of evidence synthesis.

Evaluation of the Effect of Leaf Development in Plectranthus amboinicus L. on Antimicrobial Activity and Virulence Factors of Pseudomonas aeruginosa PAO1 and Staphylococcus aureus NCTC8325.

Plectranthus amboinicus is widely recognized as a potential source of antimicrobial compounds due to the presence of bioactive components (essential oils) secreted by the glandular trichomes borne on the leaves. As such, an understanding of the effect of leaf development on the production of these essential oils (EOs) is of crucial importance to its medicinal applications. The current study represents the first comparative investigation of the effect of different stages of leaf development (lag, log, and stationary phase) upon the yield and bioactivity of phytochemicals produced. The effects of leaf extracts on the antimicrobial activity, cell surface hydrophobicity, biofilm formation, and motility of P. aeruginosa and Staphylococcus aureus were evaluated. Cryo-scanning electron microscopy was used to record the abundance and distribution of both glandular and non-glandular trichomes during leaf development. Gas chromatography-mass spectrometry analysis revealed that the potent phytochemical thymol is present primarily in log (30.28%) and stationary phase (20.89%) extracts. Log phase extracts showed the lowest minimum inhibitory concentration (25 mg/ml) when compared to other phases of development. Stationary phase extracts were shown to exhibit the highest biofilm dispersal activity against P. aeruginosa (80%), and log phase extracts against biofilms of S. aureus (59%). Log phase extracts showed the highest biofilm inhibitory activity against P. aeruginosa (66%) and S. aureus (63%). In conclusion, log phase leaf extracts of P. amboinicus exhibited a multimodal mechanism of action by displaying antimicrobial, antibiofilm activities and reducing the motility and hydrophobicity, which are important virulence factors in P. aeruginosa and S. aureus pathogenesis.

Predicting Publication of Clinical Trials Using Structured and Unstructured Data: Model Development and Validation Study.

BACKGROUND: Publication of registered clinical trials is a critical step in the timely dissemination of trial findings. However, a significant proportion of completed clinical trials are never published, motivating the need to analyze the factors behind success or failure to publish. This could inform study design, help regulatory decision-making, and improve resource allocation. It could also enhance our understanding of bias in the publication of trials and publication trends based on the research direction or strength of the findings. Although the publication of clinical trials has been addressed in several descriptive studies at an aggregate level, there is a lack of research on the predictive analysis of a trial's publishability given an individual (planned) clinical trial description. OBJECTIVE: We aimed to conduct a study that combined structured and unstructured features relevant to publication status in a single predictive approach. Established natural language processing techniques as well as recent pretrained language models enabled us to incorporate information from the textual descriptions of clinical trials into a machine learning approach. We were particularly interested in whether and which textual features could improve the classification accuracy for publication outcomes. METHODS: In this study, we used metadata from ClinicalTrials.gov (a registry of clinical trials) and MEDLINE (a database of academic journal articles) to build a data set of clinical trials (N=76,950) that contained the description of a registered trial and its publication outcome (27,702/76,950, 36% published and 49,248/76,950, 64% unpublished). This is the largest data set of its kind, which we released as part of this work. The publication outcome in the data set was identified from MEDLINE based on clinical trial identifiers. We carried out a descriptive analysis and predicted the publication outcome using 2 approaches: a neural network with a large domain-specific language model and a random forest classifier using a weighted bag-of-words representation of text. RESULTS: First, our analysis of the newly created data set corroborates several findings from the existing literature regarding attributes associated with a higher publication rate. Second, a crucial observation from our predictive modeling was that the addition of textual features (eg, eligibility criteria) offers consistent improvements over using only structured data (F1-score=0.62-0.64 vs F1-score=0.61 without textual features). Both pretrained language models and more basic word-based representations provide high-utility text representations, with no significant empirical difference between the two. CONCLUSIONS: Different factors affect the publication of a registered clinical trial. Our approach to predictive modeling combines heterogeneous features, both structured and unstructured. We show that methods from natural language processing can provide effective textual features to enable more accurate prediction of publication success, which has not been explored for this task previously.

RNA-seq analysis reveals key genes associated with seed germination of Fritillaria taipaiensis P.Y.Li by cold stratification.

Seed dormancy is an adaptive strategy for environmental evolution. However, the molecular mechanism of the breaking of seed dormancy at cold temperatures is still unclear, and the genetic regulation of germination initiated by exposure to cold temperature requires further investigation. In the initial phase of the current study, the seed coat characteristics and embryo development of Fritillaria taipaiensis P.Y.Li at different temperatures (0°C, 4°C, 10°C & 25°C) was recorded. The results obtained demonstrated that embryo elongation and the dormancy-breaking was most significantly affected at 4°C. Subsequently, transcriptome analyses of seeds in different states of dormancy, at two stratification temperatures (4°C and 25°C) was performed, combined with weighted gene coexpression network analysis (WGCNA) and metabolomics, to explore the transcriptional regulation of seed germination in F. taipaiensis at the two selected stratification temperatures. The results showed that stratification at the colder temperature (4°C) induced an up-regulation of gene expression involved in gibberellic acid (GA) and auxin biosynthesis and the down-regulation of genes related to the abscisic acid (ABA) biosynthetic pathway. Thereby promoting embryo development and the stimulation of seed germination. Collectively, these data constitute a significant advance in our understanding of the role of cold temperatures in the regulation of seed germination in F. taipaiensis and also provide valuable transcriptomic data for seed dormancy for other non-model plant species.

Evaluating the dose, indication and agreement with guidelines of antimicrobial use in companion animal practice with natural language processing.

BACKGROUND: As antimicrobial prescribers, veterinarians contribute to the emergence of MDR pathogens. Antimicrobial stewardship programmes are an effective means of reducing the rate of development of antimicrobial resistance. A key component of antimicrobial stewardship programmes is selecting an appropriate antimicrobial agent for the presenting complaint and using an appropriate dose rate for an appropriate duration. OBJECTIVES: To describe antimicrobial usage, including dose, for common indications for antimicrobial use in companion animal practice. METHODS: Natural language processing (NLP) techniques were applied to extract and analyse clinical records. RESULTS: A total of 343 668 records for dogs and 109 719 records for cats administered systemic antimicrobials from 1 January 2013 to 31 December 2017 were extracted from the database. The NLP algorithms extracted dose, duration of therapy and diagnosis completely for 133 046 (39%) of the records for dogs and 40 841 records for cats (37%). The remaining records were missing one or more of these elements in the clinical data. The most common reason for antimicrobial administration was skin disorders (n = 66 198, 25%) and traumatic injuries (n = 15 932, 19%) in dogs and cats, respectively. Dose was consistent with guideline recommendations in 73% of cases where complete clinical data were available. CONCLUSIONS: Automated extraction using NLP methods is a powerful tool to evaluate large datasets and to enable veterinarians to describe the reasons that antimicrobials are administered. However, this can only be determined when the data presented in the clinical record are complete, which was not the case in most instances in this dataset. Most importantly, the dose administered varied and was often not consistent with guideline recommendations.

ChEMU 2020: Natural Language Processing Methods Are Effective for Information Extraction From Chemical Patents.

Chemical patents represent a valuable source of information about new chemical compounds, which is critical to the drug discovery process. Automated information extraction over chemical patents is, however, a challenging task due to the large volume of existing patents and the complex linguistic properties of chemical patents. The Cheminformatics Elsevier Melbourne University (ChEMU) evaluation lab 2020, part of the Conference and Labs of the Evaluation Forum 2020 (CLEF2020), was introduced to support the development of advanced text mining techniques for chemical patents. The ChEMU 2020 lab proposed two fundamental information extraction tasks focusing on chemical reaction processes described in chemical patents: (1) chemical named entity recognition, requiring identification of essential chemical entities and their roles in chemical reactions, as well as reaction conditions; and (2) event extraction, which aims at identification of event steps relating the entities involved in chemical reactions. The ChEMU 2020 lab received 37 team registrations and 46 runs. Overall, the performance of submissions for these tasks exceeded our expectations, with the top systems outperforming strong baselines. We further show the methods to be robust to variations in sampling of the test data. We provide a detailed overview of the ChEMU 2020 corpus and its annotation, showing that inter-annotator agreement is very strong. We also present the methods adopted by participants, provide a detailed analysis of their performance, and carefully consider the potential impact of data leakage on interpretation of the results. The ChEMU 2020 Lab has shown the viability of automated methods to support information extraction of key information in chemical patents.

Effective Control against Broadleaf Weed Species Provided by Biodegradable PBAT/PLA Mulch Film Embedded with the Herbicide 2-Methyl-4-Chlorophenoxyacetic Acid (MCPA).

Biodegradable mulches are considered a promising alternative to polyethylene-based, nonbiodegradable mulch for sustainable agriculture. In the present study, a bioactive 2-methyl-4- cholorophenoxyacetic acid/poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (MCPA-PHBV) conjugate blended with biodegradable poly(butylene adipate-co-terephthalate/polylactide (PBAT/PLA) was developed and used as mulch under controlled condition greenhouse pot experiment with fava bean (Vicia faba) as the nontarget crop species. The objectives were to examine the effectiveness of sustained-release of MCPA herbicide from biodegradable mulch for broadleaf weed suppression and to assess any adverse effects of the herbicide on the nontarget species (fava bean). The energy-dispersive X-ray spectroscopy analysis (EDS) suggests that a substantial quantity of the herbicide was released from the biodegradable mulch which effectively killed the broadleaf weed species even at 1% MCPA concentration. However, the higher concentrations of the herbicide adversely affected several physiological parameters of fava bean growth and development. Stomatal conductance decreased, while leaf temperature subsequently rose (at MCPA concentrations 5, 7.5, and 10%). The quantum yield of the Photosystem II (PSII) indicates that the photosynthetic efficiency was also restricted at concentrations 7.5% and 10%. Evidently, this slow-release herbicide system worked efficiently for broadleaf weed control but at higher concentrations, resulted in adverse physiological effects on the nontarget crop species. This study has demonstrated that biodegradable mulches containing MCPA herbicide are able to effectively inhibit the growth of broad leaf weed species and may be of potential importance in a wide variety of horticultural and agricultural applications.

Use of cefovecin in dogs and cats attending first-opinion veterinary practices in Australia.

BACKGROUND: Cefovecin is a long-acting third-generation cephalosporin commonly used in veterinary medicine. Third-generation cephalosporins are critically important antimicrobials that should only be used after culture and susceptibility testing. The authors describe the common indications for cefovecin use in dogs and cats, and the frequency of culture and susceptibility testing. MATERIALS AND METHODS: A cross-sectional study was performed using clinical records extracted from VetCompass Australia. A previously described method was used to identify records containing cefovecin. The reason for cefovecin use was annotated in situ in each consultation text. RESULTS: Over a six-month period (February and September 2018), 5180 (0.4 per cent) consultations involved cefovecin administration, of which 151 were excluded. Cats were administered cefovecin more frequently than dogs (1.9 per cent of cat consultations and 0.1 per cent of dog consultations). The most common reasons for cefovecin administration to cats were cat fight injuries and abscesses (28 per cent) and dermatitis (13 per cent). For dogs, the most common reasons for cefovecin administration were surgical prophylaxis (24 per cent) and dermatitis (19 per cent). Culture and susceptibility testing were reported in 16 cases (0.3 per cent). CONCLUSION: Cefovecin is used in many scenarios in dogs and cats where antimicrobials may be either not indicated or where an antimicrobial of lower importance to human health is recommended.

Describing the antimicrobial usage patterns of companion animal veterinary practices; free text analysis of more than 4.4 million consultation records.

Antimicrobial Resistance is a global crisis that veterinarians contribute to through their use of antimicrobials in animals. Antimicrobial stewardship has been shown to be an effective means to reduce antimicrobial resistance in hospital environments. Effective monitoring of antimicrobial usage patterns is an essential part of antimicrobial stewardship and is critical in reducing the development of antimicrobial resistance. The aim of this study is to describe how frequently antimicrobials were used in veterinary consultations and identify the most frequently used antimicrobials. Using VetCompass Australia, Natural Language Processing techniques, and the Australian Strategic Technical Advisory Group's (ASTAG) Rating system to classify the importance of antimicrobials, descriptive analysis was performed on the antimicrobials prescribed in consultations from 137 companion animal veterinary clinics in Australia between 2013 and 2017 (inclusive). Of the 4,400,519 consultations downloaded there were 595,089 consultations where antimicrobials were prescribed to dogs or cats. Antimicrobials were dispensed in 145 of every 1000 canine consultations; and 38 per 1000 consultations involved high importance rated antimicrobials. Similarly with cats, 108 per 1000 consultations had antimicrobials dispensed, and in 47 per 1000 consultations an antimicrobial of high importance rating was administered. The most common antimicrobials given to cats and dogs were cefovecin and amoxycillin clavulanate, respectively. The most common topical antimicrobial and high-rated topical antimicrobial given to dogs and cats was polymyxin B. This study provides a descriptive analysis of the antimicrobial usage patterns in Australia using methods that can be automated to inform antimicrobial use surveillance programs and promote antimicrobial stewardship.

A Need for Better Understanding Is the Major Determinant for Public Perceptions of Human Gene Editing.

The CRISPR/Cas system could provide an efficient and reliable means of editing the human genome and has the potential to revolutionize modern medicine; however, rapid developments are raising complex ethical issues. There has been significant scientific debate regarding the acceptability of some applications of CRISPR/Cas, with leaders in the field highlighting the need for the lay public's views to shape expert discussion. As such, we sought to determine the factors that influence public opinion on gene editing. We created a 17-item online survey translated into 11 languages and advertised worldwide. Topic modeling was used to analyze textual responses to determine what factors influenced respondents' opinions toward human somatic or embryonic gene editing, and how this varied among respondents with differing attitudes and demographic backgrounds. A total of 3,988 free-text responses were analyzed. Respondents had a mean age of 32 (range, 11-90) years, and 37% were female. The most prevalent topics cited were Future Generations, Research, Human Editing, Children, and Health. Respondents who disagreed with gene editing for health-related purposes were more likely to cite the topic Better Understanding than those who agreed to both somatic and embryonic gene editing. Respondents from Western backgrounds more frequently discussed Future Generations, compared with participants from Eastern countries. Religious respondents did not cite the topic Religious Beliefs more frequently than did nonreligious respondents, whereas Christian respondents were more likely to cite the topic Future Generations. Our results suggest that public resistance to human somatic or embryonic gene editing does not stem from an inherent mistrust of genome modification, but rather a desire for greater understanding. Furthermore, we demonstrate that factors influencing public opinion vary greatly amongst demographic groups. It is crucial that the determinants of public attitudes toward CRISPR/Cas be well understood so that the technology does not suffer the negative public sentiment seen with previous genetic biotechnologies.

Biodegradable PBAT/PLA Blend with Bioactive MCPA-PHBV Conjugate Suppresses Weed Growth.

The herbicide 2-methyl-4-chlorophenoxyacetic acid (MCPA) conjugated with poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) was prepared via a melt transesterification route. The resultant bioactive oligomer was then mixed with a blend of polylactide (PLA) and poly(butylene adipate-co-terephthalate) (PBAT) with different loadings to manufacture films to be used as a bioactive, biodegradable mulch to deliver the herbicide to target broadleaf weed species. The biological targeting of the MCPA-PHBV conjugate in the mulch film was investigated under glasshouse conditions using faba bean (Vicia faba) as a selective (nontarget) model crop species having broadleaf morphology. The presence of the MCPA-PHBV conjugate in the biodegradable PBTA/PLA blend was shown to completely suppress the growth of broadleaf weed species while displaying only a mild effect on the growth of the model crop. The degradation of the mulch film under glasshouse conditions was quite slow. The release of the MCPA-PHBV during this process was detected using NMR, GPC, EDS, and DSC analyses, indicating that the majority of the MCPA diffused out after MCPA-PHBV conjugate bond scission. These data provide a strong "proof of concept" and show that this biodegradable, bioactive film is a good candidate for future field applications and may be of wide agricultural applicability.

VetCompass Australia: A National Big Data Collection System for Veterinary Science.

VetCompass Australia is veterinary medical records-based research coordinated with the global VetCompass endeavor to maximize its quality and effectiveness for Australian companion animals (cats, dogs, and horses). Bringing together all seven Australian veterinary schools, it is the first nationwide surveillance system collating clinical records on companion-animal diseases and treatments. VetCompass data service collects and aggregates real-time, clinical records for researchers to interrogate, delivering sustainable and cost-effective access to data from hundreds of veterinary practitioners nationwide. Analysis of these clinical records will reveal geographical and temporal trends in the prevalence of inherited and acquired diseases, identify frequently prescribed treatments, revolutionize clinical auditing, help the veterinary profession to rank research priorities, and assure evidence-based companion-animal curricula in veterinary schools. VetCompass Australia will progress in three phases: (1) roll-out of the VetCompass platform to harvest Australian veterinary clinical record data; (2) development and enrichment of the coding (data-presentation) platform; and (3) creation of a world-first, real-time surveillance interface with natural language processing (NLP) technology. The first of these three phases is described in the current article. Advances in the collection and sharing of records from numerous practices will enable veterinary professionals to deliver a vastly improved level of care for companion animals that will improve their quality of life.

Advancing the science of myocardial recovery with mechanical circulatory support: A Working Group of the National, Heart, Lung, and Blood Institute.

The medical burden of heart failure (HF) has spurred interest in clinicians and scientists to develop therapies to restore the function of a failing heart. To advance this agenda, the National Heart, Lung, and Blood Institute (NHLBI) convened a Working Group of experts from June 2 to 3, 2016, in Bethesda, Maryland, to develop NHLBI recommendations aimed at advancing the science of cardiac recovery in the setting of mechanical circulatory support (MCS). MCS devices effectively reduce volume and pressure overload that drives the cycle of progressive myocardial dysfunction, thereby triggering structural and functional reverse remodeling. Research in this field could be innovative in many ways, and the Working Group specifically discussed opportunities associated with genome-phenome systems biology approaches; genetic epidemiology; bioinformatics and precision medicine at the population level; advanced imaging modalities, including molecular and metabolic imaging; and the development of minimally invasive surgical and percutaneous bioengineering approaches. These new avenues of investigations could lead to new treatments that target phylogenetically conserved pathways involved in cardiac reparative mechanisms. A central point that emerged from the NHLBI Working Group meeting was that the lessons learned from the MCS investigational setting can be extrapolated to the broader HF population. With the precedents set by the significant effect of studies of other well-controlled and tractable subsets on larger populations, such as the genetic work in both cancer and cardiovascular disease, the work to improve our understanding of cardiac recovery and resilience in MCS patients could be transformational for the greater HF population.

A Global Social Media Survey of Attitudes to Human Genome Editing.

Ongoing breakthroughs with CRISPR/Cas-based editing could potentially revolutionize modern medicine, but there are many questions to resolve about the ethical implications for its therapeutic application. We conducted a worldwide online survey of over 12,000 people recruited via social media to gauge attitudes toward this technology and discuss our findings here.