Machine learning algorithms for systematic review: reducing workload in a preclinical review of animal studies and reducing human screening error

BACKGROUND: Here, we outline a method of applying existing machine learning (ML) approaches to aid citation screening in an on-going broad and shallow systematic review of preclinical animal studies. The aim is to achieve a high-performing algorithm comparable to human screening that can reduce human resources required for carrying out this step of a systematic review. METHODS: We applied ML approaches to a broad systematic review of animal models of depression at the citation screening stage. We tested two independently developed ML approaches which used different classification models and feature sets. We recorded the performance of the ML approaches on an unseen validation set of papers using sensitivity, specificity and accuracy. We aimed to achieve 95% sensitivity and to maximise specificity. The classification model providing the most accurate predictions was applied to the remaining unseen records in the dataset and will be used in the next stage of the preclinical biomedical sciences systematic review. We used a cross-validation technique to assign ML inclusion likelihood scores to the human screened records, to identify potential errors made during the human screening process (error analysis). RESULTS: ML approaches reached 98.7% sensitivity based on learning from a training set of 5749 records, with an inclusion prevalence of 13.2%. The highest level of specificity reached was 86%. Performance was assessed on an independent validation dataset. Human errors in the training and validation sets were successfully identified using the assigned inclusion likelihood from the ML model to highlight discrepancies. Training the ML algorithm on the corrected dataset improved the specificity of the algorithm without compromising sensitivity. Error analysis correction leads to a 3% improvement in sensitivity and specificity, which increases precision and accuracy of the ML algorithm. CONCLUSIONS: This work has confirmed the performance and application of ML algorithms for screening in systematic reviews of preclinical animal studies. It has highlighted the novel use of ML algorithms to identify human error. This needs to be confirmed in other reviews with different inclusion prevalence levels, but represents a promising approach to integrating human decisions and automation in systematic review methodology

Immobilization of the white-rot fungus Anthracophyllum discolor to degrade the herbicide atrazine

Herbicides cause environmental concerns because they are toxic and accumulate in the environment, food products and water supplies. There is a need to develop safe, efficient and economical methods to remove them from the environment, often by biodegradation. Atrazine is such herbicide. White-rot fungi have the ability to degrade herbicides of potential utility. This study formulated a novel pelletized support to immobilize the white-rot fungus Anthracophyllum discolor to improve its capability to degrade the atrazine using a biopurification system (BS). Different proportions of sawdust, starch, corn meal and flaxseed were used to generate three pelletized supports (F1, F2 and F3). In addition, immobilization with coated and uncoated pelletized supports (CPS and UPS, respectively) was assessed. UPS-F1 was determined as the most effective system as it provided high level of manganese peroxidase activity and fungal viability. The half-life (t1/2) of atrazine decreased from 14 to 6 days for the control and inoculated samples respectively. Inoculation with immobilized A. discolor produced an increase in the fungal taxa assessed by DGGE and on phenoloxidase activity determined. The treatment improves atrazine degradation and reduces migration to surface and groundwater.Grant CONICYT/FONDAP/15130015Grant FONDECYT 112096

The Populus holobiont: dissecting the effects of plant niches and genotype on the microbiome

Background: Microorganisms serve important functions within numerous eukaryotic host organisms. An understanding of the variation in the plant niche-level microbiome, from rhizosphere soils to plant canopies, is imperative to gain a better understanding of how both the structural and functional processes of microbiomes impact the health of the overall plant holobiome. Using Populus trees as a model ecosystem, we characterized the archaeal/bacterial and fungal microbiome across 30 different tissue-level niches within replicated Populus deltoides and hybrid Populus trichocarpa × deltoides individuals using 16S and ITS2 rRNA gene analyses. Results: Our analyses indicate that archaeal/bacterial and fungal microbiomes varied primarily across broader plant habitat classes (leaves, stems, roots, soils) regardless of plant genotype, except for fungal communities within leaf niches, which were greatly impacted by the host genotype. Differences between tree genotypes are evident in the elevated presence of two potential fungal pathogens, Marssonina brunnea and Septoria sp., on hybrid P. trichocarpa × deltoides trees which may in turn be contributing to divergence in overall microbiome composition. Archaeal/bacterial diversity increased from leaves, to stem, to root, and to soil habitats, whereas fungal diversity was the greatest in stems and soils. Conclusions: This study provides a holistic understanding of microbiome structure within a bioenergy relevant plant host, one of the most complete niche-level analyses of any plant. As such, it constitutes a detailed atlas or map for further hypothesis testing on the significance of individual microbial taxa within specific niches and habitats of Populus and a baseline for comparisons to other plant species

Polyclonal rabbit anti-murine plasmacytoma cell globulins induce myeloma cells apoptosis and inhibit tumour growth in mice

Multiple myelomas (MMs) are etiologically heterogeneous and there are limited treatment options; indeed, current monoclonal antibody therapies have had limited success, so more effective antibodies are urgently needed. Polyclonal antibodies are a possible alternative because they target multiple antigens simultaneously. In this study, we produced polyclonal rabbit anti-murine plasmacytoma cell immunoglobulin (PAb) by immunizing rabbits with the murine plasmacytoma cell line MPC-11. The isolated PAb bound to plasma surface antigens in several MM cell lines, inhibited their proliferation as revealed by MTT assay, and induce apoptosis as indicated by flow cytometry, microscopic observation of apoptotic changes in morphology, and DNA fragmentation on agarose gels. The cytotoxicity of PAb on MPC-11 cell lines was both dose-dependent and time-dependent; PAb exerted a 50% inhibitory effect on MPC-11 cell viability at a concentration of 200 µg/ml in 48 h. Flow cytometry demonstrated that PAb treatment significantly increased the number of apoptotic cells (48.1%) compared with control IgG (8.3%). Apoptosis triggered by PAb was confirmed by activation of caspase-3, -8, and -9. Serial intravenous or intraperitoneal injections of PAb inhibited tumour growth and prolonged survival in mice bearing murine plasmacytoma, while TUNEL assay demonstrated that PAb induced statistically significant apoptosis (P < 0.05) compared to control treatments. We conclude that PAb is an effective agent for in vitro and in vivo induction of apoptosis in multiple myeloma and that exploratory clinical trials may be warranted

Transcriptome Analysis of Neisseria meningitidis in Human Whole Blood and Mutagenesis Studies Identify Virulence Factors Involved in Blood Survival

During infection Neisseria meningitidis (Nm) encounters multiple environments within the host, which makes rapid adaptation a crucial factor for meningococcal survival. Despite the importance of invasion into the bloodstream in the meningococcal disease process, little is known about how Nm adapts to permit survival and growth in blood. To address this, we performed a time-course transcriptome analysis using an ex vivo model of human whole blood infection. We observed that Nm alters the expression of ≈30% of ORFs of the genome and major dynamic changes were observed in the expression of transcriptional regulators, transport and binding proteins, energy metabolism, and surface-exposed virulence factors. In particular, we found that the gene encoding the regulator Fur, as well as all genes encoding iron uptake systems, were significantly up-regulated. Analysis of regulated genes encoding for surface-exposed proteins involved in Nm pathogenesis allowed us to better understand mechanisms used to circumvent host defenses. During blood infection, Nm activates genes encoding for the factor H binding proteins, fHbp and NspA, genes encoding for detoxifying enzymes such as SodC, Kat and AniA, as well as several less characterized surface-exposed proteins that might have a role in blood survival. Through mutagenesis studies of a subset of up-regulated genes we were able to identify new proteins important for survival in human blood and also to identify additional roles of previously known virulence factors in aiding survival in blood. Nm mutant strains lacking the genes encoding the hypothetical protein NMB1483 and the surface-exposed proteins NalP, Mip and NspA, the Fur regulator, the transferrin binding protein TbpB, and the L-lactate permease LctP were sensitive to killing by human blood. This increased knowledge of how Nm responds to adaptation in blood could also be helpful to develop diagnostic and therapeutic strategies to control the devastating disease cause by this microorganism

Sources of potential bias when combining routine data linkage and a national survey of secondary school-aged children: a record linkage study

Background Linking survey data to administrative records requires informed participant consent. When linkage includes child data, this includes parental and child consent. Little is known of the potential impacts of introducing consent to data linkage on response rates and biases in school-based surveys. This paper assessed: i) the impact on overall parental consent rates and sample representativeness when consent for linkage was introduced and ii) the quality of identifiable data provided to facilitate linkage. Methods Including an option for data linkage was piloted in a sub-sample of schools participating in the Student Health and Wellbeing survey, a national survey of adolescents in Wales, UK. Schools agreeing to participate were randomized 2:1 to receive versus not receive the data linkage question. Survey responses from consenting students were anonymised and linked to routine datasets (e.g. general practice, inpatient, and outpatient records). Parental withdrawal rates were calculated for linkage and non-linkage samples. Multilevel logistic regression models were used to compare characteristics between: i) consenters and non-consenters; ii) successfully and unsuccessfully linked students; and iii) the linked cohort and peers within the general population, with additional comparisons of mental health diagnoses and health service contacts. Results The sub-sample comprised 64 eligible schools (out of 193), with data linkage piloted in 39. Parental consent was comparable across linkage and non-linkage schools. 48.7% (n = 9232) of students consented to data linkage. Modelling showed these students were more likely to be younger, more affluent, have higher positive mental wellbeing, and report fewer risk-related behaviours compared to non-consenters. Overall, 69.8% of consenting students were successfully linked, with higher rates of success among younger students. The linked cohort had lower rates of mental health diagnoses (5.8% vs. 8.8%) and specialist contacts (5.2% vs. 7.7%) than general population peers. Conclusions Introducing data linkage within a national survey of adolescents had no impact on study completion rates. However, students consenting to data linkage, and those successfully linked, differed from non-consenting students on several key characteristics, raising questions concerning the representativeness of linked cohorts. Further research is needed to better understand decision-making processes around providing consent to data linkage in adolescent populations

Hotel smoking policies and their implementation: a survey of California hotel managers

Introduction Most states in the U.S. permit hotels to allow smoking in some guest rooms, and only five (Indiana, Michigan, North Dakota, Vermont, and Wisconsin) require that all hotel and motel rooms be 100% smoke-free (State and local 100% smokefree hotel and motel guest room laws enacted as of July 3, 2017). Little is known, however, about how hotels’ smoking policies have been implemented. This study examined hotels’ smoking policies and their implementation. Material and Methods A telephone survey of a random sample of 383 California hotel managers was conducted. Results Overall, 60.6% of hotels reported that smoking was prohibited in all guest rooms, and 4.7% reported that smoking was prohibited everywhere on their property. While California law permitted smoking in up to 65% of guest rooms, only 6.9% of rooms were reported as smoking-permitted. Over 90% of hotels had smoking rooms scattered among nonsmoking rooms, and about half of the smoking hotels reported that guests requesting either smoking or nonsmoking rooms were sometimes assigned to the other room type. When guests smoked in nonsmoking rooms fees could be substantial, but were often uncollected. Conclusions Hotel smoking policies and their implementation fall short of protecting nonsmoking guests and workers from exposure to secondhand and thirdhand smoke. Complete indoor smoking bans for all hotels are needed to close existing loopholes. Nonsmokers who wish to protect themselves from exposure to tobacco smoke should avoid hotels that permit smoking and instead stay in completely smoke-free hotels