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ObjectivesNucleic acid amplification tests (NAATs) are highly sensitive for the detection ofChlamydia trachomatis(CT) andNeisseria gonorrhoeae(NG) DNA/ribosomal RNA (rRNA). Previous studies have demonstrated contamination of surfaces in sexual health clinics (SHCs) with CT/NG. False positive results can occur if patient samples are contaminated by environmental DNA/rRNA. This can have a dramatic impact on patients’ lives and relationships. Previous attempts to reduce contamination, through staff training alone, have been unsuccessful. We aimed to investigate environmental contamination levels in SHCs and to assess a two-armed intervention aimed at reducing surface contamination.MethodsQuestionnaires were sent to 10 SHCs. Six clinics, with differing characteristics, were selected to participate in sample collection. Each clinic followed standardised instructions to sample surfaces using a CT/NG NAAT swab. Clinics were invited to introduce the two-armed intervention. The first arm was cleaning with a chlorine-based cleaning solution once daily. The second arm involved introducing clinic-specific changes to reduce contamination.Results7/10 (70%) clinics completed the questionnaire. Overall, 88/263 (33%) swabs were positive for CT/NG. Clinics 1, 3 and 4 had high levels of contamination, with 28/64 (44%), 17/33 (52%) and 30/52 (58%) swabs testing positive, respectively. Clinics 2 and 6 had lower levels of contamination, with 7/46 (15%) and 6/35 (17%), respectively. 0/33 (0%) of swabs were positive at clinic 5 and this was the only clinic already using a chlorine-based solution to clean all surfaces and delivering twice-yearly clinic-specific infection control training. Following both intervention arms at clinic 1, 2/50 (4%, pConclusionsEnvironmental contamination is a concern in SHCs. We recommend that all SHCs monitor contamination levels and, if necessary, consider using chlorine-based cleaning products and introduce clinic-specific changes to address environmental contamination.

The deployment of machine learning (ML) models in the health care domain can increase the speed and accuracy of diagnosis and improve treatment planning and patient care. Translating academic research to applications that are deployable in clinical settings requires the ability to generalize and high reproducibility, which are contingent on a rigorous and sound methodology for the development and evaluation of ML models. This article describes the fundamental concepts and processes for ML model evaluation and highlights common workflows. It concludes with a discussion of the requirements for the deployment of ML models in clinical settings.

This article reviews the history of artificial intelligence and introduces the reader to major events that prompted interest in the field, as well as pitfalls and challenges that have slowed its development. The purpose of this article is to provide a high-level historical perspective on the development of the field over the past decades, highlighting the potential of the field for transforming health care, but also the importance of setting realistic expectations for artificial intelligence applications to avoid repeating historical cyclical trends and a third "artificial intelligence winter."

Nucleic acid amplification tests (NAATs) are highly sensitive for the detection ofQuestionnaires were sent to 10 SHCs. Six clinics, with differing characteristics, were selected to participate in sample collection. Each clinic followed standardised instructions to sample surfaces using a CT/NG NAAT swab. Clinics were invited to introduce the two-armed intervention. The first arm was cleaning with a chlorine-based cleaning solution once daily. The second arm involved introducing clinic-specific changes to reduce contamination.7/10 (70%) clinics completed the questionnaire. Overall, 88/263 (33%) swabs were positive for CT/NG. Clinics 1, 3 and 4 had high levels of contamination, with 28/64 (44%), 17/33 (52%) and 30/52 (58%) swabs testing positive, respectively. Clinics 2 and 6 had lower levels of contamination, with 7/46 (15%) and 6/35 (17%), respectively. 0/33 (0%) of swabs were positive at clinic 5 and this was the only clinic already using a chlorine-based solution to clean all surfaces and delivering twice-yearly clinic-specific infection control training. Following both intervention arms at clinic 1, 2/50 (4%, p0.0001) swabs tested positive for CT/NG. Clinic 4 introduced each arm separately. After the first intervention, 13/52 (25%, p=0.003) swabs tested positive and following the second arm 4/50 (8%, p0.0001) swabs were positive.Environmental contamination is a concern in SHCs. We recommend that all SHCs monitor contamination levels and, if necessary, consider using chlorine-based cleaning products and introduce clinic-specific changes to address environmental contamination.

Gene set analysis has been widely used to gain insight from high-throughput expression studies. Although various tools and methods have been developed for gene set analysis, there is no consensus among researchers regarding best practice(s). Most often, evaluation studies have reported contradictory recommendations of which methods are superior. Therefore, an unbiased quantitative framework for evaluations of gene set analysis methods will be valuable. Such a framework requires gene expression datasets where enrichment status of gene sets is known a priori. In the absence of such gold standard datasets, artificial datasets are commonly used for evaluations of gene set analysis methods, however, they often rely on oversimplifying assumptions that make them biased in favor of or against a given method. In this paper, we propose a quantitative framework for evaluation of gene set analysis methods by synthesizing expression datasets using real data, without relying on oversimplifying or unrealistic assumptions, while preserving complex gene–gene correlations and retaining the distribution of expression values. The utility of the quantitative approach is shown by evaluating ten widely used gene set analysis methods. An implementation of the proposed method is publicly available. We suggest using Silver to evaluate existing and new gene set analysis methods. Evaluation using Silver provides a better understanding of current methods and can aid in the development of gene set analysis methods to achieve higher specificity without sacrificing sensitivity.

Ancestors of the Antarctic icefishes (family Channichthyidae) were benthic and had no swim bladder, making it energetically expensive to rise from the ocean floor. To exploit the water column, benthopelagic icefishes were hypothesized to have evolved a skeleton with “reduced bone,” which gross anatomical data supported. Here, we tested the hypothesis that changes to icefish bones also occurred below the level of gross anatomy. Histology and micro‐CT imaging of representative craniofacial bones (i.e., ceratohyal, frontal, dentary, and articular) of extant Antarctic fish species specifically evaluated two features that might cause the appearance of “reduced bone”: bone microstructure (e.g., bone volume fraction and structure linear density) and bone mineral density (BMD, or mass of mineral per volume of bone). Measures of bone microstructure were not consistently different in bones from the icefishes Chaenocephalus aceratus and Champsocephalus gunnari, compared to the related benthic notothenioids Notothenia coriiceps and Gobionotothen gibberifrons. Some quantitative measures, such as bone volume fraction and structure linear density, were significantly increased in some icefish bones compared to homologous bones of non‐icefish. However, such differences were rare, and no microstructural measures were consistently different in icefishes across all bones and species analyzed. Furthermore, BMD was similar among homologous bones of icefish and non‐icefish Antarctic notothenioids. In summary, “reduced bone” in icefishes was not due to systemic changes in bone microstructure or BMD, raising the prospect that “reduced bone” in icefish occurs only at the gross anatomic level (i.e., smaller or fewer bones). Given that icefishes exhibit delayed skeletal development compared to non‐icefish Antarctic fishes, combining these phenotypic data with genomic data might clarify genetic changes driving skeletal heterochrony., Antarctic icefishes are amazing examples of evolutionary adaptation. Here, we show quantitatively that icefish skeletons did not change bone microstructure or bone mineral density, relative to closely related fish species.

Similarities and differences in the associations of biological entities among species can provide us with a better understanding of evolutionary relationships. Often the evolution of new phenotypes results from changes to interactions in pre-existing biological networks and comparing networks across species can identify evidence of conservation or adaptation. Gene co-expression networks (GCNs), constructed from high-throughput gene expression data, can be used to understand evolution and the rise of new phenotypes. The increasing abundance of gene expression data makes GCNs a valuable tool for the study of evolution in non-model organisms. In this paper, we cover motivations for why comparing these networks across species can be valuable for the study of evolution. We also review techniques for comparing GCNs in the context of evolution, including local and global methods of graph alignment. While some protein-protein interaction (PPI) bioinformatic methods can be used to compare co-expression networks, they often disregard highly relevant properties, including the existence of continuous and negative values for edge weights. Also, the lack of comparative datasets in non-model organisms has hindered the study of evolution using PPI networks. We also discuss limitations and challenges associated with cross-species comparison using GCNs, and provide suggestions for utilizing co-expression network alignments as an indispensable tool for evolutionary studies going forward.

BackgroundMycoplasma genitalium (Mgen) causes non-gonococcal urethritis (NGU) and is believed to cause pelvic inflammatory disease (PID). High rates of macrolide resistance are well documented globally for Mgen. In Brighton, patients with NGU and PID are tested for Mgen and test of cure (TOC) offered post-treatment.MethodsDemographic, clinical and treatment history data were collected over a 12-month period for all Mgen-positive patients in a Brighton-based genitourinary clinic.ResultsThere were 114 patients with Mgen. 18% (61/339) of men with NGU and 9% (15/160) of women with PID had Mgen. 62/114 (54%) returned for first test TOC 4 weeks after treatment. 27/62 (44%) had a positive TOC; 25/27 (92.6%) had received azithromycin first line (500 mg stat then 250 mg OD for 4 days), 1/27 (3.7%) had received moxifloxacin first line (400 mg OD for 14 days) and 1/27 (3.7%) had received doxycycline first line (100 mg BD for 7 days). 20/27 (74%) returned for a second TOC 4 weeks later. 5/20 (25%) patients were positive on second TOC; 3/5 (60%) had received azithromycin second line and 2/5 (40%) had received moxifloxacin second line. Patients were more likely to have a positive TOC if they were at risk of reinfection (9/27 positive TOC vs 3/35 negative TOC; p=0.02). Patients given moxifloxacin were more likely to have a negative TOC (1/27 positive TOC vs 9/35 negative TOC; p=0.03) than those who received other antibiotic regimens.ConclusionsTreatment failure rates for Mgen following azithromycin use are substantial, raising concerns regarding resistance. However, reinfection risk may contribute, suggesting a requirement for improved public awareness and clinician knowledge.

Simple Summary Head and neck squamous cell carcinoma (HNSCC) is the most common mucosal malignancy of the head and neck and a leading cause of cancer death. HNSCC arises from different primary anatomical locations that are typically combined during radiomic analyses assuming that the radiomic features, i.e., quantitative image-based features, are similar based on histopathologic characteristics. However, whether these quantitative features are comparable across tumor sites remains unknown. The aim of our retrospective study was to assess if systematic differences exist between radiomic features based on different tumor sites in HNSCC and how they might affect machine learning model performance in endpoint prediction. Using a population of 605 HNSCC patients, we observed significant differences in radiomic features of tumors from different locations and showed that these differences can impact machine learning model performance. This suggests that tumor site should be considered when developing and evaluating radiomics-based models. Abstract Current radiomic studies of head and neck squamous cell carcinomas (HNSCC) are typically based on datasets combining tumors from different locations, assuming that the radiomic features are similar based on histopathologic characteristics. However, molecular pathogenesis and treatment in HNSCC substantially vary across different tumor sites. It is not known if a statistical difference exists between radiomic features from different tumor sites and how they affect machine learning model performance in endpoint prediction. To answer these questions, we extracted radiomic features from contrast-enhanced neck computed tomography scans (CTs) of 605 patients with HNSCC originating from the oral cavity, oropharynx, and hypopharynx/larynx. The difference in radiomic features of tumors from these sites was assessed using statistical analyses and Random Forest classifiers on the radiomic features with 10-fold cross-validation to predict tumor sites, nodal metastasis, and HPV status. We found statistically significant differences (p-value ≤ 0.05) between the radiomic features of HNSCC depending on tumor location. We also observed that differences in quantitative features among HNSCC from different locations impact the performance of machine learning models. This suggests that radiomic features may reveal biologic heterogeneity complementary to current gold standard histopathologic evaluation. We recommend considering tumor site in radiomic studies of HNSCC.

The extensive body of research and advances in machine learning (ML) and the availability of a large volume of patient data make ML a powerful tool for producing models with the potential for widespread deployment in clinical settings. This article provides an overview of the classic supervised and unsupervised ML methods as well as fundamental concepts required for understanding how to develop generalizable and high-performing ML applications. It also describes the important steps for developing a ML model and how decisions made in these steps affect model performance and ability to generalize.

The deployment of machine learning (ML) models in the health care domain can increase the speed and accuracy of diagnosis and improve treatment planning and patient care. Translating academic research to applications that are deployable in clinical settings requires the ability to generalize and high reproducibility, which are contingent on a rigorous and sound methodology for the development and evaluation of ML models. This article describes the fundamental concepts and processes for ML model evaluation and highlights common workflows. It concludes with a discussion of the requirements for the deployment of ML models in clinical settings.

Identifying relationships between genes facilitates the comparison of different cell types at the transcriptomic level. Gene expression data such as RNA-seq can be used to construct co-expression networks, which is one means in systems biology to describe the coordinated expression patterns among genes across samples. Currently, there is no consensus as to the number of samples required to construct a reproducible gene co-expression network. Indeed, irreproducibility of gene expression experiments is a major challenge, and small sample sizes tend to be one of the major causes. However, recommending a single sample size that applies to all scenarios may not be practical. As such, we utilize a systematic, quantitative approach to study the effect of sample size on the reproducibility of constructing large, fully-connected gene co-expression networks using several correlation-based measures or mutual information. This approach does not require synthetic datasets that are constructed based on oversimplified assumptions nor is it dependent on known functional annotations. Further, we describe two similarity measures to measure consistency and use them to determine if the biological variance present within samples impacts the rate at which the networks will stabilize and compare to networks with randomly reassigned nodes. Our results show that the required number of samples to construct consistent co-expression networks could be influenced by the tissue type used to construct the networks as well as the similarity measure used to measure consistency.

Gene set analysis methods are widely used to provide insight into high-throughput gene expression data. There are many gene set analysis methods available. These methods rely on various assumptions and have different requirements, strengths and weaknesses. In this paper, we classify gene set analysis methods based on their components, describe the underlying requirements and assumptions for each class, and provide directions for future research in developing and evaluating gene set analysis methods.

Patients often present to GPs with intimate problems that they find embarrassing. GPs need to be comfortable dealing with a vast array of sexual health problems and know when to refer to specialist genitourinary medicine (GUM) physicians. Anogenital warts are common, with approximately 130 000 cases treated annually in GUM clinics throughout the UK. However, for patients who do not wish to attend GUM services, or when these are not easily accessible, a basic knowledge of current guidance is essential to ensure that patients can be effectively managed in primary care.

An effective publication-quality visualization tells a concise story from data. Methods and tools that facilitate making such visualizations are valuable to the scientific community. In this paper, we introduce pineplot, an R package for generating insightful visualizations called pine plots. Pine plots are applicable to a wide variety of datasets and create a holistic picture of the relationship between variables across different experimental conditions. A pine plot provides a means to visualize a group of symmetric matrices, each represented by triangular heat maps. Pine plots can be used to visualize large datasets for exploratory data analysis while controlling for different potentially confounding factors. The utility of the package is demonstrated by visualizing gene expression values of tissue-specific genes from RNA-seq data and the clinical factors in a liver disease and a heart disease dataset. The implementation of pineplot offers a straightforward procedure for generating pine plots; full control of the aesthetic elements of generated plots; and the possibility of augmenting generated plots with extra layers of graphical elements to further extend their usability.

Gene set analysis is a well-established approach for analyzing high-throughput gene expression data. The choice of gene set database used for gene set analysis may affect the outcome of the analysis. Therefore, understanding characteristics of these databases is vital to the success of gene set analysis. Due to the sheer size of the gene set databases, a comprehensive qualitative evaluation of them is impractical. In this paper, we quantitatively study several well-established gene set databases. We propose and use a quantitative measure for assessing the similarity between gene set databases. Also, we introduce presence score, for quantifying the degree to which a given gene is represented in a database, and permeability score, for quantifying the degree to which genes in a given list co-occur in the gene sets of a database. A maximum achievable coverage score is defined based on the permeability score. Using the maximum achievable coverage score, we propose a methodology to statistically determine whether a phenotype of interest is well-represented in a given database. To study the effect of the choice of gene set database on the result of gene set analysis and show the utility of the maximum achievable coverage score, we conduct an experiment using two widely used gene set analysis methods and three expression datasets. The results suggest that the choice of gene set database might profoundly affect the outcome of the analysis. Also, our findings show that the permeability score and maximum achievable coverage can be used to guide the selection of an appropriate gene set database for a given study.

Gene set analysis is a quantitative approach for generating biological insight from gene expression datasets. The abundance of gene set analysis methods speaks to their popularity, but raises the question of the extent to which results are affected by the choice of method. Our systematic analysis of 13 popular methods using 6 different datasets, from both DNA microarray and RNA-Seq origin, shows that this choice matters a great deal. We observed that the overall number of gene sets reported by each method differed by up to 2 orders of magnitude, and there was a bias toward reporting large gene sets with some methods. Furthermore, there was substantial disagreement between the 20 most statistically significant gene sets reported by the methods. This was also observed when expanding to the 100 most statistically significant reported gene sets. For different datasets of the same phenotype/condition, the top 20 and top 100 most significant results also showed little to no agreement even when using the same method. GAGE, PAGE, and ORA were the only methods able to achieve relatively high reproducibility when comparing the 20 and 100 most statistically significant gene sets. Biological validation on a juvenile idiopathic arthritis (JIA) dataset showed wide variation in terms of the relevance of the top 20 and top 100 most significant gene sets to known biology of the disease, where GAGE predicted the most relevant gene sets, followed by GSEA, ORA, and PAGE.

Introduction: Delta checks use two specimen test results taken in succession in order to detect test result changes greater than expected physiological variation. One of the most common and serious errors detected by delta checks is specimen mix-up errors. The positive and negative predictive values of delta checks for detecting specimen mix-up errors, however, are largely unknown. Materials and Methods: We addressed this question by first constructing a stochastic dynamic model using repeat test values for five analytes from approximately 8000 inpatients in Calgary, Alberta, Canada. The analytes examined were sodium, potassium, chloride, bicarbonate, and creatinine. The model simulated specimen mix-up errors by randomly switching a set number of pairs of second test results. Sensitivities and specificities were then calculated for each analyte for six combinations of delta check equations and cut-off values from the published literature. Results: Delta check specificities obtained from this model ranged from 50% to 99%; however the sensitivities were generally below 20% with the exception of creatinine for which the best performing delta check had a sensitivity of 82.8%. Within a plausible incidence range of specimen mix-ups the positive predictive values of even the best performing delta check equation and analyte became negligible. Conclusion: This finding casts doubt on the ongoing clinical utility of delta checks in the setting of low rates of specimen mix-ups.

As an expedition doctor in Tanzania Katie Ovens gained useful clinical and managerial experience

Background/introduction Equivocal NAAT results for Chlamydia and gonorrhoea (GC) cause treatment dilemmas for health professionals as there are no definitive management guidelines. Debate continues whether to rescreen and treat patients with equivocal results or rescreen the patient and await results before treatment. Aim(s)/objectives To investigate rescreening tests for equivocal results and establish when patients should be offered treatment. Methods A retrospective study of equivocal results from 2 GUM clinics between November 2013 and May 2014, and a third clinic between March 2010 and May 2014. HIV positive patients’ results were included. Paper notes or electronic systems were examined. Data was collected using a standardised proforma and analysed using excel software. Results 76 equivocal results (2.2% of positive results) were investigated. 62 patients (83.8%) attended recall appointments, 36 patients (58.0%) were offered treatment and rescreened, 14 (22.6%) were rescreened and awaited results prior to treatment and 2 patients (3.2%) were treated with no retest sent. 8 patients (16.1%) were treated due to a positive GC result at a second site alongside the equivocal result. Of the 54 equivocal results re-tested, 3 (5.6%) were positive and all of these resulted from equivocal GC tests (19 rescreened). All 35 rescreened equivocal Chlamydia tests were negative. Discussion/conclusion There is currently variation in how clinicians are managing equivocal results. The findings suggest that initiating treatment for Chlamydia before rescreening may result in over treatment. GC equivocal results are more likely to be positive on re-testing, thus clinicians should have a lower threshold for treating these at the time of rescreening.