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Microbial community analysis can be biased by multiple technical factors, such as storage conditions, DNA extraction, or amplification conditions. In a high-throughput laboratory that relies on samples obtained from thousands of different subjects, knowledge of the extent of subject-introduced sampling and storage variation on the outcome of the inferred microbiome, as well as the effect of laboratory-introduced variation caused by reagent batches, equipment, or operator on the consistency of these processes within the laboratory is paramount. Here, we analyzed the effect of sampling from different parts of the same stool specimen or on different consecutive days, as well as short-term storage of samples at different temperatures on microbiome profiles obtained by 16S rRNA gene amplification. Each of these factors had relatively little effect on the microbial composition. In addition, replicate amplification of 44 stool samples showed reproducible results. Finally, 363 independent replicate extractions and amplifications of a single human homogenized stool (HS) specimen showed reproducible results (average Lin’s correlation = 0.95), with little variation introduced by HS batch, operator, extraction equipment, or DNA sequencer. In all cases, variations between replicates were significantly smaller than those between individual samples; subject identity always was the largest determinant. We propose that homogenized stool specimens could be used as quality control to routinely monitor the laboratory process and to validate new methods.

The composition of the vaginal microbiome, including both the presence of pathogens involved in sexually transmitted infections (STI) as well as commensal microbiota, has been shown to have important associations for a woman’s reproductive and general health. Currently, healthcare providers cannot offer comprehensive vaginal microbiome screening, but are limited to the detection of individual pathogens, such as high-risk human papillomavirus (hrHPV), the predominant cause of cervical cancer. There is no single test on the market that combines HPV, STI, and microbiome screening. Here, we describe a novel inclusive women’s health assay that combines self-sampling with sequencing-based HPV detection and genotyping, vaginal microbiome analysis, and STI-associated pathogen detection. The assay includes genotyping and detection of 14 hrHPV types, 5 low-risk HPV types (lrHPV), as well as the relative abundance of 32 bacterial taxa of clinical importance, includingLactobacillus,Sneathia,Gardnerella, and 4 pathogens involved in STI, with high sensitivity, specificity, and reproducibility. For each of these taxa, healthy ranges were determined in a group of 50 self-reported healthy women. The hrHPV portion of the test was evaluated against the Digene High-Risk HPV HC2 DNA test with vaginal samples obtained from 185 women. Results were concordant for 181/185 of the samples (overall agreement of 97.83%, Cohen’s kappa = 0.93), with sensitivity and specificity values of 94.74% and 98.64%, respectively. Two discrepancies were caused by the Digene assay’s known cross-reactivity with low-risk HPV types, while two additional samples were found to contain hrHPV not detected by Digene. This novel assay could be used to complement conventional cervical cancer screening, because its self-sampling format can expand access among women who would otherwise not participate, and because of its additional information about the composition of the vaginal microbiome and the presence of pathogens.

The composition of the vaginal microbiome, including both the presence of pathogens involved in sexually transmitted infections (STI) as well as commensal microbiota, has been shown to have important associations for a woman´s reproductive and general health. Currently, healthcare providers cannot offer comprehensive vaginal microbiome screening, but are limited to the detection of individual pathogens, such as high-risk human papillomavirus (hrHPV), the predominant cause of cervical cancer. There is no single test on the market that combines HPV, STI, and microbiome screening. Here, we describe a novel inclusive vaginal health assay that combines self-sampling with sequencing-based HPV detection and genotyping, vaginal microbiome analysis, and STI-associated pathogen detection. The assay includes genotyping and detection of 14 hrHPV types, 5 low-risk HPV types (lrHPV), as well as the relative abundance of 31 bacterial taxa of clinical importance, including Lactobacillus, Sneathia, Gardnerella, and 3 pathogens involved in STI, with high sensitivity, specificity, and reproducibility. For each of these taxa, reference ranges were determined in a group of 50 self-reported healthy women. The HPV sequencing portion of the test was evaluated against the digene High-Risk HPV HC2 DNA test. For hrHPV genotyping, agreement was 95.3% with a kappa of 0.804 (601 samples); after removal of samples in which the digene hrHPV probe showed cross-reactivity with lrHPV types, the sensitivity and specificity of the hrHPV genotyping assay were 94.5% and 96.6%, respectively, with a kappa of 0.841. For lrHPV genotyping, agreement was 93.9% with a kappa of 0.788 (148 samples), while sensitivity and specificity were 100% and 92.9%, respectively. This novel assay could be used to complement conventional cervical cancer screening, because its self-sampling format can expand access among women who would otherwise not participate, and because of its additional information about the composition of the vaginal microbiome and the presence of pathogens. Fil: Bik, Elisabeth M.. Ubiome; Fil: Bird, Sara W.. Ubiome; Fil: Bustamante, Juan Pablo. Universidad Nacional de Entre Ríos. Instituto de Investigación y Desarrollo en Bioingeniería y Bioinformática - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación y Desarrollo en Bioingeniería y Bioinformática; Argentina Fil: Leon, Luis E.. Ubiome

To evaluate, in patients with chest pain, the diagnostic value of ST elevation (STE) in lead aVR during stress testing prior to (99m) Tc-sestamibi scanning correlating ischaemic territory with angiographic findings.Consecutive patients attending for (99m) Tc-sestamibi myocardial perfusion imaging (MPI) completed a treadmill protocol. Peak exercise ECGs were coded. STEor=0.05 mV in lead aVR was considered significant. Gated perfusion images and findings at angiography were assessed.STE in lead aVR occurred in 25% (138/557) of the patients. More patients with STE in aVR had a reversible defect on imaging compared with those who had no STE in aVR (41%, 56/138 vs 27%, 114/419, p=0.003). Defects indicating a left anterior descending artery (LAD) culprit lesion were more common in the STE in aVR group (20%, 27/138 vs 9%, 39/419, p=0.001). There was a trend towards coronary artery stenosis (70%) in a double vessel distribution involving the LAD in those patients who had STE in aVR compared with those who did not (22%, 8/37 vs 5%, 4/77, p=0.06). Logistic regression analysis demonstrated that STE in aVR (OR 1.36, p=0.233) is not an independent predictor of inducible abnormality when adjusted for STD0.1 mV (OR 1.69, p=0.026). However, using anterior wall defect as an end-point, STE in aVR (OR 2.77, p=0.008) was a predictor even after adjustment for STD (OR 1.43, p=0.281).STE in lead aVR during exercise does not diagnose more inducible abnormalities than STD alone. However, unlike STD, which is not predictive of a territory of ischaemia, STE in aVR may indicate an anterior wall defect.

In this lesson, you’ll learn how to use python with the Zotero API to interact with your Zotero library. The Zotero API is a powerful interface that would allow you to build a complete Zotero client from scratch if you so desired. But like most APIs, it works in small, discrete steps, so we have to build our way up to the complicated requests we might want to use to access our Zotero libraries. But this incremental building gives us plenty of time to learn as we go along.

In Intro to the Zotero API, you learned a little bit about Zotero; now you can access some of its functions using Python scripts. In this lesson, you will create a new item in a Zotero library and add some basic metadata such as title and date.