Evaluating intestinal biopsy tissue preservation methods to facilitate large-scale microbiome research
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PROVIDER: PRJEB74359 | ENA |
REPOSITORIES: ENA
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Project description:The investigation of wildlife gastrointestinal microbiomes by next-generation sequencing approaches is a growing field in microbial ecology and conservation. Such studies often face difficulties in sample preservation if neither freezing facilities nor liquid nitrogen (LQN) are readily available. Thus, in order to prevent microbial community changes because of bacterial growth after sampling, preservation buffers need to be applied to samples. However, the amount of microbial community variation attributable to the different preservation treatments and potentially affecting biological interpretation is hardly known. Here, we sampled feces of 11 sheep (Ovis aries sp.) by using swabs and analyzed the effect of air-drying, an inexpensive self-made nucleic acid preservation buffer (NAP), DNA/RNA Shield™, and RNAlater®, each together with freezing (for 10 days) or storing at room temperature (for 10 days) prior to 16S rRNA gene high-throughput sequencing to determine bacterial communities. Results revealed that the proportions of operational taxonomic units (OTUs) belonging to a bacterial phylum were affected by the preservation treatments, and that alpha diversities [observed OTUs, Shannon index, and phylogenetic diversity (PD)] were lower in all preservation treatments than in samples taken by forensic swabs and immediately frozen which is considered as the favored preservation treatment in the absence of any logistic constraints. Overall, NAP had better preservation qualities than RNAlater® and DNA/RNA Shield™ making this self-made buffer a valuable solution in wildlife microbiome studies.
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2021-09-20 | GSE183069 | GEO
Project description:Inflammatory Bowel Disease (IBD) is a progressive disease of the gut and consists of two types, Crohn’s Disease (CD) and Ulcerative Colitis (UC). It is a complex disease involving genetic, microbial, and environmental factors. The incidence of IBD is steadily increasing and current therapeutic options are plateauing. Thus treatments are evolving to 1. deeper levels of remission from clinical to endoscopic and histologic normalization and 2. Embrace novel targets or drug combinations. We explored whole transcriptome data generated in biopsy specimens sampled from a large cohort of adult IBD and control subjects to provide 1. a granular, objective and sensitive molecular measures of disease activity in the gut and 2. Novel molecular mechanisms and biomarkers underlying IBD pathology.
2022-09-16 | GSE193677 | GEO
Project description:A significant challenge for fMRI research is statistically controlling for false positives without omitting true effects. Although a number of traditional methods for multiple comparison correction exist, several alternative tools have been developed that do not rely on strict parametric assumptions, but instead implement alternative methods to correct for multiple comparisons. In this study, we evaluated three of these methods, Statistical non-Parametric Mapping (SnPM), 3DClustSim, and Threshold Free Cluster Enhancement (TFCE), by examining which method produced the most consistent outcomes even when spatially-autocorrelated noise was added to the original images. We assessed the false alarm rate and hit rate of each method after noise was applied to the original images.
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Project description:Population health researchers from different fields often address similar substantive questions but rely on different study designs, reflecting their home disciplines. This is especially true in studies involving causal inference, for which semantic and substantive differences inhibit interdisciplinary dialogue and collaboration. In this paper, we group nonrandomized study designs into two categories: those that use confounder-control (such as regression adjustment or propensity score matching) and those that rely on an instrument (such as instrumental variables, regression discontinuity, or differences-in-differences approaches). Using the Shadish, Cook, and Campbell framework for evaluating threats to validity, we contrast the assumptions, strengths, and limitations of these two approaches and illustrate differences with examples from the literature on education and health. Across disciplines, all methods to test a hypothesized causal relationship involve unverifiable assumptions, and rarely is there clear justification for exclusive reliance on one method. Each method entails trade-offs between statistical power, internal validity, measurement quality, and generalizability. The choice between confounder-control and instrument-based methods should be guided by these tradeoffs and consideration of the most important limitations of previous work in the area. Our goals are to foster common understanding of the methods available for causal inference in population health research and the tradeoffs between them; to encourage researchers to objectively evaluate what can be learned from methods outside one's home discipline; and to facilitate the selection of methods that best answer the investigator's scientific questions.
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Project description:BackgroundVariable selection for regression models plays a key role in the analysis of biomedical data. However, inference after selection is not covered by classical statistical frequentist theory, which assumes a fixed set of covariates in the model. This leads to over-optimistic selection and replicability issues.MethodsWe compared proposals for selective inference targeting the submodel parameters of the Lasso and its extension, the adaptive Lasso: sample splitting, selective inference conditional on the Lasso selection (SI), and universally valid post-selection inference (PoSI). We studied the properties of the proposed selective confidence intervals available via R software packages using a neutral simulation study inspired by real data commonly seen in biomedical studies. Furthermore, we present an exemplary application of these methods to a publicly available dataset to discuss their practical usability.ResultsFrequentist properties of selective confidence intervals by the SI method were generally acceptable, but the claimed selective coverage levels were not attained in all scenarios, in particular with the adaptive Lasso. The actual coverage of the extremely conservative PoSI method exceeded the nominal levels, and this method also required the greatest computational effort. Sample splitting achieved acceptable actual selective coverage levels, but the method is inefficient and leads to less accurate point estimates. The choice of inference method had a large impact on the resulting interval estimates, thereby necessitating that the user is acutely aware of the goal of inference in order to interpret and communicate the results.ConclusionsDespite violating nominal coverage levels in some scenarios, selective inference conditional on the Lasso selection is our recommended approach for most cases. If simplicity is strongly favoured over efficiency, then sample splitting is an alternative. If only few predictors undergo variable selection (i.e. up to 5) or the avoidance of false positive claims of significance is a concern, then the conservative approach of PoSI may be useful. For the adaptive Lasso, SI should be avoided and only PoSI and sample splitting are recommended. In summary, we find selective inference useful to assess the uncertainties in the importance of individual selected predictors for future applications.
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Project description:Item parameter estimates of a common item on a new test form may change abnormally due to reasons such as item overexposure or change of curriculum. A common item, whose change does not fit the pattern implied by the normally behaved common items, is defined as an outlier. Although improving equating accuracy, detecting and eliminating of outliers may cause a content imbalance among common items. Robust scale transformation methods have recently been proposed to solve this problem when only one outlier is present in the data, although it is not uncommon to see multiple outliers in practice. In this simulation study, the authors examined the robust scale transformation methods under conditions where there were multiple outlying common items. Results indicated that the robust scale transformation methods could reduce the influences of multiple outliers on scale transformation and equating. The robust methods performed similarly to a traditional outlier detection and elimination method in terms of reducing the influence of outliers while keeping adequate content balance.
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Project description:MicroRNA expression profiling in matched lesional skin samples from 25 patients with psoriasis using the miRNA analysis platform miRCURY LNATM MicroRNA array (v. 11.0) (Exiqon). Aim: To explore the effect of three different preservation methods (Formalin-fixation paraffin-embedding (FFPE), frozen (FS) and OCT-embedding (OCT)) on miRNA expression levels in matched lesional skin samples from 25 patients with psoriasis. Three-condition experiment, FS vs. FFPE, OCT vs. FFPE and FS vs. OCT. Biological replicates: 25 matched samples from patients with psoriasis. One replicate per array.
2012-03-22 | E-GEOD-28729 | biostudies-arrayexpress
Project description:Genomics and molecular imaging, along with clinical and translational research have transformed biomedical science into a data-intensive scientific endeavor. For researchers to benefit from Big Data sets, developing long-term biomedical digital data preservation strategy is very important. In this opinion article, we discuss specific actions that researchers and institutions can take to make research data a continued resource even after research projects have reached the end of their lifecycle. The actions involve utilizing an Open Archival Information System model comprised of six functional entities: Ingest, Access, Data Management, Archival Storage, Administration and Preservation Planning. We believe that involvement of data stewards early in the digital data life-cycle management process can significantly contribute towards long term preservation of biomedical data. Developing data collection strategies consistent with institutional policies, and encouraging the use of common data elements in clinical research, patient registries and other human subject research can be advantageous for data sharing and integration purposes. Specifically, data stewards at the onset of research program should engage with established repositories and curators to develop data sustainability plans for research data. Placing equal importance on the requirements for initial activities (e.g., collection, processing, storage) with subsequent activities (data analysis, sharing) can improve data quality, provide traceability and support reproducibility. Preparing and tracking data provenance, using common data elements and biomedical ontologies are important for standardizing the data description, making the interpretation and reuse of data easier. The Big Data biomedical community requires scalable platform that can support the diversity and complexity of data ingest modes (e.g. machine, software or human entry modes). Secure virtual workspaces to integrate and manipulate data, with shared software programs (e.g., bioinformatics tools), can facilitate the FAIR (Findable, Accessible, Interoperable and Reusable) use of data for near- and long-term research needs.
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Project description:BackgroundEndocytoscopy (ECS) provides a magnification of approximately 450× for real-time observation of lesion nuclei. Using ECS, we aimed to evaluate whether sufficient samples for diagnosis can be obtained during bronchoscopy. We also investigated whether ECS can enable two-class diagnosis of malignant or non-malignant transbronchial biopsy specimens in real-time during bronchoscopy.MethodsThis was a single-facility, prospective, observational, ex vivo study. Forty cases with localized peripheral pulmonary lesions underwent transbronchial biopsy with endobronchial ultrasonography using a guide sheath. Each biopsy specimen was immediately observed and evaluated endocytoscopically after the collection by the bronchoscopic procedure.ResultsThirty-seven cases were enrolled. The diagnostic accuracy achieved by ECS was 91.9% (34/37). The agreement rate between the endocytoscopic evaluation and pathological diagnosis of each specimen (170 specimens) was 65.3% (111/170). The median time required for endocytoscopic evaluation per specimen was 70 s. When we judged a specimen to be malignant a second time on ECS evaluations of five specimens in one case, pathologically malignant specimens were collected in 26 of 27 cases (96.3%).ConclusionsECS with methylene blue staining may aid in the two-class diagnosis of malignant or non-malignant transbronchial biopsy specimens during bronchoscopy. This may reduce the number of tissue biopsies.
| S-EPMC9834692 | biostudies-literature