Definition and research guide
This page combines a MeSH definition, publication statistics and selected research abstracts. It is a starting point for reading the literature, not a medical textbook or a systematic evidence review.
Content available: 3 complete abstracts (861 words), 9 references. Full article text is not stored or reproduced here; follow the PMID, DOI or PMC links to read the original sources and their access terms.
Definition
Excrement from the intestines, containing unabsorbed solids, waste products, secretions, and bacteria of the digestive system.
Definition: NLM Medical Subject Headings (MeSH) scope note (Public domain (U.S. National Library of Medicine); attribution requested). View the MeSH record.
Where it fits in the MeSH classification
- Broader topics
- Fluids and Secretions
- Narrower topics
- Meconium; Melena
- See also
- Manure; Melena
Related concepts
- Intestines
- Bacteria
- Digestive System
Research landscape
PubMed indexes 121,548 records tagged with the MeSH heading “Feces” (counts retrieved 2026-10-31). 24,847 of them (20.4%) were published from 2020 onward, and the busiest period so far is through 1999 with 44,903 records.
Reviews account for 4,490 records (3.7%) and meta-analyses for 527. Randomized controlled trials number 3,926 (3.2%) as a share of all records on the topic.
Show the data
| Period | Records |
|---|---|
| through 1999 | 44,903 |
| 2000-2009 | 19,669 |
| 2010-2019 | 32,129 |
| 2020 onward | 24,847 |
Publication-type chart data
| Type | Records | Share |
|---|---|---|
| Reviews | 4,490 | 3.7% |
| Meta-analyses | 527 | 0.4% |
| Randomized controlled trials | 3,926 | 3.2% |
Method: PubMed query "Feces"[MeSH Terms] with publication-type and date filters, retrieved 2026-10-31.
Reading the evidence in anatomy and physiology
Anatomy describes how body structures are built and physiology describes how they normally work, so most findings here are descriptive or mechanistic rather than evidence about treatment. Much of what is known comes from dissection, imaging, tissue studies and experiments in animals, and measurements in healthy volunteers can differ from those in people with disease or at different ages. Normal ranges vary between individuals and populations. Check whether a result was measured in people or in another species, the age and health of the participants, and the method used.
Evidence from open-access studies
These open-access studies come from PubMed’s best-match results for this heading (papers whose main topic it is), ranked here by citation count, with one recent addition. Each abstract is reproduced in full under its recorded open licence so that you can read the authors’ own words. They illustrate the literature; they are not a systematic summary of it. Citation counts measure attention, not reliability or clinical benefit.
| Study / authors | Year | Source label / design | Europe PMC citations | Licence / source |
|---|---|---|---|---|
| Colonoscopy and fecal immunochemical testing versus usual care in diagnostic colorectal cancer screening: the SCREESCO randomized controlled trial | 2026 | Randomized controlled trial Design not recorded in structured metadata | 7 | cc by PMID 41721101 · DOI · PMC13099434 |
| Faecal metabolites as a readout of habitual diet capture dietary interactions with the gut microbiome | 2025 | Research article Design not recorded in structured metadata | 6 | cc by PMID 41345102 · DOI · PMC12678775 |
| Detection of protozoan and helminth parasites in concentrated wet mounts of stool using a deep convolutional neural network | 2025 | Research article Design not recorded in structured metadata | 6 | cc by PMID 41117595 · DOI · PMC12607898 |
Colonoscopy and fecal immunochemical testing versus usual care in diagnostic colorectal cancer screening: the SCREESCO randomized controlled trial [1]
Abstract reading summary — source excerpts
- Opening excerpt
- There is a need to quantify the benefits and harms of colorectal cancer (CRC) screening using primary colonoscopy or fecal immunochemical testing (FIT) compared with usual care with no screening.
- Closing excerpt
- gov: NCT02078804 .
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (288 words)
There is a need to quantify the benefits and harms of colorectal cancer (CRC) screening using primary colonoscopy or fecal immunochemical testing (FIT) compared with usual care with no screening. Guidelines recommend screening in individuals aged 50-75 years using colonoscopy or FIT, and many screening programs use one-sample biennial FIT. Here we compare incidence of diagnosed CRCs and gastrointestinal and cardiovascular events between screening and usual care during the diagnostic phase of the SCREESCO trial. A randomized block method (no masking) assigned 278,280 individuals aged 60 years to once-only colonoscopy, 2 rounds of two-stool FIT with a low cutoff (10 μg g-1 feces) or usual care (control group) in a ratio of 1:6 for colonoscopy versus control and 1:2 for FIT versus control. In the analysis, 31,113 individuals were in the primary colonoscopy arm and 60,267 were in the FIT arm, and there were 186,671 primary colonoscopy controls, of whom 120,521 were also controls for comparison with the FIT arm. After a median follow-up of 4.8 years, the incidence rate of CRC was 107.9 in the colonoscopy arm and 99.9 in controls per 100,000 person-years (incidence rate ratio (IRR): 1.08, 95% confidence interval (CI): 0.91-1.28) and 96.0 in the FIT arm and 103.9 in controls (IRR: 0.92, 95% CI: 0.81-1.05). Rates of stage I-II CRC were higher in the colonoscopy arm (IRR: 1.38, 95% CI: 1.09-1.74) and in the FIT arm (IRR: 1.19, 95% CI: 0.99-1.43) versus controls. Rates of cardiovascular and gastrointestinal events were slightly higher in the intervention arms during the first year and were subsequently more similar to controls. Our findings of an increase in CRC detection implies a benefit of screening while the increase in adverse events suggests some initial harm. ClinicalTrials.gov: NCT02078804 .
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 41721101 · DOI · PMC13099434
Faecal metabolites as a readout of habitual diet capture dietary interactions with the gut microbiome [2]
Abstract reading summary — source excerpts
- Opening excerpt
- The interplay between diet and gut microbiome composition is complex.
- Closing excerpt
- Our findings reveal that faecal metabolites capture mediations between diet and the gut microbiome, advancing our understanding of diet-related disease risk and informing metabolite-based interventions.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (179 words)
The interplay between diet and gut microbiome composition is complex. Faecal metabolites, the end products of human and microbial metabolism, provide insights into these interactions. Here, we integrate faecal metabolomics, metagenomics, and habitual dietary data from 1810 individuals from the TwinsUK and 837 from the ZOE PREDICT1 cohorts. Using machine learning models, we find that faecal metabolites accurately predict reported intakes of 20 food groups (area under the curve (AUC) > 0.80 for meat, nuts and seeds, wholegrains, tea and coffee, and alcohol) and adherence to seven dietary patterns (AUC from 0.71 for the Plant-based Diet Index to 0.83 for the Dietary Approaches to Stop Hypertension score). Notably, the faecal metabolome is a stronger predictor of atherosclerotic cardiovascular disease risk (AUC = 0.86) than the Dietary Approaches to Stop Hypertension score (AUC = 0.66). We identify 414 associations between 19 food groups and 211 metabolites, that significantly correlate with microbial α-diversity and 217 species. Our findings reveal that faecal metabolites capture mediations between diet and the gut microbiome, advancing our understanding of diet-related disease risk and informing metabolite-based interventions.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 41345102 · DOI · PMC12678775
Detection of protozoan and helminth parasites in concentrated wet mounts of stool using a deep convolutional neural network [3]
Abstract reading summary — source excerpts
- Opening excerpt
- Comprehensive diagnosis of gastrointestinal parasites is largely reliant on traditional stool microscopy, despite gains in molecular diagnostics.
- Closing excerpt
- This work represents the completion of a groundbreaking effort to bring parasite screening into the technological age.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (394 words)
Comprehensive diagnosis of gastrointestinal parasites is largely reliant on traditional stool microscopy, despite gains in molecular diagnostics. Wet-mount examinations remain a significant challenge for traditional microscopy, digital microscopy, and artifical intelligence (AI). We developed and validated a deep convolutional neural network (CNN) model that provides highly sensitive detection and presumptive classification of enteric parasites. Twenty-seven different parasites were trained on a CNN model using a wide diversity of 4,049 unique parasite-positive specimens (determined by traditional microscopy) collected in the USA, Europe, Africa, and Asia. Model validation was performed with a unique holdout set. In clinical validation, AI correctly detected 250/265 positive specimens (94.3% agreement) and 94/100 negative specimens (94.0%) before discrepant resolution. AI also detected 169 additional organisms from the validation specimens that were not previously identified. These additional detections underwent further discrepant analysis to adjudicate the results by scan review and microscopy. After resolution and inclusion of newly defined true positives and false positives, the positive agreement was 472/477 (98.6%). Negative agreement was variable by organism, ranging from 91.8% to 100%. A relative limit of detection study was performed comparing AI to three technologists of varying experience using serial dilutions of specimens containing Entamoeba, Ascaris, Trichuris, and hookworm. AI consistently detected more organisms and at lower dilutions of parasites than humans, regardless of the technologist's experience. The use of AI for wet-mount analysis is highly sensitive and detects considerably more organisms than traditional microscopy alone. The use of AI simplifies the parasitology workflow and reduces the reliance on traditional microscopy.IMPORTANCEGastrointestinal parasite ova and parasite (O&P) detection from stools is a manual, labor-intensive method requiring highly trained personnel. This testing has been largely unchanged in 100 years, with the exception of minor improvements in processing and fixation techniques. O&Ps are performed worldwide on millions of stool specimens a year, making any improvements in the process highly impactful. Digital slide imaging and artificial intelligence were recently established tools by our laboratory for improving permanent trichrome stain interpretation. This work builds on that breakthrough and describes the first comprehensive wet-mount AI model development and validation. Improved diagnostic yield, analytical sensitivity, and precision were demonstrated in this work through full clinical laboratory validation studies, including a specimen collection sourced from four continents and a diversity of fixatives and preparation techniques. This work represents the completion of a groundbreaking effort to bring parasite screening into the technological age.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 41117595 · DOI · PMC12607898
Key reviews and meta-analyses
The most cited review-type papers among PubMed’s best-match results for this heading, listed with bibliographic details only. Follow the links for the full text and access terms.
- Research article Gastrointestinal Manifestations of SARS-CoV-2 Infection and Virus Load in Fecal Samples From a Hong Kong Cohort: Systematic Review and Meta-analysis [4]
- Research article Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer [5]
- Research article Fecal microbiota transplantation for Clostridium difficile infection: systematic review and meta-analysis [6]
- Research article Intestinal dysbiosis in preterm infants preceding necrotizing enterocolitis: a systematic review and meta-analysis [7]
- Research article C-Reactive Protein, Fecal Calprotectin, and Stool Lactoferrin for Detection of Endoscopic Activity in Symptomatic Inflammatory Bowel Disease Patients: A Systematic Review and Meta-Analysis [8]
- Research article Accuracy of fecal immunochemical tests for colorectal cancer: systematic review and meta-analysis [9]
References
- Westerberg M, Ludvigsson JF, Metcalfe C, Strömberg U, Blom J, Engstrand L, et al.. Colonoscopy and fecal immunochemical testing versus usual care in diagnostic colorectal cancer screening: the SCREESCO randomized controlled trial. . 2026. PMID 41721101 · DOI · PMC13099434
- Pope R, Visconti A, Zhang X, Louca P, Baleanu AF, Lin Y, et al.. Faecal metabolites as a readout of habitual diet capture dietary interactions with the gut microbiome. . 2025. PMID 41345102 · DOI · PMC12678775
- Mathison BA, Knight K, Potts J, Black B, Walker JF, Markow F, et al.. Detection of protozoan and helminth parasites in concentrated wet mounts of stool using a deep convolutional neural network. . 2025. PMID 41117595 · DOI · PMC12607898
- Cheung KS, Hung IFN, Chan PPY, Lung KC, Tso E, Liu R, et al.. Gastrointestinal Manifestations of SARS-CoV-2 Infection and Virus Load in Fecal Samples From a Hong Kong Cohort: Systematic Review and Meta-analysis. Gastroenterology. 2020. PMID 32251668 · DOI · PMC7194936
- Wirbel J, Pyl PT, Kartal E, Zych K, Kashani A, Milanese A, et al.. Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer. Nat Med. 2019. PMID 30936547 · DOI · PMC7984229
- Kassam Z, Lee CH, Yuan Y, Hunt RH. Fecal microbiota transplantation for Clostridium difficile infection: systematic review and meta-analysis. Am J Gastroenterol. 2013. PMID 23511459 · DOI
- Pammi M, Cope J, Tarr PI, Warner BB, Morrow AL, Mai V, et al.. Intestinal dysbiosis in preterm infants preceding necrotizing enterocolitis: a systematic review and meta-analysis. Microbiome. 2017. PMID 28274256 · DOI · PMC5343300
- Mosli MH, Zou G, Garg SK, Feagan SG, MacDonald JK, Chande N, et al.. C-Reactive Protein, Fecal Calprotectin, and Stool Lactoferrin for Detection of Endoscopic Activity in Symptomatic Inflammatory Bowel Disease Patients: A Systematic Review and Meta-Analysis. Am J Gastroenterol. 2015. PMID 25964225 · DOI
- Lee JK, Liles EG, Bent S, Levin TR, Corley DA. Accuracy of fecal immunochemical tests for colorectal cancer: systematic review and meta-analysis. Ann Intern Med. 2014. PMID 24658694 · DOI · PMC4189821
About this article
- Sources
- NLM Medical Subject Headings (definition, synonyms); NCBI PubMed (counts, identifiers); Europe PMC (open-access abstracts and citation counts).
- Verification
- Every reference was resolved against PubMed on 2026-10-31; title, year and DOI matched.
- Review state
- automated source verified; no human expert review has been recorded yet.
- Version
- 1, last updated 2026-10-31.
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Evidence strength
EvidenceHelix's computed view of the study designs behind this topic: how common meta-analyses, randomized trials and reviews are, how recent the literature is, and a plain-language reading. It describes the design mix of the literature. It is not a quality appraisal of any study, and it is not medical advice.