Normalization of the microbiota in patients after treatment for colonic lesions
Colorectal cancer is a worldwide health problem. Despite growing evidence that members of the gut microbiota can drive tumorigenesis, little is known about what happens to it after treatment for an adenoma or carcinoma. This study tested the hypothesis that treatment for adenoma or carcinoma alters...
Saved in:
| Published in | Microbiome Vol. 5; no. 1; pp. 150 - 10 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
BioMed Central Ltd
16.11.2017
BioMed Central BMC |
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | Colorectal cancer is a worldwide health problem. Despite growing evidence that members of the gut microbiota can drive tumorigenesis, little is known about what happens to it after treatment for an adenoma or carcinoma. This study tested the hypothesis that treatment for adenoma or carcinoma alters the abundance of bacterial populations associated with disease to those associated with a normal colon. We tested this hypothesis by sequencing the 16S rRNA genes in the feces of 67 individuals before and after treatment for adenoma (N = 22), advanced adenoma (N = 19), and carcinoma (N = 26).
There were small changes to the bacterial community associated with adenoma or advanced adenoma and large changes associated with carcinoma. The communities from patients with carcinomas changed significantly more than those with adenoma following treatment (P value < 0.001). Although treatment was associated with intrapersonal changes, the change in the abundance of individual OTUs in response to treatment was not consistent within diagnosis groups (P value > 0.05). Because the distribution of OTUs across patients and diagnosis groups was irregular, we used the random forest machine learning algorithm to identify groups of OTUs that could be used to classify pre and post-treatment samples for each of the diagnosis groups. Although the adenoma and carcinoma models could reliably differentiate between the pre- and post-treatment samples (P value < 0.001), the advanced-adenoma model could not (P value = 0.61). Furthermore, there was little overlap between the OTUs that were indicative of each treatment. To determine whether individuals who underwent treatment were more likely to have OTUs associated with normal colons we used a larger cohort that contained individuals with normal colons and those with adenomas, advanced adenomas, and carcinomas. We again built random forest models and measured the change in the positive probability of having one of the three diagnoses to assess whether the post-treatment samples received the same classification as the pre-treatment samples. Samples from patients who had carcinomas changed toward a microbial milieu that resembles the normal colon after treatment (P value < 0.001). Finally, we were unable to detect any significant differences in the microbiota of individuals treated with surgery alone and those treated with chemotherapy or chemotherapy and radiation (P value > 0.05).
By better understanding the response of the microbiota to treatment for adenomas and carcinomas, it is likely that biomarkers will eventually be validated that can be used to quantify the risk of recurrence and the likelihood of survival. Although it was difficult to identify significant differences between pre- and post-treatment samples from patients with adenoma and advanced adenoma, this was not the case for carcinomas. Not only were there large changes in pre- versus post-treatment samples for those with carcinoma, but also these changes were toward a more normal microbiota. |
|---|---|
| AbstractList | Background Colorectal cancer is a worldwide health problem. Despite growing evidence that members of the gut microbiota can drive tumorigenesis, little is known about what happens to it after treatment for an adenoma or carcinoma. This study tested the hypothesis that treatment for adenoma or carcinoma alters the abundance of bacterial populations associated with disease to those associated with a normal colon. We tested this hypothesis by sequencing the 16S rRNA genes in the feces of 67 individuals before and after treatment for adenoma (N = 22), advanced adenoma (N = 19), and carcinoma (N = 26). Results There were small changes to the bacterial community associated with adenoma or advanced adenoma and large changes associated with carcinoma. The communities from patients with carcinomas changed significantly more than those with adenoma following treatment (P value < 0.001). Although treatment was associated with intrapersonal changes, the change in the abundance of individual OTUs in response to treatment was not consistent within diagnosis groups (P value > 0.05). Because the distribution of OTUs across patients and diagnosis groups was irregular, we used the random forest machine learning algorithm to identify groups of OTUs that could be used to classify pre and post-treatment samples for each of the diagnosis groups. Although the adenoma and carcinoma models could reliably differentiate between the pre- and post-treatment samples (P value < 0.001), the advanced-adenoma model could not (P value = 0.61). Furthermore, there was little overlap between the OTUs that were indicative of each treatment. To determine whether individuals who underwent treatment were more likely to have OTUs associated with normal colons we used a larger cohort that contained individuals with normal colons and those with adenomas, advanced adenomas, and carcinomas. We again built random forest models and measured the change in the positive probability of having one of the three diagnoses to assess whether the post-treatment samples received the same classification as the pre-treatment samples. Samples from patients who had carcinomas changed toward a microbial milieu that resembles the normal colon after treatment (P value < 0.001). Finally, we were unable to detect any significant differences in the microbiota of individuals treated with surgery alone and those treated with chemotherapy or chemotherapy and radiation (P value > 0.05). Conclusions By better understanding the response of the microbiota to treatment for adenomas and carcinomas, it is likely that biomarkers will eventually be validated that can be used to quantify the risk of recurrence and the likelihood of survival. Although it was difficult to identify significant differences between pre- and post-treatment samples from patients with adenoma and advanced adenoma, this was not the case for carcinomas. Not only were there large changes in pre- versus post-treatment samples for those with carcinoma, but also these changes were toward a more normal microbiota. Keywords: Microbiota, Colorectal cancer, Polyps, Treatment, Risk factor Colorectal cancer is a worldwide health problem. Despite growing evidence that members of the gut microbiota can drive tumorigenesis, little is known about what happens to it after treatment for an adenoma or carcinoma. This study tested the hypothesis that treatment for adenoma or carcinoma alters the abundance of bacterial populations associated with disease to those associated with a normal colon. We tested this hypothesis by sequencing the 16S rRNA genes in the feces of 67 individuals before and after treatment for adenoma (N = 22), advanced adenoma (N = 19), and carcinoma (N = 26). There were small changes to the bacterial community associated with adenoma or advanced adenoma and large changes associated with carcinoma. The communities from patients with carcinomas changed significantly more than those with adenoma following treatment (P value < 0.001). Although treatment was associated with intrapersonal changes, the change in the abundance of individual OTUs in response to treatment was not consistent within diagnosis groups (P value > 0.05). Because the distribution of OTUs across patients and diagnosis groups was irregular, we used the random forest machine learning algorithm to identify groups of OTUs that could be used to classify pre and post-treatment samples for each of the diagnosis groups. Although the adenoma and carcinoma models could reliably differentiate between the pre- and post-treatment samples (P value < 0.001), the advanced-adenoma model could not (P value = 0.61). Furthermore, there was little overlap between the OTUs that were indicative of each treatment. To determine whether individuals who underwent treatment were more likely to have OTUs associated with normal colons we used a larger cohort that contained individuals with normal colons and those with adenomas, advanced adenomas, and carcinomas. We again built random forest models and measured the change in the positive probability of having one of the three diagnoses to assess whether the post-treatment samples received the same classification as the pre-treatment samples. Samples from patients who had carcinomas changed toward a microbial milieu that resembles the normal colon after treatment (P value < 0.001). Finally, we were unable to detect any significant differences in the microbiota of individuals treated with surgery alone and those treated with chemotherapy or chemotherapy and radiation (P value > 0.05). By better understanding the response of the microbiota to treatment for adenomas and carcinomas, it is likely that biomarkers will eventually be validated that can be used to quantify the risk of recurrence and the likelihood of survival. Although it was difficult to identify significant differences between pre- and post-treatment samples from patients with adenoma and advanced adenoma, this was not the case for carcinomas. Not only were there large changes in pre- versus post-treatment samples for those with carcinoma, but also these changes were toward a more normal microbiota. BackgroundColorectal cancer is a worldwide health problem. Despite growing evidence that members of the gut microbiota can drive tumorigenesis, little is known about what happens to it after treatment for an adenoma or carcinoma. This study tested the hypothesis that treatment for adenoma or carcinoma alters the abundance of bacterial populations associated with disease to those associated with a normal colon. We tested this hypothesis by sequencing the 16S rRNA genes in the feces of 67 individuals before and after treatment for adenoma (N = 22), advanced adenoma (N = 19), and carcinoma (N = 26).ResultsThere were small changes to the bacterial community associated with adenoma or advanced adenoma and large changes associated with carcinoma. The communities from patients with carcinomas changed significantly more than those with adenoma following treatment (P value < 0.001). Although treatment was associated with intrapersonal changes, the change in the abundance of individual OTUs in response to treatment was not consistent within diagnosis groups (P value > 0.05). Because the distribution of OTUs across patients and diagnosis groups was irregular, we used the random forest machine learning algorithm to identify groups of OTUs that could be used to classify pre and post-treatment samples for each of the diagnosis groups. Although the adenoma and carcinoma models could reliably differentiate between the pre- and post-treatment samples (P value < 0.001), the advanced-adenoma model could not (P value = 0.61). Furthermore, there was little overlap between the OTUs that were indicative of each treatment. To determine whether individuals who underwent treatment were more likely to have OTUs associated with normal colons we used a larger cohort that contained individuals with normal colons and those with adenomas, advanced adenomas, and carcinomas. We again built random forest models and measured the change in the positive probability of having one of the three diagnoses to assess whether the post-treatment samples received the same classification as the pre-treatment samples. Samples from patients who had carcinomas changed toward a microbial milieu that resembles the normal colon after treatment (P value < 0.001). Finally, we were unable to detect any significant differences in the microbiota of individuals treated with surgery alone and those treated with chemotherapy or chemotherapy and radiation (P value > 0.05).ConclusionsBy better understanding the response of the microbiota to treatment for adenomas and carcinomas, it is likely that biomarkers will eventually be validated that can be used to quantify the risk of recurrence and the likelihood of survival. Although it was difficult to identify significant differences between pre- and post-treatment samples from patients with adenoma and advanced adenoma, this was not the case for carcinomas. Not only were there large changes in pre- versus post-treatment samples for those with carcinoma, but also these changes were toward a more normal microbiota. Abstract Background Colorectal cancer is a worldwide health problem. Despite growing evidence that members of the gut microbiota can drive tumorigenesis, little is known about what happens to it after treatment for an adenoma or carcinoma. This study tested the hypothesis that treatment for adenoma or carcinoma alters the abundance of bacterial populations associated with disease to those associated with a normal colon. We tested this hypothesis by sequencing the 16S rRNA genes in the feces of 67 individuals before and after treatment for adenoma (N = 22), advanced adenoma (N = 19), and carcinoma (N = 26). Results There were small changes to the bacterial community associated with adenoma or advanced adenoma and large changes associated with carcinoma. The communities from patients with carcinomas changed significantly more than those with adenoma following treatment (P value < 0.001). Although treatment was associated with intrapersonal changes, the change in the abundance of individual OTUs in response to treatment was not consistent within diagnosis groups (P value > 0.05). Because the distribution of OTUs across patients and diagnosis groups was irregular, we used the random forest machine learning algorithm to identify groups of OTUs that could be used to classify pre and post-treatment samples for each of the diagnosis groups. Although the adenoma and carcinoma models could reliably differentiate between the pre- and post-treatment samples (P value < 0.001), the advanced-adenoma model could not (P value = 0.61). Furthermore, there was little overlap between the OTUs that were indicative of each treatment. To determine whether individuals who underwent treatment were more likely to have OTUs associated with normal colons we used a larger cohort that contained individuals with normal colons and those with adenomas, advanced adenomas, and carcinomas. We again built random forest models and measured the change in the positive probability of having one of the three diagnoses to assess whether the post-treatment samples received the same classification as the pre-treatment samples. Samples from patients who had carcinomas changed toward a microbial milieu that resembles the normal colon after treatment (P value < 0.001). Finally, we were unable to detect any significant differences in the microbiota of individuals treated with surgery alone and those treated with chemotherapy or chemotherapy and radiation (P value > 0.05). Conclusions By better understanding the response of the microbiota to treatment for adenomas and carcinomas, it is likely that biomarkers will eventually be validated that can be used to quantify the risk of recurrence and the likelihood of survival. Although it was difficult to identify significant differences between pre- and post-treatment samples from patients with adenoma and advanced adenoma, this was not the case for carcinomas. Not only were there large changes in pre- versus post-treatment samples for those with carcinoma, but also these changes were toward a more normal microbiota. Colorectal cancer is a worldwide health problem. Despite growing evidence that members of the gut microbiota can drive tumorigenesis, little is known about what happens to it after treatment for an adenoma or carcinoma. This study tested the hypothesis that treatment for adenoma or carcinoma alters the abundance of bacterial populations associated with disease to those associated with a normal colon. We tested this hypothesis by sequencing the 16S rRNA genes in the feces of 67 individuals before and after treatment for adenoma (N = 22), advanced adenoma (N = 19), and carcinoma (N = 26). There were small changes to the bacterial community associated with adenoma or advanced adenoma and large changes associated with carcinoma. The communities from patients with carcinomas changed significantly more than those with adenoma following treatment (P value < 0.001). Although treatment was associated with intrapersonal changes, the change in the abundance of individual OTUs in response to treatment was not consistent within diagnosis groups (P value > 0.05). Because the distribution of OTUs across patients and diagnosis groups was irregular, we used the random forest machine learning algorithm to identify groups of OTUs that could be used to classify pre and post-treatment samples for each of the diagnosis groups. Although the adenoma and carcinoma models could reliably differentiate between the pre- and post-treatment samples (P value < 0.001), the advanced-adenoma model could not (P value = 0.61). Furthermore, there was little overlap between the OTUs that were indicative of each treatment. To determine whether individuals who underwent treatment were more likely to have OTUs associated with normal colons we used a larger cohort that contained individuals with normal colons and those with adenomas, advanced adenomas, and carcinomas. We again built random forest models and measured the change in the positive probability of having one of the three diagnoses to assess whether the post-treatment samples received the same classification as the pre-treatment samples. Samples from patients who had carcinomas changed toward a microbial milieu that resembles the normal colon after treatment (P value < 0.001). Finally, we were unable to detect any significant differences in the microbiota of individuals treated with surgery alone and those treated with chemotherapy or chemotherapy and radiation (P value > 0.05). By better understanding the response of the microbiota to treatment for adenomas and carcinomas, it is likely that biomarkers will eventually be validated that can be used to quantify the risk of recurrence and the likelihood of survival. Although it was difficult to identify significant differences between pre- and post-treatment samples from patients with adenoma and advanced adenoma, this was not the case for carcinomas. Not only were there large changes in pre- versus post-treatment samples for those with carcinoma, but also these changes were toward a more normal microbiota. Colorectal cancer is a worldwide health problem. Despite growing evidence that members of the gut microbiota can drive tumorigenesis, little is known about what happens to it after treatment for an adenoma or carcinoma. This study tested the hypothesis that treatment for adenoma or carcinoma alters the abundance of bacterial populations associated with disease to those associated with a normal colon. We tested this hypothesis by sequencing the 16S rRNA genes in the feces of 67 individuals before and after treatment for adenoma (N = 22), advanced adenoma (N = 19), and carcinoma (N = 26).BACKGROUNDColorectal cancer is a worldwide health problem. Despite growing evidence that members of the gut microbiota can drive tumorigenesis, little is known about what happens to it after treatment for an adenoma or carcinoma. This study tested the hypothesis that treatment for adenoma or carcinoma alters the abundance of bacterial populations associated with disease to those associated with a normal colon. We tested this hypothesis by sequencing the 16S rRNA genes in the feces of 67 individuals before and after treatment for adenoma (N = 22), advanced adenoma (N = 19), and carcinoma (N = 26).There were small changes to the bacterial community associated with adenoma or advanced adenoma and large changes associated with carcinoma. The communities from patients with carcinomas changed significantly more than those with adenoma following treatment (P value < 0.001). Although treatment was associated with intrapersonal changes, the change in the abundance of individual OTUs in response to treatment was not consistent within diagnosis groups (P value > 0.05). Because the distribution of OTUs across patients and diagnosis groups was irregular, we used the random forest machine learning algorithm to identify groups of OTUs that could be used to classify pre and post-treatment samples for each of the diagnosis groups. Although the adenoma and carcinoma models could reliably differentiate between the pre- and post-treatment samples (P value < 0.001), the advanced-adenoma model could not (P value = 0.61). Furthermore, there was little overlap between the OTUs that were indicative of each treatment. To determine whether individuals who underwent treatment were more likely to have OTUs associated with normal colons we used a larger cohort that contained individuals with normal colons and those with adenomas, advanced adenomas, and carcinomas. We again built random forest models and measured the change in the positive probability of having one of the three diagnoses to assess whether the post-treatment samples received the same classification as the pre-treatment samples. Samples from patients who had carcinomas changed toward a microbial milieu that resembles the normal colon after treatment (P value < 0.001). Finally, we were unable to detect any significant differences in the microbiota of individuals treated with surgery alone and those treated with chemotherapy or chemotherapy and radiation (P value > 0.05).RESULTSThere were small changes to the bacterial community associated with adenoma or advanced adenoma and large changes associated with carcinoma. The communities from patients with carcinomas changed significantly more than those with adenoma following treatment (P value < 0.001). Although treatment was associated with intrapersonal changes, the change in the abundance of individual OTUs in response to treatment was not consistent within diagnosis groups (P value > 0.05). Because the distribution of OTUs across patients and diagnosis groups was irregular, we used the random forest machine learning algorithm to identify groups of OTUs that could be used to classify pre and post-treatment samples for each of the diagnosis groups. Although the adenoma and carcinoma models could reliably differentiate between the pre- and post-treatment samples (P value < 0.001), the advanced-adenoma model could not (P value = 0.61). Furthermore, there was little overlap between the OTUs that were indicative of each treatment. To determine whether individuals who underwent treatment were more likely to have OTUs associated with normal colons we used a larger cohort that contained individuals with normal colons and those with adenomas, advanced adenomas, and carcinomas. We again built random forest models and measured the change in the positive probability of having one of the three diagnoses to assess whether the post-treatment samples received the same classification as the pre-treatment samples. Samples from patients who had carcinomas changed toward a microbial milieu that resembles the normal colon after treatment (P value < 0.001). Finally, we were unable to detect any significant differences in the microbiota of individuals treated with surgery alone and those treated with chemotherapy or chemotherapy and radiation (P value > 0.05).By better understanding the response of the microbiota to treatment for adenomas and carcinomas, it is likely that biomarkers will eventually be validated that can be used to quantify the risk of recurrence and the likelihood of survival. Although it was difficult to identify significant differences between pre- and post-treatment samples from patients with adenoma and advanced adenoma, this was not the case for carcinomas. Not only were there large changes in pre- versus post-treatment samples for those with carcinoma, but also these changes were toward a more normal microbiota.CONCLUSIONSBy better understanding the response of the microbiota to treatment for adenomas and carcinomas, it is likely that biomarkers will eventually be validated that can be used to quantify the risk of recurrence and the likelihood of survival. Although it was difficult to identify significant differences between pre- and post-treatment samples from patients with adenoma and advanced adenoma, this was not the case for carcinomas. Not only were there large changes in pre- versus post-treatment samples for those with carcinoma, but also these changes were toward a more normal microbiota. |
| ArticleNumber | 150 |
| Audience | Academic |
| Author | Ruffin, Mack T. Schloss, Patrick D. Baxter, Nielson T. Rogers, Mary A. M. Sze, Marc A. |
| Author_xml | – sequence: 1 givenname: Marc A. surname: Sze fullname: Sze, Marc A. – sequence: 2 givenname: Nielson T. surname: Baxter fullname: Baxter, Nielson T. – sequence: 3 givenname: Mack T. surname: Ruffin fullname: Ruffin, Mack T. – sequence: 4 givenname: Mary A. M. surname: Rogers fullname: Rogers, Mary A. M. – sequence: 5 givenname: Patrick D. orcidid: 0000-0002-6935-4275 surname: Schloss fullname: Schloss, Patrick D. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29145893$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kktv1DAUhSNUREvpD2CDIrGBRYpfceINUlXxGKmiEo-1dePH1KMkLrYHAb-eOw-qToVIFrGuv3Oce32eVkdznF1VPafknNJevsmCUNk3hHYN4VI2_FF1wohQDZO0P7q3Pq7Ocl4RfBQVneifVMcMV22v-El1_SmmCcbwG0qIcx19XW5cPQWT4hBigTrM9S3uubnkGnxxqS7JQZmwUPuYahPHOAdTjy6jQX5WPfYwZne2_55W396_-3r5sbm6_rC4vLhqjJSkNMZb1ipOFBtoPzgHnLPB8s5aPigiDSjbSUuVaK2BthUdF94NXICzhoAAflotdr42wkrfpjBB-qUjBL0txLTUkEowo9M9EcwRa0GiUik5CC89sw5NscoNer3ded2uhwkPwNYSjAemhztzuNHL-EO3sle0b9Hg1d4gxe9rl4ueQjZuHGF2cZ01VVIy7JALRF8-QFdxnWYcFVIdU4yQreGeWgI2EGYf8VyzMdUX7eYSSddKpM7_QeFrHV4gpsUHrB8IXh8IkCnuZ1nCOme9-PL5kH1xfyh30_gbHQToDsCo5Jycv0Mo0ZuE6l1CNSZUbxKqN5rugcaEsk0e_nkY_6P8A1Js6Hs |
| CitedBy_id | crossref_primary_10_1080_19490976_2023_2244139 crossref_primary_10_3390_ijms20174145 crossref_primary_10_1136_gutjnl_2023_329429 crossref_primary_10_3389_fcimb_2023_1191126 crossref_primary_10_4174_astr_2020_99_1_44 crossref_primary_10_3390_nu15030563 crossref_primary_10_1111_jgh_15559 crossref_primary_10_4236_jbm_2023_1110017 crossref_primary_10_1016_j_crbiot_2024_100211 crossref_primary_10_3390_nu13010143 crossref_primary_10_1007_s10620_020_06126_4 crossref_primary_10_1128_mBio_01454_19 crossref_primary_10_5217_ir_2018_00013 crossref_primary_10_1111_1462_2920_14498 crossref_primary_10_24884_0042_4625_2019_178_5_132_138 crossref_primary_10_1038_s41571_023_00766_x crossref_primary_10_1099_jmm_0_001699 crossref_primary_10_2174_1389201022666210910094827 crossref_primary_10_1016_j_chom_2024_07_008 crossref_primary_10_1093_narcan_zcac011 crossref_primary_10_3390_jcm9092735 crossref_primary_10_3389_fmicb_2019_01746 crossref_primary_10_1128_msystems_00775_21 crossref_primary_10_31146_1682_8658_ecg_218_10_63_76 crossref_primary_10_1080_19490976_2022_2038863 crossref_primary_10_1111_jgh_14735 crossref_primary_10_1128_msystems_00018_22 crossref_primary_10_3748_wjg_v26_i20_2498 crossref_primary_10_3390_biomedicines9101305 crossref_primary_10_1007_s12029_023_00925_4 crossref_primary_10_1016_j_clcc_2021_10_004 crossref_primary_10_1016_j_compbiomed_2021_104556 crossref_primary_10_3389_fonc_2020_526009 crossref_primary_10_1080_00071668_2021_2003753 crossref_primary_10_1186_s12951_023_02158_w crossref_primary_10_3389_fonc_2021_745262 crossref_primary_10_1016_j_jfma_2019_01_002 crossref_primary_10_3390_cancers13123061 crossref_primary_10_1038_s41467_021_23265_y crossref_primary_10_1080_00207454_2022_2030327 crossref_primary_10_3390_microorganisms7110561 crossref_primary_10_1016_j_chom_2020_03_005 crossref_primary_10_1093_bioinformatics_btab879 crossref_primary_10_1016_j_bbrc_2024_150580 crossref_primary_10_3389_fmicb_2020_01106 crossref_primary_10_1038_s41598_018_28671_9 crossref_primary_10_3389_fmicb_2023_1236583 crossref_primary_10_1128_mBio_00630_18 crossref_primary_10_14218_ERHM_2019_00026 crossref_primary_10_3390_microorganisms12071336 |
| Cites_doi | 10.1890/12-2010.1 10.1001/jamaoncol.2015.1377 10.1158/1940-6207.CAPR-14-0129 10.3945/ajcn.112.046607 10.1186/2049-2618-2-20 10.1128/AEM.02810-10 10.1128/mSphere.00102-16 10.1128/AEM.00062-07 10.1186/s13073-016-0290-3 10.1126/science.1224820 10.1016/j.chom.2016.07.006 10.1016/j.chom.2013.07.007 10.1055/s-2006-956445 10.1055/s-0029-1242458 10.1038/ncomms5724 10.1093/nar/gkm864 10.1128/mBio.00692-13 10.1023/A:1010933404324 10.1073/pnas.1406199111 10.4161/gmic.1.3.12360 10.1093/bioinformatics/btr381 10.1038/ismej.2011.109 10.1038/ncomms7528 10.1073/pnas.1010203108 10.1038/nm.2015 10.1111/j.2517-6161.1995.tb02031.x 10.1128/mSphere.00001-15 10.1101/gr.126573.111 10.4174/astr.2014.86.3.143 10.15252/msb.20145645 10.1128/AEM.01043-13 |
| ContentType | Journal Article |
| Copyright | COPYRIGHT 2017 BioMed Central Ltd. Copyright BioMed Central 2017 The Author(s) 2017 |
| Copyright_xml | – notice: COPYRIGHT 2017 BioMed Central Ltd. – notice: Copyright BioMed Central 2017 – notice: The Author(s) 2017 |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM ISR 3V. 7X7 7XB 88E 8FE 8FH 8FI 8FJ 8FK ABUWG AFKRA AZQEC BBNVY BENPR BHPHI CCPQU DWQXO FYUFA GHDGH GNUQQ HCIFZ K9. LK8 M0S M1P M7P PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS 7X8 5PM DOA |
| DOI | 10.1186/s40168-017-0366-3 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Gale In Context: Science ProQuest Central (Corporate) Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) ProQuest SciTech Collection ProQuest Natural Science Journals ProQuest Hospital Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials Biological Science Collection ProQuest Central Natural Science Collection ProQuest One Community College ProQuest Central Korea Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) Biological Sciences ProQuest Health & Medical Collection Medical Database Biological Science Database ProQuest Central Premium ProQuest One Academic Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Publicly Available Content Database ProQuest Central Student ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest Health & Medical Research Collection Health Research Premium Collection Health and Medicine Complete (Alumni Edition) Natural Science Collection ProQuest Central Korea Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest Biological Science Collection ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest SciTech Collection ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | Publicly Available Content Database MEDLINE MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: DOA name: Directory of Open Access Journals (DOAJ) url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database – sequence: 4 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Biology |
| EISSN | 2049-2618 |
| EndPage | 10 |
| ExternalDocumentID | oai_doaj_org_article_8042e0dda60a4996b4f6f2de4730dd3c PMC5689185 A514740756 29145893 10_1186_s40168_017_0366_3 |
| Genre | Journal Article |
| GrantInformation_xml | – fundername: National Cancer Institute grantid: U01CA86400 – fundername: National Institute of General Medical Sciences grantid: R01GM099514 – fundername: NIDDK NIH HHS grantid: P30 DK034933 – fundername: National Institute of Diabetes and Digestive and Kidney Diseases grantid: P30DK034933 – fundername: ; grantid: P30DK034933 – fundername: ; grantid: U01CA86400 – fundername: ; grantid: R01GM099514 |
| GroupedDBID | 0R~ 53G 5VS 7X7 88E 8FE 8FH 8FI 8FJ AAFWJ AAHBH AAJSJ AASML AAYXX ABUWG ACGFS ADBBV ADRAZ ADUKV AENEX AFKRA AFPKN AHBYD AHYZX ALIPV ALMA_UNASSIGNED_HOLDINGS AMKLP AOIJS ASPBG BAWUL BBNVY BCNDV BENPR BFQNJ BHPHI BMC BPHCQ BVXVI C6C CCPQU CITATION DIK EBLON EBS EJD FYUFA GROUPED_DOAJ GX1 H13 HCIFZ HMCUK HYE IAG IAO IEP IHR INH INR ISR ITC KQ8 LK8 M1P M48 M7P M~E OK1 PGMZT PHGZM PHGZT PIMPY PQQKQ PROAC PSQYO RBZ ROL RPM RSV SOJ UKHRP CGR CUY CVF ECM EIF NPM PJZUB PPXIY PQGLB PMFND 3V. 7XB 8FK AHSBF AZQEC DWQXO GNUQQ K9. PKEHL PQEST PQUKI PRINS 7X8 5PM PUEGO |
| ID | FETCH-LOGICAL-c660t-cfd2593092b18beea332bd37dd3b906ca9d76d1945dca554734feb34aedc0a4a3 |
| IEDL.DBID | M48 |
| ISSN | 2049-2618 |
| IngestDate | Wed Aug 27 01:31:54 EDT 2025 Thu Aug 21 18:09:24 EDT 2025 Mon Jul 21 10:21:46 EDT 2025 Fri Jul 25 10:23:49 EDT 2025 Tue Jun 17 21:15:56 EDT 2025 Tue Jun 10 20:28:40 EDT 2025 Fri Jun 27 04:42:07 EDT 2025 Mon Jul 21 06:00:19 EDT 2025 Thu Apr 24 23:05:28 EDT 2025 Tue Jul 01 04:16:34 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | Microbiota Treatment Polyps Colorectal cancer Risk factor |
| Language | English |
| License | Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c660t-cfd2593092b18beea332bd37dd3b906ca9d76d1945dca554734feb34aedc0a4a3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0002-6935-4275 |
| OpenAccessLink | http://journals.scholarsportal.info/openUrl.xqy?doi=10.1186/s40168-017-0366-3 |
| PMID | 29145893 |
| PQID | 1972920085 |
| PQPubID | 2040205 |
| PageCount | 10 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_8042e0dda60a4996b4f6f2de4730dd3c pubmedcentral_primary_oai_pubmedcentral_nih_gov_5689185 proquest_miscellaneous_1966233234 proquest_journals_1972920085 gale_infotracmisc_A514740756 gale_infotracacademiconefile_A514740756 gale_incontextgauss_ISR_A514740756 pubmed_primary_29145893 crossref_primary_10_1186_s40168_017_0366_3 crossref_citationtrail_10_1186_s40168_017_0366_3 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2017-11-16 |
| PublicationDateYYYYMMDD | 2017-11-16 |
| PublicationDate_xml | – month: 11 year: 2017 text: 2017-11-16 day: 16 |
| PublicationDecade | 2010 |
| PublicationPlace | England |
| PublicationPlace_xml | – name: England – name: London |
| PublicationTitle | Microbiome |
| PublicationTitleAlternate | Microbiome |
| PublicationYear | 2017 |
| Publisher | BioMed Central Ltd BioMed Central BMC |
| Publisher_xml | – name: BioMed Central Ltd – name: BioMed Central – name: BMC |
| References | JJ Kozich (366_CR28) 2013; 79 FA Haggar (366_CR2) 2009; 22 G Zeller (366_CR25) 2014; 10 NT Baxter (366_CR13) 2014; 2 W Chen (366_CR17) 2012; e39743 JC Arthur (366_CR8) 2014; 5 JP Zackular (366_CR12) 2013; 4 NT Baxter (366_CR24) 2016; 8 CL O’Brien (366_CR27) 2013; e62815 AD Kostic (366_CR19) 2012; 22 Y Benjamini (366_CR35) 1995; 57 MJ Anderson (366_CR34) 2013; 83 J Abed (366_CR7) 2016; 20 S Wu (366_CR10) 2009; 15 JC Arthur (366_CR23) 2012; 338 XJ Shen (366_CR18) 2010; 1 T Wang (366_CR15) 2012; 6 L Breiman (366_CR33) 2001; 45 H-M Chen (366_CR16) 2013; 97 Q Feng (366_CR20) 2015; 6 JP Ryuk (366_CR4) 2014; 86 AD Kostic (366_CR9) 2013; 14 RL Siegel (366_CR1) 2016; 66 Q Wang (366_CR31) 2007; 73 MD Hellinger (366_CR3) 2006; 19 366_CR5 PD Schloss (366_CR32) 2011; 77 JP Zackular (366_CR26) 2014; 7 AC Goodwin (366_CR6) 2011; 108 KJ Flynn (366_CR14) 2016; 1 E Pruesse (366_CR29) 2007; 35 CM Dejea (366_CR21) 2014; 111 RC Edgar (366_CR30) 2011; 27 JP Zackular (366_CR11) 2016; 1 K Mima (366_CR22) 2015; 1 20740058 - Gut Microbes. 2010 May-Jun;1(3):138-47 21850056 - ISME J. 2012 Feb;6(2):320-9 24761423 - Ann Surg Treat Res. 2014 Mar;86(3):143-51 23650530 - PLoS One. 2013 May 01;8(5):e62815 24194538 - MBio. 2013 Nov 05;4(6):e00692-13 21421784 - Appl Environ Microbiol. 2011 May;77(10):3219-26 24967088 - Microbiome. 2014 Jun 17;2:20 27303681 - mSphere. 2015 Nov 04;1(1):null 21700674 - Bioinformatics. 2011 Aug 15;27(16):2194-200 25489084 - Proc Natl Acad Sci U S A. 2014 Dec 23;111(51):18321-6 20011326 - Clin Colon Rectal Surg. 2006 Nov;19(4):228-36 21037809 - Clin Colon Rectal Surg. 2009 Nov;22(4):191-7 17947321 - Nucleic Acids Res. 2007;35(21):7188-96 25432777 - Mol Syst Biol. 2014 Nov 28;10:766 17586664 - Appl Environ Microbiol. 2007 Aug;73(16):5261-7 25182170 - Nat Commun. 2014 Sep 03;5:4724 21876161 - Proc Natl Acad Sci U S A. 2011 Sep 13;108(37):15354-9 23954159 - Cell Host Microbe. 2013 Aug 14;14(2):207-15 26181352 - JAMA Oncol. 2015 Aug;1(5):653-61 22761885 - PLoS One. 2012;7(6):e39743 19701202 - Nat Med. 2009 Sep;15(9):1016-22 22009990 - Genome Res. 2012 Feb;22(2):292-8 26742998 - CA Cancer J Clin. 2016 Jan-Feb;66(1):7-30 27512904 - Cell Host Microbe. 2016 Aug 10;20(2):215-25 23793624 - Appl Environ Microbiol. 2013 Sep;79(17):5112-20 23553152 - Am J Clin Nutr. 2013 May;97(5):1044-52 27303740 - mSphere. 2016 May 11;1(3):null 27056827 - Genome Med. 2016 Apr 06;8(1):37 25758642 - Nat Commun. 2015 Mar 11;6:6528 22903521 - Science. 2012 Oct 5;338(6103):120-3 25104642 - Cancer Prev Res (Phila). 2014 Nov;7(11):1112-21 |
| References_xml | – volume: 83 start-page: 557 year: 2013 ident: 366_CR34 publication-title: Ecol Monogr doi: 10.1890/12-2010.1 – volume: 1 start-page: 653 year: 2015 ident: 366_CR22 publication-title: JAMA Oncol doi: 10.1001/jamaoncol.2015.1377 – volume: 7 start-page: 1112 year: 2014 ident: 366_CR26 publication-title: Cancer Prev Res (Philadelphia) doi: 10.1158/1940-6207.CAPR-14-0129 – volume: 97 start-page: 1044 year: 2013 ident: 366_CR16 publication-title: Am J Clin Nutr doi: 10.3945/ajcn.112.046607 – volume: 2 start-page: 20 year: 2014 ident: 366_CR13 publication-title: Microbiome doi: 10.1186/2049-2618-2-20 – volume: 77 start-page: 3219 year: 2011 ident: 366_CR32 publication-title: Appl Environ Microbiol doi: 10.1128/AEM.02810-10 – volume: 1 start-page: 1 year: 2016 ident: 366_CR14 publication-title: mSphere doi: 10.1128/mSphere.00102-16 – volume: 73 start-page: 5261 year: 2007 ident: 366_CR31 publication-title: Appl Environ Microbiol doi: 10.1128/AEM.00062-07 – volume: 8 start-page: 37 year: 2016 ident: 366_CR24 publication-title: Genome Med doi: 10.1186/s13073-016-0290-3 – volume: 338 start-page: 120 year: 2012 ident: 366_CR23 publication-title: Science (New York) doi: 10.1126/science.1224820 – volume: 20 start-page: 215 year: 2016 ident: 366_CR7 publication-title: Cell Host Microbe doi: 10.1016/j.chom.2016.07.006 – volume: 14 start-page: 207 year: 2013 ident: 366_CR9 publication-title: Cell Host Microbe doi: 10.1016/j.chom.2013.07.007 – volume: e62815 start-page: 8 year: 2013 ident: 366_CR27 publication-title: PloS ONE – ident: 366_CR5 – volume: 19 start-page: 228 year: 2006 ident: 366_CR3 publication-title: Clin Colon Rectal Surg doi: 10.1055/s-2006-956445 – volume: 22 start-page: 191 year: 2009 ident: 366_CR2 publication-title: Clin Colon Rectal Surg doi: 10.1055/s-0029-1242458 – volume: 5 start-page: 4724 year: 2014 ident: 366_CR8 publication-title: Nature Commun doi: 10.1038/ncomms5724 – volume: 35 start-page: 7188 year: 2007 ident: 366_CR29 publication-title: Nucleic Acids Res doi: 10.1093/nar/gkm864 – volume: 4 start-page: e00692—00613 year: 2013 ident: 366_CR12 publication-title: mBio doi: 10.1128/mBio.00692-13 – volume: 45 start-page: 5 year: 2001 ident: 366_CR33 publication-title: Mach Learn doi: 10.1023/A:1010933404324 – volume: 111 start-page: 18321 year: 2014 ident: 366_CR21 publication-title: Proc Natl Acad Sci USA doi: 10.1073/pnas.1406199111 – volume: 1 start-page: 138 year: 2010 ident: 366_CR18 publication-title: Gut Microbes doi: 10.4161/gmic.1.3.12360 – volume: 27 start-page: 2194 year: 2011 ident: 366_CR30 publication-title: Bioinformatics. (Oxford England) doi: 10.1093/bioinformatics/btr381 – volume: 6 start-page: 320 year: 2012 ident: 366_CR15 publication-title: ISME J doi: 10.1038/ismej.2011.109 – volume: e39743 start-page: 7 year: 2012 ident: 366_CR17 publication-title: PloS One – volume: 6 start-page: 6528 year: 2015 ident: 366_CR20 publication-title: Nat Commun doi: 10.1038/ncomms7528 – volume: 66 start-page: 7 year: 2016 ident: 366_CR1 publication-title: CA: Cancer J Clin – volume: 108 start-page: 15354 year: 2011 ident: 366_CR6 publication-title: Proc Natl Acad Sci USA doi: 10.1073/pnas.1010203108 – volume: 15 start-page: 1016 year: 2009 ident: 366_CR10 publication-title: Nature Med doi: 10.1038/nm.2015 – volume: 57 start-page: 289 year: 1995 ident: 366_CR35 publication-title: J Royal Stat Soc Ser B Methodol doi: 10.1111/j.2517-6161.1995.tb02031.x – volume: 1 start-page: 1 year: 2016 ident: 366_CR11 publication-title: mSphere doi: 10.1128/mSphere.00001-15 – volume: 22 start-page: 292 year: 2012 ident: 366_CR19 publication-title: Genome Res doi: 10.1101/gr.126573.111 – volume: 86 start-page: 143 year: 2014 ident: 366_CR4 publication-title: Ann Surg Treat Res doi: 10.4174/astr.2014.86.3.143 – volume: 10 start-page: 766 year: 2014 ident: 366_CR25 publication-title: Mol Syst Biol doi: 10.15252/msb.20145645 – volume: 79 start-page: 5112 year: 2013 ident: 366_CR28 publication-title: Appl Environ Microbiol doi: 10.1128/AEM.01043-13 – reference: 22761885 - PLoS One. 2012;7(6):e39743 – reference: 25432777 - Mol Syst Biol. 2014 Nov 28;10:766 – reference: 25182170 - Nat Commun. 2014 Sep 03;5:4724 – reference: 21876161 - Proc Natl Acad Sci U S A. 2011 Sep 13;108(37):15354-9 – reference: 25104642 - Cancer Prev Res (Phila). 2014 Nov;7(11):1112-21 – reference: 19701202 - Nat Med. 2009 Sep;15(9):1016-22 – reference: 25489084 - Proc Natl Acad Sci U S A. 2014 Dec 23;111(51):18321-6 – reference: 23650530 - PLoS One. 2013 May 01;8(5):e62815 – reference: 24967088 - Microbiome. 2014 Jun 17;2:20 – reference: 21850056 - ISME J. 2012 Feb;6(2):320-9 – reference: 26742998 - CA Cancer J Clin. 2016 Jan-Feb;66(1):7-30 – reference: 21421784 - Appl Environ Microbiol. 2011 May;77(10):3219-26 – reference: 27056827 - Genome Med. 2016 Apr 06;8(1):37 – reference: 27512904 - Cell Host Microbe. 2016 Aug 10;20(2):215-25 – reference: 27303681 - mSphere. 2015 Nov 04;1(1):null – reference: 20740058 - Gut Microbes. 2010 May-Jun;1(3):138-47 – reference: 22009990 - Genome Res. 2012 Feb;22(2):292-8 – reference: 23553152 - Am J Clin Nutr. 2013 May;97(5):1044-52 – reference: 26181352 - JAMA Oncol. 2015 Aug;1(5):653-61 – reference: 17947321 - Nucleic Acids Res. 2007;35(21):7188-96 – reference: 24761423 - Ann Surg Treat Res. 2014 Mar;86(3):143-51 – reference: 17586664 - Appl Environ Microbiol. 2007 Aug;73(16):5261-7 – reference: 23954159 - Cell Host Microbe. 2013 Aug 14;14(2):207-15 – reference: 23793624 - Appl Environ Microbiol. 2013 Sep;79(17):5112-20 – reference: 25758642 - Nat Commun. 2015 Mar 11;6:6528 – reference: 21037809 - Clin Colon Rectal Surg. 2009 Nov;22(4):191-7 – reference: 24194538 - MBio. 2013 Nov 05;4(6):e00692-13 – reference: 21700674 - Bioinformatics. 2011 Aug 15;27(16):2194-200 – reference: 27303740 - mSphere. 2016 May 11;1(3):null – reference: 22903521 - Science. 2012 Oct 5;338(6103):120-3 – reference: 20011326 - Clin Colon Rectal Surg. 2006 Nov;19(4):228-36 |
| SSID | ssj0000914748 |
| Score | 2.3323843 |
| Snippet | Colorectal cancer is a worldwide health problem. Despite growing evidence that members of the gut microbiota can drive tumorigenesis, little is known about... Background Colorectal cancer is a worldwide health problem. Despite growing evidence that members of the gut microbiota can drive tumorigenesis, little is... BackgroundColorectal cancer is a worldwide health problem. Despite growing evidence that members of the gut microbiota can drive tumorigenesis, little is known... Abstract Background Colorectal cancer is a worldwide health problem. Despite growing evidence that members of the gut microbiota can drive tumorigenesis,... |
| SourceID | doaj pubmedcentral proquest gale pubmed crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source |
| StartPage | 150 |
| SubjectTerms | Abundance Adenoma Adenoma - drug therapy Adenoma - microbiology Aged Analysis Bacteria Bacteria - classification Bacteria - genetics Bacteria - isolation & purification Carcinoma Carcinoma - drug therapy Carcinoma - microbiology Care and treatment Chemotherapy Colonic Polyps - drug therapy Colonic Polyps - microbiology Colorectal cancer Colorectal carcinoma Colorectal Neoplasms - drug therapy Colorectal Neoplasms - microbiology Diagnosis Feces - microbiology Female Gastrointestinal Microbiome - genetics Gastrointestinal Microbiome - physiology Genomes Humans Intestinal microflora Learning algorithms Male Medical prognosis Microbiota Microbiota (Symbiotic organisms) Middle Aged Neoplasm Recurrence, Local - drug therapy Patients Polyps Radiography Risk factor Risk Factors RNA, Ribosomal, 16S - genetics rRNA 16S Sequence Analysis, DNA Surgery Treatment Tumorigenesis Tumors |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Li9RAEG5kQfAirs_oKq0IghA2SXcq3cdVXFYPK6gLe2s6_dCB2YyYmcP-e6vSvWGCoBdPA9MVmP7ydT0mla8Yex07rykOl43o-1LGzpV9HVTZ-QY_fAg2dVucw9mF_HTZXu6N-qKesCQPnIA7VsiqUHlvobKYnUMvI8TGB4nU9F448r4Y8_aKqckH61p2UuXHmLWC4xELCaC-ra5Epw2lWASiSa__T6-8F5aWLZN7Mej0Hrubk0d-kn70IbsVhvvsdhonef2AfT6nBHSd36zkm8gxu-NXq6S1tLV8NfCsozryaTg4n_vMOSavnBSsh5Xj60D_oY0P2cXph2_vz8o8MKF0ANW2dNFjNSMq3fS16hFnIZreiw5x6nUFzmrfga-1bL2zLU0dlhGLaWlxX4iuFY_YwbAZwhPGA0SrQAvfop10rY42qipUQA8udR0KVt2gZ1xWE6ehFmszVRUKTALcIOCGADeiYG_nS34mKY2_Gb-jWzIbkgr29AVyw2RumH9xo2Cv6IYa0rkYqJHmu92No_n49Ys5aYkgmC9Bwd5ko7jBHTib30tAHEgaa2F5tLDEg-iWyze8MdkRjIamumnqMWkL9nJepiupuW0Imx3ZACahohGyYI8TzeZ9N0jkFnPKgnULAi6AWa4Mqx-TTHgLSmM29vR_IPmM3Wno6FD_Ixyxg-2vXXiO2di2fzEdvN_lhTFe priority: 102 providerName: Directory of Open Access Journals – databaseName: Health & Medical Collection dbid: 7X7 link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV3da9UwFA86EXwZfts5JYogCGFtkybN09jEMX2YoA7uW0jzMS_ctdt674P_vee0uXVF2FMhOYHm5OR8JCe_Q8iHqLxGO8xK3jRMROVYU4SaKV_Cx4dgx2yLM3l6Lr4tqkU6cOtTWuVWJw6K2ncOz8gPsDyWxsv66vDqmmHVKLxdTSU07pMHCF2GKV1qoaYzFrCFQok6XWYWtTzoIZyQmL2lGKhuyfjMHA2o_f_r5lvGaZ44ecsSnTwmu8mFpEfjmj8h90L7lDwci0r-eUa-n6EbukrvK2kXKfh49HI5Ii6tLV22NKGp9nQoEU6nbHMKLixFHOt26egq4Ela_5ycn3z59fmUpbIJzEmZr5mLHmIanuuyKeoGuM152XiuvOeNzqWz2ivpCy0q72yFtYdFhJBaWJhXboXlL8hO27XhFaFBRltLzX0FdMJVOtpY5yGXeH2pi5CRfMs94xKmOJa2WJkhtqilGRlugOEGGW54Rj5NQ65GQI27iI9xSSZCxMIeGrqbC5O2lqlB74Tceyvh7yF8a0SUsfQBJgat3GXkPS6oQbSLFtNpLuym783Xnz_MUYUCAl6TzMjHRBQ7mIGz6XUC8AEBsmaU-zNK2I5u3r2VG5PUQW_-CW9G3k3dOBJT3NrQbZBGgivKSy4y8nIUs2neJQhyBZ5lRtRMAGeMmfe0y98DWHglaw0-2d7dv_WaPCpxU2B-o9wnO-ubTXgD3ta6eTtsqb9OPShz priority: 102 providerName: ProQuest |
| Title | Normalization of the microbiota in patients after treatment for colonic lesions |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/29145893 https://www.proquest.com/docview/1972920085 https://www.proquest.com/docview/1966233234 https://pubmed.ncbi.nlm.nih.gov/PMC5689185 https://doaj.org/article/8042e0dda60a4996b4f6f2de4730dd3c |
| Volume | 5 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3di9NAEB_uA8EX8dvoWVYRBCGaZpPd5EGkJ3ecBavcWejbstmPs1BTbVrw_ntnkm254CE-BbKTkJ2d2flNMvkNwCsvbUlxOE55VcWZlyauhq6IpU3xYJ3TXbXFRJxNs_Esn-3Btr1VUGBzY2pH_aSmq8Xb37-uPqDDv28dvhDvGswRBJVkyRj3YxHzfTjEwCTJTz8HtN9uzOUwk20_rRRxcYy5QxG-c954l16kagn9_962r8Wtfk3ltSB1ehfuBHTJRp053IM9V9-HW12_yasH8GVCCHURfr1kS88Q_rEf846Maa3ZvGaBaLVhbfdwtitEZ4huGVFc13PDFo5esjUPYXp68u3jWRw6KsRGiGQdG28x3eFJmVbDosKF4DytLJfW8qpMhNGllcIOyyy3RufUljjzmG1nGueV6EzzR3BQL2v3BJgTXhei5DZHuczkpde-SFwi6MtmOXQRJFvtKRPoxqnrxUK1aUchVKdwhQpXpHDFI3izu-Rnx7XxL-FjWpKdINFktyeWq0sVvE4VuCW5xFot8Okxs6syL3xqHU4Mz3ITwUtaUEVEGDVV2lzqTdOoTxfnapSTsSCgEhG8DkJ-iTMwOvy4gHog7qye5FFPEj3V9Ie3dqO2hq6o7VtJRSh5BC92w3QlVb_VbrkhGYEolac8i-BxZ2a7eado1DmCzghkzwB7iumP1PPvLY94LooS4drT_1HCM7idkmtQAaQ4goP1auOeIxxbVwPYlzM5gMPRaHwxxuPxyeTr-aB9uTFoHfAPpBk0TQ |
| linkProvider | Scholars Portal |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3da9RAEB_qFdEX8dto1VUUQQjNZZNN8iDSasudrafUFvq2bvajHpxJbe6Q_lP-jc7kywahb30KZCdhd3Zm5ze7szMAr1xiMrLDfsjz3I9cov18bFM_MSE-jLWqibaYiclR9Ok4Pl6DP91dGAqr7NbEeqE2paY98k0qj5XRYX38_vSXT1Wj6HS1K6HRiMWePf-NLlv1bvoR5_d1GO7uHH6Y-G1VAV8LESx97QxCfh5kYT5Oc-wM52FueGIMz7NAaJWZRBj07WOjVUyleSOHHmekrNGBihTH_16D9YijKzOC9e2d2deDflcHrW-URGl7fDpOxWaFDoygeLHER2MhfD4wgHWdgP-twQVzOAzVvGD7dm_DrRa0sq1Gyu7Ami3uwvWmjOX5PfgyI-C7aG90stIxRJXs57zJ8bRUbF6wNn9rxeqi5KyPb2cImhllzi7mmi0s7d1V9-HoSlj6AEZFWdhHwKxwKhUZNzHSRTrOnHJpYANBB6bZ2HoQdNyTus1iTsU0FrL2ZlIhG4ZLZLgkhkvuwdv-k9MmhcdlxNs0JT0hZd-uX5RnJ7JVZpniSmcDY5TA3qPDmEdOuNBYHBi-5dqDlzShkvJrFBTAc6JWVSWn3w7kVkwCgjhNePCmJXIljkCr9j4E8oFScg0oNwaUuADoYXMnN7JdgCr5T108eNE305cUVFfYckU0AsEvD3nkwcNGzPpxhyjIMWJZD5KBAA4YM2wp5j_q9OSxSDNEgY8v79ZzuDE5_Lwv96ezvSdwMyQFoehKsQGj5dnKPkWst8yftQrG4PtV6_RfW_loPA |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Normalization+of+the+microbiota+in+patients+after+treatment+for+colonic+lesions&rft.jtitle=Microbiome&rft.au=Sze%2C+Marc+A&rft.au=Baxter%2C+Nielson+T&rft.au=Ruffin%2C+Mack+T&rft.au=Rogers%2C+Mary+A.+M&rft.date=2017-11-16&rft.pub=BioMed+Central+Ltd&rft.issn=2049-2618&rft.eissn=2049-2618&rft.volume=5&rft.issue=1&rft_id=info:doi/10.1186%2Fs40168-017-0366-3&rft.externalDBID=ISR&rft.externalDocID=A514740756 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2049-2618&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2049-2618&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2049-2618&client=summon |