The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Analysis of the chronic wound microbiota of 2,963 patients by 16S rDNA pyrosequencing
Open accessAbstract
The extent to which microorganisms impair wound healing is an ongoing controversy in the management of chronic wounds. Because the high diversity and extreme variability of the microbiota between individual chronic wounds lead to inconsistent findings in small cohort studies, evaluation of a large number of chronic wounds using identical sequencing and bioinformatics methods is necessary for clinicians to be able to select appropriate empiric therapies. In this study, we utilized 16S rDNA pyrosequencing to analyze the composition of the bacterial communities present in samples obtained from patients with chronic diabetic foot ulcers (N = 910), venous leg ulcers (N = 916), decubitus ulcers (N = 767), and nonhealing surgical wounds (N = 370). The wound samples contained a high proportion of Staphylococcus and Pseudomonas species in 63 and 25% of all wounds, respectively; however, a high prevalence of anaerobic bacteria and bacteria traditionally considered commensalistic was also observed. Our results suggest that neither patient demographics nor wound type influenced the bacterial composition of the chronic wound microbiome. Collectively, these findings indicate that empiric antibiotic selection need not be based on nor altered for wound type. Furthermore, the results provide a much clearer understanding of chronic wound microbiota in general; clinical application of this new knowledge over time may help in its translation to improved wound healing outcomes.
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Price, M. N., Dehal, P. S., & Arkin, A. P. (2010) · PLoS ONE · doi:10.1371/journal.pone.0009490 · Cited by 14,894
Bacterial Biofilms: A Common Cause of Persistent Infections
Costerton, J. W., Stewart, P. S., & Greenberg, E. P. (1999) · Science · doi:10.1126/science.284.5418.1318 · Cited by 12,167
Structure, function and diversity of the healthy human microbiome
The Human Microbiome Project Consortium (2012) · Nature · doi:10.1038/nature11234 · Cited by 11,726
More powerful procedures for multiple significance testing
Hochberg, Y., & Benjamini, Y. (1990) · Statistics in Medicine · doi:10.1002/sim.4780090710 · Cited by 2,715
Commensal–dendritic-cell interaction specifies a unique protective skin immune signature
Naik, S., Bouladoux, N., Linehan, J. L., Han, S.-J., Harrison, O. J., Wilhelm, C., Conlan, S., Himmelfarb, S., Byrd, A. L., Deming, C., et al. (2015) · Nature · doi:10.1038/nature14052 · Cited by 820
The Neuropathic Diabetic Foot Ulcer Microbiome Is Associated With Clinical Factors
Gardner, S. E., Hillis, S. L., Heilmann, K., Segre, J. A., & Grice, E. A. (2013) · Diabetes · doi:10.2337/db12-0771 · Cited by 320
A Staphylococcus aureus Regulatory System that Responds to Host Heme and Modulates Virulence
Torres, V. J., Stauff, D. L., Pishchany, G., Bezbradica, J. S., Gordy, L. E., Iturregui, J., Anderson, K. L., Dunman, P. M., Joyce, S., & Skaar, E. P. (2007) · Cell Host & Microbe · doi:10.1016/j.chom.2007.03.001 · Cited by 241
What lives on our skin: ecology, genomics and therapeutic opportunities of the skin microbiome
Scharschmidt, T. C., & Fischbach, M. A. (2013) · Drug Discovery Today: Disease Mechanisms · doi:10.1016/j.ddmec.2012.12.003 · Cited by 215
Rhoads, D. D., Cox, S. B., Rees, E. J., Sun, Y., & Wolcott, R. D. (2012) · BMC Infectious Diseases · doi:10.1186/1471-2334-12-321 · Cited by 167
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Falanga, V., Isseroff, R. R., Soulika, A. M., Romanelli, M., Margolis, D., Kapp, S., Granick, M., & Harding, K. (2022) · Nature Reviews Disease Primers · doi:10.1038/s41572-022-00377-3 · Cited by 1,020
Chronic wounds: Treatment consensus
Eriksson, E., Liu, P. Y., Schultz, G. S., Martins‐Green, M. M., Tanaka, R., Weir, D., Gould, L. J., Armstrong, D. G., Gibbons, G. W., Wolcott, R., et al. (2022) · Wound Repair and Regeneration · doi:10.1111/wrr.12994 · Cited by 412
Self-citationConsensus guidelines for the identification and treatment of biofilms in chronic nonhealing wounds
Schultz, G., Bjarnsholt, T., James, G. A., Leaper, D. J., McBain, A. J., Malone, M., Stoodley, P., Swanson, T., Tachi, M., Wolcott, R. D., et al. (2017) · Wound Repair and Regeneration · doi:10.1111/wrr.12590 · Cited by 337
Self-citationBiofilm-Innate Immune Interface: Contribution to Chronic Wound Formation
Versey, Z., da Cruz Nizer, W. S., Russell, E., Zigic, S., DeZeeuw, K. G., Marek, J. E., Overhage, J., & Cassol, E. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.648554 · Cited by 253
Bacterial Biofilm in Chronic Wounds and Possible Therapeutic Approaches
Cavallo, I., Sivori, F., Mastrofrancesco, A., Abril, E., Pontone, M., Di Domenico, E. G., & Pimpinelli, F. (2024) · Biology · doi:10.3390/biology13020109 · Cited by 213
Metadata
- Authors
- Wolcott, R. D., Hanson, J. D., Rees, E. J., Koenig, L. D., Phillips, C. D., Wolcott, R. A., Cox, S. B., & White, J. S.
- Venue
- Wound Repair and Regeneration
- Publisher
- Wiley
- Published
- 2015-12-10
- Volume
- 24
- Issue
- 1
- Pages
- 163-174
- ISSN
- 1067-1927 (ISSN-L)
- 1524-475X
- OpenAlex
- W1897910051
- Cited by
- 414
- References
- 9
- FWCI
- 17.37
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Wolcott, R. D., Hanson, J. D., Rees, E. J., Koenig, L. D., Phillips, C. D., Wolcott, R. A., Cox, S. B., & White, J. S. (2016). Analysis of the chronic wound microbiota of 2,963 patients by 16S rDNA pyrosequencing. Wound Repair and Regeneration. https://doi.org/10.1111/wrr.12370
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