| Literature DB >> 28646991 |
Tadashi Takeuchi1, Hiroshi Ohno2, Naoko Satoh-Takayama3.
Abstract
Although acute peritonitis is a common and severe complication associated with peritoneal dialysis, the culture-based test used as the diagnostic criterion for this disease is often too slow to allow appropriate point-of-care diagnosis of specific bacterial infection. To address this problem, Zhang et al. report the efficacy of a novel set of immune biomarkers derived from a machine-learning algorithm applied to patient data. This fingerprint could predict major pathogenic causes of peritonitis.Entities:
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Year: 2017 PMID: 28646991 DOI: 10.1016/j.kint.2017.02.027
Source DB: PubMed Journal: Kidney Int ISSN: 0085-2538 Impact factor: 10.612