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Dawa J, Walong E, Onyango C, Mathaiya J, Muturi P, Bunei M, Ochieng W, Barake W, Seixas JN, Mayieka L, Ochieng M, Omballa V, Lidechi S, Hunsperger E, Otieno NA, Ritter JM, Widdowson MA, Diaz MH, Winchell JM, Martines RB, Zaki SR, Chaves SS. Effect of Time Since Death on Multipathogen Molecular Test Results of Postmortem Specimens Collected Using Minimally Invasive Tissue Sampling Techniques. Clin Infect Dis 2021; 73:S360-S367. [PMID: 34910183 PMCID: PMC8672767 DOI: 10.1093/cid/ciab810] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Background We used postmortem minimally invasive tissue sampling (MITS) to assess the effect of time since death on molecular detection of pathogens among respiratory illness–associated deaths. Methods Samples were collected from 20 deceased children (aged 1–59 months) hospitalized with respiratory illness from May 2018 through February 2019. Serial lung and/or liver and blood samples were collected using MITS starting soon after death and every 6 hours thereafter for up to 72 hours. Bodies were stored in the mortuary refrigerator for the duration of the study. All specimens were analyzed using customized multipathogen TaqMan® array cards (TACs). Results We identified a median of 3 pathogens in each child’s lung tissue (range, 1–8; n = 20), 3 pathogens in each child’s liver tissue (range, 1–4; n = 5), and 2 pathogens in each child’s blood specimen (range, 0–4; n = 5). Pathogens were not consistently detected across all collection time points; there was no association between postmortem interval and the number of pathogens detected (P = .43) and no change in TAC cycle threshold value over time for pathogens detected in lung tissue. Human ribonucleoprotein values indicated that specimens collected were suitable for testing throughout the study period. Conclusions Results suggest that lung, liver, and blood specimens can be collected using MITS procedures up to 4 days after death in adequately preserved bodies. However, inconsistent pathogen detection in samples needs careful consideration before drawing definitive conclusions on the etiologic causes of death.
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Affiliation(s)
- Jeanette Dawa
- Washington State University, Global Health Programs (Kenya Office), Nairobi, Kenya.,College of Health Sciences, University of Nairobi, Nairobi, Kenya
| | - Edwin Walong
- College of Health Sciences, University of Nairobi, Nairobi, Kenya
| | - Clayton Onyango
- Division of Global Health Protection, Centers for Disease Control and Prevention, Nairobi, Kenya
| | - John Mathaiya
- Department of Pathology, Thika Level 5 Hospital, Kiambu County, Kenya
| | - Peter Muturi
- Washington State University, Global Health Programs (Kenya Office), Nairobi, Kenya
| | - Milka Bunei
- Washington State University, Global Health Programs (Kenya Office), Nairobi, Kenya
| | | | - Walter Barake
- College of Health Sciences, University of Nairobi, Nairobi, Kenya
| | - Josilene N Seixas
- Infectious Diseases Pathology Branch, Division of High-Consequence Pathogens and Pathology, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Lillian Mayieka
- Centre for Global Health Research, Kenya Medical Research Institute, Nairobi, Kenya
| | - Melvin Ochieng
- Centre for Global Health Research, Kenya Medical Research Institute, Nairobi, Kenya
| | - Victor Omballa
- Centre for Global Health Research, Kenya Medical Research Institute, Nairobi, Kenya
| | - Shirley Lidechi
- Centre for Global Health Research, Kenya Medical Research Institute, Nairobi, Kenya
| | - Elizabeth Hunsperger
- Division of Global Health Protection, Centers for Disease Control and Prevention, Nairobi, Kenya
| | - Nancy A Otieno
- Centre for Global Health Research, Kenya Medical Research Institute, Nairobi, Kenya
| | - Jana M Ritter
- Infectious Diseases Pathology Branch, Division of High-Consequence Pathogens and Pathology, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Marc-Alain Widdowson
- Division of Global Health Protection, Centers for Disease Control and Prevention, Nairobi, Kenya.,Institute of Tropical Medicine, Antwerp, Belgium
| | - Maureen H Diaz
- Respiratory Diseases Branch, Division of Bacterial Diseases, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Jonas M Winchell
- Respiratory Diseases Branch, Division of Bacterial Diseases, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Roosecelis B Martines
- Infectious Diseases Pathology Branch, Division of High-Consequence Pathogens and Pathology, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Sherif R Zaki
- Infectious Diseases Pathology Branch, Division of High-Consequence Pathogens and Pathology, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Sandra S Chaves
- Influenza Program, Centers for Disease Control and Prevention, Nairobi, Kenya
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Lu Y, Chen S, Wei L, Sun L, Liu H, Xu Y. A Microfluidic-Based SNP Genotyping Method for Hereditary Hearing-Loss Detection. Anal Chem 2019; 91:6111-6117. [PMID: 30917650 DOI: 10.1021/acs.analchem.9b00652] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Ying Lu
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Shan Chen
- Laboratory of ShenZhen Third People’s Hospital, ShenZhen, GuangDong 518112, China
| | - Li Wei
- CapitalBio Technology, Beijing 101111, China
| | - Lanhua Sun
- CapitalBio Technology, Beijing 101111, China
| | - Houming Liu
- Laboratory of ShenZhen Third People’s Hospital, ShenZhen, GuangDong 518112, China
| | - Youchun Xu
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
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A diagnostic and epidemiologic investigation of acute febrile illness (AFI) in Kilombero, Tanzania. PLoS One 2017; 12:e0189712. [PMID: 29287070 PMCID: PMC5747442 DOI: 10.1371/journal.pone.0189712] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2017] [Accepted: 11/30/2017] [Indexed: 12/20/2022] Open
Abstract
Introduction In low-resource settings, empiric case management of febrile illness is routine as a result of limited access to laboratory diagnostics. The use of comprehensive fever syndromic surveillance, with enhanced clinical microbiology, advanced diagnostics and more robust epidemiologic investigation, could enable healthcare providers to offer a differential diagnosis of fever syndrome and more appropriate care and treatment. Methods We conducted a year-long exploratory study of fever syndrome among patients ≥ 1 year if age, presenting to clinical settings with an axillary temperature of ≥37.5°C and symptomatic onset of ≤5 days. Blood and naso-pharyngeal/oral-pharyngeal (NP/OP) specimens were collected and analyzed, respectively, using AFI and respiratory TaqMan Array Cards (TAC) for multi-pathogen detection of 57 potential causative agents. Furthermore, we examined numerous epidemiologic correlates of febrile illness, and conducted demographic, clinical, and behavioral domain-specific multivariate regression to statistically establish associations with agent detection. Results From 15 September 2014–13 September 2015, 1007 febrile patients were enrolled, and 997 contributed an epidemiologic survey, including: 14% (n = 139) 1<5yrs, 19% (n = 186) 5-14yrs, and 67% (n = 672) ≥15yrs. AFI TAC and respiratory TAC were performed on 842 whole blood specimens and 385 NP/OP specimens, respectively. Of the 57 agents surveyed, Plasmodium was the most common agent detected. AFI TAC detected nucleic acid for one or more of seven microbial agents in 49% of AFI blood samples, including: Plasmodium (47%), Leptospira (3%), Bartonella (1%), Salmonella enterica (1%), Coxiella burnetii (1%), Rickettsia (1%), and West Nile virus (1%). Respiratory TAC detected nucleic acid for 24 different microbial agents, including 12 viruses and 12 bacteria. The most common agents detected among our surveyed population were: Haemophilus influenzae (67%), Streptococcus pneumoniae (55%), Moraxella catarrhalis (39%), Staphylococcus aureus (37%), Pseudomonas aeruginosa (36%), Human Rhinovirus (25%), influenza A (24%), Klebsiella pneumoniae (14%), Enterovirus (15%) and group A Streptococcus (12%). Our epidemiologic investigation demonstrated both age and symptomatic presentation to be associated with a number of detected agents, including, but not limited to, influenza A and Plasmodium. Linear regression of fully-adjusted mean cycle threshold (Ct) values for Plasmodium also identified statistically significant lower mean Ct values for older children (20.8), patients presenting with severe fever (21.1) and headache (21.5), as well as patients admitted for in-patient care and treatment (22.4). Conclusions This study is the first to employ two syndromic TaqMan Array Cards for the simultaneous survey of 57 different organisms to better characterize the type and prevalence of detected agents among febrile patients. Additionally, we provide an analysis of the association between adjusted mean Ct values for Plasmodium and key clinical and demographic variables, which may further inform clinical decision-making based upon intensity of infection, as observed across endemic settings of sub-Saharan Africa.
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