Serological surveillance development for tropical infectious diseases using simultaneous microsphere-based multiplex assays and finite mixture models.
Serological surveillance development for tropical infectious diseases using simultaneous microsphere-based multiplex assays and finite mixture models.
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DOI:
10.1371/journal.pntd.0003040
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发表时间:
2014
影响因子:
3.8
通讯作者:
Hirayama K
中科院分区:
文献类型:
--
作者:
Fujii Y;Kaneko S;Nzou SM;Mwau M;Njenga SM;Tanigawa C;Kimotho J;Mwangi AW;Kiche I;Matsumoto S;Niki M;Osada-Oka M;Ichinose Y;Inoue M;Itoh M;Tachibana H;Ishii K;Tsuboi T;Yoshida LM;Mondal D;Haque R;Hamano S;Changoma M;Hoshi T;Kamo K;Karama M;Miura M;Hirayama K
A strategy to combat infectious diseases, including neglected tropical diseases (NTDs), will depend on the development of reliable epidemiological surveillance methods. To establish a simple and practical seroprevalence detection system, we developed a microsphere-based multiplex immunoassay system and evaluated utility using samples obtained in Kenya. We developed a microsphere-based immuno-assay system to simultaneously measure the individual levels of plasma antibody (IgG) against 8 antigens derived from 6 pathogens: Entamoeba histolytica (C-IgL), Leishmania donovani (KRP42), Toxoplasma gondii (SAG1), Wuchereria bancrofti (SXP1), HIV (gag, gp120 and gp41), and Vibrio cholerae (cholera toxin). The assay system was validated using appropriate control samples. The assay system was applied for 3411 blood samples collected from the general population randomly selected from two health and demographic surveillance system (HDSS) cohorts in the coastal and western regions of Kenya. The immunoassay values distribution for each antigen was mathematically defined by a finite mixture model, and cut-off values were optimized. Sensitivities and specificities for each antigen ranged between 71 and 100%. Seroprevalences for each pathogen from the Kwale and Mbita HDSS sites (respectively) were as follows: HIV, 3.0% and 20.1%; L. donovani, 12.6% and 17.3%; E. histolytica, 12.8% and 16.6%; and T. gondii, 30.9% and 28.2%. Seroprevalences of W. bancrofti and V. cholerae showed relatively high figures, especially among children. The results might be affected by immunological cross reactions between W. bancrofti-SXP1 and other parasitic infections; and cholera toxin and the enterotoxigenic E. coli (ETEC), respectively. A microsphere-based multi-serological assay system can provide an opportunity to comprehensively grasp epidemiological features for NTDs. By adding pathogens and antigens of interest, optimized made-to-order high-quality programs can be established to utilize limited resources to effectively control NTDs in Africa. Monitoring the distribution of neglected tropical diseases (NTDs) is a key to controlling their spread in Africa. Currently, such surveillance is conducted independently for each NTD. To tackle this problem, we developed a microsphere-based system to permit simultaneous measurement of IgG antibody levels for antigens from six infectious diseases: Entamoeba histolytica, Leishmania donovani, Toxoplasma gondii, Wuchereria bancrofti, HIV, and Vibrio cholerae. Using this system, we conducted a serological survey using two health and demographic surveillance system (HDSS) areas in coastal and western Kenya. We randomly selected 4,600 individuals according to sex and age group, of whom 3411 agreed to participate in the study. Mathematical analyses of the distributions of the participants' reactivity to each antigen and the reactivity of the sero-positive and -negative controls indicated that this system could be used to monitor infections, especially, those associated with HIV, filariasis, toxoplasmosis, leishmaniasis, and amebiasis. For the practical development and eventual implementation of actual programs in Africa, pathogens and antigens of interest can be added to optimize made-to-order monitoring programs.
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DOI:
10.1016/s0035-9203(03)80012-3
发表时间:
2003-09-01
影响因子:
2.2
作者:
Marlet, MVL;Ritmeijer, K;Davidson, RN
通讯作者:
Davidson, RN
影响因子:
--
作者:
Pickering, Jerry W.;Larson, Matthew T.;Hill, Harry R.
通讯作者:
Hill, Harry R.
影响因子:
3.2
作者:
Njenga SM;Mwandawiro CS;Wamae CN;Mukoko DA;Omar AA;Shimada M;Bockarie MJ;Molyneux DH
通讯作者:
Molyneux DH
影响因子:
2.3
作者:
Everitt, B S
通讯作者:
Everitt, B S
影响因子:
168.9
作者:
Kotloff, Karen L.;Nataro, James P.;Levine, Myron M.
通讯作者:
Levine, Myron M.