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Updated: Jun 22, 2026

High-throughput Detection Method for Influenza Virus
Published on: February 4, 2012
Engineered Nanobodies for early and accurate diagnosis of dengue virus infection
María Florencia Pavan1, Cristian Miguel Malnero2, María Emilia Mora Alvarado1
1Laboratorio de Ingeniería de Anticuerpos. Instituto de Química Física de los Materiales, Medio Ambiente y Energía (INQUIMAE), Universidad de Buenos Aires, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Ciudad Autónoma de Buenos Aires, Argentina.
Background:
Dengue virus (DENV), a mosquito-borne flavivirus responsible for dengue disease, has emerged as an escalating global health concern, with cases increasing sharply in recent decades. In Argentina, dengue has transitioned from a sporadic disease to a recurrent epidemic, now affecting 18 of 23 provinces and exposing gaps in diagnostic capacity.
Methodology And Principal Findings:
The DENV genome encodes the non-structural protein 1 (NS1), a key biomarker for early infection detection. Given the limited access to commercially available diagnostic kits within the public health system, we developed a combined ELISA system incorporating Nanobodies designed to target NS1 across all four DENV serotypes as detection antibodies. This system demonstrates excellent discriminative performance (AUC > 0.9), with a diagnostic sensitivity of 93.6% (95% CI: 86.6-97.6%) and a specificity of 81.1% (95% CI: 70.3-89.3%). The analytical sensitivity showed strong correlation between sera pool dilutions and detected signals, with a limit of detection aligning with reported NS1 concentrations in human samples. While the system exhibits limitations in detecting NS1 from DENV-4, it successfully identified cases in patients five days post-symptom onset who were initially considered epidemiologically negative for dengue infection.
Significance:
Our results underscore the urgent need for accessible, high-precision diagnostic tools in regions facing a surge in dengue outbreaks. Additionally, they highlight the necessity of revising current diagnostic algorithms to enhance the detection of late-presenting cases.
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