mChip

Abstract: The robustness of a recently developed diagnostic microarray for influenza, the MChip, was evaluated with 16 historic subtype H1N1 influenza A viruses (A/H1N1), including A/Brevig Mission/1/1918. The matrix gene segments from all 16 viruses were successfully detected on the array. An artificial neural network trained with temporally related A/H1N1 viruses identified A/Brevig Mission/1/1918 as influenza virus A/H1N1 with 94% probability.

Abstract: Background: Early diagnosis of HIV and treatment initiation at higher CD4 counts improves outcomes and reduces transmission. However, Lesotho is not realizing the full benefits of ART because of the low proportion of men tested (40%). Public sector VMMC services, which were launched in district hospitals in February 2012 by the Lesotho MOH supported by USAID/MCHIP, include HIV testing with referral to care and treatment. The objective of this study was to better understand the contribution of VMMC services to HIV diagnosis and treatment. Methods: VMMC clients diagnosed with HIV were traced after 6 months to ascertain whether they: (1) presented to the referral HIV center, (2) had a CD4 count done and (3) were enrolled on ART. Linkages between VMMC and HIV services were assessed by comparing the proportion of HIV-infected males referred from VMMC services with those from other hospital departments. Results: Between March and September 2012, 72 men presenting for VMMC services tested positive for HIV, representing 65% of the total male tests at the hospital; 45 of these men (62.5%) received an immediate CD4 count and went to the HIV referral site; 40 (89%) were eligible for treatment and initiated ART. 27 clients did not have a CD4 count due to stock-out of reagents. Individuals who did not receive a CD4 count on the same day did not return to the HIV center. Conclusion: All VMMC clients testing positive for HIV and receiving a CD4 count on the testing day began ART. Providing VMMC services in a district hospital offering the continuum of care could increase diagnoses and treatment uptake among men, but requires an investment in communication between VMMC and ART clinics. In high HIV prevalence settings, investing in PIMA CD4 devices at integrated VMMC clinics is likely to increase male ART enrolment.

Abstract: Please cite this paper as: Heil et al. (2010) MChip, a low density microarray, differentiates among seasonal human H1N1, North American swine H1N1, and the 2009 pandemic H1N1. Influenza and Other Respiratory Viruses 4(6), 411–416. Background The MChip uses data from the hybridization of amplified viral RNA to 15 distinct oligonucleotides that target the influenza A matrix (M) gene segment. An artificial neural network (ANN) automates the interpretation of subtle differences in fluorescence intensity patterns from the microarray. The complete process from clinical specimen to identification including amplification of viral RNA can be completed in <8 hours for under US$10. Objectives The work presented here represents an effort to expand and test the capabilities of the MChip to differentiate influenza A/H1N1 of various species origin. Methods The MChip ANN was trained to recognize fluorescence image patterns of a variety of known influenza A viruses, including examples of human H1N1, human H3N2, swine H1N1, 2009 pandemic influenza A H1N1, and a wide variety of avian, equine, canine, and swine influenza viruses. Robustness of the MChip ANN was evaluated using 296 blinded isolates. Results Training of the ANN was expanded by the addition of 71 well-characterized influenza A isolates and yielded relatively high accuracy (little misclassification) in distinguishing unique H1N1 strains: nine human A/H1N1 (88·9% correct), 35 human A/H3N2 (97·1% correct), 31 North American swine A/H1N1 (80·6% correct), 14 2009 pandemic A/H1N1 (87·7% correct), and 23 negative samples (91·3% correct). Genetic diversity among the swine H1N1 isolates may have contributed to the lower success rate for these viruses. Conclusions The current study demonstrates the MChip has the capability to differentiate the genetic variations among influenza viruses with appropriate ANN training. Further selective enrichment of the ANN will improve its ability to rapidly and reliably characterize influenza viruses of unknown origin.