RESUMEN
In conventional immunoassays, a secondary antibody is used to amplify the signal generated by the binding of the primary antibody to the target analyte. Due to concerns regarding animal use and cost-inefficiency of secondary antibody productions, there is a significant demand for the development of recombinant secondary antibody mimics (rSAMs). Here, we developed rSAMs using a signal-generating enzyme, monomeric alkaline phosphatase (mALP), and antibody-binders, including monomeric streptavidin (mSA2) and mouse IgG1- or rabbit IgG-binding nanobodies (MG1Nb or RNb). The mALP-MG1Nb, mALP-RNb, and mALP-mSA2 were genetically constructed and produced in large quantities using bacterial overexpression systems, which reduced manufacturing costs and time without the use of animals. Each rSAM exhibited high and selective binding to its respective primary antibody, generating linear band signals corresponding to the amounts of target analytes in western blots. The rSAMs also successfully generated sigmoidal signal curves that increased as the sample concentration increased. Moreover, they generated stronger signals than conventional ALP-conjugated secondary antibodies and SA, particularly in the medium to high sample concentration range, in both indirect and sandwich-type indirect ELISAs at the same sample concentration. The rSAMs we developed here may provide new insights to develop novel immunoassay-based analytical and diagnostic tools.
RESUMEN
BACKGROUND: Depression is a common mental disorder that impacts millions of people across the world. However, its diagnosis is difficult due to the dependence on subjective testing. Although quantitative electroencephalography (EEG) has been investigated as a promising diagnostic tool for depression, the associated results have proven contradictory. The current study determines whether the alpha/beta (ABR), alpha/theta (ATR), and theta/beta (TBR) ratios can serve as biological markers of depression. METHODS: We used open-access EEG data from OpenNeuro to investigate power ratios in the resting state of 46 patients with depression and 75 healthy controls. Spectral data were extracted by fast Fourier transform at the theta band (4-8 Hz), alpha band (8-13 Hz), and beta band (13-32 Hz). Neural network, logistic regression, and receiver operating characteristic (ROC) curves were used to assess the diagnostic accuracies of each suggested index. Additionally, the cutoff point, sensitivity, specificity, positive predictive value, and negative predictive value at the maximized Youden index were compared for each variable. RESULTS: Decreased anterior frontal, frontal, central, parietal, occipital, and temporal ABR and decreased central and parietal TBR were observed in the depression group. The area under the curve of the ROC curves further revealed that these ratios could all effectively differentiate depression. In particular, the central, frontal, and parietal ABR exhibited high discrimination scores. Multiple logistic regression analysis demonstrated that the Beck Depression Inventory and Spielberger Trait Anxiety Inventory scores, as well as the probability of depression, increased with a decrease in the central ABR. Moreover, neural network analysis revealed that the global ABR was the most effective index for diagnosing depression among the three global EEG power ratios. CONCLUSIONS: The central, frontal, and parietal ABR represent potential biomarkers to differentiate patients with depression from healthy controls.
