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Data-driven approaches for molecular diagnostics are emerging as an alternative to perform an accurate and inexpensive multi-pathogen detection. A novel technique called Amplification Curve Analysis (ACA) has been recently developed by coupling machine learning and real-time Polymerase Chain Reaction (qPCR) to enable the simultaneous detection of multiple targets in a single reaction well. However, target classification purely relying on the amplification curve shapes faces several challenges, such as distribution discrepancies between different data sources (i.e., training vs testing). Optimisation of computational models is required to achieve higher performance of ACA classification in multiplex qPCR through the reduction of those discrepancies. Here, we proposed a novel transformer-based conditional domain adversarial network (T-CDAN) to eliminate data distribution differences between the source domain (synthetic DNA data) and the target domain (clinical isolate data). The labelled training data from the source domain and unlabelled testing data from the target domain are fed into the T-CDAN, which learns both domains' information simultaneously. After mapping the inputs into a domain-irrelevant space, T-CDAN removes the feature distribution differences and provides a clearer decision boundary for the classifier, resulting in a more accurate pathogen identification. Evaluation of 198 clinical isolates containing three types of carbapenem-resistant genes (blaNDM, blaIMP and blaOXA-48) illustrates a curve-level accuracy of 93.1% and a sample-level accuracy of 97.0% using T-CDAN, showing an accuracy improvement of 20.9% and 4.9% respectively. This research emphasises the importance of deep domain adaptation to enable high-level multiplexing in a single qPCR reaction, providing a solid approach to extend qPCR instruments' capabilities in real-world clinical applications.

Real-time digital polymerase chain reaction (qdPCR) coupled with machine learning (ML) methods has shown the potential to unlock scientific breakthroughs, particularly in the field of molecular diagnostics for infectious diseases. One promising application of this emerging field explores single fluorescent channel PCR multiplex by extracting target-specific kinetic and thermodynamic information contained in amplification curves, also known as data-driven multiplexing. However, accurate target classification is compromised by the presence of undesired amplification events and not ideal reaction conditions. Therefore, here, we proposed a novel framework to identify and filter out nonspecific and low-efficient reactions from qdPCR data using outlier detection algorithms purely based on sigmoidal trends of amplification curves. As a proof-of-concept, this framework is implemented to improve the classification performance of the recently reported data-driven multiplexing method called amplification curve analysis (ACA), using available published data where the ACA is demonstrated to screen carbapenemase-producing organisms in clinical isolates. Furthermore, we developed a novel strategy, named adaptive mapping filter (AMF), to adjust the percentage of outliers removed according to the number of positive counts in qdPCR. From an overall total of 152,000 amplification events, 116,222 positive amplification reactions were evaluated before and after filtering by comparing against melting peak distribution, proving that abnormal amplification curves (outliers) are linked to shifted melting distribution or decreased PCR efficiency. The ACA was applied to assess classification performance before and after AMF, showing an improved sensitivity of 1.2% when using inliers compared to a decrement of 19.6% when using outliers (p-value < 0.0001), removing 53.5% of all wrong melting curves based only on the amplification shape. This work explores the correlation between the kinetics of amplification curves and the thermodynamics of melting curves, and it demonstrates that filtering out nonspecific or low-efficient reactions can significantly improve the classification accuracy for cutting-edge multiplexing methodologies.

We characterized the localization, phenotype, and some functions of plasmacytoid dendritic cells (pDCs) in the human spleen. pDCs were localized in the marginal zone and the periarteriolar region. Some were also found in the red pulp. pDCs were immature by phenotypic labeling, consistently with their capacity to internalize Dextran in a functional assay. In spleens from HIV-infected patients with thrombocytopenic purpura, these characteristics were unaffected. However, an accumulation of pDCs, but not myeloid dendritic cells (mDCs), was observed in some HIV+ patients, correlating with high proviral loads. Moreover, although undetectable in most HIV− patients, interferon-α (IFN-α) production was evidenced in situ and by flow cytometry in most HIV+ patients. IFN-α was located in the marginal zone. Surprisingly, IFN-α colocalized only with few pDCs, but rather with other cells, including T and B lymphocytes, mDCs, and macrophages. Therefore, pDCs accumulated in spleens from HIV+ patients with high proviral loads, but they did not seem to be the main IFN-α producers.

We characterized the localization, phenotype, and some functions of plasmacytoid dendritic cells (pDCs) in the human spleen. pDCs were localized in the marginal zone and the periarteriolar region. Some were also found in the red pulp. pDCs were immature by phenotypic labeling, consistently with their capacity to internalize Dextran in a functional assay. In spleens from HIV-infected patients with thrombocytopenic purpura, these characteristics were unaffected. However, an accumulation of pDCs, but not myeloid dendritic cells (mDCs), was observed in some HIV+patients, correlating with high proviral loads. Moreover, although undetectable in most HIV−patients, interferon-α (IFN-α) production was evidenced in situ and by flow cytometry in most HIV+patients. IFN-α was located in the marginal zone. Surprisingly, IFN-α colocalized only with few pDCs, but rather with other cells, including T and B lymphocytes, mDCs, and macrophages. Therefore, pDCs accumulated in spleens from HIV+patients with high proviral loads, but they did not seem to be the main IFN-α producers.