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CD4 T lymphocytes play a key role in initiating the adaptive immune response, releasing cytokines that mediate numerous signal transduction pathways across the immune system. Therefore, CD4 T cell counts are widely used as an indicator of overall immunological health. HIV, one of the leading causes of death in the developing world, specifically targets and gradually depletes CD4 cells, making CD4 counts a critical metric for monitoring disease progression. As a result, accurately counting CD4 cells represents a pressing challenge in global healthcare. Flow cytometry remains the gold standard for enumerating CD4 T cells; however, flow cytometers are expensive, difficult to transport, and require skilled medical staff to prepare samples, operate the equipment, and interpret results. This highlights the critical need for novel, rapid, cost-effective, and portable methods of CD4 enumeration that are suitable for deployment in resource-limited countries. This review will survey and analyze emerging research in CD4 counting, with a focus on microfluidic systems, which represent a promising area of investigation. 

Determining nucleic acid concentrations in a sample is an important step prior to proceeding with downstream analysis in molecular diagnostics. Given the need for testing DNA amounts and its purity in many samples, including in samples with very small input DNA, there is utility of novel machine learning approaches for accurate and high-throughput DNA quantification. Here, we demonstrated the ability of a neural network to predict DNA amounts coupled to paramagnetic beads. To this end, a custom-made microfluidic chip is applied to detect DNA molecules bound to beads by measuring the impedance peak response (IPR) at multiple frequencies. We leveraged electrical measurements including the frequency and imaginary and real parts of the peak intensity within a microfluidic channel as the input of deep learning models to predict DNA concentration. Specifically, 10 different deep learning architectures are examined. The results of the proposed regression model indicate that an R_Squared of 97% with a slope of 0.68 is achievable. Consequently, machine learning models can be a suitable, fast, and accurate method to measure nucleic acid concentration in a sample. The results presented in this study demonstrate the ability of the proposed neural network to use the information embedded in raw impedance data to predict the amount of DNA concentration.