A fully automated and accurate assay of rare cell phenotypes in densely-packed fluorescently-labeled liquid biopsy images remains elusive. Methods: Employing a hybrid artificial intelligence (AI) paradigm that combines traditional rule-based morphological manipulations with modern statistical machine learning, we deployed a next generation software, ALICE (Automated Liquid Biopsy Cell Enumerator) to identify and enumerate minute amounts of tumor cell phenotypes bestrewed in massive populations of leukocytes. As a code designed for futurity, ALICE is armed with internet of things (IOT) connectivity to promote pedagogy and continuing education and also, an advanced cybersecurity system to safeguard against digital attacks from malicious data tampering. Results: By combining robust principal component analysis, random forest classifier and cubic support vector machine, ALICE was able to detect synthetic, anomalous and tampered input images with an average recall and precision of 0.840 and 0.752, respectively. In terms of phenotyping enumeration, ALICE was able to enumerate various circulating tumor cell (CTC) phenotypes with a reliability ranging from 0.725 (substantial agreement) to 0.961 (almost perfect) as compared to human analysts. Further, two subpopulations of circulating hybrid cells (CHCs) were serendipitously discovered and labeled as CHC-1 (DAPI+/CD45+/E-cadherin+/vimentin-) and CHC-2 (DAPI+ /CD45+/E-cadherin+/vimentin+) in the peripheral blood of pancreatic cancer patients. CHC-1 was found to correlate with nodal staging and was able to classify lymph node metastasis with a sensitivity of 0.615 (95% CI: 0.374-0.898) and specificity of 1.000 (95% CI: 1.000-1.000). Conclusion: This study presented a machine-learning-augmented rule-based hybrid AI algorithm with enhanced cybersecurity and connectivity for the automatic and flexibly-adapting enumeration of cellular liquid biopsies. ALICE has the potential to be used in a clinical setting for an accurate and reliable enumeration of CTC phenotypes.

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Traditional Chinese herbal medicine aiming at nourishing yin formed a distinctive school of thought in history to achieve anti-aging and longevity. In the formula Gancao nourishing yin (GCNY) decoction, all of the ingredients show antioxidant properties. However, in real clinical practice, extractions of herbs are rarely applied alone but are prescribed as the integrated formula. To investigate whether GCNY possesses anti-oxidation potential, we applied GCNY to treat rats to acquire medicated serum, which was then added on H2O2 (200 μM)-modeled human microglial cell line HMC-3 in comparison with its control serum. The results revealed that GCNY-medicated serum decreased reactive oxygen species (ROS) levels. Inflammatory cytokines such as pNF-κB p65 (ser536) and IL-6 were also decreased. Nrf2 and its pathway-related molecules, such as HO1, ABCC2, GLCM, ME1, NQO1, and TKT, were activated by H2O2 modeling while declined by treating with GCNY-medicated serum, which indicated attenuated oxidative stress of GCNY. Furthermore, mRNA-seq analysis showed 58 differential expressed genes (DEGs), which were enriched in pathways including antigen processing and presentation, longevity regulation, oxidative phosphorylation, and Parkinson’s disease progression. DEGs that were downregulated by H2O2 modeling but upregulated by GCNY treatment include CENPF, MKI67, PRR11, and TOP2A. Those targets were reported to be associated with the cell cycle and cell proliferation and belong to the category of growth factor genes. In conclusion, this study verified anti-oxidation effects of GCNY and indicated its promising application for cognitive degeneration and aging-related disorders.

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