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.

While the majority of the technologies developed for energy storage are macrosized, the reactions involved in energy storage, such as diffusion, ionic transport, and surface‐based reactions, occur on the microscale. In light of this, microfluidics with the ability to manipulate such reactions and fluids on the micrometer scale has emerged as an interesting platform for the development of energy storage systems. Herein, the advances in utilizing microfluidic technologies in energy storage and release systems are reviewed in terms of four aspects. First, miniaturized microfluidic devices to store various forms of energy such as electrochemical, biochemical, and solar energy with unique architectures and enhanced performances are discussed. Second, novel energy materials with the desired geometries and characteristics that can be fabricated via microfluidic techniques are reviewed. Third, applications enabled by such microfluidic energy storage and release systems, particularly focusing on medical, environmental, and modeling purposes, are presented. Lastly, some remaining problems and challenges and possible future works in this field are suggested.

E xosomes are nano-sized (30-150 nm) extracellular vesicles with lipid-bilayer membrane.