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Identification of lymphocyte cell types are crucial for understanding their pathophysiological roles in human diseases. Current methods for discriminating lymphocyte cell types primarily rely on labelling techniques with magnetic beads or fluorescence agents, which take time and have costs for sample preparation and may also have a potential risk of altering cellular functions. Here, we present the identification of non-activated lymphocyte cell types at the single-cell level using refractive index (RI) tomography and machine learning. From the measurements of three-dimensional RI maps of individual lymphocytes, the morphological and biochemical properties of the cells are quantitatively retrieved. To construct cell type classification models, various statistical classification algorithms are compared, and the k-NN (k = 4) algorithm was selected. The algorithm combines multiple quantitative characteristics of the lymphocyte to construct the cell type classifiers. After optimizing the feature sets via cross-validation, the trained classifiers enable identification of three lymphocyte cell types (B, CD4+ T, and CD8+ T cells) with high sensitivity and specificity. The present method, which combines RI tomography and machine learning for the first time to our knowledge, could be a versatile tool for investigating the pathophysiological roles of lymphocytes in various diseases including cancers, autoimmune diseases, and virus infections.

A synergistic application of holography and deep learning enables rapid optical screening of anthrax spores and other pathogens.

Humidity sensors are essential components in wearable electronics for monitoring of environmental condition and physical state. In this work, a unique humidity sensing layer composed of nitrogen-doped reduced graphene oxide (nRGO) fiber on colorless polyimide film is proposed. Ultralong graphene oxide (GO) fibers are synthesized by solution assembly of large GO sheets assisted by lyotropic liquid crystal behavior. Chemical modification by nitrogen-doping is carried out under thermal annealing in H (4%)/N (96%) ambient to obtain highly conductive nRGO fiber. Very small (≈2 nm) Pt nanoparticles are tightly anchored on the surface of the nRGO fiber as water dissociation catalysts by an optical sintering process. As a result, nRGO fiber can effectively detect wide humidity levels in the range of 6.1-66.4% relative humidity (RH). Furthermore, a 1.36-fold higher sensitivity (4.51%) at 66.4% RH is achieved using a Pt functionalized nRGO fiber (i.e., Pt-nRGO fiber) compared with the sensitivity (3.53% at 66.4% RH) of pure nRGO fiber. Real-time and portable humidity sensing characteristics are successfully demonstrated toward exhaled breath using Pt-nRGO fiber integrated on a portable sensing module. The Pt-nRGO fiber with high sensitivity and wide range of humidity detection levels offers a new sensing platform for wearable humidity sensors.