Type 2 diabetes mellitus is one of the most common metabolic diseases in the world. However, frequent blood glucose testing causes continual harm to diabetics, which cannot meet the needs of early diagnosis and long-term tracking of diabetes. Thus non-invasive adjuvant diagnosis methods are urgently needed, enabling early screening of the population for diabetes, the evaluation of diabetes risk, and assessment of therapeutic effects. The human eye plays an important role in painless and non-invasive approaches, because it is considered an internal organ but can be easily be externally observed. We developed an AI model to predict the probability of diabetes from scleral images taken by a specially developed instrument, which could conveniently and quickly collect complete scleral images in four directions and perform artificial intelligence (AI) analysis in 3 min without any reagent consumption or the need for a laboratory. The novel optical instrument could adaptively eliminate reflections and collected shadow-free scleral images. 177 subjects were recruited to participate in this experiment, including 127 benign subjects and 50 malignant subjects. The blood sample and sclera images from each subject was obtained. The scleral image classification model achieved a mean AUC over 0.85, which indicates great potential for early screening of practical diabetes during periodic physical checkups or daily family health monitoring. With this AI scleral features imaging and analysis method, diabetic patients’ health conditions can be rapidly, noninvasively, and accurately analyzed, which offers a platform for noninvasive forecasting, early diagnosis, and long-term monitoring for diabetes and its complications.
Quantitative phase imaging (QPI) has quickly emerged as a powerful tool for label-free living cell morphology and metabolism monitoring. However, for current QPI techniques, interference signals from different layers overlay with each other and impede nanoscale optical sectioning. This phenomenon leads to unsatisfactory performances for optically thick or complex scattering biological samples. To address this challenge, we have developed an alternative quantitative phase microscopy with computational hyperspectral interferometry. Nanoscale optical sectioning could be achieved with Fourier domain spectral decomposition. Morphological fluctuations and refractive index distribution could be reconstructed simultaneously with 89.2 nm axial resolution and 1.91 nm optical path difference sensitivity. With this method, we established a label-free cell imaging system for long-term cellular dry mass measurement and in-situ dynamic single cell monitoring. Different intrinsic cell growth characteristics of dry mass between HeLa cells and Human Cervical Epithelial Cells (HCerEpiC) were studied. The dry mass of HeLa cells consistently increased before M phase, whereas that of HCerEpiC increased and then decreased. The maximum growth rate of HeLa cells was 11.7% higher than that of HCerEpiC. We also use the proposed method and system to explore the relationship between cellular dry mass distributions and drug effects for cancer cells. The results show that cells with higher nuclear dry mass and nuclear density standard deviations were more likely to survive the chemotherapy. The presented work shows potential values for cell growth dynamics research, cell health characterization, medication guidance and adjuvant drug development.
Label free point mutation detection is particularly momentous in the area of biomedical research and clinical diagnosis since gene mutations naturally occur and bring about highly fatal diseases. In this paper, a label free and high sensitive approach is proposed for point mutation detection based on hyperspectral interferometry. A hybridization strategy is designed to discriminate a single-base substitution with sequence-specific DNA ligase. Double-strand structures will take place only if added oligonucleotides are perfectly paired to the probe sequence. The proposed approach takes full use of the inherent conformation of double-strand DNA molecules on the substrate and a spectrum analysis method is established to point out the sub-nanoscale thickness variation, which benefits to high sensitive mutation detection. The limit of detection reach 4pg/mm2 according to the experimental result. A lung cancer gene point mutation was demonstrated, proving the high selectivity and multiplex analysis capability of the proposed biosensor.
KEYWORDS: Microfluidics, Point-of-care devices, Lamps, Digital signal processing, Filtering (signal processing), Optical filters, Sensors, Digital Light Processing, Signal processing, Lithium
Point-of-care testing (POCT) for an infectious diseases is the prerequisite to control of the disease and limitation of its spread. A microfluidic chip for detection and classification of four strains of Ebola virus was developed and evaluated. This assay was based on reverse transcription loop-mediated isothermal amplification (RT-LAMP) and specific primers for Ebola Zaire virus, Ebola Sudan virus, Ebola Tai Forest virus and Ebola Bundibugyo virus were designed. The sensitivity of the microfluidic chip was under 103 copies per milliliter, as determined by ten repeated tests. This assay is unique in its ability to enable diagnosis of the Ebola infections and simultaneous typing of Ebola virus on a single chip. It offers short reaction time, ease of use and high specificity. These features should enable POCT in remote area during outbreaks of Ebola virus.
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