Eyes-like diagnosis was a traditional Chinese medicine method for many diseases, such as chronic gastritis, diabetes, hypertension etc. There was a close relationship between viscera and eyes-like. White-Eye was divided into fourteen sections, which corresponded to different viscera, so eyes-like was the reflection of status of viscera, in another words, it was an epitome of viscera health condition. In this paper, we developed a novel shadowless imaging technology and system for eyes-like diagnosis in vivo, which consisted of an optical shadowless imaging device for capturing and saving images of patients’ eyes-like, and a computer linked to the device for image processing. A character matching algorithm was developed to extract the character of white-eye in corresponding sections of eyes-like images taken by the optical shadowless imaging device, according to the character of eyes-like, whether there were viscera diseases could be learned. A series of assays were carried out, and the results verified the feasibility of eyes-like diagnosis technique.
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/mm<sup>2</sup> 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.