Logic gate-driven dual-index balanced visualization strategy for tumor metastasis diagnosis

Area of Science:

• Biomedical diagnostics
• Cytopathology
• Chemical sensing

Background:

• Exfoliated tumor cell detection is crucial for malignant tumor diagnosis.
• Current clinical cytology preparation is time-consuming, often taking over two days.
• There is a need for rapid and accurate methods to screen exfoliated tumor cells.

Purpose of the Study:

• To develop a rapid, visual diagnostic kit for distinguishing normal from exfoliated tumor cells.
• To enable initial screening of exfoliated tumor cells within 10 minutes.
• To integrate advanced algorithms for accurate tumor cell identification.

Main Methods:

• A balanced-etching visual kit utilizing Glutathione/Glucose (GSH/Glu) concentration differences was developed.
• The kit generates color cards for initial visual screening.
• Logic gates and machine learning algorithms were employed for RGB color card analysis.

Main Results:

• The balanced-etching kit successfully distinguished normal from exfoliated tumor cells.
• Initial screening was achieved within 10 minutes.
• Machine learning analysis of color cards provided accurate exfoliated tumor cell screening.
• Validation was performed on various clinical samples including urine, pleural fluids, ascites, and gastric fluids.

Conclusions:

• A novel, rapid diagnostic strategy for exfoliated tumor cells was successfully developed.
• The balanced-etching visual kit offers a fast and accurate method for initial tumor cell screening.
• This approach has the potential to significantly advance precision medicine through improved diagnostic speed and accuracy.