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Govind V. Kaigala

IBM Research – Zurich, 8803 Rüschlikon, Switzerland


Contributions

  • High-Quality Immunohistochemical Stains through Computational Assay Parameter Optimization
    High-Quality Immunohistochemical Stains through Computational Assay Parameter Optimization

    Immunohistochemistry has been an invaluable analytical method in the field of cancer diagnosis. Optimization of assay parameters governing the quality of immunostaining requires of exhaustive exploration of the parameter space, but such optimization is infeasible due to the limited availability of tissue samples. Thus, suboptimal images are being used for diagnoses. This work analyzes immunohistochemistry staining quality through staining quality indicators and proposes an innovative local staining method using the microfluidic probe technology. Consequently, the tissue is processed with parameters that result in improved signal-to-background stains. This methodology will contribute to standardize immunostaining across diagnostic laboratories and to reduce errors in diagnosis.

  • Fluidic bypass structures for improving the robustness of liquid scanning probes

    Upon analyzing failure modes and their causes in the operation of liquid scanning probes, two main modes were identified. These failure modes can be countered by a simple design element, a microfluidic bypass channel, which is straightforward to implement in most liquid scanning probe. The bypass can be operated in dc mode when filled with liquid or in ac mode when filled with gas. Each mode allows to prevent one of the two main failure modes. Presented analytical models, engineering design considerations and experimental verification enable a swift adaption of these bypass channels approach to increase operational robustness of liquid scanning probes.

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