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A miniaturized microchip-based absorbance detector was developed for portable high-performance liquid chromatography (HPLC) to test glycated hemoglobin (HbA1c). The microchip integrating a Z-shaped cell, two collimating micro-lenses and two ink-filled optical slits is small in size (30 mm × 15 mm × 7 mm). The Z-shaped cell has a cross-sectional size of 500 µm × 500 µm and a physical optical path length of 2 mm. Two collimating micro-lenses were inserted in empty grooves on both sides of the cell, one micro-lens for collimating the initial light and the other for focusing the transmitted light. Optical slits on each end of the cell were used to block the stray light. Therefore, this detector indicated a low stray light level (0.011 %) and noise level (2.5 × 10-4 AU). This detector was applied for the commercial HPLC system to detect HbA1c level, and showed a low limit of detection (0.5 µg/mL) and excellent repeatability (≤ 2.03 %). The sensitivity was enhanced by 3.4 times when the optical path length was increased from 0.5 mm to 2 mm and the stray light was blocked by optical slits. The miniaturized microchip-based absorbance detector developed shows a great potential for application in portable and compact HPLC.

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BACKGROUND: The determination of glycosylated hemoglobin (HbA1c) is crucial for diabetes diagnosis and can provide more substantial results than the simple measurement of glycemia. While there is a lack of simple methods for the determination of HbA1c using a point-of-care test (POCT) compared to glycemia measurement. In particular, high-performance liquid chromatography (HPLC) is considered the current gold standard for determining HbA1c levels. However, commercial HPLC systems usually have some sort of disadvantages such as bulky size, high-cost and need for qualified operators. Therefore, there is an urgent demand to develop a portable, and fast HbA1c detection system consuming fewer reagents. RESULTS: We present a novel microchip that integrates a micromixer, passive injector, packed column and detection cell. The integrated microchip, in which all the microstructures were formed in the CNC machining center through micro-milling, is small in size (30 mm × 70 mm × 10 mm), and can withstand 1600 psi of liquid pressure. The integrated design is beneficial to reduce the band broadening caused by dead volume. Based on the microchip, a microchip liquid chromatography (LC) system was built and applied to the analysis of HbA1c. The separation conditions of HbA1c in blood calibrator samples were optimized using the microchip LC system. Samples containing four levels of HbA1c were completely separated within 2 min in optimal gradient conditions, with an inaccuracy (<3.2 %), a coefficient of variation (c.v. < 2.1 %) and a correlation coefficient (R2 = 0.993), indicating excellent separation efficiency and reproducibility. SIGNIFICANCE: The POCT of HbA1c is critical for diabetes diagnosis. The microchip chromatography system was developed for HbA1c determination, which contains an integrated microchip and works under a gradient elution. It surpasses existing chip technology in terms of separation performance and detection speed, providing a competitive advantage for POCT of HbA1c. It is considered one important step for realizing efficient portable systems for timely and accurate diabetes diagnosis.

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Brain atlas is an important tool in the diagnosis and treatment of neurological disorders. However, due to large variations in the organizational principles of individual brains, many challenges remain in clinical applications. Brain atlas individualization network (BAI-Net) is an algorithm that subdivides individual cerebral cortex into segregated areas using brain morphology and connectomes. The presented method integrates group priors derived from a population atlas, adjusts areal probabilities using the context of connectivity fingerprints derived from the fiber-tract embedding of tractography, and provides reliable and explainable individualized brain areas across multiple sessions and scanners. We demonstrate that BAI-Net outperforms the conventional iterative clustering approach by capturing significantly heritable topographic variations in individualized cartographies. The topographic variability of BAI-Net cartographies has shown strong associations with individual variability in brain morphology, connectivity as well as higher relationship on individual cognitive behaviors and genetics. This study provides an explainable framework for individualized brain cartography that may be useful in the precise localization of neuromodulation and treatments on individual brains.

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The processing of peptide tandem mass spectrometry data involves matching observed spectra against a sequence database. The ranking and calibration of these peptide-spectrum matches can be improved substantially using a machine learning postprocessor. Here, we describe our efforts to speed up one widely used postprocessor, Percolator. The improved software is dramatically faster than the previous version of Percolator, even when using relatively few processors. We tested the new version of Percolator on a data set containing over 215 million spectra and recorded an overall reduction to 23% of the running time as compared to the unoptimized code. We also show that the memory footprint required by these speedups is modest relative to that of the original version of Percolator.

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