Cyclodextrin-NH-MIL-53 open tubular stationary phase for capillary electrochromatography enantioseparation.

A novel chiral group functionalized metal-organic framework, Cyclodextrin-NH-MIL-53, was synthesized and modified on the inner wall of a capillary column via a post-synthetic process. The prepared chiral metal-organic framework was utilized as a chiral capillary stationary phase and used in an open-tubular capillary electrochromatography method to enantioseparate several racemic amino acids. Excellent enantioseparation of five pairs of enantiomers was obtained in this chiral separation system (Resolutions of D/L-Alanine = 16.844, D/L-Cysteine = 3.617, D/L-Histidine = 9.513, D/L-Phenylalanine = 8.133, and D/L-Tryptophan = 2.778). The prepared Cyclodextrin-NH-MIL-53 and the Cyclodextrin-NH-MIL-53-based capillary columns were characterized by scanning electron microscopy, X-ray diffraction, Fourie-transform infrared spectroscopy, and circular dichroism. The chiral capillary electrochromatography conditions, such as separation conditions, amount of Cyclodextrin-NH-MIL-53, and electroosmotic flow, were optimized. This research is estimated to present a novel insight and method for the design and use of metal-organic framework-based capillaries for enantioseparation.

A chiral metal-organic framework {(HQA)(ZnCl2)(2.5H2O)}n for the enantioseparation of chiral amino acids and drugs.

Chiral metal-organic frameworks (CMOFs) with enantiomeric subunits have been employed in chiral chemistry. In this study, a CMOF formed from 6-methoxyl-(8S,9R)-cinchonan-9-ol-3-carboxylic acid (HQA) and ZnCl2, {(HQA)(ZnCl2)(2.5H2O)}n, was constructed as a chiral stationary phase (CSP) via an in situ fabrication approach and used for chiral amino acid and drug analyses for the first time. The {(HQA)(ZnCl2)(2.5H2O)}n nanocrystal and the corresponding chiral stationary phase were systematically characterised using a series of analytical techniques including scanning electron microscopy, X-ray diffraction, Fourier transform infrared spectroscopy, circular dichroism, X-ray photoelectron spectroscopy, thermogravimetric analysis, and Brunauer-Emmett-Teller surface area measurements. In open-tubular capillary electrochromatography (CEC), the novel chiral column exhibited strong and broad enantioselectivity toward a variety of chiral analytes, including 19 racemic dansyl amino acids and several model chiral drugs (both acidic and basic). The chiral CEC conditions were optimised, and the enantioseparation mechanisms are discussed. This study not only introduces a new high-efficiency member of the MOF-type CSP family but also demonstrates the potential of improving the enantioselectivities of traditional chiral recognition reagents by fully using the inherent characteristics of porous organic frameworks.

Recent applications and chiral separation development based on stationary phases in open tubular capillary electrochromatography (2019-2022).

Capillary electrochromatography (CEC) plays a significant role in chiral separation via the double separation principle, partition coefficient difference between the two phases, and electroosmotic flow-driven separation. Given the distinct properties of the inner wall stationary phase (SP), the separation ability of each SP differs from one another. Particularly, it provides large room for promising applications of open tubular capillary electrochromatography (OT-CEC). We divided the OT-CEC SPs developed over the past four years into six types: ionic liquids, nanoparticle materials, microporous materials, biomaterials, non-nanopolymers, and others, to mainly introduce their characteristics in chiral drug separation. There also added a few classic SPs that occurred within ten years as supplements to enrich the features of each SP. Additionally, we discuss their applications in metabolomics, food, cosmetics, environment, and biology as analytes in addition to chiral drugs. OT-CEC plays an increasingly significant role in chiral separation and may promote the development of capillary electrophoresis (CE) combined with other instruments in recent years, such as CE with mass spectrometry (CE/MS) and CE with ultraviolet light detector (CE/UV).

Raman spectroscopy and machine learning for the classification of breast cancers.

Breast cancer is a major health threat for women. The drug responses associated with different breast cancer subtypes have obvious effects on therapeutic outcomes; therefore, the accurate classification of breast cancer subtypes is critical. Breast cancer subtype classification has recently been examined using various methods, and Raman spectroscopy has emerged as an effective technique that can be used for noninvasive breast cancer analysis. However, the accurate and rapid classification of breast cancer subtypes currently requires a great deal of effort and experience with the processing and analysis of Raman spectra data. Here, we adopted Raman spectroscopy and machine learning techniques to simplify and accelerate the process used to distinguish normal from breast cancer cells and classify breast cancer subtypes. Raman spectra were obtained from cultured breast cancer cell lines, and the data were analyzed by two machine learning algorithms: principal component analysis (PCA)-discriminant function analysis (DFA) and PCA-support vector machine (SVM). The accuracies with which these two algorithms were able to distinguish normal breast cells from breast cancer cells were both greater than 97%, and the accuracies of breast cancer subtype classification for both algorithms were both greater than 92%. Moreover, our results showed evidence to support the use of characteristic Raman spectral features as cancer cell biomarkers, such as the intensity of intrinsic Raman bands, which increased in cancer cells. Raman spectroscopy combined with machine learning techniques provides a rapid method for breast cancer analysis able to reveal differences in intracellular compositions and molecular structures among subtypes.