Intelligent Clinical Lab for the Diagnosis of Post-Neurosurgical Meningitis Based on Machine-Learning-Aided Cerebrospinal Fluid Analysis.

Post-neurosurgical meningitis (PNM) often leads to serious consequences; unfortunately, the commonly used clinical diagnostic methods of PNM are time-consuming or have low specificity. To realize the accurate and convenient diagnosis of PNM, herein, we propose a comprehensive strategy for cerebrospinal fluid (CSF) analysis based on a machine-learning-aided cross-reactive sensing array. The sensing array involves three Eu3+-doped metal-organic frameworks (MOFs), which can generate specific fluorescence responding patterns after reacting with potential targets in CSF. Then, the responding pattern is used as learning data to train the machine learning algorithms. The discrimination confidence for artificial CSF containing different components of molecules, proteins, and cells is from 81.3 to 100%. Furthermore, the machine-learning-aided sensing array was applied in the analysis of CSF samples from post-neurosurgical patients. Only 25 µL of CSF samples was needed, and the samples could be robustly classified into "normal," "mild," or "severe" groups within 40 min. It is believed that the combination of machine learning algorithms with robust data processing capability and a lanthanide luminescent sensor array will provide a reliable alternative for more comprehensive, convenient, and rapid diagnosis of PNM.

"French fries"-like luminescent metal organic frameworks for the fluorescence determination of cytochrome c released by apoptotic cells and screening of anticancer drug activity.

A luminescent metal organic framework was prepared by encapsulating Zn-Ag-In-S quantum dots into "French fries"-like MIL-68(In) metal organic frameworks (ZAISQDs@MIL-68(In)). The ZAISQDs@MIL-68(In) had a maximum excitation wavelength at 370 nm and maximum emission wavelength at 620 nm. It was found that the ZAISQDs@MIL-68(In) was efficiently quenched by cytochrome c (Cyt c), which is an important biomarker of early cell apoptosis. The quenching mechanism was ascribed to be an inner filter effect and dynamic quenching of Cyt c towards the ZAISQDs@MIL-68(In), and the enrichment effect of MIL-68(In). Benefiting from the multiple advantages, ZAISQDs@MIL-68(In) was developed as an assay strategy of Cyt c with logarithmic relation between signal quenching and concentration in the range 0.02 to 3.5 µM. The linear equation was (F0-F)/F0 = 0.5043 + 0.2678 × logcCyt c with a detection limit of 8 nM. Cyt c released by drug induced apoptotic cells was determined by ZAISQDs@MIL-68(In), and this strategy has been utilized for the screening of anticancer drug activity. Graphical abstract Schematic representation of the synthesis of ZAISQDs@MIL-68(In) and its application for Cyt c and screening anticancer drug activity.

Lanthanide-functionalized metal-organic frameworks based ratiometric fluorescent sensor array for identification and determination of antibiotics.

Antibiotics have made great contributions to the improvement of human health and life quality. However, the current abuse of antibiotics not only has a serious impact on the environment, but also endangers people's health. For this reason, the simultaneous identification and accurate determination of as many antibiotics in the environment, food and organisms as possible is critical. Herein, a ratiometric fluorescent sensor array based on Eu3+ and Tb3+ co-doped metal-organic frameworks (MOFs) was fabricated. Benefiting from the sensitization of the organic ligands to Eu3+ and Tb3+, the reaction of MOFs with various antibiotics resulted in different responses to the ratio of fluorescent intensity at 545 nm and 616 nm (F545/F616). After these responses were differentiated by principal component analysis (PCA), totally eight kinds of 25 antibiotics were well distinguished with the existence of interfering substances. The proposed sensor array exhibited high accuracy (98%) for the identification of 48 unknown samples in water and outstanding quantitative ability for the mixture of antibiotics. Finally, the practicability of the sensor array for the analysis of real samples was proved. In this strategy, we have not only provided an efficient way for the comprehensive identification and determination of antibiotics, but also promised new opportunities for the development of ratiometric signal based sensor array.