Self-Assembly Fluorescent Cationic Cellulose Nanocomplex via Electrostatic Interaction for the Detection of Fe³⁺ Ions

In this work, an aggregation-induced emission (AIE) sensor for the detection of Fe3+ ions was fabricated through the electrostatic interaction between 1,1,2-triphenyl-2-[4-(3-sulfonatopropoxyl)-phenyl]-ethene sodium salt (SPOTPE) and quaternized cellulose (QC). The structure and properties of the SPOTPE/QC nanocomplex were studied by using 1H NMR, spectrofluorophotometer, transmission electron microscopy (TEM), and dynamic laser light scattering (DLS). An aqueous solution of SPOTPE and QC resulted in a remarkably enhanced cyan fluorescence in comparison to that of the SPOTPE solution. Strong through-space electrostatic interaction between SPOTPE and QC is the main cause for the fluorescence emerging. The fluorescence of the SPOTPE/QC solutions show good stability over a wide pH range of 5.0⁻10.0. When introducing Fe3+ ions into the SPOTPE/QC solution, the fluorescence quenched within 5 s. SPOTPE/QC solutions exhibited high selectivity and sensitivity for the detection of Fe3+ ions with ignored interferences from other ions, and the detection limit was determined to be 2.92 × 10−6 M. The quenching mechanism was confirmed to be the consequence of the binding interactions between Fe3+ ions and SPOTPE/QC complex

Aggregation-Induced Emission (AIE)-Labeled Cellulose Nanocrystals for the Detection of Nitrophenolic Explosives in Aqueous Solutions

Aggregation-induced emission (AIE) active cellulose nanocrystals (TPE-CNCs) were synthesized by attaching tetraphenylethylene (TPE) to cellulose nanocrystals (CNCs). The structure and morphology of TPE-CNCs were characterized by FT-IR, XRD, ζ-potential measurements, elemental analysis, TEM, atomic force microscopy (AFM), and dynamic laser light scattering (DLS). Fluorescent properties of TPE-CNCs were also further studied. Unlike aggregation-caused quenching (ACQ), TPE-CNCs emitted weak fluorescence in the dilute suspensions, while emitting efficiently in the aggregated states. The AIE mechanism of TPE-CNCs was attributed to the restriction of an intramolecular rotation (RIR) process in the aggregated states. TPE-CNCs displayed good dispersity in water and stable fluorescence, which was reported through the specific detection of nitrophenolic explosives in aqueous solutions by a fluorescence quenching assay. The fluorescence emissions of TPE-CNCs showed quantitative and sensitive responses to picric acid (PA), 2,4-dinitro-phenol (DNP), and 4-nitrophenol (NP), and the detection limits were 220, 250, and 520 nM, respectively. Fluorescence quenching occurred through a static mechanism via the formation of a nonfluorescent complex between TPE-CNCs and nitrophenolic analytes. A fluorescence lifetime measurement revealed that the quenching was a static process. The results demonstrated that TPE-CNCs were excellent sensors for the detection of nitrophenolic explosives in aqueous systems, which has great potential applications in chemosensing and bioimaging

Identifying the predictors of severe psychological distress by auto-machine learning methods

Social stress in daily life and the COVID-19 pandemic have greatly impacted the mental health of the population. Early detection of a predisposition to severe psychological distress is essential for timely interventions. This paper analyzed 4036 samples participating in the 2019–2020 National Health Information Trends Survey (HINTS) and identified 57 candidate predictors of severe psychological distress based on univariate chi-square and t-test analyses. Five machine learning methods, namely logistic regression (LR), automatic generalized linear models (Auto-GLM), automatic random forests (Auto-Random Forests), automatic deep neural networks (Auto-Deep learning) and automatic gradient boosting machines (Auto-GBM), were employed to model synthetic minority oversampling technique-based (SMOTE) resampled data and identify predictors of severe psychological distress. Predictors were evaluated by odds ratios in logistic models and variable importance in the other models. Forty-seven variables were identified as significant predictors of severe psychological distress, including 13 sociodemographic variables and 34 variables related to individual lifestyle and behavioral habits. Among them, new potentially relevant variables related to an individual's level of concern and trust in cancer information, exposure to health care providers, and cancer screening and awareness are included. The performance of each model was evaluated using five-fold cross-validation. The optimal model performance-wise was Auto-GBM with an accuracy of 89.75%, a precision of 89.68%, a recall of 89.31%, an F1-score of 89.48% and an AUC of 95.57%. Significant predictors of severe psychological distress were identified in this study and the value of machine learning methods in predicting severe psychological distress is demonstrated, thereby enhancing pre-prediction and clinical decision-making of severe psychological distress problems

Mesenchymal stem cells shift the pro-inflammatory phenotype of neutrophils to ameliorate acute lung injury

Abstract Background Mesenchymal stem cell (MSC) treatment plays a major role in the management of acute lung injury (ALI), and neutrophils are the initial line of defense against ALI. However, the effect of MSCs on neutrophils in ALI remains mostly unknown. Methods We investigated the characteristics of neutrophils in lung tissue of ALI mice induced by lipopolysaccharide after treatment with MSCs using single-cell RNA sequencing. Neutrophils separated from lung tissue in ALI were co-cultured with MSCs, and then samples were collected for reverse transcription-polymerase chain reaction and flow cytometry. Results During inflammation, six clusters of neutrophils were identified, annotated as activated, aged, and circulatory neutrophils. Activated neutrophils had higher chemotaxis, reactive oxygen species (ROS) production, and nicotinamide adenine dinucleotide phosphate (NADPH) oxidase scores than aged neutrophils. Circulatory neutrophils occurred mainly in healthy tissue and were characterized by higher expression of Cxcr2 and Sell. Activated neutrophils tended to exhibit higher expression of Cxcl10 and Cd47, and lower expression of Cd24a, while aged neutrophils expressed a lower level of Cd47 and higher level of Cd24a. MSC treatment shifted activated neutrophils toward an aged neutrophil phenotype by upregulating the expression of CD24, thereby inhibiting inflammation by reducing chemotaxis, ROS production, and NADPH oxidase. Conclusion We identified the immunosuppressive effects of MSCs on the subtype distribution of neutrophils and provided new insight into the therapeutic mechanism of MSC treatment in ALI