Effect of deacetylation of chitosan on the physicochemical, antioxidant and antibacterial properties activities of chitosan-mannose derivatives.

BACKGROUND: The present study investigates the physical, chemical, and antibacterial properties of water-soluble chitosan derivatives. Preparation of the water-soluble chitosan derivatives was performed by the Maillard reaction (MR) between chitosan [with the degree of deacetylation (DD) being 50%, 70%, and 90%] and mannose. No organic reagent was used in the process. Systematic evaluations of the effects of chitosan DD on the reaction extent, the structure, the composition, as well as the physicochemical properties, antioxidant properties, and bacterial inhibitory properties of the finished chitosan-mannose MR products (Mc-mrps), were carried out. RESULTS: Based on the experimental data obtained from Fourier transform infrared spectroscopy, thermogravimetric analysis, X-ray diffraction, Pyrolysis-gas chromatography-mass spectrometry analysis, and 1 H-NMR, the Mc-mrps formed from chitosan with different DDs had different structures and components. An increase in the DD of chitosan led to a significant increase in the degree of reaction, color difference (△E), and solubility (P < 0.05). The zeta potential and particle size of the Mc-mrps were also influenced by the DD of chitosan. Additionally, the antimicrobial action against Gram-positive (Staphylococcus aureus and Bacillus subtilis) and Gram-negative bacteria (Escherichia coli and Salmonella typhimurium), as well as antioxidant activity, were enhanced by the incorporation of mannose. This was also achieved by the increase of the DD of chitosan. CONCLUSION: The results of the present study suggest that chitosan was derived with mannose to yield a novel, water-soluble polysaccharide with better antioxidant and antimicrobial activities. The DD of chitosan had a significant effect on the properties of the Mc-mrp, which can serve as a reference point for the subsequent preparation and application of such derivatives. © 2023 Society of Chemical Industry.

Amine-functionalized MOF-derived carbon materials for efficient removal of Congo red dye from aqueous solutions: simulation and adsorption studies.

In this study, a novel polyethyleneimine (PEI) modified MOF-derived carbon adsorbent (PEI@MDC) was proposed, which exhibited significant adsorption capacity for Congo Red (CR) in aqueous solutions. FT-IR and XPS results showed that PEI was successfully grafted onto MDC, increasing the content of amine groups on the surface of MDC. The adsorption process conformed to the Langmuir isotherm adsorption model and pseudo-second-order kinetic equation, indicating that the adsorption of CR on PEI@MDC was covered by a single layer, and the adsorption process was controlled by chemical processes. According to the Langmuir model, the maximum adsorption capacity at 30 °C was 1723.86 mg g-1. Hydrogen bonding and electrostatic interactions between CR and PEI@MDC surface functional groups were the main mechanisms controlling the adsorption process. After five adsorption-desorption cycles, PEI@MDC still showed a high adsorption capacity for CR, indicating that the adsorbent had an excellent regeneration ability.

Radiomics Features on Computed Tomography Combined With Clinical-Radiological Factors Predicting Progressive Hemorrhage of Cerebral Contusion.

Background: Traumatic brain injury (TBI) is the main cause of death and severe disability in young adults worldwide. Progressive hemorrhage (PH) worsens the disease and can cause a poor neurological prognosis. Radiomics analysis has been used for hematoma expansion of hypertensive intracerebral hemorrhage. This study attempts to develop an optimal radiomics model based on non-contrast CT to predict PH by machine learning (ML) methods and compare its prediction performance with clinical-radiological models. Methods: We retrospectively analyzed 165 TBI patients, including 89 patients with PH and 76 patients without PH, whose data were randomized into a training set and a testing set at a ratio of 7:3. A total of 10 different machine learning methods were used to predict PH. Univariate and multivariable logistic regression analyses were implemented to screen clinical-radiological factors and to establish a clinical-radiological model. Then, a combined model combining clinical-radiological factors with the radiomics score was constructed. The area under the receiver operating characteristic curve (AUC), accuracy and F1 score, sensitivity, and specificity were used to evaluate the models. Results: Among the 10 various ML algorithms, the support vector machine (SVM) had the best prediction performance based on 12 radiomics features, including the AUC (training set: 0.918; testing set: 0.879) and accuracy (training set: 0.872; test set: 0.834). Among the clinical and radiological factors, the onset-to-baseline CT time, the scalp hematoma, and fibrinogen were associated with PH. The radiomics model's prediction performance was better than the clinical-radiological model, while the predictive nomogram combining the radiomics features with clinical-radiological characteristics performed best. Conclusions: The radiomics model outperformed the traditional clinical-radiological model in predicting PH. The nomogram model of the combined radiomics features and clinical-radiological factors is a helpful tool for PH.

A polypyrrole-coated eightfold-helical Wells-Dawson POM-based Cu-FKZ framework for enhanced colorimetric sensing.

To explore a novel colorimetric biosensor with high sensibility and selectivity, a new Wells-Dawson-type polyoxometalate (POM)-based metal-organic framework (MOF) with an eightfold helix, [Cu9(FKZ)12(H2O)8][H3P2W18O62]2·4H2O (CuFKZP2W18) (HFKZ = 1-(2,4-difluorophenyl)-1,1-bis[(1H-1,2,4-triazol-1-yl)methyl] ethanol), was successfully synthesized; then, polypyrrole (PPy) was introduced to fabricate CuFKZP2W18/PPy(n) nanocomposites (n = 7%, 15%, 30%) via a facile in situ oxidation polymerization process. All the results indicate that CuFKZP2W18/PPy(15%) as a colorimetric biosensor exhibits lower limits of detection (0.07 µM towards H2O2 and 0.627 µM towards ascorbic acid), smaller Km values (0.106 mM for H2O2 and 0.042 mM for o-phenylenediamine) and higher sensitivity (0.0227 1 µM-1 towards H2O2 and 0.0025 1 µM-1 to ascorbic acid) than most reported enzyme mimetics to the best of our knowledge.

Molybdenum oxide-based metal-organic framework/polypyrrole nanocomposites for enhancing electrochemical detection of dopamine.

To overcome the poor conductivities and promote the application in the biosensors of metal-organic frameworks (MOFs), a simple approach was employed to improve their overall conductivity by adjusting the metal centers of MOFs and coating conductive polypyrrole (PPy) in the work. An unprecedented molybdenum oxide-based three-dimensional MOFs with helical channels (CuTRZMoO4) was synthesized based on MoO4-, Cu2+ ions and 1,2,3-trz for the first time, then combined with PPy to fabricate hybrid composites (CuTRZMoO4@PPy-n) with both advantages. The CuTRZMoO4 modified glassy carbon electrode show high sensitivity for detecting the neurotransmitter dopamine (DA), and the CuTRZMoO4@PPy-2 modified glassy carbon electrode has the highest catalytical activity to DA with the linear detection range from 1 µM to 100 µM and the detection limit of 80 nM (S/N = 3) by differential pulse voltammetry (DPV). Moreover, the developed biosensor has good selectivity, reproducibility and stability. The concept behinds the new architecture to modify electrodes should promote the further development of MOF-based biosensors.