Neuroprotective Effects of Leptin on the APP/PS1 Alzheimer's Disease Mouse Model: Role of Microglial and Neuroinflammation.

Background: Microglia are closely linked to Alzheimer's disease (AD) many years ago; however, the pathological mechanisms of AD remain unclear. The purpose of this study was to determine whether leptin affected microglia in the hippocampus of young and aged male APP/PS1 mice. Objective: In a transgenic model of AD, we investigated the association between intraperitoneal injection of leptin and microglia. Methods: We intraperitoneal injection of leptin (1mg/kg) every day for one week and analyzed inflammatory markers in microglia in the hippocampus of adult (6 months) and aged (12 months) APP/PS1 mice. Results: In all leptin treatment group, the brain Aß levels were decrease. We found increased levels of IL-1ß, IL-6 and microglial activation in the hippocampus of adult mice. Using aged mice as an experimental model for chronic neuroinflammation and leptin resistance, the number of Iba-1+ microglia and the levels of IL-1ß/IL-6 in the hippocampus were greatly increased as compared to the adult. But between the leptin treatment and un-treatment, there were no difference. Conclusion: Leptin signaling would regulate the activation of microglia and the release of inflammatory factors, but it is not the only underlying mechanism in the neuroprotective effects of AD pathogenesis.

Nacre-inspired, strong, tough silk fibroin hydrogels based on biomineralization and the layer-by-layer assembly of ordered silk fabric.

Hydrogels are attractive materials with structures and functional properties similar to biological tissues and widely used in biomedical engineering. However, traditional synthetic hydrogels possess poor mechanical strength, and their applications are limited. Herein, a multidimensional material design method is developed; it includes the in situ gelation of silk fabric and nacre-inspired layer-by-layer assembly, which is used to prepare silk fibroin (SF) hydrogels. The in situ gelation method of silk fabric introduces a directionally ordered fabric network in a silk substrate, considerably enhancing the strength of hydrogels. Based on the nacre structure, the layer-by-layer assembly method enables silk hydrogels to break through the size limit and increase the thickness, realizing the longitudinal extension of the hydrogels. The application of the combined biomineralization and hot pressing method can effectively reduce interface defects and improve the interaction between organic and inorganic interfaces. The multidimensional material design method helps increase the strength (287.78 MPa), toughness (18.43 MJ m-3), and fracture energy (50.58 kJ m-2) of SF hydrogels; these hydrogels can weigh 2000 times their own weight. Therefore, SF hydrogels designed using the aforementioned combined method can realize the combination of strength and toughness and be used in biological tissue engineering and structural materials.

Cartilage structure-inspired elastic silk nanofiber network hydrogel for stretchable and high-performance supercapacitors.

Flexible supercapacitors are an important portable energy storage but suffer from low capacitance, inability to stretch, etc. Therefore, flexible supercapacitors must achieve higher capacitance, energy density, and mechanical robustness to expand the applications. Herein, a hydrogel electrode with excellent mechanical strength was created by simulating the collagen fiber network and proteoglycan in cartilage using silk nanofiber (SNF) network and polyvinyl alcohol (PVA). The Young's modulus and breaking strength of the hydrogel electrode increased by 205 % and 91 % compared with PVA hydrogel owing to the enhanced effect of the bionic structure, respectively, which are 1.22 MPa and 1.3 MPa. The fracture energy and fatigue threshold reached 1813.5 J/m2 and 1585.2 J/m2, respectively. The SNF network effectively connected carbon nanotubes (CNTs) and polypyrrole (PPy) in series, affording a capacitance of 13.62 F/cm2 and energy density of 1.2098 mWh/cm2. This capacitance is the highest among currently reported PVA hydrogel capacitors, which can maintain >95.2 % after 3000 charge-discharge cycles. This capacitance Notably, the cartilage-like structure endowed the supercapacitor with high resilience; thus, the capacitance remained >92.1 % under 150 % deformation and >93.35 % after repeated stretching (3000 times), which was far superior to that of other PVA-based supercapacitors. Overall, this effective bionic strategy can endow supercapacitors with ultrahigh capacitance and effectively ensure the mechanical reliability of flexible supercapacitors, which will help expand the applications of supercapacitors.

A turn-on fluorescence sensor for rapid sensing of ATP based on luminescence resonance energy transfer between upconversion nanoparticles and Cy3 in vivo or vitro.

Adenosine triphosphate (ATP) is an energy molecule of significant importance, and, the monitoring of ATP in living cells is considerable for the clinical diagnosis of many related diseases, including cancer. Upconversion nanoparticles (UCNPs) have recently been attracting widespread interest in biomedical applications due to their chemical and thermal stability, high sensitivity, good biocompatibility, and excellent tissue penetration. Herein, a Cy3-aptamer-cDNA- UCNPs nanosensor was synthesized, based on the luminescence resonance energy transfer (LRET) between UCNPs and Cy3 for monitoring ATP in living cells. It showed a selective sensing ability for ATP levels by changes of fluorescence intensity of UNCPs at 536 nm. The investigated biosensor showed a precise, efficient detection with sufficient selectivity which was achieved through the optimization of conditions. In the range of 1-1000 µM, the ATP-induced changes of the fluorescence intensity were linearly proportional to the ATP concentrations. Furthermore, the cytotoxicity assay revealed that the UCNPs sensor exhibited favorable biocompatibility, implicating the use of UCNPs in vivo imaging. This study highlights the potential of using a combination of UCNPs and ATP-binding aptamer to design an ATP-activatable probe for fluorescence-mediated imaging in living cells. These results implied that the nanosensor can be applicable for the monitoring of intracellular ATP by fluorescence imaging and the quantitative analysis of biological liquids.

Functionalized graphene oxide against U251 glioma cells and its molecular mechanism.

Graphene and its derivatives with exceptional properties are being exploited for drug delivery and even combined therapies for enhanced antitumor activity and reduced side effects. However, the unfavorable surface chemistry of pristine graphene and reduced graphene oxide made them take covalent and non-covalent functionalization strategies to improve their biocompatibility. Although graphene oxide (GO) is soluble in water owing to its oxygen-containing groups such as carboxylic acid and hydroxyl groups, it is highly accepted when to be modified to improve its colloidal stability in physiological buffers in the presence of salts. In this work, we functionalized GO with Pluronic F127 molecules via non-covalent interaction and found that GO and PF127/GO nanohybrid with a concentration lower than 5 µg/ml have no obvious toxic effect on human astrocytes (AS) and human glioma (U251) cells. Anti-tumor drug doxorubicin (DOX) being loaded onto the PF127/GO nanocarriers by π-π stacking exhibited a high loading capacity of 0.83 mg/mg and loading efficiency of 83%. Our study confirmed that the PF127/GO/DOX (PGD) induced a higher apoptosis rate (12.27 ± 0.06%) of U251 cells than that of free DOX (8.20 ± 0.06%) (P < 0.05). Western blotting results indicated that PGD affected the MAPK signaling pathway and induced the intrinsic pathway of apoptosis for the activation of Caspase-3 in U251 cells, which may provide more evidence for the signal pathway of tumor-targeting therapy.

Ultra-sensitive detection of malathion residues using FRET-based upconversion fluorescence sensor in food.

Malathion is an organophosphorus pesticide which could remain in agricultural products and exert irreversible harmful effects on human health. Hence, strict monitoring of malathion contents is very significant. Here, a highly sensitive fluorescent aptasensor was developed for the determination of malathion, the system was based on a cationic polymer-mediated fluorescence 'turn-off'. In this system, malathion-specific aptamers were bound to cationic polymer through electrostatic interactions. To produce fluorescence resonance energy transfer (FRET), negatively charged upconversion fluorescent nanoparticles (UCNPs) and cationic-polymer encapsulated gold nanoparticles (GNPs) were combined. This combination resulted in fluorescence quenching, and the degree of quenching was correlated with the concentration of malathion. Under optimum conditions, the fluorescence intensities were observed to decrease linearly with the rising concentration of the malathion from 0.01 to 1 µM with a detection limit of 1.42 nM. Furthermore, the developed sensor possessed good selective recognition ability for malathion and was successfully used to detect malathion in adulterated tap water and matcha samples with high accuracy.

Fabricating an Acetylcholinesterase Modulated UCNPs-Cu2+ Fluorescence Biosensor for Ultrasensitive Detection of Organophosphorus Pesticides-Diazinon in Food.

In this study, a highly sensitive upconversion fluorescence (FL) biosensor was developed for the detection of organophosphorus pesticides (OPs) based on an acetylcholinesterase (AChE) modulated FL "off-on-off" strategy. The luminescence of synthesized UCNPs could be quenched strongly by Cu2+ due to an energy transfer effect. Upon addition of AChE and acetylthiocholine (ATCh), the enzymatic hydrolysate (thiocholine) could seize Cu2+ from UCNPs-Cu2+ mixture, resulting in the quenched FL triggered on. OPs could irreversibly impede the activity of AChE, which caused the formation of thiocholine to decrease, thus, reduced the recovery of FL. Under the optimum conditions, a linear detection range from 0.1 to 50 ng/mL was achieved for the representative OPs (diazinon) with LOD of 0.05 ng/mL. Furthermore, the ability of the biosensor to detect OPs was also confirmed in adulterated environmental and agricultural samples. In validation analysis, the proposed sensor showed satisfactory results ( p > 0.05) with GC-MS.

Fast sensing of imidacloprid residue in tea using surface-enhanced Raman scattering by comparative multivariate calibration.

This study focused on the fabrication of a rapid, highly sensitive and inexpensive technique for the quantification of imidacloprid residue in green tea, based on surface-enhanced Raman scattering (SERS) using highly roughned surface flower shaped silver nanostructure (as SERS substrate) coupled with the chemometrics algorithm. The basic principle of this method is imidacloprid yielded SERS signal after adsorption on Ag-NF under laser excitation by the electromagnetic enhancement and the intensity of the peak is proportional to the concentration ranging from 1.0 × 103 to 1.0 × 10-4 µg/mL. Among the models used, the GA-PLS (Genetic algorithm-partial least square) exhibited superiority to quantify imidacloprid residue in green tea. The model achieved Rp (correlation coefficient) of 0.9702 with RPD of 4.95% in the test set and RSD for precision recorded up to 4.50%. Therefore, the proposed sensor could be employed to quantify imidacloprid residue in green tea for the safeguarding of quality and human health.

Multimodal Classification of Mild Cognitive Impairment Based on Partial Least Squares.

In recent years, increasing attention has been given to the identification of the conversion of mild cognitive impairment (MCI) to Alzheimer's disease (AD). Brain neuroimaging techniques have been widely used to support the classification or prediction of MCI. The present study combined magnetic resonance imaging (MRI), 18F-fluorodeoxyglucose PET (FDG-PET), and 18F-florbetapir PET (florbetapir-PET) to discriminate MCI converters (MCI-c, individuals with MCI who convert to AD) from MCI non-converters (MCI-nc, individuals with MCI who have not converted to AD in the follow-up period) based on the partial least squares (PLS) method. Two types of PLS models (informed PLS and agnostic PLS) were built based on 64 MCI-c and 65 MCI-nc from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The results showed that the three-modality informed PLS model achieved better classification accuracy of 81.40%, sensitivity of 79.69%, and specificity of 83.08% compared with the single-modality model, and the three-modality agnostic PLS model also achieved better classification compared with the two-modality model. Moreover, combining the three modalities with clinical test score (ADAS-cog), the agnostic PLS model (independent data: florbetapir-PET; dependent data: FDG-PET and MRI) achieved optimal accuracy of 86.05%, sensitivity of 81.25%, and specificity of 90.77%. In addition, the comparison of PLS, support vector machine (SVM), and random forest (RF) showed greater diagnostic power of PLS. These results suggested that our multimodal PLS model has the potential to discriminate MCI-c from the MCI-nc and may therefore be helpful in the early diagnosis of AD.