Gut microbiota: A potential player in psychiatric symptoms during COVID-19.

OBJECTIVES: This study aims to explore the potential interconnections among gut microbiota, COVID-19 infection, depression and anxiety disorder. Additionally, it tries to assess potential therapeutic interventions that may improve the dysbiosis of gut microbiota. METHODS: To achieve these objectives, we reviewed existing literature, encompassing studies and critical reviews that intersect the domains of gut microbiota, COVID-19, depression and anxiety disorders. RESULTS: The findings highlight a notable correlation between the dysbiosis of gut microbiota and psychiatric symptoms in the context of COVID-19. Specifically, there is a marked reduction in the populations of bacteria that generate anti-inflammatory short-chain fatty acids (SCFAs), alongside a rise in the prevalence of gut bacterial clusters linked to inflammatory processes. Furthermore, several potential treatment strategies were summarised for improving the dysbiosis. CONCLUSIONS: Gut microbiota plays a significant role in psychiatric symptoms during COVID-19, which has significant implications for the study and prevention of psychiatric symptoms in major epidemic diseases.

DNA-Guided Extracellular-Vesicle Metallization with High Catalytic Activity for Accurate Diagnosis of Pulmonary Nodules.

Sensitive and specific analysis of extracellular vesicles (EVs) offers a promising minimally invasive way to identify malignant pulmonary nodules from benign lesions. However, accurate analysis of EVs is subject to free target proteins in blood samples, which compromises the clinical diagnosis value of EVs. Here a DNA-guided extracellular-vesicle metallization (DEVM) strategy is described for ultrasensitive and specific analysis of EV protein biomarkers and classification of pulmonary nodules. The facile DEVM process mainly includes the incorporation of DNA labeled with cholesterol and thiol groups into EV membranes and subsequent deposition of Au3+ and Pt4+ to allow the DNA-functionalized EVs to be encapsulated with AuPt nanoshells. It is found that the synthesized AuPt-metallized EVs possess extrinsic peroxidase-like activity. Utilizing the feature of the catalytic metal nanoshells just growth on the EV membranes, the DEVM method enables multiparametric recognition of target proteins and EV membranes and can produce an amplified colorimetric signal, avoiding the interference of free proteins. By profiling four surface proteins of EVs from 48 patients with pulmonary nodules, the highest area under the receiver operating characteristic curve (0.9983) is obtained. Therefore, this work provides a feasible EVs analysis tool for accurate pulmonary nodules management.

Effect of Precipitates on the Mechanical Performance of 7005 Aluminum Alloy Plates.

In this study, the strength, elongation, and fatigue properties of 7005 aluminum alloy plates with different configurations of precipitates were investigated by means of tensile tests, fatigue tests, and microstructural observation. We found that the number and size of GP zones in an alloy plate matrix increased and the distribution was more uniform after the aging time was extended from 1 h to 4 h at 120 °C, which led to a rise in both strength and elongation of alloy plates with the extending aging time. The fatigue life of the alloy plates shortened slightly at first, then significantly prolonged, and then shortened again with the aging time extending from 1 h to 192 h and a fatigue stress level of 185 MPa and stress ratio (R) = 0. After aging at 120 °C for 96 h, the precipitates in the alloy plate matrix were almost all metastable η'-phase particles, which had the optimal aging strengthening effect on the alloy matrix, and the degree of mismatch between the α-Al matrix and second-phase particles was the smallest; the fatigue crack initiation and propagation resistances were the largest, leading to the best fatigue performance of alloy plates, and the fatigue life of the aluminum plate was the longest, up to 1.272 × 106 cycles. When the aging time at 120 °C was extended to 192 h, there were a small number of equilibrium η phases in the aluminum plates that were completely incoherent with the matrix and destroyed the continuity of the aluminum matrix, easily causing stress concentration. As a result, the fatigue life of alloy plates was shortened to 9.422 × 105 cycles.

A simple and smart AND-gate DNA nanoprobe for correlated enzymes tracking and cell-selective imaging.

Accurate cancer diagnosis and effective drug therapy entail sensitive and dynamic monitoring of intracellular key enzymes, since their expression level is closely related to disease progression. Simultaneous monitoring of correlated enzymes is promising to help unveiling mystery of cytobiological events during tumor progression and drug response, while is challenged by lacking of a robust and simple simultaneous detection strategy. In order to construct a simple and smart strategy which is complex design-avoided and doesn't need other auxiliary enzyme, here we develop an AND-gate strategy for simultaneously monitoring correlated enzymes which both are upregulated in cancer cells (telomerase and apurinic/apyrimidinic endonuclease 1). An innovative AND-gate DNA nanoprobe has been designed to avoid mutual interference and background noise, guaranteeing an enhanced fluorescent signal output upon catalyzation of dual enzymes. This AND-gate strategy achieves sensitive detection of two enzymes in an individual manner in test tube, through which the diagnostic potential of bladder cancer has been validated by telomerase detection in clinical urine sample. The AND-gate strategy enables specific intracellular imaging of dual enzymes in different cancer cell lines. Importantly, in contrast to traditional single-targeting strategies, AND-gate imaging of dual enzymes significantly improves cancer cell selectivity. Moreover, this strategy dynamically monitors enzymatic activity changes during chemoresistance induced by chemotherapeutic treatment. This simple and smart strategy has foreseeable prospect in the fields of disease diagnosis, drug prognosis evaluation, and precise fluorescence-guided surgery.

Portevin-Le Chatelier Characterization of Quenched Al-Mg Alloy Sheet with Different Mg Concentrations.

In the present study, the PLC characteristic parameters and DSA mechanism of Al-(2.86~9.41) Mg alloy sheets were investigated during tensile testing at room temperature with a tensile rate of 1 × 10-3 s-1. On the basis of the solution Mg concentrations in the α-Al matrix, the initial vacancy concentration, the second-phase particle configuration and the recrystallized grain configuration are almost the same by quenching treatment. The results show that the type of room-temperature tensile stress-strain curves of quenched Al-(2.86~9.41) Mg alloy sheets varied according to the Mg content. The type of stress-strain curve of the Al-2.86 Mg alloy sheet was B + C, while the type of stress-strain curve of the Al-(4.23~9.41) Mg alloy sheets was C. When the quenched Al-(2.86~9.41) Mg alloy sheets were stretched at room temperature, the strain cycle of the rectangular waves corresponding to the high stress flow ΔεTmax and stress drop amplitude Δσ on the zigzag stress-strain curve of alloy sheets increased with increasing the Mg content. Moreover, the strain cycle of ΔεTmax and Δσ on the stress-strain curve of alloy sheets increased gradually with increasing tensile deformation. The yield stress of quenched Al-(2.86~9.41) Mg alloy sheets increased gradually with increasing the Mg content. Moreover, the critical strain corresponding to yield stress εσ and the critical strain corresponding to the occurrence of the PLC shearing band εc of alloy sheets both increased with increasing the Mg content. However, the difference in flow strain value Δεc-σ between εc and εσ of alloy sheets decreased gradually with increasing the Mg content.

FMSM: a novel computational model for predicting potential miRNA biomarkers for various human diseases.

BACKGROUND: MicroRNA (miRNA) plays a key role in regulation mechanism of human biological processes, including the development of disease and disorder. It is necessary to identify potential miRNA biomarkers for various human diseases. Computational prediction model is expected to accelerate the process of identification. RESULTS: Considering the limitations of previously proposed models, we present a novel computational model called FMSM. It infers latent miRNA biomarkers involved in the mechanism of various diseases based on the known miRNA-disease association network, miRNA expression similarity, disease semantic similarity and Gaussian interaction profile kernel similarity. FMSM achieves reliable prediction performance in 5-fold and leave-one-out cross validations with area under ROC curve (AUC) values of 0.9629+/- 0.0127 and 0.9433, respectively, which outperforms the state-of-the-art competitors and classical algorithms. In addition, 19 of top 25 predicted miRNAs have been validated to have associations with Colonic Neoplasms in case study. CONCLUSIONS: A factored miRNA similarity based model and miRNA expression similarity substantially contribute to the well-performing prediction. The list of the predicted most latent miRNA biomarkers of various human diseases is publicized. It is anticipated that FMSM could serve as a useful tool guiding the future experimental validation for those promising miRNA biomarker candidates.