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Point-of-care testing (POCT) of pathogenic bacteria at low concentrations is vital to early warning of bacterial contaminations. A disposable centrifuge tube was reconstructed in this study for sensitive Salmonella detection in large-volume samples, where the entire bacterial detection progress from separation to detection, was performed within the tube. The bottom of this centrifuge tube was assembled with a flexible stirrer containing a pair of circular magnets, which was rapidly rotated using a DC motor to produce the vortex for simultaneous mixing and capture of target bacteria. Besides, immune manganese dioxide nanoflowers were synthesized and used to label target bacteria, followed by mimicking catalyze colorless TMB substrate into blue TMBox product. The product image was captured and analyzed by a smartphone App to quantitatively determine the target bacteria. This POCT centrifuge tube effectively achieved a separation efficiency of approximately 80 % for target bacteria from a 10 mL sample, enabling the detection of target bacteria within the range of 1.3 × 101 to 1.3 × 104 CFU/mL in 1 h, with a low detection limit of 13 CFU/mL. More importantly, this reconstructed centrifuge tube demonstrated its potential as a laboratory consumable for bacterial detection in routine screening owing to the features of low cost, easy operation and high integration.

Summary: Accurate detection of pathogens, particularly distinguishing between Gram-positive and Gram-negative bacteria, could improve disease treatment. Host gene expression can capture the immune system’s response to infections caused by various pathogens. Here, we present a deep neural network model, bvnGPS2, which incorporates the attention mechanism based on a large-scale integrated host transcriptome dataset to precisely identify Gram-positive and Gram-negative bacterial infections as well as viral infections. We performed analysis of 4,949 blood samples across 40 cohorts from 10 countries using our previously designed omics data integration method, iPAGE, to select discriminant gene pairs and train the bvnGPS2. The performance of the model was evaluated on six independent cohorts comprising 374 samples. Overall, our deep neural network model shows robust capability to accurately identify specific infections, paving the way for precise medicine strategies in infection treatment and potentially also for identifying subtypes of other diseases.

This study is to examine the effects of EGCG on fatty acid metabolism and their possible relationship with gut microbiota. C57BL/6J mice fed with HFD were administrated with 210 mg/kg EGCG for 12 weeks. EGCG significantly decreased saturated fatty acids (C17:0 and C22:0), increased the unsaturated fatty acids (C18:1 and C18:2) (p 0.45 or r

Different from most of other plants, peanut (Arachis hypogaea L.) is a typical geocarpic species which flowering and forming pegs (gynophores) above the ground. Pegs penetrate into soil for embryo and pod development. To investigate the molecular mechanism of geocarpy feature of peanut, the proteome profiles of aerial grown gynophores (S1), subterranean unswollen gynophores (S2), and gynophores that had just started to swell into pods (S3) were analyzed by combining 1 DE with nano LC–MS/MS approaches. The proteomic data provided valuable information for understanding pod development of peanut. The data described here can be found in the PRIDE Archive using the reference number PXD002579-81. A more comprehensive analysis of this data may be obtained from the article in BMC Plant Biology (Zhao et al., 2015 [1]).