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A rapid and cost-effective technique for identification of microorganisms was explored using fluorescence microscopy and image analysis, and classification was done with trained neural network. The microorganisms used in this study are Bacillus thuringiensis (C399), Escherichia coli K12 (ATCC 10798), Lactobacillus brevis (LJH240), Listeria innocua (C366), and Staphylococcus epidermis (LJH343). After staining the microorganisms with fluorescent dyes [diamidino-2-phenyl-indole and acridine orange (AO)], images of the microorganisms were captured using a digital camera attached to a light microscope. Geometrical, optical, and textural features were extracted from the images using image analysis. Parameters extracted from images of microorganisms stained with AO gave better results for classification of the microorganisms. From these parameters, the best identification parameters that could classify the microorganisms with higher accuracy were selected using a probabilistic neural network (PNN). PNN was then used to classify the microorganisms with a 100% accuracy using nine identification parameters. These parameters are: 45° run length non-uniformity, width, shape factor, horizontal run length non-uniformity, mean gray level intensity, ten percentile values of the gray level histogram, 99 percentile values of the gray level histogram, sum entropy, and entropy. When the five microorganisms were mixed together then, also the PNN could classify the microorganisms with 100% accuracy using these nine parameters.

SKESA is a DeBruijn graph-based de-novo assembler designed for assembling reads of microbial genomes sequenced using Illumina. Comparison with SPAdes and MegaHit shows that SKESA produces assemblies that have high sequence quality and contiguity, handles low-level contamination in reads, is fast, and produces an identical assembly for the same input when assembled multiple times with the same or different compute resources. SKESA has been used for assembling over 272,000 read sets in the Sequence Read Archive at NCBI and for real-time pathogen detection. Source code for SKESA is freely available at https://github.com/ncbi/SKESA/releases .

Novel rapid methodologies for the detection of bacteria have been recently investigated and applied. In hospital environments, infections by pathogens are very common and can cause serious health problems. Pseudomonas aeruginosa is one of the most common bacteria, which can grow in hospital equipment such as catheters and respirators. Even at low concentrations, it can cause severe infections as it is resistant to antibiotics and other treatments. Based on this subject’s relevance, this work aimed to develop a colorimetric biosensor using aptamer-functionalized gold nanoparticles for identifying P. aeruginosa. The detection mechanism is based on the color change of gold nanoparticles (AuNPs) from red to blue-purple through NaCl induction after bacteria incubation and aptamer-target binding. First, AuNPs were synthesized and characterized. The influence of aptamer and sodium chloride concentration on the agglomeration of AuNPs was investigated. Optimization of aptamer concentration and salt addition were performed. The best condition for detection was 5 µM aptamers and 200 mM of NaCl. In this case, P. aeruginosa was detected after 5 h for concentrations from 108 to 105 CFU mL−1, being 105 and 104 CFU mL−1 the detection limit for color change by the naked eye and UV–Vis spectrometry, respectively. In addition, other bacteria such as E. coli, S. typhimurium, and Enterobacteriaceae bacterium were also detected with color changing from red to gray. Finally, it was confirmed that the salt incubation time can be 2 h, and that the ideal aptamer concentration is 5 µM. Thus, the colorimetric analysis can be a simple and fast detection method for P. aeruginosa in the range of 108 to 105 CFU mL−1 to the naked eye.Key Points• A new method for rapid detection of Pseudomonas aeruginosa• Aptamers conjugated with gold nanoparticles allow pathogen detection by colorimetry• No need for previous surface modification of nanoparticles

Initially described a century ago by William Twort and Felix d’Herelle, bacteriophages are bacterial viruses found ubiquitously in nature, located wherever their host cells are present. Translated literally, bacteriophage (phage) means ‘bacteria eater’. Phages interact and infect specific bacteria while not affecting other bacteria or cell lines of other organisms. Due to the specificity of these phage–host interactions, the relationship between phages and their host cells has been the topic of much research. The advances in phage biology research have led to the exploitation of these phage–host interactions and the application of phages in the agricultural and food industry. Phages may provide an alternative to the use of antibiotics, as it is well known that the emergence of antibiotic-resistant bacterial infections has become an epidemic in clinical settings. In agriculture, pre-harvest and/or post-harvest application of phages to crops may prevent the colonisation of bacteria that are detrimental to plant or human health. In addition, the abundance of data generated from genome sequencing has allowed the development of phage-derived bacterial detection systems of foodborne pathogens. This review aims to outline the specific interactions between phages and their host and how these interactions may be exploited and applied in the food industry.

Background High-throughput sequencing enables unbiased profiling of microbial communities, universal pathogen detection, and host response to infectious diseases. However, computation times and algorithmic inaccuracies have hindered adoption. Results We present Taxonomer, an ultrafast, web-tool for comprehensive metagenomics data analysis and interactive results visualization. Taxonomer is unique in providing integrated nucleotide and protein-based classification and simultaneous host messenger RNA (mRNA) transcript profiling. Using real-world case-studies, we show that Taxonomer detects previously unrecognized infections and reveals antiviral host mRNA expression profiles. To facilitate data-sharing across geographic distances in outbreak settings, Taxonomer is publicly available through a web-based user interface. Conclusions Taxonomer enables rapid, accurate, and interactive analyses of metagenomics data on personal computers and mobile devices.

The activities and transmissions of microorganisms are closely related to human, and all kinds of diseases caused by pathogenic microorganisms have attracted attention in the world and brought many challenges to human health and public health. The traditional microbial detection technologies have characteristics of longer detection cycle and complicated processes, therefore, which can no longer meet the detection requirements in the field of public health. At present, it is the focus to develop and design a novel, rapid, and simple microbial detection method in the field of public health. Herein, this article summarized the development of aptamer biosensor technologies for detection of microorganism in the aspect of bacteria, viruses, and toxins in detail, including optical aptamer sensors such as fluorometry and colorimetry, electrochemical aptamer sensors, and other technologies combined with aptamer.Key points• Aptamer biosensor is a good platform for microbial detection.• Aptamer biosensors include optical sensors and electrochemical sensors.• Aptamer sensors have been widely used in the detection of bacteria, viruses, and other microorganisms.

Analysis of microbial data from archaeological samples is a growing field with great potential for understanding ancient environments, lifestyles, and diseases. However, high error rates have been a challenge in ancient metagenomics, and the availability of computational frameworks that meet the demands of the field is limited. Here, we propose aMeta, an accurate metagenomic profiling workflow for ancient DNA designed to minimize the amount of false discoveries and computer memory requirements. Using simulated data, we benchmark aMeta against a current state-of-the-art workflow and demonstrate its superiority in microbial detection and authentication, as well as substantially lower usage of computer memory.

The microbiological diagnosis of infection for hematological malignancy patients receiving chemotherapy or allogeneic hematopoietic stem cell transplantation (allo-HSCT) patients relies primarily on standard microbial culture, especially blood culture, which has many shortcomings, such as having low positive rates, being time-consuming and having a limited pathogenic spectrum. In this prospective observational self-controlled test accuracy study, blood, cerebrospinal fluid (CSF), and bronchoalveolar lavage fluid (BALF) samples were collected from chemotherapy or allo-HSCT patients with clinical symptoms of infections who were hospitalized at Peking University First Hospital. Possible pathogens were detected by the method based on recombinant mannan-binding lectin (MBL) magnetic bead enrichment (M1 method) and simultaneously by a standard method. The analytical sensitivity of M1 method was close to that of standard culture method. Besides, the turn-around time of M1-method was significantly shorter than that of standard culture method. Moreover, the M1 method also added diagnostic value through the detection of some clinically relevant microbes missed by the standard method. M1 method could significantly increase the detection efficiency of pathogens (including bacteria and fungi) in immunocompromised patients. Key points • The detection results of M1-method had a high coincidence rate with that of standard method • M1 method detected many pathogens which had not been found by standard clinic method

As a significant constituent in biosphere, bacteria have a great influence on human activity. The detection of pathogen bacteria is closely related to the human health. However, the traditional methods for detection of pathogenic bacteria are time-consuming and difficult for quantification, although they are practical and reliable. Therefore, novel strategies for rapid, sensitive, and cost-effective detection are in great demand. Aptamer is a kind of oligonucleotide that selected by repeated screening in vitro or systematic evolution of ligands by exponential enrichment (SELEX) technology. Over the past years, owing to high affinity and specificity of aptamers, a variety of aptamer-based biosensors have been designed and applied for pathogen detection. In this review, we have discussed the recent advances on the applications of aptamer-based biosensors in detection of pathogenic bacteria. In addition, we also point out some problems in current methods and look forward to the further development of aptamer-based biosensors for pathogen detection.

IntroductionMany bacteria are responsible for infections in humans and plants, being found in vegetables, water, and medical devices. Most bacterial detection methods are time-consuming and take days to give the result. Aptamers are a promising alternative for a quick and reliable measurement technique to detect bacteria present in food products. Selected aptamers are DNA or RNA oligonucleotides that can bind with bacteria or other molecules with affinity and specificity for the target cells by the SELEX or cell-SELEX technique. This method is based on some rounds to remove the non-ligand oligonucleotides, leaving the aptamers specific to bind to the selected bacteria. Compared with conventional methodologies, the detection approach using aptamers is a rapid, low-cost form of analysis. ObjectiveThis review summarizes obtention methods and applications of aptamers in the food industry and biotechnology. Besides, different techniques with aptamers are presented, which enable more effective target detection.ConclusionApplications of aptamers as biosensors, or the association of aptamers with nanomaterials, may be employed in analyses by colorimetric, fluorescence, or electrical devices. Additionally, more efficient ways of sample preparation are presented, which can support food safety to provide human health, with a low-cost method for contaminant detection.Key points• Aptamers are promising for detecting contaminants outbreaks.• Studies are needed to identify aptamers for different targets.