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Background Effective disinfection is a diligent step in the treatment of endodontic infections to reduce the microbial load below a critical level that promotes healing of apical periodontitis (AP). Despite its clinical importance, there is no consensus on the most effective disinfection strategy to achieve this goal. Therefore, this scoping review aimed to comprehensively review the role of current disinfection therapies in microbial reduction during root canal treatment (RCT). Methods A systematic search was conducted in PubMed, Web of Science, Scopus, and Google Scholar. Relevant clinical studies were selected based on predefined inclusion criteria. Data extraction focused on microbial load reduction, disinfection protocols, and microbial detection techniques. Results Out of 3519 retrieved articles, 148 were eligible and processed for data extraction. All included studies were clinical studies. Most of the studies were primary RCT (82.43%) and secondary RCT (15.54%). While only 3 studies (2.03%) included both primary and secondary RCT. Microbial load reduction was assessed in the majority of the studies, while few others assessed endotoxin reduction. The mean percentage of microbial load reduction was (87.24 ± 21.86). The most frequently used disinfection regimens were irrigation with; (2.5% NaOCl alone, 2.5% NaOCl followed by 17% EDTA, 2%CHX; 16.05%, 11.11%, 10.49% respectively), activation methods including; passive ultrasonic irrigation (PUI); 12.35%, photodynamic therapy (PDT); 11.11%, and laser; 11.11%) and intracanal medication (ICM) with Ca (OH)2, Ca (OH)2 + 2%CHX, or 2%CHX; 31.48%, 10.51%, 7.41% respectively. The most prevalent methods of microbial detection were culture-based (64.86%), and molecular-based methods were performed in 58.10% of the studies. Conclusion A combination of advanced irrigation techniques, appropriate ICM, and adjunctive disinfection activation techniques provides the most effective approach for both microbial and endotoxin reduction. However, complete disinfection and its influence on the validity of treatment outcomes remain a dilemma. The healing of apical periodontitis is multifactorial, and future research should focus on personalized treatment protocols based on patient and tooth specific considerations to optimize clinical outcomes.

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

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.

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.

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.

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.

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.

Bacteria, fungi, viruses, and nematodes are the major causal agents of plant diseases. These phytopathogens are responsible for about 10–40% losses in productivity and quality of food crops and horticultural produce. Although eradication of pathogens is not possible, control of plant diseases has been an area of continuous improvement/research. Use of antimicrobials, bacteriophages, and biocontrol agents, natural and synthetic agrochemicals along with best farm management practices constitute integrated measures for disease control. However, the quest for new materials continues due to pesticide resistance in the pathogens, emergence of new serotypes, and accumulation of high quantities of agrochemical contaminants in the ecosystem and associated environmental hazards, specificity of biocontrol agents, succession of pathogens during the plant growth phase, etc. The emergence of “nanotechnology,” a multidisciplinary field of research, has provided a plethora of nanomaterials for potential applications in the agricultural sector. Control of plant diseases requires agents that reduce the pathogen to manageable levels, tools for early-stage detection of pathogen, and compounds that elicit immune response in the host plants. Nanomaterials have in fact been assessed for their utility in all these approaches for disease control. The present review discusses nanomaterials for controlling phytopathogens, nanomaterials in plant disease diagnostics, and nanomaterials as elicitors of the plant immune system. These nanomaterials thus represent new weapons in the fight against the phytopathogens. Recent studies indicate that nanomaterials will be a crucial component in the agroecosystem.