Bacterial Classification Research Articles

Peningkatan Algoritma Naïve Bayes Menggunakan Algoritma Genetika Pada Klasifikasi Bakteri

In previous studies, only using the nave Bayes algorithm and resulted in an accuracy value of 80.93% and currently the accuracy value will be increased by using a genetic algorithm. Learning patterns in genetic algorithms can inform new entrants or new classifications with a faster time. Difficulties in the configuration that exist in nave Bayes can be helped by this genetic algorithm in addition to being able to provide adequate modeling to describe the system. Bacteria consists of three classifications of bacteria: based on how they obtain food, based on gram staining, and based on their shape. In this bacterial data, which consists of numerical parameters containing the sequence name, Mcg, gvh, Lip, chg, aac, alm1, alm2 and a class distributron of 336 attributes, in the class distribution there are 8 protein classes classified, namely cytoplasm (cp), membrane inside without signal sequence (im), perisplasm (pp), inside membrane with uncleavable signal sequence (IMU), outer membrane (om), outer membrane lipoprotein (OML), inside membrane lipoprotein (IML), inner membrane with cleavable signal sequence ( STI). The results of this study resulted in an accuracy value of 81.19%. Thus, it is proven that the performance of the nave Bayes algorithm can be improved by using a genetic algorithm for bacterial classification

Combined procalcitonin and hemogram parameters contribute to early differential diagnosis of Gram‐negative/Gram‐positive bloodstream infections

BackgroundHemogram parameters and procalcitonin (PCT) play auxiliary roles in the diagnosis and outcome of sepsis. However, it is not clear whether these indicators can quickly distinguish bacterial classification or guide the choice of empirical antibiotics.MethodsWe retrospectively enrolled 381 patients with bloodstream infections (BSI), divided into Gram‐positive bloodstream infections (GP‐BSI) and Gram‐negative bloodstream infections (GN‐BSI). Demographic parameters, hemogram parameters, and PCT were recorded and compared between the two groups.ResultsThe mean platelet volume (MPV), platelet distribution width (PDW), and PCT in the GN‐BSI group were significantly higher than those in the GP‐BSI group, while the platelet count (PLT), plateletcrit, platelet count‐to‐white blood cell count ratio (PWR), platelet count‐to‐neutrophil count ratio (PNR), platelet count‐to‐PCT ratio (PLT/PCT), and mean platelet volume‐to‐PCT ratio (MPV/PCT) were significantly lower in the GN‐BSI group. Multivariate stepwise logistic regression analysis revealed that the independent predictors of GN‐BSI were MPV, PWR, and PCT. The areas under the curve (AUC) for this prediction model was 0.79, with sensitivity =0.75 and specificity =0.71.ConclusionsThere were significant differences in terms of PCT, platelet parameters, and platelet‐related index‐PCT ratio between GN‐BSI and GP‐BSI. Combined PCT and hemogram parameters are more conducive to the early differential diagnosis of bacterial classification of BSI.

DNA structural and physical properties reveal peculiarities in promoter sequences of the bacterium Escherichia coli K-12

The gene transcription of bacteria starts with a promoter sequence being recognized by a transcription factor found in the RNAP enzyme, this process is assisted through the conservation of nucleotides as well as other factors governing these intergenic regions. Faced with this, the coding of genetic information into physical aspects of the DNA such as enthalpy, stability, and base-pair stacking could suggest promoter activity as well as protrude differentiation of promoter and non-promoter data. In this work, a total of 3131 promoter sequences associated to six different sigma factors in the bacterium E. coli were converted into numeric attributes, a strong set of control sequences referring to a shuffled version of the original sequences as well as coding regions is provided. Then, the parameterized genetic information was normalized, exhaustively analyzed through statistical tests. The results suggest that strong signals in the promoter sequences match the binding site of transcription factor proteins, indicating that promoter activity is well represented by its conversion into physical attributes. Moreover, the features tested in this report conveyed significant variances between promoter and control data, enabling these features to be employed in bacterial promoter classification. The results produced here may aid in bacterial promoter recognition by providing a robust set of biological inferences.

Impact of Anaerobic Antibacterial Spectrum on Cystic Fibrosis Airway Microbiome Diversity and Pulmonary Function.

The role of anaerobic organisms in the cystic fibrosis (CF) lung microbiome is unclear. Our objectives were to investigate the effect of broad (BS) versus narrow (NS) spectrum antianaerobic antibiotic activity on lung microbiome diversity and pulmonary function, hypothesizing that BS antibiotics would cause greater change in microbiome diversity without a significant improvement in lung function. Pulmonary function tests and respiratory samples were collected prospectively in persons with CF before and after treatment for pulmonary exacerbations. Treatment antibiotics were classified as BS or NS. Gene sequencing data from 16S rRNA were used for diversity analysis and bacterial genera classification. We compared the effects of BS versus NS on diversity indices, lung function and anaerobic/aerobic ratios. Statistical significance was determined by multilevel mixed-effects generalized linear models and mixed-effects regression models. Twenty patients, 6-20 years of age, experienced 30 exacerbations. BS therapy had a greater effect on beta diversity than NS therapy when comparing time points before antibiotics to after and at recovery. After antibiotics, the NS therapy group had a greater return toward baseline forced expiratory volume at 1 second and forced expiratory flow 25%-75% values than the BS group. The ratio of anaerobic/aerobic organisms showed a predominance of anaerobes in the NS group with aerobes dominating in the BS group. BS antianaerobic therapy had a greater and possibly longer lasting effect on the lung microbiome of persons with CF, without achieving the recovery of pulmonary function seen with the NS therapy. Specific antibiotic therapies may affect disease progression by changing the airway microbiome.

Temporal and Spatial Changes in Phyllosphere Microbiome of Acacia Trees Growing in Arid Environments.

Background: The evolutionary relationships between plants and their microbiomes are of high importance to the survival of plants in general and even more in extreme conditions. Changes in the plant's microbiome can affect plant development, growth, fitness, and health. Along the arid Arava, southern Israel, acacia trees (Acacia raddiana and Acacia tortilis) are considered keystone species. In this study, we investigated the ecological effects of plant species, microclimate, phenology, and seasonality on the epiphytic and endophytic microbiome of acacia trees. One hundred thirty-nine leaf samples were collected throughout the sampling year and were assessed using 16S rDNA gene amplified with five different primers (targeting different gene regions) and sequenced (150 bp paired-end) on an Illumina MiSeq sequencing platform.Results: Epiphytic bacterial diversity indices (Shannon–Wiener, Chao1, Simpson, and observed number of operational taxonomic units) were found to be nearly double compared to endophyte counterparts. Epiphyte and endophyte communities were significantly different from each other in terms of the composition of the microbial associations. Interestingly, the epiphytic bacterial diversity was similar in the two acacia species, but the canopy sides and sample months exhibited different diversity, whereas the endophytic bacterial communities were different in the two acacia species but similar throughout the year. Abiotic factors, such as air temperature and precipitation, were shown to significantly affect both epiphyte and endophytes communities. Bacterial community compositions showed that Firmicutes dominate A. raddiana, and Proteobacteria dominate A. tortilis; these bacterial communities consisted of only a small number of bacterial families, mainly Bacillaceae and Comamonadaceae in the endophyte for A. raddiana and A. tortilis, respectively, and Geodematophilaceae and Micrococcaceae for epiphyte bacterial communities, respectively. Interestingly, ~60% of the obtained bacterial classifications were unclassified below family level (i.e., “new”).Conclusions: These results shed light on the unique desert phyllosphere microbiome highlighting the importance of multiple genotypic and abiotic factors in shaping the epiphytic and endophytic microbial communities. This study also shows that only a few bacterial families dominate both epiphyte and endophyte communities, highlighting the importance of climate change (precipitation, air temperature, and humidity) in affecting arid land ecosystems where acacia trees are considered keystone species.

Human sensor-inspired supervised machine learning of smartphone-based paper microfluidic analysis for bacterial species classification

Bacteria identification has predominantly been conducted using specific bioreceptors such as antibodies or nucleic acid sequences. This approach may be inappropriate for environmental monitoring when the user does not know the target bacterial species and for screening complex water samples with many unknown bacterial species. In this work, we investigate the supervised machine learning of the bacteria-particle aggregation pattern induced by the peptide sets identified from the biofilm-bacteria interface. Each peptide is covalently conjugated to polystyrene particles and loaded together with bacterial suspensions onto paper microfluidic chips. Each peptide interacts with bacterial species to a different extent, leading to varying sizes of particle aggregation. This aggregation changes the surface tension and viscosity of the liquid flowing through the paper pores, altering the flow velocity at different extents. A smartphone camera captures this flow velocity without being affected by ambient and environmental conditions, towards a low-cost, rapid, and field-ready assay. A collection of such flow velocity data generates a unique fingerprinting profile for each bacterial species. Support vector machine is utilized to classify the species. At optimized conditions, the training model can predict the species at 93.3% accuracy out of five bacteria: Escherichia coli, Staphylococcus aureus, Salmonella Typhimurium, Enterococcus faecium, and Pseudomonas aeruginosa. Flow rates are monitored for less than 6 s and the sample-to-answer assay time is less than 10 min. The demonstrated method can open a new way of analyzing complex biological and environmental samples in a biomimetic manner with machine learning classification.

Rapid uropathogen identification using surface enhanced Raman spectroscopy active filters

Urinary tract infection is one of the most common bacterial infections leading to increased morbidity, mortality and societal costs. Current diagnostics exacerbate this problem due to an inability to provide timely pathogen identification. Surface enhanced Raman spectroscopy (SERS) has the potential to overcome these issues by providing immediate bacterial classification. To date, achieving accurate classification has required technically complicated processes to capture pathogens, which has precluded the integration of SERS into rapid diagnostics. This work demonstrates that gold-coated membrane filters capture and aggregate bacteria, separating them from urine, while also providing Raman signal enhancement. An optimal gold coating thickness of 50 nm was demonstrated, and the diagnostic performance of the SERS-active filters was assessed using phantom urine infection samples at clinically relevant concentrations (105 CFU/ml). Infected and uninfected (control) samples were identified with an accuracy of 91.1%. Amongst infected samples only, classification of three bacteria (Escherichia coli, Enterococcus faecalis, Klebsiella pneumoniae) was achieved at a rate of 91.6%.

Developing bacterial set box media containing fiqh al biah for Islamic senior high school

The limitations of bacterial visualization media make bacteria consider as abstract and difficult material to study. Furthermore, the implementation of bacteria material and the integration of Islamic values are difficult to teach. This research aimed at developing and validating the BSB (Bacterial Set Box) media containing fiqh al biah. The method used in this research was R&D (Research and Development) with 4D model (Define, Design, Develop, and Disseminate) by Thiagarajan et al. The media developed was three dimensions (3D), which was packed up in a box which contained three packages. The first box was bacterial visualization; the second box was bacterial classification; the third box was 3D box containing fiqh al biah. The field test was carried out in Al Azhar Islamic Public School 16 and MA Nurussalam Semarang. The results showed that the validation percentages were 94% (material expert), 79% (media expert), 96% (integration expert), 92% (biology teacher of Al Azhar Islamic Public School 16), and 86% (biology teacher of MA Nurussalam). A pilot scale test and a large-scale test showed very feasible. It can be concluded that BSB media containing fiqh al biah is very feasible to use.

A review of the taxonomy, genetics, and biology of the genus Escherichia and the type species Escherichia coli.

Historically, bacteriologists have relied heavily on biochemical and structural phenotypes for bacterial taxonomic classification. However, advances in comparative genomics have led to greater insights into the remarkable genetic diversity within the microbial world, and even within well-accepted species such as Escherichia coli. The extraordinary genetic diversity in E. coli recapitulates the evolutionary radiation of this species in exploiting a wide range of niches (i.e., ecotypes), including the gastrointestinal system of diverse vertebrate hosts as well as non-host natural environments (soil, natural waters, wastewater), which drives the adaptation, natural selection, and evolution of intragenotypic conspecific specialism as a strategy for survival. Over the last few years, there has been increasing evidence that many E. coli strains are very host (or niche)-specific. While biochemical and phylogenetic evidence support the classification of E. coli as a distinct species, the vast genomic (diverse pan-genome and intragenotypic variability), phenotypic (e.g., metabolic pathways), and ecotypic (host-/niche-specificity) diversity, comparable to the diversity observed in known species complexes, suggest that E. coli is better represented as a complex. Herein we review the taxonomic classification of the genus Escherichia and discuss how phenotype, genotype, and ecotype recapitulate our understanding of the biology of this remarkable bacterium.

Origin Sample Prediction and Spatial Modeling of Antimicrobial Resistance in Metagenomic Sequencing Data.

The steady elaboration of the Metagenomic and Metadesign of Subways and Urban Biomes (MetaSUB) international consortium project raises important new questions about the origin, variation, and antimicrobial resistance of the collected samples. CAMDA (Critical Assessment of Massive Data Analysis, http://camda.info/) forum organizes annual challenges where different bioinformatics and statistical approaches are tested on samples collected around the world for bacterial classification and prediction of geographical origin. This work proposes a method which not only predicts the locations of unknown samples, but also estimates the relative risk of antimicrobial resistance through spatial modeling. We introduce a new component in the standard analysis as we apply a Bayesian spatial convolution model which accounts for spatial structure of the data as defined by the longitude and latitude of the samples and assess the relative risk of antimicrobial resistance taxa across regions which is relevant to public health. We can then use the estimated relative risk as a new measure for antimicrobial resistance. We also compare the performance of several machine learning methods, such as Gradient Boosting Machine, Random Forest, and Neural Network to predict the geographical origin of the mystery samples. All three methods show consistent results with some superiority of Random Forest classifier. In our future work we can consider a broader class of spatial models and incorporate covariates related to the environment and climate profiles of the samples to achieve more reliable estimation of the relative risk related to antimicrobial resistance.

Surveillance of foodborne diseases in Taiwan: A retrospective study.

Foodborne pathogens cause diseases and death, increasing the economic burden. It needs to identify incident places, media food and pathgens. Our aim is to survey empirical data that provide a retrospective historical perspective on foodborne diseases and explore the causes and trends of outbreaks.We examined publicly available annual summary data on reported foodborne disease outbreaks in Taiwan from 2014 to 2018. We calculated the percentage of places, media food, bacteria and natural toxin sources in foodborne diseases and performed a chi-square test for difference evaluation. The higher risk of places and causes in 2018 compared with 2014 was empolyzed with univariate logistic regression.There were 26847 patients with foodborne diseases during the period from 2014 to 2018. The top 2 primary source locations of the foodborne diseases were schools and restaurants. The top 2 primary food media classifications of the foodborne diseases were boxed meals and compounded foods. The top 2 primary incident bacterial classifications of the observed foodborne diseases were Bacillus cereus and Staphylococcus aureus. The top 2 primary natural toxin classifications of the foodborne diseases were plants and histamines. The incidence of foodborne disease in military facilities, fruits and vegetables, and Staphylococcus aureus was increased in our study.Our study confirmed the high risk and increased incidence of foodborne diseases, food media classifications, bacterial classifications, and natural toxins in Taiwan. It is worthy of attention for the government health department-designed policy to promote disease prevention.

Validation of a Host Gene Expression Test for Bacterial/Viral Discrimination in Immunocompromised Hosts.

Host gene expression has emerged as a complementary strategy to pathogen detection tests for the discrimination of bacterial and viral infection. The impact of immunocompromise on host-response tests remains unknown. We evaluated a host-response test discriminating bacterial, viral, and noninfectious conditions in immunocompromised subjects. An 81-gene signature was measured using real-time-polymerase chain reaction in subjects with immunocompromise (chemotherapy, solid-organ transplant, immunomodulatory agents, AIDS) with bacterial infection, viral infection, or noninfectious illness. A regularized logistic regression model trained in immunocompetent subjects was used to estimate the likelihood of each class in immunocompromised subjects. Accuracy in the 136-subject immunocompetent training cohort was 84.6% for bacterial versus nonbacterial discrimination and 80.8% for viral versus nonviral discrimination. Model validation in 134 immunocompromised subjects showed overall accuracy of 73.9% for bacterial infection (P = .04 relative to immunocompetent subjects) and 75.4% for viral infection (P = .30). A scheme reporting results by quartile improved test utility. The highest probability quartile ruled-in bacterial and viral infection with 91.4% and 84.0% specificity, respectively. The lowest probability quartile ruled-out infection with 90.1% and 96.4% sensitivity for bacterial and viral infection, respectively. Performance was independent of the type or number of immunocompromising conditions. A host gene expression test discriminated bacterial, viral, and noninfectious etiologies at a lower overall accuracy in immunocompromised patients compared with immunocompetent patients, although this difference was only significant for bacterial infection classification. With modified interpretive criteria, a host-response strategy may offer clinically useful diagnostic information for patients with immunocompromise.

Bacterial classification and antibiotic susceptibility testing on an integrated microfluidic platform.

With the prevalence of bacterial infections and increasing levels of antibiotic resistance comes the need for rapid and accurate methods for bacterial classification (BC) and antibiotic susceptibility testing (AST). Here we demonstrate the use of the fluid handling technique digital microfluidics (DMF) for automated and simultaneous BC and AST using growth metabolic markers. Custom instrumentation was developed for this application including an integrated heating module and a machine-learning-enabled low-cost colour camera for real-time absorbance and fluorescent sample monitoring on multipurpose devices. Antibiotic dilutions along with sample handling, mixing and incubation at 37 °C were all pre-programmed and processed automatically. By monitoring the metabolism of resazurin, resorufin beta-D-glucuronide and resorufin beta-D-galactopyranoside to resorufin, BC and AST were achieved in under 18 h. AST was validated in two uropathogenic E. coli strains with antibiotics ciprofloxacin and nitrofurantoin. BC was performed independently and simultaneously with ciprofloxacin AST for E. coli, K. pneumoniae, P. mirabilis and S. aureus. Finally, a proof-of-concept multiplexed system for breakpoint testing of two antibiotics, as well as E. coli and coliform classification was investigated with a multidrug-resistant E. coli strain. All bacteria were correctly identified, while AST and breakpoint test results were in essential and category agreement with reference methods. These results show the versatility and accuracy of this all-in-one microfluidic system for analysis of bacterial growth and phenotype.

Discrimination of bacteria using whole organism fingerprinting: the utility of modern physicochemical techniques for bacterial typing.

Rapid and accurate classification and discrimination of bacteria is an important task and has been highlighted recently for rapid diagnostics using real-time results. Coupled with a recent report by Jim O'Neill [] that if left unaddressed antimicrobial resistance (AMR) in bacteria could kill 10 million people per year by 2050, which would surpass current cancer mortality, this further highlights the need for unequivocal identification of microorganisms. Whilst traditional microbiological testing has offered insights into the characterisation and identification of a wide range of bacteria, these approaches have proven to be laborious and time-consuming and are not really fit for purpose, considering the modern day speed and volume of international travel and the opportunities it creates for the spread of pathogens globally. To overcome these disadvantages, modern analytical methods, such as mass spectrometry (MS) and vibrational spectroscopy, that analyse the whole organism, have emerged as essential alternative approaches. Currently within clinical microbiology laboratories, matrix assisted laser desorption ionisation (MALDI)-MS is the method of choice for bacterial identification. This is largely down to its robust analysis as it largely measures the ribosomes which are always present irrespective of how the bacteria are cultured. However, MALDI-MS requires large amounts of biomass and infrared spectroscopy and Raman spectroscopy are attractive alternatives as these physicochemical bioanalytical techniques have the advantages of being rapid, reliable and cost-effective for analysing various types of bacterial samples, even at the single cell level. In this review, we discuss the fundamental applications, advantages and disadvantages of modern analytical techniques used for bacterial characterisation, classification and identification.

1216. Benchmarking Published Gene Signatures for Robust Infection Classification

BackgroundHost gene expression has emerged as a promising diagnostic strategy to discriminate bacterial and viral infection. Multiple gene signatures of varying size and complexity have been developed in various clinical populations. However, there has been no systematic comparison of these signatures. It is also unclear how these signatures apply to different clinical populations. This meta-analysis examined 19 published signatures, validated in 49 publicly available datasets for a total of 4750 patients. The objectives were to understand how the signatures compared to each other with respect to composition and performance, and to evaluate their performance in different patient subgroups.MethodsSignatures were characterized with respect to size, platform, and discovery population. For each of the 19 signatures, we ran leave-one-out cross-validation to generate AUCs for bacterial classification and viral classification. We then applied dataset-specific thresholds to generate accuracies, pooling patients across datasets.ResultsSignature performance varied significantly with a median AUC across all validation datasets ranging from 0.55 to 0.94 for bacterial classification and 0.79 to 0.96 for viral classification. Signature size varied (1- 341 genes) with smaller signatures generally performing more poorly for both bacterial classification (P < .001) and for viral classification (P = 0.02). Viral infection was easier to diagnose than bacterial infection (85% vs. 80% overall accuracy, respectively; P < .001). Host gene expression classifiers performed more poorly in children < 12-years compared to those older than 12-years for both bacterial infection (77% vs. 83%, respectively; P < .001) and for viral infection (82% vs. 89%, respectively; P < .001). We did not observe differences based on illness severity as defined by ICU care for either bacterial or viral infections.ConclusionWe observed significant differences among gene expression signatures for bacterial/viral discrimination, though these were not due to variations in the discovery methods or populations. Signature size directly correlated with test performance, which was generally better for the diagnosis of viral infection and in populations >12-years.Disclosures Ephraim L. Tsalik, MD, MHS, PhD, Predigen (Shareholder, Other Financial or Material Support, Founder)

Anthropogenic influence on the environmental health along Montenegro coast based on the bacterial and chemical characterization.

Coastal marine sediments are particularly exposed to human activities. The function of a coastal ecosystem is largely affected by eutrophication, wastewater discharges, chemical pollution, port activities, industry and tourism. Bacterial classification can be used as a measure in assessing the harmful effects on the ecosystem. This study provided insight into the environmental health of the coastal region of Montenegro analyzing the possible impact of PAHs and PCBs upon the bacterial community diversity and function as well as nutrients. Two stations at the shipyards were defined as very high PAH polluted together with PCB concentration exceeding threshold values. The bacterial community at the OTU level clustered together all stations except the most polluted site (SBL), the main tourist destination in Montenegro (BDV) and the estuary site (ADB) forming the independent clusters. Bacterial community based on the OTU level was driven by PAHs, TOC and silt content. The lowest richness and diversity were indicated at the site with the highest concentration of PAHs and PCBs with the highest abundance of Alphaproteobacteria followed by Gammaproteobacteria. OTUs affiliated to phyla BRC1, Dadabacteria and Spirochaetes were present with a total abundance higher than 1% only at the most polluted site indicating their persistence and possible potential for degradation of aromatic compounds. To compare functional capabilities potentially related to biodegradation of aromatic compounds and active transport systems, PICRUSt was used to predict metagenomes of the sediments. From our data, we identified specific bacterial community and predicted metabolic pathways that give us a picture of the environmental health along the coast of Montenegro, which provides us a new insight into human-induced pollution impacts on the coastal ecosystem.

Algunos parámetros biológicos en la evaluación de la estabilidad y la madurez de dos compost

Composting represents an alternative for the use of organic waste by transforming it into an organic amendment that improves the structure and fertility of soils. As it is a microbiological process, the objective was to analyze some biological parameters in the evaluation of the stability and maturity of two compost. Two compost was made with waste generated at the Simón Bolívar University one obtained from organic waste generated in the dining room and the other from plant remains from the pruning of the gardens. The parameters evaluated were temperature and microbial count, expressed as colony-forming units per gram of compost for yeast, and for aerobic and anaerobic bacteria (in turn classifying the bacteria by the Gram staining method), as well as the germination index determined by the phytotoxicity test. The results obtained allowed us to conclude that: the temperature of the compost pile is a good indicator of the evolution of the composting process, as it is closely associated with biological activity indicating the stages of the process, in the compost analyzed, the counting of microorganisms allowed to evaluate them as stable, the bacterial classification by Gram staining, represented a good indicator of the successions that occur in the composting process and was useful to help the classification of the bacteria present; the phytotoxicity test corroborated the degree of maturity and stability of the studied compost.

Systematic prediction of genes functionally associated with bacterial retrons and classification of the encoded tripartite systems.

Bacterial retrons consist of a reverse transcriptase (RT) and a contiguous non-coding RNA (ncRNA) gene. One third of annotated retrons carry additional open reading frames (ORFs), the contribution and significance of which in retron biology remains to be determined. In this study we developed a computational pipeline for the systematic prediction of genes specifically associated with retron RTs based on a previously reported large dataset representative of the diversity of prokaryotic RTs. We found that retrons generally comprise a tripartite system composed of the ncRNA, the RT and an additional protein or RT-fused domain with diverse enzymatic functions. These retron systems are highly modular, and their components have coevolved to different extents. Based on the additional module, we classified retrons into 13 types, some of which include additional variants. Our findings provide a basis for future studies on the biological function of retrons and for expanding their biotechnological applications.

Circulating 16S RNA in Biofluids: Extracellular Vesicles as Mirrors of Human Microbiome?

The human body is inhabited by around 1013 microbes composing a multicomplex system, termed microbiota, which is strongly involved in the regulation and maintenance of homeostasis. Perturbations in microbiota composition can lead to dysbiosis, which has been associated with several human pathologies. The gold-standard method to explore microbial composition is next-generation sequencing, which involves the analysis of 16S rRNA, an indicator of the presence of specific microorganisms and the principal tool used in bacterial taxonomic classification. Indeed, the development of 16S RNA sequencing allows us to explore microbial composition in several environments and human body districts and fluids, since it has been detected in “germ-free” environments such as blood, plasma, and urine of diseased and healthy subjects. Recently, prokaryotes showed to generate extracellular vesicles, which are known to be responsible for shuttling different intracellular components such as proteins and nucleic acids (including 16S molecules) by protecting their cargo from degradation. These vesicles can be found in several human biofluids and can be exploited as tools for bacterial detection and identification. In this review, we examine the complex link between circulating 16S RNA molecules and bacteria-derived vesicles.

An Automated Method for Bacterial Flora Counting Based on Image Analysis

How to quickly detect the characteristics of water quality flora, such as bacterial flora number, is a key problem of water quality safety, the traditional manual counting method has some serious defects such as low efficiency, low accuracy, easy to cause visual fatigue and hard to deal with large samples. This paper designs an automated method for bacterial flora counting based on image analysis, including such modules as pretreatment of bacterial image classification and colony counting of culture medium images by filter method (using Hough circle detection algorithm and a circle deduplication algorithm). The accuracy of experimental results is above 90%, which is in line with international standards, and the time complexity of the algorithms is up to the order of square. This method can solve the problems of heavy workload and low efficiency in traditional manual counting method.