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Summary Sequence technology advancements have led to an exponential increase in bacterial genomes, necessitating robust taxonomic classification methods. The Percentage Of Conserved Proteins (POCP), proposed initially by Qin et al. (2014), is a valuable metric for assessing prokaryote genus boundaries. Here, I introduce a computational pipeline for automated POCP calculation, aiming to enhance reproducibility and ease of use in taxonomic studies. Availability and implementation The POCP-nf pipeline uses DIAMOND for faster protein alignments, achieving similar sensitivity to BLASTP. The pipeline is implemented in Nextflow with Conda and Docker support and is freely available on GitHub under https://github.com/hoelzer/pocp. The open-source code can be easily adapted for various prokaryotic genome and protein datasets. Detailed documentation and usage instructions are provided in the repository. [ABSTRACT FROM AUTHOR]

Identifying bacterial strains is essential in microbiology for various practical applications, such as disease diagnosis and quality monitoring of food and water. Classical machine learning algorithms have been utilized to identify bacteria based on their Raman spectra. However, convolutional neural networks (CNNs) offer higher classification accuracy, but they require extensive training sets and retraining of previous untrained class targets can be costly and time-consuming. Siamese networks have emerged as a promising solution. They are composed of two CNNs with the same structure and a final network that acts as a distance metric, converting the classification problem into a similarity problem. Classical machine learning approaches, shallow and deep CNNs, and two Siamese network variants were tailored and tested on Raman spectral datasets of bacteria. The methods were evaluated based on mean sensitivity, training time, prediction time, and the number of parameters. In this comparison, Siamese-model2 achieved the highest mean sensitivity of 83.61 ± 4.73 and demonstrated remarkable performance in handling unbalanced and limited data scenarios, achieving a prediction accuracy of 73%. Therefore, the choice of model depends on the specific trade-off between accuracy, (prediction/training) time, and resources for the particular application. Classical machine learning models and shallow CNN models may be more suitable if time and computational resources are a concern. Siamese networks are a good choice for small datasets and CNN for extensive data. [ABSTRACT FROM AUTHOR]

• Inoculation with the UFRB FA72A2-1 isolate significantly improved corn growth. • Higher leaf nitrogen content, photosynthetic pigments and net photosynthesis were verified under inoculation with the isolate UFRB FA72A2-1. • Inoculations with UFRB FA34C2-2 and commercial strain, combined with the use of humic substances resulted in an increase in photosynthetic pigments and growth of corn plants. • The effects of humic substances are dose-dependent. This study investigates the morphophysiological traits and nutritional status of corn plants subjected to varying levels of humic substances, plant growth-promoting bacteria, and conventional fertilization. A controlled greenhouse experiment was conducted, involving the administration of diverse humic substance concentrations (0.0, 1.5, 3.0, 4.5, and 6.0 L ha−1), distinct bacterial strains (isolates UFRB FA72A2-1 + nitrogen supplementation, UFRB FA34C2-2 + nitrogen supplementation, and a commercial strain + nitrogen supplementation), alongside two control treatments (total nitrogen and cover), totaling 68 experimental plots. The evaluation encompassed parameters such as emergence percentage, emergence speed, emergence speed index, and emergence speed coefficient during the seedling emergence phase. Subsequently, the assessment extended to plant height, stem diameter, leaf count, leaf area, shoot biomass, root biomass, total dry mass, root volume, net photosynthesis, stomatal conductance, transpiration, as well as the quantification of total chlorophyll, carotenoids, nitrogen, and phosphorus content. Corn plants inoculated with the UFRB FA72A2-1 isolate exhibited superior growth, marked by elevated leaf nitrogen content, enhanced photosynthetic pigment concentration, and increased net photosynthesis. As for the UFRB FA34C2-2 and the commercial strain, the combined application of 1.5 and 3.0 L ha−1 of humic substances elevated the concentration of photosynthetic pigments and resulted in heightened stem height and diameter. Moreover, this combination contributed to an augmented total dry mass in the case of the commercial strain. Application of humic substances in isolation at these specified concentrations yielded a significant enhancement in net photosynthesis and shoot dry mass for corn plants. This study underscores the potential synergy between plant growth-promoting bacteria and humic substances in stimulating corn plant growth. The effect of the combined application of humic substances with plant growth-promoting bacteria depends on the dose of the humic substance and the species involved. [ABSTRACT FROM AUTHOR]

• Medicinal properties (antibacterial- and anti-HIV-1 activities) of South African mushroom species. • Rhodofomitopsis lilacinogilva and Pisolithus tinctorius caused cell membrane damage. • Gymnopilus junonius and R. lilacinogilva showed promising antimycobacterial activity. • P. tinctorius inhibited HIV-1 reverse transcriptase activity. • Chloropyllum molybdites, P. tinctorius and Pycnoporus sanguineus inhibited HIV-1 protease activity. The medicinal properties of South African mushroom species and their associated biological activities have been poorly documented. Increased resistance to antimicrobial drugs is a global problem and growing concern. Ethanol (80 %) and/or aqueous extracts of twenty-one mushroom species were screened against bacterial species infecting the respiratory system (i.e., Pseudomonas aeruginosa, Klebsiella pneumoniae, Streptococcus pyogenes, S. pneumoniae, Staphylococcus aureus and Mycobacterium tuberculosis H37), and the minimum inhibitory concentrations (MIC) determined using the p-iodonitrotetrazolium chloride (INT) and microplate Alamar Blue assays. Transmission electron microscopy (TEM) was used to detect morphological changes. Ethanol extracts were screened for HIV-1 reverse transcriptase (RT) and protease inhibition using colourimetric and fluorometric assay kits, respectively. The ethanol extracts showed better antibacterial activity against gram-positive bacteria. Extracts of Rhodofomitopsis lilacinogilva and Pisolithus tinctorius caused cell membrane damage. Gymnopilus junonius (MIC: 62.5 µg/mL) and R. lilacinogilva (MIC: 31.25 µg/mL) showed promising antimycobacterial activity. P. tinctorius (IC 50 : 1.35 µg/mL) inhibited HIV-1 reverse transcriptase activity. Chloropyllum molybdites, P. tinctorius and Pycnoporus sanguineus inhibited HIV-1 protease activity. South African mushroom species contain biologically active compounds with antibacterial- and anti-HIV-1 activities, which may play an important role in the development of novel therapies and antiretrovirals (ARVs) against respiratory associated pathogens. [ABSTRACT FROM AUTHOR]

Identification and classification of bacterial genera and species are very important for medical prevention, diagnosis, and treatment. However, due to microbial diversity and high variability in appearance, the manual classification of bacteria is a challenging and time-consuming task. This paper aims to facilitate such a troublesome task using deep learning techniques. Through the utilization of a deep learning model, specifically a Big Transfer (BiT) combined with graph Laplacian-based data cleaning and weight initialization based-rectified linear unit (WIB-Relu) activation, we have developed an accurate bacteria classification model. We have tested our proposed method on a public dataset of microscopic bacteria images, called the Digital Images of Bacteria Species (DIBaS), and achieved promising results with an accuracy of 99.11%, precision of 99.31%, recall of 99.09%, and F1 score of 99.06%, respectively. Moreover, the proposed bacteria classification performed well regardless of the size of the training data. We investigated its generalizability not only on the original dataset but also on the few shots (5-shots, 2-shots, and 1-shot) and augmented datasets.

Background: Accurate identification of bacterial communities is crucial for research applications, diagnostics, and clinical interventions. Although 16S ribosomal RNA (rRNA) gene sequencing is a widely employed technique for bacterial taxonomic classification, it often results in misclassified or unclassified bacterial taxa. This study sought to refine the full-length 16S rRNA gene sequencing protocol using the MinION sequencer, focusing on the V1–V9 regions. Our methodological enquiry examined several factors, including the number of PCR amplification cycles, choice of primers and Taq polymerase, and specific sequence databases and workflows employed. We used a microbial standard comprising eight bacterial strains (five gram-positive and three gram-negative) in known proportions as a validation control. Results: Based on the MinION protocol, we employed the microbial standard as the DNA template for the 16S rRNA gene amplicon sequencing procedure. Our analysis showed that an elevated number of PCR amplification cycles introduced PCR bias, and the selection of Taq polymerase and primer sets significantly affected the subsequent analysis. Bacterial identification at genus level demonstrated Pearson correlation coefficients ranging from 0.73 to 0.79 when assessed using BugSeq, Kraken-Silva and EPI2ME-16S workflows. Notably, the EPI2ME-16S workflow exhibited the highest Pearson correlation with the microbial standard, minimised misclassification, and increased alignment accuracy. At the species taxonomic level, the BugSeq workflow was superior, with a Pearson correlation coefficient of 0.92. Conclusions: These findings emphasise the importance of careful selection of PCR settings and a well-structured analytical framework for 16S rRNA full-length gene sequencing. The results showed a robust correlation between the predicted and observed bacterial abundances at both the genus and species taxonomic levels, making these findings applicable across diverse research contexts and with clinical utility for reliable pathogen identification. [ABSTRACT FROM AUTHOR]

One of the widely used microbiological methods to determine the toxicity of chemicals, catalysts, and other types of materials is the minimum inhibitory concentration (MIC) test. The present study aims to investigate the influence of composition of composite materials based on TiO2 and their particle size as well as bacterial type and shape based on the MIC values reported in the literature. The results show that among the 36 articles selected, most of the studies used Escherichia coli (E. coli) (26) and Staphylococcus aureus (S. aureus) (19) bacteria to determine MIC values. This study revealed that the MIC in values below 70 µg ml−1 for S. aureus was lower than that for E. coli bacteria (below 200 µg ml−1). Importantly, MIC value decreased from 60.6 to 7.66 µg ml−1 with decrease in the size of nanoparticles. It follows from the increased surface area for smaller-sized particles, thus increased interaction with bacteria during MIC test. [ABSTRACT FROM AUTHOR]

Temperature significantly impacts bacterial physiology, metabolism and cell chemistry. In this study, we analysed lipids and the total cellular biochemical profile of 74 fast‐growing Antarctic bacteria grown at different temperatures. Fatty acid diversity and temperature‐induced alterations aligned with bacterial classification—Gram‐groups, phylum, genus and species. Total lipid content, varied from 4% to 19% of cell dry weight, was genus‐ and species‐specific. Most bacteria increased lipid content at lower temperatures. The effect of temperature on the profile was complex and more species‐specific, while some common for all bacteria responses were recorded. Gram‐negative bacteria adjusted unsaturation and acyl chain length. Gram‐positive bacteria adjusted methyl branching (anteiso‐/iso‐), chain length and unsaturation. Fourier transform infrared spectroscopy analysis revealed Gram‐, genus‐ and species‐specific changes in the total cellular biochemical profile triggered by temperature fluctuations. The most significant temperature‐related alterations detected on all taxonomy levels were recorded for mixed region 1500–900 cm−1, specifically the band at 1083 cm−1 related to phosphodiester groups mainly from phospholipids (for Gram‐negative bacteria) and teichoic/lipoteichoic acids (for Gram‐positive bacteria). Some changes in protein region were detected for a few genera, while the lipid region remained relatively stable despite the temperature fluctuations. [ABSTRACT FROM AUTHOR]

Background: Frogs are critical economic animals essential to agricultural ecosystem equilibrium. However, Meningitis-like Infectious Disease (MID) often affects them in agricultural settings. While frog-associated microbiota contribute to elemental cycling and immunity, the effects of frog sex and health on gut bacteria remain understudied, and the relationship between frog habitat and soil microbes is unclear. We aimed to determine how frog sex, health status and habitat influence symbiotic bacteria and community assembly mechanism to provide guidance for sustainable frog farming and conservation. Results: We employed 16S rRNA sequencing to investigate gut microbiota differences in relation to frog sex and health status. We also compared symbiotic communities in frog-aggregation, native and soybean soil on the farm. Results showed that gut bacterial β-diversity and taxonomy were markedly influenced by frog sex and health. Healthy frogs had more robust gut bacterial metabolism than frogs infected with MID. Cooccurrence network analysis revealed that healthy female frogs had more complex microbial network structure than males; however, diseased males showed the greatest network complexity. The assembly mechanism of gut bacteria in male frogs was dominated by deterministic processes, whereas in female frogs it was dominated by stochastic processes. Among symbiotic bacteria in frog habitat soils, deterministic processes predominantly shaped the community assembly of soybean soil. In particular, soybean soil was enriched in pathogens and nitrogen functions, whereas frog-aggregation soil was markedly increased in sulphur respiration and hydrocarbon degradation. Conclusion: Our study reveals that sex mainly alters the interaction network and assembly mechanism of frog intestinal bacteria; MID infection significantly inhibits the metabolic functions of intestinal bacteria. Furthermore, diverse frog habitat soils could shape more symbiotic bacteria to benefit frog farming. Our findings provide new horizons for symbiotic bacteria among frogs, which could contribute to sustainable agriculture and ecological balance. [ABSTRACT FROM AUTHOR]

The activated sludge was collected from the Membrane BioReactor (MBR) pool of the sewage treatment system of Sanxing Town, Jintang County, Chengdu, to obtain a good population of heterotrophic nitrifying/aerobic denitrifying (HNAD) bacteria. After undergoing enrichment, isolation, and purification, the HNAD bacteria were selected using the pure culture method. The 16S rDNA molecular technology was used to determine the taxonomy of bacteria. The heterophic nitrifying ability and denitrification capacity of HNAD strains was ascertained through their growth characteristics in heterotrophic nitrification and denitrification media. The results showed that 53 HNAD strains selected from the MBR pool belonged to 2 phyla, 3 classes, 6 orders, 6 families, and 7 genera, with 26 species. Acinetobacter was the largest and dominant genus. Among these, strains numbered (bacterial strain) SW21HD14, SW21HD17, and SW21HD18 were potentially new species in the Acinetobacter genus. Each HNAD strain showed a significant heterotrophic nitrifying and aerobic denitrifying efficiency compared with the control strain (P < 0.05). Specifically, 10 strains demonstrated ammonia nitrogen degradation of greater than 70 mg·L-1 and 9 strains demonstrated nitrate nitrogen degradation above 150 mg·L-1. The HNAD bacteria, which were selected from the MBR pool of sewage treatment system of the Sanxing Town sewage treatment plant, exhibited rich diversity and strong nitrogen removal ability. These findings offered an effective strain source and theoretical basis for implementing biological denitrification technology that involves synchronous nitrification and denitrification. [ABSTRACT FROM AUTHOR]

Bees represent not only a valuable asset in agriculture, but also serve as a model organism within contemporary microbiology. The metagenomic composition of the bee superorganism has been substantially characterized. Nevertheless, traditional cultural methods served as the approach to studying brood combs in the past. Indeed, the comb microbiome may contribute to determining larval caste differentiation and hive immunity. To further this understanding, we conducted a shotgun sequencing analysis of the brood comb microbiome. While we found certain similarities regarding species diversity, it exhibits significant differentiation from all previously described hive metagenomes. Many microbiome members maintain a relatively constant ratio, yet taxa with the highest abundance level tend to be ephemeral. More than 90% of classified metagenomes were Gammaproteobacteria, Bacilli and Actinobacteria genetic signatures. Jaccard dissimilarity between samples based on bacteria genus classifications hesitate from 0.63 to 0.77, which for shotgun sequencing indicates a high consistency in bacterial composition. Concurrently, we identified antagonistic relationships between certain bacterial clusters. The presence of genes related to antibiotic synthesis and antibiotic resistance suggests potential mechanisms underlying the stability of comb microbiomes. Differences between pupal and larval combs emerge in the total metagenome, while taxa with the highest abundance remained consistent. All this suggests that a key role in the functioning of the comb microbiome is played by minor biodiversity, the function of which remains to be established experimentally. [ABSTRACT FROM AUTHOR]

Aim: This prospective observational study investigated wound area reduction (WAR) outcomes in a complex wound population composed of non-healing acute and chronic wounds. The relationship between bacterial autofluorescence signals and WAR was investigated. Area measurements were collected both manually and digitally, and both methods were compared for accuracy. Methods: Twenty-six participants with 27 wounds of varying etiologies were observed twice weekly for two weeks. Digital wound measurement, wound bacterial status assessment, and targeted debridement were performed through a point-of-care fluorescence imaging device (MolecuLight® i: X, MolecuLight Inc, Toronto, Canada). The wound area reduction (WAR) rate was calculated using baseline and last visit measurements. Statistical analyses, including t-tests, Fisher exact tests, the Wilcoxon signed rank test for method comparison, and ANOVA for bacterial subgroups, were applied as pertinent. Results: The overall average WAR was −3.80 cm2, or a decrease of 46.88% (manual measurement), and −2.62 cm2, or a 46.05% decrease (digital measurement via MolecuLight® device). There were no statistically significant differences between the WAR of acute and chronic wounds (p = 0.7877). A stepwise correlation between the WAR and bacterial status classification per fluorescence findings was observed, where persistent bacteria resulted in worse WAR outcomes. An overestimation of wound area by manual measurement was 23% on average. Conclusion: Fluorescence imaging signals were linked to WAR outcome and could be considered predictive. Wounds exhibiting bacterial loads that persisted at the end of the study period had worse WAR outcomes, while those for which management was able to effectively remove them demonstrated greater WAR. Manual measurement of the wound area consistently overestimated wound size when compared to digital measurement. However, if performed by the same operator, the overestimation was uniform enough that the WAR was calculated to be close to accurate. Notwithstanding, single wound measurements are likely to result in overestimation. [ABSTRACT FROM AUTHOR]

Objectives Our study aimed to sequence class 1 integrons in uncultured environmental bacterial cells in freshwater from suburban creeks and uncover the taxonomy of their bacterial hosts. We also aimed to characterize integron gene cassettes with altered DNA sequences relative to those from databases or literature and identify key signatures of their molecular evolution. Methods We applied a single-cell fusion PCR-based technique— e mulsion, p aired i solation and c oncatenation PCR (epicPCR)—to link class 1 integron gene cassette arrays to the phylogenetic markers of their bacterial hosts. The levels of streptomycin resistance conferred by the WT and altered aadA5 and aadA11 gene cassettes that encode aminoglycoside (3″) adenylyltransferases were experimentally quantified in an Escherichia coli host. Results Class 1 integron gene cassette arrays were detected in Alphaproteobacteria and Gammaproteobacteria hosts. A subset of three gene cassettes displayed signatures of molecular evolution, namely the gain of a regulatory 5′-untranslated region (5′-UTR), the loss of attC recombination sites between adjacent gene cassettes, and the invasion of a 5′-UTR by an IS element. Notably, our experimental testing of a novel variant of the aadA11 gene cassette demonstrated that gaining the observed 5′-UTR contributed to a 3-fold increase in the MIC of streptomycin relative to the ancestral reference gene cassette in E. coli. Conclusions Dissecting the observed signatures of molecular evolution of class 1 integrons allowed us to explain their effects on antibiotic resistance phenotypes, while identifying their bacterial hosts enabled us to make better inferences on the likely origins of novel gene cassettes and IS that invade known gene cassettes. [ABSTRACT FROM AUTHOR]

Local microbiome shifts are implicated in the development and progression of gastrointestinal cancers, and in particular, esophageal carcinoma (ESCA), which is among the most aggressive malignancies. Short-read RNA sequencing (RNAseq) is currently the leading technology to study gene expression changes in cancer. However, using RNAseq to study microbial gene expression is challenging. Here, we establish a new tool to efficiently detect viral and bacterial expression in human tissues through RNAseq. This approach employs a neural network to predict reads of likely microbial origin, which are targeted for assembly into longer contigs, improving identification of microbial species and genes. This approach is applied to perform a systematic comparison of bacterial expression in ESCA and healthy esophagi. We uncover bacterial genera that are over or underabundant in ESCA vs healthy esophagi both before and after correction for possible covariates, including patient metadata. However, we find that bacterial taxonomies are not significantly associated with clinical outcomes. Strikingly, in contrast, dozens of microbial proteins were significantly associated with poor patient outcomes and in particular, proteins that perform mitochondrial functions and iron-sulfur coordination. We further demonstrate associations between these microbial proteins and dysregulated host pathways in ESCA patients. Overall, these results suggest possible influences of bacteria on the development of ESCA and uncover new prognostic biomarkers based on microbial genes. In addition, this study provides a framework for the analysis of other human malignancies whose development may be driven by pathogens. [ABSTRACT FROM AUTHOR]

Axenic Aedes aegypti mosquitoes were colonized with bacteria from an environmental water source to compare the midgut microbiota acquired from the wild to the microbiome of insectary-reared mosquitoes, specifically over the course of blood meal digestion. 16S rRNA gene sequencing revealed that diversity, composition, and community turnover of the midgut microbiomes were distinct between the insectary and environmental groups, with the environmental microbiomes having a greater diversity and larger temporal shifts over the course of the blood meal. Metagenomic prediction from the 16S rRNA gene sequence data pointed to metabolic processes such as vitamin biosynthesis, fatty acid recycling, and fermentation pathways differentiating the functional potential of the two different microbiomes. To further test if we could identify functional traits that distinguished the two microbiomes, we performed a culture-based assay. Culturable bacteria were more abundant in the insectary microbiomes and there was very little overlap in the taxonomy of bacteria recovered from the insectary or environmental groups. The ability of the isolates to lyse blood cells was determined on blood agar plates, and only isolates from the environmental microbiome harbored the ability to perform hemolysis in culture. These data support that the differences in taxonomy observed between the two different microbiomes also result in differences in the functional potential of the community. Thus, this study demonstrates the power of the axenic mosquito model to shed light on the community ecology of the mosquito microbiome, and the potential to better represent the microbiomes of wild mosquitoes in a laboratory setting. IMPORTANCE The blood meal of the female mosquito serves as a nutrition source to support egg development, so is an important aspect of its biology. Yet, the roles the microbiome may play in blood digestion are poorly characterized. We employed axenic mosquitoes to investigate how the microbiome differs between mosquitoes reared in the insectary versus mosquitoes that acquire their microbiome from the environment. Environmental microbiomes were more diverse and showed larger temporal shifts over the course of blood digestion. Importantly, only bacteria from the environmental microbiome performed hemolysis in culture, pointing to functional differences between bacterial populations. These data highlight that taxonomic differences between the microbiomes of insectary-reared and wild mosquitoes are potentially also related to their functional ecology. Thus, axenic mosquitoes colonized with environmental bacteria offer a way to investigate the role of bacteria from the wild in mosquito processes such as blood digestion, under controlled laboratory conditions. [ABSTRACT FROM AUTHOR]

Clean drinking water access is a main factor that supports general health worldwide, where important investment is made to maintain water quality. The gene sequence of 16S rRNA was applied to study bacterial phylogenesis and classification. This study aimed to isolate new bacterial strains from wastewater environments. Thisstudy collected water samples from different wastewater sites for Baghdad Medical City's Hospital. The results of Vitek 2Compact were with a probability of99% for Escherichia coli isolates. The bacterial isolates were identified using Polymerase chain reaction (PCR) based on the universal diagnostic gene 16SrRNA, and the PCR product was obtained with a molecular weight of 1250bp. The PCR productsequencing showed 166 isolates of E. coli different from the isolates registered in the NCBI database of E. coli after alignment between them. The present study reported 16 Iraqi isolates in NCBI with an accession number: OM032663.1; OM 032664.1; OM294659.2; ON724178.1; ON724264.1; ON724331.1; ON725076.1; ON725091.1; ON725139.1; ON725141.1; ON739200.1; ON739201.1; ON739207.1; ON739208.1; ON739209.1; ON739211.1. The identity was then observed(96-100%) with strains in Gene Bank. There were many different sequence position substitutions (transition, deletion, transversion for the registered local isolates when conducting multiple alignments and comparing themwith the isolates in the Gene Bank. According to the phylogenetic tree building by joining-the neighbor method, the local Iraqi isolates clustered into sub-descents. Moreover, local isolates of E.coli appeared more convergent to Saudi Arabia isolates (KY780347.1). This may be due to the geographical proximity of the two countries to the same Arab Gulf region. The present study identified 16 isolates of E. coli from the Iraqi aquatic environment (medical wastewater). The novelty of this study was represented to monitoring the evolution of E. coli in medical wastewater. [ABSTRACT FROM AUTHOR]

Objectives: This study aims to develop a deep learning algorithm, Pneumonia-Plus, based on computed tomography (CT) images for accurate classification of bacterial, fungal, and viral pneumonia. Methods: A total of 2763 participants with chest CT images and definite pathogen diagnosis were included to train and validate an algorithm. Pneumonia-Plus was prospectively tested on a nonoverlapping dataset of 173 patients. The algorithm's performance in classifying three types of pneumonia was compared to that of three radiologists using the McNemar test to verify its clinical usefulness. Results: Among the 173 patients, area under the curve (AUC) values for viral, fungal, and bacterial pneumonia were 0.816, 0.715, and 0.934, respectively. Viral pneumonia was accurately classified with sensitivity, specificity, and accuracy of 0.847, 0.919, and 0.873. Three radiologists also showed good consistency with Pneumonia-Plus. The AUC values of bacterial, fungal, and viral pneumonia were 0.480, 0.541, and 0.580 (radiologist 1: 3-year experience); 0.637, 0.693, and 0.730 (radiologist 2: 7-year experience); and 0.734, 0.757, and 0.847 (radiologist 3: 12-year experience), respectively. The McNemar test results for sensitivity showed that the diagnostic performance of the algorithm was significantly better than that of radiologist 1 and radiologist 2 (p < 0.05) in differentiating bacterial and viral pneumonia. Radiologist 3 had a higher diagnostic accuracy than the algorithm. Conclusions: The Pneumonia-Plus algorithm is used to differentiate between bacterial, fungal, and viral pneumonia, which has reached the level of an attending radiologist and reduce the risk of misdiagnosis. The Pneumonia-Plus is important for appropriate treatment and avoiding the use of unnecessary antibiotics, and provide timely information to guide clinical decision-making and improve patient outcomes. Clinical relevance statement: Pneumonia-Plus algorithm could assist in the accurate classification of pneumonia based on CT images, which has great clinical value in avoiding the use of unnecessary antibiotics, and providing timely information to guide clinical decision-making and improve patient outcomes. Key Points: • The Pneumonia-Plus algorithm trained from data collected from multiple centers can accurately identify bacterial, fungal, and viral pneumonia. • The Pneumonia-Plus algorithm was found to have better sensitivity in classifying viral and bacterial pneumonia in comparison to radiologist 1 (5-year experience) and radiologist 2 (7-year experience). • The Pneumonia-Plus algorithm is used to differentiate between bacterial, fungal, and viral pneumonia, which has reached the level of an attending radiologist. [ABSTRACT FROM AUTHOR]

Introduction: The rumen microbial community plays a crucial role in the digestion and metabolic processes of ruminants. Although sequencing-based studies have helped reveal the diversity and functions of bacteria in the rumen, their physiological and biochemical characteristics, as well as their dynamic regulation along the digestion process in the rumen, remain poorly understood. Addressing these gaps requires pure culture studies to demystify the intricate mechanisms at play. Bacteria exhibit morphological differentiation associated with different species. Based on the difference in size or shape of microorganisms, size fractionation by filters with various pore sizes can be used to separate them. Methods: In this study, we used polyvinylidene difluoride filters with pore sizes of 300, 120, 80, 40, 20, 8, 6, 2.1, and 0.6 µm. Bacterial suspensions were successively passed through these filters for the analysis of microbial population distribution using 16S rRNA gene sequences. Results: We found that bacteria from the different pore sizes were clustered into four branches (> 120 µm, 40-120 µm, 6-20 µm, 20-40 µm, and < 0.6 µm), indicating that size fractionation had effects on enriching specific groups but could not effectively separate dominant groups by cell size alone. The species of unclassified Flavobacterium, unclassified Chryseobacterium, unclassified Delftia, Methylotenera mobilis, unclassified Caulobacteraceae, unclassified Oligella, unclassified Sphingomonas, unclassified Stenotrophomonas, unclassified Shuttleworthia, unclassified Sutterella, unclassified Alphaproteobacteria, and unclassified SR1 can be efficiently enriched or separated by size fractionation. Discussion: In this study, we investigated the diversity of sorted bacteria populations in the rumen for preliminary investigations of the relationship between the size and classification of rumen bacteria that have the potential to improve our ability to isolate and culture bacteria from the rumen in the future. [ABSTRACT FROM AUTHOR]

To investigate the degradation effect of bovine trypsin on multispecies biofilm of caries-related bacteria and provide an experimental foundation for the prevention of dental caries. Standard strains of S. mutans, S. sanguis, S. gordonii, and L. acidophilus were co-cultured to form 24 h, 48 h, and 72 h biofilms. The experimental groups were treated with bovine trypsin for 30 s, 1 min, and 3 min. Morphological observation and quantitative analysis of extracellular polymeric substances (EPS), live bacteria, and dead bacteria were conducted using the confocal laser scanning microscope (CLSM). The morphological changes of EPS and bacteria were also observed using a scanning electron microscope (SEM). When biofilm was treated for 1 min, the minimal inhibitory concentration (MIC) of bovine trypsin to reduce EPS was 0.5 mg/mL in 24 h and 48 h biofilms, and the MIC of bovine trypsin was 2.5 mg/mL in 72 h biofilms (P < 0.05). When biofilm was treated for 3 min, the MIC of bovine trypsin to reduce EPS was 0.25 mg/mL in 24 h and 48 h biofilms, the MIC of bovine trypsin was 1 mg/mL in 72 h biofilm (P < 0.05). The ratio of live-to-dead bacteria in the treatment group was significantly lower than blank group in 24 h, 48 h, and 72 h multispecies biofilms (P < 0.05). Bovine trypsin can destroy multispecies biofilm structure, disperse biofilm and bacteria flora, and reduce the EPS and bacterial biomass in vitro, which are positively correlated with the application time and concentration. [ABSTRACT FROM AUTHOR]

The aim of this study was to evaluate the surface roughness and contact angle of composite resins produced by CAD/CAM milling and three-dimensional (3D) printing for permanent restorations as well as the adhesion of S. mutans and S. sanguinis bacteria to these composites. Three CAD/CAM milling composite resins (Vita Enamic-VE, Cerasmart-CE, Lava Ultimate-LU) and three 3D printing resins (Varseo Smile Crown plus-VSC, Saremco print Crowntech-SPC, Formlabs 3B Permanent crown-FLP) were selected. Twenty samples were prepared for each group. Using a contact profilometer, the surface roughness was determined, and an optical goniometer was used to quantify the contact angle. To evaluate the bacterial adhesion, composite specimens were immersed in mucin containing artificial saliva. All samples were incubated for 24 h at 37°C in 5% CO2. CFUs were determined by counting colonies after the incubation period. Surface roughness values of test samples were the highest in the Group VSC [0.46 (0.14) µm], whereas the lowest values were found in the Group LU [0.23 (0.05) µm]. There was no statistically significant difference between the groups in contact angle values (p > 0.05). The S. mutans adhesion extent on the Group SPC was statistically higher compared to all other materials with p < 0.05. For S. sanguinis, the lowest bacterial adhesion value was recorded in Group CE (3.00 × 104 CFU/ml) and statistically significant differences were found with Group VE and VSC (p < 0.05). Different digital manufacturing techniques and material compositions can affect the surface roughnesses of composite resins. All composite resin samples have hydrophobic characteristics. Microbial adhesion of the tested composite resins may be varied depending on the bacterial species. S. mutans showed much more adhesion to these materials than S. sanguinis. [ABSTRACT FROM AUTHOR]

Bacillus species are ubiquitous in nature and have tremendous application potential in agriculture, medicine, and industry. However, the individual species of this genus vary widely in both ecological niches and functional phenotypes, which, hence, requires accurate classification of these bacteria when selecting them for specific purposes. Although analysis of the 16S rRNA gene has been widely used to disseminate the taxonomy of most bacterial species, this gene fails proper classification of Bacillus species. To circumvent this restriction, we designed novel primers and optimized them to allow exact species resolution of Bacillus species in both synthetic and natural communities using high-throughput amplicon sequencing. The primers designed for the tuf gene were not only specific for the Bacillus genus but also sufficiently discriminated species both in silico and in vitro in a mixture of 11 distinct Bacillus species. Investigating the primers using a natural soil sample, 13 dominant species were detected including Bacillus badius, Bacillus velezensis, and Bacillus mycoides as primary members, neither of which could be distinguished with 16S rRNA sequencing. In conclusion, a set of high-throughput primers were developed which allows unprecedented species-level identification of Bacillus species and aids the description of the ecological distribution of Bacilli in various natural environment. [ABSTRACT FROM AUTHOR]

Microbes are usually present as a specific microbiota, and their classification remains a challenge. MALDI-TOF MS is particularly successful in library-based microbial identification at the species level as it analyzes the molecular weight of peptides and ribosomal proteins. FT-IR allows more accurate classification of bacteria at the subspecies level due to the high sensitivity, specificity and repeatability of FT-IR signals from bacteria, which is not achievable with MALDI-TOF MS. Previous studies have shown that more accurate identification results can be obtained by the fusion of FT-IR and MALDI-TOF MS spectral data. Here, we constructed 20 groups of model microbiota samples and used FT-IR, MALDI-TOF MS, and their fusion data to classify them. Hierarchical clustering analysis (HCA) showed that the classification accuracy of FT-IR, MALDI-TOF MS, and the fusion data was 85%, 90%, and 100%, respectively. These results indicate that both FT-IR and MALDI-TOF MS can effectively classify specific microbiota, and the fusion of their spectral data could improve the classification accuracy. The FT-IR and MALDI-TOF MS data fusion strategy may be a promising technology for specific microbiota classification. [ABSTRACT FROM AUTHOR]

Reliable detection and classification of bacteria and other pathogens in the human body, animals, food, and water is crucial for improving and safeguarding public health. For instance, identifying the species and its antibiotic susceptibility is vital for effective bacterial infection treatment. Here we show that phase contrast time-lapse microscopy combined with deep learning is sufficient to classify four species of bacteria relevant to human health. The classification is performed on living bacteria and does not require fixation or staining, meaning that the bacterial species can be determined as the bacteria reproduce in a microfluidic device, enabling parallel determination of susceptibility to antibiotics. We assess the performance of convolutional neural networks and vision transformers, where the best model attained a class-average accuracy exceeding 98%. Our successful proof-of-principle results suggest that the methods should be challenged with data covering more species and clinically relevant isolates for future clinical use. Author summary: Bacterial infections are a leading cause of premature death worldwide, and growing antibiotic resistance is making treatment increasingly challenging. To effectively treat a patient with a bacterial infection, it is essential to quickly detect and identify the bacterial species and determine its susceptibility to different antibiotics. Prompt and effective treatment is crucial for the patient's survival. A microfluidic device functions as a miniature "lab-on-chip" for manipulating and analyzing tiny amounts of fluids, such as blood or urine samples from patients. Microfluidic chips with chambers and channels have been designed for quickly testing bacterial susceptibility to different antibiotics by analyzing bacterial growth. Identifying bacterial species has previously relied on killing the bacteria and applying species-specific fluorescent probes. The purpose of the herein proposed species identification is to speed up decisions on treatment options by already in the first few imaging frames getting an idea of the bacterial species, without interfering with the ongoing antibiotics susceptibility testing. We introduce deep learning models as a fast and cost-effective method for identifying bacteria species. We envision this method being employed concurrently with antibiotic susceptibility tests in future applications, significantly enhancing bacterial infection treatments. [ABSTRACT FROM AUTHOR]

Because of the two sequenced methods stated above, SG and AMP, are being used in different ways, present a deep learning methodology for taxonomic categorization of the metagenomic information which could be utilized for either. To place the suggested pipeline to a trial, 1000 16 S full-length genomes were used to generate either SG or AMP short-reads. Then, to map sequencing as matrices into such a number space, used a k-mer model. Our analysis of the existing approaches revealed several drawbacks, including limited ability to handle complex hierarchical representations of data and suboptimal feature extraction from grid-like structures. To overcome these limitations, we introduce DBNs for feature learning and dimensionality reduction, and CNNs for efficient processing of grid-like metagenomic data. Finally, a training set for every taxon was obtained by training two distinct deep learning constructions, specifically deep belief network (DBN) and convolutional neural network (CNN). This examined the proposed methodology to determine the best factor that determines and compared findings to the classification abilities offered by the RDP classifier, a standard classifier for bacterium identification. These designs outperform using RDP classifiers at every taxonomic level. So, at the genetic level, for example, both CNN and DBN achieved 91.4% accuracy using AMP short-reads, but the RDP classifier achieved 83.9% with the same information. This paper, suggested a classification method for 16 S short-read sequences created on k-mer representations and a deep learning structure, that every taxon creates a classification method. The experimental findings validate the suggested pipelines as a realistic strategy for classifying bacterium samples; as a result, the technique might be included in the most commonly used tools for the metagenomic research. According to the outcomes, it could be utilized to effectively classify either SG or AMP information. [ABSTRACT FROM AUTHOR]

An important factor dictating coral fitness is the quality of bacteria associated with corals and coral reefs. One way that bacteria benefit corals is by stimulating the larval to juvenile life cycle transition of settlement and metamorphosis. Tetrabromopyrrole (TBP) is a small molecule produced by bacteria that stimulates metamorphosis with and without attachment in a range of coral species. A standing debate remains, however, about whether TBP biosynthesis from live Pseudoalteromonas bacteria is the primary stimulant of coral metamorphosis. In this study, we create a Pseudoalteromonas sp. PS5 mutant lacking the TBP brominase gene, bmp2. Using this mutant, we confirm that the bmp2 gene is critical for TBP biosynthesis in Pseudoalteromonas sp. PS5. Mutation of this gene ablates the bacterium's ability in live cultures to stimulate the metamorphosis of the stony coral Porites astreoides. We further demonstrate that expression of TBP biosynthesis genes is strongest in stationary and biofilm modes of growth, where Pseudoalteromonas sp. PS5 might exist within surface-attached biofilms on the sea floor. Finally, we create a modular transposon plasmid for genomic integration and fluorescent labeling of Pseudoalteromonas sp. PS5 cells. Our results functionally link a TBP biosynthesis gene from live bacteria to a morphogenic effect in corals. The genetic techniques established here provide new tools to explore coral-bacteria interactions and could help to inform future decisions about utilizing marine bacteria or their products for coral restoration. [ABSTRACT FROM AUTHOR]

Average Nucleotide Identity (ANI) is becoming a standard measure for bacterial species delimitation. However, its calculation can take orders of magnitude longer than similarity estimates based on sampling of short nucleotides, compiled into so-called sketches. These estimates are widely used. However, their variable correlation with ANI has suggested that they might not be as accurate. For a where-the-rubber-meets-the-road assessment, we compared two sketching programs, mash and dashing, against ANI, in delimiting species among Esterobacterales genomes. Receiver Operating Characteristic (ROC) analysis found Area Under the Curve (AUC) values of 0.99, almost perfect species discrimination for all three measures. Subsampling to avoid over-represented species reduced these AUC values to 0.92, still highly accurate. Focused tests with ten genera, each represented by more than three species, also showed almost identical results for all methods. Shigella showed the lowest AUC values (0.68), followed by Citrobacter (0.80). All other genera, Dickeya, Enterobacter, Escherichia, Klebsiella, Pectobacterium, Proteus, Providencia and Yersinia, produced AUC values above 0.90. The species delimitation thresholds varied, with species distance ranges in a few genera overlapping the genus ranges of other genera. Mash was able to separate the E. coli + Shigella complex into 25 apparent phylogroups, four of them corresponding, roughly, to the four Shigella species represented in the data. Our results suggest that fast estimates of genome similarity are as good as ANI for species delimitation. Therefore, these estimates might suffice for covering the role of genomic similarity in bacterial taxonomy, and should increase confidence in their use for efficient bacterial identification and clustering, from epidemiological to genome-based detection of potential contaminants in farming and industry settings. [ABSTRACT FROM AUTHOR]

Sulfate/sulfite-reducing microorganisms (SRM) are ubiquitous in nature, driving the global sulfur cycle. A hallmark of SRM is the dissimilatory sulfite reductase encoded by the genes dsrAB. Based on analysis of 950 mainly metagenome-derived dsrAB -carrying genomes, we redefine the global diversity of microorganisms with the potential for dissimilatory sulfate/sulfite reduction and uncover genetic repertoires that challenge earlier generalizations regarding their mode of energy metabolism. We show: (i) 19 out of 23 bacterial and 2 out of 4 archaeal phyla harbor uncharacterized SRM, (ii) four phyla including the Desulfobacterota harbor microorganisms with the genetic potential to switch between sulfate/sulfite reduction and sulfur oxidation, and (iii) the combination as well as presence/absence of different dsrAB- types , dsrL -types and dsrD provides guidance on the inferred direction of dissimilatory sulfur metabolism. We further provide an updated dsrAB database including > 60% taxonomically resolved, uncultured family-level lineages and recommendations on existing dsrAB -targeted primers for environmental surveys. Our work summarizes insights into the inferred ecophysiology of newly discovered SRM, puts SRM diversity into context of the major recent changes in bacterial and archaeal taxonomy, and provides an up-to-date framework to study SRM in a global context. [ABSTRACT FROM AUTHOR]

Green Chemistry is a vital and crucial instrument in achieving pollution control, and it plays an important role in helping society reach the Sustainable Development Goals (SDGs). NIR (near-infrared spectroscopy) has been utilized as an alternate technique for molecular identification, making the process faster and less expensive. Near-infrared diffuse reflectance spectroscopy and Machine Learning (ML) algorithms were utilized in this study to construct identification and classification models of bacteria such as Escherichia coli, Salmonella enteritidis, Enterococcus faecalis and Listeria monocytogenes. Furthermore, divide these bacteria into Gram-negative and Gram-positive groups. The green and quick approach was created by combining NIR spectroscopy with a diffuse reflectance accessory. Using infrared spectral data and ML techniques such as principal component analysis (PCA), hierarchical cluster analysis (HCA) and K-Nearest Neighbor (KNN), It was feasible to accomplish the identification and classification of four bacteria and classify these bacteria into two groups: Gram-positive and Gram-negative, with 100% accuracy. We may conclude that our study has a high potential for bacterial identification and classification, as well as being consistent with global policies of sustainable development and green analytical chemistry. [ABSTRACT FROM AUTHOR]

[Display omitted] • The current review describes bacterial classification using SERS for the identification of pathogens for clinical application diagnosis. • This review provides a brief overview of the mechanism and evolution of the SERS diagnostic principle for pathogenic bacteria. • Discussed the several novel SERS-compatible technologies in clinical applications and computational challenges of pre-processing spectra. • Its highlights the limitations and perspectives of the SERS technique. • It summarizes the potential applications for direct bacterial diagnosis in clinical samples such as blood, urine and sputum. The rapid and reliable detection of pathogenic bacteria at an early stage is a highly significant research field for public health. However, most traditional approaches for pathogen identification are time-consuming and labour-intensive, which may cause physicians making inappropriate treatment decisions based on an incomplete diagnosis of patients with unknown infections, leading to increased morbidity and mortality. Therefore, novel methods are constantly required to face the emerging challenges of bacterial detection and identification. In particular, Raman spectroscopy (RS) is becoming an attractive method for rapid and accurate detection of bacterial pathogens in recent years, among which the newly developed surface-enhanced Raman spectroscopy (SERS) shows the most promising potential. Recent advances in pathogen detection and diagnosis of bacterial infections were discussed with focuses on the development of the SERS approaches and its applications in complex clinical settings. The current review describes bacterial classification using surface enhanced Raman spectroscopy (SERS) for developing a rapid and more accurate method for the identification of bacterial pathogens in clinical diagnosis. The initial part of this review gives a brief overview of the mechanism of SERS technology and development of the SERS approach to detect bacterial pathogens in complex samples. The development of the label-based and label-free SERS strategies and several novel SERS-compatible technologies in clinical applications, as well as the analytical procedures and examples of chemometric methods for SERS, are introduced. The computational challenges of pre-processing spectra and the highlights of the limitations and perspectives of the SERS technique are also discussed. Taken together, this systematic review provides an overall summary of the SERS technique and its application potential for direct bacterial diagnosis in clinical samples such as blood, urine and sputum, etc. [ABSTRACT FROM AUTHOR]

In multispectral digital in-line holographic microscopy (DIHM), aberrations of the optical system affect the repeatability of the reconstruction of transmittance, phase and morphology of the objects of interest. Here we address this issue first by model fitting calibration using transparent beads inserted in the sample. This step estimates the aberrations of the optical system as a function of the lateral position in the field of view and at each wavelength. Second, we use a regularized inverse problem approach (IPA) to reconstruct the transmittance and phase of objects of interest. Our method accounts for shift-variant chromatic and geometrical aberrations in the forward model. The multi-wavelength holograms are jointly reconstructed by favouring the colocalization of the object edges. The method is applied to the case of bacteria imaging in Gram-stained blood smears. It shows our methodology evaluates aberrations with good repeatability. This improves the repeatability of the reconstructions and delivers more contrasted spectral signatures in transmittance and phase, which could benefit applications of microscopy, such as the analysis and classification of stained bacteria. [ABSTRACT FROM AUTHOR]

Introduction: The pivotal roles of both abundant and rare bacteria in ecosystem function are widely acknowledged. Despite this, the diversity elevational patterns of these two bacterial taxa in different seasons and influencing factors remains underexplored, especially in the case of rare bacteria. Methods: Here, a metabarcoding approach was employed to investigate elevational patterns of these two bacterial communities in different seasons and tested the roles of soil physico-chemical properties in structuring these abundant and rare bacterial community. Results and discussion: Our findings revealed that variation in elevation and season exerted notably effects on the rare bacterial diversity. Despite the reactions of abundant and rare communities to the elevational gradient exhibited similarities during both summer and winter, distinct elevational patterns were observed in their respective diversity. Specifically, abundant bacterial diversity exhibited a roughly U-shaped pattern along the elevation gradient, while rare bacterial diversity increased with the elevational gradient. Soil moisture and N:P were the dominant factor leading to the pronounced divergence in elevational distributions in summer. Soil temperature and pH were the key factors in winter. The network analysis revealed the bacteria are better able to adapt to environmental fluctuations during the summer season. Additionally, compared to abundant bacteria, the taxonomy of rare bacteria displayed a higher degree of complexity. Our discovery contributes to advancing our comprehension of intricate dynamic diversity patterns in abundant and rare bacteria in the context of environmental gradients and seasonal fluctuations. [ABSTRACT FROM AUTHOR]

Purpose: Through a pilot study, we performed whole gut metagenomic analysis in 17 Lynch syndrome (LS) families, including colorectal cancer (CRC) patients and their healthy first-degree relatives. In a second asymptomatic LS cohort (n=150) undergoing colonoscopy-screening program, individuals with early precancerous lesions were compared to those with a normal colonoscopy. Since bacteria are organized into different networks within the microbiota, we compared related network structures in patients and controls. Experimental design: Fecal prokaryote DNA was extracted prior to colonoscopy for whole metagenome (n=34, pilot study) or 16s rRNA sequencing (validation study). We characterized bacteria taxonomy using Diamond/MEGAN6 and DADA2 pipelines and performed differential abundances using Shaman website. We constructed networks using SparCC inference tools and validated the construction's accuracy by performing qPCR on selected bacteria. Results: Significant differences in bacterial communities in LS-CRC patients were identified, with an enrichment of virulent bacteria and a depletion of symbionts compared to their first-degree relatives. Bacteria taxa in LS asymptomatic individuals with colonic precancerous lesions (n=79) were significantly different compared to healthy individuals (n=71). The main bacterial network structures, constructed based on bacteria-bacteria correlations in CRC (pilot study) and in asymptomatic precancerous patients (validation-study), showed a different pattern than in controls. It was characterized by virulent/symbiotic coexclusion in both studies and illustrated (validation study) by a higher Escherichia/Bifidobacterium ratio, as assessed by qPCR. Conclusion: Enhanced fecal virulent/symbiotic bacteria ratios influence bacterial network structures. As an early event in colon carcinogenesis, these ratios can be used to identify asymptomatic LS individual with a higher risk of CRC. [ABSTRACT FROM AUTHOR]

Introduction: To investigate the effects of different drip irrigation periods on soil microbial communities and functions. Methods: Increasing drip irrigation amount at the seedling (S), jointing (J), bell (B), tasseling (T) and grain filling (G) stages of maize were studied using no increase in irrigation amount as control (CK). Principal component analysis was conducted to comprehensively evaluate soil microbial quality following the different drip irrigation treatments. In addition, the characteristics of the community structure and the potential functional composition of soil bacteria and fungi were comparatively analyzed by combining amplicon sequencing and functional prediction methods. Results: The results indicated that MBNT15 was the most important genus for the classification of soil bacterial samples, Saitozyma was the most important genus for the classification of soil fungal samples, and fungi were more important than bacteria for the classification of soil microbial samples. Compared with fungal communities, bacterial communities exhibited high levels of functional diversity. The proportion of metabolism was the highest in the prediction of bacterial primary functions, and carbohydrate metabolism and amino acid metabolism were important functions in the prediction of bacterial secondary functions. BugBase phenotype prediction results showed that soil bacteria under B treatment had a higher number of aerobic bacteria and greater resistance to disease and stress. The J treatment had the highest number of bacteria with biofilm forms, and the J, S, and G treatments contained more potentially pathogenic bacteria but fewer stress-tolerant bacteria compared with the CK treatment. The number of Saprotroph was the largest and the number of Symbiotroph was the least. The relative abundances of Saprotroph, Pathotroph and Symbiotroph were 68.60%~74.33%, 15.76%~20.60% and 9.16%~11.13%, respectively. Discussion: The findings provide a reference for conserving water resources, improving maize yield, and predicting soil microbial metabolic potential and function by reflecting the richness of the soil microbial community structure in maize fields. [ABSTRACT FROM AUTHOR]

Background: Possible relationships between gut dysbiosis and breast cancer (BC) development and progression have been previously reported. However, the results of these metagenomics studies are inconsistent. Our study involved 88 patients diagnosed with breast cancer and 86 cancer-free control women. Participants were divided into groups based on their menopausal status. Fecal samples were collected from 47 and 41 pre- and postmenopausal newly diagnosed breast cancer patients and 51 and 35 pre- and postmenopausal controls, respectively. In this study, we performed shotgun metagenomic analyses to compare the gut microbial community between pre- and postmenopausal BC patients and the corresponding controls. Results: Firstly, we identified 12, 64, 158, and 455 bacterial taxa on the taxonomy level of phyla, families, genera, and species, respectively. Insignificant differences of the Shannon index and b-diversity were found at the genus and species levels between pre- and postmenopausal controls; the differences concerned only the Chao index at the species level. No differences in a-diversity indexes were found between pre- and postmenopausal BC patients, although b-diversity differed these subgroups at the genus and species levels. Consistently, only the abundance of single taxa differed between pre- and postmenopausal controls and cases, while the abundances of 14 and 23 taxa differed or tended to differ between premenopausal cases and controls, and between postmenopausal cases and controls, respectively. There were similar differences in the distribution of enterotypes. Of 460 bacterial MetaCyc pathways discovered, no pathways differentiated pre- and postmenopausal controls or BC patients, while two and one pathways differentiated cases from controls in the pre- and postmenopausal subgroups, respectively. Conclusion: While our findings did not reveal an association of changes in the overall microbiota composition and selected taxa with the menopausal status in cases and controls, they confirmed differences of the gut microbiota between pre- and postmenopausal BC patients and the corresponding controls. However, these differences were less extensive than those described previously. [ABSTRACT FROM AUTHOR]

Cable bacteria are unique multicellular organisms that live in the sediment at the bottom of oceans and lakes. They form long chains called filaments, with each filament consisting of thousands of cells connected together. What sets cable bacteria apart is their ability to generate energy through electrical teamwork. The cells within a filament work together to transfer electrons from hydrogen sulfide to oxygen, generating electricity in the process. This unique energy generation allows cable bacteria to thrive in environments where resources are spatially separated. Further research is needed to understand the mechanisms behind this electrical cooperation and its implications. [Extracted from the article]

In the tropics, anthropogenic activities can lead to water and wind erosion, a loss of biodiversity, and a reduction in sequestered carbon, fertility, and organic matter content in the soils concerned, potentially resulting in their degradation. This study therefore aims to identify the mechanisms used by plant species to restore degraded tropical soils and plant species characteristics that are best suited to achieve this through a critical scoping review of the peer-reviewed literature. Soil restoration leads to the re-establishment of ecosystem services and an increase in soil production potential, the regeneration of biodiversity, the stopping of organic matter losses, and the creation of favorable conditions for carbon sequestration and nitrogen fixation. The choice of appropriate plant species depends on the restoration objectives to be achieved. Five key mechanisms by which plant species contribute to restore degraded tropical soils include: (1) nitrogen fixation, (2) carbon sequestration, (3) organic matter addition, (4) structure stabilization, and (5) erosion control. The main characteristics of plant species and vegetation involved in these mechanisms are (a) the capacity to form symbiotic associations with N-fixing bacteria and mycorrhizae, (b) the production of abundant root biomass releasing litter and exudates, (c) roots having a high length density, branching intensity, and depth distribution, (d) the production of an abundant and easily decomposed above ground litter, (e) the production of a vast canopy, and (f) the presence of different vegetation strata. Targeting these characteristics will contribute to acting on several mechanisms simultaneously, which will increase the chance of success in tropical soil restoration. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new and advanced multi-feature selection method for bacterial classification that uses the salp swarm algorithm (SSA). We improve the SSA's performance by using opposition-based learning (OBL) and a local search algorithm (LSA). The proposed method has three main stages, which automate the categorization of bacteria based on their unique characteristics. The method uses a multi-feature selection approach augmented by an enhanced version of the SSA. The enhancements include using OBL to increase population diversity during the search process and LSA to address local optimization problems. The improved salp swarm algorithm (ISSA) is designed to optimize multi-feature selection by increasing the number of selected features and improving classification accuracy. We compare the ISSA's performance to that of several other algorithms on ten different test datasets. The results show that the ISSA outperforms the other algorithms in terms of classification accuracy on three datasets with 19 features, achieving an accuracy of 73.75%. Additionally, the ISSA excels at determining the optimal number of features and producing a better fit value, with a classification error rate of 0.249. Therefore, the ISSA method is expected to make a significant contribution to solving feature selection problems in bacterial analysis. [ABSTRACT FROM AUTHOR]

Identifying the nature and type of bacteria is essential in diagnosing various fatal diseases and their treatments. Biologists classify bacteria using morphological characterization from microscopic images according to their color and shape information. Therefore, an automated bacterial recognition and classification approach is required compared to a challenging and time-consuming manual process. Much research has been carried out on bacteria classification using machine learning algorithms. However, the major weakness of these conventional machine learning algorithms is that they cannot differentiate pose and deformation, have significant trainable parameters, require extensive training time, and use trial-and-error-based hyperparameter selection. In addition, the choice of optimization function to reduce the loss is also equally important. This paper presents an efficient capsule network that encodes information from orientation as an alternative to these machine learning models. The proposed model is designed with histogram-based feature sets requiring minimal parameters. Various optimization algorithms are tested to find an appropriate optimizer. 33 categories of bacteria species are classified using the proposed method. A comprehensive analysis of popular gradient descent optimizers is presented with a capsule network to strengthen testing and validation and benchmark the performance. The extensive empirical study on DiBAS datasets demonstrates that the proposed network performs 95.08% efficiency among various machine learning algorithms, including KNN, Decision Trees, Naïve Bayes and SVM. Furthermore, the proposed model achieves better accuracy with the least training parameters of 6.9 million.

Identifying bacterial strains is essential in microbiology for various practical applications, such as disease diagnosis and quality monitoring of food and water. Classical machine learning algorithms have been utilized to identify bacteria based on their Raman spectra. However, convolutional neural networks (CNNs) offer higher classification accuracy, but they require extensive training sets and retraining of previous untrained class targets can be costly and time-consuming. Siamese networks have emerged as a promising solution. They are composed of two CNNs with the same structure and a final network that acts as a distance metric, converting the classification problem into a similarity problem. Classical machine learning approaches, shallow and deep CNNs, and two Siamese network variants were tailored and tested on Raman spectral datasets of bacteria. The methods were evaluated based on mean sensitivity, training time, prediction time, and the number of parameters. In this comparison, Siamese-model2 achieved the highest mean sensitivity of 83.61 ± 4.73 and demonstrated remarkable performance in handling unbalanced and limited data scenarios, achieving a prediction accuracy of 73%. Therefore, the choice of model depends on the specific trade-off between accuracy, (prediction/training) time, and resources for the particular application. Classical machine learning models and shallow CNN models may be more suitable if time and computational resources are a concern. Siamese networks are a good choice for small datasets and CNN for extensive data.

Background: Compared to their healthy counterparts, patients with type 2 diabetes (T2D) can exhibit an altered gut microbiota composition, correlated with detrimental outcomes, including reduced insulin sensitivity, dyslipidemia, and increased markers of inflammation. However, a typical T2D microbiota profile is not established. The aim of this pilot study was to explore the gut microbiota and bacteria associated with prediabetes (pre-T2D) patients, and treatment naïve T2D patients, compared to healthy subjects. Methods: Fecal samples were collected from patients and healthy subjects (from Norway). The bacterial genomic DNA was extracted, and the microbiota analyzed utilizing the bacterial 16S rRNA gene. To secure a broad coverage of potential T2D associated bacteria, two technologies were used: The GA-map® 131-plex, utilizing 131 DNA probes complementary to pre-selected bacterial targets (covering the 16S regions V3-V9), and the LUMI-Seq™ platform, a full-length 16S sequencing technology (V1-V9). Variations in the gut microbiota between groups were explored using multivariate methods, differential bacterial abundance was estimated, and microbiota signatures discriminating the groups were assessed using classification models. Results: In total, 24 pre-T2D patients, 18 T2D patients, and 52 healthy subjects were recruited. From the LUMI-Seq™ analysis, 10 and 9 bacterial taxa were differentially abundant between pre-T2D and healthy, and T2D and healthy, respectively. From the GA-map® 131-plex analysis, 10 bacterial markers were differentially abundant when comparing pre-T2D and healthy. Several of the bacteria were short-chain fatty acid (SCFA) producers or typical opportunistic bacteria. Bacteria with similar function or associated properties also contributed to the separation of pre-T2D and T2D from healthy as found by classification models. However, limited overlap was found for specific bacterial genera and species. Conclusions: This pilot study revealed that differences in the abundance of SCFA producing bacteria, and an increase in typical opportunistic bacteria, may contribute to the variations in the microbiota separating the pre-T2D and T2D patients from healthy subjects. However, further efforts in investigating the relationship between gut microbiota, diabetes, and associated factors such as BMI, are needed for developing specific diabetes microbiota signatures. [ABSTRACT FROM AUTHOR]

Simple Summary: Untargeted shotgun DNA sequencing of fecal samples is a relatively novel approach to study the microbiome. This method allows better classification of bacteria on a species level compared to traditional 16S rRNA gene sequencing, and provides data about viruses, fungi, archaea, and functional genes. A targeted qPCR-based dysbiosis index has been recently introduced to evaluate the fecal microbiota in dogs. This study evaluated the agreement for core bacterial taxa between targeted qPCR assays and relative abundances obtained by shotgun DNA sequencing. We analyzed the fecal microbiota of 296 dogs with various clinical phenotypes using both methods. Significant correlations were found between the two methods, and the qPCR-based dysbiosis index accurately reflected shifts in the microbiome of dogs as observed by DNA shotgun sequencing. DNA shotgun sequencing is an untargeted approach for identifying changes in relative abundances, while qPCR allows reproducible quantification of specific bacteria. The canine dysbiosis index (DI) assesses the canine fecal microbiota by using a mathematical algorithm based on qPCR results. We evaluated the correlation between qPCR and shotgun sequencing using fecal samples from 296 dogs with different clinical phenotypes. While significant correlations were found between qPCR and sequencing, certain taxa were only detectable by qPCR and not by sequencing. Based on sequencing, less than 2% of bacterial species (17/1190) were consistently present in all healthy dogs (n = 76). Dogs with an abnormal DI had lower alpha-diversity compared to dogs with normal DI. Increases in the DI correctly predicted the gradual shifts in microbiota observed by sequencing: minor changes (R = 0.19, DI < 0 with any targeted taxa outside the reference interval, RI), mild-moderate changes (R = 0.24, 0 < DI < 2), and significant dysbiosis (R = 0.54, 0.73, and 0.91 for DI > 2, DI > 5, and DI > 8, respectively), compared to dogs with a normal DI (DI < 0, all targets within the RI), as higher R-values indicated larger dissimilarities. In conclusion, the qPCR-based DI is an effective indicator of overall microbiota shifts observed by shotgun sequencing in dogs. [ABSTRACT FROM AUTHOR]

Plant growth-promoting rhizobacteria (PGPRs) are bacteria that colonize the plant roots. These beneficial bacteria have an influence on plant development through multiple mechanisms, such as nutrient availability, alleviating biotic and abiotic stress, and secrete phytohormones. Therefore, their inoculation constitutes a powerful tool towards sustainable agriculture and crop production. To understand plant-PGPRs interaction we present the classification of PGPR using machine learning and meta-learning classifiers namely Support Vector Machine (SVM), Kernel Logistic Regression (KLR), meta-SVM and meta-KLR to predict the presence of Bacillus megaterium inoculated in tomato root tissues using publicly available transcriptomic data. The original dataset presents 36 significantly differentially expressed genes. As the meta-KLR achieved near-optimal performance considering all the relevant metrics, this meta learner was afterwards used to identify the informative genes (IGs). The outcomes showed 157 IGs, being present all significantly differentially expressed genes previously identified. Among the IGs, 113 were identified as tomato genes, 5 as Bacillus subtilis proteins, 1 as Escherichia coli protein and 6 were unidentified. Then, a functional enrichment analysis of the tomato IGs showed 175 biological processes, 22 molecular functions and 20 KEGG pathways involved in B. megaterium–tomato interaction. Furthermore, the biological networks study of their Arabidopsis thaliana orthologous genes identified the co-expression, predicted interaction, shared protein domains and co-localization networks. [ABSTRACT FROM AUTHOR]

Our study aims to evaluate the performance of the combination of Sysmex urine dry chemistry analyzer UC-3500 and urine particle analyzer UF-5000 in screening bacterial urinary tract infection (UTI). We analyzed 2000 urine specimens from patients with suspected UTI by using a urine dry chemistry analyzer (UC-3500) and a fully automated sediment analyzer (UF-5000). After being tested by the instrument, all specimens were sent to our clinical microbiology laboratory for culture. In addition, 600 urine specimens were selected to evaluate the accuracy of the six screening strategies established in this study. The consistency of UF-5000 bacterial classification and bacterial culture was fair (Kappa = 0.339). The counts of WBC and BACT elevated with sequential group designs (P < 0.001). The cut-off value of WBC was 32.20/μL for males (AUC, 0.942, 95%CI, 0.930–0.955) and 39.15/μL for females (AUC, 0.931, 95%CI, 0.914–0.948). The sensitivity and specificity of WBC were relatively higher than those of BACT. Strategy④ and Strategy⑥ in all six strategies had a good negative predictive value (NPV) which was 98.73%. UF-5000 bacterial classification cannot be used as a practical reference. 32.20/μL (male) and 39.15/μL (female) for WBC as well as 22.35/μL (male) and 127.25/μL (female) for BACT were used as cut-off values to effectively determine whether UTI occurs. WBC, BACT and LEU joint screening programs were suitable to rapidly and effectively exclude bacterial UTI. [ABSTRACT FROM AUTHOR]

The process of bacterial nomenclature change has evolved in complexity over time and continues to be an iterative process that is not without challenges. The importance and feasibility of such changes vary among basic researchers, clinical microbiologists, and clinicians. In recent years, clinically relevant changes have been made across Gram-positive and Gram-negative organism groups, as well as the mycobacteria. Updated clinical laboratory accreditation requirements state that clinical laboratories must update their reporting practices in the case of clinically relevant nomenclature changes. These updates may significantly affect various sectors of health care, including antimicrobial stewardship, laboratory protocols, and infection prevention procedures and policies. While regularly updating bacterial nomenclature aims to improve the accuracy and consistency of our microbial language, the potential impact of such changes must be considered. [ABSTRACT FROM AUTHOR]

Aim: To compare the isolated and identified bacterial species colonizing on the genital skin between patients with and without incontinence‐associated dermatitis. Methods: This cross‐sectional study included 102 patients with stroke admitted to an acute hospital in Japan. Swabs were collected, and bacterial species found in swabs were isolated and identified using a selective agar medium and simple identification kits. In addition to demographic information, severity of incontinence‐associated dermatitis and the total bacterial counts were measured. Results: Incontinence‐associated dermatitis was present in 53.9% of the participants. Staphylococcus aureus was found in 50% of the participants with incontinence‐associated dermatitis and only 17.9% of those without incontinence‐associated dermatitis (P = 0.0029). Bacterial species distribution by erythema and skin erosion, which denote severity of incontinence‐associated dermatitis, was different, but not significant; additionally, the total number of bacterial colonies was equivalent. Conclusions: Bacterial species distribution differed between patients with and without incontinence‐associated dermatitis, whereas the total number of bacterial colonies was equivalent. A high detection rate of S. aureus on genital skin sites potentially affects the presence of incontinence‐associated dermatitis and its severity. Geriatr Gerontol Int 2023; 23: 537–542. [ABSTRACT FROM AUTHOR]

Cultural heritage conservation extends beyond individual interests, therefore preserving patrimony artworks is a timeless endeavor. This study proposes cold atmospheric plasma as a suitable method for the treatment of paper, deteriorated by cellulolytic bacteria. Three distinct methods of treating Bacillus velezensis isolated herein from an artificially deteriorated book were applied using cold atmospheric plasma. Bacteria were cultivated on membranes, in liquid media, and directly on paper. When used for five minutes at a power of 50 W and a current density of 12 mA/cm2, the designed cold atmospheric plasma device proved successful in preventing bacterial growth and spore formation. B. velezensis spore generation is a rare mechanism that has never been observed within these circumstances before. Moreover, the cold atmospheric plasma device can suppress the development of cellulolytic bacteria without damaging the paper substrate, which is a significant step in the preservation of patrimony artworks. [ABSTRACT FROM AUTHOR]

Oncolytic bacteria are a classification of bacteria with a natural ability to specifically target solid tumors and, in the process, stimulate a potent immune response. Currently, these include species of Klebsiella, Listeria, Mycobacteria, Streptococcus/Serratia (Coley's Toxin), Proteus, Salmonella, and Clostridium. Advancements in techniques and methodology, including genetic engineering, create opportunities to "hijack" typical host–pathogen interactions and subsequently harness oncolytic capacities. Engineering, sometimes termed "domestication", of oncolytic bacterial species is especially beneficial when solid tumors are inaccessible or metastasize early in development. This review examines reported oncolytic bacteria–host immune interactions and details the known mechanisms of these interactions to the protein level. A synopsis of the presented membrane surface molecules that elicit particularly promising oncolytic capacities is paired with the stimulated localized and systemic immunogenic effects. In addition, oncolytic bacterial progression toward clinical translation through engineering efforts are discussed, with thorough attention given to strains that have accomplished Phase III clinical trial initiation. In addition to therapeutic mitigation after the tumor has formed, some bacterial species, referred to as "prophylactic", may even be able to prevent or "derail" tumor formation through anti-inflammatory capabilities. These promising species and their particularly favorable characteristics are summarized as well. A complete understanding of the bacteria–host interaction will likely be necessary to assess anti-cancer capacities and unlock the full cancer therapeutic potential of oncolytic bacteria. [ABSTRACT FROM AUTHOR]

Aim: To evaluate the microbial colonization in different dentition phases on individuals from 0 to 18 years of age belonging to families with a history of periodontitis compared to descendants of periodontally healthy parents. Materials and Methods: The offspring of subjects with periodontitis ('Perio' group) and the offspring of periodontally healthy subjects ('Healthy' group), matched for gender and age, were included in this cross‐sectional study and divided according to the dentition phase: pre‐dentate, primary, mixed and permanent. The patients were clinically assessed, and their saliva was collected. DNA was extracted, and V1–V3 and V4–V5 regions of the 16S rRNA gene were sequenced. Results: Fifty children of parents with periodontitis and 50 from healthy parents were included in the study and divided according to the dentition phase: pre‐dentate (n = 5/group), primary dentition (n = 15/group), mixed dentition (n = 15/group) and permanent dentition (n = 15/group) in each group. The microbiome composition was different between dentitions for both groups. Children of the Perio group presented a microbial diversity different from that of the Healthy group in mixed and permanent dentitions. The more intense shift in the community occurred between primary and mixed dentition in the Perio group, while the transition between mixed and permanent dentition was the period with greater changes in the microbiome for the Healthy group. Furthermore, a pathogen‐rich environment—higher prevalence and abundance of periodontitis‐associated species such as Prevotella spp., Selenomonas spp., Leptotrichia spp., Filifactor alocis, Prevotella intermedia, Treponema denticola and Tannerella forsythia— was observed in the Perio group. Conclusions: The parents' periodontal status significantly affects the microbiome composition of their offspring from an early age. The mixed dentition was the phase associated with establishing a dysbiotic and pathogen‐rich microbiome in descendants of parents with periodontitis. [ABSTRACT FROM AUTHOR]

Identification of antimicrobial resistance markers in urinary tract infections could provide a more targeted approach in the diagnosis and treatment of UTIs while reducing overall public health burdens. We describe a molecular assay as a diagnostic tool for antibiotic resistance characterization to promote faster diagnosis of antibiotic regimens compared to standard microbiology techniques. Targeted antibiotic usage for pathogenic infections remains a main goal for effective antibiotic treatment protocols and reducing the overall public health burden. Rapid identification of the pathogen(s) causing the infection and harboring the antibiotic resistance gene is also a main area of exploration for antibiotic appropriation and stewardship. Urinary tract infections are a common clinical disease and reservoir for pathogenic infection and the development of antibiotic resistance, especially in hospital- and community-acquired settings. Standard methods require urine culture, which is time consuming and relies on phenotypic characterization. A genetic diagnostic method is warranted for the rapid molecular characterization of antibiotic resistance genes to reduce inappropriate exposure to antibiotics while improving the overall treatment model for urinary tract infections. The purpose of this study is to demonstrate logical viability for real-time molecular diagnostics for early identification, active surveillance and overall targeted antibiotic stratification that is proposed as an in vitro rapid and comprehensive tool for assessing proper antibiotic stewardship in UTIs. Here, we describe a multiplex real-time fluorescence polymerase chain reaction (PCR) for probe-based detection of the top 24 antibiotic resistance genes with targeted relationships to target molecular drug classes and administered antibiotics. Multiplexed analysis based on molecular features enables rapid testing while shifting the diagnostic detection paradigm from monocentric infections towards polymicrobial infections. We utilized 366 samples from the FDA-CDC Antimicrobial Resistance Isolate Bank to test the efficacy of the assay and propose a model to infer the identity of bacterial isolates. We found that, in addition to a high level of accuracy in predicting bacterial genus classification, the assay was mostly in agreement with CDC-tested genotypic and phenotypic results. This study provides evidence for using genetic diagnostic methods, such as multiplex qPCR, in the rapid identification of antibiotic resistance (ABR) genes for the characterization and treatment of urinary tract infections. [ABSTRACT FROM AUTHOR]

Background: Dental calculus is the result of dental plaque mineralization, originating from the tooth‐associated bacterial biofilm. Recent evidence revealed that the dental calculus microbiome has a more complex composition than previously considered, including an unstructured mix of both aerobes and anaerobes bacteria. Actually, we lack information about the influence of host lifestyle factors, such as diet and health on this highly biodiverse ecosystem. Here, we provide a pilot study investigating dental calculus microbial biodiversity and its relation with the host diet. Methods: We collected 40 dental calculus samples during routine dental inspection; deoxyribonucleic acid was extracted and analyzed through 16S amplicon sequencing, while dietary information was retrieved through a questionnaire. Associations between diet and oral bacteria taxonomy and functional pathways were statistically tested. Results: Overall, microbiome composition was dominated by 10 phyla and 39 bacterial genera, which were differently distributed among samples. Cluster analysis revealed four main groups based on the taxonomic profile and two groups based on functional pathways. Each taxonomic cluster was dominated by different microbial biomarkers: Streptococcus, Rothia, Tannerella, Lautropia, and Fusobacterium. Bacteria genera and pathways were also associated with specific dietary elements, especially vegetable and fruit intake suggesting an overall effect of diet on dental calculus microbiome. Conclusions: The present study demonstrates that there exists an inter‐variability in the microbial composition of dental calculus among individuals of a rather homogeneous population. Furthermore, the observed biodiversity and microbial functions can find an association with specific dietary habits, such as a high‐fiber diet or a protein‐rich diet. [ABSTRACT FROM AUTHOR]