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The cross-species transmission of intestinal bacteria caused by mixed-group contact in animals is currently an issue of concern. Herring gulls (Larus argentatus) and common shelducks (Tadorna tadorna) often forage in mixed groups, a process which is known as sympatric mixed foraging. This study sought to explore how mixed foraging affects the gut bacteria structure in herring gulls and common shelducks on Binzhou Seashell Island, China. Illumina high-throughput sequencing was employed to analyze the intestinal bacteria of these birds during their wintering period, with the goal of understanding the impact of mixed-species interactions on gut microbiota diversity. The results showed that alpha diversity of intestinal flora in common shelducks was higher than that in herring gulls, and there was no significant difference in beta diversity between the two species. The similarity ratio of the top five abundances of bacteria at the phylum classification level reached 42.86%. Judging from the results, there is resemblance in the structure of the intestinal flora between the two species. The study not only enhances our understanding of the adaptive strategies and interactions of these two avian species within the ecosystem, but also establishes a crucial foundation for bird resource management and disease prevention and control in this region. It holds significant scientific and practical implications for fields such as ecological conservation and biodiversity. [ABSTRACT FROM AUTHOR]

The article by Christopher Williams-Wynn explores how mediatization now extends across land, sea, and air, integrating natural and technological elements into one interconnected system. This process is increasingly reflected in contemporary artistic practices, such as Jenna Sutela's work with neural networks and bacterial systems, and Pierre Huyghe's installations that blend artificial intelligence, insects, and mechanical interventions.

Understanding the intricate pathways of protein secretion in bacteria is crucial for advancing research on bacterial diseases and their potential treatments, particularly in the case of non-classical protein secretion pathways. These pathways pose unique challenges due to the complex signaling mechanisms involved. To address this, we employed advanced machine learning techniques and gathered physical–chemical characteristics of amino acids from the AA index site. Through a meticulous six-step methodology, we curated a comprehensive dataset by filtering raw genome data and juxtaposing it with a positive dataset comprising 141 proteins from authoritative literature sources. Leveraging a conventional Random Forest machine learning algorithm, we achieved an impressive accuracy rate of approximately 91% in classifying non-classical secreted proteins. This validation was conducted on a dataset of 14 positive and 92 negative proteins, resulting in a sensitivity of 91% and a specificity of 86%. Notably, our study distinguishes itself by its rapid execution of non-classical secretion pathway analyses, rendering it particularly suitable for large datasets. This speed does not compromise accuracy, allowing for comprehensive Omics analyses. Consequently, our research underscores the significance of carefully selecting appropriate descriptors and constructing a robust training dataset to enhance Omics analyses of bacterial protein secretion via non-classical pathways. For further details, please refer to the complete study available at https://github.com/santosardr/non-CSPs. [ABSTRACT FROM AUTHOR]

The current study focused on the exploration of bacterial community in heavy metal contaminated soil using culture dependent and independent approaches that might be applied to the bioremediation process. A total of 150 bacterial colonies were examined from the heavy metal-contaminated soil at initial level, and 25 isolates were chosen for further examination. Finally, twelve strains were chosen for future research based on their high levels of heavy metal resistance. The 10 bacterial strains i.e.,1K-10K were then characterized on the basis of their morphology and microscopic analysis. The morphological and biochemical characteristic relied on the basis of Bergey's manual of bacterial classification that 1K as Pseudomonas sp., 2K as Enterobacter sp., 3K as Streptococcus sp., 4K as Staphylococcus sp.,5K as Staphylococcus sp., 6K as Pseudomonas sp., 7K as Micrococcus sp., 8K as Staphylococcus sp., 9K as Staphylococcus sp. & 10K as Staphylococcus sp. In metagenomics analysis, the most prevalent bacteria present in samples were "Actinobacteria, Proteobacteria, Acidobacteria, Pseudomonas Cyanobacteria, Bacteriodes, and Sphingomonas. Among the 29 identified phyla, Chloroflexi and Tenericutes were the most dominant phyla and their relative abundance ranged from 0.75-1.00. While, on the genera level, the most abundant genera were Blautia, Rhodoplanes followed by Prevotella and Arenimonas. The relative abundance of these genera ranged from 0.75% to 1.00%. Depending on the relative abundance of taxons, the results of this study showed Proteobacteria, Synergistetes and Bacteriodes were most abundant phylum while the least abundant phylum present in heavy metal contaminated soil was Chlamydia and Tenericutes. Screening of soil-based libraries using functional and sequence-based specifications, which has disclosed information on soil microbial communities, has made it possible to identify new microbial communities. Thus, the culture dependent and independent approaches revealed that even heavy metal contaminated soil compose of diverse group of bacterial community that could be explored for bioremediation purposes. [ABSTRACT FROM AUTHOR]

Background: Oral microbiota comprises polymicrobial communities shaped by mutualistic coevolution with the host, contributing to homeostasis and regulating immune function. Nevertheless, dysbiosis of oral bacterial communities is associated with a number of clinical symptoms that ranges from infections to oral cancer. Peri-implant diseases are biofilm-associated inflammatory conditions affecting the soft and hard tissues around dental implants. Characterization and identification of the biofilm community are essential for the understanding of the pathophysiology of such diseases. For that sampling methods should be representative of the biofilm communities Therefore, there is a need to know the effect of different sampling strategies on the biofilm characterization by next generation sequencing. Methods: With the aim of selecting an appropriate microbiome sampling procedure for periimplant biofilms, next generation sequencing was used for characterizing the bacterial communities obtained by three different sampling strategies two months after transepithelial abutment placement: adjacent periodontal crevicular fluid (ToCF), crevicular fluid from transepithelial abutment (TACF) and transepithelial abutment (TA). Results: Significant differences in multiple alpha diversity indices were detected at both the OTU and the genus level between different sampling procedures. Differentially abundant taxa were detected between sample collection strategies, including peri-implant health and disease related taxa. At the community level significant differences were also detected between TACF and TA and also between TA and ToCF. Moreover, differential network properties and association patterns were identified. Conclusions: The selection of sample collection strategy can significantly affect the community composition and structure. Trial registration: This research is part of a randomized clinical trial that was designed to assess the effect of transepithelial abutment surface on the biofilm formation. The trial was registered at Trial Registration ClinicalTrials.gov under the number NCT03554876. [ABSTRACT FROM AUTHOR]

Background: Periodontitis is the sixth-most common disease worldwide. The oral microbiome composition and its association with Periodontal disease (PD) have been largely explored; however, limited studies have explored the microbial profiles of both oral and toothbrushes in patients with PD. Thus, this study aimed to ascertain the oral and toothbrushes microbial composition in high-altitude populations, hypothesizing that their correlation with periodontal health would differ from those at lower altitudes, potentially indicating links between environmental factors, microbial colonization patterns, and periodontal health in distinct geographic contexts. Methods: In the present study, we enrolled 35 individuals including 21 healthy and 14 diagnosed with PD from the Lhasa region of Tibet, China. Saliva and toothbrush samples were collected from each participant to assess the association between toothbrush usage and oral microbiome with PD using 16 S rRNA gene-specific V3-V4 regions sequencing. To assess the oral and toothbrush microbiome composition and diversity and its possible link to PD. Results: Significantly higher Alpha diversity (Shannon index) was observed between the PD group and PD toothbrushes (p = 0.00021) and between the PD group and Healthy toothbrushes (p = 0.00041). The predominant species were Proteobacteria, Bacteroidota, Firmicutes, Actinobacteria, and Fusobacteria, with genera Pseudomonas, Veillonella, Neisseria, Acinetobacter, and Haemophilus. In addition, PICRUST2 analysis unveiled 44 significant pathways differentiating the disease and healthy groups, along with 29 pathways showing significant differences between their respective toothbrush microbial profiles. The distinct oral and toothbrush microbial composition among high-altitude populations suggests potential adaptations to the challenges of high-altitude environments. Conclusion: This study emphasizes the importance of tailored dental care strategies, accounting for altitude and racial factors, to effectively manage periodontal health in these communities. Further research is warranted to investigate the specific microbial mechanisms and develop targeted interventions for optimizing oral health in populations across varying altitudes. [ABSTRACT FROM AUTHOR]

The rapid expansion of whole genome sequencing in bacterial taxonomy has revealed deep evolutionary relationships and speciation signals, but assembly methods often miss true nucleotide diversity in the ribosomal operons. Though it lacks sufficient phylogenetic signal at the species level, the 16S ribosomal RNA gene is still much used in bacterial taxonomy. In cyanobacterial taxonomy, comparisons of 16S–23S Internal Transcribed Spacer (ITS) regions are used to bridge this information gap. Although ITS rRNA region analyses are routinely being used to identify species, researchers often do not identify orthologous operons, which leads to improper comparisons. No method for delineating orthologous operon copies from paralogous ones has been established. A new method for recognizing orthologous ribosomal operons by quantifying the conserved paired nucleotides in a helical domain of the ITS, has been developed. The D1′ Index quantifies differences in the ratio of pyrimidines to purines in paired nucleotide sequences of this helix. Comparing 111 operon sequences from 89 strains of Brasilonema, four orthologous operon types were identified. Plotting D1′ Index values against the length of helices produced clear separation of orthologs. Most orthologous operons in this study were observed both with and without tRNA genes present. We hypothesize that genomic rearrangement, not gene duplication, is responsible for the variation among orthologs. This new method will allow cyanobacterial taxonomists to utilize ITS rRNA region data more correctly, preventing erroneous taxonomic hypotheses. Moreover, this work could assist genomicists in identifying and preserving evident sequence variability in ribosomal operons, which is an important proxy for evolution in prokaryotes. [ABSTRACT FROM AUTHOR]

Taxonomic classification is crucial in identifying organisms within diverse microbial communities when using metagenomics shotgun sequencing. While second-generation Illumina sequencing still dominates, third-generation nanopore sequencing promises improved classification through longer reads. However, extensive benchmarking studies on nanopore data are lacking. We systematically evaluated performance of bacterial taxonomic classification for metagenomics nanopore sequencing data for several commonly used classifiers, using standardized reference sequence databases, on the largest collection of publicly available data for defined mock communities thus far (nine samples), representing different research domains and application scopes. Our results categorize classifiers into three categories: low precision/high recall; medium precision/medium recall, and high precision/medium recall. Most fall into the first group, although precision can be improved without excessively penalizing recall with suitable abundance filtering. No definitive 'best' classifier emerges, and classifier selection depends on application scope and practical requirements. Although few classifiers designed for long reads exist, they generally exhibit better performance. Our comprehensive benchmarking provides concrete recommendations, supported by publicly available code for reassessment and fine-tuning by other scientists. [ABSTRACT FROM AUTHOR]

In this study, bacteria with the best abilities in cellulose degradation, siderophore production, phosphate solubility, and Pythium parasitica inhibition were selected from termite nests and guts. The isolate BTNASP 5-2, BTPK 5-3 and BTNA 5-1 from termite guts exhibited highest in siderophore production index (SPI) (4.16 ± 0.21), phosphate solubilizing index (PSI) (2.10 ± 0.14) and percentage inhibition of radial growth (PIRG) (67.07 ± 4.02 %), respectively. The BGNACMC 4-3 isolated from termite nest gave the highest cellulolytic index (CI) of 5.17 ± 0.24. Bacterial classification was performed using 16s rRNA gene sequencing. The isolates BTPK 5-3, BGNACMC 4-3 and BTNA 5-1 were found closely related to Bacillus cereus, whereas the bacterial isolate BTNASP 5-2 was closely related to Bacillus subtilis. It is also suggested that the Bacillus cereus exhibited a variety of biological activities, denoting the highest cellulase, phosphate-solubilizing and antifungal activities, while Bacillus subtilis produced only a siderophore. The results obtained suggest that the bacteria selected will be used to develop bio-compost to promote plant growth, leading to sustainable farming. [ABSTRACT FROM AUTHOR]

The current sea monitoring equipment's are being used for a variety of purposes around the world. Currently used vehicles have some drawbacks. The first is the high fuel cost. The Vehicle engines cost more fuel as they have to release more power and environment and pollution. As well as not being able to stay under the sea for long days, there will often be a need for vehicles to come to the surface to refuel. The second is the vibrations and noise of these vehicles. The vibrations caused by these can be detrimental to the biodiversity of the ocean. Also, the noise makes it easier for enemies to identify our vehicles. Similarly when these vehicles go under water, water waves form on the surface. With this in mind, radar can detect what a vehicle under the sea looks like. In this paper, an artificial intelligence based microbiological model was proposed to monitor the sea level. With this biological model can greatly reduce fuels. It can get more capacity than normal vehicles. As fuel consumption decreases, so it does environmental pollution and since it operates quietly and without high vibrations, there is no threat to the biodiversity of the ocean. [ABSTRACT FROM AUTHOR]

Background: Bacterial image analysis plays a vital role in various fields, providing valuable information and insights for studying bacterial structural biology, diagnosing and treating infectious diseases caused by pathogenic bacteria, discovering and developing drugs that can combat bacterial infections, etc. As a result, it has prompted efforts to automate bacterial image analysis tasks. By automating analysis tasks and leveraging more advanced computational techniques, such as deep learning (DL) algorithms, bacterial image analysis can contribute to rapid, more accurate, efficient, reliable, and standardised analysis, leading to enhanced understanding, diagnosis, and control of bacterial-related phenomena. Methods: Three object detection networks of DL algorithms, namely SSD-MobileNetV2, EfficientDet, and YOLOv4, were developed to automatically detect Escherichia coli (E. coli) bacteria from microscopic images. The multi-task DL framework is developed to classify the bacteria according to their respective growth stages, which include rod-shaped cells, dividing cells, and microcolonies. Data preprocessing steps were carried out before training the object detection models, including image augmentation, image annotation, and data splitting. The performance of the DL techniques is evaluated using the quantitative assessment method based on mean average precision (mAP), precision, recall, and F1-score. The performance metrics of the models were compared and analysed. The best DL model was then selected to perform multi-task object detections in identifying rod-shaped cells, dividing cells, and microcolonies. Results: The output of the test images generated from the three proposed DL models displayed high detection accuracy, with YOLOv4 achieving the highest confidence score range of detection and being able to create different coloured bounding boxes for different growth stages of E. coli bacteria. In terms of statistical analysis, among the three proposed models, YOLOv4 demonstrates superior performance, achieving the highest mAP of 98% with the highest precision, recall, and F1-score of 86%, 97%, and 91%, respectively. Conclusions: This study has demonstrated the effectiveness, potential, and applicability of DL approaches in multi-task bacterial image analysis, focusing on automating the detection and classification of bacteria from microscopic images. The proposed models can output images with bounding boxes surrounding each detected E. coli bacteria, labelled with their growth stage and confidence level of detection. All proposed object detection models have achieved promising results, with YOLOv4 outperforming the other models. [ABSTRACT FROM AUTHOR]

Infections with the human papillomavirus (HPV) are one of the most common sexually transmitted diseases and are the main cause of precancerous changes and cervical cancer development. However, mere infection does not guarantee the development of disease or carcinogenesis. For this to occur, high-risk types of HPV must integrate into the host cell genome, transitioning into a persistent form of infection. Following the discovery of risk factors for HPV infection and factors predisposing infection to a persistent form, the number of studies investigating the correlation between vaginal dysbiosis and infection has significantly increased. Numerous studies document a significant association between the state of vaginal bacterial flora and HPV infection, leading to the development of plentiful studies on infection pathogenesis, risk factors, prevention, and treatment methods using bacteria. In this paper, based on recently published studies and current state of science knowledge, we will outline the topic of vaginal microbiome, emphasize the importance of bacteria inhabiting the vagina for human health, and identify the problem of changes in vaginal microflora. We would like to contribute to the dissemination of knowledge about the vaginal microbiome and general knowledge about taking care of its health. We will also summarize available treatment methods based on bacterial environment and discuss those methods for which research is currently underway or should be initiated. [ABSTRACT FROM AUTHOR]

The proliferation and dissemination of antimicrobial-resistant bacteria is an increasingly global challenge and is attributed mainly to the excessive or improper use of antibiotics. Currently, the gold-standard phenotypic methodology for detecting resistant strains is agar plating, which is a time-consuming process that involves multiple subculturing steps. Genotypic analysis techniques are fast, but they require pure starting samples and cannot differentiate between viable and non-viable organisms. Thus, there is a need to develop a better method to identify and prevent the spread of antimicrobial resistance. This work presents a novel method for detecting and identifying antibiotic-resistant strains by combining a cell sorter for bacterial detection and an elastic-light-scattering method for bacterial classification. The cell sorter was equipped with safety mechanisms for handling pathogenic organisms and enabled precise placement of individual bacteria onto an agar plate. The patterning was performed on an antibiotic-gradient plate, where the growth of colonies in sections with high antibiotic concentrations confirmed the presence of a resistant strain. The antibiotic-gradient plate was also tested with an elastic-light-scattering device where each colony's unique colony scatter pattern was recorded and classified using machine learning for rapid identification of bacteria. Sorting and patterning bacteria on an antibiotic-gradient plate using a cell sorter reduced the number of subculturing steps and allowed direct qualitative binary detection of resistant strains. Elastic-light-scattering technology is a rapid, label-free, and non-destructive method that permits instantaneous classification of pathogenic strains based on the unique bacterial colony scatter pattern. Key points: • Individual bacteria cells are placed on gradient agar plates by a cell sorter • Laser-light scatter patterns are used to recognize antibiotic-resistant organisms • Scatter patterns formed by colonies correspond to AMR-associated phenotypes [ABSTRACT FROM AUTHOR]

Fungal and bacterial bioaerosols have the potential to induce biodeterioration in books and archival materials stored within libraries through their metabolic processes. The progression of these processes is dependent on microclimatic factors, including relative humidity and temperature. At the national level, research on biodeterioration in libraries and investigations into bioaerosols in indoor environments have been limited. These topics are of great significance as they have an impact on both public health and book preservation. This study aims to determine the microbial load within the indoor air of the library situated on the campus of Universidad Militar Nueva Granada in Cajicá, Colombia. Simultaneously, it seeks to investigate the microclimatic conditions of this environment. We used a MAS-100 Eco air sampler with an airflow rate of 100 L/min for a duration of 2.5 minutes. Nutrient agar and rose Bengal were employed as culture media. The samples were incubated for 8 days, and colony counting was performed on Petri dishes. For fungal identification, we used lactophenol blue staining, and Gram staining was employed for bacterial classification. Microclimatic conditions were continuously monitored using a data logger equipped with a USB connection. The results revealed microbial loads ranging from 220 to 921 CFU/m³, with temperatures fluctuating between 18.2 and 18.6°C, and relative humidity levels within the range of 60.8% to 64.7%. The identification of fungal genera, including Aspergillus, Penicillium, and Cladosporium, as well as both Gram-positive and Gram-negative bacteria, was successfully achieved. An interesting observation emerged, indicating a corresponding increase in the concentration of fungal aerosols as relative humidity levels rose. In conclusion, this study emphasizes that microclimatic conditions, especially relative humidity, may increase the risk of biodeterioration within the library. [ABSTRACT FROM AUTHOR]

Background: Diabetic foot osteomyelitis (DFO) commonly occurs secondary to ulcerations of the skin. Empirical antibiotic agents are a key element of treatment and their use is dependent on local knowledge of the microbial spectrum of diabetic foot infections. This study aimed to retrospectively analyze the local microbiological profile, including bacterial culture/sensitivity results of DFO, and compare findings with literature. This study also aimed to review the concordance of microbiology results with national guidelines for the future treatment of DFO. Methods: A retrospective review of clinical records was performed on patients who presented to the high-risk foot clinic, Townsville University Hospital, between 2018 and 2022. All patients older than 18 years and diagnosed with DFO were included. Our exclusion criteria included all other foot presentations, including trauma, vasculitis, and neoplasms. Results: On the basis of the inclusion and exclusion criteria, 124 patients with DFO were selected. Most patients in the cohort were males (70.2%), non-Indigenous (68.5%), aged 50-69 years (55.6%), and with elevated HbA1c levels (>8.6). Chronic kidney disease (39.5%) and ischemic heart disease (41.9%) were common comorbidities. Of the pertinent microbial results, Staphylococcus aureus (~76%) was the most commonly isolated Gram-positive organism. Gram-positive bacteria were significantly increased in the elderly population with DFO (P <.05). All methicillin-resistant S aureus isolates were vancomycin- and cotrimoxazole-sensitive. Pseudomonas aeruginosa was the predominant Gram-negative organism isolated (39.3%). P aeruginosa exhibited low sensitivity to ciprofloxacin. Conclusion: This study has enhanced our understanding of the various microbial species underlying DFO at our center and may be generalizable. Level of Evidence: Level IV, retrospective case series. [ABSTRACT FROM AUTHOR]

Sepsis is a life-threatening condition arising from a dysregulated host immune response to infection, leading to a substantial global health burden. The accurate identification of bacterial pathogens in sepsis is essential for guiding effective antimicrobial therapy and optimising patient outcomes. Traditional culture-based bacterial typing methods present inherent limitations, necessitating the exploration of alternative diagnostic approaches. This study reports the successful application of Fourier-transform infrared (FT-IR) spectroscopy in combination with chemometrics as a potent tool for the classification and discrimination of microbial species and strains, primarily sourced from individuals with invasive infections. These samples were obtained from various children with suspected sepsis infections with bacteria and fungi originating at different sites. We conducted a comprehensive analysis utilising 212 isolates from 14 distinct genera, comprising 202 bacterial and 10 fungal isolates. With the spectral analysis taking several weeks, we present the incorporation of quality control samples to mitigate potential variations that may arise between different sample plates, especially when dealing with a large sample size. The results demonstrated a remarkable consistency in clustering patterns among 14 genera when subjected to principal component analysis (PCA). Particularly, Candida, a fungal genus, was distinctly recovered away from bacterial samples. Principal component discriminant function analysis (PC-DFA) allowed for distinct discrimination between different bacterial groups, particularly Gram-negative and Gram-positive bacteria. Clear differentiation was also observed between coagulase-negative staphylococci (CNS) and Staphylococcus aureus isolates, while methicillin-resistant S. aureus (MRSA) was also separated from methicillin-susceptible S. aureus (MSSA) isolates. Furthermore, highly accurate discrimination was achieved between Enterococcus and vancomycin-resistant enterococci isolates with 98.4% accuracy using partial least squares-discriminant analysis. The study also demonstrates the specificity of FT-IR, as it effectively discriminates between individual isolates of Streptococcus and Candida at their respective species levels. The findings of this study establish a strong groundwork for the broader implementation of FT-IR and chemometrics in clinical and microbiological applications. The potential of these techniques for enhanced microbial classification holds significant promise in the diagnosis and management of invasive bacterial infections, thereby contributing to improved patient outcomes. [ABSTRACT FROM AUTHOR]

Background: The clinical diagnosis of neonatal sepsis remains difficult because of various challenges, such as culturing the bacteria and avoiding contamination. Therefore, this study aimed to identify bacterial pathogens in patients with clinically diagnosed neonatal sepsis by next-generation sequencing (NGS). Methods: High-throughput NGS and traditional culture identification were performed by comparing samples from newborns with neonatal sepsis with healthy control infants. All blood samples were separately inoculated into anaerobic and aerobic bottles and incubated for 7 days at 37 °C, the positive specimens were then identified. Novel bacteria identified through high-throughput NGS were analysed using polymerase chain reaction (PCR), PCR products were verified by Sanger sequencing. Wilcoxon rank-sum and chi-square tests were performed to assess the significance of differences in species abundance between groups. Subjects were clinically diagnosed and hospitalized at the Pediatrics Department of Shenzhen Seventh People's Hospital and Pediatrics Department of the Longhua Branch of Shenzhen People's Hospital. Experiments were performed at the Shenzhen Seventh People's Hospital. The experimental group comprised 45 newborns clinically diagnosed with neonatal sepsis (age: 0–28 days; 28 males, 17 females). Fifteen normal newborns aged 0–28 days (7 males, 8 females) were included as the control group. Results: High-throughput NGS showed a positivity rate of 44% (20/45) for bacteria in patients clinically diagnosed with neonatal sepsis, whereas traditional bacterial culture identification showed a positivity rate of 0% (0/45). The four main bacterial species identified were Anoxybacillus kestanbolensis, Geobacillus vulcani, Klebsiella oxytoca, and Acinetobacter guillouiae. Conclusions: A. kestanbolensis, G. vulcani, K. oxytoca, and A. guillouiae, newly discovered bacteria in patients with neonatal sepsis, were identified with high-throughput NGS. Which may result from maternal intrauterine infection or birth-canal infection and have a high clinical-cure rate. Owing to a lack of methods to culture these bacteria, their role in neonatal sepsis remains unclear. A definite diagnosis cannot rely solely on bacterial culture identification for patients with a suspected diagnosis and clinical diagnosis of neonatal sepsis and should involve other effective diagnostic techniques. [ABSTRACT FROM AUTHOR]

Two thermophilic spore-forming sulfate-reducing strains, 435T and 781, were isolated from oil and gas reservoirs in Western Siberia (Russia) about 50 years ago. Both strains were found to be neutrophilic, chemoorganotrophic, anaerobic bacteria, growing at 45–70 °C (optimum, 55–60 °C) and with 0–4.5% (w/v) NaCl (optimum, 0.5–1% NaCl). The major fatty acids were iso-C15:0, iso-C17:0, C16:0, and C18:0. In sulfate-reducing conditions, the strains utilized H2/CO2, formate, lactate, pyruvate, malate, fumarate, succinate, methanol, ethanol, propanol, butanol, butyrate, valerate, and palmitate. In 2005, based on phenotypic characteristics and a 16S rRNA gene sequence analysis, the strains were described as 'Desulfotomaculum salinum' sp. nov. However, this species was not validly published because the type strain was not deposited in two culture collections. In this study, a genomic analysis of strain 435T was carried out to determine its taxonomic affiliation. The genome size of strain 435T was 2.886 Mb with a 55.1% genomic G + C content. The average nucleotide identity and digital DNA–DNA hybridization values were highest between strain 435T and members of the genus Desulfofundulus, 78.7–93.3% and 25.0–52.2%, respectively; these values were below the species delineation cut-offs (<95–96% and <70%). The cumulative phenotypic and phylogenetic data indicate that two strains represent a novel species within the genus Desulfofundulus, for which the name Desulfofundulus salinus sp. nov. is proposed. The type strain is 435T (=VKM B-1492T = DSM 23196T). A genome analysis of strain 435T revealed the genes for dissimilatory sulfate reduction, autotrophic carbon fixation via the Wood–Ljungdahl pathway, hydrogen utilization, methanol and organic acids metabolism, and sporulation, which were confirmed by cultivation studies. [ABSTRACT FROM AUTHOR]

Traditional microbial diagnostic methods face many obstacles such as sample handling, culture difficulties, misidentification, and delays in determining susceptibility. The advent of artificial intelligence (AI) has markedly transformed microbial diagnostics with rapid and precise analyses. Nonetheless, ethical considerations accompany AI adoption, necessitating measures to uphold patient privacy, mitigate biases, and ensure data integrity. This review examines conventional diagnostic hurdles, stressing the significance of standardized procedures in sample processing. It underscores AI's significant impact, particularly through machine learning (ML), in microbial diagnostics. Recent progressions in AI, particularly ML methodologies, are explored, showcasing their influence on microbial categorization, comprehension of microorganism interactions, and augmentation of microscopy capabilities. This review furnishes a comprehensive evaluation of AI's utility in microbial diagnostics, addressing both advantages and challenges. A few case studies including SARS-CoV-2, malaria, and mycobacteria serve to illustrate AI's potential for swift and precise diagnosis. Utilization of convolutional neural networks (CNNs) in digital pathology, automated bacterial classification, and colony counting further underscores AI's versatility. Additionally, AI improves antimicrobial susceptibility assessment and contributes to disease surveillance, outbreak forecasting, and real-time monitoring. Despite a few limitations, integration of AI in diagnostic microbiology presents robust solutions, user-friendly algorithms, and comprehensive training, promising paradigm-shifting advancements in healthcare. [ABSTRACT FROM AUTHOR]

Maintaining soil biological diversity is an effective approach in facilitating Chinese fir plantations' sustainable development. However, dynamic characteristics of soil microbial and nematode communities during Chinese fir planting remain unclear, particularly on the aggregate scales, which are the main sites of soil biological activity. Therefore, the research aims to explore how stand age (3, 9, 17, and 26 years) of Chinese fir plantations affects soil microbial biomass (including bacteria, fungi, and actinomycetes, based on the phospholipid fatty acid method), nematode abundances (including bacterivores, fungivores, plant parasites, and omnivores‐predators, based on the modified Baermann funnel method), and their diversity within aggregates of differing sizes (>2, 2–1, 1–0.25, and <0.25 mm). According to the findings, both aggregate size and stand age were major factors affecting soil microbes and nematodes in Chinese fir plantations. Specifically, soil microbial biomass (mainly bacteria), nematode abundances (except fungivores), and their diversity peaked within large macro‐aggregates (>2 mm), and the ranking order for stand ages was 17 > 9 ≈ 3 > 26 years. As the main fractions of soil aggregates in Chinese fir plantations, large macro‐aggregates had suitable internal environments for soil microbial propagation and nematode growth and mainly accumulated at 17 years, which caused the prosperity of soil microbes and nematodes at this stand age. Moreover, the partial least squares path model demonstrated that the influence of stand age on soil microbes and nematodes was indirect; but rather, the aggregate composition played a direct role in shaping their communities. Therefore, in order to maintain soil biological diversity, large macro‐aggregates should be protected during the growth of Chinese fir, especially at the late stage (from 17 to 26 years). Overall, these results could provide valuable insights into Chinese fir plantations' sustainable development. [ABSTRACT FROM AUTHOR]

Background Environmental contamination is suspected to play an important role in Candida auris transmission. Understanding speed and risks of contamination after room disinfection could inform environmental cleaning recommendations. Methods We conducted a prospective multicenter study of environmental contamination associated with C. auris colonization at 6 ventilator-capable skilled nursing facilities and 1 acute care hospital in Illinois and California. Known C. auris carriers were sampled at 5 body sites followed by sampling of nearby room surfaces before disinfection and at 0, 4, 8, and 12 hours after disinfection. Samples were cultured for C. auris and bacterial multidrug-resistant organisms (MDROs). Odds of surface contamination after disinfection were analyzed using multilevel generalized estimating equations. Results Among 41 known C. auris carriers, colonization was detected most frequently on palms/fingertips (76%) and nares (71%). C. auris contamination was detected on 32.2% (66/205) of room surfaces before disinfection and 20.5% (39/190) of room surfaces by 4 hours after disinfection. A higher number of C. auris –colonized body sites was associated with higher odds of environmental contamination at every time point following disinfection, adjusting for facility of residence. In the rooms of 38 (93%) C. auris carriers co-colonized with a bacterial MDRO, 2%–24% of surfaces were additionally contaminated with the same MDRO by 4 hours after disinfection. Conclusions C. auris can contaminate the healthcare environment rapidly after disinfection, highlighting the challenges associated with environmental disinfection. Future research should investigate long-acting disinfectants, antimicrobial surfaces, and more effective patient skin antisepsis to reduce the environmental reservoir of C. auris and bacterial MDROs in healthcare settings. [ABSTRACT FROM AUTHOR]

Soybean and corn strip cropping takes full advantage of the root characteristics and plant height differences between soybean and corn, which can improve crop photosynthesis and nutrient uptake as well as capitalize on themarginal effect of corn. However, there is not enough in-depth research on the effects of soybean and corn strip cropping modes on inter-root microbial communities and crop yield traits and their correlations, and the functional genes of inter-rootmicrobes. In this study, the structural composition and functional genes of soil inter-root microbial communities under different strip cropping patterns of soybean and maize, Mode 1, Mode 2, and Mode 3, were studied using macro-genome high-throughput sequencing, and the yield traits of soybean and maize were determined and the correlation was understood, taking Zhonglian Soybean 1505 and Tiyu 108 maize as test materials at the experimental base of the Research Institute of Agricultural and Livestock Sciences, Xing'anmeng, Inner Mongolia. correlation between the two. The following results were found. Redundancy analysis found that rhizospheremicroorganisms aremainly bacteria, followed by viruses, followed by archaea, and fungi are the least. Relatively speaking, viruses and bacteria are more susceptible to the influence of planting patterns than fungi. Alpha diversity analysis shows that the diversity index changes significantly during the peak flowering period. There is no difference between the other two stages. The diversity of maize rhizosphere microorganisms under Mode 1 or Mode 2 modes is higher than that under Mode 3 modes, while the diversity of soybean rhizosphere microorganisms under Mode 3 modes is higher than the other two modes. Profiling analysis found that different growth periods and planting patterns lead to varying degrees of changes in community structure. The dominant bacterial phyla include Pseudomonas, Actinobacteria, Bacteroidetes, and Streptococcus. The dominant genera of bacteria are rhizobia, pseudomonas, erythrobacteria, and pseudomonas. The relative abundance of slow root rhizobia in the three growth stages of maize rhizosphere is relatively low. The results of yield traits showed that corn yield under Mode 1 and Mode 2 planting was significantly higher than that under pattern III; soybean yield under pattern III planting was significantly higher than that under the other two patterns, indicating that pattern I or II planting is suitable for corn production, while Mode 3 planting mode is more suitable for soybean production. Correlation analysis showed that S_Massilis_putida was significantly and positively correlated with maize yield, and S_lysobacter_capsici was highly significantly and significantly positively correlated with the number of soybean plants per square meter, and the number of plants in 2 square meters. This study contributes to our further understanding of the classification of inter-rootmicroorganisms and their functional relationships in maize and soybean under different conditions. [ABSTRACT FROM AUTHOR]

This study aimed to explore the curative effect of curcumin on liver fibrosis and its correlation with the gut–liver axis in animal models. Histological staining was utilized to conduct histological analysis of the liver and intestine. An automatic biochemical analyzer or enzyme-linked immunosorbent assay system was utilized for analyzing the biochemical indexes in mice. Western blotting was employed to examine the level of relevant proteins. Furthermore, 16S rRNA high-throughput sequencing was performed to explore the impact of curcumin on intestinal microorganisms in rats with liver fibrosis. Ultrahigh-performance liquid chromatography with quadrupole-orbitrap mass spectrometry was utilized to analyze the effect of curcumin on rat feces metabolites. Our results showed that curcumin reduced the formation of collagen fibers caused by carbon tetrachloride in a dose-dependent manner. In addition, curcumin was able to restore intestinal permeability in rats with liver fibrosis. By adopting α diversity analysis (Chao 1 index, Shannon index, and Simpson index), we observed that both the diversity and the abundance of intestinal flora in rats with liver fibrosis were increased. The principal component analysis diagram demonstrated that curcumin could enhance the abundance and diversity of intestinal flora, and also restore the composition of model rat flora, which was similar to that in normal rats, thereby correcting the imbalance of flora in rats with liver fibrosis. In addition, curcumin regulated feces metabolites and their signaling pathways, including glycerophospholipid metabolism, pantothenate and CoA biosynthesis. Our findings suggest that curcumin exhibits antiliver fibrosis effects, and its antiliver fibrosis effects might correlate with gut–liver axis. [ABSTRACT FROM AUTHOR]

Background Bacterial image analysis plays a vital role in various fields, providing valuable information and insights for studying bacterial structural biology, diagnosing and treating infectious diseases caused by pathogenic bacteria, discovering and developing drugs that can combat bacterial infections, etc. As a result, it has prompted efforts to automate bacterial image analysis tasks. By automating analysis tasks and leveraging more advanced computational techniques, such as deep learning (DL) algorithms, bacterial image analysis can contribute to rapid, more accurate, efficient, reliable, and standardised analysis, leading to enhanced understanding, diagnosis, and control of bacterial-related phenomena. Methods Three object detection networks of DL algorithms, namely SSD-MobileNetV2, EfficientDet, and YOLOv4, were developed to automatically detect Escherichia coli (E. coli) bacteria from microscopic images. The multi-task DL framework is developed to classify the bacteria according to their respective growth stages, which include rod-shaped cells, dividing cells, and microcolonies. Data preprocessing steps were carried out before training the object detection models, including image augmentation, image annotation, and data splitting. The performance of the DL techniques is evaluated using the quantitative assessment method based on mean average precision (mAP), precision, recall, and F1-score. The performance metrics of the models were compared and analysed. The best DL model was then selected to perform multi-task object detections in identifying rod-shaped cells, dividing cells, and microcolonies. Results The output of the test images generated from the three proposed DL models displayed high detection accuracy, with YOLOv4 achieving the highest confidence score range of detection and being able to create different coloured bounding boxes for different growth stages of E. coli bacteria. In terms of statistical analysis, among the three proposed models, YOLOv4 demonstrates superior performance, achieving the highest mAP of 98% with the highest precision, recall, and F1-score of 86%, 97%, and 91%, respectively. Conclusions This study has demonstrated the effectiveness, potential, and applicability of DL approaches in multi-task bacterial image analysis, focusing on automating the detection and classification of bacteria from microscopic images. The proposed models can output images with bounding boxes surrounding each detected E. coli bacteria, labelled with their growth stage and confidence level of detection. All proposed object detection models have achieved promising results, with YOLOv4 outperforming the other models.

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]

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]

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]

• 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]

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]

Background: Escherichia coli consists of a wide range of strains with huge diversity in their genome, distributed in nature and the alimentary tracts of animals and humans. This study analyzed the phylogenetic group determination and genetic diversity of E. coli strains isolated from domestic animals and human clinical samples. Methods: Twenty E. coli isolates from domestic animals were analyzed for phylogenetic grouping. Also, 100 clinical samples and 20 animal samples were evaluated by the enterobacterial repetitive intergenic consensus-polymerase chain reaction (ERIC-PCR) technique. The results and the similarity between the strains were determined based on the Dice similarity coefficient in the SAHN program of the NTSYS-pc software. Results: The frequency of phylogroups among animal samples were A = 5%, B1 = 65%, B2 = 20%, and D = 10%. Based on the ERIC-PCR results, the clinical strains were allocated into 19 clusters. Most strains were in the E7 cluster. Fifty percent of the E. coli isolated from animal specimens belonged to the E4 group, and the lowest number of strains was in the E3 and E5 (1 strain) groups. Conclusion: The results confirmed the efficiency and usefulness of the ERIC-PCR tool for the identification and classification of bacteria. Also, we demonstrated the most phylogroup among animal samples. [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]

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]

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]

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]

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]

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: 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]

Previous studies have analyzed bacterial taxa associated with rhizospheric cinnamon forest soil. Cicer montbretii Jaub. & Spach (C. montbretii), an endemic species for the region of Strandzha, was analyzed using a metagenomic approach. The diversity of endophytic microorganisms is essential for ecology, as it makes it possible to understand in detail the plant-microbial interactions. The objectives of this study were to isolate and identify endophytic bacteria from the roots of C. montbretii associated with cinnamon forest (A1) and yellow earth podzolic soils (A2). A study on endophytic bacteria in the roots of C. montbretii legume plant, done by assessing its PCR amplicon of 16S rDNA with the Illumina metagenomics technique, used to generate a total of 81,782 reads from the samples. The analyses revealed that Proteobacteria were similar in cinnamon forest and in yellow earth podzolic soils. Bacteriodota prevailed in yellow earth podzolic soils. Firmicutes and Actinobacteria were the common genera in cinnamon forest soil. The roots of the plants grown in A1 soil had the largest composition with Proteobacteria 78%, which consisted of the genus— Erwinia 13%, Pseudomonas 11%, Shingomonas 11%, Rhizobium 5%, and Bradirhyzobium 4%. In the A2 soil prevailed Proteobacteria 76% and Bacterioidota 15%. The most abandoned genus in A2 was Orchobactrum 20%, Stenotrophomonas 15%, Sphingobacterium 12%, Serratia 4%, and Flavobactrerium 4%. The α)diversity analysis indicated the richness and inverse Simpson diversity index of the bacterial endophyte communities for the root tissues to be 6.551 for A1 and 4,692 for A2 soil, respectively. The endophytic bacterial communities differed among the two soil samples, indicating that the soil and the climatic conditions considerably affected the entire endophytic community. [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]

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]

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]

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]

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]