Search Results (474)

We developed a machine-learning model for the International Classification of Diseases, 10th Revision (ICD-10) classification using data from 5108 patients. Nine features, including age, gender, BMI, and vital signs, were extracted to classify the top three ICD-10 categories: intestinal infections, tuberculosis, and other bacterial diseases. Decision trees, random forest, and XGBoost models were tested using the synthetic minority over-sampling technique (SMOTE) and class weights to minimize class imbalance. Five-fold cross-validation was used using the training and testing datasets in a data ratio of 80:20. The random forest model with class weights showed the best performance. Shapley additive explanations (SHAP) analysis highlighted body-mass index (BMI), gender, and pulse as key features. The developed model showed potential for enhancing ICD-10 classification through real-time and personalized medical applications. Full article

Bacillus cereus AS_3 and Bacillus cereus AS_5 are bacterial endophytes isolated from sterilized leaves of the medical plant Alectra sessiliflora, which were previously identified using 16S rRNA sequencing. Here, we present the whole-genome sequencing and annotation of strains AS_3 and AS_5, the first genome report of Bacillus cereus strains from A. sessiliflora. The genome of strain AS_3 has 59 contigs, 5 503 542 bp draft circular chromosome, an N₅₀ of 211,274 bp, and an average G+C content of 35.2%; whereas strain AS_5 has 38 contigs, 5,510,121 bp draft circular chromosome, an N₅₀ of 536,033 bp, and an average G+C content of 35.2%. A total of 5679 protein-coding genes, 62 genes coding for RNAs, and 122 pseudogenes in the strain AS_3 genome were identified by the National Center for Biotechnology Information Prokaryotic Annotation pipeline, whereas a total of 5688 gene protein-coding genes were identified in AS_5, with 60 genes coding for RNAs and 120 pseudogenes. Phenotypic analysis and whole-genome sequencing analysis showed that AS_3 and AS_5 share similar characteristics, including Gram-positive, motile, rod-shaped, and endospore-forming have shown a high sequence similarity with Bacillus cereus, type strain ATCC 14579^T. Strains AS_3 and AS_5 had genomic digital DNA–DNA hybridization (dDDH) with the type strain Bacillus cereus ATCC 14579^T of 85.8% and 86%, respectively, and average nucleotide identities (ANIs) of 98% and 98.01%, respectively. Phylogenomic analysis confirmed that strains AS_3 and AS_5 share very similar genomic and phenotypic characteristics, and are closely related to the type strain Bacillus cereus type strain ATCC 14579^T, supporting their classification within the Bacillus cereus species. A total of 10 secondary metabolite gene clusters, including siderophore type petrobactin, terpene type molybdenum cofactor, non-ribosomal peptide synthetase (NRPS) type bacillibactin, and β-lactone type fengycin, were predicted using AntiSMASH software (version 5.0). Putative genes potentially involved in bioremediation and endophytic lifestyle were identified in the genome analysis. Genome sequencing of Bacillus cereus AS_3 and Bacillus cereus AS_5 has provided genomic information and demonstrated potential biotechnological applications. Full article

The oral microbiome has garnered significant interest in recent years. Its profound implications for oral and systemic diseases have led to a considerable amount of research and analysis aimed at providing deeper insights into its composition. This study aimed to characterize oral bacterial communities comprehensively based on microorganisms indexed in the Human Oral Microbiome Database, which was systematically analyzed, and its taxonomic classification was used to describe the diversity of indexed bacteria in the oral cavity. A total of 522 bacteria were considered for the analysis. Among these, 49.04% were named, whereas 29.12% represent uncultivated phylotypes. The taxonomic characterization revealed that more than 80% of total taxa are distributed across five phyla: Bacillota, Bacteroidota, Actinomycetota, Pseudomonadota, and Fusobacteriota. Of these, Bacillota and Bacteroidota are the dominant ones with, respectively, 166 (31.80%) and 96 (18.39%) bacterial taxa. With the recent advances in genomics and bioinformatics, the HOMD is constantly updated, further enhancing our understanding of the bacterial community of the oral microbiome. However, the considerable diversity of the oral microbiome may present analytical challenges and the possible misperception of the implications of closely related species/subspecies in oral and systemic health. Full article

During infectious disease outbreaks, acquiring genetic data across various kingdoms offers essential information to tailor precise treatment methodologies and bolster clinical, epidemiological, and public health awareness. Metagenomics sequencing has paved the way for personalized treatment approaches and streamlined the monitoring process for both co-infections and opportunistic infections. In this study, we conducted long-read metagenomic DNA sequencing on mpox-like lesion pustules from six suspected patients who were positive and confirmed to be infected with MPXV during the MPXV subclade Ib outbreak in the Eastern Democratic Republic of the Congo. The sequenced data were taxonomically classified as bacterial, fungal, and viral in composition. Our results show a wide spectrum of microorganisms present in the lesions. Bacteria such as Corynebacterium amycolatum, Gardnerella vaginalis, Enterococcus faecium, Enterobacter clocae, Staphylococcus epidermidis, and Stenotrophomonas maltophilia were found in the lesions. The viral classification of the reads pointed out the absolute predominance of the monkeypox virus. Taken together, the outcomes of this investigation underscore the potential involvement of microorganisms in mpox lesions and the possible role that co-infections played in exacerbating disease severity and transmission during the MPXV subclade Ib outbreak. Full article

Background/Objectives: 16S ribosomal RNA sequencing has, for several years, been the main means of identifying bacterial and archaeal species. Low-throughput Sanger sequencing is often used for the detection and identification of microbial species, but this technique has several limitations. The use of high-throughput sequencers may be a good alternative to improve patient identification, especially for polyclonal infections and management. Kraken 2 and KrakenUniq are free, high-throughput tools providing a very rapid and accurate classification for metagenomic analyses. However, Kraken 2 can present false-positive results relative to KrakenUniq, which can be limiting in hospital settings requiring high levels of accuracy. The aim of this study was to establish an alternative next-generation sequencing technique to replace Sanger sequencing and to confirm that KrakenUniq is an excellent analysis tool that does not present false results relative to Kraken 2. Methods: DNA was extracted from reference bacterial samples for Laboratory Quality Controls (QCMDs) and the V2-V3 and V3-V4 regions of the 16S ribosomal gene were amplified. Amplified products were sequenced with the Illumina 16S Metagenomic Sequencing protocol with minor modifications to adapt and sequence an Illumina 16S library with a small 500-cycle nano-flow cell. The raw files (Fastq) were analyzed on a commercial Smartgene platform for comparison with Kraken 2 and KrakenUniq results. KrakenUniq was used with a standard bacterial database and with the 16S-specific Silva138, RDP11.5, and Greengenes 13.5 databases. Results: Seven of the eight (87.5%) QCMDs were correctly sequenced and identified by Sanger sequencing. The remaining QCMD, QCMD6, could not be identified through Sanger sequencing. All QCMDs were correctly sequenced and identified by MiSeq with the commercial Smartgene analysis platform. QCMD6 contained two bacteria, Acinetobacter and Klebsiella. KrakenUniq identification results were identical to those of Smartgene, whereas Kraken 2 yielded 25% false-positive results. Conclusions: If Sanger identification fails, MiSeq with a small nano-flow cell is a very good alternative for the identification of bacterial species. KrakenUniq is a free, fast, and easy-to-use tool for identifying and classifying bacterial infections. Full article

Foodborne diseases represent a significant public health challenge worldwide. This study systematically analyzed the temporal dynamics, key predictors, and seasonal patterns of pathogen-specific foodborne diseases using a dataset of 56,970 cases from Zhejiang Province, China, spanning 2014 to 2023. A comprehensive set of 91 candidate variables was constructed by integrating epidemiological, environmental, socioeconomic, and agricultural data. Lasso regression was employed to identify 41 important predictors. Based on these variables, supervised machine learning models (Random Forest and XGBoost) were trained and evaluated, achieving training set classification accuracies of 86% and 87%, respectively, demonstrating robust performance. Feature importance analysis revealed that patient age, food type, climate policy, and processing methods were the most influential determinants, highlighting the combined impact of host, exposure, and environmental factors on disease risk. The results demonstrated significant shifts in the pathogen spectrum over the past decade, including a steady decline in Vibrio parahaemolyticus, an increase in Salmonella after 2016, and persistent seasonal peaks in Norovirus and Vibrio parahaemolyticus during warmer months. Seasonal ARIMA modeling and time-series decomposition further confirmed the critical role of seasonal and trend components in bacterial incidence. Overall, this study demonstrates the value of integrating machine learning and time-series analysis for pathogen-specific surveillance, risk prediction, and targeted public health interventions. Full article

Mammals harbor diverse microbial communities across different body sites, which are crucial to physiological functions and host homeostasis. This study aimed to understand the structure and function of gut and lung microbiota of pregnant Pomona leaf-nosed bats using V3-V4 16S rRNA gene sequencing. Of the 350 bats captured using mist nets in Yunnan, nine pregnant Pomona leaf-nosed bats with similar body sizes were chosen. Gut and lung samples were aseptically collected from each bat following cervical dislocation and placed in sterile cryotubes before microbiota investigation. Microbial taxonomic annotation revealed that the phyla Firmicutes and Actinobacteriota were most abundant in the guts of pregnant bats, whereas Proteobacteria and Bacteroidota were abundant in the lungs. Family-level classification revealed that Bacillaceae, Enterobacteriaceae, and Streptococcaceae were more abundant in the guts, whereas Rhizobiaceae and Burkholderiaceae dominated the lungs. Several opportunistic and potentially pathogenic bacterial genera were present at the two body sites. Bacillus, Cronobacter, and Corynebacterium were abundant in the gut, whereas Bartonella, Burkholderia, and Mycoplasma dominated the lungs. Alpha diversity analysis (using Chao1 and Shannon indices) within sample groups examined read depth and species richness, whereas beta diversity using unweighted and weighted UniFrac distance metrics revealed distinct clustering patterns between the two groups. LEfSe analysis revealed significantly enriched bacterial taxa, indicating distinct microbial clusters within the two body sites. The two Random Forest classifiers (MDA and MDG) evaluated the importance of microbial features in the two groups. Comprehensive functional annotation provided insights into the microbiota roles in metabolic activities, human diseases, signal transduction, etc. This study contributes to our understanding of the microbiota structure and functional potential in pregnant wild bats, which may have implications for host physiology, immunity, and the emergence of diseases. Full article

Gram-negative Burkholderia bacteria are known for causing diseases in humans, animals, and plants, and high intrinsic resistance to antibiotics. Phage therapy is a promising alternative to control multidrug-resistant bacterial pathogens. Here, we present an overview of Burkholderia phage characteristics, host specificity, genomic classification, and therapeutic potentials across medical, veterinary, and agricultural systems. We evaluate the efficacy and limitations of current phage candidates, the biological and environmental barriers of phage applications, and the phage cocktail strategy. We highlight the innovations on the development of targeted phage delivery systems and the transition from the exploration of clinical phage therapy to plant disease management, advocating integrated disease control strategies. Full article

Hippophae rhamnoides subsp. sinensis Rousi has high ecological and medicinal value, and it is an important plant resource unique to the arid regions of Northwest China. Exploring the influence of climate characteristics and soil factors on the composition, diversity, and function of the rhizosphere bacterial community of Chinese seabuckthorn is of great value for developing and popularizing characteristic plant resources in the arid regions of Northwest China. In this study, the rhizosphere soil of 13 Chinese seabuckthorn distribution areas in the northwest of China was taken as the research object, the bacterial community map was constructed based on 16S rRNA gene high-throughput sequencing technology, and the species abundance composition, structural diversity, molecular co-occurrence network, and phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt), as well as the function of rhizosphere soil bacterial community, were systematically studied. Combined with Mantel test and redundancy analysis (RDA), the key habitat factors driving the rhizosphere soil bacterial community structure of Chinese seabuckthorn were explored. The results showed that: (1) The number of amplicon sequence variants (ASVs) in rhizosphere soil bacterial community of Chinese seabuckthorn was the highest in S2(3072) and the S12(3637), and the lowest in the S11(1358) and S13(1996). The rhizosphere soil bacterial community was primarily composed of Proteobacteria, Actinobacteriota, and Acidobacteriota. Except for the S6 and S11 habitats, the dominant bacterial genera were mainly Achromobacter, Acidobacter (RB41), and Sphingomonas. (2) The α and β diversity of rhizosphere soil bacterial communities of Chinese seabuckthorn across 13 distribution areas were significantly different. The number of operational taxonomic units (OTUs), Ace index, and Chao 1 index of soil bacterial community in the S12 distribution area are the highest, and they are the lowest in S11 distribution area, with significant differences. The aggregation of soil bacterial communities in the S5 and S10 distribution areas is the highest, while it is the lowest in the S6 and S11 distribution areas. (3) PICRUSt function classification of soil bacteria showed that Metabolism and Genetic Information Processing functions were the strongest across all distribution areas, with S10 exhibiting higher functional capacity than other areas and S11 showing the weakest. (4) Cluster analysis revealed that soil bacteria across the 13 distribution areas were clustered into two groups, with S10 and S12 distribution areas as one group (Group 1) and the remaining 11 distribution areas as another group (Group 2). (5) Redundancy analysis revealed that pH was the key soil environmental factor driving the rhizosphere soil bacterial community α-diversity of Chinese seabuckthorn, followed by altitude (ALT) and soil water content (SWC). In summary, Chinese seabuckthorn prefers neutral to alkaline soils, and environmental factors play an important role in driving bacterial diversity, community structure, functional profiles, and co-occurrence networks in rhizosphere soil of Chinese seabuckthorn. Full article

Background: Peptides are a class of molecules that can be presented as good antimicrobials and with mechanisms that avoid resistance, and the design of peptides with good activity can be complex and laborious. The study of their quantitative structure–activity relationships through machine learning algorithms can shed light on a rational and effective design. Methods: Information on the antimicrobial activity of peptides was collected, and their structures were characterized by molecular descriptors generation to design regression and classification models based on machine learning algorithms. The contribution of each descriptor in the generated models was evaluated by determining its relative importance and, finally, the antimicrobial activity of new peptides was estimated. Results: A structured database of antimicrobial peptides and their descriptors was obtained, with which 56 machine learning models were generated. Random Forest-based models showed better performance, and of these, regression models showed variable performance (R² = 0.339–0.574), while classification models showed good performance (MCC = 0.662–0.755 and ACC = 0.831–0.877). Those models based on bacterial groups showed better performance than those based on the entire dataset. The properties of the new peptides generated are related to important descriptors that encode physicochemical properties such as lower molecular weight, higher charge, propensity to form alpha-helical structures, lower hydrophobicity, and higher frequency of amino acids such as lysine and serine. Conclusions: Machine learning models allowed to establish the structure–activity relationships of antimicrobial peptides. Classification models performed better than regression models. These models allowed us to make predictions and new peptides with high antimicrobial potential were proposed. Full article

Soil salinity adversely affects crop growth and development, leading to reduced soil fertility and agricultural productivity. The indigenous salt-tolerant plant growth-promoting rhizobacteria (PGPR), as a sustainable microbial resource, do not only promote growth and alleviate salt stress, but also improve the soil microecology of crops. The strain H5 isolated from saline-alkali soil in Bachu of Xinjiang was studied through whole-genome analysis, functional annotation, and plant growth-promoting, salt-tolerant trait gene analysis. Phylogenetic tree analysis and 16S rDNA sequencing confirmed its classification within the genus Halomonas. Functional annotation revealed that the H5 genome harbored multiple functional gene clusters associated with plant growth promotion and salt tolerance, which were critically involved in key biological processes such as bacterial survival, nutrient acquisition, environmental adaptation, and plant growth promotion. The pot experiment under moderate salt stress demonstrated that seed inoculation with Halomonas sp. H5 not only significantly improved the agronomic traits of tomato seedlings, but also increased plant antioxidant enzyme activities under salt stress. Additionally, soil analysis revealed H5 treatment significantly decreased the total salt (9.33%) and electrical conductivity (8.09%), while significantly improving organic matter content (11.19%) and total nitrogen content (10.81%), respectively (p < 0.05). Inoculation of strain H5 induced taxonomic and functional shifts in the rhizosphere microbial community, increasing the relative abundance of microorganisms associated with plant growth-promoting and carbon and nitrogen cycles, and reduced the relative abundance of the genera Alternaria (15.14%) and Fusarium (9.76%), which are closely related to tomato diseases (p < 0.05). Overall, this strain exhibits significant potential in alleviating abiotic stress, enhancing growth, improving disease resistance, and optimizing soil microecological conditions in tomato plants. These results provide a valuable microbial resource for saline soil remediation and utilization. Full article

Background/Objectives: In recent work, we have demonstrated that principal component analysis (PCA) and Fourier Transformation Infrared (FTIR) spectra are powerful tools for analyzing the changes in microorganisms at the biomolecular level to detect changes in bacteria with resistance to antibiotics. Here biochemical structural changes in Staphylococcus aureus were analyzed over exposure time with the goal of identifying trends inside the samples that have been exposed to antibiotics for increasing amounts of time and developed resistance. Methods: All studied data was obtained from FTIR spectra of samples with induced antibiotic resistance to either Azithromycin, Oxacillin, or Trimethoprim/Sulfamethoxazole following the evolution of this development over four increasing antibiotic exposure periods. Results: The processing and data analysis with machine learning algorithms performed on this FTIR spectral database allowed for the identification of patterns across minimum inhibitory concentration (MIC) values associated with different exposure times and both clusters from hierarchical classification and PCA. Conclusions: The results enable the observation of resistance development pathways for the sake of knowing the present stage of resistance of a bacterial sample. This is carried out via machine learning methods for the purpose of faster and more effective infection treatment in healthcare settings. Full article

Background/Objectives: Cardiovascular diseases remain the leading cause of global mortality, with the gut microbiome emerging as a critical factor. This study aimed to characterize gut microbiome composition and metabolic pathways in individuals with low cardiovascular risk (LCR) compared to healthy controls to reveal insights into early disease shifts. Methods: We performed shotgun metagenomic sequencing on fecal samples from 25 LCR individuals and 25 matched healthy controls. Participants underwent a comprehensive cardiovascular evaluation. Taxonomic classification used MetaPhlAn 4, and functional profiling employed HUMAnN 3. Results: Despite similar alpha diversity, significant differences in bacterial community structure were observed between groups (PERMANOVA, p < 0.05). The LCR group showed enrichment of Faecalibacterium prausnitzii (p = 0.035), negatively correlating with atherogenic markers, including ApoB (r = −0.3, p = 0.025). Conversely, Fusicatenibacter saccharivorans positively correlated with ApoB (r = 0.4, p = 0.006). Metabolic pathway analysis revealed upregulation of nucleotide biosynthesis, glycolysis, and sugar degradation pathways in the LCR group, suggesting altered metabolic activity. Conclusions: We identified distinct gut microbiome signatures in LCR individuals that may represent early alterations associated with cardiovascular disease development. The opposing correlations between F. prausnitzii and F. saccharivorans with lipid parameters highlight their potential roles in cardiometabolic health. These findings suggest gut microbiome signatures may serve as indicators of early metabolic dysregulation preceding clinically significant cardiovascular disease. Full article

The analysis of soil nematode communities provides information on their impact on soil quality and the health of different agricultural cropping systems and soil management practices, which is necessary to evaluate their sustainability. Here, we evaluated the status of nematode communities and trophic groups’ abundance in fifteen fields hosting different cropping systems and managed according to organic or conventional practices. The nematode population densities differed significantly across cropping systems and management types covering various European climatic zones (spanning 121 to 799 individuals per sample). Population density was affected by the duration of the cropping system, with the lowest value in the vegetable cropping system (on average about 300 individuals) and the highest in the long-term fruiting system (on average more than 500 individuals). The occurrence and abundance of the different trophic groups was partly dependent on the cropping system or the management method, particularly for the bacteria, fungal and plant feeders. The taxonomical classification of a subset of samples allowed us to identify 22 genera and one family (Dorylaimidae) within the five trophic groups. Few taxa were observed in all fields and samples (i.e., Rhabditis and Cephalobus), while Aphelenchoides or Pratylenchus were present in the majority of samples. Phosphorus content was the only soil chemical parameter showing a positive correlation with total nematode population and bacterial feeders’ absolute abundance. Based on the nematological ecological indices, all three cropping systems were characterized by disturbed soil conditions, conductive and dominated by bacterivorous nematodes. This knowledge could lead to a choice of soil management practices that sustain a transition toward healthy soils. Full article

This paper describes the development of a CNN model for the analysis of chest X-rays and the automated diagnosis of pneumonia, bacterial or viral, and lung pathologies resulting from COVID-19, offering new insights for further research through the development of an AI-based diagnostic tool, which can be automatically implemented and made available for rapid differentiation between normal pneumonia and COVID-19 starting from X-ray images. The model developed in this work is capable of performing three-class classification, achieving 97.48% accuracy in distinguishing chest X-rays affected by COVID-19 from other pneumonias (bacterial or viral) and from cases defined as normal, i.e., without any obvious pathology. The novelty of our study is represented not only by the quality of the results obtained in terms of accuracy but, above all, by the reduced complexity of the model in terms of parameters and a shorter inference time compared to other models currently found in the literature. The excellent trade-off between the accuracy and computational complexity of our model allows for easy implementation on numerous embedded hardware platforms, such as FPGAs, for the creation of new diagnostic tools to support medical practice. Full article