AIMC Topic: Bacteria

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MorphoCol: An ontology-based knowledgebase for the characterisation of clinically significant bacterial colony morphologies.

Journal of biomedical informatics
BACKGROUND: One of the major concerns of the biomedical community is the increasing prevalence of antimicrobial resistant microorganisms. Recent findings show that the diversification of colony morphology may be indicative of the expression of virule...

Prediction and analysis of quorum sensing peptides based on sequence features.

PloS one
Quorum sensing peptides (QSPs) are the signaling molecules used by the Gram-positive bacteria in orchestrating cell-to-cell communication. In spite of their enormous importance in signaling process, their detailed bioinformatics analysis is lacking. ...

Predicting fecal sources in waters with diverse pollution loads using general and molecular host-specific indicators and applying machine learning methods.

Journal of environmental management
In this study we use a machine learning software (Ichnaea) to generate predictive models for water samples with different concentrations of fecal contamination (point source, moderate and low). We applied several MST methods (host-specific Bacteroide...

Fecal gut microbiota and amino acids as noninvasive diagnostic biomarkers of Pediatric inflammatory bowel disease.

Gut microbes
BACKGROUND AND AIMS: Fecal calprotectin (FCP) has limited specificity as diagnostic biomarker of pediatric inflammatory bowel disease (IBD), leading to unnecessary invasive endoscopies. This study aimed to develop and validate a fecal microbiota and ...

Meta-analysis of shotgun sequencing of gut microbiota in obese children with MASLD or MASH.

Gut microbes
Alterations in the gut microbiome affect the development and severity of metabolic dysfunction-associated steatotic liver disease (MASLD) or metabolic dysfunction-associated steatohepatitis (MASH). We analyzed microbiomes of obese children with and w...

Bacterial identification in SERS-integrated microfluidics using CNN-driven 2D classification of 1D spectra.

Talanta
Bacterial sensing involves complex and variable samples that require advanced handling and analytical methods. To address these challenges, machine learning-especially deep learning-and SERS-based microfluidics have shown great promise. While previou...

Deep learning-based detection of bacterial swarm motion using a single image.

Gut microbes
Motility is a fundamental characteristic of bacteria. Distinguishing between swarming and swimming, the two principal forms of bacterial movement, holds significant conceptual and clinical relevance. Conventionally, the detection of bacterial swarmin...

Machine-learning assisted discovery unveils novel interplay between gut microbiota and host metabolic disturbance in diabetic kidney disease.

Gut microbes
Diabetic kidney disease (DKD) is a serious healthcare dilemma. Nonetheless, the interplay between the functional capacity of gut microbiota and their host remains elusive for DKD. This study aims to elucidate the functional capability of gut microbio...

Machine learning assisted paper-based fluorescent sensor array with metal-doped multicolor carbon quantum dots for identification and inactivation of bacteria.

Talanta
Bacterial infection is a thorny threat in a variety of fields, including medicine, environment, food, and agriculture. A multifunctional platform that meets the demands of both bacterial identification and real-time inactivation is urgently needed. T...

Machine learning classification of quorum sensing-induced bacterial aggregation using flow rate assays on paper chips toward bacterial species identification in potable water sources.

Biosensors & bioelectronics
Preventing waterborne disease caused by bacteria is especially important in low-resource settings, where skilled personnel and laboratory equipment are scarce. This work reports a straightforward method for classifying bacterial species by monitoring...