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Feces

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Combining deep learning and fluorescence imaging to automatically identify fecal contamination on meat carcasses.

Scientific reports
Food safety and foodborne diseases are significant global public health concerns. Meat and poultry carcasses can be contaminated by pathogens like E. coli and salmonella, by contact with animal fecal matter and ingesta during slaughter and processing...

Fast and accurate automated recognition of the dominant cells from fecal images based on Faster R-CNN.

Scientific reports
Fecal samples can easily be collected and are representative of a person's current health state; therefore, the demand for routine fecal examination has increased sharply. However, manual operation may pollute the samples, and low efficiency limits t...

Merged Affinity Network Association Clustering: Joint multi-omic/clinical clustering to identify disease endotypes.

Cell reports
Although clinical and laboratory data have long been used to guide medical practice, this information is rarely integrated with multi-omic data to identify endotypes. We present Merged Affinity Network Association Clustering (MANAclust), a coding-fre...

Identification the source of fecal contamination for geographically unassociated samples with a statistical classification model based on support vector machine.

Journal of hazardous materials
The bacterial diversity and corresponding biological significance revealed by high-throughput sequencing contribute massive information to source tracking of fecal contamination. The performances of classification models on predicting the fecal sourc...

Developing a Neural Network Model for a Non-invasive Prediction of Histologic Activity in Inflammatory Bowel Diseases.

The Turkish journal of gastroenterology : the official journal of Turkish Society of Gastroenterology
BACKGROUND: Colonoscopy with biopsy is the "gold" standard for evaluating disease activity in inflammatory bowel diseases (IBD). Current research is geared toward finding non-invasive, cost-efficient methods that estimate disease activity. We aimed t...

Machine Learning Supports Automated Digital Image Scoring of Stool Consistency in Diapers.

Journal of pediatric gastroenterology and nutrition
BACKGROUND/AIMS: Accurate stool consistency classification of non-toilet-trained children remains challenging. This study evaluated the feasibility of automated classification of stool consistencies from diaper photos using machine learning (ML).

Further evaluation and validation of the VETSCAN IMAGYST: in-clinic feline and canine fecal parasite detection system integrated with a deep learning algorithm.

Parasites & vectors
BACKGROUND: Fecal examinations in pet cats and dogs are key components of routine veterinary practice; however, their accuracy is influenced by diagnostic methodologies and the experience level of personnel performing the tests. The VETSCAN IMAGYST s...

Host variables confound gut microbiota studies of human disease.

Nature
Low concordance between studies that examine the role of microbiota in human diseases is a pervasive challenge that limits the capacity to identify causal relationships between host-associated microorganisms and pathology. The risk of obtaining false...

Machine Learning Strategy for Gut Microbiome-Based Diagnostic Screening of Cardiovascular Disease.

Hypertension (Dallas, Tex. : 1979)
Cardiovascular disease (CVD) is the number one leading cause for human mortality. Besides genetics and environmental factors, in recent years, gut microbiota has emerged as a new factor influencing CVD. Although cause-effect relationships are not cle...

Sequence-enabled community-based microbial source tracking in surface waters using machine learning classification: A review.

Journal of microbiological methods
The development of Microbial Source Tracking (MST) technologies was borne out of necessity. This was largely due to the: 1) inadequacies of the fecal indicator bacterial paradigm, 2) fact that many fecal bacteria can survive and often grow in the env...