scholarly journals Usefulness of Machine Learning-Based Gut Microbiome Analysis for Identifying Patients with Irritable Bowels Syndrome

2020 ◽  
Vol 9 (8) ◽  
pp. 2403
Author(s):  
Hirokazu Fukui ◽  
Akifumi Nishida ◽  
Satoshi Matsuda ◽  
Fumitaka Kira ◽  
Satoshi Watanabe ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Gut Microbiome ◽  
Clinical Symptoms ◽  
Short Chain Fatty Acids ◽  
Gas Chromatography Mass Spectrometry ◽  
Healthy Controls ◽  
Fecal Microbiome ◽  
Microbiome Analysis ◽  
Model Based

Irritable bowel syndrome (IBS) is diagnosed by subjective clinical symptoms. We aimed to establish an objective IBS prediction model based on gut microbiome analyses employing machine learning. We collected fecal samples and clinical data from 85 adult patients who met the Rome III criteria for IBS, as well as from 26 healthy controls. The fecal gut microbiome profiles were analyzed by 16S ribosomal RNA sequencing, and the determination of short-chain fatty acids was performed by gas chromatography–mass spectrometry. The IBS prediction model based on gut microbiome data after machine learning was validated for its consistency for clinical diagnosis. The fecal microbiome alpha-diversity indices were significantly smaller in the IBS group than in the healthy controls. The amount of propionic acid and the difference between butyric acid and valerate were significantly higher in the IBS group than in the healthy controls (p < 0.05). Using LASSO logistic regression, we extracted a featured group of bacteria to distinguish IBS patients from healthy controls. Using the data for these featured bacteria, we established a prediction model for identifying IBS patients by machine learning (sensitivity >80%; specificity >90%). Gut microbiome analysis using machine learning is useful for identifying patients with IBS.

scholarly journals Fecal Microbiota Transplantation Modulates the Gut Flora Favoring Patients With Functional Constipation

2021 ◽  
Vol 12 ◽  
Author(s):  
Xueying Zhang ◽  
Ning Li ◽  
Qiyi Chen ◽  
Huanlong Qin
Keyword(s):  
Systemic Inflammation ◽  
Clinical Symptoms ◽  
Fecal Microbiota Transplantation ◽  
Bacterial Genome ◽  
Functional Constipation ◽  
Short Chain Fatty Acids ◽  
Gas Chromatography Mass Spectrometry ◽  
Enzyme Linked Immunosorbent Assay ◽  
Inflammatory Factor ◽  
Fecal Microbiome

Intestinal dysmotility is common in many diseases and is correlated with gut microbiota dysbiosis and systemic inflammation. Functional constipation (FC) is the most typical manifestation of intestinal hypomotility and reduces patients’ quality of life. Some studies have reported that fecal micriobiota transplantation (FMT) may be an effective and safe therapy for FC as it corrects intestinal dysbiosis. This study was conducted to evaluate how FMT remodels the gut microbiome and to determine a possible correlation between certain microbes and clinical symptoms in constipated individuals. Data were retrospectively collected on 18 patients who underwent FMT between January 1, 2019 and June 30, 2020. The fecal bacterial genome was detected by sequencing the V3–V4 hypervariable regions of the 16S rDNA gene. Fecal short chain fatty acids (SCFAs) were detected by gas chromatography-mass spectrometry, and serum inflammatory factor concentrations were detected via enzyme-linked immunosorbent assay. Comparing the changes in fecal microbiome compositions before and after FMT revealed a significant augmentation in the alpha diversity and increased abundances of some flora such as Clostridiales, Fusicatenibacter, and Paraprevotella. This was consistent with the patients experiencing relief from their clinical symptoms. Abundances of other flora, including Lachnoanaerobaculum, were decreased, which might correlate with the severity of patients’ constipation. Although no differences were found in SCFA production, the butyric acid concentration was correlated with both bacterial alterations and clinical symptoms. Serum IL-8 levels were significantly lower after FMT than at baseline, but IL-4, IL-6, IL-10, and IL-12p70 levels were not noticeably changed. This study showed how FMT regulates the intestinal microenvironment and affects systemic inflammation in constipated patients, providing direction for further research on the mechanisms of FMT. It also revealed potential microbial targets for precise intervention, which may bring new breakthroughs in treating constipation.

Visual Attention Prediction Model Based on Prominence Maps, Machine Learning and Biometric Data

2021 ◽  
Author(s):  
Helver Novoa Mendoza ◽  
William Joseph Giraldo ◽  
Emilio Granell ◽  
Faber Danilo Giraldo
Keyword(s):  
Machine Learning ◽  
Visual Attention ◽  
Prediction Model ◽  
Biometric Data ◽  
Model Based

scholarly journals Characterizing the Skin and Gut Microbiome of Alopecia Areata Patients

2020 ◽  
Vol 4 (1) ◽  
pp. 23-30
Author(s):  
Margit Juhasz ◽  
Siwei Chen ◽  
Arash Khosrovi-Eghbal ◽  
Chloe Ekelem ◽  
Yessica Landaverde ◽  
...  
Keyword(s):  
Alopecia Areata ◽  
Gut Microbiome ◽  
Healthy Controls ◽  
Healthy Individuals ◽  
Microbial Composition ◽  
Future Directions ◽  
Fecal Microbiome ◽  
Autoimmune Attack ◽  
Significant Difference ◽  
Control Study

Background: Alopecia areata (AA) is caused by autoimmune attack of the hair follicle. The exact pathogenesis is unknown, but hypotheses include innate immunity imbalance, environmental exposures, genetic predisposition, and possibly the microbiome. The objective of this study was to characterize the skin and gut microbiome of AA patients, and compare microbial composition to healthy individuals. Methods: This was a pilot, case-control study. Scalp and fecal microbiome samples were collected from 25 AA patients, and 25 age, gender, and race-matched healthy controls in Southern California with no significant difference in demographic characteristics. After library preparation and identification of bacterial and fungal taxonomy, multivariant analysis was performed to compare AA and healthy microbiomes. Results: The AA scalp microbiome was significant for decreased Clostridia and Malasseziomycetes, and the gut microbiome was significant for decreased Bacteroidia and increased Bacilli (p<0.05) compared to healthy controls. Conclusions: The composition of the AA bacterial and fungal, scalp and gut microbiome is significantly different than healthy individuals. Future directions include using this data to characterize microbial changes associated with AA patient diet, relating to disease severity, and predicting disease progression, prognosis and/or therapeutic response.

scholarly journals A Predictive Model Based on Machine Learning for the Early Detection of Late-Onset Neonatal Sepsis: Development and Observational Study (Preprint)

2019 ◽  
Author(s):  
Wongeun Song ◽  
Se Young Jung ◽  
Hyunyoung Baek ◽  
Chang Won Choi ◽  
Young Hwa Jung ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Intensive Care ◽  
High Resolution ◽  
Prediction Model ◽  
Neonatal Sepsis ◽  
Vital Sign ◽  
Prediction Models ◽  
Late Onset ◽  
Model Based

BACKGROUND Neonatal sepsis is associated with most cases of mortalities and morbidities in the neonatal intensive care unit (NICU). Many studies have developed prediction models for the early diagnosis of bloodstream infections in newborns, but there are limitations to data collection and management because these models are based on high-resolution waveform data. OBJECTIVE The aim of this study was to examine the feasibility of a prediction model by using noninvasive vital sign data and machine learning technology. METHODS We used electronic medical record data in intensive care units published in the Medical Information Mart for Intensive Care III clinical database. The late-onset neonatal sepsis (LONS) prediction algorithm using our proposed forward feature selection technique was based on NICU inpatient data and was designed to detect clinical sepsis 48 hours before occurrence. The performance of this prediction model was evaluated using various feature selection algorithms and machine learning models. RESULTS The performance of the LONS prediction model was found to be comparable to that of the prediction models that use invasive data such as high-resolution vital sign data, blood gas estimations, blood cell counts, and pH levels. The area under the receiver operating characteristic curve of the 48-hour prediction model was 0.861 and that of the onset detection model was 0.868. The main features that could be vital candidate markers for clinical neonatal sepsis were blood pressure, oxygen saturation, and body temperature. Feature generation using kurtosis and skewness of the features showed the highest performance. CONCLUSIONS The findings of our study confirmed that the LONS prediction model based on machine learning can be developed using vital sign data that are regularly measured in clinical settings. Future studies should conduct external validation by using different types of data sets and actual clinical verification of the developed model.

scholarly journals An Intelligent Rockburst Prediction Model Based on Scorecard Methodology

Minerals ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1294
Author(s):  
Honglei Wang ◽  
Zhenlei Li ◽  
Dazhao Song ◽  
Xueqiu He ◽  
Aleksei Sobolev ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
False Alarm ◽  
False Alarm Rate ◽  
Learning Model ◽  
Underground Engineering ◽  
Risk Levels ◽  
Model Based ◽  
Machine Learning Model ◽  
Sample Category

Rockburst is a serious hazard in underground engineering, and accurate prediction of rockburst risk is challenging. To construct an intelligent prediction model of rockburst risk with interpretability and high accuracy, three binary scorecards predicting different risk levels of rockburst were constructed using ChiMerge, evidence weight theory, and the logistic regression algorithm. An intelligent rockburst prediction model based on scorecard methodology (IRPSC) was obtained by integrating the three scorecards. The effects of hazard sample category weights on the missed alarm rate, false alarm rate, and accuracy of the IRPSC were analyzed. Results show that the accuracy, false alarm rate, and missed alarm rate of the IRPSC for rockburst prediction in riverside hydropower stations are 75%, 12.5%, and 12.5%, respectively. Setting higher hazard sample category weights can reduce the missed alarm rate of IRPSC, but it will lead to a higher false alarm rate. The IRPSC can adaptively adjust the threshold and weight value of the indicator and convert the abstract machine learning model into a tabular form, which overcomes the commonly black box problems of machine learning model, as well as is of great significance to the application of machine learning in rockburst risk prediction.

scholarly journals A Risk Prediction Model Based on Machine Learning for Cognitive Impairment Among Chinese Community-Dwelling Elderly People With Normal Cognition: Development and Validation Study

10.2196/20298 ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. e20298
Author(s):  
Mingyue Hu ◽  
Xinhui Shu ◽  
Gang Yu ◽  
Xinyin Wu ◽  
Maritta Välimäki ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Cognitive Impairment ◽  
Older People ◽  
Prediction Model ◽  
Elderly People ◽  
Community Dwelling ◽  
Chinese Community ◽  
Model Based ◽  
Normal Cognition

Background Identifying cognitive impairment early enough could support timely intervention that may hinder or delay the trajectory of cognitive impairment, thus increasing the chances for successful cognitive aging. Objective We aimed to build a prediction model based on machine learning for cognitive impairment among Chinese community-dwelling elderly people with normal cognition. Methods A prospective cohort of 6718 older people from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) register, followed between 2008 and 2011, was used to develop and validate the prediction model. Participants were included if they were aged 60 years or above, were community-dwelling elderly people, and had a cognitive Mini-Mental State Examination (MMSE) score ≥18. They were excluded if they were diagnosed with a severe disease (eg, cancer and dementia) or were living in institutions. Cognitive impairment was identified using the Chinese version of the MMSE. Several machine learning algorithms (random forest, XGBoost, naïve Bayes, and logistic regression) were used to assess the 3-year risk of developing cognitive impairment. Optimal cutoffs and adjusted parameters were explored in validation data, and the model was further evaluated in test data. A nomogram was established to vividly present the prediction model. Results The mean age of the participants was 80.4 years (SD 10.3 years), and 50.85% (3416/6718) were female. During a 3-year follow-up, 991 (14.8%) participants were identified with cognitive impairment. Among 45 features, the following four features were finally selected to develop the model: age, instrumental activities of daily living, marital status, and baseline cognitive function. The concordance index of the model constructed by logistic regression was 0.814 (95% CI 0.781-0.846). Older people with normal cognitive functioning having a nomogram score of less than 170 were considered to have a low 3-year risk of cognitive impairment, and those with a score of 170 or greater were considered to have a high 3-year risk of cognitive impairment. Conclusions This simple and feasible cognitive impairment prediction model could identify community-dwelling elderly people at the greatest 3-year risk for cognitive impairment, which could help community nurses in the early identification of dementia.

scholarly journals Early Life Exposure to Environmental Contaminants (BDE-47, TBBPA, and BPS) Produced Persistent Alterations in Fecal Microbiome in Adult Male Mice

2020 ◽  
Author(s):  
Matthew V Gomez ◽  
Moumita Dutta ◽  
Alexander Suvorov ◽  
Xiaojian Shi ◽  
Haiwei Gu ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Adult Male ◽  
Gut Microbiome ◽  
Corn Oil ◽  
Maternal Exposure ◽  
Gas Chromatography Mass Spectrometry ◽  
Bisphenol S ◽  
Fecal Microbiome ◽  
Chromatography Mass Spectrometry ◽  
Microbial Biomarkers

Abstract The gut microbiome is a pivotal player in toxicological responses. We investigated the effects of maternal exposure to 3 human health-relevant toxicants (BDE-47, tetrabromobisphenol [TBBPA], and bisphenol S [BPS]) on the composition and metabolite levels (bile acids [BAs] and short-chain fatty acids [SCFAs]) of the gut microbiome in adult pups. CD-1 mouse dams were orally exposed to vehicle (corn oil, 10 ml/kg), BDE-47 (0.2 mg/kg), TBBPA (0.2 mg/kg), or BPS (0.2 mg/kg) once daily from gestational day 8 to the end of lactation (postnatal day 21). 16S rRNA sequencing and targeted metabolomics were performed in feces of 20-week-old adult male pups (n = 14 − 23/group). Host gene expression and BA levels were quantified in liver. BPS had the most prominent effect on the beta-diversity of the fecal microbiome compared with TBPPA and BDE-47 (QIIME). Seventy-three taxa were persistently altered by at least 1 chemical, and 12 taxa were commonly regulated by all chemicals (most of which were from the Clostridia class and were decreased). The most distinct microbial biomarkers were S24-7 for BDE-47, Rikenellaceae for TBBPA, and Lactobacillus for BPS (LefSe). The community-wide contributions to the shift in microbial pathways were predicted using FishTaco. Consistent with FishTaco predictions, BDE-47 persistently increased fecal and hepatic BAs within the 12α hydroxylation pathway, corresponding to an up-regulation with the hepatic BA-synthetic enzyme Cyp7a1. Fecal BAs were also persistently up-regulated by TBBPA and BPS (liquid chromatography-mass spectrometry). TBBPA increased propionic acid and succinate, whereas BPS decreased acetic acid (gas chromatography-mass spectrometry). There was a general trend in the hepatic down-regulation of proinflammatory cytokines and the oxidative stress sensor target gene (Nqo1), and a decrease in G6Pdx (the deficiency of which leads to dyslipidemia). In conclusion, maternal exposure to these toxicants persistently modified the gut-liver axis, which may produce an immune-suppressive and dyslipidemia-prone signature later in life.

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