scholarly journals Predicting Hyperoxia-Induced Lung Injury from Associated Intestinal and Lung Dysbiosis in Neonatal Mice

2020 ◽  
Author(s):  
Chung-Ming Chen ◽  
Hsiu-Chu Chou ◽  
Yu-Chen Yang ◽  
Emily Chia-Yu Su
Keyword(s):  
Lung Injury ◽  
Intestinal Microbiota ◽  
Bacterial Translocation ◽  
Prediction Models ◽  
Left Lung ◽  
Area Under The Curve ◽  
Intestinal Barrier ◽  
Machine Learning Algorithms ◽  
Neonatal Mice ◽  
Neonatal Hyperoxia

Abstract Background: Newborns with respiratory disorders often require supplemental oxygen. Preclinical studies have demonstrated that hyperoxia disrupts the intestinal barrier, impairs intestinal function, and injures the lungs of newborn animals. The effects of neonatal hyperoxia on intestinal and lung microbiota and the role of the intestinal microbiota in the pathogenesis of hyperoxia-induced lung injury have not been investigated.Results: In this study, we evaluated the effect of neonatal hyperoxia on intestine and lung microbiota alterations in neonatal C57BL/6N mice reared in either room air (RA) or hyperoxia (85% O2) from postnatal days 1 to 7. On postnatal day 7, lung and intestinal microbiota were sampled from the left lung and lower gastrointestinal tract for 16S ribosomal RNA gene sequencing. Tissue from the right lung and terminal ileum were harvested for Western blot and histology analysis. Hyperoxia decreased body weight, induced intestinal injury, decreased intestinal tight junction expression, impaired lung alveolarization and angiogenesis, and increased lung cytokines in neonatal mice. Hyperoxia also altered intestinal and lung microbiota and promoted bacterial translocation from the intestine to the lung as evidenced by the presence of intestinal bacteria in the lungs of hyperoxia-exposed neonatal mice. The relative abundance of these bacterial taxa was significantly positively correlated with lung cytokines. Intestinal and lung microbiota combined with cytokines were incorporated into machine learning algorithms to develop prediction models for the classification of RA- or hyperoxia-reared mice. The experiment results demonstrated that a Bayes network achieved the best predictive performance, attaining accuracy, sensitivity, specificity, and area under the curve values of 94.4%, 88.9%, 100%, and 0.963, respectively. Selected discriminative features included lung cytokines (interleukin-1β, macrophage inflammatory protein-2, and tumor necrosis factor-α), lung microbiota (Ruminococcaceae_UCG-010, CAG-56, and Enterobacter), and intestinal microbiota (Peptococcaceae_ge, Muribaculum, Enterobacter, and Ruminococcaceae_UCG-010). Conclusions: Neonatal hyperoxia exposure during the first week of life induced intestinal and lung dysbiosis and promoted bacterial translocation from the intestine to the lung. These findings suggest that changes in the composition of the intestinal microbiota contribute to hyperoxia-induced lung injury and that the combination of intestinal and lung microbiota may indicate hyperoxia-induced lung injury in neonatal mice.

scholarly journals Predicting Hyperoxia-Induced Lung Injury from Associated Intestinal and Lung Dysbiosis in Neonatal Mice

Neonatology ◽  
2021 ◽  
pp. 106-116
Author(s):  
Chung-Ming Chen ◽  
Hsiu-Chu Chou ◽  
Yu-Chen S.H. Yang ◽  
Emily Chia-Yu Su ◽  
Yun-Ru Liu
Keyword(s):  
Lung Injury ◽  
Intestinal Microbiota ◽  
Bacterial Translocation ◽  
Left Lung ◽  
Intestinal Barrier ◽  
Intestinal Bacteria ◽  
Machine Learning Algorithms ◽  
Neonatal Mice ◽  
The Right ◽  
Neonatal Hyperoxia

Background: Preclinical studies have demonstrated that hyperoxia disrupts the intestinal barrier, changes the intestinal bacterial composition, and injures the lungs of newborn animals. Objectives: The aim of the study was to investigate the effects of hyperoxia on the lung and intestinal microbiota and the communication between intestinal and lung microbiota and to develop a predictive model for the identification of hyperoxia-induced lung injury from intestinal and lung microbiota based on machine learning algorithms in neonatal mice. Methods: Neonatal C57BL/6N mice were reared in either room air or hyperoxia (85% O2) from postnatal days 1–7. On postnatal day 7, lung and intestinal microbiota were sampled from the left lung and lower gastrointestinal tract for 16S ribosomal RNA gene sequencing. Tissue from the right lung and terminal ileum were harvested for Western blot and histology analysis. Results: Hyperoxia induced intestinal injury, decreased intestinal tight junction expression, and impaired lung alveolarization and angiogenesis in neonatal mice. Hyperoxia also altered intestinal and lung microbiota and promoted bacterial translocation from the intestine to the lung as evidenced by the presence of intestinal bacteria in the lungs of hyperoxia-exposed neonatal mice. The relative abundance of these bacterial taxa was significantly positively correlated with the increased lung cytokines. Conclusions: Neonatal hyperoxia induced intestinal and lung dysbiosis and promoted bacterial translocation from the intestine to the lung. Further studies are needed to clarify the pathophysiology of bacterial translocation to the lung.

scholarly journals Impact of Early Life Antibiotic Exposure and Neonatal Hyperoxia on the Murine Microbiome and Lung Injury

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Melissa H. Althouse ◽  
Christopher Stewart ◽  
Weiwu Jiang ◽  
Bhagavatula Moorthy ◽  
Krithika Lingappan
Keyword(s):  
Lung Injury ◽  
Beta Diversity ◽  
Alpha Diversity ◽  
Intestinal Microbiome ◽  
Rrna Gene ◽  
Antibiotic Exposure ◽  
Neonatal Mice ◽  
Intestinal Dysbiosis ◽  
Neonatal Hyperoxia

Abstract Cross talk between the intestinal microbiome and the lung and its role in lung health remains unknown. Perinatal exposure to antibiotics disrupts the neonatal microbiome and may have an impact on the preterm lung. We hypothesized that perinatal antibiotic exposure leads to long-term intestinal dysbiosis and increased alveolar simplification in a murine hyperoxia model. Pregnant C57BL/6 wild type dams and neonatal mice were treated with antibiotics before and/or immediately after delivery. Control mice received phosphate-buffered saline (PBS). Neonatal mice were exposed to 95% oxygen for 4 days or room air. Microbiome analysis was performed using 16S rRNA gene sequencing. Pulmonary alveolarization and vascularization were analyzed at postnatal day (PND) 21. Perinatal antibiotic exposure modified intestinal beta diversity but not alpha diversity in neonatal mice. Neonatal hyperoxia exposure altered intestinal beta diversity and relative abundance of commensal bacteria in antibiotic treated mice. Hyperoxia disrupted pulmonary alveolarization and vascularization at PND 21; however, there were no differences in the degree of lung injury in antibiotic treated mice compared to vehicle treated controls. Our study suggests that exposure to both hyperoxia and antibiotics early in life may cause long-term alterations in the intestinal microbiome, but intestinal dysbiosis may not significantly influence neonatal hyperoxic lung injury.

scholarly journals Machine learning improves the prediction of febrile neutropenia in Korean inpatients undergoing chemotherapy for breast cancer

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Bum-Joo Cho ◽  
Kyoung Min Kim ◽  
Sanchir-Erdene Bilegsaikhan ◽  
Yong Joon Suh
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Risk Factors ◽  
Febrile Neutropenia ◽  
Prediction Models ◽  
Learning Algorithms ◽  
Area Under The Curve ◽  
Primary Prophylaxis ◽  
Machine Learning Algorithms ◽  
Significant Difference

Abstract Febrile neutropenia (FN) is one of the most concerning complications of chemotherapy, and its prediction remains difficult. This study aimed to reveal the risk factors for and build the prediction models of FN using machine learning algorithms. Medical records of hospitalized patients who underwent chemotherapy after surgery for breast cancer between May 2002 and September 2018 were selectively reviewed for development of models. Demographic, clinical, pathological, and therapeutic data were analyzed to identify risk factors for FN. Using machine learning algorithms, prediction models were developed and evaluated for performance. Of 933 selected inpatients with a mean age of 51.8 ± 10.7 years, FN developed in 409 (43.8%) patients. There was a significant difference in FN incidence according to age, staging, taxane-based regimen, and blood count 5 days after chemotherapy. The area under the curve (AUC) built based on these findings was 0.870 on the basis of logistic regression. The AUC improved by machine learning was 0.908. Machine learning improves the prediction of FN in patients undergoing chemotherapy for breast cancer compared to the conventional statistical model. In these high-risk patients, primary prophylaxis with granulocyte colony-stimulating factor could be considered.

Intelligent Personalized Abnormality Detection for Remote Health Monitoring

2020 ◽  
Vol 16 (2) ◽  
pp. 87-109 ◽  
Author(s):  
Poorani Marimuthu ◽  
Varalakshmi Perumal ◽  
Vaidehi Vijayakumar
Keyword(s):  
Machine Learning ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
False Negative ◽  
False Negative Rate ◽  
Area Under The Curve ◽  
Ground Truth ◽  
Machine Learning Algorithms ◽  
Abnormality Detection ◽  
Remote Healthcare

Machine learning algorithms are extensively used in healthcare analytics to learn normal and abnormal patterns automatically. The detection and prediction accuracy of any machine learning model depends on many factors like ground truth instances, attribute relationships, model design, the size of the dataset, the percentage of uncertainty, the training and testing environment, etc. Prediction models in healthcare should generate a minimal false positive and false negative rate. To accomplish high classification or prediction accuracy, the screening of health status needs to be personalized rather than following general clinical practice guidelines (CPG) which fits for an average population. Hence, a personalized screening model (IPAD – Intelligent Personalized Abnormality Detection) for remote healthcare is proposed that tailored to specific individual. The severity level of the abnormal status has been derived using personalized health values and the IPAD model obtains an area under the curve (AUC) of 0.907.

scholarly journals Machine Learning Prediction Models for Mitral Valve Repairability and Mitral Regurgitation Recurrence in Patients Undergoing Surgical Mitral Valve Repair

2021 ◽  
Vol 8 (9) ◽  
pp. 117
Author(s):  
Marco Penso ◽  
Mauro Pepi ◽  
Valentina Mantegazza ◽  
Claudia Cefalù ◽  
Manuela Muratori ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mitral Valve ◽  
Prediction Models ◽  
Area Under The Curve ◽  
Mitral Valve Regurgitation ◽  
Machine Learning Algorithms ◽  
Failure Assessment ◽  
Echocardiographic Examination ◽  
Repair Procedure ◽  
Surgical Failure

Background: Mitral valve regurgitation (MR) is the most common valvular heart disease and current variables associated with MR recurrence are still controversial. We aim to develop a machine learning-based prognostic model to predict causes of mitral valve (MV) repair failure and MR recurrence. Methods: 1000 patients who underwent MV repair at our institution between 2008 and 2018 were enrolled. Patients were followed longitudinally for up to three years. Clinical and echocardiographic data were included in the analysis. Endpoints were MV repair surgical failure with consequent MV replacement or moderate/severe MR (>2+) recurrence at one-month and moderate/severe MR recurrence after three years. Results: 817 patients (DS1) had an echocardiographic examination at one-month while 295 (DS2) also had one at three years. Data were randomly divided into training (DS1: n = 654; DS2: n = 206) and validation (DS1: n = 164; DS2 n = 89) cohorts. For intra-operative or early MV repair failure assessment, the best area under the curve (AUC) was 0.75 and the complexity of mitral valve prolapse was the main predictor. In predicting moderate/severe recurrent MR at three years, the best AUC was 0.92 and residual MR at six months was the most important predictor. Conclusions: Machine learning algorithms may improve prognosis after MV repair procedure, thus improving indications for correct candidate selection for MV surgical repair.

scholarly journals Bifidobacteria may be beneficial to intestinal microbiota and reduction of bacterial translocation in mice following ischaemia and reperfusion injury

2012 ◽  
Vol 109 (11) ◽  
pp. 1990-1998 ◽  
Author(s):  
Honggang Wang ◽  
Wei Zhang ◽  
Lugen Zuo ◽  
Weiming Zhu ◽  
Bin Wang ◽  
...  
Keyword(s):  
Epithelial Cell ◽  
Intestinal Microbiota ◽  
Bacterial Translocation ◽  
Cell Apoptosis ◽  
Intestinal Barrier ◽  
Peroral Administration ◽  
Barrier Dysfunction ◽  
Mesenteric Lymph Nodes ◽  
Ischaemia And Reperfusion ◽  
Epithelial Cell Apoptosis

The aim of the present study was to determine the effect of peroral bifidobacteria on the intestinal microbiota, barrier function and bacterial translocation (BT) in a mouse model of ischaemia and reperfusion (I/R) injury. A total of twenty-four male BALB/c mice were randomly allocated into three groups: (1) sham-operated, (2) I/R and (3) I/R injury and bifidobacteria pretreatment (109colony-forming units/d). Bifidobacteria were administered daily intragastrically for 2 weeks before induction of I/R. Subsequently, samples of caecal content, intestinal mucosa, ileal segments, blood, mesenteric lymph nodes (MLN) and distant organs (liver, spleen and kidney) were prepared for examination. In the I/R model, barrier dysfunction (caecal microbiota dysbiosis, disruption of tight junction (TJ), increased epithelial cell apoptosis, disruption of mucosa and multiple erosions) in the intestine was observed, associated with increased BT to extraintestinal sites. The ratio of BT to MLN and distant organs in mice exposed to I/R injury was 62·5 %, which was significantly higher than the sham-operated group. However, pretreatment of animals with bifidobacteria prevented I/R-induced BT, reduced pro-inflammatory cytokine release, the levels of endotoxin, intestinal epithelial cell apoptosis, disruption of TJ and increased the concentration of SCFA, resulting in recovered microbiota and mucosal integrity. Bifidobacteria may be beneficial in reducing BT in I/R injury of mice. Therefore, peroral administration of bifidobacteria is a potential strategy to prevent I/R-induced BT and intestinal barrier dysfunction.

Development of Prediction Models Using Machine Learning Algorithms for Girls with Suspected Central Precocious Puberty: Retrospective Study (Preprint)

2018 ◽  
Author(s):  
Liyan Pan ◽  
Guangjian Liu ◽  
Xiaojian Mao ◽  
Huixian Li ◽  
Jiexin Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Retrospective Study ◽  
Random Forest ◽  
Precocious Puberty ◽  
Prediction Models ◽  
Central Precocious Puberty ◽  
Machine Learning Algorithms ◽  
Stimulation Test ◽  
Gnrh Analogue ◽  
Prediction Probability

BACKGROUND Central precocious puberty (CPP) in girls seriously affects their physical and mental development in childhood. The method of diagnosis—gonadotropin-releasing hormone (GnRH)–stimulation test or GnRH analogue (GnRHa)–stimulation test—is expensive and makes patients uncomfortable due to the need for repeated blood sampling. OBJECTIVE We aimed to combine multiple CPP–related features and construct machine learning models to predict response to the GnRHa-stimulation test. METHODS In this retrospective study, we analyzed clinical and laboratory data of 1757 girls who underwent a GnRHa test in order to develop XGBoost and random forest classifiers for prediction of response to the GnRHa test. The local interpretable model-agnostic explanations (LIME) algorithm was used with the black-box classifiers to increase their interpretability. We measured sensitivity, specificity, and area under receiver operating characteristic (AUC) of the models. RESULTS Both the XGBoost and random forest models achieved good performance in distinguishing between positive and negative responses, with the AUC ranging from 0.88 to 0.90, sensitivity ranging from 77.91% to 77.94%, and specificity ranging from 84.32% to 87.66%. Basal serum luteinizing hormone, follicle-stimulating hormone, and insulin-like growth factor-I levels were found to be the three most important factors. In the interpretable models of LIME, the abovementioned variables made high contributions to the prediction probability. CONCLUSIONS The prediction models we developed can help diagnose CPP and may be used as a prescreening tool before the GnRHa-stimulation test.

scholarly journals Development of Machine Learning Models for Prediction of Smoking Cessation Outcome

2021 ◽  
Vol 18 (5) ◽  
pp. 2584
Author(s):  
Cheng-Chien Lai ◽  
Wei-Hsin Huang ◽  
Betty Chia-Chen Chang ◽  
Lee-Ching Hwang
Keyword(s):  
Machine Learning ◽  
Smoking Cessation ◽  
Success Rate ◽  
Prediction Models ◽  
Smoking Status ◽  
Medical Center ◽  
Machine Learning Algorithms ◽  
Classification And Regression Tree ◽  
Support Vector ◽  
Smoking Cessation Outcome

Predictors for success in smoking cessation have been studied, but a prediction model capable of providing a success rate for each patient attempting to quit smoking is still lacking. The aim of this study is to develop prediction models using machine learning algorithms to predict the outcome of smoking cessation. Data was acquired from patients underwent smoking cessation program at one medical center in Northern Taiwan. A total of 4875 enrollments fulfilled our inclusion criteria. Models with artificial neural network (ANN), support vector machine (SVM), random forest (RF), logistic regression (LoR), k-nearest neighbor (KNN), classification and regression tree (CART), and naïve Bayes (NB) were trained to predict the final smoking status of the patients in a six-month period. Sensitivity, specificity, accuracy, and area under receiver operating characteristic (ROC) curve (AUC or ROC value) were used to determine the performance of the models. We adopted the ANN model which reached a slightly better performance, with a sensitivity of 0.704, a specificity of 0.567, an accuracy of 0.640, and an ROC value of 0.660 (95% confidence interval (CI): 0.617–0.702) for prediction in smoking cessation outcome. A predictive model for smoking cessation was constructed. The model could aid in providing the predicted success rate for all smokers. It also had the potential to achieve personalized and precision medicine for treatment of smoking cessation.

scholarly journals Models for predicting treatment efficacy of antiepileptic drugs and prognosis of treatment withdrawal in epilepsy patients

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Shijun Yang ◽  
Bin Wang ◽  
Xiong Han
Keyword(s):  
Antiepileptic Drugs ◽  
Statistical Models ◽  
Prediction Models ◽  
External Validation ◽  
Machine Learning Algorithms ◽  
Treatment Withdrawal ◽  
Patient Treatment ◽  
Recent Developments ◽  
Progression Of Disease ◽  
Patients With Epilepsy

AbstractAlthough antiepileptic drugs (AEDs) are the most effective treatment for epilepsy, 30–40% of patients with epilepsy would develop drug-refractory epilepsy. An accurate, preliminary prediction of the efficacy of AEDs has great clinical significance for patient treatment and prognosis. Some studies have developed statistical models and machine-learning algorithms (MLAs) to predict the efficacy of AEDs treatment and the progression of disease after treatment withdrawal, in order to provide assistance for making clinical decisions in the aim of precise, personalized treatment. The field of prediction models with statistical models and MLAs is attracting growing interest and is developing rapidly. What’s more, more and more studies focus on the external validation of the existing model. In this review, we will give a brief overview of recent developments in this discipline.

scholarly journals Prediction of COVID-19 severity using laboratory findings on admission: informative values, thresholds, ML model performance

BMJ Open ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. e044500
Author(s):  
Yauhen Statsenko ◽  
Fatmah Al Zahmi ◽  
Tetiana Habuza ◽  
Klaus Neidl-Van Gorkom ◽  
Nazar Zaki
Keyword(s):  
Lymphocyte Count ◽  
Prediction Models ◽  
Model Performance ◽  
Area Under The Curve ◽  
Predictive Biomarkers ◽  
Supervised Machine Learning ◽  
Study Cohort ◽  
Threshold Values ◽  
Laboratory Findings ◽  
Study Results

BackgroundDespite the necessity, there is no reliable biomarker to predict disease severity and prognosis of patients with COVID-19. The currently published prediction models are not fully applicable to clinical use.ObjectivesTo identify predictive biomarkers of COVID-19 severity and to justify their threshold values for the stratification of the risk of deterioration that would require transferring to the intensive care unit (ICU).MethodsThe study cohort (560 subjects) included all consecutive patients admitted to Dubai Mediclinic Parkview Hospital from February to May 2020 with COVID-19 confirmed by the PCR. The challenge of finding the cut-off thresholds was the unbalanced dataset (eg, the disproportion in the number of 72 patients admitted to ICU vs 488 non-severe cases). Therefore, we customised supervised machine learning (ML) algorithm in terms of threshold value used to predict worsening.ResultsWith the default thresholds returned by the ML estimator, the performance of the models was low. It was improved by setting the cut-off level to the 25th percentile for lymphocyte count and the 75th percentile for other features. The study justified the following threshold values of the laboratory tests done on admission: lymphocyte count <2.59×109/L, and the upper levels for total bilirubin 11.9 μmol/L, alanine aminotransferase 43 U/L, aspartate aminotransferase 32 U/L, D-dimer 0.7 mg/L, activated partial thromboplastin time (aPTT) 39.9 s, creatine kinase 247 U/L, C reactive protein (CRP) 14.3 mg/L, lactate dehydrogenase 246 U/L, troponin 0.037 ng/mL, ferritin 498 ng/mL and fibrinogen 446 mg/dL.ConclusionThe performance of the neural network trained with top valuable tests (aPTT, CRP and fibrinogen) is admissible (area under the curve (AUC) 0.86; 95% CI 0.486 to 0.884; p<0.001) and comparable with the model trained with all the tests (AUC 0.90; 95% CI 0.812 to 0.902; p<0.001). Free online tool at https://med-predict.com illustrates the study results.

Sign in / Sign up

Export Citation Format

Share Document