Correction: A predictive model to diagnose pregnancy in guanacos (Lama guanicoe) using non-invasive methods

Pregnancy status is usually not included in ecological studies because it is difficult to evaluate. The use of non-invasive methods to determine pregnancy, without physically restraining individuals, would enable pregnancy to be included in population studies. In this study, we evaluated sex steroid hormones in plasma and fecal samples from pregnant and non-pregnant females to develop a pregnancy predictive model for guanacos (Lama guanicoe (Müller, 1776)). Samples were obtained during live-shearing management (i.e., capture, shear, and release) of guanacos. Enzyme immunoassays were used to evaluate progesterone (P4) and estradiol (E2) concentrations in plasma and pregnanediol glucuronides (PdG) and conjugated estrogens (EC) in feces. Mean hormonal and fecal metabolite concentrations were significantly higher in pregnant females than in non-pregnant females. A linear relationship was found between each hormone and its fecal metabolite. Finally, hormonal data were combined with an independent source of pregnancy diagnosis such as abdominal ballottement to develop a logistic regression model to diagnose pregnancy in non-handled individuals. The use of predictive models and non-invasive methods might be suitable to incorporate pregnancy information in large-scale population studies on guanaco and other free-ranging ungulates.

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Non-invasive predictive model for hepatic venous pressure gradient based on a 3-dimensional computed tomography volume rendering technology

Experimental and Therapeutic Medicine ◽

10.3892/etm.2018.5816 ◽

2018 ◽

Cited By ~ 1

Author(s):

Yujen Tseng ◽

Lili Ma ◽

Tiancheng Luo ◽

Xiaoqing Zeng ◽

Na Li ◽

...

Keyword(s):

Computed Tomography ◽

Pressure Gradient ◽

Predictive Model ◽

Volume Rendering ◽

Venous Pressure ◽

Hepatic Venous Pressure Gradient ◽

3 Dimensional ◽

Non Invasive ◽

Gradient Based

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Red-Green-Blue (RGB) Profiling Of Pancreatic Mass-Elastographies: Validation Of A Predictive Model For Non-Invasive Assessment Of Malignancy

10.1055/s-0041-1724941 ◽

2021 ◽

Author(s):

M Razpotnik ◽

S Bota ◽

G Essler ◽

J Weber-Eibel ◽

M Peck-Radosavljevic

Keyword(s):

Predictive Model ◽

Pancreatic Mass ◽

Non Invasive

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950 A NEW NON-INVASIVE PREDICTIVE MODEL OF INFLAMMATORY ACTIVITY IN PATIENTS WITH TYPE I AUTOIMMUNE HEPATITIS

Journal of Hepatology ◽

10.1016/s0168-8278(12)60962-3 ◽

2012 ◽

Vol 56 ◽

pp. S371

Author(s):

K. Gutkowski ◽

T. Kacperek-Hartleb ◽

M. Hartleb ◽

M. Kajor ◽

W. Mazur ◽

...

Keyword(s):

Autoimmune Hepatitis ◽

Predictive Model ◽

Inflammatory Activity ◽

Type I ◽

Non Invasive

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A New Biomarker of Fecal Bacteria for Non-Invasive Diagnosis of Colorectal Cancer

Frontiers in Cellular and Infection Microbiology ◽

10.3389/fcimb.2021.744049 ◽

2021 ◽

Vol 11 ◽

Author(s):

Yizhou Yao ◽

Haishun Ni ◽

Xuchao Wang ◽

Qixuan Xu ◽

Jiawen Zhang ◽

...

Keyword(s):

Colorectal Cancer ◽

Predictive Model ◽

Clinical Application ◽

Sensitivity And Specificity ◽

Healthy Subjects ◽

Intestinal Flora ◽

Detection Methods ◽

Diagnostic Ability ◽

Non Invasive ◽

Specificity And Sensitivity

BackgroundThe intestinal flora is correlated with the occurrence of colorectal cancer. We evaluate a new predictive model for the non-invasive diagnosis of colorectal cancer based on intestinal flora to verify the clinical application prospects of the intestinal flora as a new biomarker in non-invasive screening of colorectal cancer.MethodsSubjects from two independent Asian cohorts (cohort I, consisting of 206 colorectal cancer and 112 healthy subjects; cohort II, consisting of 67 colorectal cancer and 54 healthy subjects) were included. A probe-based duplex quantitative PCR (qPCR) determination was established for the quantitative determination of candidate bacterial markers.ResultsWe screened through the gutMEGA database to identify potential non-invasive biomarkers for colorectal cancer, including Prevotella copri (Pc), Gemella morbillorum (Gm), Parvimonas micra (Pm), Cetobacterium somerae (Cs), and Pasteurella stomatis (Ps). A predictive model with good sensitivity and specificity was established as a new diagnostic tool for colorectal cancer. Under the best cutoff value that maximizes the sum of sensitivity and specificity, Gm and Pm had better specificity and sensitivity than other target bacteria. The combined detection model of five kinds of bacteria showed better diagnostic ability than Gm or Pm alone (AUC = 0.861, P < 0.001). These findings were further confirmed in the independent cohort II. Particularly, the combination of bacterial markers and fecal immunochemical test (FIT) improved the diagnostic ability of the five bacteria (sensitivity 67.96%, specificity 89.29%) for patients with colorectal cancer.ConclusionFecal-based colorectal cancer-related bacteria can be used as new non-invasive diagnostic biomarkers of colorectal cancer. Simultaneously, the molecular biomarkers in fecal samples are similar to FIT, have the applicability in combination with other detection methods, which is expected to improve the sensitivity of diagnosis for colorectal cancer, and have a promising prospect of clinical application.

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A PREDICTIVE MODEL FOR NON-INVASIVE EVALUATION OF LIVER FIBROSIS IN PATIENTS WITH CHRONIC HEPATITIS VIRUS INFECTION

Medical Immunology (Russia) ◽

10.15789/1563-0625-2008-4-5-405-414 ◽

2014 ◽

Vol 10 (4-5) ◽

pp. 405

Author(s):

A. A. Ostanin ◽

E. L. Gelfgadt ◽

M. V. Shipunov ◽

E. Ya. Shevela ◽

E. V. Kurganova ◽

...

Keyword(s):

Chronic Hepatitis ◽

Liver Fibrosis ◽

Virus Infection ◽

Predictive Model ◽

Hepatitis Virus ◽

Non Invasive ◽

Hepatitis Virus Infection ◽

Invasive Evaluation

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PLS Predictive Model for In-Vivo Non-Invasive Finger Touch Blood Glucose NIR Spectrosensor

10.1109/rsm52397.2021.9511583 ◽

2021 ◽

Author(s):

Katrul Nadia Basri ◽

Nur Azera Tuhaime ◽

Mohd Hafizulfika Hisham ◽

Muhammad Hafiz Laili ◽

Zalhan Md Yusof ◽

...

Keyword(s):

Blood Glucose ◽

Predictive Model ◽

Non Invasive ◽

Finger Touch

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Integrated CT Radiomics Features Could Enhance the Efficacy of 18F-FET PET for Non-Invasive Isocitrate Dehydrogenase Genotype Prediction in Adult Untreated Gliomas: A Retrospective Cohort Study

Frontiers in Oncology ◽

10.3389/fonc.2021.772703 ◽

2021 ◽

Vol 11 ◽

Author(s):

Weiyan Zhou ◽

Qi Huang ◽

Jianbo Wen ◽

Ming Li ◽

Yuhua Zhu ◽

...

Keyword(s):

Predictive Model ◽

Predictive Models ◽

Isocitrate Dehydrogenase ◽

Clinical Decision Making ◽

Model Building ◽

Machine Learning Algorithms ◽

Clinical Feature ◽

Fet Pet ◽

Non Invasive ◽

Pet Ct

PurposeWe aimed to investigate the predictive models based on O-[2-(18F)fluoroethyl]-l-tyrosine positron emission tomography/computed tomography (18F-FET PET/CT) radiomics features for the isocitrate dehydrogenase (IDH) genotype identification in adult gliomas.MethodsFifty-eight consecutive pathologically confirmed adult glioma patients with pretreatment 18F-FET PET/CT were retrospectively enrolled. One hundred and five radiomics features were extracted for analysis in each modality. Three independent radiomics models (PET-Rad Model, CT-Rad Model and PET/CT-Rad Model) predicting IDH mutation status were generated using the least absolute shrinkage and selection operator (LASSO) regression analysis based on machine learning algorithms. All-subsets regression and cross validation were applied for the filter and calibration of the predictive radiomics models. Besides, semi-quantitative parameters including maximum, peak and mean tumor to background ratio (TBRmax, TBRpeak, TBRmean), standard deviation of glioma lesion standardized uptake value (SUVSD), metabolic tumor volume (MTV) and total lesion tracer uptake (TLU) were obtained and filtered for the simple model construction with clinical feature of brain midline involvement status. The area under the receiver operating characteristic curve (AUC) was applied for the evaluation of the predictive models.ResultsThe AUC of the simple predictive model consists of semi-quantitative parameter SUVSD and dichotomized brain midline involvement status was 0.786 (95% CI 0.659-0.883). The AUC of PET-Rad Model building with three 18F-FET PET radiomics parameters was 0.812 (95% CI 0.688-0.902). The AUC of CT-Rad Model building with three co-registered CT radiomics parameters was 0.883 (95% CI 0.771-0.952). While the AUC of the combined 18F-FET PET/CT-Rad Model building with three CT and one PET radiomics features was 0.912 (95% CI 0.808-0.970). DeLong test results indicated the PET/CT-Rad Model outperformed the PET-Rad Model (p = 0.048) and simple predictive model (p = 0.034). Further combination of the PET/CT-Rad Model with the clinical feature of dichotomized tumor location status could slightly enhance the AUC to 0.917 (95% CI 0.814-0.973).ConclusionThe predictive model combining 18F-FET PET and integrated CT radiomics features could significantly enhance and well balance the non-invasive IDH genotype prediction in untreated gliomas, which is important in clinical decision making for personalized treatment.

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