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Context: Undiagnosed gestational diabetes mellitus (GDM) is a major preventable cause of stillbirth. In the United Kingdom, women are selected for diagnostic testing for GDM based on risk factors, including body mass index (BMI) > 30 kg/m2.
Objective: To improve the prediction of GDM using metabolomics.
Methods: We performed metabolomics on maternal serum from the Pregnancy Outcome Prediction (POP) study at 12 and 20 weeks of gestational age (wkGA; 185 GDM cases and 314 noncases). Predictive metabolites were internally validated using the 28 wkGA POP study serum sample and externally validated using 24- to 28-wkGA fasting plasma from the Born in Bradford (BiB) cohort (349 GDM cases and 2347 noncases). The predictive ability of a model including the metabolites was compared with BMI > 30 kg/m2 in the POP study.
Results: Forty-seven predictive metabolites were identified using the 12- and 20-wkGA samples. At 28 wkGA, 4 of these [mannose, 4-hydroxyglutamate, 1,5-anhydroglucitol, and lactosyl-N-palmitoyl-sphingosine (d18:1/16:0)] independently increased the bootstrapped area under the receiver operating characteristic curve (AUC) by >0.01. All 4 were externally validated in the BiB samples (P = 2.6 × 10-12, 2.2 × 10-13, 6.9 × 10-28, and 2.6 × 10-17, respectively). In the POP study, BMI > 30 kg/m2 had a sensitivity of 28.7% (95% CI 22.3-36.0%) and a specificity of 85.4% whereas at the same level of specificity, a predictive model using age, BMI, and the 4 metabolites had a sensitivity of 60.2% (95% CI 52.6-67.4%) and an AUC of 0.82 (95% CI 0.78-0.86).
Conclusions: We identified 4 strongly and independently predictive metabolites for GDM that could have clinical utility in screening for GDM.
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http://dx.doi.org/10.1210/clinem/dgac240 | DOI Listing |
JMIR Res Protoc
September 2025
Institute of Higher Education and Research in Healthcare, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland.
Background: In pediatric intensive care units, pain, sedation, delirium, and iatrogenic withdrawal syndrome (IWS) must be managed as interrelated conditions. Although clinical practice guidelines (CPGs) exist, new evidence needs to be incorporated, gaps in recommendations addressed, and recommendations adapted to the European context.
Objective: This protocol describes the development of the first patient- and family-informed European guideline for managing pain, sedation, delirium, and IWS by the European Society of Paediatric and Neonatal Intensive Care.
Target Oncol
September 2025
Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark.
Background: Population pharmacokinetic models can potentially provide suggestions for an initial dose and the magnitude of dose adjustment during therapeutic drug monitoring procedures of imatinib. Several population pharmacokinetic models for imatinib have been developed over the last two decades. However, their predictive performance is still unknown when extrapolated to different populations, especially children.
View Article and Find Full Text PDFJ Ultrasound Med
September 2025
Department of Ultrasound, Donghai Hospital Affiliated to Kangda College of Nanjing Medical University, Lianyungang, China.
Objective: The aim of this study is to evaluate the prognostic performance of a nomogram integrating clinical parameters with deep learning radiomics (DLRN) features derived from ultrasound and multi-sequence magnetic resonance imaging (MRI) for predicting survival, recurrence, and metastasis in patients diagnosed with triple-negative breast cancer (TNBC) undergoing neoadjuvant chemotherapy (NAC).
Methods: This retrospective, multicenter study included 103 patients with histopathologically confirmed TNBC across four institutions. The training group comprised 72 cases from the First People's Hospital of Lianyungang, while the validation group included 31 cases from three external centers.
J Thorac Imaging
September 2025
Department of Radiology, the Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University.
Purpose: To establish an explainable machine learning (ML) approach using patient-related and noncontrast chest CT-derived features to predict the contrast material arrival time (TARR) in CT pulmonary angiography (CTPA).
Materials And Methods: This retrospective study included consecutive patients referred for CTPA between September 2023 to October 2024. Sixteen clinical and 17 chest CT-derived parameters were used as inputs for the ML approach, which employed recursive feature elimination for feature selection and XGBoost with SHapley Additive exPlanations (SHAP) for explainable modeling.
J Pharm Anal
August 2025
College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
P-glycoprotein (P-gp) is a transmembrane protein widely involved in the absorption, distribution, metabolism, excretion, and toxicity (ADMET) of drugs within the human body. Accurate prediction of P-gp inhibitors and substrates is crucial for drug discovery and toxicological assessment. However, existing models rely on limited molecular information, leading to suboptimal model performance for predicting P-gp inhibitors and substrates.
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