Background: Bronchopulmonary dysplasia-associated pulmonary hypertension (BPD-PH) remains a devastating clinical complication seriously affecting the therapeutic outcome of preterm infants. Hence, early prevention and timely diagnosis prior to pathological change is the key to reducing morbidity and improving prognosis. Our primary objective is to utilize machine learning techniques to build predictive models that could accurately identify BPD infants at risk of developing PH.
Methods: The data utilized in this study were collected from neonatology departments of four tertiary-level hospitals in China. To address the issue of imbalanced data, oversampling algorithms synthetic minority over-sampling technique (SMOTE) was applied to improve the model.
Results: Seven hundred sixty one clinical records were collected in our study. Following data pre-processing and feature selection, 5 of the 46 features were used to build models, including duration of invasive respiratory support (day), the severity of BPD, ventilator-associated pneumonia, pulmonary hemorrhage, and early-onset PH. Four machine learning models were applied to predictive learning, and after comprehensive selection a model was ultimately selected. The model achieved 93.8% sensitivity, 85.0% accuracy, and 0.933 AUC. A score of the logistic regression formula greater than 0 was identified as a warning sign of BPD-PH.
Conclusions: We comprehensively compared different machine learning models and ultimately obtained a good prognosis model which was sufficient to support pediatric clinicians to make early diagnosis and formulate a better treatment plan for pediatric patients with BPD-PH.
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http://dx.doi.org/10.1186/s12931-024-02797-z | DOI Listing |
Talanta
December 2024
College of Chemistry and Chemical Engineering, Henan Key Laboratory of Function-Oriented Porous Materials, Luoyang Normal University, Luoyang, 471934, China.
An europium metal organic framework (Eu-DBPA-Phen) was synthesized using 2,5-dibromoterephthalic acid (HDBPA) and 1-10-phenanthroline (Phen) as ligands. A straightforwardc quasi-ratiometric fluorescence probe was then developed for the detection of levofloxacin (LVF) by the simplistic combination of red-emitting Eu-DBPA-Phen and the inherent blue auto-fluorescence of the target. The probe exhibits the advantages of wide linear range (0.
View Article and Find Full Text PDFMol Divers
December 2024
Institute of Physiologically Active Compounds Federal Research Center of Problems of Chemical Physics and Medicinal Chemistry, Russian Academy of Sciences, Chernogolovka, 142432, Russia.
Histone deacetylase 3 (HDAC3) inhibitors keep significant therapeutic promise for treating oncological, neurodegenerative, and inflammatory diseases. In this work, we developed robust QSAR regression models for HDAC3 inhibitory activity and acute toxicity (LD, intravenous administration in mice). A total of 1751 compounds were curated for HDAC3 activity, and 15,068 for toxicity.
View Article and Find Full Text PDFEur J Med Res
December 2024
Department of Geriatric Respiratory and Critical Care, Anhui Geriatric Institute, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui, China.
Background: This study aimed to develop predictive models with robust generalization capabilities for assessing the risk of pulmonary embolism in patients with tuberculosis using machine learning algorithms.
Methods: Data were collected from two centers and categorized into development and validation cohorts. Using the development cohort, candidate variables were selected via the Recursive Feature Elimination (RFE) method.
Anim Microbiome
December 2024
School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Background: Dogs-whether pets, rural, or stray-exhibit distinct living styles that influence their fecal microbiota and resistomes, yet these dynamics remain underexplored. This study aimed to analyze and compare the fecal microbiota and resistomes of three groups of dogs (37 pets, 20 rural, and 25 stray dogs) in Shanghai, China.
Results: Metagenomic analysis revealed substantial differences in fecal microbial composition and metabolic activities among the dog groups.
Background: A multivariate predictive model was constructed using baseline and 12-week clinical data to evaluate the rate of clearance of hepatitis B surface antigen (HBsAg) at the 48-week mark in patients diagnosed with chronic hepatitis B who are receiving treatment with pegylated interferon α (PEG-INFα).
Methods: The study cohort comprised CHB patients who received pegylated interferon treatment at Mengchao Hepatobiliary Hospital, Fujian Medical University, between January 2019 and April 2024. Predictor variables were identified (LASSO), followed by multivariate analysis and logistic regression analysis.
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