Objective: To determine which parameter of the umbilical arterial cord gas analysis, pH, base deficit (BD) or lactate has a bigger predictive ability for neonatal morbidity at term.
Method: We conducted a four-year retrospective cohort study including all non-anomalous, singleton, vertex, term births with neonatal acidemia (umbilical arterial cord gas pH ≤ 7.1). The primary outcomes were a composite neurological morbidity and a composite systemic morbidity. The predictive ability of lactate, BD and pH was compared using receiver operator characteristic (ROC) curves. Optimal cutoff values of lactate, BD and pH were estimated based on their maximal Youden Index.
Results: We identified 466 acidemic neonates who had paired and validated cord blood gas data. The ROC curve analysis revealed that pH, BD and lactate had a similar predictive ability for neurological (AUC: 0.81; 0.78; 0.83, respectively) and systemic neonatal morbidity (AUC: 0.77; 0.82; 0.82, respectively). The combination of pH ≤ 7.0 and lactate ≥ 7.0 mmol/L presented similar validity to that of pH ≤ 7.0 and BD ≥ 12 mmol/L, but both were comparable to pH alone.
Conclusions: pH, BD and lactate have similar predictive ability for adverse neonatal outcomes among acidemic neonates. Umbilical arterial lactate could replace BD as a measure of the metabolic component in acidemic neonates. However, neither BD nor lactate demonstrated in this study to improve the predictive ability of pH alone for short-term neonatal outcomes.
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http://dx.doi.org/10.1080/14767058.2016.1248936 | DOI Listing |
J Chem Inf Model
January 2025
Department of Computer Science and Technology, Shantou University, Shantou 515063, China.
The human microbiota may influence the effectiveness of drug therapy by activating or inactivating the pharmacological properties of drugs. Computational methods have demonstrated their ability to screen reliable microbe-drug associations and uncover the mechanism by which drugs exert their functions. However, the previous prediction methods failed to completely exploit the neighborhood topologies of the microbe and drug entities and the diverse correlations between the microbe-drug entity pair and the other entities.
View Article and Find Full Text PDFMethods Mol Biol
January 2025
Ecole Polytechnique Fédérale de Lausanne, School of Life Sciences, Institute of Bioengineering, Lausanne, Switzerland.
Gene expression memory-based lineage inference (GEMLI) is a computational tool allowing to predict cell lineages solely from single-cell RNA-sequencing (scRNA-seq) datasets and is publicly available as an R package on GitHub. GEMLI is based on the occurrence of gene expression memory, i.e.
View Article and Find Full Text PDFPhytopathology
January 2025
Swedish University of Agricultural Sciences, Plant Protection Biology, Alnarp, Sweden;
Transglutaminases (TGases) are enzymes highly conserved among prokaryotic and eukaryotic organisms, where their role is to catalyze protein cross-linking. One of the putative TGases of has previously been shown to be localized to the cell wall. Based on sequence similarity we were able to identify six more genes annotated as putative TGases and show that these seven genes group together in phylogenetic analysis.
View Article and Find Full Text PDFTuberk Toraks
December 2024
Clinic of Nephrology, Health Sciences University Mehmet Akif İnan Education and Research Hospital, Şanlıurfa, Türkiye.
Introduction: Pneumonia is a common symptom of coronavirus disease-2019 (COVID-19), and this study aimed to determine how analyzing initial thoracic computerized-tomography (CT) scans using semi-quantitative methods could be used to predict the outcomes for hospitalized patients.
Materials And Methods: This study looked at previously collected data from adult patients who were hospitalized with a positive test for severe acute respiratory syndrome coronavirus-2 and had CT scans of their thorax at the time of presentation. The CT scans were evaluated for the extent of lung involvement using a semi-quantitative scoring system ranging from 0 to 72.
Stroke
January 2025
Center for Brain Recovery, Boston University, MA (M.J.M., E.C., M.S., M.R.-M., S.K.).
Background: Predicting treated language improvement (TLI) and transfer to the untreated language (cross-language generalization, CLG) after speech-language therapy in bilingual individuals with poststroke aphasia is crucial for personalized treatment planning. This study evaluated machine learning models to predict TLI and CLG and identified the key predictive features (eg, patient severity, demographics, and treatment variables) aligning with clinical evidence.
Methods: Forty-eight Spanish-English bilingual individuals with poststroke aphasia received 20 sessions of semantic feature-based naming treatment in either their first or second language.
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