The hypothesis that aspects of current mother-infant interactions predict an infant's response to maternal infant-directed speech (IDS) was tested. Relative to infants of non-depressed mothers, those of depressed mothers acquired weaker voice-face associations in response to their own mothers' IDS in a conditioned-attention paradigm, although this was partially attributable to demographic differences between the two groups. The extent of fundamental frequency modulation (DeltaF(0)) in maternal IDS was smaller for infants of depressed than non-depressed mothers, but did not predict infant learning. However, Emotional Availability Scale ratings of maternal sensitivity, coded from videotapes of mothers and infants engaged in a brief play interaction, were significant predictors of infant learning, even after maternal depression, its demographic correlates, and antidepressant medication use had been taken into account. These findings are consistent with a role for experience-dependent processes in determining IDS's effects on infant learning.
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http://dx.doi.org/10.1080/15250000802706924 | DOI Listing |
Sci Rep
December 2024
Neuroscience & Neuroengineering Research Lab, Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science & Technology (IUST), Narmak, Tehran, Iran.
Currently, pain assessment using electroencephalogram signals and machine learning methods in clinical studies is of great importance, especially for those who cannot express their pain. Since newborns are among the high-risk group and always experience pain at the beginning of birth, in this research, the severity of newborns has been investigated and evaluated. Other studies related to the annoyance of newborns have used the EEG signal of newborns alone; therefore, in this study, the intensity of newborn pain was measured using the electroencephalogram signal of 107 infants who were stimulated by the heel lance in three levels: no pain, low pain and moderate pain were recorded as a single trial and evaluated.
View Article and Find Full Text PDFTomography
December 2024
Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA.
This research introduces BAE-ViT, a specialized vision transformer model developed for bone age estimation (BAE). This model is designed to efficiently merge image and sex data, a capability not present in traditional convolutional neural networks (CNNs). BAE-ViT employs a novel data fusion method to facilitate detailed interactions between visual and non-visual data by tokenizing non-visual information and concatenating all tokens (visual or non-visual) as the input to the model.
View Article and Find Full Text PDFClin Transl Sci
January 2025
Department of Pharmacy, Nanfang Hospital, Southern Medical University, Guangzhou, China.
Mycophenolic acid (MPA) is commonly used to treat autoimmune diseases in children, and therapeutic drug monitoring is recommended to ensure adequate drug exposure. However, multiple blood sampling is required to calculate the area under the plasma concentration-time curve (AUC), causing patient discomfort and waste of human and financial resources. This study aims to use machine learning and deep learning algorithms to develop a prediction model of MPA exposure for pediatric autoimmune diseases with optimizing sampling frequency.
View Article and Find Full Text PDFFront Public Health
December 2024
Department of Statistics, College of Science, Bahir Dar University, Bahir Dar, Ethiopia.
Introduction: Dynamic Bayesian networks improve the modeling of complex systems by incorporating continuous probabilistic relationships between covariates that change over time. This study aimed to analyze the complex causal links contributing to child undernutrition using dynamic Bayesian network modeling, examining both the best- and worst-case scenarios. The Young Cohort of the Ethiopian Young Lives dataset from 2002-2016 was used to analyze the complex relationships among various covariates influencing child undernutrition.
View Article and Find Full Text PDFJ Cell Mol Med
December 2024
Department of Critical Care Medicine, the First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, P. R. China.
Sevoflurane's potential impact on cognitive function and neurodevelopment, especially in susceptible populations such as infants and the elderly, has raised widespread concern. This study focuses on how sevoflurane induces ferroptosis in astrocytes and identifies solute carrier family 7 member 11 (SLC7A11) as a mediator of ferroptosis, providing new insights into sevoflurane-related neurotoxic pathways. We analysed single-cell sequencing (scRNA-seq) data from sevoflurane-exposed mice and control mice, supplemented with bulk RNA-seq data, to assess gene expression alterations.
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