We report quantitative measurements of ten parameters of nutritive sucking behavior in 91 normal full-term infants obtained using a novel device (an Orometer) and a data collection/analytical system (Suck Editor). The sucking parameters assessed include the number of sucks, mean pressure amplitude of sucks, mean frequency of sucks per second, mean suck interval in seconds, sucking amplitude variability, suck interval variability, number of suck bursts, mean number of sucks per suck burst, mean suck burst duration, and mean interburst gap duration. For analyses, test sessions were divided into 4 × 2-min segments. In single-study tests, 36 of 60 possible comparisons of ten parameters over six pairs of 2-min time intervals showed a p value of 0.05 or less. In 15 paired tests in the same infants at different ages, 33 of 50 possible comparisons of ten parameters over five time intervals showed p values of 0.05 or less. Quantification of nutritive sucking is feasible, showing statistically valid results for ten parameters that change during a feed and with age. These findings suggest that further research, based on our approach, may show clinical value in feeding assessment, diagnosis, and clinical management.
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http://dx.doi.org/10.1007/s00455-010-9305-1 | DOI Listing |
BMC Public Health
January 2025
Meizhou Hospital of Guangzhou University of Chinese Medicine, Meizhou, Guangdong, China.
Objective: Hypertension increases the prevalence of depression to a certain extent and identification and diagnosis of depression frequently pose challenges for clinicians. The study aimed to construct and validate a scoring model predicting the prevalence of depression with hypertension.
Methods: 6124 individuals with hypertension were utilized from the 2007 to 2020 National Health and Nutrition Examination Survey database (NHANES), including 645 subjects that were assessed to have depressive symptoms, 390 in the development group and 255 in the validation group.
BMC Med Imaging
January 2025
Department of Neurosurgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
Purpose: We used knowledge discovery from radiomics of T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (T1C) for assessing relapse risk in patients with high-grade meningiomas (HGMs).
Methods: 279 features were extracted from each ROI including 9 histogram features, 220 Gy-level co-occurrence matrix features, 20 Gy-level run-length matrix features, 5 auto-regressive model features, 20 wavelets transform features and 5 absolute gradient statistics features. The datasets were randomly divided into two groups, the training set (~ 70%) and the test set (~ 30%).
NPJ Biofilms Microbiomes
January 2025
Center of Reproductive Medicine, Shengjing Hospital of China Medical University, Shenyang, China.
This study aims to evaluate differences in gut microbiota structures between infertile women undergoing frozen embryo transfer (FET) with gestational diabetes mellitus (GDM) and healthy controls (HCs), and to identify potential markers. We comprehensively enrolled 193 infertile women undergoing FET (discovery cohort: 38 HCs and 31 GDM; validation cohort: 85 HCs and 39 GDM). Gut microbial profiles of the discovery cohort were investigated during the pre-pregnancy (Pre), first trimester (T1), and second trimester (T2).
View Article and Find Full Text PDFSci Rep
January 2025
Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden.
Subject-specific parameters in lumped hemodynamic models of the cardiovascular system can be estimated using data from experimental measurements, but the parameter estimation may be hampered by the variability in the input data. In this study, we investigate the influence of inter-sequence, intra-observer, and inter-observer variability in input parameters on estimation of subject-specific model parameters using a previously developed approach for model-based analysis of data from 4D Flow MRI acquisitions and cuff pressure measurements. The investigated parameters describe left ventricular time-varying elastance and aortic compliance.
View Article and Find Full Text PDFComput Biol Med
January 2025
Department of Automation, Tsinghua University, Beijing, China. Electronic address:
Background: Prognosis prediction in the intensive care unit (ICU) traditionally relied on physiological scoring systems based on clinical indicators at admission. Electrocardiogram (ECG) provides easily accessible information, with heart rate variability (HRV) derived from ECG showing prognostic value. However, few studies have conducted a comprehensive analysis of HRV-based prognostic model against established standards, which limits the application of HRV's prognostic value in clinical settings.
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