Fetal bone development in the lowland paca ( Cuniculus paca , Rodentia, Cuniculidae) determined using ultrasonography

2020 ◽  
Vol 237 (1) ◽  
pp. 105-118
Author(s):  
Gessiane Pereira da Silva ◽  
Frederico Ozanan Barros Monteiro ◽  
Thyago Habner de Souza Pereira ◽  
Sandy Estefany Rodrigues de Matos ◽  
Rafael Santos de Andrade ◽  
...  
Keyword(s):  
Bone Development ◽  
Fetal Bone

Notice of Retraction: The Effects of Season on Fetal Bone Development: A Study from Chinese Newborn Infants

2011 ◽  
Author(s):  
X. P. Liao ◽  
X. J. Zhou ◽  
C. H. Yan ◽  
W. L. Zhang ◽  
X. D. Yu
Keyword(s):  
Bone Development ◽  
Newborn Infants ◽  
Fetal Bone

scholarly journals Low maternal vitamin D status and fetal bone development: Cohort study

2009 ◽  
Vol 25 (1) ◽  
pp. 14-19 ◽  
Author(s):  
Pamela Mahon ◽  
Nicholas Harvey ◽  
Sarah Crozier ◽  
Hazel Inskip ◽  
Sian Robinson ◽  
...  
Keyword(s):  
Vitamin D ◽  
Cohort Study ◽  
Bone Development ◽  
Vitamin D Status ◽  
Maternal Vitamin ◽  
Fetal Bone ◽  
Maternal Vitamin D Status

Reduced tibial speed of sound in Chinese infants at birth compared with Caucasian peers: the effects of race, gender, and vitamin D on fetal bone development

2010 ◽  
Vol 21 (12) ◽  
pp. 2003-2011 ◽  
Author(s):  
X. P. Liao ◽  
W. L. Zhang ◽  
C. H. Yan ◽  
X. J. Zhou ◽  
P. Wang ◽  
...  
Keyword(s):  
Vitamin D ◽  
Speed Of Sound ◽  
Bone Development ◽  
Fetal Bone

scholarly journals Collagenase-3 (MMP-13) is expressed by hypertrophic chondrocytes, periosteal cells, and osteoblasts during human fetal bone development

1997 ◽  
Vol 208 (3) ◽  
pp. 387-397 ◽  
Author(s):  
Nina Johansson ◽  
Ulpu Saarialho-Kere ◽  
Kristiina Airola ◽  
Riitta Herva ◽  
Liisa Nissinen ◽  
...  
Keyword(s):  
Bone Development ◽  
Hypertrophic Chondrocytes ◽  
Fetal Bone ◽  
Periosteal Cells

Influence of Maternal Estrogen on Fetal Bone Development in the Rabbit

1968 ◽  
Vol 31 (3) ◽  
pp. 346-353 ◽  
Author(s):  
RAJA W. ABDUL-KARIM ◽  
JOHN T. PRIOR ◽  
ROBERT E. L. NESBITT
Keyword(s):  
Bone Development ◽  
Fetal Bone

Evaluation of 9.4-T MR microimaging in assessing normal and defective fetal bone development: comparison of MR imaging and histological findings

Bone ◽  
2004 ◽  
Vol 34 (4) ◽  
pp. 619-628 ◽  
Author(s):  
Yoko Ichikawa ◽  
Misa Sumi ◽  
Nobu Ohwatari ◽  
Toshihisa Komori ◽  
Tadateru Sumi ◽  
...  
Keyword(s):  
Mr Imaging ◽  
Bone Development ◽  
Histological Findings ◽  
Fetal Bone

The Role of the Perichondrium in Fetal Bone Development

2007 ◽  
Vol 1116 (1) ◽  
pp. 59-64 ◽  
Author(s):  
H. M. KRONENBERG
Keyword(s):  
Bone Development ◽  
Fetal Bone

scholarly journals Machine learning classification models for fetal skeletal development performance prediction using maternal bone metabolic proteins in goats

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7840
Author(s):  
Yong Liu ◽  
Cristian R. Munteanu ◽  
Qiongxian Yan ◽  
Nieves Pedreira ◽  
Jinhe Kang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Efficient Method ◽  
Bone Development ◽  
Late Gestation ◽  
Classification Models ◽  
Experimental Conditions ◽  
Fetal Bone ◽  
Nutritional Conditions ◽  
Feature Importance ◽  
Importance Analysis

Background In developing countries, maternal undernutrition is the major intrauterine environmental factor contributing to fetal development and adverse pregnancy outcomes. Maternal nutrition restriction (MNR) in gestation has proven to impact overall growth, bone development, and proliferation and metabolism of mesenchymal stem cells in offspring. However, the efficient method for elucidation of fetal bone development performance through maternal bone metabolic biochemical markers remains elusive. Methods We adapted goats to elucidate fetal bone development state with maternal serum bone metabolic proteins under malnutrition conditions in mid- and late-gestation stages. We used the experimental data to create 72 datasets by mixing different input features such as one-hot encoding of experimental conditions, metabolic original data, experimental-centered features and experimental condition probabilities. Seven Machine Learning methods have been used to predict six fetal bone parameters (weight, length, and diameter of femur/humerus). Results The results indicated that MNR influences fetal bone development (femur and humerus) and fetal bone metabolic protein levels (C-terminal telopeptides of collagen I, CTx, in middle-gestation and N-terminal telopeptides of collagen I, NTx, in late-gestation), and maternal bone metabolites (low bone alkaline phosphatase, BALP, in middle-gestation and high BALP in late-gestation). The results show the importance of experimental conditions (ECs) encoding by mixing the information with the serum metabolic data. The best classification models obtained for femur weight (Fw) and length (FI), and humerus weight (Hw) are Support Vector Machines classifiers with the leave-one-out cross-validation accuracy of 1. The rest of the accuracies are 0.98, 0.946 and 0.696 for the diameter of femur (Fd), diameter and length of humerus (Hd, Hl), respectively. With the feature importance analysis, the moving averages mixed ECs are generally more important for the majority of the models. The moving average of parathyroid hormone (PTH) within nutritional conditions (MA-PTH-experim) is important for Fd, Hd and Hl prediction models but its removal for enhancing the Fw, Fl and Hw model performance. Further, using one feature models, it is possible to obtain even more accurate models compared with the feature importance analysis models. In conclusion, the machine learning is an efficient method to confirm the important role of PTH and BALP mixed with nutritional conditions for fetal bone growth performance of goats. All the Python scripts including results and comments are available into an open repository at https://gitlab.com/muntisa/goat-bones-machine-learning.

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