Objectives: to compare health of mothers and neonatal among age groups, to find out the correlation between
age groups and mother and neonatal health.
Methodology: A descriptive study was carried out at delivery rooms of three teaching hospitals in Baghdad city
from Feb. 28th through May. 28th
, 2013. A purposive (non-probability) sample of 300 laboring women was selected
from delivery rooms categorized into three groups, group 1 (≤19) years, group 2 their age between (20-35) years
old and group 3 their age (≥35) years. The data were collected through the use of constructing questionnaire, an
interview technique with mothers and reviewing their medical records as means of data collection; The
questionnaire consist of 3 parts: (1) demographic data (2) health of mothers during pregnancy (pregnancy
complications) (3) neonate health status. The validity of the study questionnaire was determined through a panel
of experts and the reliability of the study questionnaire was determine by the application of pilot study.
Descriptive statistical analysis procedure (frequencies and percentages) and inferential data analysis procedure
(chi-square, P-value, split- half approach and Cronbach's Alpha correlation coefficients) were applied for data
analysis.
Results: the finding of the study indicated that the highest percentages of all the study groups were primary school
graduates, urban and housewives. More than half of mothers in group 1 (teenage mothers) and group 2 (20-35)
years were related to their husband, while the highest percentages of mothers in group 3 (advanced maternal age)
were not related to their husband. The highest percentages of complications present in group 1 (teenage mothers)
were anemia, urinary tract infection, multiple pregnancy, Oligohydramnios, poly-hydramnios, intra uterine growth
retardation, Post-term labor, low Apgar score, low birth weight, congenital anomalies, respiratory distrees
syndrome, meconium aspiration syndrome and neonatal death. The highest percentages of complications present
in group 3 (advanced maternal age) were pregnancy induced hypertension, gestational diabetes, antepartum
haemorrhage, pre-term labor, premature rupture of membrane and macrosomia. There is a statistical significant
correlation between age groups and hypertension and preterm, Apgar score, neonatal weight and respiratory
distress syndrome.
Recommendations: Care providers need to be aware about the increasing of obstetrical and neonatal health
complications in the extremities of maternal age (teenage and advanced maternal age) and adjust obstetrical and
neonatal management protocols to ensure optimal maternal and neonatal health
(16)
(18)
(14)
Abstract :H.pylori is an important cause of gastric duodenal disease, including gastric ulcers, Mucosa-associated lymphoid tissue (MALT), and gastric carcinoma. biosensors are becoming the most extensively studied discipline because the easy, rapid, low-cost, highly sensitive, and highly selective biosensors contribute to advances in next-generation medicines such as individualized medicine and ultrasensitive point-of-care detection of markers for diseases. Five of ten patients diagnosed with H.pylori ranging in age from 15–85 participated in this research. who [gastritis, duodenitis, duodenal ulcer (DU), and peptic ulcer (PU)] Suspected H.pylori colonies w
... Show More
Eco-friendly concrete is produced using the waste of many industries. It reduces the fears concerning energy utilization, raw materials, and mass-produced cost of common concrete. Several stress-strain models documented in the literature can be utilized to estimate the ultimate strength of concrete components reinforced with fibers. Unfortunately, there is a lack of data on how non-metallic fibers, such as polypropylene (PP), affect the properties of concrete, especially eco-friendly concrete. This study presents a novel approach to modeling the stress-strain behavior of eco-friendly polypropylene fiber-reinforced concrete (PFRC) using meta-heuristic particle swarm optimization (PSO) employing 26 PFRC various mixtures. The cement was partia
... Show More
(4)
(1)
(22)
(20)
(2)
(24)
(5)
During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve
... Show More
(4)
(28)
(24)