Background: Ruptured uterus is a catastrophic event to both
mother and her fetus. Apart from maternal and fetal mortality
rates, the incidence of rupture of uterus is often taken as an
index of the standard of obstetric care.
Objective: To determine the frequency, causes, management
outcome of ruptured uterus at Al Batool maternity hospital.
Method: The study was conducted in the department of
obstetrics and gynecology at Al Batool maternity hospital
Mosul-Iraq over a period of three years from October 1st 2002
to August 30th 2005. All the cases of uterine rupture presented
during the study period were recorded and managed in the
department. Data was recorded on designed forms.
Results: Thirty nine cases of ruptured uterus out of 44539
deliveries were registered, the incidence was 0.087%. Age
ranged from 15 to45 years. The majority of the patients were
grandmultipara (Para 5 and above), but 7 of the patients were
nulliparous women (17.94%). 26 ruptures (66.66%) occurred
in unscarred uterus and 13 ruptures (33.33%) occurred in
scarred uterus, cephalopelvic disproportion and obstructed
labor were the cause of rupture in 11 cases (28.2%), in
addition to the mentioned causes 11 cases of the uterine
ruptures we registered in our study (28.2%) were mishandled
by the traditional birth attendants, and in 14 cases (35.89%)
there was injudicious use of oxytocin. We had three maternal
deaths (7.69%) out of 39 patients, and only 10 fetuses out of
39(25.64%) were alive.
Conclusion: Rupture uterus is still an important cause of
maternal and perinatal mortality and morbidity in Iraq, it is
not always suspected, and hence increased vigilance is vital.
Further studies may help in the development of preventive
strategies and ensure prompt management to reduce maternal
and perinatal mortality and morbidity
The extraction of Cupressus sempervirens L. or cypress essential oil was studied in this paper. This cypress oil was extracted by using the hydro-distillation method, using a clevenger apparatus. Cupressus sempervirens L. leaves were collected from Hit city in Al-Anbar province – Iraq. The influences of three important parameters on the process of oil extraction; water which used as a solvent to the solid ratio (5:1 and 14:1 (ml solvent/g plant), temperature (30 to 100 °C) and processing time, were examined to obtain the best processing conditions to achieve the maximum yield of the essential oil. Also, the mathematical model was described to calculate the mass transfer coefficient. Therefore, the best conditions, that were obtained in
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A many risk challenge in (settings hospital) are multi- bacteria are antibiotic-resistant. Some type strains that ability adhesion surface-attached bio-film census. Fifteen MRSA isolates were considered as high biofilm producers Moreover all MRSA isolates; M3, M5, M7 and M11 produced biofilms but the thickest biofilm seen M7strain. The MIC values of N. sativa oil against clinical isolates of MRSA were between (0.25, 0.5, 0.75, 1.0) μg/ml While MRSAcin (50, 75, 100, 125) µg\ ml. All biofilms treated with MRSAcin and Nigella sativa developed a presence of live cells after cultured on plate agar with inhibition zone between MIC (18 – 15) and (14- 11)mm respectively.Yet, results showed that MRSA supernatant developed a inhibitory ef
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Manganese sulfate and Punica granatum plant extract were used to create MnO2 nanoparticles, which were then characterized using techniques like Fourier transform infrared spectroscopy, ultraviolet-visible spectroscopy, atomic force microscopy, X-ray diffraction, transmission electron microscopy, scanning electron microscopy, and energy-dispersive X-ray spectroscopy. The crystal's size was calculated to be 30.94nm by employing the Debye Scherrer equation in X-ray diffraction. MnO2 NPs were shown to be effective in adsorbing M(II) = Co, Ni, and Cu ions, proving that all three metal ions may be removed from water in one go. Ni(II) has a higher adsorption rate throughout the board. Co, Ni, and Cu ion removal efficiencies were 32.79%, 75
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The successful implementation of deep learning nets opens up possibilities for various applications in viticulture, including disease detection, plant health monitoring, and grapevine variety identification. With the progressive advancements in the domain of deep learning, further advancements and refinements in the models and datasets can be expected, potentially leading to even more accurate and efficient classification systems for grapevine leaves and beyond. Overall, this research provides valuable insights into the potential of deep learning for agricultural applications and paves the way for future studies in this domain. This work employs a convolutional neural network (CNN)-based architecture to perform grapevine leaf image classifi
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