STABILITY-INDICATING LIQUID CHROMATOGRAPHY METHOD FOR THE DETERMINATION OF PENTOXYVERINE CITRATE AND ITS DEGRADANT

Objectives: The objectives of this research were to develop and validate a HPLC method for determination of Pentoxyverine citrate (PNX) and its degradant (DG). Methods: Forced degradation studies were performed on bulk sample using alkaline (0.1M sodium hydroxide) and acid (1M hydrochloric acid. The proposed method was based on using a 150 x 4.6 mm (i. d.) (Luna, Phenomenex, Torrance, CA, USA) (5 µm particle size) reversed phase C18 column with mobile phase consisting of a mixture of methanol-10 mM sodium dihydrogen phosphate pH 4 in ratio of (60:40, v/v) and UV detection at 230 nm with flow rate of 1 mL min-1. Results: The linear calibration range was between 10-40 mg ml-1and 10-40 mg ml-1for PNX and DG respectively. The method was found to be accurate with 100.23% and 100.07% recovery for PNX and DG respectively. The limit of detection (LOD) was found to be 3.79 x10-2μg ml-1 and 4.24 x10-2μg ml-1 for PNX and DG respectively, while the limit of quantification (LOQ) was found to be 12.62 x10-2μg ml-1 and 14.12 x10-2μg ml-1 for PNX and DG respectively. PNX was found to be most stable at a pH of 5.7. Conclusion: The validation study of the proposed method was successfully carried out and the method was found to be suitable and economic for routine determination of PNX in pharmaceutical syrup, without any interference from the excipients, and in the presence of its acidic and alkaline degradation products

Productive Performance of F1- (Damascus Goat × Desert) under Traditional Management in North Kordofan

This study carried out in North Kordofan state, Western Sudan (latitudes 12:15-16:32 N and longitudes 27-32 E), during the period Feb 2018 - Aug 2021 to assessment the productive and reproductive performance of crossed F1 (Desert× Damascus) under the traditional management systems, to evaluate adaptation of crossed to environmental conditions of study area, to study milk production of the crossed goats. 44 females crossed goat F1 (Damascus×Desert goat) were selected at weaning age. Two mature Damascus bucks were used in the experiment all animals were identified using ear tags and treated against external and internal parasites. All animals will set free during the day and kept in closed pen during the night. The behavior of the animals and the adaptation on the environment was observed and recorded in addition to any problems like disease; Data were analyzed using the Statistical Package for Social Sciences, software package (SPSS, V23, 2017). In this study, the Damascus Bucks were severely affected under the conditions of nature grazing and suffered from parasites and high temperature, Bucks have died as a result of tick fever according to Anatomical symptoms, The parameters of dams varied according to litter size, Females were born as a single reached puberty at (278.2±7 days) with body weight of 20.01 kg while the twins females was puberty age (291.6±9 days), and weight was 16.91kg , The average lactation period was 120 days with average total milk production of 40.46± 1.74 kg, this study conclude that the crossed Females are late in sexual maturity, So The genetic cross as a way to improve the herd without taking into account environmental conditions does not lead to the desired results.&nbsp

An efficient algorithm for data parallelism based on stochastic optimization

Deep neural network models can achieve greater performance in numerous machine learning tasks by raising the depth of the model and the amount of training data samples. However, these essential procedures will proportionally raise the cost of training deep neural network models. Accelerating the training process of deep neural network models in a distributed computing environment has become the most often utilized strategy for developers in order to better cope with a huge quantity of training overhead. The current deep neural network model is the stochastic gradient descent (SGD) technique. It is one of the most widely used training techniques in network models, although it is prone to gradient obsolescence during parallelization, which impacts the overall convergence. The majority of present solutions are geared at high-performance nodes with minor performance changes. Few studies have taken into account the cluster environment in high-performance computing (HPC), where the performance of each node varies substantially. A dynamic batch size stochastic gradient descent approach based on performance-aware technology is suggested to address the aforesaid difficulties (DBS-SGD). By assessing the processing capacity of each node, this method dynamically allocates the minibatch of each node, guaranteeing that the update time of each iteration between nodes is essentially the same, lowering the average gradient of the node. The suggested approach may successfully solve the asynchronous update strategy’s gradient outdated problem. The Mnist and cifar10 are two widely used image classification benchmarks, that are employed as training data sets, and the approach is compared with the asynchronous stochastic gradient descent (ASGD) technique. The experimental findings demonstrate that the proposed algorithm has better performance as compared with existing algorithms

Could heart-type fatty acid binding protein predict clinical outcome in coronary artery bypass graft surgery?

Background: Detection of myocardial damage and its degree during open heart surgery were studied previously using different biomarkers. Heart fatty acid binding protein (h-FABP) was used in the diagnosis of myocardial infarction with variable results. In this study, we aimed to find the possibility of the use of this biomarker as a predictor of myocardial damage after coronary artery bypass graft (CABG) surgery. Methods: We conducted a prospective study on 47 patients who had CABG surgery. Blood samples (4 ml) were drawn from patients at 5 points: before induction of anesthesia, after aortic declamping, 1 hour after declamping, 6 hours after declamping and one day after surgery. Levels of h-FABP and creatine kinase muscle/brain (CK-MB) were estimated, and the relationship between h-FABP and operative and postoperative outcomes were recorded. Results: There were statistically significant correlations between higher levels of h-FABP measured immediately after aortic declamping and need for intra-aortic balloon (116.55 + 9.26 vs, 84.34 + 19.55 ng/ml; p= 0.022), inotropes (107.04+ 14.79 vs, 79.95 + 17.59ng/ml; p< 0.001), defibrillators (97.73 + 15.18 vs 81.59 + 20.31 ng/ml; p=0.016), and postoperative atrial fibrillation (99.94 + 17.83 vs 80.84 + 18.89ng/ml; p= 0.004). No mortality was detected in our study. h-FABP levels showed an early peak just after aortic declamping and reached baseline by postoperative day one. CK-MB peaked 1 hour after aortic declamping and remained elevated for more than 24 hours. Conclusion: h-FABP is a cardiac biomarker that could be used as a rapid indicator of ventricular dysfunction and atrial fibrillation post-CABG surgery

The effect of regular daily walking on adverse pregnancy outcomes among overweight primigravidas: a prospective cohort study

Objective: The study aims to evaluate the effect of regular daily walking on the occurrence of adverse pregnancy outcomes among overweight primigravidas. Materials and Methods: A prospective cohort study conducted at Aswan University Hospital from March 2015 to December 2016. The study included 360 overweight primigravidas, with singleton pregnancies, who were divided into two groups: Group I (control group) and Group II (study group) under supervised regular walking 5 times per week for 30 minutes, starting from 10-12 weeks of gestational age to 38-39 weeks of gestational age. The primary outcome of the study was the rate of gestational weight gain in participants in both groups. Secondary outcomes included the rate of gestational diabetes mellitus, gestational hypertension, preeclampsia, preterm labor ( 4 kg), excessive maternal weight gain (>11.5 kg), and the rate of cesarean delivery. Results: There were no significant differences between groups with regard to the basic criteria. The exercise program decreased the incidence of preeclampsia (OR=0.120; 95% CI=0.015-0.970; p=0.037), postdate (OR=0.274; 95% CI= 0.099-0.759); p=0.008), excessive weight gain (OR=0.220; 95% CI=0.114-0.424), p=0.000), and cesarean delivery (OR=0.519; 95% CI=0.316-0.841, p=0.007). Conclusion: Regular maternal walking throughout the pregnancy may be a preventive tool for preeclampsia, postdate pregnancy, excessive weight gain and may decrease the incidence of cesarean delivery in primigravidas

Pregnancy outcome according to body mass index in primigravidas: a prospective cohort study

Objective: The study aims to evaluate the association between Body Mass Index (BMI), gestational weight gain (GWG) and adverse obstetric outcomes among primigravidas. Material and methods: This was a prospective cohort study conducted at a tertiary University Hospital between June 2015 and May 2017. The study included 480 primigravidas, with singleton pregnancies, who were divided into three groups: women with a healthy weight (BMI: 18.5–24.9 kg/m2) overweight (BMI: 25 – 29.9 kg/m2) and obese (BMI ≥ 30 kg/m2). The primary outcome of the study was the rate of GWG in the participants. Secondary outcomes included the rate of gestational diabetes mellitus (GDM), gestational hypertension, pre-eclampsia, preterm labor (PTL), postdate pregnancy, fetal macrosomia and the rate of birth by cesarean (CB). Results: There were no significant differences between groups regarding the socio-demographic criteria. The rate of GWG was significantly higher in obese women versus average weight women (11.4±1.73 vs. 10.49±1.09, p=0.0001). There was an increased incidence of GDM (p=0.008), gestational hypertension (p=0.001), pre-eclampsia (p=0.0001), PTL (p=0.002), postdate (p=0.0001) and macrosomia (p=0.0001) in women who were obese compared with women with a healthy weight. Additionally, there was an increased incidence of CB with increasing body mass (p=0.0001) Conclusions: Higher BMI in primigravidas is associated with increased GWG and with adverse pregnancy outcomes such as GDM, gestational hypertension, pre-eclampsia, PTL, postdate, fetal macrosomia and cesarean birth

Does lidocaine gel produce an effective analgesia prior to copper IUD insertion? Randomized clinical trial

Background: IUD is a small contraceptive device, often containing either copper or levonorgestrel, which is inserted into the uterus. Objective of present study was to determine if lidocaine gel prior to intrauterine device (IUD) insertion decreases pain with the insertion procedure among multiparous women choosing the copper T380A-IUD.Methods: It is a randomized double-blind controlled trial carried out at Assiut Women's Health Hospital, Assiut, Egypt. Parous women eligible for Copper IUD insertion attended the Family Planning Clinic were recruited and randomized in a 1:1 ratio to lidocaine gel or placebo. Two ml of the study medications were topically placed on the cervix 3 minutes before IUD insertion. The primary outcome was the difference in pain scores using a 10-cm Visual Analogue Scale (VAS) during IUD insertion. We considered a 1.5 cm difference in VAS scores between study groups as clinically significant.Results: One hundred women consented to participate and randomized either to group I:  lidocaine group or group II: placebo group. Both groups were homogenous in baseline socio-demographic data. There was significant difference in mean pain scores for IUD placement between women who received lidocaine gel and placebo at two steps of insertion (at vulsellum application and at uterine sounding) while the rest of steps show no statistical significant difference (p=0.000). There were no statistical significant differences between both group as regard the ease of insertion, the duration of insertion and the satisfaction score after the procedure (p>0.05).Conclusions: This study depicts that the use of lidocaine gel prior to copper IUD insertion in multiparous women could partially reduce the pain during tenaculum placement and uterine sounding

Novel salted anionic-cationic polymethacrylate polymer blends for sustained release of acidic and basic drugs

Background: Since a unique matrix tablet formulation that independently controls the release of various drug types is in a great demand, the objective of this research was to develop a sustained release matrix tablet as a universal dosage form using a binary mixture of the salt forms of Eudragit polymers rather than their interpolyelectrolyte complexes. Methods: Tablets were prepared by wet granulation and compressed at different compression forces, depending on drug type. Dissolution tests were conducted using USP XXII rotating paddle apparatus at 50 rpm at 37°C in consecutive pH stages. Results: Tablets containing Ibuprofen (IB) as a model acidic drug and Metronidazole (MD) as a model basic drug showed controlled/sustained release behavior. For IB tablets containing 80% Ibuprofen and 5% (w/w) polymeric combination; the time for 50% of the drug release was about 24 hours compared to 8.5 hours for plain tablets containing 80% IB. In case of MD, the drug release extended to about 7 hours for tablets containing 80% MD and 5% (w/w) polymeric combination, compared to about 1 hour for plain tablets containing 80% MD. In terms of extending the release of medications, the dissolution profiles of the tablets containing polymeric salts forms were found to be statistically superior to tablets prepared by direct compression of the polymers in their powdered base forms, and superior to tablets containing the same polymers granulated using isopropyl alcohol. Conclusion: The findings indicated the significance of combining the polymers in their salt forms in controlling the release of various drug types from matrices

Interpretable Deep Learning for Discriminating Pneumonia from Lung Ultrasounds

Lung ultrasound images have shown great promise to be an operative point-of-care test for the diagnosis of COVID-19 because of the ease of procedure with negligible individual protection equipment, together with relaxed disinfection. Deep learning (DL) is a robust tool for modeling infection patterns from medical images; however, the existing COVID-19 detection models are complex and thereby are hard to deploy in frequently used mobile platforms in point-of-care testing. Moreover, most of the COVID-19 detection models in the existing literature on DL are implemented as a black box, hence, they are hard to be interpreted or trusted by the healthcare community. This paper presents a novel interpretable DL framework discriminating COVID-19 infection from other cases of pneumonia and normal cases using ultrasound data of patients. In the proposed framework, novel transformer modules are introduced to model the pathological information from ultrasound frames using an improved window-based multi-head self-attention layer. A convolutional patching module is introduced to transform input frames into latent space rather than partitioning input into patches. A weighted pooling module is presented to score the embeddings of the disease representations obtained from the transformer modules to attend to information that is most valuable for the screening decision. Experimental analysis of the public three-class lung ultrasound dataset (PCUS dataset) demonstrates the discriminative power (Accuracy: 93.4%, F1-score: 93.1%, AUC: 97.5%) of the proposed solution overcoming the competing approaches while maintaining low complexity. The proposed model obtained very promising results in comparison with the rival models. More importantly, it gives explainable outputs therefore, it can serve as a candidate tool for empowering the sustainable diagnosis of COVID-19-like diseases in smart healthcare