CYTOTOXIC ACTIVITY OF ALKALOID EXTRACTS OF DIFFERENT PLANTS AGAINST BREAST CANCER CELL LINE

Objectives: To study in vitro cytotoxic activity of total alkaloid extracts of Pinus sabiniana L., Phoenix dactylifera L. and Ferocactus sp. L. against breast cancer cell line Michigan Cancer Foundation-7 (MCF-7) and non-tumorigenic fetal hepatic cell line (WRL-68). Methods: Plant powder of each P. sabiniana L. leaves, P. dactylifera L. pollen grains, and Ferocactus sp. L. The leaves were extracted separately with 80% methanol, chloroform at pH 2 and pH 10 and the chloroform portion was dried to obtain the total alkaloid extracts. The total alkaloids were detected qualitatively by Mayer's, Dragendorff's and Hager's reagents and estimated quantitatively by bromocresol green spectrophotometry depending on the atropine calibration curve. The cytotoxic activity was evaluated by 3-[4, 5-dimethylthiazoyl]-2, 5-diphenyltetrazolium bromide assay. Results: The extract of P. sabiniana L. had highest total alkaloid content (164.62±2.8 mg/100 g dry weight of plant) than the other plants (P. dactylifera l., Ferocactus sp. L.), the total alkaloids of Ferocactus sp. L. and P. dactylifera L., reduced the cell viability of both cell lines, the highest reduction occurred in the concentration 400 μg/ml was 46±2.20% (MCF-7) and 56.2±2.2% (WRL-68) for Ferocactus sp. L., followed by 56.2±2.2% (MCF-7) and 57.5±3.2% (WRL-68) for P. dactylifera L. The alkaloids of P. sabiniana was very lower effects on both cell lines MCF-7, and WRL-68 was 89.3±3.44% and 90.16±2.7%, respectively, at the same concentration. Conclusion: Plant alkaloids had variable effects against cancer and normal cell lines depending on the type of alkaloid compounds and their concentration in the extract

Hungry Bone Syndrome Associated with Transient Hypoparathyroidism

We report on an infant who presented at 50 days old of age with hypocalcemic seizure, who proved to have transient hypoparathyroidism, biochemically. During the course of his therapy, he developed severe hungry bone syndrome. Hungry bone syndrome and transient hypoparathyroidism is highlighted

Levels, distribution profiles and risk assessment of chlorinated organophosphate esters in car and road dust from Basrah, Iraq

The occurrence, concentrations, and distribution profiles of chlorinated organophosphate esters (Cl-OPEs) were investigated in seventy-one car and road dust samples collected from Basrah, southern Iraq. In addition, estimated daily intakes (EDIs) via dust ingestion were assessed for toddlers, regular adults, and taxi drivers. In car dust samples, the concentrations of Σ3Cl-OPEs ranged from 4120 to 73200 ng/g (median 11700 ng/g) with tris (1,3-dichloroisopropyl) phosphate (TDCIPP) the predominant compound. In road dust samples, the concentrations of Σ3Cl-OPEs ranged from 269 to 3400 ng/g (median 373 ng/g) and 114–526 ng/g (median 222 ng/g) in urban and rural areas, respectively, with tris (2-chloroisopropyl) phosphate (TCIPP), predominant. Concentrations of Cl-OPEs in urban road dust are significantly higher (P &lt; 0.05) than those in rural road dust, suggesting commercial and industrial activity, population density, and heavy traffic may influence the concentrations. The different compositional profiles of Cl-OPEs in car and road dust may be attributed to the physicochemical properties of Cl-OPEs and the pathways through which they can be released into indoor and outdoor environments. EDI values of Cl-OPEs for the Iraqi population via car dust ingestion were in the order: toddlers &gt; taxi drivers &gt; regular adults, exceeding those via road dust by factors of 27 and 40 from urban and rural dust, respectively. For people who work as taxi drivers, EDIs were seven times higher than those of regular adults, implying that people - such as professional drivers - who spend a substantial amount of time in their vehicles may be exposed to hazardous levels of Cl-OPEs. Despite the study showing that the EDIs through dust ingestion for the three population groups were well below the reference dose (RfD) levels, further studies are recommended to assess other pathways, such as inhalation, dietary sources, and dermal absorption.</p

A Proposed Approach for Object Detection and Recognition by Deep Learning Models Using Data Augmentation

Object detection and recognition play a crucial role in computer vision applications, ranging from security systems to autonomous vehicles. Deep learning algorithms have shown remarkable performance in these tasks, but they often require large, annotated datasets for training. However, collecting such datasets can be time-consuming and costly. Data augmentation techniques provide a solution to this problem by artificially expanding the training dataset. In this study, we propose a deep learning approach for object detection and recognition that leverages data augmentation techniques. We use deep convolutional neural networks (CNNs) as the underlying architecture, specifically focusing on popular models such as You Only Look Once version 3 (YOLOv3). By augmenting the training data with various transformations, such as rotation, scaling, and flipping, we can effectively increase the diversity and size of the dataset. Our approach not only improves the robustness and generalization of the models but also reduces the risk of overfitting. By training on augmented data, the models can learn to recognize objects from different viewpoints, scales, and orientations, leading to improved accuracy and performance. We conduct extensive experiments on benchmark datasets and evaluate the performance of our approach using standard metrics such as precision, recall, and mean average precision (mAP). The experimental results demonstrate that our data augmentation-based deep learning approach achieves superior object detection and recognition accuracy compared to traditional training methods without data augmentation. We compare the average accuracy of the YOLOv3-SPP model with two other variants of the YOLOv3 algorithm: one with a feature extraction network consisting of 53 convolutional layers and the other with 13 convolutional layers. The average accuracy of the proposed model (YOLOv3-SPP) is reported as accuracy of 97%, F1-score of 96%, precision of 94%, and average Intersection over Union (IoU) of 78.04%

A Comparative Study of the Effects of Age and Smoking on Nail Growth Rate in Healthy Individuals

Background: The nail organ has an important functional and aesthetic importance. Nail Growth Rate (NGR) has attracted the attention of many investigators not only due to the importance of the nail apparatus but also as a tool to reflect health. Objective: To study the effects of smoking and age on nail growth. Patients and methods:The study was conducted at the Department of Physiology and the Department of Dermatology, College of Medicine, University of Baghdad during the period from January 2011 to May 2011. Nail growth measurement was performed by etching a T – mark on the nail plate of the right and the left thumbs with a wide bore needle. The vertical distance between the point of meeting of the T and the proximal nail fold was measured using (vernier). A second measurement was performed one month later. The difference between the 2 readings was divided by the number of days between the readings to give the NGR. Results:The NGR was measured in a total of 106 subjects. The subjects were divided into 3 groups: Group1 (Smoker male subjects): 23 subjects were included in this group. Their ages ranged between 20 and 59 years with a mean of 35.87 +11.72 years. Group 2 (Non-smoker male subjects): 34 subjects were included. Their ages ranged between 13 and 52 years with a mean of 31.76 + 10.16 years.Group 3 (Non-smoker female subjects): 49 subjects were included. Their ages ranged between 8 and 58 years with a mean of 27.49 +11.66 years. NGR measurement in: Group 1:The mean growth rate in the right thumb was 95.4 + 28.8 microns/day, and for the left thumb was 96 + 34 microns/day. Group 2:The NGR was 105.6 + 34.35, and 103.4 + 34.24 microns /day, for the right thumb and the left thumb respectively. . Group 3: The NGR was 100 .5 + 33.52 microns/day for the right thumb and 101.6 + 31.77 microns /day for the left thumb. Conclusion: Age was inversely correlated with nail growth in right and left thumbs in all groups. The duration of smoking and number of cigarettes was inversely related to nail growth, but it did not reach statistical significance

Characterizing the morbid genome of ciliopathies

Background Ciliopathies are clinically diverse disorders of the primary cilium. Remarkable progress has been made in understanding the molecular basis of these genetically heterogeneous conditions; however, our knowledge of their morbid genome, pleiotropy, and variable expressivity remains incomplete. Results We applied genomic approaches on a large patient cohort of 371 affected individuals from 265 families, with phenotypes that span the entire ciliopathy spectrum. Likely causal mutations in previously described ciliopathy genes were identified in 85% (225/265) of the families, adding 32 novel alleles. Consistent with a fully penetrant model for these genes, we found no significant difference in their “mutation load” beyond the causal variants between our ciliopathy cohort and a control non-ciliopathy cohort. Genomic analysis of our cohort further identified mutations in a novel morbid gene TXNDC15, encoding a thiol isomerase, based on independent loss of function mutations in individuals with a consistent ciliopathy phenotype (Meckel-Gruber syndrome) and a functional effect of its deficiency on ciliary signaling. Our study also highlighted seven novel candidate genes (TRAPPC3, EXOC3L2, FAM98C, C17orf61, LRRCC1, NEK4, and CELSR2) some of which have established links to ciliogenesis. Finally, we show that the morbid genome of ciliopathies encompasses many founder mutations, the combined carrier frequency of which accounts for a high disease burden in the study population. Conclusions Our study increases our understanding of the morbid genome of ciliopathies. We also provide the strongest evidence, to date, in support of the classical Mendelian inheritance of Bardet-Biedl syndrome and other ciliopathies

Development of duststorm attenuation model for microwave links

Duststorms are significant meteorological phenomenon occur for a significant percentage of time in arid and semi arid areas especially at African Sahara and Middle East. Measurements at existing microwave links show that the duststorms can potentially result in serious attenuation in signal level especially at Ku band and higher frequencies with direct impact on telecommunications system performance. Very limited research has been done to predict the attenuation even the scarcity of measured data forces the researcher to work for the duststorm prediction modeling

Linear, Deterministic, and Order-Invariant Initialization Methods for the K-Means Clustering Algorithm

Over the past five decades, k-means has become the clustering algorithm of choice in many application domains primarily due to its simplicity, time/space efficiency, and invariance to the ordering of the data points. Unfortunately, the algorithm's sensitivity to the initial selection of the cluster centers remains to be its most serious drawback. Numerous initialization methods have been proposed to address this drawback. Many of these methods, however, have time complexity superlinear in the number of data points, which makes them impractical for large data sets. On the other hand, linear methods are often random and/or sensitive to the ordering of the data points. These methods are generally unreliable in that the quality of their results is unpredictable. Therefore, it is common practice to perform multiple runs of such methods and take the output of the run that produces the best results. Such a practice, however, greatly increases the computational requirements of the otherwise highly efficient k-means algorithm. In this chapter, we investigate the empirical performance of six linear, deterministic (non-random), and order-invariant k-means initialization methods on a large and diverse collection of data sets from the UCI Machine Learning Repository. The results demonstrate that two relatively unknown hierarchical initialization methods due to Su and Dy outperform the remaining four methods with respect to two objective effectiveness criteria. In addition, a recent method due to Erisoglu et al. performs surprisingly poorly.Comment: 21 pages, 2 figures, 5 tables, Partitional Clustering Algorithms (Springer, 2014). arXiv admin note: substantial text overlap with arXiv:1304.7465, arXiv:1209.196