Lung Cancer Detection Using Artificial Neural Network

In this paper, we developed an Artificial Neural Network (ANN) for detect the absence or presence of lung cancer in human body. Symptoms were used to diagnose the lung cancer, these symptoms such as Yellow fingers, Anxiety, Chronic Disease, Fatigue, Allergy, Wheezing, Coughing, Shortness of Breath, Swallowing Difficulty and Chest pain. They were used and other information about the person as input variables for our ANN. Our ANN established, trained, and validated using data set, which its title is “survey lung cancer”. Model evaluation showed that the ANN model is able to detect the absence or presence of lung cancer with 96.67 % accuracy

Web Application for Generating a Standard Coordinated Documentation for CS Students’ Graduation Project in Gaza Universities

The computer science (CS) graduated students suffered from documenting their projects and specially from coordinating it. In addition, students’ supervisors faced difficulties with guiding their students to an efficient process of documenting. In this paper, we will offer a suggestion as a solution to the mentioned problems; that is an application to make the process of documenting computer science (CS) student graduation project easy and time-cost efficient. This solution will decrease the possibility of human mistakes and reduce the effort of documenting process

Quality of life in prostate cancer survivors in developing countries: The case of the Gaza Strip, Palestine

Background & Aim: Prostate cancer is one of the most common cancers in males and the second leading cause of cancer-related deaths in Palestine. Although, many studies were conducted in de-veloped countries to evaluate quality of life (QOL) in survivors of prostate cancer, the researchers could not find any study that was conducted in a developing country including Palestine. Therefore, the purpose of this study was to evaluate the QOL of prostate cancer survivors residing in Gaza Strip, Palestine, as an example of a developing country, and compare it with the literature. Methods & Materials: A total of a 121 men who were diagnosed with prostate cancer and live in Gaza Strip participated in this cross-sectional study. The University of California at Los Angeles Prostate-Specific Index including the RAND 36-Item Health Survey v2 was used to assess QOL of participants. Results: Age of participants’ ranged between 52 and 89 years with a mean of 71.80 (SD 7.66). The greatest majority of participants (n= 94, 77.67%) were diagnosed with prostate cancer after the age of 60. The mean scores for the entire general QOL items was 47.93 (SD= 22.46) and the mean for all Prostate Cancer Index-University of California items was 44.20 (SD= 16.16). Conclusion: Prostate cancer survivors living in Gaza Strip, Palestine have lower level of QOL than their counterparts who live in developed courtiers. These differences could be related to early screening and advanced technology used to treat prostate cancer in developed countries. Health care providers and health care policy makers need to improve provided health care services and introduce screening

End of Life-Decisions: An Islamic Perspective

Patients who live with a low quality of life and suffer from chronic pain may wish to end their suffering through different means such as active euthanasia, passive euthanasia, and physician-assisted suicide. These alternatives to end one’s own life create many ethical dilemmas for health care professionals, patients, and family members. Some of these decisions are forbidden in Islamic Religion, while others are permitted. In this paper, the authors will discuss how Islam looks at these decisions. Knowing about how Islam deals with such decisions will be of great help for health care providers who take care of Muslim patients. It will also help patients and their families in making their decisions at the end of life

Suggestions to Enhance the Scholarly Search Engine: Google Scholar

The scholarly search engine Google Scholar (G.S.) has problems that make it not a 100% trusted search engine. In this research, we discussed a few drawbacks that we noticed in Google Scholar, one of them is related to how does it perform (add articles) option for adding new articles that are related to the registered researchers. Our suggestion is an attempt for making G.S. more efficient by improving the searching method that it uses and finally having trusted statistical results

Sesamin from Cuscuta palaestina natural plant extracts: Directions for new prospective applications

The aim of this study is to disclose the potential bioactive components of Cuscuta palaestina, a native parasitic natural plant of flora palaestina and to open direction towards new prospective application. GC-MS analysis identified 18 components in the methanolic extract of C. palaestina for the first time. The most appealing among them are Sesamin and two other phytosterols (Campesterol and Stigmasterol), all of which are documented in the scientific literature for their anticancer activity. Quantitation of Sesamin extracted from C. palaestina by HPLC-PDA with the use of three organic solvents showed that the Sesamin content in the methanolic extract was the highest. Following the disclosure of Sesamin presence in C. palaestina, we raised the question of whether it is produced naturally in C. palaestina or acquired from the host plant. The quantitation of Sesamin in C. palaestina was performed while being with five different host plants, and was compared with the amount of Sesamin in C. palaestina grown alone. The findings reveal that Sesamin is an endogenous secondary metabolite in C. palaestina. Thus, further studies are required to prove if C. palaestina can be used as an alternative source of anticancer phytochemicals, mainly Sesamin, and if proteins in the Sesamin production pathway could be valid biological targets for the development of novel and selective pesticides for control/ eradication of C. palaestina and maybe some other Cuscuta species. As well, the findings from this study raise a big question of whether inferring Sesamin production in C. palaestina could reduce its attack ability to host plants.This study was supported by unrestricted grants from Al-Qasemi Academic College and the Institute of Applied Research±Galilee Society. We acknowledge the Ministry of Science, Space and Technology. We declare that the funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript

Forage yield potential of Sudan grass-cowpea irrigated mixtures in central Sudan

     The study was conducted at the Gezira University Farm, Wad Medani, Sudan, during the winter of 1998 and autumn of 1999 to investigate the effects of nine cropping systems, two rates of nitrogen and two harvesting dates on the quantity and quality of the forage produced by Sudan grass-cowpea mixtures. In both seasons, Sudan grass in around two months from sowing while cowpea flowered in about one month. Black seeded Sudan grass variety (SG2) outperformed the light brown seeded Sudan grass variety (SGI). The autumn forage yields were higher than those of winter. Mixing increased the per plant growth parameters of both crops and across seasons. In winter, the fresh forage yield of pure cowpea and the highest yielding mixture (40 SG2 + 20 CP) were comparable (averaging 6.6 t/ha), while in autumn, the highest yielding crop mixture was 30 SG2 + 30 CP (19.01 t/ha) which is not siB1ificantly different from that of pure SG2 (19.08 t/ha). The land equivalent ratio (LER) values exceeded 1.00, in both seasons, showing a clear advantage of mixtures over monocultures. The addition of 44 kg N/ha significantly increased the growth parameters and forage yield of sole Sudan grass and its mixtures with cowpea but not that of pure cowpea. Harvesting at 60 days from sowing appreciably increased growth parameters, fresh forage yield and quality of forage produced by all seeding combinations. Mixing showed significant effects on crude protein percentage, crude fibre percentage, total crude protein and total crude fibre

Prediction Heart Attack using Artificial Neural Networks (ANN)

Abstract Heart Attack is the Cardiovascular Disease (CVD) which causes the most deaths among CVDs. We collected a dataset from Kaggle website. In this paper, we propose an ANN model for the predicting whether a patient has a heart attack or not that. The dataset set consists of 9 features with 1000 samples. We split the dataset into training, validation, and testing. After training and validating the proposed model, we tested it with testing dataset. The proposed model reached an accuracy of 98.01% on the Heart Disease Dataset

Post-traumatic stress disorder among health care providers two years following the Israeli attacks against Gaza Strip in August 2014: Another call for policy intervention

This study aimed to assess the level of posttraumatic stress disorder and to examine the relationship between exposure to war stress and posttraumatic symptoms among health care providers two years following Israeli offensives against Gaza Strip in 2014. Methodology: A cross-sectional design was used for this study. We targeted all nurses and doctors working in three governmental hospitals in the Gaza Strip who worked with victims of the 2014 war, more specifically, those who were working in emergency departments, intensive care units, operating rooms, surgical departments, and burn units. A demographic sheet and Impact Event Scale-Revised were used in this study. The Impact Event Scale-Revised has three sub-scales; intrusion, avoidance, and hyper-arousal. Results: The results showed that 291 (89.3%) out of 2444 participants had scores more than 35 (threshold cut-off point) on Impact Event Scale-Revised. Scores ranged from eight to 80 with a mean of 52.71. Females had higher levels of stress (55.33) than males (50.82) and nurses (52.67) had more stress than physicians (47.38). The most frequent symptoms of trauma subscales was “Intrusion” (mean=19.99), followed by “Avoidance” (mean=17.60), and then “Hyper-arousal” (mean=14.12). Level of trauma symptoms were not affected by place of living, hospital of work, while level of education had impacted level of trauma. Conclusion: The findings showed that health care providers still suffer from severe posttraumatic symptoms two years after exposure to a prolonged war stress. This level of trauma among health care providers warrants intervention programs to reduce stress and trauma among Gaza health care providers after the war.This study aimed to assess the level of posttraumatic stress disorder and to examine the relationship between exposure to war stress and posttraumatic symptoms among health care providers two years following Israeli offensives against Gaza Strip in 2014. Methodology: A cross-sectional design was used for this study. We targeted all nurses and doctors working in three governmental hospitals in the Gaza Strip who worked with victims of the 2014 war, more specifically, those who were working in emergency departments, intensive care units, operating rooms, surgical departments, and burn units. A demographic sheet and Impact Event Scale-Revised were used in this study. The Impact Event Scale-Revised has three sub-scales; intrusion, avoidance, and hyper-arousal. Results: The results showed that 291 (89.3%) out of 2444 participants had scores more than 35 (threshold cut-off point) on Impact Event Scale-Revised. Scores ranged from eight to 80 with a mean of 52.71. Females had higher levels of stress (55.33) than males (50.82) and nurses (52.67) had more stress than physicians (47.38). The most frequent symptoms of trauma subscales was “Intrusion” (mean=19.99), followed by “Avoidance” (mean=17.60), and then “Hyper-arousal” (mean=14.12). Level of trauma symptoms were not affected by place of living, hospital of work, while level of education had impacted level of trauma. Conclusion: The findings showed that health care providers still suffer from severe posttraumatic symptoms two years after exposure to a prolonged war stress. This level of trauma among health care providers warrants intervention programs to reduce stress and trauma among Gaza health care providers after the war

Diagnostic power of resting-state fMRI for detection of network connectivity in Alzheimer's disease and mild cognitive impairment: a systematic review

Resting-state fMRI (rs-fMRI) detects functional connectivity (FC) abnormalities that occur in the brains of patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI). FC of the default mode network (DMN) is commonly impaired in AD and MCI. We conducted a systematic review aimed at determining the diagnostic power of rs-fMRI to identify FC abnormalities in the DMN of patients with AD or MCI compared with healthy controls (HCs) using machine learning (ML) methods. Multimodal support vector machine (SVM) algorithm was the commonest form of ML method utilized. Multiple kernel approach can be utilized to aid in the classification by incorporating various discriminating features, such as FC graphs based on "nodes" and "edges" together with structural MRI-based regional cortical thickness and gray matter volume. Other multimodal features include neuropsychiatric testing scores, DTI features, and regional cerebral blood flow. Among AD patients, the posterior cingulate cortex (PCC)/Precuneus was noted to be a highly affected hub of the DMN that demonstrated overall reduced FC. Whereas reduced DMN FC between the PCC and anterior cingulate cortex (ACC) was observed in MCI patients. Evidence indicates that the nodes of the DMN can offer moderate to high diagnostic power to distinguish AD and MCI patients. Nevertheless, various concerns over the homogeneity of data based on patient selection, scanner effects, and the variable usage of classifiers and algorithms pose a challenge for ML-based image interpretation of rs-fMRI datasets to become a mainstream option for diagnosing AD and predicting the conversion of HC/MCI to AD