38 research outputs found
sj-docx-2-cng-10.1177_10784535231211694 - Supplemental material for Helping a Patient With a Pre-Existing Mental Health Condition Cope With Depression and COVID-19 Using the Neuman Systems Model: A Single Intrinsic Case Study
No full textSupplemental material, sj-docx-2-cng-10.1177_10784535231211694 for Helping a Patient With a Pre-Existing Mental Health Condition Cope With Depression and COVID-19 Using the Neuman Systems Model: A Single Intrinsic Case Study by Golnaz Azami, Aliashraf Mozafari, Mohamadreza Kafashian, Sanaz Aazami and Boshra Ebrahimy in Creative Nursing</p
sj-docx-1-cng-10.1177_10784535231211694 - Supplemental material for Helping a Patient With a Pre-Existing Mental Health Condition Cope With Depression and COVID-19 Using the Neuman Systems Model: A Single Intrinsic Case Study
No full textSupplemental material, sj-docx-1-cng-10.1177_10784535231211694 for Helping a Patient With a Pre-Existing Mental Health Condition Cope With Depression and COVID-19 Using the Neuman Systems Model: A Single Intrinsic Case Study by Golnaz Azami, Aliashraf Mozafari, Mohamadreza Kafashian, Sanaz Aazami and Boshra Ebrahimy in Creative Nursing</p
Effectiveness of a nurse-led diabetes self-management education on glycosylated hemoglobin among Iranian adults with type 2 diabetes
Get PDFIn recent years, great emphasis has been placed on the role of non-pharmacological
self-management in the care of patients with diabetes. Studies have reported that
nurses, compared to other healthcare professionals, are more likely to promote
preventive healthcare seeking behaviors. The aim of this study was to investigate the
effectiveness of a nurse-led diabetes self-management education on glycosylated
hemoglobin. A two-arm parallel-group randomized controlled trial with the blinded
outcome assessors was designed. One hundred forty-two adults with type 2 diabetes
were randomized to receive either usual diabetes care (control group) or usual care
plus a nurse-led diabetes self-management education (intervention group). Duration
of the intervention was 12 weeks. The primary outcome was glycosylated hemoglobin
(HbA1c values). Secondary outcomes were changes in blood pressure, body weight,
lipid profiles, self-efficacy (efficacy expectation and outcome expectation), selfmanagement behaviors, quality of life, social support and depression. Outcome
measures were assessed at baseline and at 12 and 24 weeks post-randomization.
Patients in the intervention group showed significant improvement in HbA1c, blood
pressure, body weight, efficacy expectation, outcome expectation and diabetes selfmanagement behaviors. The beneficial effect of a nurse-led intervention continued to
accrue beyond the end of the trial resulting in sustained improvements in clinical,
lifestyle and psychosocial outcomes.
This study is registered with the Iranian Registry of Clinical Trials number
IRCT2016062528627N1
Development and psychometric evaluation of a 360-degree evaluation instrument to assess medical students’ performance in clinical settings at the emergency medicine department in Iran: a methodological study
No full textIn the Iranian context, no 360-degree evaluation tool has been developed to assess the performance of prehospital medical emergency students in clinical settings. This article describes the development of a 360-degree evaluation tool and presents its first psychometric evaluation. There were 2 steps in this study: step 1 involved developing the instrument (i.e., generating the items) and step 2 constituted the psychometric evaluation of the instrument. We performed exploratory and confirmatory factor analyses and also evaluated the instrument’s face, content, and convergent validity and reliability
Comprehensive analysis of pathways in Coronavirus 2019 (COVID-19) using an unsupervised machine learning method
No full textThe World Health Organization (WHO) introduced “Coronavirus disease 19” or “COVID-19” as a novel coronavirus in March 2020. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) requires the fast discovery of effective treatments to fight this worldwide crisis. Artificial intelligence and bioinformatics analysis pipelines can assist with finding biomarkers, explanations, and cures. Artificial intelligence and machine learning methods provide powerful infrastructures for interpreting and understanding the available data. On the other hand, pathway enrichment analysis, as a dominant tool, could help researchers discover potential key targets present in biological pathways of host cells that are targeted by SARS-CoV-2. In this work, we propose a two-stage machine learning approach for pathway analysis. During the first stage, four informative gene sets that can represent important COVID-19 related pathways are selected. These “representative genes” are associated with the COVID-19 pathology. Then, two distinctive networks were constructed for COVID-19 related signaling and disease pathways. In the second stage, the pathways of each network are ranked with respect to some unsupervised scorning method based on our defined informative features. Finally, we present a comprehensive analysis of the top important pathways in both networks. Materials and implementations are available at: https://github.com/MahnazHabibi/Pathway
Respiratory Distress and Meconium Aspiration Syndrome: Effect of Neonatal Resuscitation Program (NRP) in Ilam, Iran
No full textVision-based Cost Maps for Safe Autonomous Navigation : Design and Evaluation of Vision-based Control Barrier Functions
Get PDFDesigning safety-critical systems for unfamiliar environments is a substantial challenge in the field of robotics. Control Barrier Functions (CBF) serve as a common tool for addressing this challenge. However, the definition of CBF based on perceptual input remains a relatively unexplored and complex area of research.
This thesis extends prior work, where the authors, including the author of this thesis, introduced the innovative concept of Vision-based Control Barrier Functions (V-CBF). V-CBF defines Control Barrier Functions using perceptual input obtained from an RGBD camera, enabling the avoidance of obstacles with arbitrary shapes in unknown environments. A pivotal element of V-CBF is the 2D customized cost map, which transforms the segmented unsafe sets into an appropriate format that satisfies the requirements of CBF. The design and evaluation of these cost maps play a crucial role in the proper generation of V-CBFs. However, this aspect has not been thoroughly extensively in previous research.
The thesis embarks on a comprehensive investigation of diverse methodologies for generating these essential cost maps. The proposed methods are rigorously implemented and assessed within the CARLA simulator. To offer a thorough evaluation, both qualitative and quantitative comparisons are conducted, drawing from industry-standard ISO 22737 guidelines and custom-designed metrics within the CARLA simulator environment.
Furthermore, to substantiate the practical applicability of V-CBF, it is implemented on an industrial mobile robot. This real-world deployment serves as a tangible demonstration of the effectiveness of V-CBF in unknown environments, emphasizing its potential beyond simulated contexts. The transition from simulation to tangible real-world implementation underscores the portability and robustness of V-CBF, signifying its relevance and promise in real-world scenarios
Post-Colonial Reading of Isabel Allende’s The Japanese Lover
Get PDFThis research will explore the result of studying different aspects of identity seeking and establishing it as a liminal-prone one in a hybridized atmosphere among the colonized in terms of post-colonial discourse, based on Bhabha’s theories in his book, The Location of Culture, and on Isabel Allende’s novel, The Japanese Lover (2015). This study strives to expose the way through which the colonized characters’ identities in the novel undergo radical transformation through the third space which is heavily laced with qualities like ambivalence, stereotype, mimicry, and unhomeliness. Isabel Allende is an author whose novels mostly are an attempt to delineate the process of identity shaping particularly in the USA, since identity has always been an obsession for human which is defined based on different properties, one of which refers to the nation, culture and the territories based on Bhabha’s notion of hybridity which stems from confrontation of the cultures of the oppressor and the oppressed in the process of colonization. Generally, subject of identity in post-colonialism discourse is one in which people especially the colonized seeks for attachment. It will be divulged through this analysis that how liminal quality which is created as the consequence of colonial discourses will result in creating a space in which the oppressed one undergo radical changes in forming identity and how their identities are susceptible to alteration and likely to be unstable and fugitive
Thrombotic thrombocytopenic purpura following honeybee envenomation: A case report
Get PDFBackground: Thrombotic Thrombocytopenic Purpura (TTP) is a rare and life-threatening disorder characterized by severe thrombocytopenia, microangiopathic hemolytic anemia, fever, renal dysfunction, and neurological deficient. TTP leads to the formation of blood clots in small blood vessels throughout the body. TTP is associated with many risk factors such as pregnancy, HIV, cancer, lupus, and infections. Recently there have been few published case reports of bee sting associated TTP. Methods: A 67-year-old man from a rural area of the Southwest Province of Iran, Ilam, was referred to the academic general hospital because of fever, chills, sweating, vomiting and dizziness following the honeybee sting on his body. Results: this study showed that,multiple co-morbidities including CVD and diabetes, along with coagulation abnormalities after honeybee stings, seriously exacerbated patient hemodynamic status. Conclusion: TTP, as a major complication due to the toxic reaction of a large number of bee stings with underlying diseases in patients, should be given more attention. © 2020 Shahid Beheshti University of Medical Sciences. All rights reserved
Applications of image processing and machine learning techniques in wildlife monitoring and cancer cell characterization
No full textThis dissertation presents the application of state-of-art image classification and object detection techniques to two projects in the areas of wildlife monitoring and cancer cell characterization. The following presents a summary of the problem statement, solution approach, and major contributions for each of the two projects.
In the wildlife monitoring project, an automated vision system is proposed for animal detection in trail-camera images taken from a field under the administration of the Texas Parks and Wildlife Department. As traditional wildlife counting techniques are intrusive and labor-intensive to conduct, trail-camera imaging is a comparatively non-intrusive method for capturing wildlife activity. However, given the large volume of images produced from trail cameras, manual analysis of the images remains time-consuming and inefficient. For this purpose, a two-stage deep convolutional neural network pipeline is implemented to find animal-containing images in the first stage and then process these images to detect birds in the second stage. The animal classification system classifies animal images with 93% sensitivity and 96% specificity. The bird detection system achieves better than 93% sensitivity, 92% specificity, and 68% average Intersection-over-Union rate. This dissertation also addresses post-deployment issues related to data drift for the animal classification system as image features vary with seasonal changes. This system utilizes an automatic retraining algorithm to detect data drift and to update the system when necessary. Moreover, two statistical experiments are presented to explain the prediction behavior of the animal classification system. These experiments investigate the cues that steer the system towards a particular decision. Statistical hypothesis testing demonstrates that the presence of an animal in the input image significantly contributes to the system’s decisions.
The cell characterization study investigates the automatic detection and enumeration of circulating tumor cells in patient blood samples. Circulating tumor cells (CTCs) are invaluable biomarkers used in the early diagnosis and treatment of cancer. Microscopy images of isolated CTCs from patient blood samples are routinely acquired and analyzed for CTC detection and enumeration purposes. Due to the scarcity of CTCs in the patient blood sample, their manual characterization is a challenging task that involves a series of tedious cell staining and labeling procedures and laborious manual identification of the cells. This study proposes an automated detection and enumeration system to alleviate the lag in the enumeration process. The core of this system is an efficient and accurate convolutional neural network (CNN)-based model that performs label-free detection of MCF-7 breast cancer cells, as a proxy to CTCs, in brightfield images. The MCF-7 detection model achieves above 99% sensitivity and specificity. In addition, the average Intersection-over-Union rate of the proposed detector is better than 80%. For the training set generation, a fully automated workflow is presented that facilitates the efficient and easy labeling of brightfield images. Additionally, multiple experiments are designed and implemented to explore the prominent features that the CNN extracts and uses for distinguishing MCF-7 cells from white blood cells. The results of the experiments indicate that the designed CNN uses the size of the cell as the prominent distinguishing feature. Furthermore, if the size feature is eliminated, the CNN is still capable of extracting other features to distinguish MCF-7 cells, but with a 3% accuracy reduction. Finally, the robustness of the proposed MCF-7 detection model in the presence of various image intensity transformations is investigated. The results indicate that the F1-score of the detection model deteriorates by less than 0.2% in the presence of image intensity and contrast transformations. Therefore, the MCF-7 cell detection model has sufficient robustness with respect to variations in the intensity and contrast of the brightfield images.Embargo status: Restricted until 06/2022. To request the author grant access, click on the PDF link to the left
