Examining the moderating effect of individual-level cultural values on users’ acceptance of E-learning in developing countries: a structural equation modeling of an extended technology acceptance model

In this study, we examine the effects of individual-level culture on the adoption and acceptance of e-learning tools by students in Lebanon using a theoretical framework based on the Technology Acceptance Model (TAM). To overcome possible limitations of using TAM in developing countries, we extend TAM to include subjective norms (SN) and quality of work life constructs as additional constructs and a number of cultural variables as moderators. The four cultural dimensions of masculinity/femininity (MF), individualism/collectivism, power distance and uncertainty avoidance were measured at the individual level to enable them to be integrated into the extended TAM as moderators and a research model was developed based on previous literature. To test the hypothesised model, data were collected from 569 undergraduate and postgraduate students using e-learning tools in Lebanon via questionnaire. The collected data were analysed using the structural equation modelling technique in conjunction with multi-group analysis. As hypothesised, the results of the study revealed perceived usefulness (PU), perceived ease of use (PEOU), SN and quality of work life to be significant determinants of students’ behavioural intention (BI) towards e-learning. The empirical results also demonstrated that the relationship between SN and BI was particularly sensitive to differences in individual-cultural values, with significant moderating effects observed for all four of the cultural dimensions studied. Some moderating effects of culture were also found for both PU and PEOU, however, contrary to expectations the effect of quality of work life was not found to be moderated by MF as some previous authors have predicted. The implications of these results to both theory and practice are explored in the paper

Interventional bronchoscopy for benign tracheobronchial diseases under cardiopulmonary bypass support: case reports and literature review

The use of cardiopulmonary bypass as an adjunct to airway surgery for non-malignant diseases in adults is not well established in the UK. We are reporting two cases which demonstrate the additional benefits of using cardiopulmonary bypass during difficult bronchoscopy and complex airway stenting. The first case presents an emergency indication for cardiopulmonary bypass in a life-threatening but benign condition. The second case presented, utilises cardiopulmonary bypass standby as adjunct to a potentially life threatening procedure. A review of the literature is also provided

Multi-Method Diagnosis of CT Images for Rapid Detection of Intracranial Hemorrhages Based on Deep and Hybrid Learning

Intracranial hemorrhaging is considered a type of disease that affects the brain and is very dangerous, with high-mortality cases if there is no rapid diagnosis and prompt treatment. CT images are one of the most important methods of diagnosing intracranial hemorrhages. CT images contain huge amounts of information, requiring a lot of experience and taking a long time for proper analysis and diagnosis. Thus, artificial intelligence techniques provide an automatic mechanism for evaluating CT images to make a diagnosis with high accuracy and help radiologists make their diagnostic decisions. In this study, CT images for rapid detection of intracranial hemorrhages are diagnosed by three proposed systems with various methodologies and materials, where each system contains more than one network. The first system is proposed by three pretrained deep learning models, which are GoogLeNet, ResNet-50 and AlexNet. The second proposed system using a hybrid technology consists of two parts: the first part is the GoogLeNet, ResNet-50 and AlexNet models for extracting feature maps, while the second part is the SVM algorithm for classifying feature maps. The third proposed system uses artificial neural networks (ANNs) based on the features of the GoogLeNet, ResNet-50 and AlexNet models, whose dimensions are reduced by a principal component analysis (PCA) algorithm, and then the low-dimensional features are combined with the features of the GLCM and LBP algorithms. All the proposed systems achieved promising results in the diagnosis of CT images for the rapid detection of intracranial hemorrhages. The ANN network based on fusion of the deep feature of AlexNet with the features of GLCM and LBP reached an accuracy of 99.3%, precision of 99.36%, sensitivity of 99.5%, specificity of 99.57% and AUC of 99.84

Inequalities in higher education in low‐ and middle‐income countries:A scoping review of the literature

Motivation:  Higher  education  is  regarded  as  a  key  instrument  to  enhance  socioeconomic  mobility  andreduce inequalities. Recent literature reviews have examined inequalities in the higher education systemsof  high-income  countries,  but  less  is  known  about  the  situation  in  low-  and  middle-income  countries,where higher education is expanding fast.Purpose:  The  article  reviews  the  academic  literature  on  higher  education  in  low-  and  middle-incomecountries using a research framework inspired by social justice and capability approaches. It considers the financial,  socio-cultural,  human,  and  political  resource  domains  on  which  people  draw,  and  how  they relate to access, participation, and outcomes in higher education.Methods: A literature search for studies explicitly discussing in-country  inequalities  in  higher  education revealed  22  publications. Substantial  knowledge  gaps remain,  especially  regarding  the  political  (and decision-making)  side  of  inequalities;  the  ideologies  and  philosophies  underpinning  higher  education systems; and the linkages between resource domains, both micro and macro.Findings:  The  review  highlights  key  elements  for  policy-makers  and  researchers:  (1)  the  financial  lens alone  is  insufficient  to  understand  and  tackle  inequalities,  since  these  are  also  shaped  by  human  and other non-financial factors; (2) socio-cultural constructs are central in explaining unequal outcomes; and (3) inequalities develop throughout one’s life and need to be considered during, but also before and afterhigher education.  The scope  of  inequalities  is  wide, and  the literature  offers a  few ideas  for short-term fixes such as part-time and online education.Policy implications: Inclusive policy frameworks for higher education should include explicit goals related to (in)equality,  which  are  best  measured in  terms  of  the  extent  to  which  certain  actions  or  choices are feasible for all. Policies in these frameworks, we argue, should go beyond providing financial support, and also address socio-cultural and human resource constraints and challenges in retention, performance, and labour market outcomes. Finally, they should consider relevant contextual determinants of inequalities.</p

The Palestinian primary ciliary dyskinesia population: first results of the diagnostic, and genetic spectrum

BACKGROUND: Diagnostic testing for primary ciliary dyskinesia (PCD) started in 2013 in Palestine. We aimed to describe the diagnostic, genetic and clinical spectrum of the Palestinian PCD population. METHODS: Individuals with symptoms suggestive of PCD were opportunistically considered for diagnostic testing: nasal nitric oxide (nNO) measurement, transmission electron microscopy (TEM) and/or PCD genetic panel or whole-exome testing. Clinical characteristics of those with a positive diagnosis were collected close to testing including forced expiratory volume in 1 s (FEV1) Global Lung Index z-scores and body mass index z-scores. RESULTS: 68 individuals had a definite positive PCD diagnosis, 31 confirmed by genetic and TEM results, 23 by TEM results alone, and 14 by genetic variants alone. 45 individuals from 40 families had 17 clinically actionable variants and four had variants of unknown significance in 14 PCD genes. CCDC39, DNAH11 and DNAAF11 were the most commonly mutated genes. 100% of variants were homozygous. Patients had a median age of 10.0 years at diagnosis, were highly consanguineous (93%) and 100% were of Arabic descent. Clinical features included persistent wet cough (99%), neonatal respiratory distress (84%) and situs inversus (43%). Lung function at diagnosis was already impaired (FEV1 z-score median −1.90 (−5.0–1.32)) and growth was mostly within the normal range (z-score mean −0.36 (−3.03–2.57). 19% individuals had finger clubbing. CONCLUSIONS: Despite limited local resources in Palestine, detailed geno- and phenotyping forms the basis of one of the largest national PCD populations globally. There was notable familial homozygosity within the context of significant population heterogeneity

Multi-method diagnosis of CT images for rapid detection of intracranial hemorrhages based on deep and hybrid learning

Intracranial hemorrhaging is considered a type of disease that affects the brain and is very dangerous, with high-mortality cases if there is no rapid diagnosis and prompt treatment. CT images are one of the most important methods of diagnosing intracranial hemorrhages. CT images contain huge amounts of information, requiring a lot of experience and taking a long time for proper analysis and diagnosis. Thus, artificial intelligence techniques provide an automatic mechanism for evaluating CT images to make a diagnosis with high accuracy and help radiologists make their diagnostic decisions. In this study, CT images for rapid detection of intracranial hemorrhages are diagnosed by three proposed systems with various methodologies and materials, where each system contains more than one network. The first system is proposed by three pretrained deep learning models, which are GoogLeNet, ResNet-50 and AlexNet. The second proposed system using a hybrid technology consists of two parts: the first part is the GoogLeNet, ResNet-50 and AlexNet models for extracting feature maps, while the second part is the SVM algorithm for classifying feature maps. The third proposed system uses artificial neural networks (ANNs) based on the features of the GoogLeNet, ResNet-50 and AlexNet models, whose dimensions are reduced by a principal component analysis (PCA) algorithm, and then the low-dimensional features are combined with the features of the GLCM and LBP algorithms. All the proposed systems achieved promising results in the diagnosis of CT images for the rapid detection of intracranial hemorrhages. The ANN network based on fusion of the deep feature of AlexNet with the features of GLCM and LBP reached an accuracy of 99.3%, precision of 99.36%, sensitivity of 99.5%, specificity of 99.57% and AUC of 99.84%

Deletion of the thrombin cleavage domain of osteopontin mediates breast cancer cell adhesion, proteolytic activity, tumorgenicity, and metastasis

<p>Abstract</p> <p>Background</p> <p>Osteopontin (OPN) is a secreted phosphoprotein often overexpressed at high levels in the blood and primary tumors of breast cancer patients. OPN contains two integrin-binding sites and a thrombin cleavage domain located in close proximity to each other.</p> <p>Methods</p> <p>To study the role of the thrombin cleavage site of OPN, MDA-MB-468 human breast cancer cells were stably transfected with either wildtype OPN (468-OPN), mutant OPN lacking the thrombin cleavage domain (468-ΔTC) or an empty vector (468-CON) and assessed for <it>in vitro </it>and <it>in vivo </it>functional differences in malignant/metastatic behavior.</p> <p>Results</p> <p>All three cell lines were found to equivalently express thrombin, tissue factor, CD44, αvβ5 integrin and β1 integrin. Relative to 468-OPN and 468-CON cells, 468-ΔTC cells expressing OPN with a deleted thrombin cleavage domain demonstrated decreased cell adhesion (p < 0.001), decreased mRNA expression of MCAM, maspin and TRAIL (p < 0.01), and increased uPA expression and activity (p < 0.01) <it>in vitro</it>. Furthermore, injection of 468-ΔTC cells into the mammary fat pad of nude mice resulted in decreased primary tumor latency time (p < 0.01) and increased primary tumor growth and lymph node metastatic burden (p < 0.001) compared to 468-OPN and 468-CON cells.</p> <p>Conclusions</p> <p>The results presented here suggest that expression of thrombin-uncleavable OPN imparts an early tumor formation advantage as well as a metastatic advantage for breast cancer cells, possibly due to increased proteolytic activity and decreased adhesion and apoptosis. Clarification of the mechanisms responsible for these observations and the translation of this knowledge into the clinic could ultimately provide new therapeutic opportunities for combating breast cancer.</p