Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel Human NK Cell Line Carrying CAR Targeting EGFRvIII Induces Antitumor Effects in Glioblastoma Cells

Background/Aim: Natural killer (NK) cells are considered potential antitumor effector cells. The aim of this study was to establish a novel type of a chimeric antigen receptor (CAR) NK cell line (CAR-KHYG-1) specific for epidermal growth factor receptor variant III (EGFRvIII)-expressing tumors and investigate the anti-tumor activity of EGFRvIII-specific-CAR-KHYG-1 (EvCAR-KHYG-1). Materials and Methods: EvCAR-KHYG-1 was established by self-inactivated lentiviral-based transduction of the EvCAR gene and magnetic bead-based purification of EvCAR-expressing NK cells. The anti-tumor effects of EvCAR-KHYG-1 were evaluated using growth inhibition and apoptosis detection assays in glioblastoma (GBM) cell lines (EGFRvIII-expressing and non-expressing U87MG). Results: The findings demonstrated that EvCAR-KHYG-1 inhibited GBM cell-growth via apoptosis in an EGFRvIII-expressing specific manner. Conclusion: This is the first study to establish a CAR NK cell line based on the human NK cell line KHYG-1. Therapy with EvCAR-KHYG-1 may be an effective treatment option for GBM patients.
Purification of Stem Cells from Oral Pyogenic Granuloma Tissue

Introduction: The isolation of stem cells from pathologically damaged dental tissues has been examined only by a few studies. The purpose of this study was to evaluate the possibility of isolation of stem cells from pyogenic granuloma. Methods: Pyogenic granuloma tissues were enzymatically digested and the resulting single cells were cultured. Then, the cultured cells differentiated into adipocytes and osteoblasts cells. Flow cytometry analyses were performed on markers such as CD 90, CD 73, CD105, CD 45 and CD14. Other features were also analyzed including the effect of colony formation and potentials of differentiation into adipocytes and osteoblasts. Results: The cells derived from pyogenic granuloma tissue formed higher colonies similar to typical spindle-shaped fibroblasts. The cells were positive for mesenchymal markers such as CD 44, CD 271, CD 90, and CD 73, and negative for surface molecules such as CD 14, CD 34 and CD 45. Moreover, they successfully differentiated into adipocytes and osteoblasts. Conclusion: The cells isolated from pyogenic granuloma could form CFU fibroblastic units expressing an appropriate marker panel of the cell surface antigen and adequate differentiation potential, all of which met the Cell Therapy International Association criteria for the definition of mesenchymal stromal cells. Pyogenic granuloma contains cells with stem cell properties.
The effect of education through mobile phone short message service on promoting self-care in pre-diabetic pregnant women: A randomized controlled trial

Introduction Gestational diabetes is increasing in developed and developing countries. This study aimed to evaluate the effect of SMS messaging on the self-care among pre-diabetic pregnant women. Methods This randomized controlled trial conducted on 100 pre-diabetic pregnant women referred to Ahvaz Health Centers, Iran, Sep to Dec, 2016. Women were randomly assigned into the face to face education group and short message systems in addition to the face to face education group. Self-care variables were compared between the groups. Data was analyzed using chi-square, independent and paired t-tests. Results There was no difference regard to FBG and OGTT, but there was a significant difference between groups in physical activity level (P<0.001) and in all food groups except bread and cereals after the intervention (P<0.05). Conclusion The education through a short text message system seem couldn't control the blood sugar, but it could promote physical activity and food additives of pre-diabetic pregnant women.
HIV testing and pre-exposure prophylaxis (PrEP) use, familiarity, and attitudes among gay and bisexual men in the United States: A national probability sample of three birth cohorts

This study examined HIV testing and use, familiarity, and attitudes toward pre-exposure prophylaxis (PrEP) among HIV-negative gay and bisexual men in the United States. A national probability sample (N = 470) of three age cohorts (18–25, 34–41, and 52–59 years) completed a survey between March, 2016 and March, 2017. Most men did not meet CDC recommendations for HIV testing, and 25.2% of men in the younger cohort had never tested. Only 4.1% used PrEP across cohorts. Visiting an LGBT clinic and searching for LGBT resources online were associated with PrEP use. Men in the middle cohort were more familiar with PrEP (79%) than men in the younger (52%) and older (57%) cohorts. Bisexual and non-urban men were less familiar with PrEP. Attitudes were positive among most men (68.4%) familiar with PrEP. Findings suggest that most men potentially at risk for HIV do not meet CDC guidelines for testing, and PrEP use continues to be minimal. Efforts to educate gay and bisexual men about HIV risk and prevention need to be reinvigorated and expanded to include non-gay-identified and non-urban men.
MUAC as the sole discharge criterion from community‐based management of severe acute malnutrition in Burkina Faso

Abstract The use of mid upper arm circumference (MUAC) measurement to screen and determine eligibility for admission to therapeutic feeding programs has been established, but evidence and programmatic experience to inform guidance on the use of MUAC as a discharge criterion is limited. We present results from a large‐scale nutritional program using MUAC for admission and discharge and compare program outcomes and response to treatment when determining eligibility for discharge by proportional weight gain versus discharge by MUAC. The study population included all children admitted to the Ministry of Health therapeutic feeding program supported by Médecins Sans Frontières in northern Burkina Faso from September 2007 to December 2011 (n = 50,841). Recovery was high overall using both discharge criteria, with low risks of death, nonresponse, and transfer to inpatient care and high daily gains in weight, MUAC, weight‐for‐height Z score, and height. When discharge was made by MUAC only, recovery increased, while all adverse program outcomes and length of stay decreased, with increasing MUAC on admission. MUAC‐based programming, where MUAC is integrated into program screening, admission, and discharge, is one of several new approaches that can be used to target resources to the most at‐risk malnourished children and improve program efficiency and coherency. This analysis provides additional programmatic experience on the use of MUAC‐based discharge criterion, but more work may be needed to inform optimal discharge thresholds across settings.
Case-Control Study of Cryptosporidium Transmission in Bangladeshi Households

Abstract Background Cryptosporidium is a leading contributor to diarrheal morbidity and mortality in under-5 children worldwide. As there is no vaccine and no effective drug therapy in young children for this infection, preventing infection is critical. We undertook a pilot case-control study to define the extent of person-to-person transmission of cryptosporidiosis within an urban and a rural community in Bangladesh. Methods We enrolled 48 case families with a Cryptosporidium-infected child aged 6–18 months. Controls were age- and sex-matched Cryptosporidium-negative children in 12 households. Children and household members were followed for 8 weeks with weekly illness survey and stool testing with quantitative polymerase chain reaction for Cryptosporidium. Results In the 24 urban case families, the secondary attack rate was 35.8% (19/53) vs 0% (0/11) in controls (P = .018, χ2 test). In contrast, in the 24 rural case families, the secondary attack rate was 7.8% (5/64) vs 0% (0/21) in controls (P = .19, χ2 test). Genotyping by gp60 demonstrated infection with the same subspecies in 5 families, and evidence of transmission in 2. Serologic response to Cryptosporidium infection was associated with younger age, longer duration of infection, and Cryptosporidium hominis gp60_IbA9G3R2 infection. Conclusions In the urban site, the high rate of secondary infection and infection with the same subspecies within families suggests that person-to-person transmission is a major source of Cryptosporidium infection for young children living in this region. Molecular genotyping can be applied to determine transmission of Cryptosporidium in endemic regions. Further work is needed to understand the differences in parasite transmissibility and immunity to different genotypes.
Efficacy of different doses of ketamine as a bolus in major depressive disorder

Background: Major depressive disorder is a severe, heterogeneous, common medical illness and a leading cause of disability throughout the world that poses a significant public health issue. Previous studies have shown rapid antidepressant effects following a single administration of ketamine. This study aimed to assess the impact of route of administration and dose of ketamine for the reduction of depressive symptoms and compare the effects of different doses and methods. Methods: A double-blind clinical controlled trial was done on 100 patients with a primary diagnosis of major depressive disorder who were assigned into two groups of 50 subjects at a dose of 0.5 mg/kg and 0.75 mg/kg ketamine and each group was divided into two groups of 25 subjects following a single dose of intravenous bolus and infusion of ketamine. The patient’s severity of depression was evaluated with Hamillton Depression Rating Scale and Beck Depression Inventory scores after 2 days, 7 days, 30 days and 60 days of ketamine administration, then the results were compared between groups. Results: According to Hamilton and Beck score, the treatment response in investigated patients was 64% and 60%, respectively. Conclusions: These data suggest that ketamine effect is related to drug dose and type of administration. The dose of 0.75 mg/kg of ketamine is more effective than 0.5 mg/kg and a bolus injection of low-dose ketamine (0.5 mg/kg) is more effective than infusion and in high-dose ketamine (0.75 mg/kg), there was no difference between the methods of drug administration.
Principles for Assessing Adaptive Online Courses

Adaptive online courses are designed to automatically customize material for different users, typically based on data captured during the course. Assessing the quality of these adaptive courses, however, can be difﬁcult. Traditional assessment methods for (machine) learning algorithms, such as comparison against a ground truth, are often unavailable due to education’s unique goal of affecting both internal user knowledge, which cannot be directly measured, as well as external, measurable performance. Traditional metrics for education like quiz scores, on the other hand, do not necessarily capture the adaptive course’s ability to present the right material to different users. In this work, we present a mathematical framework for developing scalable, efﬁciently computable metrics for these courses that can be used by instructors to gauge the efﬁ-cacy of the adaptation and their course content. Our metric framework takes as input a set of quantities describing user activities in the course, and balances deﬁnitions of user consistency and over-all efﬁcacy as inferred by the quantity distributions. We support the metric deﬁnitions by comparing the results of a comprehensive statistical analysis with a sample metric evaluation on a dataset of roughly 5,000 users from an online chess platform. In doing so, we ﬁnd that our metrics yield important insights about the course that are embedded in the larger statistical analysis, as well as additional insights into student drop-off rates.
Mining User Trajectories in Electronic Text Books

Analyzing user behavior in electronic textbooks oﬀers appealing insights into how pupils interact with the book and internalize the content. Using these insights may help to personalize the book, e.g., to support users with special educational needs. Conventional approaches often focus on atomic, user-triggered events like clicks or scrolls. In this paper, we propose to view all ongoing sessions in a classroom simultaneously and cast the problem as a multi-user problem over space and time. We devise two distance measures to compare the navigation behavior of pupils in diﬀerent dimensions. Empirically, we observe that our metrics lead to interpretable clusters and serve as performance indicators
Analyzing Big Data through the lens of customer knowledge management: Evidence from a set of regional tourism experiences

Purpose This paper aims to demonstrate how customer knowledge management (CKM) can opportunely support the process of value creation from Big Data. Focusing on tourism as a knowledge-intensive industry, the paper tries to contribute to the debate on management of Big Data by proposing CKM as a meaningful approach for transforming the huge amount of data available on social networks into valuable assets for competitiveness of tourism destinations. Design/methodology/approach The paper adopts a qualitative research methodology based on multiple exploratory case studies identified in a set of digital local events related to the Apulia destination (southern Italy). Findings Research findings demonstrate that the three dimensions of CKM (knowledge for, from and about customers) could be adopted as lens for analyzing the huge amount of data created for, from and about tourist experiences and for transforming them into valuable assets supporting the competitiveness of tourism destinations. Research limitations/implications Limitations are related to the industry and the regional dimension of the sample. Accordingly, more research is necessary to prove the validity of the approach and to assure its larger replicability. Practical implications Implications for the agenda of organizations and destinations’ makers for designing and implementing knowledge-based services and products arise. Originality/value Elements of originality reside into the adoption of CKM as framework to analyze Big Data in the tourism industry.
Application of fuzzy data fusion theory in fault diagnosis of rotating machinery

In this article, the most common induction motor faults including bearing outer race defect, broken rotor bar, and short-circuit of stator windings are diagnosed with high reliability. The decentralized fuzzy-integral data fusion method is used for information fusion in feature level. In the proposed scheme, the feature vectors are constructed using signatures created by time-domain characteristics obtained from stator three-phase current measurements. Partial matching of each feature is calculated by the fuzzy c-mean classifier algorithm, and features with high diagnosis ability are fused by Choquet fuzzy integral. The technique is validated experimentally on the 4 hp induction motor of an electropump, and the results are presented.
Using Student Logs to Build Bayesian Models of Student Knowledge and Skills

Recent work on Intelligent Tutoring Systems have focused on more complicated knowledge domains, which poses challenges in automated assessment of student performance. In particular, while the system can log every user action and therefore keep track of the student’s solution state, it is unable to determine the hidden intermediate steps leading to such state or what the student is trying to achieve. In this paper, we show that this information can be acquired through data mining, along with the type, frequency and context of errors that students made. Our technique has been implemented as part of the student model in a tutor that teaches red-black trees. The system has been evaluated on three semesters of student data and analysis of the results shows that the proposed framework of error analysis can help the system in predicting student performance with good accuracy and the instructor in determining difficulties that students encounter, both individually and collectively as a class.
A Comparison Study between Different Sampling Strategies for Intrusion Detection System of Active Learning Model

Active learning aims to train an accurate model with minimum cost by labeling the most informative instances without compromising the model performance. So, choosing an efficient criterion for instance selection is the most important step. Sampling stage is the main issue in active learning for many problems such as intrusion detection system. There are many methods for sampling stage to select the informative instances, but what the method should be used to provide the most accurate to the Intrusion Detection System (IDS). So, we made a comparison between three of these methods, uncertainty sampling, Query By Committee (QBC) and expected model change. The contribution of this study is analyzing and examining three of common strategies that used to select the most informative instances to determine the best one of them. The experimental result showed that the expected model change method achieved the highest accuracy compared with uncertainty sampling and query by committee methods.
Effect of antiretroviral therapy on all-cause mortality among people living with HIV/AIDS in Ghana using Mahalanobis distant metric matching within propensity score caliper analysis: A retrospective cohort study

Several health interventions have been put in place to improve health outcomes of people living with HIV/AIDS (PLHIV) in Ghana. We evaluated the impact of Antiretroviral Therapy (ART) on all-cause mortality in Ghana using matching procedures. This was a retrospective cohort study of 12,881 HIV/AIDS patients initiated on ART at 40 sentinel sites and 199 treatment centers between 2013 and 2016 countrywide. Patients were included if they had date of ART initiation and if they had mortality outcome recorded. Mahalanobis distant metric matching within propensity score caliper and other matching procedures were used to evaluate the effectiveness of ART in reducing the risk of all-cause mortality among PLHIV in Ghana. We performed sensitivity analysis using different matching procedures including Kernel weighting adjustment and Mahalanobis distance metric matching with nearest neighbour to ascertain the robustness of our results in the presence of unmeasured covariates. The proportion of patients on ART was 60.3% (95% CI: 59.5–61.1). The total number of mortalities reported was only 734 representing 4.6% (95% CI: 4.2–4.9) of the studied population. The risk of all-cause mortality has reduced by 11.6 percentage point among HIV/AIDS patients who were on ART compared to those who were not on ART (95% CI: 9.6–13.4). ART was associated with a decreased risk of all-cause mortality. Effort being made by Government and non-Governmental organizations in support of ART treatment in Ghana should continue unabated to help reduce mortality rate and improve health outcomes among HIV/AIDS. To reduce bias to the barest minimum between treatment and intervention group when evaluating the effectiveness of health interventions, it is recommended to use matching procedures especially when the study design is not a randomized control trial.
Prediction of protein group function by iterative classification on functional relevance network

Motivation Biological experiments including proteomics and transcriptomics approaches often reveal sets of proteins that are most likely to be involved in a disease/disorder. To understand the functional nature of a set of proteins, it is important to capture the function of the proteins as a group, even in cases where function of individual proteins is not known. In this work, we propose a model that takes groups of proteins found to work together in a certain biological context, integrates them into functional relevance networks, and subsequently employs an iterative inference on graphical models to identify group functions of the proteins, which are then extended to predict function of individual proteins. Results The proposed algorithm, iterative group function prediction (iGFP), depicts proteins as a graph that represents functional relevance of proteins considering their known functional, proteomics and transcriptional features. Proteins in the graph will be clustered into groups by their mutual functional relevance, which is iteratively updated using a probabilistic graphical model, the conditional random field. iGFP showed robust accuracy even when substantial amount of GO annotations were missing. The perspective of ‘group’ function annotation opens up novel approaches for understanding functional nature of proteins in biological systems. Availability and implementation: http://kiharalab.org/iGFP/ Supplementary information Supplementary data are available at Bioinformatics online.
Nucleos(t)ide Analogs Do Not Independently Influence Hepatic Fibrosis and Portal Hypertension beyond Viral Suppression in CBDL-Induced Cirrhotic Rat

Chronic hepatitis is the major cause of liver cirrhosis and portal hypertension. Several factors affect portal pressure, including liver fibrosis, splanchnic vasodilatation, and pathologic angiogenesis. Nucleos(t)ide analogs (NUCs), the oral antiviral agents, effectively attenuate chronic hepatitis B-related liver cirrhosis and portal hypertension via viral suppression and alleviation of hepatitis. On the other hand, NUCs affect tumor necrosis factor (TNF)-α, vascular endothelial growth factor (VEGF), and nitric oxide, which participate in fibrogenesis, vasodilatation, and angiogenesis. However, whether NUCs independently influence liver fibrosis and portal hypertension beyond viral suppression is unknown. This study thus aimed to evaluate the influences of three frequently used NUCs in rats with nonviral cirrhosis. Male Sprague-Dawley rats received common bile duct ligation (CBDL) to induce cholestatic cirrhosis and portal hypertension. The rats were randomly allocated into four groups, treated by mouth with lamivudine (30 mg/kg per day), entecavir (0.09 mg/kg per day), tenofovir (50 mg/kg per day), or distilled water (vehicle control) from the 15th day after CBDL. On the 29th day, liver cirrhosis- and portal hypertension-related parameters were evaluated. The results showed that chronic NUCs treatment did not affect hemodynamic parameters, plasma TNF-α concentration, and hepatic fibrogenesis protein expressions in rats with nonviral cirrhosis. Though the mesenteric VEGF receptor 2 phosphorylation was downregulated in NUCs-treated groups, the splanchnic angiogenesis was not influenced. In conclusion, lamivudine, entecavir, and tenofovir had no additional effects on liver cirrhosis and portal hypertension in rats with nonviral cirrhosis.
What can we learn from college students' network transactions? Constructing useful features for student success prediction

Identifying at-risk students at an early stage is a challenging task for colleges and universities. In this paper, we use students’ on-campus network traffic volume to construct several useful features in predicting their first semester GPA. In particular, we build proxies for their attendance, class engagement, and out-of-class study hours based on their network traffic volume. We then test how much these network-based features can increase the performance of a model with only conventional features (e.g., demographics, high school GPA, standardized test scores, etc.). We labeled students as “above median” and “below median” students based on their first term GPA. Several machine learning models were then applied, ranging from logistic regression, SVM, and random forests, to AdaBoost. The result shows that the model with network-based features consistently outperforms the ones without, in terms of accuracy, f1 score, and AUC. Given that network activity data is readily available data in most colleges and universities, this study provides practical insights on how to build more powerful models to predict student success.