Measuring Social Media Activity of Scientific Literature: An Exhaustive Comparison of Scopus and Novel Altmetrics Big Data

This paper measures social media activity of 15 broad scientific disciplines indexed in Scopus database using Altmetric.com data. First, the presence of Altmetric.com data in Scopus database is investigated, overall and across disciplines. Second, the correlation between the bibliometric and altmetric indices is examined using Spearman correlation. Third, a zero-truncated negative binomial model is used to determine the association of various factors with increasing or decreasing citations. Lastly, the effectiveness of altmetric indices to identify publications with high citation impact is comprehensively evaluated by deploying Area Under the Curve (AUC) - an application of receiver operating characteristic. Results indicate a rapid increase in the presence of Altmetric.com data in Scopus database from 10.19% in 2011 to 20.46% in 2015. A zero-truncated negative binomial model is implemented to measure the extent to which different bibliometric and altmetric factors contribute to citation counts. Blog count appears to be the most important factor increasing the number of citations by 38.6% in the field of Health Professions and Nursing, followed by Twitter count increasing the number of citations by 8% in the field of Physics and Astronomy. Interestingly, both Blog count and Twitter count always show positive increase in the number of citations across all fields. While there was a positive weak correlation between bibliometric and altmetric indices, the results show that altmetric indices can be a good indicator to discriminate highly cited publications, with an encouragingly AUC= 0.725 between highly cited publications and total altmetric count. Overall, findings suggest that altmetrics could better distinguish highly cited publications.Comment: 34 Pages, 3 Figures, 15 Table

Poly(β-Amino Ester)-Nanoparticle Mediated Transfection of Retinal Pigment Epithelial Cells In Vitro and In Vivo

A variety of genetic diseases in the retina, including retinitis pigmentosa and leber congenital amaurosis, might be excellent targets for gene delivery as treatment. A major challenge in non-viral gene delivery remains finding a safe and effective delivery system. Poly(beta-amino ester)s (PBAEs) have shown great potential as gene delivery reagents because they are easily synthesized and they transfect a wide variety of cell types with high efficacy in vitro. We synthesized a combinatorial library of PBAEs and evaluated them for transfection efficacy and toxicity in retinal pigment epithelial (ARPE-19) cells to identify lead polymer structures and transfection formulations. Our optimal polymer (B5-S5-E7 at 60 w/w polymer∶DNA ratio) transfected ARPE-19 cells with 44±5% transfection efficacy, significantly higher than with optimized formulations of leading commercially available reagents Lipofectamine 2000 (26±7%) and X-tremeGENE HP DNA (22±6%); (p<0.001 for both). Ten formulations exceeded 30% transfection efficacy. This high non-viral efficacy was achieved with comparable cytotoxicity (23±6%) to controls; optimized formulations of Lipofectamine 2000 and X-tremeGENE HP DNA showed 15±3% and 32±9% toxicity respectively (p>0.05 for both). Our optimal polymer was also significantly better than a gold standard polymeric transfection reagent, branched 25 kDa polyethyleneimine (PEI), which achieved only 8±1% transfection efficacy with 25±6% cytotoxicity. Subretinal injections using lyophilized GFP-PBAE nanoparticles resulted in 1.1±1×103-fold and 1.5±0.7×103-fold increased GFP expression in the retinal pigment epithelium (RPE)/choroid and neural retina respectively, compared to injection of DNA alone (p = 0.003 for RPE/choroid, p<0.001 for neural retina). The successful transfection of the RPE in vivo suggests that these nanoparticles could be used to study a number of genetic diseases in the laboratory with the potential to treat debilitating eye diseases

Deep context of citations using machine‑learning models in scholarly full‑text articles

Information retrieval systems for scholarly literature rely heavily not only on text matching but on semantic- and context-based features. Readers nowadays are deeply interested in how important an article is, its purpose and how influential it is in follow-up research work. Numerous techniques to tap the power of machine learning and artificial intelligence have been developed to enhance retrieval of the most influential scientific literature. In this paper, we compare and improve on four existing state-of-the-art techniques designed to identify influential citations. We consider 450 citations from the Association for Computational Linguistics corpus, classified by experts as either important or unimportant, and further extract 64 features based on the methodology of four state-of-the-art techniques. We apply the Extra-Trees classifier to select 29 best features and apply the Random Forest and Support Vector Machine classifiers to all selected techniques. Using the Random Forest classifier, our supervised model improves on the state-of-the-art method by 11.25%, with 89% Precision-Recall area under the curve. Finally, we present our deep-learning model, the Long Short-Term Memory network, that uses all 64 features to distinguish important and unimportant citations with 92.57% accuracy

Naturally acquired antibody response to Plasmodium falciparum and Plasmodium vivax among indigenous Orang Asli communities in Peninsular Malaysia

Malaria remains a public health problem in many parts of the world. In Malaysia, the significant progress towards the national elimination programme and effective disease notification on malaria has resulted in zero indigenous human malaria cases since 2018. However, the country still needs to determine the extent of malaria exposure and transmission patterns, particularly in high-risk populations. In this study, a serological method was used to measure transmission levels of Plasmodium falciparum and Plasmodium vivax among indigenous Orang Asli communities in Kelantan, Peninsular Malaysia. A community-based cross-sectional survey was conducted in three Orang Asli communities (i.e., Pos Bihai, Pos Gob, and Pos Kuala Betis) in Kelantan from June to July 2019. Antibody responses to malaria were assessed by enzyme-linked immunosorbent assay (ELISA) using two P. falciparum (PfAMA-1 and PfMSP-119) and two P. vivax (PvAMA-1 and PvMSP-119) antigens. Age-adjusted antibody responses were analysed using a reversible catalytic model to calculate seroconversion rates (SCRs). Multiple logistic regression was used to investigate factors associated with malaria exposure. The overall malaria seroprevalence was 38.8% for PfAMA-1, 36.4% for PfMSP-119, 2.2% for PvAMA-1, and 9.3% for PvMSP-119. Between study areas, the proportion of seropositivity for any P. falciparum and P. vivax antigens was significantly highest in Pos Kuala Betis with 34.7% (p &lt; 0.001) and 13.6% (p &lt; 0.001), respectively. For all parasite antigens except for PvAMA-1, the proportion of seropositive individuals significantly increased with age (all p &lt; 0.001). Based on the SCR, there was a higher level of P. falciparum transmission than P. vivax in the study area. Multivariate regression analyses showed that living in Pos Kuala Betis was associated with both P. falciparum (adjusted odds ratio [aOR] 5.6, p &lt; 0.001) and P. vivax (aOR 2.1, p &lt; 0.001) seropositivities. Significant associations were also found between age and seropositivity to P. falciparum and P. vivax antigens. Analysis of community-based serological data helps describe the level of transmission, heterogeneity, and factors associated with malaria exposure among indigenous communities in Peninsular Malaysia. This approach could be an important adjunct tool for malaria monitoring and surveillance in low malaria transmission settings in the country

Derivation and validation of a novel risk assessment tool to identify children aged 2-59 months at risk of hospitalised pneumonia-related mortality in 20 countries

INTRODUCTION: Existing risk assessment tools to identify children at risk of hospitalised pneumonia-related mortality have shown suboptimal discriminatory value during external validation. Our objective was to derive and validate a novel risk assessment tool to identify children aged 2-59 months at risk of hospitalised pneumonia-related mortality across various settings. METHODS: We used primary, baseline, patient-level data from 11 studies, including children evaluated for pneumonia in 20 low-income and middle-income countries. Patients with complete data were included in a logistic regression model to assess the association of candidate variables with the outcome hospitalised pneumonia-related mortality. Adjusted log coefficients were calculated for each candidate variable and assigned weighted points to derive the Pneumonia Research Partnership to Assess WHO Recommendations (PREPARE) risk assessment tool. We used bootstrapped selection with 200 repetitions to internally validate the PREPARE risk assessment tool. RESULTS: A total of 27 388 children were included in the analysis (mean age 14.0 months, pneumonia-related case fatality ratio 3.1%). The PREPARE risk assessment tool included patient age, sex, weight-for-age z-score, body temperature, respiratory rate, unconsciousness or decreased level of consciousness, convulsions, cyanosis and hypoxaemia at baseline. The PREPARE risk assessment tool had good discriminatory value when internally validated (area under the curve 0.83, 95% CI 0.81 to 0.84). CONCLUSIONS: The PREPARE risk assessment tool had good discriminatory ability for identifying children at risk of hospitalised pneumonia-related mortality in a large, geographically diverse dataset. After external validation, this tool may be implemented in various settings to identify children at risk of hospitalised pneumonia-related mortality

Evidence of Submicroscopic Plasmodium knowlesi Mono-Infection in Remote Indigenous Communities in Kelantan, Peninsular Malaysia

Malaysia has maintained zero cases of indigenous human malaria since 2018. However, zoonotic malaria is still prevalent in underdeveloped areas and hard-to-reach populations. This study aimed to determine the prevalence of malaria among remote indigenous communities in Peninsular Malaysia. A cross-sectional survey was conducted in six settlements in Kelantan state, from June to October 2019. Blood samples were tested for malaria using microscopy and nested polymerase chain reaction (nPCR) targeting the Plasmodium cytochrome c oxidase subunit III (cox3) gene. Of the 1,954 individuals who appeared healthy, no malaria parasites were found using microscopy. However, nPCR revealed seven cases of Plasmodium knowlesi mono-infection (0.4%), and six out of seven infections were in the group of 19 to 40 years old (P = 0.026). No human malaria species were detected by nPCR. Analysis of the DNA sequences also showed high similarity that reflects common ancestry to other P. knowlesi isolates. These findings indicate low submicroscopic P. knowlesi infections among indigenous communities in Malaysia, requiring PCR-based surveillance to support malaria control activities in the country

Global, regional, and national incidence and mortality for HIV, tuberculosis, and malaria during 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013

BACKGROUND: The Millennium Declaration in 2000 brought special global attention to HIV, tuberculosis, and malaria through the formulation of Millennium Development Goal (MDG) 6. The Global Burden of Disease 2013 study provides a consistent and comprehensive approach to disease estimation for between 1990 and 2013, and an opportunity to assess whether accelerated progress has occured since the Millennium Declaration. METHODS: To estimate incidence and mortality for HIV, we used the UNAIDS Spectrum model appropriately modified based on a systematic review of available studies of mortality with and without antiretroviral therapy (ART). For concentrated epidemics, we calibrated Spectrum models to fit vital registration data corrected for misclassification of HIV deaths. In generalised epidemics, we minimised a loss function to select epidemic curves most consistent with prevalence data and demographic data for all-cause mortality. We analysed counterfactual scenarios for HIV to assess years of life saved through prevention of mother-to-child transmission (PMTCT) and ART. For tuberculosis, we analysed vital registration and verbal autopsy data to estimate mortality using cause of death ensemble modelling. We analysed data for corrected case-notifications, expert opinions on the case-detection rate, prevalence surveys, and estimated cause-specific mortality using Bayesian meta-regression to generate consistent trends in all parameters. We analysed malaria mortality and incidence using an updated cause of death database, a systematic analysis of verbal autopsy validation studies for malaria, and recent studies (2010-13) of incidence, drug resistance, and coverage of insecticide-treated bednets. FINDINGS: Globally in 2013, there were 1·8 million new HIV infections (95% uncertainty interval 1·7 million to 2·1 million), 29·2 million prevalent HIV cases (28·1 to 31·7), and 1·3 million HIV deaths (1·3 to 1·5). At the peak of the epidemic in 2005, HIV caused 1·7 million deaths (1·6 million to 1·9 million). Concentrated epidemics in Latin America and eastern Europe are substantially smaller than previously estimated. Through interventions including PMTCT and ART, 19·1 million life-years (16·6 million to 21·5 million) have been saved, 70·3% (65·4 to 76·1) in developing countries. From 2000 to 2011, the ratio of development assistance for health for HIV to years of life saved through intervention was US$4498 in developing countries. Including in HIV-positive individuals, all-form tuberculosis incidence was 7·5 million (7·4 million to 7·7 million), prevalence was 11·9 million (11·6 million to 12·2 million), and number of deaths was 1·4 million (1·3 million to 1·5 million) in 2013. In the same year and in only individuals who were HIV-negative, all-form tuberculosis incidence was 7·1 million (6·9 million to 7·3 million), prevalence was 11·2 million (10·8 million to 11·6 million), and number of deaths was 1·3 million (1·2 million to 1·4 million). Annualised rates of change (ARC) for incidence, prevalence, and death became negative after 2000. Tuberculosis in HIV-negative individuals disproportionately occurs in men and boys (versus women and girls); 64·0% of cases (63·6 to 64·3) and 64·7% of deaths (60·8 to 70·3). Globally, malaria cases and deaths grew rapidly from 1990 reaching a peak of 232 million cases (143 million to 387 million) in 2003 and 1·2 million deaths (1·1 million to 1·4 million) in 2004. Since 2004, child deaths from malaria in sub-Saharan Africa have decreased by 31·5% (15·7 to 44·1). Outside of Africa, malaria mortality has been steadily decreasing since 1990. INTERPRETATION: Our estimates of the number of people living with HIV are 18·7% smaller than UNAIDS's estimates in 2012. The number of people living with malaria is larger than estimated by WHO. The number of people living with HIV, tuberculosis, or malaria have all decreased since 2000. At the global level, upward trends for malaria and HIV deaths have been reversed and declines in tuberculosis deaths have accelerated. 101 countries (74 of which are developing) still have increasing HIV incidence. Substantial progress since the Millennium Declaration is an encouraging sign of the effect of global action. FUNDING: Bill & Melinda Gates Foundation