Acceptance patterns and decision-making for human papillomavirus vaccination among parents in Vietnam: an in-depth qualitative study post-vaccination

BACKGROUND: The GAVI Alliance’s decision in late 2011 to invite developing countries to apply for funding for human papillomavirus (HPV) vaccine introduction underscores the importance of understanding levels of HPV vaccine acceptance in developing country settings. In this paper, we present findings from qualitative research on parents’ rationales for vaccinating or not vaccinating their daughters (vaccine acceptance) and their decision-making process in the context of an HPV vaccination demonstration project in Vietnam (2008–2009). METHODS: We designed a descriptive qualitative study of HPV vaccine acceptability among parents of girls eligible for vaccination in four districts of two provinces in Vietnam(a). The study was implemented after each of two years of vaccinations was completed. In total, 133 parents participated in 16 focus group discussions and 27 semi-structured interviews. RESULTS: Focus group discussions and in-depth interviews with parents of girls vaccinated revealed that they were generally very supportive of immunization for disease prevention and of vaccinating girls against HPV. The involvement of the National Expanded Program of Immunization in the demonstration project lent credibility to the HPV vaccine, contributing to high levels of acceptance. For parents who declined participation, concerns about side effects, the possibility that the vaccine was experimental, and the possible impact of the vaccine on future fertility rose to the surface. In terms of the decision-making process, many parents exhibited ‘active decision-making,’ reaching out to friends, family, and opinion leaders for guidance prior to making their decision. CONCLUSION: Vietnam’s HPV vaccination experience speaks to the importance of close collaboration with the government to make the most of high levels of trust, and to reduce suspicions about new vaccines that may arise in the context of vaccine introduction in developing country settings

Class based Influence Functions for Error Detection

Influence functions (IFs) are a powerful tool for detecting anomalous examples in large scale datasets. However, they are unstable when applied to deep networks. In this paper, we provide an explanation for the instability of IFs and develop a solution to this problem. We show that IFs are unreliable when the two data points belong to two different classes. Our solution leverages class information to improve the stability of IFs. Extensive experiments show that our modification significantly improves the performance and stability of IFs while incurring no additional computational cost.Comment: Thang Nguyen-Duc, Hoang Thanh-Tung, and Quan Hung Tran are co-first authors of this paper. 12 pages, 12 figures. Accepted to ACL 202

HierarchyNet : learning to summarize source code with heterogeneous representations

Code representation is important to machine learning models in the code-related applications. Existing code summarization approaches primarily leverage Abstract Syntax Trees (ASTs) and sequential information from source code to generate code summaries while often overlooking the critical consideration of the interplay of dependencies among code elements and code hierarchy. However, effective summarization necessitates a holistic analysis of code snippets from three distinct aspects: lexical, syntactic, and semantic information. In this paper, we propose a novel code summarization approach utilizing Heterogeneous Code Representations (HCRs) and our specially designed HierarchyNet. HCRs adeptly capture essential code features at lexical, syntactic, and semantic levels within a hierarchical structure. HierarchyNet processes each layer of the HCR separately, employing a Heterogeneous Graph Transformer, a Tree-based CNN, and a Transformer Encoder. In addition, HierarchyNet demonstrates superior performance compared to fine-tuned pre-trained models, including CodeT5, and CodeBERT, as well as large language models that employ zero/few-shot settings, such as CodeLlama, StarCoder, and CodeGen. Implementation details can be found at https://github.com/FSoft-AI4Code/HierarchyNet

Analysis of the Expression of Repetitive DNA Elements in Osteosarcoma

Osteosarcoma (OS) is a rare malignant bone tumor. It affects mostly young persons and has poor outcome with the present treatment. No improvement was observed since the introduction of chemotherapy. The better understanding of osteosarcoma development could indicate better management strategy. Repetitive DNA elements were found to play a role in cancer mechanism especially in epithelial tumors but not yet analyzed in osteosarcoma. We conducted the study to analyse the expression profile of repetitive elements (RE) in osteosarcoma. Methods: Fresh bone paired (tumor and normal bone) samples were obtained from excised parts of tumors of 18 patients with osteosarcoma. We performed sequencing of RNA extracted from 36 samples (18 tumor tissues and 18 normal bone for controls), mapped raw reads to the human genome and identified the REs. EdgeR package was used to analyse the difference in expression of REs between osteosarcoma and normal bone. Results: 82 REs were found differentially expressed (FDR < 0.05) between osteosarcoma and normal bone. Out of all significantly changed REs, 35 were upregulated and 47 were downregulated. HERVs (THE1C-int, LTR5, MER57F and MER87B) and satellite elements (HSATII, ALR-alpha) were the most significantly differential expressed elements between osteosarcoma and normal tissues. These results suggest significant impact of REs in the osteosarcoma. The role of REs should be further studied to understand the mechanism they have in the genesis of osteosarcoma

Introductory programming: a systematic literature review

As computing becomes a mainstream discipline embedded in the school curriculum and acts as an enabler for an increasing range of academic disciplines in higher education, the literature on introductory programming is growing. Although there have been several reviews that focus on specific aspects of introductory programming, there has been no broad overview of the literature exploring recent trends across the breadth of introductory programming. This paper is the report of an ITiCSE working group that conducted a systematic review in order to gain an overview of the introductory programming literature. Partitioning the literature into papers addressing the student, teaching, the curriculum, and assessment, we explore trends, highlight advances in knowledge over the past 15 years, and indicate possible directions for future research

Screening of GPCR drugs for repurposing in breast cancer

Drug repurposing can overcome both substantial costs and the lengthy process of new drug discovery and development in cancer treatment. Some Food and Drug Administration (FDA)-approved drugs have been found to have the potential to be repurposed as anti-cancer drugs. However, the progress is slow due to only a handful of strategies employed to identify drugs with repurposing potential. In this study, we evaluated GPCR-targeting drugs by high throughput screening (HTS) for their repurposing potential in triple-negative breast cancer (TNBC) and drug-resistant human epidermal growth factor receptor-2-positive (HER2+) breast cancer (BC), due to the dire need to discover novel targets and drugs in these subtypes. We assessed the efficacy and potency of drugs/compounds targeting different GPCRs for the growth rate inhibition in the following models: two TNBC cell lines (MDA-MB-231 and MDA-MB-468) and two HER2+ BC cell lines (BT474 and SKBR3), sensitive or resistant to lapatinib + trastuzumab, an effective combination of HER2-targeting therapies. We identified six drugs/compounds as potential hits, of which 4 were FDA-approved drugs. We focused on β-adrenergic receptor-targeting nebivolol as a candidate, primarily because of the potential role of these receptors in BC and its excellent long-term safety profile. The effects of nebivolol were validated in an independent assay in all the cell line models. The effects of nebivolol were independent of its activation of β3 receptors and nitric oxide production. Nebivolol reduced invasion and migration potentials which also suggests its inhibitory role in metastasis. Analysis of the Surveillance, Epidemiology and End Results (SEER)-Medicare dataset found numerically but not statistically significant reduced risk of all-cause mortality in the nebivolol group. In-depth future analyses, including detailed in vivo studies and real-world data analysis with more patients, are needed to further investigate the potential of nebivolol as a repurposed therapy for BC

Research Trends in Evidence-Based Medicine: A Joinpoint Regression Analysis of More than 50 Years of Publication Data

Background Evidence-based medicine (EBM) has developed as the dominant paradigm of assessment of evidence that is used in clinical practice. Since its development, EBM has been applied to integrate the best available research into diagnosis and treatment with the purpose of improving patient care. In the EBM era, a hierarchy of evidence has been proposed, including various types of research methods, such as meta-analysis (MA), systematic review (SRV), randomized controlled trial (RCT), case report (CR), practice guideline (PGL), and so on. Although there are numerous studies examining the impact and importance of specific cases of EBM in clinical practice, there is a lack of research quantitatively measuring publication trends in the growth and development of EBM. Therefore, a bibliometric analysis was constructed to determine the scientific productivity of EBM research over decades. Methods NCBI PubMed database was used to search, retrieve and classify publications according to research method and year of publication. Joinpoint regression analysis was undertaken to analyze trends in research productivity and the prevalence of individual research methods. Findings Analysis indicates that MA and SRV, which are classified as the highest ranking of evidence in the EBM, accounted for a relatively small but auspicious number of publications. For most research methods, the annual percent change (APC) indicates a consistent increase in publication frequency. MA, SRV and RCT show the highest rate of publication growth in the past twenty years. Only controlled clinical trials (CCT) shows a non-significant reduction in publications over the past ten years. Conclusions Higher quality research methods, such as MA, SRV and RCT, are showing continuous publication growth, which suggests an acknowledgement of the value of these methods. This study provides the first quantitative assessment of research method publication trends in EBM