1,720,958 research outputs found
Linguistic changes in the transition from summaries to abstracts: The case of the Journal of Experimental Medicine
No full textThe introduction of Abstracts to replace article summaries in 1990 recognized changes to linguistic reporting that have been apparent during the century. The 1970s showed a dramatic increase in the informal language used in article abstracts and summaries. The Journal of Experimental Medicine (JEM) demonstrates an increase in first-person pronouns within article abstracts and summaries, but moves from singular to plural to represent the increase in multi-authored research works. Linguistic changes during the century also include a greater focus on the future rather than the past, and an increase in language that indicates ‘clout’ which signifies author self-confidence
Seeing to learn and learning to see: histology teaching between new technologies, old paradigms and natural cyborgs
No full textHistology is a foundational course in many life science programs. Microscopes have long been the primary instruments used in this discipline, playing a crucial role in histology education for decades. However, with the current significant technological advancements, digital tools are progressively replacing microscopes in university classrooms worldwide. Due to their expense and maintenance requirements, educators are questioning whether the use of traditional microscopes remains a practical approach to teaching this subject. This work aims to present an alternative perspective on the importance and the epistemic peculiarities of microscopes in understanding the microstructure of tissues, moving from internalist approaches to enactive perspectives. Rather than adjudicating a technological contest that many programs have already resolved pragmatically, we offer a philosophical and pedagogical reflection that clarifies what kinds of understanding are cultivated by optical and virtual practices and how those understandings align with contemporary research
A Comparative Analysis of Sentence Transformer Models for Automated Journal Recommendation Using PubMed Metadata
No full textWe present an automated journal recommendation pipeline designed to evaluate the performance of five Sentence Transformer models—all-mpnet-base-v2 (Mpnet), all-MiniLM-L6-v2 (Minilm-l6), all-MiniLM-L12-v2 (Minilm-l12), multi-qa-distilbert-cos-v1 (Multi-qa-distilbert), and all-distilroberta-v1 (roberta)—for recommending journals aligned with a manuscript’s thematic scope. The pipeline extracts domain-relevant keywords from a manuscript via KeyBERT, retrieves potentially related articles from PubMed, and encodes both the test manuscript and retrieved articles into high-dimensional embeddings. By computing cosine similarity, it ranks relevant journals based on thematic overlap. Evaluations on 50 test articles highlight mpnet’s strong performance (mean similarity score 0.71 ± 0.04), albeit with higher computational demands. Minilm-l12 and minilm-l6 offer comparable precision at lower cost, while multi-qa-distilbert and roberta yield broader recommendations better suited to interdisciplinary research. These findings underscore key trade-offs among embedding models and demonstrate how they can provide interpretable, data-driven insights to guide journal selection across varied research contexts
A biomimetic polynucleotides–hyaluronic acid hydrogel promotes wound healing in a primary gingival fibroblast model
No full textPolynucleotides (PN) have long been known as an effective supportive therapy for wound healing. The present study investigated whether a hydrogel formulation containing PN and hyaluronic acid (PN + HA) could promote wound healing in an in vitro model of gingival fibroblasts. PN promoted cell growth and viability as assessed by different assays, and PN + HA, though not significantly further increasing cell growth as compared to PN, supported the formation of dense multilayered cell nodules. PN promoted fibroblasts’ clonogenic efficiency and PN + HA further enhanced the formation of more numerous dense colonies. PN + HA appeared to significantly increase the expression of collagen 1a1 and 3a1, while not affecting proteoglycans deposition. Inter-estingly, when tested in a scratch assay, PN + HA achieved gap closure after 48 h, while cells in the comparison groups had not completely bridged the scratch even after 96 h. Taken together, these results demonstrate that PN + HA is a promising candidate for a supportive therapy to promote soft tissue healing in the oral cavity
How to Write Effective Prompts for Screening Biomedical Literature Using Large Language Models
No full textLarge language models (LLMs) have emerged as powerful tools for (semi-)automating the initial screening of abstracts in systematic reviews, offering the potential to significantly reduce the manual burden on research teams. This paper provides a broad overview of prompt engineering principles and highlights how traditional PICO (Population, Intervention, Comparison, Outcome) criteria can be converted into actionable instructions for LLMs. We analyze the trade-offs between “soft” prompts, which maximize recall by accepting articles unless they explicitly fail an inclusion requirement, and “strict” prompts, which demand explicit evidence for every criterion. Using a periodontics case study, we illustrate how prompt design affects recall, precision, and overall screening efficiency and discuss metrics (accuracy, precision, recall, F1 score) to evaluate performance. We also examine common pitfalls, such as overly lengthy prompts or ambiguous instructions, and underscore the continuing need for expert oversight to mitigate hallucinations and biases inherent in LLM outputs. Finally, we explore emerging trends, including multi-stage screening pipelines and fine-tuning, while noting ethical considerations related to data privacy and transparency. By applying systematic prompt engineering and rigorous evaluation, researchers can optimize LLM-based screening processes, allowing for faster and more comprehensive evidence synthesis across biomedical disciplines
Performance Comparison of Large Language Models for Efficient Literature Screening
No full textBackground: Systematic reviewers face a growing body of biomedical literature, making early-stage article screening increasingly time-consuming. In this study, we assessed six large language models (LLMs)—OpenHermes, Flan T5, GPT-2, Claude 3 Haiku, GPT-3.5 Turbo, and GPT-4o—for their ability to identify randomized controlled trials (RCTs) in datasets of increasing difficulty. Methods: We first retrieved articles from PubMed and used all-mpnet-base-v2 to measure semantic similarity to known target RCTs, stratifying the collection into quartiles of descending relevance. Each LLM then received either verbose or concise prompts to classify articles as “Accepted” or “Rejected”. Results: Claude 3 Haiku, GPT-3.5 Turbo, and GPT-4o consistently achieved high recall, though their precision varied in the quartile with the highest similarity, where false positives increased. By contrast, smaller or older models struggled to balance sensitivity and specificity, with some over-including irrelevant studies or missing key articles. Importantly, multi-stage prompts did not guarantee performance gains for weaker models, whereas single-prompt approaches proved effective for advanced LLMs. Conclusions: These findings underscore that both model capability and prompt design strongly affect classification outcomes, suggesting that newer LLMs, if properly guided, can substantially expedite systematic reviews
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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