Search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.

Self-assembly of amyloid β-protein (Aβ) into neurotoxic oligomers and fibrillar aggregates is a key process thought to be the proximal event leading to development of Alzheimer's disease (AD). Therefore, numerous attempts have been made to develop reagents that disrupt this process and prevent the formation of the toxic oligomers and aggregates. An attractive strategy for developing such reagents is to use peptides derived from Aβ based on the assumption that such peptides would bind to full-length Aβ, interfere with binding of additional full-length molecules, and thereby prevent formation of the toxic species. Guided by this rationale, most of the studies in the last two decades have focused on preventing formation of the core cross-β structure of Aβ amyloid fibrils using β-sheet-breaker peptides derived from the central hydrophobic cluster of Aβ. Though this approach is effective in inhibiting fibril formation, it is generally inefficient in preventing Aβ oligomerization. An alternative approach is to use peptides derived from the C-terminus of Aβ, which mediates both oligomerization and fibrillogenesis. This approach has been explored by several groups, including our own, and led to the discovery of several lead peptides with moderate to high inhibitory activity. Interestingly, the mechanisms of these inhibitory effects have been found to be diverse, and only in a small percentage of cases involved interference with β-sheet formation. Here, we review the strategy of using C-terminal fragments of Aβ as modulators of Aβ assembly and discuss the relevant challenges, therapeutic potential, and mechanisms of action of such fragments.

Abnormal protein folding and self-assembly causes over 30 cureless human diseases for which no disease-modifying therapies are available. The common side to all these diseases is formation of aberrant toxic protein oligomers and amyloid fibrils. Both types of assemblies are drug targets, yet each presents major challenges to drug design, discovery, and development. In this review, we focus on two small molecules that inhibit formation of toxic amyloid protein assemblies - the green-tea derivative (-)-epigallocatechin-3-gallate (EGCG), which was identified through a combination of epidemiologic data and a compound library screen, and the molecular tweezer CLR01, whose inhibitory activity was discovered in our group based on rational reasoning, and subsequently confirmed experimentally. Both compounds act in a manner that is not specific to one particular protein and thus are useful against a multitude of amyloidogenic proteins, yet they act via distinct putative mechanisms. CLR01 disrupts protein aggregation through specific binding to lysine residues, whereas the mechanisms underlying the activity of EGCG are only recently beginning to unveil. We discuss current in vitro and, where available, in vivo literature related to EGCG and CLR01's effects on amyloid β-protein, α-synuclein, transthyretin, islet amyloid polypeptide, and calcitonin. We also describe the toxicity, pharmacokinetics, and mechanism of action of each compound. © Versita Sp. z o.o.

Aptamers are useful molecular recognition tools in research, diagnostics, and therapy. Despite promising results in other fields, aptamer use has remained scarce in amyloid research, including Alzheimer's disease (AD). AD is a progressive neurodegenerative disease believed to be caused by neurotoxic amyloid beta-protein (Abeta) oligomers. Abeta oligomers therefore are an attractive target for development of diagnostic and therapeutic reagents. We used covalently-stabilized oligomers of the 40-residue form of Abeta (Abeta40) for aptamer selection. Despite gradually increasing the stringency of selection conditions, the selected aptamers did not recognize Abeta40 oligomers but reacted with fibrils of Abeta40, Abeta42, and several other amyloidogenic proteins. Aptamer reactivity with amyloid fibrils showed some degree of protein-sequence dependency. Significant fibril binding also was found for the naïve library and could not be eliminated by counter-selection using Abeta40 fibrils, suggesting that aptamer binding to amyloid fibrils was RNA-sequence-independent. Aptamer binding depended on fibrillogenesis and showed a lag phase. Interestingly, aptamers detected fibril formation with > or =15-fold higher sensitivity than thioflavin T (ThT), revealing substantial beta-sheet and fibril formation undetected by ThT. The data suggest that under physiologic conditions, aptamers for oligomeric forms of amyloidogenic proteins cannot be selected due to high, non-specific affinity of oligonucleotides for amyloid fibrils. Nevertheless, the high sensitivity, whereby aptamers detect beta-sheet formation, suggests that they can serve as superior amyloid recognition tools.

Those who participate in and contribute to academic publishing are affected by its evolution. Funding bodies, academic institutions, researchers and peer-reviewers, junior scholars, freelance language editors, language-editing services, and journal editors are to enforce and uphold the ethical norms on which academic publishing is founded. Deviating from such norms will challenge and threaten the scholarly reputation, academic careers, and institutional standing; reduce the publishers' true impacts; squander public funding; and erode the public trust to the academic enterprise. Rigorous review is paramount because peer-review norms guarantee that scientific findings are scrutinized before being publicized. Volunteer peer-reviewers and guest journal editors devote an immense amount of unremunerated time to reviewing papers, voluntarily serving the scientific community, and benefiting the publishers. Some mega-journals are motivated to mass-produce publications and attract the funded projects instead of maintaining the scientific rigor. The rapid development of mega-journals may diminish some traditional journals by outcompeting their impacts. Artificial intelligence (AI) tools/algorithms such as ChatGPT may be misused to contribute to the mass-production of publications which may have not been rigorously revised or peer-reviewed. Maintaining norms that guarantee scientific rigor and academic integrity enable the academic community to overcome the new challenges such as mega-journals and AI tools. [ABSTRACT FROM AUTHOR]