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We have characterized the expression of microRNAs and selected microRNA precursors within several synaptic fractions of adult mouse forebrain, including synaptoneurosomes, synaptosomes and isolated post-synaptic densities (PSDs), using methods of microRNA microarray, real time qRT-PCR, Northern blotting and immunopurification using anti-PSD95 antibody. The majority of brain microRNAs (especially microRNAs known to be expressed in pyramidal neurons) are detectably expressed in synaptic fractions, and a subset of microRNAs is significantly enriched in synaptic fractions relative to total forebrain homogenate. MicroRNA precursors are also detectable in synaptic fractions at levels that are comparable to whole tissue. Whereas mature microRNAs are predominantly associated with soluble components of the synaptic fractions, microRNA precursors are predominantly associated with PSDs. For seven microRNAs examined, there was a significant correlation between the relative synaptic enrichment of the precursor and the relative synaptic enrichment of the corresponding mature microRNA. These findings support the proposal that microRNAs are formed, at least in part, via processing of microRNA precursors locally within dendritic spines. Dicer is expressed in PSDs but is enzymatically inactive until conditions that activate calpain cause its liberation; thus, we propose that synaptic stimulation may lead to local processing of microRNA precursors in proximity to the synapse. [ABSTRACT FROM AUTHOR]

We have hypothesized that small RNAs may participate in learning and memory mechanisms. Because dendritic spines are important in synaptic plasticity and learning, we asked whether dicer, the rate-limiting enzyme in the formation of small RNAs, is enriched within dendritic spines. In adult mouse brain, dicer and the RNA-induced silencing complex (RISC) component eIF2c were expressed in the somatodendritic compartment of principal neurons and some interneurons in many regions, and dicer was enriched in dendritic spines and postsynaptic densities (PSDs). A portion of dicer and eIF2c were associated with each other and with fragile X mental retardation protein (FMRP), as assessed by co-immunoprecipitation. Calpain I treatment of recombinant dicer or immunopurified brain dicer caused a marked increase in RNAse III activity. Purified PSDs did not exhibit RNAse III activity, but calpain caused release of dicer from PSDs in an enzymatically active form, together with eIF2c. NMDA stimulation of hippocampal slices, or calcium treatment of synaptoneurosomes, caused a 75 kDa dicer fragment to appear in a calpain-dependent manner. The findings support a model whereby acute neuronal stimulation at excitatory synapses increases intracellular calcium, which activates calpain, which liberates dicer and eIF2c bound to PSDs. This supports the hypothesis that dicer could be involved in synaptic plasticity. [ABSTRACT FROM AUTHOR]

In this article, researchers present a model for estimating the probability that a pair of author names appearing on two different articles refer to the same individual. Bio-informatics research databases have dramatically accelerated the pace of discovery in the biomedical sciences. Among these, Medline is the oldest and the best curated, and arguably it contains the most scientific information insofar as it summarizes knowledge that has been published across all biomedical fields. Medline and the most popular search interface, PubMed, have devoted a great deal of attention to the comprehensive retrieval of papers according to their subject content. Thus, each paper in Medline is indexed by hierarchical controlled-vocabulary medical subject headings, and this information is used automatically in query processing by PubMed. The model uses a simple yet powerful similarity profile between a pair of articles, based on title, journal name, coauthor names, medical subject headings, language, affiliation, and name attributes. The similarity profile distribution is computed from reference sets consisting of pairs of articles containing almost exclusively author matches versus non-matches, generated in an unbiased manner. Although the match set is generated automatically and might contain a small proportion of non-matches, the model is quite robust against contamination with non-matches.

Reelin is synthesized and secreted into extracellular matrix by cortical γ-aminobutyric acid (GABA)ergic interneurons and binds with high affinity to the extracellular domain of integrin receptors expressed in dendritic shaft and spine postsynaptic densities (DSPSD) of pyramidal neurons. In heterozygous reeler mice, reelin bound to DSPSD, and the expression of Arc (activity-regulated cytoskeletal protein) is lower than in wild-type mice. We studied the effect of reelin on Arc and total protein synthesis in synaptoneurosomes (SNSs) prepared from mouse neocortex. Recombinant full-length mouse reelin displaces the high affinity (K[sub D] = 60 fM) binding of [[sup 125]I]echistatin (a competitive integrin receptor antagonist) to integrin receptors with a K[sub i] of 22 pM and with a Hill slope close to 1. Echistatin (50-100 nM) competitively antagonizes and abates reelin binding. The addition of reelin (2-40 pM) to SNSs enhances the incorporation of [[sup 35]S]methionine into Arc and other rapidly translated proteins in a concentration-dependent manner. This incorporation is virtually abolished by 50-100 nM echistatin or by 5-10 nM rapamycin, a blocker of the mammalian target of rapamycin kinase. We conclude that reelin binds with high affinity to integrin receptors expressed in SNSs and thereby activates Arc protein synthesis. [ABSTRACT FROM AUTHOR]

Stimuli that stimulate neurons elicit transcription of immediate-early genes, a process which requires local sites of chromosomal DNA to form double-strand breaks (DSBs) generated by topoisomerase IIb within a few minutes, followed by repair within a few hours. Wakefulness, exploring a novel environment, and contextual fear conditioning also elicit turn-on of synaptic genes requiring DSBs and repair. It has been reported (in non-neuronal cells) that extrachromosomal circular DNA can form at DSBs as the sites are repaired. I propose that activated neurons may generate extrachromosomal circular DNAs during repair at DSB sites, thus creating long-lasting "markers" of that activity pattern which contain sequences from their sites of origin and which regulate long-term gene expression. Although the population of extrachromosomal DNAs is diverse and overall associated with pathology, a subclass of small circular DNAs ("microDNAs," ∼100-400 bases long), largely derives from unique genomic sequences and has attractive features to act as stable, mobile circular DNAs to regulate gene expression in a sequence-specific manner. Circular DNAs can be templates for the transcription of RNAs, particularly small inhibitory siRNAs, circular RNAs and other non-coding RNAs that interact with microRNAs. These may regulate translation and transcription of other genes involved in synaptic plasticity, learning and memory. Another possible fate for mobile DNAs is to be inserted stably into chromosomes after new DSB sites are generated in response to subsequent activation events. Thus, the insertions of mobile DNAs into activity-induced genes may tend to inactivate them and aid in homeostatic regulation to avoid over-excitation, as well as providing a "counter" for a neuron's activation history. Moreover, activated neurons release secretory exosomes that can be transferred to recipient cells to regulate their gene expression. Mobile DNAs may be packaged into exosomes, released in an activity-dependent manner, and transferred to recipient cells, where they may be templates for regulatory RNAs and possibly incorporated into chromosomes. Finally, aging and neurodegenerative diseases (including Alzheimer's disease) are also associated with an increase in DSBs in neurons. It will become important in the future to assess how pathology-associated DSBs may relate to activity-induced mobile DNAs, and whether the latter may potentially contribute to pathogenesis., Competing Interests: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2023 Smalheiser.)

Systematic reviews are extremely time-consuming. The goal of this work is to assess work savings and recall for a publication type filtering strategy that uses the output of two machine learning models, Multi-Tagger and web RCT Tagger, applied retrospectively to 10 systematic reviews on drug effectiveness. Our filtering strategy resulted in mean work savings of 33.6% and recall of 98.3%. Of 363 articles finally included in any of the systematic reviews, 7 were filtered out by our strategy, but 1 "error" was actually an article using a publication type that the SR team had not pre-specified as relevant for inclusion. Our analysis suggests that automated publication type filtering can potentially provide substantial work savings with minimal loss of included articles. Publication type filtering should be personalized for each systematic review and might be combined with other filtering or ranking methods to provide additional work savings for manual triage., (©2022 AMIA - All rights reserved.)

Objective: Evidence synthesis teams, physicians, policy makers, and patients and their families all have an interest in following the outcomes of clinical trials and would benefit from being able to evaluate both the results posted in trial registries and in the publications that arise from them. Manual searching for publications arising from a given trial is a laborious and uncertain process. We sought to create a statistical model to automatically identify PubMed articles likely to report clinical outcome results from each registered trial in ClinicalTrials.gov., Materials and Methods: A machine learning-based model was trained on pairs (publications known to be linked to specific registered trials). Multiple features were constructed based on the degree of matching between the PubMed article metadata and specific fields of the trial registry, as well as matching with the set of publications already known to be linked to that trial., Results: Evaluation of the model using known linked articles as gold standard showed that they tend to be top ranked (median best rank = 1.0), and 91% of them are ranked in the top 10., Discussion: Based on this model, we have created a free, public web-based tool that, given any registered trial in ClinicalTrials.gov, presents a ranked list of the PubMed articles in order of estimated probability that they report clinical outcome data from that trial. The tool should greatly facilitate studies of trial outcome results and their relation to the original trial designs., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Objectives: To produce a systematic review (SR), reviewers typically screen thousands of titles and abstracts of articles manually to find a small number which are read in full text to find relevant articles included in the final SR. Here, we evaluate a proposed automated probabilistic publication type screening strategy applied to the randomized controlled trial (RCT) articles (i.e., those which present clinical outcome results of RCT studies) included in a corpus of previously published Cochrane reviews., Materials and Methods: We selected a random subset of 558 published Cochrane reviews that specified RCT study only inclusion criteria, containing 7113 included articles which could be matched to PubMed identifiers. These were processed by our automated RCT Tagger tool to estimate the probability that each article reports clinical outcomes of a RCT., Results: Removing articles with low predictive scores P  < 0.01 eliminated 288 included articles, of which only 22 were actually typical RCT articles, and only 18 were actually typical RCT articles that MEDLINE indexed as such. Based on our sample set, this screening strategy led to fewer than 0.05 relevant RCT articles being missed on average per Cochrane SR., Discussion: This scenario, based on real SRs, demonstrates that automated tagging can identify RCT articles accurately while maintaining very high recall. However, we also found that even SRs whose inclusion criteria are restricted to RCT studies include not only clinical outcome articles per se, but a variety of ancillary article types as well., Conclusions: This encourages further studies learning how best to incorporate automated tagging of additional publication types into SR triage workflows., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Over a decade ago, we introduced Anne O'Tate, a free, public web-based tool http://arrowsmith.psych.uic.edu/cgi-bin/arrowsmith&#95;uic/AnneOTate.cgi to support user-driven summarization, drill-down and mining of search results from PubMed, the leading search engine for biomedical literature. A set of hotlinked buttons allows the user to sort and rank retrieved articles according to important words in titles and abstracts; topics; author names; affiliations; journal names; publication year; and clustered by topic. Any result can be further mined by choosing any other button, and small search results can be expanded to include related articles. It has been deployed continuously, serving a wide range of biomedical users and needs, and over time has also served as a platform to support the creation of new tools that address additional needs. Here we describe the current, greatly expanded implementation of Anne O'Tate, which has added additional buttons to provide new functionalities: We now allow users to sort and rank search results by important phrases contained in titles and abstracts; the number of authors listed on the article; and pairs of topics that co-occur significantly more than chance. We also display articles according to NLM-indexed publication types, as well as according to 50 different publication types and study designs as predicted by a novel machine learning-based model. Furthermore, users can import search results into two new tools: e) Mine the Gap!, which identifies pairs of topics that are under-represented within set of the search results, and f) Citation Cloud, which for any given article, allows users to visualize the set of articles that cite it; that are cited by it; that are co-cited with it; and that are bibliographically coupled to it. We invite the scientific community to explore how Anne O'Tate can assist in analyzing biomedical literature, in a variety of use cases., Competing Interests: The authors declare no competing interests. Xornet, Inc. provides database and website services but has no commercial interests in the Anne O’Tate tool. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

A recent flood of publications has documented serious problems in scientific reproducibility, power, and reporting of biomedical articles, yet scientists persist in their usual practices. Why? We examined a popular and important preclinical assay, the Forced Swim Test (FST) in mice used to test putative antidepressants. Whether the mice were assayed in a naïve state vs. in a model of depression or stress, and whether the mice were given test agents vs. known antidepressants regarded as positive controls, the mean effect sizes seen in the experiments were indeed extremely large (1.5-2.5 in Cohen's d units); most of the experiments utilized 7-10 animals per group which did have adequate power to reliably detect effects of this magnitude. We propose that this may at least partially explain why investigators using the FST do not perceive intuitively that their experimental designs fall short-even though proper prospective design would require ~21-26 animals per group to detect, at a minimum, large effects (0.8 in Cohen's d units) when the true effect of a test agent is unknown. Our data provide explicit parameters and guidance for investigators seeking to carry out prospective power estimation for the FST. More generally, altering the real-life behavior of scientists in planning their experiments may require developing educational tools that allow them to actively visualize the inter-relationships among effect size, sample size, statistical power, and replicability in a direct and intuitive manner., Competing Interests: The authors have declared that no competing interests exist.

Objectives: To identify separate publications that report outcomes from the same underlying clinical trial, in order to avoid over-counting these as independent pieces of evidence., Materials and Methods: We updated our previous model by creating larger, more recent, and more diverse positive and negative training sets consisting of article pairs that were (or not) linked to the same ClinicalTrials.gov trial registry number. Features were extracted from PubMed metadata; pairwise similarity scores were modeled using logistic regression and used to form clusters of articles that are likely to arise from the same registered clinical trial., Results: Articles from the same trial were identified with high accuracy (F1 = 0.859), nominally better than the previous model (F1 = 0.843). Predicted clusters showed a low error rate of splitting of 8-11% (ie, when 2 articles belonged to the same trial but were assigned to different clusters). Performance was similar whether only randomized controlled trial articles or a more diverse set of clinical trial articles were processed., Discussion: Metadata are surprisingly accurate in predicting when 2 articles derive from the same underlying clinical trial., Conclusion: We have continued confidence in the Aggregator tool which can be accessed publicly at http://arrowsmith.psych.uic.edu/cgi-bin/arrowsmith&#95;uic/RCT&#95;Tagger.cgi., (© The Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Mining the case report literature identified an intriguing, yet neglected finding: Deoxyribonuclease I (DNase I) as a possible treatment for Alzheimer's disease. This finding is speculative, both because it is based on one patient, and because the underlying mechanism(s) of action remain obscure. However, further literature review revealed that there are several plausible mechanisms by which DNase I might affect the course of Alzheimer's disease. Given that DNase I is an FDA-approved drug, with extensive studies in both animals and man in the context of other diseases, I suggest that investigation of DNAse I in Alzheimer's disease is worthwhile., Competing Interests: The author has no conflict of interest to report.

Neural embeddings are a popular set of methods for representing words, phrases or text as a low dimensional vector (typically 50-500 dimensions). However, it is difficult to interpret these dimensions in a meaningful manner, and creating neural embeddings requires extensive training and tuning of multiple parameters and hyperparameters. We present here a simple unsupervised method for representing words, phrases or text as a low dimensional vector, in which the meaning and relative importance of dimensions is transparent to inspection. We have created a near-comprehensive vector representation of words, and selected bigrams, trigrams and abbreviations, using the set of titles and abstracts in PubMed as a corpus. This vector is used to create several novel implicit word-word and text-text similarity metrics. The implicit word-word similarity metrics correlate well with human judgement of word pair similarity and relatedness, and outperform or equal all other reported methods on a variety of biomedical benchmarks, including several implementations of neural embeddings trained on PubMed corpora. Our implicit word-word metrics capture different aspects of word-word relatedness than word2vec-based metrics and are only partially correlated (rho = 0.5-0.8 depending on task and corpus). The vector representations of words, bigrams, trigrams, abbreviations, and PubMed title + abstracts are all publicly available from http://arrowsmith.psych.uic.edu/arrowsmith&#95;uic/word&#95;similarity&#95;metrics.html for release under CC-BY-NC license. Several public web query interfaces are also available at the same site, including one which allows the user to specify a given word and view its most closely related terms according to direct co-occurrence as well as different implicit similarity metrics., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Clinical case reports are the `eyewitness reports' of medicine and provide a valuable, unique, albeit noisy and underutilized type of evidence. Generally a case report has a single main finding that represents the reason for writing up the report in the first place. In the present study, we present the results of manual annotation carried out by two individuals on 500 randomly sampled case reports. This corpus contains main finding sentences extracted from title, abstract and full-text of the same article that can be regarded as semantically related and are often paraphrases. The final reconciled corpus of 416 articles comprises an open resource for further study. This is the first step in establishing text mining models and tools that can identify main finding sentences in an automated fashion, and in measuring quantitatively how similar any two main findings are. We envision that case reports in PubMed may be automatically indexed by main finding, so that users can carry out information queries for specific main findings (rather than general topics)-and given one case report, a user can retrieve those having the most similar main findings. The metric of main finding similarity may also potentially be relevant to the modeling of paraphrasing, summarization and entailment within the biomedical literature.

Many investigators have carried out text mining of the biomedical literature for a variety of purposes, ranging from the assignment of indexing terms to the disambiguation of author names. A common approach is to define positive and negative training examples, extract features from article metadata, and employ machine learning algorithms. At present, each research group tackles each problem from scratch, and in isolation of other projects, which causes redundancy and great waste of effort. Here, we propose and describe the design of a generic platform for biomedical text mining, which can serve as a shared resource for machine learning projects, and can serve as a public repository for their outputs. We will initially focus on a specific goal, namely, classifying articles according to Publication Type, and emphasize how feature sets can be made more powerful and robust through the use of multiple, heterogeneous similarity measures as input to machine learning models. We then discuss how the generic platform can be extended to include a wide variety of other machine learning based goals and projects, and can be used as a public platform for disseminating the results of NLP tools to end-users as well.

The late Don R. Swanson was well appreciated during his lifetime as Dean of the Graduate Library School at University of Chicago, as winner of the American Society for Information Science Award of Merit for 2000, and as author of many seminal articles. In this informal essay, I will give my personal perspective on Don's contributions to science, and outline some current and future directions in literature-based discovery that are rooted in concepts that he developed.