 Reading time 10 min
TL;DR: I re-ran the IF/SJR analysis focused on publishers highlighted by MDPI as contemporaries with very high self-citation rate. Many of those publishing groups included non-profit professional societies. Like other non-profits, their IF/SJR were very low and drastically different from MDPI. This also brought IGI Global into the mix, which has been referred to as a "vanity press" publisher that contributes little beyond pumping up people's CVs with obscure academic book chapters. IGI Global and my control predatory publisher (Bentham) were the only groups with an IF/SJR comparable to MDPI. I recently shared some thoughts on what defines a modern predatory publisher. In that post (see here [1]), I defined five red flags that act as markers of predatory behaviour. These were: #1) a history of controversy over rigour of peer review, #2) rapid and homogenous article processing times, #3) Frequent email spam to submit papers to special editions or invitations to be an editor, #4) low diversity of journals citing works by a publisher, and #5) high rates of self-citation within a publisher's journal network. Overall, the post seems to have been well-received (I like to think!). One tool I proposed to help spot predatory publishing behaviour was to use the difference in how Impact Factor (IF) and Scimago Journal Rank (SJR) metrics were calculated to expose publishers with either: #4) low diversity of journals citing works by a publisher and/or #5) high rates of self-citation within a publisher's journal network. I'll reiterate that one should really judge all five red flags in making conclusions about a publisher, but today I'll focus on the IF/SJR metric.  Reading time: 15 minutes
You've no doubt heard the phrase "predatory publishing." The term was popularized by librarian Jeffrey Beall for his observation of "publishers that are ready to publish any article for payment" [1]. The term accurately describes the least professional publishing companies that typically do not get indexed by major publisher metrics analysts like Clarivate or Scopus. However the advent and explosive success of major Open-Access publishing companies like MDPI, Frontiers, or BMC, has led to murky interpretations of the term "predatory publishing." Some might even adopt the term to describe the exorbitant open-access prices of major journals like Nature or Science Advances [2]. A recent commentary by Paolo Crossetto [3] made the point to say that some of the strategies employed by the controversial publisher MDPI might better be referred to as "aggressive rent extracting" rather than predatory behaviour; the term is borrowed from economics, and refers to lawmakers receiving payments from interest groups in return for favourable legislation [4]. Crossetto added the caveat that their methods might lean towards more predatory behaviour over time [3]. But there's that word again: "predatory." What exactly does it mean to lean towards predatory behaviour? When does one cross the line from engaging in predatory behaviour to becoming a predatory publisher? Is predation really even the right analogy? I'll take some time to discuss the term "predatory publishing" here to try and hammer out what it means and what it doesn't mean in the modern era. The TL;DR summary is: the standards for predatory publishing have shifted, and so have the publishers. But there is a general idea out there of what constitutes predatory publishing. I make an effort to define the factors leading to those impressions, and describe some tools that the layman can use to inform themselves on predatory publishing behaviour.  Summary (TL;DR)
Predatory publishing is a significant issue in scientific discourse. Predatory publishers erode the integrity of science in the public eye due to accepting poor quality work or even misinformation advertised as professional investigation, and harm scientists that contribute time and money to aid these disreputable companies. Recent conversations around the Multidisciplinary Publishing Institute (MDPI) have accused MDPI of engaging in predatory practices. Notably, MDPI journals have been observed with high self-citation rates, which artificially inflate their Impact Factor compared to honest scientific discourse. Here I use a novel metric to assess whether MDPI journals have artificially inflated their Impact Factor: the ratio of Impact Factor (IF) to SCImago Journal Rank (SJR). This IF/SJR metric readily distinguishes reputable not-for-profit publishers from a known predatory publisher (Bentham Open), and also from MDPI. I further included a spectrum of for-profit and non-profit publishing companies in the analysis, including Frontiers, BMC, and PLoS. My results inform on the degree of predatory publishing by each of these groups, and indicate IF/SJR as a simple metric for assessing a publisher's citation behaviour. I suggest that an IF/SJR ratio >4 is an indication of possible predatory behaviour, and scientists approached by publishers with IF/SJR > 4 should proceed with caution. Importantly, the IF/SJR ratio can be determined easily and without relying on data hiding behind paywalls. Background The full dataset and R scripts used for the analysis are provided as a .zip file at the end of this article. Predatory publishing is a major issue in the scientific literature. Briefly: predatory publishers basically pose as legitimate scientific journals, but fail to perform the minimum rigour as curators of scientific information. Nowadays, outright fake journals are readily spotted by careful scientists, but quasi-predatory publishers can appear quite professional and are much more difficult to identify. Quasi-predatory publishers are identifiable by a few key factors (I have selected a few factors discussed in Ref [1]):
Regarding this fourth point, a journal's Impact Factor (IF) is an important metric that is used by granting agencies and scientists to judge a journal's prestige (for better or worse). The IF is determined by the average number of citations articles get when published in that journal. Importantly, the IF number can be artificially inflated by authors citing themselves or their close colleagues in a biased fashion [2]. On the surface, this gives the impression that the scientist or journal is highly productive and well-recognized. This gives predatory publishers a significant incentive to engage in self-citation practices that inflate their Impact Factor. Predatory publishers do this by citing work from their own network of journals in an unabashedly biased fashion [1]. I'll be referring to this as "Impact Factor Inflation" hereon (see [3]). Analyses like this one and studies like this one [1] have already discussed the quasi-predatory or even outright predatory behaviour of MDPI, which has grown more bold and problematic in recent years. The recommendation of Ref [1] was that scientific journal databases should remove MDPI due to predatory behaviour, indicating the degree of the issue being debated with MDPI. In particular, MDPI was seen to have significant self-citation issues, alongside 'citation cartels', networks of journals that cite each other to artificially inflate their impact factor [1]. I will add two more factors that are associated with predatory publishers:
For instance, the MDPI journal "Vaccines" recently published a vaccine misinformation piece that falsely claimed more people suffered serious complications from Covid vaccination than from Covid-19 disease (amongst a long history of other controversial pieces summarized here). However other publishers have also published controversial pieces that clearly lacked the rigour of professional peer review (e.g. see Frontiers Media here and Bentham Open here), and even highly-respected journals can slip up every now and then: remember that Science article about Arsenic being used as a DNA backbone? It is thus imperative to come up with ways to quantify the degree of predatory behaviour beyond anecdotes. IF/SJR: identifying Impact Factor inflation Drosophila geneticists benefit from decades of research and development, providing tools that can tackle literally any gene in the genome. At the click of your mouse, you can readily order flies that will express dsRNA to knock down your gene of interest for <$20 plus the cost of shipping. Most genes have putative mutant alleles disrupting gene expression or structure. Now, in the age of CRISPR, it's even easier to fill in the gaps where existing toolkits are less robust. But CRISPR involves capitol. Time and money, and energy. And after that, there is still a chance that your "good" mutation isn't as good as you thought it was. Take it from someone who's experienced this firsthand on more than one occasion: and I haven't even worked with that many flies generated using CRISPR and double gRNA! Briefly: we generated and tracked a Cecropin mutation using mutant-specific primers, but somehow failed to detect a wild-type Cecropin locus present in our Cecropin-mutant stocks caused by a bizarre recombination event. More recently, I detected that a similar double gRNA approach that also included an HDR vector (two chromosome arms that were there as guides to the locus) did not insert as one might expect... I detected this after performing maybe the 3rd or 4th routine-check PCR where I eventually realized: "hey... that band is ~100 bp larger than it should be... isn't it?" In the end, instead of replacing the locus, the HDR vector failed to do its job and inserted in the middle of the promoter. SNPs and a 10 nt indel prevented detection by existing primers for the wild-type gene. In the end, our mutant was still effectively a hypomorph (which is good, because it had a phenotype we spent a good while on), but not a full knock out (Fig 1).  Figure 1: the BaraB[LC1] mutation (Hanson and Lemaitre, 2021) did not insert as expected. This mutation disrupts the promoter but the full gene remains present and mostly unchanged. A 10nt insertion at the C-terminus overlapped the reverse primer binding site that was initially used to check for the wild-type locus. My point here is that CRISPR isn't a magic tool that always works. Beyond these bizarre instances, off-target effects are a serious concern and could affect 4% of CRISPR mutant stocks (Shu Kondo, EDRC 2019); or more, it's tough to say! In total, CRISPR is a powerful tool in the toolkit, but it requires gRNA generation, injection, mutant isolation, and in my experience something that is especially important: robust mutant validation. This process takes a couple months at best, but more likely longer. But what if you didn't have to make new mutations? What if there was already an existing mutation you could access, but not in known databases. Enter the DGRP I was motivated to pen some thoughts on Furins owing the ongoing Covid-19 pandemic. My research focuses on antimicrobial peptides (AMPs), which in Drosophila and other insects are commonly cleaved by Furin-like enzymes. Relatively little has been done to understand exactly how different Furins act, and what intracellular processes they can regulate; this is likely at least in part due to their potential to affect many cell processes simultaneously, making genetic approaches difficult and confusticating the interpretation of focused in vitro approaches.
What is Furin, and how does it relate to Sars-CoV-2? The Sars-CoV-2 (Sars2) pandemic has resulted in a great deal of interest in the unique Furin cleavage site of the Sars2 spike protein [1-3]. Furins are subtilisin-like peptidases that cleave at a predictable multibasic cleavage site typically consisting of either an RXRR or RXKR motif. Humans encode many Furin-like enzymes including human Furin and numerous proprotein convertase subtilisin/kexin type (PCSK) enzymes [4]. It has been shown that even within the RXRR motif Furins, there is substrate specificity depending on what the X is, and also some variation in preference for whether the terminal residues are RR or KR (e.g. PCSK3) [4-5]. However there is also residual activity of human Furin on RXXR sites in general (NEB: Furin), suggesting these enzymes are not absolutely specific. These cleavages are thought to occur either at the cell membrane, at the golgi, or in vesicles during subcellular trafficking [6]. There is some evidence that Sars2 infectivity is affected by the presence of the Sars2 Furin site (RRAR). In an African green monkey kidney epithelium cell line (Vero E6), loss of the Furin cleavage site is associated with improved infectivity [3]. However the direction of change owing to the presence/absence of this cleavage site in human cells and in more relevant tissues (like lung epithelium) is not clear. What is known is that such Furin sites are not unique to Sars2, but also found in MERS-CoV (RXVR), HCOV-OC43 (RSRR), and HKU1-CoV (RKRR) [7]. So it seems like Furin sites are maintained in many CoVs, despite a reduced efficiency of Furin site-containing Sars2 in Vero cells. Drosophila as a model for Furin evolution and specificity... The peer review process is often seen as something that ends at the point of publication. But it is only after publication that most conversations surrounding a paper can begin! By virtue of an extended audience, often the feedback one receives at this post-publication stage is just as valuable as the feedback received during the initial peer review process.
This was perhaps best-emphasized to me following a recent publication on antimicrobial peptide evolution in Diptera. Our reviewers for this publication were experts in insect immunity and symbiosis, and contributed valuable input to improve the manuscript as a whole. However it was only after presenting this work to a diverse audience that key questions regarding some elements of the story were asked. Herein I summarize these key questions raised, and provide answers that hopefully further improve the reader's confidence in the claims made in the publication: Hanson MA, Lemaitre B, Unckless RL. 2019. Dynamic Evolution of Antimicrobial Peptides Underscores Trade-Offs Between Immunity and Ecological Fitness. Frontiers in Immunology, 10, 2620. https://doi.org/10.3389/fimmu.2019.02620 |
AuthorMark Archives
August 2024
Categories |
RSS Feed
