<h4>Objectives</h4>Citation bias concerns the selective citation of scientific articles based on their results. We brought together all available evidence on citation bias across scientific disciplines and quantified its impact.<h4>Study design and setting</h4>An extensive search strategy was applied to the Web of Science Core Collection and Medline, yielding 52 studies in total. We classified these studies on scientific discipline, selection method, and other variables. We also performed random-effects meta-analyses to pool the effect of positive vs. negative results on subsequent citations. Finally, we checked for other determinants of citation as reported in the citation bias literature.<h4>Results</h4>Evidence for the occurrence of citation bias was most prominent in the biomedical sciences and least in the natural sciences. Articles with statistically significant results were cited 1.6 (95% confidence interval [CI] 1.3-1.8) times more often than articles with nonsignificant results. Articles in which the authors explicitly conclude to have found support for their hypothesis were cited 2.7 (CI 2.0-3.7) times as often. Article results and journal impact factor were associated with citation more often than any other reported determinant.<h4>Conclusion</h4>Similar to what we already know on publication bias, also citation bias can lead to an overrepresentation of positive results and unfounded beliefs.

Given small sample sizes, loss of animals in preclinical experiments can dramatically alter results. However, effects of attrition on distortion of results are unknown. We used a simulation study to analyze the effects of random and biased attrition. As expected, random loss of samples decreased statistical power, but biased removal, including that of outliers, dramatically increased probability of false positive results. Next, we performed a meta-analysis of animal reporting and attrition in stroke and cancer. Most papers did not adequately report attrition, and extrapolating from the results of the simulation data, we suggest that their effect sizes were likely overestimated.

Over the past few years, we have witnessed an exponential increase in the number of radiomic-related publications. In the PET literature, a PubMed search using a criterion of ‘‘(radiomic OR radiomics OR texture OR textural) AND PET’’ yielded 37 publications in 2015 increasing to 110 in 2018. Interestingly, an extensive survey of these publications demonstrates that 94% reported positive or ‘‘promising’’ results involving some sophisticated radiomic features, with large variations in the performance supporting that conclusion. As few as 6% of them clearly concluded negative results, including the paper by Saadani et al. (1) published in this issue of The Journal of Nuclear Medicine. These numbers highlight a publication bias well acknowledged in many different research fields, with an ongoing debate about the actual false-discovery rate in the medical literature (2–4). This publication bias has well-understood roots. From the editors’ standpoint, positive results are often thought to be much more exciting and valuable than negative ones and are more likely to be cited, hence favorably increasing the journal influence metrics (e.g., 2-y impact factor) (5). From the authors’ perspective, positive results are more rewarding than negative findings and might better contribute to boosting their careers. As a consequence, negative radiomic results, the dark side of radiomics, currently remain mostly unpublished. Yet, publishing negative results is a must for ethical reasons. Criteria for publishing should be the quality of the study and its statistical power, whatever the outcome. A clinically or biologically relevant question and a methodologically well-designed study should warrant publication, no matter if the null hypothesis is rejected or not. In some instances, negative studies might be even more impactful than positive ones, as they may challenge existing paradigms and invite investigators to focus research efforts on different paths. The conclusion of the Saadani et al. paper (1) is that although B-rapidly accelerated fibrosarcoma (BRAF) mutation drives the mitogen-activated protein kinase pathway and glucose metabolism in some cancers, BRAF valine 600 status could not be successfully detected using radiomic features calculated from 18F-FDG PET/CT in melanoma patients. This observation should be an incentive to further explore the connection between the genetic mutations or pathway alterations and their macroscopic consequences detectable using our in vivo imaging devices. It is reasonable to expect that some biologic dysfunctions resulting from mutations will produce a cascade of events that might ultimately yield a signal detectable by our exquisite molecular imaging scanners. However, both the magnitude and the spatiotemporal extent of the biologic effect will determine our ability to identify an abnormality from in vivo images using a given radiotracer. Investigations of the relationships between the triggering signal (here a mutation) and the net observable result (here a change in tumor glucose metabolism) are absolutely needed for two reasons: first, to establish realistic expectations regarding the potential power of radiomic features; second, to use radiomic observations as a driver to formulate more precise biologic assumptions regarding the underlying processes and subsequently test them. Advancing that field will require the publication of both positive and negative radiomic results. One could argue that given the overwhelming number of radiomicrelated publications reporting positive results, which might be the trees that hide the forest, the publication of negative results in that domain will be practically challenging. What we need are methodologically sound, properly powered, and robust radiomic studies addressing a biologically driven hypothesis and described in such a way that independent investigators can reproduce and confirm the findings. Indeed, replication has often more scientific value than original dis

Diagnosing systemic lupus erythematosus (SLE) may be difficult in cases of negative results for antinuclear antibodies (ANAs) and anti-double stranded DNA (dsDNA) antibodies, which is known as seronegative SLE. Additionally, in patients with HIV infection, the diagnosis of SLE is made complicated by the overlap of symptoms and the possibility of false negative results on antibody tests. Herein, we report the case of a 24-year-old female with HIV infection on anti-retroviral therapy who presented with vesicles and plaques over the malar area and ulcers over the roof of the mouth. Antibody tests for ANAs and dsDNA were negative. She was initially treated for herpes simplex with a secondary infection, but the symptoms did not improve. She ultimately died from acute myocardial infarction while awaiting results of direct immunofluorescence, which revealed the deposition of immunoglobulin (Ig) M, IgG, and C3 along the basement membrane, thus enabling a diagnosis of SLE. Therefore, SLE can be difficult to diagnose in patients with HIV, and other diagnostic criteria should be considered when suspecting SLE and treating these patients. Additionally, we also present our experience with ChatGPT (OpenAI LP, OpenAI Inc., San Francisco, CA, USA) in academic publishing and its pros and cons.

In a previous Editorial (Weintraub 2015), I stated that the Journal of Insect Science would accept well-supported manuscripts with negative data. I feel that the publication of negative results is important, in fact, imperative, as I’ve outlined below. 1. Scientific thinking is not always open minded and without bias; accepted theories can be hard to overthrow—and I am referring here to scientists, not the general public. Publishing only selective information, i.e. positive results, does not allow one to visualize/understand the whole of the situation. However, by publishing negative data or results that contradict the establish way of thinking, we may more quickly come to a new understanding of the situation, whatever it may be. 2. Negative results are important for the broader field where they are relevant, helping to interpret positive results that may have been obtained in related studies. 3. If negative results are not reported, a nonproductive or flawed concept may continue to receive support from agencies, diverting funding from potentially more fruitful endeavors. How much funding has gone into supporting an idea that looks good on paper but does not come to fruition? And, how many times has this been repeated by funding agencies in different countries around the world? If the initial failure had been reported, the granting agencies could channel that money into other projects. 4. The reporting of negative results can help other scientists adjust their research plans and increase their chances of success. Once an idea has been developed and a team has moved to actually performing the research, they will work in the most logical fashion; A to B, B to C, validating each step. At this point there is no reason to “think outside the box”. When …

Techniques of sperm selection, improvement and separation in an in vitro fertilization program. The Authors present a review of the various techniques used for improving the fertility potential of sperm within the context of an in vitro fertilization (IVF) program. After a brief description of the mechanisms leading to normal in vivo fertilization, they discuss the different methods of selecting and improving sperm for IVF. They conclude that centrifugation on discontinuous Percoll gradients would seem to be the most efficient separation method from all points of view, while the addition of pharmacological agents to improve sperm quality and motility lead to extremely unsatisfactory results. Published in Acta Europaea fertilitatis

experimental results, they had certain unsatisfactory qualities stemming from their neglect of the relativistic creation and annihilation of particles. Quantum mechanics, also known as quantum physics, is the fundamental physical theory that describes the behavior of matter and of light; the behaviors it models typically occur at and below the scale of atoms, and have been described as peculiar and mysterious. Its concepts and methods have been applied across many disciplines, including quantum chemistry, quantum biology, quantum field theory, qu Quantum mechanics has had enormous success in explaining many of the features of our universe, with regard to small-scale and discrete quantities and interactions which…