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Innovative techniques, such as environmental DNA (eDNA) metabarcoding, are now promoting broader biodiversity monitoring at unprecedented scales, because of the reduction in time, presumably lower cost, and methodological efficiency. Our goal was to assess the efficiency of established inventory techniques (live-trapping grids, pitfall traps, camera trapping, mist netting) as well as eDNA for detecting Amazonian mammals. For terrestrial small mammals, we used 32 live-trapping grids based on Sherman and Tomahawk traps (total effort of 10,368 trap-nights); in addition to 16 pitfall traps (1,408 trap-nights). For bats, we used mist nets at 8 sites (4,800 net hours). For medium and large mammals, we used 72 camera trap stations (5,208 camera-days). We identified vertebrate and mammal taxa based on eDNA analysis (12S region, with V05 and Mamm01 markers) from water samples, including a total of 11 3-km transects for stagnant water sampling and seven small streams for running water sampling. A total of 106 mammal species were recorded. Building on sample-based rarefaction and extrapolation curves, both trapping grids and pitfall were successful, recording 91.16% and 82.1% of the expected species for these techniques (~22 and ~9 species), and 16.98% and 6.60% of the total recorded mammal species, respectively. Mist nets recorded 83.2% of the expected bat species (~48), and 34.91% of the total recorded species. Camera trapping recorded 99.2% of the predicted large- and medium-sized species (~31), and 33.02% of the total recorded species. eDNA recorded 75.4% of the expected mammal species for this technique (~68), and 47.0% of the total recorded species. eDNA resulted in a useful tool that saves on effort and reduces sampling costs. This study is among the first to show the large potential of eDNA metabarcoding for assessing Amazonian mammal communities, providing, in combination with conventional techniques, a rapid overview of mammal diversity with broad applications to monitoring, management and conservation. By including appropriate genetic markers and updated reference databases, eDNA metabarcoding method can be extended to the whole vertebrate community.

A microneedle (MN) is a painless and minimally invasive drug delivery device initially developed in 1976. As microneedle technology evolves, microneedles with different shapes (cone and pyramid) and forms (solid, drug-coated, hollow, dissolvable and hydrogel-based microneedles) have been developed. The main objective of this review is the applications of microneedles in biomedical areas. Firstly, the classifications and manufacturing of microneedle are briefly introduced so that we can learn the advantages and fabrications of different MNs. Secondly, research of microneedles in biomedical therapy such as drug delivery systems, diagnoses of disease, as well as wound repair and cancer therapy are overviewed. Finally, the safety and the vision of the future of MNs are discussed.

Background Due to clinical and methodological diversity, clinical studies included in meta-analyses often differ in ways that lead to differences in treatment effects across studies. Meta-regression analysis is generally recommended to explore associations between study-level characteristics and treatment effect, however, three key pitfalls of meta-regression may lead to invalid conclusions. Our aims were to determine the frequency of these three pitfalls of meta-regression analyses, examine characteristics associated with the occurrence of these pitfalls, and explore changes between 2002 and 2012. Methods A meta-epidemiological study of studies including aggregate data meta-regression analysis in the years 2002 and 2012. We assessed the prevalence of meta-regression analyses with at least 1 of 3 pitfalls: ecological fallacy, overfitting, and inappropriate methods to regress treatment effects against the risk of the analysed outcome. We used logistic regression to investigate study characteristics associated with pitfalls and examined differences between 2002 and 2012. Results Our search yielded 580 studies with meta-analyses, of which 81 included meta-regression analyses with aggregated data. 57 meta-regression analyses were found to contain at least one pitfall (70%): 53 were susceptible to ecological fallacy (65%), 14 had a risk of overfitting (17%), and 5 inappropriately regressed treatment effects against the risk of the analysed outcome (6%). We found no difference in the prevalence of meta-regression analyses with methodological pitfalls between 2002 and 2012, nor any study-level characteristic that was clearly associated with the occurrence of any of the pitfalls. Conclusion The majority of meta-regression analyses based on aggregate data contain methodological pitfalls that may result in misleading findings.

Having gained a tremendous amount of popularity since its introduction in 2006, tract-based spatial statistics (TBSS) can now be considered as the standard approach for voxel-based analysis (VBA) of diffusion tensor imaging (DTI) data. Aiming to improve the sensitivity, objectivity, and interpretability of multi-subject DTI studies, TBSS includes a skeletonization step that alleviates residual image misalignment and obviates the need for data smoothing. Although TBSS represents an elegant and user-friendly framework that tackles numerous concerns existing in conventional VBA methods, it has limitations of its own, some of which have already been detailed in recent literature. In this work, we present general methodological considerations on TBSS and report on pitfalls that have not been described previously. In particular, we have identified specific assumptions of TBSS that may not be satisfied under typical conditions. Moreover, we demonstrate that the existence of such violations can severely affect the reliability of TBSS results. With TBSS being used increasingly, it is of paramount importance to acquaint TBSS users with these concerns, such that a well-informed decision can be made as to whether and how to pursue a TBSS analysis. Finally, in addition to raising awareness by providing our new insights, we provide constructive suggestions that could improve the validity and increase the impact of TBSS drastically. •We investigate tract-based spatial statistics (TBSS) considering potential pitfalls.•TBSS is not tract-specific and we show how this may falsify results.•User defined parameters strongly influence the final TBSS-derived results.•We provide suggestions that improve the validity and increase the impact of TBSS.

Images document scientific discoveries and are prevalent in modern biomedical research. Microscopy imaging in particular is currently undergoing rapid technological advancements. However, for scientists wishing to publish obtained images and image-analysis results, there are currently no unified guidelines for best practices. Consequently, microscopy images and image data in publications may be unclear or difficult to interpret. Here, we present community-developed checklists for preparing light microscopy images and describing image analyses for publications. These checklists offer authors, readers and publishers key recommendations for image formatting and annotation, color selection, data availability and reporting image-analysis workflows. The goal of our guidelines is to increase the clarity and reproducibility of image figures and thereby to heighten the quality and explanatory power of microscopy data. Community-developed checklists offer best-practice guidance for biologists preparing light microscopy images and describing image analyses for publications.

To understand change in global biodiversity patterns requires large‐scale, long‐term monitoring. The ability to draw meaningful comparison across studies is severely hampered by extensive variation in the design of the sampling equipment and how it is used. Here, we present a meta‐analysis and description highlighting this variation in a common, widely used entomological survey technique. We report a decline in the completeness of methodological reporting over a 20‐year period, while there has been no clear reduction in the methodological variation between researchers using pitfall traps for arthropod sampling. There is a growing need for improved comparability between studies to facilitate the generation of large‐scale, long‐term biodiversity datasets. However, our results show that, counterproductive to this goal, over the last 20 years there has little progress in reducing the methodological variation. We propose a standardized pitfall trap design for the study of ground‐active arthropods. In addition, we provide a table to promote a more standardized reporting of the key methodological variables. Widespread adoption of more standardized methods and reporting would facilitate more nuanced analysis of biodiversity change. The use of pitfall trapping for monitoring ground‐active arthropods is a common technique in ecological research, but suffers from extensive variation between researchers and reporting of the methodology is often incomplete. This review highlights this variation in both trap design and reporting completeness and proposes a standardized pitfall trap design. It is hoped that uptake of this design would facilitate future comparison between studies and allow investigation of biodiversity at larger spatial and temporal scales than would be easily achievable from individual researchers.

Background The rapidly expanding clinical adaptation of prostate-specific membrane antigen (PSMA)-targeted PET imaging in the evaluation of patients with prostate cancer has placed an increasing onus on understanding both the potential pearls of interpretation as well as limitations of this new technique. As with any new molecular imaging modality, accurate characterization of abnormalities on PSMA-targeted PET imaging can be accomplished only if one is aware of the normal distribution pattern, physiological variants of radiotracer uptake, and potential sources of false-positive and false-negative imaging findings. In recent years, a growing number of reports have come to light describing incidental non-prostatic benign or malignant pathologies with high uptake on PSMA-targeted PET imaging. In this review, we have summarized the published literature regarding the potential pearls and technical and interpretive pitfalls of this imaging modality. Knowledge of these limitations can increase the confidence of interpreting physicians and thus improve patient care. Conclusions As PSMA-targeted PET is expected to be evaluated in larger prospective trials, the dissemination of potential diagnostic pitfalls and the biologic underpinning of those findings will be of increased importance.

Recent advancements in next-generation sequencing (NGS) have provided the foundation for modern studies into the composition of microbial communities. The use of these NGS methods allows for the detection and identification of (‘difficult-to-culture’) microorganisms using a culture-independent strategy. In the field of routine clinical diagnostics however, the application of NGS is currently limited to microbial strain typing for epidemiological purposes only, even though the implementation of NGS for microbial community analysis may yield clinically important information. This lack of NGS implementation is due to many different factors, including issues relating to NGS method standardization and result reproducibility. In this review article, the authors provide a general introduction to the most widely used NGS methods currently available (i.e., targeted amplicon sequencing and shotgun metagenomics) and the strengths and weaknesses of each method is discussed. The focus of the publication then shifts toward 16S rRNA gene NGS methods, which are currently the most cost-effective and widely used NGS methods for research purposes, and are therefore more likely to be successfully implemented into routine clinical diagnostics in the short term. In this respect, the experimental pitfalls and biases created at each step of the 16S rRNA gene NGS workflow are explained, as well as their potential solutions. Finally, a novel diagnostic microbiota profiling platform (‘MYcrobiota’) is introduced, which was developed by the authors by taking into consideration the pitfalls, biases, and solutions explained in this article. The development of the MYcrobiota, and future NGS methodologies, will help pave the way toward the successful implementation of NGS methodologies into routine clinical diagnostics.

Large language models (LLMs) are exhibiting remarkable performance in clinical contexts, with exemplar results ranging from expert-level attainment in medical examination questions to superior accuracy and relevance when responding to patient queries compared to real doctors replying to queries on social media. The deployment of LLMs in conventional health care settings is yet to be reported, and there remains an open question as to what evidence should be required before such deployment is warranted. Early validation studies use unvalidated surrogate variables to represent clinical aptitude, and it may be necessary to conduct prospective randomized controlled trials to justify the use of an LLM for clinical advice or assistance, as potential pitfalls and pain points cannot be exhaustively predicted. This viewpoint states that as LLMs continue to revolutionize the field, there is an opportunity to improve the rigor of artificial intelligence (AI) research to reward innovation, conferring real benefits to real patients.

Common to neuroendocrine neoplasms of the pancreas is their expression of synaptophysin, chromogranin A, and/or INSM1. They differ, however, in their histological differentiation and molecular profile. Three groups can be distinguished: well-differentiated neuroendocrine neoplasms (neuroendocrine tumors), poorly differentiated neuroendocrine neoplasms (neuroendocrine carcinomas), and mixed neuroendocrine-non-neuroendocrine neoplasms. However, the expression of synaptophysin and, to a lesser extent, also chromogranin A is not restricted to the neuroendocrine neoplasms, but may also be in a subset of non-neuroendocrine epithelial and non-epithelial neoplasms. This review provides the essential criteria for the diagnosis of pancreatic neuroendocrine neoplasms including diagnostic clues for the distinction of high-grade neuroendocrine tumors from neuroendocrine carcinomas and an algorithm avoiding diagnostic pitfalls in the delineation of non-neuroendocrine neoplasms with neuroendocrine features from pancreatic neuroendocrine neoplasms.