Can the crowd judge truthfulness? A longitudinal study on recent misinformation about COVID-19.

Recently, the misinformation problem has been addressed with a crowdsourcing-based approach: to assess the truthfulness of a statement, instead of relying on a few experts, a crowd of non-expert is exploited. We study whether crowdsourcing is an effective and reliable method to assess truthfulness during a pandemic, targeting statements related to COVID-19, thus addressing (mis)information that is both related to a sensitive and personal issue and very recent as compared to when the judgment is done. In our experiments, crowd workers are asked to assess the truthfulness of statements, and to provide evidence for the assessments. Besides showing that the crowd is able to accurately judge the truthfulness of the statements, we report results on workers' behavior, agreement among workers, effect of aggregation functions, of scales transformations, and of workers background and bias. We perform a longitudinal study by re-launching the task multiple times with both novice and experienced workers, deriving important insights on how the behavior and quality change over time. Our results show that workers are able to detect and objectively categorize online (mis)information related to COVID-19; both crowdsourced and expert judgments can be transformed and aggregated to improve quality; worker background and other signals (e.g., source of information, behavior) impact the quality of the data. The longitudinal study demonstrates that the time-span has a major effect on the quality of the judgments, for both novice and experienced workers. Finally, we provide an extensive failure analysis of the statements misjudged by the crowd-workers.

Assessment of hepatic steatosis based on needle biopsy images from deceased donor livers.

BACKGROUND: Assessment of hepatic steatosis (HS) before transplantation requires the pathologist to read a graft biopsy. A simple method based on the evaluation of images from tissue samples with a smartphone could expedite and facilitate the liver selection. This study aims to assess the degree of HS by analysing photographic images from liver needle biopsy samples. METHODS: Thirty-three biopsy-images were acquired with a smartphone. Image processing was carried out using ImageJ: background subtraction, conversion to HSB colour space, segmentation of the biopsy area, and evaluation of statistical features of Hue, Saturation, Brightness, Red, Green, and Blue channels on the biopsy area. After feature extraction, correlations were made with gold standard HS percentage assessed at two levels (frozen-section vs glass-slide). Sensitivity, specificity, and accuracy were calculated for each feature. RESULTS: Correlations were found for H, S, R. The sensitivity, specificity, and accuracy of the final classifier based on the K* algorithm were 94%, 92%, 94%. LIMITATIONS: Accuracy assessment was performed considering macrovesicular steatosis on specimens with mostly < 30% HS. CONCLUSIONS: The steatosis assessment based on needle biopsy images, proved to be an effective and promising method. Deep learning approaches could also be experimented with a larger set of images.

Toward a Harmonized WHO Family of International Classifications Content Model.

An overarching WHO-FIC Content Model will allow uniform modeling of classifications in the WHO Family of International Classifications (WHO-FIC) and promote their joint use. We provide an initial conceptualization of such a model.

Expression and modulation of S100A4 protein by human mast cells.

S100A4 protein is expressed in fibroblasts during tissue remodelling and in cancer stem cells and it induces the metastatic spread of tumor cells. In mast cells (MCs) S100A4 have been found in some pathological conditions, but its function in normal MCs remains to be described. The purpose of this study was to characterize the cellular localization of the S100A4 protein in MCs of human tissues with inflammatory or tumor disorders and, to determine the consequence of reducing its expression in MC response. We found that tissue resident MCs stained positive to S100A4. Both human HMC-1 cell line and resting CD34+-derived MCs expressed S100A4, whose levels were differentially modulated upon MC activation. Downregulation of the S100A4 protein resulted in MC growth inhibition, enhanced apoptosis and deregulation of MMP-1 and MMP-10 production. Our results suggest that S100A4 is also playing a role in the MC life cycle and functions.

Teaching Digital Pathology: The International School of Digital Pathology and Proposed Syllabus.

Digital pathology is an interdisciplinary field where competency in pathology, laboratory techniques, informatics, computer science, information systems, engineering, and even biology converge. This implies that teaching students about digital pathology requires coverage, expertise, and hands-on experience in all these disciplines. With this in mind, a syllabus was developed for a digital pathology summer school aimed at professionals in the aforementioned fields, as well as trainees and doctoral students. The aim of this communication is to share the context, rationale, and syllabus for this school of digital pathology.