Exploring the experiences of minoritised groups at life sciences conferences in the UK: new perspectives and actions to improve inclusivity in the sector.

Attending and participating in scientific research meetings and conferences is a key mechanism for researchers to share information and knowledge, build networks, and establish relationships and collaborations to support career development. In the UK, researchers from minoritised or underrepresented groups, may have a different experience at a conference than their peers. As a high profile provider of genomics-focussed life science conferences, Wellcome Connecting Science is committed to ensuring that our events are as inclusive as possible.

digitalMALDI: A Single-Particle-Based Mass Spectrometric Detection System for Biomolecules.

The development of a real-time system for characterizing individual biomolecule-containing aerosol particles presents a transformative opportunity to monitor respiratory conditions, including infections and lung diseases. Existing molecular assay technologies, although effective, rely on costly reagents, are relatively slow, and face challenges in multiplexing, limiting their use for real-time applications. To overcome these challenges, we developed digitalMALDI, a laser-based mass spectrometry system designed for single-particle characterization.

3D histology reveals that immune response to pancreatic precancers is heterogeneous and depends on global pancreas structure.

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer for which few effective therapies exist. Immunotherapies specifically are ineffective in pancreatic cancer, in part due to its unique stromal and immune microenvironment. Pancreatic intraepithelial neoplasia, or PanIN, is the main precursor lesion to PDAC.

Power-law growth models explain incidences and sizes of pancreatic cancer precursor lesions and confirm spatial genomic findings.

Pancreatic ductal adenocarcinoma is a rare but lethal cancer. Recent evidence suggests that pancreatic intraepithelial neoplasia (PanIN), a microscopic precursor lesion that gives rise to pancreatic cancer, is larger and more prevalent than previously believed. Better understanding of the growth-law dynamics of PanINs may improve our ability to understand how a miniscule fraction makes the transition to invasive cancer.

PanIN or IPMN? Redefining Lesion Size in 3 Dimensions.

Pancreatic ductal adenocarcinoma (PDAC) develops from 2 known precursor lesions: a majority (∼85%) develops from pancreatic intraepithelial neoplasia (PanIN), and a minority develops from intraductal papillary mucinous neoplasms (IPMNs). Clinical classification of PanIN and IPMN relies on a combination of low-resolution, 3-dimensional (D) imaging (computed tomography, CT), and high-resolution, 2D imaging (histology). The definitions of PanIN and IPMN currently rely heavily on size.

3D genomic mapping reveals multifocality of human pancreatic precancers.

Pancreatic intraepithelial neoplasias (PanINs) are the most common precursors of pancreatic cancer, but their small size and inaccessibility in humans make them challenging to study. Critically, the number, dimensions and connectivity of human PanINs remain largely unknown, precluding important insights into early cancer development. Here, we provide a microanatomical survey of human PanINs by analysing 46 large samples of grossly normal human pancreas with a machine-learning pipeline for quantitative 3D histological reconstruction at single-cell resolution.

Pathology of Chronic Mycoplasma ovipneumoniae Carriers in a Declining Bighorn Sheep (Ovis canadensis) Population.

Bighorn sheep (Ovis canadensis) across North America commonly experience population-limiting epizootics of respiratory disease. Although many cases of bighorn sheep pneumonia are polymicrobial, Mycoplasma ovipneumoniae is most frequently associated with all-age mortality events followed by years of low recruitment. Chronic carriage of M.

Three-dimensional assessments are necessary to determine the true, spatially-resolved composition of tissues.

Methods for spatially resolved cellular profiling using thinly cut sections have enabled in-depth quantitative tissue mapping to study inter-sample and intra-sample differences in normal human anatomy and disease onset and progression. These methods often profile extremely limited regions, which may impact the evaluation of heterogeneity due to tissue sub-sampling. Here, we applied CODA, a deep learning-based tissue mapping platform, to reconstruct the three-dimensional (3D) microanatomy of grossly normal and cancer-containing human pancreas biospecimens obtained from individuals who underwent pancreatic resection.

Power-law growth models explain incidences and sizes of pancreatic cancer precursor lesions and confirm spatial genomic findings.

Pancreatic ductal adenocarcinoma is a rare but lethal cancer. Recent evidence reveals that pancreatic intraepithelial neoplasms (PanINs), the microscopic precursor lesions in the pancreatic ducts that can give rise to invasive pancreatic cancer, are significantly larger and more prevalent than previously believed. Better understanding of the growth law dynamics of PanINs may improve our ability to understand how a miniscule fraction of these lesions makes the transition to invasive cancer.

Learning with limited target data to detect cells in cross-modality images.

Deep neural networks have achieved excellent cell or nucleus quantification performance in microscopy images, but they often suffer from performance degradation when applied to cross-modality imaging data. Unsupervised domain adaptation (UDA) based on generative adversarial networks (GANs) has recently improved the performance of cross-modality medical image quantification. However, current GAN-based UDA methods typically require abundant target data for model training, which is often very expensive or even impossible to obtain for real applications.

Three-dimensional genomic mapping of human pancreatic tissue reveals striking multifocality and genetic heterogeneity in precancerous lesions.

Pancreatic intraepithelial neoplasia (PanIN) is a precursor to pancreatic cancer and represents a critical opportunity for cancer interception. However, the number, size, shape, and connectivity of PanINs in human pancreatic tissue samples are largely unknown. In this study, we quantitatively assessed human PanINs using CODA, a novel machine-learning pipeline for 3D image analysis that generates quantifiable models of large pieces of human pancreas with single-cell resolution.

Cybersecurity and Information Assurance for the Clinical Laboratory.

Background: Network-connected medical devices have rapidly proliferated in the wake of recent global catalysts, leaving clinical laboratories and healthcare organizations vulnerable to malicious actors seeking to ransom sensitive healthcare information. As organizations become increasingly dependent on integrated systems and data-driven patient care operations, a sudden cyberattack and the associated downtime can have a devastating impact on patient care and the institution as a whole. Cybersecurity, information security, and information assurance principles are, therefore, vital for clinical laboratories to fully prepare for what has now become inevitable, future cyberattacks.

Low-Resource Adversarial Domain Adaptation for Cross-Modality Nucleus Detection.

Due to domain shifts, deep cell/nucleus detection models trained on one microscopy image dataset might not be applicable to other datasets acquired with different imaging modalities. Unsupervised domain adaptation (UDA) based on generative adversarial networks (GANs) has recently been exploited to close domain gaps and has achieved excellent nucleus detection performance. However, current GAN-based UDA model training often requires a large amount of unannotated target data, which may be prohibitively expensive to obtain in real practice.

CODA: quantitative 3D reconstruction of large tissues at cellular resolution.

A central challenge in biology is obtaining high-content, high-resolution information while analyzing tissue samples at volumes relevant to disease progression. We address this here with CODA, a method to reconstruct exceptionally large (up to multicentimeter cubed) tissues at subcellular resolution using serially sectioned hematoxylin and eosin-stained tissue sections. Here we demonstrate CODA's ability to reconstruct three-dimensional (3D) distinct microanatomical structures in pancreas, skin, lung and liver tissues.

Interactive Multimedia Reporting Technical Considerations: HIMSS-SIIM Collaborative White Paper.

Despite technological advances in the analysis of digital images for medical consultations, many health information systems lack the ability to correlate textual descriptions of image findings linked to the actual images. Images and reports often reside in separate silos in the medical record throughout the process of image viewing, report authoring, and report consumption. Forward-thinking centers and early adopters have created interactive reports with multimedia elements and embedded hyperlinks in reports that connect the narrative text with the related source images and measurements.

Trends in dermatology eponyms.

Background: Eponyms are ubiquitous in dermatology; however, their usage trends have not been studied.

Objective: To characterize the usage of eponyms in dermatology from 1880 to 2020.

Methods: Candidate eponyms were collected from a textbook and an online resource.

A Biopython-based method for comprehensively searching for eponyms in Pubmed.

Eponyms are common in medicine; however, their usage has varied between specialties and over time. A search of specific eponyms will reveal the frequency of usage within a medical specialty. While usage of eponyms can be studied by searching PubMed, manual searching can be time-consuming.

Multispecialty Enterprise Imaging Workgroup Consensus on Interactive Multimedia Reporting Current State and Road to the Future: HIMSS-SIIM Collaborative White Paper.

Diagnostic and evidential static image, video clip, and sound multimedia are captured during routine clinical care in cardiology, dermatology, ophthalmology, pathology, physiatry, radiation oncology, radiology, endoscopic procedural specialties, and other medical disciplines. Providers typically describe the multimedia findings in contemporaneous electronic health record clinical notes or associate a textual interpretative report. Visual communication aids commonly used to connect, synthesize, and supplement multimedia and descriptive text outside medicine remain technically challenging to integrate into patient care.

Artificial intelligence for automating the measurement of histologic image biomarkers.

Artificial intelligence has been applied to histopathology for decades, but the recent increase in interest is attributable to well-publicized successes in the application of deep-learning techniques, such as convolutional neural networks, for image analysis. Recently, generative adversarial networks (GANs) have provided a method for performing image-to-image translation tasks on histopathology images, including image segmentation. In this issue of the JCI, Koyuncu et al.

DETECTION OF DEER ATADENOVIRUS A DNA IN DAM AND OFFSPRING PAIRS OF ROCKY MOUNTAIN MULE DEER (ODOCOILEUS HEMIONUS HEMIONUS) AND ROCKY MOUNTAIN ELK (CERVUS CANADENSIS NELSONI).

Adenovirus hemorrhagic disease affects primarily mule deer (Odocoileus hemionus), white-tailed deer (Odocoileus virginianus), Rocky Mountain elk (Cervus canadensis nelsoni), and moose (Alces alces) in their first year of life. The method by which the causative virus, Deer atadenovirus A, is maintained in the environment and transmitted to neonates is unknown. In this study, we investigated the potential transmission of the virus from dam to offspring in Rocky Mountain mule deer (Odocoileus hemionus hemionus) and elk in western Wyoming, US.

Bidirectional Mapping-Based Domain Adaptation for Nucleus Detection in Cross-Modality Microscopy Images.

Cell or nucleus detection is a fundamental task in microscopy image analysis and has recently achieved state-of-the-art performance by using deep neural networks. However, training supervised deep models such as convolutional neural networks (CNNs) usually requires sufficient annotated image data, which is prohibitively expensive or unavailable in some applications. Additionally, when applying a CNN to new datasets, it is common to annotate individual cells/nuclei in those target datasets for model re-learning, leading to inefficient and low-throughput image analysis.

Eponyms in clinical chemistry.

Background: Eponyms are commonly used in medicine, but there are no specific studies of the use of eponyms in clinical chemistry.

Methods: Clinical chemistry eponyms were manually collected from books, review articles and journal articles from 1847 through 2020. Eponym usage was examined by searching titles and abstracts in PubMed.