Leveraging weak complementary labels enhances semantic segmentation of hepatocellular carcinoma and intrahepatic cholangiocarcinoma.

In this paper we present a deep learning segmentation approach to classify and quantify the two most prevalent primary liver cancers - hepatocellular carcinoma and intrahepatic cholangiocarcinoma - from hematoxylin and eosin (H&E) stained whole slide images. While semantic segmentation of medical images typically requires costly pixel-level annotations by domain experts, there often exists additional information which is routinely obtained in clinical diagnostics but rarely utilized for model training. We propose to leverage such weak information from patient diagnoses by deriving complementary labels that indicate to which class a sample cannot belong to.

Dissecting AI-based mutation prediction in lung adenocarcinoma: A comprehensive real-world study.

Introduction: Molecular profiling of lung cancer is essential to identify genetic alterations that predict response to targeted therapy. While deep learning shows promise for predicting oncogenic mutations from whole tissue images, existing studies often face challenges such as limited sample sizes, a focus on earlier stage patients, and insufficient analysis of robustness and generalizability.

Methods: This retrospective study evaluates factors influencing mutation prediction accuracy using the large Heidelberg Lung Adenocarcinoma Cohort (HLCC), a cohort of 2356 late-stage FFPE samples.

Management of a Traumatic Collapse of the First Carpometacarpal Joint After Trapeziectomy With Suture Suspensionplasty: A Case Report.

Arthritis of the first carpometacarpal (CMC) joint is a common pathology hand surgeons encounter. Treatment begins with conservative measures, but when they fail, surgery is a viable option for providing relief to patients. The most widely used surgical technique is CMC arthroplasty with ligament reconstruction and tendon interposition (LRTI).

[Explainable artificial intelligence in pathology].

With the advancements in precision medicine, the demands on pathological diagnostics have increased, requiring standardized, quantitative, and integrated assessments of histomorphological and molecular pathological data. Great hopes are placed in artificial intelligence (AI) methods, which have demonstrated the ability to analyze complex clinical, histological, and molecular data for disease classification, biomarker quantification, and prognosis estimation. This paper provides an overview of the latest developments in pathology AI, discusses the limitations, particularly concerning the black box character of AI, and describes solutions to make decision processes more transparent using methods of so-called explainable AI (XAI).

TTF-1 status in early-stage lung adenocarcinoma is an independent predictor of relapse and survival superior to tumor grading.

Objectives: Thyroid transcription factor 1 (TTF-1) is a well-established independent prognostic factor in lung adenocarcinoma (LUAD), irrespective of stage. This study aims to determine if TTF-1's prognostic impact is solely based on histomorphological differentiation (tumor grading) or if it independently relates to a biologically more aggressive phenotype. We analyzed a large bi-centric LUAD cohort to accurately assess TTF-1's prognostic value in relation to tumor grade.

Toward Explainable Artificial Intelligence for Precision Pathology.

The rapid development of precision medicine in recent years has started to challenge diagnostic pathology with respect to its ability to analyze histological images and increasingly large molecular profiling data in a quantitative, integrative, and standardized way. Artificial intelligence (AI) and, more precisely, deep learning technologies have recently demonstrated the potential to facilitate complex data analysis tasks, including clinical, histological, and molecular data for disease classification; tissue biomarker quantification; and clinical outcome prediction. This review provides a general introduction to AI and describes recent developments with a focus on applications in diagnostic pathology and beyond.

From Clustering to Cluster Explanations via Neural Networks.

A recent trend in machine learning has been to enrich learned models with the ability to explain their own predictions. The emerging field of explainable AI (XAI) has so far mainly focused on supervised learning, in particular, deep neural network classifiers. In many practical problems, however, the label information is not given and the goal is instead to discover the underlying structure of the data, for example, its clusters.

Exposure to tobacco, alcohol and 'Junk food' content in reality TV programmes broadcast in the UK between August 2019-2020.

Background: Exposure to alcohol, tobacco and foods high in fat, sugar or salt (HFSS) content in media is a risk factor for smoking, alcohol use and HFSS consumption in young people. We report an analysis of tobacco, alcohol and HFSS content in a sample of reality TV programmes broadcast on TV and video-on-demand services throughout a 1-year period.

Methods: We used 1-min interval coding to quantify content in all episodes of 20 different reality TV programmes between August 2019 and August 2020 and estimated population exposure to a sample of these programmes using viewing data and UK population estimates.

Mechanically matching the rheological properties of brain tissue for drug-delivery in human glioblastoma models.

Peptide functionalized hyaluronic acid (HA) cross-linked by cucurbit[8]uril (CB[8]), a new class of drug-delivery reservoirs, is used to enable improved drug bioavailability for glioblastoma tumors in patient-derived xenograft (PDX) models. The mechanical and viscoelastic properties of native human and mouse tissues are measured over 8 h via oscillatory rheology under physiological conditions. Treatment with drug-loaded hydrogels allowed for a significant survival impact of 45 % (55.

Translocations Constitute Ependymoma Chromatin Remodeling and Transcription Factors.

()-a gene of unknown function-partners with a variety of transcriptional coactivators in translocations that drive supratentorial ependymoma, a frequently lethal brain tumor. Understanding the function of is key to developing therapies that inhibit these fusion proteins. Here, using a combination of transcriptomics, chromatin immunoprecipitation sequencing, and proteomics, we interrogated a series of deletion-mutant genes to identify a tripartite transformation mechanism of ZFTA-containing fusions, including: spontaneous nuclear translocation, extensive chromatin binding, and SWI/SNF, SAGA, and NuA4/Tip60 HAT chromatin modifier complex recruitment.

Trends in vaccine investment in middle income countries.

Although a proven and effective preventive health measure, childhood immunization programs remain vulnerable to budgetary pressures. Sustainable financing of immunization programs is an important issue that presents a challenge for middle-income countries (MIC) in particular, in part due to technological advances meaning more vaccines are available. This study aimed to analyse trends in immunization program investment across 15 MIC selected based on availability of data, income level classification, and regional representativeness.

Silica cloaking of adenovirus enhances gene delivery while reducing immunogenicity.

Viral gene therapy is a means of delivering genes to replace malfunctioning ones, to kill cancer cells, or to correct genetic mutations. This technology is emerging as a powerful clinical tool; however, it is still limited by viral tropism, uptake and clearance by the liver, and most importantly an immune response. To overcome these challenges, we sought to merge the robustness of viral gene expression and the versatility of nanoparticle technology.

Identification of Peptide Mimotope Ligands for Natalizumab.

Mimotope peptides selected from combinatorial peptide libraries can be used as capture reagents for immunoassay detection of therapeutic monoclonal antibodies (mAbs). We report the use of phage display libraries to identify peptide ligands (Veritopes) that bind natalizumab, a therapeutic mAb indicated for use in multiple sclerosis. PKNPSKF is identified as a novel natalizumab-binding motif, and peptides containing this motif demonstrated utility as capture reagents in enzyme-linked immunosorbent assays (ELISAs).

Individualized Dosing of Therapeutic Monoclonal Antibodies-a Changing Treatment Paradigm?

The introduction of monoclonal antibodies (mAbs) to the treatment of inflammatory bowel disease (IBD) was an important medical milestone. MAbs have been demonstrated as safe and efficacious treatments of IBD. However, a large percentage of patients either fail to respond initially or lose response to therapy after a period of treatment.

A Comparative Study of Receptor-Targeted Magnetosome and HSA-Coated Iron Oxide Nanoparticles as MRI Contrast-Enhancing Agent in Animal Cancer Model.

Magnetosomes are specialized organelles arranged in intracellular chains in magnetotactic bacteria. The superparamagnetic property of these magnetite crystals provides potential applications as contrast-enhancing agents for magnetic resonance imaging. In this study, we compared two different nanoparticles that are bacterial magnetosome and HSA-coated iron oxide nanoparticles for targeting breast cancer.

The Nature of Selection on the Major Histocompatibility Complex.

Only natural selection can account for the extreme genetic diversity of genes of the major histocompatibility complex (MHC). Although the structure and function of classic MHC genes is well understood at the molecular and cellular levels, there is controversy about how MHC diversity is selectively maintained. The diversifying selection can be driven by pathogen interactions and inbreeding avoidance mechanisms.

Molecular and Spectroscopic Characterization of Aspergillus flavipes and Pseudomonas putida L-Methionine γ-Lyase in Vitro.

Pseudomonas putida L-methionine γ-lyase (PpMGL) has been recognized as an efficient anticancer agent, however, its antigenicity and stability remain as critical challenges for its clinical use. From our studies, Aspergillus flavipes L-methionine γ-lyase (AfMGL) displayed more affordable biochemical properties than PpMGL. Thus, the objective of this work was to comparatively assess the functional properties of AfMGL and PpMGL via stability of their internal aldimine linkage, tautomerism of pyridoxal 5'-phosphate (PLP) and structural stability responsive to physicochemical factors.

Economic impact of onabotulinumtoxinA for overactive bladder with urinary incontinence in Europe.

Background: Overactive bladder (OAB) is a common condition that has a significant impact on patients' health-related quality-of-life and is associated with a substantial economic burden to healthcare systems. OnabotulinumtoxinA has a well-established efficacy and safety profile as a treatment for OAB; however, the economic impact of using onabotulinumtoxinA has not been well described.

Methods: An economic model was developed to assess the budget impact associated with OAB treatment in France, Germany, Italy, Spain and the UK, using onabotulinumtoxinA alongside best supportive care (BSC)-comprising incontinence pads and/or anticholinergic use and/or clean intermittent catheterisation (CIC)-vs BSC alone.

Clinical and economic impact of rivaroxaban on the burden of atrial fibrillation: The case study of Japan.

Objectives: Atrial fibrillation (AF) affects an estimated 1.5 million individuals in Japan, increasing their stroke risk and imposing considerable costs on the Japanese healthcare system. To reduce stroke incidence, guidelines recommend using anticoagulants in moderate-to-high risk non-valvular AF (NVAF) patients; however, many patients receive no treatment, aspirin only, or remain poorly-controlled on vitamin K antagonists (VKAs) due to high VKA discontinuation rates and non-adherence to guidelines.

Selection of DNA nanoparticles with preferential binding to aggregated protein target.

High affinity and specificity are considered essential for affinity reagents and molecularly-targeted therapeutics, such as monoclonal antibodies. However, life's own molecular and cellular machinery consists of lower affinity, highly multivalent interactions that are metastable, but easily reversible or displaceable. With this inspiration, we have developed a DNA-based reagent platform that uses massive avidity to achieve stable, but reversible specific recognition of polyvalent targets.

Reduction in resource use with the misoprostol vaginal insert vs the dinoprostone vaginal insert for labour induction: a model-based analysis from a United Kingdom healthcare perspective.

Background: In view of the increasing pressure on the UK's maternity units, new methods of labour induction are required to alleviate the burden on the National Health Service, while maintaining the quality of care for women during delivery. A model was developed to evaluate the resource use associated with misoprostol vaginal inserts (MVIs) and dinoprostone vaginal inserts (DVIs) for the induction of labour at term.

Methods: The one-year Markov model estimated clinical outcomes in a hypothetical cohort of 1397 pregnant women (parous and nulliparous) induced with either MVI or DVI at Southmead Hospital, Bristol, UK.

Signatures of tumour immunity distinguish Asian and non-Asian gastric adenocarcinomas.

Objective: Differences in gastric cancer (GC) clinical outcomes between patients in Asian and non-Asian countries has been historically attributed to variability in clinical management. However, recent international Phase III trials suggest that even with standardised treatments, GC outcomes differ by geography. Here, we investigated gene expression differences between Asian and non-Asian GCs, and if these molecular differences might influence clinical outcome.