Pathologie (Heidelberg, Germany)最新文献

Background: Mass spectrometry (MS)-based proteomics is rapidly transforming pathology research and diagnostics by enabling comprehensive studies of protein expression and post-translational modifications (PTMs).

Objective: This article discusses recent advancements in MS-based proteomics, focusing on emerging technologies in sample preparation, MS instrumentation, and data analysis. These developments are scrutinized for their applications in clinical cohort studies and molecular pathology diagnostics.

Materials and methods: The article reviews innovations in automated sample preparation, chromatography systems, advanced MS technologies, and proteomic data analysis in the context of pathology. Specific applications such as liquid biopsy, spike-in heavy peptide panels, immunopeptidomics, and PTM screening are highlighted alongside opportunities for data integration.

Results: Recent technological improvements have significantly increased the throughput, precision, and scope of proteomic studies, enabling the analysis of large clinical cohorts and small specimens with unprecedented sensitivity. Advanced MS techniques have broadened applications, opening new avenues for discovery and diagnosis of marker proteins and therapeutic targets.

Conclusion: Advancements in MS-based proteomics have created new opportunities in clinical research and diagnostics. By facilitating more comprehensive and integrated analyses of proteomes, these technologies are set to play a pivotal role in the future of personalized medicine and pathology research.

A 28-year-old female patient developed a 5.5 cm progressive thyroid nodule that was surgically removed due to local symptoms. Histologically, a solitary fibrous tumor (SFT) in a thyroid adenoma was diagnosed. The tumor had a specific NAB2-STAT6 fusion.Solitary fibrous tumors rarely occur in the thyroid gland and are described as both primary and secondary manifestations. The diagnosis, prognosis assessment and treatment decision are carried out analogously to SFT of other localizations.

The limited availability of suitable animal models and cell lines often impedes experimental cancer research. Wet-laboratory experiments are also time-consuming and cost-intensive. In this review, we present an in silico modeling strategy, namely, Boolean network (BN) models, and demonstrate how it could be applied to streamline experimental design and to focus the effort of experimental read-outs. Boolean network models allow for the dynamic analysis of large molecular signaling pathways and their crosstalks. After establishing and validating a specific tumor model, mechanistic insights into the tumor cell behavior can be gained by studying the trajectories of different tumor phenotypes. Also, tumor driver and drug target screenings can be performed. These automatic screenings can help to identify new intervention targets and putative biomarkers for tumor evolution, hence guiding new wet-laboratory experiments. The goal of this round-up is to demonstrate how to establish, validate, and use BN modeling and its crosstalks in classic wet-laboratory research using a chronic lymphocytic leukemia (CLL) BN model.

Background: The field of pathology is evolving with the integration of advanced and artificial-intelligence-powered diagnostic technologies. However, there remains a significant gap in clearly outlining the key considerations for the effective implementation of these innovations into clinical care.

Objectives: The aim of this review was to identify and address the essential aspects required to bridge the implementation gap of new diagnostic technologies in pathology.

Material and methods: This review synthesizes key elements from relevant scientific journals, organizational websites, and practical examples from pathology practice. The findings are presented as a structured framework of six key elements, supported by an infographic and illustrative cases from clinical settings.

Results: The key elements are: (1) Innovation depends more on the people driving it than on the work it demands, highlighting the importance of team collaboration and communication; (2) in-depth knowledge of the delivery system emphasizing the importance of care, IT, and administrative layers is crucial; (3) data-driven decision-making in healthcare transformation is central, with an emphasis on the process of converting real-world data (RWD) into actionable real-world evidence (RWE); (4) a proven approach for practice transformation uses a structured (utilization management strategy, UMS) framework; (5) a balanced approach toward financial sustainability, including local and systemic financial strategies, is important; and (6)  ensuring safe and effective progress requires a new, collaborative definition of regulatory science, aligning innovation with regulatory oversight to support technological advancements.

Conclusion: These key aspects offer a foundational framework for integrating new technologies into healthcare. Although not exhaustive, overlooking them would miss a significant opportunity to enhance patient care.