Parallel to and after publication of the WHO 2024 classification of head and neck tumors, several developments concerning known existing salivary gland tumor entities, but also proposing new evolving tumor entities have been published. This review article describes the most important new developments in salivary gland pathology published through 2022–2025, that were not included in the 5th edition of the WHO Classification of Head and Neck Tumours 2024. This review summarizes these recent developments in both the benign and the malignant tumor categories. Among the recently proposed entities are palisading adenocarcinoma, microcribriform adenocarcinoma, fenestrating adenocarcinoma and skin-analogue poroid carcinoma. Developments in existing carcinoma entities include recognition of mucoacinar carcinoma as subtype of mucoepidermoid carcinoma (MAML2-fused), mucoepidermoid carcinoma without squamous cell differentiation, metatypical adenoid cystic carcinoma, and adenoid cystic carcinoma with prominent tubular hypereosinophilia. In the benign tumor category, recognition of pleomorphic adenoma with canalicular/trabecular phenotype driven by HMGA2 fusions, triphasic basal cell adenoma with S100 protein-positive "stroma", characterized by CTNNB1 mutations, metaplastic Warthin tumor with KRAS mutations and delineation of thymus-like phenotype in non-sebaceous lymphadenoma with recurrent CYLD mutations are the main highlights. Emerging concepts include benign tumor with ductal and papillary morphology (sialadenopapillary ductal tumor). Finally, new grading schemes have been developed/ proposed for acinic cell carcinoma and secretory carcinoma.
Coal is a major global energy resource, but its extraction raises significant environmental and health concerns. Mining activities release large amounts of particulate matter, including nanoparticles (NPs), into the environment. These NPs are hazardous due to their content of polycyclic aromatic hydrocarbons (PAHs), metals, oxides, and other compounds capable of disrupting cellular and molecular processes. This study evaluated the cytotoxic and genotoxic effects of coal-derived NPs on V79 and HaCaT cell lines, focusing on their impact on DNA stability and the mechanisms responsible for cellular damage. NPs were isolated using an acid-based separation method and applied to cells at concentrations of 50, 150, and 300 μg/mL. Atomic force microscopy (AFM) provided topographical characterization, while dynamic light scattering (DLS) confirmed their tendency to agglomerate. Scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM-EDS) confirmed NP morphology and elemental composition, including carbon, oxygen, iron, calcium, silicon, aluminum, and copper. Cytotoxicity was assessed using resazurin and sulforhodamine B assays, and genotoxicity was evaluated using the comet assay, micronucleus test, and γH2AX immunostaining. Results showed a clear dose-dependent effect, with coal NPs inducing genomic instability and increased cell mortality, mainly through apoptosis. These findings highlight the importance of characterizing coal-derived NPs to better assess their environmental and health risks, particularly regarding respiratory diseases.
The large volume of abdominal computed tomography (CT) scans1,2 coupled with the shortage of radiologists3-6 have intensified the need for automated medical image analysis tools. Previous state-of-the-art approaches for automated analysis leverage vision-language models (VLMs) that jointly model images and radiology reports7-12. However, current medical VLMs are generally limited to 2D images and short reports. Here to overcome these shortcomings for abdominal CT interpretation, we introduce Merlin, a 3D VLM that learns from volumetric CT scans, electronic health record data and radiology reports. This approach is enabled by a multistage pretraining framework that does not require additional manual annotations. We trained Merlin using a high-quality clinical dataset of paired CT scans (>6 million images from 15,331 CT scans), diagnosis codes (>1.8 million codes) and radiology reports (>6 million tokens). We comprehensively evaluated Merlin on 6 task types and 752 individual tasks that covered diagnostic, prognostic and quality-related tasks. The non-adapted (off-the-shelf) tasks included zero-shot classification of findings (30 findings), phenotype classification (692 phenotypes) and zero-shot cross-modal retrieval (image-to-findings and image-to-impression). The model-adapted tasks included 5-year chronic disease prediction (6 diseases), radiology report generation and 3D semantic segmentation (20 organs). We validated Merlin at scale, with internal testing on 5,137 CT scans and external testing on 44,098 CT scans from 3 independent sites and 2 public datasets. The results demonstrated high generalization across institutions and anatomies. Merlin outperformed 2D VLMs, CT foundation models and off-the-shelf radiology models. We also computed scaling laws and conducted ablation studies to identify optimal training strategies. We release our trained models, code and dataset for 25,494 pairs of abdominal CT scans and radiology reports. Our results demonstrate how Merlin may assist in the interpretation of abdominal CT scans and mitigate the burden on radiologists while simultaneously adding value for future biomarker discovery and disease risk stratification.
Individuals who are immunocompromised (IC) with COVID-19 have consistently been associated with worse clinical outcomes than individuals who are non-immunocompromised (non-IC). The aim of this study was to describe and compare in-hospital outcomes between individuals who are IC and non-IC hospitalised with COVID-19, with a particular focus on mortality across periods defined by the predominant circulating SARS-CoV-2 variants. This is an observational, retrospective, descriptive study based on a secondary database from Brazil's Unified Health System. Patients hospitalised with laboratory-confirmed COVID-19 were included. Descriptive statistics were used to summarize the cohort; categorical variables were expressed as frequencies and percentages, and continuous variables as mean ± standard deviation. Univariate logistic regression analyses were conducted to estimate the association between immunocompromised status and two key outcomes: in-hospital mortality and ICU admission, stratified by dominant SARS-CoV-2 variant. Results were reported as odds ratios (OR) with 95
