The global incidence of thyroid cancer (TC) has increased significantly in recent decades. Traditionally, this trend has been attributed to overdiagnosis resulting from the widespread use of imaging and improved diagnostic techniques, but increasing evidence suggests that current epidemiological patterns may not be fully explained by diagnostic factors alone. Most importantly, increasing rates of larger and more advanced tumors have been reported in several populations. This narrative review discusses the evidence on possible drivers of rising TC incidence: obesity and metabolic dysfunction, environmental exposures such as air pollution, and endocrine-disrupting chemicals (EDCs) and ionizing radiation, as well as molecular and genetic mechanisms involved in thyroid carcinogenesis. The role of advances in diagnostics and their contribution to overdiagnosis are also discussed. Current available data support a multifactorial model with better detection coupled with changes in environmental, metabolic and biological risk factors. A better understanding of the relative contribution and interaction of these determinants may help refine diagnostic strategies, reduce unnecessary interventions, and improve the prevention and management of TC.

Advances in digital pathology and artificial intelligence (AI) are significantly transforming the approach to analyzing the tumor microenvironment (TME) in gastrointestinal cancers (GICs). The TME consists of tumor cells, stromal components, and immune cells. It plays a key role in disease progression, treatment response, and patient prognosis. This review discusses the most important TME biomarkers, such as tumor budding (TB), tumor-infiltrating lymphocytes (TILs), and tertiary lymphoid structures (TLSs), with emphasis on their prognostic and predictive significance. Traditional histopathological assessment of these parameters is limited by subjectivity, intraobserver variability, and time-consuming nature. In this context, AI-based tools enable automated, quantitative, and more reproducible analysis of entire histological sections. Deep learning models allow the accurate detection and classification of structures and also analysis of their spatial organization. They provide new biological insights unavailable in routine diagnostics. The integration of imaging data with molecular and clinical information leads to the development of personalized medicine. Despite numerous advantages, the implementation of AI in clinical practice continues to face challenges related to standardization, data availability, and model interpretability.

Ocular diseases are a significant public health problem worldwide and represent one of the leading causes of disability. The prevalence of visual impairment is steadily increasing, largely driven by the ageing population. Oxidative stress, chronic inflammation and neurodegenerative processes underlie the pathogenesis of many eye diseases, including age-related macular degeneration (AMD), diabetic retinopathy, glaucoma, and cataracts, contributing to progressive vision loss and functional impairment. Carotenoids such as lutein, zeaxanthin, meso-zeaxanthin, β-carotene, lycopene, and astaxanthin exhibit multidirectional biological effects, including antioxidant, anti-inflammatory, and neuroprotective properties. These compounds are selectively accumulated in the tissues of the eye, especially in the retina and macula, where they neutralise reactive oxygen species, modulate inflammatory pathways, stabilise mitochondrial function and support the survival and function of retinal ganglion cells and photoreceptors. The aim of our review was to provide a comprehensive review of current data regarding the mechanisms of action of carotenoids and their potential clinical significance in the prevention and treatment of retinal, optic nerve, lens, and eye surface diseases, including dry eye syndrome and Meibomian gland dysfunction. We identified results of available experimental and clinical research and outlined that an adequate supply of carotenoids in the diet or in the form of supplementation may support the protection of eye structures against oxidative and inflammatory damage, improve visual performance and potentially slow disease progression. However, high-quality prospective clinical trials are necessary to conclusively assess their therapeutic efficacy and establish evidence-based recommendations for clinical practice.

Mushroom-related intoxications pose a distinctive challenge for forensic medicine because early manifestations are non-specific, latency may be prolonged, and co-exposures can obscure the mechanism of death. This narrative review summarizes key toxic and psychoactive fungi and their principal compounds, spanning organ-toxic syndromes (amatoxins, orellanine) and functional neuropsychiatric intoxications—acute, predominantly functional effects causing impairment rather than organ failure (psilocybin/psilocin, ibotenic acid/muscimol). We propose an integrated diagnostic workflow combining exposure history, biochemical markers of organ injury, mycological assessment, and confirmatory toxicology. Particular emphasis is placed on postmortem interpretation: toxin instability and biotransformation, conjugation, matrix effects, postmortem redistribution (central vs. femoral blood), and postmortem fungal colonization that may alter analyte profiles or generate misleading metabolites. Because robust lethality thresholds are unavailable for most mushroom toxins, conclusions should rely on a multi-source synthesis of scene information, autopsy/histopathology, and time-dependent matrix selection (urine, gastric contents/vomitus, bile, and selected tissues; kidney for late orellanine confirmation). We review current screening and confirmatory methods—ELISA; LC-MS/MS, LC-HRMS/MS, GC-MS—and highlight pre-analytical requirements (rapid sampling, cold storage) to reduce false negatives. Finally, we discuss emerging directions such as point-of-care tests, portable mass spectrometry, and DNA barcoding for species identification.