The following narrative review discusses the use of deep learning and 3D modeling in facial reconstruction from skeletal remains, focusing on accuracy, algorithmic bias, and evidential reliability. Forensic facial reconstruction (FFR) is a multidisciplinary field combining anthropology, medicine, and visual sciences to approximate the facial appearance of unidentified individuals from skeletal remains. Traditional manual methods, based on anatomical knowledge and facial soft tissue thickness (FSTT) measurements, are limited by subjectivity, labor intensity, and inter-expert variability. This narrative review summarizes contemporary AI-assisted approaches, with emphasis on convolutional neural networks (CNNs), generative adversarial networks (GANs), variational autoencoders (VAEs), and diffusion models, which enable probabilistic prediction of facial morphology while accounting for demographic variables such as sex, age, and population ancestry. Key challenges affecting reconstruction accuracy—including dataset limitations, population-specific variability, and algorithmic bias—are discussed, alongside quantitative validation methods and concerns regarding model transparency. Legal and ethical considerations, such as privacy, biometric data protection, and the need for explainable AI (XAI) frameworks, are highlighted. Future perspectives include hybrid expert–AI workflows, the development of globally representative datasets, and the integration of multimodal data sources, including DNA phenotyping, 3D morphometrics, and biomechanical modeling. These advances aim to create standardized, interpretable, and biologically informed frameworks that enable AI to support expert judgment and enhance the reliability of forensic facial reconstructions.

Micro- and nanoplastics (MNPs) are emerging environmental contaminants of increasing relevance to human health. Growing evidence suggests that, following ingestion, inhalation, or, less convincingly, dermal exposure, MNPs may cross biological barriers, enter lymphatic and vascular compartments, and reach the liver. Owing to portal blood flow, sinusoidal architecture and Kupffer cell activity, the liver appears to be one of the principal sites of early particle sequestration. Human biomonitoring, ex vivo and postmortem studies have detected MNPs in blood and multiple organs, including the liver, although the currently available evidence remains limited and methodologically heterogeneous. Their identification relies on multistep analytical procedures that integrate sample pretreatment with FTIR, Raman spectroscopy, LD-IR, Py-GC-MS and supplementary imaging methods. However, each of these techniques presents significant limitations, particularly in the analysis of nanoplastics. Experimental studies indicate that MNPs may induce hepatic injury through oxidative stress, mitochondrial impairment, endoplasmic reticulum stress, inflammation, DNA damage, dysregulated lipid metabolism and disruption of the gut–liver axis, consequently contributing to steatosis, cholestatic anomalies and fibrosis. Consequently, MNPs should be considered potential contributors to liver pathology, although more comprehensive human data are still required.

The introduction of immune checkpoint inhibitors (ICIs) into the treatment of melanoma has significantly reduced mortality over the past decade. However, therapeutic benefit is not observed in all patients, and treatment may be associated with severe adverse events. Therefore, identifying patients who are most likely to benefit from immunotherapy remains of critical importance. Currently used biomarkers, such as programmed death-ligand 1 (PD-L1) expression and manual assessment of tumour-infiltrating lymphocytes (TILs), have limited predictive value. This narrative review provides a critical appraisal of studies employing digital pathology tools, multiplex and spatial techniques (including multiplex immunofluorescence, imaging mass cytometry, and digital spatial profiling), as well as machine learning algorithms for predicting response to ICIs in patients with melanoma. Available evidence suggests that the highest predictive value may be achieved by approaches integrating quantitative assessment of immune infiltration with information on its spatial distribution, functional state, and interactions within the tumour microenvironment. Particular relevance may be attributed to features associated with the “immune-inflamed”, “immune-excluded”, and “immune-desert” phenotypes, the presence of tertiary lymphoid structures, and the organisation of local immune niches. In addition, this review highlights key limitations in the interpretation of current data, including lack of methodological standardisation, data heterogeneity, and insufficient validation. Directions for future research necessary for the implementation of these approaches into routine clinical practice are also discussed.