Introduction and objective: The superior thyroid artery (STA) is one of the two paired arteries that constitute the arterial supply to the thyroid. In the traditional approach, it is described as a branch of the external carotid artery, originating directly above the bifurcation of the common...

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.

Small-cell lung carcinoma (SCLC) is one of the most aggressive and therapeutically challenging malignancies. It is characterised by rapid progression, early metastasis and frequent relapse. Despite considerable advances in molecular oncology, effective biomarkers for prognosis and treatment response remain elusive. In this review, we summarise and discuss recent evidence on microRNAs (miRNAs) as central regulators of SCLC biology and their potential clinical applications. A narrative review of the literature was conducted. Search of PubMed and Scopus databases identified 14 miRNAs, including miR-7-5p, miR-22-3p, miR-134, miR-181b, miR-200b, miR-335, miR-335-5p, miR-495, miR-24-3p, miR-30a-5p, miR-30a-3p, miR-100, miR-1 and miR-494, which are linked to tumour progression, therapy resistance and metastasis. These molecules influence several signalling cascades, including PI3K/Akt, Hippo, TGF-β, PARP1-mediated DNA repair and autophagy. Their abnormal expression correlates with patient outcome and may enable plasma- or exosome-based non-invasive monitoring. In particular, strategies that restore or inhibit miRNA activity using mimics or antagomiRs show promise in improving drug sensitivity and complementing current treatment options. Overall, emerging evidence supports the integration of miRNA profiling into precision oncology for SCLC, with the aim of refining diagnosis, risk assessment and therapeutic decision-making.