This study involves the histological analysis of samples taken during autopsies in cases of COVID-19 related death to evaluate the inflammatory cytokine response and the tissue localization of the virus in various organs. In all the selected cases, SARS-CoV-2 RT-PCR on swabs collected from the upper (nasopharynx and oropharynx) and/or the lower respiratory (trachea and primary bronchi) tracts were positive. Tissue localization of SARS-CoV-2 was detected using antibodies against the nucleoprotein and the spike protein. Overall, we tested the hypothesis that the overexpression of proinflammatory cytokines plays an important role in the development of COVID-19-associated pneumonia by estimating the expression of multiple cytokines (IL-1β, IL-6, IL-10, IL-15, TNF-α, and MCP-1), inflammatory cells (CD4, CD8, CD20, and CD45), and fibrinogen. Immunohistochemical staining showed that endothelial cells expressed IL-1β in lung samples obtained from the COVID-19 group (p < 0.001). Similarly, alveolar capillary endothelial cells showed strong and diffuse immunoreactivity for IL-6 and IL-15 in the COVID-19 group (p < 0.001). TNF-α showed a higher immunoreactivity in the COVID-19 group than in the control group (p < 0.001). CD8 + T cells where more numerous in the lung samples obtained from the COVID-19 group (p < 0.001). Current evidence suggests that a cytokine storm is the major cause of acute respiratory distress syndrome (ARDS) and multiple organ failure and is consistently linked with fatal outcomes.

Guillain-Barre syndrome (GBS) is an acute immune-mediated disease of the peripheral nerves and nerve roots (polyradiculoneuropathy) that is usually elicited by various infections. We present a case of GBS after receiving the second dose of Pfizer-COVID 19 vaccine. Diagnosis was made after performing an accurate clinical examination, electromyoneurography and laboratory tests. In particular, anti-ganglioside antibodies have tested positive. During this pandemic with ongoing worldwide mass vaccination campaign, it is critically important for clinicians to rapidly recognize neurological complications or other side effects associated with COVID-19 vaccination.

The aim of this cross‐sectional study was to test the accuracy of the third molar maturity index (I_3M) cut‐off value (0.08) to distinguish between individuals above and below the adult age of legal responsibility (18 years) in a Russian population. A sample of 571 digital panoramic radiographs of healthy Russian minors and young adults (363 females and 208 males), aged between 14 and 24 years, was evaluated. The lower left third molars were analyzed by applying the cut‐off value of 0.08 determined by Cameriere et al. (2008). Lin’s concordance correlation coefficient (ρ_c) and Cohen’s kappa coefficient (κ) showed that repeatability and reproducibility are high for both intra‐ and interobserver errors. The I_3M value decreased with age in both sexes. Age distribution gradually decreases as I_3M increases in both girls and boys. In the male group, molar maturity stages 0‐0.04, 0.04‐0.08, 0.08‐0.3, 0.3‐0.5, and 0.9‐1.4 were reached slightly earlier than in the female group. The results demonstrated that sensitivity is 0.96 in boys and 0.93 in girls; associated specificity values were both 0.98. The cut‐off value of I_3M is statistically robust and thus valid for forensic application in a Russian population to determine whether or not a subject has reached 18 years of age. Finally, we compared our results with those of other studies in which the same I_3M cut‐off value was tested on different populations. The method is novel as it is reliable and easily reproducible, thus ensuring a universal way of comparing the results obtained (based on a cut‐off value) among many populations, in order to develop an ever‐increasing database.

Background/Objectives: Artificial intelligence (AI) is revolutionizing the healthcare industry, improving diagnoses, treatments, and clinical processes. However, its integration poses significant cybersecurity risks, including data breaches, algorithmic opacity, and vulnerabilities in AI-controlled medical devices. This narrative review analyzes these threats and evaluates blockchain technology as a potential mitigation strategy within a Clinical Risk Management framework. Methods: The literature search was conducted on PubMed, Scopus, and Web of Science, considering peer-reviewed publications from 2000 to January 2025. 1,204 articles were identified. Inclusion criteria included studies on cybersecurity risks in healthcare, blockchain applications in the clinical setting, and regulatory references (eg, General Data Protection Regulation). Conference abstracts, non-English articles, and non-peer-reviewed contributions were excluded. To ensure methodological rigor, the Scale for the Assessment of Narrative Review Articles criteria were applied. Results: The thematic analysis highlighted recurring critical issues: difficulties with informed consent, unauthorized access to sensitive data, and systemic vulnerabilities in hospital digital infrastructures. Blockchain presents a promising solution thanks to its decentralization, immutability, and transparency. Integration with smart contracts enables dynamic consent management, secure data sharing, and real-time monitoring of medical devices. Permissioned networks improve traceability and regulatory compliance, while Layer 2 solutions and optimized consent protocols address scalability challenges. Conclusion: Despite its potential, blockchain adoption faces obstacles: high costs, regulatory rigidity, and poor acceptance among healthcare professionals. The review highlights the need for pilot projects, interdisciplinary collaboration, and regulatory updates for effective integration. Combining AI and blockchain in Clinical Risk Management can transform clinical risk management from reactive to proactive, improving patient safety, data governance, and accountability.

The COVID-19 pandemic raised concerns about the potential for co-infection or over-infection with other respiratory infections, as they can complicate the diagnosis, treatment and prognosis of the disease. This is also a challenge for forensic pathologists, who may come across cases where the presence of co-infection or over-infection is suspected or confirmed, and it is important that they take this into account when determining the cause of death. The aim of this systematic review is to analyse the prevalence of each specific pathogen co-infecting or over-infecting patients with SARS-CoV-2 infection. In total, 575 studies were selected from the Scopus and Pub-Med online databases and 8 studies were included in a meta-analysis. Male gender, advanced age and nursing home care are risk factors associated with the development of co-infection, whereas age, tachypnoea, hypoxaemia and bacterial infection are predictors of mortality. Overall, however, having a SARS-CoV-2 infection does not represent a real risk for the development of co-infections/super-infections.

The incorporation of artificial intelligence (AI) in health care offers revolutionary enhancements in patient diagnostics, clinical processes, and overall access to services. Nevertheless, this technological transition brings forth various new, intricate risks that pose challenges to current safety and ethical norms. This research explores the ability of enterprise risk management as an all‐encompassing framework to tackle these arising risks, providing both a forward‐looking and responsive strategy designed for the health care industry. At the core of this method are instruments that together seek to proactively uncover and address AI‐related weaknesses like algorithmic bias, system failures, and data privacy issues. On the reactive side, it incorporates incident reporting systems and root cause analysis, tools that enable health care providers to quickly address unexpected events and consistently improve AI implementation procedures. However, some application difficulties still exist. The unclear, “black box” characteristics of numerous AI models hinder transparency and responsibility, prompting inquiries about the clarity of AI‐generated choices and their adherence to ethical benchmarks in patient treatment. The research highlights that with the progress of AI technologies, the enterprise risk management framework also needs to evolve, addressing these new complexities while promoting a culture focused on safety in health care settings.