Background: This study presents a comprehensive analysis of cholesteatoma of the middle ear, focusing on its clinical presentation, diagnostic imaging, and treatment outcomes. Cholesteatomas are defined by the keratinized squamous epithelium within the middle ear, leading to significant bone erosion, often affecting the ossicular chain and surrounding structures. Methods: The study explores various mechanisms involved in cholesteatoma progression, including enzymatic lysis, inflammatory responses, and neurotrophic disturbances. The study conducted a retrospective clinical and statistical review of 580 patients over a 20-year period (2003–2023), highlighting the role of advanced imaging, including computed tomography (CT) and diffusion-weighted magnetic resonance imaging (DWI), in preoperative planning and postoperative follow-up. Results: Findings revealed that early detection and intervention are crucial in preventing severe complications such as intracranial infection and hearing loss. Surgical treatment primarily involved tympanoplasty and mastoidectomy, with a recurrence rate of 1.55% within two years. The study underscores the importance of integrating imaging advancements into clinical decision-making to enhance patient outcomes and suggests further investigation into molecular mechanisms underlying cholesteatoma progression and recurrence. Histopathological and microbiological analysis was performed to identify pathological patterns and microbial agents. Conclusions: The study highlights the importance of early diagnosis and intervention to prevent complications such as intracranial infections and permanent hearing loss, while also emphasizing the role of advanced imaging techniques in the management and long-term monitoring of cholesteatoma patients.

Antibiotic misuse in Romania has exacerbated the issue of antibiotic resistance, as patients often use antibiotics without proper medical consultation. This study aimed to assess the resistance of prevalent bacteria to different antibiotics. In this observational study conducted over six months, we analyzed 31 pediatric patients aged from 12 days to 13 years using the disk diffusion method. We identified 31 bacterial isolates, including 8 Gram-negative and 8 Gram-positive strains, with the most common being Pseudomonas aeruginosa, Escherichia coli, Streptococcus pneumoniae, methicillin-resistant Staphylococcus aureus, Streptococcus species, and Elizabethkingia meningoseptica. Our findings revealed that the most effective antibiotics were linezolid, ertapenem, and teicoplanin. In contrast, nearly all tested bacteria exhibited resistance to penicillin, followed by oxacillin and ampicillin. Resistance to cephalosporins varied with generation, showing higher resistance to lower-generation cephalosporins. The study highlights significant antibiotic resistance among common bacterial pathogens in Romanian pediatric patients, emphasizing the urgent need for controlled antibiotic use and alternative treatment strategies to combat this growing issue. Effective antibiotics such as linezolid and ertapenem offer potential solutions, whereas reliance on penicillin and lower-generation cephalosporins is increasingly futile.

Background: Artificial intelligence (AI) and machine learning (ML) have rapidly expanded across the continuum of cardiopulmonary resuscitation (CPR), with growing evidence of their contribution to improving early recognition, intervention quality, and post-cardiac arrest outcomes. This narrative review synthesizes the current advancements and challenges in AI/ML-enhanced resuscitation science. Methods: A targeted literature search was conducted in Web of Science for the period 2018–2025 using the keywords “artificial intelligence” and “cardiopulmonary resuscitation”. The search identified studies addressing AI/ML applications across the resuscitation pathway, which were reviewed and categorized according to the American Heart Association’s Chain of Survival—prevention and preparedness, activation of the emergency response system, high-quality CPR including early defibrillation, advanced resuscitation interventions, post-cardiac arrest care, and recovery. Results: The literature demonstrates substantial promise for AI/ML in several domains: (1) early recognition and timely activation of emergency medical services through real-time detection algorithms; (2) optimization of high-quality CPR, including feedback systems, automated assessment of chest compressions, and prediction of defibrillation success; (3) support for advanced resuscitation interventions, such as rhythm classification, prognostication, and intra-arrest decision support; (4) post-cardiac arrest care, including outcome prediction and neuroprognostication; and (5) integrative and cross-domain approaches that link multiple phases of resuscitation into end-to-end AI-supported systems. Emerging work also highlights the role of AI in education and training, with applications in simulation, assessment, and skill reinforcement. Conclusions: AI/ML technologies hold significant potential to augment clinical performance across all links of the Chain of Survival. Their effective implementation requires attention to ethical considerations, data representativeness, and real-world validation. Future research should prioritize multicenter datasets, transparency, bias mitigation, and clinically embedded evaluation frameworks to ensure that AI/ML systems support safe, equitable, and high-impact resuscitation care.