• B. bronchiseptica has rarely been isolated from humans despite exposure to animals. • Working with farm animals, COPD or AIDS are risk factors to develop pneumonia. • B. bronchiseptica pneumonia may lead to misdiagnosis with other infections.

To study the association between detection of the Clostridioides difficile gene encoding the binary toxin (CDT) and direct detection of toxinB (TcdB) from feces with the appearance of serious disease, complications, or recurrence in a prospective series of cases. A total of 220 confirmed cases were included, using a two-step algorithm: an initial study to detect the enzyme, glutamate dehydrogenase (GDH), followed, in cases of positivity, by detection of the tcdB. tcdB-positive patients were investigated for the presence of CDT and TcdB. Outcome variables were severe disease, the modified Illinois C. difficile infection (CDI) prognostic risk index (ZAR score), the appearance of complications (need for colectomy, CDI-related death, or toxic megacolon) and recurrence. Patients who tested positive for the presence of TcdB in feces were found to have greater disease severity than those who tested negative, with a ZAR score of 35.4% vs. 23% (p = .048), a higher recurrence rate (14.6% vs. 5.9%, p = .032), and a tendency for higher number of complications (20.7% vs. 11.5%), although without reaching statistical significance (p = .053). When presence of CDT was analyzed, higher frequencies of severe disease (39.2% vs. 21.2%, p = .005), complications and recurrence (21.6% vs. 10.9%, p = .037 and 14.9% vs. 5.8%, p = .029; respectively) were observed in patients where CDT was detected. TcdB and CDT act as prognostic markers of the appearance of serious disease, complications or recurrence in cases of CDI. Simultaneous detection of both markers, TcdB and CDT, had a greater impact on the prognosis than when they were detected separately.

This study sought to determine the survival duration of patients who underwent palliative sedation, comparing those who received prescriptions from referring physicians versus on-call physicians. It included all patients over 18 years old who died in the Palliative Care, Internal Medicine, and Oncology units at the Hospital Universitario of Jerez de la Frontera between 1 January 2019, and 31 December 2019. Various factors were analyzed, including age, gender, oncological or non-oncological disease, type of primary tumor and refractory symptoms. Statistical analysis was employed to compare survival times between patients who received palliative sedation from referring physicians and those prescribed by on-call physicians, while accounting for other potential confounding variables. This study revealed that the median survival time after the initiation of palliative sedation was 25 h, with an interquartile range of 8 to 48 h. Notably, if the sedation was prescribed by referring physicians, the median survival time was 30 h, while it decreased to 17 h when prescribed by on-call physicians (RR 0.357; 95% CI 0.146–0.873; p = 0.024). Furthermore, dyspnea as a refractory symptom was associated with a shorter survival time (RR 0.307; 95% CI 0.095–0.985; p = 0.047). The findings suggest that the on-call physician often administered palliative sedation to rapidly deteriorating patients, particularly those experiencing dyspnea, which likely contributed to the shorter survival time following sedation initiation. This study underscores the importance of careful patient selection and prompt initiation of palliative sedation to alleviate suffering.

Background/Objectives: Traumatic brain injury (TBI), especially mild TBI (mTBI), is frequently caused by traffic accidents, falls, or sports injuries. Although computed tomography (CT) is the gold standard for diagnosis, overuse can lead to unnecessary radiation exposure, increased healthcare costs, and emergency department saturation. Blood-based biomarkers have emerged as potential tools to optimize CT scan use. This systematic review aims to evaluate recent evidence on the role of specific blood biomarkers in guiding CT decisions in patients with mTBI. Methods: A systematic search was conducted in the PubMed, Cochrane, and CINAHL databases for studies published between 2020 and 2024. Inclusion criteria focused on adult patients with mTBI evaluated using both CT imaging and at least one of the following biomarkers: glial fibrillary acidic protein (GFAP), ubiquitin carboxy-terminal hydrolase L1 (UCH-L1), and S100 calcium-binding protein B (S100B). After screening, six studies were included in the final review. Results: All included studies reported high sensitivity and negative predictive value for the selected biomarkers in detecting clinically relevant intracranial lesions. GFAP and UCH-L1, particularly in combination, consistently identified low-risk patients who could potentially forgo CT scans. While S100B also showed high sensitivity, discrepancies in cutoff values across studies highlighted the need for harmonization. Conclusions: Blood biomarkers such as GFAP, UCH-L1, and S100B demonstrate strong potential to reduce unnecessary CT imaging in mTBI by identifying patients at low risk of significant brain injury. Future research should focus on standardizing biomarker thresholds and validating protocols to support their integration into clinical practice guidelines.

Background/Objectives: Artificial intelligence (AI) tools, particularly large language models (LLMs) such as ChatGPT-4o, are gaining prominence in medicine. While their diagnostic capabilities have been explored across various oncologic domains, their role in clinical decision-making within multidisciplinary tumor boards (MTBs) remains largely unexamined in urologic oncology. This study evaluates the performance of ChatGPT-4o as a decision-support tool in a real-world MTB setting by comparing its recommendations with those of expert clinicians. Materials and Methods: A retrospective study was conducted using 98 anonymized clinical cases discussed by a urologic MTB between June 2024 and February 2025. An independent urologist entered the same cases into ChatGPT-4o using a standardized prompt replicating real-world presentation. Two certified urologists independently assessed the model’s responses. Agreement was analyzed overall and by tumor type, disease stage, clinical context, and treatment strategy. Results: ChatGPT-4o fully agreed with the MTB in 56.1% of cases, was correct but incomplete in 23.5%, and provided partially accurate but flawed recommendations in 18.4%. Overall concordance between ChatGPT-4o and the MTB yielded a Cohen’s kappa of 0.61, indicating moderate-to-good agreement. Discrepancies were most common in metastatic prostate cancer, often due to misclassification of tumor burden or errors in treatment sequencing. Highest agreement rates were observed in bladder and renal tumors, and in standardized therapeutic scenarios such as radiotherapy. Conclusions: ChatGPT-4o demonstrated moderate alignment with expert MTB decisions and performed best in well-defined clinical contexts. While it cannot replace multidisciplinary expertise, it may serve as a supportive tool to enhance access to standardized oncologic care.

The integration of 3D printing and artificial intelligence is transforming healthcare management by driving innovations in personalized care, supply chain operations, and clinical workflows. This review offers a comprehensive overview and in-depth analysis of recent (2018–2025) applications where AI technologies enhance 3D printing within healthcare. We explore how AI-powered design and optimization facilitate the creation of patient-specific medical devices, implants, and even bioprinted tissues, while intelligent process control increases both quality and efficiency. Additionally, we examine regulatory and ethical considerations, including the evolution of frameworks for AI-enabled devices, as well as challenges in data governance, validation, and equitable access. The review takes a global perspective, presenting real-world case studies that showcase both successful implementations and ongoing challenges. We also discuss various perspectives and controversies, such as the balance between innovation and safety in autonomous AI design, and highlight areas where further research is needed. In contrast to previous narrative reviews that focus solely on clinical applications or technical aspects, this review uniquely evaluates the combined impact of AI and 3D printing on healthcare management—including cost-effectiveness, governance, decision-making processes, and point-of-care manufacturing. This work is particularly valuable for hospital administrators, clinical operations leaders, health policymakers, and biomedical innovation teams seeking to understand the broader implications of AI-enhanced 3D printing in healthcare management. Nevertheless, despite promising advancements, the field is constrained by heterogeneous evidence, a lack of standardized evaluation metrics, and insufficient long-term outcome data, which together limit the ability to fully assess the sustained impact of AI-integrated 3D printing in healthcare environments.