Citations

Background: Post-treatment prognosis and monitoring are critical for determining the timing of salvage treatment in glioblastoma patients but has been challenging due to difficulties differentiating progression from treatment effects in conventional images. This exploratory study aimed to establish the correlation of radiomics image features from time series of amino acid tracer 18F-DOPA PET images, with outcomes, using machine learning and dimension reduction analysis. Methods: 18F-DOPA PET images were collected for a patient cohort with wild-type IDH and unmethylated MGMT who underwent dose-escalated radiation therapy. Quantitative features were derived from the high uptake region (T/N > 2.0) in pre- and post-radiation therapy follow-up 18F-DOPA PET images. A customized workflow was utilized for pre-selecting predictive features, followed by manifold learning. Machine learning algorithms were employed to establish associations between imaging features and remaining survival (RS), defined as the time between a follow-up scan and date of death. Results: The ML models exhibited 81–83% ROC_AUC in predicting RS evaluated on an independent test dataset. A RS map is proposed for monitoring tumor alterations through serial 18F-DOPA PET scans, demonstrating superior sensitivity and better correlation with survival compared to the RANO criteria. Conclusions: Our study demonstrates that ML models utilizing FU 18F-DOPA PET images have the potential to effectively predict future survival outcomes in patients with glioblastoma treated with dose-escalated radiation therapy. The capability to assess changes in tumor over time through imaging can potentially assist in patient stratification and the selection of salvage treatments, while also aiding in distinguishing treatment effects from genuine tumor progression.

Clinical trial enrichment is the targeted recruitment of prospective individual patients with defined clinical characteristics who are likely to benefit from newly developed or repurposed drugs. This process is central to the success of clinical trials together with patient management and regulatory compliance. A main challenge in clinical trial enrichment lies in the recognition of a priori clinical parameters and information that informs drug efficacy or toxicity, particularly when intended for a broader unseen population. Although Artificial Intelligence (AI) approaches, especially large language models (LLMs), have been employed in many aspects of clinical trials, to our knowledge, there is no AI method that has been developed which offers a prospective prediction and assesses the extent to which a given therapeutic intervention benefits an unseen population. Here, we offer an outlook on how to build Artificial Clinic Intelligence (ACI), a generative AI (GAI)-powered modeling platform for modeling clinical trial enrichment. ACI generates synthetic patient data and models clinical trial enrichment to inform clinicians on key clinical parameters that are enriched in prospective patients prior to accrual.

Brain tumor-related epilepsy (BTRE) is a common and debilitating symptom of central nervous system (CNS) tumors. The epileptogenic zone, defined as cortex responsible for seizure generation, is located at the peritumoral region for most tumors, and lower-grade intrinsic brain tumors have the highest seizure incidence. Surgery is often the most effective treatment for the reduction in seizures in BTRE. However, surgical decisions have historically often been made exclusively for oncologic reasons, with less emphasis on seizure control. Surgical approaches for all tumor types are reviewed, highlighting relevant risk factors. Adjunctive tools during surgery, such as intraoperative electrocorticography (ECoG), may help identify and remove surrounding brain areas which are epileptogenic. Minimally invasive surgery is also gaining traction, given its utility in treating seizures deep-seated tumors. This review explores epileptogenic brain tumors, surgery for BTRE, and emerging strategies to better achieve seizure control.

Purpose of review The rapid growth and integration of artificial intelligence in healthcare has the potential to revolutionize all fields of medicine, including anesthesiology. This review summarizes the role of artificial intelligence in anesthesiology, highlighting how it can enhance patient safety, workflow efficiency, and research capabilities, while also examining the potential risks and ethical issues of introducing this new technology. Recent findings Evolving applications in anesthesiology include closed-loop anesthetic delivery, risk stratification, intraoperative monitoring, natural language processing for literature synthesis, and predictive modeling for adverse events. While these tools offer significant promise for improving efficiency and safety, emerging risks, such as algorithmic bias, lack of transparency, hallucinations, and automation complacency – must be carefully addressed. Regulatory frameworks, clinician education, and transparent model development are necessary to ensure responsible integration. Summary Artificial intelligence is poised to transform anesthetic care, but it must be developed and deployed with caution. Clinician oversight, robust validation, and ethical safeguards are essential to ensure artificial intelligence enhances, rather than replaces, clinical judgment. Strategic adoption can improve patient outcomes, reduce preventable harm, and streamline perioperative workflows.

Extracorporeal membrane oxygenation (ECMO) use during liver transplantation is a complex and evolving aspect of critical care. This retrospective analysis presents the largest North American case series to date, offering a detailed comparison of survival outcomes in liver transplant (LT) patients supported by ECMO. A total of 15 patients were supported with ECMO, eight were supported on venoarterial (VA) ECMO and the remaining seven on venovenous (VV) ECMO. The median duration of ECMO support was 10 days. Mean intensive care unit (ICU) and hospital stays were 34.8 (interquartile range [IQR]: 1–158) and 49.2 days (IQR: 1–121), respectively. Overall survival was 33.3%, comparable to published rates (20–30%). Survival was based on ECMO initiation timing: preoperative 33.3% (1/3); intraoperative 50% (3/6); postoperative, 16.6% (1/6). All the patients (five) discharged from hospital were alive at 1 year follow-up. Significant ECMO-related complications included acute kidney injury (AKI) requiring continuous renal replacement therapy (CRRT) (40%, n = 6/15), neurological complications (40%, n = 6/15), and bleeding requiring transfusion (26.6%, n = 4/15). Literature review suggests better outcomes with intraoperative or postoperative ECMO. While ECMO use is increasing in LT, outcomes remain poor. Improved survival may depend on careful patient selection, optimal timing of initiation, and distinguishing reversible pathology from irreversible one.

Background/Objectives: Erythromelalgia is a rare condition characterized by burning pain, redness, and warmth primarily in the extremities, usually worsened by heat and alleviated by cold. The objective of this study was to identify the top 10 priorities in pediatric erythromelalgia from multiple perspectives, including clinicians, people with lived experience of childhood-onset erythromelalgia, and their family members. Methods: A modified James Lind Alliance Priority-Setting Process was conducted. The top priorities were identified through four phases: (1) an international online survey to gather priorities, (2) data processing, (3) an interim prioritization online survey, and (4) a virtual workshop to set the final priorities. Results: In phase 1, 185 potential priorities were submitted by 74 respondents (53% patients, 24% family members, and 23% clinicians) that were developed into 68 unique research questions (phase 2). In phase 3, of the 68 questions, 50 were rated for importance by 58 participants (38% patients, 36% family members, and 26% clinicians), reducing the list to 25 questions. In phase 4, the top 10 was reached through consensus by 12 participants (33% patients, 25% family members, and 42% clinicians) across Canada, South Africa, the United States of America, and the United Kingdom. Conclusions: The final priorities focused on the treatment of erythromelalgia, understanding underlying mechanisms, the association of erythromelalgia with various body systems, and generating awareness. This list is the first international patient-centered research agenda for childhood-onset erythromelalgia and a call to action from key partners to improve future research and care.

Galactic cosmic ray (GCR) radiation is a major barrier to human space exploration beyond Earth’s magnetic field protection. Mesenchymal stem cells (MSCs) are found in all organs and play a critical role in repair and regeneration of tissue. We engineered bone marrow-derived MSCs and evaluated their response to ionizing radiation exposure. Epidermal growth factor receptor (EGFR) expression by certain types of cancers has been shown to induce radioresistance. In this study, we tested the feasibility of transfecting MSCs to overexpress EGFR (eMSC-EGFR) and their capacity to tolerate and recover from X-ray exposure. Quantitative real-time PCR (qRT-PCR) and immunoblotting results confirmed the efficient transfection of EGFR into MSCs and EGFR protein production. eMSC-EGFR maintained characteristics of human MSCs as outlined by the International Society for Cell & Gene Therapy. Then, engineered MSCs were exposed to various dose rates of X-ray (1–20 Gy) to assess the potential radioprotective role of EGFR overexpression in MSCs. Post-irradiation analysis included evaluation of morphology, cell proliferation, viability, tumorigenic potential, and DNA damage. eMSC-EGFR showed signs of radioresistance compared to naïve MSCs when assessing relative proliferation one week following exposure to 1–8 Gy X-rays, and significantly lower DNA damage content 24 h after exposure to 4 Gy. We establish for the first time the efficient generation of EGFR overexpressing MSCs as a model for enhancing the human body to tolerate and recover from moderate dose radiation injury in long-term manned space travel.

Background/Objectives: Muscle aging is a complex, dynamic process that impairs overall metabolism and physiological function. The molecular mechanisms underlying declines in muscle performance and metabolic efficiency remain poorly understood, largely due to the time and resource demands of traditional model organisms. The hawk moth Manduca sexta offers a promising alternative, with a short adult lifespan (~10 days) and notable similarities to vertebrate muscle systems, making it well-suited for time-course molecular dissection of muscle aging. Methods: In this study, we performed high-resolution temporal analysis of muscle tissues from aging M. sexta, spanning the physiomuscular aging process from middle age to advanced age. Results: We observed decreased expression of genes involved in fatty acid β-oxidation, ATP synthase subunits, superoxide dismutase, glutathione S-transferases, and heat shock proteins. In contrast, genes associated with proteolysis, catabolic processes, insulin signaling, akirin, titin, high-affinity choline transporters, and vesicular acetylcholine transporters were increased in expression. Conclusions: These changes suggest a shift toward increased proteolysis and protein catabolism with age. Our findings support the use of M. sexta as a complementary model for muscle aging research. However, it remains unclear whether the observed gene expression changes are driven by intrinsic, sex-specific age-related muscle aging or confounded by potential starvation effects in older males.

Retrieval-augmented generation (RAG) quality depends on how source documents are segmented before indexing; fixed-length chunks can split concepts or add noise, reducing precision. We evaluated whether proposition, semantic, and adaptive chunking improve accuracy and relevance for safer clinical decision support. Using a curated domain knowledge base with Gemini 1.0 Pro, we built four otherwise identical RAG pipelines that differed only in the chunking strategy: adaptive length, proposition, semantic, and a fixed token-dependent baseline. Thirty common postoperative rhinoplasty questions were submitted to each pipeline. Outcomes included medical accuracy and clinical relevance (3-point Likert scale) and retrieval precision, recall, and F1; group differences were tested with ANOVA and Tukey post hoc analyses. Adaptive chunking achieved the highest accuracy—87% (Likert 2.37 ± 0.72) versus baseline 50% (1.63 ± 0.72; p = 0.001)—and the highest relevance (93%, 2.90 ± 0.40). Retrieval metrics were strongest with adaptive (precision 0.50, recall 0.88, F1 0.64) versus baseline (0.17, 0.40, 0.24). Proposition and semantic strategies improved all metrics relative to baseline, though less than adaptive. Aligning chunks to logical topic boundaries yielded more accurate, relevant answers without modifying the language model, offering a model-agnostic, data-source-neutral lever to enhance the safety and utility of LLM-based clinical decision support.