Explore the vast range of titles available.

The purpose of this review is to summarize the current understanding of the therapeutic effect of stem cell-based therapies, including hematopoietic stem cells, for the treatment of ischemic heart damage. Following PRISMA guidelines, we conducted electronic searches in MEDLINE, and EMBASE. We screened 592 studies, and included RCTs, observational studies, and cohort studies that examined the effect of hematopoietic stem cell therapy in adult patients with heart failure. Studies that involved pediatric patients, mesenchymal stem cell therapy, and non-heart failure (HF) studies were excluded from our review. Out of the 592 studies, 7 studies met our inclusion criteria. Overall, administration of hematopoietic stem cells (via intracoronary or myocardial infarct) led to positive cardiac outcomes such as improvements in pathological left-ventricular remodeling, perfusion following acute myocardial infarction, and NYHA symptom class. Additionally, combined death, rehospitalization for heart failure, and infarction were significantly lower in patients treated with bone marrow-derived hematopoietic stem cells. Our review demonstrates that hematopoietic stem cell administration can lead to positive cardiac outcomes for HF patients. Future studies should aim to increase female representation and non-ischemic HF patients.

Large language models (LLMs) are routinely used by physicians and patients for medical advice, yet their clinical safety profiles remain poorly characterized. We present NOHARM (Numerous Options Harm Assessment for Risk in Medicine), a benchmark using 100 real primary care-to-specialist consultation cases to measure frequency and severity of harm from LLM-generated medical recommendations. NOHARM covers 10 specialties, with 12,747 expert annotations for 4,249 clinical management options. Across 31 LLMs, potential for severe harm from LLM recommendations occurs in up to 22.2% (95% CI 21.6-22.8%) of cases, with harm of omission accounting for 76.6% (95% CI 76.4-76.8%) of errors. Safety performance is only moderately correlated (r = 0.61-0.64) with existing AI and medical knowledge benchmarks. The best models outperform generalist physicians on safety (mean difference 9.7%, 95% CI 7.0-12.5%), and a diverse multi-agent approach improves safety compared to solo models (mean difference 8.0%, 95% CI 4.0-12.1%). Therefore, despite strong performance on existing evaluations, widely used AI models can produce severely harmful medical advice at nontrivial rates, underscoring clinical safety as a distinct performance dimension necessitating explicit measurement.

This study evaluates the capacity of large language models (LLMs) to generate structured clinical consultation templates for electronic consultation. Using 145 expert-crafted templates developed and routinely used by Stanford's eConsult team, we assess frontier models -- including o3, GPT-4o, Kimi K2, Claude 4 Sonnet, Llama 3 70B, and Gemini 2.5 Pro -- for their ability to produce clinically coherent, concise, and prioritized clinical question schemas. Through a multi-agent pipeline combining prompt optimization, semantic autograding, and prioritization analysis, we show that while models like o3 achieve high comprehensiveness (up to 92.2\\%), they consistently generate excessively long templates and fail to correctly prioritize the most clinically important questions under length constraints. Performance varies across specialties, with significant degradation in narrative-driven fields such as psychiatry and pain medicine. Our findings demonstrate that LLMs can enhance structured clinical information exchange between physicians, while highlighting the need for more robust evaluation methods that capture a model's ability to prioritize clinically salient information within the time constraints of real-world physician communication.

Large language models (LLMs) and medical AI tools are routinely used by physicians and patients for medical advice, yet their clinical safety profiles remain poorly characterized. We present NOHARM (Numerous Options Harm Assessment for Risk in Medicine), a 1,100-task benchmark of primary care-to-specialist consultation cases to measure the frequency and severity of potentially harmful errors from LLM-generated medical consultation recommendations. NOHARM covers 10 specialties, with 12,747 expert annotations for 4,249 clinical management options. Across 20 notable LLMs and 4 widely used retrieval-augmented generation (RAG) clinical AI tools, direct application of recommendations carried potential for severe harm in up to 24.6% of cases, with errors of omission accounting for more than 80% of severe errors. Harm potential was not uniform across systems, with clinical AI tools outperforming generalist LLMs, and multi-agent AI teaming further improving performance in generalist models. In a randomized study of 101 U.S.-licensed generalist physicians, AI assistance improved physician performance compared to conventional resources. However, AI-assisted physicians frequently omitted valuable AI-generated recommendations and still scored lower than many AI systems alone. Had those recommendations been incorporated, combined human-AI responses would have outperformed both the human and AI system as used, suggesting complementary strengths and unrealized potential in human-AI teaming. Collectively, these results show that despite strong performance on medical knowledge benchmarks, widely used AI tools can produce medical consultation advice with the potential for severe harm, and highlight the need for explicit measurement of clinical safety. The benchmark and leaderboard are publicly available to support ongoing evaluation and improvement of AI systems used for clinical care.

Large language models (LLMs) are routinely used by physicians and patients for medical advice, yet their clinical safety profiles remain poorly characterized. We present NOHARM (Numerous Options Harm Assessment for Risk in Medicine), a benchmark using 100 real primary care-to-specialist consultation cases to measure frequency and severity of harm from LLM-generated medical recommendations. NOHARM covers 10 specialties, with 12,747 expert annotations for 4,249 clinical management options. Across 31 LLMs, potential for severe harm from LLM recommendations occurs in up to 22.2% (95% CI 21.6-22.8%) of cases, with harm of omission accounting for 76.6% (95% CI 76.4-76.8%) of errors. Safety performance is only moderately correlated (r = 0.61-0.64) with existing AI and medical knowledge benchmarks. The best models outperform generalist physicians on safety (mean difference 9.7%, 95% CI 7.0-12.5%), and a diverse multi-agent approach improves safety compared to solo models (mean difference 8.0%, 95% CI 4.0-12.1%). Therefore, despite strong performance on existing evaluations, widely used AI models can produce severely harmful medical advice at nontrivial rates, underscoring clinical safety as a distinct performance dimension necessitating explicit measurement.

Carbon and nitrogen are crucial elements for life and must be efficiently regenerated in a circular economy. Biomass streams at the end of their useful life, such as sewage sludge, are difficult to recycle even though they contain organic carbon and nitrogen components. Gasification is an emerging technology to utilize such challenging waste streams and produce syngas that can be further processed into, e.g., Fischer-Tropsch fuels, methane, or methanol. Here, the objective is to investigate if nitrogen can be recovered from product gas cleaning in a dual fluidized bed (DFB) after gasification of softwood pellets to form yeast biomass. Yeast biomass is a protein-rich product, which can be used for food and feed applications. An aqueous solution containing ammonium at a concentration of 66 mM was obtained and by adding other nutrients it enables the growth of the methylotrophic yeast Komagataella phaffii to form 6.2 g.L −1 dry yeast biomass in 3 days. To further integrate the process, it is discussed how methanol can be obtained from syngas by chemical catalysis, which is used as a carbon source for the yeast culture. Furthermore, different gas compositions derived from the gasification of biogenic feedstocks including sewage sludge, bark, and chicken manure are evaluated for their ability to yield methanol and yeast biomass. The different feedstocks are compared based on their potential to yield methanol and ammonia, which are required for the generation of yeast biomass. It was found that the gasification of bark and chicken manure yields a balanced carbon and nitrogen source for the formation of yeast biomass. Overall, a novel integrated process concept based on renewable, biogenic feedstocks is proposed connecting gasification with methanol synthesis to enable the formation of protein-rich yeast biomass.