High-flow nasal cannula oxygen (HFNC) is a relatively new therapy used in adults with respiratory failure. Whether it is superior to conventional oxygen therapy (COT) or to noninvasive mechanical ventilation (NIV) remains unclear. The aim of the present study was to investigate whether HFNC was superior to either COT or NIV in adult acute respiratory failure patients. A review of the literature was conducted from the electronic databases from inception up to 20 October 2016. Only randomized clinical trials comparing HFNC with COT or HFNC with NIV were included. The intubation rate was the primary outcome; secondary outcomes included the mechanical ventilation rate, the rate of escalation of respiratory support and mortality. Eleven studies that enrolled 3459 patients (HFNC, n = 1681) were included. There were eight studies comparing HFNC with COT, two comparing HFNC with NIV, and one comparing all three. HFNC was associated with a significant reduction in intubation rate (OR 0.52, 95% CI 0.34 to 0.79, P = 0.002), mechanical ventilation rate (OR 0.56, 95% CI 0.33 to 0.97, P = 0.04) and the rate of escalation of respiratory support (OR 0.45, 95% CI 0.31 to 0.67, P < 0.0001) when compared to COT. There was no difference in mortality between HFNC and COT utilization (OR 1.01, 95% CI 0.67 to 1.53, P = 0.96). When HFNC was compared to NIV, there was no difference in the intubation rate (OR 0.96; 95% CI 0.66 to 1.39, P = 0.84), the rate of escalation of respiratory support (OR 1.00, 95% CI 0.77 to 1.28, P = 0.97) or mortality (OR 0.85, 95% CI 0.43 to 1.68, P = 0.65). Compared to COT, HFNC reduced the rate of intubation, mechanical ventilation and the escalation of respiratory support. When compared to NIV, HFNC showed no better outcomes. Large-scale randomized controlled trials are necessary to prove our findings. PROSPERO International prospective register of systematic reviews on May 25, 2016 registration no. CRD42016039581 .

Angiotensin converting enzyme 2 (ACE2), a transmembrane glycoprotein, is an important part of the renin-angiotensin system (RAS). In the COVID-19 epidemic, it was found to be the receptor of severe acute respiratory syndrome coronavirus 2 (SARS-COV-2). ACE2 maintains homeostasis by inhibiting the Ang II-AT1R axis and activating the Ang I (1-7)-MasR axis, protecting against lung, heart and kidney injury. In addition, ACE2 helps transport amino acids across the membrane. ACE2 sheds from the membrane, producing soluble ACE2 (sACE2). Previous studies have pointed out that sACE2 plays a role in the pathology of the disease, but the underlying mechanism is not yet clear. Recent studies have confirmed that sACE2 can also act as the receptor of SARS-COV-2, mediating viral entry into the cell and then spreading to the infective area. Elevated concentrations of sACE2 are more related to disease. Recombinant human ACE2, an exogenous soluble ACE2, can be used to supplement endogenous ACE2. It may represent a potent COVID-19 treatment in the future. However, the specific administration concentration needs to be further investigated.

Background: With the rapid development of artificial intelligence, large language models (LLMs) have shown strong capabilities in natural language understanding, reasoning, and generation, attracting much research interest in applying LLMs to health and medicine. Critical care medicine (CCM) provides diagnosis and treatment for patients with critical illness who often require intensive monitoring and interventions in intensive care units (ICUs). Whether LLMs can be applied to CCM, and whether they can operate as ICU experts in assisting clinical decision-making rather than “stochastic parrots,” remains uncertain. Objective: This scoping review aims to provide a panoramic portrait of the application of LLMs in CCM, identifying the advantages, challenges, and future potential of LLMs in this field. Methods: This study was conducted in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. Literature was searched across 7 databases, including PubMed, Embase, Scopus, Web of Science, CINAHL, IEEE Xplore, and ACM Digital Library, from the first available paper to August 22, 2025. Results: From an initial 2342 retrieved papers, 41 were selected for final review. LLMs played an important role in CCM through the following 3 main channels: clinical decision support, medical documentation and reporting, and medical education and doctor-patient communication. Compared to traditional artificial intelligence models, LLMs have advantages in handling unstructured data and do not require manual feature engineering. Meanwhile, applying LLMs to CCM has faced challenges, including hallucinations and poor interpretability, sensitivity to prompts, bias and alignment challenges, and privacy and ethical issues. Conclusions: Although LLMs are not yet ICU experts, they have the potential to become valuable tools in CCM, helping to improve patient outcomes and optimize health care delivery. Future research should enhance model reliability and interpretability, improve model training and deployment scalability, integrate up-to-date medical knowledge, and strengthen privacy and ethical guidelines, paving the way for LLMs to fully realize their impact in critical care. Trial Registration: OSF Registries yn328; https://osf.io/yn328/

Background: There is wide heterogeneity in sepsis in causative pathogens, host response, organ dysfunction, and outcomes. Clinical and biologic phenotypes of sepsis are proposed, but the role of pathogen data on sepsis classification is unknown.Methods: We conducted a secondary analysis of the Recombinant Human Activated Protein C (rhAPC) Worldwide Evaluation in Severe Sepsis (PROWESS) Study. We used latent class analysis (LCA) to identify sepsis phenotypes using, (i) only clinical variables (“host model”) and, (ii) combining clinical with microbiology variables (e.g., site of infection, culture-derived pathogen type, and anti-microbial resistance characteristics, “host-pathogen model”). We describe clinical characteristics, serum biomarkers, and outcomes of host and host-pathogen models. We tested the treatment effects of rhAPC by phenotype using Kaplan-Meier curves.Results: Among 1,690 subjects with severe sepsis, latent class modeling derived a 4-class host model and a 4-class host-pathogen model. In the host model, alpha type (N = 327, 19%) was younger and had less shock; beta type (N=518, 31%) was older with more comorbidities; gamma type (N = 532, 32%) had more pulmonary dysfunction; delta type (N = 313, 19%) had more liver, renal and hematologic dysfunction and shock. After the addition of microbiologic variables, 772 (46%) patients changed phenotype membership, and the median probability of phenotype membership increased from 0.95 to 0.97 (P < 0.01). When microbiology data were added, the contribution of individual variables to phenotypes showed greater change for beta and gamma types. In beta type, the proportion of abdominal infections (from 20 to 40%) increased, while gamma type patients had an increased rate of lung infections (from 50 to 78%) with worsening pulmonary function. Markers of coagulation such as d-dimer and plasminogen activator inhibitor (PAI)-1 were greater in the beta type and lower in the gamma type. The 28 day mortality was significantly different for individual phenotypes in host and host-pathogen models (both P < 0.01). The treatment effect of rhAPC obviously changed in gamma type when microbiology data were added (P-values of log rank test changed from 0.047 to 0.780).Conclusions: Sepsis host phenotype assignment was significantly modified when microbiology data were added to clinical variables, increasing cluster cohesiveness and homogeneity.

Purpose: The high mortality rate associated with the critical stages of severe fever with thrombocytopenia syndrome (SFTS) does not have effective treatment. We aimed to evaluate the 28-day mortality and potential impact of glucocorticoid therapy in these patients. Patients and Methods: This retrospective observational study included participants from the intensive care unit between July 2019 and April 2023. The participants were categorized into glucocorticoid (GC) and non-GC groups. Propensity score matching (PSM) was employed to ensure comparability between groups. We used Cox proportional hazard models to examine mortality risk associated with GC use, Kaplan–Meier survival analyses for overall survival, stratified Cox proportional hazard models for subgroup analyses, and likelihood ratio tests to examine interactions between subgroups. Results: Of 218 patients with SFTS (median age, 71 years; male, 49.1%), 61.9% required mechanical ventilation, 58.3% received GC treatment, and the 28-day mortality rate was 61.5%. After PSM, there were 58 patients in each group; post-PSM analysis revealed improved 28-day mortality rates with GC treatment, particularly for patients with Glasgow coma scale (GCS) score < 13 (hazard ratio [HR], 95% confidence interval [CI] for GCS score: 9– 12: 0.39, 0.17– 0.88, p=0.024 and for GCS score: 3– 8: 0.09, 0.02– 0.35, p=0.001); lactate levels > 2 mmol/L (0.35, 0.15– 0.83, p=0.017); and norepinephrine usage (0.26, 0.13– 0.49, p< 0.001). Combining antiviral (0.41, 0.22– 0.78, p=0.006) or immunoglobulin therapy (0.22, 0.1– 0.51, p< 0.001) with GC treatment significantly decreased the 28-day mortality rates, compared with GC monotherapy. Conclusion: Using GCs reduced the high 28-day mortality rate in the patients, especially with low GCS score, high lactate levels, norepinephrine intake, and on antiviral or immunoglobulin therapy.