Halloween drives an influx of ophthalmic emergencies and infections caused by nonprescription contact lenses, masks and more. Higher plasma-to-red cell transfusion ratios resulted in significantly lower mortality in trauma-induced critical bleeding. LAP surgery achieved PFS comparable to open surgery in noncurable stage IV colorectal cancer. For patients with APN-complicated urosepsis, emergent PCN enables earlier definitive URSL and reduces hospital stay duration. Incorporating metformin in burn metabolic care may help control hyperglycemia and broader metabolic dysfunction. Higher lactate-to-hematocrit ratio (LHR) values were associated with significantly worse short- and long-term outcomes. Tailoring antibiotic administration timing to patient risk in sepsis care may improve outcomes and reduce overtreatment. Inflammatory markers such as the systemic inflammation response index can indicate preeclampsia-related acute kidney injury. An intubating intensivist intervention in a community hospital ICU significantly decreased ED physician-performed ETIs. Dr. Kameelah Phillips discusses strategies for treating postpartum hemorrhage and research priorities to improve outcomes.  Patients with Fournier gangrene experienced much longer LOS and higher costs than patients treated for non-perineal NSTIs. A recent review highlights sepsis-induced immunosuppression as a key driver of mortality. Patients starting dialysis after graft failure have more complications and would benefit from a multidisciplinary approach. Dr. Kameelah Phillips discusses causes of postpartum hemorrhage and why the issue is so prevalent in the US. A trauma-informed CBT intervention decreased parental perceptions of child vulnerability in a high-risk NICU population. Many patients admitted with angioedema who are stable after the first few hours can be safely managed outside the ICU. Incorporating ALC into the SOFA score improves prognostic accuracy for patients with sepsis. For older adults, the RSVpreF vaccine reduced hospitalizations for RSV-related respiratory tract disease. Machine learning models effectively predicted in-hospital mortality among intensive care unit patients with lymphoma. ML models meaningfully predicted hospitalization and urosepsis risk for outpatients with UTI.