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Invasive pulmonary aspergillosis (IPA) is a life-threatening fungal infection traditionally associated with severely immunocompromised hosts, particularly those with hematologic malignancies. However, its epidemiological profile has shifted in recent years, with a rising incidence among critically ill patients in intensive care units (ICUs), many of whom lack classical risk factors. This change is driven by increased use of corticosteroids and immunomodulatory therapies, the growing prevalence of chronic lung disease, and severe viral pneumonias such as influenza and COVID-19. In these patients, airway epithelial injury, immune dysregulation, and mechanical ventilation facilitate fungal invasion even in the absence of profound immunosuppression. Corticosteroids play a central role in IPA pathogenesis. While they limit hyperinflammation, they simultaneously impair fungal clearance by suppressing NF-κB signaling, downregulating TNF-α production, and promoting IL-10 secretion, resulting in a Th2-skewed immune profile. Neutrophil recruitment persists but becomes dysregulated, contributing to tissue injury rather than effective pathogen elimination. Corticosteroids may also directly enhance Aspergillus growth, further compounding risk. Diagnosis of IPA in ICU patients remain challenging because radiological hallmarks such as the halo sign are uncommon, and distinguishing colonization from invasive disease is difficult. Serum and bronchoalveolar lavage galactomannan, β-D-glucan assays, and PCR can improve early detection, but no single test is definitive in this heterogeneous population. As much as possible, high-quality lower respiratory tract samples should be obtained. Furthermore, effective treatment requires not only timely diagnosis, but also careful selection of antifungal taking into consideration pharmacologic challenges of ICU patients and pharmacodynamics of antifungals. Recognition of high-risk patients such as those receiving corticosteroids, those with chronic lung disease, severe viral pneumonia, or requiring invasive ventilation is critical to improve outcomes. Mortality in this group can exceed that of neutropenic patients, underscoring the need for heightened clinical suspicion and timely antifungal therapy. A deeper understanding of the immunopathogenesis of IPA in non-neutropenic patients, particularly the dual effects of corticosteroids on inflammation and host defense, may inform risk stratification and guide earlier intervention. Enhanced surveillance, prompt diagnostic workup, and judicious use of immunomodulatory therapy represent key strategies to mitigate the rising burden of this devastating infection in ICU settings.

Background: Empirical antibiotic therapy (EAT) in febrile neutropenia (FN) remains challenging due to multidrug-resistant (MDR) Gram-negative bacteria, often leading to inappropriate empirical antibiotic therapy (IEAT). Objective: To demonstrate that risk stratification based on machine learning (ML) and prior colonisation with MDR bacteria may support the tailoring of EAT in patients with haematological malignancies. Design: Retrospective proof-of-concept cohort study. Methods: All consecutive FN episodes in patients with haematological malignancies were retrospectively included from January 2020 to March 2023 at a tertiary-level university hospital. We compared real-world, clinician-driven empirical antibiotic use with a simulated approach guided by an ML-based risk stratification model combined with prior colonisation data. The main outcomes were antibiotic selection and rates of IEAT. Results: A total of 553 FN episodes in 398 haematological patients were analysed. Bloodstream infection (BSI) occurred in 141/553 episodes (25.5%). Anti-pseudomonal (PsA) beta-lactams were prescribed in 515/553 episodes (93.1%), with carbapenems in 406/553 (73.4%). The clinician-driven approach resulted in 16/70 (22.9%) GNB-BSI episodes receiving IEAT. The ML plus colonisation-guided approach would have reduced the use of meropenem by 29.7% (−2.08 days; 95% CI, −2.42 to −1.73; p  < 0.001) and anti-PsA beta-lactams by 6.7% (−0.47 days; 95% CI, −0.76 to −0.19; p  = 0.001), and would also have led to a reduction in the rate of IEAT from 16/70 (22.9%) to 6/70 (8.6%) ( p  = 0.035). Conclusion: ML-based risk stratification combined with colonisation status would allow for personalised antibiotic therapy in FN, potentially reducing IEAT and improving antimicrobial use. These results support integrating these tools into clinical practice.

Background/Objectives: No reliable noninvasive biomarkers are available to predict RT-induced cardiotoxicity. Because the pericardial sac is a fast responder to cardiac injury, we investigated whether RT-induced radiographic pericardial changes might serve as early imaging biomarkers for late cardiotoxicity. Methods: We performed a retrospective study of 476 patients (210 males, 266 females; median age, 69 years; median follow-up, 26.7 months) treated with chemo-RT for small cell and non-small cell lung cancers at one single institution from 2009 to 2020. The heart and its 4 mm outmost layer (representing the pericardial sac) were contoured on standard-of-care baseline CTs. Six-month post-RT follow-up CTs were deformably registered on the baseline CTs. Data were harmonized for the effect of contrast. We labeled voxels as Fat, Fluid, Heme, Fibrous, and Calcification using Hounsfield units (HUs). We studied pericardial HU-change histograms as well as volume change and voxel-based mass change in each tissue composition. Results: Pericardial HU-change histograms had skewed distributions with a mean that was significantly correlated with mean pericardial dose. Voxels within Fluid, Heme, and Fibrous had mass changes consistent with the dose. In Kaplan–Meier curves, Fibrous and Heme volume changes (translating into thickening and effusion), Fat mass change, mean doses to heart and pericardium, history of cardiac disease, and being male were significantly associated with shorter survival, whereas thickening and effusion were significantly associated with shorter time to a post-RT cardiovascular disease diagnosis. Conclusions: Pericardium composition distribution has dose-dependent changes detectable on standard-of-care CTs at around 6 months post-RT and may serve as surrogate markers for clinically relevant cardiotoxicity. The findings should be validated with additional research.

Post-transplant diabetes mellitus (PTDM) is associated with a higher risk of adverse outcomes. We aimed to describe the proportion of patients with diabetes prior to solid organ transplantation (SOT) and post-transplant diabetes mellitus (PTDM) in three time periods (early-likely PTDM: 0–45 days; 46–365 days and >365 days) post-transplant and to estimate possible risk factors associated with PTDM in each time-period. Additionally, we compared the risk of death and causes of death in patients with diabetes prior to transplant, PTDM, and non-diabetes patients. A total of 959 SOT recipients (heart, lung, liver, and kidney) transplanted at University Hospital of Copenhagen between 2010 and 2015 were included. The highest PTDM incidence was observed at 46–365 days after transplant in all SOT recipients. Age and the Charlson Comorbidity Index (CCI Score) in all time periods were the two most important risk factors for PTDM. Compared to non-diabetes patients, SOT recipients with pre-transplant diabetes and PTDM patients had a higher risk of all-cause mortality death (aHR: 1.77, 95% CI: 1.16–2.69 and aHR: 1.89, 95% CI: 1.17–3.06 respectively). Pre-transplant diabetes and PTDM patients had a higher risk of death due to cardiovascular diseases and cancer, respectively, when compared to non-diabetes patients.

Organ-at-risk contouring is still a bottleneck in radiotherapy, with many deep learning methods falling short of promised results when evaluated on clinical data. We investigate the accuracy and time-savings resulting from the use of an interactive-machine-learning method for an organ-at-risk contouring task. We compare the method to the Eclipse contouring software and find strong agreement with manual delineations, with a dice score of 0.95. The annotations created using corrective-annotation also take less time to create as more images are annotated, resulting in substantial time savings compared to manual methods, with hearts that take 2 minutes and 2 seconds to delineate on average, after 923 images have been delineated, compared to 7 minutes and 1 seconds when delineating manually. Our experiment demonstrates that interactive-machine-learning with corrective-annotation provides a fast and accessible way for non computer-scientists to train deep-learning models to segment their own structures of interest as part of routine clinical workflows. Source code is available at \\href{https://github.com/Abe404/RootPainter3D}{this HTTPS URL}.