Aspergillosis in immunocompromised individuals is a serious and potentially life-threatening infection, as the weakened immune system cannot effectively fight the Aspergillus fungus. This review provides an in-depth examination of aspergillosis in patients with various conditions that compromise immunity, including hematological disorders, HIV, SARS-CoV-2 pneumonia, influenza, and those who have undergone solid organ transplants. The clinical manifestations of aspergillosis are influenced by factors such as the host’s underlying comorbidities, immune response, and immune suppression due to medications or treatments. The review delves into the epidemiology of aspergillosis, exploring various risk factors that predispose individuals to infection. It also discusses the wide range of clinical symptoms, highlighting the challenges in diagnosis and the importance of early detection. The review contrasts traditional diagnostic approaches with emerging molecular techniques, emphasizing the role of advanced diagnostics in improving outcomes. A proposed clinical decision-making flowchart is provided to assist healthcare professionals in managing suspected cases of aspergillosis. In addition to diagnostic challenges, the review addresses antifungal prophylaxis, pre-emptive therapy, and the growing concern of pharmacological resistance to antifungal agents. It concludes with a discussion of future research directions, underscoring the need for improved therapeutic strategies and preventative measures in immunocompromised patients to reduce the burden of this severe fungal infection.

Background: Meningoencephalitis is a complex inflammatory condition of the CNS that can result in significant morbidity and mortality in critically ill adults. Accurate and timely neuromonitoring is essential for guiding management and improving outcomes. This study aimed to descriptively evaluate the prognostic value of early TCCD monitoring, particularly the pulsatility index, and its integration with conventional and perfusion MRI in patients with meningoencephalitis. Methods: We present an observational, retrospective, cohort study involving ten adult patients (median age 56 years, IQR 45.5–68.5; mean 55.9, range 35–76) with neurological syndromes caused by suspected or confirmed infectious meningoencephalitis. Etiologies included bacterial meningitis/meningoencephalitis (50%), viral meningoencephalitis (10%), neurotoxoplasmosis (10%), progressive multifocal leukoencephalopathy (10%), and undetermined origin (20%). Patients underwent TCCD and MRI within 24 h. In five cases, standard MRI sequences were acquired, while in the remaining five, perfusion imaging was performed using Arterial Spin Labelling (ASL). A favorable outcome was defined as survival with neurological recovery (Glasgow Outcome Scale > 5) at ICU discharge. Results: TCCD-derived PI provided valuable information on cerebral hemodynamics. PI values ≤ 1.25 were associated with favorable clinical outcomes and symmetrical MRI findings. Conversely, PI > 1.25 correlated with poor prognosis and often preceded MRI-detectable structural damage. When combined with ASL, PI mirrored the detected perfusion asymmetries and was associated with poor prognosis in fatal cases. Conclusions: Bedside TCCD can offer real-time assessment of cerebrovascular dynamics and, when integrated with conventional and ASL MRI, could enhance the understanding of pathophysiological processes in meningoencephalitis, supporting timely and informed decisions in neurocritical care.

Background: Early diagnosis of Invasive Pulmonary Aspergillosis (IPA) in non-neutropenic ICU patients remains challenging due to the low specificity of radiological findings and the limited reliability of individual biomarkers. This diagnostic uncertainty often delays antifungal treatment and contributes to the high mortality associated with IPA. We aimed to develop a clinically interpretable model for early bedside risk stratification. Methods: We conducted a retrospective study including 298 ICU patients. A Logistic LASSO regression approach was used to identify the most informative predictors of IPA and to develop a parsimonious predictive model. Model performance was assessed through internal exploration and robustness testing under noise perturbation. Results: Three routinely available clinical variables were retained in the final model: pulmonary galactomannan, non-specific pulmonary infiltrates, and patient age, reflecting fungal burden, lung involvement, and host vulnerability, respectively, and identifying patients with a higher probability of culture-positive Aspergillus detection within a clinical context compatible with IPA. The final model showed promising discriminative performance, with an AUC of 0.836 ± 0.075, sensitivity of 0.72, and specificity of 0.74. Despite relying on only three routinely available clinical variables, the model maintained stable performance during internal exploration and robustness analyses. Conclusions: Within this cohort, the combination of positive pulmonary galactomannan, radiological infiltrates, and advanced age identifies a subgroup of ICU patients with a higher probability of microbiological positivity and clinical suspicion of IPA, supporting early risk assessment rather than definitive diagnosis. This simple and transparent predictive model may support early clinical evaluation and risk stratification of ICU patients and assist clinicians in identifying patients requiring further diagnostic assessment.