Among patients suffering from coronavirus disease 2019 (COVID-19) syndrome, one of the worst possible scenarios is represented by the critical lung damage caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2)-induced cytokine storm, responsible for a potentially very dangerous hyperinflammatory condition. Within such a context, interleukin-6 (IL-6) plays a key pathogenic role, thus being a suitable therapeutic target. Indeed, the IL-6-receptor antagonist tocilizumab, already approved for treatment of refractory rheumatoid arthritis, is often used to treat patients with severe COVID-19 symptoms and lung involvement. Therefore, the aim of this review article is to focus on the rationale of tocilizumab utilization in the SARS-CoV-2-triggered cytokine storm, as well as to discuss current evidence and future perspectives, especially with regard to ongoing trials referring to the evaluation of tocilizumab’s therapeutic effects in patients with life-threatening SARS-CoV-2 infection.

It is estimated that antimicrobial resistance (AMR) is responsible for nearly 5 million human deaths worldwide each year and will reach 10 million by 2050. Carbapenem-resistant Acinetobacter baumannii (CRAB) infections represent the fourth-leading cause of death attributable to antimicrobial resistance globally, but a standardized therapy is still lacking. Among the antibiotics under consideration, Sulbactam/durlobactam seems to be the best candidate to replace current back-bone agents. Cefiderocol could play a pivotal role within combination therapy regimens. Due to toxicity and the pharmacokinetics/pharmacodynamics (PK/PD) limitations, colistin (or polymyxin B) should be used as an alternative agent (when no other options are available). Tigecycline (or minocycline) and fosfomycin could represent suitable partners for both NBLs. Randomized clinical trials (RCTs) are needed to better evaluate the role of NBLs in CRAB infection treatment and to compare the efficacy of tigecycline and fosfomycin as partner antibiotics. Synergism should be tested between NBLs and “old” drugs (rifampicin and trimethoprim/sulfamethoxazole). Huge efforts should be made to accelerate pre-clinical and clinical studies on safer polymyxin candidates with improved lung activity, as well as on the iv rifabutin formulation. In this narrative review, we focused the antibiotic treatment of CRAB infections in view of newly developed β-lactam agents (NBLs).

Background: Retracted articles represent research withdrawn from the existing body of literature after publication. Research articles may be retracted for several reasons ranging from honest errors to intentional misconduct. They should not be used as reliable sources, and it is unclear why they are cited occasionally by other articles. This study hypothesized that several mechanisms may contribute to citing retracted literature and aimed to analyze the characteristics of articles citing retracted literature in anesthesiology and critical care. Methods: Using the Retraction Watch database, we retrieved retracted articles on anesthesiology and intensive care medicine up to August 16, 2021, and identified the papers citing these retracted articles. A survey designed to investigate the reasons for citing these articles was sent to the corresponding authors of the citing papers. Results: We identified 478 retracted articles, 220 (46%) of which were cited at least once. We contacted 1297 corresponding authors of the papers that cited these articles, 417 (30%) of whom responded to our survey and were included in the final analysis. The median number of authors in the analyzed articles was five, and the median elapsed time from retraction to citation was 3 yr. Most of the corresponding authors (372, 89%) were unaware of the retracted status of the cited article, mainly because of inadequate notification of the retraction status in journals and/or databases and the use of stored copies. Conclusions: The corresponding authors were generally unaware of the retraction of the cited article, usually because of inadequate identification of the retracted status in journals and/or web databases and the use of stored copies. Awareness of this phenomenon and rigorous control of the cited references before submitting a paper are of fundamental importance in research. Editor’s Perspective: What We Already Know about This Topic: What This Article Tells Us That Is New:

The COVID-19 pandemic may have had an effect on antimicrobial resistance. We compared the prevalence of ESKAPE multidrug-resistant (MDR) bacterial infections in COVID-19 affected/unaffected patients admitted to intensive care units (ICU) or infectious disease units at the “Mater Domini” University Hospital of Catanzaro between 1 March 2020 and 31 July 2021. Moreover, an analysis of MDR rates in ICU comparing the pre-pandemic period with the pandemic period was performed, and the possible consequence on in-hospital mortality was explored. One hundred and eighty-four ESKAPE isolates were analyzed from 362 SARS-CoV-2 positive and 199 negative patients. In total, 116 out of 171 Gram-negative isolates were classified as MDR, and a higher frequency was observed in COVID-19 compared with non-COVID-19 patients (74.2% vs. 60.3%; p = 0.052). A higher rate of MDR ESKAPE bacteria was observed in COVID-19 patients admitted to the ICU compared with COVID-19 unaffected patients admitted to the same ward in 2019 (88% vs. 80.4%; p = 0.186). Acinetobacter baumannii was the main pathogen in COVID-19 patients (58.7%), where it was the most frequent cause of bloodstream infection with the highest mortality rate (68.7%). Increase in MDR appeared to be associated with COVID-19 but only in the ICU setting. Acinetobacter baumannii was associated with the risk of death, indicating the importance of implementing infection control measures urgently.

Asthma is a highly heterogeneous respiratory disease that, in its severe forms, is characterized by persistent symptoms, frequent exacerbations, and a significant impact on patients’ quality of life. Despite high-dose inhaled corticosteroids and long-acting bronchodilators, a subset of patients remains uncontrolled, necessitating advanced therapeutic strategies. The advent of biologic therapies has revolutionized the management of severe asthma, offering targeted interventions based on the underlying inflammatory endotypes, primarily T2-high and T2-low. However, selecting the most appropriate biologic remains challenging due to overlapping phenotypic features and the limited availability of validated biomarkers. This narrative review explores the clinical utility of key biomarkers, including blood eosinophils, fractional exhaled nitric oxide (FeNO), periostin, and total and specific IgE, in guiding biologic therapy. All the information provided is based on an extensive literature search conducted on PubMed. We also examine the clinical characteristics and comorbidities that influence therapeutic choices. Furthermore, we present a practical decision-making platform, including a clinical table matching phenotypes with biologic agents, such as omalizumab, mepolizumab, benralizumab, dupilumab, and tezepelumab. By integrating biomarker analysis with clinical assessment, based on current guidelines and our extensive real-life experience, we aim to offer a logical framework to help clinicians select the most suitable biologic treatment for patients with uncontrolled severe asthma. Future research should focus on identifying novel biomarkers, refining patient stratification, and evaluating long-term outcomes to further advance precision medicine in the management of severe asthma.

Background/Objectives: Artificial intelligence (AI), machine-learning (ML), and deep-learning (DL) models are increasingly used for prognostic prediction in intensive care unit (ICU) sepsis, but their clinical readiness remains uncertain. This systematic review aimed to evaluate AI-, ML-, and DL-based models for mortality and clinically relevant outcome prediction in adult ICU patients with sepsis or septic shock. Methods: PubMed/MEDLINE, Scopus, and the Cochrane Library were searched up to April 2026. Eligible studies included adult ICU sepsis or septic shock cohorts evaluating AI/ML/DL-based prognostic models. Screening, full-text assessment, and data extraction were performed independently by two reviewers. Outcomes, model families, validation strategies, discrimination, calibration, clinical utility, explainability, comparative performance versus conventional severity scores, risk of bias, and reporting completeness were synthesized. Risk of bias was assessed using PROBAST domains supplemented by PROBAST + AI considerations, and reporting completeness was evaluated according to TRIPOD/TRIPOD + AI domains. Results: Seventy-five studies were included, comprising 50 PubMed-derived and 25 additional Scopus-derived studies. AUROC or C-statistic was extractable in 64 studies, external validation was reported in 27, prospective evaluation in three, calibration in 38, decision-curve analysis or clinical utility assessment in 37, and explainability in 64. Across 17 directly extractable within-study comparisons from nine studies, AI/ML models usually, but not uniformly, achieved higher discrimination than conventional severity scores, with a median paired ΔAUROC of +0.108 (IQR, +0.082 to +0.148; range, −0.013 to +0.203). Externally validated fixed-horizon models showed clinically relevant but heterogeneous discrimination across sepsis phenotypes, with stronger evidence in selected sepsis-induced coagulopathy cohorts and more variable transportability in respiratory and liver-injury subgroups. However, 45 studies were judged at high risk of bias, mainly because of limitations in the analysis domain. Conclusions: AI/ML models for adult ICU sepsis show a recurrent signal of prognostic discrimination and often perform comparably to or better than conventional severity scores in directly extractable within-study comparisons; however, this signal should be interpreted cautiously given clinical and methodological heterogeneity, limited prospective validation, incomplete calibration, and frequent high risk of bias. The strongest evidence comes from externally validated, phenotype-specific models, although routine clinical implementation remains limited by heterogeneous endpoints, incomplete calibration, insufficient prospective validation, and scarce workflow-level evaluation. Future studies should shift from retrospective AUROC optimization toward calibrated, externally validated, clinically actionable, and workflow-integrated decision-support tools tested in prospective ICU settings.