Background: The efficacy of benralizumab has been broadly demonstrated in severe eosinophilic asthma (SEA), but only few real-life studies evaluated its long-term effects. Here we present novel data from the ANANKE study in which a large cohort of SEA patients was treated for up to 96 weeks. Methods: ANANKE (NCT04272463) is an observational retrospective Italian study investigating the key characteristics of SEA patients (collected during the 12 months prior to benralizumab initiation) and the clinical outcomes during benralizumab treatment (annual exacerbation rate [AER], lung function, asthma control, OCS use, healthcare resource utilization). A post hoc analysis was also conducted in groups of patients based on history of previous biologic therapy (bio-experienced versus naïve patients). Analyses were descriptive only. Results: Before benralizumab initiation, evaluable SEA patients (N = 162, 61.1% females, mean age 56.0 ± 12.7) showed a median blood eosinophil count (BEC) of 600 cells/mm³ (IQR: 430–890). Patients experienced frequent exacerbations (annualized exacerbation rate [AER]: 4.10, severe AER: 0.98), with impaired lung function and poor asthma control (median ACT score: 14) despite 25.3% reported oral corticosteroid (OCS) use. Nasal polyposis was present in 53.1% patients; 47.5% patients were atopic. After 96 weeks since the start of benralizumab, nearly 90% patients were still on treatment; benralizumab dramatically decreased exacerbations (AER: − 94.9%; severe AER: − 96.9%), improved respiratory parameters (median increase in pre-bronchodilator forced expiratory volume [pre-BD FEV1]: + 400 mL) and asthma control (median ACT score: 23) while eliminating OCS in 60% patients. Importantly, benralizumab effects were either maintained or progressively improved over time, accompanied by a nearly complete depletion of BEC. Benralizumab reduced AER both in naïve (any AER: − 95.9%; severe AER: − 97.5%) and bio-experienced patients (any AER: − 92.4%; severe AER: − 94.0%). Conclusions: Profound and sustained improvements in all asthma outcomes were observed with benralizumab. The correct identification of patients’ eosinophilic-driven asthma phenotype was essential to ensure the achievement of such remarkable results. Trial registration: ClinicalTrials.gov Identifier: NCT04272463.

The human immune system is closely linked to microbiota such as a complex symbiotic relationship during the coevolution of vertebrates and microorganisms. The transfer of microorganisms from the mother’s microbiota to the newborn begins before birth during gestation and is considered the initial phase of the intestinal microbiota (IM). The gut is an important site where microorganisms can establish colonies. The IM contains polymicrobial communities, which show complex interactions with diet and host immunity. The tendency towards dysbiosis of the intestinal microbiota is influenced by local but also extra-intestinal factors such as inflammatory processes, infections, or a septic state that can aggravate it. Pathogens could trigger an immune response, such as proinflammatory responses. In addition, changes in the host immune system also influence the intestinal community and structure with additional translocation of pathogenic and non-pathogenic bacteria. Finally, local intestinal inflammation has been found to be an important factor in the growth of pathogenic microorganisms, particularly in its role in sepsis. The aim of this article is to be able to detect the current knowledge of the mechanisms that can lead to dysbiosis of the intestinal microbiota and that can cause bacterial translocation with a risk of infection or septic state and vice versa.

Metabolic syndrome (MetS) is a combination of metabolic disorders that concurrently act as factors promoting systemic pathologies such as atherosclerosis or diabetes mellitus. It is now believed to encompass six main interacting conditions: visceral fat, imbalance of lipids (dyslipidemia), hypertension, insulin resistance (with or without impairing both glucose tolerance and fasting blood sugar), and inflammation. In the last 10 years, there has been a progressive interest through scientific research investigations conducted in the field of metabolomics, confirming a trend to evaluate the role of the metabolome, particularly the intestinal one. The intestinal microbiota (IM) is crucial due to the diversity of microorganisms and their abundance. Consequently, IM dysbiosis and its derivate toxic metabolites have been correlated with MetS. By intervening in these two factors (dysbiosis and consequently the metabolome), we can potentially prevent or slow down the clinical effects of the MetS process. This, in turn, may mitigate dysregulations of intestinal microbiota axes, such as the lung axis, thereby potentially alleviating the negative impact on respiratory pathology, such as the chronic obstructive pulmonary disease. However, the biomolecular mechanisms through which the IM influences the host’s metabolism via a dysbiosis metabolome in both normal and pathological conditions are still unclear. In this study, we seek to provide a description of the knowledge to date of the IM and its metabolome and the factors that influence it. Furthermore, we analyze the interactions between the functions of the IM and the pathophysiology of major metabolic diseases via local and systemic metabolome’s relate endotoxemia.

Artificial intelligence algorithms have become extensively utilized in survival analysis for high-dimensional, multi-source data. However, due to their complexity, these methods often yield poorly interpretable outcomes, posing challenges in the analysis of several conditions. One of these conditions is obstructive sleep apnea, a sleep disorder characterized by the simultaneous occurrence of comorbidities. Survival analysis provides a potential solution for assessing and categorizing the severity of obstructive sleep apnea, aiding personalized treatment strategies. Given the critical role of time in such scenarios and considering limitations in model interpretability, time-dependent explainable artificial intelligence algorithms have been developed in recent years for direct application to basic Machine Learning models, such as Cox regression and survival random forest. Our work aims to enhance model selection in OSA survival analysis using time-dependent XAI for Machine Learning and Deep Learning models. We developed an end-to-end pipeline, training several survival models and selecting the best performers. Our top models—Cox regression, Cox time, and logistic hazard—achieved good performance, with C-index scores of 0.81, 0.78, and 0.77, and Brier scores of 0.10, 0.12, and 0.11 on the test set. We applied SurvSHAP methods to Cox regression and logistic hazard to investigate their behavior. Although the models showed similar performance, our analysis established that the results of the log hazard model were more reliable and useful in clinical practice compared to those of Cox regression in OSA scenarios.

Patients with severe chronic obstructive pulmonary disease (COPD) are unable to exercise at high intensities for sufficiently long periods of time to obtain true physiological training effects. It therefore appears sensible to increase training duration at sub-maximal exercise intensities to optimize the benefit of exercise training. We compared the effects on exercise tolerance of two endurance cycloergometer submaximal exercise protocols with different cumulative training loads (one (G1) versus two (G2) daily 40 min training sessions) both implemented over 20 consecutive days in 149 patients with COPD (forced expiratory volume at first second (FEV₁): 39% predicted) admitted to an inpatient pulmonary rehabilitation program. Patients in G2 exhibited greater improvement (p = 0.011) in submaximal endurance time (from 258 (197) to 741 (662) sec) compared to G1 (from 303 (237) to 530 (555) sec). Clinically meaningful improvements in health-related quality of life, 6MWT, and chronic dyspnea were not different between groups. Doubling the volume of endurance training is feasible and can lead to an additional benefit on exercise tolerance. Future studies may investigate the applicability and benefits of this training strategy in the outpatient or community-based pulmonary rehabilitation settings to amplify the benefits of exercise interventions.

As part of the Italian Health Service the respiratory ICS Maugeri network were reconfigured and several in-hospital programs were suspended to be substituted by workforce and facilities reorganization for acute and post-acute COVID-19 care need. The present review shows the time course variation of respiratory ICS network in terms of admissions diagnosis and outcomes. A comparative review of the admissions and outcome measures data (anthropometric, admission diagnosis, provenience, comorbidities, disability, symptoms, effort tolerance, disease impact, length of stay and discharge destinations) over 1 year period (March 2020-March 2021) was undertaken and compared to retrospective data from a corresponding 1 year (March 2019-March 2020) period to determine the impact of the network relocation on the delivery of pulmonary specialist rehabilitation to patients with complex needs during the pandemic episode. One of the changes implemented at the respiratory Maugeri network was the relocation of the Pulmonary Rehabilitation units from its 351 beds base to a repurposed 247 beds and a reduction in total number of admitted patients (n=3912 in pre-COVID time; n=2089 in post COVID time). All respiratory diagnosis, except COVID sequelae, decreased (chronic respiratory failure-CRF, COPD, obstructive sleep apnoea syndrome-OSAS, interstitial lung disease-ILD, tracheostomized patients and other mixed diseases decreased of 734, 705, 157, 87, 79 and 326 units respectively). During the pandemic time, 265 post COVID sequelae with CRF were admitted for rehabilitation (12.62%), % of patients coming from acute hospital increased, LOS and NIV use remained stable while CPAP indication decreased. Disease impact, dyspnea and effort tolerance as their improvements after rehabilitation, were similar in the two periods. Only baseline disability, expressed by Barthel index, seems higher in the 2° observation time as its improvement. Hospital deaths and transfers to acute hospitals were higher during pandemic crisis while home destination decreased. This review demonstrated impact of coronavirus pandemic situation, specifically the relocation of the respiratory inpatient rehabilitation wards in a huge Italian network.