There is a need of consensus about the pulmonary rehabilitation (PR) in patients with COVID-19 after discharge from acute care. To facilitate the knowledge of the evidence and its translation into practice, we developed suggestions based on experts’ opinion. A steering committee identified areas and questions sent to experts. Other international experts participated to a RAND Delphi method in reaching consensus and proposing further suggestions. Strong agreement in suggestions was defined when the mean agreement was >7 (1 = no agreement and 9 = maximal agreement). Panelists response rate was >95%. Twenty-three questions from 4 areas: Personnel protection equipment, phenotypes, assessments, interventions, were identified and experts answered with 121 suggestions, 119 of which received high level of concordance. The evidence-based suggestions provide the clinicians with current evidence and clinical experts opinion. This framework can be used to facilitate clinical decision making within the context of the individual patient. Further studies will evaluate the clinical usefulness of these suggestions.

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.

Background: An association between systemic corticosteroids and tuberculosis (TB) is reported in the literature. Here within, we conducted a systematic review and meta-analysis to evaluate the effects of inhaled corticosteroids (ICS) on the risk of TB in patients with obstructive lung diseases. Methods: The review was registered on PROSPERO (CRD42018095874). PubMed, CENTRAL, Scopus and Web of Science were searched from inception to September 2018. Papers reporting cases of incident TB in patients with obstructive lung diseases were included; studies without data on ICS use were excluded. Simultaneous use of oral corticosteroids (OCS) and population attributable fraction (PAF) for TB from ICS exposure were also assessed. Data were analyzed using a generic inverse variance method with a random-effects model. ORs with 95% CI were estimated. Results: Out of 4044 retrieved papers, 9 articles evaluating adult patients only were included in the review. 36,351 patients were prescribed ICS, while 147,171 were not. Any ICS use was associated with an increased risk of TB versus no ICS use (OR=1.46; 95% CI 1.06 to 2.01; p=0.02; I²=96%). A similar result was also found for current ICS use versus prior/no ICS use, as well as for high, moderate and low ICS dose versus no ICS. When simultaneous OCS use was evaluated, the independent contribution of ICS was confirmed only in patients not on OCS (OR=1.63; 95% CI 1.05 to 2.52; p=0.03; I²=94%). Only 0.49% of all TB cases could be attributable to ICS exposure. Conclusions: Despite the association between ICS and TB, the contribution of this risk factor to the epidemiology of TB seems to be limited. As a consequence, no population-based interventions are warranted. Rather, this risk should be taken into account on an individual basis, particularly in those patients with a high risk of progression from LTBI to TB.

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.

Pulmonary nocardiosis (PN) is a rare but severe disease caused by Nocardia spp. Despite the traditional description as opportunistic infection, case reports and case series of pulmonary nocardiosis have recently been reported in immunocompetent patients too, in particular among people with chronic pulmonary diseases such as advanced Chronic Obstructive Pulmonary Disease (COPD).

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.