Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) 1 is a 2019 novel coronavirus, which only in the European area has led to more than 300,000 cases with at least 21,000 deaths. This manuscript aims to speculate that the manipulation of the microbial patterns through the use of probiotics and dietary fibers consumption may contribute to reduce inflammation and strengthen the immune system response in COVID-19 infection. The reviews of this paper are available via the supplemental material section.

Introduction: Obstructive sleep apnea (OSA) is a serious and prevalent medical condition with major consequences for health and safety. Excessive daytime sleepiness (EDS) is a common—but not universal—accompanying symptom. The purpose of this literature analysis is to understand whether the presence/absence of EDS is associated with different physiopathologic, prognostic, and therapeutic outcomes in OSA patients.Methods: Articles in English published in PubMed, Medline, and EMBASE between January 2000 and June 2017, focusing on no-EDS OSA patients, were critically reviewed.Results: A relevant percentage of OSA patients do not complain of EDS. EDS is a significant and independent predictor of incident cardiovascular disease (CVD) and is associated with all-cause mortality and an increased risk of metabolic syndrome and diabetes. Male gender, younger age, high body mass index, are predictors of EDS. The positive effects of nasal continuous positive airway pressure (CPAP) therapy on blood pressure, insulin resistance, fatal and non-fatal CVD, and endothelial dysfunction risk factors have been demonstrated in EDS-OSA patients, but results are inconsistent in no-EDS patients. The most sustainable cause of EDS is nocturnal hypoxemia and alterations of sleep architecture, including sleep fragmentation. These changes are less evident in no-EDS patients that seem less susceptible to the cortical effects of apneas.Conclusions: There is no consensus if we should consider OSA as a single disease with different phenotypes with or without EDS, or if there are different diseases with different genetic/epigenetic determinants, pathogenic mechanisms, prognosis, and treatment.The small number of studies focused on this issue indicates the need for further research in this area. Clinicians must carefully assess the presence or absence of EDS and decide accordingly the treatment. This approach could improve combination therapy targeted to a patient's specific pathology to enhance both efficacy and long-term adherence to OSA treatment and significantly reduce the social, economic, and health negative impact of OSA.

Chronic obstructive pulmonary disease (COPD) is a debilitating lung disease associated with loss of lung function, poorer quality of life, co-morbidities, significant mortality, and higher health care costs. Frequent acute exacerbations of COPD are sudden worsening of symptoms, the nature of which is associated with bacterial or viral infections. However, one-third of exacerbations remain of undetermined origin. Although it is largely discussed and controversial, current guidelines recommend treatment of exacerbations with bronchodilators, antibiotics, and systemic corticosteroids; this is despite being associated with limited benefits in term of reducing mortality, side effects and without paying attention to the heterogeneity of these exacerbations. Increasing evidence suggests that the lung microbiota plays an important role in COPD and numerous studies have reported differences in the microbiota between healthy and disease states, as well as between exacerbations and stable COPD, leading to the hypothesis that frequent acute exacerbation is more likely to experience significant changes in lung microbiota composition. These findings will need further examination to explain the causes of lung dysbiosis, namely microbial composition, the host response, including the recruitment of eosinophils, lifestyle, diet, cigarette smoking and the use of antibiotics and corticosteroids. It is now important to assess: 1) Whether alterations in the lung microbiota contribute to disease pathogenesis, especially in exacerbations of unknown origin; 2) The role of eosinophils; and 3) Whether the microbiota of the lung can be manipulated therapeutically to improve COPD exacerbation event and disease progression. In summary, we hypothesize that the alterations of the lung microbiota may explain the undetermined origins of exacerbations and that there is an urgent need to facilitate the design of intervention studies that aim at conserving the lung microbial flora.

Introduction Outcome predictions of patients with congenital diaphragmatic hernia (CDH) still have some limitations in the prenatal estimate of postnatal pulmonary hypertension (PH). We propose applying Machine Learning (ML), and Deep Learning (DL) approaches to fetuses and newborns with CDH to develop forecasting models in prenatal epoch, based on the integrated analysis of clinical data, to provide neonatal PH as the first outcome and, possibly: favorable response to fetal endoscopic tracheal occlusion (FETO), need for Extracorporeal Membrane Oxygenation (ECMO), survival to ECMO, and death. Moreover, we plan to produce a (semi)automatic fetus lung segmentation system in Magnetic Resonance Imaging (MRI), which will be useful during project implementation but will also be an important tool itself to standardize lung volume measures for CDH fetuses. Methods and analytics Patients with isolated CDH from singleton pregnancies will be enrolled, whose prenatal checks were performed at the Fetal Surgery Unit of the Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico (Milan, Italy) from the 30th week of gestation. A retrospective data collection of clinical and radiological variables from newborns’ and mothers’ clinical records will be performed for eligible patients born between 01/01/2012 and 31/12/2020. The native sequences from fetal magnetic resonance imaging (MRI) will be collected. Data from different sources will be integrated and analyzed using ML and DL, and forecasting algorithms will be developed for each outcome. Methods of data augmentation and dimensionality reduction (feature selection and extraction) will be employed to increase sample size and avoid overfitting. A software system for automatic fetal lung volume segmentation in MRI based on the DL 3D U-NET approach will also be developed. Ethics and dissemination This retrospective study received approval from the local ethics committee (Milan Area 2, Italy). The development of predictive models in CDH outcomes will provide a key contribution in disease prediction, early targeted interventions, and personalized management, with an overall improvement in care quality, resource allocation, healthcare, and family savings. Our findings will be validated in a future prospective multicenter cohort study. Registration The study was registered at ClinicalTrials.gov with the identifier NCT04609163.

Results: The expression of 395 genes was associated with the risk of progression. Twenty-three genes reflecting both B-cell biology and tumour microenvironment were retained to build a predictive model.The model identified a population at increased risk of progression in the training cohort (P < 0.001).The locked model was tested in the 3 independent validation cohorts.In the overall validation cohort, 26% of all patients (122/461) were identified as being at high risk of progression. The median PFS was 3.1 and 10.8 years in the high-and low-risk groups, respectively (p < 0.001, Figure 1). The risk of lymphoma progression at 2 years was twice higher in the high-risk group (38% versus 19%, P < 0.001).In a multivariate analysis, the score predicted PFS independently of anti-CD20 maintenance treatment and of the FLIPI score (Hazard Ratio for the combined cohort, 2.30; 95% CI, 1.72-3.08). In particular, the model stratified patients with high-risk FLIPI into groups with markedly distinct outcome (median PFS of 2.1 vs 6.6 years; log-rank test, p < 0.001).Additional unsupervised analyses of the training cohort expression data confirmed that both tumour cells and microenvironment features impacted FL prognosis. Namely, a centroblast-associated signature had adverse prognostic significance, further strengthening the biological rationale of this model. Conclusion:Using the largest study evaluating molecular prognostic biomarkers in FL patients, we developed a robust 23-gene expression-based predictor of PFS, applicable to routinely available FFPE biopsies from FL patients at diagnosis. In patients treated initially with rituximab-chemotherapy, this model identifies patients with a high risk of early progression.

L’apnea ostruttiva del sonno (OSAS) è una malattia cronica eccessivamente sotto-diagnosticata con un’alta prevalenza negli adulti. L’OSAS sta diventando un problema sociale significativo perché associata ad un peggioramento della qualità della vita ed un aumento della mortalità. Il rapporto costo-efficacia nella gestione diagnostica e terapeutica dell’OSAS è un problema strategico per contrastare la crescente domanda di test oggettivi. I pazienti OSAS che non presentano comorbilità clinicamente evidenti devono essere studiati utilizzando un sistema semplificato e poco costoso, come l’Home Sleep Testing (HST). Al contrario, la Sleep Laboratory Polisomnography (PSG) rimane il gold standard per la gestione dei pazienti con OSAS in presenza di comorbidità. Occorre sottolineare che l’uso di HST potrebbe portare ad una diagnosi errata in soggetti OSAS non ben selezionati. Questa breve rassegna si propone di offrire argomenti di riflessione sulla corretta diagnosi e trattamento dell’OSAS, in rapporto ai dati di prevalenza e alle ricadute sui costi/benefici sociali della malattia. Attualmente non può essere solo il rapporto costo/efficacia a definire il modello organizzativo adottato per la gestione dell’OSAS, in quanto si rendono necessari ulteriori studi prospettici a lungo termine, volti a validare in maniera definitiva tale rapporto nonché il confronto tra il trattamento con modelli di gestione ospedaliera versus l’assistenza domiciliare.

To illustrate a new technological advance in the standard drug-induced sleep endoscopy (DISE) model, a new machine was used, the Experimental 5 Video Stream System (5VsEs), which is capable of simultaneously visualizing all the decisional parameters on a single monitor, and recording and storing them in a single uneditable video. The DISE procedure was performed on 48 obstructive sleep apnea (OSA) or snoring patients. The parameters simultaneously recorded on a single monitor are (1) the pharmacokinetics and pharmacodynamics of propofol (through the target controlled infusion (TCI) pump monitor), (2) the endoscopic upper airway view, (3) the polygraphic pattern, and (4) the level of sedation (through the bispectral index (BIS) value). In parallel to the BIS recording, the middle latency auditory evoked potential (MLAEP) was also recorded and provided. Recorded videos from the 5VsEs machine were re-evaluated six months later by the same clinician and a second clinician to evaluate the concordance of the therapeutic indications between the two. After the six-month period, the same operator confirmed all their clinical decisions for 45 out of 48 videos. Three videos were no longer evaluable for technical reasons, so were excluded from further analysis. The comparison between the two operators showed a complete adherence in 98% of cases. The 5VsEs machine provides a multiparametric evaluation setting, defined as an “all in one glance” strategy, which allows a faster and more effective interpretation of all the simultaneous parameters during the DISE procedure, improving the diagnostic accuracy, and providing a more accurate post-analysis, as well as legal and research advantages.

Breast cancer is the leading cause of cancer deaths worldwide in women. This aggressive tumor can be categorized into two main groups—in situ and infiltrative, with the latter being the most common malignant lesions. The current use of magnetic resonance imaging (MRI) was shown to provide the highest sensitivity in the detection and discrimination between benign vs. malignant lesions, when interpreted by expert radiologists. In this article, we present the prototype of a computer-aided detection/diagnosis (CAD) system that could provide valuable assistance to radiologists for discrimination between in situ and infiltrating tumors. The system consists of two main processing levels—(1) localization of possibly tumoral regions of interest (ROIs) through an iterative procedure based on intensity values (ROI Hunter), followed by a deep-feature extraction and classification method for false-positive rejection; and (2) characterization of the selected ROIs and discrimination between in situ and invasive tumor, consisting of Radiomics feature extraction and classification through a machine-learning algorithm. The CAD system was developed and evaluated using a DCE–MRI image database, containing at least one confirmed mass per image, as diagnosed by an expert radiologist. When evaluating the accuracy of the ROI Hunter procedure with respect to the radiologist-drawn boundaries, sensitivity to mass detection was found to be 75%. The AUC of the ROC curve for discrimination between in situ and infiltrative tumors was 0.70.