196,573 research outputs found
Oxidative stress during exercise: further proof that being lean is detrimental for chronic obstructive pulmonary disease patients
No full textDifferential diagnosis between usual interstitial pneumonia (UIP) and nonspecific interstitial pneumonia (NSIP) assessed by high-resolution computed tomography (HRCT)
No full textDifferential diagnosis between usual interstitial pneumonia (UIP) and nonspecific interstitial pneumonia (NSIP) assessed by high-resolution computed tomography (HRCT). Radiologia Medica, vol. 19, n. 5-6, 2005, pp. 472-487 Bna C, Zompatori M, Poletti V, Spaggiari E, Chetta A, Calabro E, Ormitti F, Berti E, Cancellieri A, Chilosi M. Sezione di Scienze Radiologiche, Dipartimento di Scienze Cliniche, Universita degli studi di Parma, Parma, Italy. PURPOSE: The aim of this study was to assess the accuracy of high-resolution CT in the differential diagnosis between UIP and NSIP, and the correlations with histological and functional findings. MATERIALS AND METHODS: Patients underwent thin-collimation spiral CT (1 mm), with 10-mm interval. Pulmonary function was assessed with a pneumotacograph and body plethysmograph connected with a computer for data analysis. Three pathologists, blinded to the clinical and functional data, provided a histological diagnosis based on established criteria reported in the literature. The study group only included patients with a histological diagnosis of either UIP or NSIP. RESULTS: We achieved a correct diagnosis of NSIP in 86.6% of cases (76.4% sensitivity; 84.6% specificity), whereas UIP was correctly diagnosed in 73.3% of cases (84.6% sensitivity; 76.5% specificity). An 80% agreement was achieved between the HRCT and histological findings in the whole case series (73% sensitivity, 87% specificity, p<0.01). CONCLUSIONS: The most important finding of our study was that a ground glass appearance equal to or greater than 15% is highly suggestive of NSIP. Therefore, our results could be useful to confirm a suggested diagnosis of NSIP
Broncopneumopatia cronica ostruttiva e condizioni cliniche che intervengono nella sua storia naturale: definizioni
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Sars-cov-2 neutralizing antibodies: A network meta-analysis across vaccines
No full textBackground: There are no studies providing head-to-head comparison across SARS-CoV-2 vaccines. Therefore, we compared the efficacy of candidate vaccines in inducing neutralizing antibodies against SARS-CoV-2. Methods: A network meta-analysis was performed to compare the peak levels of SARS-CoV-2 neutralizing antibodies across candidate vaccines. Data were reported as standardized mean difference (SMD) since the outcome was assessed via different metrics and methods across the studies. Results: Data obtained from 836 healthy adult vaccine recipients were extracted from 11 studies. BBIBP-CorV, AZD1222, BNT162b2, New Crown COVID-19, and Sputnik V induced a very large effect on the level of neutralizing antibodies (SMD > 1.3); CoVLP, Coro-naVac, NVX-CoV2373, and Ad5-nCoV induced a large effect (SMD > 0.8 to ≤1.3); and Ad26.COV2.S induced a medium effect (SMD > 0.5 to ≤0.8). BBIBP-CorV and AZD122 were more effective (p < 0.05) than Ad26.COV2.S, Ad5–nCoV, mRNA-1237, CoronaVac, NVX–CoV2373, CoVLP, and New Crown COVID-19; New Crown COVID-19 was more effective (p < 0.05) than Ad26.COV2.S, Ad5–nCoV, and mRNA-1237; CoronaVac was more effective (p < 0.05) than Ad26.COV2.S and Ad5–nCoV; and Sputnik V and BNT162b2 were more effective (p < 0.05) than Ad26.COV2.S. In recipients aged ≤60 years, AZD1222, BBIBP-CorV, and mRNA-1237 were the most effective candidate vaccines. Conclusion: All the candidate vaccines induced significant levels of SARS-CoV-2 neutralizing antibodies, but only AZD1222 and mRNA-1237 were certainly tested in patients aged ≥70 years. Compared with AZD1222, BNT162b and mRNA-1237 have the advantage that they can be quickly re-engineered to mimic new mutations of SARS-CoV-2
A SARS-CoV-2 host infection model network based on genomic human Transcription Factors (TFs) depletion
No full textIn December 2019 a new beta-coronavirus was isolated and characterized by sequencing samples from pneumonia patients in Wuhan, Hubei Province, China. Coronaviruses are positive-sense RNA viruses widely distributed among different animal species and humans in which they cause respiratory, enteric, liver and neurological symptomatology. Six species of coronavirus have been described (HCoV-229E, HCoV-OC43, HCoV-NL63 and HCoV-HKU1) that cause cold-like symptoms in immunocompetent or immunocompromised subjects and two strains of sometimes fatal zoonotic origin that cause severe acute respiratory syndrome (SARS-CoV and MERS-CoV). The SARS-CoV-2 strain is the emerging seventh member of the coronavirus family, which is actually determining a global emergency. In silico analysis is a promising approach for understanding biological events in complex diseases and due to serious worldwide emergency and serious threat to global health, it is extremely important to use bioinformatics methods able to study an emerging pathogen like SARS-CoV-2. Herein, we report on in silico comparative analysis between complete genome of SARS-CoV, MERS-CoV, HCoV-OC43 and SARS-CoV-2 strains, to identify the occurrence of specific conserved motifs on viral genomic sequences which should be able to bind and therefore induce a subtraction of host's Transcription Factors (TFs) which lead to a depletion, an effect comparable to haploinsufficiency (a genetic dominant condition in which a single copy of wild-type allele at a locus, in heterozygous combination with a variant allele, is insufficient to produce the correct quantity of transcript and, therefore, of protein, for a correct standard phenotypic expression). In this competitive scenario, virus versus host, the proposed in silico protocol identified the TFs same as the distribution of TFBSs (Transcription Factor Binding Sites) on analyzed viral strains, potentially able to influence genes and pathways with biological functions confirming that this approach could brings useful insights regarding SARS-CoV-2. According to our results obtained by this in silico approach it is possible to hypothesize that TF-binding motifs could be of help in the explanation of the complex and heterogeneous clinical presentation in SARS-CoV-2 and subsequently predict possible interactions regarding metabolic pathways, and drug or target relationships
Dexamethasone in patients hospitalized with COVID-19: Whether, when and to whom
No full textA clinical interpretation of the Randomized Evaluation of COVID-19 Therapy (RECOVERY) study was performed to provide a useful tool to understand whether, when, and to whom dexamethasone should be administered during hospitalization for COVID-19. A post hoc analysis of data published in the preliminary report of the RECOVERY study was performed to calculate the person-based number needed to treat (NNT) and number needed to harm (NNH) of 6 mg dexamethasone once daily for up to 10 days vs. usual care with respect to mortality. At day 28, the NNT of dexamethasone vs. usual care was 36.0 (95%CI 24.9–65.1, p < 0.05) in all patients, 8.3 (95%CI 6.0–13.1, p < 0.05) in patients receiving invasive mechanical ventilation, and 34.6 (95%CI 22.1–79.0, p < 0.05) in patients receiving oxygen only (with or without noninvasive ventilation). Dexamethasone increased mortality compared with usual care in patients not requiring oxygen supplementation, leading to a NNH value of 26.7 (95%CI 18.1–50.9, p < 0.05). NNT of dexamethasone vs. usual care was 17.3 (95%CI 14.9–20.6) in subjects <70 years, 27.0 (95%CI 18.5–49.8) in men, and 16.2 (95%CI 13.2–20.8) in patients in which the onset of symptoms was >7 days. Dexamethasone is effective in male subjects < 70 years that require invasive mechanical ventilation experiencing symptoms from >7 days and those patients receiving oxygen without invasive mechanical ventilation; it should be avoided in patients not requiring respiratory support
Experimental drugs in clinical trials for COPD: artificial intelligence via machine learning approach to predict the successful advance from early-stage development to approval
No full textIntroduction: Therapeutic advances in drug therapy of chronic obstructive pulmonary disease (COPD) really effective in suppressing the pathological processes underlying the disease deterioration are still needed. Artificial Intelligence (AI) via Machine Learning (ML) may represent an effective tool to predict clinical development of investigational agents. Areal covered: Experimental drugs in Phase I and II development for COPD from early 2014 to late 2022 were identified in the ClinicalTrials.gov database. Different ML models, trained from prior knowledge on clinical trial success, were used to predict the probability that experimental drugs will successfully advance toward approval in COPD, according to Bayesian inference as follows: ≤25% low probability, >25% and ≤50% moderate probability, >50% and ≤75% high probability, and >75% very high probability. Expert opinion: The Artificial Neural Network and Random Forest ML models indicated that, among the current experimental drugs in clinical trials for COPD, only the bifunctional muscarinic antagonist - β2-adrenoceptor agonists (MABA) navafenterol and batefenterol, the inhaled corticosteroid (ICS)/MABA fluticasone furoate/batefenterol, and the bifunctional phosphodiesterase (PDE) 3/4 inhibitor ensifentrine resulted to have a moderate to very high probability of being approved in the next future, however not before 2025
Chirurgia riduttiva dell’enfisema polmonare: indicazioni all’intervento e valutazione fisiopatologica
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