Background: Bronchoalveolar lavage (BAL) may be performed using a hand-held syringe or wall suction. Objectives: The aim was to study BAL volume and diagnostic yields based on BAL technique. Methods: A total of 220 consecutive patients undergoing BAL at our center were included. Manual aspiration was performed in 115 patients (group 1), and wall suction (<50 mm Hg of negative pressure) was used in 105 patients (group 2). All bronchoscopies were performed under conscious sedation applying topical anesthesia with lidocaine. Three 50-ml sterile saline aliquots were instilled in all patients. Results: The mean total amount of fluid recovered was 67 ± 20 ml in group 1 and 55 ± 22 ml in group 2 (p < 0.001). More patients in the manual aspiration group met American Thoracic Society criteria (recovery of ≥30% of instilled fluid) for an optimal BAL (81 vs. 59%; p < 0.001). The quantity of recovered fluid was also related to BAL location (p < 0.001) and radiologic findings (p = 0.002). Forty-eight (22%) BALs were diagnostic (23 in group 1 and 25 in group 2), including 37 positive bacterial cultures, 6 positive stains for Pneumocystis, and 5 cases of malignancy. No statistically significant difference in diagnostic yield was observed between the two groups. A BAL diagnosis was more likely in patients with certain radiologic (p = 0.033) and endoscopic findings (p = 0.001). When taking into account all bronchoscopic techniques performed during the procedure (e.g. biopsies, brushing, etc.), bronchoscopy was diagnostic in 37% of patients. Conclusions: Manual aspiration is superior to wall suction during BAL yielding a larger quantity of aspirate. Diagnostic yields are similar for both techniques.
Chemical compounds can be represented as attributed graphs. An attributed graph is a mathematical model of an object composed of two types of representations: nodes and edges. Nodes are individual components, and edges are relations between these components. In this case, pharmacophore-type node descriptions are represented by nodes and chemical bounds by edges. If we want to obtain the bioactivity dissimilarity between two chemical compounds, a distance between attributed graphs can be used. The Graph Edit Distance allows computing this distance, and it is defined as the cost of transforming one graph into another. Nevertheless, to define this dissimilarity, the transformation cost must be properly tuned. The aim of this paper is to analyse the structural-based screening methods to verify the quality of the Harper transformation costs proposal and to present an algorithm to learn these transformation costs such that the bioactivity dissimilarity is properly defined in a ligand-based virtual screening application. The goodness of the dissimilarity is represented by the classification accuracy. Six publicly available datasets—CAPST, DUD-E, GLL&GDD, NRLiSt-BDB, MUV and ULS-UDS—have been used to validate our methodology and show that with our learned costs, we obtain the highest ratios in identifying the bioactivity similarity in a structurally diverse group of molecules.
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