Search

Rationale: Lung biopsy (LBx) has a relevant role in the prediction of prognosis of interstitial lung diseases (ILDs), but its impact on the clinical management of patients remains unexplored. Objectives: This study evaluates whether LBx may change the therapeutic strategy and assesses the effect of diagnostic reclassification after LBx on long-term prognosis. Methods: We evaluated the LBx of 426 consecutive patients with ILDs, without a definite usual interstitial pneumonia pattern on high-resolution computed tomographic imaging. A total of 266 patients underwent transbronchial lung cryobiopsy (TBLC), and 160 patients underwent surgical lung biopsy (SLB). The multidisciplinary team (MDT) determined a diagnosis with high or low confidence, and a management strategy, both before and after the LBx data. Results: Final MDT diagnoses were 189 idiopathic pulmonary fibrosis (IPF), 143 non-IPF fibrotic ILDs, and 94 nonfibrotic ILDs. LBx data changed the management strategy in 145 cases (34%), with similar results for TBLC and SLB (the treatment strategy changed in 31.5% of TBLC cases, 84/266, P

Learning a disentangled representation of a distribution in a completely unsupervised way is a challenging task that has drawn attention recently. In particular, much focus has been put in separating factors of variation ( i.e. attributes) within the latent code of a Generative Adversarial Network. Achieving that permits to control the presence or absence of those factors in the generated samples by simply editing a small portion of the latent code. Nevertheless, existing methods that perform very well in a noise-to-image setting often fail when dealing with a real data distribution, i.e. when the discovered attributes need to be applied to real images. On the other side, some methods are able to extract and apply a style to a sample, but struggle to maintain its content and identity, while others are not able to locally apply attributes and end up to achieve only a global manipulation of the original image. In this paper, we propose a completely ( i.e. truly) unsupervised method that is able to extract a disentangled set of attributes from a data distribution and apply them to new samples from the same distribution by preserving their content. This is achieved using an image-to-image Generative Adversarial Network that maps an image and a random set of continuous attributes to a new image that includes those attributes. Indeed, these attributes are initially unknown and they are discovered during training by maximizing the mutual information between the generated samples and the attributes’ vector. Finally, the obtained disentangled set of continuous attributes can be used to freely manipulate the input samples. We prove the effectiveness of our method over a series of datasets and show its application on various tasks, such as attribute editing, data augmentation and style transfer.

Docking is a fundamental problem in computational biology that seeks to predict a ligand's binding mode and affinity to a target protein. The accurate prediction of protein-ligand interactions is crucial for drug discovery and design. However, the large search space size and the complexity of the underlying physical interactions make docking a challenging task requiring advanced computational techniques. Here, we review the PLANTS method, based on the ant colony optimization algorithm, that determines the optimal pose and ranks a set of molecules based on their binding affinity. PLANTS analyzes each ligand individually, then for each candidate, it solves a minimization problem. As an alternative, we propose PLANTS+, which considers all energy functions collectively and solves one single minimization problem. Our benchmark test results indicate that PLANTS+ outperforms PLANTS in accuracy and efficiency.

Background: Transbronchial lung cryobiopsy (TBLC) has been introduced recently in the diagnosis of interstitial lung diseases. We aimed to evaluate the prognostic significance of the distinction between idiopathic pulmonary fibrosis and other interstitial lung diseases with the use of TBLC data in multidisciplinary team (MDT) diagnosis. Methods: In this single-centre, retrospective, investigator-initiated comparative study, we evaluated consecutive patients without a definite usual interstitial pneumonia pattern on high-resolution CT, who presented to the GB Morgagni Hospital (Forlì, Italy), and who underwent TBLC (Jan 1, 2011, to Dec 31, 2014) or surgical lung biopsy (SLB; Jan 1, 2002, to Dec 31, 2016). Three pathologists reviewed the specimens, masked to clinical information. MDT evaluation was done before and after biopsy. The primary endpoint was the prognostic significance of the MDT diagnostic separation between idiopathic pulmonary fibrosis and other interstitial lung diseases in patients undergoing TBLC. Mortality was evaluated by means of Cox regression analysis. Findings: We evaluated 500 consecutive cases, 426 of which were included: 266 had TBLC and 160 had SLB. 189 patients had idiopathic pulmonary fibrosis, 143 had other fibrotic interstitial lung diseases, and 94 had non-fibrotic interstitial lung diseases. Patients undergoing TBLC had more comorbidities and better preserved lung function compared with those undergoing SLB; among patients with a final MDT diagnosis of idiopathic pulmonary fibrosis, patients undergoing TBLC were older, had more comorbidities, and had a different post-biopsy treatment profile than those who received SLB. The distinction between idiopathic pulmonary fibrosis and other interstitial lung diseases made by MDT diagnosis on the basis of TBLC biopsy had clear prognostic significance, with a 5-year transplant-free survival of 68% (95% CI 57–76) in patients with an MDT idiopathic pulmonary fibrosis diagnosis based on TBLC compared with 93% (87–96) in patients without an idiopathic pulmonary fibrosis diagnosis based on TBLC (hazard ratio 5·28, 95% CI 2·72–10·04; p

We present a method to estimate the transition rates of molecular systems under different environmental conditions which cause the formation or the breaking of bonds and require the sampling of the Grand Canonical Ensemble. For this purpose, we model the molecular system in terms of probable "scenarios", governed by different potential energy functions, which are separately sampled by classical MD simulations. Reweighting the canonical distribution of each scenario according to specific environmental variables, we estimate the grand canonical distribution, then we use the Square Root Approximation (SqRA) method to discretize the Fokker-Planck operator into a rate matrix and the robust Perron Cluster Cluster Analysis (PCCA+) method to coarse-grain the kinetic model. This permits to efficiently estimate the transition rates of conformational states as functions of environmental variables, for example, the local pH at a cell membrane. In this work we formalize the theoretical framework of the procedure and we present a numerical experiment comparing the results with those provided by a constant-pH method based on non-equilibrium Molecular Dynamics Monte Carlo simulations. The method is relevant for the development of new drug design strategies which take into account how the cellular environment influences biochemical processes.

Some patients experience pulmonary sequelae after SARS-CoV-2 infection, ranging from self-limited abnormalities to major lung diseases. Morphological analysis of lung tissue may help our understanding of pathogenic mechanisms and help to provide consistent personalised management. The aim of this study was to ascertain morphological and immunomolecular features of lung tissue. Transbronchial lung cryobiopsy was carried out in patients with persistent symptoms and computed tomography suggestive of residual lung disease after recovery from SARS-CoV-2 infection. 164 patients were referred for suspected pulmonary sequelae after COVID-19; 10 patients with >5% parenchymal lung disease underwent lung biopsy. The histological pattern of lung disease was not homogeneous and three different case clusters could be identified, which was mirrored by their clinical and radiological features. Cluster 1 (“chronic fibrosing”) was characterised by post-infection progression of pre-existing interstitial pneumonias. Cluster 2 (“acute/subacute injury”) was characterised by different types and grades of lung injury, ranging from organising pneumonia and fibrosing nonspecific interstitial pneumonia to diffuse alveolar damage. Cluster 3 (“vascular changes”) was characterised by diffuse vascular increase, dilatation and distortion (capillaries and venules) within otherwise normal parenchyma. Clusters 2 and 3 had immunophenotypical changes similar to those observed in early/mild COVID-19 pneumonias (abnormal expression of STAT3 in hyperplastic pneumocytes and PD-L1, IDO and STAT3 in endothelial cells). This is the first study correlating histological/immunohistochemical patterns with clinical and radiological pictures of patients with post-COVID lung disease. Different phenotypes with potentially different underlying pathogenic mechanisms have been identified.

Molecular dynamics (MD) are extremely complex, yet understanding the slow components of their dynamics is essential to understanding their macroscopic properties. To achieve this, one models the MD as a stochastic process and analyses the dominant eigenfunctions of the associated Fokker–Planck operator, or of closely related transfer operators. So far, the calculation of the discretized operators requires extensive MD simulations. The square-root approximation of the Fokker–Planck equation is a method to calculate transition rates as a ratio of the Boltzmann densities of neighboring grid cells times a flux, and can in principle be calculated without a simulation. In a previous work we still used MD simulations to determine the flux. Here, we propose several methods to calculate the exact or approximate flux for various grid types, and thus estimate the rate matrix without a simulation. Using model potentials we test computational efficiency of the methods, and the accuracy with which they reproduce the dominant eigenfunctions and eigenvalues. For these model potentials, rate matrices with up to O ( 1 0 6 ) states can be obtained within seconds on a single high-performance compute server if regular grids are used.

Background: Therapies that slow IPF progression are now available and recent studies suggest that the use of anti-fibrotic therapy may reduce IPF mortality. Our aim was to evaluate whether, to what extent and for which factors the survival of IPF in a real life setting has changed in the last 15 years. Methods: Historical eye is an observational study of a large cohort of consecutive IPF patients diagnosed and treated in a referral centre for ILDs with prospective intention. Results: The study comprised 634 patients. The year 2012 identifies the time point of mortality shift ( HR 0.58, CI 0.46-0.63, p Conclusions: Antifibrotic drugs significantly impact hospitalizations, acute exacerbations and IPF survival. After the introduction of cryobiopsy and antifibrotic drugs the prognosis of IPF patients has significantly improved together with our ability to detect IPF at an earlier stage.

Introduction: despite the accumulating data on clinical and imaging features of COVID-19, its pathogenesis is still poorly understood. The majority of data regarding morphology and molecular changes regards post-mortem analysis; however, the value of performing lung biopsy on live patients more than autopsies on deceased patients is significantly high, as they may provide an insight into the complexity of the disease pathophysiology. Objectives: we examine the morphologic and molecular features of patients affected by COVID-19 who underwent lung biopsy at different times in the course of the disease: either at an earlier or at a later stage. Methods: comparative, multicenter, prospective, observational study. 24 patients were recruited. Lung samples were obtained by either cryobiopsy or TBB or SLB. Results: in most “early” phases of pneumonia the morphological changes did not match with typical DAD pattern; the exudative phase with jaline membranes and alveolar collapse were absent. Alveolar damage was patchy, varying from focal AECII hyperplasia to extensive nodular proliferation. The involvement of linear AECI was only partial; in advanced cases consistent remnants of normally looking AECI were present. A wide panel of immunohistochemical markers were just applied in samples (cytokeratin-7, cytokeratin 5/6, TTF1, CD34, Tubulin beta-3, alfa-SMA, CD163, CD14, CD20, CD138, CD3, CD4, CD8, T-bet, FoxP3, Gata-3, PD1, TCF1). Molecules involved in cell signaling, cell proliferation and activation were also investigated (beta-Catenin, phospho-STAT3, PDL-1, IDO, HLA-DR, CD25, gamma-fibrinogen).

Objectives: We sought to evaluate the association between age and presentation of SARS-COV2 infection Methods: Clinical presentation and radiological data of all consecutive COVID19 patients admitted to our Hospital between 01-03-2020 and 30-04-2020 were analyzed SARS-CoV-2 positive patients were diagnosed by either swab test or bronchoalveolar lavage Patients without proved SARS-CoV-2 infection or without HRCT scan were excluded All HRCT were reviewed and classified according to RSNA Classification of SARS-CoV-2 pneumonia(l) Results: Over the study period, 179 patients met the inclusion criteria Mean age was 67 years (SD 15 1, range 19-96);55 8% (N= 100) were male Of them, 22 patients (12%) had atypical clinical presentation The more frequent atypical presentations were cardiovascular, including syncope (N= 5, 22%) and heart failure (N= 5, 22%) HRCT was Typical in 151 (84 4%) patients, Indeterminate in 14 (7 8%), Atypical in 12 (6 7%), and Negative in 2 (0 01%) Among elderly patients (>70 years old) accounting for 44% of the total population (N=80), HRTC was more frequently Atypical (11/80 patients, 13 75%) compared to younger patients (

BackgroundPathogenesis of Coronavirus disease 2019 (Covid-19) is poorly understood. Most histologic studies come from post-mortem analysis, with existing data indicating that histologic features of acute respiratory distress syndrome are typically present in fatal cases. However, this observation may be misleading, due to confounding factors in pre-terminal disease, including injury resulting from prolonged mechanical ventilation. Ante-mortem lung biopsy may provide major pathogenetic insights, potentially providing a basis for novel treatment approaches.AimThis comparative, multicenter, prospective, observational study was planned to identify ante-mortem histological profile and immunohistochemical features of lung tissue in patients with Covid-19 in early and late phases of the disease, including markers of inflammatory cells and major pathways involved in the cytokine storm triggering.MethodsEnrolled patients underwent lung biopsy, according to the study protocol approved by local Ethical Committee, either within 15 days of the first symptoms appearing (early phase) or after >15 days (more advanced disease). Key exclusion criteria were excessive or uncorrectable bleeding risk and cardiovascular disease with heart failure. Lung samples were obtained by conventional transbronchial biopsy, trans-bronchial lung cryobiopsy or surgical lung biopsy.Results23 patients were enrolled: 12 patients underwent lung biopsy within 15 days and 11 patients more than 15 days after the onset of symptoms. Early biopsies were characterized by spots of patchy acute lung injury (ALI) with alveolar type II cells hyperplasia and significant vascular abnormalities (disordered angiogenesis with alveolar capillary hyperplasia, luminal enlargement and thickened walls of pulmonary venules, perivascular CD4-T-cell infiltration), with no hyaline membranes. In the later stages, the alveolar architecture appeared disrupted, with areas of organizing ALI, venular congestion and capillary thromboembolic microangiopathy. Striking phenotypic features were demonstrated in hyperplastic pneumocytes and endothelial cells, including the expression of phospho-STAT3 and molecules involved in immunoinhibitory signals (PD-L1 and IDO-1). Alveolar macrophages exhibited macrophage-related markers (CD68, CD11c, CD14) together with unusual markers, such as DC-Lamp/CD208, CD206, CD123/IL3AR.ConclusionA morphologically distinct “Covid pattern” was identified in the earlier stages of the disease, with prominent epithelial and endothelial cell abnormalities, that may be potentially reversible, differing strikingly from findings in classical diffuse alveolar damage. These observations may have major therapeutic implications, justifying studies of early interventions aimed at mitigating inflammatory organ injury.

We have combined infrared (IR) experiments with molecular dynamics (MD) simulations in solution at finite temperature to analyse the vibrational signature of the small floppy peptide Alanine-Leucine. IR spectra computed from first-principles MD simulations exhibit no distinct differences between conformational clusters of α -helix or β -sheet-like folds with different orientations of the bulky leucine side chain. All computed spectra show two prominent bands, in good agreement with the experiment, that are assigned to the stretch vibrations of the carbonyl and carboxyl group, respectively. Variations in band widths and exact maxima are likely due to small fluctuations in the backbone torsion angles.

In this article, we propose an adaptive importance sampling scheme for dynamical quantities of complex systems, which are metastable. The main idea of this article is to combine metadynamics, an al...

Background: Therapies that slow IPF progression are now available and recent studies suggest that the use of anti-fibrotic therapy may reduce IPF mortality. Our aim was to evaluate whether, to what extent and for which factors the survival of IPF in a real life setting has changed in the last 15 years. Methods: Historical eye is an observational study of a large cohort of consecutive IPF patients diagnosed and treated in a referral centre for ILDs with prospective intention. Findings: The study comprised 634 patients. The year 2012 identifies the time point of mortality shift ( HR 0.58, CI 0.46-0.63, p

Conformational dynamics is essential to biomolecular processes. Markov State Models (MSMs) are widely used to elucidate dynamic properties of molecular systems from unbiased Molecular Dynamics (MD). However, the implementation of reweighting schemes for MSMs to analyze biased simulations is still at an early stage of development. Several dynamical reweighing approaches have been proposed, which can be classified as approaches based on (i) Kramers rate theory, (ii) rescaling of the probability density flux, (iii) reweighting by formulating a likelihood function, (iv) path reweighting. We present the state-of-the-art and discuss the methodological differences of these methods, their limitations and recent applications.

Background and Objectives: Transbronchial lung cryobiopsy(TBLC) has recently been proposed as an alternative to surgical lung biopsy(SLB), avoiding surgery in more than 80% of ILDs cases. Our study aims to evaluate impact of TBLC in real life setting of a referral ILDs center. Methods: Retrospective review of 634 patients referred to our center(2002-2016). We analyzed time trends of biopsies methods and num of IPF diagnosis per year; complications, hospitalization days and mortality rates related to TBLC and SLB were evaluated. Results: 288(36%) had histological UIP/IPF diagnosis. 133 underwent TBLC and 95 SLB (8 had TBLC before SLB). Since TBLC has been introduced in 2011, the amount of lung biopsy triplicated and TBLC has significantly increased over time (Fig1). Hospitalization time was significantly lower for TBLC compared to SLB(2.39 vs 4.88days, p Conclusions: After 2011 the number of overall IPF diagnosis increased, along with the number of histologic confirmation of UIP/IPF by TBLC, whereas SLB significantly decreased. Compared to SLB we observed lower rates of complications, hospitalization and mortality with TBLC in IPF patients.

Background: IPAF (interstitial lung disease with autoimmune features) is a research category recently introduced with limited data on prognosis and natural history. The aim of this study was to evaluate the characteristics of patients meeting IPAF criteria in a cohort of patients previously diagnosed with IPF. Methods: We retrospectively analyzed the medical records of IPF diagnoses at or center from 2002 until 2016. We reassigned IPF diagnoses according to the 2018 ATS/ERS guidelines and IPAF diagnoses according to ERS statement. We extracted data regarding demographics, medications, smoking history, laboratory results (autoimmunity) and pulmonary function. Results: Among 360 IPF-diagnoses, we identified: Group A composed of 289 IPF-only patients; Group B composed of 49 IPF patients with positive auto-antibodies (including anti-thyroid); Group C (IPF/IPAF) composed of 22 patients meeting IPAF criteria. IPF/IPAF patients were more frequently female (40%) (compared respectively to 17%females in group A and 30% in group B, p= 0.005). Conclusions: Reflux and arthralgia were the most frequent clinical manifestations in the IPAF group (respectively 23% and 55%), whereas in IPF-only group reflux was described in 26%(p 0.0065) and arthralgia in 2% of cases (p

Starting from the lock-and-key model [1], models of ligand–target binding have been extended to acknowledge the role of conformational flexibility. In current models, the targets are assumed to fluctuate between several different confor- mations. In the conformational selection model [2], one of these conformations is the active state, i.e. the conformation which is assumed in complex with the ligand. e ligand then “selects” this conformation from the ensemble and stabi- lizes it by forming a complex. By contrast, in the induced-fit model [3], the active conformation is not sampled by the apo-target. Instead, the receptor and the lig- and form an unspecific encounter complex. is weak complex then triggers a conformational rearrangement in the receptor, which leads to the fully formed complex. While examples of mechanisms have been found [4–7], most binding processes fall somewhere in between these two extremes. e conformational selection and the induced-fit model neglect the confor- mational dynamics of the ligand, which is justified if the ligand is either rigid or its dynamics is fast compared to the dynamics of the target. However, this is not always a valid assumption. In particular, peptides and peptidomimetics exhibit a complex and often slow conformational dynamics. It is increasingly recognized that the flexibility of the target and the ligand and their mutual inter- action are crucial factors in the ligand-binding process. us, to systematically vary the thermodynamic and kinetic properties of a drug molecule, not only bind- ing affinities but also dynamics need to be taken into account. Experimentally, it is difficult to characterize the full conformational ensem- ble and its dynamics. However, an increase in computer power combined with improved algorithms has rendered molecular dynamics (MD) simulations as use- ful tools in structure-based drug design [8]. With progress in distributed com- puting [9], special purpose computers [10], and graphics processing unit (GPU) devices [11], trajectories of several tens in microseconds are now accessible on a routine basis. A visual inspection of these trajectories is sometimes not feasible, due to the shear size of the data set, and almost always unrewarding, because it does not yield a quantitative description of the system.1 While statistical analyses of the trajectory for the stationary properties of the system have been used routinely for decades, methods which yield a model of the dynamics have matured only recently. Markov State Models (MSMs) [12–17], in which the dynamics is approx- imated as a Markovian jump process between distinct microstates, are the most widely used dynamic models. MSMs have been used to improve ensemble dock- ing, to optimize a specific conformation in a ligand, to identify cryptic allosteric sites, and to characterize ligand-binding processes as well as inactive-to-active transitions in signaling proteins. We do not aim at a comprehensive survey of the literature on this subject, nor do we focus on specific results. Our goal is to explain the different ways in which MSMs can be helpful in structure-based design.

Metadynamics is a computational method to explore the phase space of a molecular system. Gaussian functions are added along relevant coordinates on the fly during a molecular-dynamics simulation to force the system to escape from minima in the potential energy function. The dynamics in the resulting trajectory are however unphysical and cannot be used directly to estimate dynamical properties of the system. Girsanov reweighting is a recent method used to construct the Markov State Model (MSM) of a system subjected to an external perturbation. With the combination of these two techniques-metadynamics/Girsanov-reweighting-the unphysical dynamics in a metadynamics simulation can be reweighted to obtain the MSM of the unbiased system. We demonstrate the method on a one-dimensional diffusion process, alanine dipeptide, and the hexapeptide Val-Gly-Val-Ala-Pro-Gly (VGVAPG). The results are in excellent agreement with the MSMs obtained from direct unbiased simulations of these systems. We also apply metadynamics/Girsanov-reweighting to a β-hairpin peptide, whose dynamics is too slow to efficiently explore its phase space by direct simulation.

Automatic vectorization of fashion hand-drawn sketches is a crucial task performed by fashion industries to speed up their workflows. Performing vectorization on hand-drawn sketches is not an easy task, and it requires a first crucial step that consists in extracting precise and thin lines from sketches that are potentially very diverse (depending on the tool used and on the designer capabilities and preferences). This paper proposes a system for automatic vectorization of fashion hand-drawn sketches based on Pearson's Correlation Coefficient with multiple Gaussian kernels in order to enhance and extract curvilinear structures in a sketch. The use of correlation grants invariance to image contrast and lighting, making the extracted lines more reliable for vectorization. Moreover, the proposed algorithm has been designed to equally extract both thin and wide lines with changing stroke hardness, which are common in fashion hand-drawn sketches. It also works for crossing lines, adjacent parallel lines and needs very few parameters (if any) to run. The efficacy of the proposal has been demonstrated on both hand-drawn sketches and images with added artificial noise, showing in both cases excellent performance w.r.t. the state of the art.

The sensitivity of molecular dynamics on changes in the potential energy function plays an important role in understanding the dynamics and function of complex molecules. We present a method to obtain path ensemble averages of a perturbed dynamics from a set of paths generated by a reference dynamics. It is based on the concept of path probability measure and the Girsanov theorem, a result from stochastic analysis to estimate a change of measure of a path ensemble. Since Markov state models (MSMs) of the molecular dynamics can be formulated as a combined phase-space and path ensemble average, the method can be extended to reweight MSMs by combining it with a reweighting of the Boltzmann distribution. We demonstrate how to efficiently implement the Girsanov reweighting in a molecular dynamics simulation program by calculating parts of the reweighting factor "on the fly" during the simulation, and we benchmark the method on test systems ranging from a two-dimensional diffusion process and an artificial many-body system to alanine dipeptide and valine dipeptide in implicit and explicit water. The method can be used to study the sensitivity of molecular dynamics on external perturbations as well as to reweight trajectories generated by enhanced sampling schemes to the original dynamics.

In recent years, for the analysis of molecular processes, the estimation of time-scales and transition rates, has become fundamental. Estimating the transition rates between molecular conformations is - from a mathematical point of view - an invariant subspace projection problem. We present a method to project the infinitesimal generator acting on function space to a low-dimensional rate matrix. This projection can be performed in two steps. First, we discretize the conformational space in a Voronoi tessellation, then the transition rates between adjacent cells is approximated by the geometric average of the Boltzmann weights of the Voronoi cells. This method demonstrates that there is a direct relation between the potential energy surface of molecular structures and the transition rates of conformational changes. We will show also that this approximation is correct and converges to the generator of the Smoluchowski equation in the limit of infinitely small Voronoi cells. We present results for a two dimensional diffusion process and Alanine dipeptide as high-dimensional system.