Tensor-SqRA: Modeling the transition rates of interacting molecular systems in terms of potential energies.

Estimating the rate of rare conformational changes in molecular systems is one of the goals of molecular dynamics simulations. In the past few decades, a lot of progress has been done in data-based approaches toward this problem. In contrast, model-based methods, such as the Square Root Approximation (SqRA), directly derive these quantities from the potential energy functions. In this article, we demonstrate how the SqRA formalism naturally blends with the tensor structure obtained by coupling multiple systems, resulting in the tensor-based Square Root Approximation (tSqRA). It enables efficient treatment of high-dimensional systems using the SqRA and provides an algebraic expression of the impact of coupling energies between molecular subsystems. Based on the tSqRA, we also develop the projected rate estimation, a hybrid data-model-based algorithm that efficiently estimates the slowest rates for coupled systems. In addition, we investigate the possibility of integrating low-rank approximations within this framework to maximize the potential of the tSqRA.

Historical eye on IPF: a cohort study redefining the mortality scenario.

Rationale: Therapies that slow idiopathic pulmonary fibrosis (IPF) progression are now available and recent studies suggest that the use of antifibrotic therapy may reduce IPF mortality. Objectives: The aim of the study 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 center for ILDs with prospective intention. We recruited all consecutive IPF patients seen at GB Morgagni Hospital, Forlì, Italy between January 2002 and December 2016 (15 years). We used survival analysis methods to describe and model the time to death or lung transplant and Cox regression to model prevalent and incident patient characteristics (time-dependent Cox models were fitted). Measurements and main 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 < 0.001). In the more recent cohort, more patients had better preserved lung function, underwent cryobiopsy instead of surgery, and were treated with antifibrotics. Highly significant negative prognostic factors were lung cancer (HR 4.46, 95% CI 3.3-6, p < 0.001), hospitalizations (HR 8.37, 95% CI 6.5-10.7, p < 0.001), and acute exacerbations (HR 8.37, 95% CI 6.52-10.7, p < 0.001). The average antifibrotic treatment effect estimated using propensity score matching showed a significant effect in the reduction of all-cause mortality (ATE coeff -0.23, SE 0.04, p < 0.001), acute exacerbations (ATE coeff -0.15, SE 0.04, p < 0.001), and hospitalizations (ATE coeff -0.15, SE 0.04, p < 0.001) but no effect on lung cancer risk (ATE coeff -0.03, SE 0.03, p = 0.4). Conclusion: 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.

Assessing transition rates as functions of environmental variables.

We present a method to estimate the transition rates of molecular systems under different environmental conditions that 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 use the Square Root Approximation method to discretize the Fokker-Planck operator into a rate matrix and the robust Perron Cluster Cluster Analysis method to coarse-grain the kinetic model. This permits efficiently estimating 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 that take into account how the cellular environment influences biochemical processes.

Clinical, radiological and pathological findings in patients with persistent lung disease following SARS-CoV-2 infection.

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.

Impact of Lung Biopsy Information on Treatment Strategy of Patients with Interstitial Lung Diseases.

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 < 0.001, and in 38% of SLB, 61/160, P < 0.001). After LBx, the MDT was less inclined to "wait and see" (from 15% to 4% of cases, P < 0.001) or to prescribe steroids only (from 54% to 37%, P < 0.001) and was more confident to treat with antifibrotics (from 23% to 44%, P < 0.001) or immunosuppressive drugs (from 7% to 14%, P < 0.001). The therapeutic strategy changed in 70% of reclassified cases (60/85) and in 59% of cases in which LBx increased the MDT confidence (84/142). Reclassification significantly impacted the outcome. The cases classified as non-IPF by clinician and radiologist and then reclassified to be IPF after LBx showed a significantly worse survival compared with non-IPF confirmed cases (adjusted hazard ratio [HR], 3.8; 95% confidence interval [CI], 1.75-8.3); P = 0.001. Cases initially classified as IPF and then reclassified as non-IPF after LBx showed a better prognosis compared with IPF confirmed cases (HR, 0.41; 95% CI, 0.18-0.94; P = 0.03). Conclusions: Reclassification of cases with LBx data increased diagnostic confidence and changed the therapeutic strategy in one-third of cases. Pathologic reclassification of cases refined prognosis prediction. Patients classified as non-IPF by clinician and radiologist and then reclassified IPF after LBx had worse prognosis compared with the non-IPF confirmed cases.

Markov models from the square root approximation of the Fokker-Planck equation: calculating the grid-dependent flux.

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 [Formula: see text] states can be obtained within seconds on a single high-performance compute server if regular grids are used.

MetalGAN: Multi-domain label-less image synthesis using cGANs and meta-learning.

Image synthesis is currently one of the most addressed image processing topic in computer vision and deep learning fields of study. Researchers have tackled this problem focusing their efforts on its several challenging problems, e.g. image quality and size, domain and pose changing, architecture of the networks, and so on. Above all, producing images belonging to different domains by using a single architecture is a very relevant goal for image generation. In fact, a single multi-domain network would allow greater flexibility and robustness in the image synthesis task than other approaches. This paper proposes a novel architecture and a training algorithm, which are able to produce multi-domain outputs using a single network. A small portion of a dataset is intentionally used, and there are no hard-coded labels (or classes). This is achieved by combining a conditional Generative Adversarial Network (cGAN) for image generation and a Meta-Learning algorithm for domain switch, and we called our approach MetalGAN. The approach has proved to be appropriate for solving the multi-domain label-less problem and it is validated on facial attribute transfer, using CelebA dataset.

Prognostic value of transbronchial lung cryobiopsy for the multidisciplinary diagnosis of idiopathic pulmonary fibrosis: a retrospective validation study.

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<0·0001). This distinction remained statistically significant in a multivariate analysis controlling for age, sex, smoking status, comorbidities, pulmonary function, and high-resolution CT patterns (p=0·02). INTERPRETATION: TBLC makes an important diagnostic contribution in interstitial lung disease, on the basis of the prognostic distinction between idiopathic pulmonary fibrosis and other interstitial lung diseases when TBLC findings are included in multidisciplinary diagnosis. FUNDING: None.

Dynamical reweighting methods for Markov models.

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.

Estimation of the infinitesimal generator by square-root approximation.

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 a high-dimensional system.

Girsanov reweighting for metadynamics simulations.

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 ß-hairpin peptide, whose dynamics is too slow to efficiently explore its phase space by direct simulation.