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Background: Nipple-sparing mastectomies (NSMs) and implant-based breast reconstructions have evolved from 2-stage reconstructions with tissue expansion and implant exchange to direct-to-implant procedures. In this study, we tested safety and efficacy of polyurethane-based implants according to standard assessment tools., Objectives: This study aimed to test safety and feasibility of polyurethane-coated implants with standardized assessment employing internationally acknowledged evaluation criteria., Methods: Cases of NSMs followed by breast reconstruction in 1 stage with immediate prepectoral polyurethane-coated implant placement were retrospectively reviewed. Preoperative characteristics of the population have been collected. Adherence to quality assurance criteria of the Association of Breast Surgery-British Association of Plastic Reconstructive and Aesthetic Surgeons was verified. Complications were assessed with the Clavien Dindo classification, modified for the breast. Rippling, implant rotation, and malposition were also evaluated., Results: Sixty-three consecutive patients underwent 74 NSMs and immediate breast reconstruction with micro polyurethane foam-coated anatomic implants. In 5 cases we had unplanned readmissions with return to the operating room under general anesthesia (6.7%) and implant loss within 3 months from breast reconstruction (5 implants, 6.7%). Postoperative complications according to Clavien Dindo were grade 1 in 6 cases (8.1%), grade 2 in 3 cases (4%), and 3b in 5 cases (6.7%)., Conclusions: Polyurethane-coated implants may prevent rotation and malposition and capsular contracture in the short term. Unplanned readmission rates and implant loss rates in the short term may be slightly higher.See the abstract translated into Hindi, Portuguese, Korean, German, Italian, Arabic, Chinese, and Taiwanese online here: https://doi.org/10.1093/asj/sjad301., (© The Author(s) 2023. Published by Oxford University Press on behalf of The Aesthetic Society. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

All severe cases of SARS-CoV-2 infections are characterized by a high risk of disease progression towards ARDS, leading to a bad outcome. Respiratory symptoms in COVID-19 patients often do not correspond to disease's worsening. In our sample, median age was 74 years (72-75) and 54% were men. The median period of hospitalization was 9 days. Firstly, we observed a significant asynchronous trend of neutrophil-to-lymphocyte ratio (NLR) and C-reactive protein (CRP) in 764 selected among 963 patients, who were consecutively recruited in two hospitals (Cannizzaro, S. Marco) in Catania, Italy. NLR values in deceased patients showed an increase from baseline over time. By contrast, CRP tended to fall from baseline to median day of hospitalization in all three subgroups, but steeply increased at the end of hospitalization only in ICU-admitted patients. Then, we evaluated the relationships between NLR and CRP as continuous variables with PaO 2 /FiO 2 ratio (P/F). NLR was an independent predictor of mortality (HR: 1.77, p < 0.0001), while ICU admission was more significantly associated with CRP (HR: 1.70, p < 0.0001). Finally, age, neutrophils, CRP, and lymphocytes are significantly and directly linked to P/F, while the influence of inflammation on P/F, reflected by CRP, was also mediated by neutrophils.

Background: The role of extracorporeal life support (ECLS) in primary cardiogenic shock (PCS) is well established. In this study, we evaluated the impact of etiology on outcomes., Methods: Between January 2009 and March 2013, we implanted a total of 249 patients with ECLS; we focused on 64 patients for whom peripheral ECLS was the treatment for PCS. Of these, 37 cases (58%) were "acute" (mostly acute myocardial infarction: 39%); 27 (42%) had an exacerbation of "chronic" heart failure (dilated cardiomyopathy: 30%; post-ischemic cardiomyopathy: 9%; and congenital: 3%)., Results: In the group with chronic etiology, 23 patients were bridged to a left ventricular assist device (52%) or heart transplantation (33%). In the group with acute etiology, ECLS was used as a bridge-to-transplantation in 3 patients (8%), a bridge-to-bridge in 9 (24%), and a bridge-to-recovery in 18 (49%). One patient in each group was bridged to conventional surgery. Recovery of cardiac function was achieved in only the group with acute primary cardiogenic shock (18 vs 0 patients, P = .0001). A mean flow during support of ≤60% of the theoretic flow (body surface area × 2.4) was a predictor of successful weaning (P = .02). Median duration of ECLS support was 7 days (range: 2-11.5 days). Nine patients (14%) died during support; 30-day overall survival was 80% (51 of 64 patients); and 59% of patients were discharged, in whom survival at 48 months was 90%. Thirty-day survival was correlated with duration of ECLS support., Conclusions: In "chronic" heart failure, ECLS represents a bridge to a ventricular assist device or heart transplantation, whereas in "acute" settings, it offers a considerable chance of recovery, and is often the only required therapy., (Copyright © 2015 The American Association for Thoracic Surgery. Published by Elsevier Inc. All rights reserved.)

Computer Assisted Structure Elucidation has been used for decades to discover the chemical structure of unknown compounds. In this work we introduce the first open source structure generator, Open Molecule Generator (OMG), which for a given elemental composition produces all non-isomorphic chemical structures that match that elemental composition. Furthermore, this structure generator can accept as additional input one or multiple non-overlapping prescribed substructures to drastically reduce the number of possible chemical structures. Being open source allows for customization and future extension of its functionality. OMG relies on a modified version of the Canonical Augmentation Path, which grows intermediate chemical structures by adding bonds and checks that at each step only unique molecules are produced. In order to benchmark the tool, we generated chemical structures for the elemental formulas and substructures of different metabolites and compared the results with a commercially available structure generator. The results obtained, i.e. the number of molecules generated, were identical for elemental compositions having only C, O and H. For elemental compositions containing C, O, H, N, P and S, OMG produces all the chemically valid molecules while the other generator produces more, yet chemically impossible, molecules. The chemical completeness of the OMG results comes at the expense of being slower than the commercial generator. In addition to being open source, OMG clearly showed the added value of constraining the solution space by using multiple prescribed substructures as input. We expect this structure generator to be useful in many fields, but to be especially of great importance for metabolomics, where identifying unknown metabolites is still a major bottleneck.

Background: We consider the possibility of engineering metabolic pathways in a chassis organism in order to synthesize novel target compounds that are heterologous to the chassis. For this purpose, we model metabolic networks through hypergraphs where reactions are represented by hyperarcs. Each hyperarc represents an enzyme-catalyzed reaction that transforms set of substrates compounds into product compounds. We follow a retrosynthetic approach in order to search in the metabolic space (hypergraphs) for pathways (hyperpaths) linking the target compounds to a source set of compounds., Results: To select the best pathways to engineer, we have developed an objective function that computes the cost of inserting a heterologous pathway in a given chassis organism. In order to find minimum-cost pathways, we propose in this paper two methods based on steady state analysis and network topology that are to the best of our knowledge, the first to enumerate all possible heterologous pathways linking a target compounds to a source set of compounds. In the context of metabolic engineering, the source set is composed of all naturally produced chassis compounds (endogenuous chassis metabolites) and the target set can be any compound of the chemical space. We also provide an algorithm for identifying precursors which can be supplied to the growth media in order to increase the number of ways to synthesize specific target compounds., Conclusions: We find the topological approach to be faster by several orders of magnitude than the steady state approach. Yet both methods are generally scalable in time with the number of pathways in the metabolic network. Therefore this work provides a powerful tool for pathway enumeration with direct application to biosynthetic pathway design.

Background: Synthetic biology is used to develop cell factories for production of chemicals by constructively importing heterologous pathways into industrial microorganisms. In this work we present a retrosynthetic approach to the production of therapeutics with the goal of developing an in situ drug delivery device in host cells. Retrosynthesis, a concept originally proposed for synthetic chemistry, iteratively applies reversed chemical transformations (reversed enzyme-catalyzed reactions in the metabolic space) starting from a target product to reach precursors that are endogenous to the chassis. So far, a wider adoption of retrosynthesis into the manufacturing pipeline has been hindered by the complexity of enumerating all feasible biosynthetic pathways for a given compound., Results: In our method, we efficiently address the complexity problem by coding substrates, products and reactions into molecular signatures. Metabolic maps are represented using hypergraphs and the complexity is controlled by varying the specificity of the molecular signature. Furthermore, our method enables candidate pathways to be ranked to determine which ones are best to engineer. The proposed ranking function can integrate data from different sources such as host compatibility for inserted genes, the estimation of steady-state fluxes from the genome-wide reconstruction of the organism's metabolism, or the estimation of metabolite toxicity from experimental assays. We use several machine-learning tools in order to estimate enzyme activity and reaction efficiency at each step of the identified pathways. Examples of production in bacteria and yeast for two antibiotics and for one antitumor agent, as well as for several essential metabolites are outlined., Conclusions: We present here a unified framework that integrates diverse techniques involved in the design of heterologous biosynthetic pathways through a retrosynthetic approach in the reaction signature space. Our engineering methodology enables the flexible design of industrial microorganisms for the efficient on-demand production of chemical compounds with therapeutic applications.