Cronache tecniche di ingegneria dalla Toscana: "Adeguamento sismico di un edificio a due piani a Firenze: taglio di due pilastri centrali con messa in forza di rinforzo in acciaio" Progettista delle strutture: Prof. Ing. Paolo SPINELLI Collaborazione progetto strutturale: Dott. Ing. Alessio MARGIOTTA Direzione lavori: Dott. Ing. Massimiliano CECCONI

All’interno dei locali di un edificio esistente, situato a Firenze e costituito da due piani fuori terra, vi è stata l’esigenza di realizzare uno spazio atto ad ospitare conferenze, congressi e summit dei vertici dirigenziali dell’azienda: si doveva quindi realizzare un nuovo Auditorium a piano terra e i relativi locali dovevano essere riorganizzati in modo tale da rispondere alle nuove esigenze funzionali. Il maggior ostacolo da superare era rappresentato dalla presenza di due pilastri dell’edificio che risultavano incompatibili con le esigenze di visibilità e fruibilità della sala. D’altro canto la loro rimozione non era facile in quanto senza di essi si sarebbe avuta una luce libera di quasi 15 metri e per l’appunto essi appartenevano ad una campata che, oltre a portare il solaio di piano primo di luce 9.90 metri, si faceva carico anche delle travi di copertura aventi luce 20 m; Si è deciso di seguire la strada dell’eliminazione dei due pilastri e del conseguente rinforzo della campata con un’opportuna struttura in acciaio affiancata a quella in C.A. esistente

Artificial Intelligence in the Intensive Care Unit

AbstractThe diffusion of electronic health records collecting large amount of clinical, monitoring, and laboratory data produced by intensive care units (ICUs) is the natural terrain for the application of artificial intelligence (AI). AI has a broad definition, encompassing computer vision, natural language processing, and machine learning, with the latter being more commonly employed in the ICUs. Machine learning may be divided in supervised learning models (i.e., support vector machine [SVM] and random forest), unsupervised models (i.e., neural networks [NN]), and reinforcement learning. Supervised models require labeled data that is data mapped by human judgment against predefined categories. Unsupervised models, on the contrary, can be used to obtain reliable predictions even without labeled data. Machine learning models have been used in ICU to predict pathologies such as acute kidney injury, detect symptoms, including delirium, and propose therapeutic actions (vasopressors and fluids in sepsis). In the future, AI will be increasingly used in ICU, due to the increasing quality and quantity of available data. Accordingly, the ICU team will benefit from models with high accuracy that will be used for both research purposes and clinical practice. These models will be also the foundation of future decision support system (DSS), which will help the ICU team to visualize and analyze huge amounts of information. We plea for the creation of a standardization of a core group of data between different electronic health record systems, using a common dictionary for data labeling, which could greatly simplify sharing and merging of data from different centers

Utilization of Water Resources and Sustainable Crop Production

BIM is representing a shift in the traditional process of building delivery. Its adoption in US reached 71% in 2012 rising from 17% in 2007; moreover, Europe is going to adopt BIM for public contracting as promoted by the European Union Public Procurement Directive. Meanwhile, BIM is widely diffused in UK and Northern Europe, as it includes a more accurate documentation, less rework and shorter project timelines. The use of BIM to provide data for energy performance evaluation and sustainability assessment is defined Green BIM and pioneering design organizations are adopting this approach to enable integrated design, construction and maintenance towards Net Zero Energy buildings. Green BIM includes Building Energy Modelling dealing with project energy performance to identify options optimising building energy efficiency during the life cycle. By allowing revisions during the design phase, project teams can ensure that customers' green ambitions beyond regulation compliance can be realized, together with technical and economic requirements. Thus, BIM can provide information to support the calculation of a number of credit points to define goal levels of sustainability related to rating systems. The aim of the paper is to investigate the opportunity to include the "green dimension" in BIM considering the more diffused rating systems.</p

Wearable Health Technology for Preoperative Risk Assessment in Elderly Patients: The WELCOME Study

Preoperative identification of high-risk groups has been extensively studied to improve patients’ outcomes. Wearable devices, which can track heart rate and physical activity data, are starting to be evaluated for patients’ management. We hypothesized that commercial wearable devices (WD) may provide data associated with preoperative evaluation scales and tests, to identify patients with poor functional capacity at increased risk for complications. We conducted a prospective observational study including seventy-year-old patients undergoing two-hour surgeries under general anesthesia. Patients were asked to wear a WD for 7 days before surgery. WD data were compared to preoperatory clinical evaluation scales and with a 6-min walking test (6MWT). We enrolled 31 patients, with a mean age of 76.1 (SD ± 4.9) years. There were 11 (35%) ASA 3–4 patients. 6MWT results averaged 328.9 (SD ± 99.5) m. Daily steps and 2 as recorded using WD and were associated with 6MWT performance (R = 0.56, p = 0.001 and r = 0.58, p = 0.006, respectively) and clinical evaluation scales. This is the first study to evaluate WD as preoperative evaluation tools; we found a strong association between 6MWT, preoperative scales, and WD data. Low-cost wearable devices are a promising tool for the evaluation of cardiopulmonary fitness. Further research is needed to validate WD in this setting

Targeted next-generation sequencing helps to decipher the genetic and phenotypic heterogeneity of hypertrophic cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is mainly associated with myosin, heavy chain 7 (MYH7) and myosin binding protein C, cardiac (MYBPC3) mutations. In order to better explain the clinical and genetic heterogeneity in HCM patients, in this study, we implemented a target-next generation sequencing (NGS) assay. An Ion AmpliSeq Custom Panel for the enrichment of 19 genes, of which 9 of these did not encode thick/intermediate and thin myofilament (TTm) proteins and, among them, 3 responsible of HCM phenocopy, was created. Ninety-two DNA samples were analyzed by the Ion Personal Genome Machine: 73 DNA samples (training set), previously genotyped in some of the genes by Sanger sequencing, were used to optimize the NGS strategy, whereas 19 DNA samples (discovery set) allowed the evaluation of NGS performance. In the training set, we identified 72 out of 73 expected mutations and 15 additional mutations: the molecular diagnosis was achieved in one patient with a previously wild-type status and the pre-excitation syndrome was explained in another. In the discovery set, we identified 20 mutations, 5 of which were in genes encoding non-TTm proteins, increasing the diagnostic yield by approximately 20%: a single mutation in genes encoding non-TTm proteins was identified in 2 out of 3 borderline HCM patients, whereas co-occuring mutations in genes encoding TTm and galactosidase alpha (GLA) altered proteins were characterized in a male with HCM and multiorgan dysfunction. Our combined targeted NGS-Sanger sequencing-based strategy allowed the molecular diagnosis of HCM with greater efficiency than using the conventional (Sanger) sequencing alone. Mutant alleles encoding non-TTm proteins may aid in the complete understanding of the genetic and phenotypic heterogeneity of HCM: co-occuring mutations of genes encoding TTm and non-TTm proteins could explain the wide variability of the HCM phenotype, whereas mutations in genes encoding only the non-TTm proteins are identifiable in patients with a milder HCM status

Multiple Routes and Milestones in the Folding of HIV–1 Protease Monomer

Proteins fold on a time scale incompatible with a mechanism of random search in conformational space thus indicating that somehow they are guided to the native state through a funneled energetic landscape. At the same time the heterogeneous kinetics suggests the existence of several different folding routes. Here we propose a scenario for the folding mechanism of the monomer of HIV–1 protease in which multiple pathways and milestone events coexist. A variety of computational approaches supports this picture. These include very long all-atom molecular dynamics simulations in explicit solvent, an analysis of the network of clusters found in multiple high-temperature unfolding simulations and a complete characterization of free-energy surfaces carried out using a structure-based potential at atomistic resolution and a combination of metadynamics and parallel tempering. Our results confirm that the monomer in solution is stable toward unfolding and show that at least two unfolding pathways exist. In our scenario, the formation of a hydrophobic core is a milestone in the folding process which must occur along all the routes that lead this protein towards its native state. Furthermore, the ensemble of folding pathways proposed here substantiates a rational drug design strategy based on inhibiting the folding of HIV–1 protease

Early mobilisation in critically ill COVID-19 patients: a subanalysis of the ESICM-initiated UNITE-COVID observational study

Background Early mobilisation (EM) is an intervention that may improve the outcome of critically ill patients. There is limited data on EM in COVID-19 patients and its use during the first pandemic wave. Methods This is a pre-planned subanalysis of the ESICM UNITE-COVID, an international multicenter observational study involving critically ill COVID-19 patients in the ICU between February 15th and May 15th, 2020. We analysed variables associated with the initiation of EM (within 72 h of ICU admission) and explored the impact of EM on mortality, ICU and hospital length of stay, as well as discharge location. Statistical analyses were done using (generalised) linear mixed-effect models and ANOVAs. Results Mobilisation data from 4190 patients from 280 ICUs in 45 countries were analysed. 1114 (26.6%) of these patients received mobilisation within 72 h after ICU admission; 3076 (73.4%) did not. In our analysis of factors associated with EM, mechanical ventilation at admission (OR 0.29; 95% CI 0.25, 0.35; p = 0.001), higher age (OR 0.99; 95% CI 0.98, 1.00; p ≤ 0.001), pre-existing asthma (OR 0.84; 95% CI 0.73, 0.98; p = 0.028), and pre-existing kidney disease (OR 0.84; 95% CI 0.71, 0.99; p = 0.036) were negatively associated with the initiation of EM. EM was associated with a higher chance of being discharged home (OR 1.31; 95% CI 1.08, 1.58; p = 0.007) but was not associated with length of stay in ICU (adj. difference 0.91 days; 95% CI − 0.47, 1.37, p = 0.34) and hospital (adj. difference 1.4 days; 95% CI − 0.62, 2.35, p = 0.24) or mortality (OR 0.88; 95% CI 0.7, 1.09, p = 0.24) when adjusted for covariates. Conclusions Our findings demonstrate that a quarter of COVID-19 patients received EM. There was no association found between EM in COVID-19 patients' ICU and hospital length of stay or mortality. However, EM in COVID-19 patients was associated with increased odds of being discharged home rather than to a care facility. Trial registration ClinicalTrials.gov: NCT04836065 (retrospectively registered April 8th 2021)

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

Clinical and organizational factors associated with mortality during the peak of first COVID‑19 wave: the global UNITE‑COVID study.

Purpose: To accommodate the unprecedented number of critically ill patients with pneumonia caused by coronavirus disease 2019 (COVID-19) expansion of the capacity of intensive care unit (ICU) to clinical areas not previously used for critical care was necessary. We describe the global burden of COVID-19 admissions and the clinical and organizational characteristics associated with outcomes in critically ill COVID-19 patients. Methods: Multicenter, international, point prevalence study, including adult patients with SARS-CoV-2 infection confirmed by polymerase chain reaction (PCR) and a diagnosis of COVID-19 admitted to ICU between February 15th and May 15th, 2020. Results: 4994 patients from 280 ICUs in 46 countries were included. Included ICUs increased their total capacity from 4931 to 7630 beds, deploying personnel from other areas. Overall, 1986 (39.8%) patients were admitted to surge capacity beds. Invasive ventilation at admission was present in 2325 (46.5%) patients and was required during ICU stay in 85.8% of patients. 60-day mortality was 33.9% (IQR across units: 20%–50%) and ICU mortality 32.7%. Older age, invasive mechanical ventilation, and acute kidney injury (AKI) were associated with increased mortality. These associations were also confirmed specifically in mechanically ventilated patients. Admission to surge capacity beds was not associated with mortality, even after controlling for other factors Conclusions: ICUs responded to the increase in COVID-19 patients by increasing bed availability and staff, admitting up to 40% of patients in surge capacity beds. Although mortality in this population was high, admission to a surge capacity bed was not associated with increased mortality. Older age, invasive mechanical ventilation, and AKI were identified as the strongest predictors of mortalitypost-print871 K