Fabrication and Characterization of Quinary High Entropy-Ultra-High Temperature Diborides” Ceramics

Due to their inherently chemical complexity and their refractory nature, the obtainment of highly dense and single-phase High Entropy (HE) diborides represents a very hard target to achieve. In this framework, homogeneous (Hf0.2Nb0.2Ta0.2Mo0.2Ti0.2)B2, (Hf0.2Zr0.2Ta0.2Mo0.2Ti0.2)B2, and (Hf0.2Zr0.2Nb0.2Mo0.2Ti0.2)B2 ceramics with high relative densities (97.4, 96.5 and 98.2 %, respectively) are successfully produced by Spark Plasma Sintering (SPS) using powders prepared by Self-propagating High-temperature Synthesis (SHS). Although the latter technique does not lead to the complete conversion of initial precursors into the prescribed HE phases, such goal is fully reached after SPS (1950°C/20min/20 MPa). The three HE products show similar, even better in some cases, mechanical properties compared to ceramics with the same nominal composition attained using alternative processing methods. Superior Vickers hardness and elastic modulus values are found for the (Hf0.2Nb0.2Ta0.2Mo0.2Ti0.2)B2 and (Hf0.2Zr0.2Ta0.2Mo0.2Ti0.2)B2 systems, i.e. 28.1 GPa/538.5 GPa and 28.08 GPa/498.1 GPa, respectively, in spite of the correspondingly higher residual po-rosities (1.2 and 2.2 vol.%, respectively). In contrast, the third ceramic, not containing Tantalum, displays lower values of these two properties (25.1 GPa/404.5 GPa). However, the corresponding fracture toughness (8.84 MPa m1/2) is relatively higher. This fact can be likely ascribed to the smaller residual porosity (0.3 vol.%) of the sintered material

Modeling Of Metabolic Heat Regenerated Temperature Swing Adsorption (MTSA) Subassembly For Prototype Design

This paper describes modeling methods for the three core components of a Metabolic heat regenerated Temperature Swing Adsorption (MTSA) subassembly: a sorbent bed, a sublimation (cooling) heat exchanger (SHX), and a condensing icing (warming) heat exchanger (CIHX). The primary function of the MTSA, removing carbon dioxide from a space suit Portable Life Support System (PLSS) ventilation loop, is performed via the sorbent bed. The CIHX is used to heat the sorbent bed for desorption and to remove moisture from the ventilation loop while the SHX is alternately employed to cool the sorbent bed via sublimation of a spray of water at low pressure to prepare the reconditioned bed for the next cycle. This paper describes subsystem heat a mass transfer modeling methodologies relevant to the description of the MTSA subassembly in Thermal Desktop and SINDA/FLUINT. Several areas of particular modeling interest are discussed. In the sorbent bed, capture of the translating carbon dioxide (CO2) front and associated local energy and mass balance in both adsorbing and desorbing modes is covered. The CIHX poses particular challenges for modeling in SINDA/FLUINT as accounting for solids states in fluid submodels are not a native capability. Methods for capturing phase change and latent heat of ice as well as the transport properties across a layer of low density accreted frost are developed. This extended modeling capacity is applicable to temperatures greater than 258 K. To extend applicability to the minimum device temperature of 235 K, a method for a mapped transformation of temperatures from below the limit temperatures to some value above is given along with descriptions for associated material property transformations and the resulting impacts to total heat and mass transfer. Similar considerations are given for the SHX along with functional relationships for areal sublimation rates as limited by flow mechanics in t1he outlet duct

Design and Assembly of an Integrated Metabolic Heat Regenerated Temperature Swing Adsorption (MTSA) Subassembly Engineering Development Unit

Metabolic heat regenerated Temperature Swing Adsorption (MTSA) technology is being developed for thermal and carbon dioxide (CO2) control for a Portable Life Support System (PLSS), as well as water recycling. The core of the MTSA technology is a sorbent bed that removes CO2 from the PLSS ventilation loop gas via a temperature swing. A Condensing Icing Heat eXchanger (CIHX) is used to warm the sorbent while also removing water from the ventilation loop gas. A Sublimation Heat eXchanger (SHX) is used to cool the sorbent. Research was performed to explore an MTSA designed for both lunar and Martian operations. Previously the sorbent bed, CIHX, and SHX had been built and tested individually on a scale relevant to PLSS operations, but they had not been done so as an integrated subassembly. Design and analysis of an integrated subassembly was performed based on this prior experience and an updated transient system model. Focus was on optimizing the design for Martian operations, but the design can also be used in lunar operations. An Engineering Development Unit (EDU) of an integrated MTSA subassembly was assembled based on the design. Its fabrication is discussed. Some details on the differences between the as-assembled EDU and the future flight unit are considered

Simulated Lunar Testing of Metabolic Heat Regenerated Temperature Swing Adsorption Technology

Metabolic heat regenerated Temperature Swing Adsorption (MTSA) technology is being developed for thermal and carbon dioxide (CO2) control for a Portable Life Support System (PLSS), as well as water recycling. An Engineering Development Unit (EDU) of the MTSA subassembly was designed and assembled for optimized Martian operations, but also meets system requirements for lunar operations. For lunar operations the MTSA sorption cycle is driven via a vacuum swing between suit ventilation loop pressure and lunar vacuum. The focus of this effort is operations and testing in a simulated lunar environment. This environment was simulated in Paragon s EHF vacuum chamber. The objective of this testing was to evaluate the full cycle performance of the MTSA Subassembly EDU, and to assess CO2 loading and pressure drop of the wash coated aluminum reticulated foam sorbent bed. The lunar testing proved out the feasibility of pure vacuum swing operation, making MTSA a technology that can be tested and used on the Moon prior to going to Mars. Testing demonstrated better than expected CO2 loading on the sorbent and nearly replicates the equilibrium data from the sorbent manufacturer. This had not been achieved in any of the previous sorbent loading tests performed by Paragon. Subsequently, the increased performance of the sorbent bed design indicates future designs will require less mass and volume than the current EDU rendering MTSA as very competitive for Martian PLSS applications

Design and Assembly of an Integrated Metabolic Heat Regenerated Temperature Swing Adsorption (MTSA) Subassembly Engineering Development Unit

Metabolic heat regenerated Temperature Swing Adsorption (MTSA) technology is being developed for thermal and carbon dioxide (CO2) control for a Portable Life Support System (PLSS), as well as water recycling. The core of the MTSA technology is a sorbent bed that removes CO2 from the PLSS ventilation loop gas via a temperature swing. A Condensing Ice Heat eXchanger (CIHX) is used to warm the sorbent while also removing water from the ventilation loop gas. A Sublimation Heat eXchanger (SHX) is used to cool the sorbent. Research was performed to explore an MTSA designed for both lunar and Martian operations. Previously each the sorbent bed, CIHX, and SHX had been built and tested individually on a scale relevant to PLSS operations, but they had not been done so as an integrated subassembly. Design and analysis of an integrated subassembly was performed based on this prior experience and an updated transient system model. Focus was on optimizing the design for Martian operations, but the design can also be used in lunar operations. An Engineering Development Unit (EDU) of an integrated MTSA subassembly was assembled based on the design. Its fabrication is discussed. Some details on the differences between the as-assembled EDU to the future flight unit are considered

MicroRNA Expression Data Reveals a Signature of Kidney Damage following Ischemia Reperfusion Injury

Ischemia reperfusion injury (IRI) is a leading cause of acute kidney injury, a common problem worldwide associated with significant morbidity and mortality. We have recently examined the role of microRNAs (miRs) in renal IRI using expression profiling. Here we conducted mathematical analyses to determine if differential expression of miRs can be used to define a biomarker of renal IRI. Principal component analysis (PCA) was combined with spherical geometry to determine whether samples that underwent renal injury as a result of IRI can be distinguished from controls based on alterations in miR expression using our data set consisting of time series measuring 571 miRs. Using PCA, we examined whether changes in miR expression in the kidney following IRI have a distinct direction when compared to controls based on the trajectory of the first three principal components (PCs) for our time series. We then used Monte Carlo methods and spherical geometry to assess the statistical significance of these directions. We hypothesized that if IRI and control samples exhibit distinct directions, then miR expression can be used as a biomarker of injury. Our data reveal that the pattern of miR expression in the kidney following IRI has a distinct direction based on the trajectory of the first three PCs and can be distinguished from changes observed in sham controls. Analyses of samples from immunodeficient mice indicated that the changes in miR expression observed following IRI were lymphocyte independent, and therefore represent a kidney intrinsic response to injury. Together, these data strongly support the notion that IRI results in distinct changes in miR expression that can be used as a biomarker of injury

Generator breast datamart\u2014the novel breast cancer data discovery system for research and monitoring: Preliminary results and future perspectives

Background: Artificial Intelligence (AI) is increasingly used for process management in daily life. In the medical field AI is becoming part of computerized systems to manage information and encourage the generation of evidence. Here we present the development of the application of AI to IT systems present in the hospital, for the creation of a DataMart for the management of clinical and research processes in the field of breast cancer. Materials and methods: A multidisciplinary team of radiation oncologists, epidemiologists, medical oncologists, breast surgeons, data scientists, and data management experts worked together to identify relevant data and sources located inside the hospital system. Combinations of open-source data science packages and industry solutions were used to design the target framework. To validate the DataMart directly on real-life cases, the working team defined tumoral pathology and clinical purposes of proof of concepts (PoCs). Results: Data were classified into \u201cNot organized, not \u2018ontologized\u2019 data\u201d, \u201cOrganized, not \u2018ontologized\u2019 data\u201d, and \u201cOrganized and \u2018ontologized\u2019 data\u201d. Archives of real-world data (RWD) identified were platform based on ontology, hospital data warehouse, PDF documents, and electronic reports. Data extraction was performed by direct connection with structured data or text-mining technology. Two PoCs were performed, by which waiting time interval for radiotherapy and performance index of breast unit were tested and resulted available. Conclusions: GENERATOR Breast DataMart was created for supporting breast cancer pathways of care. An AI-based process automatically extracts data from different sources and uses them for generating trend studies and clinical evidence. Further studies and more proof of concepts are needed to exploit all the potentials of this system

A robust method to quantify low molecular weight contaminants in heparin: detection of tris(2-n-butoxyethyl) phosphate

Recently, oversulfated chondroitin sulfate (OSCS) was identified in contaminated heparin preparations, which were linked to several adverse clinical events and deaths. Orthogonal analytical techniques, namely nuclear magnetic resonance (NMR) and capillary electrophoresis (CE), have since been applied by several authors for the evaluation of heparin purity and safety. NMR identification and quantification of residual solvents and non-volatile low molecular contaminants with USP acceptance levels of toxicity was achieved 40-fold faster than the traditional GC-headspace technique, which takes similar to 120 min against similar to 3 min to obtain a (1)H NMR spectrum with a signal/noise ratio of at least 1000/1. the procedure allowed detection of Class 1 residual solvents at 2 ppm and quantification was possible above 10 ppm. 2D NMR techniques (edited-HSQC (1)H/(13)C) permitted visualization of otherwise masked EDTA signals at 3.68/59.7 ppm and 3.34/53.5 ppm, which may be overlapping mononuclear heparin signals, or those of ethanol and methanol. Detailed NMR and ESI-MS/MS studies revealed a hitherto unknown contaminant, tris(2-n-butoxyethyl) phosphate (TBEP), which has potential health risks.Brazilian agency Fundacao AraucariaBrazilian agency FINEP (PRONEX-CARBOIDRATOS, PADCT II/SBIO)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Univ Fed Parana, Dept Bioquim & Biol Mol, BR-81531980 Curitiba, PR, BrazilIst Ric Chim & Biochim G Ronzoni, I-20133 Milan, ItalyUniversidade Federal de São Paulo, Dept Bioquim & Biol Mol, BR-04044020 São Paulo, SP, BrazilUniv Liverpool, Sch Biol Sci, Liverpool L69 7ZB, Merseyside, EnglandUniversidade Federal de São Paulo, Dept Bioquim & Biol Mol, BR-04044020 São Paulo, SP, BrazilWeb of Scienc

A machine-learning parsimonious multivariable predictive model of mortality risk in patients with Covid-19

The COVID-19 pandemic is impressively challenging the healthcare system. Several prognostic models have been validated but few of them are implemented in daily practice. The objective of the study was to validate a machine-learning risk prediction model using easy-to-obtain parameters to help to identify patients with COVID-19 who are at higher risk of death. The training cohort included all patients admitted to Fondazione Policlinico Gemelli with COVID-19 from March 5, 2020, to November 5, 2020. Afterward, the model was tested on all patients admitted to the same hospital with COVID-19 from November 6, 2020, to February 5, 2021. The primary outcome was in-hospital case-fatality risk. The out-of-sample performance of the model was estimated from the training set in terms of Area under the Receiving Operator Curve (AUROC) and classification matrix statistics by averaging the results of fivefold cross validation repeated 3-times and comparing the results with those obtained on the test set. An explanation analysis of the model, based on the SHapley Additive exPlanations (SHAP), is also presented. To assess the subsequent time evolution, the change in paO2/FiO2 (P/F) at 48 h after the baseline measurement was plotted against its baseline value. Among the 921 patients included in the training cohort, 120 died (13%). Variables selected for the model were age, platelet count, SpO2, blood urea nitrogen (BUN), hemoglobin, C-reactive protein, neutrophil count, and sodium. The results of the fivefold cross-validation repeated 3-times gave AUROC of 0.87, and statistics of the classification matrix to the Youden index as follows: sensitivity 0.840, specificity 0.774, negative predictive value 0.971. Then, the model was tested on a new population (n = 1463) in which the case-fatality rate was 22.6%. The test model showed AUROC 0.818, sensitivity 0.813, specificity 0.650, negative predictive value 0.922. Considering the first quartile of the predicted risk score (low-risk score group), the case-fatality rate was 1.6%, 17.8% in the second and third quartile (high-risk score group) and 53.5% in the fourth quartile (very high-risk score group). The three risk score groups showed good discrimination for the P/F value at admission, and a positive correlation was found for the low-risk class to P/F at 48 h after admission (adjusted R-squared = 0.48). We developed a predictive model of death for people with SARS-CoV-2 infection by including only easy-to-obtain variables (abnormal blood count, BUN, C-reactive protein, sodium and lower SpO2). It demonstrated good accuracy and high power of discrimination. The simplicity of the model makes the risk prediction applicable for patients in the Emergency Department, or during hospitalization. Although it is reasonable to assume that the model is also applicable in not-hospitalized persons, only appropriate studies can assess the accuracy of the model also for persons at home