Extensive reuse of soda-lime waste glass in fly ash-based geopolymers

The possibility of extensive incorporation of soda-lime waste glass in the synthesis of fly ash-based geopolymers was investigated. Using waste glass as silica supplier avoids the use of water glass solution as chemical activator. The influence of the addition of waste glass on the microstructure and strength of fly ash-based geopolymers was studied through microstructural and mechanical characterization. Leaching analyses were also carried out. The samples were developed changing the SiO2/Al2O3 molar ratio and the molarity of the sodium hydroxide solution used as alkaline activator. The results suggest that increasing the amount of waste glass as well as increasing the molarity of the solution lead to the formation of zeolite crystalline phases and an improvement of the mechanical strength. Leaching results confirmed that the new geopolymers have the capability to immobilize heavy metal ions

Organizational health and quality of life: survey among ambulance nurses in prehospital emergency care

Background: The workplace plays a central role in causing stress and different kinds of syndromes and diseases. More generally, organizational procedures and practices could have an impact on nurses’ quality of life. Although several studies have investigated this link, none of them considered nurses working in prehospital emergency care. Objectives: To investigate the role of organizational health factors that affect the quality of life and psychosomatic complaints of ambulance nurses.Method: Our sample included 411 ambulance nurses. Workers were administered two questionnaires to assess organizational health and quality of life. Descriptive and correlational analyses were used to test our assumptions. Conclusion: Several organizational health dimensions provided an explanation for the complaints reported by nurses working in prehospital emergency care in terms of quality of life and psychosomatic disorders.The results allowed identification of possible interventions focusing on specific duties and organizational aspects that would improve the quality of nurses’ health

ESICM LIVES 2017 : 30th ESICM Annual Congress. September 23-27, 2017.

INTRODUCTION. Unplanned readmission to intensive care is highly undesirable in that it contributes to increased variance in care, disruption, difficulty in resource allocation and may increase length of stay and mortality particularly if subject to delays. Unlike the ICU admission from the ward, readmission prediction has received relatively little attention, perhaps in part because at the point of ICU discharge, full physiological information is systematically available to the clinician and so it is expected that readmission should be largely due to unpredictable factors. However it may be that there are multidimensional trends that are difficult for the clinician to perceive that may nevertheless be predictive of readmission. OBJECTIVES. We investigated whether machine learning (ML) techniques could be used to improve on the simple published SWIFT score [1] for the prediction of unplanned readmission to ICU within 48 hours. METHODS. We extracted systolic BP, pulse pressure, heart and respiration rate, temperature, SpO2, bilirubin, creatinine, INR, lactate, white cell count, platelet count, pH, FiO2, and total Glasgow Coma Score from ICU stays of over 2000 adult patients from our hospital electronic patient record system. We trained our own custom multidimensional / time-sensitive algorithmic ML system to predict failed discharges defined as either readmission or unexpected death within 48 hours of discharge. We used 10-fold cross validation to assess performance. We also assessed the effect of augmenting our system by transfer learning (TL) with 44,000 additional cases from the MIMIC III database. RESULTS. The SWIFT score performed relatively poorly with an AUROC of around 0.6 which our ML system trained on local data was also able to match. However when augmented with an additional dataset by TL, the AUROC for the ML system improved statistically and clinically significantly to over 0.7. CONCLUSIONS. Machine learning is able to improve on predictors based on simple multiple logistic regression. Thus there is likely to be information in the trends and in combinations of variables. A disadvantage with this technique is that ML approaches require large amounts of data for training. However, ML approaches can be improved by TL. Basing prediction models on locally derived data augmented by TL is a potentially novel approach to generating tools that customised to the institution yet can exploit the potential power of ML algorithms. REFERENCES [1] Gajic O, Malinchoc M, Comfere TB, et al. The Stability and Workload Index for Transfer score predicts unplanned intensive care unit patient readmission: initial development and validation. Crit Care Med. 2008;36(3):676–82. Grant Acknowledgement This work was internally funded

DNA vaccines against ErbB2+ Carcinomas: From mice to humans.

DNA vaccination exploits a relatively simple and flexible technique to generate an immune response against microbial and tumor-associated antigens (TAAs). Its effectiveness is enhanced by the application of an electrical shock in the area of plasmid injection (electroporation). In our studies we exploited a sophisticated electroporation device approved for clinical use (Cliniporator, IGEA, Carpi, Italy). As the target antigen is an additional factor that dramatically modulates the efficacy of a vaccine, we selected ErbB2 receptor as a target since it is an ideal oncoantigen. It is overexpressed on the cell membrane by several carcinomas for which it plays an essential role in driving their progression. Most oncoantigens are self-tolerated molecules. To circumvent immune tolerance we generated two plasmids (RHuT and HuRT) coding for chimeric rat/human ErbB2 proteins. Their immunogenicity was compared in wild type mice naturally tolerant for mouse ErbB2, and in transgenic mice that are also tolerant for rat or human ErbB2. In several of these mice, RHuT and HuRT elicited a stronger anti-tumor response than plasmids coding for fully human or fully rat ErbB2. The ability of heterologous moiety to blunt immune tolerance could be exploited to elicit a significant immune response in patients. A clinical trial to delay the recurrence of ErbB2+ carcinomas of the oral cavity, oropharynx and hypopharynx is awaiting the approval of the Italian authorities

Detection of lithium plating in li-ion cell anodes using realistic automotive fast-charge profiles

The widespread use of electric vehicles is nowadays limited by the “range anxiety” of the customers. The drivers’ main concerns are related to the kilometric range of the vehicle and to the charging time. An optimized fast-charge profile can help to decrease the charging time, without degrading the cell performance and reducing the cycle life. One of the main reasons for battery capacity fade is linked to the Lithium plating phenomenon. This work investigates two methodologies, i.e., three-electrode cell measurement and internal resistance evolution during charging, for detecting the Lithium plating conditions. From this preliminary analysis, it was possible to develop new Multi-Stage Constant-Current profiles, designed to improve the performance in terms of charging time and cells capacity retention with respect to a reference profile. Four new profiles were tested and compared to a reference. The results coming from the new profiles demonstrate a simultaneous improvement in terms of charging time and cycling life, showing the reliability of the implemented methodology in preventing Lithium plating

Modeling heat transport in crystals and glasses from a unified lattice-dynamical approach

We introduce a novel approach to model heat transport in solids, based on the Green-Kubo theory of linear response. It naturally bridges the Boltzmann kinetic approach in crystals and the Allen-Feldman model in glasses, leveraging interatomic force constants and normal-mode linewidths computed at mechanical equilibrium. At variance with molecular dynamics, our approach naturally and easily accounts for quantum mechanical effects in energy transport. Our methodology is carefully validated against results for crystalline and amorphous silicon from equilibrium molecular dynamics and, in the former case, from the Boltzmann transport equation