Altruism in medical education: assessing attitudes of hospital in-patients towards face-to-face contact with medical students during the COVID-19 pandemic

Abstract Background Limited research indicated patients were largely amenable to seeing medical students pre-pandemic. However, the COVID-19 pandemic has highlighted the potential risk of nosocomial transmission and harm to patients from students. Patient opinions regarding these risks remain unexplored, which impacts elicitation of informed consent. We aim to identify these, and explore whether reflection on the risks and benefits of direct student interaction influenced patients’ attitudes. For guidance, we further explored measures to reduce perceived infection risk. Method We designed an original questionnaire for a cross-sectional study, completed by 200 inpatients from 25 wards between 18/02 and 16/03/2022 at Derriford Hospital, Plymouth. Patients in intensive care, with active COVID-19 infection or unable to comprehend the study information were excluded. The responses of a guardian were recorded for inpatients under 16. 17 questions were included - the initial question, reporting willingness to talk with and be examined by students, was repeated following nine questions exploring risks and benefits of student interaction. A further four questions addressed reducing the perceived infection risk. Data is summarised using frequencies and percentages, and with Wilcoxon signed-rank and rank-sum tests of association. Results 85.4% (169/198) of participants gave an initial positive response to seeing medical students, and despite a third of participants changing their response 87.9% (174/197) remained willing after the survey resulting in no significant change. Furthermore, 87.2% (41/47) of those who perceived themselves at severe risk of harm from COVID-19 remained happy to see students. Participants reported reassurance knowing students were: fully vaccinated (76.0%); wearing masks (71.5%); lateral flow test negative within the last week (68.0%) and wearing gloves and gown (63.5%). Conclusion This study demonstrated the willingness of patients to engage in medical education despite recognised risks. Patient reflection on the risks and benefits of student interaction did not significantly reduce numbers willing to see students. Even those perceiving a risk of serious harm remained happy to have direct student contact – a demonstration of altruism in medical education. This suggests informed consent should include discussion of infection control measures, risks and benefits to patients and students, and offer alternatives to direct inpatient contact. </jats:sec

Synthesis and characterization of metal (M=Al or Ga) 2-phosphino (phenolate/benzenethiolate) complexes and their electrochemical behavior in the presence of CO2

A series of Group 13 complexes MLX2 (M = Al or Ga, L = SC6H4-2-PtBu2 or OC6H4-2-PtBu2, X = Me or C6F5) have been synthesized and characterized by multinuclear NMR spectroscopy and single crystal X-ray diffraction. Reactions of Me3Al or Me3Ga with an equivalent of either 2-tBu2P(C6H4)OH (1) or 2-tBu2P(C6H4)SH (5) resulted in the formation of four new (2,3,6, and 7), 4-coordinate dimethyl chelate (S,P or O,P) complexes via methane elimination. The dimethyl gallium complexes (3 and 7) underwent a further reaction with excess B(C6F5)3, and through ligand exchange (methyl/pentafluorophenyl), resulted in the disubstituted bis(pentafluorophenyl) analogs (4 and 8). Cyclic voltammetry (CV) experiments for all compounds in the presence of and the absence of (1–8) CO2 were performed. For compounds showing cathodic reduction waves under CO2 (2,3,4, and 6), bulk electrolysis experiments were performed. Electrochemical studies indicate that, for several compounds, a transient CO2 adduct is formed which undergoes a one-electron, irreversible (or partially irreversible) reduction to form an unstable radical anion

Landslide susceptibility mapping at VAZ watershed (Iran) using an artificial neural network model: a comparison between multilayer perceptron (MLP) and radial basic function (RBF) algorithms

Landslide susceptibility and hazard assessments are the most important steps in landslide risk mapping. The main objective of this study was to investigate and compare the results of two artificial neural network (ANN) algorithms, i.e., multilayer perceptron (MLP) and radial basic function (RBF) for spatial prediction of landslide susceptibility in Vaz Watershed, Iran. At first, landslide locations were identified by aerial photographs and field surveys, and a total of 136 landside locations were constructed from various sources. Then the landslide inventory map was randomly split into a training dataset 70 % (95 landslide locations) for training the ANN model and the remaining 30 % (41 landslides locations) was used for validation purpose. Nine landslide conditioning factors such as slope, slope aspect, altitude, land use, lithology, distance from rivers, distance from roads, distance from faults, and rainfall were constructed in geographical information system. In this study, both MLP and RBF algorithms were used in artificial neural network model. The results showed that MLP with Broyden–Fletcher–Goldfarb–Shanno learning algorithm is more efficient than RBF in landslide susceptibility mapping for the study area. Finally the landslide susceptibility maps were validated using the validation data (i.e., 30 % landslide location data that was not used during the model construction) using area under the curve (AUC) method. The success rate curve showed that the area under the curve for RBF and MLP was 0.9085 (90.85 %) and 0.9193 (91.93 %) accuracy, respectively. Similarly, the validation result showed that the area under the curve for MLP and RBF models were 0.881 (88.1 %) and 0.8724 (87.24 %), respectively. The results of this study showed that landslide susceptibility mapping in the Vaz Watershed of Iran using the ANN approach is viable and can be used for land use planning

Rate-Dependent Nucleation and Growth of NaO2 in Na-O2 Batteries

Understanding the oxygen reduction reaction kinetics in the presence of Na ions and the formation mechanism of discharge product(s) is key to enhancing Na–O2 battery performance. Here we show NaO2 as the only discharge product from Na–O2 cells with carbon nanotubes in 1,2-dimethoxyethane from X-ray diffraction and Raman spectroscopy. Sodium peroxide dihydrate was not detected in the discharged electrode with up to 6000 ppm of H2O added to the electrolyte, but it was detected with ambient air exposure. In addition, we show that the sizes and distributions of NaO2 can be highly dependent on the discharge rate, and we discuss the formation mechanisms responsible for this rate dependence. Micron-sized (∼500 nm) and nanometer-scale (∼50 nm) cubes were found on the top and bottom of a carbon nanotube (CNT) carpet electrode and along CNT sidewalls at 10 mA/g, while only micron-scale cubes (∼2 μm) were found on the top and bottom of the CNT carpet at 1000 mA/g, respectively.Seventh Framework Programme (European Commission) (Marie Curie International Outgoing Fellowship, 2007-2013))National Science Foundation (U.S.) (MRSEC Program, award number DMR-0819762)Robert Bosch GmbH (Bosch Energy Research Network (BERN) Grant)China Clean Energy Research Center-Clean Vehicles Consortium (CERC-CVC) (award number DE-PI0000012)Skolkovo Institute of Science and Technology (Skoltech-MIT Center for Electochemical Energy Storage

Landslide susceptibility mapping using support vector machine and GIS at the Golestan province, Iran

The main goal of this study is to produce landslide susceptibility map using GIS-based support vector machine (SVM) at Kalaleh Township area of the Golestan province, Iran. In this paper, six different types of kernel classifiers such as linear, polynomial degree of 2, polynomial degree of 3, polynomial degree of 4, radial basis function (RBF) and sigmoid were used for landslide susceptibility mapping. At the first stage of the study, landslide locations were identified by aerial photographs and field surveys, and a total of 82 landslide locations were extracted from various sources. Of this, 75% of the landslides (61 landslide locations) are used as training dataset and the rest was used as (21 landslide locations) the validation dataset. Fourteen input data layers were employed as landslide conditioning factors in the landslide susceptibility modelling. These factors are slope degree, slope aspect, altitude, plan curvature, profile curvature, tangential curvature, surface area ratio (SAR), lithology, land use, distance from faults, distance from rivers, distance from roads, topographic wetness index (TWI) and stream power index (SPI). Using these conditioning factors, landslide susceptibility indices were calculated using support vector machine by employing six types of kernel function classifiers. Subsequently, the results were plotted in ArcGIS and six landslide susceptibility maps were produced. Then, using the success rate and the prediction rate methods, the validation process was performed by comparing the existing landslide data with the six landslide susceptibility maps. The validation results showed that success rates for six types of kernel models varied from 79% to 87%. Similarly, results of prediction rates showed that RBF (85%) and polynomial degree of 3 (83%) models performed slightly better than other types of kernel (polynomial degree of 2 = 78%, sigmoid = 78%, polynomial degree of 4 = 78%, and linear = 77%) models. Based on our results, the differences in the rates (success and prediction) of the six models are not really significant. So, the produced susceptibility maps will be useful for general land-use planning

Specific Heat and Magnetoresistance of Y-, Tl- and Bi-Type High-Temperature Superconductors

Specific heat of polycrystalline DyBa$\text{}_{2}$Cu$\text{}_{3}$O$\text{}_{7}$ and Tl$\text{}_{0.58}$Pb $\text{}_{0.42}$Sr$\text{}_{1.6}$Ba$\text{}_{0.4}$Ca$\text{}_{2}$Cu$\text{}_{3}$O$\text{}_{9}$ samples, as well as the single crystal of Bi$\text{}_{2}$Sr$\text{}_{2}$CaCu$\text{}_{2}$O$\text{}_{8}$ have been measured within the temperature interval from 50 to 250 K. For Dy- and Tl-specimens the pronounced jump in specific heat and apparent contribution from Gaussian fluctuations of superconducting order parameter close to T$\text{}_{c}$ have been observed. In contrary, for Bi-specimen only a rounded maximum within a broad interval around T$\text{}_{c}$ has been detected. Magnetoresistance measurements as a function of temperature just below T$\text{}_{c}$ have been carried out for Dy- and Tl-samples and the slopes of upper critical fields have been determined. The data have been analysed within a frame of Ginzburg-Landau-Abrikosov- Gorkov theory with additional Gaussian-like fluctuation term. The electronic specific heat coefficients γ, and the coherence length χ have been obtained