Automation of an atomic force microscope via Arduino

The Dimension 3000 AFM used in this work was kindly donated by Prof. Nicholas D. Spencer, and facilitated by Prof. Lucio Isa, and Dr. Shivaprakash N. Ramakrishna, from ETH-Zurich. We thank Prof. David Cuartielles for encouraging us to publish this work in this special issue on Arduino Science Hardware. We also thank Llorenc Mercadal Fernandez for frutiful discussions and ideas, and the BiblioMaker unit in the Faculty of Sciences of the University of Granada for their help in 3D printing the gears used here. MAFR acknowledges support by the project PID2020-116615RA-I00 funded by MCIN/AEI/10.13039/501100011033, and the EMERGIA grant with reference EMC21_00008 funded by Consejeria de Universidad, Investigacion e Innovacion de la Junta de Andalucia, and by FEDER "ERDF A way of making Europe". JGGF and CLMM acknowledge support from grant A1S35536 by Conacyt Mexico.The Atomic Force Microscopy is a very versatile technique that allows to characterize surfaces by acquiring topographies with sub-nanometer resolution. This technique often overcomes the problems and capabilities of electron microscopy when characterizing few nanometers thin coatings over solid substrates. They are expensive, in the half million dollar range for standard units, and therefore it is often difficult to upgrade to new units with improved characteristics. One of these improvements, motorization and automation of the measurements is very interesting to sample different parts of a substrate in an unattended way. Here we report a low cost upgrade under 60 $ to a Dimension 3000 AFM based on a control unit using an Arduino Leonardo. It enables to acquire dozens or hundreds of images automatically by mimicking keyboard shortcuts and interfacing the AFM PCI card.MCIN/AEI PID2020-116615RA-I00Consejeria de Universidad, Investigacion e Innovacion de la Junta de Andalucia EMC21_00008Marie Curie ActionsConsejo Nacional de Ciencia y Tecnologia (CONACyT) A1S3553

Compilación de Proyectos de Investigacion de 1984-2002

Instituto Politecnico Nacional. UPIICS

Evolution over Time of Ventilatory Management and Outcome of Patients with Neurologic Disease∗

OBJECTIVES: To describe the changes in ventilator management over time in patients with neurologic disease at ICU admission and to estimate factors associated with 28-day hospital mortality. DESIGN: Secondary analysis of three prospective, observational, multicenter studies. SETTING: Cohort studies conducted in 2004, 2010, and 2016. PATIENTS: Adult patients who received mechanical ventilation for more than 12 hours. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Among the 20,929 patients enrolled, we included 4,152 (20%) mechanically ventilated patients due to different neurologic diseases. Hemorrhagic stroke and brain trauma were the most common pathologies associated with the need for mechanical ventilation. Although volume-cycled ventilation remained the preferred ventilation mode, there was a significant (p < 0.001) increment in the use of pressure support ventilation. The proportion of patients receiving a protective lung ventilation strategy was increased over time: 47% in 2004, 63% in 2010, and 65% in 2016 (p < 0.001), as well as the duration of protective ventilation strategies: 406 days per 1,000 mechanical ventilation days in 2004, 523 days per 1,000 mechanical ventilation days in 2010, and 585 days per 1,000 mechanical ventilation days in 2016 (p < 0.001). There were no differences in the length of stay in the ICU, mortality in the ICU, and mortality in hospital from 2004 to 2016. Independent risk factors for 28-day mortality were age greater than 75 years, Simplified Acute Physiology Score II greater than 50, the occurrence of organ dysfunction within first 48 hours after brain injury, and specific neurologic diseases such as hemorrhagic stroke, ischemic stroke, and brain trauma. CONCLUSIONS: More lung-protective ventilatory strategies have been implemented over years in neurologic patients with no effect on pulmonary complications or on survival. We found several prognostic factors on mortality such as advanced age, the severity of the disease, organ dysfunctions, and the etiology of neurologic disease