Retention of Student Pharmacists\u27 Knowledge and Skills Regarding Overdose Management with Naloxone

BACKGROUND: Overdose education and naloxone training was recently implemented into the required curriculum of the College of Pharmacy at the University of Rhode Island. The objective of this study was to compare the retention of knowledge between student pharmacists who received a didactic lecture only versus student pharmacists who received the same lecture plus a skills-based objective structured clinical examination (OSCE) with a standardized patient actor. METHODS: Students in their first-professional year (P1) of the Doctor of Pharmacy program (n = 129) and students in their second-professional (P2) year (n = 123) attended a required lecture on opioid overdose, including detailed naloxone training. P2 students were additionally required to participate in an OSCE assessment following the didactic lecture component. An anonymous, voluntary survey was offered to all students approximately 6 months later. A Chi-Square or Fisher\u27s Exact Test was performed on the survey responses to assess any difference in the responses between the two groups. RESULTS: A total of 99 P1 students (76.7%) and 116 P2 students (94.3%) completed the survey. P1 students were found to be more knowledgeable regarding the duration of naloxone action and identification of risk factors for opioid overdose. P2 students were found to be more knowledgeable regarding non-medical ways patients may obtain opioids and the correct order of emergency response during a suspected opioid overdose... Conclusions: P2 students did not demonstrate superior retention of information regarding naloxone and opioid use disorder on survey questions compared with P1 students. There was a trend towards P2 students feeling more confident in their ability to counsel patients for overdose prevention and reporting disagreement with the statement that overdose prevention for people who use drugs is a waste of time and money compared with the P1 students, but these did not reach statistical significance. Since the opioid crisis continues unabated, naloxone training using OSCE and didactic methods remain an on-going required part of the pharmacy curriculum

The PRISMA Hand II: A Sensorized Robust Hand for Adaptive Grasp and In-Hand Manipulation

Although substantial progresses have been made in building anthropomorphic robotic hands, lack of mechanical robustness, dexterity and force sensation still restrains wide adoption of robotic prostheses. This paper presents the design and preliminary evaluation of the PRISMA hand II, which is a mechanically robust anthropomorphic hand developed at the PRISMA Lab of University of Naples Federico II. The hand is highly underactuated, as the 19 finger joints are driven by three motors via elastic tendons. Nevertheless, the hand can performs not only adaptive grasps but also in-hand manipulation. The hand uses rolling contact joints, which is compliant in multiple directions. Force sensor are integrated to each fingertip in order to provide force feedback during grasping and manipulation. Preliminary experiments have been performed to evaluate the hand. Results show that the hand can perform various grasps and in-hand manipulation, while the structure can withstand severe disarticulation. This suggests that the proposed design can be a viable solution for robust and dexterous prosthetic hands

Calibration of tactile/force sensors for grasping with the PRISMA Hand II

The PRISMA Hand II is a mechanically robust anthropomorphic hand developed at PRISMA Lab, University of Naples Federico II. The hand is highly underactuated, three motors drive 19 joints via elastic tendons. Thanks to its particular mechanical design, the hand can perform not only adaptive grasps but also in-hand manipulation. Each fingertip integrates a tactile/force sensor, based on optoelectronic technology, to provide tactile/force feedback during grasping and manipulation, particularly useful with deformable objects. The paper briefly describes the mechanical design and sensor technology of the hand and proposes a calibration procedure for tactile/force sensors. A comparison between different models of Neural Networks architectures, suitable for sensors calibration, is shown. Experimental tests are provided to choose the optimal tactile sensing suite. Finally, experiments for the regulation of the forces are made to show the effectiveness of calibrated sensors

Dry acellular oesophageal matrix prepared by supercritical carbon dioxide

The research leading to these results received funding from Cassa di Risparmio di Trento e Rovereto (CaRiTRo) within the research project “Supercritical decellularization of engineered tissues for clinical application”, biomedical science section, 2013. PDC is supported by NIHR Professorship and the Catapult Cell Therapy, UK. NMP is supported by the European Research Council (ERC StG Ideas 2011 BIHSNAM no. 279985 on ‘Bio-Inspired hierarchical super-nanomaterials’, ERC PoC 2013 KNOTOUGH no. 632277 on ‘Super-tough knotted fibres’, ERC PoC 2015 SILKENE no. 693670 on ’Bionic silk with graphene or other nanomaterials spun by silkworms’) and by the European Commission under the Graphene Flagship (WP14 ‘Polymer Composites’, no. 696656). NE thanks Lorenza Lazzari for the donation of BM-MSCs from the Cell Factory Bank (Milan-Italy)

Planning of efficient trajectories in robotized assembly of aerostructures exploiting kinematic redundancy

Aerospace production volumes have increased over time and robotic solutions have been progressively introduced in the aeronautic assembly lines to achieve high-quality standards, high production rates, flexibility and cost reduction. Robotic workcells are sometimes characterized by robots mounted on slides to increase the robot workspace. The slide introduces an additional degree of freedom, making the system kinematically redundant, but this feature is rarely used to enhance performances. The paper proposes a new concept in trajectory planning, that exploits the redundancy to satisfy additional requirements. A dynamic programming technique is adopted, which computes optimized trajectories, minimizing or maximizing the performance indices of interest. The use case is defined on the LABOR (Lean robotized AssemBly and cOntrol of composite aeRostructures) project which adopts two cooperating six-axis robots mounted on linear axes to perform assembly operations on fuselage panels. Considering the needs of this workcell, unnecessary robot movements are minimized to increase safety, the mechanical stiffness is maximized to increase stability during the drilling operations, collisions are avoided, while joint limits and the available planning time are respected. Experiments are performed in a simulation environment, where the optimal trajectories are executed, highlighting the resulting performances and improvements with respect to non-optimized solutions

Discrete fully probabilistic design: towards a control pipeline for the synthesis of policies from examples

We present the principled design of a control pipeline for the synthesis of policies from examples data. The pipeline, based on a discretized design, expounds the algorithm introduced in [1] to synthesize policies from examples for constrained, stochastic and nonlinear systems. The pipeline: (i) does not need the constraints to be fulfilled in the possibly noisy example data; (ii) enables control synthesis even when the data are collected from an example system that is different from the one under control. The design is benchmarked on an example that involves controlling an inverted pendulum with actuation constraints. The data that are used to synthesize the policy are collected from a pendulum that: (i) is different from the one under control; (ii) does not satisfy the actuation constraints

Time-Optimal Trajectory Planning With Interaction With the Environment

Optimal motion planning along prescribed paths can be solved with several techniques, but most of them do not take into account the wrenches exerted by the end-effector when in contact with the environment. When a dynamic model of the environment is not available, no consolidated methodology exists to consider the effect of the interaction. Regardless of the specific performance index to optimize, this article proposes a strategy to include external wrenches in the optimal planning algorithm, considering the task specifications. This procedure is instantiated for minimum-time trajectories and validated on a real robot performing an interaction task under admittance control. The results prove that the inclusion of end-effector wrenches affects the planned trajectory, in fact modifying the manipulator's dynamic capability