Academic literature on the topic 'Best airpods cleaning kit'

Data collection and cleaning workflows implement highly repetitive but extremely important processes. In this article, we introduce iefieldkit, a package developed to standardize and simplify best practices for high-quality primary data collection across the World Bank’s Development Research Group Impact Evaluations department. iefieldkit automates error-checking for electronic Open Data Kit-based survey modules such as those implemented in SurveyCTO; duplicate checking and resolution; data cleaning, including renaming, labeling, recoding, and survey harmonization; and codebook creation.

Data collection and cleaning workflows implement highly repetitive but extremely important processes. In this article, we describe an update to iefieldkit, a package developed to standardize and simplify best practices for high-quality primary data collection across the World Bank’s Development Impact Evaluation department. The first release of iefieldkit provided workflows to automate error checking for Open Data Kit-based survey modules, duplicate management, data cleaning, and codebook creation. This update to the package includes improved commands to document and implement data point corrections, verify the structure or contents of data using codebooks, and create replicationready data through automated variable subsetting.

Objectives: To evaluate the efficacy of double manual cleaning (DMC) with enzymatic followed by alkaline detergent for removing biofilm on hinged surgical instruments compared to automated cleaning by the washer-disinfector. Methods: Biofilm of Staphylococcus aureus (ATCC 25923) was formed in vitro on hemostatic forceps (Fig. 1). Biofilm-covered forceps were rinsed in distilled water and subjected to one of the following cleaning regimes (n = 5 forceps each): Group 1 forceps were soaked in sterile water for 5 minutes. Group 2-DMC forceps were soaked in enzymatic detergent, brushed 5 times on each face, rinsed with filtrated water (0.2 µm), soaked in alkaline detergent, brushed 5 times each face, rinsed with filtrated water (0.2 µm), and dried with sterile cloth. For group 3-DMC plus hinge inner brushing (n = 5), the forceps were soaked in detergents and brushed as in group 2, including hinge inner brushing (2-mm lumen brush) (Fig. 1). In group 4 (automated cleaning in a washer/disinfector), forceps were prewashed, washed once, washed again, rinsed, thermally rinsed, and dried. After the treatments, forceps were evaluated for microbial load (counting of colony-forming units), residual protein (BCA protein assay kit), and biofilm (scanning electron microscopy). Results: There was no statistically significant differences between the microbial load and protein level contaminating the forceps subjected to DMC (group 2) and the positive control group. The DMC with hinge inner brushing group (group 3) and the automated cleaning group (group 4) demonstrated a significantly reduced microbial load: reduction averages of 2.8 log 10 (P = .038) and 7.6 log10 (P ≤ .001), respectively. The protein level remaining on the forceps also significantly decreased: 2.563 μg (P = .016) and 1,453 μg (P = .001), respectively, compared to the positive control group. There was no statistically significant difference between DMC with hinge inner brushing and automated cleaning (groups 3 and 4) for all of the tests performed. None of the cleaning methods completely removed biofilm and/or soil from the forceps hinge internal region (Fig. 1). Conclusions: Automated cleaning had the best efficacy for removing biofilm. However, DMC with hinge inner brushing was an acceptable alternative cleaning method for sterilizing service units with only manual cleaning available, as is the case in most low- and middle-income countries. Neither automated nor any manual cleaning regimes were able to completely remove biofilm and soil from the forceps hinged area, and the amount of protein left after automated and DMC plus hinge brushing was higher than the recommended. Cleaning is the most important step for the reprocessing of reusable medical devices; thus, efforts must be undertaken to improve cleaning in different social and economic realities and scenarios.Funding: This study was supported by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – CAPES.Disclosures: None

An otorhinolaryngologist (ORL) or general practitioner diagnoses ear disease based on ear image information. However, general practitioners refer patients to ORL for chronic ear disease because the image of ear disease has high complexity, variety, and little difference between diseases. An artificial intelligence-based approach is needed to make it easier for doctors to diagnose ear diseases based on ear image information, such as the Convolutional Neural Network (CNN). This paper describes how CNN was designed to generate CNN models used to classify ear diseases. The model was developed using an ear image dataset from the practice of an ORL at the University of Mataram Teaching Hospital. This work aims to find the best CNN model for classifying ear diseases applicable to android mobile devices. Furthermore, the best CNN model is deployed for an Android-based application integrated with the Endoscope Ear Cleaning Tool Kit for registering patient ear images. The experimental results show 83% accuracy, 86% precision, 86% recall, and 4ms inference time. The application produces a System Usability Scale of 76.88% for testing, which shows it is easy to use. This achievement shows that the model can be developed and integrated into an ENT expert system. In the future, the ENT expert system can be operated by workers in community health centres/clinics to assist leading health them in diagnosing ENT diseases early.

ABSTRACT The requirement for validation of allergen cleaning processes is increasing. The use of lateral flow devices (LFDs) to identify allergens has rapidly expanded, but the best practices for use of these devices are still developing. The goal of this study was to compare commercially available milk-specific LFDs and a general protein identification method. Five milk proteins and seven milk-derived ingredients were tested at several concentrations with eight milk-specific LFDs and a general protein identification kit. Nonfat dry milk (NFDM) was prepared at 100 to 10,000 ppm of milk protein and analyzed by the LFDs to determine the concentration at which a false-negative result (overload concentration or hook effect) was obtained. NFDM was also prepared in 0.025 M phosphate-buffered saline (pH 7.4, 0.85% NaCl) and applied to stainless steel panels (100, 30, 10, or 3 μg of NFDM protein) with various drying methods and sampled with various swab methods to determine the level of detectability. Several total milk LFD kits did not detect whey proteins or whey-derived ingredients. The overload concentration of the various kits ranged from 100 to 10,000 ppm of milk protein. The small dynamic range observed for some kits would necessitate multiple dilutions of a sample to ensure that the result would fall within the range of detection. For swab sampling of stainless steel for LFD analysis, milk protein residues from surfaces onto which the residues were dried with high heat were more difficult to detect than were residues dried with low heat. No differences in sensitivity were observed as a result of moistening the residue or the swab before sampling. These results highlight the importance of understanding the detection capabilities of LFDs as indicated by the variability in the performance of the milk-specific LFDs tested.

Abstract The aim of this study was to compare the efficiency of four final irrigation protocols in smear layer removal and bacterial inhibition in root canal systems. Thirty roots inoculated with Enterococcus faecalis were prepared with ProTaper Universal files. The teeth were disinfected by conventional needle irrigation, sonic agitation using the EndoActivator device, passive ultrasonic irrigation, or an M3 Max file. Teeth with no root canal preparation served as blank controls for the establishment of the infection baseline. Teeth with preparation but no final irrigation served as a post-instrumentation baseline. After the final irrigation, the teeth were sectioned in half. One half of each tooth was examined by scanning electron microscopy (SEM) to assess smear layer removal using a five-point scale. The other half was examined by confocal laser scanning microscopy (CLSM) using the LIVE/DEAD BackLight bacterial viability kit to evaluate the depth of bacterial survival in dentinal tubules. SEM analysis revealed no significant difference in smear layer removal throughout the whole canal among the EA, PUI, and M3 Max groups (P > 0.05). CLSM revealed that PUI achieved the greatest bacterial inhibition depth in the coronal ((174.27 ± 31.63) μm), middle ((160.94 ± 37.77) μm), and apical ((119.53 ± 28.49) μm) thirds of the canal (all P < 0.05 vs. other groups). According to this comprehensive SEM and CLSM evaluation, PUI appears to have the best infection control ability in root canal systems.

Total Airport Management (TAM) is an operational concept that brings together the main stakeholders at an airport to collaboratively develop a plan for the airport operations, the so-called Airport Operations Plan (AOP). This AOP contains scheduled and targeted times as well as resource plans for all flight operations related events such as on- or off-block times, runway scheduling, ground handling resource planning, etc. In case of a disruptive event at the airport the stakeholders will collaboratively develop a strategy on how to best solve the issues such that the impact on operations overall is minimized. Such disruptive events could for example be related to weather, strikes, or security incidents.The flight related processes, e.g. stand/gate planning, turnaround planning, runway assignments, flight scheduling are forming a closely-knit process network. Each of these processes is under the control of one of the stakeholders, and therefore it is very difficult for them to predict the effect of any decision they might take on the overall operations. This necessitates a process on how to analyze the effects of different stakeholder options on the overall operation before actually taking a decision and activating it in the AOP. This process is called a what-if process and is seen as a vital enabler for an efficient and successful TAM. In the context of the SESAR TAM projects an operational concept for performance-based airport management in case of meteorologically caused disruptions has been developed along with tool prototypes to support the operators with what-if functionality. In the latest TAM project (PJ04) this concept has been validated to reach Technology Readiness Level (TRL) 6 (prototype in operational environment) and both performance and human factors benefits of the approach have been demonstrated with a validation exercise taking place in the Airport Control and Operations Center Simulator (ACCES) at DLR in Braunschweig with operational experts from Oslo Gardermoen airport. In this validation exercise the stakeholders were confronted with a forecasted winter weather situation typical for the airport, where de-icing and runway cleaning is required. The weather forecast and actual weather conditions during the exercise were based on real recorded data and a newly developed MET alerting service provided de-icing and snow alerts with increased accuracy as the actual time of the event approached. The first section of the paper will address the operational concept with a focus on the use of the what-if functionality. Then a brief description of the newly developed tools is given, explaining processes and essential tools enabling effective what-if analysis, the prediction algorithm required to perform what-if analysis and the graphical user interface for the interaction between stakeholders and tools. Next the validation exercise will be described, followed by a presentation of the results, which clearly show benefits both in airport performance as well as the human factors situation awareness, workload, trust, teamwork and usability. Especially the human factors related results, which have been obtained both from standard and bespoke questionnaires, will be explained in detail. The results obtained from the exercise are very promising and suggestions for future research will be provided to conclude the paper.