The mTOR regulated RNA-binding protein LARP1 requires PABPC1 for guided mRNA interaction.

The mammalian target of rapamycin (mTOR) is a critical regulator of cell growth, integrating multiple signalling cues and pathways. Key among the downstream activities of mTOR is the control of the protein synthesis machinery. This is achieved, in part, via the co-ordinated regulation of mRNAs that contain a terminal oligopyrimidine tract (TOP) at their 5'ends, although the mechanisms by which this occurs downstream of mTOR signalling are still unclear. We used RNA-binding protein (RBP) capture to identify changes in the protein-RNA interaction landscape following mTOR inhibition. Upon mTOR inhibition, the binding of LARP1 to a number of mRNAs, including TOP-containing mRNAs, increased. Importantly, non-TOP-containing mRNAs bound by LARP1 are in a translationally-repressed state, even under control conditions. The mRNA interactome of the LARP1-associated protein PABPC1 was found to have a high degree of overlap with that of LARP1 and our data show that PABPC1 is required for the association of LARP1 with its specific mRNA targets. Finally, we demonstrate that mRNAs, including those encoding proteins critical for cell growth and survival, are translationally repressed when bound by both LARP1 and PABPC1.

Trainable segmentation for transmission electron microscope images of inorganic nanoparticles.

We present a trainable segmentation method implemented within the python package ParticleSpy. The method takes user labelled pixels, which are used to train a classifier and segment images of inorganic nanoparticles from transmission electron microscope images. This implementation is based on the trainable Waikato Environment for Knowledge Analysis (WEKA) segmentation, but is written in python, allowing a large degree of flexibility and meaning it can be easily expanded using other python packages. We find that trainable segmentation offers better accuracy than global or local thresholding methods and requires as few as 100 user-labelled pixels to produce an accurate segmentation. Trainable segmentation presents a balance of accuracy and training time between global/local thresholding and neural networks, when used on transmission electron microscope images of nanoparticles. We also quantitatively investigate the effectiveness of the components of trainable segmentation, its filter kernels and classifiers, in order to demonstrate the use cases for the different filter kernels in ParticleSpy and the most accurate classifiers for different data types. A set of filter kernels is identified that are effective in distinguishing particles from background but that retain dissimilar features. In terms of classifiers, we find that different classifiers perform optimally for different image contrast; specifically, a random forest classifier performs best for high-contrast ADF images, but that QDA and Gaussian Naïve Bayes classifiers perform better for low-contrast TEM images.

Measurement of the size, shape and composition of nanoparticles is routinely performed using transmission electron microscopy and related techniques. Typically, distinguishing particles from the background in an image is performed using the intensity of each pixel, creating two sets of pixels to separate particles from background. However, this separation of intensity can be difficult if the contrast in an image is low, or if the intensity of the background varies significantly. In this study, an approach that takes into account additional image features (such as boundaries and texture) was investigated to study electron microscope images of metallic nanoparticles. In this 'trainable segmentation' approach, the user labels examples of particle and background pixels in order to train a machine learning algorithm to distinguish between particles and background. The performance of different machine learning algorithms was investigated, in addition to the effect of using different features to aid the segmentation. Overall, a trainable segmentation approach was found to perform better than use of an intensity threshold to distinguish between particles and background in electron microscope images.

Evaluation of a preoperative personalized risk communication tool: a prospective before-and-after study.

PURPOSE: Patients want personalized information before surgery; most do not receive personalized risk estimates. Inadequate information contributes to poor experience and medicolegal complaints. We hypothesized that exposure to the Personalized Risk Evaluation and Decision Making in Preoperative Clinical Assessment (PREDICT) app, a personalized risk communication tool, would improve patient knowledge and satisfaction after anesthesiology consultations compared with standard care. METHODS: We conducted a prospective clinical study (before-after design) and used patient-reported data to calculate personalized risks of morbidity, mortality, and expected length of stay using a locally calibrated National Surgical Quality Improvement Program risk calculator embedded in the PREDICT app. In the standard care (before) phase, the application's materials and output were not available to participants; in the PREDICT app (after) phase, personalized risks were communicated. Our primary outcome was knowledge score after the anesthesiology consultation. Secondary outcomes included patient satisfaction, anxiety, feasibility, and acceptability. RESULTS: We included 183 participants (90 before; 93 after). Compared with standard care phase, the PREDICT app phase had higher post-consultation: knowledge of risks (14.3% higher; 95% confidence interval [CI], 6.5 to 22.0; P < 0.001) and satisfaction (0.8 points; 95% CI, 0.1 to 1.4; P = 0.03). Anxiety was unchanged (- 1.9%; 95% CI, - 4.2 to 0.5; P = 0.13). Acceptability was high for patients and anesthesiologists. CONCLUSION: Exposure to a patient-facing, personalized risk communication app improved knowledge of personalized risk and increased satisfaction for adults before elective inpatient surgery. TRIAL REGISTRATION: www.clinicaltrials.gov (NCT03422133); registered 5 February 2018.

RéSUMé: OBJECTIF: Les patients veulent disposer d'informations personnalisées avant leur chirurgie, mais la plupart d'entre eux ne reçoivent pas d'estimations de leur risque personnalisées. Des informations inadéquates contribuent à une mauvaise expérience et à des plaintes médicolégales. Nous avons émis l'hypothèse qu'une exposition à l'application PREDICT (Personalized Risk Evaluation and Decision Making in Preoperative Clinical Assessment), un outil de communication du risque personnalisé, améliorerait les connaissances et la satisfaction des patients après leurs consultations en anesthésiologie comparativement à des soins standard. MéTHODE: Nous avons réalisé une étude clinique prospective (de type avant-après) et utilisé les données rapportées par les patients afin de calculer leur risque personnalisé de morbidité et de mortalité, ainsi que la durée de séjour anticipée à l'aide d'un calculateur de risque tiré du Programme national d'amélioration de la qualité chirurgicale que nous avons calibré localement et intégré à l'application PREDICT. Dans la phase de soins standard (avant), le contenu et les résultats de l'application n'étaient pas divulgués aux participants; dans la phase comportant l'application PREDICT (après), les risques personnalisés étaient communiqués. Notre critère d'évaluation principal était le score des connaissances des patients après la consultation en anesthésiologie. Les critères d'évaluation secondaires comprenaient la satisfaction des patients et leur niveau d'anxiété ainsi que la faisabilité et l'acceptabilité d'une telle approche. RéSULTATS: Nous avons inclus 183 participants (90 avant; 93 après). Comparativement à la phase de soins standard, la phase avec l'application PREDICT a démontré un niveau plus élevé de connaissances des risques post consultation (14,3 % plus élevé; intervalle de confiance [IC] 95 %, 6,5 à 22,0; P < 0,001) et de satisfaction (0,8 point; IC 95 %, 0,1 à 1,4; P = 0,03). L'anxiété est demeurée inchangée (− 1,9 %; IC 95 %, − 4,2 à 0,5; P = 0,13). L'acceptabilité était élevée, tant chez les patients que chez les anesthésiologistes. CONCLUSION: L'exposition des patients à une application de communication du risque personnalisé a amélioré leurs connaissances de leur risque personnalisé et augmenté la satisfaction des adultes avant une chirurgie non urgente et non ambulatoire. ENREGISTREMENT DE L'éTUDE: www.clinicaltrials.gov (NCT03422133); enregistrée le 5 février 2018.

Modernizing Immunization Practice Through the Use of Cloud Based Platforms.

Collection of timely and accurate immunization information is essential for effective immunization programs. Current immunization information systems have important limitations that impact the ability to collect this data. Based on our experience releasing a national immunization app we describe a cloud-based platform that would allow individuals to store their records digitally and exchange these records with public health information systems thus improving the quality of immunization information held by individuals and public health officials.

Using Mobile Apps to Communicate Vaccination Records: A City-wide Evaluation with a National Immunization App, Maternal Child Registry and Public Health Authorities.

Medicine is experiencing a paradigm shift, where patients are increasingly involved in the management of their health data. We created a mobile app which permitted parental reporting of immunization status to public health authorities. We describe app use as a proxy for feasibility and acceptability as well as data utility for public health surveillance. The evaluation period ran from April 27, 2015, to April 18, 2017, during which time 2,653 unique children's records were transmitted, containing 36,105 vaccinations. Our findings suggest that mobile immunization reporting is feasible and may be an acceptable complement to existing reporting methods. Measures of data utility suggest that mobile reporting could enable more accurate assessments of vaccine coverage.

Feasibility and Limitations of Vaccine Two-Dimensional Barcoding Using Mobile Devices.

BACKGROUND: Two-dimensional (2D) barcoding has the potential to enhance documentation of vaccine encounters at the point of care. However, this is currently limited to environments equipped with dedicated barcode scanners and compatible record systems. Mobile devices may present a cost-effective alternative to leverage 2D vaccine vial barcodes and improve vaccine product-specific information residing in digital health records. OBJECTIVE: Mobile devices have the potential to capture product-specific information from 2D vaccine vial barcodes. We sought to examine the feasibility, performance, and potential limitations of scanning 2D barcodes on vaccine vials using 4 different mobile phones. METHODS: A unique barcode scanning app was developed for Android and iOS operating systems. The impact of 4 variables on the scan success rate, data accuracy, and time to scan were examined: barcode size, curvature, fading, and ambient lighting conditions. Two experimenters performed 4 trials 10 times each, amounting to a total of 2160 barcode scan attempts. RESULTS: Of the 1832 successful scans performed in this evaluation, zero produced incorrect data. Five-millimeter barcodes were the slowest to scan, although only by 0.5 seconds on average. Barcodes with up to 50% fading had a 100% success rate, but success rate deteriorated beyond 60% fading. Curved barcodes took longer to scan compared with flat, but success rate deterioration was only observed at a vial diameter of 10 mm. Light conditions did not affect success rate or scan time between 500 lux and 20 lux. Conditions below 20 lux impeded the device's ability to scan successfully. Variability in scan time was observed across devices in all trials performed. CONCLUSIONS: 2D vaccine barcoding is possible using mobile devices and is successful under the majority of conditions examined. Manufacturers utilizing 2D barcodes should take into consideration the impact of factors that limit scan success rates. Future studies should evaluate the effect of mobile barcoding on workflow and vaccine administrator acceptance.

Fabricating patient-specific 3D printed drill guides to treat femoral head avascular necrosis.

BACKGROUND: Femoral head avascular necrosis (AVN), or death of femoral head tissue due to a lack of blood supply, is a leading cause of total hip replacement for non-geriatric patients. Core decompression (CD) is an effective treatment to re-establish blood flow for patients with AVN. Techniques aimed at improving its efficacy are an area of active research. We propose the use of 3D printed drill guides to accurately guide therapeutic devices for CD. METHODS: Using femur sawbones, image processing software, and 3D modeling software, we created a custom-built device with pre-determined drill trajectories and tested the feasibility of the 3D printed drill guides for CD. A fellowship trained orthopedic surgeon used the drill guide to position an 8 ga, 230 mm long decompression device in the three synthetic femurs. CT scans were taken of the sawbones with the drill guide and decompression device. CT scans were processed in the 3D modeling software. Descriptive statistics measuring the angular and needle-tip deviation were compared to the original virtually planned model. RESULTS: Compared to the original 3D model, the trials had a mean displacement of 1.440 ± 1.03 mm and a mean angle deviation of 1.093 ± 0.749º. CONCLUSIONS: The drill guides were demonstrated to accurately guide the decompression device along its predetermined drill trajectory. Accuracy was assessed by comparing values to literature-reported values and considered AVN lesion size. This study demonstrates the potential use of 3D printing technology to improve the efficacy of CD techniques.

Using Computed Tomography-Based Three-dimensional Modeling and Computer Navigation for Minimally Invasive Core Decompression and Adjuvant Orthobiologic Therapy of Femoral Head Avascular Necrosis.

Avascular necrosis of the femoral head is a debilitating condition that can lead to femoral head collapse. Core decompression with adjuvant cellular therapies, such as bone marrow aspirate concentrate, delays disease progression and improves outcomes. However, inconsistent results in the literature may be due to limitations in surgical technique and difficulty in targeting the necrotic lesions. Here, we present a surgical technique utilizing computed tomography-based three-dimensional modeling and instrument tracking to guide the therapy to the center of the lesion. This method minimizes the number of attempts to reach the lesion and confirms the three-dimensional positioning of the instrumentation within the lesion. Our technique may improve the outcomes of core decompression and adjuvant therapy and prevent or delay hip collapse in patients with femoral head avascular necrosis.

Fabricating Patient-Specific 3D Printed Drill Guides to Treat Femoral Head Avascular Necrosis.

Background: Femoral head avascular necrosis (AVN), or death of femoral head tissue due to a lack of blood supply, is a leading cause of total hip replacement for non-geriatric patients. Core decompression (CD) is an effective treatment to re-establish blood flow for patients with AVN. Techniques aimed at improving its efficacy are an area of active research. We propose the use of 3D printed drill guides to accurately guide therapeutic devices for CD. Methods: Using femur sawbones, image processing software, and 3D modeling software, we created a custom-built device with pre-determined drill trajectories and tested the feasibility of the 3D printed drill guides for CD. A fellowship trained orthopedic surgeon used the drill guide to position an 8 ga, 230 mm long decompression device in the three synthetic femurs. CT scans were taken of the sawbones with the drill guide and decompression device. CT scans were processed in the 3D modeling software. Descriptive statistics measuring the angular and needle-tip deviation were compared to the original virtually planned model. Results: Compared to the original 3D model, the trials had a mean displacement of 1.440±1.03 mm and a mean angle deviation of 1.093±0.749°. Conclusions: The drill guides were demonstrated to accurately guide the decompression device along its predetermined drill trajectory. Accuracy was assessed by comparing values to literature-reported values and considered AVN lesion size. This study demonstrates the potential use of 3D printing technology to improve the efficacy of CD techniques.

Comparing the Use of a Mobile App and a Web-Based Notification Platform for Surveillance of Adverse Events Following Influenza Immunization: Randomized Controlled Trial.

BACKGROUND: Vaccine safety surveillance is a core component of vaccine pharmacovigilance. In Canada, active, participant-centered vaccine surveillance is available for influenza vaccines and has been used for COVID-19 vaccines. OBJECTIVE: The objective of this study is to evaluate the effectiveness and feasibility of using a mobile app for reporting participant-centered seasonal influenza adverse events following immunization (AEFIs) compared to a web-based notification system. METHODS: Participants were randomized to influenza vaccine safety reporting via a mobile app or a web-based notification platform. All participants were invited to complete a user experience survey. RESULTS: Among the 2408 randomized participants, 1319 (54%) completed their safety survey 1 week after vaccination, with a higher completion rate among the web-based notification platform users (767/1196, 64%) than among mobile app users (552/1212, 45%; P<.001). Ease-of-use ratings were high for the web-based notification platform users (99% strongly agree or agree) and 88.8% of them strongly agreed or agreed that the system made reporting AEFIs easier. Web-based notification platform users supported the statement that a web-based notification-only approach would make it easier for public health professionals to detect vaccine safety signals (91.4%, agreed or strongly agreed). CONCLUSIONS: Participants in this study were significantly more likely to respond to a web-based safety survey rather than within a mobile app. These results suggest that mobile apps present an additional barrier for use compared to the web-based notification-only approach. TRIAL REGISTRATION: ClinicalTrials.gov NCT05794113; https://clinicaltrials.gov/show/NCT05794113.