Loss-of-function of ALDH3B2 transdifferentiates human pancreatic duct cells into beta-like cells.

Replenishment of pancreatic beta cells is a key to the cure for diabetes. Beta cells regeneration is achieved predominantly by self-replication especially in rodents, but it was also shown that pancreatic duct cells can transdifferentiate into beta cells. How pancreatic duct cells undergo transdifferentiated and whether we could manipulate the transdifferentiation to replenish beta cell mass is not well understood.

Resident Impact of the Single Site Order Restricting Staff Mobility across Long-Term Care Homes in British Columbia, Canada.

The Single Site Order (SSO)-a policy restricting staff from working at multiple long-term care (LTC) homes-was mandated by the Public Health Agency of Canada to control the spread of COVID-19 in LTC homes, where nearly 70% of COVID-19-related deaths in Canada occurred. This mixed methods study assesses the impact of the SSO on LTC residents in British Columbia. Interviews were conducted (residents (n = 6), family members (n = 9), staff (n = 18), and leadership (n = 10) from long-term care homes (n = 4)) and analyzed using thematic analysis.

A Mixed Methods Study to Implement the Synergy Tool and Evaluate Its Impact on Long-Term Care Residents.

Background: There are ongoing workforce challenges with the delivery of long-term care (LTC), such as staffing decisions based on arbitrary standards. The Synergy tool, a resident-centered approach to staffing, provides objective, real-time acuity and dependency scores (Synergy scores) for residents. The purpose of this study was to implement and evaluate the impact of the Synergy tool on LTC delivery.

Impact of the single site order in LTC: exacerbation of an overburdened system.

Background: The long-term care (LTC) sector has been at the epicentre of COVID-19 in Canada. This study aimed to understand the impact that the Single Site Order (SSO) had on staff and leadership in four LTC homes in the Lower Mainland of British Columbia, Canada.

Methods: A mixed method study was conducted by analyzing administrative staffing data.

Effects of Robotics Education on Young Children's Cognitive Development: a Pilot Study with Eye-Tracking.

The emerging field of robotics education (RE) is a new and rapidly growing subject area worldwide. It may provide a playful and novel learning environment for children to engage with all aspects of science, technology, engineering, and mathematics (STEM) learning. The purpose of this research is to examine how robotics learning activities may affect the cognitive abilities and cognitive processes of 6-8 years old children.

Implementation of the Synergy Tool: A Potential Intervention to Relieve Health Care Worker Burnout.

(1) Background: Healthcare workers experienced rising burnout rates during and after the COVID-19 pandemic. A practice-academic collaboration between health services researchers and the surgical services program of a Canadian tertiary-care urban hospital was used to develop, implement and evaluate a potential burnout intervention, the Synergy tool. (2) Methods: Using participatory action research methods, this project involved four key phases: (I) an environmental scan and a baseline survey assessment, (II), a workshop, (III) Synergy tool implementation and (IV) a staffing plan workshop.

Priority Nursing Populations for Mental Health Support Before and During COVID-19: A Survey Study of Individual and Workplace Characteristics.

Background: Nursing is a high-risk profession and nurses' exposure to workplace risk factors such as heavy workloads and inadequate staffing is well documented. The COVID-19 pandemic has exacerbated nurses' exposure to workplace risk factors, further deteriorating their mental health. Therefore, it is both timely and important to determine nursing groups in greatest need of mental health interventions and supports.

Learning From Synthetic CT Images via Test-Time Training for Liver Tumor Segmentation.

Automatic liver tumor segmentation could offer assistance to radiologists in liver tumor diagnosis, and its performance has been significantly improved by recent deep learning based methods. These methods rely on large-scale well-annotated training datasets, but collecting such datasets is time-consuming and labor-intensive, which could hinder their performance in practical situations. Learning from synthetic data is an encouraging solution to address this problem.

Weakly Supervised Liver Tumor Segmentation Using Couinaud Segment Annotation.

Automatic liver tumor segmentation is of great importance for assisting doctors in liver cancer diagnosis and treatment planning. Recently, deep learning approaches trained with pixel-level annotations have contributed many breakthroughs in image segmentation. However, acquiring such accurate dense annotations is time-consuming and labor-intensive, which limits the performance of deep neural networks for medical image segmentation.

Investigation of F and Zr Isotopes Self-Absorption and Dose Rate Parameters for PET Imaging.

This work concerns study of self-absorption factor (SAF) and dose rate constants of zirconium-89 (Zr) for the purpose of radiation protection in positron emission tomography (PET) and to compare them with those of F-deoxyglucose (F-FDG). We analyzed the emitted energy spectra by F and Zr through anthropomorphic phantom and calculated the absorbed energy using Monte Carlo method. The dose rate constants for both radionuclides were estimated with 2 different fluence-to-effective dose conversion coefficients.

Describing the Mental Health State of Nurses in British Columbia: A Province-Wide Survey Study.

A cross-sectional province-wide survey study of 3,978 British Columbia (BC) nurses was conducted to explore the mental health state of the nursing workforce in BC. About one third of nurses reported depression and anxiety; about half reported symptoms of post-traumatic stress disorder and at least one third reported high levels of one or more dimensions of burnout. Mental health problems were about 1.

Artificial intelligence in prediction of non-alcoholic fatty liver disease and fibrosis.

Artificial intelligence (AI) has become increasingly widespread in our daily lives, including healthcare applications. AI has brought many new insights into better ways we care for our patients with chronic liver disease, including non-alcoholic fatty liver disease and liver fibrosis. There are multiple ways to apply the AI technology on top of the conventional invasive (liver biopsy) and noninvasive (transient elastography, serum biomarkers, or clinical prediction models) approaches.

Importance-aware personalized learning for early risk prediction using static and dynamic health data.

Objective: Accurate risk prediction is important for evaluating early medical treatment effects and improving health care quality. Existing methods are usually designed for dynamic medical data, which require long-term observations. Meanwhile, important personalized static information is ignored due to the underlying uncertainty and unquantifiable ambiguity.

Nurses' Workplace Conditions Impacting Their Mental Health during COVID-19: A Cross-Sectional Survey Study.

Among health workers, nurses are at the greatest risk of COVID-19 exposure and mortality due to their workplace conditions, including shortages of personal protective equipment (PPE), insufficient staffing, and inadequate safety precautions. The purpose of this study was to examine the impact of COVID-19 workplace conditions on nurses' mental health outcomes. A cross-sectional correlational design was used.

Explainable Uncertainty-Aware Convolutional Recurrent Neural Network for Irregular Medical Time Series.

Influenced by the dynamic changes in the severity of illness, patients usually take examinations in hospitals irregularly, producing a large volume of irregular medical time-series data. Performing diagnosis prediction from the irregular medical time series is challenging because the intervals between consecutive records significantly vary along time. Existing methods often handle this problem by generating regular time series from the irregular medical records without considering the uncertainty in the generated data, induced by the varying intervals.

Temporal matrix completion with locally linear latent factors for medical applications.

Regular medical records are useful for medical practitioners to analyze and monitor patient's health status especially for those with chronic disease. However, such records are usually incomplete due to unpunctuality and absence of patients. In order to resolve the missing data problem over time, tensor-based models have been developed for missing data imputation in recent papers.

Workplace Violence among British Columbia Nurses Across Different Roles and Contexts.

Workplace violence in healthcare settings is on the rise, particularly against nurses. Most healthcare violence research is in acute care settings. The purpose of this paper is to present descriptive findings on the prevalence of types and sources of workplace violence among nurses in different roles (i.

A Hybrid Residual Network and Long Short-Term Memory Method for Peptic Ulcer Bleeding Mortality Prediction.

The prediction of patient mortality, which can detect high-risk patients, is a significant yet challenging problem in medical informatics. Thanks to the wide adoption of electronic health records (EHRs), many data-driven methods have been proposed to forecast mortality. However, most existing methods do not consider correlations between static and dynamic data, which contain significant information about mutual influences between these data.

Machine learning model to predict recurrent ulcer bleeding in patients with history of idiopathic gastroduodenal ulcer bleeding.

Background: Patients with a history of Helicobacter pylori-negative idiopathic bleeding ulcers have an increased risk of recurring ulcer complications.

Aim: To build a machine learning model to identify patients at high risk for recurrent ulcer bleeding.

Methods: Data from a retrospective cohort of 22 854 patients (training cohort) diagnosed with peptic ulcer disease in 2007-2016 were analysed to build a model (IPU-ML) to predict recurrent ulcer bleeding.

Dynamic Graph Co-Matching for Unsupervised Video-based Person Re-Identification.

Cross-camera label estimation from a set of unlabelled training data is an extremely important component in unsupervised person re-identification (re-ID) systems. With the estimated labels, existing advanced supervised learning methods can be leveraged to learn discriminative re-ID models. In this paper, we utilize the graph matching technique for accurate label estimation due to its advantages in optimal global matching and intra-camera relationship mining.

Deprescribing benzodiazepine receptor agonists: Evidence-based clinical practice guideline.

Objective: To develop an evidence-based guideline to help clinicians make decisions about when and how to safely taper and stop benzodiazepine receptor agonists (BZRAs); to focus on the highest level of evidence available and seek input from primary care professionals in the guideline development, review, and endorsement processes.

Methods: The overall team comprised 8 clinicians (1 family physician, 2 psychiatrists, 1 clinical psychologist, 1 clinical pharmacologist, 2 clinical pharmacists, and 1 geriatrician) and a methodologist; members disclosed conflicts of interest. For guideline development, a systematic process was used, including the GRADE (Grading of Recommendations Assessment, Development and Evaluation) approach.

Process Monitoring in the Intensive Care Unit: Assessing Patient Mobility Through Activity Analysis with a Non-Invasive Mobility Sensor.

Throughout a patient's stay in the Intensive Care Unit (ICU), accurate measurement of patient mobility, as part of routine care, is helpful in understanding the harmful effects of bedrest [1]. However, mobility is typically measured through observation by a trained and dedicated observer, which is extremely limiting. In this work, we present a video-based automated mobility measurement system called .

A Qualitative Study of How Health Coaches Support Patients in Making Health-Related Decisions and Behavioral Changes.

Purpose: Although health coaches are a growing resource for supporting patients in making health decisions, we know very little about the experience of health. We undertook a qualitative study of how health coaches support patients in making decisions and implementing changes to improve their health.

Methods: We conducted 6 focus groups (3 in Spanish and 3 in English) with 25 patients and 5 friends or family members, followed by individual interviews with 42 patients, 17 family members, 17 health coaches, and 20 clinicians.

Measuring Patient Mobility in the ICU Using a Novel Noninvasive Sensor.

Objectives: To develop and validate a noninvasive mobility sensor to automatically and continuously detect and measure patient mobility in the ICU.

Design: Prospective, observational study.

Setting: Surgical ICU at an academic hospital.