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Abstract Background A prolonged first episode of psychosis (FEP) without adequate treatment is a predictor of poor clinical, functional, and health outcomes and significant economic burden. Team-based “coordinated specialty care” (CSC) for early psychosis (EP) has established effectiveness in promoting clinical and functional recovery. However, California’s CSC program implementation has been unsystematic and could benefit from standardizing its processes and data collection infrastructure. To address this, we established a consortium of EP clinics across the state via a Learning Health Care Network (LHCN) framework to develop the Early Psychosis Intervention Network of California (EPI-CAL). EPI-CAL’s LHCN developed a core battery of evidence-based measures for service users and family members and linked them together using a unique data collection and visualization application, Beehive. Methods and objectives EPI-CAL’s LHCN collects, visualizes, and aggregates data at the individual and clinic level for EP programs across California via Beehive. Beehive was designed to: (1) collect outcomes data from service users receiving care at EP programs and their support persons, (2) provide the data to providers on a secure web-based dashboard to support measurement-based care, and (3) allow data to be used for program or research analysis. We will (1) determine the feasibility of implementing an LHCN across a diverse, decentralized network of early psychosis programs, (2) determine if the implementation of an LHCN increases the delivery of measurement-based care, and (3) determine if the implementation of measurement-based care is associated with significant improvements in key service user outcomes. EPI-CAL’s network will contribute data to the Early Psychosis Intervention Network (EPINET) program. Discussion The current study aims to establish an LHCN of EP clinics in California that implements harmonized data collection using Beehive and assesses the feasibility of establishing such a network. Our goal is for this harmonized data collection approach to be used to inform decisions and develop learning opportunities for service users, staff, and administrators, and to improve outcomes for service users and their supporters in CSC care. Further, the data will enable programs and research teams to examine what elements of care lead to program success and improved treatment outcomes for service users. Clinical trials registration www.ClinicalTrials.gov , identifier NCT04007510; registered 07/05/2019.

BackgroundNurses comprise over half of the global health care workforce, and the nursing care they provide is critical for the global population's health. High patient volumes and increased medical complexity have increased the workload and stress of nurses. As a result, the health of nurses is often negatively impacted. Wearables are used within the health care setting to assess patient outcomes; however, efforts to synthesize the use of wearable devices focusing on nurses’ health are limited. ObjectiveThe primary objective of our integrative review is to synthesize available data concerning the utility of wearable devices for evaluating or improving (or both) the health of nurses. MethodsWe are conducting an integrative review synthesizing data specific to wearable devices and nurses’ health. The research question for this review aims to answer how wearable devices are used to evaluate health outcomes among nurses. We searched the following electronic databases from inception until July 2022: PubMed, Embase, CINAHL, Web of Science, IEEE Explore, and AS&T. Titles and abstracts were imported into Covidence software, where citations were screened and duplicates removed. Title and abstract screening has been completed; however, full-text screening has not been started. Further screening is being conducted independently and in duplicate by 2 teams of 2 reviewers each. These reviewers will extract data independently. ResultsSearch strategies have been developed, and data were extracted from 6 databases. After the removal of duplicates, we collected 8603 studies for title and abstract screening. Two independent reviewers conducted the title and abstract review, and after resolving conflicts, 277 full-text articles are available for review to determine whether they meet the inclusion criteria. ConclusionsThis integrative review will provide synthesized data to inform nurses and other stakeholders about the extent of wearable device–related work done with nurses and provide direction for future research. International Registered Report Identifier (IRRID)DERR1-10.2196/48178

With the aim of producing a 3D representation of tumors, imaging and molecular annotation of xenografts and tumors (IMAXT) uses a large variety of modalities in order to acquire tumor samples and produce a map of every cell in the tumor and its host environment. With the large volume and variety of data produced in the project, we developed automatic data workflows and analysis pipelines. We introduce a research methodology where scientists connect to a cloud environment to perform analysis close to where data are located, instead of bringing data to their local computers. Here, we present the data and analysis infrastructure, discuss the unique computational challenges and describe the analysis chains developed and deployed to generate molecularly annotated tumor models. Registration is achieved by use of a novel technique involving spherical fiducial marks that are visible in all imaging modalities used within IMAXT. The automatic pipelines are highly optimized and allow to obtain processed datasets several times quicker than current solutions narrowing the gap between data acquisition and scientific exploitation.

Persistent cognitive fatigue (CF) is the most reported symptom in Post-Acute Sequelae of SARS-CoV-2 Infection (PASC), but little is known about its underlying neural basis. This pilot study examined fMRI brain activation patterns during a fatiguing task in those with and without PASC. We hypothesized that individuals with PASC would show changes in CF-related brain activation within fatigue network. Participants were 10 adults with PASC and persistent CF and 10 age- and gender-matched healthy controls. The 2-back working memory task was used during fMRI to induce CF. Patients with PASC reported greater CF, as measured using a Visual Analogue Scale of Fatigue (VAS-F), throughout the task. The relationship of brain activation in the fatigue network to increased CF during the fatiguing task did not differ between groups. There were, however, more areas inside and outside the fatigue network that were activated in the PASC group as reported CF increased. The relationship between brain activation and scores on the 2-back did differ between groups, with the PASC group showing more frontal activation. Findings suggest that individuals with PASC and CF may need to exert greater mental effort during demanding cognitive tasks, reflected in recruitment of a broader network of brain regions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

Importance: Predicting neurocognitive deficits using complex auditory assessments could change how cognitive dysfunction is identified, and monitored over time. Detecting cognitive impairment in people living with HIV (PLWH) is important for early intervention, especially in low- to middle-income countries where most cases exist. Auditory tests relate to neurocognitive test results, but the incremental predictive capability beyond demographic factors is unknown., Objective: Use machine learning to predict neurocognitive deficits, using auditory tests and demographic factors., Setting: The Infectious Disease Center in Dar es Salaam, Tanzania., Participants: Participants were 939 Tanzanian individuals from Dar es Salaam living with and without HIV who were part of a longitudinal study. Patients who had only one visit, a positive history of ear drainage, concussion, significant noise or chemical exposure, neurological disease, mental illness, or exposure to ototoxic antibiotics (e.g., gentamycin), or chemotherapy were excluded. This provided 478 participants (349 PLWH, 129 HIV-negative). Participant data were randomized to training and test sets for machine learning., Main Outcome(s) and Measure(s): The main outcome was whether auditory variables combined with relevant demographic variables could predict neurocognitive dysfunction (defined as a score of <26 on the Kiswahili Montreal Cognitive Assessment) better than demographic factors alone. The performance of predictive machine learning algorithms was primarily evaluated using the area under the receiver operational characteristic curve. Secondary metrics for evaluation included F1 scores, accuracies, and the Youden's indices for the algorithms., Results: The percentage of individuals with cognitive deficits was 36.2% (139 PLWH and 34 HIV-negative). The Gaussian and kernel naïve Bayes classifiers were the most predictive algorithms for neurocognitive impairment. Algorithms trained with auditory variables had average area under the curve values of 0.91 and 0.87, F1 scores (metric for precision and recall) of 0.81 and 0.76, and average accuracies of 86.3% and 81.9% respectively. Algorithms trained without auditory variables as features were statistically worse (p < .001) in both the primary measure of area under the curve (0.82/0.78) and the secondary measure of accuracy (72.3%/74.5%) for the Gaussian and kernel algorithms respectively., Conclusions and Relevance: Auditory variables improved the prediction of cognitive function. Since auditory tests are easy-to-administer and often naturalistic tasks, they may offer objective measures or predictors of neurocognitive performance suitable for many global settings. Further research and development into using machine learning algorithms for predicting cognitive outcomes should be pursued., Competing Interests: NO authors have competing interests., (Copyright: © 2024 Niemczak et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)