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Host cell damage is a key parameter for research in infection biology, drug testing, and substance safety screening. In this study, we introduce a luciferase reporter system as a new and reliable assay to measure cell damage and validate it with the pathogenic yeast, Candida albicans , as a test case. We transduced human epithelial cell lines with a lentiviral vector to stably express an optimized luciferase enzyme, Nanoluc. Upon cell damage, the release of cytoplasmic luciferase into the extracellular space can be easily detected by a luminometer. We used the luciferase reporter system to investigate the damage caused by C. albicans to different newly generated epithelial reporter cell lines. We found that fungus-induced cell damage, as determined by established methods, correlated tightly with the release of the luciferase. The new luciferase reporter system is a simple, sensitive, robust, and inexpensive method for measuring host cell damage and has a sensitivity comparable to the standard assay, release of lactate dehydrogenase. It is suitable for high-throughput studies of pathogenesis mechanisms of any microbe, for antimicrobial drug screening, and many other applications.IMPORTANCEWe present a quick, easy, inexpensive, and reliable assay to measure damage to mammalian cells. To this end, we created reporter cell lines which artificially express luciferase, an enzyme that can be easily detected in the supernatant when these cells are damaged. We used infections with the well-investigated fungal pathogen of humans, Candida albicans , as a test case of our system. Using our reporter, we were able to recapitulate the known effects of strain variability, gene deletions, and antifungal treatments on host cell damage. This easily adaptable reporter system can be used to screen for damage in infection models with different microbial species, assay cell-damaging potential of substances, discover new non-toxic antibiotics, and many other damage-based applications., Competing Interests: Work from this paper has been used to register the patent "Cytotoxicity assay for detecting cellular damage" (L31002DE) with the Deutsches Patent-und Markenamt authors R.V., M.T., V.T., S.B., and B.H. and Leibniz-HKI as beneficiaries.

Background: Deep brain stimulation (DBS) is a standard treatment for movement disorders, epilepsy, and others, yet its influence on postprocedural sleep quality remains an under-researched topic., Study Objectives: We performed a systematic review and meta-analysis of all DBS effects on sleep., Methods: The use of preferred reporting items for systematic reviews and meta-analyses guidelines (PRISMA) was utilized. We extracted demographic data, disease type/duration, DBS target, stimulation laterality (unilateral vs bilateral), follow-up lengths, and sleep pre/post-op measurements with polysomnography or across four standard sleep scales. The Cochrane methodology for evaluating RCTs was employed using the risk of bias assessments, data synthesis, and statistical methods, including forest plots (risk ratio; M-H random effects; 95% CI)., Results: Sixty-three studies were included in the overall analysis, representing 3022 patients. In a subgroup meta-analysis of subthalamic nucleus (STN) DBS for Parkinson's disease (PD), patients showed significant sleep improvement at three but not 12 months postoperatively with PDSS, at 12 but not 3 months with Epworth sleep scale, and at 6 months with nonmotor symptom scale. Pittsburgh sleep quality index (PSQI) showed no significant improvement in sleep at any time. Bilateral DBS showed significantly more improvement than unilateral DBS in the PSQI at 6 but not 3 months. Polysomnography showed significant sleep improvement at 1 week but not at 3 or 6 months. Most studies showed no significant sleep improvement for globus pallidus internus, centromedian thalamus, and ventral intermediate nucleus DBS., Conclusions: STN-DBS for PD likely improves sleep; however, significant standardization in sleep scale outcome reporting and follow-up time is needed to effectively determine the target-dependent effects of DBS surgery on sleep., (© The Author(s) 2024. Published by Oxford University Press on behalf of Sleep Research Society.)

Magic angle spinning (MAS) dynamic nuclear polarization (DNP) increases the signal intensity of solid-state nuclear magnetic resonance. DNP typically uses continuous wave (CW) microwave irradiation close to the resonance frequency of unpaired electron spins. In this study, we demonstrate that frequency-chirped microwaves improve DNP performance under MAS. By modulating the gyrotron anode potential, we generate a train of microwave chirps with a maximum bandwidth of 310 MHz and a maximum incident power on the spinning sample of 18 W. We characterize the efficiency of chirped DNP using the following polarizing agents: TEMTriPol-1, AsymPolPOK, AMUPol, and Finland trityl. The effects of different chirp widths and periods are analyzed at different MAS frequencies and microwave powers. Furthermore, we show that chirped DNP can be combined with electron decoupling to improve signal intensity by 59%, compared to CW DNP without electron decoupling, using Finland trityl as a polarizing agent.

Competing Interests: Disclosures Dr Di Biase is a consultant for Biosense Webster, Boston Scientific, Stereotaxis, and St. Jude Medical; and has received speaker honoraria from Medtronic, AtriCure, EPiEP, and Biotronik. Dr Natale is a consultant for Biosense Webster, Stereotaxis, and Abbott Medical; and has received speaker honoraria/travel from Medtronic, AtriCure, Biotronik, and Janssen. All other authors have no conflicts of interest to disclose.

Background: Electronic health record (EHR)-based patient messages can contribute to burnout. Messages with a negative tone are particularly challenging to address. In this perspective, we describe our initial evaluation of large language model (LLM)-generated responses to negative EHR patient messages and contend that using LLMs to generate initial drafts may be feasible, although refinement will be needed., Methods: A retrospective sample ( n  = 50) of negative patient messages was extracted from a health system EHR, de-identified, and inputted into an LLM (ChatGPT). Qualitative analyses were conducted to compare LLM responses to actual care team responses., Results: Some LLM-generated draft responses varied from human responses in relational connection, informational content, and recommendations for next steps. Occasionally, the LLM draft responses could have potentially escalated emotionally charged conversations., Conclusion: Further work is needed to optimize the use of LLMs for responding to negative patient messages in the EHR., Competing Interests: S.L.B. reports equipment support from Topcon and Optomed, and consulting fees from Topcon, outside the submitted work. C.A.L. reports equity from consulting with Doximity. M.T.-S., M.M., and A.M.S. report no relevant conflicts., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Importance: Timely tests are warranted to assess the association between generative artificial intelligence (GenAI) use and physicians' work efforts., Objective: To investigate the association between GenAI-drafted replies for patient messages and physician time spent on answering messages and the length of replies., Design, Setting, and Participants: Randomized waiting list quality improvement (QI) study from June to August 2023 in an academic health system. Primary care physicians were randomized to an immediate activation group and a delayed activation group. Data were analyzed from August to November 2023., Exposure: Access to GenAI-drafted replies for patient messages., Main Outcomes and Measures: Time spent (1) reading messages, (2) replying to messages, (3) length of replies, and (4) physician likelihood to recommend GenAI drafts. The a priori hypothesis was that GenAI drafts would be associated with less physician time spent reading and replying to messages. A mixed-effects model was used., Results: Fifty-two physicians participated in this QI study, with 25 randomized to the immediate activation group and 27 randomized to the delayed activation group. A contemporary control group included 70 physicians. There were 18 female participants (72.0%) in the immediate group and 17 female participants (63.0%) in the delayed group; the median age range was 35-44 years in the immediate group and 45-54 years in the delayed group. The median (IQR) time spent reading messages in the immediate group was 26 (11-69) seconds at baseline, 31 (15-70) seconds 3 weeks after entry to the intervention, and 31 (14-70) seconds 6 weeks after entry. The delayed group's median (IQR) read time was 25 (10-67) seconds at baseline, 29 (11-77) seconds during the 3-week waiting period, and 32 (15-72) seconds 3 weeks after entry to the intervention. The contemporary control group's median (IQR) read times were 21 (9-54), 22 (9-63), and 23 (9-60) seconds in corresponding periods. The estimated association of GenAI was a 21.8% increase in read time (95% CI, 5.2% to 41.0%; P = .008), a -5.9% change in reply time (95% CI, -16.6% to 6.2%; P = .33), and a 17.9% increase in reply length (95% CI, 10.1% to 26.2%; P < .001). Participants recognized GenAI's value and suggested areas for improvement., Conclusions and Relevance: In this QI study, GenAI-drafted replies were associated with significantly increased read time, no change in reply time, significantly increased reply length, and some perceived benefits. Rigorous empirical tests are necessary to further examine GenAI's performance. Future studies should examine patient experience and compare multiple GenAIs, including those with medical training.

Background: Effective primary care necessitates follow-up actions by the patient beyond the visit. Prior research suggests room for improvement in patient adherence., Objective: This study sought to understand patients' views on their primary care visits, the plans generated therein, and their self-reported adherence after 3 months., Methods: As part of a large multisite cluster randomized pragmatic trial in 3 health care organizations, patients completed 2 surveys-the first within 7 days after the index primary care visit and another 3 months later. For this analysis of secondary outcomes, we combined the results across all study participants to understand patient adherence to care plans. We recorded patient characteristics and survey responses. Cross-tabulation and chi-square statistics were used to examine bivariate associations, adjusting for multiple comparisons when appropriate. We used multivariable logistic regression to assess how patients' intention to follow, agreement, and understanding of their plans impacted their plan adherence, allowing for differences in individual characteristics. Qualitative content analysis was conducted to characterize the patient's self-reported plans and reasons for adhering (or not) to the plan 3 months later., Results: Of 2555 patients, most selected the top box option (9=definitely agree) that they felt they had a clear plan (n=2011, 78%), agreed with the plan (n=2049, 80%), and intended to follow the plan (n=2108, 83%) discussed with their provider at the primary care visit. The most common elements of the plans reported included reference to exercise (n=359, 14.1%), testing (laboratory, imaging, etc; n=328, 12.8%), diet (n=296, 11.6%), and initiation or adjustment of medications; (n=284, 11.1%). Patients who strongly agreed that they had a clear plan, agreed with the plan, and intended to follow the plan were all more likely to report plan completion 3 months later (P<.001) than those providing less positive ratings. Patients who reported plans related to following up with the primary care provider (P=.008) to initiate or adjust medications (P≤.001) and to have a specialist visit were more likely to report that they had completely followed the plan (P=.003). Adjusting for demographic variables, patients who indicated intent to follow their plan were more likely to follow-through 3 months later (P<.001). Patients' reasons for completely following the plan were mainly that the plan was clear (n=1114, 69.5%), consistent with what mattered (n=1060, 66.1%), and they were determined to carry through with the plan (n=887, 53.3%). The most common reasons for not following the plan were lack of time (n=217, 22.8%), having decided to try a different approach (n=105, 11%), and the COVID-19 pandemic impacted the plan (n=105, 11%)., Conclusions: Patients' initial assessment of their plan as clear, their agreement with the plan, and their initial willingness to follow the plan were all strongly related to their self-reported completion of the plan 3 months later. Patients whose plans involved lifestyle changes were less likely to report that they had "completely" followed their plan., Trial Registration: ClinicalTrials.gov NCT03385512; https://clinicaltrials.gov/study/NCT03385512., International Registered Report Identifier (irrid): RR2-10.2196/30431., (©Cheryl D Stults, Kathleen M Mazor, Michael Cheung, Bernice Ruo, Martina Li, Amanda Walker, Cassandra Saphirak, Florin Vaida, Sonal Singh, Kimberly A Fisher, Rebecca Rosen, Robert Yood, Lawrence Garber, Christopher Longhurst, Gene Kallenberg, Edward Yu, Albert Chan, Marlene Millen, Ming Tai-Seale. Originally published in Journal of Participatory Medicine (https://jopm.jmir.org), 14.03.2024.)

Objective: Physicians of all specialties experienced unprecedented stressors during the COVID-19 pandemic, exacerbating preexisting burnout. We examine burnout's association with perceived and actionable electronic health record (EHR) workload factors and personal, professional, and organizational characteristics with the goal of identifying levers that can be targeted to address burnout., Materials and Methods: Survey of physicians of all specialties in an academic health center, using a standard measure of burnout, self-reported EHR work stress, and EHR-based work assessed by the number of messages regarding prescription reauthorization and use of a staff pool to triage messages. Descriptive and multivariable regression analyses examined the relationship among burnout, perceived EHR work stress, and actionable EHR work factors., Results: Of 1038 eligible physicians, 627 responded (60% response rate), 49.8% reported burnout symptoms. Logistic regression analysis suggests that higher odds of burnout are associated with physicians feeling higher level of EHR stress (odds ratio [OR], 1.15; 95% confidence interval [CI], 1.07-1.25), having more prescription reauthorization messages (OR, 1.23; 95% CI, 1.04-1.47), not feeling valued (OR, 3.38; 95% CI, 1.69-7.22) or aligned in values with clinic leaders (OR, 2.81; 95% CI, 1.87-4.27), in medical practice for ≤15 years (OR, 2.57; 95% CI, 1.63-4.12), and sleeping for <6 h/night (OR, 1.73; 95% CI, 1.12-2.67)., Discussion: Perceived EHR stress and prescription reauthorization messages are significantly associated with burnout, as are non-EHR factors such as not feeling valued or aligned in values with clinic leaders. Younger physicians need more support., Conclusion: A multipronged approach targeting actionable levers and supporting young physicians is needed to implement sustainable improvements in physician well-being., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Cryogenic magic angle spinning (MAS) is a standard technique utilized for dynamic nuclear polarization (DNP) in solid-state nuclear magnetic resonance (NMR). Here we describe the optimization and implementation of a stator for cryogenic MAS with 9.5 mm diameter spherical rotors, allowing for DNP experiments on large sample volumes. Designs of the stator and rotor for cryogenic MAS build on recent advancements of MAS spheres and take a step further to incorporate sample insert and eject and a temperature-independent spinning stability of ± 1  Hz. At a field of 7 T and spinning at 2.0 kHz with a sample temperature of 105-107 K, DNP enhancements of 256 and 200 were observed for 124 and 223  µ L sample volumes, respectively, each consisting of 4 M 13 C, 15 N-labeled urea and 20 mM AMUPol in a glycerol-water glassy matrix., Competing Interests: The ETH Zürich has intellectual property protection on the inventions included in this paper. Alexander B. Barnes has patents related to this work filed by Washington University in Saint Louis (62/703,278 filed on 25 July 2018 and 62/672,840 filed on 17 May 2018). The authors declare no other competing interests., (Copyright: © 2023 Lauren E. Price et al.)

Objective: Occupational health (OH) documentation has traditionally been separate from health system electronic health records (EHRs), but this can create patient safety and care continuity challenges. Herein, we describe outcomes and challenges of such integration including how one health system managed compliance with laws, regulations, and ethical principles concerning digital privacy., Methods: Occupational health integration with the enterprise EHR at the University of California San Diego Health was started in June 2021 and completed in December 2021., Results: Integrating with the enterprise EHR allowed for a secure telehealth system, faster visit times, digitization of questionnaires medical clearance forms, and improved reporting capabilities., Conclusions: Integration and interoperability are fundamental building blocks to any OH EHR solution and will allow for evaluation of worker population trends, and targeted interventions to improve worker health status., Competing Interests: Conflicts of interest: K.S. is the owner of MedLaw LLC, employee of the State of Maine, and employee of P&G. He conducts business with LiveWell, WorkWell, and is a consultant for various industries and startup companies. A.S. received compensation from Taylor & Francis media company for a textbook. The authors report no conflicts of interest., (Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American College of Occupational and Environmental Medicine.)

Current remote monitoring of COVID-19 patients relies on manual symptom reporting, which is highly dependent on patient compliance. In this research, we present a machine learning (ML)-based remote monitoring method to estimate patient recovery from COVID-19 symptoms using automatically collected wearable device data, instead of relying on manually collected symptom data. We deploy our remote monitoring system, namely eCOVID, in two COVID-19 telemedicine clinics. Our system utilizes a Garmin wearable and symptom tracker mobile app for data collection. The data consists of vitals, lifestyle, and symptom information which is fused into an online report for clinicians to review. Symptom data collected via our mobile app is used to label the recovery status of each patient daily. We propose a ML-based binary patient recovery classifier which uses wearable data to estimate whether a patient has recovered from COVID-19 symptoms. We evaluate our method using leave-one-subject-out (LOSO) cross-validation, and find that Random Forest (RF) is the top performing model. Our method achieves an F1-score of 0.88 when applying our RF-based model personalization technique using weighted bootstrap aggregation. Our results demonstrate that ML-assisted remote monitoring using automatically collected wearable data can supplement or be used in place of manual daily symptom tracking which relies on patient compliance.

Importance: Physician burnout is an ongoing epidemic; electronic health record (EHR) use has been associated with burnout, and the burden of EHR inbasket messages has grown in the context of the COVID-19 pandemic. Understanding how EHR inbasket messages are associated with physician burnout may uncover new insights for intervention strategies., Objective: To evaluate associations between EHR inbasket message characteristics and physician burnout., Design, Setting, and Participants: Cross-sectional study in a single academic medical center involving physicians from multiple specialties. Data collection took place April to September 2020, and data were analyzed September to December 2020., Exposures: Physicians responded to a survey including the validated Mini-Z 5-point burnout scale., Main Outcomes and Measures: Physician burnout according to the self-reported burnout scale. A sentiment analysis model was used to calculate sentiment scores for EHR inbasket messages extracted for participating physicians. Multivariable modeling was used to model risk of physician burnout using factors such as message characteristics, physician demographics, and clinical practice characteristics., Results: Of 609 physicians who responded to the survey, 297 (48.8%) were women, 343 (56.3%) were White, 391 (64.2%) practiced in outpatient settings, and 428 (70.28%) had been in medical practice for 15 years or less. Half (307 [50.4%]) reported burnout (score of 3 or higher). A total of 1 453 245 inbasket messages were extracted, of which 630 828 (43.4%) were patient messages. Among negative messages, common words included medical conditions, expletives and/or profanity, and words related to violence. There were no significant associations between message characteristics (including sentiment scores) and burnout. Odds of burnout were significantly higher among Hispanic/Latino physicians (odds ratio [OR], 3.44; 95% CI, 1.18-10.61; P = .03) and women (OR, 1.60; 95% CI, 1.13-2.27; P = .01), and significantly lower among physicians in clinical practice for more than 15 years (OR, 0.46; 95% CI, 0.30-0.68; P < .001)., Conclusions and Relevance: In this cross-sectional study, message characteristics were not associated with physician burnout, but the presence of expletives and violent words represents an opportunity for improving patient engagement, EHR portal design, or filters. Natural language processing represents a novel approach to understanding potential associations between EHR inbasket messages and physician burnout and may also help inform quality improvement initiatives aimed at improving patient experience.

Objectives: To improve diabetic retinopathy (DR) screening rates through a primary care-based "teleretina" screening program incorporating clinical informatics tools., Study Design: Quality improvement study at an academic institution., Methods: Existing DR screening workflows using in-person eye examinations were analyzed via a needs assessment. We identified gaps, which clarified the need for expanding DR screening to primary care settings. We developed informatics tools and described associated challenges and solutions. We also longitudinally monitored imaging volume and quality., Results: The needs assessment identified several gaps in baseline DR screening workflows. Health information technology (IT) considerations for the new primary care-based teleretina screening program included integrating the new program with existing information systems, facilitating care coordination, and decreasing barriers to adoption by incorporating automation and other features aimed at decreasing end-user burden. We successfully developed several tools fulfilling these goals, including integration with the ophthalmology picture and archiving communication system, a customized aggregated report in the electronic health record to monitor screenings, automation of billing and health maintenance documentation, and automated results notification to primary care physicians. Of 316 primary care patients screened between October 2020 and July 2021, 73 (23%) were found to have ocular pathology, including DR, glaucoma, age-related macular degeneration, and a range of other eye conditions that were previously undiagnosed., Conclusions: New models of health care delivery, including telemedicine workflows, have become increasingly important for complex diabetic care coordination and require substantial health IT engagement. This program illustrates how clinical informatics tools can make substantial contributions to improving diabetes care.

Objective To detail the implementation, benefits and challenges of onboarding campus-based health services onto a health system's electronic health record. Participants UC San Diego Student Health and Well-Being offers medical services to over 39,000 students. UC San Diego Health is an academic medical center. Methods 20 workstreams and 9 electronic modules, systems, or interfaces were converted to new electronic systems. Results 36,023 student-patient medical records were created. EHR-integration increased security while creating visibility to 19,700 shared patient visits and records from 236 health systems across the country over 6 months. Benefits for the COVID-19 response included access to screening tools, decision support, telehealth, patient alerting system, reporting and analytics, COVID-19 dashboard, and increased testing capabilities. Conclusion Integration of an interoperable EHR between neighboring campus-based health services and an affiliated academic medical center can streamline case management, improve quality and safety, and increase access to valuable health resources in times of need. Pertinent examples during the COVID-19 pandemic included uninterrupted and safe provision of clinical services through access to existing telehealth platforms and increased testing capacity.