Parkinson's Disease Progression and Exposure to Contaminated Water at Camp Lejeune.

BACKGROUND: We recently reported an increased risk of Parkinson's disease (PD) in service members who resided at Marine Base Camp Lejeune, North Carolina, when water supplies were contaminated with trichloroethylene and other volatile organic compounds (VOCs). Prior studies suggest that environmental exposures may affect PD phenotype or progression, but this has not been reported for VOCs. OBJECTIVE: The objective of this study was to test whether PD progression is faster in individuals exposed to VOCs in water at Camp Lejeune. METHODS: A cohort of 172,128 marines residing at Camp Lejeune between 1975 and 1985 was previously assembled. We identified individuals with PD in Veterans Health Administration and Medicare databases between 2000 and 2021. Using estimates derived by the US Agency for Toxic Substances and Disease Registry, we classified individuals as exposed or unexposed to VOCs in residential water. We used Kaplan-Meier and Cox regression models to test differences between exposed and unexposed groups in the time from PD diagnosis until psychosis, fracture, fall, or death. RESULTS: Among 270 persons with PD, 177 (65.6%) were exposed to VOCs in residential water. Median cumulative exposure was 4970 µg/L-months, >50-fold the permissible level. Time until psychosis, fracture, and fall were all shorter in the exposed group, with adjusted hazard ratios (HRs) exceeding 2: psychosis HR, 2.19 (95% confidence interval [CI]: 0.99-4.83); fracture HR, 2.44 (95% CI: 0.91-6.55); and fall HR, 2.64 (95% CI: 0.97-7.21). A significant dose response was observed for time to fall (P trend, 0.032). No differences were observed for time until death. CONCLUSIONS: PD progression may be faster in persons exposed to trichloroethylene and other VOCs in water decades earlier. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Enhancing Diagnostic Support for Chiari Malformation and Syringomyelia: A Comparative Study of Contextualized ChatGPT Models.

OBJECTIVES: The rapidly increasing adoption of large language models in medicine has drawn attention to potential applications within the field of neurosurgery. This study evaluates the effects of various contextualization methods on ChatGPT's ability to provide expert-consensus aligned recommendations on the diagnosis and management of Chiari Malformation and Syringomyelia. METHODS: Native GPT4 and GPT4 models contextualized using various strategies were asked questions revised from the 2022 Chiari and Syringomyelia Consortium International Consensus Document. ChatGPT-provided responses were then compared to consensus statements using reviewer assessments of 1) responding to the prompt, 2) agreement of ChatGPT response with consensus statements, 3) recommendation to consult with a medical professional, and 4) presence of supplementary information. Flesch-Kincaid, SMOG, word count, and Gunning-Fog readability scores were calculated for each model using the quanteda package in R. RESULTS: Relative to GPT4, all contextualized GPTs demonstrated increased agreement with consensus statements. PDF+Prompting and Prompting models provided the most elevated agreement scores of 19 of 24 and 23 of 24, respectively, versus 9 of 24 for GPT4 (p=.021, p=.001). A trend toward improved readability was observed when comparing contextualized models at large to ChatGPT4, with significant decreases in average word count (180.7 vs 382.3, p<.001) and Flesch-Kincaid Reading Ease score (11.7 vs 17.2, p=.033). CONCLUSIONS: The enhanced performance observed in response to ChatGPT4 contextualization suggests broader applications of large language models in neurosurgery than what the current literature indicates. This study provides proof of concept for the use of contextualized GPT models in neurosurgical contexts and showcases the easy accessibility of improved model performance.

A Bibliometric Analysis of the 20 Most Cited Articles on Sacrococcygeal Chordomas.

This study aims to summarize sacrococcygeal chordoma literature through bibliometric analysis and to offer insights into key studies to guide clinical practices and future research. The Web of Science database was searched using the terms "sacral chordoma", "chordomas of the sacrum", "chordomas of the sacral spine", "chordomas of the sacrococcygeal region", "coccygeal chordoma", and "coccyx chordoma". Articles were analyzed for citation count, authorship, publication date, journal, research area tags, impact factor, and evidence level. The median number of citations was 75 (range: 53-306). The primary publication venue was the International Journal of Radiation Oncology, Biology, Physics. Most works, published between 1999 and 2019, featured a median journal impact factor of 3.8 (range: 2.1-7) and predominantly fell under the research area tag, radiation, nuclear medicine, and imaging. Of these articles, 19 provided clinical data with predominantly level III evidence, and one was a literature review. This review highlights the increasing volume of sacrococcygeal chordoma publications over the past two decades, indicating evolving treatment methods and interdisciplinary patient care. Advances in radiation, particularly intensity-modulated radiation therapy (IMRT) and proton beam therapy, are believed to be propelling research growth, and the lack of level I evidence underscores the need for more rigorous studies to refine treatment protocols for sacrococcygeal chordomas.

Ethical Incorporation of Artificial Intelligence into Neurosurgery: A Generative Pretrained Transformer Chatbot-Based, Human-Modified Approach.

INTRODUCTION: Artificial intelligence (AI) has become increasingly used in neurosurgery. Generative pretrained transformers (GPTs) have been of particular interest. However, ethical concerns regarding the incorporation of AI into the field remain underexplored. We delineate key ethical considerations using a novel GPT-based, human-modified approach, synthesize the most common considerations, and present an ethical framework for the involvement of AI in neurosurgery. METHODS: GPT-4, ChatGPT, Bing Chat/Copilot, You, Perplexity.ai, and Google Bard were queried with the prompt "How can artificial intelligence be ethically incorporated into neurosurgery?". Then, a layered GPT-based thematic analysis was performed. The authors synthesized the results into considerations for the ethical incorporation of AI into neurosurgery. Separate Pareto analyses with 20% threshold and 10% threshold were conducted to determine salient themes. The authors refined these salient themes. RESULTS: Twelve key ethical considerations focusing on stakeholders, clinical implementation, and governance were identified. Refinement of the Pareto analysis of the top 20% most salient themes in the aggregated GPT outputs yielded 10 key considerations. Additionally, from the top 10% most salient themes, 5 considerations were retrieved. An ethical framework for the use of AI in neurosurgery was developed. CONCLUSIONS: It is critical to address the ethical considerations associated with the use of AI in neurosurgery. The framework described in this manuscript may facilitate the integration of AI into neurosurgery, benefitting both patients and neurosurgeons alike. We urge neurosurgeons to use AI only for validated purposes and caution against automatic adoption of its outputs without neurosurgeon interpretation.

Occupational Pesticide Exposure in Parkinson's Disease Related to GBA and LRRK2 Variants.

Background: The penetrance of common genetic risk variants for Parkinson's disease (PD) is low. Pesticide exposure increases PD risk, but how exposure affects penetrance is not well understood. Objective: To determine the relationship between occupational pesticide exposure and PD in people with LRRK2 and GBA risk variants. Methods: Participants of the Parkinson's Progression Markers Initiative (PPMI) with a LRRK2-G2019 S or GBA risk variant provided information about occupational pesticide exposure. We compared exposure in carriers with and without PD. Among carriers with PD, we used Cox proportional hazard models to compare time-to impairment in balance, cognition, and activities of daily living (ADLs) between participants with and without prior occupational pesticide exposure. Results: 378 participants with a risk variant provided exposure information; 176 with LRRK2-G2019 S (54 with and 122 without PD) and 202 with GBA variants (47 with and 155 without PD). Twenty-six participants reported pesticide exposure. People with a GBA variant and occupational pesticide exposure had much higher odds of PD (aOR: 5.4, 95% CI 1.7-18.5, p < 0.01). People with a LRRK2 variant and a history of occupational pesticide exposure had non-significantly elevated odds of PD (aOR 1.3, 95% CI 0.4-4.6, p = 0.7). Among those with PD, pesticide exposure was associated with a higher risk of balance problems and cognitive impairment in LRRK2-PD and functional impairment in GBA-PD, although associations were not statistically significant. Conclusions: Occupational pesticide exposure may increase penetrance of GBA-PD and may be associated with faster symptom progression. Further studies in larger cohorts are necessary.

A meta-analysis on global change drivers and the risk of infectious disease.

Anthropogenic change is contributing to the rise in emerging infectious diseases, which are significantly correlated with socioeconomic, environmental and ecological factors1. Studies have shown that infectious disease risk is modified by changes to biodiversity2-6, climate change7-11, chemical pollution12-14, landscape transformations15-20 and species introductions21. However, it remains unclear which global change drivers most increase disease and under what contexts. Here we amassed a dataset from the literature that contains 2,938 observations of infectious disease responses to global change drivers across 1,497 host-parasite combinations, including plant, animal and human hosts. We found that biodiversity loss, chemical pollution, climate change and introduced species are associated with increases in disease-related end points or harm, whereas urbanization is associated with decreases in disease end points. Natural biodiversity gradients, deforestation and forest fragmentation are comparatively unimportant or idiosyncratic as drivers of disease. Overall, these results are consistent across human and non-human diseases. Nevertheless, context-dependent effects of the global change drivers on disease were found to be common. The findings uncovered by this meta-analysis should help target disease management and surveillance efforts towards global change drivers that increase disease. Specifically, reducing greenhouse gas emissions, managing ecosystem health, and preventing biological invasions and biodiversity loss could help to reduce the burden of plant, animal and human diseases, especially when coupled with improvements to social and economic determinants of health.

A Closed-Loop Falls Monitoring and Prevention App for Multiple Sclerosis Clinical Practice: Human-Centered Design of the Multiple Sclerosis Falls InsightTrack.

BACKGROUND: Falls are common in people with multiple sclerosis (MS), causing injuries, fear of falling, and loss of independence. Although targeted interventions (physical therapy) can help, patients underreport and clinicians undertreat this issue. Patient-generated data, combined with clinical data, can support the prediction of falls and lead to timely intervention (including referral to specialized physical therapy). To be actionable, such data must be efficiently delivered to clinicians, with care customized to the patient's specific context. OBJECTIVE: This study aims to describe the iterative process of the design and development of Multiple Sclerosis Falls InsightTrack (MS-FIT), identifying the clinical and technological features of this closed-loop app designed to support streamlined falls reporting, timely falls evaluation, and comprehensive and sustained falls prevention efforts. METHODS: Stakeholders were engaged in a double diamond process of human-centered design to ensure that technological features aligned with users' needs. Patient and clinician interviews were designed to elicit insight around ability blockers and boosters using the capability, opportunity, motivation, and behavior (COM-B) framework to facilitate subsequent mapping to the Behavior Change Wheel. To support generalizability, patients and experts from other clinical conditions associated with falls (geriatrics, orthopedics, and Parkinson disease) were also engaged. Designs were iterated based on each round of feedback, and final mock-ups were tested during routine clinical visits. RESULTS: A sample of 30 patients and 14 clinicians provided at least 1 round of feedback. To support falls reporting, patients favored a simple biweekly survey built using REDCap (Research Electronic Data Capture; Vanderbilt University) to support bring-your-own-device accessibility-with optional additional context (the severity and location of falls). To support the evaluation and prevention of falls, clinicians favored a clinical dashboard featuring several key visualization widgets: a longitudinal falls display coded by the time of data capture, severity, and context; a comprehensive, multidisciplinary, and evidence-based checklist of actions intended to evaluate and prevent falls; and MS resources local to a patient's community. In-basket messaging alerts clinicians of severe falls. The tool scored highly for usability, likability, usefulness, and perceived effectiveness (based on the Health IT Usability Evaluation Model scoring). CONCLUSIONS: To our knowledge, this is the first falls app designed using human-centered design to prioritize behavior change and, while being accessible at home for patients, to deliver actionable data to clinicians at the point of care. MS-FIT streamlines data delivery to clinicians via an electronic health record-embedded window, aligning with the 5 rights approach. Leveraging MS-FIT for data processing and algorithms minimizes clinician load while boosting care quality. Our innovation seamlessly integrates real-world patient-generated data as well as clinical and community-level factors, empowering self-care and addressing the impact of falls in people with MS. Preliminary findings indicate wider relevance, extending to other neurological conditions associated with falls and their consequences.

Application of machine learning and artificial intelligence in the diagnosis and classification of polycystic ovarian syndrome: a systematic review.

Introduction: Polycystic Ovarian Syndrome (PCOS) is the most common endocrinopathy in women of reproductive age and remains widely underdiagnosed leading to significant morbidity. Artificial intelligence (AI) and machine learning (ML) hold promise in improving diagnostics. Thus, we performed a systematic review of literature to identify the utility of AI/ML in the diagnosis or classification of PCOS. Methods: We applied a search strategy using the following databases MEDLINE, Embase, the Cochrane Central Register of Controlled Trials, the Web of Science, and the IEEE Xplore Digital Library using relevant keywords. Eligible studies were identified, and results were extracted for their synthesis from inception until January 1, 2022. Results: 135 studies were screened and ultimately, 31 studies were included in this study. Data sources used by the AI/ML interventions included clinical data, electronic health records, and genetic and proteomic data. Ten studies (32%) employed standardized criteria (NIH, Rotterdam, or Revised International PCOS classification), while 17 (55%) used clinical information with/without imaging. The most common AI techniques employed were support vector machine (42% studies), K-nearest neighbor (26%), and regression models (23%) were the commonest AI/ML. Receiver operating curves (ROC) were employed to compare AI/ML with clinical diagnosis. Area under the ROC ranged from 73% to 100% (n=7 studies), diagnostic accuracy from 89% to 100% (n=4 studies), sensitivity from 41% to 100% (n=10 studies), specificity from 75% to 100% (n=10 studies), positive predictive value (PPV) from 68% to 95% (n=4 studies), and negative predictive value (NPV) from 94% to 99% (n=2 studies). Conclusion: Artificial intelligence and machine learning provide a high diagnostic and classification performance in detecting PCOS, thereby providing an avenue for early diagnosis of this disorder. However, AI-based studies should use standardized PCOS diagnostic criteria to enhance the clinical applicability of AI/ML in PCOS and improve adherence to methodological and reporting guidelines for maximum diagnostic utility. Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42022295287.

Impact of the Dopamine System on Long-Term Cognitive Impairment in Parkinson Disease: An Exploratory Study.

Background: Little is known about the impact of the dopamine system on development of cognitive impairment (CI) in Parkinson disease (PD). Objectives: We used data from a multi-site, international, prospective cohort study to explore the impact of dopamine system-related biomarkers on CI in PD. Methods: PD participants were assessed annually from disease onset out to 7 years, and CI determined by applying cut-offs to four measures: (1) Montreal Cognitive Assessment; (2) detailed neuropsychological test battery; (3) Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) cognition score; and (4) site investigator diagnosis of CI (mild cognitive impairment or dementia). The dopamine system was assessed by serial Iodine-123 Ioflupane dopamine transporter (DAT) imaging, genotyping, and levodopa equivalent daily dose (LEDD) recorded at each assessment. Multivariate longitudinal analyses, with adjustment for multiple comparisons, determined the association between dopamine system-related biomarkers and CI, including persistent impairment. Results: Demographic and clinical variables associated with CI were higher age, male sex, lower education, non-White race, higher depression and anxiety scores and higher MDS-UPDRS motor score. For the dopamine system, lower baseline mean striatum dopamine transporter values (P range 0.003-0.005) and higher LEDD over time (P range <0.001-0.01) were significantly associated with increased risk for CI. Conclusions: Our results provide preliminary evidence that alterations in the dopamine system predict development of clinically-relevant, cognitive impairment in Parkinson's disease. If replicated and determined to be causative, they demonstrate that the dopamine system is instrumental to cognitive health status throughout the disease course. TRIAL REGISTRATION: Parkinson's Progression Markers Initiative is registered with ClinicalTrials.gov (NCT01141023).

The Thyroid Hormone Axis and Female Reproduction.

Thyroid function affects multiple sites of the female hypothalamic-pituitary gonadal (HPG) axis. Disruption of thyroid function has been linked to reproductive dysfunction in women and is associated with menstrual irregularity, infertility, poor pregnancy outcomes, and gynecological conditions such as premature ovarian insufficiency and polycystic ovarian syndrome. Thus, the complex molecular interplay between hormones involved in thyroid and reproductive functions is further compounded by the association of certain common autoimmune states with disorders of the thyroid and the HPG axes. Furthermore, in prepartum and intrapartum states, even relatively minor disruptions have been shown to adversely impact maternal and fetal outcomes, with some differences of opinion in the management of these conditions. In this review, we provide readers with a foundational understanding of the physiology and pathophysiology of thyroid hormone interactions with the female HPG axis. We also share clinical insights into the management of thyroid dysfunction in reproductive-aged women.

Density declines, richness increases, and composition shifts in stream macroinvertebrates.

Documenting trends of stream macroinvertebrate biodiversity is challenging because biomonitoring often has limited spatial, temporal, and taxonomic scopes. We analyzed biodiversity and composition of assemblages of >500 genera, spanning 27 years, and 6131 stream sites across forested, grassland, urban, and agricultural land uses throughout the United States. In this dataset, macroinvertebrate density declined by 11% and richness increased by 12.2%, and insect density and richness declined by 23.3 and 6.8%, respectively, over 27 years. In addition, differences in richness and composition between urban and agricultural versus forested and grassland streams have increased over time. Urban and agricultural streams lost the few disturbance-sensitive taxa they once had and gained disturbance-tolerant taxa. These results suggest that current efforts to protect and restore streams are not sufficient to mitigate anthropogenic effects.

Temporal novelty detection and multiple timescale integration drive Drosophila orientation dynamics in temporally diverse olfactory environments.

To survive, insects must effectively navigate odor plumes to their source. In natural plumes, turbulent winds break up smooth odor regions into disconnected patches, so navigators encounter brief bursts of odor interrupted by bouts of clean air. The timing of these encounters plays a critical role in navigation, determining the direction, rate, and magnitude of insects' orientation and speed dynamics. Disambiguating the specific role of odor timing from other cues, such as spatial structure, is challenging due to natural correlations between plumes' temporal and spatial features. Here, we use optogenetics to isolate temporal features of odor signals, examining how the frequency and duration of odor encounters shape the navigational decisions of freely-walking Drosophila. We find that fly angular velocity depends on signal frequency and intermittency-the fraction of time signal can be detected-but not directly on durations. Rather than switching strategies when signal statistics change, flies smoothly transition between signal regimes, by combining an odor offset response with a frequency-dependent novelty-like response. In the latter, flies are more likely to turn in response to each odor hit only when the hits are sparse. Finally, the upwind bias of individual turns relies on a filtering scheme with two distinct timescales, allowing rapid and sustained responses in a variety of signal statistics. A quantitative model incorporating these ingredients recapitulates fly orientation dynamics across a wide range of environments and shows that temporal novelty detection, when combined with odor motion detection, enhances odor plume navigation.

Early detection of Parkinson's disease through enriching the electronic health record using a biomedical knowledge graph.

Introduction: Early diagnosis of Parkinson's disease (PD) is important to identify treatments to slow neurodegeneration. People who develop PD often have symptoms before the disease manifests and may be coded as diagnoses in the electronic health record (EHR). Methods: To predict PD diagnosis, we embedded EHR data of patients onto a biomedical knowledge graph called Scalable Precision medicine Open Knowledge Engine (SPOKE) and created patient embedding vectors. We trained and validated a classifier using these vectors from 3,004 PD patients, restricting records to 1, 3, and 5 years before diagnosis, and 457,197 non-PD group. Results: The classifier predicted PD diagnosis with moderate accuracy (AUC = 0.77 ± 0.06, 0.74 ± 0.05, 0.72 ± 0.05 at 1, 3, and 5 years) and performed better than other benchmark methods. Nodes in the SPOKE graph, among cases, revealed novel associations, while SPOKE patient vectors revealed the basis for individual risk classification. Discussion: The proposed method was able to explain the clinical predictions using the knowledge graph, thereby making the predictions clinically interpretable. Through enriching EHR data with biomedical associations, SPOKE may be a cost-efficient and personalized way to predict PD diagnosis years before its occurrence.

Risk of Parkinson Disease Among Service Members at Marine Corps Base Camp Lejeune.

Importance: An increased risk of Parkinson disease (PD) has been associated with exposure to the solvent trichloroethylene (TCE), but data are limited. Millions of people in the US and worldwide are exposed to TCE in air, food, and water. Objective: To test whether the risk of PD is higher in veterans who served at Marine Corps Base Camp Lejeune, whose water supply was contaminated with TCE and other volatile organic compounds (VOCs), compared with veterans who did not serve on that base. Design, Setting, and Participants: This population-based cohort study examined the risk for PD among all Marines and Navy personnel who resided at Camp Lejeune, North Carolina (contaminated water) (n = 172 128), or Camp Pendleton, California (uncontaminated water) (n = 168 361), for at least 3 months between 1975 and 1985, with follow-up from January 1, 1997, until February 17, 2021. Veterans Health Administration and Medicare databases were searched for International Classification of Diseases diagnostic codes for PD or other forms of parkinsonism and related medications and for diagnostic codes indicative of prodromal disease. Parkinson disease diagnoses were confirmed by medical record review. Exposures: Water supplies at Camp Lejeune were contaminated with several VOCs. Levels were highest for TCE, with monthly median values greater than 70-fold the permissible amount. Main Outcome and Measures: Risk of PD in former residents of Camp Lejeune relative to residents of Camp Pendleton. In those without PD or another form of parkinsonism, the risk of being diagnosed with features of prodromal PD were assessed individually and cumulatively using likelihood ratio tests. Results: Health data were available for 158 122 veterans (46.4%). Demographic characteristics were similar between Camp Lejeune (5.3% women, 94.7% men; mean [SD] attained age of 59.64 [4.43] years; 29.7% Black, 6.0% Hispanic, 67.6% White; and 2.7% other race and ethnicity) and Camp Pendleton (3.8% women, 96.2% men; mean [SD] age, 59.80 [4.62] years; 23.4% Black, 9.4% Hispanic, 71.1% White, and 5.5% other race and ethnicity). A total of 430 veterans had PD, with 279 from Camp Lejeune (prevalence, 0.33%) and 151 from Camp Pendleton (prevalence, 0.21%). In multivariable models, Camp Lejeune veterans had a 70% higher risk of PD (odds ratio, 1.70; 95% CI, 1.39-2.07; P < .001). No excess risk was found for other forms of neurodegenerative parkinsonism. Camp Lejeune veterans also had a significantly increased risk of prodromal PD diagnoses, including tremor, anxiety, and erectile dysfunction, and higher cumulative prodromal risk scores. Conclusions and Relevance: The study's findings suggest that the risk of PD is higher in persons exposed to TCE and other VOCs in water 4 decades ago. Millions worldwide have been and continue to be exposed to this ubiquitous environmental contaminant.

A Case of Pure Pericarditis Associated With COVID-19: Application of Classical Clinical Evaluation for Differential Diagnosis.

Pericarditis of varying severity is being recognized as a rare complication of the COVID-19 infection. We present a patient with an electrocardiogram (EKG) and physical exam findings that initially seemed to most likely be pericarditis related to the COVID-19 infection. The differential diagnosis was a bit difficult because it included ST-segment elevation myocardial infarction (STEMI) due to some EKG changes and early repolarization changes that were rather robust. Treatment options for STEMI could cause severe harm if the process turned out to be pericarditis. Treatment options for pericarditis could cause severe harm if the process turned out to be STEMI. And treatment options for early repolarization might be no treatment at all, which could cause harm if the process turned out to be STEMI or pericarditis. In this case, a correct diagnosis was very important to ensure a good clinical outcome. We would like to share our thought processes in the management of this case.

"No matter what your story is, there is a place for you in science": Students' Ability to Relate to Scientists Positively Shifts after Scientist Spotlight Assignments, Especially for First-Generation Students and Women.

We evaluate the impact of a low-stakes easy-to-implement course-level intervention, Scientist Spotlight assignments, which feature personal and professional stories of diverse scientists. This work extends previous studies by examining whether shifts in relatability differ across student identities, particularly students who identify as first-generation students, a population that has not been the focus of previous investigations of this intervention. Using paired pre- and postcourse data from four implementations in an introductory biology course, we report a significant, positive shift in undergraduate students' self-reported ability to relate to scientists, and concomitant shifts in how students describe scientists after completing four or six Scientist Spotlight assignments.Importantly, our data demonstrate a disproportionate, positive shift for first-generation college students and for students who identify as female, a novel contribution to the body of literature investigating the Scientist Spotlight intervention. This study, along with previous reports of similar shifts in varying institutional contexts across different populations of learners, provides a strong argument that instructors interested in diversifying their course content to include representations of diverse scientists to enhance students' ability to identify a range of "types of people" who do science can do so successfully through incorporation of a small number of Spotlight assignments.

Bayesian network-based risk assessment of synthetic biology: Simulating CRISPR-Cas9 gene drive dynamics in invasive rodent management.

Gene drive technology has been proposed to control invasive rodent populations as an alternative to rodenticides. However, this approach has not undergone risk assessment that meets criteria established by Gene Drives on the Horizon, a 2016 report by the National Academies of Sciences, Engineering, and Medicine. To conduct a risk assessment of gene drives, we employed the Bayesian network-relative risk model to calculate the risk of mouse eradication on Southeast Farallon Island using a CRISPR-Cas9 homing gene drive construct. We modified and implemented the R-based model "MGDrivE" to simulate and compare 60 management strategies for gene drive rodent management. These scenarios spanned four gene drive mouse release schemes, three gene drive homing rates, three levels of supplemental rodenticide dose, and two timings of rodenticide application relative to gene drive release. Simulation results showed that applying a supplemental rodenticide simultaneously with gene drive mouse deployment resulted in faster eradication of the island mouse population. Gene drive homing rate had the highest influence on the overall probability of successful eradication, as increased gene drive accuracy reduces the likelihood of mice developing resistance to the CRISPR-Cas9 homing mechanism.

Enhancing Clinical Information Display to Improve Patient Encounters: Human-Centered Design and Evaluation of the Parkinson Disease-BRIDGE Platform.

BACKGROUND: People with Parkinson disease (PD) have a variety of complex medical problems that require detailed review at each clinical encounter for appropriate management. Care of other complex conditions has benefited from digital health solutions that efficiently integrate disparate clinical information. Although various digital approaches have been developed for research and care in PD, no digital solution to personalize and improve communication in a clinical encounter is readily available. OBJECTIVE: We intend to improve the efficacy and efficiency of clinical encounters with people with PD through the development of a platform (PD-BRIDGE) with personalized clinical information from the electronic health record (EHR) and patient-reported outcome (PRO) data. METHODS: Using human-centered design (HCD) processes, we engaged clinician and patient stakeholders in developing PD-BRIDGE through three phases: an inspiration phase involving focus groups and discussions with people having PD, an ideation phase generating preliminary mock-ups for feedback, and an implementation phase testing the platform. To qualitatively evaluate the platform, movement disorders neurologists and people with PD were sent questionnaires asking about the technical validity, usability, and clinical relevance of PD-BRIDGE after their encounter. RESULTS: The HCD process led to a platform with 4 modules. Among these, 3 modules that pulled data from the EHR include a longitudinal module showing motor ratings over time, a display module showing the most recently collected clinical rating scales, and another display module showing relevant laboratory values and diagnoses; the fourth module displays motor symptom fluctuation based on an at-home diary. In the implementation phase, PD-BRIDGE was used in 17 clinical encounters for patients cared for by 1 of 11 movement disorders neurologists. Most patients felt that PD-BRIDGE facilitated communication with their clinician (n=14, 83%) and helped them understand their disease trajectory (n=11, 65%) and their clinician's recommendations (n=11, 65%). Neurologists felt that PD-BRIDGE improved their ability to understand the patients' disease course (n=13, 75% of encounters), supported clinical care recommendations (n=15, 87%), and helped them communicate with their patients (n=14, 81%). In terms of improvements, neurologists noted that data in PD-BRIDGE were not exhaustive in 62% (n=11) of the encounters. CONCLUSIONS: Integrating clinically relevant information from EHR and PRO data into a visually efficient platform (PD-BRIDGE) can facilitate clinical encounters with people with PD. Developing new modules with more disparate information could improve these complex encounters even further.

Association of Motor and Nonmotor Symptoms With Health-Related Quality of Life in a Large Online Cohort of People With Parkinson Disease.

BACKGROUND AND OBJECTIVES: There is growing interest in health-related quality of life (HRQOL) as a comprehensive view of the patient's well-being, guiding concept for the treating clinician, and therapeutic trial outcome measure for patients with Parkinson disease (PwPD). The key determinants of HRQOL have not been investigated in large populations of PwPD. Our objective was to evaluate correlates of HRQOL in a large, online cohort of PwPD. METHODS: As part of an ongoing online cohort study, we performed a cross-sectional analysis at enrollment of 23,058 PwPD. We conducted univariate and stepwise multivariate linear regression analyses of HRQOL as measured by the EQ-5D-5L tool. In addition, we performed an interaction analysis to evaluate heterogeneity of the effect of motor symptoms on HRQOL and Spearman correlation analysis to evaluate the association of nonmotor symptoms with HRQOL. RESULTS: In the multivariate linear regression model, participants with moderate or severe depression, more severe motor symptoms, and a higher burden of medical comorbidities had the most substantially decreased HRQOL as measured by the EQ index (ß -0.11, -0.18, -0.02, -0.01, respectively; p < 0.001 for all). An interaction analysis showed that more severe motor symptoms had a higher effect on individuals with female sex, lower educational level, lower income, more severe depression, or more severe cognitive impairment (p ≤ 0.01 for interaction terms). Neuropsychiatric symptoms and falls had the most negative associations with HRQOL (ρ -0.31 to 0.37; p < 0.0001). DISCUSSION: Potentially treatable motor and nonmotor symptoms, particularly neuropsychiatric symptoms, account for a large amount of the variation in HRQOL in PwPD. Motor symptoms may have differential effects on HRQOL in different demographic and clinical subpopulations, highlighting important areas for future health disparities research. Our findings provide targets for clinician intervention and future research on symptom management to optimize HRQOL in PD. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that motor and neuropsychiatric symptoms are associated with HRQOL in PwPD.