Efficacy of a Mobile App-Based Intervention for Young Adults With Anxiety Disorders: A Randomized Clinical Trial.

Importance: Anxiety disorders are prevalent and undertreated among young adults. Digital mental health interventions for anxiety are promising but limited by a narrow range of therapeutic components and low user engagement. Objective: To investigate the efficacy of and engagement with Maya, a scalable, self-guided, comprehensive mobile cognitive behavioral therapy (CBT) intervention with embedded engagement features, comparing the effects of 3 incentive conditions. Design, Setting, and Participants: This randomized clinical trial recruited young adults aged 18 to 25 years with anxiety disorders through online advertisements and outpatient psychiatry clinics at Weill Cornell Medicine. Enrollment was between June 16, 2021, and November 11, 2022. Data analysis was performed from December 21, 2022, to June 14, 2024. Intervention: Participants received a 6-week program of the intervention and were randomized to 1 of 3 different text message-based incentive conditions (gain-framed, loss-framed, or gain-social support). Main Outcomes and Measures: The primary outcome was change in anxious symptoms from baseline to end of treatment, as measured by the Hamilton Anxiety Rating Scale (HAM-A). The Anxiety Sensitivity Index and the Leibowitz Social Anxiety Scale scores were secondary measures. Results: The sample consisted of 59 participants (mean [SD] age, 23.1 [1.9] years; 46 [78%] female; 22 [37%] Asian, 3 [5%] Black, 5 [8%] Hispanic or Latino, 1 [2%] American Indian or Alaska Native, 25 [42%] White, and 6 [10%] >1 race; 32 [54%] college-educated and 12 [20%] graduate or professional school-educated; mean [SD] baseline HAM-A score, 15.0 [6.5]). Anxiety, measured by HAM-A, decreased across conditions from baseline to end of the intervention (mean difference, -5.64; 95% CI, -7.23 to -4.05), and symptomatic improvement was maintained at the week 12 follow-up (baseline to follow-up mean difference, -5.67; 95% CI, -7.29 to -4.04). However, there was no evidence that change in anxiety differed by incentive condition (loss-framed vs gain-social support mean difference, -1.40; 95% CI, -4.72 to 1.93; gain-framed vs gain-social support mean difference, 1.38; 95% CI, -1.19 to 3.96). Secondary anxiety measures (Anxiety Sensitivity Index and Liebowitz Social Anxiety Scale scores) showed a similar pattern of improvement, with no evidence of differences between incentive conditions. Participants completed most of the 12 sessions (mean [SD], 10.8 [2.1]; 95% CI, 10.3-11.4), and User Mobile Application Rating Scale app quality ratings exceeded the published threshold for acceptability at all study visits. There was no evidence that either session completion or app quality ratings differed by incentive condition. Conclusions and Relevance: In this randomized clinical trial of an app-based intervention for anxiety, the primary hypothesis that improvement in anxiety would be greatest in the condition using gain of points plus social incentives was not supported; however, the results suggest that a CBT application incorporating a full suite of CBT skills and embedded user engagement features was efficacious in improving symptoms in young adults with anxiety disorders. Given these findings, digital interventions represent a promising step toward wider dissemination of high-quality, evidence-based interventions. Trial Registration: ClinicalTrials.gov Identifier: NCT05130281.

Race-Ethnicity, Rurality, and Age in Prospective Preferences and Concerns Regarding Closed-Loop Implanted Neural Devices.

OBJECTIVE: Responsive and human-centered neurotechnology development requires attention to public perceptions, particularly among groups underserved by existing treatments. METHODS: The authors conducted a preregistered nationally representative survey (https://osf.io/ej9h2) using the NORC at the University of Chicago AmeriSpeak panel. One vignette compared an implanted neural device with surgical resection in a scenario involving epilepsy, and another compared an implanted neural device with medications in a scenario involving mood disorders. The survey also contained questions about respondents' confidence that a device would be available if needed and confidence that enough research has been conducted among people like themselves. Responses were entered into nested survey-weighted logistic regression models, including a base demographic model (to test the overall effect of demographic factors) and an adjusted model that also included socioeconomic, religious and political, and health care access predictors. RESULTS: A total of 1,047 adults responded to the survey, which oversampled Black non-Hispanic (N=214), Hispanic (N=210), and rural (N=219) Americans. In the base demographic model, older Americans were more likely to prefer an implanted device in the two scenarios, and non-Hispanic Black Americans were less likely than non-Hispanic White Americans to prefer a device; rural Americans were less confident than urban or suburban Americans in having access, and non-Hispanic Black and rural Americans were less confident that enough research has been conducted among people like themselves. In adjusted models, income was a key mediator, partially explaining the effect of age and the contrast between Black and White non-Hispanic respondents on preferences for a device in the epilepsy scenario and fully explaining the effect of rurality on confidence in access. CONCLUSIONS: Demographic differences in prospective preferences and concerns highlight the importance of including members of underserved communities in neurotechnology development.

Prevalence and correlates of irritability among U.S. adults.

This study aimed to characterize the prevalence of irritability among U.S. adults, and the extent to which it co-occurs with major depressive and anxious symptoms. A non-probability internet survey of individuals 18 and older in 50 U.S. states and the District of Columbia was conducted between November 2, 2023, and January 8, 2024. Regression models with survey weighting were used to examine associations between the Brief Irritability Test (BITe5) and sociodemographic and clinical features. The survey cohort included 42,739 individuals, mean age 46.0 (SD 17.0) years; 25,001 (58.5%) identified as women, 17,281 (40.4%) as men, and 457 (1.1%) as nonbinary. A total of 1218(2.8%) identified as Asian American, 5971 (14.0%) as Black, 5348 (12.5%) as Hispanic, 1775 (4.2%) as another race, and 28,427 (66.5%) as white. Mean irritability score was 13.6 (SD 5.6) on a scale from 5 to 30. In linear regression models, irritability was greater among respondents who were female, younger, had lower levels of education, and lower household income. Greater irritability was associated with likelihood of thoughts of suicide in logistic regression models adjusted for sociodemographic features (OR 1.23, 95% CI 1.22-1.24). Among 1979 individuals without thoughts of suicide on the initial survey assessed for such thoughts on a subsequent survey, greater irritability was also associated with greater likelihood of thoughts of suicide being present (adjusted OR 1.17, 95% CI 1.12-1.23). The prevalence of irritability and its association with thoughts of suicide suggests the need to better understand its implications among adults outside of acute mood episodes.

Research Letter: Characterizing spin in psychiatric clinical research literature using large language models.

Importance: Spin is a common form of biased reporting that misrepresents study results in publications as more positive than an objective assessment would indicate, but its prevalence in psychiatric journals is unknown. Objective: To apply a large language model to characterize the extent to which original reports of pharmacologic and non-pharmacologic interventions in psychiatric journals reflect spin. Design: We identified abstracts from studies published between 2013 and 2023 in 3 high-impact psychiatric journals describing randomized trials or meta-analyses of interventions. Main Outcome and Measure: Presence or absence of spin estimated by a large language model (GPT4-turbo, turbo-2024-04-09), validated using gold standard abstracts with and without spin. Results: Among a total of 663 abstracts, 296 (44.6%) exhibited possible or probable spin - 230/529 (43.5%) randomized trials, 66/134 (49.3%) meta-analyses; 148/310 (47.7%) for medication, 107/238 (45.0%) for psychotherapy, and 41/115 (35.7%) for other interventions. In a multivariable logistic regression model, reports of randomized trials, and non-pharmacologic/non-psychotherapy interventions, were less likely to exhibit spin, as were more recent publications. Conclusions and Relevance: A substantial subset of psychiatric intervention abstracts in high-impact journals may contain results presented in a potentially misleading way, with the potential to impact clinical practice. The success in automating spin detection via large language models may facilitate identification and revision to minimize spin in future publications.

Trust in Physicians and Hospitals During the COVID-19 Pandemic in a 50-State Survey of US Adults.

Importance: Trust in physicians and hospitals has been associated with achieving public health goals, but the increasing politicization of public health policies during the COVID-19 pandemic may have adversely affected such trust. Objective: To characterize changes in US adults' trust in physicians and hospitals over the course of the COVID-19 pandemic and the association between this trust and health-related behaviors. Design, Setting, and Participants: This survey study uses data from 24 waves of a nonprobability internet survey conducted between April 1, 2020, and January 31, 2024, among 443 455 unique respondents aged 18 years or older residing in the US, with state-level representative quotas for race and ethnicity, age, and gender. Main Outcome and Measure: Self-report of trust in physicians and hospitals; self-report of SARS-CoV-2 and influenza vaccination and booster status. Survey-weighted regression models were applied to examine associations between sociodemographic features and trust and between trust and health behaviors. Results: The combined data included 582 634 responses across 24 survey waves, reflecting 443 455 unique respondents. The unweighted mean (SD) age was 43.3 (16.6) years; 288 186 respondents (65.0%) reported female gender; 21 957 (5.0%) identified as Asian American, 49 428 (11.1%) as Black, 38 423 (8.7%) as Hispanic, 3138 (0.7%) as Native American, 5598 (1.3%) as Pacific Islander, 315 278 (71.1%) as White, and 9633 (2.2%) as other race and ethnicity (those who selected "Other" from a checklist). Overall, the proportion of adults reporting a lot of trust for physicians and hospitals decreased from 71.5% (95% CI, 70.7%-72.2%) in April 2020 to 40.1% (95% CI, 39.4%-40.7%) in January 2024. In regression models, features associated with lower trust as of spring and summer 2023 included being 25 to 64 years of age, female gender, lower educational level, lower income, Black race, and living in a rural setting. These associations persisted even after controlling for partisanship. In turn, greater trust was associated with greater likelihood of vaccination for SARS-CoV-2 (adjusted odds ratio [OR], 4.94; 95 CI, 4.21-5.80) or influenza (adjusted OR, 5.09; 95 CI, 3.93-6.59) and receiving a SARS-CoV-2 booster (adjusted OR, 3.62; 95 CI, 2.99-4.38). Conclusions and Relevance: This survey study of US adults suggests that trust in physicians and hospitals decreased during the COVID-19 pandemic. As lower levels of trust were associated with lesser likelihood of pursuing vaccination, restoring trust may represent a public health imperative.

Inequities in atrial fibrillation trials: An analysis of participant race, ethnicity, and sex over time.

BACKGROUND: Despite the importance of racial and ethnic representation in clinical trials, limited data exist about the enrollment trends of these groups in atrial fibrillation (AF) trials over time. OBJECTIVE: The purpose of this study was to examine the characteristics of contemporary AF clinical trials and to evaluate their association with race and ethnicity over time. METHODS: We performed a systematic search of all completed AF trials registered in ClinicalTrials.gov from conception to December 31, 2023, and manually extracted composition of race/ethnicity. We stratified trials by study characteristics, including impact factor, publication status, funding source, and location. We calculated the participation to prevalence ratio (PPR) by dividing the percentage of non-White participants by the percentage of non-White participants in the disease population (PPR of 0.8-1.2 suggests proportional representation) over time. RESULTS: We identified 277 completed AF trials encompassing a total of 1,933,441 adults, with a median proportion of non-White at 12% (interquartile range, 6%-27%), 121 (43.7%) device focused, and 184 (66.4%) funded by industry. Only 36.1% of trials reported comprehensive race information. Overall, non-White participants were underrepresented (PPR = 0.511; P < .001), including Black (PPR = 0.263) and Hispanic (PPR = 0.337) participants. The proportion of non-White participants did not change significantly between 2000 and 2023 (11% vs 9%; P = .343). CONCLUSION: Despite greater awareness, race/ethnicity reporting and representation of non-White groups in AF clinical trials are poor and have not improved significantly over time. These findings demand additional recruitment efforts and novel recruitment policies to ensure adequate representation of these demographic subgroups in future AF clinical trials.

Who are we missing? Language exclusivity of patient-reported outcomes in atrial fibrillation clinical trials.

INTRODUCTION: Patient-reported outcomes (PROs) are increasingly used to evaluate quality of life (QoL) in Atrial Fibrillation (AF) patients, providing crucial insights in clinical trials. This study examines the frequency of PRO use in AF trials and the linguistic accessibility of AF-specific PROs. BACKGROUND: As the United States becomes more multilingual, ensuring PROs are available in various languages is vital. The number of people speaking a language other than English at home has tripled from 23.1 million in 1980 to 67.8 million in 2019. This diversity necessitates the availability of PROs in multiple languages for inclusive clinical assessments. METHODS: We queried ClinicalTrials.gov for all US interventional AF trials up to November 28, 2023, reviewing each for PRO usage as primary or secondary outcomes. We identified the five most common AF-specific and generic PROs, extracting their available translations and original languages from published sources. RESULTS: Of 233 identified trials, 191 had associated publications, with 180 (94.2%) conducted solely in English. Only one trial (0.4%) used an AF-specific PRO as a primary outcome, compared to four (1.7%) with a generic PRO. Ten trials (4.3%) used AF-specific PROs as secondary endpoints, versus 22 (9.4%) using generic PROs. AF-specific PROs had significantly fewer translations than generic PROs (11.2 vs. 148.8; p < .001). The AF Effect on Quality-of-Life (AFEQT) was available in 24 languages, with limited translations in commonly spoken US languages like Arabic and Asian languages. CONCLUSION: The limited availability of AF-specific PRO translations highlights a barrier to inclusive AF clinical trials. Expanding translations for AF-specific PROs is crucial for equitable QoL assessments.

Heterogeneity in Antidepressant Treatment and Major Depressive Disorder Outcomes Among Clinicians.

Importance: While abundant work has examined patient-level differences in antidepressant treatment outcomes, little is known about the extent of clinician-level differences. Understanding these differences may be important in the development of risk models, precision treatment strategies, and more efficient systems of care. Objective: To characterize differences between outpatient clinicians in treatment selection and outcomes for their patients diagnosed with major depressive disorder across academic medical centers, community hospitals, and affiliated clinics. Design, Setting, and Participants: This was a longitudinal cohort study using data derived from electronic health records at 2 large academic medical centers and 6 community hospitals, and their affiliated outpatient networks, in eastern Massachusetts. Participants were deidentified clinicians who billed at least 10 International Classification of Diseases, Ninth Revision (ICD-9) or Tenth Revision (ICD-10) diagnoses of major depressive disorder per year between 2008 and 2022. Data analysis occurred between September 2023 and January 2024. Main Outcomes and Measures: Heterogeneity of prescribing, defined as the number of distinct antidepressants accounting for 75% of prescriptions by a given clinician; proportion of patients who did not return for follow-up after an index prescription; and proportion of patients receiving stable, ongoing antidepressant treatment. Results: Among 11 934 clinicians treating major depressive disorder, unsupervised learning identified 10 distinct clusters on the basis of ICD codes, corresponding to outpatient psychiatry as well as oncology, obstetrics, and primary care. Between these clusters, substantial variability was identified in the proportion of selective serotonin reuptake inhibitors, selective norepinephrine reuptake inhibitors, and tricyclic antidepressants prescribed, as well as in the number of distinct antidepressants prescribed. Variability was also detected between clinician clusters in loss to follow-up and achievement of stable treatment, with the former ranging from 27% to 69% and the latter from 22% to 42%. Clinician clusters were significantly associated with treatment outcomes. Conclusions and Relevance: Groups of clinicians treating individuals diagnosed with major depressive disorder exhibit marked differences in prescribing patterns as well as longitudinal patient outcomes defined by electronic health records. Incorporating these group identifiers yielded similar prediction to more complex models incorporating individual codes, suggesting the importance of considering treatment context in efforts at risk stratification.

Stratifying Risk for Postpartum Depression at Time of Hospital Discharge.

Objective: Postpartum depression (PPD) represents a major contributor to postpartum morbidity and mortality. Beyond efforts at routine screening, risk stratification models could enable more targeted interventions in settings with limited resources. Thus, we aimed to develop and estimate the performance of a generalizable risk stratification model for PPD in patients without a history of depression using information collected as part of routine clinical care. Methods: We performed a retrospective cohort study of all individuals who delivered between 2017 and 2022 in one of two large academic medical centers and six community hospitals. An elastic net model was constructed and externally validated to predict PPD using sociodemographic factors, medical history, and prenatal depression screening information, all of which was known before discharge from the delivery hospitalization. Results: The cohort included 29,168 individuals; 2,703 (9.3%) met at least one criterion for postpartum depression in the 6 months following delivery. In the external validation data, the model had good discrimination and remained well-calibrated: area under the receiver operating characteristic curve 0.721 (95% CI: 0.707-0.734), Brier calibration score 0.088 (95% CI: 0.084 - 0.092). At a specificity of 90%, the positive predictive value was 28.0% (95% CI: 26.0-30.1%), and the negative predictive value was 92.2% (95% CI: 91.8-92.7%). Conclusions: These findings demonstrate that a simple machine-learning model can be used to stratify the risk for PPD before delivery hospitalization discharge. This tool could help identify patients within a practice at the highest risk and facilitate individualized postpartum care planning regarding the prevention of, screening for, and management of PPD at the start of the postpartum period and potentially the onset of symptoms.

Maternal SARS-CoV-2 impacts fetal placental macrophage programs and placenta-derived microglial models of neurodevelopment.

BACKGROUND: The SARS-CoV-2 virus activates maternal and placental immune responses. Such activation in the setting of other infections during pregnancy is known to impact fetal brain development. The effects of maternal immune activation on neurodevelopment are mediated at least in part by fetal brain microglia. However, microglia are inaccessible for direct analysis, and there are no validated non-invasive surrogate models to evaluate in utero microglial priming and function. We have previously demonstrated shared transcriptional programs between microglia and Hofbauer cells (HBCs, or fetal placental macrophages) in mouse models. METHODS AND RESULTS: We assessed the impact of maternal SARS-CoV-2 on HBCs isolated from 24 term placentas (N = 10 SARS-CoV-2 positive cases, 14 negative controls). Using single-cell RNA-sequencing, we demonstrated that HBC subpopulations exhibit distinct cellular programs, with specific subpopulations differentially impacted by SARS-CoV-2. Assessment of differentially expressed genes implied impaired phagocytosis, a key function of both HBCs and microglia, in some subclusters. Leveraging previously validated models of microglial synaptic pruning, we showed that HBCs isolated from placentas of SARS-CoV-2 positive pregnancies can be transdifferentiated into microglia-like cells (HBC-iMGs), with impaired synaptic pruning behavior compared to HBC models from negative controls. CONCLUSION: These findings suggest that HBCs isolated at birth can be used to create personalized cellular models of offspring microglial programming.

Dimensional Measures of Psychopathology in Children and Adolescents Using Large Language Models.

BACKGROUND: To enable greater use of National Institute of Mental Health Research Domain Criteria (RDoC) in real-world settings, we applied large language models (LLMs) to estimate dimensional psychopathology from narrative clinical notes. METHODS: We conducted a cohort study using health records from individuals age ≤18 years evaluated in the psychiatric emergency department of a large academic medical center between November 2008 and March 2015. Outcomes were hospital admission and length of emergency department stay. RDoC domains were estimated using a Health Insurance Portability and Accountability Act-compliant LLM (gpt-4-1106-preview) and compared with a previously validated token-based approach. RESULTS: The cohort included 3059 individuals (median age 16 years [interquartile range, 13-18]; 1580 [52%] female, 1479 [48%] male; 105 [3.4%] identified as Asian, 329 [11%] as Black, 288 [9.4%] as Hispanic, 474 [15%] as other race, and 1863 [61%] as White), of whom 1695 (55%) were admitted. Correlation between LLM-extracted RDoC scores and the token-based scores ranged from small to medium as assessed by Kendall's tau (0.14-0.22). In logistic regression models adjusting for sociodemographic and clinical features, admission likelihood was associated with greater scores on all domains, with the exception of the sensorimotor domain, which was inversely associated (p < .001 for all adjusted associations). Tests for bias suggested modest but statistically significant differences in positive valence scores by race (p < .05 for Asian, Black, and Hispanic individuals). CONCLUSIONS: An LLM extracted estimates of 6 RDoC domains in an explainable manner, which were associated with clinical outcomes. This approach can contribute to a new generation of prediction models or biological investigations based on dimensional psychopathology.

Chemically Driven Sintering of Colloidal Cu Nanocrystals for Multiscale Electronic and Optical Devices.

Emerging applications of Internet of Things (IoT) technologies in smart health, home, and city, in agriculture and environmental monitoring, and in transportation and manufacturing require materials and devices with engineered physical properties that can be manufactured by low-cost and scalable methods, support flexible forms, and are biocompatible and biodegradable. Here, we report the fabrication and device integration of low-cost and biocompatible/biodegradable colloidal Cu nanocrystal (NC) films through room temperature, solution-based deposition, and sintering, achieved via chemical exchange of NC surface ligands. Treatment of organic-ligand capped Cu NC films with solutions of shorter, environmentally benign, and noncorrosive inorganic reagents, namely, SCN- and Cl-, effectively removes the organic ligands, drives NC grain growth, and limits film oxidation. We investigate the mechanism of this chemically driven sintering by systemically varying the Cu NC size, ligand reagent, and ligand treatment time and follow the evolution of their structure and electrical and optical properties. Cl--treated, 4.5 nm diameter Cu NC films yield the lowest DC resistivity, only 3.2 times that of bulk Cu, and metal-like dielectric functions at optical frequencies. We exploit the high conductivity of these chemically sintered Cu NC films and, in combination with photo- and nanoimprint-lithography, pattern multiscale structures to achieve high-Q radio frequency (RF) capacitive sensors and near-infrared (NIR) resonant optical metasurfaces.

Nonvolatile Control of Valley Polarized Emission in 2D WSe2-AlScN Heterostructures.

Achieving robust and electrically controlled valley polarization in monolayer transition metal dichalcogenides (ML-TMDs) is a frontier challenge for realistic valleytronic applications. Theoretical investigations show that the integration of 2D materials with ferroelectrics is a promising strategy; however, an experimental demonstration has remained elusive. Here, we fabricate ferroelectric field-effect transistors using a ML-WSe2 channel and an Al0.68Sc0.32N (AlScN) ferroelectric dielectric and experimentally demonstrate efficient tuning as well as non-volatile control of valley polarization. We measure a large array of transistors and obtain a maximum valley polarization of ∼27% at 80 K with stable retention up to 5400 s. The enhancement in the valley polarization is ascribed to the efficient exciton-to-trion (X-T) conversion and its coupling with an out-of-plane electric field, viz., the quantum-confined Stark effect. This changes the valley depolarization pathway from strong exchange interactions to slow spin-flip intervalley scattering. Our research demonstrates a promising approach for achieving non-volatile control over valley polarization for practical valleytronic device applications.

Transcranial Direct Current Stimulation to Ameliorate Post-Stroke Cognitive Impairment.

Post-stroke cognitive impairment is a common and disabling condition with few effective therapeutic options. After stroke, neural reorganization and other neuroplastic processes occur in response to ischemic injury, which can result in clinical improvement through spontaneous recovery. Neuromodulation through transcranial direct current stimulation (tDCS) is a promising intervention to augment underlying neuroplasticity in order to improve cognitive function. This form of neuromodulation leverages mechanisms of neuroplasticity post-stroke to optimize neural reorganization and improve function. In this review, we summarize the current state of cognitive neurorehabilitation post-stroke, the practical features of tDCS, its uses in stroke-related cognitive impairment across cognitive domains, and special considerations for the use of tDCS in the post-stroke patient population.

Hofbauer cells and fetal brain microglia share transcriptional profiles and responses to maternal diet-induced obesity.

Maternal immune activation is associated with adverse offspring neurodevelopmental outcomes, many mediated by in utero microglial programming. As microglia remain inaccessible throughout development, identification of noninvasive biomarkers reflecting fetal brain microglial programming could permit screening and intervention. We used lineage tracing to demonstrate the shared ontogeny between fetal brain macrophages (microglia) and fetal placental macrophages (Hofbauer cells) in a mouse model of maternal diet-induced obesity, and single-cell RNA-seq to demonstrate shared transcriptional programs. Comparison with human datasets demonstrated conservation of placental resident macrophage signatures between mice and humans. Single-cell RNA-seq identified common alterations in fetal microglial and Hofbauer cell gene expression induced by maternal obesity, as well as sex differences in these alterations. We propose that Hofbauer cells, which are easily accessible at birth, provide insights into fetal brain microglial programs and may facilitate the early identification of offspring vulnerable to neurodevelopmental disorders.

Multistate, Ultrathin, Back-End-of-Line-Compatible AlScN Ferroelectric Diodes.

The growth in data generation necessitates efficient data processing technologies to address the von Neumann bottleneck in conventional computer architecture. Memory-driven computing, which integrates nonvolatile memory (NVM) devices in a 3D stack, is gaining attention, with CMOS back-end-of-line (BEOL)-compatible ferroelectric (FE) diodes being ideal due to their two-terminal design and inherently selector-free nature, facilitating high-density crossbar arrays. Here, we demonstrate BEOL-compatible, high-performance FE diodes scaled to 5, 10, and 20 nm FE Al0.72Sc0.28N/Al0.64Sc0.36N films. Through interlayer (IL) engineering, we show substantial improvements in the on/off ratios (>166 times) and rectification ratios (>176 times) in these scaled devices. These characteristics also enable 5-bit multistate operation with a stable retention. We also experimentally and theoretically demonstrate the counterintuitive result that the inclusion of an IL can lead to a decrease in the ferroelectric switching voltage of the device. An in-depth analysis into the device transport mechanisms is performed, and our compact model aligns seamlessly with the experimental results. Our results suggest the possibility of using scaled AlxSc1-xN FE diodes for high-performance, low-power, embedded NVM.

Optimal timing of hip and knee arthroplasty after bariatric surgery: A systematic review.

Background: Obesity is a risk factor for the development of osteoarthritis and contributes to the increasing demand for total joint arthroplasty (TJA). Because a lower preoperative weight decreases the risk of complications after TJA, and because bariatric surgery (BS) can reduce weight and comorbidity burden, orthopedic surgeons often recommend BS prior to TJA in patients with obesity. However, the optimal timing of TJA after BS in terms of complications, revisions and dislocations is unknown. Methods: PubMed, Embase and Cochrane CENTRAL databases were systematically searched for any type of study reporting rates of complications, revisions and dislocations in patients who had TJA after BS. The included studies' quality was assessed using the Newcastle-Ottawa Scale. Results: Out of the 16 studies eligible for review, eight registry-based retrospective studies of high to moderate quality compared different time periods between BS and TJA and overall their results suggest little differences in complication rates. The remaining eight retrospective studies evaluated only one time period and had moderate to poor quality. Overall, there were no clear differences in outcomes after TJA for the different time frames between BS and TJA. Conclusion: The results of this systematic review suggest that there is limited and insufficient high-quality evidence to determine the optimal timing of TJA after BS in terms of the rates of complications, revisions and dislocations. Given this lack of evidence, timing of TJA after BS will have to be decided by weighing the individual patients' risk factors against the expected benefits of TJA.

Genome-Wide Association Study of Treatment-Resistant Depression: Shared Biology With Metabolic Traits.

OBJECTIVE: Treatment-resistant depression (TRD) occurs in roughly one-third of all individuals with major depressive disorder (MDD). Although research has suggested a significant common variant genetic component of liability to TRD, with heritability estimated at 8% when compared with non-treatment-resistant MDD, no replicated genetic loci have been identified, and the genetic architecture of TRD remains unclear. A key barrier to this work has been the paucity of adequately powered cohorts for investigation, largely because of the challenge in prospectively investigating this phenotype. The objective of this study was to perform a well-powered genetic study of TRD. METHODS: Using receipt of electroconvulsive therapy (ECT) as a surrogate for TRD, the authors applied standard machine learning methods to electronic health record data to derive predicted probabilities of receiving ECT. These probabilities were then applied as a quantitative trait in a genome-wide association study of 154,433 genotyped patients across four large biobanks. RESULTS: Heritability estimates ranged from 2% to 4.2%, and significant genetic overlap was observed with cognition, attention deficit hyperactivity disorder, schizophrenia, alcohol and smoking traits, and body mass index. Two genome-wide significant loci were identified, both previously implicated in metabolic traits, suggesting shared biology and potential pharmacological implications. CONCLUSIONS: This work provides support for the utility of estimation of disease probability for genomic investigation and provides insights into the genetic architecture and biology of TRD.