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Introduction: Existing theories and empirical works link phone use with anxiety; however, most leverage subjective self-reports of phone use (e.g., validated questionnaires) that may not correspond well with true behavior. Moreover, most works linking phone use with anxiety do not interrogate associations within a temporal framework. Accordingly, the present study sought to investigate the utility of passively sensed phone use as a longitudinal predictor of anxiety symptomatology within a population particularly vulnerable to experiencing anxiety. Methods: Using data from the GLOBEM study, which continuously collected longitudinal behavioral data from a college cohort of N = 330 students, weekly PHQ-4 anxiety subscale scores across 3 years (2019–2021) were paired with median daily phone use records from the 2 weeks prior to anxiety self-report completion. Phone use was operationalized through unlock duration which was passively curated via Apple’s “Screen Time” feature. GPS-tracked location data was further utilized to specify whether an individual’s phone use was at home or away from home. Within-individual and temporal associations between phone use and anxiety were modeled within an ordinal mixed-effects logistic regression framework. Results: While there was no significant association between anxiety levels and either median total phone use or median phone use at home, participants in the top quartile of median phone use away from home were predicted to exhibit clinically significant anxiety levels 20% more frequently than participants in the bottom quartile during the first study year; however, this association weakened across successive years. Importantly, these associations remained after controlling for age, physical activity, sleep, and baseline anxiety levels and were not recapitulated when operationalizing phone use with unlock frequency. Conclusions: These findings suggest that phone use may be leveraged as a means of mitigating or coping with anxiety in social situations outside the home, while pandemic-related developments may also have attenuated this behavior later in the study. Nevertheless, the present results suggest promise in interrogating a larger suite of objectively measured phone use behaviors within the context of social anxiety.

Abstract Depression and anxiety frequently co-occur with opioid use disorder (OUD) yet are often overlooked in standard OUD treatments. This study evaluated the feasibility, acceptability, and preliminary effectiveness of a mobile application designed to address these symptoms in individuals receiving medications for OUD (MOUD). A randomized controlled trial recruited N = 63 adults with OUD who received MOUD and screened positive for moderate depression or generalized anxiety. Participants were randomized to an app-based digital intervention or treatment-as-usual for 4 weeks, and completed follow-ups at 4 and 8 weeks. Primary outcomes were self-reported severity measures for depression and generalized anxiety, and urine drug screens (UDS). Secondary outcomes included self-reported OUD severity, craving intensity, and digital biomarkers derived from passive smartphone sensors. The application was well-received (median app rating = 4/5 stars). The intervention group showed significant reductions in depressive and generalized anxiety symptoms post-intervention and at 8 weeks follow-up (d > 0.70), with large (d = 0.78) and moderate (d = 0.38) effect sizes, respectively, compared to controls. Both groups exhibited substantial decreases in self-reported severity of opioid use symptoms (d > 2.50). UDS suggested similar between-group adherence to MOUD, with a marginal decrease in opioid (MOP) use in the intervention group and increase in controls, yielding medium between group effect sizes (d = 0.44). Passive sensor data suggested significant increases in social connectedness in the intervention group, evidenced by a significant rise in incoming and outgoing calls and text connections. Initial evidence supports the feasibility and acceptability of a digital intervention for treating anxiety and depressive symptoms in persons receiving MOUD. While underpowered to confidently determine statistical significance beyond directionality, the intervention showed promise in reducing depressive and anxiety symptoms, suggesting its potential as a cost-effective and scalable adjunctive therapy alongside standard OUD treatment. Due to the preliminary nature of this pilot study, further research with sample sizes permitting greater statistical power is needed to confirm findings and explore long-term effects.

Abstract Suicidal thought and behavior (STB) is highly stigmatized and taboo. Prone to censorship, yet pervasive online, STB risk detection may be improved through development of uniquely insightful digital markers. Focusing on Sanctioned Suicide, an online pro-choice suicide forum, this work derived 17 egocentric network features to capture dynamics of social interaction and engagement within this uniquely uncensored community. Using network data generated from over 3.2 million unique interactions of N = 192 individuals, n = 48 of which were determined to be highest risk users (HRUs), a machine learning classification model was trained, validated, and tested to predict HRU status. Model prediction dynamics were analyzed using introspection techniques to uncover patterns in feature influence and highlight social phenomena. The model achieved a test AUC = 0.73 ([0.61, 0.85], 95% CI), suggesting that network-based socio-behavioral patterns of online interaction can signal for heightened suicide risk. Transitivity, density, and in-degree centrality were among the most important features driving this performance. Moreover, predicted HRUs tended to be targets of social exchanges with lesser frequency and possessed egocentric networks with “small world” network properties. Through the implementation of an underutilized method on an unlikely data source, findings support future incorporation of network-based social interaction features in descriptive, predictive, and preventative STB research.

BackgroundExisting interventions for co-occurring depression and cannabis use often do not treat both disorders simultaneously and can result in higher rates of symptom relapse. Traditional in-person interventions are often difficult to obtain due to financial and time limitations, which may further prevent individuals with co-occurring depression and cannabis use from receiving adequate treatment. Digital interventions can increase the scalability and accessibility for these individuals, but few digital interventions exist to treat both disorders simultaneously. Targeting transdiagnostic processes of these disorders with a digital intervention—specifically positive valence system dysfunction—may yield improved access and outcomes. ObjectiveRecent research has highlighted a need for the inclusion of individuals with lived experiences to assist in the co-design of interventions to enhance scalability and relevance of an intervention. Thus, the purpose of this study is to describe the process of eliciting feedback from individuals with elevated depressed symptoms and cannabis use and co-designing a digital intervention, Amplification of Positivity—Cannabis Use Disorder (AMP-C), focused on improving positive valence system dysfunction in these disorders. MethodsTen individuals who endorsed moderate to severe depressive symptoms and regular cannabis use (2-3×/week) were recruited online via Meta ads. Using a mixed methods approach, participants completed a 1-hour mixed methods interview over Zoom (Zoom Technologies Inc) where they gave their feedback and suggestions for the development of a mental health app, based on an existing treatment targeting positive valence system dysfunction, for depressive symptoms and cannabis use. The qualitative approach allowed for a broader investigation of participants’ wants and needs regarding the engagement and scalability of AMP-C, and the quantitative approach allowed for specific ratings of intervention components to be potentially included. ResultsParticipants perceived the 13 different components of AMP-C as overall helpful (mean 3.9-4.4, SD 0.5-1.1) and interesting (mean 4.0-4.9, SD 0.3-1.1) on a scale from 1 (not at all) to 5 (extremely). They gave qualitative feedback for increasing engagement in the app, including adding a social component, using notifications, and being able to track their symptoms and progress over time. ConclusionsThis study highlights the importance of including individuals with lived experiences in the development of interventions, including digital interventions. This inclusion resulted in valuable feedback and suggestions for improving the proposed digital intervention targeting the positive valence system, AMP-C, to better match the wants and needs of individuals with depressive symptoms and cannabis use.

Abstract Introduction Synovial sarcoma is one of the most common soft tissue sarcomas in children. Guidelines regarding the adequate extent of resection margins and the role of re‐resection are lacking. We sought to evaluate the adequate resection margin and the role of re‐resection in predicting outcomes in children with synovial sarcomas. Methods A cohort of 36 patients less than 18 years of age at diagnosis who were treated for localized synovial sarcoma at three tertiary pediatric hospitals between January 2004 and December 2020 were included in this study. Patient and tumor demographics, treatment information, and margin status after surgical resection were collected from the medical record. Clinical, treatment, and surgical characteristics, as well as outcomes including hazard ratios (HRs), event‐free survival (EFS), and overall survival (OS) were compared by resection margins group and re‐resection status. Results Patients in the R1 resection group were significantly more likely to relapse or die compared to patients in the R0 resection group. However, there was no significant difference in EFS (HR 0.52, p = 0.54) or OS (HR 1.56, p = 0.719) in R0 patients with less than 5 mm margins compared to R0 patients with more than 5 mm margins. Patients with R1 on initial or re‐resection had significantly worse OS than patients who had R0 resection on initial or re‐resection (HR = 10.12, p = 0.005). Conclusion This study re‐affirms that R0 resection is an independent prognostic predictor of better OS/EFS in pediatric synovial sarcoma. Second, our study extends this finding to report negative margins on initial resection or re‐resection is associated with better OS/EFS than positive margins on initial resection or re‐resection. Lastly, we found that there is no difference in outcomes associated with re‐resection or

Abstract The 2021 summer upwelling season off the United States Pacific Northwest coast was unusually strong leading to widespread near-bottom, low-oxygen waters. During summer 2021, an unprecedented number of ship- and underwater glider-based measurements of dissolved oxygen were made in this region. Near-bottom hypoxia, that is dissolved oxygen less than 61 µmol kg−1 and harmful to marine animals, was observed over nearly half of the continental shelf inshore of the 200-m isobath, covering 15,500 square kilometers. A mid-shelf ribbon with near-bottom, dissolved oxygen less than 50 µmol kg−1 extended for 450 km off north-central Oregon and Washington. Spatial patterns in near-bottom oxygen are related to the continental shelf width and other features of the region. Maps of near-bottom oxygen since 1950 show a consistent trend toward lower oxygen levels over time. The fraction of near-bottom water inshore of the 200-m isobath that is hypoxic on average during the summer upwelling season increases over time from nearly absent (2%) in 1950–1980, to 24% in 2009–2018, compared with 56% during the anomalously strong upwelling conditions in 2021. Widespread and increasing near-bottom hypoxia is consistent with increased upwelling-favorable wind forcing under climate change.

Neuroblastoma accounts for approximately 15% of pediatric cancer-related deaths despite intensive multimodal therapy. This is due, in part, to high rates of metastatic disease at diagnosis and disease relapse. A better understanding of tumor biology of aggressive, pro-metastatic phenotypes is necessary to develop novel, more effective therapeutics against neuroblastoma. Phosphatidylinositol 3,4,5-trisphosphate-dependent Rac exchanger 1 (P-Rex1) has been found to stimulate migration, invasion, and metastasis in several adult malignancies. However, its role in neuroblastoma is currently unknown. In the present study, we found that P-Rex1 is upregulated in pro-metastatic murine models of neuroblastoma, as well as human neuroblastoma metastases. Correspondingly, silencing of P-Rex1 was associated with decreased migration and invasion in vitro. This was associated with decreased AKT-mTOR and ERK2 activity, dysregulation of Rac, and diminished secretion of matrix metalloproteinases. Furthermore, increased P-Rex1 expression was associated with inferior relapse-free and overall survival via tissue microarray and Kaplan-Meier survival analysis of a publicly available clinical database. Together, these findings suggest that P-Rex1 may be a novel therapeutic target and potential prognostic factor in neuroblastoma.

Abstract Major Depressive Disorder (MDD) presents considerable challenges to diagnosis and management due to symptom variability across time. Only recent work has highlighted the clinical implications for interrogating depression symptom variability. Thus, the present work investigates how sociodemographic, comorbidity, movement, and sleep data is associated with long-term depression symptom variability. Participant information included (N = 939) baseline sociodemographic and comorbidity data, longitudinal, passively collected wearable data, and Patient Health Questionnaire-9 (PHQ-9) scores collected over 12 months. An ensemble machine learning approach was used to detect long-term depression symptom variability via: (i) a domain-driven feature selection approach and (ii) an exhaustive feature-inclusion approach. SHapley Additive exPlanations (SHAP) were used to interrogate variable importance and directionality. The composite domain-driven and exhaustive inclusion models were both capable of moderately detecting long-term depression symptom variability (r = 0.33 and r = 0.39, respectively). Our results indicate the incremental predictive validity of sociodemographic, comorbidity, and passively collected wearable movement and sleep data in detecting long-term depression symptom variability.

2022 marked the third consecutive La Niña and extended the longest consecutive stretch of negative Oceanic Niño Index since 1998-2001. While physical and biological conditions in winter and spring largely adhered to prior La Niña conditions, summer and fall were very different. Similar to past La Niña events, in winter and spring coastal upwelling was either average or above average, temperature average or below average, salinity generally above average. In summer and fall, however, upwelling and temperature were generally average or slightly below average, salinity was close to average and chlorophyll a was close to average. Again, as during prior La Niña events, biomass of northern/southern copepods was above/below average off Oregon in winter, and body size of North Pacific krill in northern California was above average in winter. By contrast, later in the year the abundance of northern krill dropped off Oregon while southern copepods increased and body sizes of North Pacific krill fell in northern California. Off Oregon and Washington abundances of market squid and Pacific pompano (indicators of warm, non-typical La Niña conditions) were high. In the 20th century, Northern anchovy recruitment tended to be high during cold conditions, but despite mostly warm conditions from 2015-2021 anchovy populations boomed and remained high in 2022. Resident seabird reproductive success, which tended in the past to increase during productive La Niña conditions was highly variable throughout the system as common murre and pelagic cormorant, experienced complete reproductive failure at Yaquina Head, Oregon while Brandt’s cormorant reproduction was average. At three sampling locations off central California, however, common murre reproduction was close to or above average while both pelagic and Brandt’s cormorant were above average. California sealion reproduction has been above average each year since 2016, and pup weight was also above average in 2022, likely in response not to La Niña or El Niño but continuous high abundance of anchovy. The highly variable and often unpredictable physical and biological conditions in 2022 highlight a growing recognition of disconnects between basin-scale indices and local conditions in the CCE. “July-December 2022 is the biggest outlier from individual “strong” La Niña (events) ever going back to the 50s.” – Nate Mantua

Abstract Background Depression remains a global health problem, with its prevalence rising worldwide. Digital biomarkers are increasingly investigated to initiate and tailor scalable interventions targeting depression. Due to the steady influx of new cases, focusing on treatment alone will not suffice; academics and practitioners need to focus on the prevention of depression (i.e., addressing subclinical depression). Aim With our study, we aim to (i) develop digital biomarkers for subclinical symptoms of depression, (ii) develop digital biomarkers for severity of subclinical depression, and (iii) investigate the efficacy of a digital intervention in reducing symptoms and severity of subclinical depression. Method Participants will interact with the digital intervention BEDDA consisting of a scripted conversational agent, the slow-paced breathing training Breeze, and actionable advice for different symptoms. The intervention comprises 30 daily interactions to be completed in less than 45 days. We will collect self-reports regarding mood, agitation, anhedonia (proximal outcomes; first objective), self-reports regarding depression severity (primary distal outcome; second and third objective), anxiety severity (secondary distal outcome; second and third objective), stress (secondary distal outcome; second and third objective), voice, and breathing. A subsample of 25% of the participants will use smartwatches to record physiological data (e.g., heart-rate, heart-rate variability), which will be used in the analyses for all three objectives. Discussion Digital voice- and breathing-based biomarkers may improve diagnosis, prevention, and care by enabling an unobtrusive and either complementary or alternative assessment to self-reports. Furthermore, our results may advance our understanding of underlying psychophysiological changes in subclinical depression. Our study also provides further evidence regarding the efficacy of standalone digital health interventions to prevent depression. Trial registration Ethics approval was provided by the Ethics Commission of ETH Zurich (EK-2022-N-31) and the study was registered in the ISRCTN registry (Reference number: ISRCTN38841716, Submission date: 20/08/2022).

Background The socially unattractive and stigmatizing nature of suicidal thought and behavior (STB) makes it especially susceptible to censorship across most modern digital communication platforms. The ubiquitous integration of technology with day-to-day life has presented an invaluable opportunity to leverage unprecedented amounts of data to study STB, yet the complex etiologies and consequences of censorship for research within mainstream online communities render an incomplete picture of STB manifestation. Analyses targeting online written content of suicidal users in environments where fear of reproach is mitigated may provide novel insight into modern trends and signals of STB expression. Methods Complete written content of N = 192 users, including n = 48 identified as potential suicide completers/highest-risk users (HRUs), on the pro-choice suicide forum, Sanctioned Suicide, was modeled using a combination of lexicon-based topic modeling (EMPATH) and exploratory network analysis techniques to characterize and highlight prominent aspects of censorship-free suicidal discourse. Results Modeling of over 2 million tokens across 37,136 forum posts found higher frequency of positive emotion and optimism among HRUs, emphasis on methods seeking and sharing behaviors, prominence of previously undocumented jargon, and semantics related to loneliness and life adversity. Conclusion This natural language processing (NLP)- and network-driven exposé of online STB subculture uncovered trends that deserve further attention within suicidology as they may be able to bolster detection, intervention, and prevention of suicidal outcomes and exposures.

Abstract Background Retroperitoneal undifferentiated sarcomas are rare, highly aggressive mesenchymal tumors with limited available treatment options. Surgical resection ± chemoradiation remains standard of care. However, in unique cases, challenging tumor biology, advanced tumor stage, and the morbidity required of an R0 resection can prohibit management and ultimate cure. Targeted therapies have an increasing role in this setting due to advances in molecular analytics. Case presentation Herein, we describe the management of an 11-year-old female with a chemotherapy-refractory large retroperitoneal sarcoma encasing the infrarenal aorta with a large acquired aortic pseudoaneurysm. This is the first report describing the complex approach to a retroperitoneal NTRK-fusion ( +) undifferentiated sarcoma confounded by an acquired aortic pseudoaneurysm in a pediatric patient. Preoperative considerations, intra-operative technique, postoperative management, adjuvant therapies and a brief review of the literature are discussed. Conclusion Overall, a holistic understanding of the tumor biology and a cohesive multidisciplinary approach is integral to the care and long-term management of these cases.

Abstract Purpose JQ1 is a bromo- and extraterminal (BET) domain inhibitor that downregulates MYC expression and impairs the DNA damage response. Poly (ADP-ribose) polymerase (PARP) inhibitors prevent DNA damage sensing and repair. We hypothesized that JQ1 would promote a DNA repair-deficient phenotype that sensitizes neuroblastoma cells to PARP inhibition. Methods Four human neuroblastoma cell lines were examined: two MYCN-amplified (BE(2)-C and IMR-32), and two non-MYCN-amplified (SK-N-SH and SH-SY5Y). Cells were treated with JQ1 (BET inhibitor), Olaparib (PARP inhibitor), or in combination to assess for therapeutic synergy of JQ1 and Olaparib. Treated cells were harvested and analyzed. Quantitative assessment of combination treatment synergy was performed using the median effect principle of Chou and Talalay. Results Combination treatment with Olaparib decreased the IC50 of JQ1 by 19.9-fold, 2.0-fold, 12.1-fold, and 2.0-fold in the BE(2)-C, IMR-32, SK-N-SH, and SH-SY5Y cell lines, respectively. In the MYCN-amplified cell lines, BE(2)-C and IMR-32, combination treatment decreased gene expression of MYCN relative to single-drug treatment alone or control. Combination treatment decreased protein expression of DNA repair proteins Ku80 and RAD51, led to accumulation of DNA damage marker phospho-histone H2A.X, and increased caspase activity. In the non-MYCN-amplified cell lines, SK-N-SH and SH-SY5Y, combination treatment induced G0/G1 cell cycle arrest. Conclusions Combination BET and PARP inhibition synergistically inhibited neuroblastoma tumorigenesis in vitro. In MYCN-amplified neuroblastoma cells, this effect may be induced by downregulation of MYCN transcription, defects in DNA repair, accumulation of DNA damage, and apoptosis. In non-MYCN-amplified cell lines, combination treatment induced cell cycle arrest.

Abstract Background Generalized anxiety disorder (GAD) is a highly prevalent condition. Monitoring GAD symptoms requires substantial time, effort, and cost. The development of digital phenotypes of GAD may enable new scalable, timely, and inexpensive assessments of GAD symptoms. Method The current study used passive movement data collected within a large national cohort (N = 264) to assess GAD symptom severity. Results Using one week of movement data, machine learning models accurately predicted GAD symptoms across a continuum (r = 0.511) and accurately detected those individuals with elevated GAD symptoms (AUC = 0.892, 70.0% Sensitivity, 95.5% Specificity, Brier Score = 0.092). Those with a risk score at the 90th percentile or above had 21 times the odds of having elevated GAD symptoms compared to those with lower risk scores. The risk score was most strongly associated with irritability, worry controllability, and restlessness (individual rs > 0.5). The risk scores for GAD were also discriminant of major depressive disorder symptom severity (r = 0.190). Limitations The current study examined the detection of GAD symptom severity rather than the prediction of GAD symptom severity across time. Furthermore, the instant sample of data did not include nighttime actigraphy, as participants were not asked to wear the actigraphs at night. Conclusions These results suggest that artificial intelligence can effectively utilize wearable movement data collected in daily life to accurately infer risk of GAD symptoms.

BackgroundMultiple digital data sources can capture moment-to-moment information to advance a robust understanding of opioid use disorder (OUD) behavior, ultimately creating a digital phenotype for each patient. This information can lead to individualized interventions to improve treatment for OUD. ObjectiveThe aim is to examine patient engagement with multiple digital phenotyping methods among patients receiving buprenorphine medication for OUD. MethodsThe study enrolled 65 patients receiving buprenorphine for OUD between June 2020 and January 2021 from 4 addiction medicine programs in an integrated health care delivery system in Northern California. Ecological momentary assessment (EMA), sensor data, and social media data were collected by smartphone, smartwatch, and social media platforms over a 12-week period. Primary engagement outcomes were meeting measures of minimum phone carry (≥8 hours per day) and watch wear (≥18 hours per day) criteria, EMA response rates, social media consent rate, and data sparsity. Descriptive analyses, bivariate, and trend tests were performed. ResultsThe participants’ average age was 37 years, 47% of them were female, and 71% of them were White. On average, participants met phone carrying criteria on 94% of study days, met watch wearing criteria on 74% of days, and wore the watch to sleep on 77% of days. The mean EMA response rate was 70%, declining from 83% to 56% from week 1 to week 12. Among participants with social media accounts, 88% of them consented to providing data; of them, 55% of Facebook, 54% of Instagram, and 57% of Twitter participants provided data. The amount of social media data available varied widely across participants. No differences by age, sex, race, or ethnicity were observed for any outcomes. ConclusionsTo our knowledge, this is the first study to capture these 3 digital data sources in this clinical population. Our findings demonstrate that patients receiving buprenorphine treatment for OUD had generally high engagement with multiple digital phenotyping data sources, but this was more limited for the social media data. International Registered Report Identifier (IRRID)RR2-10.3389/fpsyt.2022.871916

Summary: Cellular metabolism is tightly regulated by growth factor signaling, which promotes metabolic rewiring to support growth and proliferation. While growth factor-induced transcriptional and post-translational modes of metabolic regulation have been well defined, whether post-transcriptional mechanisms impacting mRNA stability regulate this process is less clear. Here, we present the ZFP36/L1/L2 family of RNA-binding proteins and mRNA decay factors as key drivers of metabolic regulation downstream of acute growth factor signaling. We quantitatively catalog metabolic enzyme and nutrient transporter mRNAs directly bound by ZFP36 following growth factor stimulation—many of which encode rate-limiting steps in metabolic pathways. Further, we show that ZFP36 directly promotes the mRNA decay of Enolase 2 (Eno2), altering Eno2 protein expression and enzymatic activity, and provide evidence of a ZFP36/Eno2 axis during VEGF-stimulated developmental retinal angiogenesis. Thus, ZFP36-mediated mRNA decay serves as an important mode of metabolic regulation downstream of growth factor signaling within dynamic cell and tissue states.

Abstract Background This PRISMA systematic literature review examined the use of digital data collection methods (including ecological momentary assessment [EMA], experience sampling method [ESM], digital biomarkers, passive sensing, mobile sensing, ambulatory assessment, and time-series analysis), emphasizing on digital phenotyping (DP) to study depression. DP is defined as the use of digital data to profile health information objectively. Aims Four distinct yet interrelated goals underpin this study: (a) to identify empirical research examining the use of DP to study depression; (b) to describe the different methods and technology employed; (c) to integrate the evidence regarding the efficacy of digital data in the examination, diagnosis, and monitoring of depression and (d) to clarify DP definitions and digital mental health records terminology. Results Overall, 118 studies were assessed as eligible. Considering the terms employed, “EMA”, “ESM”, and “DP” were the most predominant. A variety of DP data sources were reported, including voice, language, keyboard typing kinematics, mobile phone calls and texts, geocoded activity, actigraphy sensor-related recordings (i.e., steps, sleep, circadian rhythm), and self-reported apps’ information. Reviewed studies employed subjectively and objectively recorded digital data in combination with interviews and psychometric scales. Conclusions Findings suggest links between a person’s digital records and depression. Future research recommendations include (a) deriving consensus regarding the DP definition and (b) expanding the literature to consider a person’s broader contextual and developmental circumstances in relation to their digital data/records.

Background With a rapidly expanding gap between the need for and availability of mental health care, artificial intelligence (AI) presents a promising, scalable solution to mental health assessment and treatment. Given the novelty and inscrutable nature of such systems, exploratory measures aimed at understanding domain knowledge and potential biases of such systems are necessary for ongoing translational development and future deployment in high-stakes healthcare settings. Methods We investigated the domain knowledge and demographic bias of a generative, AI model using contrived clinical vignettes with systematically varied demographic features. We used balanced accuracy (BAC) to quantify the model’s performance. We used generalized linear mixed-effects models to quantify the relationship between demographic factors and model interpretation. Findings We found variable model performance across diagnoses; attention deficit hyperactivity disorder, posttraumatic stress disorder, alcohol use disorder, narcissistic personality disorder, binge eating disorder, and generalized anxiety disorder showed high BAC (0.70 ≤ BAC ≤ 0.82); bipolar disorder, bulimia nervosa, barbiturate use disorder, conduct disorder, somatic symptom disorder, benzodiazepine use disorder, LSD use disorder, histrionic personality disorder, and functional neurological symptom disorder showed low BAC (BAC ≤ 0.59). Interpretation Our findings demonstrate initial promise in the domain knowledge of a large AI model, with performance variability perhaps due to the more salient hallmark symptoms, narrower differential diagnosis, and higher prevalence of some disorders. We found limited evidence of model demographic bias, although we do observe some gender and racial differences in model outcomes mirroring real-world differential prevalence estimates.

BackgroundThe impacts of the COVID-19 pandemic on mental health worldwide and in the United States have been well documented. However, there is limited research examining the long-term effects of the pandemic on mental health, particularly in relation to pervasive policies such as statewide mask mandates and political party affiliation. ObjectiveThe goal of this study was to examine whether statewide mask mandates and political party affiliations yielded differential changes in mental health symptoms across the United States by leveraging state-specific internet search query data. MethodsThis study leveraged Google search queries from March 24, 2020, to March 29, 2021, in each of the 50 states in the United States. Of the 50 states, 39 implemented statewide mask mandates—with 16 of these states being Republican—to combat the spread of COVID-19. This study investigated whether mask mandates were associated differentially with mental health in states with and without mandates by exploring variations in mental health search queries across the United States. In addition, political party affiliation was examined as a potential covariate to determine whether mask mandates had differential associations with mental health in Republican and Democratic states. Generalized additive mixed models were implemented to model associations among mask mandates, political party affiliation, and mental health search volume for up to 7 months following the implementation of a mask mandate. ResultsThe results of generalized additive mixed models revealed that search volume for “restless” significantly increased following a mask mandate across all states, whereas the search volume for “irritable” and “anxiety” increased and decreased, respectively, following a mandate for Republican states in comparison with Democratic states. Most mental health search terms did not exhibit significant changes in search volume in relation to mask mandate implementation. ConclusionsThese findings suggest that mask mandates were associated nonlinearly with significant changes in mental health search behavior, with the most notable associations occurring in anxiety-related search terms. Therefore, policy makers should consider monitoring and providing additional support for these mental health symptoms following the implementation of public health–related mandates such as mask mandates. Nevertheless, these results do not provide evidence for an overwhelming impact of mask mandates on population-level mental health in the United States.

BackgroundCannabis misuse in young adults is a major public health concern. An important predictor of continued use is cannabis craving. Due to the time-varying nature of cravings, brief momentary interventions delivered while cravings are elevated may improve the use of strategies to cope with cravings and reduce cannabis use. ObjectiveThe goal of this manuscript is to describe a formative study to develop coping strategy messages for use in a subsequent intervention. MethodsYoung adults (aged 19-25 years; n=20) who reported using cannabis >10 of the past 30 days recruited via social media participated in this formative study. Participants rated an initial set of 15 mindfulness and 15 distraction coping strategies on a scale from 1 to 4 (very low degree to very high degree) for clarity, usefulness, and tone. They also provided comments about the content. ResultsParticipants found the initial distraction messages slightly clearer than mindfulness (mean 3.5, SD 0.4 and mean 3.4, SD 0.4, respectively), both were comparable in tone (mean 3.2, SD 0.5 and mean 3.2, SD 0.4, respectively), and mindfulness messages were more useful than distraction (mean 3.0, SD 0.5 and mean 2.8, SD 0.6, respectively). Of the 30 messages, 29 received a rating of very low or low (3 participants or received a comment suggesting a change. We revised all these messages based on this feedback, and the participants rated the revised messages approximately 2 weeks later. Participants earned US $10 for completing the first and US $20 for the second survey. The ratings improved on usefulness (especially the distraction items) with very little change in clarity and tone. The top 10 messages of each coping type (mindfulness and distraction) were identified by overall average rating (collapsed across all 3 dimensions: all rated >3.0). The final items were comparable in clarity (distraction mean 3.6, SD 0.4; mindfulness mean 3.6, SD 0.4), tone (distraction mean 3.4, SD 0.4; mindfulness mean 3.4, SD 0.4), and usefulness (distraction mean 3.1, SD 0.5; mindfulness mean 3.2, SD 0.5). ConclusionsThe inclusion of end users in the formative process of developing these messages was valuable and resulted in improvements to the content of the messages. The majority of the messages were changed in some way including the removal of potentially triggering language. These messages were subsequently used in an ecological momentary intervention.

The current manuscript is a commentary on “Mobile phone–based interventions for mental health: A systematic meta-review of 14 meta-analyses of randomized controlled trials”. Although embedded within a nuanced discussion, one of the primary conclusions readers have taken from the meta-analysis was “we failed to find convincing evidence in support of any mobile phone–based intervention on any outcome”, which seems to contradict the entirety of the evidence presented when taken out of context of the methods applied. In evaluating whether the area produced “convincing evidence of efficacy,” the authors used a standard that appeared destined to fail. Specifically, the authors required “no evidence of publication bias”, which is a standard that would be unlikely to be found in any area of psychology or medicine. Second, the authors required low to moderate heterogeneity in effect sizes when comparing interventions with fundamentally different and entirely dissimilar target mechanisms. However absent these 2 untenable criteria, the authors actually found highly suggestive evidence of efficacy (N > 1,000, p < .000001) in (1) anxiety; (2) depression; (3) smoking cessation; (4) stress; and (5) quality of life. Perhaps the appropriate conclusions would be that existing syntheses of data testing smartphone intervention suggests that these interventions are promising, but additional work is needed to separate what types of interventions and mechanisms are more promising. Evidence syntheses will be useful as the field matures, but such syntheses should focus on smartphone treatments that are created equal (i.e., similar intent, features, goals, and linkages in a continuum of care model) or use standards for evidence that promote rigorous evaluation while allowing identification of resources that can help those in need.

BackgroundImmunization rates for children and adults are rising, but coverage levels have not reached optimal goals. As a result, vaccine-preventable diseases still occur. In an era of increasing complexity of immunization schedules, rising expectations about the performance of primary care, and large demands on primary care providers, it is important to understand and promote interventions that work in primary care settings to increase immunization coverage. One common theme across immunization programs in many nations involves the challenge of implementing a population-based approach and identifying all eligible recipients, for example the children who should receive the measles vaccine. However, this issue is gradually being addressed through the availability of immunization registries and electronic health records. A second common theme is identifying the best strategies to promote high vaccination rates. Three types of strategies have been studied: (1) patient-oriented interventions, such as patient reminder or recall, (2) provider interventions, and (3) system interventions, such as school laws. One of the most prominent intervention strategies, and perhaps best studied, involves patient reminder or recall systems. This is an update of a previously published review.ObjectivesTo evaluate and compare the effectiveness of various types of patient reminder and recall interventions to improve receipt of immunizations.Search methodsWe searched CENTRAL, MEDLINE, Embase and CINAHL to January 2017. We also searched grey literature and trial registers to January 2017.Selection criteriaWe included randomized trials, controlled before and after studies, and interrupted time series evaluating immunization-focused patient reminder or recall interventions in children, adolescents, and adults who receive immunizations in any setting. We included no-intervention control groups, standard practice activities that did not include immunization patient reminder or recall, media-based activities aimed at promoting immunizations, or simple practice-based awareness campaigns. We included receipt of any immunizations as eligible outcome measures, excluding special travel immunizations. We excluded patients who were hospitalized for the duration of the study period.Data collection and analysisWe used the standard methodological procedures expected by Cochrane and the Cochrane Effective Practice and Organisation of Care (EPOC) Group. We present results for individual studies as relative rates using risk ratios, and risk differences for randomized trials, and as absolute changes in percentage points for controlled before-after studies. We present pooled results for randomized trials using the random-effects model.Main resultsThe 75 included studies involved child, adolescent, and adult participants in outpatient, community-based, primary care, and other settings in 10 countries.Patient reminder or recall interventions, including telephone and autodialer calls, letters, postcards, text messages, combination of mail or telephone, or a combination of patient reminder or recall with outreach, probably improve the proportion of participants who receive immunization (risk ratio (RR) of 1.28, 95% confidence interval (CI) 1.23 to 1.35; risk difference of 8%) based on moderate certainty evidence from 55 studies with 138,625 participants.Three types of single-method reminders improve receipt of immunizations based on high certainty evidence: the use of postcards (RR 1.18, 95% CI 1.08 to 1.30; eight studies; 27,734 participants), text messages (RR 1.29, 95% CI 1.15 to 1.44; six studies; 7772 participants), and autodialer (RR 1.17, 95% CI 1.03 to 1.32; five studies; 11,947 participants). Two types of single-method reminders probably improve receipt of immunizations based on moderate certainty evidence: the use of telephone calls (RR 1.75, 95% CI 1.20 to 2.54; seven studies; 9120 participants) and letters to patients (RR 1.29, 95% CI 1.21 to 1.38; 27 studies; 81,100 participants).Based on high certainty evidence, reminders improve receipt of immunizations for childhood (RR 1.22, 95% CI 1.15 to 1.29; risk difference of 8%; 23 studies; 31,099 participants) and adolescent vaccinations (RR 1.29, 95% CI 1.17 to 1.42; risk difference of 7%; 10 studies; 30,868 participants). Reminders probably improve receipt of vaccinations for childhood influenza (RR 1.51, 95% CI 1.14 to 1.99; risk difference of 22%; five studies; 9265 participants) and adult influenza (RR 1.29, 95% CI 1.17 to 1.43; risk difference of 9%; 15 studies; 59,328 participants) based on moderate certainty evidence. They may improve receipt of vaccinations for adult pneumococcus, tetanus, hepatitis B, and other non-influenza vaccinations based on low certainty evidence although the confidence interval includes no effect of these interventions (RR 2.08, 95% CI 0.91 to 4.78; four studies; 8065 participants).Authors' conclusionsPatient reminder and recall systems, in primary care settings, are likely to be effective at improving the proportion of the target population who receive immunizations.

In late 2020, models predicted that a strong La Niña would take place for the first time since 2013, and we assessed whether physical and biological indicators in 2021 were similar to past La Niñas in the California Current Ecosystem (CCE). The Pacific Decadal Oscillation and Oceanic Niño Index indeed remained negative throughout 2021; the North Pacific Gyre Oscillation Index, however, remained strongly negative. The seventh largest marine heatwave on record was unexpectedly present from April to the end of 2021; however, similar to past La Niñas, this mass of warm water mostly remained seaward of the continental shelf. As expected from past La Niñas, upwelling and chlorophyll were mostly high and sea surface temperature was low throughout the CCE; however, values were close to average south of Point Conception. Similar to past La Niñas, abundances of lipid-rich, northern copepods off Oregon increased. In northern California, unlike past La Niñas, the body size of North Pacific krill (Euphausia pacifica) was close to average. Predictably, overall krill abundance was above average in far northern California but, unexpectedly, below average south of Cape Mendocino. Off Oregon, similar to past La Niñas, larval abundances of three of six coastal species rose, while five of six southern/offshore taxa decreased in 2021. Off California, as expected based on 2020, Northern Anchovy (Engraulis mordax) were very abundant, while Pacific Sardine (Sardinops sagax) were low. Similar to past La Niñas, market squid (Doryteuthis opalescens) and young of the year (YOY) Pacific Hake (Merluccius pacificus), YOY sanddabs (Citharichthys spp.), and YOY rockfishes (Sebastes spp.) increased. Southern mesopelagic (e.g., Panama lightfish Vinciguerria lucetia, Mexican lampfish Triphoturus mexicanus) larvae decreased as expected but were still well above average, while northern mesopelagic (e.g., northern lampfish Stenobrachius leucopsarus) larvae increased but were still below average. In line with predictions, most monitored bird species had above-average reproduction in Oregon and California. California sea lion (Zalophus californianus) pup count, growth, and weight were high given the abundant Anchovy forage. The CCE entered an enduring La Niña in 2021, and assessing the responses of various ecosystem components helped articulate aspects of the system that are well understood and those that need further study.

IntroductionDespite existing work examining the effectiveness of smartphone digital interventions for schizophrenia at the group level, response to digital treatments is highly variable and requires more research to determine which persons are most likely to benefit from a digital intervention.Materials and methodsThe current work utilized data from an open trial of patients with psychosis (N = 38), primarily schizophrenia spectrum disorders, who were treated with a psychosocial intervention using a smartphone app over a one-month period. Using an ensemble of machine learning models, pre-intervention data, app use data, and semi-structured interview data were utilized to predict response to change in symptom scores, engagement patterns, and qualitative impressions of the app.ResultsMachine learning models were capable of moderately (r = 0.32–0.39, R2 = 0.10–0.16, MAEnorm = 0.13–0.29) predicting interaction and experience with the app, as well as changes in psychosis-related psychopathology.ConclusionThe results suggest that individual smartphone digital intervention engagement is heterogeneous, and symptom-specific baseline data may be predictive of increased engagement and positive qualitative impressions of digital intervention in patients with psychosis. Taken together, interrogating individual response to and engagement with digital-based intervention with machine learning provides increased insight to otherwise ignored nuances of treatment response.

Abstract Post-traumatic stress disorder (PTSD) is characterized by complex, heterogeneous symptomology, thus detection outside traditional clinical contexts is difficult. Fortunately, advances in mobile technology, passive sensing, and analytics offer promising avenues for research and development. The present study examined the ability to utilize Global Positioning System (GPS) data, derived passively from a smartphone across seven days, to detect PTSD diagnostic status among a cohort (N = 185) of high-risk, previously traumatized women. Using daily time spent away and maximum distance traveled from home as a basis for model feature engineering, the results suggested that diagnostic group status can be predicted out-of-fold with high performance (AUC = 0.816, balanced sensitivity = 0.743, balanced specificity = 0.8, balanced accuracy = 0.771). Results further implicate the potential utility of GPS information as a digital biomarker of the PTSD behavioral repertoire. Future PTSD research will benefit from application of GPS data within larger, more diverse populations.

Abstract Managing ecological systems for resilience can increase their capacity to maintain key functions even under global change. Oxygenated coldwater (oxythermal) habitat in lakes is an important ecological resource that is threatened by both climate change and eutrophication. Here, we quantify the resilience of oxythermal habitat in over 10,000 glacial lakes in the upper Midwestern United States to climate change and watershed disturbance and classify lakes for conservation prioritization based on their current conditions and resilience. Oxythermal habitat was predicted by lake morphometry, July air temperatures, and watershed land use. Temperatures are projected to increase by mid‐century, and the magnitude of warming, its effect on oxythermal habitat, and the uncertainty surrounding that effect varied among lakes. Under mid‐century climate conditions, the number of lakes containing suitable coldwater habitat was predicted to decline by 67%, while the number of lakes with unsuitable habitat was predicted to increase by over 200%. Lakes varied in the amount of temperature increase that they could sustain without a resultant change in habitat tier (i.e., their climate resilience). Median climate resilience was 4.3°C, with some lakes capable of remaining in their habitat tier even with temperature increases up to 14°C. Changing watershed land use was predicted to influence oxythermal habitat in 24% of lakes (n = 2391). We used the magnitude of increase in watershed development that a lake could sustain while remaining in its current habitat class as a measure of its resilience to watershed disturbance. Conversely, decreased watershed development may improve oxythermal habitat conditions and push a lake into an improved condition, and this value represented a lake's restoration potential. We classified lakes into seven management classes based on their current oxythermal habitat conditions and the resilience of oxythermal habitat to climate and watershed disturbance. To facilitate management on individual lakes, we also assessed the vulnerability and resilience of individual lakes and the uncertainty surrounding these estimates. By quantifying the resilience of lakes and how it is influenced by local action across a multistate region, we can prioritize conservation action across multiple scales to maintain the critical habitat and ecosystem function of glacial lakes.

Introduction Secondary traumatic stress is highly prevalent among nurses, especially among nurses working within the emergency department (ED). Reducing healthcare worker secondary traumatic stress is important for ensuring the delivery of high quality, safe patient care. This paper reports on the development and implementation of a secondary traumatic stress reduction program. Methods We used an adaption of a 5-week intervention based on the Accelerated Recovery Program to test whether there would be a reduction in secondary traumatic stress in a pilot sample of nine ED nurses. Outcomes were assessed using the Secondary Traumatic Stress Scale (STSS), Somatic Symptoms Scale (SSS), and Compassion Satisfaction subscale (CSS) measures. Results Eight of nine nurses were able to complete at least three of the five sessions. Results indicate significant change in STSS (F[5,23] = 4.22, p = .007) and SSS (F[3,15] = 4.42, p = .02) scores, but not CSS (F[5,23] = 0.83, p = .54) scores. Pairwise comparisons revealed that the beneficial effects of the program happened early. For both STSS and SSS, scores at sessions 1 and 2 were generally higher than subsequent sessions. We also found a trend for continued effects on STSS at a four-month follow-up (t 23 = 1.95, p = .064). Conclusion Overall, results indicate the 5-week program was associated with a significant reduction in secondary traumatic stress and related somatic symptoms in healthcare workers.

Abstract Generalized anxiety disorder (GAD) and major depressive disorder (MDD) are highly prevalent and impairing problems, but frequently go undetected, leading to substantial treatment delays. Electronic health records (EHRs) collect a great deal of biometric markers and patient characteristics that could foster the detection of GAD and MDD in primary care settings. We approached the problem of predicting MDD and GAD using a novel machine learning pipeline to re-analyze data from an observational study. The pipeline constitutes an ensemble of algorithmically distinct machine learning methods, including deep learning. A sample of 4,184 undergraduate students completed the study, undergoing a general health screening and completing a psychiatric assessment for MDD and GAD. After explicitly excluding all psychiatric information, 59 biomedical and demographic features from the general health survey in addition to a set of engineered features were used for model training. We assessed the model's performance on a held-out test set and found an AUC of 0.73 (sensitivity: 0.66, specificity: 0.7) and 0.67 (sensitivity: 0.55, specificity: 0.7) for GAD, and MDD, respectively. Additionally, we used advanced techniques (SHAP values) to illuminate which features had the greatest impact on prediction for each disease. The top predictive features for MDD were being satisfied with living conditions and having public health insurance. The top predictive features for GAD were vaccinations being up to date and marijuana use. Our results indicate moderate predictive performance for the application of machine learning methods in detection of GAD and MDD based on EHR data. By identifying important predictors of GAD and MDD, these results may be used in future research to aid in the early detection of MDD and GAD.

Viruses have been re-engineered for many applications but the ability to build asymmetric capsids in vitro has been lacking. Here, the authors report the design of asymmetric subunits with different growth rates leading to capsids with discrete patches and potential for distinct surface chemistries.

IntroductionAcross the U.S., the prevalence of opioid use disorder (OUD) and the rates of opioid overdoses have risen precipitously in recent years. Several effective medications for OUD (MOUD) exist and have been shown to be life-saving. A large volume of research has identified a confluence of factors that predict attrition and continued substance use during substance use disorder treatment. However, much of this literature has examined a small set of potential moderators or mediators of outcomes in MOUD treatment and may lead to over-simplified accounts of treatment non-adherence. Digital health methodologies offer great promise for capturing intensive, longitudinal ecologically-valid data from individuals in MOUD treatment to extend our understanding of factors that impact treatment engagement and outcomes.MethodsThis paper describes the protocol (including the study design and methodological considerations) from a novel study supported by the National Drug Abuse Treatment Clinical Trials Network at the National Institute on Drug Abuse (NIDA). This study (D-TECT) primarily seeks to evaluate the feasibility of collecting ecological momentary assessment (EMA), smartphone and smartwatch sensor data, and social media data among patients in outpatient MOUD treatment. It secondarily seeks to examine the utility of EMA, digital sensing, and social media data (separately and compared to one another) in predicting MOUD treatment retention, opioid use events, and medication adherence [as captured in electronic health records (EHR) and EMA data]. To our knowledge, this is the first project to include all three sources of digitally derived data (EMA, digital sensing, and social media) in understanding the clinical trajectories of patients in MOUD treatment. These multiple data streams will allow us to understand the relative and combined utility of collecting digital data from these diverse data sources. The inclusion of EHR data allows us to focus on the utility of digital health data in predicting objectively measured clinical outcomes.DiscussionResults may be useful in elucidating novel relations between digital data sources and OUD treatment outcomes. It may also inform approaches to enhancing outcomes measurement in clinical trials by allowing for the assessment of dynamic interactions between individuals' daily lives and their MOUD treatment response.Clinical Trial RegistrationIdentifier: NCT04535583.

BackgroundSensors embedded in smartphones allow for the passive momentary quantification of people’s states in the context of their daily lives in real time. Such data could be useful for alleviating the burden of ecological momentary assessments and increasing utility in clinical assessments. Despite existing research on using passive sensor data to assess participants’ moment-to-moment states and activity levels, only limited research has investigated temporally linking sensor assessment and self-reported assessment to further integrate the 2 methodologies. ObjectiveWe investigated whether sparse movement-related sensor data can be used to train machine learning models that are able to infer states of individuals’ work-related rumination, fatigue, mood, arousal, life engagement, and sleep quality. Sensor data were only collected while the participants filled out the questionnaires on their smartphones. MethodsWe trained personalized machine learning models on data from employees (N=158) who participated in a 3-week ecological momentary assessment study. ResultsThe results suggested that passive smartphone sensor data paired with personalized machine learning models can be used to infer individuals’ self-reported states at later measurement occasions. The mean R2 was approximately 0.31 (SD 0.29), and more than half of the participants (119/158, 75.3%) had an R2 of ≥0.18. Accuracy was only slightly attenuated compared with earlier studies and ranged from 38.41% to 51.38%. ConclusionsPersonalized machine learning models and temporally linked passive sensing data have the capability to infer a sizable proportion of variance in individuals’ daily self-reported states. Further research is needed to investigate factors that affect the accuracy and reliability of the inference.

High-risk neuroblastoma requires multimodal treatment including systemic chemotherapy, surgical resection, radiation therapy, stem cell transplant, and immunotherapy. Surgeons play a vital role in obtaining local control of neuroblastoma and must therefore be knowledgeable about this complex pathology. This article provides a review of the optimal timing and extent of resection, the impact of various image-defined risk factors on surgical planning, and surgical approaches and techniques to enhance the resection of tumors in different anatomic locations.

An amendment to this paper has been published and can be accessed via the original article.

Previous research has demonstrated that adults with comorbid depressive and anxiety disorders are significantly more likely to show pathological use of drugs or alcohol. Few studies, however, have examined associations of this type in children. A better understanding of the relationships between affective disorders and substance experimentation in childhood could help clarify the complex ways in which pathological substance use symptoms develop early in life. The present study included 11,785 children (Mage = 9.9) participating in the Adolescent Brain Cognitive Development (ABCD) study. Depressive and anxiety disorder diagnoses were evaluated as concurrent predictors of experimentation with alcohol and tobacco. A series of linear regressions revealed that children with either depressive or anxiety disorders were significantly more likely to experiment with alcohol or tobacco. However, children with both depressive and anxiety diagnoses were not more likely to experiment than children without a diagnosis. These results suggest that anxiety or depressive diagnoses in childhood may be associated with a greater likelihood of substance experimentation, but severe psychological distress may suppress these effects.

Social network analysis (SNA) is an increasingly popular and effective tool for modeling psychological phenomena. Through application to the personality literature, social networks, in conjunction with passive, non-invasive sensing technologies, have begun to offer powerful insight into personality state variability. Resultant constructions of social networks can be utilized alongside machine learning-based frameworks to uniquely model personality states. Accordingly, this work leverages data from a previously published study to combine passively collected wearable sensor information on face-to-face, workplace social interactions with ecological momentary assessments of personality state. Data from 54 individuals across six weeks was used to explore the relative importance of 26 unique structural and nodal social network features in predicting individual changes in each of the Big Five (5F) personality states. Changes in personality state were operationalized by calculating the weekly root mean square of successive differences (RMSSD) in 5F state scores measured daily via self-report. Using only SNA-derived features from wearable sensor data, boosted tree-based machine learning models explained, on average, approximately 28–30% of the variance in individual personality state change. Model introspection implicated egocentric features as the most influential predictors across 5F-specific models, with network efficiency, constraint, and effective size measures among the most important. Feature importance profiles for each 5F model partially echoed previous empirical findings. Results support future efforts focusing on egocentric components of SNA and suggest particular investment in exploring efficiency measures to model personality fluctuations within the workplace setting.

Standard treatment for patients with high-risk neuroblastoma remains multimodal therapy including chemoradiation, surgical resection, and autologous stem cell rescue. Immunotherapy has demonstrated success in treating many types of cancers; however, its use in pediatric solid tumors has been limited by low tumor mutation burdens. Gastrin-releasing peptide receptor (GRP-R) is overexpressed in numerous malignancies, including poorly-differentiated neuroblastoma. Monoclonal antibodies (mAbs) to GRP-R have yet to be developed but could serve as a potential novel immunotherapy. This preclinical study aims to evaluate the efficacy of a novel GRP-R mAb immunotherapy against neuroblastoma. We established four candidate anti-GRP-R mAbs by screening a single-chain variable fragment (scFv) library. GRP-R mAb-1 demonstrated the highest efficacy with the lowest EC50 at 4.607 ng/ml against GRP-R expressing neuroblastoma cells, blocked the GRP-ligand activation of GRP-R and its downstream PI3K/AKT signaling. This resulted in functional inhibition of cell proliferation and anchorage-independent growth, indicating that mAb-1 has an antagonist inhibitory role on GRP-R. To examine the antibody-dependent cellular cytotoxicity (ADCC) of GRP-R mAb-1 on neuroblastoma, we co-cultured neuroblastoma cells with natural killer (NK) cells versus GRP-R mAb-1 treatment alone. GRP-R mAb-1 mediated ADCC effects on neuroblastoma cells and induced release of IFNγ by NK cells under co-culture conditions in vitro. The cytotoxic effects of mAb-1 were confirmed with the secretion of cytotoxic granzyme B from NK cells and the reduction of mitotic tumor cells in vivo using a murine tumor xenograft model. In summary, GRP-R mAb-1 demonstrated efficacious anti-tumor effects on neuroblastoma cells in preclinical models. Importantly, GRP-R mAb-1 may be an efficacious, novel immunotherapy in the treatment of high-risk neuroblastoma patients.

BackgroundChatbots have the potential to provide cost-effective mental health prevention programs at scale and increase interactivity, ease of use, and accessibility of intervention programs. ObjectiveThe development of chatbot prevention for eating disorders (EDs) is still in its infancy. Our aim is to present examples of and solutions to challenges in designing and refining a rule-based prevention chatbot program for EDs, targeted at adult women at risk for developing an ED. MethodsParticipants were 2409 individuals who at least began to use an EDs prevention chatbot in response to social media advertising. Over 6 months, the research team reviewed up to 52,129 comments from these users to identify inappropriate responses that negatively impacted users’ experience and technical glitches. Problems identified by reviewers were then presented to the entire research team, who then generated possible solutions and implemented new responses. ResultsThe most common problem with the chatbot was a general limitation in understanding and responding appropriately to unanticipated user responses. We developed several workarounds to limit these problems while retaining some interactivity. ConclusionsRule-based chatbots have the potential to reach large populations at low cost but are limited in understanding and responding appropriately to unanticipated user responses. They can be most effective in providing information and simple conversations. Workarounds can reduce conversation errors.

BackgroundThe digital era has ushered in an unprecedented volume of readily accessible information, including news coverage of current events. Research has shown that the sentiment of news articles can evoke emotional responses from readers on a daily basis with specific evidence for increased anxiety and depression in response to coverage of the recent COVID-19 pandemic. Given the primacy and relevance of such information exposure, its daily impact on the mental health of the general population within this modality warrants further nuanced investigation. ObjectiveUsing the COVID-19 pandemic as a subject-specific example, this work aimed to profile and examine associations between the dynamics of semantic affect in online local news headlines and same-day online mental health term search behavior over time across the United States. MethodsUsing COVID-19–related news headlines from a database of online news stories in conjunction with mental health–related online search data from Google Trends, this paper first explored the statistical and qualitative affective properties of state-specific COVID-19 news coverage across the United States from January 23, 2020, to October 22, 2020. The resultant operationalizations and findings from the joint application of dictionary-based sentiment analysis and the circumplex theory of affect informed the construction of subsequent hypothesis-driven mixed effects models. Daily state-specific counts of mental health search queries were regressed on circumplex-derived features of semantic affect, time, and state (as a random effect) to model the associations between the dynamics of news affect and search behavior throughout the pandemic. Search terms were also grouped into depression symptoms, anxiety symptoms, and nonspecific depression and anxiety symptoms to model the broad impact of news coverage on mental health. ResultsExploratory efforts revealed patterns in day-to-day news headline affect variation across the first 9 months of the pandemic. In addition, circumplex mapping of the most frequently used words in state-specific headlines uncovered time-agnostic similarities and differences across the United States, including the ubiquitous use of negatively valenced and strongly arousing language. Subsequent mixed effects modeling implicated increased consistency in affective tone (SpinVA β=–.207; P

Introduction: Online social networking data (SN) is a contextually and temporally rich data stream that has shown promise in the prediction of suicidal thought and behavior. Despite the clear advantages of this digital medium, predictive modeling of acute suicidal ideation (SI) currently remains underdeveloped. SN data, in conjunction with robust machine learning algorithms, may offer a promising way forward. Methods: We applied an ensemble machine learning model on a previously published dataset of adolescents on Instagram with a prior history of lifetime SI (N = 52) to predict SI within the past month. Using predictors that capture language use and activity within this SN, we evaluated the performance of our out-of-sample, cross-validated model against previous efforts and leveraged a model explainer to further probe relative predictor importance and subject-level phenomenology. Results: Linguistic and SN data predicted acute SI with an accuracy of 0.702 (sensitivity = 0.769, specificity = 0.654, AUC = 0.775). Model introspection showed a higher proportion of SN-derived predictors with substantial impact on prediction compared with linguistic predictors from structured interviews. Further analysis of subject-specific predictor importance uncovered potentially informative trends for future acute SI risk prediction. Conclusion: Application of ensemble learning methodologies to SN data for the prediction of acute SI may mitigate the complexities and modeling challenges of SI that exist within these time scales. Future work is needed on larger, more heterogeneous populations to fine-tune digital biomarkers and more robustly test external validity.

BackgroundThe number of smartphone apps that focus on the prevention, diagnosis, and treatment of depression is increasing. A promising approach to increase the effectiveness of the apps while reducing the individual’s burden is the use of just-in-time adaptive intervention (JITAI) mechanisms. JITAIs are designed to improve the effectiveness of the intervention and reduce the burden on the person using the intervention by providing the right type of support at the right time. The right type of support and the right time are determined by measuring the state of vulnerability and the state of receptivity, respectively. ObjectiveThe aim of this study is to systematically assess the use of JITAI mechanisms in popular apps for individuals with depression. MethodsWe systematically searched for apps addressing depression in the Apple App Store and Google Play Store, as well as in curated lists from the Anxiety and Depression Association of America, the United Kingdom National Health Service, and the American Psychological Association in August 2020. The relevant apps were ranked according to the number of reviews (Apple App Store) or downloads (Google Play Store). For each app, 2 authors separately reviewed all publications concerning the app found within scientific databases (PubMed, Cochrane Register of Controlled Trials, PsycINFO, Google Scholar, IEEE Xplore, Web of Science, ACM Portal, and Science Direct), publications cited on the app’s website, information on the app’s website, and the app itself. All types of measurements (eg, open questions, closed questions, and device analytics) found in the apps were recorded and reviewed. ResultsNone of the 28 reviewed apps used JITAI mechanisms to tailor content to situations, states, or individuals. Of the 28 apps, 3 (11%) did not use any measurements, 20 (71%) exclusively used self-reports that were insufficient to leverage the full potential of the JITAIs, and the 5 (18%) apps using self-reports and passive measurements used them as progress or task indicators only. Although 34% (23/68) of the reviewed publications investigated the effectiveness of the apps and 21% (14/68) investigated their efficacy, no publication mentioned or evaluated JITAI mechanisms. ConclusionsPromising JITAI mechanisms have not yet been translated into mainstream depression apps. Although the wide range of passive measurements available from smartphones were rarely used, self-reported outcomes were used by 71% (20/28) of the apps. However, in both cases, the measured outcomes were not used to tailor content and timing along a state of vulnerability or receptivity. Owing to this lack of tailoring to individual, state, or situation, we argue that the apps cannot be considered JITAIs. The lack of publications investigating whether JITAI mechanisms lead to an increase in the effectiveness or efficacy of the apps highlights the need for further research, especially in real-world apps.