Preclinical Characterization of AZD9574, a Blood-Brain Barrier Penetrant Inhibitor of PARP1

PURPOSE

We evaluated the properties and activity of AZD9574, a blood-brain barrier (BBB) penetrant selective inhibitor of PARP1, and assessed its efficacy and safety alone and in combination with temozolomide (TMZ) in preclinical models.

EXPERIMENTAL DESIGN

AZD9574 was interrogated in vitro for selectivity, PARylation inhibition, PARP-DNA trapping, the ability to cross the BBB, and the potential to inhibit cancer cell proliferation. In vivo efficacy was determined using subcutaneous as well as intracranial mouse xenograft models. Mouse, rat, and monkey were used to assess AZD9574 BBB penetration and rat models were used to evaluate potential hematotoxicity for AZD9574 monotherapy and the TMZ combination.

RESULTS

AZD9574 demonstrated PARP1-selectivity in fluorescence anisotropy, PARylation, and PARP-DNA trapping assays and in vivo experiments demonstrated BBB penetration. AZD9574 showed potent single agent efficacy in preclinical models with homologous recombination repair deficiency in vitro and in vivo. In an O6-methylguanine-DNA methyltransferase (MGMT)-methylated orthotopic glioma model, AZD9574 in combination with TMZ was superior in extending the survival of tumor-bearing mice compared with TMZ alone.

CONCLUSIONS

The combination of three key features-PARP1 selectivity, PARP1 trapping profile, and high central nervous system penetration in a single molecule-supports the development of AZD9574 as the best-in-class PARP inhibitor for the treatment of primary and secondary brain tumors. As documented by in vitro and in vivo studies, AZD9574 shows robust anticancer efficacy as a single agent as well as in combination with TMZ. AZD9574 is currently in a phase I trial (NCT05417594). See related commentary by Lynce and Lin, p. 1217.

Differences in Discounting Behavior and Brain Responses for Food and Money Reward

Most neuroeconomic research seeks to understand how value influences decision-making. The influence of reward type is less well understood. We used functional magnetic resonance imaging (fMRI) to investigate delay discounting of primary (i.e., food) and secondary rewards (i.e., money) in 28 healthy, normal-weighted participants (mean age = 26.77; 18 females). To decipher differences in discounting behavior between reward types, we compared how well-different option-based statistical models (exponential, hyperbolic discounting) and attribute-wise heuristic choice models (intertemporal choice heuristic, dual reasoning and implicit framework theory, trade-off model) captured the reward-specific discounting behavior. Contrary to our hypothesis of different strategies for different rewards, we observed comparable discounting behavior for money and food (i.e., exponential discounting). Higher k values for food discounting suggest that individuals decide more impulsive if confronted with food. The fMRI revealed that money discounting was associated with enhanced activity in the right dorsolateral prefrontal cortex, involved in executive control; the right dorsal striatum, associated with reward processing; and the left hippocampus, involved in memory encoding/retrieval. Food discounting, instead, was associated with higher activity in the left temporoparietal junction suggesting social reinforcement of food decisions. Although our findings do not confirm our hypothesis of different discounting strategies for different reward types, they are in line with the notion that reward types have a significant influence on impulsivity with primary rewards leading to more impulsive choices.

Identifying determinants of varenicline adherence using the Theoretical Domains framework: a rapid review

BACKGROUND

Adhering to varenicline has been shown to significantly improve the chances of successfully quitting smoking, with studies indicating a twofold increase in 6-month quit rates. However, despite its potential benefits, many individuals struggle with maintaining good adherence to varenicline; thus there is a need to develop scalable strategies to help people adhere. As a first step to inform the development of an intervention to improve adherence to varenicline, we conducted a rapid literature review to identify: 1) modifiable barriers and facilitators to varenicline adherence, and 2) behaviour change techniques associated with increased adherence to varenicline.

METHODS

We searched MEDLINE, Embase, APA PsycINFO, CINAHL, and the Cochrane Central Register of Controlled Trials for relevant studies published between 2006 and 2022. Search terms included "varenicline," "smoking cessation," and "adherence," and their respective subject headings and synonyms. We screened and included studies reporting modifiable determinants of adherence to varenicline and then assessed quality, extracted modifiable determinants and mapped them to the Theoretical Domains Framework version 2 and the Behaviour Change Technique Taxonomy version 1.

RESULTS

A total of 1,221 titles were identified through the database searches; 61 met the eligibility criteria. Most of the studies were randomized controlled trials and predominantly focused on barriers to varenicline. Only nine studies explicitly mentioned behaviour change techniques used to help varenicline adherence. Eight domains were identified as barriers to varenicline adherence (behavioural regulation, memory, goals, intentions, beliefs about capabilities, beliefs about consequences, optimism/pessimism, and environmental context) and five as facilitators (knowledge, behavioural regulation, beliefs about capabilities, social influences, and environmental context).

CONCLUSIONS

This study identifies barriers and facilitators that should be addressed when developing a complex adherence intervention tailored to patients' needs based on modifiable determinants of medication adherence, some of which are under- used by existing adherence interventions. The findings from this review will inform the design of a theory-based healthbot planned to improve varenicline adherence in people undergoing smoking cessation treatment.

SYSTEMATIC REVIEW REGISTRATION

This study was registered with PROSPERO (# CRD42022321838).

AI Conversational Agent to Improve Varenicline Adherence: Protocol for a Mixed Methods Feasibility Study

BACKGROUND

Varenicline is a pharmacological intervention for tobacco dependence that is safe and effective in facilitating smoking cessation. Enhanced adherence to varenicline augments the probability of prolonged smoking abstinence. However, research has shown that one-third of people who use varenicline are nonadherent by the second week. There is evidence showing that behavioral support helps with medication adherence. We have designed an artificial intelligence (AI) conversational agent or health bot, called "ChatV," based on evidence of what works as well as what varenicline is, that can provide these supports. ChatV is an evidence-based, patient- and health care provider-informed health bot to improve adherence to varenicline. ChatV has been programmed to provide medication reminders, answer questions about varenicline and smoking cessation, and track medication intake and the number of cigarettes.

OBJECTIVE

This study aims to explore the feasibility of the ChatV health bot, to examine if it is used as intended, and to determine the appropriateness of proceeding with a randomized controlled trial.

METHODS

We will conduct a mixed methods feasibility study where we will pilot-test ChatV with 40 participants. Participants will be provided with a standard 12-week varenicline regimen and access to ChatV. Passive data collection will include adoption measures (how often participants use the chatbot, what features they used, when did they use it, etc). In addition, participants will complete questionnaires (at 1, 4, 8, and 12 weeks) assessing self-reported smoking status and varenicline adherence, as well as questions regarding the acceptability, appropriateness, and usability of the chatbot, and participate in an interview assessing acceptability, appropriateness, fidelity, and adoption. We will use "stop, amend, and go" progression criteria for pilot studies to decide if a randomized controlled trial is a reasonable next step and what modifications are required. A health equity lens will be adopted during participant recruitment and data analysis to understand and address the differences in uptake and use of this digital health solution among diverse sociodemographic groups. The taxonomy of implementation outcomes will be used to assess feasibility, that is, acceptability, appropriateness, fidelity, adoption, and usability. In addition, medication adherence and smoking cessation will be measured to assess the preliminary treatment effect. Interview data will be analyzed using the framework analysis method.

RESULTS

Participant enrollment for the study will begin in January 2024.

CONCLUSIONS

By using predetermined progression criteria, the results of this preliminary study will inform the determination of whether to advance toward a larger randomized controlled trial to test the effectiveness of the health bot. Additionally, this study will explore the acceptability, appropriateness, fidelity, adoption, and usability of the health bot. These insights will be instrumental in refining the intervention and the health bot.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05997901; https://classic.clinicaltrials.gov/ct2/show/NCT05997901.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/53556.

Establishing the consistency of a voice recognition symbol digit modalities test analogue

BACKGROUND

We previously demonstrated the convergent validity of a fully automated voice recognition analogue of the Symbol Digit Modalities Test (VR-SDMT) for evaluating processing speed in people with multiple sclerosis (pwMS).

OBJECTIVE/METHODS

We aimed to replicate these results in 54 pwMS and 18 healthy controls (HCs), demonstrating the VR-SDMT's reliability.

RESULTS

Significant correlations were found between the VR-SDMT and the traditional oral SDMT in the multiple sclerosis (MS) (r = -0.771, p < 0.001) and HC (r = -0.785, p < 0.001) groups.

CONCLUSION

Taken collectively, our two studies demonstrate the reliability and validity of the VR-SDMT for assessing processing speed in pwMS.

A French Version of a Voice Recognition Symbol Digit Modalities Test Analog

We previously showed that a fully automated voice recognition analog of the Symbol Digit Modalities Test (VR-SDMT) is sensitive in detecting processing speed deficits in people with multiple sclerosis (pwMS). We subsequently developed a French language version and administered it to 49 French-Canadian pwMS and 29 matched healthy control (HC) subjects. Significant correlations between the VR-SDMT and traditional oral SDMT were found in the MS (r = -0.716, p < 0.001) and HC (r = -0.623, p < 0.001) groups. These findings in French replicate our previous findings and confirm the utility of voice recognition software in assessing cognition in pwMS.

A Motivational Interviewing Chatbot With Generative Reflections for Increasing Readiness to Quit Smoking: Iterative Development Study

BACKGROUND

The motivational interviewing (MI) approach has been shown to help move ambivalent smokers toward the decision to quit smoking. There have been several attempts to broaden access to MI through text-based chatbots. These typically use scripted responses to client statements, but such nonspecific responses have been shown to reduce effectiveness. Recent advances in natural language processing provide a new way to create responses that are specific to a client's statements, using a generative language model.

OBJECTIVE

This study aimed to design, evolve, and measure the effectiveness of a chatbot system that can guide ambivalent people who smoke toward the decision to quit smoking with MI-style generative reflections.

METHODS

Over time, 4 different MI chatbot versions were evolved, and each version was tested with a separate group of ambivalent smokers. A total of 349 smokers were recruited through a web-based recruitment platform. The first chatbot version only asked questions without reflections on the answers. The second version asked the questions and provided reflections with an initial version of the reflection generator. The third version used an improved reflection generator, and the fourth version added extended interaction on some of the questions. Participants' readiness to quit was measured before the conversation and 1 week later using an 11-point scale that measured 3 attributes related to smoking cessation: readiness, confidence, and importance. The number of quit attempts made in the week before the conversation and the week after was surveyed; in addition, participants rated the perceived empathy of the chatbot. The main body of the conversation consists of 5 scripted questions, responses from participants, and (for 3 of the 4 versions) generated reflections. A pretrained transformer-based neural network was fine-tuned on examples of high-quality reflections to generate MI reflections.

RESULTS

The increase in average confidence using the nongenerative version was 1.0 (SD 2.0; P=.001), whereas for the 3 generative versions, the increases ranged from 1.2 to 1.3 (SD 2.0-2.3; P<.001). The extended conversation with improved generative reflections was the only version associated with a significant increase in average importance (0.7, SD 2.0; P<.001) and readiness (0.4, SD 1.7; P=.01). The enhanced reflection and extended conversations exhibited significantly better perceived empathy than the nongenerative conversation (P=.02 and P=.004, respectively). The number of quit attempts did not significantly change between the week before the conversation and the week after across all 4 conversations.

CONCLUSIONS

The results suggest that generative reflections increase the impact of a conversation on readiness to quit smoking 1 week later, although a significant portion of the impact seen so far can be achieved by only asking questions without the reflections. These results support further evolution of the chatbot conversation and can serve as a basis for comparison against more advanced versions.

Health-Related Quality of Life after Combined External Beam and Either High Dose Rate (HDR) or Low Dose Rate (LDR) Brachytherapy: Does the Rectal Dose from the LDR Brachytherapy Make a Difference?

PURPOSE/OBJECTIVE(S)

The recently reported randomized Phase III trial comparing health related quality of life (HRQOL) after combined external beam radiation therapy (EBRT) and either HDR or LDR brachytherapy (BT) found a significant decline in the EPIC Bowel domain HRQOL score at 24- 48 months after treatment in the LDR arm of the trial. As all patients in the trial received the same EBRT dose, and HDR rectal dose was strictly controlled to be <9.5 Gy to 1cc of rectal wall (RD1cc), we investigated whether the variable rectal dose from the LDR component of treatment was related to the decline in Bowel HRQOL for these patients.

MATERIALS/METHODS

A total of 195 men with upper tier intermediate or high-risk prostate cancer were assigned by a random number generator to receive either an HDR (15 Gy, n = 108) or LDR (110Gy, n = 87) brachytherapy boost combined with 46Gy/23 fractions EBRT. All LDR patients had 1 month post implant quality assurance using CT-MRI fusion. The Expanded Prostate Cancer Composite (EPIC) questionnaire was used to evaluate HRQOL at baseline, q3 mo for 1 year, q6mo for 3 yr and then annually. A multivariate linear regression model was used to investigate the dose-response relationship between EPIC bowel domain score at 24- 48 months and RD1cc.

RESULTS

With a median follow up of 48 months, the previous analysis confirmed the expected time course of acute bowel/urinary symptoms, with LDR showing more prolonged decline in HRQOL bowel domain at 3 and 6 months, but equivalence to HDR by 12 months. HRQOL urinary domain remained equivalent from 12-60 mo. The decline in the HRQOL bowel domain observed for LDR patients from 24-48 mo was analyzed for the 79 patients with sufficient data. The mean baseline HRQOL bowel domain score was 92 and fell on average to 85 at 24-48 mo. Mean RD1cc for the LDR patients was 82Gy (SD 22 Gy), with a maximum value of 129 Gy. In this range of rectal doses, a 20Gy increase in RD1cc, was associated on average with a 1.5-point decrease in EPIC HRQOL bowel domain score (p = 0.21).

CONCLUSION

The rectal dose received by the LDR patients showed a non-significant dose-response with the EPIC Bowel domain HRQOL score. This confirms the accepted rectal dose constraints for LDR brachytherapy but does not explain the observed decline in bowel scores from 24-48 months.

Autoimmune Connective Tissue Diseases: Systemic Lupus Erythematosus and Rheumatoid Arthritis

Systemic lupus erythematosus and rheumatoid arthritis are just 2 of several autoimmune connective tissue diseases that are primarily chronic in nature but can present to the emergency department by virtue of an acute exacerbation of disease. Beyond an acute exacerbation of disease, their predilection for invading multiple organ systems lends itself to the potential for patients presenting to the emergency department with either a single or isolated symptom or a myriad of signs and/or symptoms indicative of a degree of disease complexity and severity that warrant timely recognition and resuscitation.

Evidence of Antineutrinos from Distant Reactors Using Pure Water at SNO

The SNO+ Collaboration reports the first evidence of reactor antineutrinos in a Cherenkov detector. The nearest nuclear reactors are located 240 km away in Ontario, Canada. This analysis uses events with energies lower than in any previous analysis with a large water Cherenkov detector. Two analytical methods are used to distinguish reactor antineutrinos from background events in 190 days of data and yield consistent evidence for antineutrinos with a combined significance of 3.5σ.

Cocreation of a conversational agent to help patients adhere to their varenicline treatment: A study protocol

Objective

Varenicline is the most efficacious approved smoking cessation medication, making it one of the most cost-effective clinical interventions for reducing tobacco-related morbidity and mortality. Adhering to varenicline is strongly associated with smoking cessation. Healthbots have the potential to help people adhere to their medications by scaling up evidence-based behavioral interventions. In this protocol, we outline how we will follow the UK's Medical Research Council's guidance to codesign a theory-informed, evidence-based, and patient-centered healthbot to help people adhere to varenicline.

Methods

The study will utilize the Discover, Design and Build, and Test framework and will include three phases: (a) a rapid review and interviews with 20 patients and 20 healthcare providers to understand barriers and facilitators to varenicline adherence (Discover phase); (b) Wizard of Oz test to design the healthbot and get a sense of the questions that chatbot has to be able to answer (Design phase); and (c) building, training, and beta-testing the healthbot (Building and Testing phases) where the Nonadoption, Abandonment, Scale-up, Spread, and Sustainability framework will be used to develop the healthbot using the simplest sensible solution, and 20 participants will beta test the healthbot. We will use the Capability, Opportunity, Motivation-Behavior (COM-B) model of behavior change and its associated framework, the Theoretical Domains Framework, to organize the findings.

Conclusions

The present approach will enable us to systematically identify the most appropriate features for the healthbot based on a well-established behavioral theory, the latest scientific evidence, and end users' and healthcare providers' knowledge.

Predicting Generalized Anxiety Disorder from Impromptu Speech Transcripts Using Context-Aware Transformer-Based Neural Networks: Model Evaluation study (Preprint)

BACKGROUND

The ability to automatically detect anxiety disorders from speech could be useful as a screening tool for an anxiety disorder. Prior studies have shown that individual words in textual transcripts of speech have an association with anxiety severity. Transformer-based neural networks are models that have been recently shown to have powerful predictive capabilities based on the context of more than one input word. Transformers detect linguistic patterns and can be separately trained to make specific predictions based on these patterns.

OBJECTIVE

This study aimed to determine whether a transformer-based language model can be used to screen for generalized anxiety disorder from impromptu speech transcripts.

METHODS

A total of 2000 participants provided an impromptu speech sample in response to a modified version of the Trier Social Stress Test (TSST). They also completed the Generalized Anxiety Disorder 7-item (GAD-7) scale. A transformer-based neural network model (pretrained on large textual corpora) was fine-tuned on the speech transcripts and the GAD-7 to predict whether a participant was above or below a screening threshold of the GAD-7. We reported the area under the receiver operating characteristic curve (AUROC) on the test data and compared the results with a baseline logistic regression model using the Linguistic Inquiry and Word Count (LIWC) features as input. Using the integrated gradient method to determine specific words that strongly affect the predictions, we inferred specific linguistic patterns that influence the predictions.

RESULTS

The baseline LIWC-based logistic regression model had an AUROC value of 0.58. The fine-tuned transformer model achieved an AUROC value of 0.64. Specific words that were often implicated in the predictions were also dependent on the context. For example, the first-person singular pronoun "I" influenced toward an anxious prediction 88% of the time and a nonanxious prediction 12% of the time, depending on the context. Silent pauses in speech, also often implicated in predictions, influenced toward an anxious prediction 20% of the time and a nonanxious prediction 80% of the time.

CONCLUSIONS

There is evidence that a transformer-based neural network model has increased predictive power compared with the single word-based LIWC model. We also showed that the use of specific words in a specific context-a linguistic pattern-is part of the reason for the better prediction. This suggests that such transformer-based models could play a useful role in anxiety screening systems.

Mass Spectrometry Detection and Imaging of a Non‐Covalent Protein–Drug Complex in Tissue from Orally Dosed Rats

Here, we demonstrate detection by mass spectrometry of an intact protein–drug complex directly from liver tissue from rats that had been orally dosed with the drug. The protein–drug complex comprised fatty acid binding protein 1, FABP1, non‐covalently bound to the small molecule therapeutic bezafibrate. Moreover, we demonstrate spatial mapping of the [FABP1+bezafibrate] complex across a thin section of liver by targeted mass spectrometry imaging. This work is the first demonstration of in situ mass spectrometry analysis of a non‐covalent protein–drug complex formed in vivo and has implications for early stage drug discovery by providing a route to target‐drug characterization directly from the physiological environment.

Mass Spectrometry Detection and Imaging of a Non-Covalent Protein-Drug Complex in Tissue from Orally Dosed Rats

Here, we demonstrate detection by mass spectrometry of an intact protein-drug complex directly from liver tissue from rats that had been orally dosed with the drug. The protein-drug complex comprised fatty acid binding protein 1, FABP1, non-covalently bound to the small molecule therapeutic bezafibrate. Moreover, we demonstrate spatial mapping of the [FABP1+bezafibrate] complex across a thin section of liver by targeted mass spectrometry imaging. This work is the first demonstration of in situ mass spectrometry analysis of a non-covalent protein-drug complex formed in vivo and has implications for early stage drug discovery by providing a route to target-drug characterization directly from the physiological environment.

The feasibility and effectiveness of a novel online mental health literacy course in supporting university student mental health: a pilot study

BACKGROUND

There is a need for effective universal approaches to promote and support university student mental health that are scalable and sustainable. In this pilot study we assess the feasibility and acceptability of a fully-digitalized, comprehensive mental health literacy course co-created with and tailored to the needs of undergraduate students. We also explore preliminary associations with mental health and positive behaviour change.

METHODS

An accredited online mental health literacy course was developed using state-of-the-art pedagogical principles and a reverse mentorship approach. The course was offered as an interdisciplinary undergraduate elective. Students completed an online survey before and after the 12-week course that collected demographic information and assessed mental health knowledge, emotional self-awareness, mental health, stigma, and health-related behaviors using validated measures. Dependent group t-tests were used to compare pre- and post-course levels of knowledge, mental health, sleep quality and substance use. Mental health outcomes of students who completed the course were compared to an age and sex-matched sample of students not enrolled in the course and who completed the same survey measures over the same academic year. Multivariable linear regression was used to examine the effect of course participation on outcomes at follow-up.

RESULTS

The course had good uptake and was positively reviewed by participants. Specifically, students found the course engaging, relevant, and applicable, and agreed they would recommend it to their peers. Among course participants there was improvement in mental health knowledge (p < 0.001) and emotional self-awareness (p = 0.02) at course completion. Compared to the matched comparison group, taking the course was associated with reduced alcohol (β = - 0.41, p = 0.01) and cannabis use (β = - 0.35, p = 0.03), and improved sleep quality (β = 1.56, p = 0.09) at the end of the term.

CONCLUSIONS

Findings suggest that delivering mental health literacy as an online accredited course may be an acceptable and effective way of promoting university student mental health through improved knowledge, emotional self-awareness, and healthy lifestyle choices. As the course is expanded to larger and more diverse student cohorts we will be able to further examine the short and long-term effectiveness of the course in supporting student mental health and the underlying mechanisms.

Acoustic and Linguistic Features of Impromptu Speech and Their Association With Anxiety: Validation Study

BACKGROUND

The measurement and monitoring of generalized anxiety disorder requires frequent interaction with psychiatrists or psychologists. Access to mental health professionals is often difficult because of high costs or insufficient availability. The ability to assess generalized anxiety disorder passively and at frequent intervals could be a useful complement to conventional treatment and help with relapse monitoring. Prior work suggests that higher anxiety levels are associated with features of human speech. As such, monitoring speech using personal smartphones or other wearable devices may be a means to achieve passive anxiety monitoring.

OBJECTIVE

This study aims to validate the association of previously suggested acoustic and linguistic features of speech with anxiety severity.

METHODS

A large number of participants (n=2000) were recruited and participated in a single web-based study session. Participants completed the Generalized Anxiety Disorder 7-item scale assessment and provided an impromptu speech sample in response to a modified version of the Trier Social Stress Test. Acoustic and linguistic speech features were a priori selected based on the existing speech and anxiety literature, along with related features. Associations between speech features and anxiety levels were assessed using age and personal income as covariates.

RESULTS

Word count and speaking duration were negatively correlated with anxiety scores (r=-0.12; P<.001), indicating that participants with higher anxiety scores spoke less. Several acoustic features were also significantly (P<.05) associated with anxiety, including the mel-frequency cepstral coefficients, linear prediction cepstral coefficients, shimmer, fundamental frequency, and first formant. In contrast to previous literature, second and third formant, jitter, and zero crossing rate for the z score of the power spectral density acoustic features were not significantly associated with anxiety. Linguistic features, including negative-emotion words, were also associated with anxiety (r=0.10; P<.001). In addition, some linguistic relationships were sex dependent. For example, the count of words related to power was positively associated with anxiety in women (r=0.07; P=.03), whereas it was negatively associated with anxiety in men (r=-0.09; P=.01).

CONCLUSIONS

Both acoustic and linguistic speech measures are associated with anxiety scores. The amount of speech, acoustic quality of speech, and gender-specific linguistic characteristics of speech may be useful as part of a system to screen for anxiety, detect relapse, or monitor treatment.

Screening for Generalized Anxiety Disorder from Acoustic and Linguistic Features of Impromptu Speech: Prediction Model Evaluation Study (Preprint)

Background

Frequent interaction with mental health professionals is required to screen, diagnose, and track mental health disorders. However, high costs and insufficient access can make frequent interactions difficult. The ability to assess a mental health disorder passively and at frequent intervals could be a useful complement to the conventional treatment. It may be possible to passively assess clinical symptoms with high frequency by characterizing speech alterations collected using personal smartphones or other wearable devices. The association between speech features and mental health disorders can be leveraged as an objective screening tool.

Objective

This study aimed to evaluate the performance of a model that predicts the presence of generalized anxiety disorder (GAD) from acoustic and linguistic features of impromptu speech on a larger and more generalizable scale than prior studies did.

Methods

A total of 2000 participants were recruited, and they participated in a single web-based session. They completed the Generalized Anxiety Disorder-7 item scale assessment and provided an impromptu speech sample in response to a modified version of the Trier Social Stress Test. We used the linguistic and acoustic features that were found to be associated with anxiety disorders in previous studies along with demographic information to predict whether participants fell above or below the screening threshold for GAD based on the Generalized Anxiety Disorder-7 item scale threshold of 10. Separate models for each sex were also evaluated. We reported the mean area under the receiver operating characteristic (AUROC) from a repeated 5-fold cross-validation to evaluate the performance of the models.

Results

A logistic regression model using only acoustic and linguistic speech features achieved a significantly greater prediction accuracy than a random model did (mean AUROC 0.57, SD 0.03; P<.001). When separately assessing samples from female participants, we observed a mean AUROC of 0.55 (SD 0.05; P=.01). The model constructed from the samples from male participants achieved a mean AUROC of 0.57 (SD 0.07; P=.002). The mean AUROC increased to 0.62 (SD 0.03; P<.001) on the all-sample data set when demographic information (age, sex, and income) was included, indicating the importance of demographics when screening for anxiety disorders. The performance also increased for the female sample to a mean of 0.62 (SD 0.04; P<.001) when using demographic information (age and income). An increase in performance was not observed when demographic information was added to the model constructed from the male samples.

Conclusions

A logistic regression model using acoustic and linguistic speech features, which have been suggested to be associated with anxiety disorders in prior studies, can achieve above-random accuracy for predicting GAD. Importantly, the addition of basic demographic variables further improves model performance, suggesting a role for speech and demographic information to be used as automated, objective screeners of GAD.

Shear viscosity at finite baryon densities

We use the excluded volume Hadron Resonance Gas (HRG) model with the most up-to-date hadron list to calculate ηT/w at low temperatures and at finite baryon densities ρB. This ηT/w is then matched to a QCD-based shear viscosity calculation of the QGP for different profiles of ηT/w across T,μB including cross-over and critical point transitions. When compared to ideal hydrodynamic trajectories across T,μB, we find that the ηT/w(T,μB) profiles would require initial conditions at much larger baryon density to reach the same freeze-out point.

Autoimmune Connective Tissue Diseases: Systemic Lupus Erythematosus and Rheumatoid Arthritis

Systemic lupus erythematosus and rheumatoid arthritis are just 2 of several autoimmune connective tissue diseases that are primarily chronic in nature but can present to the emergency department by virtue of an acute exacerbation of disease. Beyond an acute exacerbation of disease, their predilection for invading multiple organ systems lends itself to the potential for patients presenting to the emergency department with either a single or isolated symptom or a myriad of signs and/or symptoms indicative of a degree of disease complexity and severity that warrant timely recognition and resuscitation.

Automated Screening for Social Anxiety, Generalized Anxiety, and Depression From Objective Smartphone-Collected Data: Cross-sectional Study

Background

The lack of access to mental health care could be addressed, in part, through the development of automated screening technologies for detecting the most common mental health disorders without the direct involvement of clinicians. Objective smartphone-collected data may contain sufficient information about individuals’ behaviors to infer their mental states and therefore screen for anxiety disorders and depression.

Objective

The objective of this study is to compare how a single set of recognized and novel features, extracted from smartphone-collected data, can be used for predicting generalized anxiety disorder (GAD), social anxiety disorder (SAD), and depression.

Methods

An Android app was designed, together with a centralized server system, to collect periodic measurements of objective smartphone data. The types of data included samples of ambient audio, GPS location, screen state, and light sensor data. Subjects were recruited into a 2-week observational study in which the app was run on their personal smartphones. The subjects also completed self-report severity measures of SAD, GAD, and depression. The participants were 112 Canadian adults from a nonclinical population. High-level features were extracted from the data of 84 participants, and predictive models of SAD, GAD, and depression were built and evaluated.

Results

Models of SAD and depression achieved a significantly greater screening accuracy than uninformative models (area under the receiver operating characteristic means of 0.64, SD 0.13 and 0.72, SD 0.12, respectively), whereas models of GAD failed to be predictive. Investigation of the model coefficients revealed key features that were predictive of SAD and depression.

Conclusions

We demonstrate the ability of a common set of features to act as predictors in the models of both SAD and depression. This suggests that the types of behaviors that can be inferred from smartphone-collected data are broad indicators of mental health, which can be used to study, assess, and track psychopathology simultaneously across multiple disorders and diagnostic boundaries.

Receipt of Next-generation Genomic Sequencing among Patients with Metastatic Colorectal Cancer (mCRC) in a Real-World Cohort

Purpose of the Study: Disparities in genomic precision medicine approaches, through molecular profiling or next- generation sequencing (NGS), by race/ethnicity, insurance, and poverty have been identified in lung cancer, but not mCRC. Our goal was to examine disparities in receipt of NGS in patients with mCRC. Methods: We used all-payer electronic health record (EHR)-derived de-identified data from the Flatiron Health database generated from routine clinical care across the United States. Our study population included 26,524 patients with mCRC during the years 2013–2020. In addition to date of NGS testing, the FH-EHR data include demographics (age, sex, and race/ethnicity), payer type, and Eastern Cooperative Oncology Group (ECOG) performance status. We conducted descriptive analyses and multivariable logistic regression analysis to identify correlates of receipt of NGS within 6 months of metastatic diagnosis. Results: Among the 26,524 people with mCRC, 45% (n = 11,946) were women, 48% (n = 12,732) had a Commercial Health Plan, and the majority were seen in a community practice (92%) vs academic hospitals. Over 70% of the patients were White, 12% Black or African-American (AA), and 14% Other. Thirty-three percent (n = 8,821) of patients had documentation in the EHR of having received NGS. After simultaneously adjusting for other factors in the model, older age (ORper year increase: 0.97, 95% CI: 0.96–0.98) and Black/AA race (OR: 0.74, 95% CI: 0.68–0.81), compared to White, was associated with lower odds of receiving NGS testing. Conversely, female sex, better ECOG performance status, later calendar year, being seen in an academic practice, and having a Commercial Health Plan were associated with greater odds of receiving NGS. Conclusions: Our findings indicate that NGS is not received uniformly by all patients with mCRC. Future analyses will incorporate receipt of individual molecular biomarker tests, as recommended by professional societies, as well as their results (e.g., KRAS, NRAS, BRAF, MMR/MSI), treatment information, and survival.

Method to Investigate the Distribution of Water-Soluble Drug-Delivery Systems in Fresh Frozen Tissues Using Imaging Mass Cytometry

Imaging mass cytometry (IMC) offers the opportunity to image metal- and heavy halogen-containing xenobiotics in a highly multiplexed experiment with other immunochemistry-based reagents to distinguish uptake into different tissue structures or cell types. However, in practice, many xenobiotics are not amenable to this analysis, as any compound which is not bound to the tissue matrix will delocalize during aqueous sample-processing steps required for IMC analysis. Here, we present a strategy to perform IMC experiments on a water-soluble polysarcosine-modified dendrimer drug-delivery system (S-Dends). This strategy involves two consecutive imaging acquisitions on the same tissue section using the same instrumental platform, an initial laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MSI) experiment followed by tissue staining and a standard IMC experiment. We demonstrated that settings can be found for the initial ablation step that leave sufficient residual tissue for subsequent antibody staining and visualization. This workflow results in lateral resolution for the S-Dends of 2 μm followed by imaging of metal-tagged antibodies at 1 μm.

Smartphone-Detected Ambient Speech and Self-Reported Measures of Anxiety and Depression: Exploratory Observational Study

Background

The ability to objectively measure the severity of depression and anxiety disorders in a passive manner could have a profound impact on the way in which these disorders are diagnosed, assessed, and treated. Existing studies have demonstrated links between both depression and anxiety and the linguistic properties of words that people use to communicate. Smartphones offer the ability to passively and continuously detect spoken words to monitor and analyze the linguistic properties of speech produced by the speaker and other sources of ambient speech in their environment. The linguistic properties of automatically detected and recognized speech may be used to build objective severity measures of depression and anxiety.

Objective

The aim of this study was to determine if the linguistic properties of words passively detected from environmental audio recorded using a participant’s smartphone can be used to find correlates of symptom severity of social anxiety disorder, generalized anxiety disorder, depression, and general impairment.

Methods

An Android app was designed to collect periodic audiorecordings of participants’ environments and to detect English words using automatic speech recognition. Participants were recruited into a 2-week observational study. The app was installed on the participants’ personal smartphones to record and analyze audio. The participants also completed self-report severity measures of social anxiety disorder, generalized anxiety disorder, depression, and functional impairment. Words detected from audiorecordings were categorized, and correlations were measured between words counts in each category and the 4 self-report measures to determine if any categories could serve as correlates of social anxiety disorder, generalized anxiety disorder, depression, or general impairment.

Results

The participants were 112 adults who resided in Canada from a nonclinical population; 86 participants yielded sufficient data for analysis. Correlations between word counts in 67 word categories and each of the 4 self-report measures revealed a strong relationship between the usage rates of death-related words and depressive symptoms (r=0.41, P<.001). There were also interesting correlations between rates of word usage in the categories of reward-related words with depression (r=–0.22, P=.04) and generalized anxiety (r=–0.29, P=.007), and vision-related words with social anxiety (r=0.31, P=.003).

Conclusions

In this study, words automatically recognized from environmental audio were shown to contain a number of potential associations with severity of depression and anxiety. This work suggests that sparsely sampled audio could provide relevant insight into individuals’ mental health.

Engaging Unmotivated Smokers to Move Toward Quitting: Design of Motivational Interviewing-Based Chatbot Through Iterative Interactions

BACKGROUND

At any given time, most smokers in a population are ambivalent with no motivation to quit. Motivational interviewing (MI) is an evidence-based technique that aims to elicit change in ambivalent smokers. MI practitioners are scarce and expensive, and smokers are difficult to reach. Smokers are potentially reachable through the web, and if an automated chatbot could emulate an MI conversation, it could form the basis of a low-cost and scalable intervention motivating smokers to quit.

OBJECTIVE

The primary goal of this study is to design, train, and test an automated MI-based chatbot capable of eliciting reflection in a conversation with cigarette smokers. This study describes the process of collecting training data to improve the chatbot's ability to generate MI-oriented responses, particularly reflections and summary statements. The secondary goal of this study is to observe the effects on participants through voluntary feedback given after completing a conversation with the chatbot.

METHODS

An interdisciplinary collaboration between an MI expert and experts in computer engineering and natural language processing (NLP) co-designed the conversation and algorithms underlying the chatbot. A sample of 121 adult cigarette smokers in 11 successive groups were recruited from a web-based platform for a single-arm prospective iterative design study. The chatbot was designed to stimulate reflections on the pros and cons of smoking using MI's running head start technique. Participants were also asked to confirm the chatbot's classification of their free-form responses to measure the classification accuracy of the underlying NLP models. Each group provided responses that were used to train the chatbot for the next group.

RESULTS

A total of 6568 responses from 121 participants in 11 successive groups over 14 weeks were received. From these responses, we were able to isolate 21 unique reasons for and against smoking and the relative frequency of each. The gradual collection of responses as inputs and smoking reasons as labels over the 11 iterations improved the F1 score of the classification within the chatbot from 0.63 in the first group to 0.82 in the final group. The mean time spent by each participant interacting with the chatbot was 21.3 (SD 14.0) min (minimum 6.4 and maximum 89.2). We also found that 34.7% (42/121) of participants enjoyed the interaction with the chatbot, and 8.3% (10/121) of participants noted explicit smoking cessation benefits from the conversation in voluntary feedback that did not solicit this explicitly.

CONCLUSIONS

Recruiting ambivalent smokers through the web is a viable method to train a chatbot to increase accuracy in reflection and summary statements, the building blocks of MI. A new set of 21 smoking reasons (both for and against) has been identified. Initial feedback from smokers on the experience shows promise toward using it in an intervention.

Clinical profiles and incident heart failure in cardiomyopathies: a population-based linked electronic health record cohort study

Background

Cardiomyopathies frequently cause heart failure (HF), however their prevalence in the general population and the natural history of incident HF across the spectrum of cardiomyopathy phenotypes is poorly understood. Improved understanding will help guide rational selection of diagnostic tests and accelerate the recognition of underlying causes of HF.

Purpose

To estimate the prevalence of cardiomyopathies using electronic health records; to compare clinical characteristics between patients with cardiomyopathy phenotypes; and to describe the temporal relationship between diagnosis of cardiomyopathy and incident HF.

Methods

A population-based cohort of patients with cardiomyopathy (n=4058) was provided by the UK Clinical Practice Research Datalink (CPRD) from a denominator sample of ∼9 million individuals. Patients were phenotyped into groups according to ESC criteria: hypertrophic cardiomyopathy (HCM), dilated cardiomyopathy (DCM), arrhythmogenic right ventricular cardiomyopathy (ARVC) and restrictive cardiomyopathy (RCM). An additional group of transthyretin amyloid cardiomyopathy (ATTR-CM) was reported separately. Point prevalence was estimated for each cardiomyopathy subtype and clinical characteristics defined. An index date at first diagnosis of HF was determined for each patient and the time from/to first diagnosis of cardiomyopathy calculated relative to the index date and presented graphically.

Results

DCM was the most common cardiomyopathy phenotype among women and men with 3.4 and 7.7 cases per 10,000 population, respectively. The 2-fold increase in prevalence among men was consistent across DCM, HCM and RCM; the reverse trend was observed for ARVC which was found in 2.3 per 10,000 women and 1.1 per 10,000 men. At the time of first diagnosis of cardiomyopathy, most patients with ATTR-CM (73.5%), DCM (71.0%) and RCM (71.3%) had pre-existing HF though this proportion fell to 41.0% in ARVC and 31.0% in HCM. In relation to incident HF, a diagnosis of HCM and DCM were recorded earliest at a mean −2.2 years (SE 0.2) and −0.6 years (SE 0.1), respectively. We observed a clustering of diagnoses of RCM (mean −0.2 years, SE 0.4) and ARVC (mean 0.1 years, SE 0.1) around the time of onset of heart failure, whereas a diagnosis of ATTR-CM was first recorded at a mean of 0.9 years (SE 0.2) following the onset of heart failure.

Conclusions

Most diagnoses of ATTR-CM, DCM and RCM were preceded by clinical expression of HF whereas most people with ARVC or HCM developed HF after their cardiomyopathy diagnosis. Our findings in ARVC and HCM suggest a more indolent course with respect to cardiac function or better recognition in an asymptomatic phase. The clustering of a diagnosis of heart failure around the time of diagnosis of cardiomyopathy highlights a need for greater awareness of specific aetiologies of heart failure in routine practice and suggests opportunities for presymptomatic or earlier diagnosis.

Temporality of HF in cardiomyopathies

Funding Acknowledgement

Type of funding source: Private company. Main funding source(s): Pfizer

The Relationship Between Smartphone-Recorded Environmental Audio and Symptomatology of Anxiety and Depression: Exploratory Study

BACKGROUND

Objective and continuous severity measures of anxiety and depression are highly valuable and would have many applications in psychiatry and psychology. A collective source of data for objective measures are the sensors in a person's smartphone, and a particularly rich source is the microphone that can be used to sample the audio environment. This may give broad insight into activity, sleep, and social interaction, which may be associated with quality of life and severity of anxiety and depression.

OBJECTIVE

This study aimed to explore the properties of passively recorded environmental audio from a subject's smartphone to find potential correlates of symptom severity of social anxiety disorder, generalized anxiety disorder, depression, and general impairment.

METHODS

An Android app was designed, together with a centralized server system, to collect periodic measurements of the volume of sounds in the environment and to detect the presence or absence of English-speaking voices. Subjects were recruited into a 2-week observational study during which the app was run on their personal smartphone to collect audio data. Subjects also completed self-report severity measures of social anxiety disorder, generalized anxiety disorder, depression, and functional impairment. Participants were 112 Canadian adults from a nonclinical population. High-level features were extracted from the environmental audio of 84 participants with sufficient data, and correlations were measured between the 4 audio features and the 4 self-report measures.

RESULTS

The regularity in daily patterns of activity and inactivity inferred from the environmental audio volume was correlated with the severity of depression (r=-0.37; P<.001). A measure of sleep disturbance inferred from the environmental audio volume was also correlated with the severity of depression (r=0.23; P=.03). A proxy measure of social interaction based on the detection of speaking voices in the environmental audio was correlated with depression (r=-0.37; P<.001) and functional impairment (r=-0.29; P=.01). None of the 4 environmental audio-based features tested showed significant correlations with the measures of generalized anxiety or social anxiety.

CONCLUSIONS

In this study group, the environmental audio was shown to contain signals that were associated with the severity of depression and functional impairment. Associations with the severity of social anxiety disorder and generalized anxiety disorder were much weaker in comparison and not statistically significant at the 5% significance level. This work also confirmed previous work showing that the presence of voices is associated with depression. Furthermore, this study suggests that sparsely sampled audio volume could provide potentially relevant insight into subjects' mental health.

Hybrid Eye-Tracking on a Smartphone with CNN Feature Extraction and an Infrared 3D Model

This paper describes a low-cost, robust, and accurate remote eye-tracking system that uses an industrial prototype smartphone with integrated infrared illumination and camera. Numerous studies have demonstrated the beneficial use of eye-tracking in domains such as neurological and neuropsychiatric testing, advertising evaluation, pilot training, and automotive safety. Remote eye-tracking on a smartphone could enable the significant growth in the deployment of applications in these domains. Our system uses a 3D gaze-estimation model that enables accurate point-of-gaze (PoG) estimation with free head and device motion. To accurately determine the input eye features (pupil center and corneal reflections), the system uses Convolutional Neural Networks (CNNs) together with a novel center-of-mass output layer. The use of CNNs improves the system’s robustness to the significant variability in the appearance of eye-images found in handheld eye trackers. The system was tested with 8 subjects with the device free to move in their hands and produced a gaze bias of 0.72°. Our hybrid approach that uses artificial illumination, a 3D gaze-estimation model, and a CNN feature extractor achieved an accuracy that is significantly (400%) better than current eye-tracking systems on smartphones that use natural illumination and machine-learning techniques to estimate the PoG.

Hypertrophic cardiomyopathy MYH7 mutation R723G alters mRNA secondary structure

Point mutation R723G in the MYH7 gene causes hypertrophic cardiomyopathy (HCM). Heterozygous patients with this mutation exhibit a comparable allelic imbalance of the MYH7 gene. On average 67% of the total MYH7 mRNA are derived from the MYH7^R723G-allele and 33% from the MYH7^WT allele. Mechanisms underlying mRNA allelic imbalance are largely unknown. We suggest that a different mRNA lifetime of the alleles may cause the allelic drift in R723G patients. A potent regulator of mRNA lifetime is its secondary structure. To test for alterations in the MYH7^R723G mRNA structure we used selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) analysis. We show significantly different SHAPE reactivity of wild-type and MYH7^R723G RNA, which is in accordance with bioinformatically predicted structures. Thus, we provide the first experimental evidence for mRNA secondary structure alterations by the HCM point mutation. We assume that this may result in a prolonged lifetime of MYH7^R723G mRNA in vivo and subsequently in the determined allelic imbalance.

P909 Diagnostic evaluation of rheumatic heart disease in aborigonal population

Background

Among Indigenous Australians, rates of Acute Rheumatic Fever (ARF) continue to be among the highest in the world. Diagnosis remains a clinical decision based on identification of major and minor manifestations of the illness. Treatment involves lengthy prophylaxis and should continue for a minimum of 10 years.

ARF can cause permanent damage to the heart known as rheumatic heart disease (RHD).

We therefore utilised echocardiography as a diagnostic tool incorporating Tissue Quantification Backscatter expressed in decibels (dB) and global LV work efficiency estimated from left ventricle (LV) pressure-strain loops to identify rheumatic changes of the Mitral Valve and help improve early diagnosis of RHD.

Method

Data from patients with suspected RHD (n = 14), and age matched controls (n = 10) underwent Mitral Valve Backscatter Analysis (MVBS). MVBS was expressed as a ratio % (MVBS ratio %) by dividing the average MVBS and the average blood pool value expressed in decibels (dB). Furthermore LV function was utilised via 2D longitudinal strain and indices of myocardial work were derived.

Result

MVBS ratio % was significantly higher in the control group as compared to the RHD group (p = 0.001) (fig1). Of the RHD group echocardiography parameters showed there were no significant mitral valve stenosis or regurgitation. Correlates of LV function included: Global work Index (GWI), Global longitudinal Strain (GLS) and Global work efficiency (GWE). Of the above correlates the control group showed Backscatter vs GLS (r= -0.89, p = 0.001), the RHD group: Backscatter vs GLS (r = 0.52. p = 0.12). Within the RHD group the ratio vs GWE (r= 0.57, p = 0.09) these results showed a trend to significance.

Conclusions

Currently diagnosis of RHD remains a clinical decision based on the identification of major and minor manifestations. In addition treatment involves prophylaxis injections for a minimum of ten years. Of this group there were no significant echocardiography changes, rather clinical manifestation to derive RHD.

This study shows that calibrated MVBS ratio % and determinants of myocardial work may be a promising quantitative tool to detect early manifestation of RHD potentially aiding an early treatment plan and thus reducing the clinical burden of monthly penicillin injections for a ten year period.

Abstract P909 Figure. RHD and Myocardial correlates

Liquid Chromatographic Determination of Maleic Hydrazide in Technical and Formulated Products: Collaborative Study

Fourteen collaborating laboratories assayed maleic hydrazide (MH), 6-hydroxypyridazin-3(2H)-one, in technical and formulated products by reversed-phase liquid chromatography (LC) with sulfanilic acid as an internal standard. The active MH in the samples (6 lots) ranged from 16% (expressed as the potassium salt) to 98% (MH in the technical). A small amount of 1 M KOH was added to the technical MH and analytical standards to create the potassium salt of the analyte which is soluble in water. Test samples and standards were extracted with water containing the internal standard before analysis by LC on a C8 column with an ion-pairing eluting solution and UV detection at 254 nm. The concentration of MH was calculated by comparing the peak area response ratios of the analyte and the internal standard with those in the analytical standard solution. Eleven laboratories weighed each test sample twice with single analysis. Three laboratories weighed each sample once and made duplicate injections on the LC system. The data were analyzed using the 11 laboratories' results. A second data analysis was done including all laboratory results using a Youden pair approach, selecting one of 2 duplicate assay values randomly for each laboratory and sample. In the first data analysis, the repeatability standard deviation ranged from 0.07 to 1.39%; reproducibility standard deviation ranged from 0.22 to 1.39%. In the second data analysis (using all laboratory data), repeatability standard deviation ranged from 0.09 to 0.86%; reproducibility standard deviation ranged from 0.22 to 1.31%.

Complex regional pain syndrome: a narrative review for the practising clinician

Complex regional pain syndrome (CRPS) is a life-altering condition that usually affects the extremities after a trauma or nerve injury. The physiologic changes that occur as a result of the inciting injury are complex, as the name of the syndrome implies. The pain and disability associated with CRPS often lead to psychological co-morbidities that create a vicious cycle of pain, isolation, and depression. We review recent developments in the understanding of CRPS and advancements in management of this syndrome. Further research in targeting specific mechanisms involved in the pathophysiology of CRPS should lead to prevention of this condition.

Extracellular vesicles induce minimal hepatotoxicity and immunogenicity

Extracellular vesicles (EVs) mediate cellular communication through the transfer of active biomolecules, raising interest in using them as biological delivery vehicles for therapeutic drugs. For drug delivery applications, it is important to understand the intrinsic safety and toxicity liabilities of EVs. Nanoparticles, including EVs, typically demonstrate significant accumulation in the liver after systemic administration in vivo. We confirmed uptake of EVs derived from Expi293F cells into HepG2 cells and did not detect any signs of hepatotoxicity measured by cell viability, functional secretion of albumin, plasma membrane integrity, and mitochondrial and lysosomal activity even at high exposures of up to 5 × 1010 EVs per mL. Whole genome transcriptome analysis was used to measure potential effects on the gene expression in the recipient HepG2 cells at 24 h following exposure to EVs. Only 0.6% of all genes were found to be differentially expressed displaying less than 2-fold expression change, with genes related to inflammation or toxicity being unaffected. EVs did not trigger any proinflammatory cytokine response in HepG2 cells. However, minor changes were noted in human blood for interleukin (IL)-8, IL-6, and monocyte chemotactic protein 1 (MCP-1). Administration of 5 × 1010 Expi293F-derived EVs to BALB/c mice did not result in any histopathological changes or increases of liver transaminases or cytokine levels, apart from a modest increase in keratinocyte chemoattractant (KC). The absence of any significant toxicity associated with EVs in vitro and in vivo supports the prospective use of EVs for therapeutic applications and for drug delivery.

RUNX represses Pmp22 to drive neurofibromagenesis

Patients with neurofibromatosis type 1 (NF1) are predisposed to develop neurofibromas, but the underlying molecular mechanisms of neurofibromagenesis are not fully understood. We showed dual genetic deletion of Runx1 and Runx3 in Schwann cells (SCs) and SC precursors delayed neurofibromagenesis and prolonged mouse survival. We identified peripheral myelin protein 22 (Pmp22/Gas3) related to neurofibroma initiation. Knockdown of Pmp22 with short hairpin RNAs increased Runx1^fl/fl;Runx3^fl/fl;Nf1^fl/fl;DhhCre tumor-derived sphere numbers and enabled significantly more neurofibroma-like microlesions on transplantation. Conversely, overexpression of Pmp22 in mouse neurofibroma SCs decreased cell proliferation. Mechanistically, RUNX1/3 regulated alternative promoter usage and induced levels of protein expression of Pmp22 to control SC growth. Last, pharmacological inhibition of RUNX/core-binding factor β (CBFB) activity significantly reduced neurofibroma volume in vivo. Thus, we identified a signaling pathway involving RUNX1/3 suppression of Pmp22 in neurofibroma initiation and/or maintenance. Targeting disruption of RUNX/CBFB interaction might provide a novel therapy for patients with neurofibroma.

Influence of impaired selective motor control on gait in children with cerebral palsy

PURPOSE

Spastic cerebral palsy (CP) is characterized by four neuromuscular deficits: weakness, short muscle-tendon unit, muscle spasticity and impaired selective motor control (SMC). We examined the influence of impaired SMC on gait in children with bilateral spastic CP. Delineating the influence of neuromuscular deficits on gait abnormalities can guide surgical and therapeutic interventions to reduce long-term debilitating effects of CP.

METHODS

The relationship between impaired SMC and gait was assessed using multivariate linear regression analysis of Selective Control Assessment of the Lower Extremity (SCALE) in relation to stance phase knee flexion and temporal-spatial gait parameters calculated using 3D kinematics for 57 children with bilateral spastic CP, ages seven to 11 years.

RESULTS

Mean SCALE values were 5.8 (0 to 10, sd 3.0) and 5.7 (0 to 10, sd 2.9) for right and left legs, respectively. Multivariate linear regression models, including right and left SCALE and height, significantly predicted right and left knee flexion at initial contact (R = 0.479, p = 0.003; R = 0.452, p = 0.007, respectively) and right and left knee flexion in midstance (R = 0.428, p = 0.013; R = 0.407, p = 0.022, respectively). The model significantly predicted right and left step length (R = 0.645, p = 0.000; R = 0.523, p = 0.001, respectively) and predicted gait velocity (R = 0.444, p = 0.008). The model including SCALE did not predict step width.

CONCLUSION

Results indicate impaired SMC predicts increased knee flexion at initial contact, and reduces step length and velocity. Understanding the influence of impaired SMC on gait can inform decisions regarding therapy and surgery, such as hamstring lengthening.

LEVEL OF EVIDENCE

Level II Retrospective Study.

Patient Willingness to Consent to Mobile Phone Data Collection for Mental Health Apps: Structured Questionnaire

BACKGROUND

It has become possible to use data from a patient's mobile phone as an adjunct or alternative to the traditional self-report and interview methods of symptom assessment in psychiatry. Mobile data-based assessment is possible because of the large amounts of diverse information available from a modern mobile phone, including geolocation, screen activity, physical motion, and communication activity. This data may offer much more fine-grained insight into mental state than traditional methods, and so we are motivated to pursue research in this direction. However, passive data retrieval could be an unwelcome invasion of privacy, and some may not consent to such observation. It is therefore important to measure patients' willingness to consent to such observation if this approach is to be considered for general use.

OBJECTIVE

The aim of this study was to measure the ownership rates of mobile phones within the patient population, measure the patient population's willingness to have their mobile phone used as an experimental assessment tool for their mental health disorder, and, finally, to determine how likely patients would be to provide consent for each individual source of mobile phone-collectible data across the variety of potential data sources.

METHODS

New patients referred to a tertiary care mood and anxiety disorder clinic from August 2016 to October 2017 completed a survey designed to measure their mobile phone ownership, use, and willingness to install a mental health monitoring app and provide relevant data through the app.

RESULTS

Of the 82 respondents, 70 (85%) reported owning an internet-connected mobile phone. When asked about installing a hypothetical mobile phone app to assess their mental health disorder, 41% (33/80) responded with complete willingness to install with another 43% (34/80) indicating potential willingness to install such an app. Willingness to give permissions for specific types of data varied by data source, with respondents least willing to consent to audio recording and analysis (19% [15/80] willing respondents, 31% [25/80] potentially willing) and most willing to consent to observation of the mobile phone screen being on or off (46% [36/79] willing respondents and 23% [18/79] potentially willing).

CONCLUSIONS

The patients surveyed had a high incidence of ownership of internet-connected mobile phones, which suggests some plausibility for the general approach of mental health state inference through mobile phone data. Patients were also relatively willing to consent to data collection from sources that were less personal but expressed less willingness for the most personal communication and location data.

Accurate Model-Based Point of Gaze Estimation on Mobile Devices

The most accurate remote Point of Gaze (PoG) estimation methods that allow free head movements use infrared light sources and cameras together with gaze estimation models. Current gaze estimation models were developed for desktop eye-tracking systems and assume that the relative roll between the system and the subjects' eyes (the 'R-Roll') is roughly constant during use. This assumption is not true for hand-held mobile-device-based eye-tracking systems. We present an analysis that shows the accuracy of estimating the PoG on screens of hand-held mobile devices depends on the magnitude of the R-Roll angle and the angular offset between the visual and optical axes of the individual viewer. We also describe a new method to determine the PoG which compensates for the effects of R-Roll on the accuracy of the POG. Experimental results on a prototype infrared smartphone show that for an R-Roll angle of 90 ° , the new method achieves accuracy of approximately 1 ° , while a gaze estimation method that assumes that the R-Roll angle remains constant achieves an accuracy of 3.5 ° . The manner in which the experimental PoG estimation errors increase with the increase in the R-Roll angle was consistent with the analysis. The method presented in this paper can improve significantly the performance of eye-tracking systems on hand-held mobile-devices.

Taking the tester out of the SDMT: A proof of concept fully automated approach to assessing processing speed in people with MS

Background:

One factor hindering the widespread use of cognitive testing for people with multiple sclerosis (pwMS) is the need for a tester to administer tests.

Objective:

To undertake a proof of concept study assessing the feasibility of a fully automated speech recognition version of the Symbol Digit Modalities Test (auto-SDMT) in detecting abnormalities in processing speed in pwMS.

Methods:

A sample of 50 pwMS and 32 matched healthy control (HC) subjects was tested with the auto-SDMT and the Brief International Cognitive Assessment for MS (BICAMS).

Results:

The percentages of MS participants impaired on the auto-SDMT and the traditional oral SDMT were 34% and 32%, respectively. Excellent convergent validity was found between the two tests (MS: r = −0.806, p < 0.001 and HC: r = −0.629, p < 0.001). The auto-SDMT had a similar sensitivity and specificity to the traditional oral SDMT in predicting overall impairment on the BICAMS.

Conclusion:

The auto-SDMT is a sensitive measure for detecting processing speed deficits in pwMS. The test, the first entirely computer administrated oral response version of the SDMT, uses speech recognition technology, thereby eliminating the need for a human tester. Replication of the results is required in a larger representative sample of pwMS.

Dietary factors and pediatric multiple sclerosis: A case-control study

BACKGROUND

The role of diet in multiple sclerosis (MS) is largely uncharacterized, particularly as it pertains to pediatric-onset disease.

OBJECTIVE

To determine the association between dietary factors and MS in children.

METHODS

Pediatric MS patients and controls were recruited from 16 US centers (MS or clinically isolated syndrome onset before age 18, <4 years from symptom onset and at least 2 silent lesions on magnetic resonance imaging). The validated Block Kids Food Screener questionnaire was administered 2011-2016. Chi-squared test compared categorical variables, Kruskal-Wallis test compared continuous variables, and multivariable logistic regression analysis was performed.

RESULTS

In total, 312 cases and 456 controls were included (mean ages 15.1 and 14.4 years). In unadjusted analyses, there was no difference in intake of fats, proteins, carbohydrates, sugars, fruits, or vegetables. Dietary iron was lower in cases ( p = 0.04), and cases were more likely to consume below recommended guidelines of iron (77.2% of cases vs 62.9% of controls, p < 0.001). In multivariable analysis, iron consumption below recommended guidelines was associated with MS (odds ratio = 1.80, p < 0.01).

CONCLUSION

Pediatric MS cases may be less likely to consume sufficient iron compared to controls, and this warrants broader study to characterize a temporal relationship. No other significant difference in intake of most dietary factors was found.

An Anatomic Index for the Severity of Ocular Injuries

Ocular injuries are a frequent cause of monocular blindness and cause disfigurement and discomfort. We developed a measure of severity for eye injuries using a multi-attribute utility (MAU) model. The severity index scoring was applied to eye injuries that presented at hospitals in Wisconsin, U.S.A. The resulting distribution of severities was compatible with that seen by general eye care physicians. A severity scale provides a means of comparing the severity of injuries from a wide variety of traumatic sources (e.g. automobile crashes, combat injuries, occupational accidents, etc.) and is useful in evaluating preventive and public health measurements.