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).

Racial and ethnic associations with interstitial lung disease and healthcare utilization in patients with systemic sclerosis.

Rationale: Racial and ethnic differences in presentation and outcomes have been reported in systemic sclerosis (SSc) and SSc-interstitial lung disease (ILD). However, diverse cohorts and additional modeling can improve understanding of risk features and outcomes, which is important for reducing associated disparities. Objectives: To determine if there are racial/ethnic differences associated with SSc-ILD risk and age; time intervals between SSc and ILD, and with emergency department (ED) visit or hospitalization rates. Methods: A retrospective cohort study using electronic health record data from an integrated health system, over a 5.5 year period was conducted using clinical and sociodemographic variables, models were generated with sequential adjustments for these variables. Logistic regression models were used to examine the association of covariates with ILD and age at SSc-ILD. Healthcare outcomes were analyzed with complementary log-log regression models. Results: The cohort included 756 adults (83.6% female, 80.3% non-Hispanic White) with SSc with a mean age of 59 years. Overall, 33.7% of patients in the cohort had an ILD code, with increased odds for Asian (odds ratio [OR], 2.59; 95% confidence interval [CI], 1.29, 5.18; P =.007) compared to White patients. The age in years of patients with SSc-ILD was younger for Hispanic (mean difference, -6.5; 95% CI, -13, -0.21; P = 0.04) and Black/African American patients (-10; 95% CI -16, -4.9; P <0.001) compared to White patients. Black/African American patients were more likely to have an ILD code before an SSc code (59% compared to 20.6% of White patients), and had the shortest interval from SSc to ILD (3 months). Black/African American (HR, 2.59; 95% CI 1.47, 4.49; P =0.001) and Hispanic patients (HR 2.29; 95% CI 1.37, 3.82; P =0.002) had higher rates of an ED visit. Conclusion: In this study, SSc-ILD presentation and outcomes differed by racial/ethnic group (increased odds of SSc-ILD, younger age at SSc-ILD, and preceding diagnosis with respect to SSc, rates of ED visit), some of which was attenuated with adjustment for clinical and sociodemographic characteristics. Differing presentation may be driven by social drivers of health (SDOH), autoantibody profiles, or other key unmeasured factors contributing to susceptibility and severity.

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.

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.

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.

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.

Bronchial epithelial gene expression and interstitial lung abnormalities.

INTRODUCTION: Interstitial lung abnormalities (ILA) often represent early fibrotic changes that can portend a progressive fibrotic phenotype. In particular, the fibrotic subtype of ILA is associated with increased mortality and rapid decline in lung function. Understanding the differential gene expression that occurs in the lungs of participants with fibrotic ILA may provide insight into development of a useful biomarker for early detection and therapeutic targets for progressive pulmonary fibrosis. METHODS: Measures of ILA and gene expression data were available in 213 participants in the Detection of Early Lung Cancer Among Military Personnel (DECAMP1 and DECAMP2) cohorts. ILA was defined using Fleischner Society guidelines and determined by sequential reading of computed tomography (CT) scans. Primary analysis focused on comparing gene expression in ILA with usual interstitial pneumonia (UIP) pattern with those with no ILA. RESULTS: ILA was present in 51 (24%) participants, of which 16 (7%) were subtyped as ILA with a UIP pattern. One gene, pro platelet basic protein (PPBP) and seventeen pathways (e.g. TNF-α signalling) were significantly differentially expressed between those with a probable or definite UIP pattern of ILA compared to those without ILA. 16 of these 17 pathways, but no individual gene, met significance when comparing those with ILA to those without ILA. CONCLUSION: Our study demonstrates that abnormal inflammatory processes are apparent in the bronchial airway gene expression profiles of smokers with and without lung cancer with ILA. Future studies with larger and more diverse populations will be needed to confirm these findings.

Comparing reintroduction strategies for the endangered San Francisco gartersnake (Thamnophis sirtalis tetrataenia) using demographic models.

For endangered species persisting in a few populations, reintroductions to unoccupied habitat are a popular conservation action to increase viability in the long term. Identifying the reintroduction strategy that is most likely to result in viable founder and donor populations is essential to optimally use resources available for conservation. The San Francisco gartersnake (Thamnophis sirtalis tetrataenia) is an endangered sub-species that persists in a small number of populations in a highly urbanized region of California. Most of the extant populations of San Francisco gartersnakes have low adult abundance and effective population size, heightening the need for establishment of more populations for insurance against the risk of extinction. We used simulations from demographic models to project the probability of quasi-extinction for reintroduced populations of San Francisco gartersnakes based on the release of neonate, juvenile, adult, or mixed-age propagules. Our simulation results indicated that the release of head-started juveniles resulted in the greatest viability of reintroduced populations, and that releases would need to continue for at least 15 years to ensure a low probability of quasi-extinction. Releasing captive-bred juvenile snakes would also have less effect on the viability of the donor population, compared to strategies that require more adult snakes to be removed from the donor population for translocation. Our models focus on snake demography, but the genetic makeup of donor, captive, and reintroduced populations will also be a major concern for any proposed reintroduction plan. This study demonstrates how modeling can be used to inform reintroduction strategies for highly imperiled species.

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.

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.

Development and validation of algorithms to build an electronic health record based cohort of patients with systemic sclerosis.

OBJECTIVES: To evaluate methods of identifying patients with systemic sclerosis (SSc) using International Classification of Diseases, Tenth Revision (ICD-10) codes (M34*), electronic health record (EHR) databases and organ involvement keywords, that result in a validated cohort comprised of true cases with high disease burden. METHODS: We retrospectively studied patients in a healthcare system likely to have SSc. Using structured EHR data from January 2016 to June 2021, we identified 955 adult patients with M34* documented 2 or more times during the study period. A random subset of 100 patients was selected to validate the ICD-10 code for its positive predictive value (PPV). The dataset was then divided into a training and validation sets for unstructured text processing (UTP) search algorithms, two of which were created using keywords for Raynaud's syndrome, and esophageal involvement/symptoms. RESULTS: Among 955 patients, the average age was 60. Most patients (84%) were female; 75% of patients were White, and 5.2% were Black. There were approximately 175 patients per year with the code newly documented, overall 24% had an ICD-10 code for esophageal disease, and 13.4% for pulmonary hypertension. The baseline PPV was 78%, which improved to 84% with UTP, identifying 788 patients likely to have SSc. After the ICD-10 code was placed, 63% of patients had a rheumatology office visit. Patients identified by the UTP search algorithm were more likely to have increased healthcare utilization (ICD-10 codes 4 or more times 84.1% vs 61.7%, p < .001), organ involvement (pulmonary hypertension 12.7% vs 6% p = .011) and medication use (mycophenolate use 28.7% vs 11.4%, p < .001) than those identified by the ICD codes alone. CONCLUSION: EHRs can be used to identify patients with SSc. Using unstructured text processing keyword searches for SSc clinical manifestations improved the PPV of ICD-10 codes alone and identified a group of patients most likely to have SSc and increased healthcare needs.

Predicting Generalized Anxiety Disorder From Impromptu Speech Transcripts Using Context-Aware Transformer-Based Neural Networks: Model Evaluation Study.

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.

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.

Suspected Interstitial Lung Disease in COPDGene Study.

Rationale: Although interstitial lung abnormalities (ILA), specific patterns of incidentally-detected abnormal density on computed tomography, have been associated with abnormal lung function and increased mortality, it is unclear if a subset with incidental interstitial lung disease (ILD) accounts for these adverse consequences. Objectives: To define the prevalence and risk factors of suspected ILD and assess outcomes. Methods: Suspected ILD was evaluated in the COPDGene (Chronic Obstructive Pulmonary Disease Genetic Epidemiology) study, defined as ILA and at least one additional criterion: definite fibrosis on computed tomography, FVC less than 80% predicted, or DLCO less than 70% predicted. Multivariable linear, longitudinal, and Cox proportional hazards regression models were used to assess associations with St. George's Respiratory Questionnaire, 6-minute-walk test, supplemental oxygen use, respiratory exacerbations, and mortality. Measurements and Main Results: Of 4,361 participants with available data, 239 (5%) had evidence for suspected ILD, whereas 204 (5%) had ILA without suspected ILD. In multivariable analyses, suspected ILD was associated with increased St. George's Respiratory Questionnaire score (mean difference [MD], 3.9 points; 95% confidence interval [CI], 0.6-7.1; P = 0.02), reduced 6-minute-walk test (MD, -35 m; 95% CI, -56 m to -13 m; P = 0.002), greater supplemental oxygen use (odds ratio [OR], 2.3; 95% CI, 1.1-5.1; P = 0.03) and severe respiratory exacerbations (OR, 2.9; 95% CI, 1.1-7.5; P = 0.03), and higher mortality (hazard ratio, 2.4; 95% CI, 1.2-4.6; P = 0.01) compared with ILA without suspected ILD. Risk factors associated with suspected ILD included self-identified Black race (OR, 2.0; 95% CI, 1.1-3.3; P = 0.01) and pack-years smoking history (OR, 1.2; 95% CI, 1.1-1.3; P = 0.0005). Conclusions: Suspected ILD is present in half of those with ILA in COPDGene and is associated with exercise decrements and increased symptoms, supplemental oxygen use, severe respiratory exacerbations, and mortality.

Screening for Generalized Anxiety Disorder From Acoustic and Linguistic Features of Impromptu Speech: Prediction Model Evaluation Study.

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.

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.

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.