Identifying depression-related topics in smartphone-collected free-response speech recordings using an automatic speech recognition system and a deep learning topic model

BACKGROUND

Prior research has associated spoken language use with depression, yet studies often involve small or non-clinical samples and face challenges in the manual transcription of speech. This paper aimed to automatically identify depression-related topics in speech recordings collected from clinical samples.

METHODS

The data included 3919 English free-response speech recordings collected via smartphones from 265 participants with a depression history. We transcribed speech recordings via automatic speech recognition (Whisper tool, OpenAI) and identified principal topics from transcriptions using a deep learning topic model (BERTopic). To identify depression risk topics and understand the context, we compared participants' depression severity and behavioral (extracted from wearable devices) and linguistic (extracted from transcribed texts) characteristics across identified topics.

RESULTS

From the 29 topics identified, we identified 6 risk topics for depression: 'No Expectations', 'Sleep', 'Mental Therapy', 'Haircut', 'Studying', and 'Coursework'. Participants mentioning depression risk topics exhibited higher sleep variability, later sleep onset, and fewer daily steps and used fewer words, more negative language, and fewer leisure-related words in their speech recordings.

LIMITATIONS

Our findings were derived from a depressed cohort with a specific speech task, potentially limiting the generalizability to non-clinical populations or other speech tasks. Additionally, some topics had small sample sizes, necessitating further validation in larger datasets.

CONCLUSION

This study demonstrates that specific speech topics can indicate depression severity. The employed data-driven workflow provides a practical approach for analyzing large-scale speech data collected from real-world settings.

Microneedling versus microcoring: A review of percutaneous collagen induction for the face and neck

BACKGROUND

Microneedling (MN) and microcoring (MCT) are both methods used for percutaneous collagen induction. This minimally invasive technique involves creating controlled damage in cutaneous tissue to induce neocollagenesis and neoelastogenesis. MN utilizes solid microneedles and is commonly combined with radiofrequency (RF) to add thermal energy, while MCT involves hollow microneedles capable of removing excess tissue without inducing scar formation.

AIMS

The purpose of this review was to summarize recent literature for MN and MCT, with the goal of assisting clinical decision making regarding the use of these technologies.

METHODS

PubMed search was conducted for relevant articles published within the last 10 years. Scoping literature review was then performed with pertinent findings reported.

RESULTS

Existing literature investigating MCT is sparse. Limited data on in vivo, human effects of this technology exist. Two out of 14 studies in this review pertained to MCT.

CONCLUSION

Additional high-powered clinical studies are needed to guide future cosmetic treatments with MN and MCT.

Multilingual markers of depression in remotely collected speech samples: A preliminary analysis

BACKGROUND

Speech contains neuromuscular, physiological and cognitive components, and so is a potential biomarker of mental disorders. Previous studies indicate that speaking rate and pausing are associated with major depressive disorder (MDD). However, results are inconclusive as many studies are small and underpowered and do not include clinical samples. These studies have also been unilingual and use speech collected in controlled settings. If speech markers are to help understand the onset and progress of MDD, we need to uncover markers that are robust to language and establish the strength of associations in real-world data.

METHODS

We collected speech data in 585 participants with a history of MDD in the United Kingdom, Spain, and Netherlands as part of the RADAR-MDD study. Participants recorded their speech via smartphones every two weeks for 18 months. Linear mixed models were used to estimate the strength of specific markers of depression from a set of 28 speech features.

RESULTS

Increased depressive symptoms were associated with speech rate, articulation rate and intensity of speech elicited from a scripted task. These features had consistently stronger effect sizes than pauses.

LIMITATIONS

Our findings are derived at the cohort level so may have limited impact on identifying intra-individual speech changes associated with changes in symptom severity. The analysis of features averaged over the entire recording may have underestimated the importance of some features.

CONCLUSIONS

Participants with more severe depressive symptoms spoke more slowly and quietly. Our findings are from a real-world, multilingual, clinical dataset so represent a step-change in the usefulness of speech as a digital phenotype of MDD.

Challenges in Using mHealth Data From Smartphones and Wearable Devices to Predict Depression Symptom Severity: Retrospective Analysis

BACKGROUND

Major depressive disorder (MDD) affects millions of people worldwide, but timely treatment is not often received owing in part to inaccurate subjective recall and variability in the symptom course. Objective and frequent MDD monitoring can improve subjective recall and help to guide treatment selection. Attempts have been made, with varying degrees of success, to explore the relationship between the measures of depression and passive digital phenotypes (features) extracted from smartphones and wearables devices to remotely and continuously monitor changes in symptomatology. However, a number of challenges exist for the analysis of these data. These include maintaining participant engagement over extended time periods and therefore understanding what constitutes an acceptable threshold of missing data; distinguishing between the cross-sectional and longitudinal relationships for different features to determine their utility in tracking within-individual longitudinal variation or screening individuals at high risk; and understanding the heterogeneity with which depression manifests itself in behavioral patterns quantified by the passive features.

OBJECTIVE

We aimed to address these 3 challenges to inform future work in stratified analyses.

METHODS

Using smartphone and wearable data collected from 479 participants with MDD, we extracted 21 features capturing mobility, sleep, and smartphone use. We investigated the impact of the number of days of available data on feature quality using the intraclass correlation coefficient and Bland-Altman analysis. We then examined the nature of the correlation between the 8-item Patient Health Questionnaire (PHQ-8) depression scale (measured every 14 days) and the features using the individual-mean correlation, repeated measures correlation, and linear mixed effects model. Furthermore, we stratified the participants based on their behavioral difference, quantified by the features, between periods of high (depression) and low (no depression) PHQ-8 scores using the Gaussian mixture model.

RESULTS

We demonstrated that at least 8 (range 2-12) days were needed for reliable calculation of most of the features in the 14-day time window. We observed that features such as sleep onset time correlated better with PHQ-8 scores cross-sectionally than longitudinally, whereas features such as wakefulness after sleep onset correlated well with PHQ-8 longitudinally but worse cross-sectionally. Finally, we found that participants could be separated into 3 distinct clusters according to their behavioral difference between periods of depression and periods of no depression.

CONCLUSIONS

This work contributes to our understanding of how these mobile health-derived features are associated with depression symptom severity to inform future work in stratified analyses.

Remote Administration of ADHD-Sensitive Cognitive Tasks: A Pilot Study

OBJECTIVE

We assessed the feasibility and validity of remote researcher-led administration and self-administration of modified versions of two cognitive tasks sensitive to ADHD, a four-choice reaction time task (Fast task) and a combined Continuous Performance Test/Go No-Go task (CPT/GNG), through a new remote measurement technology system.

METHOD

We compared the cognitive performance measures (mean and variability of reaction times (MRT, RTV), omission errors (OE) and commission errors (CE)) at a remote baseline researcher-led administration and three remote self-administration sessions between participants with and without ADHD (n = 40).

RESULTS

The most consistent group differences were found for RTV, MRT and CE at the baseline researcher-led administration and the first self-administration, with 8 of the 10 comparisons statistically significant and all comparisons indicating medium to large effect sizes.

CONCLUSION

Remote administration of cognitive tasks successfully captured the difficulties with response inhibition and regulation of attention, supporting the feasibility and validity of remote assessments.

Long-term participant retention and engagement patterns in an app and wearable-based multinational remote digital depression study

Recent growth in digital technologies has enabled the recruitment and monitoring of large and diverse populations in remote health studies. However, the generalizability of inference drawn from remotely collected health data could be severely impacted by uneven participant engagement and attrition over the course of the study. We report findings on long-term participant retention and engagement patterns in a large multinational observational digital study for depression containing active (surveys) and passive sensor data collected via Android smartphones, and Fitbit devices from 614 participants for up to 2 years. Majority of participants (67.6%) continued to remain engaged in the study after 43 weeks. Unsupervised clustering of participants' study apps and Fitbit usage data showed 3 distinct engagement subgroups for each data stream. We found: (i) the least engaged group had the highest depression severity (4 PHQ8 points higher) across all data streams; (ii) the least engaged group (completed 4 bi-weekly surveys) took significantly longer to respond to survey notifications (3.8 h more) and were 5 years younger compared to the most engaged group (completed 20 bi-weekly surveys); and (iii) a considerable proportion (44.6%) of the participants who stopped completing surveys after 8 weeks continued to share passive Fitbit data for significantly longer (average 42 weeks). Additionally, multivariate survival models showed participants' age, ownership and brand of smartphones, and recruitment sites to be associated with retention in the study. Together these findings could inform the design of future digital health studies to enable equitable and balanced data collection from diverse populations.

The Feasibility of Implementing Remote Measurement Technologies in Psychological Treatment for Depression: Mixed Methods Study on Engagement

BACKGROUND

Remote measurement technologies (RMTs) such as smartphones and wearables can help improve treatment for depression by providing objective, continuous, and ecologically valid insights into mood and behavior. Engagement with RMTs is varied and highly context dependent; however, few studies have investigated their feasibility in the context of treatment.

OBJECTIVE

A mixed methods design was used to evaluate engagement with active and passive data collection via RMT in people with depression undergoing psychotherapy. We evaluated the effects of treatment on 2 different types of engagement: study attrition (engagement with study protocol) and patterns of missing data (engagement with digital devices), which we termed data availability. Qualitative interviews were conducted to help interpret the differences in engagement.

METHODS

A total of 66 people undergoing psychological therapy for depression were followed up for 7 months. Active data were gathered from weekly questionnaires and speech and cognitive tasks, and passive data were gathered from smartphone sensors and a Fitbit (Fitbit Inc) wearable device.

RESULTS

The overall retention rate was 60%. Higher-intensity treatment (χ21=4.6; P=.03) and higher baseline anxiety (t56.28=-2.80, 2-tailed; P=.007) were associated with attrition, but depression severity was not (t50.4=-0.18; P=.86). A trend toward significance was found for the association between longer treatments and increased attrition (U=339.5; P=.05). Data availability was higher for active data than for passive data initially but declined at a sharper rate (90%-30% drop in 7 months). As for passive data, wearable data availability fell from a maximum of 80% to 45% at 7 months but showed higher overall data availability than smartphone-based data, which remained stable at the range of 20%-40% throughout. Missing data were more prevalent among GPS location data, followed by among Bluetooth data, then among accelerometry data. As for active data, speech and cognitive tasks had lower completion rates than clinical questionnaires. The participants in treatment provided less Fitbit data but more active data than those on the waiting list.

CONCLUSIONS

Different data streams showed varied patterns of missing data, despite being gathered from the same device. Longer and more complex treatments and clinical characteristics such as higher baseline anxiety may reduce long-term engagement with RMTs, and different devices may show opposite patterns of missingness during treatment. This has implications for the scalability and uptake of RMTs in health care settings, the generalizability and accuracy of the data collected by these methods, feature construction, and the appropriateness of RMT use in the long term.

The utility of wearable devices in assessing ambulatory impairments of people with multiple sclerosis in free-living conditions

BACKGROUND AND OBJECTIVES

Multiple sclerosis (MS) is a progressive inflammatory and neurodegenerative disease of the central nervous system affecting over 2.5 million people globally. In-clinic six-minute walk test (6MWT) is a widely used objective measure to evaluate the progression of MS. Yet, it has limitations such as the need for a clinical visit and a proper walkway. The widespread use of wearable devices capable of depicting patients' activity profiles has the potential to assess the level of MS-induced disability in free-living conditions.

METHODS

In this work, we extracted 96 features in different temporal granularities (from minute-level to day-level) from wearable data and explored their utility in estimating 6MWT scores in a European (Italy, Spain, and Denmark) MS cohort of 337 participants over an average of 10 months' duration. We combined these features with participants' demographics using three regression models including elastic net, gradient boosted trees and random forest. In addition, we quantified the individual feature's contribution using feature importance in these regression models, linear mixed-effects models, generalized estimating equations, and correlation-based feature selection (CFS).

RESULTS

The results showed promising estimation performance with R² of 0.30, which was derived using random forest after CFS. This model was able to distinguish the participants with low disability from those with high disability. Furthermore, we observed that the minute-level (≤ 8 minutes) step count, particularly those capturing the upper end of the step count distribution, had a stronger association with 6MWT. The use of a walking aid was indicative of ambulatory function measured through 6MWT.

CONCLUSIONS

This study demonstrates the utility of wearables devices in assessing ambulatory impairments in people with MS in free-living conditions and provides a basis for future investigation into the clinical relevance.

Biopsychosocial Response to the COVID-19 Lockdown in People with Major Depressive Disorder and Multiple Sclerosis

BACKGROUND

Changes in lifestyle, finances and work status during COVID-19 lockdowns may have led to biopsychosocial changes in people with pre-existing vulnerabilities such as Major Depressive Disorders (MDDs) and Multiple Sclerosis (MS).

METHODS

Data were collected as a part of the RADAR-CNS (Remote Assessment of Disease and Relapse-Central Nervous System) program. We analyzed the following data from long-term participants in a decentralized multinational study: symptoms of depression, heart rate (HR) during the day and night; social activity; sedentary state, steps and physical activity of varying intensity. Linear mixed-effects regression analyses with repeated measures were fitted to assess the changes among three time periods (pre, during and post-lockdown) across the groups, adjusting for depression severity before the pandemic and gender.

RESULTS

Participants with MDDs (N = 255) and MS (N = 214) were included in the analyses. Overall, depressive symptoms remained stable across the three periods in both groups. A lower mean HR and HR variation were observed between pre and during lockdown during the day for MDDs and during the night for MS. HR variation during rest periods also decreased between pre- and post-lockdown in both clinical conditions. We observed a reduction in physical activity for MDDs and MS upon the introduction of lockdowns. The group with MDDs exhibited a net increase in social interaction via social network apps over the three periods.

CONCLUSIONS

Behavioral responses to the lockdown measured by social activity, physical activity and HR may reflect changes in stress in people with MDDs and MS. Remote technology monitoring might promptly activate an early warning of physical and social alterations in these stressful situations. Future studies must explore how stress does or does not impact depression severity.

Temporal Evolution of Multiday, Epileptic Functional Networks Prior to Seizure Occurrence

Epilepsy is one of the most common neurological disorders, characterized by the occurrence of repeated seizures. Given that epilepsy is considered a network disorder, tools derived from network neuroscience may confer the valuable ability to quantify the properties of epileptic brain networks. In this study, we use well-established brain network metrics (i.e., mean strength, variance of strength, eigenvector centrality, betweenness centrality) to characterize the temporal evolution of epileptic functional networks over several days prior to seizure occurrence. We infer the networks using long-term electroencephalographic recordings from 12 people with epilepsy. We found that brain network metrics are variable across days and show a circadian periodicity. In addition, we found that in 9 out of 12 patients the distribution of the variance of strength in the day (or even two last days) prior to seizure occurrence is significantly different compared to the corresponding distributions on all previous days. Our results suggest that brain network metrics computed fromelectroencephalographic recordings could potentially be used to characterize brain network changes that occur prior to seizures, and ultimately contribute to seizure warning systems.

Associations Between Depression Symptom Severity and Daily-Life Gait Characteristics Derived From Long-Term Acceleration Signals in Real-World Settings: Retrospective Analysis

BACKGROUND

Gait is an essential manifestation of depression. However, the gait characteristics of daily walking and their relationships with depression have yet to be fully explored.

OBJECTIVE

The aim of this study was to explore associations between depression symptom severity and daily-life gait characteristics derived from acceleration signals in real-world settings.

METHODS

We used two ambulatory data sets (N=71 and N=215) with acceleration signals collected by wearable devices and mobile phones, respectively. We extracted 12 daily-life gait features to describe the distribution and variance of gait cadence and force over a long-term period. Spearman coefficients and linear mixed-effects models were used to explore the associations between daily-life gait features and depression symptom severity measured by the 15-item Geriatric Depression Scale (GDS-15) and 8-item Patient Health Questionnaire (PHQ-8) self-reported questionnaires. The likelihood-ratio (LR) test was used to test whether daily-life gait features could provide additional information relative to the laboratory gait features.

RESULTS

Higher depression symptom severity was significantly associated with lower gait cadence of high-performance walking (segments with faster walking speed) over a long-term period in both data sets. The linear regression model with long-term daily-life gait features (R²=0.30) fitted depression scores significantly better (LR test P=.001) than the model with only laboratory gait features (R²=0.06).

CONCLUSIONS

This study indicated that the significant links between daily-life walking characteristics and depression symptom severity could be captured by both wearable devices and mobile phones. The daily-life gait patterns could provide additional information for predicting depression symptom severity relative to laboratory walking. These findings may contribute to developing clinical tools to remotely monitor mental health in real-world settings.

Predictors of engagement with remote sensing technologies for symptom measurement in Major Depressive Disorder

BACKGROUND

Remote sensing for the measurement and management of long-term conditions such as Major Depressive Disorder (MDD) is becoming more prevalent. User-engagement is essential to yield any benefits. We tested three hypotheses examining associations between clinical characteristics, perceptions of remote sensing, and objective user engagement metrics.

METHODS

The Remote Assessment of Disease and Relapse - Major Depressive Disorder (RADAR-MDD) study is a multicentre longitudinal observational cohort study in people with recurrent MDD. Participants wore a FitBit and completed app-based assessments every two weeks for a median of 18 months. Multivariable random effects regression models pooling data across timepoints were used to examine associations between variables.

RESULTS

A total of 547 participants (87.8% of the total sample) were included in the current analysis. Higher levels of anxiety were associated with lower levels of perceived technology ease of use; increased functional disability was associated with small differences in perceptions of technology usefulness and usability. Participants who reported higher system ease of use, usefulness, and acceptability subsequently completed more app-based questionnaires and tended to wear their FitBit activity tracker for longer. All effect sizes were small and unlikely to be of practical significance.

LIMITATIONS

Symptoms of depression, anxiety, functional disability, and perceptions of system usability are measured at the same time. These therefore represent cross-sectional associations rather than predictions of future perceptions.

CONCLUSIONS

These findings suggest that perceived usability and actual use of remote measurement technologies in people with MDD are robust across differences in severity of depression, anxiety, and functional impairment.

Remote Assessment of Disease and Relapse in Major Depressive Disorder (RADAR-MDD): Recruitment, retention, and data availability in a longitudinal remote measurement study

Introduction

Major Depressive Disorder (MDD) is prevalent, often chronic, and requires ongoing monitoring of symptoms to track response to treatment and identify early indicators of relapse. Remote Measurement Technologies (RMT) provide an exciting opportunity to transform the measurement and management of MDD, via data collected from inbuilt smartphone sensors and wearable devices alongside app-based questionnaires and tasks.

Objectives

To describe the amount of data collected during a multimodal longitudinal RMT study, in an MDD population.

Methods

RADAR-MDD is a multi-centre, prospective observational cohort study. People with a history of MDD were provided with a wrist-worn wearable, and several apps designed to: a) collect data from smartphone sensors; and b) deliver questionnaires, speech tasks and cognitive assessments and followed-up for a maximum of 2 years.

Results

A total of 623 individuals with a history of MDD were enrolled in the study with 80% completion rates for primary outcome assessments across all timepoints. 79.8% of people participated for the maximum amount of time available and 20.2% withdrew prematurely. Data availability across all RMT data types varied depending on the source of data and the participant-burden for each data type. We found no evidence of an association between the severity of depression symptoms at baseline and the availability of data. 110 participants had > 50% data available across all data types, and thus able to contribute to multiparametric analyses.

Conclusions

RADAR-MDD is the largest multimodal RMT study in the field of mental health. Here, we have shown that collecting RMT data from a clinical population is feasible.

Disclosure

No significant relationships.

Longitudinal Relationships Between Depressive Symptom Severity and Phone-Measured Mobility: Dynamic Structural Equation Modeling Study

BACKGROUND

The mobility of an individual measured by phone-collected location data has been found to be associated with depression; however, the longitudinal relationships (the temporal direction of relationships) between depressive symptom severity and phone-measured mobility have yet to be fully explored.

OBJECTIVE

We aimed to explore the relationships and the direction of the relationships between depressive symptom severity and phone-measured mobility over time.

METHODS

Data used in this paper came from a major EU program, called the Remote Assessment of Disease and Relapse-Major Depressive Disorder, which was conducted in 3 European countries. Depressive symptom severity was measured with the 8-item Patient Health Questionnaire (PHQ-8) through mobile phones every 2 weeks. Participants' location data were recorded by GPS and network sensors in mobile phones every 10 minutes, and 11 mobility features were extracted from location data for the 2 weeks prior to the PHQ-8 assessment. Dynamic structural equation modeling was used to explore the longitudinal relationships between depressive symptom severity and phone-measured mobility.

RESULTS

This study included 2341 PHQ-8 records and corresponding phone-collected location data from 290 participants (age: median 50.0 IQR 34.0, 59.0) years; of whom 215 (74.1%) were female, and 149 (51.4%) were employed. Significant negative correlations were found between depressive symptom severity and phone-measured mobility, and these correlations were more significant at the within-individual level than the between-individual level. For the direction of relationships over time, Homestay (time at home) (φ=0.09, P=.01), Location Entropy (time distribution on different locations) (φ=-0.04, P=.02), and Residential Location Count (reflecting traveling) (φ=0.05, P=.02) were significantly correlated with the subsequent changes in the PHQ-8 score, while changes in the PHQ-8 score significantly affected (φ=-0.07, P<.001) the subsequent periodicity of mobility.

CONCLUSIONS

Several phone-derived mobility features have the potential to predict future depression, which may provide support for future clinical applications, relapse prevention, and remote mental health monitoring practices in real-world settings.

Fitbeat: COVID-19 estimation based on wristband heart rate using a contrastive convolutional auto-encoder

This study proposes a contrastive convolutional auto-encoder (contrastive CAE), a combined architecture of an auto-encoder and contrastive loss, to identify individuals with suspected COVID-19 infection using heart-rate data from participants with multiple sclerosis (MS) in the ongoing RADAR-CNS mHealth research project. Heart-rate data was remotely collected using a Fitbit wristband. COVID-19 infection was either confirmed through a positive swab test, or inferred through a self-reported set of recognised symptoms of the virus. The contrastive CAE outperforms a conventional convolutional neural network (CNN), a long short-term memory (LSTM) model, and a convolutional auto-encoder without contrastive loss (CAE). On a test set of 19 participants with MS with reported symptoms of COVID-19, each one paired with a participant with MS with no COVID-19 symptoms, the contrastive CAE achieves an unweighted average recall of 95.3 % , a sensitivity of 100 % and a specificity of 90.6 % , an area under the receiver operating characteristic curve (AUC-ROC) of 0.944, indicating a maximum successful detection of symptoms in the given heart rate measurement period, whilst at the same time keeping a low false alarm rate.

Remote Assessment of Disease and Relapse in Major Depressive Disorder (RADAR-MDD): recruitment, retention, and data availability in a longitudinal remote measurement study

BACKGROUND

Major Depressive Disorder (MDD) is prevalent, often chronic, and requires ongoing monitoring of symptoms to track response to treatment and identify early indicators of relapse. Remote Measurement Technologies (RMT) provide an opportunity to transform the measurement and management of MDD, via data collected from inbuilt smartphone sensors and wearable devices alongside app-based questionnaires and tasks. A key question for the field is the extent to which participants can adhere to research protocols and the completeness of data collected. We aimed to describe drop out and data completeness in a naturalistic multimodal longitudinal RMT study, in people with a history of recurrent MDD. We further aimed to determine whether those experiencing a depressive relapse at baseline contributed less complete data.

METHODS

Remote Assessment of Disease and Relapse - Major Depressive Disorder (RADAR-MDD) is a multi-centre, prospective observational cohort study conducted as part of the Remote Assessment of Disease and Relapse - Central Nervous System (RADAR-CNS) program. People with a history of MDD were provided with a wrist-worn wearable device, and smartphone apps designed to: a) collect data from smartphone sensors; and b) deliver questionnaires, speech tasks, and cognitive assessments. Participants were followed-up for a minimum of 11 months and maximum of 24 months.

RESULTS

Individuals with a history of MDD (n = 623) were enrolled in the study,. We report 80% completion rates for primary outcome assessments across all follow-up timepoints. 79.8% of people participated for the maximum amount of time available and 20.2% withdrew prematurely. We found no evidence of an association between the severity of depression symptoms at baseline and the availability of data. In total, 110 participants had > 50% data available across all data types.

CONCLUSIONS

RADAR-MDD is the largest multimodal RMT study in the field of mental health. Here, we have shown that collecting RMT data from a clinical population is feasible. We found comparable levels of data availability in active and passive forms of data collection, demonstrating that both are feasible in this patient group.

The Association Between Home Stay and Symptom Severity in Major Depressive Disorder: Preliminary Findings From a Multicenter Observational Study Using Geolocation Data From Smartphones

BACKGROUND

Most smartphones and wearables are currently equipped with location sensing (using GPS and mobile network information), which enables continuous location tracking of their users. Several studies have reported that various mobility metrics, as well as home stay, that is, the amount of time an individual spends at home in a day, are associated with symptom severity in people with major depressive disorder (MDD). Owing to the use of small and homogeneous cohorts of participants, it is uncertain whether the findings reported in those studies generalize to a broader population of individuals with MDD symptoms.

OBJECTIVE

The objective of this study is to examine the relationship between the overall severity of depressive symptoms, as assessed by the 8-item Patient Health Questionnaire, and median daily home stay over the 2 weeks preceding the completion of a questionnaire in individuals with MDD.

METHODS

We used questionnaire and geolocation data of 164 participants with MDD collected in the observational Remote Assessment of Disease and Relapse-Major Depressive Disorder study. The participants were recruited from three study sites: King's College London in the United Kingdom (109/164, 66.5%); Vrije Universiteit Medisch Centrum in Amsterdam, the Netherlands (17/164, 10.4%); and Centro de Investigación Biomédica en Red in Barcelona, Spain (38/164, 23.2%). We used a linear regression model and a resampling technique (n=100 draws) to investigate the relationship between home stay and the overall severity of MDD symptoms. Participant age at enrollment, gender, occupational status, and geolocation data quality metrics were included in the model as additional explanatory variables. The 95% 2-sided CIs were used to evaluate the significance of model variables.

RESULTS

Participant age and severity of MDD symptoms were found to be significantly related to home stay, with older (95% CI 0.161-0.325) and more severely affected individuals (95% CI 0.015-0.184) spending more time at home. The association between home stay and symptoms severity appeared to be stronger on weekdays (95% CI 0.023-0.178, median 0.098; home stay: 25th-75th percentiles 17.8-22.8, median 20.9 hours a day) than on weekends (95% CI -0.079 to 0.149, median 0.052; home stay: 25th-75th percentiles 19.7-23.5, median 22.3 hours a day). Furthermore, we found a significant modulation of home stay by occupational status, with employment reducing home stay (employed participants: 25th-75th percentiles 16.1-22.1, median 19.7 hours a day; unemployed participants: 25th-75th percentiles 20.4-23.5, median 22.6 hours a day).

CONCLUSIONS

Our findings suggest that home stay is associated with symptom severity in MDD and demonstrate the importance of accounting for confounding factors in future studies. In addition, they illustrate that passive sensing of individuals with depression is feasible and could provide clinically relevant information to monitor the course of illness in patients with MDD.

Investigating the Use of Digital Health Technology to Monitor COVID-19 and Its Effects: Protocol for an Observational Study (Covid Collab Study)

BACKGROUND

The ubiquity of mobile phones and increasing use of wearable fitness trackers offer a wide-ranging window into people's health and well-being. There are clear advantages in using remote monitoring technologies to gain an insight into health, particularly under the shadow of the COVID-19 pandemic.

OBJECTIVE

Covid Collab is a crowdsourced study that was set up to investigate the feasibility of identifying, monitoring, and understanding the stratification of SARS-CoV-2 infection and recovery through remote monitoring technologies. Additionally, we will assess the impacts of the COVID-19 pandemic and associated social measures on people's behavior, physical health, and mental well-being.

METHODS

Participants will remotely enroll in the study through the Mass Science app to donate historic and prospective mobile phone data, fitness tracking wearable data, and regular COVID-19-related and mental health-related survey data. The data collection period will cover a continuous period (ie, both before and after any reported infections), so that comparisons to a participant's own baseline can be made. We plan to carry out analyses in several areas, which will cover symptomatology; risk factors; the machine learning-based classification of illness; and trajectories of recovery, mental well-being, and activity.

RESULTS

As of June 2021, there are over 17,000 participants-largely from the United Kingdom-and enrollment is ongoing.

CONCLUSIONS

This paper introduces a crowdsourced study that will include remotely enrolled participants to record mobile health data throughout the COVID-19 pandemic. The data collected may help researchers investigate a variety of areas, including COVID-19 progression; mental well-being during the pandemic; and the adherence of remote, digitally enrolled participants.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/32587.

Predicting Depressive Symptom Severity Through Individuals' Nearby Bluetooth Device Count Data Collected by Mobile Phones: Preliminary Longitudinal Study

BACKGROUND

Research in mental health has found associations between depression and individuals' behaviors and statuses, such as social connections and interactions, working status, mobility, and social isolation and loneliness. These behaviors and statuses can be approximated by the nearby Bluetooth device count (NBDC) detected by Bluetooth sensors in mobile phones.

OBJECTIVE

This study aimed to explore the value of the NBDC data in predicting depressive symptom severity as measured via the 8-item Patient Health Questionnaire (PHQ-8).

METHODS

The data used in this paper included 2886 biweekly PHQ-8 records collected from 316 participants recruited from three study sites in the Netherlands, Spain, and the United Kingdom as part of the EU Remote Assessment of Disease and Relapse-Central Nervous System (RADAR-CNS) study. From the NBDC data 2 weeks prior to each PHQ-8 score, we extracted 49 Bluetooth features, including statistical features and nonlinear features for measuring the periodicity and regularity of individuals' life rhythms. Linear mixed-effect models were used to explore associations between Bluetooth features and the PHQ-8 score. We then applied hierarchical Bayesian linear regression models to predict the PHQ-8 score from the extracted Bluetooth features.

RESULTS

A number of significant associations were found between Bluetooth features and depressive symptom severity. Generally speaking, along with depressive symptom worsening, one or more of the following changes were found in the preceding 2 weeks of the NBDC data: (1) the amount decreased, (2) the variance decreased, (3) the periodicity (especially the circadian rhythm) decreased, and (4) the NBDC sequence became more irregular. Compared with commonly used machine learning models, the proposed hierarchical Bayesian linear regression model achieved the best prediction metrics (R2=0.526) and a root mean squared error (RMSE) of 3.891. Bluetooth features can explain an extra 18.8% of the variance in the PHQ-8 score relative to the baseline model without Bluetooth features (R2=0.338, RMSE=4.547).

CONCLUSIONS

Our statistical results indicate that the NBDC data have the potential to reflect changes in individuals' behaviors and statuses concurrent with the changes in the depressive state. The prediction results demonstrate that the NBDC data have a significant value in predicting depressive symptom severity. These findings may have utility for the mental health monitoring practice in real-world settings.

Relationship Between Major Depression Symptom Severity and Sleep Collected Using a Wristband Wearable Device: Multicenter Longitudinal Observational Study

Background

Sleep problems tend to vary according to the course of the disorder in individuals with mental health problems. Research in mental health has associated sleep pathologies with depression. However, the gold standard for sleep assessment, polysomnography (PSG), is not suitable for long-term, continuous monitoring of daily sleep, and methods such as sleep diaries rely on subjective recall, which is qualitative and inaccurate. Wearable devices, on the other hand, provide a low-cost and convenient means to monitor sleep in home settings.

Objective

The main aim of this study was to devise and extract sleep features from data collected using a wearable device and analyze their associations with depressive symptom severity and sleep quality as measured by the self-assessed Patient Health Questionnaire 8-item (PHQ-8).

Methods

Daily sleep data were collected passively by Fitbit wristband devices, and depressive symptom severity was self-reported every 2 weeks by the PHQ-8. The data used in this paper included 2812 PHQ-8 records from 368 participants recruited from 3 study sites in the Netherlands, Spain, and the United Kingdom. We extracted 18 sleep features from Fitbit data that describe participant sleep in the following 5 aspects: sleep architecture, sleep stability, sleep quality, insomnia, and hypersomnia. Linear mixed regression models were used to explore associations between sleep features and depressive symptom severity. The z score was used to evaluate the significance of the coefficient of each feature.

Results

We tested our models on the entire dataset and separately on the data of 3 different study sites. We identified 14 sleep features that were significantly (P<.05) associated with the PHQ-8 score on the entire dataset, among them awake time percentage (z=5.45, P<.001), awakening times (z=5.53, P<.001), insomnia (z=4.55, P<.001), mean sleep offset time (z=6.19, P<.001), and hypersomnia (z=5.30, P<.001) were the top 5 features ranked by z score statistics. Associations between sleep features and PHQ-8 scores varied across different sites, possibly due to differences in the populations. We observed that many of our findings were consistent with previous studies, which used other measurements to assess sleep, such as PSG and sleep questionnaires.

Conclusions

We demonstrated that several derived sleep features extracted from consumer wearable devices show potential for the remote measurement of sleep as biomarkers of depression in real-world settings. These findings may provide the basis for the development of clinical tools to passively monitor disease state and trajectory, with minimal burden on the participant.

Remote Assessment of Disease and Relapse in Epilepsy: Protocol for a Multicenter Prospective Cohort Study

Background

In recent years, a growing body of literature has highlighted the role of wearable and mobile remote measurement technology (RMT) applied to seizure detection in hospital settings, whereas more limited evidence has been produced in the community setting. In clinical practice, seizure assessment typically relies on self-report, which is known to be highly unreliable. Moreover, most people with epilepsy self-identify factors that lead to increased seizure likelihood, including mood, behavior, sleep pattern, and cognitive alterations, all of which are amenable to measurement via multiparametric RMT.

Objective

The primary aim of this multicenter prospective cohort study is to assess the usability, feasibility, and acceptability of RMT in the community setting. In addition, this study aims to determine whether multiparametric RMT collected in populations with epilepsy can prospectively estimate variations in seizure occurrence and other outcomes, including seizure frequency, quality of life, and comorbidities.

Methods

People with a diagnosis of pharmacoresistant epilepsy will be recruited in London, United Kingdom, and Freiburg, Germany. Participants will be asked to wear a wrist-worn device and download ad hoc apps developed on their smartphones. The apps will be used to collect data related to sleep, physical activity, stress, mood, social interaction, speech patterns, and cognitive function, both passively from existing smartphone sensors (passive remote measurement technology [pRMT]) and actively via questionnaires, tasks, and assessments (active remote measurement technology [aRMT]). Data will be collected continuously for 6 months and streamed to the Remote Assessment of Disease and Relapse-base (RADAR-base) server.

Results

The RADAR Central Nervous System project received funding in 2015 from the Innovative Medicines Initiative 2 Joint Undertaking under Grant Agreement No. 115902. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation program and European Federation of Pharmaceutical Industries and Associations. Ethical approval was obtained in London from the Bromley Research Ethics Committee (research ethics committee reference: 19/LO/1884) in January 2020. The first participant was enrolled on September 30, 2020. Data will be collected until September 30, 2021. The results are expected to be published at the beginning of 2022.

Conclusions

RADAR Epilepsy aims at developing a framework of continuous data collection intended to identify ictal and preictal states through the use of aRMT and pRMT in the real-life environment. The study was specifically designed to evaluate the clinical usefulness of the data collected via new technologies and compliance, technology acceptability, and usability for patients. These are key aspects to successful adoption and implementation of RMT as a new way to measure and manage long-term disorders.

International Registered Report Identifier (IRRID)

PRR1-10.2196/21840

Using Smartphones and Wearable Devices to Monitor Behavioral Changes During COVID-19

BACKGROUND

In the absence of a vaccine or effective treatment for COVID-19, countries have adopted nonpharmaceutical interventions (NPIs) such as social distancing and full lockdown. An objective and quantitative means of passively monitoring the impact and response of these interventions at a local level is needed.

OBJECTIVE

We aim to explore the utility of the recently developed open-source mobile health platform Remote Assessment of Disease and Relapse (RADAR)-base as a toolbox to rapidly test the effect and response to NPIs intended to limit the spread of COVID-19.

METHODS

We analyzed data extracted from smartphone and wearable devices, and managed by the RADAR-base from 1062 participants recruited in Italy, Spain, Denmark, the United Kingdom, and the Netherlands. We derived nine features on a daily basis including time spent at home, maximum distance travelled from home, the maximum number of Bluetooth-enabled nearby devices (as a proxy for physical distancing), step count, average heart rate, sleep duration, bedtime, phone unlock duration, and social app use duration. We performed Kruskal-Wallis tests followed by post hoc Dunn tests to assess differences in these features among baseline, prelockdown, and during lockdown periods. We also studied behavioral differences by age, gender, BMI, and educational background.

RESULTS

We were able to quantify expected changes in time spent at home, distance travelled, and the number of nearby Bluetooth-enabled devices between prelockdown and during lockdown periods (P<.001 for all five countries). We saw reduced sociality as measured through mobility features and increased virtual sociality through phone use. People were more active on their phones (P<.001 for Italy, Spain, and the United Kingdom), spending more time using social media apps (P<.001 for Italy, Spain, the United Kingdom, and the Netherlands), particularly around major news events. Furthermore, participants had a lower heart rate (P<.001 for Italy and Spain; P=.02 for Denmark), went to bed later (P<.001 for Italy, Spain, the United Kingdom, and the Netherlands), and slept more (P<.001 for Italy, Spain, and the United Kingdom). We also found that young people had longer homestay than older people during the lockdown and fewer daily steps. Although there was no significant difference between the high and low BMI groups in time spent at home, the low BMI group walked more.

CONCLUSIONS

RADAR-base, a freely deployable data collection platform leveraging data from wearables and mobile technologies, can be used to rapidly quantify and provide a holistic view of behavioral changes in response to public health interventions as a result of infectious outbreaks such as COVID-19. RADAR-base may be a viable approach to implementing an early warning system for passively assessing the local compliance to interventions in epidemics and pandemics, and could help countries ease out of lockdown.

P1727 Large coronary artery to pulmonary artery fistula as primary source of pulmonary blood supply in tetralogy of fallot with pulmonary atresia

Funding Acknowledgements

Not Applicable

OnBehalf

Not Applicable

Introduction

Pulmonary blood supply in patients of Tetralogy of Fallot with pulmonary atresia is usually from patent arterial duct or major aortopulmonary collaterals (MAPCAs) arising from descending thoracic aorta. We describe a case in which large coronary to pulmonary artery fistula was the primary source of pulmonary blood supply.

Case Report

A 17 years old female was referred to our hospital for diagnostic workup of suspected congenital heart disease. She was previously undiagnosed and now complains of progressive shortness of breath for last few months.

On physical examination she was non-dysmorphic with oxygen saturation of ∼ 77 % in room air, blood pressure of ∼ 117/72 mmHg, pulse rate of ∼ 89 beats per minute and respiratory rate of ∼ 24 breaths per minute. She was clinically cyanosed with grade 3 clubbing and polycythemic. Cardiovascular examination revealed quiet precordium with normally placed apex beat, grade 2 parasternal heave with single second heart sound and grade 3/6 continuous murmur along left mid sternal border.

Twelve lead electrocardiogram (ECG) showed normal sinus rhythm, right axis deviation and right ventricular hypertrophy. There was no evidence of ischemia. Chest X-ray revealed "boat shaped heart" with oligaemic lung fields. Transthoracic echocardiography showed large conoventricular ventricular septal defect with bidirectional flow. There was aortic over-ride with dilated left main coronary artery. No forward flow was seen across right ventricular outflow tract. Considering hugely dilated left main coronary artery, suspicion of coronary to pulmonary artery fistula was made and cardiac computed tomography followed by conventional angiography was done, both confirmed the diagnosis of Tetralogy of Fallot with pulmonary atresia and large coronary artery to main pulmonary artery fistula as a primary pulmonary blood supply. Two small collaterals (MAPCAs) were also identified supplying small part of right and left lungs.

Conclusion

This case highlights unusual source of pulmonary blood supply in Tetralogy of Fallot with pulmonary atresia. Correct pre-operative diagnosis is essential for appropriate surgical planning and better outcome.

Abstract P1727 Figure. TOF-PA with CA to PA Fistula

RADAR-Base: Open Source Mobile Health Platform for Collecting, Monitoring, and Analyzing Data Using Sensors, Wearables, and Mobile Devices

Background

With a wide range of use cases in both research and clinical domains, collecting continuous mobile health (mHealth) streaming data from multiple sources in a secure, highly scalable, and extensible platform is of high interest to the open source mHealth community. The European Union Innovative Medicines Initiative Remote Assessment of Disease and Relapse-Central Nervous System (RADAR-CNS) program is an exemplary project with the requirements to support the collection of high-resolution data at scale; as such, the Remote Assessment of Disease and Relapse (RADAR)-base platform is designed to meet these needs and additionally facilitate a new generation of mHealth projects in this nascent field.

Objective

Wide-bandwidth networks, smartphone penetrance, and wearable sensors offer new possibilities for collecting near-real-time high-resolution datasets from large numbers of participants. The aim of this study was to build a platform that would cater for large-scale data collection for remote monitoring initiatives. Key criteria are around scalability, extensibility, security, and privacy.

Methods

RADAR-base is developed as a modular application; the backend is built on a backbone of the highly successful Confluent/Apache Kafka framework for streaming data. To facilitate scaling and ease of deployment, we use Docker containers to package the components of the platform. RADAR-base provides 2 main mobile apps for data collection, a Passive App and an Active App. Other third-Party Apps and sensors are easily integrated into the platform. Management user interfaces to support data collection and enrolment are also provided.

Results

General principles of the platform components and design of RADAR-base are presented here, with examples of the types of data currently being collected from devices used in RADAR-CNS projects: Multiple Sclerosis, Epilepsy, and Depression cohorts.

Conclusions

RADAR-base is a fully functional, remote data collection platform built around Confluent/Apache Kafka and provides off-the-shelf components for projects interested in collecting mHealth datasets at scale.

Remote assessment of disease and relapse in major depressive disorder (RADAR-MDD): a multi-centre prospective cohort study protocol

BACKGROUND

There is a growing body of literature highlighting the role that wearable and mobile remote measurement technology (RMT) can play in measuring symptoms of major depressive disorder (MDD). Outcomes assessment typically relies on self-report, which can be biased by dysfunctional perceptions and current symptom severity. Predictors of depressive relapse include disrupted sleep, reduced sociability, physical activity, changes in mood, prosody and cognitive function, which are all amenable to measurement via RMT. This study aims to: 1) determine the usability, feasibility and acceptability of RMT; 2) improve and refine clinical outcome measurement using RMT to identify current clinical state; 3) determine whether RMT can provide information predictive of depressive relapse and other critical outcomes.

METHODS

RADAR-MDD is a multi-site prospective cohort study, aiming to recruit 600 participants with a history of depressive disorder across three sites: London, Amsterdam and Barcelona. Participants will be asked to wear a wrist-worn activity tracker and download several apps onto their smartphones. These apps will be used to either collect data passively from existing smartphone sensors, or to deliver questionnaires, cognitive tasks, and speech assessments. The wearable device, smartphone sensors and questionnaires will collect data for up to 2-years about participants' sleep, physical activity, stress, mood, sociability, speech patterns, and cognitive function. The primary outcome of interest is MDD relapse, defined via the Inventory of Depressive Symptomatology- Self-Report questionnaire (IDS-SR) and the World Health Organisation's self-reported Composite International Diagnostic Interview (CIDI-SF).

DISCUSSION

This study aims to provide insight into the early predictors of major depressive relapse, measured unobtrusively via RMT. If found to be acceptable to patients and other key stakeholders and able to provide clinically useful information predictive of future deterioration, RMT has potential to change the way in which depression and other long-term conditions are measured and managed.

An independent shopping experience for wheelchair users through augmented reality and RFID

People with physical and mobility impairments continue to struggle to attain independence in the performance of routine activities and tasks. For example, browsing in a store and interacting with products located beyond an arm's length may be impossible without the enabling intervention of a human assistant. This research article describes a study undertaken to design, develop, and evaluate potential interaction methods for motor-impaired individuals, specifically those who use wheelchairs. Our study includes a user-centered approach, and a categorization of wheelchair users based upon the severity of their disability and their individual needs. We designed and developed access solutions that utilize radio frequency identification (RFID), augmented reality (AR), and touchscreen technologies in order to help people who use wheelchairs to carry out certain tasks autonomously. In this way, they have been empowered to go shopping independently, free from reliance upon the assistance of others. A total of 18 wheelchair users participated in the completed study.

On the efficiency of VBSCF algorithms, a comment on "An efficient algorithm for energy gradients and orbital optimization in valence bond theory"

We comment on the paper [Song et al., J. Comput. Chem. 2009, 30, 399]. and discuss the efficiency of the orbital optimization and gradient evaluation in the Valence Bond Self Consistent Field (VBSCF) method. We note that Song et al. neglect to properly reference Broer et al., who published an algorithm [Broer and Nieuwpoort, Theor. Chim. Acta 1988, 73, 405] to use a Fock matrix to compute a matrix element between two different determinants, which can be used for an orbital optimization. Further, Song et al. publish a misleading comparison with our VBSCF algorithm [Dijkstra and van Lenthe, J. Chem. Phys. 2000, 113, 2100; van Lenthe et al., Mol. Phys. 1991, 73, 1159] to enable them to favorably compare their algorithm with ours. We give detail timings in terms of different orbital types in the calculation and actual timings for the example cases.

How to Involve Undergraduate Medical Students in Psychiatric Research

The poster will address the important issue of how we can use opportunities in teaching our medical students how to take a wider view of psychiatry and learn to ‘think outside the box’ thus broadening their vision, enabling them to challenge presently held concepts, while at the same time learning the basic tenets of our profession.

Clearly, this is done by involving our students in clinical research based and audit based activities. However not all schools or teachers are comfortable with doing this, while the medical curriculum is broad, and there is a risk that students ‘only study for exams’.

Research based activities, including simple things such as using basic it skills to do a literature search for a review article or carrying out a useful clinical audit, using a unit held database, are however things which students can easily do, and these can lead to publishable case reports, posters, or ever articles in peer reviewed journals.

The poster will illustrate how we developed research activities with students at Cambridge University Clinical School. It shall discuss the advantages, difficulties, and indeed enjoyment of carrying out such activities.

Peri-operative silent myocardial ischaemia in patients undergoing lower limb joint replacement surgery: an indicator of postoperative morbidity or mortality?

One hundred and twenty-seven patients undergoing major lower limb joint replacement surgery were studied to determine the incidence of silent myocardial ischemia and to ascertain any link between pre-operative cardiac risk factors, silent myocardial ischaemia and postoperative morbidity. Patients underwent ambulatory ECG monitoring for 4 days (on the pre-operative night and for 3 days postoperatively). Postoperative cardiorespiratory symptomatology and morbidity was assessed by questionnaire at 3 months. Eighty-seven patients had risk factors for silent myocardial ischaemia; 42 patients (30 with risk factors) had peri-operative silent myocardial ischaemia. The median ischaemic loads (range) were 1.04 (0.32-13.31) min.h-1 pre-operatively and 5.53 (0.26-56.39), 6.69 (0.04-42.71) and 1.23 (0.1-53.74) min.h-1 on postoperative days 1-3, respectively. Risk factors did not predict the occurrence of silent myocardial ischaemia or an increased ischaemic load pre-operatively or overall postoperatively. New symptoms (chest pain, palpitations, breathlessness or fatigue) were associated with both silent myocardial ischaemia and ischaemic load (p < 0.05). Thus cardiac risk factors do not predict the occurrence of silent myocardial ischaemia or adverse outcome. Peri-operative silent myocardial ischaemia was associated with increased postoperative fatigue.

Effects of modulation of tyrosine phosphorylation on brush border enzyme activity in human Caco-2 intestinal epithelial cells

Intestinal epithelial cell differentiation is closely regulated during normal cell renewal, maturation, and malignant transformation. Since tyrosine phosphorylation influences differentiation in other cell types and has been reported to vary between crypt cells to differentiated villus tip cells, we investigated the influence of tyrosine phosphorylation in colonocyte differentiation, by using human colonic Caco-2 cells as a model and expression of the brush border enzymes alkaline phosphatase (AKP) and dipeptidyl peptidase (DPDD) as differentiation markers. We studied three tyrosine kinase inhibitors with different modes of action and specificities, viz., genistein, erbstatin analog (EA), and tyrphostin, and the tyrosine phosphatase inhibitor sodium orthovanadate. AKP- and DPDD-specific activities were assayed in protein-matched cell lysates by synthetic substrate digestion. We also correlated the effects of these agents on brush border enzyme activity with tyrosine phosphorylation of phosphoproteins by Western blotting. Genistein (5-75 mg/ml) dose-dependently stimulated AKP and DPDD with a maximal stimulation at 75 mg/ml by 158.6+/- 17.5% and 228.6+/-37.1% of control values, respectively (n=12, P<0.001). The inactive analog genistin had no effect. Tyrphostin (25 mM) similarly stimulated AKP and DPDD by 138. 6+/-6.6% and 131.8+/-1.5% of control values (n=12, P<0.001). Unexpectedly, EA (0.1-10 mM) had the opposite effect, inhibiting AKP- and DPDD-specific activity significantly at 10 mM with a maximal 14.8+/-6.4% and 26.5+/-2.5% of control values (n=12, each P<0.001). Sodium orthovanadate had a discordant effect on these two differentiation markers. Orthovanadate dose-dependently increased AKP to a maximal 188.5+/-16.1% of basal activity at 1.5 mM but decreased DPDD activity at 1.5 mM to 47.2+/-3.8% (n=9, P<0.001 each). The effects of each agent were preserved when proliferation was blocked with mitomycin C, suggesting that the modulation of phenotype by these agents was independent of any effects of proliferation. The tyrosine phosphorylation of several phosphoprotein bands was affected differently by these agents. In particular, the tyrosine phosphorylation of one 70-kDa to 71-kDa band was increased by genistein and tyrophostin but deceased by EA. The different effects of these modulators of tyrosine kinase activity raise the possibility that at least two independent enzymes or pathways regulating tyrosine phosphorylation modulate intestinal epithelial differentiation. Furthermore, tyrosine phosphorylation of the 70-kDa to 71-kDa phosphoprotein may be important in the intracellular signaling by which intestinal epithelial cell differentiation is controlled.

Regulation of human colonic cell line proliferation and phenotype by sodium butyrate

Colonic butyrate may maintain mucosal differentiation and oppose carcinogenesis. We characterized butyrate effects on differentiation, proliferation, and matrix interactions in Caco-2 and SW620 human colonic cells. Differentiation was assessed by brush border enzyme activity and doubling time by serial cell counts. Motility across matrix proteins was quantitated by monolayer expansion and correlated with adhesiveness to matrix. Integrin subunit surface pools were measured by immunoprecipitation. Butyrate-stimulated differentiation inhibited proliferation and was significantly more potent than acetate in this regard. Butyrate also inhibited motility across collagen I, collagen IV, and laminin, as well as decreasing adhesiveness to these matrices and beta 1, alpha 1, and alpha 2 integrin subunit surface expression. Butyrate acts in cultured cells at clinically relevant concentrations to oppose classical malignant behavior, inhibiting proliferation and motility while promoting differentiation. Since butyrate is derived from fermentation of dietary fiber, such mechanisms may contribute to the apparent protective action of fiber against colon carcinogenesis.

Topoisomerase II inhibition differentially modulates Caco-2 intestinal epithelial cell phenotype

Caco-2 intestinal epithelial cells differentiate spontaneously after confluence when contact inhibition slows proliferation. We hypothesized that such reversible differentiation might be dependent on DNA synthesis and repair. We studied the effects of the topoisomerase II inhibitor etoposide on Caco-2 proliferation and on the differentiation markers alkaline phosphatase and dipeptidyl dipeptidase specific activity, as well as cell motility. Etoposide (0.3-10 microM) dose-dependently inhibited proliferation and alkaline phosphatase activity. However, etoposide (0.7-3 microM dose-dependently stimulated dipeptidyl dipeptidase activity. Above this concentration, dipeptidyl dipeptidase was also inhibited. Similar effects on enzyme activity were observed when proliferation was blocked with mitomycin C. Etoposide (1-10 microM) also dose-dependently inhibited cell motility. The selective stimulation of dipeptidyl dipeptidase activity by etoposide may offer a clue to the regulation of intestinal brush border enzyme expression at the molecular level.

Association of rectal prolapse with colorectal cancer

BACKGROUND

Although screening for colorectal cancer facilitates earlier detection and improves survival, cost-effective screening requires identification of patients at high risk for colorectal cancer. Rectal prolapse has not been clearly linked to colorectal carcinoma. Whether patients with rectal prolapse should be screened for colorectal cancer is therefore unclear.

METHODS

We retrospectively identified 70 consecutive patients treated for rectal prolapse at a community hospital during a period of 16 years and monitored for an average of 4.4 +/- 2.7 years and 350 patients of similar age treated for chronic unrelated conditions in the same institution during a similar period. We determined their incidence of colorectal cancer and sought demographic correlates.

RESULTS

The prevalence of rectosigmoid carcinoma among patients with prolapse was 5.7% during the study. Only 1.4% of the comparative group had colorectal cancer. Thus patients with rectal prolapse exhibited a 4.2-fold (95% confidence interval, 1.1 to 16.0) relative risk for colorectal cancer over the comparative group (p < 0.02).

CONCLUSIONS

To the extent to which these patients represent the population of patients with rectal prolapse, routine initial screening of patients with symptomatic rectal prolapse by use of flexible sigmoidoscopy may be appropriate.

Restitution at the cellular level: regulation of the migrating phenotype

Intestinal epithelial cells migrating across a mucosal defect are generally described as dedifferentiated, a term that suggests a loss of regulatory biology. Since cell biology may be more readily studied in established cell lines than in vivo, a model is developed using the human Caco-2 intestinal epithelial cell migrating across matrix proteins. This resembles in vivo models of mucosal healing in its sheet migration and loss of the brush border enzymes, which are conventional markers for intestinal epithelial differentiation. Immunohistochemical studies of migrating Caco-2 cells suggest, however, that the rearrangements of cytoskeletal, cell-cell and cell-matrix proteins during migration are not random but seem adapted to the migratory state. Indeed, Caco-2 migration may be substantially regulated by a variety of physiologic and pharmacologic stimuli and differentiation, measured by the specific activity of the intestinal epithelial brush border enzymes alkaline phosphatase and dipeptidyl dipeptidase, may be independently pharmacologically programmed during the stimulation or inhibition of cell motility.

Primary thrombocythaemia presenting as postpartum haemorrhage: a case report

A young primigravida presented with postpartum haemorrhage with no apparent cause following a low forceps delivery. She was extremely pale with gross hepatosplenomegaly. Hysterectomy was performed following three episodes of disseminated intravascular coagulation. Investigations revealed an extremely high platelet count with poor aggregatory function. A diagnosis of primary thrombocythaemia was made.

Glutamine modulates phenotype and stimulates proliferation in human colon cancer cell lines

Glutamine supplementation has been advocated for patients requiring parenteral nutritional support. However, the direct effect of glutamine on neoplastic cells is poorly understood. We therefore investigated the effects of glutamine on the proliferation, differentiation, and cell-matrix interactions of two human colon carcinoma cell lines (Caco-2 and SW620) adapted to glutamine-free media. Doubling times were calculated by logarithmic transformation of serial cell counts. Alkaline phosphatase, cathepsin C (dipeptidyl peptidase), lactase, and isomaltase expression (markers of differentiation) were assayed by digestion of synthetic substrates. Adhesion to matrix proteins was assessed by colorimetric quantitation of toluidine blue staining of adherent cells. Surface expression of Caco-2 receptors for matrix proteins (integrins) was studied by biotinylation and immunoprecipitation with specific antibodies. Glutamine (1-10 mM) dose-dependently stimulated Caco-2 proliferation on all matrices studied with maximal effect at 7 mM. For instance, Caco-2 doubling time on collagen IV decreased by 57 +/- 0.2% (SE) (P < 0.001). Glutamine inhibited the expression of all four digestive enzymes with maximal inhibition ranging from 10 to 40% (P < 0.05 for all). Adhesion to matrix proteins was markedly diminished (51 +/- 1%, P < 0.01) by glutamine (5 mM) treatment, correlating with decreased alpha 2 and beta 1 integrin subunit surface expression. Glutamine had similar effects on SW620 cells, stimulating proliferation, inhibiting digestive enzyme expression, and diminishing both adhesion and integrin surface expression. Glutamine supplementation modulates the phenotype of at least two human colon carcinoma cell lines, increasing proliferation, decreasing differentiation, and decreasing adhesion to matrix proteins in association with decreased integrin expression. Although the mechanisms of these effects await elucidation, such characteristics would appear to predict more aggressive tumor behavior and raise the possibility that nutritional supplementation with glutamine may be deleterious in patients with cancer.

The anatomy of the auricular conchae of cattle, sheep and deer

The anatomy of the auricular conchae of ruminants has been studied by intravascular injection of radiopaque latex, by dissection and by histological sectioning. Drawings were made of the vascular network. The arrangement of nerves and blood vessels in association with the ridges on the rostral (concave) surface was observed. The spaces between the ridges are relatively free of larger blood vessels, nerves, hair and sebaceous glands. This distribution is discussed in relation to the insertion of ear tags.

A comparative study of prokallikreins and kallikreins from rat pancreatic tissue and juice

Two zymogens, designated prokallikreins A and B, were isolated from homogenates of rat pancreatic tissue. The two forms of prokallikrein were found to be very similar in size and charge properties. They gave rise to very similar kallikreins on activation with exogenous trypsin. Differences in carbohydrate content of the two zymogens were probably responsible for differences seen in their behaviour on ion-exchange chromatography and immunoelectrophoresis. In contrast, only one form of prokallikrein was isolated from rat pancreatic juice. It showed almost identical behaviour on ion-exchange chromatography and identical mobility on electrophoresis to prokallikrein A. Thus it can be tentatively suggested that it is prokallikrein A which is secreted into the pancreatic juice and represents the physiologically important zymogen.

A quantitative study of the levels of glandular kallikrein in normal and diabetic rats

The concentrations of kallikrein, as measured by specific radioimmunoassay, in pancreatic tissue of normal, alloxan-, and streptozotocin-diabetic rats were found to be essentially the same. In contrast, the levels of kallikrein in the submandibular glands of the diabetic rats were significantly less than that of the normal rats. However, there were no such differences in the levels of acid phosphatase or alpha-amylase. Thus, the experimentally induced diabetic state would seem to involve a specific reduction in submandibular kallikrein.