78 research outputs found
Maternal Cystatin C Serum is Higher in Women with Severe Preeclampsia
Objective: To determine the comparison between maternal cystatin C serum in severe preeclampsia and normal pregnancy.
Method: This was an observational study with cross sectional analytic approach. The subjects are sixty women with severe preeclampsia and normal pregnancy who met inclusion criteria. The maternal serum level of cystatin C was automatically measured with Particle Enhanced Nephelometric Assay (PENIA).
Result: Mean serum level of cystatin C in severe preeclampsia was 1.169 ± 0.311 mg/l. Mean serum level of cystatin C in normal pregnancy was 0.929 ± 0.166. There was a significant differences between maternal serum levels of cystatin C in women with severe preeclampsia compared with women with normal pregnancy.
Conclusion: There was a significant differences between maternal serum levels of cystatin C in severe preeclampsia compared with normal pregnancy.
Keywords: cystatin C, endotheliosis glomerulus, severe preeclam
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
The Relationship between Major Depression Symptom Severity and Sleep Collected Using a Wristband Wearable Device: Multi-centre Longitudinal Observational Study
Research in mental health has implicated sleep pathologies with depression.
However, the gold standard for sleep assessment, polysomnography, 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. The main aim of this study
was to devise and extract sleep features, from data collected using a wearable
device, and analyse their correlation with depressive symptom severity and
sleep quality, as measured by the self-assessed Patient Health Questionnaire
8-item. Daily sleep data were collected passively by Fitbit wristband devices,
and depressive symptom severity was self-reported every two weeks by the PHQ-8.
The data used in this paper included 2,812 PHQ-8 records from 368 participants
recruited from three study sites in the Netherlands, Spain, and the UK.We
extracted 21 sleep features from Fitbit data which describe sleep in the
following five 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-test
was used to evaluate the significance of the coefficient of each feature. We
tested our models on the entire dataset and individually on the data of three
different study sites. We identified 16 sleep features that were significantly
correlated with the PHQ-8 score on the entire dataset. Associations between
sleep features and the PHQ-8 score varied across different sites, possibly due
to the difference in the populations
Remote smartphone-based speech collection: acceptance and barriers in individuals with major depressive disorder
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
Identifying depression-related topics in smartphone-collected free-response speech recordings using an automatic speech recognition system and a deep learning topic model
Language use has been shown to correlate with depression, but large-scale
validation is needed. Traditional methods like clinic studies are expensive.
So, natural language processing has been employed on social media to predict
depression, but limitations remain-lack of validated labels, biased user
samples, and no context. Our study identified 29 topics in 3919
smartphone-collected speech recordings from 265 participants using the Whisper
tool and BERTopic model. Six topics with a median PHQ-8 greater than or equal
to 10 were regarded as risk topics for depression: No Expectations, Sleep,
Mental Therapy, Haircut, Studying, and Coursework. To elucidate the topic
emergence and associations with depression, we compared behavioral (from
wearables) and linguistic characteristics across identified topics. The
correlation between topic shifts and changes in depression severity over time
was also investigated, indicating the importance of longitudinally monitoring
language use. We also tested the BERTopic model on a similar smaller dataset
(356 speech recordings from 57 participants), obtaining some consistent
results. In summary, our findings demonstrate specific speech topics may
indicate depression severity. The presented data-driven workflow provides a
practical approach to collecting and analyzing large-scale speech data from
real-world settings for digital health research
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
Expression and analysis of the glycosylation properties of recombinant human erythropoietin expressed in Pichia pastoris
The Pichia pastoris expression system was used to produce recombinant human erythropoietin, a protein synthesized by the adult kidney and responsible for the regulation of red blood cell production. The entire recombinant human erythropoietin (rhEPO) gene was constructed using the Splicing by Overlap Extension by PCR (SOE-PCR) technique, cloned and expressed through the secretory pathway of the Pichia expression system. Recombinant erythropoietin was successfully expressed in P. pastoris. The estimated molecular mass of the expressed protein ranged from 32 kDa to 75 kDa, with the variation in size being attributed to the presence of rhEPO glycosylation analogs. A crude functional analysis of the soluble proteins showed that all of the forms were active in vivo
Recommended from our members
Associations between depression symptom severity and daily-life gait characteristics derived from long-term acceleration signals in real-world settings
Background
Gait is an essential manifestation of depression. However, the gait characteristics of daily walking and their relationships with depression are yet to be fully explored.
Objective: This study aimed 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 datasets (N=71 and N=215) whose acceleration signals were 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-effect models were used to explore the associations between daily-life gait features and depression symptom severity measured by GDS-15 and 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 found to be significantly associated with lower gait cadence of high-performance walking (segments with faster walking speed) over a long-term period in both datasets. The linear regression model with long-term daily-life gait features ( =0.30) fitted depression scores significantly better (LR test: P value = .001) than the model with only laboratory gait features ( =0.06).
Conclusion
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
- …