The mitochondria-targeted Kaempferol nanoparticle ameliorates severe acute pancreatitis.

Kaempferol (KA), an natural antioxidant of traditional Chinese medicine (TCM), is extensively used as the primary treatment for inflammatory digestive diseases with impaired redox homeostasis. Severe acute pancreatitis (SAP) was exacerbated by mitochondrial dysfunction and abundant ROS, which highlights the role of antioxidants in targeting mitochondrial function. However, low bioavailability and high dosage of KA leading to unavoidable side effects limits clinical transformation. The mechanisms of KA with poor bioavailability largely unexplored, hindering development of the efficient strategies to maximizing the medicinal effects of KA. Here, we engineered a novel thioketals (TK)-modified based on DSPE-PEG2000 liposomal codelivery system for improving bioavailability and avoiding side effects (denotes as DSPE-TK-PEG2000-KA, DTM@KA NPs). We demonstrated that the liposome exerts profound impacts on damaging intracellular redox homeostasis by reducing GSH depletion and activating Nrf2, which synergizes with KA to reinforce the inhibition of inadequate fission, excessive mitochondrial fusion and impaired mitophagy resulting in inflammation and apoptosis; and then, the restored mitochondrial homeostasis strengthens ATP supply for PAC renovation and homeostasis. Interestingly, TK bond was proved as the main functional structure to improve the above efficacy of KA compared with the absence of TK bond. Most importantly, DTM@KA NPs obviously suppresses PAC death with negligible side effects in vitro and vivo. Mechanismly, DTM@KA NPs facilitated STAT6-regulated mitochondrial precursor proteins transport via interacting with TOM20 to further promote Drp1-dependent fission and Pink1/Parkin-regulated mitophagy with enhanced lysosomal degradation for removing damaged mitochondria in PAC and then reduce inflammation and apoptosis. Generally, DTM@KA NPs synergistically improved mitochondrial homeostasis, redox homeostasis, energy metabolism and inflammation response via regulating TOM20-STAT6-Drp1 signaling and promoting mitophagy in SAP. Consequently, such a TCM's active ingredients-based nanomedicine strategy is be expected to be an innovative approach for SAP therapy.

Chemical Reaction of FA Cations Enables Efficient and Stable Perovskite Solar Cells.

Organometal halide perovskite solar cells (PSCs) have received great attention owing to a rapid increase in power conversion efficiency (PCE) over the last decade. However, the deficit of long-term stability is a major obstacle to the implementation of PSCs in commercialization. The defects in perovskite films are considered as one of the primary causes. To address this issue, isocyanic acid (HNCO) is introduced as an additive into the perovskite film, in which the added molecules form covalent bonds with FA cations via a chemical reaction. This chemical reaction gives rise to an efficient passivation on the perovskite film, resulting in an improved film quality, a suppressed non-radiation recombination, a facilitated carrier transport, and optimization of energy band levels. As a result, the HNCO-based PSCs achieve a high PCE of 24.41% with excellent storage stability both in an inert atmosphere and in air. Different from conventional passivation methods based on coordination effects, this work presents an alternative chemical reaction for defect passivation, which opens an avenue toward defect-mitigated PSCs showing enhanced performance and stability.

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.

BioisoIdentifier: an online free tool to investigate local structural replacements from PDB.

Within the realm of contemporary medicinal chemistry, bioisosteres are empirically used to enhance potency and selectivity, improve adsorption, distribution, metabolism, excretion and toxicity profiles of drug candidates. It is believed that bioisosteric know-how may help bypass granted patents or generate novel intellectual property for commercialization. Beside the synthetic expertise, the drug discovery process also depends on efficient in silico tools. We hereby present BioisoIdentifier (BII), a web server aiming to uncover bioisosteric information for specific fragment. Using the Protein Data Bank as source, and specific substructures that the user attempt to surrogate as input, BII tries to find suitable fragments that fit well within the local protein active site. BII is a powerful computational tool that offers the ligand design ideas for bioisosteric replacing. For the validation of BII, catechol is conceived as model fragment attempted to be replaced, and many ideas are successfully offered. These outputs are hierarchically grouped according to structural similarity, and clustered based on unsupervised machine learning algorithms. In summary, we constructed a user-friendly interface to enable the viewing of top-ranking molecules for further experimental exploration. This makes BII a highly valuable tool for drug discovery. The BII web server is freely available to researchers and can be accessed at http://www.aifordrugs.cn/index/ . Scientific Contribution: By designing a more optimal computational process for mining bioisosteric replacements from the publicly accessible PDB database, then deployed on a web server for throughly free access for researchers. Additionally, machine learning methods are applied to cluster the bioisosteric replacements searched by the platform, making a scientific contribution to facilitate chemists' selection of appropriate bioisosteric replacements. The number of bioisosteric replacements obtained using BII is significantly larger than the currently available platforms, which expanding the search space for effective local structural replacements.

Nanosized Prussian blue and its analogs for bioimaging and cancer theranostics.

Prussian blue (PB) nanoparticles (NPs) and Prussian blue analogs (PBAs) can form metal-organic frameworks through the programmable coordination of ferrous ions with cyanide. PB and PBAs represent a burgeoning class of hybrid functional nano-systems with a wide-ranging application spectrum encompassing biomedicine, cancer diagnosis, and therapy. A comprehensive overview of recent advancements is crucial for gaining insights for future research. In this context, we reviewed the synthesis techniques and surface modification strategies employed to tailor the dimensions, morphology, and attributes of PB NPs. Subsequently, we explored advanced biomedical utilities of PB NPs, encompassing photoacoustic imaging, magnetic resonance imaging, ultrasound (US) imaging, and multimodal imaging. In particular, the application of PB NPs-mediated photothermal therapy, photodynamic therapy, and chemodynamic therapy to cancer treatment was reviewed. Based on the literature, we envision an evolving trajectory wherein the future of Prussian blue-driven biological applications converge into an integrated theranostic platform, seamlessly amalgamating bioimaging and cancer therapy. STATEMENT OF SIGNIFICANCE: Prussian blue, an FDA-approved coordinative pigment with a centuries-long legacy, has paved the way for Prussian blue nanoparticles (PB NPs), renowned for their remarkable biocompatibility and biosafety. These PB NPs have found their niche in biomedicine, playing crucial roles in both diagnostics and therapeutic applications. The comprehensive review goes beyond PB NP-based cancer therapy. Alongside in-depth coverage of PB NP synthesis and surface modifications, the review delves into their cutting-edge applications in the realm of biomedical imaging, encompassing techniques such as photoacoustic imaging, magnetic resonance imaging, ultrasound imaging, and multimodal imaging.

Mesoporous Silica Nanoparticles for the Uptake of Toxic Antimony from Aqueous Matrices.

Contamination of water sources by toxic antimony Sb(III) ions poses a threat to clean water supplies. In this regard, we have prepared a mesoporous silica nanoparticle (MSN)-derived adsorbent by reverse microemulsion polymerization, using cetyltrimethylammonium chloride (CTAC) and triethanolamine (TEA) as co-templates. The physical and chemical properties were characterized using advanced tools. The MSN exhibits a higher surface area of up to 713.72 m2·g-1, a pore volume of 1.02 cm3·g-1, and a well-ordered mesoporous nanostructure with an average pore size of 4.02 nm. The MSN has a high adsorption capacity for toxic Sb(III) of 27.96 mg·g-1 at pH 6.0 and 298 K. The adsorption data followed the Langmuir isotherm, while the kinetics of adsorption followed the pseudo-second-order model. Interestingly, the effect of coexisting iron showed a promoting effect on Sb(III) uptake, while the presence of manganese slightly inhibited the adsorption process. The recyclability of the MSN adsorbent was achieved using a 0.5 M HCl eluent and reused consecutively for three cycles with a more than 50% removal efficiency. Moreover, the characterization data and batch adsorption study indicated physical adsorption of Sb(III) by mesopores and chemical adsorption due to silicon hydroxyl groups.

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.

Identified Isosteric Replacements of Ligands' Glycosyl Domain by Data Mining.

Biologically equivalent replacements of key moieties in molecules rationalize scaffold hopping, patent busting, or R-group enumeration. Yet, this information may depend upon the expert-defined space, and might be subjective and biased toward the chemistries they get used to. Most importantly, these practices are often informatively incomplete since they are often compromised by a try-and-error cycle, and although they depict what kind of substructures are suitable for the replacement occurrence, they fail to explain the driving forces to support such interchanges. The protein data bank (PDB) encodes a receptor-ligand interaction pattern and could be an optional source to mine structural surrogates. However, manual decoding of PDB has become almost impossible and redundant to excavate the bioisosteric know-how. Therefore, a text parsing workflow has been developed to automatically extract the local structural replacement of a specific structure from PDB by finding spatial and steric interaction overlaps between the fragments in endogenous ligands and particular ligand fragments. Taking the glycosyl domain for instance, a total of 49 520 replacements that overlap on nucleotide ribose were identified and categorized based on their SMILE codes. A predominately ring system, such as aliphatic and aromatic rings, was observed; yet, amide and sulfonamide replacements also occur. We believe these findings may enlighten medicinal chemists on the structure design and optimization of ligands using the bioisosteric replacement strategy.

Stakeholder network for developing open innovation practice of China's manufacturing enterprises.

The study aims to investigate the impact of stakeholder network characteristics on a company's open innovation performance. To investigate the innovation performance of a company. This study not only demonstrates the impact of stakeholder network characteristics on a firm's open innovation performance but also provides empirical evidence for accelerating the construction of an innovation ecology at the national and industry levels and using innovation networks to promote firm innovation performance. Panel data from 1507 listed manufacturing firms in China from 2008 to 2018 are used. A particular focus is on the role of absorptive capacity in the relationship. The results show that centrality, stability, and stakeholder network size are positively correlated or have an inverted U-shaped relationship with the firm's open innovation performance. The results show that the centrality, stability and stakeholder network size are positively correlated or have an inverse U-shaped relationship with the firm's open innovation performance, while the effects of stakeholder network density on the firm's open innovation performance are not significant. Furthermore, absorptive capacity is found to play a moderating role in the inverted U-shaped relationship between the former two factors, and the inverted U-shaped relationship between stakeholder network characteristics and a firm's open innovation performance is also significant under the conditions of different technology levels and different types of firms.

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.

Versatile Magnetic Mesoporous Carbon Derived Nano-Adsorbent for Synchronized Toxic Metal Removal and Bacterial Disinfection from Water Matrices.

Contamination of water resources by toxic metals and opportunistic pathogens remains a serious challenge. The development of nano-adsorbents with desired features to tackle this problem is a continuously evolving field. Here, magnetic mesoporous carbon nanospheres grafted by antimicrobial polyhexamethylene biguanidine (PHMB) are reported. Detailed mechanistic investigations reveal that the electrostatic stabilizer modified magnetic nanocore interfaced mesoporous shell can be programmatically regulated to tune the size and related morphological properties. The core-shell nano-adsorbent shows tailorable shell thickness (≈20-55 nm), high surface area (363.47 m2 g-1 ), pore volume (0.426 cm3 g-1 ), radially gradient pores (11.26 nm), and abundant biguanidine functionality. Importantly, the nano-adsorbent has high adsorption capacity for toxic thallium (Tl(I) ions (≈559 mg g-1 ), excellent disinfection against Staphylococcus aureus and Escherichia coli (>99.99% at 2 and 2.5 µg mL-1 ), ultrafast disinfection kinetics rate (>99.99% within ≈4 min), and remarkable regeneration capability when exposed to polluted water matrices. The Tl(I) removal is attributed to surface complexation and physical adsorption owing to open ended mesopores, while disinfection relies on contact of terminal biguanidines with phospholipid head groups of membrane. The significance of this work lies in bringing up effective synchronic water purification technology to combat pathogenic microorganisms and toxic metal.

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.

Porphyrin Centered Paclitaxel Tetrameric Prodrug Nanoassemblies as Tumor-Selective Theranostics for Synergized Breast Cancer Therapy.

Although having undergone decades of development, nanoparticulate drug delivery vehicles for efficient cancer therapy remain a challenge, confined by low drug loading, instability, and poor cancer tissue selectivity. A self-assembled prodrug, the combination of prodrug strategy and the self-assembly merits, represents a special chemical entity which spontaneously organizes into supramolecular composites with defined architecture, therefore also providing a strategy to develop new medications. Paclitaxel (PTX) is still among the most generally prescribed chemotherapeutics in oncology but is restricted by poor solubility. Although photodynamic therapy, with its noninvasive features and barely developed drug resistance, signifies an alternative tool to suppress life-threatening cancer, sole use hardly fulfills its potential. To this end, a reduction-activatable heterotetrameric prodrug with the photosensitizer is synthesized, then formulated into self-assembled nanoparticles (NPs) for tumor imaging and combined chemo- and photodynamic therapy. Coating the NPs with amphiphilic polymer distearylphosphatidylethanolamine-polyethylene glycol-arginine-glycine-aspartate (DSPE-PEG-RGD) offers high stability and enables cancer tissue targeting. The as-prepared NPs enlighten disease cells and reveal more potent cytotoxicity comparing to PTX and the photosensitizer alone. Furthermore, the NPs selectively accumulates into tumors and synergistically inhibits tumor proliferation with reduced side effects in mice.

Deciphering the Morphology Change and Performance Enhancement for Perovskite Solar Cells Induced by Surface Modification.

Organic-inorganic perovskite solar cells (PSCs) have achieved great attention due to their expressive power conversion efficiency (PCE) up to 25.7%. To improve the photovoltaic performance of PSCs, interface engineering between the perovskite and hole transport layer (HTL) is a widely used strategy. Following this concept, benzyl trimethyl ammonium chlorides (BTACls) are used to modify the wet chemical processed perovskite film in this work. The BTACl-induced low dimensional perovskite is found to have a bilayer structure, which efficiently decreases the trap density and improves the energy level alignment at the perovskite/HTL interface. As a result, the BTACl-modified PSCs show an improved PCE compared to the control devices. From device modeling, the reduced charge carrier recombination and promoted charge carrier transfer at the perovskite/HTL interface are the cause of the open-circuit (Voc ) and fill factor (FF) improvement, respectively. This study gives a deep understanding for surface modification of perovskite films from a perspective of the morphology and the function of enhancing photovoltaic performance.

Enzyme-triggered smart antimicrobial drug release systems against bacterial infections.

The rapid emergence and spread of drug-resistant bacteria, as one of the most pressing public health threats, are declining our arsenal of available antimicrobial drugs. Advanced antimicrobial drug delivery systems that can achieve precise and controlled release of antimicrobial agents in the microenvironment of bacterial infections will retard the development of antimicrobial resistance. A variety of extracellular enzymes are secreted by bacteria to destroy physical integrity of tissue during their invasion of host body, which can be utilized as stimuli to trigger "on-demand" release of antimicrobials. In the past decade, such bacterial enzyme responsive drug release systems have been intensively studied but few review has been released. Herein, we systematically summarize the recent progress of smart antimicrobial drug delivery systems triggered by bacteria secreted enzymes such as lipase, hyaluronidase, protease and antibiotic degrading enzymes. The perspectives and existing key issues of this field will also be discussed to fuel the innovative research and translational application in the future.

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.

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.

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.

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.