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.

IGFBP7 promotes endothelial cell repair in the recovery phase of acute lung injury.

IGFBP7 has been found to play an important role in inflammatory diseases, such as acute lung injury (ALI). However, the role of IGFBP7 in different stages of inflammation remains unclear. Transcriptome sequencing was used to identify the regulatory genes of IGFBP7, and endothelial IGFBP7 expression was knocked down using Aplnr-Dre mice to evaluate the endothelial proliferation capacity. The expression of proliferation-related genes was detected by Western blotting and RT-PCR assays. In the present study, we found that knockdown of IGFBP7 in endothelial cells significantly decreases the expression of endothelial cell proliferation-related genes and cell number in the recovery phase but not in the acute phase of ALI. Mechanistically, using bulk-RNA sequencing and CO-IP, we found that IGFBP7 promotes phosphorylation of FOS and subsequently up-regulates YAP1 molecules, thereby promoting endothelial cell proliferation. This study indicated that IGFBP7 has diverse roles in different stages of ALI, which extends the understanding of IGFBP7 in different stages of ALI and suggests that IGFBP7 as a potential therapeutic target in ALI needs to take into account the period specificity of ALI.

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.

Longitudinal Assessment of Seasonal Impacts and Depression Associations on Circadian Rhythm Using Multimodal Wearable Sensing: Retrospective Analysis.

BACKGROUND: Previous mobile health (mHealth) studies have revealed significant links between depression and circadian rhythm features measured via wearables. However, the comprehensive impact of seasonal variations was not fully considered in these studies, potentially biasing interpretations in real-world settings. OBJECTIVE: This study aims to explore the associations between depression severity and wearable-measured circadian rhythms while accounting for seasonal impacts. METHODS: Data were sourced from a large longitudinal mHealth study, wherein participants' depression severity was assessed biweekly using the 8-item Patient Health Questionnaire (PHQ-8), and participants' behaviors, including sleep, step count, and heart rate (HR), were tracked via Fitbit devices for up to 2 years. We extracted 12 circadian rhythm features from the 14-day Fitbit data preceding each PHQ-8 assessment, including cosinor variables, such as HR peak timing (HR acrophase), and nonparametric features, such as the onset of the most active continuous 10-hour period (M10 onset). To investigate the association between depression severity and circadian rhythms while also assessing the seasonal impacts, we used three nested linear mixed-effects models for each circadian rhythm feature: (1) incorporating the PHQ-8 score as an independent variable, (2) adding seasonality, and (3) adding an interaction term between season and the PHQ-8 score. RESULTS: Analyzing 10,018 PHQ-8 records alongside Fitbit data from 543 participants (n=414, 76.2% female; median age 48, IQR 32-58 years), we found that after adjusting for seasonal effects, higher PHQ-8 scores were associated with reduced daily steps (ß=-93.61, P<.001), increased sleep variability (ß=0.96, P<.001), and delayed circadian rhythms (ie, sleep onset: ß=0.55, P=.001; sleep offset: ß=1.12, P<.001; M10 onset: ß=0.73, P=.003; HR acrophase: ß=0.71, P=.001). Notably, the negative association with daily steps was more pronounced in spring (ß of PHQ-8 × spring = -31.51, P=.002) and summer (ß of PHQ-8 × summer = -42.61, P<.001) compared with winter. Additionally, the significant correlation with delayed M10 onset was observed solely in summer (ß of PHQ-8 × summer = 1.06, P=.008). Moreover, compared with winter, participants experienced a shorter sleep duration by 16.6 minutes, an increase in daily steps by 394.5, a delay in M10 onset by 20.5 minutes, and a delay in HR peak time by 67.9 minutes during summer. CONCLUSIONS: Our findings highlight significant seasonal influences on human circadian rhythms and their associations with depression, underscoring the importance of considering seasonal variations in mHealth research for real-world applications. This study also indicates the potential of wearable-measured circadian rhythms as digital biomarkers for depression.

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.

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.

Macrodomain Binding Compound MRS 2578 Inhibits Alphavirus Replication.

Alphaviruses are positive-strand RNA viruses causing febrile disease. Macrodomain-containing proteins, involved in ADP-ribose-mediated signaling, are encoded by both host cells and several virus groups, including alphaviruses. In this study, compound MRS 2578 that targets the human ADP-ribose glycohydrolase MacroD1 inhibited Semliki Forest virus production as well as viral RNA replication and replicase protein expression. The inhibitor was similarly active in alphavirus trans-replication systems, indicating that it targets the viral RNA replication stage.

[Study on the quality evaluation of electrocardiogram signal in wearable physiological monitoring system].

As a novel technology, wearable physiological parameter monitoring technology represents the future of monitoring technology. However, there are still many problems in the application of this kind of technology. In this paper, a pilot study was conducted to evaluate the quality of electrocardiogram (ECG) signals of the wearable physiological monitoring system (SensEcho-5B). Firstly, an evaluation algorithm of ECG signal quality was developed based on template matching method, which was used for automatic and quantitative evaluation of ECG signals. The algorithm performance was tested on a randomly selected 100 h dataset of ECG signals from 100 subjects (15 healthy subjects and 85 patients with cardiovascular diseases). On this basis, 24-hour ECG data of 30 subjects (7 healthy subjects and 23 patients with cardiovascular diseases) were collected synchronously by SensEcho-5B and ECG Holter. The evaluation algorithm was used to evaluate the quality of ECG signals recorded synchronously by the two systems. Algorithm validation results: sensitivity was 100%, specificity was 99.51%, and accuracy was 99.99%. Results of controlled test of 30 subjects: the median (Q1, Q3) of ECG signal detected by SensEcho-5B with poor signal quality time was 8.93 (0.84, 32.53) minutes, and the median (Q1, Q3) of ECG signal detected by Holter with poor signal quality time was 14.75 (4.39, 35.98) minutes (Rank sum test, P=0.133). The results show that the ECG signal quality algorithm proposed in this paper can effectively evaluate the ECG signal quality of the wearable physiological monitoring system. Compared with signal measured by Holter, the ECG signal measured by SensEcho-5B has the same ECG signal quality. Follow-up studies will further collect physiological data of large samples in real clinical environment, analyze and evaluate the quality of ECG signals, so as to continuously optimize the performance of the monitoring system.

A General Strategy for Hollow Metal-Phytate Coordination Complex Micropolyhedra Enabled by Cation Exchange.

The ability to incorporate functional metal ions (Mn+ ) into metal-organic coordination complexes adds remarkable flexibility in the synthesis of multifunctional organic-inorganic hybrid materials with tailorable electronic, optical, and magnetic properties. We report the cation-exchanged synthesis of a diverse range of hollow Mn+ -phytate (PA) micropolyhedra via the use of hollow Co2+ -PA polyhedral networks as templates at room temperature. The attributes of the incoming Mn+ , namely Lewis acidity and ionic radius, control the exchange of the parent Co2+ ions and the degree of morphological deformation of the resulting hollow micropolyhedra. New functions can be obtained for both completely and partially exchanged products, as supported by the observation of Ln3+ (Ln3+ =Tb3+ , Eu3+ , and Sm3+ ) luminescence from as-prepared hollow Ln3+ -PA micropolyhedra after surface modification with dipicolinic acid as an antenna. Moreover, Fe3+ - and Mn2+ -PA polyhedral complexes were employed as magnetic contrast agents.

Adenosine analogs bearing phosphate isosteres as human MDO1 ligands.

The human O-acetyl-ADP-ribose deacetylase MDO1 is a mono-ADP-ribosylhydrolase involved in the reversal of post-translational modifications. Until now MDO1 has been poorly characterized, partly since no ligand is known besides adenosine nucleotides. Here, we synthesized thirteen compounds retaining the adenosine moiety and bearing bioisosteric replacements of the phosphate at the ribose 5'-oxygen. These compounds are composed of either a squaryldiamide or an amide group as the bioisosteric replacement and/or as a linker. To these groups a variety of substituents were attached such as phenyl, benzyl, pyridyl, carboxyl, hydroxy and tetrazolyl. Biochemical evaluation showed that two compounds, one from both series, inhibited ADP-ribosyl hydrolysis mediated by MDO1 in high concentrations.

Structural Isosteres of Phosphate Groups in the Protein Data Bank.

We developed a computational workflow to mine the Protein Data Bank for isosteric replacements that exist in different binding site environments but have not necessarily been identified and exploited in compound design. Taking phosphate groups as examples, the workflow was used to construct 157 data sets, each composed of a reference protein complexed with AMP, ADP, ATP, or pyrophosphate as well other ligands. Phosphate binding sites appear to have a high hydration content and large size, resulting in U-shaped bioactive conformations recurrently found across unrelated protein families. A total of 16 413 replacements were extracted, filtered for a significant structural overlap on phosphate groups, and sorted according to their SMILES codes. In addition to the classical isosteres of phosphate, such as carboxylate, sulfone, or sulfonamide, unexpected replacements that do not conserve charge or polarity, such as aryl, aliphatic, or positively charged groups, were found.

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.

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.

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.

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.

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.

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.

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.

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.