CRNN-Refined Spatiotemporal Transformer for Dynamic MRI reconstruction.

Magnetic Resonance Imaging (MRI) plays a pivotal role in modern clinical practice, providing detailed anatomical visualization with exceptional spatial resolution and soft tissue contrast. Dynamic MRI, aiming to capture both spatial and temporal characteristics, faces challenges related to prolonged acquisition times and susceptibility to motion artifacts. Balancing spatial and temporal resolutions becomes crucial in real-world clinical scenarios. In the realm of dynamic MRI reconstruction, while Convolutional Recurrent Neural Networks (CRNNs) struggle with long-range dependencies, CRNNs require extensive iterations, impacting efficiency. Transformers, known for their effectiveness in high-dimensional imaging, are underexplored in dynamic MRI reconstruction. Additionally, prevailing algorithms fall short of achieving superior results in demanding generative reconstructions at high acceleration rates. This research proposes a novel approach for dynamic MRI reconstruction, named CRNN-Refined Spatiotemporal Transformer Network (CST-Net). The spatiotemporal Transformer initiates reconstruction, modeling temporal and spatial correlations, followed by refinement using the CRNN. This integration mitigates inaccuracies caused by damaged frames and reduces CRNN iterations, enhancing computational efficiency without compromising reconstruction quality. Our study compares the performance of the proposed CST-Net at 6 × and 12 × undersampling rates, showcasing its superiority over existing algorithms. Particularly, in challenging 25× generative reconstructions, the CST-Net outperforms current methods. The comparison includes experiments under both radial and Cartesian undersampling patterns. In conclusion, CST-Net successfully addresses the limitations inherent in existing generative reconstruction algorithms, thereby paving the way for further exploration and optimization of Transformer-based approaches in dynamic MRI reconstruction. Code and Datasets can be available: https://github.com/XWangBin/CST-Net.

A novel neuroprotective method against ischemic stroke by accelerating the drainage of brain interstitial fluid.

Ischemic stroke is a leading cause of death and disability worldwide. Inflammatory response after stroke determines the outcome of ischemic injury. A recent study has reported an efficient method, epidural arterial implantation (EAI), for accelerating interstitial fluid (ISF) drainage, which provides a promising strategy to clear pro-inflammatory cytokines in the brain extracellular space (ECS). In this study, the method of EAI was modified (m-EAI) to control its function of accelerating the ISF drainage at different time points following ischemic attack. The neuroprotective effect of m-EAI on ischemic stroke was evaluated with the transient middle cerebral artery occlusion (tMCAO) rat model. The results demonstrated the accumulation of IL-1ß, IL-6, and TNF-α was significantly decreased by activating m-EAI at 7 d before and immediately after ischemic attack in tMCAO rats, accompanied with decreased infarct volume and improved neurological function. This study consolidates the hypothesis of exacerbated ischemic damage by inflammatory response and provides a new perspective to treat encephalopathy via brain ECS. Further research is essential to investigate whether m-EAI combined with neuroprotective drugs could enhance the therapeutic effect on ischemic stroke.

Exploring Unlabeled Data in Multiple Aspects for Semi-Supervised MRI Segmentation.

Background: MRI segmentation offers crucial insights for automatic analysis. Although deep learning-based segmentation methods have attained cutting-edge performance, their efficacy heavily relies on vast sets of meticulously annotated data. Methods: In this study, we propose a novel semi-supervised MRI segmentation model that is able to explore unlabeled data in multiple aspects based on various semi-supervised learning technologies. Results: We compared the performance of our proposed method with other deep learning-based methods on 2 public datasets, and the results demonstrated that we have achieved Dice scores of 90.3% and 89.4% on the LA and ACDC datasets, respectively. Conclusions: We explored the synergy of various semi-supervised learning technologies for MRI segmentation, and our investigation will inspire research that focuses on designing MRI segmentation models.

Dynamic changes in brain glymphatic function during preoperative chemotherapy in breast cancer patients.

OBJECTIVE: The current study aimed to investigate the dynamic changes in brain glymphatic function during chemotherapy in breast cancer patients (BCP) and their correlation with cognitive function. MATERIALS AND METHODS: A total of 40 healthy female participants (control group) and 80 female BCP were included. Various cognitive assessment tools were used to evaluate cognitive function. Diffusion tensor imaging along the perivascular space was employed to measure brain glymphatic function. RESULTS: Following chemotherapy, BCP exhibited a significant decline in various cognitive scores. After chemotherapy, the along the perivascular space index, a parameter indicating brain glymphatic function, was slightly higher than that at baseline and the control group levels and was correlated with cognitive scores. CONCLUSION: This study unveiled a close relationship between the dynamic changes in brain glymphatic function after chemotherapy and cognitive function in BCP. Our findings contribute to a deeper understanding of the brain mechanisms underlying chemotherapy-related cognitive impairment and provide a theoretical basis for future interventions and treatments. In addition, they offer a new perspective for exploring the relationship between brain function and cognitive states.

[Ursolic acid improved demyelination and interstitial fluid drainage disorders in schizophrenia mice].

OBJECTIVE: To unveil the pathological changes associated with demyelination in schizophrenia (SZ) and its consequential impact on interstitial fluid (ISF) drainage, and to investigate the therapeutic efficacy of ursolic acid (UA) in treating demyelination and the ensuing abnormalities in ISF drainage in SZ. METHODS: Female C57BL/6J mice, aged 6-8 weeks and weighing (20±2) g, were randomly divided into three groups: control, SZ model, and UA treatment. The control group received intraperitoneal injection (ip) of physiological saline and intragastric administration (ig) of 1% carboxymethylcellulose sodium (CMC-Na). The SZ model group was subjected to ip injection of 2 mg/kg dizocilpine maleate (MK-801) and ig administration of 1% CMC-Na. The UA treatment group underwent ig administration of 25 mg/kg UA and ip injection of 2 mg/kg MK-801. The treatment group received UA pretreatment via ig administration for one week, followed by a two-week drug intervention for all the three groups. Behavioral assessments, including the open field test and prepulse inhibition experiment, were conducted post-modeling. Subsequently, changes in the ISF partition drainage were investigated through fluorescent tracer injection into specific brain regions. Immunofluorescence analysis was employed to examine alterations in aquaporin 4 (AQP4) polarity distribution in the brain and changes in protein expression. Myelin reflex imaging using Laser Scanning Confocal Microscopy (LSCM) was utilized to study modifications in myelin within the mouse brain. Quantitative data underwent one-way ANOVA, followed by TukeyHSD for post hoc pairwise comparisons between the groups. RESULTS: The open field test revealed a significantly longer total distance [(7 949.39±1 140.55) cm vs. (2 831.01±1 212.72) cm, P < 0.001] and increased central area duration [(88.43±22.06) s vs. (56.85±18.58) s, P=0.011] for the SZ model group compared with the controls. The UA treatment group exhibited signifi-cantly reduced total distance [(2 415.80±646.95) cm vs. (7 949.39±1 140.55) cm, P < 0.001] and increased central area duration [(54.78±11.66) s vs. (88.43±22.06) s, P=0.007] compared with the model group. Prepulse inhibition test results demonstrated a markedly lower inhibition rate of the startle reflex in the model group relative to the controls (P < 0.001 for both), with the treatment group displaying significant improvement (P < 0.001 for both). Myelin sheath analysis indicated significant demyelination in the model group, while UA treatment reversed this effect. Fluorescence tracing exhibited a significantly larger tracer diffusion area towards the rostral cortex and reflux area towards the caudal thalamus in the model group relative to the controls [(13.93±3.35) mm2 vs. (2.79±0.94) mm2, P < 0.001 for diffusion area; (2.48±0.38) mm2 vs. (0.05±0.12) mm2, P < 0.001 for reflux area], with significant impairment of drainage in brain regions. The treatment group demonstrated significantly reduced tracer diffusion and reflux areas [(7.93±2.48) mm2 vs. (13.93±3.35) mm2, P < 0.001 for diffusion area; (0.50±0.30) mm2 vs. (2.48±0.38) mm2, P < 0.001 for reflux area]. Immunofluorescence staining revealed disrupted AQP4 polarity distribution and reduced AQP4 protein expression in the model group compared with the controls [(3 663.88±733.77) µm2 vs. (13 354.92±4 054.05) µm2, P < 0.001]. The treatment group exhibited restored AQP4 polarity distribution and elevated AQP4 protein expression [(11 104.68±3 200.04) µm2 vs. (3 663.88±733.77) µm2, P < 0.001]. CONCLUSION: UA intervention ameliorates behavioral performance in SZ mice, Thus alleviating hyperactivity and anxiety symptoms and restoring sensorimotor gating function. The underlying mechanism may involve the improvement of demyelination and ISF drainage dysregulation in SZ mice.

Alzheimer's disease diagnosis from multi-modal data via feature inductive learning and dual multilevel graph neural network.

Multi-modal data can provide complementary information of Alzheimer's disease (AD) and its development from different perspectives. Such information is closely related to the diagnosis, prevention, and treatment of AD, and hence it is necessary and critical to study AD through multi-modal data. Existing learning methods, however, usually ignore the influence of feature heterogeneity and directly fuse features in the last stages. Furthermore, most of these methods only focus on local fusion features or global fusion features, neglecting the complementariness of features at different levels and thus not sufficiently leveraging information embedded in multi-modal data. To overcome these shortcomings, we propose a novel framework for AD diagnosis that fuses gene, imaging, protein, and clinical data. Our framework learns feature representations under the same feature space for different modalities through a feature induction learning (FIL) module, thereby alleviating the impact of feature heterogeneity. Furthermore, in our framework, local and global salient multi-modal feature interaction information at different levels is extracted through a novel dual multilevel graph neural network (DMGNN). We extensively validate the proposed method on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and experimental results demonstrate our method consistently outperforms other state-of-the-art multi-modal fusion methods. The code is publicly available on the GitHub website. (https://github.com/xiankantingqianxue/MIA-code.git).

Efficacy of Citicoline Delivered via Brain Extracellular Space against Experimental Acute Ischemic Stroke in Rats.

Objective: Citicoline can be used to reduce acute ischemic stroke injury via venous infusion, however, its protective effects in the brain extracellular space remain largely unknown. Herein, we investigated the brain protective effects of citicoline administered via the brain extracellular space and sought precise effective dosage range that can protect against ischemic injury after experimental ischemic stroke in rats. Methods: Fifty-six Sprague-Dawley rats were randomly divided into control, intraperitoneal (IP), caudate-putamen (CPu)-25, CPu-40, CPu-50, CPu-60 and CPu-75 groups based on the infusion site and concentration of citicoline. Two hours after the administration of citicoline, the rats were subjected to a permanent middle cerebral artery occlusion to mimic acute ischemic stroke. Then, the brain infarct volume in rats after stroke was measured and their neurological deficiency was evaluated to explain the protective effects and effective dosage range of citicoline. Results: Compared to the control and IP groups, brain infarct volume of rats in CPu-40, CPu-50, and CPu-60 groups is significant smaller. Furthermore, the brain infarct volume of rats in CPu-50 is the least. Conclusions: Here, we showed that citicoline can decrease the brain infarct volume, thus protecting the brain from acute ischemic stroke injury. We also found that the appropriate effective citicoline dose delivered via the brain extracellular space is 50 mM. Our study provides novel insights into the precise treatment of acute ischemic stroke by citicoline via the brain extracellular space, further guiding the treatment of brain disease.

Exploring the biological basis of CT imaging features in pancreatic neuroendocrine tumors: a two-center study.

Objective.Medical imaging offered a non-invasive window to visualize tumors, with radiomics transforming these images into quantitative data for tumor phenotyping. However, the intricate web linking imaging features, clinical endpoints, and tumor biology was mostly uncharted. This study aimed to unravel the connections between CT imaging features and clinical characteristics, including tumor histopathological grading, clinical stage, and endocrine symptoms, alongside immunohistochemical markers of tumor cell growth, such as the Ki-67 index and nuclear mitosis rate.Approach.We conducted a retrospective analysis of data from 137 patients with pancreatic neuroendocrine tumors who had undergone contrast-enhanced CT scans across two institutions. Our study focused on three clinical factors: pathological grade, clinical stage, and endocrine symptom status, in addition to two immunohistochemical markers: the Ki-67 index and the rate of nuclear mitosis. We computed both predefined (2D and 3D) and learning-based features (via sparse autoencoder, or SAE) from the scans. To unearth the relationships between imaging features, clinical factors, and immunohistochemical markers, we employed the Spearman rank correlation along with the Benjamini-Hochberg method. Furthermore, we developed and validated radiomics signatures to foresee these clinical factors.Main results.The 3D imaging features showed the strongest relationships with clinical factors and immunohistochemical markers. For the association with pathological grade, the mean absolute value of the correlation coefficient (CC) of 2D, SAE, and 3D features was 0.3318 ± 0.1196, 0.2149 ± 0.0361, and 0.4189 ± 0.0882, respectively. While for the association with Ki-67 index and rate of nuclear mitosis, the 3D features also showed higher correlations, with CC as 0.4053 ± 0.0786 and 0.4061 ± 0.0806. In addition, the 3D feature-based signatures showed optimal performance in clinical factor prediction.Significance.We found relationships between imaging features, clinical factors, and immunohistochemical markers. The 3D features showed higher relationships with clinical factors and immunohistochemical markers.

PCNet: Prior Category Network for CT Universal Segmentation Model.

Accurate segmentation of anatomical structures in Computed Tomography (CT) images is crucial for clinical diagnosis, treatment planning, and disease monitoring. The present deep learning segmentation methods are hindered by factors such as data scale and model size. Inspired by how doctors identify tissues, we propose a novel approach, the Prior Category Network (PCNet), that boosts segmentation performance by leveraging prior knowledge between different categories of anatomical structures. Our PCNet comprises three key components: prior category prompt (PCP), hierarchy category system (HCS), and hierarchy category loss (HCL). PCP utilizes Contrastive Language-Image Pretraining (CLIP), along with attention modules, to systematically define the relationships between anatomical categories as identified by clinicians. HCS guides the segmentation model in distinguishing between specific organs, anatomical structures, and functional systems through hierarchical relationships. HCL serves as a consistency constraint, fortifying the directional guidance provided by HCS to enhance the segmentation model's accuracy and robustness. We conducted extensive experiments to validate the effectiveness of our approach, and the results indicate that PCNet can generate a high-performance, universal model for CT segmentation. The PCNet framework also demonstrates a significant transferability on multiple downstream tasks. The ablation experiments show that the methodology employed in constructing the HCS is of critical importance. The prompt and HCS can be accessed at https://github.com/PKU-MIPET/PCNet.

UBES: Unified scatter correction using ultrafast Boltzmann equation solver for conebeam CT.

A semi-analytical solution to the unified Boltzmann equation is constructed to exactly describe the scatter distribution on a flat-panel detector for high-quality conebeam CT (CBCT) imaging. The solver consists of three parts, including the phase space distribution estimator, the effective source constructor and the detector signal extractor. Instead of the tedious Monte Carlo solution, the derived Boltzmann equation solver achieves ultrafast computational capability for scatter signal estimation by combining direct analytical derivation and time-efficient one-dimensional numerical integration over the trajectory along each momentum of the photon phase space distribution. The execution of scatter estimation using the proposed ultrafast Boltzmann equation solver (UBES) for a single projection is finalized in around 0.4 seconds. We compare the performance of the proposed method with the state-of-the-art schemes, including a time-expensive Monte Carlo (MC) method and a conventional kernel-based algorithm using the same dataset, which is acquired from the CBCT scans of a head phantom and an abdominal patient. The evaluation results demonstrate that the proposed UBES method achieves comparable correction accuracy compared with the MC method, while exhibits significant improvements in image quality over learning and kernel-based methods. With the advantages of MC equivalent quality and superfast computational efficiency, the UBES method has the potential to become a standard solution to scatter correction in high-quality CBCT reconstruction.

Quantitative analysis of molecular transport in the extracellular space using physics-informed neural network.

The brain extracellular space (ECS), an irregular, extremely tortuous nanoscale space located between cells or between cells and blood vessels, is crucial for nerve cell survival. It plays a pivotal role in high-level brain functions such as memory, emotion, and sensation. However, the specific form of molecular transport within the ECS remain elusive. To address this challenge, this paper proposes a novel approach to quantitatively analyze the molecular transport within the ECS by solving an inverse problem derived from the advection-diffusion equation (ADE) using a physics-informed neural network (PINN). PINN provides a streamlined solution to the ADE without the need for intricate mathematical formulations or grid settings. Additionally, the optimization of PINN facilitates the automatic computation of the diffusion coefficient governing long-term molecule transport and the velocity of molecules driven by advection. Consequently, the proposed method allows for the quantitative analysis and identification of the specific pattern of molecular transport within the ECS through the calculation of the Péclet number. Experimental validation on two datasets of magnetic resonance images (MRIs) captured at different time points showcases the effectiveness of the proposed method. Notably, our simulations reveal identical molecular transport patterns between datasets representing rats with tracer injected into the same brain region. These findings highlight the potential of PINN as a promising tool for comprehensively exploring molecular transport within the ECS.

Phototherapy for age-related brain diseases: Challenges, successes and future.

Brain diseases present a significant obstacle to both global health and economic progress, owing to their elusive pathogenesis and the limited effectiveness of pharmaceutical interventions. Phototherapy has emerged as a promising non-invasive therapeutic modality for addressing age-related brain disorders, including stroke, Alzheimer's disease (AD), and Parkinson's disease (PD), among others. This review examines the recent progressions in phototherapeutic interventions. Firstly, the article elucidates the various wavelengths of visible light that possess the capability to penetrate the skin and skull, as well as the pathways of light stimulation, encompassing the eyes, skin, veins, and skull. Secondly, it deliberates on the molecular mechanisms of visible light on photosensitive proteins, within the context of brain disorders and other molecular pathways of light modulation. Lastly, the practical application of phototherapy in diverse clinical neurological disorders is indicated. Additionally, this review presents novel approaches that combine phototherapy and pharmacological interventions. Moreover, it outlines the limitations of phototherapeutics and proposes innovative strategies to improve the treatment of cerebral disorders.

Federated Domain Adaptation via Transformer for Multi-Site Alzheimer's Disease Diagnosis.

In multi-site studies of Alzheimer's disease (AD), the difference of data in multi-site datasets leads to the degraded performance of models in the target sites. The traditional domain adaptation method requires sharing data from both source and target domains, which will lead to data privacy issue. To solve it, federated learning is adopted as it can allow models to be trained with multi-site data in a privacy-protected manner. In this paper, we propose a multi-site federated domain adaptation framework via Transformer (FedDAvT), which not only protects data privacy, but also eliminates data heterogeneity. The Transformer network is used as the backbone network to extract the correlation between the multi-template region of interest features, which can capture the brain abundant information. The self-attention maps in the source and target domains are aligned by applying mean squared error for subdomain adaptation. Finally, we evaluate our method on the multi-site databases based on three AD datasets. The experimental results show that the proposed FedDAvT is quite effective, achieving accuracy rates of 88.75%, 69.51%, and 69.88% on the AD vs. NC, MCI vs. NC, and AD vs. MCI two-way classification tasks, respectively.

Understanding the research on tracking, diagnosing, and intervening in sleep disorders using mHealth apps: Bibliometric analysis and systematic reviews.

Objectives: In solving the global challenge of sleep disorders, Mobile Health app is one of the important means to monitor, diagnose, and intervene in sleep disorders. This study aims to (1) summarize the status and trends of research in this field; (2) assess the production and usage of sleep mHealth apps; (3) calculate the conversion rate of grants that the proportion of newly developed apps from being funded and developed to published on application stores. Methods: Using bibliometric and content analysis methods, based on "Research Paper-Product Output-Product Application" chain and considering the "Research Grants" of articles, we conducted a systematic review of eight databases, to identify relevant studies over the last decade. Results: Over the past decade, 1399 authors published 313 papers in 182 journals and conferences. The number of publications increased with an average annual growth of 41.6%. The current focus area is research using cognitive behavioral therapy to intervene in sleep. Sleep-staging tracking is a shortcoming of this field. A total 368 sleep mHealth apps (233 newly developed and 135 existing) were examined in 313 papers; 323 grants supported 178 articles (56.9%). Only 12 of the newly developed apps are used in the real world, resulting in a 9% grant conversion rate. Conclusions: In the last decade, the field of tracking, diagnosing, and intervening in sleep disorders using mHealth apps has shown a trend of rapid development. However, the conversion rate of products from being funded and developed for use by end-users is low.

Effect of Physiotherapy Interventions on Motor Symptoms in People With Parkinson's Disease: A Systematic Review and Meta-Analysis.

OBJECTIVE: To evaluate the effectiveness of different types of physiotherapy interventions in people with Parkinson's disease (PD). DESIGN: Systematic review and meta-analysis of randomized controlled trials (RCTs). METHODS: Five databases (PubMed, Embase, Cochrane Library, CINAHL and Web of Science Core Collection) were searched for relevant RCTs published from database inception to July 14, 2022. Reviewers independently screened the literature, extracted data, and assessed the literature quality according to the Cochrane Collaboration Risk of Bias Tool and PEDro Scale. This meta-analysis was conducted using RevMan 5.4.1 and reported in compliance with the PRISMA statement. RESULTS: Forty-two RCTs with 2,530 participants were included. Across all types of physiotherapy, strength training, mind-body exercise, aerobic exercise, and non-invasive brain stimulation (NiBS) were effective in improving motor symptoms as measured by the (Movement Disorders Society-) Unified PD Scale, whereas balance and gait training (BGT) and acupuncture were not. The pooled results showed that the change in mind-body exercise (MD = -5.36, 95% CI [-7.97 to -2.74], p < .01, I2 = 68%) and NiBS (MD = -4.59, 95% CI [-8.59 to -0.59], p = .02, I2 = 78%) reached clinical threshold, indicating clinically meaningful improvements. Considering the effectiveness of the interventions on motor symptoms, balance, gait and functional mobility, mind-body exercise was recommended the most. CONCLUSIONS: Exercise appears to be a better form of physiotherapy than NiBS and acupuncture for improving motor function. Mind-body exercise showed beneficial effects on motor symptoms, balance, gait and functional mobility in people with PD, and is worthy of being promoted.

Epidural Pulsation Accelerates the Drainage of Brain Interstitial Fluid.

Unhindered transportation of substances in the brain extracellular space (ECS) is essential for maintaining brain function. Regulation of transportation is a novel strategy for treating ECS blockage-related brain diseases, but few techniques have been developed to date. In this study, we established a novel approach for accelerating the drainage of brain interstitial fluid (ISF) in the ECS using minimally invasive surgery, in which a branch of the external carotid artery is separated and implanted epidurally (i.e., epidural arterial implantation [EAI]) to promote a pulsation effect on cerebrospinal fluid (CSF) in the frontoparietal region. Tracer-based magnetic resonance imaging was used to evaluate the changes in ISF drainage in rats 7 and 15 days post-EAI. The drainage of the traced ISF from the caudate nucleus to ipsilateral cortex was significantly accelerated by EAI. Significant increases in the volume fraction of the ECS and molecular diffusion rate were demonstrated using the DECS-mapping technique, which may account for the mechanisms underlying the changes in brain ISF. This study provides a novel perspective for encephalopathy treatment via the brain ECS.

Mining the Influencing Factors and Their Asymmetrical Effects of mHealth Sleep App User Satisfaction From Real-world User-Generated Reviews: Content Analysis and Topic Modeling.

BACKGROUND: Sleep disorders are a global challenge, affecting a quarter of the global population. Mobile health (mHealth) sleep apps are a potential solution, but 25% of users stop using them after a single use. User satisfaction had a significant impact on continued use intention. OBJECTIVE: This China-US comparison study aimed to mine the topics discussed in user-generated reviews of mHealth sleep apps, assess the effects of the topics on user satisfaction and dissatisfaction with these apps, and provide suggestions for improving users' intentions to continue using mHealth sleep apps. METHODS: An unsupervised clustering technique was used to identify the topics discussed in user reviews of mHealth sleep apps. On the basis of the two-factor theory, the Tobit model was used to explore the effect of each topic on user satisfaction and dissatisfaction, and differences in the effects were analyzed using the Wald test. RESULTS: A total of 488,071 user reviews of 10 mainstream sleep apps were collected, including 267,589 (54.8%) American user reviews and 220,482 (45.2%) Chinese user reviews. The user satisfaction rates of sleep apps were poor (China: 56.58% vs the United States: 45.87%). We identified 14 topics in the user-generated reviews for each country. In the Chinese data, 13 topics had a significant effect on the positive deviation (PD) and negative deviation (ND) of user satisfaction. The 2 variables (PD and ND) were defined by the difference between the user rating and the overall rating of the app in the app store. Among these topics, the app's sound recording function (ß=1.026; P=.004) had the largest positive effect on the PD of user satisfaction, and the topic with the largest positive effect on the ND of user satisfaction was the sleep improvement effect of the app (ß=1.185; P<.001). In the American data, all 14 topics had a significant effect on the PD and ND of user satisfaction. Among these, the topic with the largest positive effect on the ND of user satisfaction was the app's sleep promotion effect (ß=1.389; P<.001), whereas the app's sleep improvement effect (ß=1.168; P<.001) had the largest positive effect on the PD of user satisfaction. The Wald test showed that there were significant differences in the PD and ND models of user satisfaction in both countries (all P<.05), indicating that the influencing factors of user satisfaction with mHealth sleep apps were asymmetrical. Using the China-US comparison, hygiene factors (ie, stability, compatibility, cost, and sleep monitoring function) and 2 motivation factors (ie, sleep suggestion function and sleep promotion effects) of sleep apps were identified. CONCLUSIONS: By distinguishing between the hygiene and motivation factors, the use of sleep apps in the real world can be effectively promoted.

Disulfide-containing polymer delivery of C527 and a Platinum(IV) prodrug selectively inhibited protein ubiquitination and tumor growth on cisplatin resistant and patient-derived liver cancer models.

USP1 (Ubiquitin-specific protease 1) is closely related to the prognosis of patients with liver cancer and plays an important role in DNA damage repair. C527 is a selective USP1 inhibitor (USP1i), which can regulate the protein ubiquitination to effectively inhibit the proliferation of cancer cells. However, its clinical application is hindered due to the poor water solubility and lack of tumor targeting. Moreover, the efficacy of single use of USP1i is still limited. Herein, a glutathione (GSH) sensitive amphiphilic polymer (poly (2-HD-co-HPMDA)-mPEG, PHHM) with disulfide bonds in the main chain was designed to encapsulate the USP1i as well as platinum (IV) prodrug (Pt (IV)-C12), resulting in the formation of composite nanoparticles, i.e., NP-Pt-USP1i. NP-Pt-USP1i can inhibit the DNA damage repair by targeting USP1 by the encapsulated USP1i, which ultimately increases the sensitivity of tumor cells to cisplatin and enhances the anti-cancer efficacy of cisplatin. Finally, an intraperitoneal tumor mice model and a patient-derived xenograft (PDX) of liver cancer mice model were established to prove that NP-Pt-USP1i could effectively inhibit the tumor growth. This work further validated the possibility of therapeutically target USP1 by USP1i in combination with DNA damaging alkylating agents, which could become a promising cancer treatment modality in the future.

Glutathione-Responsive Nanoparticles of Camptothecin Prodrug for Cancer Therapy.

Camptothecin (CPT) is a potent chemotherapeutic agent for various cancers, but the broader application of CPT is still hindered by its poor bioavailability and systemic toxicity. Here, a prodrug that releases CPT in response to glutathione (GSH), which is commonly overexpressed by cancer cells is reported. Through assembling with PEGylated lipids, the prodrug is incorporated within as-assembled nanoparticles, affording CPT with a prolonged half-life in blood circulation, enhanced tumor targetingability, and improved therapeutic efficacy. Furthermore, such prodrug nanoparticles can also promote dendritic cell maturation and tumor infiltration of CD8+ T cells, providing a novel strategy to improve the therapeutic efficacy of CPT.