Revolutionizing Neurocare: Biomimetic Nanodelivery Via Cell Membranes.

Brain disorders represent a significant challenge in medical science due to the formidable blood-brain barrier (BBB), which severely limits the penetration of conventional therapeutics, hindering effective treatment strategies. This review delves into the innovative realm of biomimetic nanodelivery systems, including stem cell-derived nanoghosts, tumor cell membrane-coated nanoparticles, and erythrocyte membrane-based carriers, highlighting their potential to circumvent the BBB's restrictions. By mimicking native cell properties, these nanocarriers emerge as a promising solution for enhancing drug delivery to the brain, offering a strategic advantage in overcoming the barrier's selective permeability. The unique benefits of leveraging cell membranes from various sources is evaluated and advanced technologies for fabricating cell membrane-encapsulated nanoparticles capable of masquerading as endogenous cells are examined. This enables the targeted delivery of a broad spectrum of therapeutic agents, ranging from small molecule drugs to proteins, thereby providing an innovative approach to neurocare. Further, the review contrasts the capabilities and limitations of these biomimetic nanocarriers with traditional delivery methods, underlining their potential to enable targeted, sustained, and minimally invasive treatment modalities. This review is concluded with a perspective on the clinical translation of these biomimetic systems, underscoring their transformative impact on the therapeutic landscape for intractable brain diseases.

Discovery of 2,4-diarylaminopyrimidine derivatives bearing sulfonamide moiety as novel FAK inhibitors.

Two series of 2,4-diarylaminopyrimidine derivatives containing sulfonamide moiety were designed and synthesized for screening as inhibitors of focal adhesion kinase (FAK). Most compounds significantly inhibited the enzymatic activities of FAK, and the best compound was 7b (IC50 = 0.27 nM). A majority of aminoethyl sulfonamide derivatives could effectively inhibit the proliferation of human cancer cell lines (HCT116, A549, MDA-MB-231 and Hela) expressing high levels of FAK. Particularly, compounds 7b, 7c, and 7o exhibited more significant efficacy against all of four cancer cell lines within concentrations of 1.5 µM. Furthermore, these three compounds displayed higher selectivity of cancer cells over normal cells (SI value > 14), compared to the positive control TAE226 (SI value = 1.63). Interestingly, introduction of dithiocarbamate moiety to the aminoethyl sulfonamide derivatives can indeed improve the antiproliferative activities against A549 cells. Especially, compound 8d demonstrated most significant cytotoxicity activity against A549 cells with an IC50 value of 0.08 µM, which is 20-fold superior to parent compound 7k. Additionally, compound 7b, which display the best anti-FAK potency, can inhibit the clone formation and migration of HCT-116 cells, and cause cell cycle arrest at G2/M phase, inducing apoptosis by promoting ROS production. Overall, these results suggest that 7b is a valuable FAK inhibitor that deserves further optimization to improve its druggability.

Examining the U-shaped relationship of sleep duration and systolic blood pressure with risk of cardiovascular events using a novel recursive gradient scanning model.

Background: Observational studies have suggested U-shaped relationships between sleep duration and systolic blood pressure (SBP) with risks of many cardiovascular diseases (CVDs), but the cut-points that separate high-risk and low-risk groups have not been confirmed. We aimed to examine the U-shaped relationships between sleep duration, SBP, and risks of CVDs and confirm the optimal cut-points for sleep duration and SBP. Methods: A retrospective analysis was conducted on NHANES 2007-2016 data, which included a nationally representative sample of participants. The maximum equal-odds ratio (OR) method was implemented to obtain optimal cut-points for each continuous independent variable. Then, a novel "recursive gradient scanning method" was introduced for discretizing multiple non-monotonic U-shaped independent variables. Finally, a multivariable logistic regression model was constructed to predict critical risk factors associated with CVDs after adjusting for potential confounders. Results: A total of 26,691 participants (48.66% were male) were eligible for the current study with an average age of 49.43 ± 17.69 years. After adjusting for covariates, compared with an intermediate range of sleep duration (6.5-8.0 h per day) and SBP (95-120 mmHg), upper or lower values were associated with a higher risk of CVDs [adjusted OR (95% confidence interval) was 1.20 (1.04-1.40) for sleep duration and 1.17 (1.01-1.36) for SBP]. Conclusions: This study indicates U-shaped relationships between SBP, sleep duration, and risks of CVDs. Both short and long duration of sleep/higher and lower BP are predictors of cardiovascular outcomes. Estimated total sleep duration of 6.5-8.0 h per day/SBP of 95-120 mmHg is associated with lower risk of CVDs.

Differential diagnosis between dilated cardiomyopathy and ischemic cardiomyopathy based on variational mode decomposition and high order spectra analysis.

The clinical manifestations of ischemic cardiomyopathy (ICM) bear resemblance to dilated cardiomyopathy (DCM). The definitive diagnosis of DCM necessitates the identification of invasive, costly, and contraindicated coronary angiography. Many diagnostic studies of cardiovascular disease have tried modal decomposition based on electrocardiogram (ECG) signals. However, these studies ignored the connection between modes and other fields, thus limiting the interpretability of modes to ECG signals and the classification performance of models. This study proposes a classification algorithm based on variational mode decomposition (VMD) and high order spectra, which decomposes the preprocessed ECG signal and extracts its first five modes obtained through VMD. After that, these modes are estimated for their corresponding bispectrums, and the feature vector is composed of fifteen features including bispectral, frequency, and nonlinear features based on this. Finally, a dataset containing 75 subjects (38 DCM, 37 ICM) is classified and compared using random forest (RF), decision tree, support vector machine, and K-nearest neighbor. The results show that, in comparison to previous approaches, the technique proposed provides a better categorization for DCM and ICM of ECG signals, which delivers 98.21% classification accuracy, 98.22% sensitivity, and 98.19% specificity. And mode 3 always has the best performance among single mode. The proposed computerized framework significantly improves automatic diagnostic performance, which can help relieve the working pressure on doctors, possible economic burden and health threaten.

Factors associated with treatment outcomes of patients with drug-resistant tuberculosis in China: A retrospective study using competing risk model.

Objectives: Drug-resistant tuberculosis remains a serious public health problem worldwide, particularly in developing countries, including China. This study determined treatment outcomes among a cohort in Guangzhou, China, and identified factors associated with them. Methods: We initiated a retrospective study using drug-resistant TB data in Guangzhou from 2016 to 2020, managed by Guangzhou Chest Hospital. A competing risk model was used to identify the factors associated with treatment failure and death, as well as loss to follow-up (LTFU). Results: A total of 809 patients were included in the study, of which 281 were under treatment. Of the remaining 528 who had clear treatment outcomes, the number and proportion of treatment success, treatment failure, death, and LTFU were 314 (59.5%), 14 (2.7%), 32 (6.0%), and 168 (31.8%), respectively. Being older and having cavities involving the upper lungs were risk factors for treatment failure and death, while non-Guangzhou household registration and interprovincial mobility were risk factors associated with LTFU. Conclusion: Treatment failure and death were significantly associated with cavitation in the lungs, and LTFU was significantly associated with household registration and geographical mobility. Early identification of factors associated with different treatment outcomes is extremely important for policymakers, health experts, and researchers to implement appropriate strategies and measures to treat and manage the TB-infected population in China.

Residual Effect of Texting to Promote Medication Adherence for Villagers with Schizophrenia in China: 18-Month Follow-up Survey After the Randomized Controlled Trial Discontinuation.

BACKGROUND: Reducing the treatment gap for mental health in low- and middle-income countries is a high priority. Even with treatment, adherence to antipsychotics is rather low. Our integrated intervention package significantly improved medication adherence within 6 months for villagers with schizophrenia in resource-poor communities in rural China. However, considering the resource constraint, we need to test whether the effect of those behavior-shaping interventions may be maintained even after the suspension of the intervention. OBJECTIVE: The aim of this study is to explore the primary outcome of adherence and other outcomes at an 18-month follow-up after the intervention had been suspended. METHODS: In a 6-month randomized trial, 277 villagers with schizophrenia were randomized to receive either a government community mental health program (686 Program) or the 686 Program plus Lay health supporters, e-platform, award, and integration (LEAN), which included health supporters for medication or care supervision, e-platform access for sending mobile SMS text messaging reminders and education message, a token gift for positive behavior changes (eg, continuing taking medicine), and integrating the e-platform with the existing 686 Program. After the 6-month intervention, both groups received only the 686 Program for 18 months (phase 2). Outcomes at both phases included antipsychotic medication adherence, functioning, symptoms, number of rehospitalization, suicide, and violent behaviors. The adherence and functioning were assessed at the home visit by trained assessors. We calculated the adherence in the past 30 days by counting the percentage of dosages taken from November to December 2018 by unannounced home-based pill counts. The functioning was assessed using the World Health Organization Disability Assessment Schedule 2.0. The symptoms were evaluated using the Clinical Global Impression-Schizophrenia during their visits to the 686 Program psychiatrists. Other outcomes were routinely collected in the 686 Program system. We used intention-to-treat analysis, and missing data were dealt with using multiple imputation. The generalized estimating equation model was used to assess program effects on adherence, functioning, and symptoms. RESULTS: In phase 1, antipsychotic adherence and rehospitalization incidence improved significantly. However, in phase 2, the difference of the mean of antipsychotic adherence (adjusted mean difference 0.05, 95% CI -0.06 to 0.16; P=.41; Cohen d effect size=0.11) and rehospitalization incidence (relative risk 0.65, 95% CI 0.32-1.33; P=.24; number needed to treat 21.83, 95% CI 8.30-34.69) was no longer statistically significant, and there was no improvement in other outcomes in either phase (P≥.05). CONCLUSIONS: The simple community-based LEAN intervention could not continually improve adherence and reduce the rehospitalization of people with schizophrenia. Our study inclined to suggest that prompts for medication may be necessary to maintain medication adherence for people with schizophrenia, although we cannot definitively exclude other alternative interpretations.

Feature Extraction of the Brain's Dynamic Complex Network Based on EEG and a Framework for Discrimination of Pediatric Epilepsy.

Most of the current complex network studies about epilepsy used the electroencephalogram (EEG) to directly construct the static complex network for analysis and discarded the dynamic characteristics. This study constructed the dynamic complex network on EEG from pediatric epilepsy and pediatric control when they were asleep by the sliding window method. Dynamic features were extracted and incorporated into various machine learning classifiers to explore their classification performances. We compared these performances between the static and dynamic complex network. In the univariate analysis, the initially insignificant topological characteristics in the static complex network can be transformed to be significant in the dynamic complex network. Under most connectivity calculation methods between leads, the accuracy of using dynamic complex network features for discrimination was higher than that of static complex network features. Particularly in the imaginary part of the coherency function (iCOH) method under the full-frequency band, the discrimination accuracies of most machine learning classifiers were higher than 95%, and the discrimination accuracies in the higher-frequency band (beta-frequency band) and the full-frequency band were higher than that of the lower-frequency bands. Our proposed method and framework could efficiently summarize more time-varying features in the EEG and improve the accuracies of the discrimination of the machine learning classifiers more than using static complex network features.

Manganese nanodepot augments host immune response against coronavirus.

Interferon (IFN) responses are central to host defense against coronavirus and other virus infections. Manganese (Mn) is capable of inducing IFN production, but its applications are limited by nonspecific distributions and neurotoxicity. Here, we exploit chemical engineering strategy to fabricate a nanodepot of manganese (nanoMn) based on Mn2+. Compared with free Mn2+, nanoMn enhances cellular uptake and persistent release of Mn2+ in a pH-sensitive manner, thus strengthening IFN response and eliciting broad-spectrum antiviral effects in vitro and in vivo. Preferentially phagocytosed by macrophages, nanoMn promotes M1 macrophage polarization and recruits monocytes into inflammatory foci, eventually augmenting antiviral immunity and ameliorating coronavirus-induced tissue damage. Besides, nanoMn can also potentiate the development of virus-specific memory T cells and host adaptive immunity through facilitating antigen presentation, suggesting its potential as a vaccine adjuvant. Pharmacokinetic and safety evaluations uncover that nanoMn treatment hardly induces neuroinflammation through limiting neuronal accumulation of manganese. Therefore, nanoMn offers a simple, safe, and robust nanoparticle-based strategy against coronavirus. Electronic Supplementary Material: Supplementary material (RNA-seq data analysis, IFN and ISGs examination, in vitro viral infection, flow cytometry, ICP-MS, DHE staining, and detection of inflammatory factors) is available in the online version of this article at 10.1007/s12274-020-3243-5.

Mobile Texting and Lay Health Supporters to Improve Schizophrenia Care in a Resource-Poor Community in Rural China (LEAN Trial): Randomized Controlled Trial Extended Implementation.

BACKGROUND: Schizophrenia is a severe and disabling condition that presents a dire health equity challenge. Our initial 6-month trial (previously reported) using mobile texting and lay health supporters, called LEAN, significantly improved medication adherence from 0.48 to 0.61 (adjusted mean 0.11, 95% CI 0.03 to 0.20, P=.007) for adults with schizophrenia living in a resource-poor village in rural China. OBJECTIVE: We explored the effectiveness of our texting program in improving participants' medication adherence, functioning, and symptoms in an extended implementation of the intervention after its initial phase. METHODS: In an approximated stepped-wedge wait-list design randomized controlled trial, 277 community-dwelling villagers with schizophrenia were assigned 1:1 in phase 1 into intervention and wait-list control groups. The intervention group received (1) lay health supporters (medication or care supervisors), (2) e-platform (mobile-texting reminders and education message) access, (3) a token gift for positive behavioral changes, and (4) integration with the existing government community-mental health program (the 686 Program) while the wait-listed control group initially only received the 686 Program. Subsequently (in the extended period), both groups received the LEAN intervention plus the 686 Program. The primary outcome was antipsychotic medication adherence (percentage of dosages taken over the past month assessed by unannounced home-based pill counts). The secondary outcomes were symptoms measured during visits to 686 Program psychiatrists using the Clinical Global Impression scale for schizophrenia and functioning measured by trained student assessors using the World Health Organization Disability Assessment Schedule 2.0. Other outcomes included data routinely collected in the 686 Program system (refill records, rehospitalization due to schizophrenia, death for any reason, suicide, wandering, and violent behaviors). We used intention-to-treat analysis and missing data were imputed. A generalized estimating equation model was used to assess program effects on antipsychotics medication adherence, symptoms, and functioning. RESULTS: Antipsychotics medication adherence improved from 0.48 in the control period to 0.58 in the extended intervention period (adjusted mean difference 0.11, 95% CI 0.04 to 0.19; P=.004). We also noted an improvement in symptoms (adjusted mean difference -0.26, 95% CI -0.50 to -0.02; P=.04; Cohen d effect size 0.20) and a reduction in rehospitalization (0.37, 95% CI 0.18 to 0.76; P=.007; number-needed-to-treat 8.05, 95% CI 4.61 to 21.41). There was no improvement in functioning (adjusted mean difference 0.02, 95% CI -0.01 to 0.06; P=.18; Cohen d effect size 0.04). CONCLUSIONS: In an extended implementation, our intervention featuring mobile texting messages and lay health workers in a resource-poor community setting was more effective than the 686 Program alone in improving medication adherence, improving symptoms, and reducing rehospitalization. TRIAL REGISTRATION: Chinese Clinical Trial Registry; ChiCTR-ICR-15006053 https://tinyurl.com/y5hk8vng.

Effect of Hormone Replacement Therapy on Bone Mineral Density and Body Composition in Chinese Adolescent and Young Adult Turner Syndrome Patients.

A longitudinal observational study was performed comparing BMD and body composition in Turner syndrome girls before and after 1 year of HRT treatment. Whole body BMD, femur neck BMD, total hip BMD, and lean mass were significantly increased, but there was no difference in fat mass, and lumbar spine BMD. Purpose: Low bone mineral density (BMD) is one of the major health problems in Turner syndrome (TS) patients, and a certain percentage of TS girls are treated with hormone replacement therapy (HRT) to improve their BMD, among other health benefits. While it is generally accepted that HRT improves BMD and body composition in adolescent and young adult TS patients, studies of HRT in Chinese TS patients are limited. Methods: To investigate the effects of HRT in Chinese TS girls, we performed a longitudinal observational study which compared measurement of BMD and body composition by dual energy X-ray absorptiometry (DXA) using a Lunar DXA densitometer in 20 Chinese adolescent and young adult TS patients (average age = 18) before and after 1 year of HRT treatment. Results: Whole body BMD (0.85 vs. 0.87 g/cm2, P < 0.001), femur neck BMD (0.6 vs. 0.62 g/cm2, P = 0.02), total hip BMD (0.68 vs. 0.71 g/cm2, P = 0.003) and whole body lean mass (30.39 vs. 31.66 kg, P = 0.002) were significantly increased in these patients after 1 year HRT treatment, but there was no difference in whole body fat mass, android:gynoid ratio and lumbar spine BMD. Conclusions: In summary, our study found that HRT was an effective way to increase whole body BMD, femur neck BMD, total hip BMD and whole body lean mass in Chinese TS girls, with no effect on whole body fat mass, android:gynoid ratio or lumbar spine BMD.