Structure-activity relationship of dual inhibitors containing maleimide and imidazole motifs against glutaminyl cyclase and glycogen synthase kinase-3ß.

Alzheimer's disease (AD) is a major cause of dementia and one of the most common chronic diseases affecting the aging population. Because AD is considered a public health priority, there is a critical need to discover novel and effective agents for the treatment of this condition. In view of the known contribution of up-regulated glutaminyl cyclase (QC) and glycogen synthase kinase-3ß (GSK-3ß) to the initiation of AD, we previously evaluated a series of dual inhibitors containing maleimide and imidazole motifs as potential anti-AD agents. Here, we assessed another series of hybrids containing maleimide and imidazole motifs to gain an in-depth understanding of the structure-activity relationship (SAR). Based on the primary screening, the introduction of 5-methyl imidazole at one side of the molecule did not enhance the QC-specific inhibitory activity of these hybrids (2, IC50 = 1.22 µM), although the potency was increased by 2' substitution on the maleimide motif at the other side of the molecule. Interestingly, compounds containing 5-methyl imidazole exhibited stronger GSK-3ß-specific inhibitory activity (2, IC50 = 0.0021 µM), and the electron-withdrawing group and 2' and 3' substitution were favorable. Further investigation of substitutions on the maleimide motif in compounds 14-35 revealed that QC-specific inhibition in the presence of piperidine was improved by introduction of a methoxy group (R2). Increasing the linker length and introduction of a methoxy group (R2) also increased the GSK-3ß-specific inhibitory potency. These findings were further confirmed by molecular docking analysis of 33 and 24 with QC and GSK-3ß. Overall, these hybrids exhibited enhanced inhibitory potency against both QC and GSK-3ß, highlighting an important strategy for improving the potency of hybrids as dual-targeting anti-AD agents.

High-dimensional mediation analysis for continuous outcome with confounders using overlap weighting method in observational epigenetic study.

BACKGROUND: Mediation analysis is a powerful tool to identify factors mediating the causal pathway of exposure to health outcomes. Mediation analysis has been extended to study a large number of potential mediators in high-dimensional data settings. The presence of confounding in observational studies is inevitable. Hence, it's an essential part of high-dimensional mediation analysis (HDMA) to adjust for the potential confounders. Although the propensity score (PS) related method such as propensity score regression adjustment (PSR) and inverse probability weighting (IPW) has been proposed to tackle this problem, the characteristics with extreme propensity score distribution of the PS-based method would result in the biased estimation. METHODS: In this article, we integrated the overlapping weighting (OW) technique into HDMA workflow and proposed a concise and powerful high-dimensional mediation analysis procedure consisting of OW confounding adjustment, sure independence screening (SIS), de-biased Lasso penalization, and joint-significance testing underlying the mixture null distribution. We compared the proposed method with the existing method consisting of PS-based confounding adjustment, SIS, minimax concave penalty (MCP) variable selection, and classical joint-significance testing. RESULTS: Simulation studies demonstrate the proposed procedure has the best performance in mediator selection and estimation. The proposed procedure yielded the highest true positive rate, acceptable false discovery proportion level, and lower mean square error. In the empirical study based on the GSE117859 dataset in the Gene Expression Omnibus database using the proposed method, we found that smoking history may lead to the estimated natural killer (NK) cell level reduction through the mediation effect of some methylation markers, mainly including methylation sites cg13917614 in CNP gene and cg16893868 in LILRA2 gene. CONCLUSIONS: The proposed method has higher power, sufficient false discovery rate control, and precise mediation effect estimation. Meanwhile, it is feasible to be implemented with the presence of confounders. Hence, our method is worth considering in HDMA studies.

Developing Deep LSTMs With Later Temporal Attention for Predicting COVID-19 Severity, Clinical Outcome, and Antibody Level by Screening Serological Indicators Over Time.

OBJECTIVE: The clinical course of COVID-19, as well as the immunological reaction, is notable for its extreme variability. Identifying the main associated factors might help understand the disease progression and physiological status of COVID-19 patients. The dynamic changes of the antibody against Spike protein are crucial for understanding the immune response. This work explores a temporal attention (TA) mechanism of deep learning to predict COVID-19 disease severity, clinical outcomes, and Spike antibody levels by screening serological indicators over time. METHODS: We use feature selection techniques to filter feature subsets that are highly correlated with the target. The specific deep Long Short-Term Memory (LSTM) models are employed to capture the dynamic changes of disease severity, clinical outcome, and Spike antibody level. We also propose deep LSTMs with a TA mechanism to emphasize the later blood test records because later records often attract more attention from doctors. RESULTS: Risk factors highly correlated with COVID-19 are revealed. LSTM achieves the highest classification accuracy for disease severity prediction. Temporal Attention Long Short-Term Memory (TA-LSTM) achieves the best performance for clinical outcome prediction. For Spike antibody level prediction, LSTM achieves the best permanence. CONCLUSION: The experimental results demonstrate the effectiveness of the proposed models. The proposed models can provide a computer-aided medical diagnostics system by simply using time series of serological indicators.

Outlier detection in spatial error models using modified thresholding-based iterative procedure for outlier detection approach.

BACKGROUND: Outliers, data points that significantly deviate from the norm, can have a substantial impact on statistical inference and provide valuable insights in data analysis. Multiple methods have been developed for outlier detection, however, almost all available approaches fail to consider the spatial dependence and heterogeneity in spatial data. Spatial data has diverse formats and semantics, requiring specialized outlier detection methodology to handle these unique properties. For now, there is limited research exists on robust spatial outlier detection methods designed specifically under the spatial error model (SEM) structure. METHOD: We propose the Spatial-Θ-Iterative Procedure for Outlier Detection (Spatial-Θ-IPOD), which utilizes a mean-shift vector to identify outliers within the SEM. Our method enables an effective detection of spatial outliers while also providing robust coefficient estimates. To assess the performance of our approach, we conducted extensive simulations and applied it to a real-world empirical study using life expectancy data from multiple countries. RESULTS: Simulation results showed that the masking and JD (Joint Detection) indicators of our Spatial-Θ-IPOD method outperformed several commonly used methods, even in high-dimensional scenarios, demonstrating stable performance. Conversely, the Θ-IPOD method proved to be ineffective in detecting outliers when spatial correlation was present. Moreover, our model successfully provided reliable coefficient estimation alongside outlier detection. The proposed method consistently outperformed other models (both robust and non-robust) in most cases. In the empirical study, our proposed model successfully detected outliers and provided valuable insights in the modeling process. CONCLUSIONS: Our proposed Spatial-Θ-IPOD offers an effective solution for detecting spatial outliers for SEM while providing robust coefficient estimates. Notably, our approach showcases its relative superiority even in the presence of high leverage points. By successfully identifying outliers, our method enhances the overall understanding of the data and provides valuable insights for further analysis.

Monocular BEV Perception of Road Scenes Via Front-to-Top View Projection.

HD map reconstruction is crucial for autonomous driving. LiDAR-based methods are limited due to expensive sensors and time-consuming computation. Camera-based methods usually need to perform road segmentation and view transformation separately, which often causes distortion and missing content. To push the limits of the technology, we present a novel framework that reconstructs a local map formed by road layout and vehicle occupancy in the bird's-eye view given a front-view monocular image only. We propose a front-to-top view projection (FTVP) module, which takes the constraint of cycle consistency between views into account and makes full use of their correlation to strengthen the view transformation and scene understanding. In addition, we apply multi-scale FTVP modules to propagate the rich spatial information of low-level features to mitigate spatial deviation of the predicted object location. Experiments on public benchmarks show that our method achieves various tasks on road layout estimation, vehicle occupancy estimation, and multi-class semantic estimation, at a performance level comparable to the state-of-the-arts, while maintaining superior efficiency.

Discovery of potential scaffolds for glutaminyl cyclase inhibitors: Virtual screening, synthesis, and evaluation.

Glutaminyl cyclase (QC) plays a crucial role in the early stages of Alzheimer's disease (AD), thus inhibition of QC may be a promising strategy for the treatment of early AD. Therefore, QC inhibitors with novel chemical scaffolds may contribute to the development of additional anti-AD agents. We conducted a virtual screening of 3 million compounds from the Chemdiv and Enamine databases, to discover potential scaffolds for QC inhibitors. Three scaffolds, 120974, 147706, and 141449, were selected from this structure-based virtual screening through a combination of pharmacophore modeling, a receptor-ligand pharmacophore model, and the GALAHAD model, and furtherly filtered by chelation with zinc ion and docking properties. Consequently, three compounds, 1, 2, and 3, were designed and synthesized based on these three scaffolds, respectively. The IC50 of compounds 1 and 3 against QC were 14.19 ± 4.21 and 4.34 ± 0.35 µM, respectively. Our results indicate that the new scaffolds selected using a virtual screening process exhibit potential as novel QC inhibitors.

Healthy life expectancy for 202 countries up to 2030: Projections with a Bayesian model ensemble.

Background: Healthy life expectancy (HLE) projections are required for optimising social and health service management in the future. Existing studies on the topic were usually conducted by selecting a single model for analysis. We thus aimed to use an ensembled model to project the future HLE for 202 countries/region. Methods: We obtained data on age-sex-specific HLE and the sociodemographic index (SDI) level of 202 countries from 1990 to 2019 from the Global Burden of Disease (GBD) database and used a probabilistic Bayesian model comprised of 21 forecasting models to predict their HLE in 2030. Results: In general, HLE is projected to increase in all 202 countries, with the least probability of 82.4% for women and 81.0% for men. Most of the countries with the lowest projected HLE would be located in Africa. Women in Singapore have the highest projected HLE in 2030, with a 94.5% probability of higher than 75.2 years, which is the highest HLE in 2019 across countries. Maldives, Kuwait, and China are projected to have a probability of 49.3%, 41.2% and 31.6% to be the new entries of the top ten countries with the highest HLE for females compared with 2019. Men in Singapore are projected to have the highest HLE at birth in 2030, with a 93.4% probability of higher than 75.2 years. Peru and Maldives have a probability of 48.7% and 35.3% being new top ten countries in male's HLE. The female advantage in HLE will shrink by 2030 in 117 countries, especially in most of the high SDI and European countries. Conclusions: HLE will likely continue to increase in most countries and regions worldwide in the future. More attention needs to be paid to combatting obesity, chronic diseases, and specific infectious diseases, especially in African and some Pacific Island countries. Although gender gaps may not be fully bridged, HLE could partially mitigate and even eliminate them through economic development and improvements in health care.

Explainable Machine Learning with Pairwise Interactions for Predicting Conversion from Mild Cognitive Impairment to Alzheimer's Disease Utilizing Multi-Modalities Data.

BACKGROUND: Predicting cognition decline in patients with mild cognitive impairment (MCI) is crucial for identifying high-risk individuals and implementing effective management. To improve predicting MCI-to-AD conversion, it is necessary to consider various factors using explainable machine learning (XAI) models which provide interpretability while maintaining predictive accuracy. This study used the Explainable Boosting Machine (EBM) model with multimodal features to predict the conversion of MCI to AD during different follow-up periods while providing interpretability. METHODS: This retrospective case-control study is conducted with data obtained from the ADNI database, with records of 1042 MCI patients from 2006 to 2022 included. The exposures included in this study were MRI biomarkers, cognitive scores, demographics, and clinical features. The main outcome was AD conversion from aMCI during follow-up. The EBM model was utilized to predict aMCI converting to AD based on three feature combinations, obtaining interpretability while ensuring accuracy. Meanwhile, the interaction effect was considered in the model. The three feature combinations were compared in different follow-up periods with accuracy, sensitivity, specificity, and AUC-ROC. The global and local explanations are displayed by importance ranking and feature interpretability plots. RESULTS: The five-years prediction accuracy reached 85% (AUC = 0.92) using both cognitive scores and MRI markers. Apart from accuracies, we obtained features' importance in different follow-up periods. In early stage of AD, the MRI markers play a major role, while for middle-term, the cognitive scores are more important. Feature risk scoring plots demonstrated insightful nonlinear interactive associations between selected factors and outcome. In one-year prediction, lower right inferior temporal volume (<9000) is significantly associated with AD conversion. For two-year prediction, low left inferior temporal thickness (<2) is most critical. For three-year prediction, higher FAQ scores (>4) is the most important. During four-year prediction, APOE4 is the most critical. For five-year prediction, lower right entorhinal volume (<1000) is the most critical feature. CONCLUSIONS: The established glass-box model EBMs with multimodal features demonstrated a superior ability with detailed interpretability in predicting AD conversion from MCI. Multi features with significant importance were identified. Further study may be of significance to determine whether the established prediction tool would improve clinical management for AD patients.

Is Increasing Diet Diversity of Animal-Source Foods Related to Better Health-Related Quality of Life among Chinese Men and Women?

Diet plays a crucial role in regulating individuals' lifestyles and is closely related to health. The intake of animal-sourced foods (ASF) provides the human body with high-quality protein and various micronutrients. This study aimed to investigate whether the diversity of animal foods has a positive impact on the health-related quality of life (HRQoL) among residents. The data came from the Shaanxi baseline survey of the Northwest Chinese Regional Ethnic Cohort Study, which recruited more than 100 thousand participants aged 35 to 74 from five provinces between June 2018 and May 2019. A total of 39,997 participants in Shaanxi (mean age: 50 years; 64% women) were finally included in this current study. The animal source food diet diversity score (ASFDDS) was established based on the frequency of consuming pork, mutton, beef, poultry, seafood, eggs, pure milk, and yogurt. The physical component score (PCS) and mental component score (MCS), ranging from 0 to 100 on the 12-Item Short Form Survey (SF-12), were used to assess participants' HRQoL. Better PCS/MCS was defined as scores higher than the 90th percentile. The results showed that men had a higher intake of ASF and ASFDDS than women. After adjusting for potential confounders, compared with those who never or rarely consumed animal foods, the likelihood of having better PCS and MCS increased by 16% (OR = 1.16, 95%CI: 1.01-1.34) and 24% (OR = 1.24, 95%CI: 1.03-1.448), respectively, in men with an ASFDDS ≥ 2. In women, a 34% increase (OR = l.34, 95%CI: 116-l.54) likelihood for better PCS was observed for an ASFDDS ≥ 2, but no association was observed for MCS. Increasing each specific animal source's food intake was associated with better PCS after adjusting for all covariates. However, for MCS, positive associations were only observed in seafood consumption among men and eggs among women. Restricted cubic splines showed a substantial dose-response association between intake frequency of animal-source foods and PCS, both in men and women. The study suggests that a diverse intake of animal-sourced foods can potentially improve the HRQoL of Chinese adults.

Gender-specific association of body fat mass with muscle meat-vegetable intake ratio in Shaanxi, China.

BACKGROUND AND OBJECTIVES: The effects of muscle meat and vegetable intake on body fat mass remain unclear in the general population. This study aimed to investigate the association of body fat mass and fat dis-tribution with a muscle meat-vegetable intake (MMV) ratio. METHODS AND STUDY DESIGN: In total, 29,271 par-ticipants aged 18-80 years were recruited from the Shaanxi cohort of the Regional Ethnic Cohort Study in Northwest China. The associations of muscle meat, vegetable and MMV ratio, as the independent variable, with body mass index (BMI), waist circumference, total body fat percentage (TBF) and visceral fat (VF), as dependent variables were evaluated by gender-specific linear regression models. RESULTS: There was 47.9% of men whose MMV ratio was greater than or equal to 1 and this figure was about 35.7% for women. For men, higher muscle meat intake was associated with higher TBF (standardized coefficient [ß], 0.508; 95% CI, 0.187-0.829), higher vegetable intake was associated with lower VF (ß, -0.109; 95% CI, -0.206 - -0.011), and higher MMV ratio was associated with higher BMI (ß, 0.195; 95% CI, 0.039-0.350) and VF (ß, 0.523; 95% CI, 0.209-0.838). For women, both higher muscle meat consumption and MMV ratio were associated with all fat mass markers, but vegetable intake was not correlated with body fat mass markers. The positive association of MMV on body fat mass was more pronounced in higher MMV ratio group, with both men and women. The intake of pork, mutton and beef was associated positively with fat mass markers but no such as-sociation was observed for poultry or seafood. CONCLUSIONS: An increased intake of muscle meat or a higher MMV ratio was associated with increased body fat, especially among women, and such impact may mainly be attributed to increasing intake of pork, beef and mutton. The dietary MMV ratio could be thus a useful parameter for nutritional intervention.

Predicting Six-Month Re-Admission Risk in Heart Failure Patients Using Multiple Machine Learning Methods: A Study Based on the Chinese Heart Failure Population Database.

Since most patients with heart failure are re-admitted to the hospital, accurately identifying the risk of re-admission of patients with heart failure is important for clinical decision making and management. This study plans to develop an interpretable predictive model based on a Chinese population for predicting six-month re-admission rates in heart failure patients. Research data were obtained from the PhysioNet portal. To ensure robustness, we used three approaches for variable selection. Six different machine learning models were estimated based on selected variables. The ROC curve, prediction accuracy, sensitivity, and specificity were used to evaluate the performance of the established models. In addition, we visualized the optimized model with a nomogram. In all, 2002 patients with heart failure were included in this study. Of these, 773 patients experienced re-admission and a six-month re-admission incidence of 38.61%. Based on evaluation metrics, the logistic regression model performed best in the validation cohort, with an AUC of 0.634 (95%CI: 0.599-0.646) and an accuracy of 0.652. A nomogram was also generated. The established prediction model has good discrimination ability in predicting. Our findings are helpful and could provide useful information for the allocation of healthcare resources and for improving the quality of survival of heart failure patients.

Instrumental variable-based high-dimensional mediation analysis with unmeasured confounders for survival data in the observational epigenetic study.

Background: High dimensional mediation analysis is frequently conducted to explore the role of epigenetic modifiers between exposure and health outcome. However, the issue of high dimensional mediation analysis with unmeasured confounders for survival analysis in observational study has not been well solved. Methods: In this study, we proposed an instrumental variable based approach for high dimensional mediation analysis with unmeasured confounders in survival analysis for epigenetic study. We used the Sobel's test, the Joint test, and the Bootstrap method to test the mediation effect. A comprehensive simulation study was conducted to decide the best test strategy. An empirical study based on DNA methylation data of lung cancer patients was conducted to illustrate the performance of the proposed method. Results: Simulation study suggested that the proposed method performed well in the identifying mediating factors. The estimation of the mediation effect by the proposed approach is also reliable with less bias compared with the classical approach. In the empirical study, we identified two DNA methylation signatures including cg21926276 and cg26387355 with a mediation effect of 0.226 (95%CI: 0.108-0.344) and 0.158 (95%CI: 0.065-0.251) between smoking and lung cancer using the proposed approach. Conclusion: The proposed method obtained good performance in simulation and empirical studies, it could be an effective statistical tool for high dimensional mediation analysis.

Purification, Characterization and Bioactivity of Different Molecular-Weight Fractions of Polysaccharide Extracted from Litchi Pulp.

Litchi polysaccharides are a kind of macromolecular polymers with various biological activities and a wide range of molecular weights. In this study, two separate fractions, with average molecular weights of 378.67 kDa (67.33%) and 16.96 kDa (6.95%), which were referred to as LP1 and LP2, respectively, were separated using an ultrafiltration membrane. Their physicochemical properties, and immunomodulatory and prebiotic activity were compared. The results revealed that LP2 contained more neutral sugar, arabinose, galactose and rhamnose, but less uronic acid, protein, mannose and glucose than LP1. Compared with LP1, LP2 possessed higher solubility and lower apparent viscosity. LP2 exhibited stronger stimulation on macrophage secretion of NO, TNF-α and IL-6, as well as better proliferation of Lactobacillus plantarum, Leuconostoc mesenteroides, Lactobacillus casei and Bifidobacterium adolescentis. These results suggest that an ultrafiltration membrane might be used to prepare a highly-active polysaccharide fraction from litchi pulp that may be used for food or drug development.

ERAP2 as a potential biomarker for predicting gemcitabine response in patients with pancreatic cancer.

OBJECTIVE: Pancreatic cancer is one of the most malignant tumors, with rapid metastasis, high mortality rate, and difficult early screening. Currently, gemcitabine is a first-line drug for pancreatic cancer patients, but its clinical effect is limited due to drug resistance. It is particularly important to further identify biomarkers associated with gemcitabine resistance to improve the sensitivity of gemcitabine treatment. METHODS: Drug sensitivity data and the corresponding transcript data derived from the Genomics of Drug Sensitivity in Cancer (GDSC) database for correlation analysis was adopted to obtain genes related to gemcitabine sensitivity. Moreover, the survival model of pancreatic cancer patients treated with gemcitabine in The Cancer Genome Atlas (TCGA) database was utilized to obtain key genes. Multiple in vitro assays were performed to verify the function of the key biomarker. RESULTS: Endoplasmic Reticulum Aminopeptidase 2 (ERAP2) was identified as a biomarker promoting gemcitabine resistance, and its high expression resulted in a worse prognosis. Besides, gemcitabine significantly increased the mRNA and protein levels of ERAP2 in pancreatic cancer cells. Additionally, ERAP2 knockdown suppressed tumorigenesis and potentiated gemcitabine-induced growth, migration and invasion inhibition in human pancreatic cancer cells. CONCLUSIONS: ERAP2 may be a novel key biomarker for gemcitabine sensitivity and diagnosis, thus providing an effective therapeutic strategy for pancreatic cancer treatment.

Hybrid Cell Membrane-Functionalized Biomimetic Nanoparticles for Targeted Therapy of Osteosarcoma.

PURPOSE: In order to prepare a biomimetic nano-carrier which has inflammatory chemotaxis, homologous targeting and reduce immune clearance, for targeted chemotherapy of osteosarcoma, we fabricated the paclitaxel-loaded poly(lactic-co-glycolic) acid (PLGA) nanoparticles coated with 143B-RAW hybrid membrane (PTX-PLGA@[143B-RAW] NPs) and evaluate its anti-cancer efficacy in vitro and vivo. METHODS: PTX-PLGA@[143B-RAW] NPs were prepared by the ultrasonic method and were characterized by size, zeta potential, polymer dispersion index (PDI), Coomassie bright blue staining, transmission electron microscopy (TEM) and high performance liquid chromatography (HPLC). Cellular uptake, cell viability assay, flow cytometry and chemotactic effect of PTX-PLGA@[143B-RAW] NPs were evaluated in vitro. Biodistribution, anti-cancer therapeutic efficacy and safety of PTX-PLGA@[143B-RAW] NPs were evaluated in 143B osteosarcoma xenograft mice. RESULTS: The hybrid membrane successfully coated onto the surface of PLGA nanoparticles. PTX-PLGA@[143B-RAW] NPs had a drug loading capacity of 4.24 ± 0.02% and showed targeting ability to osteosarcoma. PTX-PLGA@[143B-RAW] NPs showed high cellular uptake and improved anti-cancer efficacy against 143B cells. More importantly, PTX-PLGA@[143B-RAW] NPs treatment suppressed tumor growth in tumor-bearing mice with minimal damage to normal tissues. CONCLUSION: PTX-PLGA@[143B-RAW] NPs could be used for targeted drug delivery and osteosarcoma therapy.

Recent progress of macrophage vesicle-based drug delivery systems.

Nanoparticle drug delivery systems (NDDSs) are promising platforms for efficient delivery of drugs. In the past decades, many nanomedicines have received clinical approval and completed translation. With the rapid advance of nanobiotechnology, natural vectors are emerging as novel strategies to carry and delivery nanoparticles and drugs for biomedical applications. Among diverse types of cells, macrophage is of great interest for their essential roles in inflammatory and immune responses. Macrophage-derived vesicles (MVs), including exosomes, microvesicles, and those from reconstructed membranes, may inherit the chemotactic migration ability and high biocompatibility. The unique properties of MVs make them competing candidates as novel drug delivery systems for precision nanomedicine. In this review, the advantages and disadvantages of existing NDDSs and MV-based drug delivery systems (MVDDSs) were compared. Then, we summarized the potential applications of MVDDSs and discuss future perspectives. The development of MVDDS may provide avenues for the treatment of diseases involving an inflammatory process.

Comparative evaluation of methods for isolating small extracellular vesicles derived from pancreatic cancer cells.

BACKGROUND: Small extracellular vesicles (sEVs) are nanosized vesicles involved in cell-to-cell communication. sEVs have been widely studied for clinical applications such as early detection of diseases and as therapeutics. Various methods for sEVs isolation are been using, but different methods may result in different qualities of sEVs and impact downstream analysis and applications. Here, we compared current isolation methods and performed a comparative analysis of sEVs from supernatant of cultured pancreatic cancer cells. METHODS: Ultracentrifugation, ultrafiltration and co-precipitation as concentration methods were firstly evaluated for yield, size, morphology and protein level of pellets. Then, isolate sEVs obtained by four different purification methods: size exclusion chromatography, density gradient ultracentrifugation, ultracentrifugation, and immunoaffinity capturing, were analysed and compared. RESULTS: For the concentration process, ultracentrifugation method obtained high quality and high concentration of pellets. For the purification process, immunoaffinity capturing method obtained the purest sEVs with less contaminants, while density gradient ultracentrifugation-based method obtained sEVs with the smallest size. Proteomic analysis revealed distinct protein contents of purified sEVs from different methods. CONCLUSIONS: For isolating sEVs derived from supernatant of cultured pancreatic cancer cell line, ultracentrifugation-based method is recommended for concentration of sEVs, density gradient ultracentrifugation-based method may be applied for obtaining purified sEVs with controlled size, immunoaffinity capturing may be suitable for studies requiring sEVs with high purity but may loss subtypes of sEVs without specific protein marker.

Repurposing FDA-Approved Compounds for the Discovery of Glutaminyl Cyclase Inhibitors as Drugs Against Alzheimer's Disease.

Alzheimer's disease (AD) is one of the most common neurodegenerative causes of dementia, the pathology of which is still not much clear. It's challenging to discover the disease modifying agents for the prevention and treatment of AD over the years. Emerging evidence has been accumulated to reveal the crucial role of up-regulated glutaminyl cyclase (QC) in the initiation of AD. In the current study, the QC inhibitory potency of a library consisting of 1621 FDA-approved compounds was assessed. A total of 54 hits, 3.33 % of the pool, exhibited QC inhibitory activities. The Ki of the top 5 compounds with the highest QC inhibitory activities were measured. Among these selected hits, compounds affecting neuronal signaling pathways and other mechanisms were recognized. Moreover, several polyphenol derivatives with QC inhibitory activities were also identified. Frameworks and subsets contained in these hits were analyzed. Taken together, our results may contribute to the discovery and development of novel QC inhibitors as potential anti-AD agents.

3,5,4'-Trimethoxy-trans-stilbene loaded microemulsion for cutaneous melanoma therapy by transdermal drug delivery.

For therapy of skin cancer, transdermal administration has been a potential way to enhance chemotherapy. However, the drug delivery efficacy remained unsatisfactory because of the physiological barriers from the skin to the tumor, which hindered the effect of 3,5,4'-trimethoxy-trans-stilbene (BTM), a drug that has toxicity to cancer. Herein, we prepared an oil-in-water (O/W) microemulsion to load BTM (BTM-ME) for transdermal therapy of melanoma. BTM-ME was characterized by size, zeta potential, and polymer disperse index (PDI). B16F10 melanoma cell line was used for cell experiments and animal models. And cell uptake, viability assay, and flow cytometry were to test the cell internalization and the ability of BTM-ME to induce cancer cell apoptosis. Skin penetration testing was to detect its penetration efficiency to the skin. And tumor-bearing mice were used to prove the improvement of anti-cancer efficacy of BTM-ME with the combination of Taxol. BTM was successfully loaded in O/W microemulsion, with a drug loading capacity of 24.82 mg/mL. BTM-ME can penetrate the skin and increase the retention of BTM in the epidermis. And the combination of Taxol and BTM-ME effectively suppressed tumor growth and has lower toxicity to normal organs. BTM-ME provides adjuvant therapy to cutaneous melanoma and the combination of Taxol and BTM-ME has the clinical potential for skin cancer therapy. Graphical abstract.

Extracellular vesicles derived from different sources of mesenchymal stem cells: therapeutic effects and translational potential.

Mesenchymal stem cells (MSCs) were known to have excellent properties in cell therapy. However, the risk of immune rejection associated with cell transplant therapy hampers its use. Extracellular vesicles secreted by MSCs derived from different sources that contain therapeutic molecules such as RNA and proteins, which is a novel strategy for cell-free therapy. Recently, researches show EVs from MSCs (MSC-EVs) of different sources have special functions and effects on different diseases. Here, we collected these researches and compared them to each other. In addition, their potential and possible application in clinical treatment are described.