Proteomics of human platelet lysates and insight from animal studies on platelet protein diffusion to hippocampus upon intranasal administration.

Human platelet lysates (HPLs) from allogeneic platelet concentrates (PCs) are biomaterials, which are rich in various trophic factors, increasingly used in regenerative medicine and biotherapy. Understanding how preparation methods influence the HPL protein profile, biological function, and clinical outcomes is crucial. Our study sheds light on the proteomes and functionality of different HPLs, with the aim of advancing their scientifically grounded clinical applications. To achieve this, PCs suspended in plasma underwent three distinct processing methods, resulting in seven HPL types. We used three characterization techniques: label-free proteomics and tandem mass tag (TMT)-based quantitative proteomics, both before and after the immunodepletion of abundant plasma proteins. Bioinformatic tools assessed the proteome, and western blotting validated our quantitative proteomics data. Subsequent pre-clinical studies with fluorescent labeling and label-free proteomics were used as a proof of concept for brain diffusion. Our findings revealed 1441 proteins detected using the label-free method, 952 proteins from the TMT experiment before and after depletion, and 1114 proteins from the subsequent TMT experiment on depleted HPLs. Most detected proteins were cytoplasmic, playing key roles in catalysis, hemostasis, and immune responses. Notably, the processing methodologies significantly influenced HPL compositions, their canonical pathways, and, consequently, their functionality. Each HPL exhibited specific abundant proteins, providing valuable insight for tailored clinical applications. Immunoblotting results for selected proteins corroborated our quantitative proteomics data. The diffusion and differential effects to the hippocampus of a neuroprotective HPL administered intranasally to mice were demonstrated. This proteomics study advances our understanding of HPLs, suggesting ways to standardize and customize their production for better clinical efficacy in regenerative medicine and biotherapy. Proteomic analyses also offered objective evidence that HPPL, upon intranasal delivery, not only effectively diffuses to the hippocampus but also alters protein expression in mice, bolstering its potential as a treatment for memory impairments.

CdSe/ZnS Quantum Dots' Impact on In Vitro Actin Dynamics.

Quantum dots (QDs) are a novel type of nanomaterial that has unique optical and physical characteristics. As such, QDs are highly desired because of their potential to be used in both biomedical and industrial applications. However, the mass adoption of QDs usage has raised concerns among the scientific community regarding QDs' toxicity. Although many papers have reported the negative impact of QDs on a cellular level, the exact mechanism of the QDs' toxicity is still unclear. In this investigation, we study the adverse effects of QDs by focusing on one of the most important cellular processes: actin polymerization and depolymerization. Our results showed that QDs act in a biphasic manner where lower concentrations of QDs stimulate the polymerization of actin, while high concentrations of QDs inhibit actin polymerization. Furthermore, we found that QDs can bind to filamentous actin (F-actin) and cause bundling of the filament while also promoting actin depolymerization. Through this study, we found a novel mechanism in which QDs negatively influence cellular processes and exert toxicity.

Enhancing Nasopharyngeal Carcinoma Survival Prediction: Integrating Pre- and Post-Treatment MRI Radiomics with Clinical Data.

Recurrences are frequent in nasopharyngeal carcinoma (NPC) despite high remission rates with treatment, leading to considerable morbidity. This study aimed to develop a prediction model for NPC survival by harnessing both pre- and post-treatment magnetic resonance imaging (MRI) radiomics in conjunction with clinical data, focusing on 3-year progression-free survival (PFS) as the primary outcome. Our comprehensive approach involved retrospective clinical and MRI data collection of 276 eligible NPC patients from three independent hospitals (180 in the training cohort, 46 in the validation cohort, and 50 in the external cohort) who underwent MRI scans twice, once within 2 months prior to treatment and once within 10 months after treatment. From the contrast-enhanced T1-weighted images before and after treatment, 3404 radiomics features were extracted. These features were not only derived from the primary lesion but also from the adjacent lymph nodes surrounding the tumor. We conducted appropriate feature selection pipelines, followed by Cox proportional hazards models for survival analysis. Model evaluation was performed using receiver operating characteristic (ROC) analysis, the Kaplan-Meier method, and nomogram construction. Our study unveiled several crucial predictors of NPC survival, notably highlighting the synergistic combination of pre- and post-treatment data in both clinical and radiomics assessments. Our prediction model demonstrated robust performance, with an accuracy of AUCs of 0.66 (95% CI: 0.536-0.779) in the training cohort, 0.717 (95% CI: 0.536-0.883) in the testing cohort, and 0.827 (95% CI: 0.684-0.948) in validation cohort in prognosticating patient outcomes. Our study presented a novel and effective prediction model for NPC survival, leveraging both pre- and post-treatment clinical data in conjunction with MRI features. Its constructed nomogram provides potentially significant implications for NPC research, offering clinicians a valuable tool for individualized treatment planning and patient counseling.

CD69 Signaling in Eosinophils Induces IL-10 Production and Apoptosis via the Erk1/2 and JNK Pathways, Respectively.

INTRODUCTION: Eosinophils contribute to the pathogenesis of allergic diseases, including asthma, allergic rhinitis, and atopic dermatitis. We previously reported that human tissue eosinophils have high CD69 expression compared to blood eosinophils, and its expression is correlated with disease severity and the number of infiltrated eosinophils. However, biological CD69 signaling activity in eosinophils remains unclear. METHODS: CD69 expression on lung tissue eosinophils obtained from mice with ovalbumin-induced asthma was measured using flow cytometry. CD69 crosslinking was performed on eosinophils purified from the spleen of IL-5 transgenic mice to investigate CD69 signaling and its function in eosinophils. Then, qPCR, Western blot, enzyme-linked immunosorbent assay, and survival assay results were analyzed. RESULTS: Surface CD69 expression on lung tissue eosinophils in the asthma mice model was 2.91% ± 0.76%, whereas no expression was detected in the healthy group. CD69-expressed eosinophils intrinsically have an upregulation of IL-10 mRNA expression. Moreover, CD69 crosslinking induced further pronounced IL-10 production and apoptosis; these responses were mediated via the Erk1/2 and JNK pathways, respectively. CONCLUSIONS: Our results suggested that CD69+ eosinophils play an immunoregulator role in type 2 inflammation, whereas activated tissue eosinophils contribute to the pathogenesis of asthma.

Extracellular vesicles purified from serum-converted human platelet lysates offer strong protection after cardiac ischaemia/reperfusion injury.

Extracellular vesicles (EVs) from cultured cells or bodily fluids have been demonstrated to show therapeutic value following myocardial infarction. However, challenges in donor variation, EV generation and isolation methods, and material availability have hindered their therapeutic use. Here, we show that human clinical-grade platelet concentrates from a blood establishment can be used to rapidly generate high concentrations of high purity EVs from sero-converted platelet lysate (SCPL-EVs) with minimal processing, using size-exclusion chromatography. Processing removed serum carrier proteins, coagulation factors and complement proteins from the original platelet lysate and the resultant SCPL-EVs carried a range of trophic factors and multiple recognised cardioprotective miRNAs. As such, SCPL-EVs protected rodent and human cardiomyocytes from hypoxia/re-oxygenation injury and stimulated angiogenesis of human cardiac microvessel endothelial cells. In a mouse model of myocardial infarction with reperfusion, SCPL-EV delivery using echo-guided intracavitary percutaneous injection produced large improvements in cardiac function, reduced scar formation and promoted angiogenesis. Since platelet-based biomaterials are already widely used clinically, we believe that this therapy could be rapidly suitable for a human clinical trial.

Detection of Abnormality in Coronary Artery Magnetic Resonance Imaging using Bit Plane Slicing and Deep Learning.

INTRODUCTION: This paper presents a novel approach for detecting abnormality in coronary arteries using MRI data in RGB images. The study evaluates the test accuracy of the weak classifiers and the test accuracy and F1 score of the strong classifier. METHODS: The method involves separating the image into information planes, including R, G, and B color space, or bit-planes, and training a VGG-like convolutional neural network model on each plane separately, referred to as a "weak classifier." The classification results of these planes are aggregated using a proposed soft voting method, forming a "strong classifier," with the weights for the aggregation determined by the model's performance on the training set. RESULTS: The results indicate that the strong classifier achieves a test accuracy and F1 score of around 68% to 74% on our private coronary artery dataset. Moreover, by aggregating the top three highest bit-plane levels in a grayscale image, the accuracy is slightly lower than that of the three color spaces but requires a significantly smaller CNN model of nearly 4M parameters. CONCLUSION: The potential of bit-planes in reducing model storage costs is suggested. This approach holds promise for improving the detection of abnormalities in coronary arteries using MRI data.

The Impact of Cadmium Selenide Zinc Sulfide Quantum Dots on the Proteomic Profile of Saccharomyces cerevisiae.

Quantum dots (QDs) have been highly sought after in the past few decades for their potential to be used in many biomedical applications. However, QDs' cytotoxicity is still a major concern that limits the incorporation of QDs into cutting-edge technologies. Thus, it is important to study and understand the mechanism by which QDs exert their toxicity. Although many studies have explored the cytotoxicity of quantum dots through the transcriptomic level and reactive species generation, the impact of quantum dots on the expression of cellular protein remains unclear. Using Saccharomyces cerevisiae as a model organism, we studied the effect of cadmium selenide zinc sulfide quantum dots (CdSe/ZnS QDs) on the proteomic profile of budding yeast cells. We found a total of 280 differentially expressed proteins after 6 h of CdSe/ZnS QDs treatment. Among these, 187 proteins were upregulated, and 93 proteins were downregulated. The majority of upregulated proteins were found to be associated with transcription/RNA processing, intracellular trafficking, and ribosome biogenesis. On the other hand, many of the downregulated proteins are associated with cellular metabolic pathways and mitochondrial components. Through this study, the cytotoxicity of CdSe/ZnS QDs on the proteomic level was revealed, providing a more well-rounded knowledge of QDs' toxicity.

Microscopic Video-Based Grouped Embryo Segmentation: A Deep Learning Approach.

PURPOSE: The primary aim of this research is to enhance the utilization of advanced deep learning (DL) techniques in the domain of in vitro fertilization (IVF) by presenting a more refined approach to the segmentation and organization of microscopic embryos. This study also seeks to establish a comprehensive embryo database that can be employed for future research and educational purposes. METHODS: This study introduces an advanced methodology for embryo segmentation and organization using DL. The approach comprises three primary steps: Embryo Segmentation Model, Segmented Embryo Image Organization, and Clear and Blur Image Classification. The proposed approach was rigorously evaluated on a sample of 5182 embryos extracted from 362 microscopic embryo videos. RESULTS: The study's results show that the proposed method is highly effective in accurately segmenting and organizing embryo images. This is evidenced by the high mean average precision values of 1.0 at an intersection over union threshold of 0.5 and across the range of 0.5 to 0.95, indicating a robust object detection capability that is vital in the IVF process. Segmentation of images based on various factors such as the day of development, patient, growth medium, and embryo facilitates easy comparison and identification of potential issues. Finally, appropriate threshold values for clear and blur image classification are proposed. CONCLUSION: The suggested technique represents an indispensable stage of data preparation for IVF training and education. Furthermore, this study provides a solid foundation for future research and adoption of DL in IVF, which is expected to have a significant positive impact on IVF outcomes.

Interactions between Quantum Dots and G-Actin.

Quantum dots (QDs) are a type of nanoparticle with excellent optical properties, suitable for many optical-based biomedical applications. However, the potential of quantum dots to be used in clinical settings is limited by their toxicity. As such, much effort has been invested to examine the mechanism of QDs' toxicity. Yet, the current literature mainly focuses on ROS- and apoptosis-mediated cell death induced by QDs, which overlooks other aspects of QDs' toxicity. Thus, our study aimed to provide another way by which QDs negatively impact cellular processes by investigating the possibility of protein structure and function modification upon direct interaction. Through shotgun proteomics, we identified a number of QD-binding proteins, which are functionally associated with essential cellular processes and components, such as transcription, translation, vesicular trafficking, and the actin cytoskeleton. Among these proteins, we chose to closely examine the interaction between quantum dots and actin, as actin is one of the most abundant proteins in cells and plays crucial roles in cellular processes and structural maintenance. We found that CdSe/ZnS QDs spontaneously bind to G-actin in vitro, causing a static quenching of G-actin's intrinsic fluorescence. Furthermore, we found that this interaction favors the formation of a QD-actin complex with a binding ratio of 1:2.5. Finally, we also found that CdSe/ZnS QDs alter the secondary structure of G-actin, which may affect G-actin's function and properties. Overall, our study provides an in-depth mechanistic examination of the impact of CdSe/ZnS QDs on G-actin, proposing that direct interaction is another aspect of QDs' toxicity.

The fuzzy Kullback-Leibler divergence for estimating parameters of the probability distribution in fuzzy data: an application to classifying Vietnamese Herb Leaves.

In this paper, we address the challenge of estimating probability distributions which are typically represented by parameter-based values. However, this estimation is prone to errors and does not comprehensively capture the nature of real-world data. Additionally, real-world data often follows a mixed form of probability distributions, where sub-datasets may contain incomplete information. To enhance flexibility, especially in classification problems, we propose a new method for describing parameters estimated through Bayesian statistics. Our method introduces fuzzy parameters and assesses the similarity between probability distributions using the fuzzy extended Kullback-Leibler divergence. We demonstrate the practical application of our approach in Vietnamese Herb Leaves classification. By incorporating fuzzy parameters and leveraging Bayesian statistics, our method provides more robust estimations of probability distributions and enables improved flexibility in classification tasks.

Current Advances in the Biomedical Applications of Quantum Dots: Promises and Challenges.

Quantum dots (QDs) are a type of nanoparticle with exceptional photobleaching-resistant fluorescence. They are highly sought after for their potential use in various optical-based biomedical applications. However, there are still concerns regarding the use of quantum dots. As such, much effort has been invested into understanding the mechanisms behind the behaviors of QDs, so as to develop safer and more biocompatible quantum dots. In this mini-review, we provide an update on the recent advancements regarding the use of QDs in various biomedical applications. In addition, we also discuss# the current challenges and limitations in the use of QDs and propose a few areas of interest for future research.

Red CdSe/ZnS QDs' Intracellular Trafficking and Its Impact on Yeast Polarization and Actin Filament.

Quantum dots are nanoparticles (2-10 nm) that emit strong and tunable fluorescence. Quantum dots have been heavily used in high-demand commercialized products, research, and for medical purposes. Emerging concerns have demonstrated the negative impact of quantum dots on living cells; however, the intracellular trafficking of QDs in yeast cells and the effect of this interaction remains unclear. The primary goal of our research is to investigate the trafficking path of red cadmium selenide zinc sulfide quantum dots (CdSe/ZnS QDs) in Saccharomyces cerevisiae and the impact QDs have on yeast cellular dynamics. Using cells with GFP-tagged reference organelle markers and confocal microscopy, we were able to track the internalization of QDs. We found that QDs initially aggregate at the exterior of yeast cells, enter the cell using clathrin-receptor-mediated endocytosis, and distribute at the late Golgi/trans-Golgi network. We also found that the treatment of red CdSe/ZnS QDs resulted in growth rate reduction and loss of polarized growth in yeast cells. Our RNA sequence analysis revealed many altered genes. Particularly, we found an upregulation of DID2, which has previously been associated with cell cycle arrest when overexpressed, and a downregulation of APS2, a gene that codes for a subunit of AP2 protein important for the recruitment of proteins to clathrin-mediated endocytosis vesicle. Furthermore, CdSe/ZnS QDs treatment resulted in a slightly delayed endocytosis and altered the actin dynamics in yeast cells. We found that QDs caused an increased level of F-actin and a significant reduction in profilin protein expression. In addition, there was a significant elevation in the amount of coronin protein expressed, while the level of cofilin was unchanged. Altogether, this suggests that QDs favor the assembly of actin filaments. Overall, this study provides a novel toxicity mechanism of red CdSe/ZnS QDs on yeast actin dynamics and cellular processes, including endocytosis.

Neuroprotective activity of a virus-safe nanofiltered human platelet lysate depleted of extracellular vesicles in Parkinson's disease and traumatic brain injury models.

Brain administration of human platelet lysates (HPL) is a potential emerging biotherapy of neurodegenerative and traumatic diseases of the central nervous system. HPLs being prepared from pooled platelet concentrates, thereby increasing viral risks, manufacturing processes should incorporate robust virus-reduction treatments. We evaluated a 19 ± 2-nm virus removal nanofiltration process using hydrophilic regenerated cellulose hollow fibers on the properties of a neuroprotective heat-treated HPL (HPPL). Spiking experiments demonstrated >5.30 log removal of 20-22-nm non-enveloped minute virus of mice-mock particles using an immuno-quantitative polymerase chain reaction assay. The nanofiltered HPPL (NHPPL) contained a range of neurotrophic factors like HPPL. There was >2 log removal of extracellular vesicles (EVs), associated with decreased expression of pro-thrombogenic phosphatidylserine and procoagulant activity. LC-MS/MS proteomics showed that ca. 80% of HPPL proteins, including neurotrophins, cytokines, and antioxidants, were still found in NHPPL, whereas proteins associated with some infections and cancer-associated pathways, pro-coagulation and EVs, were removed. NHPPL maintained intact neuroprotective activity in Lund human mesencephalic dopaminergic neuron model of Parkinson's disease (PD), stimulated the differentiation of SH-SY5Y neuronal cells and showed preserved anti-inflammatory function upon intranasal administration in a mouse model of traumatic brain injury (TBI). Therefore, nanofiltration of HPL is feasible, lowers the viral, prothrombotic and procoagulant risks, and preserves the neuroprotective and anti-inflammatory properties in neuronal pre-clinical models of PD and TBI.

Quantum Dots and Their Interaction with Biological Systems.

Quantum dots are nanocrystals with bright and tunable fluorescence. Due to their unique property, quantum dots are sought after for their potential in several applications in biomedical sciences as well as industrial use. However, concerns regarding QDs' toxicity toward the environment and other biological systems have been rising rapidly in the past decade. In this mini-review, we summarize the most up-to-date details regarding quantum dots' impacts, as well as QDs' interaction with mammalian organisms, fungal organisms, and plants at the cellular, tissue, and organismal level. We also provide details about QDs' cellular uptake and trafficking, and QDs' general interactions with biological structures. In this mini-review, we aim to provide a better understanding of our current standing in the research of quantum dots, point out some knowledge gaps in the field, and provide hints for potential future research.

Effects of Extracellular Vesicles Secreted by TGFß-Stimulated Umbilical Cord Mesenchymal Stem Cells on Skin Fibroblasts by Promoting Fibroblast Migration and ECM Protein Production.

Umbilical cord-derived mesenchymal stem cells (UCMSCs) have been illustrated for their roles in immunological modulation and tissue regeneration through the secretome. Additionally, culture conditions can trigger the secretion of extracellular vesicles (EVs) into extracellular environments with significant bioactivities. This study aims to investigate the roles of three EV sub-populations released by UCMSCs primed with transforming growth factor ß (TGFß) and their capacity to alter dermal fibroblast functions for skin aging. Results show that three EV sub-populations, including apoptotic bodies (ABs), microvesicles (MVs), and exosomes (EXs), were separated from conditioned media. These three EVs carried growth factors, such as FGF-2, HGF, and VEGF-A, and did not express noticeable effects on fibroblast proliferation and migration. Only EX from TGFß-stimulated UCMSCs exhibited a better capacity to promote fibroblasts migrating to close scratched wounds than EX from UCMSCs cultured in the normal condition from 24 h to 52 h. Additionally, mRNA levels of ECM genes (COL I, COL III, Elastin, HAS II, and HAS III) were detected with lower levels in fibroblasts treated with EVs from normal UCMSCs or TGFß-stimulated UCMSCs compared to EV-depleted condition. On the contrary, the protein levels of total collagen and elastin released by fibroblasts were greater in the cell groups treated with EVs compared to EV-depleted conditions; particularly elastin associated with TGFß-stimulated UCMSCs. These data indicate the potential roles of EVs from UCMSCs in protecting skin from aging by promoting ECM protein production.

Characterization of Isolated Aerobic Denitrifying Bacteria and Their Potential Use in the Treatment of Nitrogen-Polluted Aquaculture Water.

Ammonia and nitrite treatments are the critical steps that must be done to ensure the healthy growth of aquatic animals. The nitrification process is often used for nitrogen removal purposes due to its efficiency and environmentally friendly properties. However, the varied growth rate between ammonia and nitrite-oxidizing bacteria can cause nitrite accumulation, leading to the massive mortality of aquatic animals at high concentrations. Therefore, this study aimed to integrate the fast-growing heterotrophic nitrite-reducing bacteria with nitrifying bacteria to achieve a quicker nitrite removal activity. The two denitrifying Bacillus sp. ST20 and Bacillus sp. ST26 were screened from shrimp ponds in Bac Lieu province, Vietnam. Obtained results showed that under anoxic conditions, the nitrite removal efficiency of these two strains reached 68.5-82% at nitrite initial concentration of 20 mgN-NO2/L after 72 h. Higher efficiency of over 95% was gained under oxic conditions. Hence, it enabled the use of denitrifying and nitrifying bacteria-co-immobilized carriers for ammonia oxidation and nitrite reduction simultaneously in a single-aerated bioreactor. A total of over 96% nitrogen content was removed during the bioreactor operation, despite the increase of inputting nitrogen concentration from 40 to 200 mgN/L. Moreover, the suitable conditions for bacterial growth and nitrite conversion activity of the ST20 and ST26 were detected as 15‰ salinity and 35 °C. The isolates also utilized various C-sources for growth, hence widening their applicability. The present study suggested that the isolated aerobic denitrifying bacteria are potentially used for the complete removal of nitrogen compounds from aquaculture wastewater.

Genomic insights into the circulation of pandemic fluoroquinolone-resistant extra-intestinal pathogenic Escherichia coli ST1193 in Vietnam.

Extra-intestinal pathogenic Escherichia coli (ExPEC) ST1193, a globally emergent fluoroquinolone-resistant clone, has become an important cause of bloodstream infections (BSIs) associated with significant morbidity and mortality. Previous studies have reported the emergence of fluoroquinolone-resistant ExPEC ST1193 in Vietnam; however, limited data exist regarding the genetic structure, antimicrobial resistance (AMR) determinants and transmission dynamics of this pandemic clone. Here, we performed genomic and phylogenetic analyses of 46 ST1193 isolates obtained from BSIs and healthy individuals in Ho Chi Minh City, Vietnam, to investigate the pathogen population structure, molecular mechanisms of AMR and potential transmission patterns. We further examined the phylogenetic structure of ST1193 isolates in a global context. We found that the endemic E. coli ST1193 population was heterogeneous and highly dynamic, largely driven by multiple strain importations. Several well-supported phylogenetic clusters (C1-C6) were identified and associated with distinct blaCTX-M variants, including blaCTXM-27 (C1-C3, C5), blaCTXM-55 (C4) and blaCTXM-15 (C6). Most ST1193 isolates were multidrug-resistant and carried an extensive array of AMR genes. ST1193 isolates also exhibited the ability to acquire further resistance while circulating in Vietnam. There were phylogenetic links between ST1193 isolates from BSIs and healthy individuals, suggesting these organisms may both establish long-term colonization in the human intestinal tract and induce infections. Our study uncovers factors shaping the population structure and transmission dynamics of multidrug-resistant ST1193 in Vietnam, and highlights the urgent need for local One Health genomic surveillance to capture new emerging ExPEC clones and to better understand the origins and transmission patterns of these pathogens.

Machine Learning-Based Prediction of Drug-Drug Interactions for Histamine Antagonist Using Hybrid Chemical Features.

The requesting of detailed information on new drugs including drug-drug interactions or targets is often unavailable and resource-intensive in assessing adverse drug events. To shorten the common evaluation process of drug-drug interactions, we present a machine learning framework-HAINI to predict DDI types for histamine antagonist drugs using simplified molecular-input line-entry systems (SMILES) combined with interaction features based on CYP450 group as inputs. The data used in our research consisted of approved drugs of histamine antagonists that are connected to 26,344 DDI pairs from the DrugBank database. Various classification algorithms such as Naive Bayes, Decision Tree, Random Forest, Logistic Regression, and XGBoost were used with 5-fold cross-validation to approach a large-scale DDIs prediction among histamine antagonist drugs. The prediction performance shows that our model outperformed previously published works on DDI prediction with the best precision of 0.788, a recall of 0.921, and an F1-score of 0.838 among 19 given DDIs types. An important finding of the study is that our prediction is based solely on the SMILES and CYP450 and thus can be applied at the early stage of drug development.