Phospholipid-Anchored Ligand Conjugation on Extracellular Vesicles for Enhanced Cancer Targeting.

Extracellular vesicles (EVs) are recognized as potential candidates for next-generation drug delivery systems. However, the inherent cancer-targeting efficiency is unsatisfactory, necessitating surface modification to attach cell-binding ligands. By utilizing phospholipase D from Streptomyces in combination with maleimide-containing primary alcohol, the authors successfully anchored ligands onto milk-derived EVs (mEVs), overcoming the issues of ligand leakage or functional alteration seen in traditional methods. Quantitative nano-flow cytometry demonstrated that over 90% of mEVs are effectively modified with hundreds to thousands of ligands. The resulting mEV formulations exhibited remarkable long-term stability in conjugation proportion, ligand number, size distribution, and particle concentration, even after months of storage. It is further shown that conjugating transferrin onto mEVs significantly enhanced cellular uptake and induced pronounced cytotoxic effects when loaded with paclitaxel. Overall, this study presents a highly efficient, stable, cost-effective, and scalable ligand conjugation approach, offering a promising strategy for targeted drug delivery of EVs.

Study on high-precision three-dimensional reconstruction of pulmonary lesions and surrounding blood vessels based on CT images.

The adoption of computerized tomography (CT) technology has significantly elevated the role of pulmonary CT imaging in diagnosing and treating pulmonary diseases. However, challenges persist due to the complex relationship between lesions within pulmonary tissue and the surrounding blood vessels. These challenges involve achieving precise three-dimensional reconstruction while maintaining accurate relative positioning of these elements. To effectively address this issue, this study employs a semi-automatic precise labeling process for the target region. This procedure ensures a high level of consistency in the relative positions of lesions and the surrounding blood vessels. Additionally, a morphological gradient interpolation algorithm, combined with Gaussian filtering, is applied to facilitate high-precision three-dimensional reconstruction of both lesions and blood vessels. Furthermore, this technique enables post-reconstruction slicing at any layer, facilitating intuitive exploration of the correlation between blood vessels and lesion layers. Moreover, the study utilizes physiological knowledge to simulate real-world blood vessel intersections, determining the range of blood vessel branch angles and achieving seamless continuity at internal blood vessel branch points. The experimental results achieved a satisfactory reconstruction with an average Hausdorff distance of 1.5 mm and an average Dice coefficient of 92%, obtained by comparing the reconstructed shape with the original shape,the approach also achieves a high level of accuracy in three-dimensional reconstruction and visualization. In conclusion, this study is a valuable source of technical support for the diagnosis and treatment of pulmonary diseases and holds promising potential for widespread adoption in clinical practice.

MolProphet: A One-Stop, General Purpose, and AI-Based Platform for the Early Stages of Drug Discovery.

Artificial intelligence (AI) is an effective tool to accelerate drug discovery and cut costs in discovery processes. Many successful AI applications are reported in the early stages of small molecule drug discovery. However, most of those applications require a deep understanding of software and hardware, and focus on a single field that implies data normalization and transfer between those applications is still a challenge for normal users. It usually limits the application of AI in drug discovery. Here, based on a series of robust models, we formed a one-stop, general purpose, and AI-based drug discovery platform, MolProphet, to provide complete functionalities in the early stages of small molecule drug discovery, including AI-based target pocket prediction, hit discovery and lead optimization, and compound targeting, as well as abundant analyzing tools to check the results. MolProphet is an accessible and user-friendly web-based platform that is fully designed according to the practices in the drug discovery industry. The molecule screened, generated, or optimized by the MolProphet is purchasable and synthesizable at low cost but with good drug-likeness. More than 400 users from industry and academia have used MolProphet in their work. We hope this platform can provide a powerful solution to assist each normal researcher in drug design and related research areas. It is available for everyone at https://www.molprophet.com/.

Refractive Index Determination of Individual Viruses and Small Extracellular Vesicles in Aqueous Media Using Nano-Flow Cytometry.

The refractive index (RI) is a fundamental physical property of materials. Although measurement of the RI of biological nanoparticles (BNPs) in aqueous media is of great importance to basic research and biomedical applications, it is hampered by their tiny size, large intrinsic heterogeneity, and weak scattering. Here, we report the development of a label-free technique that can determine the RI of individual viruses and small extracellular vesicles (sEVs) with high precision and an analysis rate up to 10 000 particles per minute. This was achieved via the combination of high-sensitivity light-scattering detection by nanoflow cytometry (nFCM) and the Mie theory calculation. With the measured RIs for T7 virions, T7 capsids, and sEVs, the concentrations of nucleic acid in viral particles and protein in the lumen of sEVs were estimated. Furthermore, building upon a simplified core-shell model, the RIs of sEVs ranging from 40 to 200 nm were obtained. By using these RIs, a statistically robust size distribution of sEVs was acquired in minutes with accuracy and resolution matched closely with those of cryo-TEM measurements. Our approach could become an important tool in the RI determination of single BNPs.

Intelligent Labeling of Tumor Lesions Based on Positron Emission Tomography/Computed Tomography.

Positron emission tomography/computed tomography (PET/CT) plays a vital role in diagnosing tumors. However, PET/CT imaging relies primarily on manual interpretation and labeling by medical professionals. An enormous workload will affect the training samples' construction for deep learning. The labeling of tumor lesions in PET/CT images involves the intersection of computer graphics and medicine, such as registration, a fusion of medical images, and labeling of lesions. This paper extends the linear interpolation, enhances it in a specific area of the PET image, and uses the outer frame scaling of the PET/CT image and the least-squares residual affine method. The PET and CT images are subjected to wavelet transformation and then synthesized in proportion to form a PET/CT fusion image. According to the absorption of 18F-FDG (fluoro deoxy glucose) SUV in the PET image, the professionals randomly select a point in the focus area in the fusion image, and the system will automatically select the seed point of the focus area to delineate the tumor focus with the regional growth method. Finally, the focus delineated on the PET and CT fusion images is automatically mapped to CT images in the form of polygons, and rectangular segmentation and labeling are formed. This study took the actual PET/CT of patients with lymphatic cancer as an example. The semiautomatic labeling of the system and the manual labeling of imaging specialists were compared and verified. The recognition rate was 93.35%, and the misjudgment rate was 6.52%.

Intelligent Mining of Urban Ventilation Corridors Based on High-Precision Oblique Photographic Images.

With the advancement of urbanization and the impact of industrial pollution, the issue of urban ventilation has attracted increasing attention. Research on urban ventilation corridors is a hotspot in the field of urban planning. Traditional studies on ventilation corridors mostly focus on qualitative or simulated research on urban climate issues such as the intensified urban heat island effect, serious environmental pollution, and insufficient climate adaptability. Based on the high-precision urban remote sensing image data obtained by aeromagnetic oblique photography, this paper calculates the frontal area density of the city with reference to the urban wind statistics. Based on the existing urban patterns, template matching technology was used to automatically excavate urban ventilation corridors, which provides scientific and reasonable algorithmic support for the rapid construction of potential urban ventilation corridor paths. It also provides technical methods and decision basis for low-carbon urban planning, ecological planning and microclimate optimization design. This method was proved to be effective through experiments in Deqing city, Zhejiang Province, China.

Light-Scattering Sizing of Single Submicron Particles by High-Sensitivity Flow Cytometry.

Rapid and reliable size measurement of single submicron particles (100-1000 nm) is important for quality control of particulate matter, biomedical research, environmental study, and drug delivery system development. Though direct measurement of the elastically scattered light from individual submicron particles represents the simplest method for particle size measurement, the inadequate instrument sensitivity and complicated relationship between scattering intensity and particle size render it a great challenge. Combining the superior sensitivity of a laboratory-built high-sensitivity flow cytometer (HSFCM) in the side scattering (SSC) detection of single nanoparticles and the great efforts in synthesizing 38 highly monodisperse silica spheres ranging from 180 to 880 nm with small size intervals, here we report the first comprehensive comparison between the experimentally measured and Mie theory calculated intensities of light scattered by single submicron particles. Good agreements were observed for both the silica spheres and polystyrene beads at both the perpendicular and the parallel polarizations of the incident laser beam. Compared with perpendicular polarization, parallel polarization can resolve differently sized beads better due to the continuously increased scattering intensity with particle size. The predictive capability of the simple numerical model constructed in present work can be exploited to allow us to foresee scattering behavior on flow cytometers. More importantly, the linear correlation between the measured and the calculated scattering intensities enables us to develop a method that can measure the particle size of submicron particles with the precision and accuracy of Mie theory rather than a calibration curve fitted by several sparsely separated size reference standards. Comparable sizing resolution and accuracy to those of electron microscopy were demonstrated for Gram-positive bacteria Staphylococcus aureus. The as-developed method shows great potential in guiding the accurate size measurement of submicron particles.

Label-Free Analysis of Single Viruses with a Resolution Comparable to That of Electron Microscopy and the Throughput of Flow Cytometry.

Viruses are by far the most abundant biological entities on our planet, yet existing characterization methods are limited by either their speed or lack of resolution. By applying a laboratory-built high-sensitivity flow cytometer (HSFCM) to precisely quantify the extremely weak elastically scattered light from single viral particles, we herein report the label-free analysis of viruses with a resolution comparable to that of electron microscopy and the throughput of flow cytometry. The detection of single viruses with diameters down to 27 nm is described. T7 and lambda bacteriophages, which differ in size by as little as 4 nm, could be baseline-resolved. Moreover, subtle structural differences of the same viral particles can be discriminated. Using monodisperse silica nanoparticles as the size reference standards, the virus sizes measured by the HSFCM are in agreement with the equivalent particle diameters derived from their structural dimensions. The HSFCM opens a new avenue for virus characterization.

Corrigendum to 'Characterization of lipid-based nanomedicines at the single-particle level' [Fundamental Research 3 (2023) 488-504].

[This corrects the article DOI: 10.1016/j.fmre.2022.09.011.].

An analytical approach for unsupervised learning rate estimation using rectified linear units.

Unsupervised learning based on restricted Boltzmann machine or autoencoders has become an important research domain in the area of neural networks. In this paper mathematical expressions to adaptive learning step calculation for RBM with ReLU transfer function are proposed. As a result, we can automatically estimate the step size that minimizes the loss function of the neural network and correspondingly update the learning step in every iteration. We give a theoretical justification for the proposed adaptive learning rate approach, which is based on the steepest descent method. The proposed technique for adaptive learning rate estimation is compared with the existing constant step and Adam methods in terms of generalization ability and loss function. We demonstrate that the proposed approach provides better performance.

Nano-Proteolysis Targeting Chimeras (Nano-PROTACs) in Cancer Therapy.

Proteolysis-targeting chimeras (PROTACs) are heterobifunctional molecules that have the capability to induce specific protein degradation. While playing a revolutionary role in effectively degrading the protein of interest (POI), PROTACs encounter certain limitations that impede their clinical translation. These limitations encompass off-target effects, inadequate cell membrane permeability, and the hook effect. The advent of nanotechnology presents a promising avenue to surmount the challenges associated with conventional PROTACs. The utilization of nano-proteolysis targeting chimeras (nano-PROTACs) holds the potential to enhance specific tissue accumulation, augment membrane permeability, and enable controlled release. Consequently, this approach has the capacity to significantly enhance the controllable degradation of target proteins. Additionally, they enable a synergistic effect by combining with other therapeutic strategies. This review comprehensively summarizes the structural basis, advantages, and limitations of PROTACs. Furthermore, it highlights the latest advancements in nanosystems engineered for delivering PROTACs, as well as the development of nano-sized PROTACs employing nanocarriers as linkers. Moreover, it delves into the underlying principles of nanotechnology tailored specifically for PROTACs, alongside the current prospects of clinical research. In conclusion, the integration of nanotechnology into PROTACs harbors vast potential in enhancing the anti-tumor treatment response and expediting clinical translation.

Debittering of Emblica (Phyllanthus emblica L.) fruit powder: Preparation and biological activity.

Emblica, also known as Phyllanthus emblica L., is a drug homologous food that is rich in polyphenols with various biological activities. However, its bitterness and astringency pose a significant challenge to its utilization in food products. In this study, we aimed to identify the optimal conditions for debittering Emblica. Our findings revealed that the best debittering conditions were: temperature = 50 °C, pH = 4, α-l-rhamnosidase concentration 200 U/g, and time = 5 h. High-performance liquid chromatography (HPLC) and molecular docking analysis revealed that enzymatic hydrolysis partially removed bitterness compounds. The results of antioxidant activity, xanthine oxidase, and α-glucosidase inhibitory activity assays confirmed that the Emblica fruit powder still exhibited good biological activity after enzymatic debitterization. Moreover, gastric fluids treatment might contribute to the above enhancing effect of enzymatic hydrolysates of Emblica. This study provided a theoretical basis for promoting the processing and utilization of Emblica fruit powder, as well as understanding its biological activity.

A cooperative combination of non-targeted metabolomics and electronic tongue evaluation reveals the dynamic changes in metabolites and sensory quality of radish during pickling.

Pickled radish is a traditional fermented food with a unique flavor after long-term preservation. This study analyzed the organoleptic and chemical characteristics of pickled radish from different years to investigate quality changes during pickling. The results showed that the sourness, saltiness, and aftertaste-bitterness increased after pickling, and bitterness and astringency decreased. The levels of free amino acids, soluble sugars, total phenols, and total flavonoids initially decreased during pickling but increased with prolonged pickling. The diversity of organic acids also increased over time. Through non-targeted metabolomics analysis, 349 differential metabolites causing metabolic changes were identified to affect the quality formation of pickled radish mainly through amino acid metabolism, phenylpropane biosynthesis and lipid metabolism. Correlation analysis showed that L*, soluble sugars, lactic acid, and acetic acid were strongly associated with taste quality. These findings provide a theoretical basis for standardizing and scaling up traditional pickled radish production.

Comparison of Lipid Composition between Quasipaa spinosa Oil and Rana catesbeiana Oil and Its Effect on Lipid Accumulation in Caenorhabditis elegans.

Frog oil has been recognized for its nutritional and medicinal value. However, there is limited research on the role of frog oil in preventing obesity. In this study, we aimed to investigate the lipid composition of Quasipaa spinosa oil (QSO) and Rana catesbeiana oil (RCO) using lipidomics analysis. We compared the lipid accumulation effects of these two kinds of frog oils and soybean oil (SO) in Caenorhabditis elegans (C. elegans). Additionally, we determined the gene expression related to lipid metabolism and used the nhr-49 mutant (RB1716) and sir-2.1 mutant (VC199) for validation experiments. The results showed that the lipid composition of QSO and RCO was significantly different (p < 0.05), and QSO was rich in more polyunsaturated fatty acids (PUFAs). After feeding C. elegans, the lipid accumulation of the QSO group was the lowest among the three dietary oil groups. In addition, compared with RCO and SO, QSO significantly inhibited the production of malondialdehyde (MDA) and increased the activity of superoxide dismutase (SOD). The effects of three kinds of dietary oils on the fatty acid composition of C. elegans were significantly different. Compared with SO and RCO, QSO significantly up-regulated (p < 0.05) the expression of sir-2.1 and ech-1 genes. The results showed that QSO might reduce lipid accumulation through the SIRT1 and nuclear hormone signaling pathways. Such a situation was verified experimentally by the nhr-49 mutant (RB1716) and sir-2.1 mutant (VC199). This study proposed a new functional oil, laying the groundwork for developing functional foods from Quasipaa spinosa.

Preparation and identification of antioxidant peptides from Quasipaa spinosa skin through two-step enzymatic hydrolysis and molecular simulation.

Frog skin, a by-product of Quasipaa Spinosa farming, is rich in protein and potentially a valuable raw material for obtaining antioxidant peptides. This study used papain combined with acid protease to digest frog skin in a two-step enzymatic hydrolysis method. Based on a single factor and response surface experiments, experimental conditions were optimized, and the degree of hydrolysis was 30 %. A frog skin hydrolysate (QSPH-Ⅰ-3) was obtained following ultrafiltration and gel filtration chromatography. IC50 for DPPH, ABTS, and hydroxyl radical scavenging capacities were 1.68 ± 0.05, 1.20 ± 0.14 and 1.55 ± 0.11 mg/mL, respectively. Peptide sequences (17) were analyzed and, through molecular docking, peptides with low binding energies for KEAP1 were identified, which might affect the NRF2-KEAP1 pathway. These findings suggest protein hydrolysates and antioxidant peptide derivatives might be used in functional foods.

Synergistic effects of trace sulfadiazine and corrosion scales on disinfection by-product formation in bulk water of cast iron pipe.

The effects of trace sulfadiazine (SDZ) and cast-iron corrosion scales on the disinfection by-product (DBP) formation in drinking water distribution systems (DWDSs) were investigated. The results show that under the synergistic effect of trace SDZ (10 µg/L) and magnetite (Fe3O4), higher DBP concentration occurred in the bulk water with the transmission and distribution of the drinking water. Microbial metabolism-related substances, one of the important DBP precursors, increased under the SDZ/Fe3O4 condition. It was found that Fe3O4 induced a faster microbial extracellular electron transport (EET) pathway, resulting in a higher microbial regrowth activity. On the other hand, the rate of chlorine consumption was quite high, and the enhanced microbial EET based on Fe3O4 eliminated the need for microorganisms to secrete excessive extracellular polymeric substances (EPS). More importantly, EPS could be continuously secreted due to the higher microbial activity. Finally, high reactivity between EPS and chlorine disinfectant resulted in the continuous formation of DBPs, higher chlorine consumption, and lower EPS content. Therefore, more attention should be paid to the trace antibiotics polluted water sources and cast-iron corrosion scale composition in the future. This study reveals the synergistic effects of trace antibiotics and corrosion scales on the DBP formation in DWDSs, which has important theoretical significance for the DBP control of tap water.

Characterization of lipid-based nanomedicines at the single-particle level.

Lipid-based nanomedicines (LBNMs), including liposomes, lipid nanoparticles (LNPs) and extracellular vesicles (EVs), are recognized as one of the most clinically acceptable nano-formulations. However, the bench-to-bedside translation efficiency is far from satisfactory, mainly due to the lack of in-depth understanding of their physical and biochemical attributes at the single-particle level. In this review, we first give a brief introduction of LBNMs, highlighting some milestones and related scientific and clinical achievements in the past several decades, as well as the grand challenges in the characterization of LBNMs. Next, we present an overview of each category of LBNMs as well as the core properties that largely dictate their biological characteristics and clinical performance, such as size distribution, particle concentration, morphology, drug encapsulation and surface properties. Then, the recent applications of several analytical techniques including electron microscopy, atomic force microscopy, fluorescence microscopy, Raman microscopy, nanoparticle tracking analysis, tunable resistive pulse sensing and flow cytometry on the single-particle characterization of LBNMs are thoroughly discussed. Particularly, the comparative advantages of the newly developed nano-flow cytometry that enables quantitative analysis of both the physical and biochemical characteristics of LBNMs smaller than 40 nm with high throughput and statistical robustness are emphasized. The overall aim of this review article is to illustrate the importance, challenges and achievements associated with single-particle characterization of LBNMs.

miR-494-5p mediates the antioxidant activity of EPA by targeting the mitochondrial elongation factor 1 gene MIEF1 in HepG2 cells.

Eicosapentaenoic acid (EPA) shows antioxidant activity, which may be attributed to its regulatory effect on microRNA expression. Our preliminary study indicated that EPA upregulated miR-494-5p, which was possibly involved in the regulation of cellular stress responses. The current study aimed to address whether miR-494-5p was targeted by EPA to regulate cellular oxidative stress and its possible functional mechanism. The results showed that miR-494-5p mediated the antioxidant effect of EPA and miR-494-5p reduction deteriorated EPA-induced increase in the cellular antioxidant capacity of HepG2 cells. Moreover, the mitochondrial elongation factor 1 (MIEF1) gene was a target gene of miR-494-5p. Both miR-494-5p overexpression and MIEF1 knockdown significantly enhanced cellular antioxidant capacity, as indicated by a reduction in the reactive oxygen species level and an increase in the total cellular antioxidant capacity, along with enhancing antioxidant enzymes. Thus, miR-494-5p and MIEF1 had opposite effects on cellular antioxidant capacity. Furthermore, their regulatory effects on oxidative stress may have been attributed to modulation of mitochondrial function, biogenesis and homeostasis. Taken together, the findings indicated that miR-494-5p mediated EPA activity and promoted cellular antioxidant capacity by inhibiting the expression of MIEF1, which further modulated mitochondrial structure and activity. This study may provide novel insights into the post-translational regulation of antioxidation reactions, which involves the coordinated control of mitochondria.

Quantification of Available Ligand Density on the Surface of Targeted Liposomal Nanomedicines at the Single-Particle Level.

Active targeting has been hailed as one of the most promising strategies to further enhance the therapeutic efficacy of liposomal nanomedicines. Owing to the critical role of ligand density in mediating cellular uptake and the intrinsic heterogeneity of liposomal formulations, precise quantification of the surface ligand density on a single-particle basis is of fundamental importance. In this work, we report a method to simultaneously measure the particle size and the number of ligands on the same liposomal nanoparticles by nanoflow cytometry. Then the ligand density for each individual liposome can be determined. With an analysis rate up to 10 000 particles per minute, a statistically representative distribution of ligand density could be determined in minutes. By utilizing fluorescently labeled recombinant receptors as the detection probe against the conjugated ligands, only those available for cell targeting can be exclusively detected. The influence of ligand input, conjugation strategy, and the polyethylene glycol spacer length on the available ligand density of folate-modified liposomes was investigated. The correlation between the available ligand density and cell targeting capability was assessed in a quantitative perspective for liposomes modified with three different targeting moieties. The optimal ligand density was determined to be 0.5-2.0, 0.7, and 0.2 ligand per 100 nm2 for folate-, transferrin-, and HER2-antibody-conjugated liposomes, respectively. These optimal values agreed well with the spike density of the natural counterparts, viruses. The as-developed approach is generally applicable to a wide range of active-targeting nanocarriers.

Characterization of xanthine oxidase inhibitory activities of phenols from pickled radish with molecular simulation.

Pickled radish is a general source of natural bioactive compounds that include phenols. Here, we used molecular docking, fluorescence quenching, circular dichroism spectroscopy and molecular dynamics simulations to identify potential inhibitors against xanthine oxidase from a library of pickled radish compounds. The most effective compounds were selected for validation through in vitro experiments including enzyme activity inhibition tests, and cell-based assays. Molecular docking results revealed that 2,6-Dihydroxyacetophenone, 4-Hydroxyphenethyl alcohol, and 4-Hydroxybenzaldehyde exhibited significant effects on xanthine oxidase inhibition. Three phenols have varying degrees of inhibition on xanthine oxidase, which is driven by hydrophobic interactions and hydrogen bonds and affects the secondary structure and hydrophobic homeostasis of xanthine oxidase. The stability of xanthine oxidase inhibition by three phenols was analyzed by molecular dynamics simulation. Finally, cellular experiments confirmed that three phenols reduced uric acid levels by inhibiting the xanthine oxidase enzyme activity of BRL 3A cells.