Green synthesis of ZnFe2O4@ZnO nanocomposites using Chrysanthemum spp. floral waste for photocatalytic dye degradation.

The occurrence of textile dyeing wastewater discharged into the environment has been recently increasing, resulting in harmful effects on living organisms and human health. The use of green nanoparticles for water decontamination has received much attention. Floral waste can be extracted with the release of natural compounds, which act as reducing and stabilizing agents during the biosynthesis of nanoparticles. Herein, we report the utilization of Chrysanthemum spp. floral waste extract to synthesize green ZnFe2O4@ZnO (ZFOZx) nanocomposites for the photocatalytic degradation of Congo red under solar light irradiation. The various molar ratio of ZnFe2O4 (0-50%) was incorporated into ZnO nanoparticles. The surface area of green ZFOZx nanocomposites was found to increase (7.41-42.66 m2 g-1) while their band gap energy decreased from 1.98 eV to 1.92 eV. Moreover, the results exhibited the highest Congo red dye degradation efficiency of 94.85% at a concentration of 5.0 mg L-1, and a catalyst dosage of 0.33 g L-1. The •O2- reactive species played a vital role in the photocatalytic degradation of Congo red dye. Green ZFOZ3 nanocomposites had good recyclability with at least three cycles, and an excellent stability. The germination results showed that wastewater treated by ZFOZ3 was safe enough for bean seed germination. We expect that this work contributes significantly to developing novel green bio-based nanomaterials for environmental remediation as well as reducing the harm caused by flower wastes.

Facile synthesis of CoFe2O4@MIL-53(Al) nanocomposite for fast dye removal: Adsorption models, optimization and recyclability.

The global occurrence of textile dyes pollution has recently emerged, posing a serious threat to ecological systems. To abate dye contamination, we here developed a novel magnetic porous CoFe2O4@MIL-53(Al) nanocomposite by incorporating magnetic CoFe2O4 nanoparticles with MIL-53(Al) metal-organic framework. This nanocomposite possessed a surface area of 197.144 m2 g-1 and a pore volume of 0.413 cm3 g-1. The effect of contact time (5-120 min), concentration (5-50 mg L-1), dosage (0.1-1.0 g L-1), and pH (2-10) on Congo red adsorption was clarified. CoFe2O4@MIL-53(Al) could remove 95.85% of Cong red dye from water with an accelerated kinetic rate of 0.6544 min-1 within 10 min. The kinetic and isotherm models showed the predominance of Bangham and Temkin. According to Langmuir, the maximum uptake capacities of CoFe2O4@MIL-53(Al), CoFe2O4, and MIL-53(Al) adsorbents were 43.768, 17.982, and 15.295 mg g-1, respectively. CoFe2O4@MIL-53(Al) was selected to optimize Cong red treatment using Box-Behnken experimental design. The outcomes showed that CoFe2O4@MIL-53(Al) achieved the highest experimental uptake capacity of 35.919 mg g-1 at concentration (29.966 mg L-1), time (14.926 min), and dosage (0.486 g L-1). CoFe2O4@MIL-53(Al) could treat dye mixture (methylene blue, methyl orange, Congo red, malachite green, and crystal violet) with an outstanding removal efficiency of 81.24% for 30 min, and could be reused up to five cycles. Therefore, novel recyclable and stable CoFe2O4@MIL-53(Al) is recommended to integrate well with real dye treatments systems.

Generating reaction trees with cascaded variational autoencoders.

To develop useful drugs and materials, chemists synthesize diverse molecules by trying various reactants and reaction routes. Toward automating this process, we propose a deep generative model, called cascaded variational autoencoder (casVAE), for synthesizable molecular design. It generates a reaction tree, where the reactants are chosen from commercially available compounds and the synthesis route is constructed as a tree of reaction templates. The first part of casVAE is designed to generate a molecule called a surrogate product, while the second part constructs a reaction tree that synthesizes it. In benchmarking, casVAE showed its ability to generate reaction trees that yield high-quality and synthesizable molecules. An implementation of casVAE is publicly available at https://github.com/tsudalab/rxngenerator.

Recent advances and prospects of computational methods for metabolite identification: a review with emphasis on machine learning approaches.

MOTIVATION: Metabolomics involves studies of a great number of metabolites, which are small molecules present in biological systems. They play a lot of important functions such as energy transport, signaling, building block of cells and inhibition/catalysis. Understanding biochemical characteristics of the metabolites is an essential and significant part of metabolomics to enlarge the knowledge of biological systems. It is also the key to the development of many applications and areas such as biotechnology, biomedicine or pharmaceuticals. However, the identification of the metabolites remains a challenging task in metabolomics with a huge number of potentially interesting but unknown metabolites. The standard method for identifying metabolites is based on the mass spectrometry (MS) preceded by a separation technique. Over many decades, many techniques with different approaches have been proposed for MS-based metabolite identification task, which can be divided into the following four groups: mass spectra database, in silico fragmentation, fragmentation tree and machine learning. In this review paper, we thoroughly survey currently available tools for metabolite identification with the focus on in silico fragmentation, and machine learning-based approaches. We also give an intensive discussion on advanced machine learning methods, which can lead to further improvement on this task.

ADAPTIVE: leArning DAta-dePendenT, concIse molecular VEctors for fast, accurate metabolite identification from tandem mass spectra.

MOTIVATION: Metabolite identification is an important task in metabolomics to enhance the knowledge of biological systems. There have been a number of machine learning-based methods proposed for this task, which predict a chemical structure of a given spectrum through an intermediate (chemical structure) representation called molecular fingerprints. They usually have two steps: (i) predicting fingerprints from spectra; (ii) searching chemical compounds (in database) corresponding to the predicted fingerprints. Fingerprints are feature vectors, which are usually very large to cover all possible substructures and chemical properties, and therefore heavily redundant, in the sense of having many molecular (sub)structures irrelevant to the task, causing limited predictive performance and slow prediction. RESULTS: We propose ADAPTIVE, which has two parts: learning two mappings (i) from structures to molecular vectors and (ii) from spectra to molecular vectors. The first part learns molecular vectors for metabolites from given data, to be consistent with both spectra and chemical structures of metabolites. In more detail, molecular vectors are generated by a model, being parameterized by a message passing neural network, and parameters are estimated by maximizing the correlation between molecular vectors and the corresponding spectra in terms of Hilbert-Schmidt Independence Criterion. Molecular vectors generated by this model are compact and importantly adaptive (specific) to both given data and task of metabolite identification. The second part uses input output kernel regression (IOKR), the current cutting-edge method of metabolite identification. We empirically confirmed the effectiveness of ADAPTIVE by using a benchmark data, where ADAPTIVE outperformed the original IOKR in both predictive performance and computational efficiency. AVAILABILITY AND IMPLEMENTATION: The code will be accessed through http://www.bic.kyoto-u.ac.jp/pathway/tools/ADAPTIVE after the acceptance of this article.

Decellularized Porcine Epiphyseal Plate-Derived Extracellular Matrix Powder: Synthesis and Characterization.

The aim of this study was to develop a porcine epiphyseal plate-derived extracellular matrix powder (PEPEP) for epiphyseal plate regeneration. PEPEP was characterized by chemical assay to determine the contents of DNA and epiphyseal plate complex chemical components (glycosaminoglycan and hydroxyproline). The effects of PEPEP on the viability, proliferation, and differentiation of human bone marrow mesenchymal stem cells (hBMSCs) were also evaluated. hBMSCs cultured in PEPEP exhibited a good distribution with excellent viability after 72 h, demonstrating the ability of PEPEP to support hBMSC proliferation. At week 4 and 6 in vitro, the PEPEP + hBMSCs structure showed chondrogenic ability and an increase in expression of collagen type I, type II, and type X. PEPEP showed a promising ability to enhance cartilage formation and promote chondrocyte differentiation, maturation, and hypertrophy. The results provide insights into the feasibility of PEPEP as a potential material for tissue engineering applications.

Potential Application of Gold Nanospheres as a Surface Plasmon Resonance Based Sensor for In-Situ Detection of Residual Fungicides.

It is essential to develop a simple and sensitive method to rapidly detect residual fungicides in agricultural products to protect human health. So far, little studies have been reported on potential application of gold nanospheres (AuNSps) as a surface plasmon resonance based sensor for in-situ detection of residual fungicides. Therefore, in this study, we investigated the potential application of AuNSps as a surface plasmon resonance based sensor for in-situ detection of fungicides. AuNSps were successfully synthesized via a seed-mediated method with some modifications. Firstly, gold nanoseeds were made during the reduction of chloroauric acid by trisodium citrate dihydrate (TSC). Then, AuNSps were grown from the seeds by using HAuCl4, TSC and EDTA. AuNSps were subsequently dropped on a glass substrate before covered by thiophanate methyl, a broad-spectrum systemic fungicide. The AuNSps coated glass substrate was subsequently dried in the air for further surface-enhanced Raman spectroscopy (SERS) measurements. Optical properties, shape and size of AuNSps were confirmed by UV-vis spectroscopy, XRD, SEM-EDX and TEM. The results showed that AuNSps were successfully synthesized with the size of 53 nm, and their resonance peak was located at 560 nm. The Raman signal intensity of thiophanate methyl covered on AuNSps is higher than that without AuNSps, indicating SERS effects of AuNSps deposited glass substrate.

SIMPLE: Sparse Interaction Model over Peaks of moLEcules for fast, interpretable metabolite identification from tandem mass spectra.

Motivation: Recent success in metabolite identification from tandem mass spectra has been led by machine learning, which has two stages: mapping mass spectra to molecular fingerprint vectors and then retrieving candidate molecules from the database. In the first stage, i.e. fingerprint prediction, spectrum peaks are features and considering their interactions would be reasonable for more accurate identification of unknown metabolites. Existing approaches of fingerprint prediction are based on only individual peaks in the spectra, without explicitly considering the peak interactions. Also the current cutting-edge method is based on kernels, which are computationally heavy and difficult to interpret. Results: We propose two learning models that allow to incorporate peak interactions for fingerprint prediction. First, we extend the state-of-the-art kernel learning method by developing kernels for peak interactions to combine with kernels for peaks through multiple kernel learning (MKL). Second, we formulate a sparse interaction model for metabolite peaks, which we call SIMPLE, which is computationally light and interpretable for fingerprint prediction. The formulation of SIMPLE is convex and guarantees global optimization, for which we develop an alternating direction method of multipliers (ADMM) algorithm. Experiments using the MassBank dataset show that both models achieved comparative prediction accuracy with the current top-performance kernel method. Furthermore SIMPLE clearly revealed individual peaks and peak interactions which contribute to enhancing the performance of fingerprint prediction. Availability and implementation: The code will be accessed through http://mamitsukalab.org/tools/SIMPLE/.

PEGylated PAMAM dendrimers loading oxaliplatin with prolonged release and high payload without burst effect.

Oxaliplatin (OXA) was coupled to PEGylated polyamidoamine dendrimers of fourth generation (G4-PEG@OXA) in the comparison to PEGylated ones of odd generation (G3.5-PEG@OXA). Proton nuclear magnetic resonance and Fourier-transform infrared spectroscopy were used to confirm the successful incorporation of OXA as well as the synthesis of carrier systems. Both two types of carrier systems exhibited in sphere nanoparticle shape with size of less than 100 nm that was in the range being able to cause toxicity on cancer cells. The average drug loading efficiency (DLE) of G4-PEG@OXA was obtained at 84.63% that was higher than DLE of G3.5-PEG of 75.69%. The release kinetic of G4-PEG@OXA and G3.5-PEG@OXA did not show any burst release phenomenon while free OXA was released over 40% at the first hour. The sustainable release of OXA was achieved when it was encapsulated in these carriers, but the G4 generation liberated OXA (3.4%-6.4%) slower than G3.5 one (11.9%-22.8%). The in vitro cytotoxicities of G4-PEG@OXA were evaluated in HeLa cell lines using resazurin assay and live/dead staining test. Although the free OXA showed a rather moderate killing ability, the G4-PEG@OXA still displayed the low viability of HeLa that was better to the result of G3.5-PEG@OXA due to released OXA amount. The benefit of this system was to overcome the burst release phenomenon to minimize OXA toxicity without compromising its efficiency.

Grafting of Poly(poly(ethylene glycol) methacrylate) onto Halloysite Nanotubes via Surface-Initiated Atom Transfer Radical Polymerization.

A synthetic strategy for the functionalization of halloysite nanotubes (HNTs) with poly(poly(ethylene glycol) methacrylate) (PPEGMA) via surface-initiated atom transfer radical polymerization (SIATRP). The covalent immobilization of PPEGMA was confirmed by FT-IR analysis. The preparation of the nanohybrids was further evidenced by the XPS and EDX studies. The morphologies of functionalized HNTs were investigated by FE-SEM analysis. TGA results suggested that the nanohybrids can be used in high temperature applications. Thus, this newly developed surface functionalization protocol offers the tailoring of the HNTs surface with wide functionalities and is potentially useful to the biomedical applications.

Modified Carboxyl-Terminated PAMAM Dendrimers as Great Cytocompatible Nano-Based Drug Delivery System.

Polyamidoamine (PAMAM) dendrimers are extensively researched as potential drug delivery system thanks to their desirable features such as controlled and stable structures, and ease of functionalization onto their surface active groups. However, there have been concerns about the toxicity of full generation dendrimers and risks of premature clearance from circulation, along with other physical drawbacks presented in previous formulations, including large particle sizes and low drug loading efficiency. In our study, carboxyl-terminated PAMAM dendrimer G3.5 was grafted with poly (ethylene glycol) methyl ether (mPEG) to be employed as a nano-based drug delivery system with great cytocompatibility for the delivery of carboplatin (CPT), a widely prescribed anticancer drug with strong side effects so that the drug will be effectively entrapped and not exhibit uncontrolled outflow from the open structure of unmodified PAMAM G3.5. The particles formed were spherical in shape and had the optimal size range (around 36 nm) that accommodates high drug entrapment efficiency. Surface charge was also determined to be almost neutral and the system was cytocompatible. In vitro release patterns over 24 h showed a prolonged CPT release compared to free drug, which correlated to the cytotoxicity assay on malignant cell lines showing the lack of anticancer effect of CPT/mPEG-G3.5 compared with CPT.

Enhanced tissue adhesiveness of injectable gelatin hydrogels through dual catalytic activity of horseradish peroxidase.

Development of bioadhesives with tunable mechanical strength, high adhesiveness, biocompatibility, and injectability is greatly desirable in all surgeries to replace or complement the sutures and staples. Herein, the dual catalytic activity of horseradish peroxidase is exploited to in situ form the hydroxyphenyl propionic acid-gelatin/thiolated gelatin (GH/GS) adhesive hydrogels including two alternative crosslinks (phenol-phenol and disulfide bonds) with fast gelation (few seconds - several minutes) and improved physicochemical properties. Their elastic moduli increase from 6.7 to 10.3 kPa by adding GS polymer that leads to the better stability of GH/GS hydrogels than GH ones. GH/GS adhesive strength is respectively 6.5-fold and 15.8-fold higher than GH-only and fibrin glue that is due to additional disulfide linkages between hydrogels and tissues. Moreover, in vitro cell study with human dermal fibroblast showed the cell-compatibility of GH/GS hydrogels. Taken together, GH/GS hydrogels can be considered as promising potential adhesive materials for various biomedical applications.

Preparation, Characterization and Antifungal Properties of Chitosan-Silver Nanoparticles Synergize Fungicide Against Pyricularia oryzae.

Rice (Oryza sativa L.) is one of the major staple food crops of nearly two-third of the world's population. However, rice blast caused by fungus Pyricularia oryzae is generally considered the most serious disease of cultivated rice worldwide due to its extensive distribution and destructiveness under favourable climatic conditions. In this report, the combination between chitosan (CS) and silver (Ag), Ag@CS, was introduced for antifungal activity against Pyricularia oryzae extracted from blast-infected leaves. In detail, Ag@CS nanoparticles (NPs) were first synthesized and further mixed with Trihexad 700 WP (Tri), Ag@CS-Tri, against the fungus by agar diffusion method. The prepared Ag@CS-Tri NPs were characterized by Fourier transform infrared (FTIR), dynamic light scattering (DLS), transmission electron microscopy (TEM), X-ray diffraction (XRD), and thermogravimetric analysis (TGA). In aqueous condition, Ag@CS-Tri NPs were successfully prepared and existed as spherical NPs with particle size of 17.26 ± 0.89 nm, which is an ideal size for their uptake into plant cells, indicating that the size of their parentally Ag@CS NPs is small enough to combine Tri, and their diameter is large enough to effectively penetrate the cellular membrane and kill fungi. More importantly, the antifungal property of Ag@CS-Tri NPs was significantly increased with inhibition zone around 25 nm compared with only around 12 nm of Ag@CS at the same concentration of Ag (2 ppm) and CS (4000 ppm). These results demonstrated that the synergistic effect of Tri and Ag@CS NPs can be a potential candidate with high antifungal activity for the use of antibiotics in agriculture.

Correction: Nguyen et al. Green Silver Nanoparticles Formed by Phyllanthus urinaria, Pouzolzia zeylanica, and Scoparia dulcis Leaf Extracts and the Antifungal Activity. Nanomaterials 2020, 10, 542.

In the original publication [...].

Optimization of hydrothermal synthesis conditions of Bidens pilosa-derived NiFe2O4@AC for dye adsorption using response surface methodology and Box-Behnken design.

The presence of stable and hazardous organic dyes in industrial effluents poses significant risks to both public health and the environment. Activated carbons and biochars are widely used adsorbents for removal of these pollutants, but they often have several disadvantages such as poor recoverability and inseparability from water in the post-adsorption process. Incorporating a magnetic component into activated carbons can address these drawbacks. This study aims to optimizing the production of NiFe2O4-loaded activated carbon (NiFe2O4@AC) derived from a Bidens pilosa biomass source through a hydrothermal method for the adsorption of Rhodamine B (RhB), methyl orange (MO), and methyl red (MR) dyes. Response surface methodology (RSM) and Box-Behnken design (BBD) were applied to analyze the key synthesis factors such as NiFe2O4 loading percentage (10-50%), hydrothermal temperature (120-180 °C), and reaction time (6-18 h). The optimized condition was found at a NiFe2O4 loading of 19.93%, a temperature of 135.55 °C, and a reaction time of 16.54 h. The optimum NiFe2O4@AC demonstrated excellent sorption efficiencies of higher than 92.98-97.10% against all three dyes. This adsorbent was characterized, exhibiting a well-developed porous structure with a high surface area of 973.5 m2 g-1. Kinetic and isotherm were studied with the best fit of pseudo-second-order, and Freundlich or Temkin. Qmax values were determined to be 204.07, 266.16, and 177.70 mg g-1 for RhB, MO, and MR, respectively. By selecting HCl as an elution, NiFe2O4@AC could be efficiently reused for at least 4 cycles. Thus, the Bidens pilosa-derived NiFe2O4@AC can be a promising material for effective and recyclable removal of dye pollutants from wastewater.

Synthesis of magnetic MFe2O4 (M = Ni, Co, Zn, Fe) supported on porous carbons derived from Bidens pilosa weed and their adsorptive comparison of toxic dyes.

Bidens pilosa is classified as an invasive plant and has become a problematic weed to many agricultural crops. This species strongly germinates, grows and reproduces and competing for nutrients with local plants. To lessen the influence of Bidens pilosa, therefore, converting this harmful species into carbon materials as adsorbents in harm-to-wealth and valorization strategies is required. Here, we synthesized a series of magnetic composites based on MFe2O4 (M = Ni, Co, Zn, Fe) supported on porous carbon (MFOAC) derived from Bidens pilosa by a facile hydrothermal method. The Bidens pilosa carbon was initially activated by condensed H3PO4 to increase the surface chemistry. We observed that porous carbon loaded NiFe2O4 (NFOAC) reached the highest surface area (795.7 m2 g-1), followed by CoFe2O4/AC (449.1 m2 g-1), Fe3O4/AC (426.1 m2 g-1), ZnFe2O4/AC (409.5 m2 g-1). Morphological results showed nanoparticles were well-dispersed on the surface of carbon. RhB, MO, and MR dyes were used as adsorbate to test the adsorption by MFOAC. Effect of time (0-360 min), concentration (5-50 mg L-1), dosage (0.05-0.2 g L-1), and pH (3-9) on dyes adsorption onto MFOAC was investigated. It was found that NFOAC obtained the highest maximum adsorption capacity against dyes, RhB (107.96 mg g-1) < MO (148.05 mg g-1) < MR (153.1 mg g-1). Several mechanisms such as H bonding, π-π stacking, cation-π interaction, and electrostatic interaction were suggested. With sufficient stability and capacity, NFOAC can be used as potential adsorbent for real water treatment systems.

Development and Characterization of Quercetin-Loaded Polymeric Liposomes with Gelatin-Poly(ethylene glycol)-Folic Acid Coating to Increase Their Long-Circulating and Anticancer Activity.

Liposomes as drug-delivery systems have been researched and applied in multiple scientific reports and introduced as patented products with interesting therapeutic properties. Despite various advantages, this drug carrier faces major difficulties in its innate stability, cancer cell specificity, and control over the release of hydrophobic drugs, particularly quercetin, a naturally derived drug that carries many desirable characteristics for anticancer treatment. To improve the effectiveness of liposomes to deliver quercetin by tackling and mitigating the mentioned hurdles, we developed a strategy to establish the ability to passively target cancerous cells, as well as to increase the bioavailability of loaded drugs by incorporating poly(ethylene glycol), gelatin, and folic acid moieties to modify the liposomal system's surface. This research developed a chemically synthesized gelatin, poly(ethylene glycol), and folic acid as a single polymer to coat drug-loaded liposome systems. Liposomes were coated with gelatin-poly(ethylene glycol)-folic acid by electrostatic interaction, characterized by their size, morphology, ζ potential, drug loading efficiency, infrared structures, differential scanning calorimetry spectra, and drug-releasing profiles, and then evaluated for their cytotoxicity to MCF-7 breast cancer cells, as well as cellular uptake, analyzed by confocal imaging to further elaborate on the in vitro behavior of the coated liposome. The results indicated an unusual change in size with increased coating materials, followed by increased colloidal stability, ζ potential, and improved cytotoxicity to cancer cells, as shown by the cellular viability test with MCF-7. Cellular uptake also confirmed these results, providing data for the effects of biopolymer coating, while confirming that folic acid can increase the uptake of liposome by cancer cells. In consideration of such results, the modified gelatin-poly(ethylene glycol)-folic acid-coated liposome can be a potential system in delivering the assigned anticancer compound. This modified biopolymer showed excellent properties as a coating material and should be considered for further practical applications in the future.

Differentiable optimization layers enhance GNN-based mitosis detection.

Automatic mitosis detection from video is an essential step in analyzing proliferative behaviour of cells. In existing studies, a conventional object detector such as Unet is combined with a link prediction algorithm to find correspondences between parent and daughter cells. However, they do not take into account the biological constraint that a cell in a frame can correspond to up to two cells in the next frame. Our model called GNN-DOL enables mitosis detection by complementing a graph neural network (GNN) with a differentiable optimization layer (DOL) that implements the constraint. In time-lapse microscopy sequences cultured under four different conditions, we observed that the layer substantially improved detection performance in comparison with GNN-based link prediction. Our results illustrate the importance of incorporating biological knowledge explicitly into deep learning models.

New frontiers of invasive plants for biosynthesis of nanoparticles towards biomedical applications: A review.

Above 1000 invasive species have been growing and developing ubiquitously on Earth. With extremely vigorous adaptability, strong reproduction, and spreading powers, invasive species have posed an alarming threat to indigenous plants, water quality, soil, as well as biodiversity. It was estimated that an economic loss of billions of dollars or equivalent to 1 % of gross domestic product as a consequence of lost crops, control efforts, and damage costs caused by invasive plants in the United States. While eradicating invasive plants from the ecosystems is practically infeasible, taking advantage of invasive plants as a sustainable, locally available, and zero-cost source to provide valuable phytochemicals for bionanoparticles fabrication is worth considering. Here, we review the harms, benefits, and role of invasive species as important botanical sources to extract natural compounds such as piceatannol, resveratrol, and quadrangularin-A, flavonoids, and triterpenoids, which are linked tightly to the formation and application of bionanoparticles. As expected, the invasive plant-mediated bionanoparticles have exhibited outstanding antibacterial, antifungal, anticancer, and antioxidant activities. The mechanism of biomedical activities of the invasive plant-mediated bionanoparticles was insightfully addressed and discussed. We also expect that this review not only contributes to efforts to combat invasive plant species but also opens new frontiers of bionanoparticles in the biomedical applications, therapeutic treatment, and smart agriculture.