Mass Transfer Analysis of Growth and Substance Metabolism of NSCs Cultured in Collagen-Based Scaffold In Vitro

The aim of this study is to analyze the growth and substance metabolism of neural stem cells (NSCs) cultured in biological collagen-based scaffolds. Mass transfer and metabolism model of glucose, lactic acid, and dissolved oxygen (DO) were established and solved on MATLAB platform to obtain the concentration distributions of DO, glucose, and lactic acid in culture system, respectively. Calculation results showed that the DO influenced their normal growth and metabolism of NSCs mostly in the in vitro culture within collagen-based scaffolds. This study also confirmed that 2-mm thickness of collagen scaffold was capable of in vitro cultivation and growth of NSCs with an inoculating density of 1?×?10⁶ cells/mL.     

Plasma-Induced Synthesis of CuO Nanofibers and ZnO Nanoflowers in Water

Fiber-shaped cupric oxide (CuO) nanoparticles and flower-shaped ZnO nanoparticles were facilely synthesized by plasma-induced technique directly from copper and zinc electrode pair in water, respectively. The phase composition, morphologies and optical property of nanoparticles have been investigated by energy dispersive X-ray analysis, X-ray powder diffraction, transmission electron microscopy and UV–vis. The in situ analysis by an optical emission spectroscopy clarified the formation mechanism. Plasma was generated from the discharge between a metal electrode pair in water by a pulse direct current power. CuO and ZnO nanoparticles were synthesized via almost the same formation mechanism, which were prepared via the rapid energetic radicals’ bombardment to electrodes’ surface, atom vapour diffusion, plasma expansion, solution medium condensation, and in situ oxygen reaction and further growth. This novel plasma-induced technique will become a potential application in nanomaterials synthesis.     

Synthesis of Nano-hydroxyapatite via Microbial Method and Its Characterization

Nanoparticles of hydroxyapatite were successfully synthesized by microbial method at ambient temperature and pressure, using calcium chloride and specific substrate as reactants. The compositional and morphological properties of products of the syntheses were studied by means of X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), transmission electron microscopy (TEM), and thermogravimetric analysis (TGA). The characterization data obtained showed that the phase composition, functional groups, and surface morphology of samples obtained by microbial method were mainly similar to that by chemical precipitation method. The hydroxyapatite powder was shown to be nanometer-grade in size and sphere-like in shape.     

Preparation of raspberry-like silica microcapsules via sulfonated polystyrene template and aniline medium assembly method

Raspberry-like composite particles and microcapsules were prepared with anionic sulfonated polystyrene (PSS) particles as templates and cationic aniline monomer as assembly medium. With the help of the sulfonated microgel shells, aniline and silica particles could not only adsorbed onto template surfaces but also go inward shells and finally form microcapsules with varied silica shell thickness. The sulfonation extent of PSS particles first climbed up and then decreased with sulfonation time due to the competition of sulfonation reaction and PSS chain detachment. The silica content in composite particles and shell thickness of microcapsules followed similar trend with sulfonation extent. The choice of aniline as assembly medium was checked by comparing with methyl methacrylate and [2-(methacryloyloxy) ethyl] trimethylammonium chloride, and it was found that the cationic and water-insoluble properties of aniline are both important for the composite efficiency.     

Preparation of a visible light-driven Bi2O3–TiO2 composite photocatalyst by an ethylene glycol-assisted sol–gel method,and its photocatalytic properties

A visible light-driven Bi₂O₃–TiO₂ composite photocatalyst was prepared by an ethylene glycol-assisted sol–gel method in which ethylene glycol acted as a polycondensation agent to capture metal ions by reacting with bismuth and titanium sources via a complex polycondensation pathway. The photocatalyst was characterized by X-ray photoelectron spectroscopy, X-ray diffraction, acquisition of N₂ adsorption–desorption isotherms, transmission electron microscopy, and UV–visible diffuse reflectance spectroscopy. The results revealed that the Bi₂O₃–TiO₂ composite was of smaller particle size, greater specific surface area, and had stronger absorbance in the visible light region than pure TiO₂. The photocatalytic activity of the as-prepared catalyst was evaluated by degradation of rhodamine B under visible light irradiation (λ > 400 nm); the as-prepared Bi₂O₃–TiO₂ composite was substantially more active than pure TiO₂. This was ascribed to the high surface area and the heterojunction structure.     

ChemStable: a web server for rule-embedded naïve Bayesian learning approach to predict compound stability

Predicting compound chemical stability is important because unstable compounds can lead to either false positive or to false negative conclusions in bioassays. Experimental data (COMDECOM) measured from DMSO/H₂O solutions stored at 50 °C for 105 days were used to predicted stability by applying rule-embedded naïve Bayesian learning, based upon atom center fragment (ACF) features. To build the naïve Bayesian classifier, we derived ACF features from 9,746 compounds in the COMDECOM dataset. By recursively applying naïve Bayesian learning from the data set, each ACF is assigned with an expected stable probability (p _s ) and an unstable probability (p _uns ). 13,340 ACFs, together with their p _s and p _uns data, were stored in a knowledge base for use by the Bayesian classifier. For a given compound, its ACFs were derived from its structure connection table with the same protocol used to drive ACFs from the training data. Then, the Bayesian classifier assigned p _s and p _uns values to the compound ACFs by a structural pattern recognition algorithm, which was implemented in-house. Compound instability is calculated, with Bayes’ theorem, based upon the p _s and p _uns values of the compound ACFs. We were able to achieve performance with an AUC value of 84 % and a tenfold cross validation accuracy of 76.5 %. To reduce false negatives, a rule-based approach has been embedded in the classifier. The rule-based module allows the program to improve its predictivity by expanding its compound instability knowledge base, thus further reducing the possibility of false negatives. To our knowledge, this is the first in silico prediction service for the prediction of the stabilities of organic compounds.     

Structural insight into exosite binding and discovery of novel exosite inhibitors of botulinum neurotoxin serotype A through in silico screening

Botulinum neurotoxin serotype A (BoNT/A) is the most lethal toxin among the Tier 1 Select Agents. Development of potent and selective small molecule inhibitors against BoNT/A zinc metalloprotease remains a challenging problem due to its exceptionally large substrate binding surface and conformational plasticity. The exosites of the catalytic domain of BoNT/A are intriguing alternative sites for small molecule intervention, but their suitability for inhibitor design remains largely unexplored. In this study, we employed two recently identified exosite inhibitors, D-chicoric acid and lomofungin, to probe the structural features of the exosites and molecular mechanisms of synergistic inhibition. The results showed that D-chicoric acid favors binding at the α-exosite, whereas lomofungin preferentially binds at the β-exosite by mimicking the substrate β-sheet binding interaction. Molecular dynamics simulations and binding interaction analysis of the exosite inhibitors with BoNT/A revealed key elements and hotspots that likely contribute to the inhibitor binding and synergistic inhibition. Finally, we performed database virtual screening for novel inhibitors of BoNT/A targeting the exosites. Hits C1 and C2 showed non-competitive inhibition and likely target the α- and β-exosites, respectively. The identified exosite inhibitors may provide novel candidates for structure-based development of therapeutics against BoNT/A intoxication.     

Predicting Regioselectivity in Radical C−H Functionalization of Heterocycles through Machine Learning

Radical C−H bond functionalization provides a versatile approach for elaborating heterocyclic compounds. The synthetic design of this transformation relies heavily on the knowledge of regioselectivity, while a quantified and efficient regioselectivity prediction approach is still elusive. Herein, we report the feasibility of using a machine learning model to predict the transition state barrier from the computed properties of isolated reactants. This enables rapid and reliable regioselectivity prediction for radical C−H bond functionalization of heterocycles. The Random Forest model with physical organic features achieved 94.2 % site accuracy and 89.9 % selectivity accuracy in the out-of-sample test set. The prediction performance was further validated by comparing the machine learning results with additional substituents, heteroarene scaffolds and experimental observations. This work revealed that the combination of mechanism-based computational statistics and machine learning model can serve as a useful strategy for selectivity prediction of organic transformations.     

Sequence-Dependent DNA Functionalization of Upconversion Nanoparticles and Their Programmable Assemblies

DNA-modified lanthanide-doped upconversion nanoparticles (DNA-UCNPs) that combine the functions of DNA and the optical features of UCNPs have shown great promise in a wide range of fields. However, challenges remain in precisely tethering and orienting the DNA strands on the UCNP surface. Herein, we systematically investigate the sequence dependence of DNAs in their interactions with UCNPs, and reveal that poly-cytosine (poly-C) has high affinity for the UCNP surface. A general approach to synthesize monodispersed DNA-UCNP conjugates is developed using poly-C-containing diblock DNA strands. The poly-C segment of the DNA strand binds to the surfaces of UCNPs and the second segment is oriented perpendicularly on the UCNP surface, making the DNA-UCNPs highly stable and monodispersed in aqueous solution. The dense layer of DNA on the UCNP surface enables the programmable assembly of UCNPs with other DNA-functionalized nanoparticles or DNA origamis through hybridization, resulting in the formation of well-organized complex structures.     

Exploring the Limits of Transition-Metal Fluorination at High Pressures

Fluorination is a proven method for challenging the limits of chemistry, both structurally and electronically. Here we explore computationally how pressures below 300 GPa affect the fluorination of several transition metals. A plethora of new structural phases are predicted along with the possibility for synthesizing four unobserved compounds: TcF₇, CdF₃, OsF₈, and IrF₈. The Ir and Os octaflourides are both predicted to be stable as quasi-molecular phases with an unusual cubic ligand coordination, and both compounds formally correspond to a high oxidation state of +8. Electronic-structure analysis reveals that otherwise unoccupied 6p levels are brought down in energy by the combined effects of pressure and a strong ligand field. The valence expansion of Os and Ir enables ligand-to-metal F 2p→M 6p charge transfer that strengthens M−F bonds and decreases the overall bond polarity. The lower stability of IrF₈, and the instability of PtF₈ and several other compounds below 300 GPa, is explained by the occupation of M−F antibonding orbitals in octafluorides with a metal-valence-electron count exceeding 8.