Synthesis and Investigation of Novel CHCA-Derived Matrices for Matrix-Assisted Laser Desorption/Ionization Mass Spectrometric Analysis of Lipids

A significant area of study and upgrading for increasing sensitivity and general performances of matrix-assisted laser-desorption ionization (MALDI) mass spectrometry (MS) is related to matrix design. Several efforts have been made to address the challenge of low-mass-region interference-free for metabolomics analysis and specifically for lipidomics. To this aim, rationally designed matrices as 4-chloro-α-cyanocinnamic acid (ClCCA) were introduced and reported to provide enhanced analytical performances. We have taken this rational design one step further by developing and optimizing new MALDI matrices with a range of modifications on the CHCA core, involving different functionalities and substituents. Of particular interest was the understanding of the electron-withdrawing (e.g., nitro-) or donating (e.g., methoxy-) effects along with the extent of conjugation on the ionization efficiency. In the present work, ten matrices were designed on a reasonable basis, synthesized, and characterized by NMR and UV spectroscopies and laser desorption ionization. With the assistance of these putative MALDI matrices, samples containing phospholipids (PL), and neutral di-/tri-acylglycerols (DAG, TAG) were investigated using milk, fish, blood, and human plasma extracts. In comparison with CHCA and ClCCA, four of them, viz. [(2E,4E)-2-cyano-5-(4-methoxyphenyl)penta-2,4-dienoic acid] (1), [(2E,4E)-2-cyano-5-(4-nitrophenyl)penta-2,4-dienoic acid] (2), [(E)-2-cyano-3-(6-methoxynaphthalen-2-yl)acrylic acid] (6) and [(E)-2-cyano-3-(naphthalen-2-yl)acrylic acid] (7) displayed good to even excellent performances as MALDI matrices in terms of ionization capability, interference-free spectra, S/N ratio, and reproducibility. Especially compound 7 (cyano naphthyl acrylic acid, CNAA) was the election matrix for PL analysis and matrix 2 (cyano nitrophenyl dienoic acid, CNDA) for neutral lipids such as DAG and TAG in positive ion mode.     

Stereospecific Photoredox-Catalyzed Vinylations to Functionalized Alkenes and C-Glycosides**

We report an efficient radical-mediated C−C coupling through photoredox-catalyzed reactions of 4-alkyl-dihydropyridines (DHPs) and vinylbenziodoxol(on)es (VBX, VBO). This transition-metal-free and mild photocatalytic method has excellent functional group tolerance and affords vinylated products in good yields, with complete retention of the alkene configuration. The utility of the methodology is demonstrated by the diastereoselective synthesis of C-vinyl glycosides. Preliminary mechanistic studies suggest that the C−C bond formation is stereospecific and proceeds through a concerted radical coupling transition state.     

A General Organophotoredox Strategy to Difluoroalkyl Bicycloalkane (CF2-BCA) Hybrid Bioisosteres**

Here, we report a general approach to the synthesis of the difluoroalkyl bicycloalkanes (CF₂-BCAs), as structural surrogates of aryl ketones and ethers. The chemistry is driven by a dihydrobenzoacridine photocatalyst, that engages in a catalytic electron-donor acceptor (EDA) complex, or directly reduces the fluorinated substrate. These two convergent manifolds lead to the generation of the R-CF₂ radical, that reacts with the [1.1.1]- or [3.1.1.]-propellane. The method is extremely general, and extendable to complex bioactive molecules (30 examples, up to 87 % yield). The structural features of the CF₂-BCP hybrid bioisostere were investigated by single crystal X-ray. Finally, we synthesised a CF₂-BCP analogue of a Leukotriene A₄ hydrolase inhibitor, replacing the original aryl ether motif. In silico docking studies indicated that this new analogue maintains the same arrangement within the enzyme pocket, profiling the use of the CF₂-BCA hybrid bioisostere in medicinal chemistry settings.     

An enriched mixture model for functional clustering

There is an increasingly rich literature about Bayesian nonparametric models for clustering functional observations. Most recent proposals rely on infinite-dimensional characterizations that might lead to overly complex cluster solutions. In addition, while prior knowledge about the functional shapes is typically available, its practical exploitation might be a difficult modeling task. Motivated by an application in e-commerce, we propose a novel enriched Dirichlet mixture model for functional data. Our proposal accommodates the incorporation of functional constraints while bounding the model complexity. We characterize the prior process through a urn scheme to clarify the underlying partition mechanism. These features lead to a very interpretable clustering method compared to available techniques. Moreover, we employ a variational Bayes approximation for tractable posterior inference to overcome computational bottlenecks.