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Aberrant glycosylation patterns of glycoproteins and glycolipids have long been recognized as one the major hallmarks of cancer cells that has led to numerous glycoconjugate vaccine attempts. These abnormal glycosylation profiles mostly originate from the lack of key glycosyltransferases activities, mutations, over expressions, or modifications of the requisite chaperone for functional folding. Due to their relative structural simplicity, O -linked glycans of the altered mucin family of glycoproteins have been particularly attractive in the design of tumor associated carbohydrate-based vaccines. Several such glycoconjugate vaccine formulations have generated potent monoclonal anti-carbohydrate antibodies useful as diagnostic and immunotherapies in the fight against cancer. Paradoxically, glycoproteins related to enveloped viruses also express analogous N - and O -linked glycosylation patterns. However, due to the fact that viruses are not equipped with the appropriate glycosyl enzyme machinery, they need to hijack that of the infected host cells. Although the resulting N -linked glycans are very similar to those of normal cells, some of their O -linked glycan patterns often share the common structural simplicity to those identified on tumor cells. Consequently, given that both cancer cells and viral glycoproteins share both common N - and O -linked glycoepitopes, glycoconjugate vaccines could be highly attractive to generate potent immune responses to target both conditions.

Glycodendrimers have attracted considerable interest in the field of dendrimer sciences owing to their plethora of implications in biomedical applications. This is primarily due to the fact that cell surfaces expose a wide range of highly diversified glycan architectures varying by the nature of the sugars, their number, and their natural multiantennary structures. This particular situation has led to cancer cell metastasis, pathogen recognition and adhesion, and immune cell communications that are implicated in vaccine development. The diverse nature and complexity of multivalent carbohydrate–protein interactions have been the impetus toward the syntheses of glycodendrimers. Since their inception in 1993, chemical strategies toward glycodendrimers have constantly evolved into highly sophisticated methodologies. This review constitutes the first part of a series of papers dedicated to the design, synthesis, and biological applications of heterofunctional glycodendrimers. Herein, we highlight the most common synthetic approaches toward these complex molecular architectures and present modern applications in nanomolecular therapeutics and synthetic vaccines.

Obesity is increasingly prevalent and associated with increased risk of developing type 2 diabetes, cardiovascular diseases, and cancer. Magnetic resonance imaging (MRI) is an accurate method for determination of body fat volume and distribution. However, quantifying body fat from numerous MRI slices is tedious and time-consuming. Here we developed a deep learning-based method for measuring visceral and subcutaneous fat in the abdominal region of mice. Congenic mice only differ from C57BL/6 (B6) Apoe knockout (Apoe-/-) mice in chromosome 9 that is replaced by C3H/HeJ genome. Male congenic mice had lighter body weight than B6-Apoe-/- mice after being fed 14 weeks of Western diet. Axial and coronal T1-weighted sequencing at 1-mm-thickness and 1-mm-gap was acquired with a 7T Bruker ClinScan scanner. A deep learning approach was developed for segmenting visceral and subcutaneous fat based on the U-net architecture made publicly available through the open-source ANTsRNet library-a growing repository of well-known neural networks. The volumes of subcutaneous and visceral fat measured through our approach were highly comparable with those from manual measurements. The Dice score, root-mean-square error (RMSE), and correlation analysis demonstrated the similarity between two methods in quantifying visceral and subcutaneous fat. Analysis with the automated method showed significant reductions in volumes of visceral and subcutaneous fat but not non-fat tissues in congenic mice compared to B6 mice. These results demonstrate the accuracy of deep learning in quantification of abdominal fat and its significance in determining body weight.

This paper consists of a deep analysis and data comparison of the main strategies undertaken for achieving truly reversible capture of carbon dioxide involving optimized gas uptakes while affording weakest retention strength. So far, most strategies failed because the estimated amount of CO2 produced by equivalent energy was higher than that captured. A more viable and sustainable approach in the present context of a persistent fossil fuel-dependent economy should be based on a judicious compromise between effective CO2 capture with lowest energy for adsorbent regeneration. The most relevant example is that of so-called promising technologies based on amino adsorbents which unavoidably require thermal regeneration. In contrast, OH-functionalized adsorbents barely reach satisfactory CO2 uptakes but act as breathing surfaces affording easy gas release even under ambient conditions or in CO2-free atmospheres. Between these two opposite approaches, there should exist smart approaches to tailor CO2 retention strength even at the expense of the gas uptake. Among these, incorporation of zero-valent metal and/or OH-enriched amines or amine-enriched polyol species are probably the most promising. The main findings provided by the literature are herein deeply and systematically analysed for highlighting the main criteria that allow for designing ideal CO2 adsorbent properties.

Glycoconjugate vaccines provide effective prophylaxis against bacterial infections. To date, however, no commercial vaccine has been available in which the key carbohydrate antigens are produced synthetically. We describe the large-scale synthesis, pharmaceutical development, and clinical evaluation of a conjugate vaccine composed of a synthetic capsular polysaccharide antigen of Haemophilus influenzae type b (Hib). The vaccine was evaluated in clinical trials in Cuba and showed long-term protective antibody titers that compared favorably to licensed products prepared with the Hib polysaccharide extracted from bacteria. This demonstrates that access to synthetic complex carbohydrate-based vaccines is feasible and provides a basis for further development of similar approaches for other human pathogens.

This study investigates the capability of sequence-to-sequence machine learning (ML) architectures in an effort to develop streamflow forecasting tools for Canadian watersheds. Such tools are useful to inform local and region-specific water management and flood forecasting related activities. Two powerful deep-learning variants of the Recurrent Neural Network were investigated, namely the standard and attention-based encoder-decoder long short-term memory (LSTM) models. Both models were forced with past hydro-meteorological states and daily meteorological data with a look-back time window of several days. These models were tested for 10 different watersheds from the Ottawa River watershed, located within the Great Lakes Saint-Lawrence region of Canada, an economic powerhouse of the country. The results of training and testing phases suggest that both models are able to simulate overall hydrograph patterns well when compared to observational records. Between the two models, the attention model significantly outperforms the standard model in all watersheds, suggesting the importance and usefulness of the attention mechanism in ML architectures, not well explored for hydrological applications. The mean performance accuracy of the attention model on unseen data, when assessed in terms of mean Nash–Sutcliffe Efficiency and Kling-Gupta Efficiency is, respectively, found to be 0.985 and 0.954 for these watersheds. Streamflow forecasts with lead times of up to 5 days with the attention model demonstrate overall skillful performance with well above the benchmark accuracy of 70%. The results of the study suggest that the encoder–decoder LSTM, with attention mechanism, is a powerful modelling choice for developing streamflow forecasting systems for Canadian watersheds.

Pollution from organic molecules is a major environmental issue that needs to be addressed because of the negative impacts of both the harmfulness of the molecule structures and the toxicity that can spread through natural media. This is mainly due to their unavoidable partial oxidation under exposure to air and solar radiation into diverse derivatives. Even when insoluble, the latter can be dispersed in aqueous media through solvatation and/or complexation with soluble species. Coagulation–flocculation, biological water treatments or adsorption on solids cannot result in a total elimination of organic pollutants. Chemical degradation by chlorine and/or oxygen-based oxidizing agents is not a viable approach due to incomplete mineralization into carbon dioxide and other oxides. A more judicious strategy resides in mimicking natural oxidation under ambient conditions. Soils and aqueous clay suspensions are known to display adsorptive and catalytic properties, and slow and complete self-regeneration can be achieved in an optimum time frame with a much slower pollution throughput. A deep knowledge of the behavior of aluminosilicates and of oxidizing species in soils and aquatic media allows us to gain an understanding of their roles in natural oxidative processes. Their individual and combined contributions will be discussed in the present critical analysis of the reported literature.

This brief review highlights systematic progress in the design of synthetic glycolipid (neoglycolipids) analogs evolving from the conventional architectures of natural glycosphingolipids and gangliosides. Given that naturally occurring glycolipids are composed of only one hydrophilic sugar head-group and two hydrophobic lipid tails embedded in the lipid bilayers of the cell membranes, they usually require extraneous lipids (phosphatidylcholine, cholesterol) to confer their stability. In order to obviate the necessity for these additional stabilizing ingredients, recent investigations have merged dendrimer chemistry with that of neoglycolipid syntheses. This singular approach has provided novel glycoarchitectures allowing reconsidering the necessity for the traditional one to two hydrophilic/hydrophobic ratio. An emphasis has been provided in the recent design of modular arborescent neoglycolipid syntheses coined glycodendrimersomes.

Vanillin-based lactoside derivatives were synthetized using phase-transfer catalyzed reactions from per-O-acetylated lactosyl bromide. The aldehyde group of the vanillin moiety was then modified to generate a series of related analogs having variable functionalities in the para- position of the aromatic residue. The corresponding unprotected lactosides, obtained by Zemplén transesterification, were regioselectively 3′-O-sulfated using tin chemistry activation followed by treatment with sulfur trioxide-trimethylamine complex (Men3N-SO3). Additional derivatives were also prepared from the vanillin’s aldehyde using a Knoevenagel reaction to provide extended α, β-unsaturated carboxylic acid which was next reduced to the saturated counterpart.

Glycan recognition by sugar receptors (lectins) is intimately involved in many aspects of cell physiology. However, the factors explaining the exquisite selectivity of their functional pairing are not yet fully understood. Studies toward this aim will also help appraise the potential for lectin-directed drug design. With the network of adhesion/growth-regulatory galectins as therapeutic targets, the strategy to recruit synthetic chemistry to systematically elucidate structure-activity relationships is outlined, from monovalent compounds to glyco-clusters and glycodendrimers to biomimetic surfaces. The versatility of the synthetic procedures enables to take examining structural and spatial parameters, alone and in combination, to its limits, for example with the aim to produce inhibitors for distinct galectin(s) that exhibit minimal reactivity to other members of this group. Shaping spatial architectures similar to glycoconjugate aggregates, microdomains or vesicles provides attractive tools to disclose the often still hidden significance of nanometric aspects of the different modes of lectin design (sequence divergence at the lectin site, differences of spatial type of lectin-site presentation). Of note, testing the effectors alone or in combination simulating (patho)physiological conditions, is sure to bring about new insights into the cooperation between lectins and the regulation of their activity.