Imprinted Polymers with Affinity for Phosphorylated Peptides and Proteins

The present invention relates to a method of separating or extracting phosphorylated amino acids, peptides or proteins with a molecularly imprinted polymer and to the preparation of said molecularly imprinted polymer as well as the use of molecularly imprinted polymer for separating or extracting phosphorylated amino acids, peptides or proteins

Développement d’une méthode de dosage automatisé du méthotrexate dans le plasma humain par couplage direct à la chromatographie liquide de l’extraction sur phase solide à empreintes moléculaires

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Molecularly imprinted surfaces using surface-bound templates

The invention claimed is: 1. A method of producing a hierarchical molecularly-imprinted material, comprising: (a) synthesizing at least one peptide corresponding to an epitope of a target peptide or target protein by attaching a first amino acid to modified surfaces of the pores of a disposable surface-modified porous support, followed by attaching one or more amino acids to said first amino acid to produce said at least one peptide attached to said surfaces; (b) providing a selected monomer mixture; (c) contacting said monomer mixture with said support surface-attached peptide so that the monomer mixture enters the pores of the porous support; (d) initiating polymerisation or at least one crosslinking reaction of said monomer mixture to yield a polymer; and (e) dissolving or degrading said at least one support surface-attached peptide and said support; to provide a polymer material comprising a hierarchical molecular imprint of the epitope synthesized in step (a) and the porous support, wherein the epitope is a peptide that corresponds to only part of the target peptide or protein. 2. A method according to claim 1, wherein said target peptide is a dipeptide or oligopeptide. 3. A method according to claim 1, wherein step (d) is conducted with the aid of at least one factor consisting of crosslinking agents, heat, and ultraviolet irradiation. 4. A method according to claim 1, wherein said epitope of a target peptide is selected from the group consisting of FMOC-Phe-Gly-Si, H-Phe-Gly-Si, FMOC-Phe-Gly-OH, H-Phe-Gly-NH.sub.2, H-Phe-Gly-Gly-Phe-OH (SEQ ID NO:1), and H-Gly-Phe-OH. 5. A method according to claim 1, wherein said disposable surface modified support is modified silica or controlled pore glass (CPG). 6. A method according to claim 1, wherein said monomer mixture comprises monomers selected from the group consisting of styrene/divinyl benzene, methacrylates, acrylates, acrylamides, methacrylamides and combinations thereof. 7. A method of using a molecularly-imprinted material, comprising: producing a molecularly-imprinted material according to claim 1; and using said molecularly-imprinted material as an affinity phase for the separation of biological macromolecules or oligomers. 8. A method according to claim 7, wherein said biological macromolecules or oligomers are selected from the group consisting of peptides, polypeptides, oligopeptides, proteins, nucleic acids, oligonucleotides, polynucleotides, saccharides, oligosaccharides, and polysaccharides. 9. A chromatographic stationary phase, comprising a molecularly imprinted material produced according to claim 1, wherein said peptide, oligosaccharide or oligonucleotide of step (c) is selected from the group consisting of FMOC-Phe-Gly-Si, H-Phe-Gly-Si, FMOC-Phe-Gly-OH, H-Phe-Gly-NH.sub.2, H-Phe-Gly-Gly-Phe-OH (SEQ ID NO:1), and H-Gly-Phe-OH

Deep Learning for Distinguishing Normal versus Abnormal Chest Radiographs and Generalization to Unseen Diseases

Chest radiography (CXR) is the most widely-used thoracic clinical imaging modality and is crucial for guiding the management of cardiothoracic conditions. The detection of specific CXR findings has been the main focus of several artificial intelligence (AI) systems. However, the wide range of possible CXR abnormalities makes it impractical to build specific systems to detect every possible condition. In this work, we developed and evaluated an AI system to classify CXRs as normal or abnormal. For development, we used a de-identified dataset of 248,445 patients from a multi-city hospital network in India. To assess generalizability, we evaluated our system using 6 international datasets from India, China, and the United States. Of these datasets, 4 focused on diseases that the AI was not trained to detect: 2 datasets with tuberculosis and 2 datasets with coronavirus disease 2019. Our results suggest that the AI system generalizes to new patient populations and abnormalities. In a simulated workflow where the AI system prioritized abnormal cases, the turnaround time for abnormal cases reduced by 7-28%. These results represent an important step towards evaluating whether AI can be safely used to flag cases in a general setting where previously unseen abnormalities exist

ELIXR: Towards a general purpose X-ray artificial intelligence system through alignment of large language models and radiology vision encoders

Our approach, which we call Embeddings for Language/Image-aligned X-Rays, or ELIXR, leverages a language-aligned image encoder combined or grafted onto a fixed LLM, PaLM 2, to perform a broad range of tasks. We train this lightweight adapter architecture using images paired with corresponding free-text radiology reports from the MIMIC-CXR dataset. ELIXR achieved state-of-the-art performance on zero-shot chest X-ray (CXR) classification (mean AUC of 0.850 across 13 findings), data-efficient CXR classification (mean AUCs of 0.893 and 0.898 across five findings (atelectasis, cardiomegaly, consolidation, pleural effusion, and pulmonary edema) for 1% (~2,200 images) and 10% (~22,000 images) training data), and semantic search (0.76 normalized discounted cumulative gain (NDCG) across nineteen queries, including perfect retrieval on twelve of them). Compared to existing data-efficient methods including supervised contrastive learning (SupCon), ELIXR required two orders of magnitude less data to reach similar performance. ELIXR also showed promise on CXR vision-language tasks, demonstrating overall accuracies of 58.7% and 62.5% on visual question answering and report quality assurance tasks, respectively. These results suggest that ELIXR is a robust and versatile approach to CXR AI

An enantioselective imprinted receptor for Z-glutamate exhibiting a binding induced color change

New MIP for the specific recongnition of oxyanions and in particular of dihydrofolate reductase inhibitors such as methotrexate. The strong binding is detected by a colour change and this advocates applications in the field of sensors for the recognition of molecules of pharmaceutical interest. The recognition of oxyanions can also be exploited for the selective analysis of peptides and proteins such as biopharmaceuticals. Nuovo polimero per l’analisi qualitativa (riconoscimento specifico) di ossianioni, in particolare di inibitori della diidrofolatoreduttasi come il metotrexato. La presenza di legame forte, dovuto ad una interazione stechiometrica monomero-templato, è visibile ad occhio nudo (intenso colore giallo) fenomeno interessante applicazioni nel campo dei sensori per molecole di interesse farmaceutico. Si preconizza di estendere l’uso di tale polimero anche nel riconoscimento di molecole di interesse biologico (peptidi, piccole proteine) che contengono la funzione ossianionica