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1,060 results on '"Luchinat C."'

6. Transthyretin conjugated with a tafamidis derivative

Author

Cerofolini, L., primary, Vasa, K., additional, Bianconi, E., additional, Salobehaj, M., additional, Cappelli, G., additional, Licciardi, G., additional, Perez-Rafols, A., additional, Padilla Cortes, L.D., additional, Antonacci, S., additional, Rizzo, D., additional, Ravera, E., additional, Calderone, V., additional, Parigi, G., additional, Luchinat, C., additional, Macchiarulo, A., additional, Menichetti, S., additional, and Fragai, M., additional

Published
2023
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8. Combining Solid-State NMR with Structural and Biophysical Techniques to Design Challenging Protein-Drug Conjugates

Author

Cerofolini, L., Vasa, K., Bianconi, E., Salobehaj, M., Cappelli, G., Bonciani, A., Licciardi, G., Perez-Rafols, A., Padilla-Cortes, L., Antonacci, S., Rizzo, D., Ravera, E., Viglianisi, C., Calderone, V., Parigi, G., Luchinat, C., Macchiarulo, A., Menichetti, S., and Fragai, M.

Subjects
Structural Biology, Drug Design, Protein-Drug Conjugates, Drug Delivery, NMR Spectroscopy
Published
2023

9. Early downregulation of hsa-miR-144-3p in serum from drug-naïve Parkinson's disease patients

Author

Zago E., Dal Molin A., Dimitri G. M., Xumerle L., Pirazzini C., Bacalini M. G., Maturo M. G., Azevedo T., Spasov S., Gomez-Garre P., Perinan M. T., Jesus S., Baldelli L., Sambati L., Calandra Buonaura G., Garagnani P., Provini F., Cortelli P., Mir P., Trenkwalder C., Mollenhauer B., Franceschi C., Lio P., Nardini C., Adarmes-Gomez A., Bartoletti-Stella A., Bhatia K. P., Marta B. -T., Boninsegna C., Broli M., Dolores B. -R., Calandra-Buonaura G., Capellari S., Carrion-Claro M., Cilea R., Clayton R., Molin A. D., De Luca S., De Massis P., Doykov I., Escuela-Martin R., Fabbri G., Gabellini A., Giuliani C., Guaraldi P., Hagg S., Hallqvist J., Halsband C., Heywood W., Houlden H., Huertas I., Jylhava J., Labrador-Espinosa M. A., Licari C., Luchinat C., Macias D., Macri S., Magrinelli F., Rodriguez J. F. M., Massimo D., Mengozzi G., Meoni G., Mignani F., Milazzo M., Mills K., Nassetti S. A., Pedersen N. L., Perinan-Tocino M. T., Ravaioli F., Sala C., Scaglione C. L. M., Schade S., Schreglmann S., Strom S., Tejera-Parrado C., Tenori L., Turano P., Valzania F., Ortega R. V., Williams D., Apollo - University of Cambridge Repository, Zago E., Dal Molin A., Dimitri G.M., Xumerle L., Pirazzini C., Bacalini M.G., Maturo M.G., Azevedo T., Spasov S., Gomez-Garre P., Perinan M.T., Jesus S., Baldelli L., Sambati L., Calandra Buonaura G., Garagnani P., Provini F., Cortelli P., Mir P., Trenkwalder C., Mollenhauer B., Franceschi C., Lio P., Nardini C., Adarmes-Gomez A., Bartoletti-Stella A., Bhatia K.P., Marta B.-T., Boninsegna C., Broli M., Dolores B.-R., Calandra-Buonaura G., Capellari S., Carrion-Claro M., Cilea R., Clayton R., Molin A.D., De Luca S., De Massis P., Doykov I., Escuela-Martin R., Fabbri G., Gabellini A., Giuliani C., Guaraldi P., Hagg S., Hallqvist J., Halsband C., Heywood W., Houlden H., Huertas I., Jylhava J., Labrador-Espinosa M.A., Licari C., Luchinat C., Macias D., Macri S., Magrinelli F., Rodriguez J.F.M., Massimo D., Mengozzi G., Meoni G., Mignani F., Milazzo M., Mills K., Nassetti S.A., Pedersen N.L., Perinan-Tocino M.T., Ravaioli F., Sala C., Scaglione C.L.M., Schade S., Schreglmann S., Strom S., Tejera-Parrado C., Tenori L., Turano P., Valzania F., Ortega R.V., and Williams D.

Subjects
Male, Aging, Molecular biology, Science, Immunology, Article, Cohort Studies, 03 medical and health sciences, 0302 clinical medicine, Medical research, Humans, Parkinson, 030304 developmental biology, Aged, 0303 health sciences, Multidisciplinary, Biological techniques, Parkinson Disease, Middle Aged, 3. Good health, nervous system diseases, Computational biology and bioinformatics, MicroRNAs, Neurology, ageing, Medicine, Female, 030217 neurology & neurosurgery, Biomarkers
Abstract

Advanced age represents one of the major risk factors for Parkinson's Disease. Recent biomedical studies posit a role for microRNAs, also known to be remodelled during ageing. However, the relationship between microRNA remodelling and ageing in Parkinson's Disease, has not been fully elucidated. Therefore, the aim of the present study is to unravel the relevance of microRNAs as biomarkers of Parkinson's Disease within the ageing framework. We employed Next Generation Sequencing to profile serum microRNAs from samples informative for Parkinson's Disease (recently diagnosed, drug-naïve) and healthy ageing (centenarians) plus healthy controls, age-matched with Parkinson's Disease patients. Potential microRNA candidates markers, emerging from the combination of differential expression and network analyses, were further validated in an independent cohort including both drug-naïve and advanced Parkinson's Disease patients, and healthy siblings of Parkinson's Disease patients at higher genetic risk for developing the disease. While we did not find evidences of microRNAs co-regulated in Parkinson's Disease and ageing, we report that hsa-miR-144-3p is consistently down-regulated in early Parkinson's Disease patients. Moreover, interestingly, functional analysis revealed that hsa-miR-144-3p is involved in the regulation of coagulation, a process known to be altered in Parkinson's Disease. Our results consistently show the down-regulation of hsa-mir144-3p in early Parkinson's Disease, robustly confirmed across a variety of analytical and experimental analyses. These promising results ask for further research to unveil the functional details of the involvement of hsa-mir144-3p in Parkinson's Disease., This work was supported by the Horizon 2020 Framework Programme (Grant number 634821, PROPAG-AGING).

Published
2022

10. NMR Spectroscopy Combined with Machine Learning Approaches for Age Prediction in Healthy and Parkinson’s Disease Cohorts through Metabolomic Fingerprints

Author

Dimitri, GM, Meoni, G, Tenori, L, Luchinat, C, Lió, P, Dimitri, GM [0000-0002-2728-4272], Meoni, G [0000-0002-8608-4641], Tenori, L [0000-0001-6438-059X], Luchinat, C [0000-0003-2271-8921], and Apollo - University of Cambridge Repository

Subjects
Fluid Flow and Transfer Processes, metabolomics aging, Process Chemistry and Technology, Parkinson's disease, machine learning, spectrum, metabolites, lipids, Parkinson’s disease, biological age, General Engineering, Computer Science Applications, General Materials Science, Instrumentation
Abstract

Peer reviewed: True, Biological aging can be affected by several factors such as drug treatments and pathological conditions. Metabolomics can help in the estimation of biological age by analyzing the differences between predicted and actual chronological age in different subjects. In this paper, we compared three different and well-known machine learning approaches—SVM, ElasticNet, and PLS—to build a model based on the 1H-NMR metabolomic data of serum samples, able to predict chronological age in control individuals. Then, we tested these models in two pathological cohorts of de novo and advanced PD patients. The discrepancies observed between predicted and actual age in patients are interpreted as a sign of a (pathological) biological aging process.

Published
2022

11. A framework for validating AI in precision medicine: considerations from the European ITFoC consortium

Author

Tsopra, R, Fernandez, X, Luchinat, C, Alberghina, L, Lehrach, H, Vanoni, M, Dreher, F, Sezerman, O, Cuggia, M, de Tayrac, M, Miklasevics, E, Itu, L, Geanta, M, Ogilvie, L, Godey, F, Boldisor, C, Campillo-Gimenez, B, Cioroboiu, C, Ciusdel, C, Coman, S, Hijano Cubelos, O, Itu, A, Lange, B, Le Gallo, M, Lespagnol, A, Mauri, G, Soykam, H, Rance, B, Turano, P, Tenori, L, Vignoli, A, Wierling, C, Benhabiles, N, Burgun, A, Tsopra R., Fernandez X., Luchinat C., Alberghina L., Lehrach H., Vanoni M., Dreher F., Sezerman O. U., Cuggia M., de Tayrac M., Miklasevics E., Itu L. M., Geanta M., Ogilvie L., Godey F., Boldisor C. N., Campillo-Gimenez B., Cioroboiu C., Ciusdel C. F., Coman S., Hijano Cubelos O., Itu A., Lange B., Le Gallo M., Lespagnol A., Mauri G., Soykam H. O., Rance B., Turano P., Tenori L., Vignoli A., Wierling C., Benhabiles N., Burgun A., Tsopra, R, Fernandez, X, Luchinat, C, Alberghina, L, Lehrach, H, Vanoni, M, Dreher, F, Sezerman, O, Cuggia, M, de Tayrac, M, Miklasevics, E, Itu, L, Geanta, M, Ogilvie, L, Godey, F, Boldisor, C, Campillo-Gimenez, B, Cioroboiu, C, Ciusdel, C, Coman, S, Hijano Cubelos, O, Itu, A, Lange, B, Le Gallo, M, Lespagnol, A, Mauri, G, Soykam, H, Rance, B, Turano, P, Tenori, L, Vignoli, A, Wierling, C, Benhabiles, N, Burgun, A, Tsopra R., Fernandez X., Luchinat C., Alberghina L., Lehrach H., Vanoni M., Dreher F., Sezerman O. U., Cuggia M., de Tayrac M., Miklasevics E., Itu L. M., Geanta M., Ogilvie L., Godey F., Boldisor C. N., Campillo-Gimenez B., Cioroboiu C., Ciusdel C. F., Coman S., Hijano Cubelos O., Itu A., Lange B., Le Gallo M., Lespagnol A., Mauri G., Soykam H. O., Rance B., Turano P., Tenori L., Vignoli A., Wierling C., Benhabiles N., and Burgun A.

Abstract

Background: Artificial intelligence (AI) has the potential to transform our healthcare systems significantly. New AI technologies based on machine learning approaches should play a key role in clinical decision-making in the future. However, their implementation in health care settings remains limited, mostly due to a lack of robust validation procedures. There is a need to develop reliable assessment frameworks for the clinical validation of AI. We present here an approach for assessing AI for predicting treatment response in triple-negative breast cancer (TNBC), using real-world data and molecular -omics data from clinical data warehouses and biobanks. Methods: The European “ITFoC (Information Technology for the Future Of Cancer)” consortium designed a framework for the clinical validation of AI technologies for predicting treatment response in oncology. Results: This framework is based on seven key steps specifying: (1) the intended use of AI, (2) the target population, (3) the timing of AI evaluation, (4) the datasets used for evaluation, (5) the procedures used for ensuring data safety (including data quality, privacy and security), (6) the metrics used for measuring performance, and (7) the procedures used to ensure that the AI is explainable. This framework forms the basis of a validation platform that we are building for the “ITFoC Challenge”. This community-wide competition will make it possible to assess and compare AI algorithms for predicting the response to TNBC treatments with external real-world datasets. Conclusions: The predictive performance and safety of AI technologies must be assessed in a robust, unbiased and transparent manner before their implementation in healthcare settings. We believe that the consideration of the ITFoC consortium will contribute to the safe transfer and implementation of AI in clinical settings, in the context of precision oncology and personalized care.

Published
2021

12. Risk assessment of Disease Recurrence (DR) through serum metabolomics analysis in elderly patients (pts) with early breast cancer (eBC)

Author

Lisanti, C., primary, Risi, E., additional, Vignoli, A., additional, Becheri, D., additional, Biagioni, C., additional, Paderi, A., additional, Cappadona, S., additional, Del Monte, F., additional, Moretti, E., additional, Sanna, G., additional, Livraghi, L., additional, Malorni, L., additional, Benelli, M., additional, Puglisi, F., additional, Mottino, G., additional, Luchinat, C., additional, Tenori, L., additional, and Biganzoli, L., additional

Published
2022
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13. 148P Serum metabolomics based risk assessment of disease recurrence in elderly patients with early breast cancer (eBC)

Author

Risi, E., primary, Vignoli, A., additional, Lisanti, C., additional, Biagioni, C., additional, Paderi, A., additional, Cappadona Sciammetta, S., additional, Del Monte, F., additional, Moretti, E., additional, Sanna, G., additional, Livraghi, L., additional, Malorni, L., additional, Benelli, M., additional, Puglisi, F., additional, Luchinat, C., additional, Tenori, L., additional, and Biganzoli, L., additional

Published
2022
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18. Early downregulation of hsa-miR-144-3p in serum from drug-naïve Parkinson’s disease patients

Author

Zago, E., Dal Molin, A., Dimitri, G. M., Xumerle, L., Pirazzini, C., Bacalini, M. G., Maturo, M. G., Azevedo, T., Spasov, S., Gomez-Garre, P., Perinan, M. T., Jesus, S., Baldelli, L., Sambati, L., Calandra-Buonaura, G., Garagnani, P., Provini, F., Cortelli, P., Mir, P., Trenkwalder, C., Mollenhauer, B., Franceschi, C., Lio, P., Nardini, C., Adarmes-Gomez, A., Bartoletti-Stella, A., Bhatia, K. P., Marta, B. -T., Boninsegna, C., Broli, M., Dolores, B. -R., Capellari, S., Carrion-Claro, M., Cilea, R., Clayton, R., Molin, A. D., De Luca, S., De Massis, P., Doykov, I., Escuela-Martin, R., Fabbri, G., Gabellini, A., Giuliani, C., Guaraldi, P., Hagg, S., Hallqvist, J., Halsband, C., Heywood, W., Houlden, H., Huertas, I., Jylhava, J., Labrador-Espinosa, M. A., Licari, C., Luchinat, C., Macias, D., Macri, S., Magrinelli, F., Rodriguez, J. F. M., Massimo, D., Mengozzi, G., Meoni, G., Mignani, F., Milazzo, M., Mills, K., Nassetti, S. A., Pedersen, N. L., Perinan-Tocino, M. T., Ravaioli, F., Sala, C., Scaglione, C. L. M., Schade, S., Schreglmann, S., Strom, S., Tejera-Parrado, C., Tenori, L., Turano, P., Valzania, F., Ortega, R. V., and Williams, D.

Subjects
hsa‑miR‑144‑3p, serum, Parkinson’s disease patients
Published
2022

22. Reliability of quantitative diffusion weighted imaging: a large multicenter and multiparametric study based on a unsupervised clustering analysis

Author

Mazzoni, L.N., primary, Fedeli, L., additional, Belli, G., additional, Ciccarone, A., additional, Coniglio, A., additional, Esposito, M., additional, Giannelli, M., additional, Sghedoni, R., additional, Tarducci, R., additional, Altabella, L., additional, Belligotti, E., additional, Benelli, M., additional, Bernardi, L., additional, Betti, M., additional, Caivano, R., additional, Carnì, M., additional, Chiappiniello, A., additional, Cimolai, S., additional, Cretti, F., additional, Fulcheri, C., additional, Gasperi, C., additional, Giacometti, M., additional, Levrero, F., additional, Lizio, D., additional, Maieron, M., additional, Marzi, S., additional, Mascaro, L., additional, Mazzocchi, S., additional, Meliadò, G., additional, Morzenti, S., additional, Niespolo, A., additional, Nocetti, L., additional, Noferini, L., additional, Oberhofer, N., additional, Orsingher, L., additional, Quattrocchi, M.G., additional, Ricci, A., additional, Savini, A., additional, Taddeucci, A., additional, Testa, C., additional, Tortoli, P., additional, Luchinat, C., additional, Tenori, L., additional, Gobbi, G., additional, Gori, C., additional, and Busoni, S., additional

Published
2021
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23. Multicenter comparison of MR scanners for quantitative diffusion weighted imaging: apparent diffusion coefficient dependence on acquisition plan and spatial position – preliminary results

Author

Fedeli, L., primary, Belli, G., additional, Ciccarone, A., additional, Coniglio, A., additional, Esposito, M., additional, Giannelli, M., additional, Sghedoni, R., additional, Tarducci, R., additional, Altabella, L., additional, Belligotti, E., additional, Benelli, M., additional, Bernardi, L., additional, Betti, M., additional, Caivano, R., additional, Carni, M., additional, Chiappiniello, A., additional, Cimolai, S., additional, Cretti, F., additional, Fulcheri, C., additional, Gasperi, C., additional, Giacometti, M., additional, Levrero, F., additional, Lizio, D., additional, Maieron, M., additional, Marzi, S., additional, Mascaro, L., additional, Mazzocchi, S., additional, Meliado, G., additional, Morzenti, S., additional, Niespolo, A., additional, Nocetti, L., additional, Noferini, L., additional, Oberhofer, N., additional, Orsingher, L., additional, Quattrocchi, M.G., additional, Ricci, A., additional, Savini, A., additional, Taddeucci, A., additional, Testa, C., additional, Tortoli, P., additional, Luchinat, C., additional, Tenori, L., additional, Gobbi, G., additional, Gori, C., additional, Busoni, S., additional, and Mazzoni, L.N., additional

Published
2021
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31. Stromal interaction molecule 1 coiled-coil 1 fragment

Author

Rathner, P., primary, Cerofolini, L., additional, Ravera, E., additional, Bechmann, M., additional, Grabmayr, H., additional, Fahrner, M., additional, Fragai, M., additional, Romanin, C., additional, Luchinat, C., additional, and Mueller, N., additional

Published
2020
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32. Mixing Abeta(1-40) and Abeta(1-42) peptides generates unique amyloid fibrils

Author

Cerofolini, L., primary, Ravera, E., additional, Bologna, S., additional, Wiglenda, T., additional, Boddrich, A., additional, Purfurst, B., additional, Benilova, A., additional, Korsak, M., additional, Gallo, G., additional, Rizzo, D., additional, Gonnelli, L., additional, Fragai, M., additional, De Strooper, B., additional, Wanker, E.E., additional, and Luchinat, C., additional

Published
2020
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35. The Cr(sup)IIL reduction of [2Fe-2S] ferredoxins and site of attachment of Cr(sup)III using (sup)1H NMR and site-directed mutagenesis

Author

Im, S.-C., Worrall, J.A.R., Liu, G., Aliverti, A., Zanetti, G., Luchinat, C., Bertini, I, and Sykes, A.G.

Subjects
Oxidation-reduction reaction -- Analysis, Chromium -- Analysis, Ligands -- Usage, Amino acids -- Structure-activity relationships, Nuclear magnetic resonance spectroscopy -- Usage, Chemistry
Abstract

Results show that six residues at site A 19-22, 27, and 30 of ferredoxin(sup)III-ferredoxin(sup)III ferredoxinI and II from parsley and spinach bind to the reduced Cr(sup)IIL as Cr(sup)IIIL. Data further indicate that this attachment at site A results in the production of a large reduction potential.

Published
2000

36. Protein-protein interactions

Author

Di Savino, A., Ubbink, M., Luchinat, C., Parigi, G., Ravera, E., Luchinat, C., Parigi, G., and Ravera, E.

Subjects
chemistry.chemical_compound, Paramagnetism, Electron transfer, Chemistry, Chemical physics, Docking (molecular), Dimer, Intermolecular force, Protein complex formation, Crystal structure, Protein–protein interaction
Abstract

Paramagnetic NMR methods are excellently suited for the study of protein–protein complexes in solution. Intermolecular pseudocontact shifts (PCSs), residual dipolar couplings (RDCs) and paramagnetic relaxations enhancements (PREs) can be used, ideally in combination, for docking proteins and determining their orientation in the complex. PCSs can be used for breaking the structure symmetry in dimer complexes. PCSs also can be applied to detect structural differences in proteins and protein complexes in solution in comparison to crystal structures. RDCs are sensitive to the degree of alignment of both partners in a protein complex and are thus very useful to detect dynamics within complexes. PREs can detect states in which nuclei approach a paramagnetic centre closely, even if it exists only for a small fraction of the time. Thus, PREs are used to detect minor states and characterize ensembles. PRE studies have been the foundation for characterizing encounter states and the process of protein complex formation. In weak complexes, such as are found in electron transfer chains, proteins can be in an encounter state for a large fraction of the complex lifetime. Paramagnetic NMR tools thus have found many applications for studying protein complexes, and more may be on the horizon.

Published
2018

38. Mixing Aβ(1-40) and Aβ(1-42) peptides generates unique amyloid fibrils

Author

Cerofolini, L., Ravera, E., Bologna, S., Wiglenda, T., Boddrich, A., Purfurst, B., Benilova, I., Korsak, M., Gallo, G., Rizzo, D., Gonnelli, L., Fragai, M., De Strooper, B., Wanker, E. E., and Luchinat, C.

Subjects
Solid-state NMR spectroscopy, Beta-amyloid, fibrils
Published
2020

46. Sarcolab pilot study into skeletal muscle's adaptation to long-term spaceflight

Author

Rittweger J, Albracht K, Flück M, Ruoss S, Brocca L, Longa E, Moriggi M, Seynnes O, Di Giulio I, Tenori L, Vignoli A, Capri M, Gelfi C, Luchinat C, Franceschi C, Bottinelli R, Cerretelli P, Narici M., and Rittweger J, Albracht K, Flück M, Ruoss S, Brocca L, Longa E, Moriggi M, Seynnes O, Di Giulio I, Tenori L, Vignoli A, Capri M, Gelfi C, Luchinat C, Franceschi C, Bottinelli R, Cerretelli P, Narici M.

Subjects
skeletal-muscle, Spaceflight
Abstract

Spaceflight causes muscle wasting. The Sarcolab pilot study investigated two astronauts with regards to plantar flexor muscle size, architecture, and function, and to the underlying molecular adaptations in order to further the understanding of muscular responses to spaceflight and exercise countermeasures. Two crew members (A and B) spent 6 months in space. Crew member A trained less vigorously than B. Postflight, A showed substantial decrements in plantar flexor volume, muscle architecture, in strength and in fiber contractility, which was strongly mitigated in B. The difference between these crew members closely reflected FAK-Y397 abundance, a molecular marker of muscle's loading history. Moreover, crew member A showed downregulation of contractile proteins and enzymes of anaerobic metabolism, as well as of systemic markers of energy and protein metabolism. However, both crew members exhibited decrements in muscular aerobic metabolism and phosphate high energy transfer. We conclude that countermeasures can be effective, particularly when resistive forces are of sufficient magnitude. However, to fully prevent space-related muscular deterioration, intersubject variability must be understood, and intensive exercise countermeasures programs seem mandatory. Finally, proteomic and metabolomic analyses suggest that exercise benefits in space may go beyond mere maintenance of muscle mass, but rather extend to the level of organismic metabolism.

Published
2018

47. Redox chemistry of superoxide dismutase. Cyclic voltammetry of wild-type enzymes and mutants on functionally relevant residues

Author

Azab, H.A., Banci, L., Borsari, M., Luchinat, C., Sola, M., and Viezzoli, M.S.

Subjects
Enzymes, Chemistry
Abstract

The reduction potentials of human and bovine copper(II)-zinc(II) superoxide dismutases, their mutants, and azide and cyanide derivatives were determined by cyclic voltammetry. The results showed E values of 0.36, 0.32 and 0.37 for human, bovine and human-AS enzymes, respectively, at pH 7.4. Negative reduction potentials were obtained for all the mutants investigated.

Published
1992

50. A metabolomic recurrence score for risk-stratification of elderly patients (pts) with early colorectal cancer (eCRC)

Author

Donato, S Di, primary, Vignoli, A., additional, Mori, E., additional, Tenori, L., additional, Malorni, L., additional, Cantafio, S., additional, Mottino, G., additional, Becheri, D., additional, Mccartney, A., additional, Biagioni, C., additional, De Monte, F.l., additional, Leo, A Di, additional, Luchinat, C., additional, and Biganzoli, L., additional

Published
2019
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