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968 results on '"A, Tardáguila"'

1. Small data deep learning methodology for in-field disease detection

Author

Herrera-Poyato, David, Domínguez-Rull, Jacinto, Montes, Rosana, Hernánde, Inés, Barrio, Ignacio, Poblete-Echeverria, Carlos, Tardaguila, Javier, Herrera, Francisco, and Herrera-Poyatos, Andrés

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Early detection of diseases in crops is essential to prevent harvest losses and improve the quality of the final product. In this context, the combination of machine learning and proximity sensors is emerging as a technique capable of achieving this detection efficiently and effectively. For example, this machine learning approach has been applied to potato crops -- to detect late blight (Phytophthora infestans) -- and grapevine crops -- to detect downy mildew. However, most of these AI models found in the specialised literature have been developed using leaf-by-leaf images taken in the lab, which does not represent field conditions and limits their applicability. In this study, we present the first machine learning model capable of detecting mild symptoms of late blight in potato crops through the analysis of high-resolution RGB images captured directly in the field, overcoming the limitations of other publications in the literature and presenting real-world applicability. Our proposal exploits the availability of high-resolution images via the concept of patching, and is based on deep convolutional neural networks with a focal loss function, which makes the model to focus on the complex patterns that arise in field conditions. Additionally, we present a data augmentation scheme that facilitates the training of these neural networks with few high-resolution images, which allows for development of models under the small data paradigm. Our model correctly detects all cases of late blight in the test dataset, demonstrating a high level of accuracy and effectiveness in identifying early symptoms. These promising results reinforce the potential use of machine learning for the early detection of diseases and pests in agriculture, enabling better treatment and reducing their impact on crops., Comment: 9 pages

Published
2024

3. Misexpression of inactive genes in whole blood is associated with nearby rare structural variants

Author

Vanderstichele, Thomas, Burnham, Katie L., de Klein, Niek, Tardaguila, Manuel, Howell, Brittany, Walter, Klaudia, Kundu, Kousik, Koeppel, Jonas, Lee, Wanseon, Tokolyi, Alex, Persyn, Elodie, Nath, Artika P., Marten, Jonathan, Petrovski, Slavé, Roberts, David J., Di Angelantonio, Emanuele, Danesh, John, Berton, Alix, Platt, Adam, Butterworth, Adam S., Soranzo, Nicole, Parts, Leopold, Inouye, Michael, Paul, Dirk S., and Davenport, Emma E.

Published
2024
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8. What to Expect (and What Not) from Dual-Energy CT Imaging Now and in the Future?

Author

Roberto García-Figueiras, Laura Oleaga, Jordi Broncano, Gonzalo Tardáguila, Gabriel Fernández-Pérez, Eliseo Vañó, Eloísa Santos-Armentia, Ramiro Méndez, Antonio Luna, and Sandra Baleato-González

Subjects
computed tomography, dual-energy CT, iodine map, material, Photography, TR1-1050, Computer applications to medicine. Medical informatics, R858-859.7, Electronic computers. Computer science, QA75.5-76.95
Abstract

Dual-energy CT (DECT) imaging has broadened the potential of CT imaging by offering multiple postprocessing datasets with a single acquisition at more than one energy level. DECT shows profound capabilities to improve diagnosis based on its superior material differentiation and its quantitative value. However, the potential of dual-energy imaging remains relatively untapped, possibly due to its intricate workflow and the intrinsic technical limitations of DECT. Knowing the clinical advantages of dual-energy imaging and recognizing its limitations and pitfalls is necessary for an appropriate clinical use. The aims of this paper are to review the physical and technical bases of DECT acquisition and analysis, to discuss the advantages and limitations of DECT in different clinical scenarios, to review the technical constraints in material labeling and quantification, and to evaluate the cutting-edge applications of DECT imaging, including artificial intelligence, qualitative and quantitative imaging biomarkers, and DECT-derived radiomics and radiogenomics.

Published
2024
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11. Review and Evaluation of European National Clinical Practice Guidelines for the Treatment and Management of Active Charcot Neuro-Osteoarthropathy in Diabetes Using the AGREE-II Tool Identifies an Absence of Evidence-Based Recommendations

Author

Nichola Renwick, Jennifer Pallin, Rasmus Bo Jansen, Catherine Gooday, Aroa Tardáguila-Garcia, Irene Sanz-Corbalán, Anastasios Tentolouris, Alexandra Jirkovská, Armin Koller, Anna Korzon-Burakowska, Nina Petrova, and Frances Game

Subjects
Diseases of the endocrine glands. Clinical endocrinology, RC648-665
Abstract

Conclusions: European national CPGs for diabetes provide limited recommendations on active CNO. All guidelines showcased deficits in their methodology, suggesting that more rigorous methods should be employed for diabetes CPG development across Europe.

Published
2024
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19. Diagnostic Accuracy of Dermatoscopy Versus Microbiological Culture and Polymerase Chain Reaction in the Diagnosis of Onychomycosis: A Cross‐Sectional Study.

Author

Navarro‐Pérez, David, Tardáguila‐García, Aroa, García‐Oreja, Sara, León‐Herce, Diego, Álvaro‐Afonso, Francisco Javier, and Lázaro‐Martínez, José Luis

Subjects
*MICROBIAL cultures, *POLYMERASE chain reaction, *DERMOSCOPY, *ONYCHOMYCOSIS, *SYMPTOMS
Abstract

Background: Several clinical signs in dermatoscopy are very characteristic of onychomycosis and can be a quick complement for the diagnosis of onychomycosis. Objectives: The aim of this study was to evaluate the diagnostic accuracy of dermatoscopy compared to microbiological culture and polymerase chain reaction (PCR), as well as the clinical signs associated with onychomycosis. Methods: The clinical signs of 125 patients were assessed cross‐sectionally using dermatoscopy, and a positive or negative result was assigned. A sample was then taken for PCR and microbiological culture. Results: Of the 125 patients, 69.6% (87/125) had positive results when both laboratory tests were combined. When they were not combined, the prevalence was lower at 48% (60/125) with PCR and at 43.2% (54/125) with culture. Furthermore, 76.8% (96/125) were classified as positive with dermatoscopy with a sensitivity of 1, a specificity of 0.76, positive predictive value of 0.91 and negative predictive value of 1 (with 95% confidence intervals). Of the 96 dermatoscopy‐positive samples, 36 were negative with PCR (p < 0.001), 42 were negative with culture (p < 0.001) and nine were negative when both tests were combined (p < 0.001). Clinical signs that were significantly associated with the presence of onychomycosis were subungual hyperkeratosis (dermatoscopy: p = 0.004, odds ratio (OR) = 2.438; PCR + microbiological culture: p = 0.004, OR = 3.221), subungual detritus (p = 0.033, OR = 3.01, only with dermatoscopy) and dermatophytoma (dermatoscopy: p = 0.049, OR = 3.02; PCR + microbiological culture: p = 0.022, OR = 2.40). Conclusions: The results suggest that dermatoscopy is a good tool for the diagnosis of onychomycosis but should be used as a complementary test or for screening patients to be sampled for laboratory testing. The combination of the three tests can lead to a reduction of false‐positive and false‐negative clinical and laboratory results. This allows for early diagnosis and specific treatment based on test results. [ABSTRACT FROM AUTHOR]

Published
2024
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23. Image analysis with deep learning for early detection of downy mildew in grapevine

Author

European Commission, Universidad de La Rioja, Gobierno de La Rioja, Gutiérrez, Salvador [0000-0002-8205-9772], Hernández, Inés, Gutiérrez, Salvador, Tardáguila, Javier, European Commission, Universidad de La Rioja, Gobierno de La Rioja, Gutiérrez, Salvador [0000-0002-8205-9772], Hernández, Inés, Gutiérrez, Salvador, and Tardáguila, Javier

Abstract

Downy mildew is a major disease of the grapevine that can severely reduce crop quality and yield. Its assessment in the laboratory is time-consuming, usually carried out by experts, and can require expensive and complex tools. For this reason, there is an opportunity to apply sensor technologies and artificial intelligence to plant disease detection. In this study, deep learning applied to RGB images was investigated to early detect downy mildew and the infection stage in grapevine leaf discs under laboratory conditions. Leaf discs of Tempranillo grapevine variety from 3 to 9 days post-inoculation located in Petri dishes were imaged using controlled conditions. Leaf disc images were extracted using computer vision techniques. Convolutional Neural Networks were used to classify the infected and healthy discs and to identify the disease infection. 10-fold cross-validation was used to evaluate the network results and Grad-CAM was used to interpret model prediction. An accuracy around 99% and a f1-score of 0.99 was achieved in downy mildew detection after DPI 3. An accuracy of 81% and a f1-score of 0.77 was obtained in infection stage identification. The developed method offered objective, rapid and accurate results, giving the possibility of early detecting downy mildew in grapevine leaf discs using low-cost techniques.

Published
2024

26. What to Expect (and What Not) from Dual-Energy CT Imaging Now and in the Future?

Author

García-Figueiras, Roberto, Oleaga, Laura, Broncano, Jordi, Tardáguila, Gonzalo, Fernández-Pérez, Gabriel, Vañó, Eliseo, Santos-Armentia, Eloísa, Méndez, Ramiro, Luna, Antonio, and Baleato-González, Sandra

Subjects
COMPUTER-assisted image analysis (Medicine), COMPUTED tomography, ENERGY levels (Quantum mechanics), DUAL energy CT (Tomography), RADIOMICS
Abstract

Dual-energy CT (DECT) imaging has broadened the potential of CT imaging by offering multiple postprocessing datasets with a single acquisition at more than one energy level. DECT shows profound capabilities to improve diagnosis based on its superior material differentiation and its quantitative value. However, the potential of dual-energy imaging remains relatively untapped, possibly due to its intricate workflow and the intrinsic technical limitations of DECT. Knowing the clinical advantages of dual-energy imaging and recognizing its limitations and pitfalls is necessary for an appropriate clinical use. The aims of this paper are to review the physical and technical bases of DECT acquisition and analysis, to discuss the advantages and limitations of DECT in different clinical scenarios, to review the technical constraints in material labeling and quantification, and to evaluate the cutting-edge applications of DECT imaging, including artificial intelligence, qualitative and quantitative imaging biomarkers, and DECT-derived radiomics and radiogenomics. [ABSTRACT FROM AUTHOR]

Published
2024
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27. Review and Evaluation of European National Clinical Practice Guidelines for the Treatment and Management of Active Charcot Neuro‐Osteoarthropathy in Diabetes Using the AGREE‐II Tool Identifies an Absence of Evidence‐Based Recommendations

Author

Renwick, Nichola, Pallin, Jennifer, Bo Jansen, Rasmus, Gooday, Catherine, Tardáguila-Garcia, Aroa, Sanz-Corbalán, Irene, Tentolouris, Anastasios, Jirkovská, Alexandra, Koller, Armin, Korzon-Burakowska, Anna, Petrova, Nina, Game, Frances, and Catrina, Sergiu

Subjects
DIABETIC foot, DIABETES complications, DELAYED diagnosis, WORD frequency, FOOT care, FOOT diseases
Abstract

Background: Charcot neuro‐osteoarthropathy (CNO) is a rare but devastating complication of diabetes associated with high rates of morbidity; yet, many nonfoot specialists are unaware of it, resulting in missed and delayed diagnosis. Clinical practice guidelines (CPGs) have proven useful in improving quality of care and standardizing practice in diabetes and diabetic foot care. However, little is known about the consistency in recommendations for identification and management of active CNO. Aim: The aim of this study is to review European national diabetes CPGs for the diagnosis and management of active CNO and to assess their methodological rigor and transparency. Methods: A systematic search was performed to identify diabetes national CPGs across Europe. Guidelines in any language were reviewed to explore whether they provided a definition for active CNO and recommendations for diagnosis, monitoring, and management. Methodological rigor and transparency were assessed using the Appraisal of Guidelines for Research and Evaluation (AGREE‐II) tool, which comprises 23 key items organized within six domains with an overall guideline assessment score of ≥ 60% considered to be of adequate quality to recommend use. Each guideline was assessed by two reviewers, and inter‐rater agreement (Kendall's W) was calculated for AGREE‐II scores. Results: Seventeen CPGs met the inclusion criteria. Breadth of CNO content varied across guidelines (median (IQR) word count: 327; Q1 = 151; Q3 = 790), and 53% provided a definition for active CNO. Recommendations for diagnosis and monitoring were provided by 82% and 53%, respectively, with offloading being the most common management recommendation (88%). Four guidelines (24%) reached threshold for recommendation for use in clinical practice (≥ 60%) with the scope and purpose domain scoring highest (mean (SD): 67%, ± 23%). The remaining domains had average scores ranging between 19% and 53%. Inter‐rater agreement was strong (W = 0.882; p < 0.001). Conclusions: European national CPGs for diabetes provide limited recommendations on active CNO. All guidelines showcased deficits in their methodology, suggesting that more rigorous methods should be employed for diabetes CPG development across Europe. [ABSTRACT FROM AUTHOR]

Published
2024
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40. tappAS: a comprehensive computational framework for the analysis of the functional impact of differential splicing

Author

Lorena de la Fuente, Ángeles Arzalluz-Luque, Manuel Tardáguila, Héctor del Risco, Cristina Martí, Sonia Tarazona, Pedro Salguero, Raymond Scott, Alberto Lerma, Ana Alastrue-Agudo, Pablo Bonilla, Jeremy R. B. Newman, Shunichi Kosugi, Lauren M. McIntyre, Victoria Moreno-Manzano, and Ana Conesa

Subjects
Biology (General), QH301-705.5, Genetics, QH426-470
Abstract

Abstract Recent advances in long-read sequencing solve inaccuracies in alternative transcript identification of full-length transcripts in short-read RNA-Seq data, which encourages the development of methods for isoform-centered functional analysis. Here, we present tappAS, the first framework to enable a comprehensive Functional Iso-Transcriptomics (FIT) analysis, which is effective at revealing the functional impact of context-specific post-transcriptional regulation. tappAS uses isoform-resolved annotation of coding and non-coding functional domains, motifs, and sites, in combination with novel analysis methods to interrogate different aspects of the functional readout of transcript variants and isoform regulation. tappAS software and documentation are available at https://app.tappas.org .

Published
2020
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41. Inteligencia artificial y visión por ordenador para evaluar los componentes del rendimiento de la vid en viñedos comerciales

Author

Íñiguez Rubén, Poblete-Echeverría Carlos, Hernández Inés, Gutiérrez Salvador, Barrio Ignacio, and Tardáguila Javier

Subjects
Microbiology, QR1-502, Physiology, QP1-981, Zoology, QL1-991
Abstract

Resumen La estimación del rendimiento es muy importante para la industria vitivinícola, ya que proporciona información útil para la gestión de viñedos y bodegas. Los efectos del cambio climático, como el aumento de las temperaturas y la menor disponibilidad de agua, pueden afectar a los componentes del rendimiento del viñedo. En general, la previsión tradicional del rendimiento se basaba en el recuento manual y destructivo de los racimos y el peso de las bayas. Los métodos convencionales no proporcionan una estimación precisa y son lentos, caros y laboriosos. En este estudio, se utilizaron métodos novedosos que emplean tecnologías digitales como el uso de la detección próxima, la visión por ordenador y la inteligencia artificial para la estimación del rendimiento en viñedos comerciales. La visión por ordenador se utilizó para la detección automática de diferentes características del dosel y para la calibración de ecuaciones de regresión para la predicción del rendimiento por cepa. La inteligencia artificial se utilizó para el recuento automático de racimos. Los resultados mostraron que el algoritmo de aprendizaje profundo fue capaz de detectar racimos con una alta precisión. En conclusión, nuestros resultados demostraron la aplicabilidad de estos nuevos métodos para evaluar los componentes del rendimiento en viñedos comerciales.

Published
2023
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42. Bacterial Diversity and Antibiotic Resistance in Patients with Diabetic Foot Osteomyelitis

Author

Francisco Javier Álvaro-Afonso, Yolanda García-Álvarez, Aroa Tardáguila-García, Marta García-Madrid, Mateo López-Moral, and José Luis Lázaro-Martínez

Subjects
diabetic foot, diabetic foot ulcers, diabetic foot infection, diabetic foot osteomyelitis, microbiology, antibiotic resistance, Therapeutics. Pharmacology, RM1-950
Abstract

This study analysed the bacterial diversity, antibiotic susceptibility, and resistance in patients with complications of diabetic foot osteomyelitis (DFO). A retrospective observational study was carried out between September 2019 and September 2022 and involved 215 outpatients with a diagnosis of DFO at a specialized diabetic foot unit. A total of 204 positive bone cultures were isolated, including 62.7% monomicrobial cultures, and 37.3% were formed with at least two microorganisms. We observed that Proteus spp., Coagulase-negative staphylococci (CoNS), Staphylococcus aureus, Pseudomonas aeruginosa, Escherichia coli, and Corynebacterium were the most frequently isolated microorganisms and accounted for more than 10% of the DFO cases. With stratification by Gram-positive (GP) and Gram-negative (GN) bacteria, we observed that 91.6% of cultures presented at least one GP bacteria species, and 50.4% presented at least one GN bacteria species. The most common GP species were CoNS (29%), S. aureus (25.8%), and Corynebacterium spp. (14%). The most frequent GN species consisted of Proteus spp. (32%), P. aeruginosa (23.3%), and E. coli (17.5%). The main antibiotics with resistance to GP-dominated infections were penicillins without β-lactamase inhibitor, and those in GN-dominated infections were sulfonamides and penicillins without β-lactamase. Significant differences were not observed in mean healing time in DFU with acute osteomyelitis (12.76 weeks (4.50;18)) compared to chronic osteomyelitis (15.31 weeks (7;18.25); p = 0.101) and when comparing cases with soft tissue infection (15.95 (6;20)) and those without such an infection (16.59 (7.25;19.75), p = 0.618). This study shows that when treatment of DFO is based on early surgical treatment, the type of DFO and the presence of soft infection are not associated with different or worse prognoses.

Published
2023
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46. Post-anaesthesia pulmonary complications after use of muscle relaxants (POPULAR): a multicentre, prospective observational study

Author

Abad Gurumeta, Alfredo, Abernethy, Caroline, Abigail, Patrick, Achaibar, Kira, Adam, Emily, Afshari, Arash, Agudelo Montoya, M. Elizabeth, Akgün, Fatma Nur, Aletti, Gabriele, Alkış, Neslihan, Allan, Katie, Allan, Ashley, Allaouchiche, Bernard, Allcock, Clare, Almasy, Emoke, Amey, Isobel, Amigoni, Maria, Andersen, Elin, Andersson, Peder, Anipchenko, Natalya, Antunes, Pedro, Armstrong, Earlene, Aslam, Tayyba Naz, Aslin, Bjorn, Assunção, José Pedro, Ausserer, Julia, Avvai, Mary, Awad, Nahla, Ayas Montero, Begoña, Ayuso, Mercedes, Azevedo, Patricia, Badarau, Victoria, Badescu, Roxana, Baiardo Redaelli, Martina, Baird, Colin, Baird, Yolanda, Baker, Tim, Balaji, Packianathaswamy, Bălan, Cristina, Balandin, Alina, Balescu-Arion, Carmen, Baliuliene, Vilda, Baltasar Isabel, Jorge, Baluch, Saif Nasr, Bandrabur, Daniela, Bankewitz, Carla, Barber, Katrina, Barbera, Francesco, Barcraft-Barnes, Helena, Barletti, Valentina, Barnett, Gill, Baron, Kirsty, Barros, Ana, Barsan, Victoria, Bartlett, Pauline, Batistaki, Chrysanthi, Baumgarten, Georg, Baytas, Volkan, Beauchamp, Nigel, Becerra Cayetano, Isabel A., Bell, Stephanie, Bellandi, Mattia, Belletti, Alessandro, Belmonte Cuenca, Julio, Benitez-Cano, Adela, Beretta, Luigi, Berger, Marc, Bergmann, Nicole, Bergmark, Kristina, Bermudez Lopez, Maria, Bernotaite, Monika, Beurskens, Charlotte, Bidd, Heena, Bifulco, Francesca, Bignami, Elena, Bilic, Aleksandar, Bilskiene, Diana, Bischoff, Petra, Bishop, Luke, Bjonness, Therese, Blaylock, Hether, Blethyn, Kate, Blincoe, Thomas, Blokhin, Ivan, Blunt, Nadia, Boer, Christa, Bois, Grégory, Bonicolini, Eleonora, Booth, Joanna, Borecka-Kedzierska, Miroslawa, Borstnar, Katarina, Borys, Michał, Boselli, Emmanuel, Bouvet, Lionel, Bouwman, Arthur, Bowen, Leonora, Bowrey, Sarah, Boxall, Leigh, Božić, Teodora, Bradley, Tom, Branco, Teresa, Brazzi, Luca, Brazzoni, Marcella, Brear, Tracy, Brogly, Nicolas, Brohi, Farooq, Broms, Jacob, Bubliauskas, Andrius, Bucolo, Gea Erika, Buerkle, Hartmut, Buggy, Donal, Buhre, Wolfgang, Bukauskas, Tomas, Butturini, Francesco, Byttner, Anders, Cabrera Díaz, Itahísa, Calderon, Adriana, Calhau, Ricardo, Callejo, Angel, Cammu, Guy, Campesato, Manuela, Can, Özlem S, Candeias, Margarida, Cantor, Andreea, Carise, Elsa, Carmona, Cristina, Carreteiro, Joana, Carrieri, Cosima, Carter, Anna, Casal, Manuela, Casanova, Irene, Cascella, Marco, Casero, Luis M., Casiraghi, Guiseppina Maria, Castelo-Branco, Laila, Castro Arranz, Carlos, Cernea, Daniela Denisa, Cervantes, Jesoporiol, Chandler, Ben, Charnock, Robert, Chatzimicali, Aikaterini, Chinery, Elane, Chishti, Ahmed, Chondhury, Priyakam, Christie, Emily, Christodoudiles, George, Ciardo, Stefano, Cimpeanu, Luminata, Cindea, Iulia, Cinnella, Gilda, Clark, Sebastian, Clayton, Matthew, Cocu, Simona, Collyer, Thomas, Colvin, Carie, Cope, Sean, Copeta, Filomena, Copotoiu, Sanda-Maria, Correia de Barros, Filinto, Corso, Ruggero Massimo, Cortegiani, Andrea, Costa, Gabriela, Cowton, Amanda, Cox, Nicolas, Craig, James, Cricca, Valentina, Cronin, John, Cunha, Mariana, Cuomo, Arturo, Curley, Katherine, Czuczwar, Mirosław, Dabrowska, Domenika, Damster, Sabrine, Danguy des Déserts, Marc, Daniliuc, Aura, Danninger, Thomas, Darwish, Imad, Dascalu, Corina, Davies, Kirsty, Davies, Simon, De Boer, Hans, De Flaviis, Adelisa, De Selincourt, Gabrielle, Deana, Cristian, Debaene, Bertrand, Debreceni, Gabor, Dedhia, Jatin, Delgado Garcia, Isabel, Della Rocca, Giorgio, Delroy-Buelles, Llana, Desai, Tejal, Dhillon, Parveen, Di Giacinto, Ida, Di Mauro, Piero, Diaz Gomez, Tamara V., Dimitrovski, Aleksandar, Dinic, Vesna, Dîrzu, Dan-Sebastian, Divander, Mona Britt, Dolinar, Janez, Domingues, Susana, Doolan, James, Downes, Charlotte, Dragoescu, Nicoleta Alice, Droc, Gabriela, Dum, Elisabeth, Dumitrescu, Alexandra, Duncan, Louise, Dzurňáková, Paul, Eberl, Susanne, Edwards, Jayne, Edwards, Mark, Ekelund, Kim, Ekengren, Patrik, Elghouty, Eyad, Ellerkmann, Richard, Ellis, Helen, Elme, Andreas, Ernst, Thomas, Errando, Carlos Luis, Estenes, Simao, Ewaldsson, Callis, Farid, Nahla, Featherstone, James, Febres, Daniela, Fedorov, Sergey, Feggeler, Johanna, Feijten, Prisca, Fellmann, Tobias, Fernandez Candil, Juan, Fernandez Castineira, Ana, Fernández Castineira, Juan, Fernando, Aruna, Ferrando, Carlos, Ferreira, Leonia, Ferreira, Patrick, Feyling, Anders, Filipescu, Daniela, Fleischer, Andreas, Floris, Leda, Foerster, Urs, Fox, Benjamin, Franke, Uwe, Frasca, Denis, Frey, Christian, Frost, Victoria, Fullin, Giorgio, Fumagalli, Jacopo, Furneval, Julie, Fusari, Maurizio, Gallacher, Stuart, Galushka, Svetlana, Gambale, Giorgio, Gambino, Irene, Garcia-Perez, Maria Luisa, Garg, Sanjeev, Garlak, Justyna, Gavranovic, Zeljka, Gavrilov, Roman, Gaynor, Lames, Gecaj Gashi, Agreta, Georghiou, Maria, Gerjevic, Bozena, Gferer, Gudrun, Giarratano, Antonino, Gibson, Andy, Gievski, Vanja, Giles, Julian, Gillberg, Lars, Gilowska, Katarzyna, Gilsanz Rodriguez, Fernando, Gioia, Antonio, Giovannoni, Cecilia, Girotra, Vandana, Gkinas, Dimitrios, Gkiokas, George, Godoroja, Daniela, Goebel, Ulrich, Goel, Vandana, Gonzalez, Matilde, Goranovic, Tatjana, Gornik-Wlaszczuk, Ewa, Gosavi, Smita, Gottfridsson, peter, Gottschalk, André, Granell, Manuel, Granstrom, Anna, Grassetto, Alberto, Greenwood, Anna, Grigoras, Ioana, Grintescu, Ioana, Gritsan, Alexey, Gritsan, Galina, Grynyuk, Andriy, Guadagnin, Giovanni Maria, Guarnieri, Marcello, Güçlü, Çiğdem, Guerrero Diez, Maria, Gunenc, Ferim, Günther, Ulf, Gupta, Pawan, Guttenthaler, Vera, Hack, Yvonne, Hafisayena, Ade, Hagau, Natalia, Haldar, Jagannath, Hales, Dawn, Hancı, Volkan, Hanna-Jumma, Sameer, Harazim, Hana, Harlet, Pierre, Harper, Daniel, Harris, Benjamin, Harvey, Orla, Hashimi, Medita, Hawkins, Lesley, Hayes, Conrad, Heaton, James, Heier, Tom, Helliwell, Laurence, Hemmes, Sabrine, Henderson, Kate, Hermanides, Jeroen, Hermanns, Henning, Herrera Hueso, Berta, Hestenes, Siv, Hettiarachchi, Roshane, Highgate, Judith, Hodgson, Keith, Hoelbling, Daniel, Holland, Jonathan, Horhota, Lucian, Hormis, Anil, Hribar, Renata, Hua, Alina, Humphreys, Sally, Humphries, Ryan, Humpliková, Simona, Hunt, Janez, Husnain, Ali, Hussein, Ahmed, Hyams, Benjamin, Iannuccelli, Fabrizio, Ilette, Katie, Ilyas, Carl, Inan, Turgay, India, Immaculada, Ionițăv, Victor, Irwin, Foo, Jain, Vipul, Janez, Benedikt, Jankovic, Radmilo, Jenkins, Sarah, Jenko, Matej, Jimenez, Raquel, Jiménez Gomez, Bárbara, Joachim, Sugganthi, Joelsson-Alm, Eva, John, John, Jonikaite, Lina, Jovic, Miomir, Jungwirth, Bettina, Junke, Etienne, Kabakov, Borys, Kadaoui, Salah-Din, Kanski, Andrzej, Karadag, Süheyla, Karbonskiene, Aurika, Karjagin, Juri, Kasnik, Darja, Katanolli, Fatos, Katsika, Eleni, Kaufmann, Kai, Keane, Helen, Kelly, Martin, Kent, Melanie, Keraitiene, Grazina, Khudhur, Ahmed, Khuenl-Brady, Karin, Kidd, Laurie, King, Siobhan, Kirchgäßner, Katharina, Klancir, Tino, Klucniks, Andris, Knotzer, Johann, Knowlden, Peter, Koers, Lena, Kompan, Janez, Koneti, Kiran K, Kooij, Fabian, Koolen, Eric, Koopman - van Gemert, Anna Wilhelmina Margaretha Maria, Kopp, Kristen, Korfiotis, Dimitrios, Korolkov, Oleg, Kosinová, Martina, Köstenberger, Markus, Kotzinger, Oskar, Kovačević, Marko, Kranke, Peter, Kranke, Eva, Kraus, Christiane, Kraus, Stephanie, Kubitzek, Christiane, Kucharski, Rafal, Kucukguclu, Semih, Kudrashou, Allaksandr, Kumar, Vinayak, Kummen, Live, Kunit, Cornelia, Kushakovsky, Vlad, Kuvaki, Bahar, Kuzmanovska, Biljana, Kyttari, Aikaterina, Landoni, Giovanni, Lau, Gary, Lazarev, Konstantin, Legett, Samantha, Legrottaglie, Anna Maria, Leonardi, Silvia, Leong, Maria, Lercher, Helene, Leuvrey, Matthieu, Leva, Brigitte, Levstek, Meta, Limb, James, Lindholm, Espen, Linton, Fiona, Liperi, Corradero, Lipski, Fabian, Lirk, Philipp, Lisi, Alberto, Lišková, Katarina, Lluch Oltra, Aitana, Loganathan, Vinothan, Lombardi, Stefania, Lopez, Eloisa, Lopez Rodríguez, Maria, Lorenzini, Laura, Lowicka, Malgorzata, Lugovoy, Alexander, Luippold, Madeleine, Lumb, Andrew, Macas, Andrius, Macgregor, Mark, Machado, Humberto, Maciariello, Maria, Madeira, Isabel, Maitan, Stefan, Majewski, Jacek, Maldini, Branka, 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Agostina, Wade, Andrew, Wallberg, Hanna, Wallden, Jakob, Wallner, Johann, Walther Sturesson, Louise, Watson, Davina, Weber, Stefan, Wegiel Leskiewiq, Anna, Weller, Debbie, Wensing, Carine, Werkmann, Markus, Westberg, Henrik, Wikström, Erik, Williams, Benedict, Wilson, Robin, Wirth, Steffen, Wittmann, Maria, Wood, Laura, Wright, Stella, Zachoval, Christian, Zambon, Massimo, Zampieri, Silvia, Zampone, Salvatore, Zangrillo, Alberto, Zani, Gianluca, Zavackiene, Asta, Zieglerder, Raphael, Zonneveldt, Harry, Zsisku, Lajos, Zucker, Tom-Philipp, Żukowski, Maciej, Zuleika, Mehrun, Zupanĕiĕ, Darja, Kirmeier, Eva, Eriksson, Lars I, Lewald, Heidrun, Jonsson Fagerlund, Malin, Hoeft, Andreas, Hollmann, Markus, Meistelman, Claude, Hunter, Jennifer M, Ulm, Kurt, and Blobner, Manfred

Published
2019
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50. Optimal management of diabetic foot osteomyelitis: challenges and solutions

Author

Lázaro Martínez JL, García Álvarez Y, Tardáguila-García A, and García Morales E

Subjects
diabetic foot, diabetic foot infection, bone infection, diabetic foot ulcers, Specialties of internal medicine, RC581-951
Abstract

José Luis Lázaro Martínez, Yolanda García Álvarez, Aroa Tardáguila-García, Esther García MoralesInstituto de Investigación Sanitaria del Hospital Clínico San Carlos, Universidad Complutense de Madrid, Madrid 28040, SpainPurpose: Diabetic foot osteomyelitis (DFO) is the most frequent infection associated with diabetic foot ulcers, occurs in >20% of moderate infections and 50%–60% of severe infections, and is associated with high rates of amputation. DFO represents a challenge in both diagnosis and therapy, and many consequences of its condition are related to late diagnosis, delayed referral, or ill-indicated treatment. This review aimed to analyze the current evidence on DFO management and to discuss advantages and disadvantages of different treatment options.Methods: A narrative review of the evidence was begun by searching Medline and PubMed databases for studies using the keywords “management”, “diabetic foot”, “osteomyelitis”, and “diabetic foot osteomyelitis” from 2008 to 2018.Results: We found a great variety of studies focusing on both medical and surgical therapies showing a similar rate of effectiveness and outcomes; however, the main factors in choosing one over the other seem to be associated with the presence of soft-tissue infection or ischemia and the clinical presentation of DFO.Conclusion: Further randomized controlled trials with large samples and long-term follow-up are necessary to demonstrate secondary outcomes, such as recurrence, recurrent ulceration, and reinfection associated with both medical and surgical options.Keywords: diabetic foot, diabetic foot infection, bone infection, diabetic foot ulcers

Published
2019

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