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5. Metformin alleviates lung-endothelial hyperpermeability by regulating cofilin-1/PP2AC pathway

Author

M. Rizwan Siddiqui, Narsa M. Reddy, Hafeez M. Faridi, Mohd Shahid, and Thomas P. Shanley

Subjects
metformin, PP2A, cofilin-1, vascular endothelial cells, acute lung injury, Therapeutics. Pharmacology, RM1-950
Abstract

Background: Microvascular endothelial hyperpermeability is an earliest pathological hallmark in Acute Lung Injury (ALI), which progressively leads to Acute Respiratory Distress Syndrome (ARDS). Recently, vascular protective and anti-inflammatory effect of metformin, irrespective of glycemic control, has garnered significant interest. However, the underlying molecular mechanism(s) of metformin’s barrier protective benefits in lung-endothelial cells (ECs) has not been clearly elucidated. Many vascular permeability-increasing agents weakened adherens junctions (AJ) integrity by inducing the reorganization of the actin cytoskeleton and stress fibers formation. Here, we hypothesized that metformin abrogated endothelial hyperpermeability and strengthen AJ integrity via inhibiting stress fibers formation through cofilin-1-PP2AC pathway.Methods: We pretreated human lung microvascular ECs (human-lung-ECs) with metformin and then challenged with thrombin. To investigate the vascular protective effects of metformin, we studied changes in ECs barrier function using electric cell-substrate impedance sensing, levels of actin stress fibers formation and inflammatory cytokines IL-1β and IL-6 expression. To explore the downstream mechanism, we studied the Ser3-phosphorylation-cofilin-1 levels in scramble and PP2AC-siRNA depleted ECs in response to thrombin with and without metformin pretreatment.Results: In-vitro analyses showed that metformin pretreatment attenuated thrombin-induced hyperpermeability, stress fibers formation, and the levels of inflammatory cytokines IL-6 and IL-β in human-lung-ECs. We found that metformin mitigated Ser3-phosphorylation mediated inhibition of cofilin-1 in response to thrombin. Furthermore, genetic deletion of PP2AC subunit significantly inhibited metformin efficacy to mitigate thrombin-induced Ser3-phosphorylation cofilin-1, AJ disruption and stress fibers formation. We further demonstrated that metformin increases PP2AC activity by upregulating PP2AC-Leu309 methylation in human-lung-ECs. We also found that the ectopic expression of PP2AC dampened thrombin-induced Ser3-phosphorylation-mediated inhibition of cofilin-1, stress fibers formation and endothelial hyperpermeability.Conclusion: Together, these data reveal the unprecedented endothelial cofilin-1/PP2AC signaling axis downstream of metformin in protecting against lung vascular endothelial injury and inflammation. Therefore, pharmacologically enhancing endothelial PP2AC activity may lead to the development of novel therapeutic approaches for prevention of deleterious effects of ALI on vascular ECs.

Published
2023
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10. Podocyte-specific deletion of miR-146a increases podocyte injury and diabetic kidney disease

Author

Xiaobo Li, Ishwarya Venkatesh, Veronica Villanueva, Huiting Wei, Terese Geraghty, Anugraha Rajagopalan, Richard W. Helmuth, Mehmet M. Altintas, Hafeez M. Faridi, and Vineet Gupta

Subjects
podocytes, MicroRNA, miR-146a, diabetic nephropathy, glomerular disease, Medicine (General), R5-920
Abstract

Diabetic glomerular injury is a major complication of diabetes mellitus and is the leading cause of end stage renal disease (ESRD). Healthy podocytes are essential for glomerular function and health. Injury or loss of these cells results in increased proteinuria and kidney dysfunction and is a common finding in various glomerulopathies. Thus, mechanistic understanding of pathways that protect podocytes from damage are essential for development of future therapeutics. MicroRNA-146a (miR-146a) is a negative regulator of inflammation and is highly expressed in myeloid cells and podocytes. We previously reported that miR-146a levels are significantly reduced in the glomeruli of patients with diabetic nephropathy (DN). Here we report generation of mice with selective deletion of miR-146a in podocytes and use of these mice in models of glomerular injury. Induction of glomerular injury in C57BL/6 wildtype mice (WT) and podocyte-specific miR-146a knockout (Pod-miR146a–/–) animals via administration of low-dose lipopolysaccharide (LPS) or nephrotoxic serum (NTS) resulted in increased proteinuria in the knockout mice, suggesting that podocyte-expressed miR-146a protects these cells, and thus glomeruli, from damage. Furthermore, induction of hyperglycemia using streptozotocin (STZ) also resulted in an accelerated development of glomerulopathy and a rapid increase in proteinuria in the knockout animals, as compared to the WT animals, further confirming the protective role of podocyte-expressed miR-146a. We also confirmed that the direct miR-146a target, ErbB4, was significantly upregulated in the diseased glomeruli and erlotinib, an ErbB4 and EGFR inhibitor, reducedits upregulation and the proteinuria in treated animals. Primary miR146–/– podocytes from these animals also showed a basally upregulated TGFβ-Smad3 signaling in vitro. Taken together, this study shows that podocyte-specific miR-146a is imperative for protecting podocytes from glomerular damage, via modulation of ErbB4/EGFR, TGFβ, and linked downstream signaling.

Published
2022
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18. CARBON-SEQUESTERING FERTILIZERS USAGE TO BOOST POTASSIUM EFFICIENCY IN WHEAT GROWTH UNDER SALINE CONDITIONS.

Author

HAFEEZ, M., TAHIR, M. A., NOORKA, I. R., SABAH, N., SARWAR, G., and GUL, S.

Subjects
*POTASSIUM fertilizers, *FERTILIZERS, *FERTILIZER application, *SOIL salinity, *POTASSIUM, *NUTRITIONAL status, *WHEAT, *WINTER wheat
Abstract

Potassium (K) is a crucial element required for the healthy growth of plants, as it activates many enzymatic reactions in the plant body. Nowadays, K-deficiency is widespread worldwide. The high cost of potassium fertilization and less awareness about the pros of external nutrition from K fertilizer application are possible reasons for K-deficient soils in Pakistan. Efforts are ongoing to improve the potassium use efficiency (KUE) and bioavailability of K from commercial potassium sources. Therefore, a field experiment commenced to minimize the bioavailable K losses using carbon sequestering fertilizer (CSF) under saline soil conditions (4 dS m-1) using wheat as a test crop in Pind Dadan Khan, Punjab, Pakistan. The experiment ran in a three-replication randomized complete block design (RCBD) statistical scheme using four treatments, i.e., T1 = Control; T2 = Filter cake press mud (FCP) as CSF; T3 = Sulphate of potash (SOP); and T4 = SOP + FCP, using two wheat varieties, namely, Faisalabad 2008 (FSD-08) and Chakwal-50. Results implied that SOP application in combination with FCP (T4) performed best treatments with Faisalabad 2008 as the better variety than Chakwal-50 under saline growth environment. Maximum spike length (8.85 cm), the number of grains/spike (45), shoot dry weight (2.75 g), soil K contents (310 ppm), soil C content (1.03%), chlorophyll content (2.18 µmol m-2), and H-ATPs (99.5) resulted in Faisalabad 2008 when applied with the combination of SOP and FCP (T4). Thus, the combined application of mineral and organic sources of CSF improved the wheat growth parameters and nutritional status of the soil. [ABSTRACT FROM AUTHOR]

Published
2024
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22. E-248 Rescue intracranial stenting for failed posterior circulation thrombectomy: analysis from saint study

Author

Mohammaden, M, primary, Tarek, M, additional, Haussen, D, additional, Fifi, J, additional, Matsoukas, S, additional, Farooqui, M, additional, Ortega-Gutierrez, S, additional, Zevallos, C, additional, Galecio-Castillo, M, additional, Hassan, A, additional, Tekle, W, additional, Al-Bayati, A, additional, Salem, M, additional, Burkhardt, J, additional, Pukenas, B, additional, Cortez, G, additional, Hanel, R, additional, Aghaebrahim, A, additional, Sauvageau, E, additional, Hafeez, M, additional, Kan, P, additional, Tanweer, O, additional, Jumaa, M, additional, Zaidi, S, additional, Oliver, M, additional, Sheth, S, additional, Nahhas, M, additional, Salazar-Marioni, S, additional, Khaldi, A, additional, Li, H, additional, Kuybu, O, additional, Abdalkader, M, additional, Klein, P, additional, Peng, S, additional, Alaraj, A, additional, Nguyen, T, additional, and Nogueira, R, additional

Published
2023
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23. P-017 Fusiform vs non-fusiform posterior circulation aneurysms treated with flow diversion: a multicenter study

Author

Vivanco-Suarez, J, primary, Rodriguez-Calienes, A, additional, Cortez, G, additional, Nishi, H, additional, Pereira, V, additional, Costa, M, additional, Feigen, C, additional, Altschul, D, additional, Matsoukas, S, additional, Fifi, J, additional, Hafeez, M, additional, Kan, P, additional, Kühn, A, additional, Puri, A, additional, Rabinovic, M, additional, Wakhloo, A, additional, Khandelwal, P, additional, Lu, Y, additional, Galecio-Castillo, M, additional, Alva, C, additional, Farooqui, M, additional, Hanel, R, additional, and Ortega-Gutierrez, S, additional

Published
2023
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27. Upregulated Angiogenesis Is Incompetent to Rescue Dilated Cardiomyopathy Phenotype in Mice

Author

Mohammed Arif, Perwez Alam, Rafeeq PH Ahmed, Raghav Pandey, Hafeez M Faridi, and Sakthivel Sadayappan

Subjects
dilated cardiomyopathy, angiogenesis, MicroRNA-210, Cytology, QH573-671
Abstract

Dilated cardiomyopathy (DCM) is characterized by pathologic cardiac remodeling resulting in chambers enlargement and impaired heart contractility. Previous reports and our in-silico analysis support the association of DCM phenotype and impaired tissue angiogenesis. Here, we explored whether the modulation in cardiac angiogenesis partly intervenes or rescues the DCM phenotype in mice. Here, a DCM mouse model [α-tropomyosin 54 (α-TM54) mutant] was crossbred with microRNA-210 transgenic mice (210-TG) to develop microRNA-210 (miR-210) overexpressing α-TM54 mutant mice (TMx210). Contrary to wild-type (WT) and 210-TG mice, a significant increase in heart weight to body weight ratio in aged mixed-gender TMx210 and DCM mice was recorded. Histopathological analysis revealed signs of pathological cardiac remodeling such as myocardial disarray, myofibrillar loss, and interstitial fibrosis in DCM and TMx210 mice. Contrary to WT and DCM, a significant increase in angiogenic potential was observed in TMx210 and 210-TG mice hearts which is reflected by higher blood vessel density and upregulated proangiogenic vascular endothelial growth factor-A. The echocardiographic assessment showed comparable cardiac dysfunction in DCM and TMx210 mice as compared to WT and 210-TG. Overall, the present study concludes that miR-210 mediated upregulated angiogenesis is not sufficient to rescue the DCM phenotype in mice.

Published
2021
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29. Fusion Model For Covid-19 Diagnosis Using Chest X-Ray Images

Author

Mohammed Hafeez M. K, M. Sharmila Kumari, Hana Fathima, Haleemathul Shifa Tahseen, Shrama K, and Vaishnavi N Shetty

Subjects
COVID-1, Convolutional neural network, Pre- processing, Feature extraction, Fusion model Classification
Abstract

The COVID-19 pandemic increased at an exponential rate and faced a major problem due to the restricted accessibility of rapid test kits. The radio-imaging approach recommends that the images comprise important data related to coronaviruses. The proposed FM-HCF-DLF model is comprised of Gaussian filtering-based pre-processing, a fusion model for feature extraction and classification. The proposed FM model is a combination of handcrafted features with the help of local binary patterns (LBP), deep learning (DL) features and the convolutional neural network (CNN)-based Inception v3 technique using the Adam optimizer. The multilayer perceptron (MLP) is employed to carry out the classification process. The proposed FM-HCF-DLF model used a chest X-ray dataset for experimentation purposes. The experimental outcomes yielded superior performance with a maximum sensitivity of 93.61%, specificity of 94.56%, the precision of 94.85%, the accuracy of 94.08%, F score of 93.2% and kappa value of 93.5%., {"references":["1.\tFan Y, Zhao K, Shi Z, Zhou P (2019) Bat coronaviruses in China. Viruses. 11:210","2.\tChen Y, Liu Q, Guo D (2020) Emerging coronaviruses: genome structure, replication, and pathogenesis. J Med Virol 92:418–423","3.\tSetio AAA, Ciompi F, Litjens G, Gerke P, Jacobs C, van Riel SJ et al (2016) Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks. IEEE Trans Med Imaging 35(5):1160–1169","4.\tXia K, Yin H, Qian P, Jiang Y, Wang S (2019) Liver semantic segmentation algorithm based on improved deep adversarial networks in combination of weighted loss function on abdominal CT images. IEEE Access 7:96349–96358","5.\tPezeshk A, Hamidian S, Petrick N, Sahiner B (2019) 3-D convolutional neural networks for automatic detection of pulmonary nodules in chest CT. IEEE J Biomed Health Inform 23(5):2080–2090","6.\tPrakasa E (2016) Texture feature extraction by applying local binary pattern (LBP). INKOM J 9(2):45–48","7. Nardelli P, Jimenez-Carretero D, Bermejo-Pelaez D, Washko GR, Rahaghi FN, Ledesma-Carbayo MJ et al (2018) Pulmonary artery–vein classification in CT images using deep learning. IEEE Trans Med Imaging 37(11):2428–2440."]}

Published
2023
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30. Fake Currency Detection using Machine Learning Algorithm

Author

Mohammed Hafeez M. K, M. Sharmila Kumari, Muhammed Sahil, Abdul Khadar Sawad, Ibarhim Bathisha D, and Ankush

Subjects
Machine Learning Algorithm, DFDs, UV light
Abstract

A huge deal of counterfeit currency has been printed recently, which has hurt society greatly. Therefore, the creation of a method to identify fraudulent currency has become essential. By using their image, our proposed system will employ a method to identify counterfeit cash notes traded in our nation. Our work will offer the necessary adaptability and compatibility for the majority of individuals, as well as dependable accuracy for the detection of counterfeit currencies. To make this application effective, we are employing the logistic regression algorithm. Using a machine learning algorithm, this work will identify several significant properties in notes that will establish the currency note's uniqueness. This application makes it simple to spot fraudulent notes and reduces their availability on the market., {"references":["D. Kumar and S. Chauhan, \"Indian fake Currency Detection using computer vision\", International Research Journal of Engineering and Technology, vol. 7, no. 5, pp. 2870- 2874, 2020.","Ankush Singh, Prof. Ketaki Bhoyar, Ankur Pandey, Prashant Mankani, Aman Tekriwal Detection of fake currency using image processingInternational Journal of Engineering Research & Technology (IJERT), Vol. 8 Issue 12, December-2019.","S. Arya and M. Sasiku mar, \"Fake Currency Detection,\" 2019 Int. Conf. Recent Adv. Energy-Efficient Comput. Commun. ICRAECC 2019, PP. 2019-2022, 10.1109/ICRAECC43874.2019.89949 68. 2019, doi:","M. N. Rathore and J. Sagar, \"A Review on Fake currency detection using feature extraction,\" vol. 10, no. 11, pp. 407–411, 2019.","M. N. Shende and P. P. Patil, \"A Review on Fake Currency Detection using Image Processing,\" Int. J. Futur. Revolut. Comput. Sci. Commun. Eng., vol. 4, no. 1, pp. 391–393, 2018.","A. Upadhyaya, V. Shokeen, and G. Srivastava, \"Analysis of counterfeit currency detection techniques for classification mode 1,\" 2018 4th Int. Conf. Comput. Commun. Autom. ICCCA 2018, pp. 1-6, 2018, doi: 10.1109/CCAA.2018.8777704.","M. A. Gaikwad, V. V Bhosle, and V. D. Patil, \"Automatic Indian New Fake Currency Detection Technique,\" Int. J. Eng. Res. Technol., vol. 6, no. 11, pp. 84-87, 2017.","P. Ponishjino, K. Antony, S. Kumar, and S. Jebakumar, \"Bogus currency authorization using HSV techniques,\" Proc. Int. Conf. Electron. Commun. Aerosp. Technol. ICECA 2017, vol. 2017- January, pp. 179-183, 2017, doi:10.1109/ICECA.2017.8203667.","Goodfellow I, Bengio Y, Courville A, Bengio Y. 2016. Deep Learning, volume 1. Cambridge: MIT Press.","D. Alekhya, G. DeviSuryaPrabha and G. Venkata Durga Rao, Fake Currency Detection Using Image Processing and Other Standard Methods, International Journal of Research in Computer and Communication Technology, Vol 3, Issue 1, January-2014","PM.Deborah. P, C.Soniya PrathapDetection of fake currency using image processing-International Journal of Innovative Science, Engineering & Technology (IJERT), Vol. 1 Issue 10, December-2014.","Rubeena mirza and veenti nanda, Design and Implementation of Indian Paper Currency Authentication System Based on Feature Extraction by Edge Based Segmentation Using Sobel Operator, IJERD, Volume 3, Issue 2 (August 2012), PP. 41-46"]}

Published
2023
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31. Design and Analysis of Deep-Learning based Iris Recognition System

Author

K, Mohammed Hafeez M., M. Sharmila Kumari, Bashi Rasheed, Fathima Joura, Haneena Hyder, and Zameer, Mohammad

Subjects
Iris recognition preprocessing, Deep learning, Convolutional Neural Network (CNN), Feature extraction(FE)
Abstract

Biometrics The existing iris identification techniques that have been published over the years primarily rely on particular circumstances, such as the distance at which the images are acquired and the setting of constant staring, which necessitates extensive user collaboration. These segment/normalize based and "phase based" method encountersnumerous issues when representing data reduces the disparities caused by heavy eyelash occlusion, motion blurs, motion translations, scale, rotations, pupillary dilation, and irregular reflections in the area around the user's eyes in situations where there is no assurance of collaboration. By analyzing the displacements between the respective patches in pairs of iris pictures, convolutional neural network-based deep learning classification models (CNN) are able to accurately distinguish between genuine and imposter comparisons using iris segmentation, even in extremely noisy environments. When compared to other biometric recognition systems, the iris texture's ability to differ across identical twins' eyes and between the left and right eyes of the same person gives it a more secure method of authentication. In the studies, we took into account three well-known data sets (CASIA, Polaris), coming to the conclusion that the suggested algorithm is effective, especially in situations when precisely segmenting the iris is difficult., {"references":["J. G. Daugman, \"High confidence visual recognition of persons by a test of statistical independence,\" in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no. 11, pp. 1148-1161, Nov. 1993, doi: 10.1109/34.244676.","J. G. Daugman, \"How iris recognition works,\" IEEE Transactions on Circuits and Systems for Video Technology, vol.14, no. 1, pp. 21-30, 2004.","IIT Delhi Iris Image Database. [Online]. Available: https://www4.comp.polyu.edu.hk/~ csajaykr/IITD/Database_Iris. htm","Chinese Academy of Sciences Institute of Automation. (Aug. 2017). CASIA Iris Image Database. [Online]. Available: http://biometrics.idealtest.org","S. Arora and M. P. S. Bhatia, \"A Computer Vision System for Iris Recognition Based on Deep Learning,\" IEEE 8th International Advance Computing Conference (IACC), Greater Noida, India, 2018, pp. 157-161, doi: 10.1109/IADCC.2018.8692114","S. Liu and W. Deng, \"Very deep convolutional neural networkbased image classification using small training sample size,\" The 3rd IAPR Asian Conference on Pattern Recognition (ACPR), Kuala Lumpur, 2015, pp. 730-734, doi: 10.1109/ACPR.2015.7486599.","Mingxing Tan and Quoc V. Le, \" EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks,\" International Conference on Machine Learning, 2019, arXiv:1905.11946.","M. Tan, R. Pang and Q. V. Le, \"EfficientDet: Scalable and Efficient Object Detection,\" IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 2020, pp. 10778- 10787, doi: 10.1109/CVPR42600.2020.01079.","S. Arora and M. P. S. Bhatia, \"A Computer Vision System for Iris Recognition Based on Deep Learning,\" IEEE 8th International Advance Computing Conference (IACC), Greater Noida, India, 2018, pp. 157-161, doi: 10.1109/IADCC.2018.8692114","Yingda Xia et al., \"Synthesize then Compare: Detecting Failures and Anomalies for Semantic Segmentation\" arXiv:2003.08440v2 [cs.CV] 8 Sep 2020"]}

Published
2023
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33. Search and Rescue Robotics - From Theory to Practice

Author

Cubber, Geert De, primary, Doroftei, Daniela, additional, Rudin, Konrad, additional, Berns, Karsten, additional, Matos, Anibal, additional, Serrano, Daniel, additional, Sanchez, Jose Manuel, additional, Govindaraj, Shashank, additional, Bedkowski, Janusz, additional, Roda, Rui, additional, Silva, Eduardo, additional, Ourevitch, Stephane, additional, Wagemans, Rene, additional, Lobo, Victor, additional, Cardoso, Guerreiro, additional, Chintamani, Keshav, additional, Gancet, Jeremi, additional, Stupler, Pascal, additional, Nezhadfard, Atabak, additional, Tosa, Massimo, additional, Balta, Haris, additional, Almeida, Jose, additional, Martins, Alfredo, additional, Ferreira, Hugo, additional, Ferreira, Bruno, additional, Alves, Jose, additional, Dias, Andre, additional, Fioravanti, Stefano, additional, Bertin, Daniele, additional, Moreno, German, additional, Cordero, Jose, additional, Marques, Mario Monteiro, additional, Grati, Alberto, additional, Chaudhary, Hafeez M., additional, Sheers, Bart, additional, Riobo, Yudani, additional, Letier, Pierre, additional, Jimenez, Mario Nunez, additional, Esbri, Miguel Angel, additional, Musialik, Pawel, additional, Badiola, Irune, additional, Goncalves, Ricardo, additional, Coelho, Antonio, additional, Pfister, Thomas, additional, Majek, Karol, additional, Pelka, Michal, additional, Maslowski, Andrzej, additional, and Baptista, Ricardo, additional

Published
2017
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35. Chapter Tactical Communications for Cooperative SAR Robot Missions

Author

Sanchez, Jose, Chaudhary, Hafeez M., Riobó, Yudani, and Bueno Cordero, José

Subjects
communications, mesh, contention, optimisation, middleware, propagation, Ambulance & rescue services
Abstract

This chapter describes how the ICARUS communications (COM) team defined, developed and implemented an integrated wireless communication system to ensure an interoperable and dependable networking capability for both human and robotic search and rescue field teams and crisis managers. It starts explaining the analysis of the requirements and the context of the project, the existing solutions and the design of the ICARUS communication system to fulfil all the project needs. Next, it addresses the implementation process of the required networking capabilities, and finally, it explains how the ICARUS communication system and associated tools have been integrated in the overall mission systems and have been validated to provide reliable communications for real‐time information sharing during search and rescue operations in hostile conditions.

Published
2021
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41. LB007 Intrasaccular flow disruption for ruptured aneurysms: an international multicenter study

Author

Diestro, J, primary, Dibas, M, additional, Adeeb, N, additional, Regenhardt, R, additional, Vranic, J, additional, Guenego, A, additional, Lay, S, additional, Renieri, L, additional, Sundararajan, S, additional, Premat, K, additional, El Namaani, K, additional, Saliou, G, additional, Möhlenbruch, M, additional, Lylyk, I, additional, Foreman, P, additional, Vachhani, J, additional, Župančić, V, additional, Hafeez, M, additional, Rutledge, C, additional, Rai, H, additional, Tutino, V, additional, Mirshahi, S, additional, Ghozy, S, additional, Harker, P, additional, Alotaibi, N, additional, Rabinov, J, additional, Spears, J, additional, Parra-Farinas, C, additional, Patel, A, additional, and Dmytriw, A, additional

Published
2022
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42. E-141 Predictors of favorable clinical outcome in posterior circulation acute ischemic stroke | insights from star

Author

Hafeez, M, primary, Essibayi, M, additional, Raper, D, additional, Al Kasab, S, additional, Burks, J, additional, Alsbrook, D, additional, Park, M, additional, Dumont, T, additional, Townsend, R, additional, Starke, R, additional, Arthur, A, additional, Goyal, N, additional, Maier, I, additional, Wolfe, S, additional, Mascitelli, J, additional, Fargen, K, additional, Spiotta, A, additional, and Kan, P, additional

Published
2022
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43. E-119 Predictors of first pass efficacy in posterior circulation acute ischemic strokes

Author

Hafeez, M, primary, Essibayi, M, additional, Raper, D, additional, Al Kasab, S, additional, Burks, J, additional, Alsbrook, D, additional, Park, M, additional, Dumont, T, additional, Townsend, R, additional, Starke, R, additional, Arthur, A, additional, Goyal, N, additional, Maier, I, additional, Wolfe, S, additional, Mascitelli, J, additional, Fargen, K, additional, Spiotta, A, additional, and Kan, P, additional

Published
2022
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45. IMPACT-Global Hip Fracture Audit: Nosocomial infection, risk prediction and prognostication, minimum reporting standards and global collaborative audit. Lessons from an international multicentre study of 7,090 patients conducted in 14 nations during the COVID-19 pandemic

Author

Hall, A, Clement, N, Ojeda-Thies, C, Maclullich, A, Toro, G, Johansen, A, White, T, Duckworth, A, Abdul-Jabar, H, Abu-Rajab, R, Abugarja, A, Adam, K, Aguado Hernández, H, Améstica Lazcano, G, Anderson, S, Ansar, M, Antrobus, J, Aragón Achig, E, Archunan, M, Arrieta Salinas, M, Ashford-Wilson, S, Assens Gibert, C, Athanasopoulou, K, Awadelkarim, M, Baird, S, Bajada, S, Balakrishnan, S, Balasubramanian, S, Ballantyne, J, Bárcena Goitiandia, L, Barkham, B, Barmpagianni, C, Barres-Carsi, M, Barrett, S, Baskaran, D, Bell, J, Bell, K, Bell, S, Bellelli, G, Benchimol, J, Boietti, B, Boswell, S, Braile, A, Brennan, C, Brent, L, Brooke, B, Bruno, G, Burahee, A, Burns, S, Calabrò, G, Campbell, L, Carabelli, G, Carnegie, C, Carretero Cristobal, G, Caruana, E, Cassinello Ogea, M, Castellanos Robles, J, Castillon, P, Chakrabarti, A, Cecere, A, Chen, P, Clarke, J, Collins, G, Corrales Cardenal, J, Corsi, M, Cózar Adelantado, G, Craxford, S, Crooks, M, Cuarental-García, J, Cuthbert, R, Dall, G, Daskalakis, I, De Cicco, A, Diana, D, Demaria, P, Dereix, J, Díaz Jiménez, J, Dinamarca Montecinos, J, Do Le, H, Donoso Coppa, J, Drosos, G, Duffy, A, East, J, Eastwood, D, Elbahari, H, Elias de Molins Peña, C, Elmamoun, M, Emmerson, B, Escobar Sánchez, D, Faimali, M, Farré-Mercadé, M, Farrow, L, Fayez, A, Fell, A, Fenner, C, Ferguson, D, Finlayson, L, Flores Gómez, A, Freeman, N, French, J, Gabardo Calvo, S, Gagliardo, N, Garcia Albiñana, J, García Cruz, G, García de Cortázar Antolín, U, García Virto, V, Gealy, S, Gil Caballero, S, Gill, M, González González, M, Gopireddy, R, Guntley, D, Gurung, B, Guzmán Rosales, G, Haddad, N, Hafeez, M, Haller, P, Halligan, E, Hardie, J, Hawker, I, Helal, A, Herrera Cruz, M, Herreros Ruiz-Valdepeñas, R, Horton, J, Howells, S, Howieson, A, Hughes, L, Hünicken Torrez, F, Hurtado Ortega, A, Huxley, P, Hamid, H, Ilahi, N, Iliadis, A, Inman, D, Jadhao, P, Jandoo, R, Jawad, L, Jayatilaka, M, Jenkins, P, Jeyapalan, R, Johnson, D, Johnston, A, Joseph, S, Kapoor, S, Karagiannidis, G, Karanam, K, Kattakayam, F, Konarski, A, Kontakis, G, Labrador Hernández, G, Lancaster, V, Landi, G, Le, B, Liew, I, Logishetty, K, Lopez Marquez, A, Lopez, J, Lum, J, Macpherson, G, Madan, S, Mahroof, S, Malik-Tabassum, K, Mallina, R, Maqsood, A, Marson, B, Martin Legorburo, M, Martin-Perez, E, Martínez Jiménez, T, Martinez Martin, J, Mayne, A, Mayor, A, Mcalinden, G, Mclean, L, Mcdonald, L, Mcintyre, J, Mckay, P, Mckean, G, Mcshane, H, Medici, A, Meeke, C, Meldrum, E, Mendez, M, Mercer, S, Merino Perez, J, Mesa-Lampré, M, Mighton, S, Milne, K, Mohamed Yaseen, M, Moppett, I, Mora, J, Morales-Zumel, S, Moreno Fenoll, I, Mousa, A, Murray, A, Murray, E, Nair, R, Neary, F, Negri, G, Negus, O, Newham-Harvey, F, Ng, N, Nightingale, J, Noor Mohamed Anver, S, Nunag, P, O'Hare, M, Ollivere, B, Ortés Gómez, R, Owens, A, Page, S, Palloni, V, Panagiotopoulos, A, Panagiotopoulos, E, Panesar, P, Papadopoulos, A, Spyridon, P, Pareja Sierra, T, Park, C, Parwaiz, H, Paterson-Byrne, P, Patton, S, Pearce, J, Porter, M, Pellegrino, A, Pèrez Cuellar, A, Pezzella, R, Phadnis, A, Pinder, C, Piper, D, Powell-Bowns, M, Prieto Martín, R, Probert, A, Ramesh, A, Ramírez de Arellano, M, Renton, D, Rickman, S, Robertson, A, Roche Albero, A, Rodrigo Verguizas, J, Rodríguez Couso, M, Rooney, J, Sáez-López, P, Saldaña-Díaz, A, Santulli, A, Sanz Pérez, M, Sarraf, K, Scarsbrook, C, Scott, C, Scott, J, Shah, S, Sharaf, S, Sharma, S, Shirley, D, Siano, A, Simpson, J, Singh, A, Sinnett, T, Sisodia, G, Smith, P, Sophena Bert, E, Steel, M, Stewart, A, Stewart, C, Sugand, K, Sullivan, N, Sweeting, L, Symes, M, Tan, D, Tancredi, F, Tatani, I, Thomas, P, Thomson, F, Toner, N, Tong, A, Toro, A, Tosounidis, T, Tottas, S, Trinidad Leo, A, Tucker, D, Vemulapalli, K, Ventura Garces, D, Vernon, O, Viveros Garcia, J, Ward, A, Ward, K, Watson, K, Weerasuriya, T, Wickramanayake, U, Wilkinson, H, Windley, J, Wood, J, Wynell-Mayow, W, Zatti, G, Zeiton, M, Zurrón Lobato, M, Hall, Andrew J., Clement, Nicholas D., Ojeda-Thies, Cristina, MacLullich, Alasdair MJ., Toro, Giuseppe, Johansen, Antony, White, Tim O., Duckworth, Andrew D., Abdul-Jabar, Hani, Abu-Rajab, Rashid, Abugarja, Ahmed, Adam, Karen, Aguado Hernández, Héctor J., Améstica Lazcano, Gedeón, Anderson, Sarah, Ansar, Mahmood, Antrobus, Jonathan, Aragón Achig, Esteban Javier, Archunan, Maheswaran, Arrieta Salinas, Mirentxu, Ashford-Wilson, Sarah, Assens Gibert, Cristina, Athanasopoulou, Katerina, Awadelkarim, Mohamed, Baird, Stuart, Bajada, Stefan, Balakrishnan, Shobana, Balasubramanian, Sathishkumar, Ballantyne, James A., Bárcena Goitiandia, Leopoldo, Barkham, Benjamin, Barmpagianni, Christina, Barres-Carsi, Mariano, Barrett, Sarah, Baskaran, Dinnish, Bell, Jean, Bell, Katrina, Bell, Stuart, Bellelli, Giuseppe, Benchimol, Javier Alberto, Boietti, Bruno Rafael, Boswell, Sally, Braile, Adriano, Brennan, Caitlin, Brent, Louise, Brooke, Ben, Bruno, Gaetano, Burahee, Abdus, Burns, Shirley, Calabrò, Giampiero, Campbell, Lucy, Carabelli, Guido Sebastian, Carnegie, Carol, Carretero Cristobal, Guillermo, Caruana, Ethan, Cassinello Ogea, M. a Concepción, Castellanos Robles, Juan, Castillon, Pablo, Chakrabarti, Anil, Cecere, Antonio Benedetto, Chen, Ping, Clarke, Jon V., Collins, Grace, Corrales Cardenal, Jorge E., Corsi, Maurizio, Cózar Adelantado, Gara María, Craxford, Simon, Crooks, Melissa, Cuarental-García, Javier, Cuthbert, Rory, Dall, Graham, Daskalakis, Ioannis, De Cicco, Annalisa, Diana, de la Fuente de Dios, Demaria, Pablo, Dereix, John, Díaz Jiménez, Julian, Dinamarca Montecinos, José Luis, Do Le, Ha Phuong, Donoso Coppa, Juan Pablo, Drosos, Georgios, Duffy, Andrew, East, Jamie, Eastwood, Deborah, Elbahari, Hassan, Elias de Molins Peña, Carmen, Elmamoun, Mamoun, Emmerson, Ben, Escobar Sánchez, Daniel, Faimali, Martina, Farré-Mercadé, Maria Victòria, Farrow, Luke, Fayez, Almari, Fell, Adam, Fenner, Christopher, Ferguson, David, Finlayson, Louise, Flores Gómez, Aldo, Freeman, Nicholas, French, Jonathan, Gabardo Calvo, Santiago, Gagliardo, Nicola, Garcia Albiñana, Joan, García Cruz, Guillermo, García de Cortázar Antolín, Unai, García Virto, Virginia, Gealy, Sophie, Gil Caballero, Sandra Marcela, Gill, Moneet, González González, María Soledad, Gopireddy, Rajesh, Guntley, Diane, Gurung, Binay, Guzmán Rosales, Guadalupe, Haddad, Nedaa, Hafeez, Mahum, Haller, Petra, Halligan, Emer, Hardie, John, Hawker, Imogen, Helal, Amr, Herrera Cruz, Mariana, Herreros Ruiz-Valdepeñas, Ruben, Horton, James, Howells, Sean, Howieson, Alan, Hughes, Luke, Hünicken Torrez, Flavia Lorena, Hurtado Ortega, Ana, Huxley, Peter, Hamid, Hytham K. S., Ilahi, Nida, Iliadis, Alexis, Inman, Dominic, Jadhao, Piyush, Jandoo, Rajan, Jawad, Lucy, Jayatilaka, Malwattage Lara Tania, Jenkins, Paul J., Jeyapalan, Rathan, Johnson, David, Johnston, Andrew, Joseph, Sarah, Kapoor, Siddhant, Karagiannidis, Georgios, Karanam, Krishna Saga, Kattakayam, Freddy, Konarski, Alastair, Kontakis, Georgios, Labrador Hernández, Gregorio, Lancaster, Victoria, Landi, Giovanni, Le, Brian, Liew, Ignatius, Logishetty, Kartik, Lopez Marquez, Andrew Carlomaria Daniel, Lopez, Judit, Lum, Joann, Macpherson, Gavin J., Madan, Suvira, Mahroof, Sabreena, Malik-Tabassum, Khalid, Mallina, Ravi, Maqsood, Afnan, Marson, Ben, Martin Legorburo, M José, Martin-Perez, Encarna, Martínez Jiménez, Tania, Martinez Martin, Javier, Mayne, Alistair, Mayor, Amy, McAlinden, Gavan, McLean, Lucille, McDonald, Lorna, McIntyre, Joshua, McKay, Pamela, McKean, Greg, McShane, Heather, Medici, Antonio, Meeke, Chelsea, Meldrum, Evonne, Mendez, Mijail, Mercer, Scott, Merino Perez, Josu, Mesa-Lampré, María-Pilar, Mighton, Shuna, Milne, Kirsty, Mohamed Yaseen, Muhammed, Moppett, Iain, Mora, Jesus, Morales-Zumel, Sira, Moreno Fenoll, Irene Blanca, Mousa, Adham, Murray, Alastair W., Murray, Elspeth V., Nair, Radhika, Neary, Fiona, Negri, Giacomo, Negus, Oliver, Newham-Harvey, Fiona, Ng, Nigel, Nightingale, Jess, Noor Mohamed Anver, Sumiya, Nunag, Perrico, O'Hare, Matthew, Ollivere, Ben, Ortés Gómez, Raquel, Owens, AnneMarie, Page, Siobhan, Palloni, Valentina, Panagiotopoulos, Andreas, Panagiotopoulos, Elias, Panesar, Paul, Papadopoulos, Antonios, Spyridon, Papagiannis, Pareja Sierra, Teresa, Park, Chang, Parwaiz, Hammad, Paterson-Byrne, Paul, Patton, Sam, Pearce, Jack, Porter, Marina, Pellegrino, Achille, Pèrez Cuellar, Arturo, Pezzella, Raffaele, Phadnis, Ashish, Pinder, Charlotte, Piper, Danielle, Powell-Bowns, Matilda, Prieto Martín, Rocío, Probert, Annabel, Ramesh, Ashwanth, Ramírez de Arellano, Manuel Vicente Mejía, Renton, Duncan, Rickman, Stephen, Robertson, Alastair, Roche Albero, Adrian, Rodrigo Verguizas, José Alberto, Rodríguez Couso, Myriam, Rooney, Joanna, Sáez-López, Pilar, Saldaña-Díaz, Andres, Santulli, Adriano, Sanz Pérez, Marta Isabel, Sarraf, Khaled M., Scarsbrook, Christine, Scott, Chloe E. H., Scott, Jennifer, Shah, Sachi, Sharaf, Sharief, Sharma, Sidharth, Shirley, Denise, Siano, Antonio, Simpson, James, Singh, Abhinav, Singh, Amit, Sinnett, Tim, Sisodia, Gurudatt, Smith, Philomena, Sophena Bert, Eugenia, Steel, Michael, Stewart, Avril, Stewart, Claire, Sugand, Kapil, Sullivan, Niall, Sweeting, Lauren, Symes, Michael, Tan, Dylan Jun Hao, Tancredi, Francesco, Tatani, Irini, Thomas, Philip, Thomson, Fraser, Toner, Niamh S., Tong, Anna, Toro, Antonio, Tosounidis, Theodoros, Tottas, Stylianos, Trinidad Leo, Andrea, Tucker, Damien, Vemulapalli, Krishna, Ventura Garces, Diego, Vernon, Olivia Katherine, Viveros Garcia, Juan Carlos, Ward, Alex, Ward, Kirsty, Watson, Kate, Weerasuriya, Thisara, Wickramanayake, Udara, Wilkinson, Hannah, Windley, Joseph, Wood, Janet, Wynell-Mayow, William, Zatti, Giovanni, Zeiton, Moez, Zurrón Lobato, Miriam, Hall, A, Clement, N, Ojeda-Thies, C, Maclullich, A, Toro, G, Johansen, A, White, T, Duckworth, A, Abdul-Jabar, H, Abu-Rajab, R, Abugarja, A, Adam, K, Aguado Hernández, H, Améstica Lazcano, G, Anderson, S, Ansar, M, Antrobus, J, Aragón Achig, E, Archunan, M, Arrieta Salinas, M, Ashford-Wilson, S, Assens Gibert, C, Athanasopoulou, K, Awadelkarim, M, Baird, S, Bajada, S, Balakrishnan, S, Balasubramanian, S, Ballantyne, J, Bárcena Goitiandia, L, Barkham, B, Barmpagianni, C, Barres-Carsi, M, Barrett, S, Baskaran, D, Bell, J, Bell, K, Bell, S, Bellelli, G, Benchimol, J, Boietti, B, Boswell, S, Braile, A, Brennan, C, Brent, L, Brooke, B, Bruno, G, Burahee, A, Burns, S, Calabrò, G, Campbell, L, Carabelli, G, Carnegie, C, Carretero Cristobal, G, Caruana, E, Cassinello Ogea, M, Castellanos Robles, J, Castillon, P, Chakrabarti, A, Cecere, A, Chen, P, Clarke, J, Collins, G, Corrales Cardenal, J, Corsi, M, Cózar Adelantado, G, Craxford, S, Crooks, M, Cuarental-García, J, Cuthbert, R, Dall, G, Daskalakis, I, De Cicco, A, Diana, D, Demaria, P, Dereix, J, Díaz Jiménez, J, Dinamarca Montecinos, J, Do Le, H, Donoso Coppa, J, Drosos, G, Duffy, A, East, J, Eastwood, D, Elbahari, H, Elias de Molins Peña, C, Elmamoun, M, Emmerson, B, Escobar Sánchez, D, Faimali, M, Farré-Mercadé, M, Farrow, L, Fayez, A, Fell, A, Fenner, C, Ferguson, D, Finlayson, L, Flores Gómez, A, Freeman, N, French, J, Gabardo Calvo, S, Gagliardo, N, Garcia Albiñana, J, García Cruz, G, García de Cortázar Antolín, U, García Virto, V, Gealy, S, Gil Caballero, S, Gill, M, González González, M, Gopireddy, R, Guntley, D, Gurung, B, Guzmán Rosales, G, Haddad, N, Hafeez, M, Haller, P, Halligan, E, Hardie, J, Hawker, I, Helal, A, Herrera Cruz, M, Herreros Ruiz-Valdepeñas, R, Horton, J, Howells, S, Howieson, A, Hughes, L, Hünicken Torrez, F, Hurtado Ortega, A, Huxley, P, Hamid, H, Ilahi, N, Iliadis, A, Inman, D, Jadhao, P, Jandoo, R, Jawad, L, Jayatilaka, M, Jenkins, P, Jeyapalan, R, Johnson, D, Johnston, A, Joseph, S, Kapoor, S, Karagiannidis, G, Karanam, K, Kattakayam, F, Konarski, A, Kontakis, G, Labrador Hernández, G, Lancaster, V, Landi, G, Le, B, Liew, I, Logishetty, K, Lopez Marquez, A, Lopez, J, Lum, J, Macpherson, G, Madan, S, Mahroof, S, Malik-Tabassum, K, Mallina, R, Maqsood, A, Marson, B, Martin Legorburo, M, Martin-Perez, E, Martínez Jiménez, T, Martinez Martin, J, Mayne, A, Mayor, A, Mcalinden, G, Mclean, L, Mcdonald, L, Mcintyre, J, Mckay, P, Mckean, G, Mcshane, H, Medici, A, Meeke, C, Meldrum, E, Mendez, M, Mercer, S, Merino Perez, J, Mesa-Lampré, M, Mighton, S, Milne, K, Mohamed Yaseen, M, Moppett, I, Mora, J, Morales-Zumel, S, Moreno Fenoll, I, Mousa, A, Murray, A, Murray, E, Nair, R, Neary, F, Negri, G, Negus, O, Newham-Harvey, F, Ng, N, Nightingale, J, Noor Mohamed Anver, S, Nunag, P, O'Hare, M, Ollivere, B, Ortés Gómez, R, Owens, A, Page, S, Palloni, V, Panagiotopoulos, A, Panagiotopoulos, E, Panesar, P, Papadopoulos, A, Spyridon, P, Pareja Sierra, T, Park, C, Parwaiz, H, Paterson-Byrne, P, Patton, S, Pearce, J, Porter, M, Pellegrino, A, Pèrez Cuellar, A, Pezzella, R, Phadnis, A, Pinder, C, Piper, D, Powell-Bowns, M, Prieto Martín, R, Probert, A, Ramesh, A, Ramírez de Arellano, M, Renton, D, Rickman, S, Robertson, A, Roche Albero, A, Rodrigo Verguizas, J, Rodríguez Couso, M, Rooney, J, Sáez-López, P, Saldaña-Díaz, A, Santulli, A, Sanz Pérez, M, Sarraf, K, Scarsbrook, C, Scott, C, Scott, J, Shah, S, Sharaf, S, Sharma, S, Shirley, D, Siano, A, Simpson, J, Singh, A, Sinnett, T, Sisodia, G, Smith, P, Sophena Bert, E, Steel, M, Stewart, A, Stewart, C, Sugand, K, Sullivan, N, Sweeting, L, Symes, M, Tan, D, Tancredi, F, Tatani, I, Thomas, P, Thomson, F, Toner, N, Tong, A, Toro, A, Tosounidis, T, Tottas, S, Trinidad Leo, A, Tucker, D, Vemulapalli, K, Ventura Garces, D, Vernon, O, Viveros Garcia, J, Ward, A, Ward, K, Watson, K, Weerasuriya, T, Wickramanayake, U, Wilkinson, H, Windley, J, Wood, J, Wynell-Mayow, W, Zatti, G, Zeiton, M, Zurrón Lobato, M, Hall, Andrew J., Clement, Nicholas D., Ojeda-Thies, Cristina, MacLullich, Alasdair MJ., Toro, Giuseppe, Johansen, Antony, White, Tim O., Duckworth, Andrew D., Abdul-Jabar, Hani, Abu-Rajab, Rashid, Abugarja, Ahmed, Adam, Karen, Aguado Hernández, Héctor J., Améstica Lazcano, Gedeón, Anderson, Sarah, Ansar, Mahmood, Antrobus, Jonathan, Aragón Achig, Esteban Javier, Archunan, Maheswaran, Arrieta Salinas, Mirentxu, Ashford-Wilson, Sarah, Assens Gibert, Cristina, Athanasopoulou, Katerina, Awadelkarim, Mohamed, Baird, Stuart, Bajada, Stefan, Balakrishnan, Shobana, Balasubramanian, Sathishkumar, Ballantyne, James A., Bárcena Goitiandia, Leopoldo, Barkham, Benjamin, Barmpagianni, Christina, Barres-Carsi, Mariano, Barrett, Sarah, Baskaran, Dinnish, Bell, Jean, Bell, Katrina, Bell, Stuart, Bellelli, Giuseppe, Benchimol, Javier Alberto, Boietti, Bruno Rafael, Boswell, Sally, Braile, Adriano, Brennan, Caitlin, Brent, Louise, Brooke, Ben, Bruno, Gaetano, Burahee, Abdus, Burns, Shirley, Calabrò, Giampiero, Campbell, Lucy, Carabelli, Guido Sebastian, Carnegie, Carol, Carretero Cristobal, Guillermo, Caruana, Ethan, Cassinello Ogea, M. a Concepción, Castellanos Robles, Juan, Castillon, Pablo, Chakrabarti, Anil, Cecere, Antonio Benedetto, Chen, Ping, Clarke, Jon V., Collins, Grace, Corrales Cardenal, Jorge E., Corsi, Maurizio, Cózar Adelantado, Gara María, Craxford, Simon, Crooks, Melissa, Cuarental-García, Javier, Cuthbert, Rory, Dall, Graham, Daskalakis, Ioannis, De Cicco, Annalisa, Diana, de la Fuente de Dios, Demaria, Pablo, Dereix, John, Díaz Jiménez, Julian, Dinamarca Montecinos, José Luis, Do Le, Ha Phuong, Donoso Coppa, Juan Pablo, Drosos, Georgios, Duffy, Andrew, East, Jamie, Eastwood, Deborah, Elbahari, Hassan, Elias de Molins Peña, Carmen, Elmamoun, Mamoun, Emmerson, Ben, Escobar Sánchez, Daniel, Faimali, Martina, Farré-Mercadé, Maria Victòria, Farrow, Luke, Fayez, Almari, Fell, Adam, Fenner, Christopher, Ferguson, David, Finlayson, Louise, Flores Gómez, Aldo, Freeman, Nicholas, French, Jonathan, Gabardo Calvo, Santiago, Gagliardo, Nicola, Garcia Albiñana, Joan, García Cruz, Guillermo, García de Cortázar Antolín, Unai, García Virto, Virginia, Gealy, Sophie, Gil Caballero, Sandra Marcela, Gill, Moneet, González González, María Soledad, Gopireddy, Rajesh, Guntley, Diane, Gurung, Binay, Guzmán Rosales, Guadalupe, Haddad, Nedaa, Hafeez, Mahum, Haller, Petra, Halligan, Emer, Hardie, John, Hawker, Imogen, Helal, Amr, Herrera Cruz, Mariana, Herreros Ruiz-Valdepeñas, Ruben, Horton, James, Howells, Sean, Howieson, Alan, Hughes, Luke, Hünicken Torrez, Flavia Lorena, Hurtado Ortega, Ana, Huxley, Peter, Hamid, Hytham K. S., Ilahi, Nida, Iliadis, Alexis, Inman, Dominic, Jadhao, Piyush, Jandoo, Rajan, Jawad, Lucy, Jayatilaka, Malwattage Lara Tania, Jenkins, Paul J., Jeyapalan, Rathan, Johnson, David, Johnston, Andrew, Joseph, Sarah, Kapoor, Siddhant, Karagiannidis, Georgios, Karanam, Krishna Saga, Kattakayam, Freddy, Konarski, Alastair, Kontakis, Georgios, Labrador Hernández, Gregorio, Lancaster, Victoria, Landi, Giovanni, Le, Brian, Liew, Ignatius, Logishetty, Kartik, Lopez Marquez, Andrew Carlomaria Daniel, Lopez, Judit, Lum, Joann, Macpherson, Gavin J., Madan, Suvira, Mahroof, Sabreena, Malik-Tabassum, Khalid, Mallina, Ravi, Maqsood, Afnan, Marson, Ben, Martin Legorburo, M José, Martin-Perez, Encarna, Martínez Jiménez, Tania, Martinez Martin, Javier, Mayne, Alistair, Mayor, Amy, McAlinden, Gavan, McLean, Lucille, McDonald, Lorna, McIntyre, Joshua, McKay, Pamela, McKean, Greg, McShane, Heather, Medici, Antonio, Meeke, Chelsea, Meldrum, Evonne, Mendez, Mijail, Mercer, Scott, Merino Perez, Josu, Mesa-Lampré, María-Pilar, Mighton, Shuna, Milne, Kirsty, Mohamed Yaseen, Muhammed, Moppett, Iain, Mora, Jesus, Morales-Zumel, Sira, Moreno Fenoll, Irene Blanca, Mousa, Adham, Murray, Alastair W., Murray, Elspeth V., Nair, Radhika, Neary, Fiona, Negri, Giacomo, Negus, Oliver, Newham-Harvey, Fiona, Ng, Nigel, Nightingale, Jess, Noor Mohamed Anver, Sumiya, Nunag, Perrico, O'Hare, Matthew, Ollivere, Ben, Ortés Gómez, Raquel, Owens, AnneMarie, Page, Siobhan, Palloni, Valentina, Panagiotopoulos, Andreas, Panagiotopoulos, Elias, Panesar, Paul, Papadopoulos, Antonios, Spyridon, Papagiannis, Pareja Sierra, Teresa, Park, Chang, Parwaiz, Hammad, Paterson-Byrne, Paul, Patton, Sam, Pearce, Jack, Porter, Marina, Pellegrino, Achille, Pèrez Cuellar, Arturo, Pezzella, Raffaele, Phadnis, Ashish, Pinder, Charlotte, Piper, Danielle, Powell-Bowns, Matilda, Prieto Martín, Rocío, Probert, Annabel, Ramesh, Ashwanth, Ramírez de Arellano, Manuel Vicente Mejía, Renton, Duncan, Rickman, Stephen, Robertson, Alastair, Roche Albero, Adrian, Rodrigo Verguizas, José Alberto, Rodríguez Couso, Myriam, Rooney, Joanna, Sáez-López, Pilar, Saldaña-Díaz, Andres, Santulli, Adriano, Sanz Pérez, Marta Isabel, Sarraf, Khaled M., Scarsbrook, Christine, Scott, Chloe E. H., Scott, Jennifer, Shah, Sachi, Sharaf, Sharief, Sharma, Sidharth, Shirley, Denise, Siano, Antonio, Simpson, James, Singh, Abhinav, Singh, Amit, Sinnett, Tim, Sisodia, Gurudatt, Smith, Philomena, Sophena Bert, Eugenia, Steel, Michael, Stewart, Avril, Stewart, Claire, Sugand, Kapil, Sullivan, Niall, Sweeting, Lauren, Symes, Michael, Tan, Dylan Jun Hao, Tancredi, Francesco, Tatani, Irini, Thomas, Philip, Thomson, Fraser, Toner, Niamh S., Tong, Anna, Toro, Antonio, Tosounidis, Theodoros, Tottas, Stylianos, Trinidad Leo, Andrea, Tucker, Damien, Vemulapalli, Krishna, Ventura Garces, Diego, Vernon, Olivia Katherine, Viveros Garcia, Juan Carlos, Ward, Alex, Ward, Kirsty, Watson, Kate, Weerasuriya, Thisara, Wickramanayake, Udara, Wilkinson, Hannah, Windley, Joseph, Wood, Janet, Wynell-Mayow, William, Zatti, Giovanni, Zeiton, Moez, and Zurrón Lobato, Miriam

Abstract

Aims: This international study aimed to assess: 1) the prevalence of preoperative and postoperative COVID-19 among patients with hip fracture, 2) the effect on 30-day mortality, and 3) clinical factors associated with the infection and with mortality in COVID-19-positive patients. Methods: A multicentre collaboration among 112 centres in 14 countries collected data on all patients presenting with a hip fracture between 1st March-31st May 2020. Demographics, residence, place of injury, presentation blood tests, Nottingham Hip Fracture Score, time to surgery, management, ASA grade, length of stay, COVID-19 and 30-day mortality status were recorded. Results: A total of 7090 patients were included, with a mean age of 82.2 (range 50–104) years and 4959 (69.9%) being female. Of 651 (9.2%) patients diagnosed with COVID-19, 225 (34.6%) were positive at presentation and 426 (65.4%) were positive postoperatively. Positive COVID-19 status was independently associated with male sex (odds ratio (OR) 1.38, p = 0.001), residential care (OR 2.15, p < 0.001), inpatient fall (OR 2.23, p = 0.003), cancer (OR 0.63, p = 0.009), ASA grades 4 (OR 1.59, p = 0.008) or 5 (OR 8.28, p < 0.001), and longer admission (OR 1.06 for each increasing day, p < 0.001). Patients with COVID-19 at any time had a significantly lower chance of 30-day survival versus those without COVID-19 (72.7% versus 92.6%, p < 0.001). COVID-19 was independently associated with an increased 30-day mortality risk (hazard ratio (HR) 2.83, p < 0.001). Increasing age (HR 1.03, p = 0.028), male sex (HR 2.35, p < 0.001), renal disease (HR 1.53, p = 0.017), and pulmonary disease (HR 1.45, p = 0.039) were independently associated with a higher 30-day mortality risk in patients with COVID-19 when adjusting for confounders. Conclusion: The prevalence of COVID-19 in hip fracture patients during the first wave of the pandemic was 9%, and was independently associated with a three-fold increased 30-day mortality risk.

Published
2022

48. Metformin alleviates lung-endothelial hyperpermeability by regulating cofilin-1/PP2AC pathway.

Author

Siddiqui, M. Rizwan, Reddy, Narsa M., Faridi, Hafeez M., Shahid, Mohd, and Shanley, Thomas P.

Subjects
ENDOTHELIAL cells, LUNGS, METFORMIN, ADULT respiratory distress syndrome, ELECTRIC impedance, ADHERENS junctions
Abstract

Background: Microvascular endothelial hyperpermeability is an earliest pathological hallmark in Acute Lung Injury (ALI), which progressively leads to Acute Respiratory Distress Syndrome (ARDS). Recently, vascular protective and anti-inflammatory effect of metformin, irrespective of glycemic control, has garnered significant interest. However, the underlying molecular mechanism(s) of metformin's barrier protective benefits in lung-endothelial cells (ECs) has not been clearly elucidated. Many vascular permeability-increasing agents weakened adherens junctions (AJ) integrity by inducing the reorganization of the actin cytoskeleton and stress fibers formation. Here, we hypothesized that metformin abrogated endothelial hyperpermeability and strengthen AJ integrity via inhibiting stress fibers formation through cofilin-1-PP2AC pathway. Methods: We pretreated human lung microvascular ECs (human-lung-ECs) with metformin and then challenged with thrombin. To investigate the vascular protective effects of metformin, we studied changes in ECs barrier function using electric cell-substrate impedance sensing, levels of actin stress fibers formation and inflammatory cytokines IL-1β and IL-6 expression. To explore the downstream mechanism, we studied the Ser3-phosphorylation-cofilin-1 levels in scramble and PP2AC-siRNA depleted ECs in response to thrombin with and without metformin pretreatment. Results: In-vitro analyses showed that metformin pretreatment attenuated thrombin-induced hyperpermeability, stress fibers formation, and the levels of inflammatory cytokines IL-6 and IL-β in human-lung-ECs. We found that metformin mitigated Ser3-phosphorylation mediated inhibition of cofilin-1 in response to thrombin. Furthermore, genetic deletion of PP2AC subunit significantly inhibited metformin efficacy to mitigate thrombin-induced Ser3-phosphorylation cofilin-1, AJ disruption and stress fibers formation. We further demonstrated that metformin increases PP2AC activity by upregulating PP2AC-Leu309 methylation in human-lung-ECs. We also found that the ectopic expression of PP2AC dampened thrombin-induced Ser3-phosphorylation-mediated inhibition of cofilin-1, stress fibers formation and endothelial hyperpermeability. Conclusion: Together, these data reveal the unprecedented endothelial cofilin-1/ PP2AC signaling axis downstream of metformin in protecting against lung vascular endothelial injury and inflammation. Therefore, pharmacologically enhancing endothelial PP2AC activity may lead to the development of novel therapeutic approaches for prevention of deleterious effects of ALI on vascular ECs. [ABSTRACT FROM AUTHOR]

Published
2023
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50. Growth, physiological, biochemical and molecular changes in plants induced by magnetic fields: A review.

Author

Hafeez, M. B., Zahra, N., Ahmad, N., Shi, Z., Raza, A., Wang, X., Li, J., and Wicke, S.

Subjects
*MAGNETIC fields, *GEOMAGNETISM, *SUSTAINABLE agriculture, *METABOLITES, *CROP yields
Abstract

The Earth's geomagnetic field (GMF) is an inescapable environmental factor for plants that affects all growth and yield parameters. Both strong and weak magnetic fields (MF), as compared to the GMF, have specific roles in plant growth and development. MF technology is an eco‐friendly technique that does not emit waste or generate harmful radiation, nor require any external power supply, so it can be used in sustainable modern agriculture. Thus, exposure of plants to MF is a potential affordable, reusable and safe practice for enhancing crop productivity by changing physiological and biochemical processes. However, the effect of MF on plant physiological and biochemical processes is not yet well understood. This review describes the effects of altering MF conditions (higher or lower values than the GMF) on physiological and biochemical processes of plants. The current contradictory and inconsistent outcomes from studies on varying effects of MF on plants could be related to species and/or MF exposure time and intensity. The reviewed literature suggests MF have a role in changing physiological processes, such as respiration, photosynthesis, nutrient uptake, water relations and biochemical attributes, including genes involved in ROS, antioxidants, enzymes, proteins and secondary metabolites. MF application might efficiently increase growth and yield of many crops, and as such, should be the focus for future research. [ABSTRACT FROM AUTHOR]

Published
2023
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