Structural relations among implicit theories, achievement goals, and performance in writing

inexistenteinfo:eu-repo/semantics/publishedVersio

Model-based design of MADIX under bulk and solution conditions

Macromolecular design by interchange of xanthates (MADIX) is a less studied controlled radical polymerization technique from a mechanistic and modeling point of view. In this contribution, MADIX of styrene and chain extension toward the synthesis of block copolymers is investigated, with azobisisobutyronitrile as conventional radical initiator and O-ethylxanthyl ethyl propionate as initial RAFT agent (R0X). Degenerative transfer coefficients for both the exchange with R0X and macro-RAFT agent are reported and their difference is highlighted to be relevant for the kinetic description. The model validity is supported by measurement of end-group functionality (EGF) data considering elemental analysis. Novel mechanistic insights are that in contrast to typical reversible addition fragmentation chain transfer (RAFT) polymerizations the macroradical CLD follows a Schulz-Flory distribution and that both during the homopolymerization and the chain extensions an exchange, so with monomer incorporation, only takes place once [1]. [1] D.J.G. Devlaminck, P.H.M. Van Steenberge, M.-F. Reyniers, D.R. D’hooge, Polym Chem. 2017, 8, 694

The influence of distributed leadership on teachers' organizational commitment: a multilevel approach

In the present study the effects of a cooperative leadership team, distributed leadership, participative decision-making, and context variables on teachers' organizational commitment are investigated. Multilevel analyses on data from 1522 teachers indicated that 9% of the variance in teachers' organizational commitment is attributable to differences between schools. The analyses revealed that especially the presence of a cooperative leadership team and the amount of leadership support played a significantly positive key role in predicting teachers' organizational commitment. Also, participative decision-making and distribution of the supportive leadership function had a significant positive impact on teachers' organizational commitment. In contrast, distribution of the supervisory leadership function and teachers' job experience had a significant negative impact

The relation between school leadership from a distributed perspective and teachers' organizational commitment: examining the source of the leadership function

Purpose: In this study the relationship between school leadership and teachers’ organizational commitment is examined by taking into account a distributed leadership perspective. The relation between teachers’ organizational commitment and contextual variables of teachers’ perceptions of the quality and the source of the supportive and supervisory leadership function, participative decision making, and cooperation within the leadership team are examined. Research Design: A survey was set up involving 1,522 teachers from 46 large secondary schools in Flanders (Belgium). Because the data in the present study have an inherent hierarchical structure, that is, teachers are nested into schools, hierarchical linear modeling techniques are applied. Findings: The analyses reveal that 9% of the variance in teachers’ organizational commitment is attributable to differences between schools. Teachers’ organizational commitment is mainly related to quality of the supportive leadership, cooperation within the leadership team, and participative decision making. Who performed the supportive leadership function plays only a marginally significant positive role. The quality of the supervisory leadership function and the role of the leadership team members in this function were not significantly related to teachers’ organizational commitment. Conclusions: The implications of the findings are that to promote teachers’ organizational commitment teachers should feel supported by their leadership team and that this leadership team should be characterized by group cohesion, role clarity, and goal orientedness. Recommendations for further research are provided

Viability and Burden of Leishmania in Extralesional Sites during Human Dermal Leishmaniasis

Understanding of the dynamics and distribution of Leishmania in the human host is fundamental to the targeting of control measures and their evaluation. Amplification of parasite gene sequences in clinical samples from cutaneous leishmaniasis patients has provided evidence of Leishmania in blood, other tissues and sites distinct from the lesion and of persistence of infection after clinical resolution of disease. However, there is uncertainty about the interpretation of the presence of Leishmania DNA as indicative of viable parasites. Because RNA is short-lived and labile, its presence provides an indicator of viability. We amplified Leishmania 7SLRNA, a molecule involved in intracellular protein translocation, to establish viability and estimate parasite load in blood monocytes, tonsil swab samples, and tissue fluid from healthy skin of patients with dermal leishmaniasis. Results showed that during active dermal leishmaniasis, viable Leishmania are present in blood monocytes, tonsils and normal skin in quantities similar to that in lesions, demonstrating widespread dissemination of infection and subclinical involvement of tissues beyond the lesion site. Leishmania 7SLRNA will be useful in deciphering the role of human infection in transmission

REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs

[EN] Glaucoma is one of the leading causes of irreversible but preventable blindness in working age populations. Color fundus photography (CFP) is the most cost-effective imaging modality to screen for retinal disorders. However, its application to glaucoma has been limited to the computation of a few related biomarkers such as the vertical cup-to-disc ratio. Deep learning approaches, although widely applied for medical image analysis, have not been extensively used for glaucoma assessment due to the limited size of the available data sets. Furthermore, the lack of a standardize benchmark strategy makes difficult to compare existing methods in a uniform way. In order to overcome these issues we set up the Retinal Fundus Glaucoma Challenge, REFUGE (https://refuge.grand-challenge.org), held in conjunction with MIC-CAI 2018. The challenge consisted of two primary tasks, namely optic disc/cup segmentation and glaucoma classification. As part of REFUGE, we have publicly released a data set of 1200 fundus images with ground truth segmentations and clinical glaucoma labels, currently the largest existing one. We have also built an evaluation framework to ease and ensure fairness in the comparison of different models, encouraging the development of novel techniques in the field. 12 teams qualified and participated in the online challenge. This paper summarizes their methods and analyzes their corresponding results. In particular, we observed that two of the top-ranked teams outperformed two human experts in the glaucoma classification task. Furthermore, the segmentation results were in general consistent with the ground truth annotations, with complementary outcomes that can be further exploited by ensembling the results.This work was supported by the Christian Doppler Research Association, the Austrian Federal Ministry for Digital and Economic Affairs and the National Foundation for Research, Technology and Development, J.I.O is supported by WWTF (Medical University of Vienna: AugUniWien/FA7464A0249, University of Vienna: VRG12- 009). Team Masker is supported by Natural Science Foundation of Guangdong Province of China (Grant 2017A030310647). Team BUCT is partially supported by the National Natural Science Foundation of China (Grant 11571031). The authors would also like to thank REFUGE study group for collaborating with this challenge.Orlando, JI.; Fu, H.; Breda, JB.; Van Keer, K.; Bathula, DR.; Diaz-Pinto, A.; Fang, R.... (2020). REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs. Medical Image Analysis. 59:1-21. https://doi.org/10.1016/j.media.2019.101570S12159Abramoff, M. D., Garvin, M. K., & Sonka, M. (2010). Retinal Imaging and Image Analysis. IEEE Reviews in Biomedical Engineering, 3, 169-208. doi:10.1109/rbme.2010.2084567Abràmoff, M. D., Lavin, P. T., Birch, M., Shah, N., & Folk, J. C. (2018). Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. npj Digital Medicine, 1(1). doi:10.1038/s41746-018-0040-6Al-Bander, B., Williams, B., Al-Nuaimy, W., Al-Taee, M., Pratt, H., & Zheng, Y. (2018). Dense Fully Convolutional Segmentation of the Optic Disc and Cup in Colour Fundus for Glaucoma Diagnosis. Symmetry, 10(4), 87. doi:10.3390/sym10040087Almazroa, A., Burman, R., Raahemifar, K., & Lakshminarayanan, V. (2015). Optic Disc and Optic Cup Segmentation Methodologies for Glaucoma Image Detection: A Survey. Journal of Ophthalmology, 2015, 1-28. doi:10.1155/2015/180972Burlina, P. M., Joshi, N., Pekala, M., Pacheco, K. D., Freund, D. E., & Bressler, N. M. (2017). Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks. JAMA Ophthalmology, 135(11), 1170. doi:10.1001/jamaophthalmol.2017.3782Carmona, E. J., Rincón, M., García-Feijoó, J., & Martínez-de-la-Casa, J. M. (2008). Identification of the optic nerve head with genetic algorithms. Artificial Intelligence in Medicine, 43(3), 243-259. doi:10.1016/j.artmed.2008.04.005Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research, 16, 321-357. doi:10.1613/jair.953Christopher, M., Belghith, A., Bowd, C., Proudfoot, J. A., Goldbaum, M. H., Weinreb, R. N., … Zangwill, L. M. (2018). Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs. Scientific Reports, 8(1). doi:10.1038/s41598-018-35044-9De Fauw, J., Ledsam, J. R., Romera-Paredes, B., Nikolov, S., Tomasev, N., Blackwell, S., … Ronneberger, O. (2018). Clinically applicable deep learning for diagnosis and referral in retinal disease. Nature Medicine, 24(9), 1342-1350. doi:10.1038/s41591-018-0107-6Decencière, E., Zhang, X., Cazuguel, G., Lay, B., Cochener, B., Trone, C., … Klein, J.-C. (2014). FEEDBACK ON A PUBLICLY DISTRIBUTED IMAGE DATABASE: THE MESSIDOR DATABASE. Image Analysis & Stereology, 33(3), 231. doi:10.5566/ias.1155DeLong, E. R., DeLong, D. M., & Clarke-Pearson, D. L. (1988). Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach. Biometrics, 44(3), 837. doi:10.2307/2531595European Glaucoma Society Terminology and Guidelines for Glaucoma, 4th Edition - Part 1Supported by the EGS Foundation. (2017). British Journal of Ophthalmology, 101(4), 1-72. doi:10.1136/bjophthalmol-2016-egsguideline.001Farbman, Z., Fattal, R., Lischinski, D., & Szeliski, R. (2008). Edge-preserving decompositions for multi-scale tone and detail manipulation. ACM Transactions on Graphics, 27(3), 1-10. doi:10.1145/1360612.1360666Fu, H., Cheng, J., Xu, Y., Wong, D. W. K., Liu, J., & Cao, X. (2018). Joint Optic Disc and Cup Segmentation Based on Multi-Label Deep Network and Polar Transformation. IEEE Transactions on Medical Imaging, 37(7), 1597-1605. doi:10.1109/tmi.2018.2791488Gómez-Valverde, J. J., Antón, A., Fatti, G., Liefers, B., Herranz, A., Santos, A., … Ledesma-Carbayo, M. J. (2019). Automatic glaucoma classification using color fundus images based on convolutional neural networks and transfer learning. Biomedical Optics Express, 10(2), 892. doi:10.1364/boe.10.000892Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., … Webster, D. R. (2016). Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA, 316(22), 2402. doi:10.1001/jama.2016.17216Hagiwara, Y., Koh, J. E. W., Tan, J. H., Bhandary, S. V., Laude, A., Ciaccio, E. J., … Acharya, U. R. (2018). Computer-aided diagnosis of glaucoma using fundus images: A review. Computer Methods and Programs in Biomedicine, 165, 1-12. doi:10.1016/j.cmpb.2018.07.012Haleem, M. S., Han, L., van Hemert, J., & Li, B. (2013). Automatic extraction of retinal features from colour retinal images for glaucoma diagnosis: A review. Computerized Medical Imaging and Graphics, 37(7-8), 581-596. doi:10.1016/j.compmedimag.2013.09.005Holm, S., Russell, G., Nourrit, V., & McLoughlin, N. (2017). DR HAGIS—a fundus image database for the automatic extraction of retinal surface vessels from diabetic patients. Journal of Medical Imaging, 4(1), 014503. doi:10.1117/1.jmi.4.1.014503Joshi, G. D., Sivaswamy, J., & Krishnadas, S. R. (2011). Optic Disk and Cup Segmentation From Monocular Color Retinal Images for Glaucoma Assessment. IEEE Transactions on Medical Imaging, 30(6), 1192-1205. doi:10.1109/tmi.2011.2106509Kaggle, 2015. Diabetic Retinopathy Detection. https://www.kaggle.com/c/diabetic-retinopathy-detection. [Online; accessed 10-January-2019].Kumar, J. R. H., Seelamantula, C. S., Kamath, Y. S., & Jampala, R. (2019). Rim-to-Disc Ratio Outperforms Cup-to-Disc Ratio for Glaucoma Prescreening. Scientific Reports, 9(1). doi:10.1038/s41598-019-43385-2Lavinsky, F., Wollstein, G., Tauber, J., & Schuman, J. S. (2017). The Future of Imaging in Detecting Glaucoma Progression. Ophthalmology, 124(12), S76-S82. doi:10.1016/j.ophtha.2017.10.011Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324. doi:10.1109/5.726791Li, Z., He, Y., Keel, S., Meng, W., Chang, R. T., & He, M. (2018). Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs. Ophthalmology, 125(8), 1199-1206. doi:10.1016/j.ophtha.2018.01.023Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., … Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60-88. doi:10.1016/j.media.2017.07.005Liu, S., Graham, S. L., Schulz, A., Kalloniatis, M., Zangerl, B., Cai, W., … You, Y. (2018). A Deep Learning-Based Algorithm Identifies Glaucomatous Discs Using Monoscopic Fundus Photographs. Ophthalmology Glaucoma, 1(1), 15-22. doi:10.1016/j.ogla.2018.04.002Lowell, J., Hunter, A., Steel, D., Basu, A., Ryder, R., Fletcher, E., & Kennedy, L. (2004). Optic Nerve Head Segmentation. IEEE Transactions on Medical Imaging, 23(2), 256-264. doi:10.1109/tmi.2003.823261Maier-Hein, L., Eisenmann, M., Reinke, A., Onogur, S., Stankovic, M., Scholz, P., … Kopp-Schneider, A. (2018). Why rankings of biomedical image analysis competitions should be interpreted with care. Nature Communications, 9(1). doi:10.1038/s41467-018-07619-7Miri, M. S., Abramoff, M. D., Lee, K., Niemeijer, M., Wang, J.-K., Kwon, Y. H., & Garvin, M. K. (2015). Multimodal Segmentation of Optic Disc and Cup From SD-OCT and Color Fundus Photographs Using a Machine-Learning Graph-Based Approach. IEEE Transactions on Medical Imaging, 34(9), 1854-1866. doi:10.1109/tmi.2015.2412881Niemeijer, M., van Ginneken, B., Cree, M. J., Mizutani, A., Quellec, G., Sanchez, C. I., … Abramoff, M. D. (2010). Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs. IEEE Transactions on Medical Imaging, 29(1), 185-195. doi:10.1109/tmi.2009.2033909Odstrcilik, J., Kolar, R., Budai, A., Hornegger, J., Jan, J., Gazarek, J., … Angelopoulou, E. (2013). Retinal vessel segmentation by improved matched filtering: evaluation on a new high‐resolution fundus image database. IET Image Processing, 7(4), 373-383. doi:10.1049/iet-ipr.2012.0455Orlando, J. I., Prokofyeva, E., & Blaschko, M. B. (2017). A Discriminatively Trained Fully Connected Conditional Random Field Model for Blood Vessel Segmentation in Fundus Images. IEEE Transactions on Biomedical Engineering, 64(1), 16-27. doi:10.1109/tbme.2016.2535311Park, S. J., Shin, J. Y., Kim, S., Son, J., Jung, K.-H., & Park, K. H. (2018). A Novel Fundus Image Reading Tool for Efficient Generation of a Multi-dimensional Categorical Image Database for Machine Learning Algorithm Training. Journal of Korean Medical Science, 33(43). doi:10.3346/jkms.2018.33.e239Poplin, R., Varadarajan, A. V., Blumer, K., Liu, Y., McConnell, M. V., Corrado, G. S., … Webster, D. R. (2018). Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nature Biomedical Engineering, 2(3), 158-164. doi:10.1038/s41551-018-0195-0Porwal, P., Pachade, S., Kamble, R., Kokare, M., Deshmukh, G., Sahasrabuddhe, V., & Meriaudeau, F. (2018). Indian Diabetic Retinopathy Image Dataset (IDRiD): A Database for Diabetic Retinopathy Screening Research. Data, 3(3), 25. doi:10.3390/data3030025Prokofyeva, E., & Zrenner, E. (2012). Epidemiology of Major Eye Diseases Leading to Blindness in Europe: A Literature Review. Ophthalmic Research, 47(4), 171-188. doi:10.1159/000329603Raghavendra, U., Fujita, H., Bhandary, S. V., Gudigar, A., Tan, J. H., & Acharya, U. R. (2018). Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus images. Information Sciences, 441, 41-49. doi:10.1016/j.ins.2018.01.051Reis, A. S. C., Sharpe, G. P., Yang, H., Nicolela, M. T., Burgoyne, C. F., & Chauhan, B. C. (2012). Optic Disc Margin Anatomy in Patients with Glaucoma and Normal Controls with Spectral Domain Optical Coherence Tomography. Ophthalmology, 119(4), 738-747. doi:10.1016/j.ophtha.2011.09.054Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., … Fei-Fei, L. (2015). ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision, 115(3), 211-252. doi:10.1007/s11263-015-0816-ySchmidt-Erfurth, U., Sadeghipour, A., Gerendas, B. S., Waldstein, S. M., & Bogunović, H. (2018). Artificial intelligence in retina. Progress in Retinal and Eye Research, 67, 1-29. doi:10.1016/j.preteyeres.2018.07.004Sevastopolsky, A. (2017). Optic disc and cup segmentation methods for glaucoma detection with modification of U-Net convolutional neural network. Pattern Recognition and Image Analysis, 27(3), 618-624. doi:10.1134/s1054661817030269Taha, A. A., & Hanbury, A. (2015). Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Medical Imaging, 15(1). doi:10.1186/s12880-015-0068-xThakur, N., & Juneja, M. (2018). Survey on segmentation and classification approaches of optic cup and optic disc for diagnosis of glaucoma. Biomedical Signal Processing and Control, 42, 162-189. doi:10.1016/j.bspc.2018.01.014Tham, Y.-C., Li, X., Wong, T. Y., Quigley, H. A., Aung, T., & Cheng, C.-Y. (2014). Global Prevalence of Glaucoma and Projections of Glaucoma Burden through 2040. Ophthalmology, 121(11), 2081-2090. doi:10.1016/j.ophtha.2014.05.013Johnson, S. S., Wang, J.-K., Islam, M. S., Thurtell, M. J., Kardon, R. H., & Garvin, M. K. (2018). Local Estimation of the Degree of Optic Disc Swelling from Color Fundus Photography. Lecture Notes in Computer Science, 277-284. doi:10.1007/978-3-030-00949-6_33Trucco, E., Ruggeri, A., Karnowski, T., Giancardo, L., Chaum, E., Hubschman, J. P., … Dhillon, B. (2013). Validating Retinal Fundus Image Analysis Algorithms: Issues and a Proposal. Investigative Opthalmology & Visual Science, 54(5), 3546. doi:10.1167/iovs.12-10347Vergara, I. A., Norambuena, T., Ferrada, E., Slater, A. W., & Melo, F. (2008). StAR: a simple tool for the statistical comparison of ROC curves. BMC Bioinformatics, 9(1). doi:10.1186/1471-2105-9-265Wu, Z., Shen, C., & van den Hengel, A. (2019). Wider or Deeper: Revisiting the ResNet Model for Visual Recognition. Pattern Recognition, 90, 119-133. doi:10.1016/j.patcog.2019.01.006Zheng, Y., Hijazi, M. H. A., & Coenen, F. (2012). Automated «Disease/No Disease» Grading of Age-Related Macular Degeneration by an Image Mining Approach. Investigative Opthalmology & Visual Science, 53(13), 8310. doi:10.1167/iovs.12-957

Gravitational waves from single neutron stars: an advanced detector era survey

With the doors beginning to swing open on the new gravitational wave astronomy, this review provides an up-to-date survey of the most important physical mechanisms that could lead to emission of potentially detectable gravitational radiation from isolated and accreting neutron stars. In particular we discuss the gravitational wave-driven instability and asteroseismology formalism of the f- and r-modes, the different ways that a neutron star could form and sustain a non-axisymmetric quadrupolar "mountain" deformation, the excitation of oscillations during magnetar flares and the possible gravitational wave signature of pulsar glitches. We focus on progress made in the recent years in each topic, make a fresh assessment of the gravitational wave detectability of each mechanism and, finally, highlight key problems and desiderata for future work.Comment: 39 pages, 12 figures, 2 tables. Chapter of the book "Physics and Astrophysics of Neutron Stars", NewCompStar COST Action 1304. Minor corrections to match published versio

Joint hypermobility in children with idiopathic scoliosis: SOSORT award 2011 winner

<p>Abstract</p> <p>Background</p> <p>Generalized joint hypermobility (JHM) refers to increased joint mobility with simultaneous absence of any other systemic disease. JHM involves proprioception impairment, increased frequency of pain within joints and tendency to injure soft tissues while performing physical activities. Children with idiopathic scoliosis (IS) often undergo intensive physiotherapy requiring good physical capacities. Further, some physiotherapy methods apply techniques that increase joint mobility and thus may be contraindicated.</p> <p>The aim of this paper was to assess JHM prevalence in children with idiopathic scoliosis and to analyze the relationship between JHM prevalence and the clinical and radiological parameters of scoliosis. The methods of assessment of generalized joint hypermobility were also described.</p> <p>Materials and methods</p> <p>This case-control study included 70 subjects with IS, aged 9-18 years (mean 13.2 ± 2.2), Cobb angle range 10°-53° (mean 24.3 ± 11.7), 34 presenting single curve thoracic scoliosis and 36 double curve thoracic and lumbar scoliosis. The control group included 58 children and adolescents aged 9-18 years (mean 12.6 ± 2.1) selected at random. The presence of JHM was determined using Beighton scale complemented with the questionnaire by Hakim and Grahame. The relationship between JHM and the following variables was evaluated: curve severity, axial rotation of the apical vertebra, number of curvatures (single versus double), number of vertebrae within the curvature (long versus short curves), treatment type (physiotherapy versus bracing) and age.</p> <p>Statistical analysis was performed with Statistica 8.1 (StatSoft, USA). The Kolmogorov-Smirnov test, U Mann-Whitney test, Chi²test, Pearson and Spermann correlation rank were conducted. The value <it>p </it>= 0.05 was adopted as the level of significance.</p> <p>Results</p> <p>JHM was diagnosed in more than half of the subjects with idiopathic scoliosis (51.4%), whilst in the control group it was diagnosed in only 19% of cases (<it>p </it>= 0.00015). A significantly higher JHM prevalence was observed in both girls (<it>p </it>= 0.0054) and boys (<it>p </it>= 0.017) with IS in comparison with the corresponding controls. No significant relation was found between JHM prevalence and scoliosis angular value (<it>p </it>= 0.35), apical vertebra rotation (<it>p </it>= 0.86), the number of vertebrae within curvature (<it>p </it>= 0.8), the type of applied treatment (<it>p </it>= 0.55) and the age of subjects (<it>p </it>= 0.79). JHM prevalence was found to be higher in children with single curve scoliosis than in children with double curve scoliosis (<it>p </it>= 0.03).</p> <p>Conclusions</p> <p>JHM occurs more frequently in children with IS than in healthy sex and age matched controls. No relation of JHM with radiological parameters, treatment type and age was found. Systematically searched in IS children, JHM should be taken into account when physiotherapy is planned.</p

The role of alveolar type II cells in swine leptospirosis

Abstract: This study aimed to investigate a possible relationship between alveolar type II cells and the inflammatory response to infection with Leptospira spp., and thus comprise a further element that can be involved in the pathogenesis of lung injury in naturally infected pigs. The study group consisted of 73 adult pigs that were extensively reared and slaughtered in Teresina, Piauí state, and Timon, Maranhão state, Brazil. The diagnosis of leptospirosis was made using the microscopic agglutination test (MAT) aided by immunohistochemistry and polymerase chain reaction. The MAT registered the occurrence of anti-Leptospira antibodies in 10.96% (8/73) of the pigs. Immunohistochemistry allowed for the visualization of the Leptospira spp. antigen in the lungs of 87.67% (64/73) of the pigs. There was hyperplasia of bronchus-associated lymphoid tissue and circulatory changes, such as congestion of alveolar septa, parenchymal hemorrhage and edema within the alveoli. Lung inflammation was more intense (p = 0.0312) in infected animals, which also showed increased thickening of the alveolar septa (p = 0.0006). Evaluation of alveolar type II (ATII) cells using an anti-TTF-1 (Thyroid Transcription Factor-1) antibody showed that there were more immunostained cells in the non-infected pigs (53.8%) than in the infected animals (46.2%) and that there was an inverse correlation between TTF-1 positive cells and the inflammatory infiltrate. There was no amplification of Leptospira DNA in the lung samples, but leptospiral DNA amplification was observed in the kidneys. The results of this study showed that a relationship exists between a decrease in alveolar type II cells and a leptospire infection. Thus, this work points to the importance of studying the ATII cells as a potential marker of the level of lung innate immune response during leptospirosis in pigs