8 research outputs found
A Case of Paroxysmal Supraventricular Tachycardia Clinical and Electrocardiographic Appraisal
No full textParoxysmal supraventricular tachycardia refers to a clinical syndrome characterized by rapid regular tachycardia with abrupt onset and termination which originates from or conducts through the atria or atrio-ventricular node. (1) We report 40- year- old male patient who underwent mitral valve replacement for rheumatic heart disease severe mitral stenosis with St Jude’s Valve who presented with recurrent palpitations. Systematic clinical and electrocardiographic analysis aids in precise non- invasive diagnosis prior to detailed electrophysiological studies
Unusual Cause of Congestive Heart Failure with Severe Mitral Regurgitation
No full textSub mitral aneurysm is a rare cardiac anomaly with varied clinical manifestations, usually due to congenital defect adjacent to posterior leaflet of mitral valve. We report 50-year-old male patient with submitral aneurysm who presented with features suggestive of congestive heart failure and severe mitral regurgitation. Echo cardiography and cardiac MRI aid in precise non-invasive diagnosis
Quality assessment of user-generated video using camera motion
No full textWith user-generated video (UGV) becoming so popular on
theWeb, the availability of a reliable quality assessment (QA) measure of UGV is necessary for improving the users’ quality of experience in videobased application. In this paper, we explore QA of UGV based on how much irregular camera motion it contains with low-cost manner. A blockmatch
based optical flow approach has been employed to extract camera motion features in UGV, based on which, irregular camera motion is calculated and automatic QA scores are given. Using a set of UGV clips from benchmarking datasets as a showcase, we observe that QA scores from the proposed automatic method and subjective method fit well.
Further, the automatic method reports much better performance than the random run. These confirm the satisfaction of the automatic QA scores indicating the quality of the UGV when only considering visual
camera motion. Furthermore, it also shows that the UGV quality can be assessed automatically for improving the end users quality of experience in video-based applications
A QoE adaptive management system for high definition video streaming over wireless networks
No full text[EN] The development of the smart devices had led to demanding high-quality streaming videos over wireless communications. In Multimedia technology, the Ultra-High Definition (UHD) video quality has an important role due to the smart devices that are capable of capturing and processing high-quality video content. Since delivery of the high-quality video stream over the wireless networks adds challenges to the end-users, the network behaviors 'factors such as delay of arriving packets, delay variation between packets, and packet loss, are impacted on the Quality of Experience (QoE). Moreover, the characteristics of the video and the devices are other impacts, which influenced by the QoE. In this research work, the influence of the involved parameters is studied based on characteristics of the video, wireless channel capacity, and receivers' aspects, which collapse the QoE. Then, the impact of the aforementioned parameters on both subjective and objective QoE is studied. A smart algorithm for video stream services is proposed to optimize assessing and managing the QoE of clients (end-users). The proposed algorithm includes two approaches: first, using the machine-learning model to predict QoE. Second, according to the QoE prediction, the algorithm manages the video quality of the end-users by offering better video quality. As a result, the proposed algorithm which based on the least absolute shrinkage and selection operator (LASSO) regression is outperformed previously proposed methods for predicting and managing QoE of streaming video over wireless networks.This work has been partially supported by the "Ministerio de Economia y Competitividad" in the "Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento" with in the Project under Grant TIN2017-84802-C2-1-P. This study has been partially done in the computer science departments at the (University of Sulaimani and Halabja).Taha, M.; Canovas, A.; Lloret, J.; Ali, A. (2021). A QoE adaptive management system for high definition video streaming over wireless networks. Telecommunication Systems. 77(1):63-81. https://doi.org/10.1007/s11235-020-00741-2S6381771Cisco report, Last Accessed July 1, 2017. [Available Online]: http://bit.ly/2gg1F6BKitamura, M., Shirai, D., Kaneko, K., Murooka, T., Sawabe, T., Fujii, T., & Takahara, A. (2011). Beyond 4K: 8K 60p live video streaming to multiple sites. Future Generation Computer Systems, 27(7), 952–959. https://doi.org/10.1016/j.future.2010.11.025.Juluri, P., Tamarapalli, V., & Medhi, D. (2016). Measurement of quality of experience of video-on-demand services: A survey. IEEE Communications Surveys & Tutorials, 18(1), 401–418.Frnda, J., Voznak, M., & Sevcik, L. (2016). Impact of packet loss and delay variation on the quality of real-time video streaming. Telecommunication Systems, 62(2), 265–275.Al-Jobouri, L., Fleury, M., & Ghanbari, M. (2016). Broadband wireless video streaming: achieving unicast and multicast IPTV in a practical manner. Multimedia Tools and Applications, Springer, 75(11), 6403–6430. https://doi.org/10.1007/s11042-015-2577-6.Moorthy, A. K., & Bovik, A. C. (2011). Visual quality assessment algorithms: What does the future hold? Multimedia Tools and Applications, 51(2), 675–696. https://doi.org/10.1007/s11042-010-0640-x.T.-L. Chin, T.-Y. Chen, C.-C. Huang, T.-R. (2015). Hsiang. Scalable video streaming for multicast in wireless networks. In Intelligent Signal Processing and Communication Systems (ISPACS), International Symposium on, IEEE, Nusa Dua, Indonesia, pp. 182–187, Nov. 2015. https://doi.org/10.1109/ISPACS.2015.7432762Shmueli, R., Hadar, O., Huber, R., Maltz, M., & Huber, M. (2008). Effects of an encoding scheme on perceived video quality transmitted over lossy internet protocol networks. Transactions on Broadcasting, IEEE, 54(3), 628–640.Seufert, M., Egger, S., Slanina, M., Zinner, T., Hoßfeld, T., & Tran-Gia, P. (2015). A survey on quality of experience of HTTP adaptive streaming. IEEE Communications Surveys & Tutorials, 17(1), 469–492.Ge, C., Wang, N., Foster, G., & Wilson, M. (2017). Towards QoE-assured 4K Video-on-Demand Delivery through Mobile Edge Virtualization with Adaptive Prefetching. Transactions on Multimedia, IEEE, PP(99), 1–1. https://doi.org/10.1109/TMM.2017.2735301.Lim, W.-S., Kim, D.-W., & Suh, Y.-J. (2012). Design of efficient multicast protocol for IEEE 802.11 n WLANs and cross-layer optimization for scalable video streaming. Transactions on Mobile Computing, IEEE, 11(5), 780–792. https://doi.org/10.1109/TMC.2011.95.Su, G.-M., Su, X., Bai, Y., Wang, M., Vasilakos, A. V., & Wang, H. (2016). QoE in video streaming over wireless networks: perspectives and research challenges. Wireless networks, Springer, 22(5), 571–1593. https://doi.org/10.1007/s11276-015-1028-7.Lloret, J., Garcia, M., Atenas, M., & Canovas, A. (2010). A QoE management system to improve the IPTV networks. International Journal of Communication Systems, 24(1), 118–138. https://doi.org/10.1002/dac.1145.Richards, A., Rogers, G., Antoniades, M., Witana, V. (1998) Mapping user level QoS from a single parameter. In Second IFIP/IEEE International Conference of Management of Multimedia Networks and Services, (pp. 16–18) Versailles, France, November 1998.Soh, K., & Iah, S. (2001). Subjectively assessing method for audiovisual quality using equivalent signal-to-noise ratio conversion. Transactions of the Institute of Electronics, Information and Communication Engineers, J84A(11), 1305–1313.Sedano, I., Brunnström, K., Kihl, M., & Aurelius, A. (2014). Full-reference video quality metric assisted the development of no-reference bitstream video quality metrics for real-time network monitoring. EURASIP Journal on Image and Video Processing, Springer, 2014(1), 4.Hadizadeh, H., & Bajic, I. V. (2017). Full-reference objective quality assessment of tone-mapped images. Transactions on Multimedia, IEEE, PP(99), 1–1. https://doi.org/10.1109/TMM.2017.2740023.Wang, H., Chan, M. C., & Ooi, W. T. (2015). Wireless multicast for zoomable video streaming. ACM Transactions on Multimedia, Computing Communications, and Applications (TOMM), 12(1), 5. https://doi.org/10.1145/2801123.Seshadrinathan, K., Soundararajan, R., Bovik, A. C., & Cormack, L. K. (2010). Study of subjective and objective quality assessment of video. IEEE transactions on Image Processing, 19(6), 1427–1441. https://doi.org/10.1109/TIP.2010.2042111.Taha, M., Jimenez, J. M., Canovas, A., & Lloret, J. (2018). Intelligent Algorithm for Enhancing MPEG-DASH QoE in eMBMS. Network Protocols and Algorithms, 9(3–4), 94–114. https://doi.org/10.5296/npa.v9i3-4.12573.Taha, M., Lloret, J., Canovas, A., & Garcia, L. (2017). Survey of transportation of adaptive multimedia streaming service in internet. Network Protocols and Algorithms, 9(1–2), 85–125. https://doi.org/10.5296/npa.v9i1-2.12412.Mateos-Cañas, I., Sendra, S., Lloret, J., & Jimenez, J. M. (2017). Autonomous video compression system for environmental monitoring. Network Protocols and Algorithms. https://doi.org/10.5296/npa.v9i1-2.12386.Vega, M. T., Perra, C., De Turck, F., & Liotta, A. (2018). A Review of Predictive Quality of Experience Management in Video Streaming Services. IEEE Transactions on Broadcasting, 64(2), 432–445.Taha, M., Lloret, J., Ali, A., & Garcia, L. (2018). Adaptive video streaming testbed design for performance study and assessment of QoE. International Journal of Communication Systems, 31(9), e3551. https://doi.org/10.1002/dac.3551.Cánovas, A., Taha, M., Lloret, J., & Tomas, J. (2019). A cognitive network management system to improve QoE in stereoscopic IPTV service. International Journal of Communication Systems, 32(12), e3992.Abdullah, M. T. (2018). Smart client-server protocol and architecture for adaptive multimedia streaming. PhD diss., UniversitatPolitècnica de València
An automated model for the assessment of QoE of adaptive video streaming over wireless networks
No full text[EN] Nowadays, heterogeneous devices are widely utilizing Hypertext Transfer Protocol (HTTP) to transfer the data. Furthermore, HTTP adaptive video streaming (HAS) technology transmits the video data over wired and wireless networks. In adaptive technology services, a client's application receives a streaming video through the adaptation of its quality to the network condition. However, such a technology has increased the demand for Quality of Experience (QoE) in terms of prediction and assessment. It can also cause a challenging behavior regarding subjective and objective QoE evaluations of HTTP adaptive video over time since each Quality of Service (QoS) parameter affects the QoE of end-users separately. This paper introduces a methodology design for the evaluation of subjective QoE in adaptive video streaming over wireless networks. Besides, some parameters are considered such as video characteristics, segment length, initial delay, switch strategy, stalls, as well as QoS parameters. The experiment's evaluation demonstrated that objective metrics can be mapped to the most significant subjective parameters for user's experience. The automated model could function to demonstrate the importance of correlation for network behaviors' parameters. Consequently, it directly influences the satisfaction of the end-user's perceptual quality. In comparison with other recent related works, the model provided a positive Pearson Correlation value. Simulated results give a better performance between objective Structural Similarity (SSIM) and subjective Mean Opinion Score (MOS) evaluation metrics for all video test samples.This work has been partially supported by the "Ministerio de Economia y Competitividad" in the "Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento" within the Project under Grant TIN2017-84802-C2-1-P. This study has been partially done in the computer science departments at the (University of Sulaimani and Halabja).Taha, M.; Ali, A.; Lloret, J.; Gondim, PRL.; Canovas, A. (2021). An automated model for the assessment of QoE of adaptive video streaming over wireless networks. Multimedia Tools and Applications. 80(17):26833-26854. https://doi.org/10.1007/s11042-021-10934-9S26833268548017Abar T, Letaifa AB, Elasmi S (2018) Enhancing QoE based on machine learning and DASH in SDN networks. 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA), IEEE, Krakow, Poland, pp. 258–263, https://doi.org/10.1109/WAINA.2018.00095Absolute Category Rating (ACR) ITU-R BT.500–12. Recommendation ITU-R BT.500-13Methodology for the subjective assessment of the quality of television pictures (Jan. 2012), [Accessed November 2019 Online]Barman N, Martini MG (2019) QoE modeling for HTTP adaptive video streaming–a survey and open challenges. IEEE Access 7:30831–30859. https://doi.org/10.1109/ACCESS.2019.2901778Barman N, Zadtootaghaj S, Schmidt S, Martini MG, Möller S (2018) An objective and subjective quality assessment study of passive gaming video streaming. International Journal of Network Management, pp. e2054. DOI: https://doi.org/10.1002/nem.2054Barman N, Jammeh E, Ghorashi SA, Martini MG (2019) No-reference video quality estimation based on machine learning for passive gaming video streaming applications. IEEE Access 7:74511–74527. https://doi.org/10.1109/ACCESS.2019.2920477Bulkan U, Dagiuklas T (2019) Predicting quality of experience for online video service provisioning. Multimed Tools Appl 78(13):18787–18811. https://doi.org/10.1007/s11042-019-7164-9Chen Y, Wu K, Zhang Q (2014) From QoS to QoE: a tutorial on video quality assessment. IEEE Commun Surveys Tutorials 17(2):1126–1165. https://doi.org/10.1109/COMST.2014.2363139Chikkerur S, Sundaram V, Reisslein M, Karam LJ (2011) Objective video quality assessment methods: a classification, review, and performance comparison. IEEE Trans Broadcast 57(2):165–182. https://doi.org/10.1109/TBC.2011.2104671Cisco report, [Accessed November 2019 Online]: http://bit.ly/2gg1F6BClaeys M, Latre S, Famaey J, De Turck F (2014) Design and evaluation of a self-learning HTTP adaptive video streaming client. IEEE Commun Lett 18(4):716–719. https://doi.org/10.1109/LCOMM.2014.020414.132649Cofano G, De Cicco L, Zinner T, Nguyen-Ngoc A, Tran-Gia P, Mascolo S (2016) Design and experimental evaluation of network-assisted strategies for HTTP adaptive streaming. In Proceedings of the 7th International Conference on Multimedia Systems, pp. 1–12. https://doi.org/10.1145/2910017.2910597Duanmu Z, Zeng K, Ma K, Rehman A, Wang Z (2016) A quality-of-experience index for streaming video. IEEE J Selected Topics Sign Process 11(1):154–166. https://doi.org/10.1109/10.1109/JSTSP.2016.2608329García L, Lloret J, Turro C, Taha M (2016) QoE assesment of MPEG-DASH in polimedia e-learning system. International Conference on Advances in Computing, Communications and Informatics (ICACCI), IEEE, pp. 1117–1123. DOI: https://doi.org/10.1109/ICACCI.2016.7732194García B, López-Fernández L, Gortázar F, Gallego M (2019) Practical Evaluation of VMAF Perceptual Video Quality for WebRTC Aepplications. Journal of Electronics, vol. 9, no. 2. https://doi.org/10.3390/electronics8080854Guan-MingSu XS, Bai Y, Wang M, Vasilakos AV, Wang H (2016) QoE in video streaming over wireless networks: perspectives and research challenges. Wirel Netw 22(5):1571–1593. https://doi.org/10.1007/s11276-015-1028-7Gutterman C, Guo K, Arora S, Wang X, Wu L, Katz-Bassett E, Zussman G (2019) Requet: Real-time QoE detection for encrypted YouTube traffic. In Proceedings of the 10th ACM Multimedia Systems Conference, pp. 48–59. https://doi.org/10.1145/3304109.3306226Huawei report, [Accessed December 2019 Online]: https://www.huawei.com/minisite/hwmbbf15/img/video_coverage_whitepaper_en.pdf.ITU-T Rec. P.1203. (2017) Parametric bitstream-based quality assessment of progressive download and adaptive audiovisual streaming services over reliable transport. ITU-T Rec. P.1203Juluri P, Tamarapalli V, Medhi D (2015) Measurement of quality of experience of video-on-demand services: a survey. IEEE Commun Surveys Tutorials 18(1):401–418. https://doi.org/10.1109/COMST.2015.2401424Juluri P, Tamarapalli V, Medhi D (2016) Measurement of quality of experience of video-on-demand services: a survey. IEEE Commun Surveys Tutorials 18(1):401–418. https://doi.org/10.1109/COMST.2015.2401424Liu X, Nan Z, Richard Yu F, Chen Y, Tang J, Leung VCM (2018) Cooperative video transmission strategies via caching in small-cell networks. IEEE Trans Veh Technol 67(12):12204–12217. https://doi.org/10.1109/TVT.2018.2874258MingfuLi C-LY, Shao-Yu L (2018) Real-time QoE monitoring system for video streaming services with adaptive media playout. Int J Digital Multimed Broadcast 2018:1–11. https://doi.org/10.1155/2018/2619438Mok R, Luo X, Chan E, Chang R (2012) QDASH: A QoE-aware DASH system. Proceedings of the 3rd Multimedia Systems Conference, pp. 11–22. https://doi.org/10.1145/2155555.2155558Moorthy AK, Seshadrinathan K, Soundararajan R, Bovik AC (2010) Wireless video quality assessment: a study of subjective scores and objective algorithms. IEEE trans Circuits Syst Vid Technol 20(4):587–599. https://doi.org/10.1109/TCSVT.2010.2041829Nam H, Kim K-H, Schulzrinne H (2016) QoE matters more than QoS: Why people stop watching cat videos. In IEEE INFOCOM 2016-The 35th Annual IEEE International Conference on Computer Communications, IEEE, pp. 1–9. https://doi.org/10.1109/INFOCOM.2016.7524426Nunome T, Mizutani K (2019) The joint effect of wireless LAN reliable groupcast and a rate-adaptation mechanism on QoE of audio and video transmission. in Proc. 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ACM Trans Multimed Comput Commun Appl (TOMM) 14(2):1–29. https://doi.org/10.1145/3165266Poojary S, El-Azouzi R, Altman E, Sunny A, Triki I, Haddad M, Jimenez T, Valentin S, Tsilimantos D (2018) Analysis of QoE for adaptive video streaming over wireless networks. 16th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt), IEEE, pp. 1–8, https://doi.org/10.23919/WIOPT.2018.8362846Retal S, Bagaa M, Taleb T, Flinck H (2017) Content delivery network slicing: QoE and cost awareness. IEEE International Conference on Communications (ICC), IEEE, pp. 1–6, https://doi.org/10.1109/ICC.2017.7996499Rodrigues F, Sousa I, Queluz MP, Rodrigues A (2018) QoE-aware scheduling algorithm for adaptive HTTP video delivery in wireless networks. Wireless Communications and Mobile computing, vol. 2018. 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Enhancing non-centrifugal cane sugar's clarification process to obtain a low-sugar sweetener
No full textConsidering the negative health consequences of sugar consumption, the polyols have emerged as promising alternative sweeteners due to their negligible caloric contribution and considerably sweetening capacity. The Stirred-tank (STBR) and shaken flask (SFB) fermentation processes have been proposed in recent researches, as well as, cheaper alternative carbon sources, such as Non-centrifugal Sugar Cane (NCS). This study investigated the use of Non-centrifugal Cane Sugar (NCS), a traditional Colombian product, as a low-cost substrate for producing an alternative sweetener with functional properties and suitable physical quality standards. The research was conducted in two stages. In the first stage, the sugar cane juice clarification process was improved by using three natural flocculants ("Cadillo Blanco," "Balso," "Guásimo") and three pH levels (5,5; 6,0; 6,5). Statistical analysis identified "Cadillo Blanco" and a pH of 5,5 as the best treatment, enhancing polyphenol content (2,11 ± 0,18 mg GAE/g NCS), antioxidant properties (DPPH IC50: 707,79 ± 6.27 mg/L; FRAP: 10,2 ± 0.06 mg Trolox eq./g NCS), and reducing acrylamide (29 µg/kg) and glucose levels (0,051 ± 0.004 g glucose/g NCS), while maintaining acceptable color parameters. In the second stage, the biotechnological production of a low-sugar sweetener enriched with polyols was evaluated. A factorial design assessed the effects of two substrates (the improved NCS treatment from first stage and commercial NCS) and two fermentation methods (STBR and SFB). Manufactured NCS in STBR fermentation resulted in the highest concentrations of polyols (L-arabitol: 159,07 ± 0,002 mg/g; erythritol: 17,45 ± 0,001 mg/g), remnant polyphenols (0,96 ± 0,075 mg GAE/g), and antioxidant capacity (DPPH IC50: 860,8 ± 21,4 mg of sample/L; FRAP: 7,78 ± 0,071 mg Trolox eq./g of sample), with suitable physical properties (hygroscopicity: 18,09 ± 1,10g/100g; solubility: 86,1 ± 2,8%). This study highlights NCS as a promising raw material for functional sweetener production, aligning with global trends for healthier food systems.Maestrí
Caracterización clínica y genética de una muestra de pacientes colombianos con Epilepsia y análisis de posibles factores moleculares intervinientes en la respuesta a fármacos
No full text132 páginas : gráficasEpilepsy is defined by the International League Against Epilepsy (ILAE) as a chronic disease characterized by a predisposition to the occurrence of epileptic seizures that affect around 50 million people according to WHO, with Colombia being the third largest Latin American country prevalence It is estimated that up to 40% of the different types of epilepsy are of genetic origin and gene expression may be directly or indirectly involved with neurobiological mechanisms that are responsible for therapeutic failure during the epileptogenesis process due to the interindividual variability of response to antiepileptic drugs, promoting the existence of phenotypically refractory patients and contributing to the emergence of drug resistance.
This research work is a descriptive cross-sectional observational study consisting of a sample of 29 patients diagnosed with epilepsy that meets the inclusion criteria. A clinical and paraclinical characterization of the patients was carried out, and subsequently the specific genetic variants were identified to perform a bioinformatic approach through which possible target proteins and signaling pathways involved in the drug response mechanism were possible.
The most frequent epilepsy was of a generalized type with 34.48% and the most frequent neurological comorbidity was the global developmental delay with 65.5%. In 20 patients, the result of the molecular study was abnormal, identifying 60 genetic variants of which 86.7%, that is, 52 variants were classified as VOUS and 36 explained the phenotype of the patients. In addition, 13.3%, that is, 8 variants, were classified as probably pathogenic variants and 7 of these explained the phenotype of the patients. Through the bioinformatic analysis of the candidate genes, create 15 biological networks, of which in 7 networks corresponding to the genes CACNA1H, CNTN2, TSC1, EPM2A, SCN1A, KCNQ3 and PRICKLE1 were found the possible devices for the response to medications and other potential therapeutic targets were proposed.La epilepsia es definida por la Liga Internacional Contra la Epilepsia (ILAE) como una enfermedad crónica que se caracteriza por una predisposición a la aparición de crisis epilépticas que afecta alrededor de 50 millones de personas según la OMS, siendo Colombia el tercer país latinoamericano con mayor prevalencia. Se estima que hasta un 40% de los distintos tipos de epilepsia son de origen genético y la expresión de genes puede estar involucrada directa o indirectamente con los mecanismos neurobiológicos que durante el proceso de epileptogénesis son responsables del fallo terapéutico gracias a la variabilidad interindividual de respuesta a fármacos antiepilépticos, promoviendo la existencia de pacientes fenotípicamente refractarios y contribuyendo con la aparición de farmacorresistencia.
Este trabajo de investigación es un estudio observacional descriptivo de corte transversal que consta de una muestra de 29 pacientes diagnosticados con epilepsia que cumplían los criterios de inclusión. Se realizó una caracterización clínica y paraclínica de los pacientes, y posteriormente se identificaron variantes genéticas seleccionadas para realizar una aproximación bioinformática mediante la cual fueron propuestas posibles proteínas diana y vías de señalización involucradas en el mecanismo de respuesta a fármacos.
La epilepsia más frecuente fue de tipo generalizado con un 34.48% y la comorbilidad neurológica más frecuente fue el retardo global del desarrollo con un 65.5%. En 20 pacientes el resultado del estudio molecular fue anormal, identificándose 60 variantes genéticas de las cuales un 86,7 %, es decir 52 variantes fueron clasificadas como VOUS y 36 explicaban el fenotipo de los pacientes. Adicionalmente el 13,3 % es decir 8 variantes, fueron clasificadas como variantes probablemente patogénicas y 7 de estas explicaban el fenotipo de los pacientes. A través del análisis bioinformático de los genes candidatos se crearon 15 redes biológicas, de las cuales en 7 redes correspondientes a los genes CACNA1H, CNTN2, TSC1, EPM2A, SCN1A, KCNQ3 y PRICKLE1 fueron hallados posibles mecanismos para explicar la respuesta a fármacos y se propusieron potenciales blancos terapéuticos.PregradoMédico(a) Cirujan
Caracterización clínica y genética de una muestra de pacientes colombianos con Epilepsia y análisis de posibles factores moleculares intervinientes en la respuesta a fármacos
No full text132 páginas : gráficasEpilepsy is defined by the International League Against Epilepsy (ILAE) as a chronic disease characterized by a predisposition to the occurrence of epileptic seizures that affect around 50 million people according to WHO, with Colombia being the third largest Latin American country prevalence It is estimated that up to 40% of the different types of epilepsy are of genetic origin and gene expression may be directly or indirectly involved with neurobiological mechanisms that are responsible for therapeutic failure during the epileptogenesis process due to the interindividual variability of response to antiepileptic drugs, promoting the existence of phenotypically refractory patients and contributing to the emergence of drug resistance.
This research work is a descriptive cross-sectional observational study consisting of a sample of 29 patients diagnosed with epilepsy that meets the inclusion criteria. A clinical and paraclinical characterization of the patients was carried out, and subsequently the specific genetic variants were identified to perform a bioinformatic approach through which possible target proteins and signaling pathways involved in the drug response mechanism were possible.
The most frequent epilepsy was of a generalized type with 34.48% and the most frequent neurological comorbidity was the global developmental delay with 65.5%. In 20 patients, the result of the molecular study was abnormal, identifying 60 genetic variants of which 86.7%, that is, 52 variants were classified as VOUS and 36 explained the phenotype of the patients. In addition, 13.3%, that is, 8 variants, were classified as probably pathogenic variants and 7 of these explained the phenotype of the patients. Through the bioinformatic analysis of the candidate genes, create 15 biological networks, of which in 7 networks corresponding to the genes CACNA1H, CNTN2, TSC1, EPM2A, SCN1A, KCNQ3 and PRICKLE1 were found the possible devices for the response to medications and other potential therapeutic targets were proposed.La epilepsia es definida por la Liga Internacional Contra la Epilepsia (ILAE) como una enfermedad crónica que se caracteriza por una predisposición a la aparición de crisis epilépticas que afecta alrededor de 50 millones de personas según la OMS, siendo Colombia el tercer país latinoamericano con mayor prevalencia. Se estima que hasta un 40% de los distintos tipos de epilepsia son de origen genético y la expresión de genes puede estar involucrada directa o indirectamente con los mecanismos neurobiológicos que durante el proceso de epileptogénesis son responsables del fallo terapéutico gracias a la variabilidad interindividual de respuesta a fármacos antiepilépticos, promoviendo la existencia de pacientes fenotípicamente refractarios y contribuyendo con la aparición de farmacorresistencia.
Este trabajo de investigación es un estudio observacional descriptivo de corte transversal que consta de una muestra de 29 pacientes diagnosticados con epilepsia que cumplían los criterios de inclusión. Se realizó una caracterización clínica y paraclínica de los pacientes, y posteriormente se identificaron variantes genéticas seleccionadas para realizar una aproximación bioinformática mediante la cual fueron propuestas posibles proteínas diana y vías de señalización involucradas en el mecanismo de respuesta a fármacos.
La epilepsia más frecuente fue de tipo generalizado con un 34.48% y la comorbilidad neurológica más frecuente fue el retardo global del desarrollo con un 65.5%. En 20 pacientes el resultado del estudio molecular fue anormal, identificándose 60 variantes genéticas de las cuales un 86,7 %, es decir 52 variantes fueron clasificadas como VOUS y 36 explicaban el fenotipo de los pacientes. Adicionalmente el 13,3 % es decir 8 variantes, fueron clasificadas como variantes probablemente patogénicas y 7 de estas explicaban el fenotipo de los pacientes. A través del análisis bioinformático de los genes candidatos se crearon 15 redes biológicas, de las cuales en 7 redes correspondientes a los genes CACNA1H, CNTN2, TSC1, EPM2A, SCN1A, KCNQ3 y PRICKLE1 fueron hallados posibles mecanismos para explicar la respuesta a fármacos y se propusieron potenciales blancos terapéuticos.PregradoMédico(a) Cirujan
