Análisis de la espectroscopia Raman para la detección de la diabetes

Trabajo de investigaciónEl objetivo de este trabajo de grado es proporcionar información sobre una técnica ya implementada en la literatura para diagnosticar la diabetes. Para lograr esto lo primero que se realiza es recopilar información sobre las técnicas convencionales empleadas para dicho diagnóstico, seguidamente se expone la técnica espectroscopia Raman, teniendo en cuenta su definición, antecedentes, etapas del sistema y protocolos empleados en las muestras de sangre.INTRODUCCIÓN 1. GENERALIDADES 2. TÉCNICAS PARA REALIZAR DIAGNÓSTICO DE DIABETES MEDIANTE ANÁLISIS DE SANGRE 3. ETAPAS DE LA ESPECTROSCOPIA RAMAN PARA EL DIAGNÓSTICO DE DIABETES 4. IMPLEMENTACIÓN DE LA ESPECTROSCOPIA DE RAMAN PARA EL DIAGNÓSTICO DE DIABETES 5. COMPARAR LAS PRUEBAS REALIZADAS DE LA ESPECTROSCOPIA RAMAN CON LA BASE TEÓRICA REFERENTE A LA ESPECTROSCOPIA RAMAN DIVULGADA EN LA LITERATURA SOBRE EL ANÁLISIS DE LAS CÉLULAS DE LOS GLÓBULOS ROJOS DE LA SANGRE 6. CONCLUSIONES 7. TRABAJO A FUTURO 8. ESTRATEGIAS DE COMUNICACIÓN PARA LA TRANSFERENCIA DE RESULTADOS BIBLIOGRAFÍA ANEXOSPregradoIngeniero Electrónic

Suppression of Estrogen Receptor Transcriptional Activity by Connective Tissue Growth Factor

Secreted growth factors have been shown to stimulate the transcriptional activity of estrogen receptors (ER) that are responsible for many biological processes. However, whether these growth factors physically interact with ER remains unclear. Here, we show for the first time that connective tissue growth factor (CTGF) physically and functionally associates with ER. CTGF interacted with ER both in vitro and in vivo. CTGF interacted with ER DNA-binding domain. ER interaction region in CTGF was mapped to the thrombospondin type I repeat, a cell attachment motif. Overexpression of CTGF inhibited ER transcriptional activity as well as the expression of estrogen-responsive genes, including pS2 and cathepsin D. Reduction of endogenous CTGF with CTGF small interfering RNA enhanced ER transcriptional activity. The interaction between CTGF and ER is required for the repression of estrogen-responsive transcription by CTGF. Moreover, CTGF reduced ER protein expression, whereas the CTGF mutant that did not repress ER transcriptional activity also did not alter ER protein levels. The results suggested the transcriptional regulation of estrogen signaling through interaction between CTGF and ER, and thus may provide a novel mechanism by which cross-talk between secreted growth factor and ER signaling pathways occurs

Label-free determination of liver cancer stages using surface-enhanced Raman scattering coupled with preferential adsorption of hydroxyapatite microspheres

Label-free albumin targeted analysis method by utilizing HAp to adsorb–release HSA, with the conjunction of SERS for screening liver cancer at different tumor (T) stages.</jats:p

DWS-YOLO: A Lightweight Detector for Blood Cell Detection

ABSTRACTPeripheral blood cell detection is an essential component of medical practice and is used to diagnose and treat diseases, as well as to monitor the progress of therapies. Our objective is to construct an efficient deep learning model for peripheral blood cell analysis that achieves an optimized balance between inference speed, computational complexity, and detection accuracy. In this article, we propose the DWS-YOLO blood detector, which is a lightweight blood detector. Our model includes several improved modules, including the lightweight C3 module, the increased combined attention mechanism, the Scylla-IoU loss function, and the improved soft non-maximum suppression. Improved attention, loss function, and suppression enhance detection accuracy, while lightweight C3 module reduces computation time. The experiment results demonstrate that our proposed modules can enhance a detector’s detection performance, and obtain new state-of-the-art (SOTA) results and excellent robustness performance on the BCCD dataset. On the white blood cell detection dataset (Raabin-WBC), the proposed detector’s generalization performance was confirmed to be satisfactory. Our proposed blood detector achieves high detection accuracy while requiring few computational resources and is very suitable for resource-limited but efficient medical device environments, providing a reliable and advanced solution for blood detection that greatly improves the efficiency and effectiveness of peripheral blood cell analysis in clinical practice