Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551039
Pertenecen a esta colección Tesis y Trabajos de grado de las Maestrías correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
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- Quality assessment and validation of digital PCR (dPCR) for grapevine virus diagnosis(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12-11) Hernández Pérez, Daniella María Joselyn; Díaz Lara, Alfredo; emipsanchez; Carrillo Tripp, Jimena; Rodríguez García, Manuel; School of Engineering and Sciences; Campus MonterreyGrapevine is a highly economically important crop in Mexico. However, it can be affected by several pathogens, including viruses that can cause significant crop losses. It is important to identify early the infected plants to manage the disease correctly and prevent economic losses. Traditional detection methods have drawbacks, such as limited sensibility and accuracy. Digital PCR (dPCR) is an innovative method that claims to be more sensitive and reproducible than the routine method for virus identification: quantitative PCR (qPCR). This study assesses reverse transcription dPCR (RT-dPCR) as a method for the detection and quantification of RNA grapevine viruses focusing on grapevine virus A (GVA), grapevine fanleaf virus (GFLV), grapevine leafroll-associated virus 3 (GLRaV-3), and grapevine Pinot gris virus (GPGV). This assessment was performed using positive controls and comparing the limit of detection (LoD) results of RT-dPCR against the results obtained by RT-qPCR. ANOVA results showed that the PCR technique (RT-dPCR or RT-qPCR) and the virus (GVA, GLFV, GPGV, and GLRaV-3) were statistically significant in the results of the comparison of LoD. Furthermore, the replicates were non-significant according to ANOVA, showing high repeatability in both RT-qPCR and RT-dPCR. Tukey test demonstrated that RT-dPCR is significantly more sensitive than RT-qPCR, with a statistically reliable difference of 95% trust, especially in low-viral-load viruses such GPGV, which detection showed also to be statistically different than the other viruses. Additionally, a field study was performed to identify the presence or absence of each virus in 45 grapevine samples evaluated with RT-qPCR and RT-dPCR. Several false negative results were generated by RT-qPCR, which only reported positive results to 62.5% of the GVA infected samples, 85.7% of the samples contaminated with GLRaV-3, 38.9% of GPGV positive samples and for GFLV only 12.5% of the infected samples were identified. These results confirm the effectiveness of RT-dPCR as a sensitive method for RNA virus detection in grapevine, enabling early diagnosis and optimal management of viral infections in grapevine crops.
- A comparison between Machine Learning models for OSA detection based on ECG signal(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2023-12-04) Espinosa Varela, Miguel Angel; Ponce Cruz, Pedro; emimmayorquin; Ponce, Pedro; Rojas, Mario; Borja, Vicente; Mata, Omar; Molina, Arturo; School of Engineering and Sciences; Campus Ciudad de México; Rojas Hernández, MarioOSA is a one of the most common sleep disorders nowadays, which is diagnosed by a Polysomnography (PSG) study. Even though this is the golden standard to diagnose OSA, it is time consuming, very expensive and there are not many specialized centers to conduct it, this implies that fewer patients are diagnosed. The development of new solutions at a lower cost and in less time would allow more patients to be diagnosed and treated promptly. There are solutions that enable the diagnosis of OSA through monitoring signals from the human body, including an auto-diagnosis. However, these solutions do not aim to perform screening on the most significant parameters along with the best model for making predictions. The main objective of this tesis is to make a comparison between 27 Machine Learning (ML) models in order to find the best model to diagnose OSA. It also aims to find which are the most representative parameters in OSA detection. By doing a frequency-domain, time-domain and non-linear domain analysis to extract them from the RR intervals, and with a wilcoxon test and correlation matrix, select the most useful ones. The results showed that the best model was Support Vector Machine (SVM) with an accuracy, balanced accuracy, ROC AUC and F1 Score of 0.97. The most significant parameters found were: RR tri index, LF/HF ratio, alpha 2, HF\%, SDNN and RMSSD. This solution can be integrated into current clinical cases for a quick OSA diagnosis. Proposal does not aim to replace PSG for a complete and accurate diagnosis, but to be a pre diagnosis accessible to a larger number of patients. Health providers can implement this solution and reduce the number of patients in the waiting list. Also, this proposal would make research in OSA diagnosis more accessible and provide a framework that can be the starting point to other researchs.