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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- Efecto de las lipoproteínas de alta densidad (HDL) sobre la preservación de la función de secreción de insulina en un modelo murino de diabetes inducida por estreptozotocina(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-06-11) Lozano Belsaguy, Patricio; Pérez Méndez, Óscar Armando; emimmayorquin; Campus MonterreyLa secreción de insulina en la diabetes se ve afectada por la disminución en la cantidad y función de las células , trayendo como consecuencia el aumento crónico de los niveles de glucosa plasmática. Estudios realizados in vitro han demostrado que la incubación con lipoproteínas de alta densidad (HDL) estimula una mayor secreción de insulina; sin embargo, este efecto no se ha descrito in vivo. En la presente tesis, se demuestra el aumento de la concentración de insulina en plasma y la reducción en los niveles de glucosa en un modelo murino de diabetes inducida por estreptozotocina y tratado con HDL. El mecanismo propuesto sugiere que las HDL, para mantener un equilibrio lipídico, llevan a cabo un eflujo de membrana para aumentar la tasa de recuperación de lípidos que se fusionan a la membrana en el proceso de exocitosis, complementando así el mecanismo fisiológico de la endocitosis. Con el objetivo de analizar cuáles de los componentes de las HDL y a qué concentración pueden aumentar este efecto, se construyeron proteoliposomas utilizando el pool de apolipoproteínas presentes en las HDL, y estos fueron caracterizados fisicoquímicamente.
- Analysis of data-driven diabetes subgroups using machine learning for prediction of glycemic control and complications(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2023-06-14) Nasser Kadamani, Sharif; Montesinos Silva, Luis Arturo; puemcuervo, emimayorquin; Aguilar Salinas, Carlos Alberto; Tamez Peña, José Gerardo; Treviño Alvarado, Víctor Manuel; School of Engineering and Sciences; Campus Monterrey; Santos Díaz, AlejandroDiabetes is a long-term illness that affects the metabolism and causes high blood sugar levels. This can result in severe harm to vital organs such as blood vessels, the heart, kidneys, eyes, and nerves over time. Type 2 diabetes is the most prevalent form, typically found in adults, and it develops when the pancreas is unable to produce enough insulin or when the body is unable to effectively use the insulin it produces, resulting in insulin resistance. Machine learning principles have been applied to develop algorithms that support predictive models for the risk of developing certain diseases, including diabetes, which has become a global pandemic. The management of this disease following a uniform treatment algorithm is usually linked to evolved treatment failure and the development of diabetic complications, so it has become necessary to create algorithms that consider the clinical characterization of the patient, which would be possible due to the recent advancements in the knowledge of the genomic architecture of diabetes and its complications. This research work aims to use machine learning techniques for classification to study the subgroups, generated through clustering techniques proposed by previous studies, to develop a continuation that allows the finding of patterns within subgroups for the generation of a model able to predict complications and the response to certain treatments of a patient given its clinical characterization, that is phenotypic data. This was done in collaboration with the Unidad de Investigación de Enfermedades Metabólicas (UIEM), which is part of the Instituto Nacional de Ciencias Medicas y Nutrición Salvador Zubirán (INCMNSZ), which provided data from cohorts of diabetic patients, as well as medical knowledge and feedback from health professionals. In this study, five clusters or data-driven diabetes subgroups were identified in a Mexican cohort based on six clinical characteristics. It is shown that it is possible to obtain similar subgroups to those found in other studies by using METS-IR and METS-VF instead of HOMA-IR and HOMA2-β formulas. These subgroups can be associated with different risks of complications, and progression rates from treatments, which might have relevance in advancing toward precision medicine. However, after including these subgroups in the training of classification models to predict glycemic control and assess the presence of complications, the performance of the models remained similar, that is no noticeable improvement was presented. This study showed that it is possible to build models that predict glycemic control after three months of initiating treatment with good performances, as well as create models that classify the presence of neuropathy. On the other hand, the generation of classification models for nephropathy and retinopathy did not yield acceptable performances.
- Evaluation of the functional properties and antidiabetic potential of synbiotic dairy snacks based on Petit Suisse cheese and skyr-like yogurt models(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2023-05-05) Zepeda Hernández, Andrea; García Cayuela, Tomás; emimmayorquin; School of Engineering and Sciences; Campus Monterrey; García Amézquita, Luis EduardoSynbiotic dairy products offer stability to functional components with health benefits by modulating the gut microbiota. Type 2 Diabetes Mellitus (T2DM) is the most common form of diabetes, and it is an inflammatory disease associated with gut dysbiosis, hyperglycemia, inflammatory markers, insulin resistance (IR), oxidative stress, hypercholesterolemia, and relative insulin deficiency. In Mexico, it is the second cause of death after cardiovascular diseases. Fermented dairy serves as an effective vehicle for bioactive compounds through gut tract due to the protective properties of food matrix. Some probiotics have demonstrated potential in improving lipid metabolism, such as reducing cholesterol levels and diabetes-related diseases. Based on this information, the objective of this work was to evaluate the functional and antidiabetic properties of two synbiotics dairy models based on a Petit Suisse cheese (PSC) and a skyr-type yogurt (STY) while assessing the impact of adding functional ingredients. Petit Suisse cheese (PSC) is a fresh cheese produced by coagulating milk with rennet, and specific bacteria, or a combination of these elements. Diets that include synbiotic foods with high antioxidant capacity are associated with a reduced risk of diseases such as IR and prediabetes. Blueberries are a rich source of polyphenols and anthocyanins, which are bioactive molecules known for their high antioxidant activity. A PSC base was prepared by adding a starter culture (2%), probiotics (0.5-1%), and prebiotic ingredients (1-2%). Three formulations of PSC, namely PSC1 (without blueberry ingredients), PSC2 (with only blueberry syrup), and PSC3 (with blueberry bagasse and syrup), were developed. These formulations were selected for evaluation of their technological profile through physicochemical analysis (pH, titratable acidity, viscosity, syneresis), proximal analysis, and a sensory evaluation involving 100 participants to assess acceptability. Subsequently, the antidiabetic and functional activity of the formulations were determined before and after an in vitro digestion. The antioxidant profile (ABTS, TPC, and TAC), enzymatic assays (α-amylase and α-glucosidase inhibition), and in vitro cellular assays (antioxidant and anti-inflammatory capacity) were conducted. The addition of blueberry-based ingredients was found to enhance the functional and bioactive profile of the PSC formulations, even after digestion. Among the formulations, PSC3 demonstrated the best acceptability, antidiabetic potential, and antioxidant response. It exhibited the highest content of high molecular weight dietary fiber (8.10 ± 0.60 g/100 d.w.). In the ABTS assay, PSC3 showed the highest antioxidant capacity (60.03 ± 1.12 mg ascorbic acid equivalents/100 g d.w.) and TPC (33% higher than the control PSC1) after digestion. Moreover, PSC3 displayed the best antidiabetic potential in terms of α-amylase and α-glucosidase inhibition, with 61% to 75% inhibition in water-soluble (WS) extracts. It also demonstrated the highest antioxidant and anti-inflammatory capacity in in vitro cell models at a concentration of 100 μg/mL. On the other hand, skyr, an Icelandic fermented dairy product, is gaining popularity due to its distinct sensory characteristics and low-fat high-protein content. There is also increasing interest in the health benefits associated with heat-killed bacteria, now referred to as postbiotics. In this study, three strains (Lactiplantibacillus plantarum 299v (LP299V), Lactiplantibacillus plantarum LPK, and Lactobacillus acidophilus 5 (LA5)) were selected based on their antioxidant capacity and potential to reduce cholesterol levels through bile salt hydrolase (BSH) activity. These strains were used in the production of skyr-type yogurt (STY) using skimmed milk, rennet, and inulin. The STY samples included LA5 (S1), LP299V (S2), and LPK (S3). A sensory analysis was conducted by a panel of 100 participants, followed by the evaluation of the bioactive profile and microbiological viability after in vitro digestion. Additionally, a heat treatment was applied to assess the potential of postbiotics (indicated by the addition of "P" to each code name). Besides the same parameters described in PSC, BSH activity, phagocytic activity, and cytokine expression were also evaluated. All STY with probiotic strains showed a better microbiological viability at the end of digestion. For texture, S2 was preferred. The heat treatment did not have a negative effect on the functional and antidiabetic properties of STY; in fact, it improved the antioxidant potential. PS1 exhibited the highest inhibitory activity against α-glucosidase in both extracts (67%), and PS1 and PS3 showed the highest inhibitory activity against α-amylase (58-81% in both extracts). Lastly, S1 exhibited the highest BSH activity (0.24 U/mL). Cellular models supported the antioxidant capacity, anti-inflammatory capacity, and immunomodulatory potential. In conclusion, synbiotic dairy products, such as PS cheese and skyr, offer a promising approach for delivering functional compounds with health benefits. The addition of blueberry ingredients in PS cheese enhances its antioxidant and antidiabetic properties, while skyr fermented with specific probiotic strains demonstrates potential as a functional dairy product for managing cholesterol levels and promoting antidiabetic effects. These findings highlight the importance of gut microbiota modulation and the incorporation of functional ingredients in dairy matrices to enhance their bioactive profiles. Further research is needed to explore the potential health benefits of fermented dairy products in different matrices and to evaluate their effects on various cellular models and signaling pathways of interest. Additionally, investigations into the bioactivity of peptides, inhibition of carbohydrates-hydrolyzing enzymes, and postbiotic components can contribute to a better understanding of the functional potential of these dairy products.
- Development of a type two diabetes predictive model for mexicans applying to electronic health records dataset retrieved from National Public Data (ENSANUT 2018)(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-12-02) Fregoso Aparicio, Luis Martín; Noguez Monroy, Juana Julieta; puemcuervo; Cantú Ortiz, Francisco Javier; González Mendoza, Miguel; García García, José Antonio; School of Engineering and Sciences; Campus Estado de México; Montesinos Silva, Luis ArturoDiabetes mellitus is a chronic and severe disease that occurs when the glucose levels in the blood rise above the limits because the body of the patient cannot produce insulin hormone or the amount is insufficient. Likewise, when the produced hormone is not able to be used efficiently. The American Diabetes Association establish to diagnosis Diabetes when the test of HbA1c is higher or equal to 6.5\%. Likewise, if basal fasting blood glucose (GB) is higher than 126 mg/dL or blood glucose 2 hours after an oral glucose tolerance test with 75 g of glucose (SOG) is greater or equal to 200 mg/dL. Type 2 diabetes (T2D), formerly known as adult-onset diabetes, is a form of diabetes characterized by high blood sugar, insulin resistance, and a relative lack of insulin. In Mexico, ten-point four percent of the population had diabetes in 2016, compared with 7\% of the population in 2006. In the past years, Machine Learning has been used to create a predictive model for the onset of type 2 diabetes, making it achievable to develop one for the Mexican population. The model should have the capacity to detect undiagnosed diabetics, applying a national public dataset of diabetes mellitus 2 in Mexico (ENSANUT 2018). The objective is to develop a predictive model of type 2 diabetes for Mexicans as a support tool helping primary care physicians make a timely diagnosis, preventing the onset of diabetes or its complications, detecting diabetes early with higher accuracy than the few Mexican models. A systematic review with 91 studies is performed to detect possible optimal machine learning techniques and features to create novel type 2 diabetes predictive models. Based on the PRISMA methodology combined with the methodology of Keele University and Durham University. The related work section results found that tree-type clusters of machine learning algorithms developed the best predictive models. There are five possible models Decision Tree, Random Forest, Gradient Boosting Tree, K-Nearest Neighborhood, and Logistic Regression to choose for classification diabetes. The database selected for the model is the National Health and Nutrition Survey (ENSANUT 2018), a tool that shows the general health and nutrition conditions of a representative sample of the population of Mexico. It is divided into several datasets joined by a unique ID created with values of their variables. The target (HEMGLICLASS) is a binary categorical variable which zero corresponds to a healthy person, and one is diabetic, and the complete database has 11639 samples and 55 attributes. After cleaning it and balancing the samples for diabetics and healthy, the final database has 21696 observations and 26 variables composed of the surveyed's categorization eating habits and their corresponding blood chemistry test values. Based on their metrics, after performing a model selection and optimization applying to the ENSANUT database, from the techniques described in the systematic review, Random Forest Classifier has the best metric for the prediction and could be interpreted it the physicians. The proposed model is a Random Forest with the default values with fifteen attributes from the original ENSANUT database. The attributes are related to the values of the testing blood measurements as the classical models and add new features like the intake of vegetables and fruits during the whole week as a protector or the enhancer in the case of an excessive intake of meat milky products or candies. Once the model was done, it was validated with the actual data to assure that the performance of the accuracy and AUC(ROC) keep higher than the 90 percent further other three metrics also are estimated. The results are accuracy: (0.90 $\pm$ 0.154), F1-Score: (0.86 $\pm$ 0.286) Precision: ( 0.94 $\pm$ 0.069), Sensitivity: (0.87 $\pm$ 0.294), and AUC(ROC): (0.92 $\pm$ 0.191). For proving the superior prediction capacity of the new model versus the Olimpia Arrellano-Campos model, equality of the means test with unknown variances is done with the T-student as estimator and p-value as the criterion to reject. The result is a p-value equal to 0.00572, demonstrating the improvement in the capacity of prediction by the model. Finally, the relevance of this model is the possibility to anticipate a diagnosis before the onset of symptoms, and even in the long term, anticipate the development of chronic complications. The model reflected this importance showing the complexity inherent to the detection of diabetes, generating a tool as simple as possible to support physicians in making a diagnosis. The ideal is to predict the onset before it is possible to call a pre-diabetic stage, but this model offers the possibility to generate a diagnosis near this stage.