Identificación de biomarcadores de fibrilación auricular empleando métodos estadísticos e inteligencia artificial

Trabajo fin de máster en Bioinformática y Biología ComputacionalLas arritmias cardiacas tienen un peso considerable en la morbilidad y mortalidad en las enfermedades del corazón, generando más de un cuarto de millón de muertes al año en los Estados Unidos. Las arritmias pueden ocurrir durante la edad temprana, o pueden surgir más adelante debido a alguna enfermedad o el envejecimiento. La prueba más común utilizada para diagnosticar una arritmia es un electrocardiograma (ECG), el cual registra las diferencias de potencial eléctrico generadas por el corazón. Ciertas alteraciones en el patrón normal de la actividad eléctrica del corazón son indicativas de patologías cardíacas. Entre los distintos tipos de arritmias cardíacas la Fibrilación Auricular (FA) es la más común, y está asociada al envejecimiento. En el presente proyecto se analizaron más de 320.000 electrocardiogramas (ECGs) registrados en la base de datos del Hospital Universitario de La Princesa desde el año 2007 en formato XML, con el objeto de determinar biomarcadores y la generación de modelos predictivos de FA a partir de ECGs normales. Inicialmente se procedió con el estudio de la estructura de los archivos XML, y la identificación de la información de interés y sensible que pudiera identificar al paciente. Mediante un script en Bash la base de datos fue anonimizada, eliminando toda la información que pudiera identificar a los pacientes y generando nuevos números de identificación en una base de datos alterna. Posteriormente, con herramientas de análisis masivo se identificó, de forma anonimizada, aquellos pacientes que al menos tienen un ECG en FA y que a su vez presenten ECGs previos en Ritmo Sinusal (RS) normal (grupo de casos), al igual que pacientes que solo tienen registrados ECGs en RS (grupo control). El análisis masivo de más de 444 variables de ECGs en RS entre el grupo control y casos se llevó acabo por sexo y edad (de 40 a 49, 50 a 59, 60 a 69, 70 a 79, más de 80 años y el conjunto completo), y tomando en cuenta el tiempo entre ECGs. Una vez establecidos los grupos de estudio, se realizó un análisis estadístico para determinar si estos grupos presentaban diferencias significativas con respecto la edad, sexo y distancia entre ECGs, y se ajustaron para eliminar dichas diferencias. Seguido de esto, se llevó a cabo un análisis univariante para identificar de entre las más de 444 variables aquellas que presentan diferencias significativas entre casos y controles, y seguidamente con estas variables se construyeron modelos predictivos empleando los algoritmos de “Extreme Gradient Boosting” (XGBoost) y “Support Vector Machines” (SVM). Los resultados de exactitud obtenidos de estos ensayos se encuentran alrededor del 60%. Con el objeto de mejorar los resultados se empleó el método “Sequential Forward Floating Selection” (SFFS) o Selección secuencial flotante hacia adelante, el cual es otro método para la selección del conjunto de variables relevantes, obteniendo una mejoría en la exactitud del alrededor del 2

Kur'an-ı Kerim'de kıllet ve kesret kavramları

Kıllet (azlık) ve kesret (çokluk), hakkıyla araştırmaya ve incelenmeye tabi tutulmayan Kur’an’ın çağdaş konuları arasında yer almaktadır. Araştırmacılar, bu konuda yazmaya devam ediyorlar. Fakat yapılan araştırmalar yeterli değildir. İmam Zeyd b. Ali, bu konuda ilk görüş beyan eden kimselerin başında gelir. Kıllet sözcüğü, zillet, izzet ve nefy anlamına gelir. Kesret kavramı ise çokluk, izzet anlamına gelip sayılar hakkında da kullanılır. Kıllet kelimesi Kur’an’da “kalle”, “akallet”, “yukallilüküm” “kalilün” “kalilen” gibi formlarda kullanılır. Kesret kavramı da “kesura”, “kesurat”, “kesiran”, “eksar” “tekasür” gibi pek çok kalıpta kullanılır. Kıllet ve kesretin ölçüsü şudur: Konu İslâm dairesi içinde olduğu zaman şüphesiz ki, çoğunluk olan topluluğun görüşü haktır. Fakat konu Müslüman ve Müslüman olmayanları içerecek şekilde genel olduğu takdirde bu durumda hak ehli olan Müslümanların yeryüzünde bulunan insanlara nispetle az olur. Kıllet, Kur’an’da pek çok anlamda zikredilmiştir. Bazıları şunlardır: Dünya malı ve hazları, adet, miktar, zikrin, şükrün ve tefekkürün azlığı. Kesret de Kur’an’da pek çok anlamda kullanılmıştır. Bazıları şunlardır: Adet, miktar, iman etmeyenlerin çokluğu, çokça bağışlama, çokça şükretmeme, düşünmeyenlerin fazla olması, zanna göre hareket edenlerin sayısının fazla olması, zikretmeyi çokça teşvik etme, haktan hoşlanmayanların sayısının çok olması, gafllerin çok olması, insanlardan cinlere uyanların çok olması ve fasıkların fazla olması

Insight on physicochemical properties governing peptide MS1 response in HPLC-ESI-MS/MS: A deep learning approach

Accurate and absolute quantification of peptides in complex mixtures using quantitative mass spectrometry (MS)-based methods requires foreground knowledge and isotopically labeled standards, thereby increasing analytical expenses, time consumption, and labor, thus limiting the number of peptides that can be accurately quantified. This originates from differential ionization efficiency between peptides and thus, understanding the physicochemical properties that influence the ionization and response in MS analysis is essential for developing less restrictive label-free quantitative methods. Here, we used equimolar peptide pool repository data to develop a deep learning model capable of identifying amino acids influencing the MS1 response. By using an encoder-decoder with an attention mechanism and correlating attention weights with amino acid physicochemical properties, we obtain insight on properties governing the peptide-level MS1 response within the datasets. While the problem cannot be described by one single set of amino acids and properties, distinct patterns were reproducibly obtained. Properties are grouped in three main categories related to peptide hydrophobicity, charge, and structural propensities. Moreover, our model can predict MS1 intensity output under defined conditions based solely on peptide sequence input. Using a refined training dataset, the model predicted log-transformed peptide MS1 intensities with an average error of 9.7 ± 0.5% based on 5-fold cross validation, and outperformed random forest and ridge regression models on both log-transformed and real scale data. This work demonstrates how deep learning can facilitate identification of physicochemical properties influencing peptide MS1 responses, but also illustrates how sequence-based response prediction and label-free peptide-level quantification may impact future workflows within quantitative proteomics

Gigartina pilot scale protein extract BUP

Seaweeds are attracting substantial interest as novel sources of sustainable food protein, as they are established sources of industrial hydrocolloids and have reasonable protein content. In this study we investigate the protein composition and quality of a seaweed protein extract (SPE) from Gigartina radula obtained as a side-stream from industrial carrageenan production. The SPE displayed low (<2%), but pH-dependent, aqueous solubility due to the harsh (heat and extreme pH) employed during extraction. Solubility was improved using buffer and detergent to facilitate proteomic characterization by quantitative LC-MS/MS. Proteomics revealed that the SPE was dominated by proteins related to light harvest and particularly rich in phycobiliproteins (44% relative molar abundance), where phycoerythrin was the most abundant (28%). Based on subcellular localization of identified proteins, the extraction method was evaluated as good for release of cellular protein. The SPE was found to be rich in essential amino acids (36-41%) and particularly in branched chain amino acids (22-24%) and thereby a potential source of nutritional food protein. Using bioinformatic prediction and structural modelling, we found that abundant proteins in the SPE contained high potential, novel emulsifier peptides with amphiphilic properties required to stabilize an oil/water interface. Based on this study, Gigartina could serve as a good candidate for extraction of sustainable and nutritious food protein and possibly be further processed to obtain a hydrolysate with good emulsifying properties for use in foods