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Colorectal cancer (CRC) is a significant global health issue where early detection is crucial for improving treatment outcomes and survival rates. This comprehensive review assesses the utility of stool-based tests in CRC screening, including traditional fecal occult blood tests (FOBT), both chemical (gFOBT) and immunochemical techniques (FIT), as well as multitarget stool DNA (mt-sDNA) as a novel and promising biomarker. The advancements, limitations and the impact of false positives and negatives of these methods are examined. The review analyzed various studies on current screening methods, focusing on laboratory tests and biomarkers. Findings indicate that while FIT and mt-sDNA tests offer enhanced sensitivity and specificity over traditional guaiac-based FOBT, they also come with higher costs and potential for increased false positives. FIT shows better patient adherence due to its ease to use, but incorrect usage and interpretation of FOBT can lead to significant diagnostic errors. In conclusion, despite the improvements in FOBT methods like FIT in CRC detection, careful consideration of each method’s benefits and drawbacks is essential. Effective CRC screening programs should combine various methods tailored to specific population needs, aiming for early detection and reduced mortality rates.

Hepatocellular carcinoma (HCC) presents high mortality rates worldwide, with limited evidence on prognostic factors at diagnosis. This study evaluates the utility of common scores incorporating albumin as predictors of mortality at HCC diagnosis using Machine Learning techniques. They are also compared to other scores and variables commonly used. A retrospective cohort study was conducted with 191 patients from Virgen de la Luz Hospital of Cuenca and University Hospital of Guadalajara. Demographic, analytical, and tumor-specific variables were included. Various Machine Learning algorithms were implemented, with eXtreme Gradient Boosting (XGB) as the reference method. In the predictive model developed, the Barcelona Clinic Liver Cancer score was the best predictor of mortality, closely followed by the Platelet-Albumin-Bilirubin and Albumin-Bilirubin scores. Albumin levels alone also showed high relevance. Other scores, such as C-Reactive Protein/albumin and Child-Pugh performed less effectively. XGB proved to be the most accurate method across the metrics analyzed, outperforming other ML algorithms. In conclusion, the Barcelona Clinic Liver Cancer, Platelet-Albumin-Bilirubin and Albumin-Bilirubin scores are highly reliable for assessing survival at HCC diagnosis. The XGB-developed model proved to be the most reliable for this purpose compared to the other proposed methods.

Artificial intelligence (AI) systems are already being used in various healthcare areas. Similarly, they can offer many advantages in hospital emergency services. The objective of this work is to demonstrate that through the novel use of AI, a trained system can be developed to detect patients at potential risk of infection in a new pandemic more quickly than standardized triage systems. This identification would occur in the emergency department, thus allowing for the early implementation of organizational preventive measures to block the chain of transmission. Materials and Methods: In this study, we propose the use of a machine learning system in emergency department triage during pandemics to detect patients at the highest risk of death and infection using the COVID-19 era as an example, where rapid decision making and comprehensive support have becoming increasingly crucial. All patients who consecutively presented to the emergency department were included, and more than 89 variables were automatically analyzed using the extreme gradient boosting (XGB) algorithm. Results: The XGB system demonstrated the highest balanced accuracy at 91.61%. Additionally, it obtained results more quickly than traditional triage systems. The variables that most influenced mortality prediction were procalcitonin level, age, and oxygen saturation, followed by lactate dehydrogenase (LDH) level, C-reactive protein, the presence of interstitial infiltrates on chest X-ray, and D-dimer. Our system also identified the importance of oxygen therapy in these patients. Conclusions: These results highlight that XGB is a useful and novel tool in triage systems for guiding the care pathway in future pandemics, thus following the example set by the well-known COVID-19 pandemic.

Electroencephalography (EEG) has been a fundamental technique in the identification of health conditions since its discovery. This analysis specifically centers on machine learning (ML) and deep learning (DL) methodologies designed for the analysis of electroencephalogram (EEG) data to categorize individuals with Alzheimer's Disease (AD) into two groups: Moderate or Advanced Alzheimer's dementia.Our study is based on a comprehensive database comprising 668 volunteers from 5 different hospitals, collected over a decade. This diverse dataset enables better training and validation of our results. Among the methods evaluated, the CNN (deep learning) approach outperformed others, achieving a remarkable classification accuracy of 97.45% for patients with Moderate Alzheimer's Dementia (ADM) and 97.03% for patients with Advanced Alzheimer's Dementia (ADA). Importantly, all the compared methods were rigorously assessed under identical conditions. The proposed DL model, specifically CNN, effectively extracts time domain features from EEG data in time, resulting in a significant reduction in learnable parameters and data redundancy.

El proceso de adopción de niños menores de edad para familias homoparentales, dentro del ordenamiento jurídico ecuatoriano no ha podido ser regulado, en vista a que sigue siendo discriminatorio para personas del mismo sexo. El Código de la Niñez y Adolescencia (2015) estipula que los niños, niñas y adolescentes tienen derecho a ser adoptados y formar un hogar diferente del biológico. En este aspecto, la investigación se centra en el objetivo de Identificar la contradicción normativa existente que deriva en una vulneración de derechos constitucionales como el de igualdad para las personas que conforman parejas del mismo sexo, dados los obstáculos procedimentales existentes. Entendiéndose entonces, que existe discriminación para las parejas homosexuales. La presente investigación tiene un enfoque cualitativo, donde se empleó el método analítico-sintético, además del método inductivo, los cuales ayudaron a analizar el objeto de estudio de manera subjetiva. Por lo que, se obtuvo como resultados que los derechos de las parejas homosexuales se ven transgredidos por una falta de normativa que regule la adopción de niños a parejas homosexuales. Palabras clave: Adopción de niños, discriminación, familias homoparentales, legislación ecuatoriana, vulneración de los derechos. ABSTRACT The adoption process of minor children for homoparental families, within the Ecuadorian legal system, has not been able to be regulated, given that it continues to be discriminatory for people of the same sex. The Childhood and Adolescence Code (2015) stipulates that children and adolescents have the right to be adopted and form a home other than their biological one. In this regard, the research focuses on the objective of identifying the existing regulatory contradiction that leads to a violation of constitutional rights such as equality for people who make up same-sex couples, given the existing procedural obstacles. Understanding then, that there is discrimination for homosexual couples. The present investigation has a qualitative approach, where the analytical-synthetic method was used, in addition to the inductive method, which will help to analyze the object of study subjectively. Therefore, the results obtained were that the rights of homosexual couples are violated by a lack of regulations that regulate the adoption of children by homosexual couples. Keywords: Adoption of children, discrimination, homoparental families, Ecuadorian legislation, violation of rights.

Metabolic dysfunction-associated steatotic liver disease (MASLD) currently represents the predominant cause of chronic liver disease and is closely linked to a significant increase in the risk of hepatocellular carcinoma (HCC), even in the absence of liver cirrhosis. In this retrospective multicenter study, machine learning (ML) methods were employed to investigate the relationship between metabolic profile and prognosis at diagnosis in a total of 219 HCC patients. The eXtreme Gradient Boosting (XGB) method demonstrated superiority in identifying mortality predictors in our patients. Etiology was the most determining prognostic factor followed by Barcelona Clinic Liver Cancer (BCLC) and Eastern Cooperative Oncology Group (ECOG) classifications. Variables related to the development of hepatic steatosis and metabolic syndrome, such as elevated levels of alkaline phosphatase (ALP), uric acid, obesity, alcohol consumption, and high blood pressure (HBP), had a significant impact on mortality prediction. This study underscores the importance of metabolic syndrome as a determining factor in the progression of HCC secondary to MASLD. The use of ML techniques provides an effective tool to improve risk stratification and individualized therapeutic management in these patients.

El traslado de los servidores judiciales dentro de la legislación ecuatoriana forma parte de una problemática que no ha sido tomada en cuenta desde el punto de vista normativo, sino, más bien, en un sentido práctico, haciendo referencia a los conflictos que se han generado a raíz de los traslados de forma potestativa, lo cual acarrea a vulneraciones de derechos como al debido proceso en su garantía de motivación. Por lo que, este artículo tiene como objetivo analizar el traslado de los de los servidores judiciales frente al debido proceso en el Ecuador, para lo cual se empleó el enfoque cualitativo junto al método hermenéutico para la revisión bibliográfica y documental que contribuyeron a la resolución del problema donde se constata que el traslado de los servidores judiciales no cuenta con un debido proceso que garantice un cambio efectivo que no afecte su proyecto de vida ni a sus derechos. Palabras clave: Traslado, servidores judiciales, debido proceso. ABSTRACT The transfer of judicial servants under Ecuadorian law is part of a problem that has not been addressed from a normative point of view, but rather from a practical point of view, referring to conflicts that have arisen as a result of the transfer, which leads to violations of rights such as due process and guarantees of reasons. Therefore, the aim of this article is to analyse the transfer of judicial servants in relation to due process in Ecuador. To this end, a qualitative approach was used together with the hermeneutic method for the literature and documentary review, which contributed to the resolution of the problem, where it is found that the transfer of judicial servants does not have due process to guarantee an effective change that does not affect their Project life and their rights. Keywords: Transfer, judicial servants, due process.

The COVID-19 pandemic demonstrated the need to develop strategies to control a new viral infection. However, the different characteristics of the health system and population of each country and hospital would require the implementation of self-systems adapted to their characteristics. The objective of this work was to determine predictors that should identify the most severe patients with COVID-19 infection. Given the poor situation of the hospitals in the first wave, the analysis of the data from that period with an accurate and fast technique can be an important contribution. In this regard, machine learning is able to objectively analyze data in hourly sets and is used in many fields. This study included 291 patients admitted to a hospital in Spain during the first three months of the pandemic. After screening seventy-one features with machine learning methods, the variables with the greatest influence on predicting mortality in this population were lymphocyte count, urea, FiO2, potassium, and serum pH. The XGB method achieved the highest accuracy, with a precision of >95%. Our study shows that the machine learning-based system can identify patterns and, thus, create a tool to help hospitals classify patients according to their severity of illness in order to optimize admission.

Background: Hepatocellular carcinoma (HCC) accounts for 75% of primary liver tumors. Controlling risk factors associated with its development and implementing screenings in risk populations does not seem sufficient to improve the prognosis of these patients at diagnosis. The development of a predictive prognostic model for mortality at the diagnosis of HCC is proposed. Methods: In this retrospective multicenter study, the analysis of data from 191 HCC patients was conducted using machine learning (ML) techniques to analyze the prognostic factors of mortality that are significant at the time of diagnosis. Clinical and analytical data of interest in patients with HCC were gathered. Results: Meeting Milan criteria, Barcelona Clinic Liver Cancer (BCLC) classification and albumin levels were the variables with the greatest impact on the prognosis of HCC patients. The ML algorithm that achieved the best results was random forest (RF). Conclusions: The development of a predictive prognostic model at the diagnosis is a valuable tool for patients with HCC and for application in clinical practice. RF is useful and reliable in the analysis of prognostic factors in the diagnosis of HCC. The search for new prognostic factors is still necessary in patients with HCC.

Hepatocellular carcinoma (HCC) is the most common primary liver tumor and is associated with high mortality rates. Approximately 80% of cases occur in cirrhotic livers, posing a significant challenge for appropriate therapeutic management. Adequate screening programs in high-risk groups are essential for early-stage detection. The extent of extrahepatic tumor spread and hepatic functional reserve are recognized as two of the most influential prognostic factors. In this retrospective multicenter study, we utilized machine learning (ML) methods to analyze predictors of mortality at the time of diagnosis in a total of 208 patients. The eXtreme gradient boosting (XGB) method achieved the highest values in identifying key prognostic factors for HCC at diagnosis. The etiology of HCC was found to be the variable most strongly associated with a poorer prognosis. The widely used Barcelona Clinic Liver Cancer (BCLC) classification in our setting demonstrated superiority over the TNM classification. Although alpha-fetoprotein (AFP) remains the most commonly used biological marker, elevated levels did not correlate with reduced survival. Our findings suggest the need to explore new prognostic biomarkers for individualized management of these patients.

Systemic Lupus Erythematosus (SLE) is a multifaceted autoimmune ailment that impacts multiple bodily systems and manifests with varied clinical manifestations. Early detection is considered the most effective way to save patients’ lives, but detecting severe SLE activity in its early stages is proving to be a formidable challenge. Consequently, this work advocates the use of Machine Learning (ML) algorithms for the diagnosis of SLE flares in the context of infections. In the pursuit of this research, the Random Forest (RF) method has been employed due to its performance attributes. With RF, our objective is to uncover patterns within the patient data. Multiple ML techniques have been scrutinized within this investigation. The proposed system exhibited around a 7.49% enhancement in accuracy when compared to k-Nearest Neighbors (KNN) algorithm. In contrast, the Support Vector Machine (SVM), Binary Linear Discriminant Analysis (BLDA), Decision Trees (DT) and Linear Regression (LR) methods demonstrated inferior performance, with respective values around 81%, 78%, 84% and 69%. It is noteworthy that the proposed method displayed a superior area under the curve (AUC) and balanced accuracy (both around 94%) in comparison to other ML approaches. These outcomes underscore the feasibility of crafting an automated diagnostic support method for SLE patients grounded in ML systems.

The impact of SARS-CoV-2 infection remains substantial on a global scale, despite widespread vaccination efforts, early therapeutic interventions, and an enhanced understanding of the disease’s underlying mechanisms. At the same time, a significant number of patients continue to develop severe COVID-19, necessitating admission to intensive care units (ICUs). This study aimed to provide evidence concerning the most influential predictors of mortality among critically ill patients with severe COVID-19, employing machine learning (ML) techniques. To accomplish this, we conducted a retrospective multicenter investigation involving 684 patients with severe COVID-19, spanning from 1 June 2020 to 31 March 2023, wherein we scrutinized sociodemographic, clinical, and analytical data. These data were extracted from electronic health records. Out of the six supervised ML methods scrutinized, the extreme gradient boosting (XGB) method exhibited the highest balanced accuracy at 96.61%. The variables that exerted the greatest influence on mortality prediction encompassed ferritin, fibrinogen, D-dimer, platelet count, C-reactive protein (CRP), prothrombin time (PT), invasive mechanical ventilation (IMV), PaFi (PaO2/FiO2), lactate dehydrogenase (LDH), lymphocyte levels, activated partial thromboplastin time (aPTT), body mass index (BMI), creatinine, and age. These findings underscore XGB as a robust candidate for accurately classifying patients with COVID-19.

Cholecystectomy and Metabolic-associated steatotic liver disease (MASLD) are prevalent conditions in gastroenterology, frequently co-occurring in clinical practice. Cholecystectomy has been shown to have metabolic consequences, sharing similar pathological mechanisms with MASLD. A database of MASLD patients who underwent cholecystectomy was analysed. This study aimed to develop a tool to identify the risk of liver fibrosis after cholecystectomy. For this purpose, the extreme gradient boosting (XGB) algorithm was used to construct an effective predictive model. The factors associated with a better predictive method were platelet level, followed by dyslipidaemia and type-2 diabetes (T2DM). Compared to other ML methods, our proposed method, XGB, achieved higher accuracy values. The XGB method had the highest balanced accuracy (93.16%). XGB outperformed KNN in accuracy (93.16% vs. 84.45%) and AUC (0.92 vs. 0.84). These results demonstrate that the proposed XGB method can be used as an automatic diagnostic aid for MASLD patients based on machine-learning techniques.

Resumen: Introducción: A pesar de las recomendaciones de las actuales Guías de Práctica Clínica, la radiografía de tórax sigue siendo una prueba diagnóstica ampliamente utilizada en la evaluación de lactantes con bronquiolitis aguda (BA). No obstante, su reproductibilidad en estos pacientes no ha sido muy estudiada. En la presente investigación, se evalúan radiografías describiéndose su calidad técnica, hallazgos radiológicos en las mismas y se aportan nuevas evidencias sobre la concordancia entre observadores. Método: Sobre un total de 281 lactantes ingresados por bronquiolitis aguda, se realizaron 140 radiografías de tórax. Se evaluó la presencia o ausencia de 10 signos radiográficos previamente consensuados por 12 médicos de diferentes especialidades. El nivel de concordancia entre dos observadores y en grupos de tres o más, fue estudiado mediante el índice kappa de Cohen y de Fleiss, respectivamente. Resultados: Únicamente en el 8,5% de las radiografías se evidenciaron signos de BA complicada. La concordancia entre observadores en grupos de tres o más fue mediana y con escasa variabilidad (kappa: 0,20-0,40), sin embargo, entre dos observadores, cada médico observador frente al radiólogo de referencia, la variabilidad fue más amplia, (kappa: –0,20-0,60). Este nivel de concordancia se relacionaba con factores tales como el signo a evaluar, la especialidad médica y el grado de experiencia profesional, entre otros. Conclusión: Los bajos niveles de concordancia entre observadores y su amplia variabilidad, convierten a la radiografía de tórax en una herramienta diagnóstica poco fiable y no recomendable para la evaluación de lactantes con BA. Abstract: Introduction: Despite the recommendations of the current Clinical Practice Guidelines, the chest x-ray continues to be a widely used diagnostic test in the assessment of infants with acute bronchiolitis (AB). However, there have not been many studies that have assessed its reproducibility in these patients. In the present study, an evaluation is made on the radiographs, describing their quality, their radiological findings, and provides new evidence on the agreement between observers. Method: Out of a total of 281 infants admitted due to acute bronchiolitis, 140 chest x-rays were performed. Twelve doctors from different specialities evaluated the presence or absence of 10 radiological signs previously agreed by consensus. The level of agreement between 2 observers, and in groups of 3 or more, were analysed using the Cohen and Fleiss kappa index, respectively. Results: Only 8.5% of the radiographs showed evidence of a complicated AB. The between-observer agreement in groups of 3 or more was medium, and with little variability (kappa: 0.20-0.40). However, between 2 observers, each observer against radiologist, the variability was wider, (kappa: –0.20-0.60). This level of agreement was associated with factors including, the sign to evaluate, the medical specialty, and level of professional experience. Conclusion: The low levels of agreement between observers and the wide variability, makes the chest x-ray an unreliable diagnostic tool, and is not recommended for the assessment of infants with AB.

Introduction: Despite the recommendations of the current Clinical Practice Guidelines, the chest x-ray continues to be a widely used diagnostic test in the assessment of infants with acute bronchiolitis (AB). However, there have not been many studies that have assessed its reproducibility in these patients. In the present study, an evaluation is made on the radiographs, describing their quality, their radiological findings, and provides new evidence on the agreement between observers. Method: Out of a total of 281 infants admitted due to acute bronchiolitis, 140 chest x-rays were performed. Twelve doctors from different specialities evaluated the presence or absence of 10 radiological signs previously agreed by consensus. The level of agreement between 2 observers, and in groups of 3 or more, were analysed using the Cohen and Fleiss kappa index, respectively. Results: Only 8.5% of the radiographs showed evidence of a complicated AB. The between-observer agreement in groups of 3 or more was medium, and with little variability (kappa: 0.20−0.40). However, between 2 observers, specialists in radiology, the variability was wider, (kappa: −0.20 to 0.60). This level of agreement was associated with factors including, the sign to evaluate, the medical specialty, and level of professional experience. Conclusion: The low levels of agreement between observers and the wide variability, makes the chest x-ray an unreliable diagnostic tool, and is not recommended for the assessment of infants with AB. Resumen: Introducción: A pesar de las recomendaciones de las actuales Guías de Práctica Clínica, la radiografía de tórax sigue siendo una prueba diagnóstica ampliamente utilizada en la evaluación de lactantes con bronquiolitis aguda. No obstante, su reproductibilidad en estos pacientes no ha sido muy estudiada. En la presente investigación, se evalúan radiografías describiéndose su calidad técnica, hallazgos radiológicos en las mismas y se aportan nuevas evidencias sobre la concordancia entre observadores. Método: Sobre un total de 281 lactantes ingresados por bronquiolitis aguda, 140 radiografías de tórax fueron realizadas. 12 médicos de diferentes especialidades evaluaron la presencia o ausencia de 10 signos radiográficos previamente consensuados. El nivel de concordancia entre 2 observadores y en grupos de 3 o más, fue estudiado mediante el índice kappa de Cohen y de Fleiss respectivamente. Resultados: Únicamente en el 8,5% de las radiografías se evidenciaron signos de BA complicada. La concordancia entre observadores en grupos de 3 o más fue mediana y con escasa variabilidad (kappa: 0,20−0,40), sin embargo, entre 2 observadores, especialista con radiólogo de referencia, la variabilidad fue más amplia, (kappa: −0,20 to 0,60). Este nivel de concordancia se relacionaba con factores tales como el signo a evaluar, la especialidad médica y el grado de experiencia profesional, entre otros. Conclusión: Los bajos niveles de concordancia entre observadores y su amplia variabilidad, convierten a la radiografía de tórax en una herramienta diagnóstica poco fiable y no recomendable para la evaluación de lactantes con BA.

El presente artículo recoge un estudio doctrinario en el ámbito jurídico, social y constitucional, y tiene como objetivo general, analizar críticamente al derecho de inclusión laboral de las personas con discapacidad, dentro del ordenamiento legal ecuatoriano. La investigación de carácter cualitativo se fundamentan en la exegética para recaudar información mediante el análisis e interpretación de los articulados de la Constitución, el Código de Trabajo y la Ley Orgánica de Discapacidades, donde se recogen los derechos y garantías que poseen estas personas en el ámbito social, laboral, cultural y de otra índole, así como en el análisis de contenido empleado en el estudio de los textos contenidos en libros, artículos científicos, tesis y otros materiales bibliográficos. Entre los principales hallazgos se significa que Ecuador cuenta con un marco normativo garante de los derechos de las personas discapacitadas, entre ellos la inclusión laboral, a pesar de ello no se ha logrado una plena incorporación de estas personas a la vida laboral con la consecuente mejora de su calidad de vida e inserción social.

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