Exploring the intersection of obesity and gender in COVID-19 outcomes in hospitalized Mexican patients: a comparative analysis of risk profiles using unsupervised machine learning.

Introduction: Obesity and gender play a critical role in shaping the outcomes of COVID-19 disease. These two factors have a dynamic relationship with each other, as well as other risk factors, which hinders interpretation of how they influence severity and disease progression. This work aimed to study differences in COVID-19 disease outcomes through analysis of risk profiles stratified by gender and obesity status. Methods: This study employed an unsupervised clustering analysis, using Mexico's national COVID-19 hospitalization dataset, which contains demographic information and health outcomes of patients hospitalized due to COVID-19. Patients were segmented into four groups by obesity and gender, with participants' attributes and clinical outcome data described for each. Then, Consensus and PAM clustering methods were used to identify distinct risk profiles based on underlying patient characteristics. Risk profile discovery was completed on 70% of records, with the remaining 30% available for validation. Results: Data from 88,536 hospitalized patients were analyzed. Obesity, regardless of gender, was linked with higher odds of hypertension, diabetes, cardiovascular diseases, pneumonia, and Intensive Care Unit (ICU) admissions. Men tended to have higher frequencies of ICU admissions and pneumonia and higher mortality rates than women. Within each of the four analysis groups (divided based on gender and obesity status), clustering analyses identified four to five distinct risk profiles. For example, among women with obesity, there were four profiles; those with a hypertensive profile were more likely to have pneumonia, and those with a diabetic profile were most likely to be admitted to the ICU. Conclusion: Our analysis emphasizes the complex interplay between obesity, gender, and health outcomes in COVID-19 hospitalizations. The identified risk profiles highlight the need for personalized treatment strategies for COVID-19 patients and can assist in planning for patterns of deterioration in future waves of SARS-CoV-2 virus transmission. This research underscores the importance of tackling obesity as a major public health concern, given its interplay with many other health conditions, including infectious diseases such as COVID-19.

Classification of Thyroid Tumors Based on DNA Methylation Patterns.

Background: Alterations in DNA methylation are stable epigenetic events that can serve as clinical biomarkers. The aim of this study was to analyze methylation patterns among various follicular cell-derived thyroid neoplasms to identify disease subtypes and help understand and classify thyroid tumors. Methods: We employed an unsupervised machine learning method for class discovery to search for distinct methylation patterns among various thyroid neoplasms. Our algorithm was not provided with any clinical or pathological information, relying exclusively on DNA methylation data to classify samples. We analyzed 810 thyroid samples (n = 256 for discovery and n = 554 for validation), including benign and malignant tumors, as well as normal thyroid tissue. Results: Our unsupervised algorithm identified that samples could be classified into three subtypes based solely on their methylation profile. These methylation subtypes were strongly associated with histological diagnosis (p < 0.001) and were therefore named normal-like, follicular-like, and papillary thyroid carcinoma (PTC)-like. Follicular adenomas, follicular carcinomas, oncocytic adenomas, and oncocytic carcinomas clustered together forming the follicular-like methylation subtype. Conversely, classic papillary thyroid carcinomas (cPTC) and tall cell PTC clustered together forming the PTC-like subtype. These methylation subtypes were also strongly associated with genomic drivers: 98.7% BRAFV600E-driven cancers were PTC like, whereas 96.0% RAS-driven cancers had a follicular-like methylation pattern. Interestingly, unlike other diagnoses, follicular variant PTC (FVPTC) samples were split into two methylation clusters (follicular like and PTC like), indicating a heterogeneous group likely to be formed by two distinct diseases. FVPTC samples with a follicular-like methylation pattern were enriched for RAS mutations (36.4% vs. 8.0%; p < 0.001), whereas FVPTC- with PTC-like methylation patterns were enriched for BRAFV600E mutations (52.0% vs. 0%, Fisher exact p = 0.004) and RET fusions (16.0% vs. 0%, Fisher exact p = 0.003). Conclusions: Our data provide novel insights into the epigenetic alterations of thyroid tumors. Since our classification method relies on a fully unsupervised machine learning approach for subtype discovery, our results offer a robust background to support the classification of thyroid neoplasms based on methylation patterns.

Identifying two distinct subphenotypes of patent ductus arteriosus in preterm infants using machine learning.

To use unsupervised machine learning to identify potential subphenotypes of preterm infants with patent ductus arteriosus (PDA). The study was conducted retrospectively at a neonatal intensive care unit in Brazil. Patients with a gestational age < 28 weeks who had undergone at least one echocardiogram within the first two weeks of life and had PDA size > 1.5 or LA/AO ratio > 1.5 were included. Agglomerative hierarchical clustering on principal components was used to divide the data into different clusters based on common characteristics. Two distinct subphenotypes of preterm infants with hemodynamically significant PDA were identified: "inflamed," characterized by high leukocyte, neutrophil, and neutrophil-to-lymphocyte ratio, and "respiratory acidosis," characterized by low pH and high pCO2 levels.    Conclusions: This study suggests that there may be two distinct subphenotypes of preterm infants with hemodynamically significant PDA: "inflamed" and "respiratory acidosis." By dividing the population into different subgroups based on common characteristics, it is possible to get a more nuanced understanding of the effectiveness of PDA interventions. What is Known: • Treatment of PDA in preterm infants has been controversial. • Stratification of preterm infants with PDA into subgroups is important in order to determine the best treatment. What is New: • Unsupervised machine learning was used to identify two subphenotypes of preterm infants with hemodynamically significant PDA. • The 'inflamed' cluster was characterized by higher values of leukocyte, neutrophil, and neutrophil-to-lymphocyte ratio. The 'respiratory acidosis' cluster was characterized by lower pH values and higher pCO2 values.

Comparación de dos tipos de clasificación en dispepsia funcional; distrés postprandial o dolor epigástrico vs. un análisis multidimensional por conglomerados utilizando aprendizaje no supervisado

Los métodos de inteligencia artificial utilizando herramientas de aprendizaje no supervisado pueden apoyar la resolución de problemas al establecer patrones de agrupación o clasificación no identificados, que permiten tipificar subgrupos para manejos más individualizados. Existen pocos estudios que permiten conocer la influencia de síntomas digestivos y extradigestivos en la tipificación dispepsia funcional; esta investigación realizó un análisis de aprendizaje no supervisado por conglomerados basándose en dichos síntomas, para discriminar subtipos de dispepsia y comparar con una de las clasificaciones actualmente más aceptadas. Se realizó un análisis exploratorio de conglomerados en adultos con dispepsia funcional según síntomas digestivos, extradigestivos y emocionales. Se conformaron patrones de agrupación de tal manera que dentro de cada grupo existiera homogeneidad en cuanto a los valores adoptados por cada variable. El método de análisis de conglomerados fue bietápico y los resultados del patrón de clasificación se compararon con una de las clasificaciones más aceptadas de dispepsia funcional. De 184 casos, 157 cumplieron con criterios de inclusión. El análisis de conglomerados excluyó 34 casos no clasificables. Los pacientes con dispepsia de tipo 1 (conglomerado uno), presentaron mejoría al tratamiento en el 100% de los casos, solo una minoría presentaron síntomas depresivos. Los pacientes con dispepsia de tipo 2 (conglomerado dos) presentaron una mayor probabilidad de falla al tratamiento con inhibidor de bomba de protones, padecieron con mayor frecuencia trastornos de sueño, ansiedad, depresión, fibromialgia, limitaciones físicas o dolor crónico de naturaleza no digestiva. Esta clasificación de dispepsia por análisis de clúster establece una visión más holística de la dispepsia en la cual características extradigestivas, síntomas afectivos, presencia o no de trastornos de sueño y de dolor crónico permiten discriminar el comportamiento y respuesta al manejo de primera línea.

Artificial intelligence methods using unsupervised learning tools can support problem solving by establishing unidentified grouping or classification patterns that allow typing subgroups for more individualized management. There are few studies that allow us to know the influence of digestive and extra-digestive symptoms in the classification of functional dyspepsia. This research carried out a cluster unsupervised learning analysis based on these symptoms to discriminate subtypes of dyspepsia and compare with one of the currently most accepted classifications. An exploratory cluster analysis was carried out in adults with functional dyspepsia according to digestive, extra-digestive and emotional symptoms. Grouping patterns were formed in such a way that within each group there was homogeneity in terms of the values adopted by each variable. The cluster analysis method was two-stage and the results of the classification pattern were compared with one of the most accepted classifications of functional dyspepsia. Of 184 cases, 157 met the inclusion criteria. The cluster analysis excluded 34 unclassifiable cases. Patients with type 1 dyspepsia (cluster one) presented improvement after treatment in 100% of cases, only a minority presented depressive symptoms. Patients with type 2 dyspepsia (cluster two) presented a higher probability of failure to treatment with proton pump inhibitor, suffered more frequently from sleep disorders, anxiety, depression, fibromyalgia, physical limitations or chronic pain of a non-digestive nature. This classification of dyspepsia by cluster analysis establishes a more holistic vision of dyspepsia in which extradigestive characteristics, affective symptoms, presence or absence of sleep disorders and chronic pain allow discriminating behavior and response to first-line management.

Body Composition Profiles of Applicants to a Physical Education and Sports Major in Southeastern Mexico.

This study aimed to determine the body composition profile of candidates applying for a Physical Education and Sports major. 327 young adults (F: 87, M: 240) participated in this cross-sectional study. Nutritional status and body composition analysis were performed, and the profiles were generated using an unsupervised machine learning algorithm. Body mass index (BMI), percentage of fat mass (%FM), percentage of muscle mass (%MM), metabolic age (MA), basal metabolic rate (BMR), and visceral fat level (VFL) were used as input variables. BMI values were normal-weight although VFL was significantly higher in men (<0.001; η2 = 0.104). MA was positively correlated with BMR (0.81 [0.77, 0.85]; p < 0.01), BMI (0.87 [0.84, 0.90]; p < 0.01), and VFL (0.77 [0.72, 0.81]; p < 0.01). The hierarchical clustering analysis revealed two significantly different age-independent profiles: Cluster 1 (n = 265), applicants of both sexes that were shorter, lighter, with lower adiposity and higher lean mass; and, Cluster 2 (n = 62), a group of overweight male applicants with higher VFL, taller, with lower %MM and estimated energy expended at rest. We identified two profiles that might help universities, counselors and teachers/lecturers to identify applicants in which is necessary to increase physical activity levels and improve dietary habits.

Heterogeneity in gender dysphoria in a Brazilian sample awaiting gender-affirming surgery: a data-driven analysis.

BACKGROUND: Population heterogeneity and the lack of clinical and sociodemographic information in transgender individuals with gender dysphoria (GD) remains a challenge for specialized services in mental health and surgical procedures. It aimed to identify and describe profiles in a sample waiting for gender-affirming surgery. METHODS: A sample of 100 outpatients with GD was assessed through a structured interview, Emotion Regulation Difficulty Scale (DERS), Ruminative Response Scale (RRS), Depression, Anxiety and Stress Scale (DASS-21) and Life Satisfaction scale (SWLS). Cluster analysis was used to identify different profile categories. RESULTS: Two subgroups with different profiles were identified: with less clinical severity (LCS) and with high clinical severity (HCS) on emotional dysregulation, acute symptoms of depression, anxiety, stress and association with mental rumination. The HCS cluster had greater vulnerability in terms of psychiatric history, use of psychotropic drugs, HIV positive, child abuse and suicidal behavior. CONCLUSION: Different profiles were found regarding the vulnerability to mental health in a sample of transgender people with GD who seek a public hospital service for the same clinical-surgical objective. Longitudinal studies are essential to monitor the impact of these contrasts and to target personalized therapeutic approaches in the prevention of psychiatric disorders.

Profiling Physical Fitness of Physical Education Majors Using Unsupervised Machine Learning.

The academic curriculum has shown to promote sedentary behavior in college students. This study aimed to profile the physical fitness of physical education majors using unsupervised machine learning and to identify the differences between sexes, academic years, socioeconomic strata, and the generated profiles. A total of 542 healthy and physically active students (445 males, 97 females; 19.8 [2.2] years; 66.0 [10.3] kg; 169.5 [7.8] cm) participated in this cross-sectional study. Their indirect VO2max (Cooper and Shuttle-Run 20 m tests), lower-limb power (horizontal jump), sprint (30 m), agility (shuttle run), and flexibility (sit-and-reach) were assessed. The participants were profiled using clustering algorithms after setting the optimal number of clusters through an internal validation using R packages. Non-parametric tests were used to identify the differences (p < 0.05). The higher percentage of the population were freshmen (51.4%) and middle-income (64.0%) students. Seniors and juniors showed a better physical fitness than first-year students. No significant differences were found between their socioeconomic strata (p > 0.05). Two profiles were identified using hierarchical clustering (Cluster 1 = 318 vs. Cluster 2 = 224). The matching analysis revealed that physical fitness explained the variation in the data, with Cluster 2 as a sex-independent and more physically fit group. All variables differed significantly between the sexes (except the body mass index [p = 0.218]) and the generated profiles (except stature [p = 0.559] and flexibility [p = 0.115]). A multidimensional analysis showed that the body mass, cardiorespiratory fitness, and agility contributed the most to the data variation so that they can be used as profiling variables. This profiling method accurately identified the relevant variables to reinforce exercise recommendations in a low physical performance and overweight majors.

Unsupervised Machine Learning Applied to Seismic Interpretation: Towards an Unsupervised Automated Interpretation Tool.

Seismic interpretation is a fundamental process for hydrocarbon exploration. This activity comprises identifying geological information through the processing and analysis of seismic data represented by different attributes. The interpretation process presents limitations related to its high data volume, own complexity, time consumption, and uncertainties incorporated by the experts' work. Unsupervised machine learning models, by discovering underlying patterns in the data, can represent a novel approach to provide an accurate interpretation without any reference or label, eliminating the human bias. Therefore, in this work, we propose exploring multiple methodologies based on unsupervised learning algorithms to interpret seismic data. Specifically, two strategies considering classical clustering algorithms and image segmentation methods, combined with feature selection, were evaluated to select the best possible approach. Additionally, the resultant groups of the seismic data were associated with groups obtained from well logs of the same area, producing an interpretation with aggregated lithologic information. The resultant seismic groups correctly represented the main seismic facies and correlated adequately with the groups obtained from the well logs data.

Reducing the heterogeneity in hepatocellular carcinoma. A cluster analysis based on clinical variables in patients treated at a quaternary care hospital.

INTRODUCTION AND AIMS: Even though the term hepatocellular carcinoma designates the most common type of primary liver cancer, the disease has a high level of heterogeneity due to its etiology, geographic variation, behavior, and association with specific genetic alterations. The aim of the present study was to establish, through a cluster analysis, the clinical characteristics that enable homogeneous conglomerates to be defined. MATERIALS AND METHODS: An exploratory cluster analysis was developed utilizing the K-means method for sub-classifying 119 cases of patients with hepatocellular carcinoma. Sixty-two of those patients met the inclusion criteria, as well as none of the exclusion criteria. For the cluster analysis, an n-dimensional space was defined, in which n was equal to the number of variables included in the study (n = 17). The spatial coordinates corresponded to any possible magnitude between the minimum and maximum values of the variables analyzed (age, sex, tumor volume, AFP, AST, DB, Alb, Na, INR, Cr, HBV, HCV, OH, NASH, cirrhosis, multiple tumors, and neotumor). RESULTS: Four patterns with homogeneous clinical characteristics were identified, in which age at presentation, history of hepatitis B virus infection, altered liver profile with cholestatic dominance, and low albumin levels were associated with an apparently worse outcome. CONCLUSIONS: How heterogeneity in hepatocellular carcinoma could be reduced was shown through utilizing an unsupervised learning method to define specific subgroups, in whom known pathophysiologic mechanisms could better explain tumor behavior and define the determining prognostic factors related to the subgroups.

Reducing the heterogeneity in hepatocellular carcinoma. A cluster analysis based on clinical variables in patients treated at a quaternary care hospital. / Disminuyendo la heterogeneidad en hepatocarcinoma. Análisis de clúster por variables clínicas en pacientes atendidos en una institución de cuarto nivel de complejidad.

INTRODUCTION AND OBJECTIVE: Even though the term hepatocellular carcinoma designates the most common type of primary liver cancer, the disease has a high level of heterogeneity due to its etiology, geographic variation, behavior, and association with specific genetic alterations. The aim of the present study was to establish, through a cluster analysis, the clinical characteristics that enable homogeneous conglomerates to be defined. MATERIALS AND METHODS: An exploratory cluster analysis was developed utilizing the K-means method for sub-classifying 119 cases of patients with hepatocellular carcinoma. Sixty-two of those patients met the inclusion criteria, as well as none of the exclusion criteria. For the cluster analysis, an n-dimensional space was defined, in which n was equal to the number of variables included in the study (n = 17). The spatial coordinates corresponded to any possible magnitude between the minimum and maximum values of the variables analyzed (age, sex, tumor volume, AFP, AST, DB, Alb, Na, INR, Cr, HBV, HCV, OH, NASH, cirrhosis, multiple tumors, and neotumor). RESULTS: Four patterns with homogeneous clinical characteristics were identified, in which age at presentation, history of hepatitis B virus infection, altered liver profile with cholestatic dominance, and low albumin levels were associated with an apparently worse outcome. CONCLUSION: How heterogeneity in hepatocellular carcinoma could be reduced was shown through utilizing an unsupervised learning method to define specific subgroups, in whom known pathophysiologic mechanisms could better explain tumor behavior and define the determining prognostic factors related to the subgroups.

Molecular mechanism driving retroperitoneal adipocyte hypertrophy and hyperplasia in response to a high-sugar diet.

SCOPE: We have previously shown an increase in adipocyte size and lipid content in retroperitoneal white adipose tissue (rWAT) induced by an 8-week high-sugar diet (HSD). In this study, we assessed the effect of a HSD on the transcriptional activity of adipogenic genes in a time-course study to provide insight regarding the genetic networks involved in the rWAT response to dietary sugar. METHODS AND RESULTS: Weaned male Wistar rats were fed a standard chow diet or HSD (68% carbohydrates) for 4, 8 or 12 weeks, and rWAT was removed for histopathology and PCR array (adipogenesis) analyses. The HSD induced adipocyte hypertrophy and hyperplasia in rWAT after 12 weeks of ingestion. Additionally, the HSD altered serum VLDL-cholesterol, triacylglycerol and glucometabolic parameters. Hierarchical clustering revealed HSD-induced changes in the expression patterns of the tested gene set. Pathway analysis, which used the enrichment analysis algorithm of the Thompson Reuters MetaCore platform, associated a cluster of differentially expressed genes with canonical pathways related to regulating adipocyte differentiation and proliferation (p-value < 10(-7)). CONCLUSION: HSD feeding post-weaning increased both the adipocyte size and number by simultaneously up-regulating pro-adipogenic signals (the PPARγ pathway) and down-regulating anti-adipogenic signals (Wnt pathway) in young adults.