COADREADx: A comprehensive algorithmic dissection of colorectal cancer unravels salient biomarkers and actionable insights into its discrete progression.

Background: Colorectal cancer is a common condition with an uncommon burden of disease, heterogeneity in manifestation, and no definitive treatment in the advanced stages. Renewed efforts to unravel the genetic drivers of colorectal cancer progression are paramount. Early-stage detection contributes to the success of cancer therapy and increases the likelihood of a favorable prognosis. Here, we have executed a comprehensive computational workflow aimed at uncovering the discrete stagewise genomic drivers of colorectal cancer progression. Methods: Using the TCGA COADREAD expression data and clinical metadata, we constructed stage-specific linear models as well as contrast models to identify stage-salient differentially expressed genes. Stage-salient differentially expressed genes with a significant monotone trend of expression across the stages were identified as progression-significant biomarkers. The stage-salient genes were benchmarked using normals-augmented dataset, and cross-referenced with existing knowledge. The candidate biomarkers were used to construct the feature space for learning an optimal model for the digital screening of early-stage colorectal cancers. The candidate biomarkers were also examined for constructing a prognostic model based on survival analysis. Results: Among the biomarkers identified are: CRLF1, CALB2, STAC2, UCHL1, KCNG1 (stage-I salient), KLHL34, LPHN3, GREM2, ADCY5, PLAC2, DMRT3 (stage-II salient), PIGR, HABP2, SLC26A9 (stage-III salient), GABRD, DKK1, DLX3, CST6, HOTAIR (stage-IV salient), and CDH3, KRT80, AADACL2, OTOP2, FAM135B, HSP90AB1 (top linear model genes). In particular the study yielded 31 genes that are progression-significant such as ESM1, DKK1, SPDYC, IGFBP1, BIRC7, NKD1, CXCL13, VGLL1, PLAC1, SPERT, UPK2, and interestingly three members of the LY6G6 family. Significant monotonic linear model genes included HIGD1A, ACADS, PEX26, and SPIB. A feature space of just seven biomarkers, namely ESM1, DHRS7C, OTOP3, AADACL2, LPHN3, GABRD, and LPAR1, was sufficient to optimize a RandomForest model that achieved > 98% balanced accuracy (and performant recall) of cancer vs. normal on external validation. Design of an optimal multivariate model based on survival analysis yielded a prognostic panel of three stage-IV salient genes, namely HOTAIR, GABRD, and DKK1. Based on the above sparse signatures, we have developed COADREADx, a web-server for potentially assisting colorectal cancer screening and patient risk stratification. COADREADx provides uncertainty measures for its predictions and needs clinical validation. It has been deployed for experimental non-commercial use at: https://apalanialab.shinyapps.io/coadreadx/.

Occupational stress and body composition of hospital workers: a follow-up study.

This study sought to analyze the influence of occupational stress on the body composition of hospital workers after one year of follow-up. This prospective cohort study included 218 workers from one of the leading private hospitals in the municipality of Santo Antônio de Jesus, Recôncavo da Bahia region, Northeast Brazil. Body composition was analyzed by proxy (Body Mass Index and Waist Circumference) and Bioelectrical Impedance Analysis. The primary exposure adopted in the present study was the perception of occupational stress, assessed with the adapted and reduced version of the Job Content Questionnaire evaluating demand and control dimensions. The covariates were work characteristics; biological characteristics; sociodemographic characteristics and lifestyle. Statistical analyses were performed using descriptive, bivariate and multivariate analysis. At the first stage of the study, we identified that 55.96% (n = 122) of workers had high work demand and 25.22% (n = 55) had low control. Among those who had high demand and low control at the beginning of the study, the majority were women, non-white, with low educational and income levels, sleeping less than 7 h/day. After 12 months of follow-up, the median value for demand continued as 13 (IQR: 5-25) and for control, it increased to 16 (IQR: 9-23). In this second moment of the study, 62.38% (n = 136) of workers showed high demand and 45.87% (n = 100) low control. The characteristics of workers with high demand and low control were similar to those of the first moment. The results indicate that high demand and low control at work are risk factors for changes in body mass index, fat mass and fat-free mass in hospital workers. This study shows the importance and need for clinical and epidemiological assessments regarding the body composition of professionals working in hospitals, since high rates of overweight and obesity are triggers of chronic health problems such as dyslipidemia, diabetes mellitus and cardiovascular diseases, among others. Therefore, managers must promote adequate working conditions and understand the need for periodic body composition assessments.

Tabular deep learning: a comparative study applied to multi-task genome-wide prediction.

PURPOSE: More accurate prediction of phenotype traits can increase the success of genomic selection in both plant and animal breeding studies and provide more reliable disease risk prediction in humans. Traditional approaches typically use regression models based on linear assumptions between the genetic markers and the traits of interest. Non-linear models have been considered as an alternative tool for modeling genomic interactions (i.e. non-additive effects) and other subtle non-linear patterns between markers and phenotype. Deep learning has become a state-of-the-art non-linear prediction method for sound, image and language data. However, genomic data is better represented in a tabular format. The existing literature on deep learning for tabular data proposes a wide range of novel architectures and reports successful results on various datasets. Tabular deep learning applications in genome-wide prediction (GWP) are still rare. In this work, we perform an overview of the main families of recent deep learning architectures for tabular data and apply them to multi-trait regression and multi-class classification for GWP on real gene datasets. METHODS: The study involves an extensive overview of recent deep learning architectures for tabular data learning: NODE, TabNet, TabR, TabTransformer, FT-Transformer, AutoInt, GANDALF, SAINT and LassoNet. These architectures are applied to multi-trait GWP. Comprehensive benchmarks of various tabular deep learning methods are conducted to identify best practices and determine their effectiveness compared to traditional methods. RESULTS: Extensive experimental results on several genomic datasets (three for multi-trait regression and two for multi-class classification) highlight LassoNet as a standout performer, surpassing both other tabular deep learning models and the highly efficient tree based LightGBM method in terms of both best prediction accuracy and computing efficiency. CONCLUSION: Through series of evaluations on real-world genomic datasets, the study identifies LassoNet as a standout performer, surpassing decision tree methods like LightGBM and other tabular deep learning architectures in terms of both predictive accuracy and computing efficiency. Moreover, the inherent variable selection property of LassoNet provides a systematic way to find important genetic markers that contribute to phenotype expression.

Analysis of single-cell CRISPR perturbations indicates that enhancers predominantly act multiplicatively.

A single gene may have multiple enhancers, but how they work in concert to regulate transcription is poorly understood. To analyze enhancer interactions throughout the genome, we developed a generalized linear modeling framework, GLiMMIRS, for interrogating enhancer effects from single-cell CRISPR experiments. We applied GLiMMIRS to a published dataset and tested for interactions between 46,166 enhancer pairs and corresponding genes, including 264 "high-confidence" enhancer pairs. We found that enhancer effects combine multiplicatively but with limited evidence for further interactions. Only 31 enhancer pairs exhibited significant interactions (false discovery rate <0.1), none of which came from the high-confidence set, and 20 were driven by outlier expression values. Additional analyses of a second CRISPR dataset and in silico enhancer perturbations with Enformer both support a multiplicative model of enhancer effects without interactions. Altogether, our results indicate that enhancer interactions are uncommon or have small effects that are difficult to detect.

Estimands and Cumulative Incidence Function Regression in Clinical Trials: Some New Results on Interpretability and Robustness.

Regression analyses based on transformations of cumulative incidence functions are often adopted when modeling and testing for treatment effects in clinical trial settings involving competing and semi-competing risks. Common frameworks include the Fine-Gray model and models based on direct binomial regression. Using large sample theory we derive the limiting values of treatment effect estimators based on such models when the data are generated according to multiplicative intensity-based models, and show that the estimand is sensitive to several process features. The rejection rates of hypothesis tests based on cumulative incidence function regression models are also examined for null hypotheses of different types, based on which a robustness property is established. In such settings supportive secondary analyses of treatment effects are essential to ensure a full understanding of the nature of treatment effects. An application to a palliative study of individuals with breast cancer metastatic to bone is provided for illustration.

Methodology for the generation of normative data for the U.S. adult Spanish-speaking population: A Bayesian approach.

BACKGROUND: Hispanics are the largest growing ethnic minority group in the U.S. Despite significant progress in providing norms for this population, updated normative data are essential. OBJECTIVE: To present the methodology for a study generating normative neuropsychological test data for Spanish-speaking adults living in the U.S. using Bayesian inference as a novel approach. METHODS: The sample consisted of 253 healthy adults from eight U.S. regions, with individuals originating from a diverse array of Latin American countries. To participate, individuals must have met the following criteria: were between 18 and 80 years of age, had lived in the U.S. for at least 1 year, self-identified Spanish as their dominant language, had at least one year of formal education, were able to read and write in Spanish at the time of evaluation, scored≥23 on the Mini-Mental State Examination, <10 on the Patient Health Questionnaire- 9, and <10 on the Generalized Anxiety Disorder scale. Participants completed 12 neuropsychological tests. Reliability statistics and norms were calculated for all tests. CONCLUSION: This is the first normative study for Spanish-speaking adults in the U.S. that uses Bayesian linear or generalized linear regression models for generating norms in neuropsychology, implementing sociocultural measures as possible covariates.

The Relationship Between Rates of Cannabis Use and Covid-19 Infection Rates During the Pandemic: An Analysis of Canada's National Cannabis Survey.

Background: The well-documented relationship between mental health and substance use is corroborated by recent research on the impacts of the Covid-19 pandemic on cannabis use behavior. Social isolation, anxiety, depression, stress, and boredom are all linked to the greater prevalence of cannabis and other substance use. Objectives: To better understand the relationship between infection rates in Canada and cannabis use behavior, this research examines the prevalence and frequency of cannabis use across health regions in all 10 provinces at the height of the pandemic. Methods: Our analyses linked data from the National Cannabis Survey with Covid-19 case rates and cannabis availability through legal retail outlets at the end of 2020, 2 years after cannabis legalization came into effect. Hierarchical generalized linear models were employed, controlling for age, gender, SES, mental health, the number of cannabis stores per square kilometer, and prevalence of cannabis use in each health region prior to the pandemic. Results: Even after controlling for other predictors, our models show that those residing where infection rates are higher are more likely to use cannabis and use it more often. Conclusions: The findings of this study support investing in better-targeted harm reduction measures in areas hit hardest by the pandemic to address contributing societal conditions. The implications are noteworthy for drug policy observers in North America and other global jurisdictions pursuing evidence-based public health approaches to regulating cannabis and other substance use.

The prediction limits of the National Adult Reading Test and its abbreviated and international variants.

OBJECTIVE: Premorbid tests estimate cognitive ability prior to neurological condition onset or brain injury. Tests requiring oral pronunciation of visually presented irregular words, such as the National Adult Reading Test (NART), are commonly used due to robust evidence that word familiarity is well-preserved across a range of neurological conditions and correlates highly with intelligence. Our aim is to examine the prediction limits of NART variants to assess their ability to accurately estimate premorbid IQ. METHOD: We examine the prediction limits of 13 NART variants, calculate which IQ classification system categories are reachable in principle, and consider the proportion of the adult population in the target country falling outside the predictable range. RESULTS: Many NART variants cannot reach higher or lower IQ categories due to floor/ceiling effects and inherent limitations of linear regression (used to convert scores to predicted IQ), restricting clinical accuracy in evaluating premorbid ability (and thus the magnitude of impairment). For some variants this represents a sizeable proportion of the target population. CONCLUSIONS: Since both higher and lower IQ categories are unreachable in principle, we suggest that future NART variants consider polynomial or broken-stick fitting (or similar methods) and suggest that prediction limits should be routinely reported.

Joint modeling of an outcome variable and integrated omics datasets using GLM-PO2PLS.

In many studies of human diseases, multiple omics datasets are measured. Typically, these omics datasets are studied one by one with the disease, thus the relationship between omics is overlooked. Modeling the joint part of multiple omics and its association to the outcome disease will provide insights into the complex molecular base of the disease. Several dimension reduction methods which jointly model multiple omics and two-stage approaches that model the omics and outcome in separate steps are available. Holistic one-stage models for both omics and outcome are lacking. In this article, we propose a novel one-stage method that jointly models an outcome variable with omics. We establish the model identifiability and develop EM algorithms to obtain maximum likelihood estimators of the parameters for normally and Bernoulli distributed outcomes. Test statistics are proposed to infer the association between the outcome and omics, and their asymptotic distributions are derived. Extensive simulation studies are conducted to evaluate the proposed model. The method is illustrated by modeling Down syndrome as outcome and methylation and glycomics as omics datasets. Here we show that our model provides more insight by jointly considering methylation and glycomics.

Ditching the norm: Using alternative distributions for biological data analysis.

Most classical statistical tests assume data are normally distributed. If this assumption is not met, researchers often turn to non-parametric methods. These methods have some drawbacks, and if no suitable non-parametric test exists, a normal distribution may be used inappropriately instead. A better option is to select a distribution appropriate for the data from dozens available in modern software packages. Selecting a distribution that represents the data generating process is a crucial but overlooked step in analysing data. This paper discusses several alternative distributions and the types of data that they are suitable for.

Nationwide Analysis of the Relationship Between Low Ambient Temperature and Acute Aortic Dissection-related Hospitalizations.

AIMS: Acute aortic dissection (AAD) is a life-threatening cardiovascular emergency. Therefore, identifying modifiable risk factors for AAD is of great public health significance. An association between ambient temperature (AT) and AAD has been reported; however, not all findings have been elucidated. This study examined the association between AAD-related hospitalization and AT using data from the Japanese Registry of All Cardiac and Vascular Diseases Diagnostic Procedure Combination (JROAD-DPC), which is a nationwide claims-based database. METHODS: This nationwide time-stratified case-crossover study evaluated data of hospitalized patients with AAD from 1,119 certified hospitals between 2012 and 2020 using the JROAD-DPC database. Conditional logistic regression and distributed lag non-linear models were used to investigate the association between average daily temperature and AAD-related hospitalization. RESULTS: Among the 96,812 cases analyzed. The exposure-response curve between AT and AAD-related hospitalization showed an increase in the odds ratio for lower temperatures, with a peak at timed -10°C (odds ratio: 2.28, 95% confidence interval: 1.92-2.71, compared with that at 20°C). The effects of temperature on lag days 0 and 1 were also significant.Stratified analyses showed a greater association between AT and AAD-related hospitalization for the following variables: older age (≥75 years), female sex (44.4%, the mean age ± SD was 76 ± 12 years), low body mass index (<22), winter season, and warmer regions. CONCLUSIONS: Low AT is associated with an increased risk of AAD-related hospitalization. Several susceptible groups are affected by cold temperatures and have a higher risk of hospitalization.

This study examined the association between hospitalization due to acute aortic dissection (AAD) and ambient temperature using data from the Japanese Registry of All Cardiac and Vascular Diseases Diagnostic Procedure Combination, which is a nationwide claims-based database. Key findings:Low ambient temperature is associated with an increased risk of AAD-related hospitalization, with a greater association with older age and female sex.Other predisposing factors for the above association include lower body mass index, winter seasons, and warmer regions.

Integrated analysis of remote sensing with meteorological and health data for allergic rhinitis forecasting in Tianjin.

Long time series of vegetation monitoring can be carried out by remote sensing data, the level of urban greening is objectively described, and the spatial characteristics of plant pollen are indirectly understood. Pollen is the main allergen in patients with seasonal allergic rhinitis. Meteorological factors affect the release and diffusion of pollen. Therefore, studying of the complex relationship between meteorological factors and allergic rhinitis is essential for effective prevention and treatment of the disease. In this study, we leverage remote sensing data for a comprehensive decade-long analysis of urban greening in Tianjin, which exhibits an annual increase in vegetative cover of 0.51 per annum, focusing on its impact on allergic rhinitis through changes in pollen distribution. Utilizing high-resolution imagery, we quantify changes in urban Fractional Vegetation Coverage (FVC) and its correlation with pollen types and allergic rhinitis cases. Our analysis reveals a significant correlation between FVC trends and pollen concentrations, with a surprising value of 0.71, highlighting the influence of urban greenery on allergenic pollen levels. We establish a robust connection between the seasonal patterns of pollen outbreaks and allergic rhinitis consultations, with a noticeable increase in consultations during high pollen seasons. our findings indicate a higher allergenic potential of herbaceous compared to woody vegetation. This nuanced understanding underscores the importance of pollen sensitivity, alongside concentration, in driving allergic rhinitis incidents. Utilizing a Generalized Linear Model, significant features influencing the number of visits for allergic rhinitis (P < 0.05) were identified. Both GLM and LSTM models were employed to forecast the visitation volumes for rhinitis during the spring and summer-autumn of 2022. Upon validation, it was found that the R² values between the simulated and actual values for both GLM and LSTM models surpassed the 95% confidence threshold. Moreover, the R² values for the summer-autumn seasons (GLM: 0.56, LSTM: 0.72) were higher than those for spring (GLM: 0.22, LSTM: 0.47). Comparing the errors between the simulated and actual values of GLM and LSTM models, LSTM exhibited higher simulation precision in both spring and summer-autumn seasons, demonstrating superior simulation performance. Overall, our study pioneers the integration of remote sensing with meteorological and health data for allergic rhinitis forecasting. This integrative approach provides valuable insights for public health planning, particularly in urban settings, and lays the groundwork for advanced, location-specific allergenic pollen forecasting and mitigation strategies.

The spike-and-slab lasso and scalable algorithm to accommodate multinomial outcomes in variable selection problems.

Spike-and-slab prior distributions are used to impose variable selection in Bayesian regression-style problems with many possible predictors. These priors are a mixture of two zero-centered distributions with differing variances, resulting in different shrinkage levels on parameter estimates based on whether they are relevant to the outcome. The spike-and-slab lasso assigns mixtures of double exponential distributions as priors for the parameters. This framework was initially developed for linear models, later developed for generalized linear models, and shown to perform well in scenarios requiring sparse solutions. Standard formulations of generalized linear models cannot immediately accommodate categorical outcomes with > 2 categories, i.e. multinomial outcomes, and require modifications to model specification and parameter estimation. Such modifications are relatively straightforward in a Classical setting but require additional theoretical and computational considerations in Bayesian settings, which can depend on the choice of prior distributions for the parameters of interest. While previous developments of the spike-and-slab lasso focused on continuous, count, and/or binary outcomes, we generalize the spike-and-slab lasso to accommodate multinomial outcomes, developing both the theoretical basis for the model and an expectation-maximization algorithm to fit the model. To our knowledge, this is the first generalization of the spike-and-slab lasso to allow for multinomial outcomes.

Bite Me: Bark Stripping Showed Negligible Effect on Volume Growth of Norway Spruce in Latvia.

Over the past few decades, increasing populations of cervid species in the Baltic region have reduced the quality and vitality of cultivated Norway spruce (Picea abies (L.) Karst.) stands. This study evaluated the effect of bark stripping on the volume growth of spruce trees in Latvia. Data collection took place in two forest stands. In each stand, 20 Norway spruce trees were sampled, 10 with visible bark damage scars and 10 control trees. Stem discs were collected from control trees at specified heights (0 m, 0.5 m, 1 m, 1.3 m, and 2 m, and then at one-metre intervals up to the top) and from damaged trees at additional specific points relative to the damage. Each disc was sanded and scanned; tree ring widths were measured in 16 radial directions using WinDendro 2012a software. Annual volume growth reconstruction was performed for each tree. Changes in relative volume growth were analysed in interaction with scar parameters, tree type (damaged/control), and pre-damage volume using linear regression models. The significance of parameter interactions was assessed using analysis of variance (ANOVA). Pairwise comparisons of estimated marginal means (EMMs) were conducted using Tukey's HSD post hoc test. No significant effect of bark stripping on the total stem volume increment was detected. However, the length of bark stripping scars had a significant impact on relative volume growth in the lower parts of the stems. These findings underscore the importance of further research examining a broader spectrum of cervid damage intensity and the effects of repeated damage on tree survival and growth.

Curve progression following selective and nonselective spinal fusion for adolescent idiopathic scoliosis: are selective fusions stable?

PURPOSE: The purpose of this study is to compare postoperative outcomes between selective and non-selective fusions longitudinally over the first five postoperative years. METHODS: Patient parameters were retrieved from a multicenter, prospective, database. Patients with Lenke 1-6, B and C deformities were included. Patients were stratified into 2 groups: selective fusion (SF), if the last instrumented vertebra (LIV) was at or cranial to the lumbar apex, or non-selective fusion (NSF). Differences in coronal and sagittal radiographic outcomes were assessed with generalized linear models (GLMs) at 1-, 2- and 5- year postoperative outcomes. Five-year postoperative categorical radiographic outcomes, flexibility, scoliosis research society scores (SRS), and reoperation rates were compared between groups. Matched cohorts were created for subgroup analysis. RESULTS: 416 (SF:261, NF:155) patients, including 353 females were included in this study. The mean preoperative thoracic and lumbar Cobb angles were 57.3 ± 8.9 and 45.3 ± 8.0, respectively. GLMs demonstrated greater postoperative coronal deformity in the SF group (p < 0.01); however, the difference between groups did not change overtime (p > 0.05) indicating a relatively stable postoperative deformity correction. The SF group had a greater incidence of lumbar Cobb ≥ 26 degrees (p < 0.01). The NSF group demonstrated worse forward and lateral flexibility at 5-year postoperative outcome (p < 0.05). There was no difference in postoperative SRS scores between the SF and NSF groups. Reoperation rates were similar between groups. CONCLUSION: Selective fusion results in greater coronal plane deformity; however, this deformity does not progress significantly over time compared to non-selective fusion. Selective spinal fusion may be a beneficial option for a larger subset of patients than previously identified. LEVEL OF EVIDENCE: III.

Effect of Temperature and Precipitation on Acute Appendicitis Incidence in Seoul: A Time Series Regression Analysis.

PURPOSE: This study aimed to investigate the relationship between meteorological factors, specifically temperature and precipitation, and the incidence of appendicitis in Seoul, South Korea. METHODS: Using data from the National Health Insurance Service spanning 2010-2020, the study analyzed 165,077 appendicitis cases in Seoul. Time series regression modeling with distributed-lag non-linear models was employed. RESULTS: Regarding acute appendicitis and daily average temperature, the incidence rate ratio (IRR) showed an increasing trend from approximately - 10 °C to 10 °C. At temperatures above 10 °C, the increase was more gradual. The IRR approached a value close to 1 at temperatures below - 10 °C and above 30 °C. Both total and complicated appendicitis exhibited similar trends. Increased precipitation was negatively associated with the incidence of total acute appendicitis around the 50 mm/day range, but not with complicated appendicitis. CONCLUSIONS: The findings suggest that environmental factors, especially temperature, may play a role in the occurrence of appendicitis. This research underscores the potential health implications of global climate change and the need for further studies to understand the broader impacts of environmental changes on various diseases.

Simultaneous Inference of Multiple Binary Endpoints in Biomedical Research: Small Sample Properties of Multiple Marginal Models and a Resampling Approach.

In biomedical research, the simultaneous inference of multiple binary endpoints may be of interest. In such cases, an appropriate multiplicity adjustment is required that controls the family-wise error rate, which represents the probability of making incorrect test decisions. In this paper, we investigate two approaches that perform single-step p $p$ -value adjustments that also take into account the possible correlation between endpoints. A rather novel and flexible approach known as multiple marginal models is considered, which is based on stacking of the parameter estimates of the marginal models and deriving their joint asymptotic distribution. We also investigate a nonparametric vector-based resampling approach, and we compare both approaches with the Bonferroni method by examining the family-wise error rate and power for different parameter settings, including low proportions and small sample sizes. The results show that the resampling-based approach consistently outperforms the other methods in terms of power, while still controlling the family-wise error rate. The multiple marginal models approach, on the other hand, shows a more conservative behavior. However, it offers more versatility in application, allowing for more complex models or straightforward computation of simultaneous confidence intervals. The practical application of the methods is demonstrated using a toxicological dataset from the National Toxicology Program.

Oral Health Differences Between Middle-Aged and Older Adults in South Korea.

INTRODUCTION AND AIMS: The oral health characteristics of middle-aged and older adults exhibit variations. This study identifies the various factors associated with oral health among middle-aged and older adults through a subgroup analysis by age group of data representative of the South Korean population. METHODS: We examined influencing factors: demographic, socioeconomic, dental, physical attributes, psychological, and mental attributes. Oral health was assessed using the Geriatric Oral Health Assessment Index. The participants were divided into two groups: those under 65 years of age (middle-aged) and those over 65 years of age (older adults). We used multiple linear regression analysis and dominance analysis to determine the dominant factors associated with oral health. RESULTS: A total of 6369 participants were aged 69.2 ± 9.8 years on average, and 57.5% were women. Dominance analysis revealed that lower educational levels and activity difficulty caused by diseases were significantly associated with both groups. Moreover, depressive symptoms were the foremost adverse factor linked to oral health in the middle-aged (P < .001, standardized beta [ß] = -4.30, general dominance index [GDI] = 19.00) and older (P < .001, ß = -0.30, GDI = 10.70) adults. The number of teeth exhibited the most positive association with oral health in both middle-aged (P < .001, ß = 0.20, GDI = 5.30) and older (P < .001, ß = 0.23, GDI = 7.40) adults. However, cognitive function, dental visits, body mass index, severe pain, functional limitations, and cognitive function exhibited distinct patterns between the age groups. CONCLUSION: Depressive symptoms and the number of teeth significantly influence oral health in middle-aged and older adults, though the impact varies by age. These findings stress the importance of tailored strategies considering age-specific attributes for effective oral health improvement. CLINICAL RELEVANCE: Enhancing oral health requires healthcare providers to prioritize monitoring age-specific risk factors. Further, educational plans should highlight the importance of preventive oral care and regular dental visits.

Identifying Key Genes Involved in Axillary Lymph Node Metastasis in Breast Cancer Using Advanced RNA-Seq Analysis: A Methodological Approach with GLMQL and MAS.

Our study aims to address the methodological challenges frequently encountered in RNA-Seq data analysis within cancer studies. Specifically, it enhances the identification of key genes involved in axillary lymph node metastasis (ALNM) in breast cancer. We employ Generalized Linear Models with Quasi-Likelihood (GLMQLs) to manage the inherently discrete and overdispersed nature of RNA-Seq data, marking a significant improvement over conventional methods such as the t-test, which assumes a normal distribution and equal variances across samples. We utilize the Trimmed Mean of M-values (TMMs) method for normalization to address library-specific compositional differences effectively. Our study focuses on a distinct cohort of 104 untreated patients from the TCGA Breast Invasive Carcinoma (BRCA) dataset to maintain an untainted genetic profile, thereby providing more accurate insights into the genetic underpinnings of lymph node metastasis. This strategic selection paves the way for developing early intervention strategies and targeted therapies. Our analysis is exclusively dedicated to protein-coding genes, enriched by the Magnitude Altitude Scoring (MAS) system, which rigorously identifies key genes that could serve as predictors in developing an ALNM predictive model. Our novel approach has pinpointed several genes significantly linked to ALNM in breast cancer, offering vital insights into the molecular dynamics of cancer development and metastasis. These genes, including ERBB2, CCNA1, FOXC2, LEFTY2, VTN, ACKR3, and PTGS2, are involved in key processes like apoptosis, epithelial-mesenchymal transition, angiogenesis, response to hypoxia, and KRAS signaling pathways, which are crucial for tumor virulence and the spread of metastases. Moreover, the approach has also emphasized the importance of the small proline-rich protein family (SPRR), including SPRR2B, SPRR2E, and SPRR2D, recognized for their significant involvement in cancer-related pathways and their potential as therapeutic targets. Important transcripts such as H3C10, H1-2, PADI4, and others have been highlighted as critical in modulating the chromatin structure and gene expression, fundamental for the progression and spread of cancer.

Impact of Bariatric Surgery on metabolic health in a Uruguayan cohort and the emerging predictive role of FSTL1.

Obesity poses significant challenges, necessitating comprehensive strategies for effective intervention. Bariatric Surgery (BS) has emerged as a crucial therapeutic approach, demonstrating success in weight loss and comorbidity improvement. This study aimed to evaluate the outcomes of BS in a cohort of 48 Uruguayan patients and investigate the interplay between BS and clinical and metabolic features, with a specific focus on FSTL1, an emerging biomarker associated with obesity and inflammation. We quantitatively analyzed BS outcomes and constructed linear models to identify variables impacting BS success. The study revealed the effectiveness of BS in improving metabolic and clinical parameters. Importantly, variables correlating with BS success were identified, with higher pre-surgical FSTL1 levels associated with an increased effect of BS on BMI reduction. FSTL1 levels were measured from patient plasma using an ELISA kit pre-surgery and six months after. This research, despite limitations of a small sample size and limited follow-up time, contributes valuable insights into understanding and predicting the success of BS, highlighting the potential role of FSTL1 as a useful biomarker in obesity.