Spatiotemporal Distribution, Time to Treatment Outcome Clustering and Determinants of Esophageal Cancer in Ethiopia, a Scoping Study.

INTRODUCTION: Esophageal cancer was the eighth and sixth leading cause of morbidity of all cancers in the world, and the 15th and 12th in Ethiopia, respectively. There is a lack of comprehensive data regarding Ethiopia's esophageal cancer hotspot, treatment outcome clustering, and other factors. OBJECTIVE: This scoping review was designed to understand the extent and type of existing evidence regarding spatiotemporal distribution, time to treatment outcome clustering, and determinants of esophageal cancer in Ethiopia up to March 28, 2023. METHODS: Three-step search strategies were employed for the scoping review from March 15 to 28, 2023. Targeted databases included PubMed/Medline, PubMed Central (PMC), Google Scholar, Hinari, and Cochrane for published studies and different websites for unpublished studies for evidence synthesis. Data were extracted using the Joanna Briggs Institute (JBI) manual format. RESULTS: Our final analysis comprised 17 (16 quantitative and 1 qualitative) studies. Three studies attempted to depict the country's temporal distribution, whereas 12 studies showed the spatial distribution of esophageal cancer by proportion. The regional state of Oromia recorded a high percentage of cases. Numerous risk factors linked to the tumor have been identified in 8 investigations. Similarly, 5 studies went into detail regarding the likelihood of survival and the factors that contribute to malignancy, while 2 studies covered the results of disease-related treatments. CONCLUSIONS: The substantial body of data that underpins this finding supports the fact that esophageal cancer has several risk factors and that its prevalence varies greatly across the country and among regions. Surgery, radiotherapy, or chemotherapy helped the patient live longer. However, no research has investigated which treatment is best for boosting patient survival and survival clustering. Therefore, research with robust models for regional distribution, clustering of time to treatment outcomes, and drivers of esophageal cancer will be needed.

The review was based on 17 studies searched from five electronic databases, and six additional sources. Esophageal cancer incidence varies across the nation (from region to region). The median survival time of esophageal cancer cases were four months, and six months. No study investigated the better treatment that improved the survival of patients with esophageal cancer. A contradicting report were found about the link b/n khat chewing and esophageal cancer. The temporal distribution of the tumor was controversial.

Biclustering analysis on tree-shaped time-series single cell gene expression data of Caenorhabditis elegans.

BACKGROUND: In recent years, gene clustering analysis has become a widely used tool for studying gene functions, efficiently categorizing genes with similar expression patterns to aid in identifying gene functions. Caenorhabditis elegans is commonly used in embryonic research due to its consistent cell lineage from fertilized egg to adulthood. Biologists use 4D confocal imaging to observe gene expression dynamics at the single-cell level. However, on one hand, the observed tree-shaped time-series datasets have characteristics such as non-pairwise data points between different individuals. On the other hand, the influence of cell type heterogeneity should also be considered during clustering, aiming to obtain more biologically significant clustering results. RESULTS: A biclustering model is proposed for tree-shaped single-cell gene expression data of Caenorhabditis elegans. Detailedly, a tree-shaped piecewise polynomial function is first employed to fit non-pairwise gene expression time series data. Then, four factors are considered in the objective function, including Pearson correlation coefficients capturing gene correlations, p-values from the Kolmogorov-Smirnov test measuring the similarity between cells, as well as gene expression size and bicluster overlapping size. After that, Genetic Algorithm is utilized to optimize the function. CONCLUSION: The results on the small-scale dataset analysis validate the feasibility and effectiveness of our model and are superior to existing classical biclustering models. Besides, gene enrichment analysis is employed to assess the results on the complete real dataset analysis, confirming that the discovered biclustering results hold significant biological relevance.

Multi-Input data ASsembly for joint Analysis (MIASA): A framework for the joint analysis of disjoint sets of variables.

The joint analysis of two datasets [Formula: see text] and [Formula: see text] that describe the same phenomena (e.g. the cellular state), but measure disjoint sets of variables (e.g. mRNA vs. protein levels) is currently challenging. Traditional methods typically analyze single interaction patterns such as variance or covariance. However, problem-tailored external knowledge may contain multiple different information about the interaction between the measured variables. We introduce MIASA, a holistic framework for the joint analysis of multiple different variables. It consists of assembling multiple different information such as similarity vs. association, expressed in terms of interaction-scores or distances, for subsequent clustering/classification. In addition, our framework includes a novel qualitative Euclidean embedding method (qEE-Transition) which enables using Euclidean-distance/vector-based clustering/classification methods on datasets that have a non-Euclidean-based interaction structure. As an alternative to conventional optimization-based multidimensional scaling methods which are prone to uncertainties, our qEE-Transition generates a new vector representation for each element of the dataset union [Formula: see text] in a common Euclidean space while strictly preserving the original ordering of the assembled interaction-distances. To demonstrate our work, we applied the framework to three types of simulated datasets: samples from families of distributions, samples from correlated random variables, and time-courses of statistical moments for three different types of stochastic two-gene interaction models. We then compared different clustering methods with vs. without the qEE-Transition. For all examples, we found that the qEE-Transition followed by Ward clustering had superior performance compared to non-agglomerative clustering methods but had a varied performance against ultrametric-based agglomerative methods. We also tested the qEE-Transition followed by supervised and unsupervised machine learning methods and found promising results, however, more work is needed for optimal parametrization of these methods. As a future perspective, our framework points to the importance of more developments and validation of distance-distribution models aiming to capture multiple-complex interactions between different variables.

Evaluation of a walking school bus program: a cluster randomized controlled trial.

BACKGROUND: The purpose of this study was to investigate the effects of a walking school bus intervention on children's active commuting to school. METHODS: We conducted a randomized controlled trial (RCT) in Houston, Texas (Year 1) and Seattle, Washington (Years 2-4) from 2012 to 2016. The study had a two-arm, cluster randomized design comparing the intervention (walking school bus and education materials) to the control (education materials) over one school year October/November - May/June). Twenty-two schools that served lower income families participated. Outcomes included percentage of days students' active commuting to school (primary, measured via survey) and moderate-to-vigorous physical activity (MVPA, measured via accelerometry). Follow-up took place in May or June. We used linear mixed-effects models to estimate the association between the intervention and outcomes of interest. RESULTS: Total sample was 418 students [Mage=9.2 (SD = 0.9) years; 46% female], 197 (47%) in the intervention group. The intervention group showed a significant increase compared with the control group over time in percentage of days active commuting (ß = 9.04; 95% CI: 1.10, 16.98; p = 0.015) and MVPA minutes/day (ß = 4.31; 95% CI: 0.70, 7.91; p = 0.02). CONCLUSIONS: These findings support implementation of walking school bus programs that are inclusive of school-age children from lower income families to support active commuting to school and improve physical activity. TRAIL REGISTRATION: This RCT is registered at clinicaltrials.gov (NCT01626807).

Aspergillus hubkae, a Novel Species Isolated from a Patient with Probable Invasive Pulmonary Aspergillosis.

A 50-year-old man, previously diagnosed with pulmonary tuberculosis and lung cavities, presented with symptoms including fever, shortness of breath, and cough. A pulmonary CT scan revealed multiple cavities, consolidation and tree-in-bud in the upper lungs. Further investigation through direct examination of bronchoalveolar lavage fluid showed septate hyphae with dichotomous acute branching. Subsequent isolation and morphological analysis identified the fungus as belonging to Aspergillus section Nigri. The patient was diagnosed with probable invasive pulmonary aspergillosis and successfully treated with a three-month oral voriconazole therapy. Phylogenetic analysis based on partial ß-tubulin, calmodulin and RNA polymerase second largest subunit sequences revealed that the isolate represents a putative new species related to Aspergillus brasiliensis, and is named Aspergillus hubkae here. Antifungal susceptibility testing demonstrated that the isolate is resistant to itraconazole but susceptible to voriconazole. This phenotypic and genetic characterization of A. hubkae, along with the associated case report, will serve as a valuable resource for future diagnoses of infections caused by this species. It will also contribute to more precise and effective patient management strategies in similar clinical scenarios.

Analyzing the transition from two-vehicle collisions to chain reaction crashes: A hybrid approach using random parameters logit model, interpretable machine learning, and clustering.

Chain reaction crashes (CRC) begin with a two-vehicle collision and rapidly intensify as more vehicles get directly involved. CRCs result in more extensive damage compared to two-vehicle crashes and understanding the progression of a two-vehicle collision into a CRC can unveil preventive strategies that have received less attention. In this study, to align with recent research direction and overcome the limitations of econometric and machine learning (ML) modelling, a hybrid approach is adopted. Moreover, to tackle the existing challenges in crash analysis, addressing unobserved heterogeneity in ML, and exploring random parameter effects and interactions more precisely, a new approach is proposed. To achieve this, a hybrid random parameter logit model and interpretable ML, joint with prior latent class clustering is implemented. Notably, this is the first attempt at using a clustering with hybrid modeling. The significant risk factors, their critical values, distinct effects, and interactions are interpreted using both marginal effects and the SHAP (SHapley Additive exPlanations) method across clusters. This study utilizes crash, traffic, and geometric data from eleven suburban freeways in Iran collected over a 5-year period. The overall results indicate an increased risk of CRC in congested traffic, higher traffic variation, and on horizontal curves combined with longitudinal slopes. Some parameters exhibit distinct or fluctuating effects, which are discussed across different conditions or considering interactions. For instance, during nighttime, heightened congestion on 2-lane freeways, increased traffic variation in less congested conditions, and adverse weather combined with horizontal curves and slopes pose risks. During daytime, increased traffic variation within highly congested sections, higher proportion of heavy vehicle traffic in moderately congested sections, and two lanes in each direction coupled with curves, elevate the levels of risk. The results of this study provide a better understanding of risk factors impact across different conditions, which are usable for policy makers.

Wildfires and climate justice: future wildfire events predicted to disproportionally impact socioeconomically vulnerable communities in North Carolina.

Wildfire events are becoming increasingly common across many areas of the United States, including North Carolina (NC). Wildfires can cause immediate damage to properties, and wildfire smoke conditions can harm the overall health of exposed communities. It is critical to identify communities at increased risk of wildfire events, particularly in areas with that have sociodemographic disparities and low socioeconomic status (SES) that may exacerbate incurred impacts of wildfire events. This study set out to: (1) characterize the distribution of wildfire risk across NC; (2) implement integrative cluster analyses to identify regions that contain communities with increased vulnerability to the impacts of wildfire events due to sociodemographic characteristics; (3) provide summary-level statistics of populations with highest wildfire risk, highlighting SES and housing cost factors; and (4) disseminate wildfire risk information via our online web application, ENVIROSCAN. Wildfire hazard potential (WHP) indices were organized at the census tract-level, and distributions were analyzed for spatial autocorrelation via global and local Moran's tests. Sociodemographic characteristics were analyzed via k-means analysis to identify clusters with distinct SES patterns to characterize regions of similar sociodemographic/socioeconomic disparities. These SES groupings were overlayed with housing and wildfire risk profiles to establish patterns of risk across NC. Resulting geospatial analyses identified areas largely in Southeastern NC with high risk of wildfires that were significantly correlated with neighboring regions with high WHP, highlighting adjacent regions of high risk for future wildfire events. Cluster-based analysis of SES factors resulted in three groups of regions categorized through distinct SES profiling; two of these clusters (Clusters 2 and 3) contained indicators of high SES vulnerability. Cluster 2 contained a higher percentage of younger (<5 years), non-white, Hispanic and/or Latino residents; while Cluster 3 had the highest mean WHP and was characterized by a higher percentage of non-white residents, poverty, and less than a high school education. Counties of particular SES and WHP-combined vulnerability include those with majority non-white residents, tribal communities, and below poverty level households largely located in Southeastern NC. WHP values per census tract were dispersed to the public via the ENVIROSCAN application, alongside other environmentally-relevant data.

Comprehensive analysis of human monocyte subsets using full-spectrum flow cytometry and hierarchical marker clustering.

Introduction: Exploring monocytes' roles within the tumor microenvironment is crucial for crafting targeted cancer treatments. Methods: This study unveils a novel methodology utilizing four 20-color flow cytometry panels for comprehensive peripheral immune system phenotyping, specifically targeting classical, intermediate, and non-classical monocyte subsets. Results: By applying advanced dimensionality reduction techniques like t-distributed stochastic neighbor embedding (tSNE) and FlowSom analysis, we performed an extensive profiling of monocytes, assessing 50 unique cell surface markers related to a wide range of immunological functions, including activation, differentiation, and immune checkpoint regulation. Discussion: This in-depth approach significantly refines the identification of monocyte subsets, directly supporting the development of personalized immunotherapies and enhancing diagnostic precision. Our pioneering panel for monocyte phenotyping marks a substantial leap in understanding monocyte biology, with profound implications for the accuracy of disease diagnostics and the success of checkpoint-inhibitor therapies. Key findings include revealing distinct marker expression patterns linked to tumor progression and providing new avenues for targeted therapeutic interventions.

The future of rapid and automated single-cell data analysis using reference mapping.

As the number of single-cell datasets continues to grow rapidly, workflows that map new data to well-curated reference atlases offer enormous promise for the biological community. In this perspective, we discuss key computational challenges and opportunities for single-cell reference-mapping algorithms. We discuss how mapping algorithms will enable the integration of diverse datasets across disease states, molecular modalities, genetic perturbations, and diverse species and will eventually replace manual and laborious unsupervised clustering pipelines.

Clustering evaluation of water quality for various classes of in-flow rivers in connected brackish lakes.

Brackish waters and estuaries at the lower reaches of rivers accumulate organic matter and nutrients from various sources in the watershed. Sufficient light and shallow water depth stimulate phytoplankton growth, resulting in a more diversified ecosystem with higher trophic levels. For effective watershed management, it is crucial to characterize the water quality of all rivers, including small and medium-sized ones. Our field survey assessed water quality parameters in 26 inflow rivers surrounding Lakes Shinji and Nakaumi, two consolidated brackish lakes in Japan. The parameters included water temperature, salinity, chlorophyll-a, and nutrients. The study used hierarchical clustering. The Silhouette Index was used to assess clustering outcomes and identify any difficulties in dispersion across clusters. The 26 rivers surrounding Lakes Shinji and Nakaumi were classified into six groups based on their water quality characteristics. This classification distinguishes itself from earlier subjective methods that relied on geographical factors. The new approach identifies a need for improved management of river water quality. The results of the cluster analysis provide valuable insights for future management initiatives. It is important to consider these findings alongside established watershed criteria.

Identification of inflammatory clusters in long-COVID through analysis of plasma biomarker levels.

Mechanisms underlying long COVID remain poorly understood. Patterns of immunological responses in individuals with long COVID may provide insight into clinical phenotypes. Here we aimed to identify these immunological patterns and study the inflammatory processes ongoing in individuals with long COVID. We applied an unsupervised hierarchical clustering approach to analyze plasma levels of 42 biomarkers measured in individuals with long COVID. Logistic regression models were used to explore associations between biomarker clusters, clinical variables, and symptom phenotypes. In 101 individuals, we identified three inflammatory clusters: a limited immune activation cluster, an innate immune activation cluster, and a systemic immune activation cluster. Membership in these inflammatory clusters did not correlate with individual symptoms or symptom phenotypes, but was associated with clinical variables including age, BMI, and vaccination status. Differences in serologic responses between clusters were also observed. Our results indicate that clinical variables of individuals with long COVID are associated with their inflammatory profiles and can provide insight into the ongoing immune responses.

Prediction of tuberculosis clusters in the riverine municipalities of the Brazilian Amazon with machine learning.

OBJECTIVE: Tuberculosis (TB) is the second most deadly infectious disease globally, posing a significant burden in Brazil and its Amazonian region. This study focused on the "riverine municipalities" and hypothesizes the presence of TB clusters in the area. We also aimed to train a machine learning model to differentiate municipalities classified as hot spots vs. non-hot spots using disease surveillance variables as predictors. METHODS: Data regarding the incidence of TB from 2019 to 2022 in the riverine town was collected from the Brazilian Health Ministry Informatics Department. Moran's I was used to assess global spatial autocorrelation, while the Getis-Ord GI* method was employed to detect high and low-incidence clusters. A Random Forest machine-learning model was trained using surveillance variables related to TB cases to predict hot spots among non-hot spot municipalities. RESULTS: Our analysis revealed distinct geographical clusters with high and low TB incidence following a west-to-east distribution pattern. The Random Forest Classification model utilizes six surveillance variables to predict hot vs. non-hot spots. The machine learning model achieved an Area Under the Receiver Operator Curve (AUC-ROC) of 0.81. CONCLUSION: Municipalities with higher percentages of recurrent cases, deaths due to TB, antibiotic regimen changes, percentage of new cases, and cases with smoking history were the best predictors of hot spots. This prediction method can be leveraged to identify the municipalities at the highest risk of being hot spots for the disease, aiding policymakers with an evidenced-based tool to direct resource allocation for disease control in the riverine municipalities.

High-risk space-time clusters of mortality in children 5 to 14 years of age in Mato Grosso state, Brazil. / Clusters Espaço-Temporais de Alto Risco da Mortalidade Infantojuvenil em Mato Grosso, Brasil.

The study aimed to detect high-risk areas for deaths of children and adolescents 5 to 14 years of age in the state of Mato Grosso, Brazil, from 2009 to 2020. This was an exploratory ecological study with municipalities as the units of analysis. Considering mortality data from the Mortality Information System (SIM) and demographic data from the Brazilian Institute of Geography and Statistics (IBGE), the study used multivariate statistics to identify space-time clusters of excess mortality risk in this age group. From 5 to 9 years of age, two clusters with high mortality risk were detected; the most likely located in the state's southern mesoregion (RR: 1.6; LRT: 8,53). Among the 5 clusters detected in the 10-14-year age group, the main cluster was in the state's northern mesoregion (RR: 2,26; LRT: 7,84). A reduction in mortality rates was observed in the younger age group and an increase in these rates in the older group. The identification of these clusters, whose analysis merits replication in other parts of Brazil, is the initial stage in the investigation of possible factors associated with morbidity and mortality in this group, still insufficiently explored, and for planning adequate interventions.

O objetivo deste estudo é detectar as áreas de maior risco para óbitos de crianças e adolescentes de 5 a 14 anos no estado de Mato Grosso entre os anos de 2009 e 2020. Estudo ecológico, tipo exploratório, cuja unidade de análise foram os municípios. Considerando dados de mortalidade do SIM e os demográficos do IBGE, o estudo utilizou a estatística multivariada para a identificação dos clusters espaço-temporais de sobrerrisco de mortalidade nesta faixa etária. Dos 5 aos 9 anos, dois clusters de alto risco de mortalidade foram detectados; o mais provável localizado na mesorregião sul (RR: 1,6; LRV: 8,53). Dentre os 5 clusters detectados na faixa etária dos 10 aos 14 anos, o principal foi localizado na mesorregião norte (RR: 2,26; LRV: 7,84). Foi identificada redução das taxas de mortalidade na faixa etária mais jovem e aumento destas taxas na faixa etária mais velha. A identificação destes clusters, cuja análise merece ser replicada a outras partes do território nacional, é a etapa inicial para a investigação de possíveis fatores associados à morbi-mortalidade deste grupo ainda pouco explorado e para o planejamento de intervenções adequadas.

Analysis of factors influencing the degree of accidental injury of bicycle riders considering data heterogeneity and imbalance.

Bicycle safety has emerged as a pressing concern within the vulnerable transportation community. Numerous studies have been conducted to identify the significant factors that contribute to the severity of cyclist injuries, yet the findings have been subject to uncertainty due to unobserved heterogeneity and class imbalance. This research aims to address these issues by developing a model to examine the impact of key factors on cyclist injury severity, accounting for data heterogeneity and imbalance. To incorporate unobserved heterogeneity, a total of 3,895 bicycle accidents were categorized into three homogeneous sub-accident clusters using Latent Class Cluster Analysis (LCA). Additionally, five over-sampling techniques were employed to mitigate the effects of data imbalance in each accident cluster category. Subsequently, Bayesian Network (BN) structure learning algorithms were utilized to construct 32 BN models after pairing the accident data from the four accident cluster types before and after sampling. The optimal BN models for each accident cluster type provided insights into the key factors associated with cyclist injury severity. The results indicate that the key factors influencing serious cyclist injuries vary heterogeneously across different accident clusters. Female cyclists, adverse weather conditions such as rain and snow, and off-peak periods were identified as key factors in several subclasses of accident clusters. Conversely, factors such as the week of the accident, characteristics of the trafficway, the season, drivers failing to yield to the right-of-way, distracted cyclists, and years of driving experience were found to be key factors in only one subcluster of accident clusters. Additionally, factors such as the time of the crash, gender of the cyclist, and weather conditions exhibit varying levels of heterogeneity across different accident clusters, and in some cases, exhibit opposing effects.

Investigating oral somatosensory perception and oral symptoms of head and neck cancer patients: insights on eating behaviour.

PURPOSE: Sensory alterations and oral manifestations are prevalent among head and neck cancer (HNC) patients. While taste and smell alterations have been thoroughly investigated, studies on their oral somatosensory perception remain limited. Building upon our previous publication that primarily focused on objective somatosensory measurements, the present work examined self-reported sensory perception, including somatosensation and oral symptoms, in HNC patients and evaluated their link with eating behaviour. METHODS: A cross-sectional study was conducted using self-reported questionnaires on sensory perception, oral symptoms, sensory-related food preference, and eating behaviour among HNC patients (n = 30). Hierarchical clustering analysis was performed to categorise patients based on their sensory perception. Correlations between oral symptoms score, sensory perception, sensory-related food preference, and eating behaviour were explored. RESULTS: Two distinct sensory profiles of patients were identified: no alteration (n = 14) and alteration (n = 16) group. The alteration group showed decreased preference towards several sensory modalities, especially the somatosensory. Concerning eating behaviour, more patients in the alteration group agreed to negatively connotated statements (e.g. having food aversion and eating smaller portions), demonstrating greater eating difficulties. In addition, several oral symptoms related to salivary dysfunction were reported. These oral symptoms were correlated with sensory perception, sensory-related food preference, and eating behaviour. CONCLUSION: This study presented evidence demonstrating that sensory alterations in HNC patients are not limited to taste and smell but cover somatosensory perception and are linked to various aspects of eating. Moreover, patients reported experiencing several oral symptoms. Those with sensory alterations and oral symptoms experienced more eating difficulties.

An inversion-based clustering approach for complex clusters.

BACKGROUND: The choice of an appropriate similarity measure plays a pivotal role in the effectiveness of clustering algorithms. However, many conventional measures rely solely on feature values to evaluate the similarity between objects to be clustered. Furthermore, the assumption of feature independence, while valid in certain scenarios, does not hold true for all real-world problems. Hence, considering alternative similarity measures that account for inter-dependencies among features can enhance the effectiveness of clustering in various applications. METHODS: In this paper, we present the Inv measure, a novel similarity measure founded on the concept of inversion. The Inv measure considers the significance of features, the values of all object features, and the feature values of other objects, leading to a comprehensive and precise evaluation of similarity. To assess the performance of our proposed clustering approach that incorporates the Inv measure, we evaluate it on simulated data using the adjusted Rand index. RESULTS: The simulation results strongly indicate that inversion-based clustering outperforms other methods in scenarios where clusters are complex, i.e., apparently highly overlapped. This showcases the practicality and effectiveness of the proposed approach, making it a valuable choice for applications that involve complex clusters across various domains. CONCLUSIONS: The inversion-based clustering approach may hold significant value in the healthcare industry, offering possible benefits in tasks like hospital ranking, treatment improvement, and high-risk patient identification. In social media analysis, it may prove valuable for trend detection, sentiment analysis, and user profiling. E-commerce may be able to utilize the approach for product recommendation and customer segmentation. The manufacturing sector may benefit from improved quality control, process optimization, and predictive maintenance. Additionally, the approach may be applied to traffic management and fleet optimization in the transportation domain. Its versatility and effectiveness make it a promising solution for diverse fields, providing valuable insights and optimization opportunities for complex and dynamic data analysis tasks.

Lung cancer clustering by identification of similarities and discrepancies of DNA copy numbers using maximal information coefficient.

Lung cancer is the second most diagnosed cancer and the first cause of cancer related death for men and women in the United States. Early detection is essential as patient survival is not optimal and recurrence rate is high. Copy number (CN) changes in cancer populations have been broadly investigated to identify CN gains and deletions associated with the cancer. In this research, the similarities between cancer and paired peripheral blood samples are identified using maximal information coefficient (MIC) and the spatial locations with substantially high MIC scores in each chromosome are used for clustering analysis. The results showed that a sizable reduction of feature set can be obtained using only a subset of locations with high MIC values. The clustering performance was evaluated using both true rate and normalized mutual information (NMI). Clustering results using the reduced feature set outperformed the performance of clustering using entire feature set in several chromosomes that are highly associated with lung cancer with several identified oncogenes.

Exploring the mystery of colon cancer from the perspective of molecular subtypes and treatment.

The molecular categorization of colon cancer patients remains elusive. Gene set enrichment analysis (GSEA), which investigates the dysregulated genes among tumor and normal samples, has revealed the pivotal role of epithelial-to-mesenchymal transition (EMT) in colon cancer pathogenesis. In this study, we employed multi-clustering method for grouping data, resulting in the identification of two clusters characterized by varying prognostic outcomes. These two subgroups not only displayed disparities in overall survival (OS) but also manifested variations in clinical variables, genetic mutation, and gene expression profiles. Using the nearest template prediction (NTP) method, we were able to replicate the molecular classification effectively within the original dataset and validated it across multiple independent datasets, underscoring its robust repeatability. Furthermore, we constructed two prognostic signatures tailored to each of these subgroups. Our molecular classification, centered on EMT, hold promise in offering fresh insights into the therapy strategies and prognosis assessment for colon cancer.

COVID-19 patient profiles over four waves in Barcelona metropolitan area: A clustering approach.

OBJECTIVES: Identifying profiles of hospitalized COVID-19 patients and explore their association with different degrees of severity of COVID-19 outcomes (i.e. in-hospital mortality, ICU assistance, and invasive mechanical ventilation). The findings of this study could inform the development of multiple care intervention strategies to improve patient outcomes. METHODS: Prospective multicentre cohort study during four different waves of COVID-19 from March 1st, 2020 to August 31st, 2021 in four health consortiums within the southern Barcelona metropolitan region. From a starting point of over 292 demographic characteristics, comorbidities, vital signs, severity scores, and clinical analytics at hospital admission, we used both clinical judgment and supervised statistical methods to reduce to the 36 most informative completed covariates according to the disease outcomes for each wave. Patients were then grouped using an unsupervised semiparametric method (KAMILA). Results were interpreted by clinical and statistician team consensus to identify clinically-meaningful patient profiles. RESULTS: The analysis included nw1 = 1657, nw2 = 697, nw3 = 677, and nw4 = 787 hospitalized-COVID-19 patients for each of the four waves. Clustering analysis identified 2 patient profiles for waves 1 and 3, while 3 profiles were determined for waves 2 and 4. Patients allocated in those groups showed a different percentage of disease outcomes (e.g., wave 1: 15.9% (Cluster 1) vs. 31.8% (Cluster 2) for in-hospital mortality rate). The main factors to determine groups were the patient's age and number of obese patients, number of comorbidities, oxygen support requirement, and various severity scores. The last wave is also influenced by the massive incorporation of COVID-19 vaccines. CONCLUSION: Our study suggests that a single care model at hospital admission may not meet the needs of hospitalized-COVID-19 adults. A clustering approach appears to be appropriate for helping physicians to differentiate patients and, thus, apply multiple care intervention strategies, as another way of responding to new outbreaks of this or future diseases.

Patterns of cognitive-emotional change after cognitive-behavioural treatment in emotional disorders: A 12-month longitudinal cluster analysis.

INTRODUCTION: The aim of this study was to use cluster analysis based on the trajectory of five cognitive-emotional processes (worry, rumination, metacognition, cognitive reappraisal and expressive suppression) over time to explore differences in clinical and performance variables in primary care patients with emotional symptoms. METHODS: We compared the effect of adding transdiagnostic cognitive-behavioural therapy (TD-CBT) to treatment as usual (TAU) according to cluster membership and sought to determine the variables that predicted cluster membership. 732 participants completed scales about cognitive-emotional processes, anxiety and depressive symptoms, functioning, and quality of life (QoL) at baseline, posttreatment, and at 12 months. Longitudinal cluster analysis and logistic regression analyses were carried out. RESULTS: A two-cluster solution was chosen as the best fit, named as "less" or "more" improvement in cognitive-emotional processes. Individuals who achieved more improvement in cognitive-emotional processes showed lower emotional symptoms and better QoL and functioning at all three time points. TAU+TD-CBT, income level, QoL and anxiety symptoms were significant predictors of cluster membership. CONCLUSIONS: These results underscore the value of adding TD-CBT to reduce maladaptive cognitive-emotional regulation strategies. These findings highlight the importance of the processes of change in therapy and demonstrate the relevance of the patient's cognitive-emotional profile in improving treatment outcomes.