A Role for Systemic Inflammation in Stroke-Associated Infection and the Long-Term Prognosis of Acute Ischemic Stroke: A Mediation Analysis.

Background and Purpose: The dynamic systemic inflammation level and stroke-associated infection (SAI) are related to the prognosis of acute ischemic stroke (AIS). We aimed to explore whether the systemic inflammatory response index (SIRI), systemic immune inflammation index (SII), and their dynamic changes possess predictability for SAI and long-term prognosis. Methods: A total of 1804 AIS patients without intravenous thrombolysis in two hospitals were included. We explored the relationship between SIRI, SII, and their dynamic changes and outcomes by constructing clusters. The mediating effects of SAI between prognosis and systemic inflammation were further evaluated. Results: Each SD increase in the concentration of SIRI exhibited a significant correlation with the risk of poor functional outcome, mortality, and functional dependency. Through K-means clustering analysis, patients with dramatically elevated or decreased systemic inflammation levels of SIRI (OR: 2.293, 95% CI: 1.279-4.109) and SII (OR: 3.165, 95% CI: 1.627-6.156) within 7 days had a higher risk of functional outcome. Through mediation analysis, SAI mediated the association between systemic inflammation and poor prognosis (SIRI: 33.73%, SII: 16.01%). Conclusion: Dramatically changing dynamics of SIRI and SII were significantly associated with a higher risk of poor prognosis in AIS patients. SAI mediated the association between systemic inflammation and prognosis at 1 year.

Association of dietary habits with general and abdominal obesity in Korean children and adolescents: cluster analysis of nationwide population survey data.

Introduction: Childhood obesity is a growing global health concern, but few studies have investigated dietary factors specifically related to obesity and abdominal obesity in children and adolescents. Herein, we aimed to identify the dietary factors affecting childhood obesity in Korean children and adolescents. Methods: Data from the Korea National Health and Nutrition Survey (KNHANES) VIII were analyzed using K-means clustering analysis to identify distinct clusters based on nine variables related to dietary habit, nutritional status, and nutritional education. Multiple logistic regression analysis was used to examine the association between incident obesity risk and the different clusters. We enrolled 2,290 participants aged 6-18 years, and separated them into two distinct clusters; Healthy and Unhealthy Dietary Habit Groups, clusters 1 and 2, respectively. Results: Cluster 1 was characterized by a lower obesity prevalence, healthier dietary habits (regular breakfast consumption; fruit and vegetable, reduced total energy, and lower protein and fat intakes), and greater nutritional education than Cluster 2. After adjusting for confounders, compared with Cluster 1, Cluster 2 demonstrated a significantly higher prevalence (OR [95% CI]) of both general and abdominal obesity (1.49 [1.05-2.13], p=0.027 and 1.43 [1.09-1.88], p=0.009). Discussion: Maintaining optimal dietary quality and patterns are crucial to prevent childhood obesity. Further research is warranted to explore specific dietary interventions tailored to different clusters to effectively address childhood obesity.

Mental health issues of higher education students reflected in academic research: A text mining study.

Objective: This study investigated mental health issues among higher education students to identify key concepts, topics, and trends over three periods of time: Period 1 (2000-2009), Period 2 (2010-2019), and Period 3 (2020-May 2024). Methods: The study collected 11,732 bibliographic records from Scopus and Web of Science, published between January 2000 and May 2024, and employed textual analysis methods, including keyword co-occurrence analysis, cluster analysis, and topic modeling. Results: In Period 1, general topics related to mental health disorders and treatments were identified. Period 2 showed prominence of well-being and help-seeking, as well as the emergence of digital mental health. Period 3 emphasized the impact of COVID-19 and increased technology usage. Conclusions: Based on the findings, we discussed the significance of the study and practical implications for clinicians and policymakers, as well as methodological implications for researchers. Additionally, the limitations of the study and future research were addressed.

Identifying subtypes of Type 2 diabetes mellitus based on real-world electronic medical record data in China.

AIMS: To replicate the European subtypes of type 2 diabetes mellitus (T2DM) in Chinese diabetes population, and investigate the risk of complications in different subtypes. METHODS: A diabetes cohort using real-world patient data was constructed and clustering was employed to subgroup the T2DM patients. Kaplan-Meier analysis and the Cox model were used to analyze the association between diabetes subtypes and the risk of complications. RESULTS: A total of 2,652 T2DM patients with complete clustering data were extracted. Among them, 466 (17.57 %) were classified as severe insulin-deficient diabetes (SIDD), 502 (18.93 %) as severe insulin-resistant diabetes (SIRD), 672 (25.34 %) as mild obesity-related diabetes (MOD), and 1,012 (38.16 %) as mild age-related diabetes (MARD). The risk of chronic kidney disease (CKD) and diabetic retinopathy (DR) were different in the four subtypes. Compared with MARD, SIRD had a higher risk of CKD (HR 2.01 [1.03, 3.91]), and SIDD had a higher risk of DR (HR 2.17 [1.12, 4.20]). The risk of stroke and coronary events had no difference. CONCLUSIONS: The European T2DM subtypes can be replicated in Chinese diabetes population. The risk of CKD and DR varied among different subtypes, indicating that proper interventions can be taken to prevent specific complications in different subtypes.

Classification of papyrus fragments by chemometric analysis of near-infrared spectra.

Papyrus has been used for millennia to record information, for sophisticated works of art as well as mundane notes. The collection, identification, and translation of papyrus fragments therefore opens a gateway into the past. To aid the efforts to access the history recorded in papyri, we investigated the suitability of NIR spectroscopy to perform two tasks: One is to support the authentication of ancient papyri, by differentiation of papyri that were manufactured more recently and subjected to accelerated ageing to resemble the originals. The other is the extensive task to piece together papyrus fragments into readable texts again. In museums around the world, more than 100,000 ancient papyrus fragments still wait for their proper assembly, deciphering and publication. The papyrus writing-ground was analysed by near-infrared (NIR) spectroscopy, and the spectra were evaluated using principal component analysis (PCA), hierarchical cluster analysis (HCA), partial least squares discriminant analysis (PLS-DA), and self-organizing maps (SOM). Cluster analysis and PLS-DA proved to be useful tools for distinguishing modern papyri from ancient papyri which were provided by collections in Vienna and Leipzig. Neither natural nor accelerated ageing affected the classification. A PLS-DA classification model, constructed from NIR spectra of 89 model scores, detected recent Papyri samples with 100 % sensitivity and specificity, even after accelerated ageing. The identification of groups of fragments of ancient papyri based on NIR spectra and chemometry is not straightforward. HCA, which focuses on the differences between samples, only grouped the fragments of 4 out of 20 papyri correctly. SOM, which rather focuses on the similarities, grouped 6 sets of fragments correctly. An automated grouping of fragments remains difficult, since the fragments themselves are heterogeneous while similarities between unrelated ancient papyri can be large.

Classification of Patients Based on Dyspnea and Desaturation During Exercise in Interstitial Lung Disease.

BACKGROUND: Dyspnea and desaturation during exercise are essential assessment items for pulmonary rehabilitation. Characterizing patients using these 2 factors may be important for providing more effective pulmonary rehabilitation. This study aimed to categorize subjects with interstitial lung disease (ILD) using dyspnea and desaturation at the end of the 6-min walk test (6MWT). METHODS: This was a retrospective study including 230 stable subjects with ILD who underwent 6MWT in our out-patient department at a general hospital in Japan. The modified Borg scale and oxygen saturation determined by SpO2 at the end of the 6MWT were used for cluster analysis using the k-means method with k = 4. RESULTS: Subjects were classified into 4 characteristic clusters. SpO2 at the end of the 6MWT was lower in cluster 4 (80.5 ± 3.0%) than in clusters 1 (94.3 ± 2.0%), 2 (94.3 ± 1.9%), and 3 (87.9 ± 1.8%) and was lower in cluster 3 than in clusters 1 and 2. The modified Borg scale score at the end of the 6MWT was higher in clusters 2 (4 [3-8]), 3 (3 [0-9]), and 4 (4 [0-7]) than in cluster 1 (0.5 [0-2.0]) and was higher in cluster 2 than in cluster 3. CONCLUSIONS: Subjects with ILD were classified into 4 characteristic clusters using dyspnea and SpO2 at the end of the 6MWT. The 4 clusters are characterized as follows: Cluster 1 had mild desaturation and mild dyspnea; cluster 2 had mild desaturation and severe dyspnea; cluster 3 had both moderate desaturation and dyspnea, and cluster 4 had both severe desaturation and dyspnea. These classification data offer insight for individualized pulmonary rehabilitation for patients with ILD.

Unlocking high-value football fans: unsupervised machine learning for customer segmentation and lifetime value.

Introduction: In the modern competitive landscape of football, clubs are increasingly leveraging data-driven decision-making to strengthen their commercial positions, particularly against rival clubs. The strategic allocation of resources to attract and retain profitable fans who exhibit long-term loyalty is crucial for advancing a club's marketing efforts. While the Recency, Frequency, and Monetary (RFM) customer segmentation technique has seen widespread application in various industries for predicting customer behavior, its adoption within the football industry remains underexplored. This study aims to address this gap by introducing an adjusted RFM approach, enhanced with the Analytic Hierarchy Process (AHP) and unsupervised machine learning, to effectively segment football fans based on Customer Lifetime Value (CLV). Methods: This research employs a novel weighted RFM method where the significance of each RFM component is quantified using the AHP method. The study utilizes a dataset comprising 500,591 anonymized merchandising transactions from Amsterdamsche Football Club Ajax (AFC Ajax). The derived weights for the RFM variables are 0.409 for Monetary, 0.343 for Frequency, and 0.248 for Recency. These weights are then integrated into a clustering framework using unsupervised machine learning algorithms to segment fans based on their weighted RFM values. The simple weighted sum approach is subsequently applied to estimate the CLV ranking for each fan, enabling the identification of distinct fan segments. Results: The analysis reveals eight distinct fan clusters, each characterized by unique behaviors and value contributions: The Golden Fans (clusters 1 and 2) exhibit the most favourable scores across the recency, frequency, and monetary metrics, making them relatively the most valuable. They are critical to the club's profitability and should be rewarded through loyalty programs and exclusive services. The Promising segment (cluster 3) shows potential to ascend to Golden Fan status with increased spending. Targeted marketing campaigns and incentives can stimulate this transition. The Needs Attention segment (cluster 4) are formerly loyal fans whose engagement has diminished. Re-engagement strategies are vital to prevent further churn. The New Fans segment (clusters 5 and 6) are fans who have recently transacted and show potential for growth with proper engagement and personalized offerings. Lastly, the Churned/Low Value segment (clusters 7 and 8) are fans who relatively contribute the least and may require price incentives to potentially re-engage, though they hold relatively lower priority compared to other segments. Discussion: The findings validate the proposed method's utility through its application to AFC Ajax's Customer Relationship Management (CRM) data and provides a robust framework for fan segmentation in the football industry. The approach offers actionable insights that can significantly enhance marketing strategies by identifying and prioritizing high-value segments based on the club's preferences and requirements. By maintaining the loyalty of Golden Fans and nurturing the Promising segment, football clubs can achieve substantial gains in profitability and fan engagement. Additionally, the study underscores the necessity of re-engaging formerly loyal fans and fostering new fans' growth to enable long-term commercial success. This methodology not only aims to bridge a research gap, but also equips marketing practitioners with data-driven tools for effective and efficient customer segmentation in the football industry.

Identification of type 1 diabetes risk phenotypes using an outcome-guided clustering analysis.

AIMS/HYPOTHESIS: Although statistical models for predicting type 1 diabetes risk have been developed, approaches that reveal the heterogeneity of the at-risk population by identifying clinically meaningful clusters are lacking. We aimed to identify and characterise clusters of islet autoantibody-positive individuals who share similar characteristics and type 1 diabetes risk. METHODS: We tested a novel outcome-guided clustering method in initially non-diabetic autoantibody-positive relatives of individuals with type 1 diabetes, using the TrialNet Pathway to Prevention study data (n=1123). The outcome of the analysis was the time to development of type 1 diabetes, and variables in the model included demographic characteristics, genetics, metabolic factors and islet autoantibodies. An independent dataset (the Diabetes Prevention Trial of Type 1 Diabetes Study) (n=706) was used for validation. RESULTS: The analysis revealed six clusters with varying type 1 diabetes risks, categorised into three groups based on the hierarchy of clusters. Group A comprised one cluster with high glucose levels (median for glucose mean AUC 9.48 mmol/l; IQR 9.16-10.02) and high risk (2-year diabetes-free survival probability 0.42; 95% CI 0.34, 0.51). Group B comprised one cluster with high IA-2A titres (median 287 DK units/ml; IQR 250-319) and elevated autoantibody titres (2-year diabetes-free survival probability 0.73; 95% CI 0.67, 0.80). Group C comprised four lower-risk clusters with lower autoantibody titres and glucose levels (with 2-year diabetes-free survival probability ranging from 0.84-0.99 in the four clusters). Within group C, the clusters exhibit variations in characteristics such as glucose levels, C-peptide levels and age. A decision rule for assigning individuals to clusters was developed. Use of the validation dataset confirmed that the clusters can identify individuals with similar characteristics. CONCLUSIONS/INTERPRETATION: Demographic, metabolic, immunological and genetic markers may be used to identify clusters of distinctive characteristics and different risks of progression to type 1 diabetes among autoantibody-positive individuals with a family history of type 1 diabetes. The results also revealed the heterogeneity in the population and complex interactions between variables.

Using clustering of genetic variants in Mendelian randomization to interrogate the causal pathways underlying multimorbidity from a common risk factor.

Mendelian randomization (MR) is an epidemiological approach that utilizes genetic variants as instrumental variables to estimate the causal effect of an exposure on a health outcome. This paper investigates an MR scenario in which genetic variants aggregate into clusters that identify heterogeneous causal effects. Such variant clusters are likely to emerge if they affect the exposure and outcome via distinct biological pathways. In the multi-outcome MR framework, where a shared exposure causally impacts several disease outcomes simultaneously, these variant clusters can provide insights into the common disease-causing mechanisms underpinning the co-occurrence of multiple long-term conditions, a phenomenon known as multimorbidity. To identify such variant clusters, we adapt the general method of agglomerative hierarchical clustering to multi-sample summary-data MR setup, enabling cluster detection based on variant-specific ratio estimates. Particularly, we tailor the method for multi-outcome MR to aid in elucidating the causal pathways through which a common risk factor contributes to multiple morbidities. We show in simulations that our "MR-AHC" method detects clusters with high accuracy, outperforming the existing methods. We apply the method to investigate the causal effects of high body fat percentage on type 2 diabetes and osteoarthritis, uncovering interconnected cellular processes underlying this multimorbid disease pair.

Determination of the physiochemical characteristics of traditional doenjang produced in Chungnam region in South Korea.

Traditional doenjang characteristically differs from commercial doenjang, as the former involves a long and natural fermentation process. This study determined the physiochemical characteristics of traditional doenjang produced in Chungnam region. Two commercial and thirteen traditional doenjang products were characterized in terms of color, moisture content, pH, °Brix, salinity, acid value, titratable acidity, NH2-N content, alcohol content, and total and reducing sugar contents. The traditional samples significantly differed from the commercial samples in terms of color, moisture, °Brix, acid value, and in alcohol, NH2-N content, and total sugar contents (p < 0.05), and the traditional samples were characteristically similar to those previously analyzed. Moreover, the samples produced in different cities could be clustered based on their physiochemical characteristics. The observed differences among the traditional samples were attributed to fermentation conditions, namely, duration and temperature, as these differences were not correlated with ingredient ratio.

Traditional Processing Can Enhance the Medicinal Effects of Polygonatum cyrtonema by Inducing Significant Chemical Changes in the Functional Components in Its Rhizomes.

The aims of this study were to explore the significant chemical changes in functional components induced by the traditional processing method and evaluate whether this method based on nine cycles of steaming and drying can effectively enhance the medicinal effects of Polygonatum cyrtonema rhizome. A global analysis on dynamic changes in secondary metabolites during nine processing cycles was performed, and the significantly differentially accumulated secondary metabolites were initially identified based on the secondary metabolome. Unsupervised principal component analysis (PCA), hierarchical clustering analysis (HCA), and orthogonal partial least squares discriminant analysis (OPLA-DA) on secondary metabolites clearly showed that processing significantly increased the global accumulation of secondary metabolites in processed P. cyrtonema rhizomes compared to unprocessed crude rhizomes. The first six processing cycles induced drastic changes in the accumulation of functional components, while the last three did not induce further changes. The accumulations of most functional components were significantly enhanced after the first three cycles and stabilized after six cycles; meanwhile, the first three cycles also led to numerous new components. However, the enhancing effects were unavoidably reversed or weakened under continued processing lasting 6-9 cycles. Furthermore, continued processing also reduced the contents of a small number of original components to undetectable levels. Processing induced some significantly enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, among which the first three processing cycles enhanced the synthesis of various secondary metabolites and significantly affected the metabolisms of amino acids. In conclusion, this study not only reveals that processing can effectively enhance the medicinal effects, by two main mechanisms including enhancing chemical synthesis and inducing structural transformation of functional components, but also provides theoretical guidance for the optimization of the traditional processing method based on nine cycles of steaming and drying for achieving optimal effects on enhancing the medicinal effects of P. cyrtonema rhizome.

NIH funding and journal selection: trends in dermatology publication models across tiers from 2021 to 2023.

This study examines the influence of National Institutes of Health (NIH) funding on the publication choices of dermatologists, particularly in terms of journal tiers and pay-to-publish (P2P) versus free-to-publish (F2P) models. Utilizing k-means clustering for journal ranking based on SCImago Journal Rank, h-index, and Impact Factor, journals were categorized into three tiers and 54,530 dermatology publications from 2021 to 2023 were analyzed. Authors were classified as Top NIH Funded or Non-Top NIH Funded according to Blue Ridge Institute for Medical Research rankings. The study finds significant differences in publication patterns, with Top NIH Funded researchers in Tier I journals demonstrating a balanced use of P2P and F2P models, while they preferred F2P models in Tier II and III journals. Non-Top NIH Funded authors, however, opted for P2P models more frequently across all tiers. These data suggest NIH funding allows researchers greater flexibility to publish in higher-tier journals despite publication fees, while prioritizing F2P models in lower-tier journals. Such a pattern indicates that funding status plays a critical role in strategic publication decisions, potentially impacting research visibility and subsequent funding. The study's dermatology focus limits broader applicability, warranting further research to explore additional factors like geographic location, author gender, and research design.

Investigation of prognostic values of immune infiltration and LGMN expression in the microenvironment of osteosarcoma.

BACKGROUND: Osteosarcoma (OS), the most common primary malignant bone tumor, predominantly affects children and young adults and is characterized by high invasiveness and poor prognosis. Despite therapeutic advancements, the survival rate remains suboptimal, indicating an urgent need for novel biomarkers and therapeutic targets. This study aimed to investigate the prognostic significance of LGMN expression and immune cell infiltration in the tumor microenvironment of OS. METHODS: We performed an integrative bioinformatics analysis utilizing the GEO and TARGET-OS databases to identify differentially expressed genes (DEGs) associated with LGMN in OS. We conducted Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) to explore the biological pathways and functions. Additionally, we constructed protein-protein interaction (PPI) networks, a competing endogenous RNA (ceRNA) network, and applied the CIBERSORT algorithm to quantify immune cell infiltration. The diagnostic and prognostic values of LGMN were evaluated using the area under the receiver operating characteristic (ROC) curve and Cox regression analysis. Furthermore, we employed Consensus Clustering Analysis to explore the heterogeneity within OS samples based on LGMN expression. RESULTS: The analysis revealed significant upregulation of LGMN in OS tissues. DEGs were enriched in immune response and antigen processing pathways, suggesting LGMN's role in immune modulation within the TME. The PPI and ceRNA network analyses provided insights into the regulatory mechanisms involving LGMN. Immune cell infiltration analysis indicated a correlation between high LGMN expression and increased abundance of M2 macrophages, implicating an immunosuppressive role. The diagnostic AUC for LGMN was 0.799, demonstrating its potential as a diagnostic biomarker. High LGMN expression correlated with reduced overall survival (OS) and progression-free survival (PFS). Importantly, Consensus Clustering Analysis identified two distinct subtypes of OS, highlighting the heterogeneity and potential for personalized medicine approaches. CONCLUSIONS: Our study underscores the prognostic value of LGMN in osteosarcoma and its potential as a therapeutic target. The identification of LGMN-associated immune cell subsets and the discovery of distinct OS subtypes through Consensus Clustering Analysis provide new avenues for understanding the immunosuppressive TME of OS and may aid in the development of personalized treatment strategies. Further validation in larger cohorts is warranted to confirm these findings.

Clustering analysis for classifying fake real estate listings.

With the rapid growth of online property rental and sale platforms, the prevalence of fake real estate listings has become a significant concern. These deceptive listings waste time and effort for buyers and sellers and pose potential risks. Therefore, developing effective methods to distinguish genuine from fake listings is crucial. Accurately identifying fake real estate listings is a critical challenge, and clustering analysis can significantly improve this process. While clustering has been widely used to detect fraud in various fields, its application in the real estate domain has been somewhat limited, primarily focused on auctions and property appraisals. This study aims to fill this gap by using clustering to classify properties into fake and genuine listings based on datasets curated by industry experts. This study developed a K-means model to group properties into clusters, clearly distinguishing between fake and genuine listings. To assure the quality of the training data, data pre-processing procedures were performed on the raw dataset. Several techniques were used to determine the optimal value for each parameter of the K-means model. The clusters are determined using the Silhouette coefficient, the Calinski-Harabasz index, and the Davies-Bouldin index. It was found that the value of cluster 2 is the best and the Camberra technique is the best method when compared to overlapping similarity and Jaccard for distance. The clustering results are assessed using two machine learning algorithms: Random Forest and Decision Tree. The observational results have shown that the optimized K-means significantly improves the accuracy of the Random Forest classification model, boosting it by an impressive 96%. Furthermore, this research demonstrates that clustering helps create a balanced dataset containing fake and genuine clusters. This balanced dataset holds promise for future investigations, particularly for deep learning models that require balanced data to perform optimally. This study presents a practical and effective way to identify fake real estate listings by harnessing the power of clustering analysis, ultimately contributing to a more trustworthy and secure real estate market.

Enhancing home delivery of emergency medicine and medical supplies through clustering and simulation techniques: A case study of COVID-19 home isolation in Bangkok.

This study investigates the enhancement of the home delivery distribution network for COVID-19 Home Isolation (HI) kits during the Delta variant outbreak of the SARS-CoV-2 virus in Bangkok Metropolitan Area, Thailand. It addresses challenges related to limited resources and delays in delivering HI kits, which can exacerbate symptoms and increase mortality rates. A k-means clustering approach is utilized to optimize the assignment of service areas within the COVID-19 HI program, while discrete event simulation (DES) evaluates potential changes in the home delivery logistics network. Real-world data from the peak outbreak is used to determine the optimal allocation of resources and propose a new logistics network based on proximity to patients' residences. Experimental results demonstrate a significant 44.29 % improvement in overall performance and a substantial 40.80 % decrease in maximum service time. The findings offer theoretical and managerial implications for effective HI management, supporting practitioners and policymakers in mitigating the impact of future outbreaks.

Correlation coefficients between normal wiggly hesitant fuzzy sets and their applications.

The multi-criteria decision-making (MCDM) field has long sought tools capable of adeptly capturing the intricacies of human decision-making amidst uncertainty. Hesitant fuzzy sets (HFS) have become a cornerstone in the MCDM field due to their ability to capture the intricacies of human decision-making under uncertainty. Nonetheless, we identified a significant gap in traditional HFS formulations, which often fail to fully harness the nuanced and implicit preferences of decision-makers (DMs). This shortcoming can lead to suboptimal decision outcomes in complex and uncertain environments. We introduce the normal wiggly hesitant fuzzy set (NWHFS), a novel construct that encapsulates both explicit and implicit preferences within a more representative framework. This study pioneers the development of new correlation coefficients for NWHFSs, offering a robust quantitative measure to elucidate the intricate relationships between variables. Our findings demonstrate that NWHFSs significantly enhance the MCDM process, providing a nuanced perspective that traditional HFS models cannot match. The proposed correlation coefficients not only reveal the concealed preferences of DMs but also broaden the decision-making spectrum, offering a more profound understanding of the relationships between alternatives and criteria. We illustrate the superiority of our approach through comparative analysis with existing methods, highlighting its ability to discern subtleties that other models overlook. Moreover, we integrate NWHFSs into clustering analysis, showcasing their potential to classify data sources with shared attributes effectively. This integration is particularly noteworthy for its ability to navigate complex datasets, offering a new dimension in data mining and resource retrieval. In essence, our study redefines the MCDM paradigm by introducing NWHFSs and their correlation coefficients, setting a new standard for decision-making accuracy and insight. The implications of our work extend beyond theory, offering practical solutions to real-world decision-making challenges.

A clustering mining method for sports behavior characteristics of athletes based on the ant colony optimization.

This study aims to enhance the precision of analyzing athlete behavior characteristics, thereby optimizing sports training and competitive strategies. This study introduces an innovative Ant Colony Optimization (ACO) clustering model designed to address the high-dimensional clustering issues in athlete behavior data by simulating the path selection mechanism of ants searching for food. The development process of this model includes fine-tuning ACO parameters, optimizing for features specific to sports data, and comparing it with traditional clustering algorithms, and similar research models based on the neural network, support vector machines, and deep learning. The results indicate that the ACO model significantly outperforms the comparison algorithms in terms of silhouette coefficient (0.72) and Davies-Bouldin index (1.05), demonstrating higher clustering effectiveness and model stability. Particularly noteworthy is the recall rate (0.82), a key performance indicator, where the ACO model accurately captures different behavioral characteristics of athletes, validating its effectiveness and reliability in athlete behavior analysis. The innovation lies not only in the application of the ACO algorithm to address practical issues in the field of sports but also in showcasing the advantages of the ACO algorithm in handling complex, high-dimensional sports data. However, its generality and efficiency on a larger scale or different types of sports data still need further validation. In conclusion, through the introduction and optimization of the ACO clustering model, this study provides a novel and effective approach for a deeper understanding and analysis of athlete behavior characteristics. This study holds significant importance in advancing sports science research and practical applications.

Distinct phenotype and prognosis of immune-mediated necrotizing myopathy based on clinical-serological-pathological classification.

OBJECTIVE: The aim of the study was to investigate the characteristics and prognosis of patients with immune-mediated necrotizing myopathy (IMNM) based on clinical, serological and pathological classification. METHODS: A total of 138 patients with IMNM who met the 2018 European Neuromuscular Center criteria for IMNM including 62 anti-SRP, 32 anti-HMGCR-positive and 44 myositis specific antibody-negative were involved in the study. All patients were followed up and evaluated remission and relapse. Clustering analysis based on clinical, serological, and pathological parameters was used to define subgroups. RESULTS: Clustering analysis classified IMNM into three clusters. Cluster 1 patients (n = 35) had the highest CK levels, the shortest disease course, severe muscle weakness, and more inflammation infiltration in muscle biopsy. Cluster 2 patients (n = 79) had the lowest CK level and moderate inflammation infiltrate. Cluster 3 patients (n = 24) had the youngest age of onset, the longest disease course and the least frequency of inflammatory infiltration. Patients in cluster 3 had the longest time-to-remission (median survival time: 61[18.3, 103.7] vs 20.5[16.2, 24.9] and 27[19.6, 34.3] months) and shortest relapse-free time than those in cluster 1 and 2 (median remission time 95%CI: 34[19.9, 48.0] vs 73[49.0, 68.7] and 73[48.4, 97.6] months). Patients with age of onset >55 years, more regeneration of muscle fibers, more CD4+T infiltration, and MAC deposition had more favorable outcomes regarding time to achieving remission. CONCLUSIONS: Stratification combining clinical, serological, and pathological features could distinguish phenotypes and prognosis of IMNM. The pathological characteristics may impact the long-term prognosis of patients with IMNM.

Clustering analysis of HRCT parameters measured using a texture-based automated system: relationship with clinical outcomes of IPF.

PURPOSE: The extent of honeycombing and reticulation predict the clinical prognosis of IPF. Emphysema, consolidation, and ground glass opacity are visible in HRCT scans. To date, there have been few comprehensive studies that have used these parameters. We conducted automated quantitative analysis to identify predictive parameters for clinical outcomes and then grouped the subjects accordingly. METHODS: CT images were obtained while patients held their breath at full inspiration. Parameters were analyzed using an automated lung texture quantification system. Cluster analysis was conducted on 159 IPF patients and clinical profiles were compared between clusters in terms of survival. RESULTS: Kaplan-Meier analysis revealed that survival rates declined as fibrosis, reticulation, honeycombing, consolidation, and emphysema scores increased. Cox regression analysis revealed that reticulation had the most significant impact on survival rate, followed by honeycombing, consolidation, and emphysema scores. Hierarchical and K-means cluster analyses revealed 3 clusters. Cluster 1 (n = 126) with the lowest values for all parameters had the longest survival duration, and relatively-well preserved FVC and DLCO. Cluster 2 (n = 15) with high reticulation and consolidation scores had the lowest FVC and DLCO values with a predominance of female, while cluster 3 (n = 18) with high honeycombing and emphysema scores predominantly consisted of male smokers. Kaplan-Meier analysis revealed that cluster 2 had the lowest survival rate, followed by cluster 3 and cluster 1. CONCLUSION: Automated quantitative CT analysis provides valuable information for predicting clinical outcomes, and clustering based on these parameters may help identify the high-risk group for management.

Directly selecting cell-type marker genes for single-cell clustering analyses.

In single-cell RNA sequencing (scRNA-seq) studies, cell types and their marker genes are often identified by clustering and differentially expressed gene (DEG) analysis. A common practice is to select genes using surrogate criteria such as variance and deviance, then cluster them using selected genes and detect markers by DEG analysis assuming known cell types. The surrogate criteria can miss important genes or select unimportant genes, while DEG analysis has the selection-bias problem. We present Festem, a statistical method for the direct selection of cell-type markers for downstream clustering. Festem distinguishes marker genes with heterogeneous distribution across cells that are cluster informative. Simulation and scRNA-seq applications demonstrate that Festem can sensitively select markers with high precision and enables the identification of cell types often missed by other methods. In a large intrahepatic cholangiocarcinoma dataset, we identify diverse CD8+ T cell types and potential prognostic marker genes.