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.

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.

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.

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.

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.

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 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.

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.

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.

Characterisation of colorectal cancer by hierarchical clustering analyses for five stroma-related markers.

Evidence for the tumour-supporting capacities of the tumour stroma has accumulated rapidly in colorectal cancer (CRC). Tumour stroma is composed of heterogeneous cells and components including cancer-associated fibroblasts (CAFs), small vessels, immune cells, and extracellular matrix proteins. The present study examined the characteristics of CAFs and collagen, major components of cancer stroma, by immunohistochemistry and Sirius red staining. The expression status of five independent CAF-related or stromal markers, decorin (DCN), fibroblast activation protein (FAP), podoplanin (PDPN), alpha-smooth muscle actin (ACTA2), and collagen, and their association with clinicopathological features and clinical outcomes were analysed. Patients with DCN-high tumours had a significantly worse 5-year survival rate (57.3% versus 79.0%; p = 0.044). Furthermore, hierarchical clustering analyses for these five markers identified three groups that showed specific characteristics: a solid group (cancer cell-rich, DCNLowPDPNLow); a PDPN-dominant group (DCNMidPDPNHigh); and a DCN-dominant group (DCNHighPDPNLow), with a significant association with patient survival (p = 0.0085). Cox proportional hazards model identified the PDPN-dominant group (hazard ratio = 0.50, 95% CI = 0.26-0.96, p = 0.037) as a potential favourable factor compared with the DCN-dominant group. Of note, DCN-dominant tumours showed the most advanced pT stage and contained the lowest number of CD8+ and FOXP3+ immune cells. This study has revealed that immunohistochemistry and special staining of five stromal factors with hierarchical clustering analyses could be used for the prognostication of patients with CRC. Cancer stroma-targeting therapies may be candidate treatments for patients with CRC.

Phenotypic clustering of patients hospitalized in intensive cardiac care units: Insights from the ADDICT-ICCU study.

BACKGROUND: Intensive cardiac care units (ICCUs) were created to manage ventricular arrhythmias after acute coronary syndromes, but have diversified to include a more heterogeneous population, the characteristics of which are not well depicted by conventional methods. AIMS: To identify ICCU patient subgroups by phenotypic unsupervised clustering integrating clinical, biological, and echocardiographic data to reveal pathophysiological differences. METHODS: During 7-22 April 2021, we recruited all consecutive patients admitted to ICCUs in 39 centers. The primary outcome was in-hospital major adverse events (MAEs; death, resuscitated cardiac arrest or cardiogenic shock). A cluster analysis was performed using a Kamila algorithm. RESULTS: Of 1499 patients admitted to the ICCU (69.6% male, mean age 63.3±14.9 years), 67 (4.5%) experienced MAEs. Four phenogroups were identified: PG1 (n=535), typically patients with non-ST-segment elevation myocardial infarction; PG2 (n=444), younger smokers with ST-segment elevation myocardial infarction; PG3 (n=273), elderly patients with heart failure with preserved ejection fraction and conduction disturbances; PG4 (n=247), patients with acute heart failure with reduced ejection fraction. Compared to PG1, multivariable analysis revealed a higher risk of MAEs in PG2 (odds ratio [OR] 3.13, 95% confidence interval [CI] 1.16-10.0) and PG3 (OR 3.16, 95% CI 1.02-10.8), with the highest risk in PG4 (OR 20.5, 95% CI 8.7-60.8) (all P<0.05). CONCLUSIONS: Cluster analysis of clinical, biological, and echocardiographic variables identified four phenogroups of patients admitted to the ICCU that were associated with distinct prognostic profiles. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT05063097.

Delineating the Heterogeneity of Alzheimer's Disease and Mild Cognitive Impairment Using Normative Models of Dynamic Brain Functional Networks.

BACKGROUND: Alzheimer's disease (AD), which has been identified as the most common type of dementia, presents considerable heterogeneity in its clinical manifestations. Early intervention at the stage of mild cognitive impairment (MCI) holds potential in AD prevention. However, characterizing the heterogeneity of neurobiological abnormalities and identifying MCI subtypes pose significant challenges. METHODS: We constructed sex-specific normative age models of dynamic brain functional networks and mapped the deviations of the brain characteristics for individuals from multiple datasets, including 295 patients with AD, 441 patients with MCI, and 1160 normal control participants. Then, based on these individual deviation patterns, subtypes for both AD and MCI were identified using the clustering method, and their similarities and differences were comprehensively assessed. RESULTS: Individuals with AD and MCI were clustered into 2 subtypes, and these subtypes exhibited significant differences in their intrinsic brain functional phenotypes and spatial atrophy patterns, as well as in disease progression and cognitive decline trajectories. The subtypes with positive deviations in AD and MCI shared similar deviation patterns, as did those with negative deviations. There was a potential transformation of MCI with negative deviation patterns into AD, and participants with MCI had a more severe cognitive decline rate. CONCLUSIONS: In this study, we quantified neurophysiological heterogeneity by analyzing deviation patterns from the dynamic functional connectome normative model and identified disease subtypes of AD and MCI using a comprehensive resting-state functional magnetic resonance imaging multicenter dataset. The findings provide new insights for developing early prevention and personalized treatment strategies for AD.

The Influence of Pain Hypersensitivity and Psychological Factors on Pain and Disability in the Transition From Acute to Chronic Low Back Pain: A Longitudinal Exploratory Investigation and Cluster Analysis.

Pain hypersensitivity is present in some people with acute low back pain (LBP) and thought to be involved in the development of chronic LBP. Early evidence suggests that pain hypersensitivity in acute LBP precedes poor long-term outcome. We aimed to examine whether the presence of pain hypersensitivity in acute LBP influenced recovery status at 6 months and differentiated how pain and disability changed over time. Participants with acute nonspecific LBP (<6 weeks after pain onset, N = 118) were included in this longitudinal study. Quantitative sensory testing, including pressure and heat pain thresholds, and conditioned pain modulation and questionnaires were compared at baseline and longitudinally (at 3 and 6 months) between recovered and unrecovered participants. Using k-means clustering, we identified subgroups based on baseline sensory measures alone, and in combination with psychological factors, and compared pain and disability outcomes between subgroups. Sensory measures did not differ at baseline or longitudinally between recovered (N = 50) and unrecovered (N = 68) participants. Subgrouping based on baseline sensory measures alone did not differentiate pain or disability outcomes at any timepoint. Participants with high psychological distress at baseline (N = 19) had greater disability, but not pain, at all timepoints than those with low psychological distress, regardless of the degrees of pain sensitivity. Our findings suggest that pain hypersensitivity in acute LBP does not precede poor recovery at 6 months or differentiate how pain and disability change over time. High psychological distress during acute LBP is associated with unremitting and pronounced disability, while pain severity is unaffected. PERSPECTIVE: Pain hypersensitivity is thought to be involved in the transition to chronic LBP. Contradictory to prevailing hypothesis, our findings suggest pain hypersensitivity alone in acute LBP does not precede poor recovery. High psychological distress in acute LBP has a stronger influence than pain hypersensitivity on long-term disability, but not pain outcomes.

Multivariate Analysis of Solubility Parameters for Drug-Polymer Miscibility Assessment in Preparing Raloxifene Hydrochloride Amorphous Solid Dispersions.

The success of obtaining solid dispersions for solubility improvement invariably depends on the miscibility of the drug and polymeric carriers. This study aimed to categorize and select polymeric carriers via the classical group contribution method using the multivariate analysis of the calculated solubility parameter of RX-HCl. The total, partial, and derivate parameters for RX-HCl were calculated. The data were compared with the results of excipients (N = 36), and a hierarchical clustering analysis was further performed. Solid dispersions of selected polymers in different drug loads were produced using solvent casting and characterized via X-ray diffraction, infrared spectroscopy and scanning electron microscopy. RX-HCl presented a Hansen solubility parameter (HSP) of 23.52 MPa1/2. The exploratory analysis of HSP and relative energy difference (RED) elicited a classification for miscible (n = 11), partially miscible (n = 15), and immiscible (n = 10) combinations. The experimental validation followed by a principal component regression exhibited a significant correlation between the crystallinity reduction and calculated parameters, whereas the spectroscopic evaluation highlighted the hydrogen-bonding contribution towards amorphization. The systematic approach presented a high discrimination ability, contributing to optimal excipient selection for the obtention of solid solutions of RX-HCl.

A study on evaluation of english hybrid teaching courses based on AHP and K-means.

In hybrid English teaching, there are many courses and various kinds of assessment, which put higher requirements for teachers' accurate and objective curriculum evaluation. This article adopts the clustering method of unsupervised learning to adapt to more data and give the evaluation method a specific generalization ability. A curriculum evaluation system based on AHP and clustering is proposed. Through hierarchical analysis values of online and offline average grades and final offline assessment scores, multiple hierarchical analysis is carried out, and the K-means method is adopted to refine course evaluation, and non-iterative calculation is carried out for non-deterministic numerical data to complete the final assessment of grades. Based on the sample test of the school's data in recent years, this article finds that the proposed method can distinguish different categories of students well, and the absolute error of K-means classification is less than 0.5. The proposed method can ensure the accurate evaluation of colleges and universities and reduce teachers' burden.

Integrated morphometric and molecular approaches to screen hybrid from wild Labeo rohita and Labeo catla parent populations.

BACKGROUND: Interspecific hybrids of rohu (Labeo rohita) and catla (Labeo catla) are common, especially in India due to constrained breeding. These hybrids must segregate from their wild parents as part of conservational strategies. This study intended to screen the hybrids from wild rohu and catla parents using both morphometric and molecular approaches. METHODS & RESULTS: The carp samples were collected from Jharkhand and West Bengal, India. The correlation and regression analysis of morphometric features are considered superficial but could be protracted statistically by clustering analysis and further consolidated by nucleotide variations of one mitochondrial and one nuclear gene to differentiate hybrids from their parents. Out of 21 morphometric features, 6 were used for clustering analysis that exhibited discrete separation among rohu, catla, and their hybrids when the data points were plotted in a low-dimensional 2-D plane using the first 2 principal components. Out of 40 selected single nucleotide polymorphism (SNP) positions of the COX1 gene, hybrid showed 100% similarity with catla. Concerning SNP similarity of the 18S rRNA nuclear gene, the hybrid showed 100% similarity with rohu but not with catla; exhibiting its probable parental inheritance. CONCLUSIONS: Along with morphometric analysis, the SNP comparison study together points towards strong evidence of interspecific hybridization between rohu and catla, as these hybrids share both morphological and molecular differences with either parent. However, this study will help screen the hybrids from their wild parents, as a strategy for conservational management.

Deciphering the Systemic Impact of Herbal Medicines on Allergic Rhinitis: A Network Pharmacological Approach.

Allergic rhinitis (AR) is a systemic allergic disease that has a considerable impact on patients' quality of life. Current treatments include antihistamines and nasal steroids; however, their long-term use often causes undesirable side effects. In this context, traditional Asian medicine (TAM), with its multi-compound, multi-target herbal medicines (medicinal plants), offers a promising alternative. However, the complexity of these multi-compound traits poses challenges in understanding the overall mechanisms and efficacy of herbal medicines. Here, we demonstrate the efficacy and underlying mechanisms of these multi-compound herbal medicines specifically used for AR at a systemic level. We utilized a modified term frequency-inverse document frequency method to select AR-specific herbs and constructed an herb-compound-target network using reliable databases and computational methods, such as the Quantitative Estimate of Drug-likeness for compound filtering, STITCH database for compound-target interaction prediction (with a high confidence score threshold of 0.7), and DisGeNET and CTD databases for disease-gene association analysis. Through this network, we conducted AR-related targets and pathway analyses, as well as clustering analysis based on target-level information of the herbs. Gene ontology enrichment analysis was conducted using a protein-protein interaction network. Our research identified 14 AR-specific herbs and analyzed whether AR-specific herbs are highly related to previously known AR-related genes and pathways. AR-specific herbs were found to target several genes related to inflammation and AR pathogenesis, such as PTGS2, HRH1, and TBXA2R. Pathway analysis revealed that AR-specific herbs were associated with multiple AR-related pathways, including cytokine signaling, immune response, and allergic inflammation. Additionally, clustering analysis based on target similarity identified three distinct subgroups of AR-specific herbs, corroborated by a protein-protein interaction network. Group 1 herbs were associated with the regulation of inflammatory responses to antigenic stimuli, while Group 2 herbs were related to the detection of chemical stimuli involved in the sensory perception of bitter taste. Group 3 herbs were distinctly associated with antigen processing and presentation and NIK/NF-kappa B signaling. This study decodes the principles of TAM herbal configurations for AR using a network pharmacological approach, providing a holistic understanding of drug effects beyond specific pathways.

Scientometric Research and Critical Analysis of Gait and Balance in Older Adults.

Gait and balance have emerged as a critical area of research in health technology. Gait and balance studies have been affected by the researchers' slow follow-up of research advances due to the absence of visual inspection of the study literature across decades. This study uses advanced search methods to analyse the literature on gait and balance in older adults from 1993 to 2022 in the Web of Science (WoS) database to gain a better understanding of the current status and trends in the field for the first time. The study analysed 4484 academic publications including journal articles and conference proceedings on gait and balance in older adults. Bibliometric analysis methods were applied to examine the publication year, number of publications, discipline distribution, journal distribution, research institutions, application fields, test methods, analysis theories, and influencing factors in the field of gait and balance. The results indicate that the publication of relevant research documents has been steadily increasing from 1993 to 2022. The United States (US) exhibits the highest number of publications with 1742 articles. The keyword "elderly person" exhibits a strong citation burst strength of 18.04, indicating a significant focus on research related to the health of older adults. With a burst factor of 20.46, Harvard University has made impressive strides in the subject. The University of Pittsburgh displayed high research skills in the area of gait and balance with a burst factor of 7.7 and a publication count of 103. The research on gait and balance mainly focuses on physical performance evaluation approaches, and the primary study methods include experimental investigations, computational modelling, and observational studies. The field of gait and balance research is increasingly intertwined with computer science and artificial intelligence (AI), paving the way for intelligent monitoring of gait and balance in the elderly. Moving forward, the future of gait and balance research is anticipated to highlight the importance of multidisciplinary collaboration, intelligence-driven approaches, and advanced visualization techniques.

Clustering Pseudo Time Series: Exploring Trajectories in the Ageing Process.

Investigating the natural ageing process typically involves the use of extensive longitudinal datasets that can capture changes associated with the progression of ageing. However, they are often resource-intensive and time-consuming to conduct. Cross-sectional data, on the other hand, provides a snapshot of a population at many different ages and can capture many disease processes but do not incorporate the time dimension. Pseudo time series can be reconstructed from cross sectional data, with the aim to explore dynamic processes (such as the ageing process). In this paper we focus on employing pseudo time series analysis on cross-sectional population data that we constrain using age information to create realistic trajectories of people with different degrees of cardiovascular disease. We then use clustering methods to construct and label trajectory-based phenotypes, aiming to enhance our understanding of ageing and disease progression.