Precision probiotics supplement strategy in aging population based on gut microbiome composition.

With the increasing prevalence of age-related chronic diseases burdening healthcare systems, there is a pressing need for innovative management strategies. Our study focuses on the gut microbiota, essential for metabolic, nutritional, and immune functions, which undergoes significant changes with aging. These changes can impair intestinal function, leading to altered microbial diversity and composition that potentially influence health outcomes and disease progression. Using advanced metagenomic sequencing, we explore the potential of personalized probiotic supplements in 297 older adults by analyzing their gut microbiota. We identified distinctive Lactobacillus and Bifidobacterium signatures in the gut microbiota of older adults, revealing probiotic patterns associated with various population characteristics, microbial compositions, cognitive functions, and neuroimaging results. These insights suggest that tailored probiotic supplements, designed to match individual probiotic profile, could offer an innovative method for addressing age-related diseases and functional declines. Our findings enhance the existing evidence base for probiotic use among older adults, highlighting the opportunity to create more targeted and effective probiotic strategies. However, additional research is required to validate our results and further assess the impact of precision probiotics on aging populations. Future studies should employ longitudinal designs and larger cohorts to conclusively demonstrate the benefits of tailored probiotic treatments.

Consensus clustering with missing labels (ccml): a consensus clustering tool for multi-omics integrative prediction in cohorts with unequal sample coverage.

Multi-omics data integration is a complex and challenging task in biomedical research. Consensus clustering, also known as meta-clustering or cluster ensembles, has become an increasingly popular downstream tool for phenotyping and endotyping using multiple omics and clinical data. However, current consensus clustering methods typically rely on ensembling clustering outputs with similar sample coverages (mathematical replicates), which may not reflect real-world data with varying sample coverages (biological replicates). To address this issue, we propose a new consensus clustering with missing labels (ccml) strategy termed ccml, an R protocol for two-step consensus clustering that can handle unequal missing labels (i.e. multiple predictive labels with different sample coverages). Initially, the regular consensus weights are adjusted (normalized) by sample coverage, then a regular consensus clustering is performed to predict the optimal final cluster. We applied the ccml method to predict molecularly distinct groups based on 9-omics integration in the Karolinska COSMIC cohort, which investigates chronic obstructive pulmonary disease, and 24-omics handprint integrative subgrouping of adult asthma patients of the U-BIOPRED cohort. We propose ccml as a downstream toolkit for multi-omics integration analysis algorithms such as Similarity Network Fusion and robust clustering of clinical data to overcome the limitations posed by missing data, which is inevitable in human cohorts consisting of multiple data modalities. The ccml tool is available in the R language (https://CRAN.R-project.org/package=ccml, https://github.com/pulmonomics-lab/ccml, or https://github.com/ZhoulabCPH/ccml).

Improving replicability in single-cell RNA-Seq cell type discovery with Dune.

BACKGROUND: Single-cell transcriptome sequencing (scRNA-Seq) has allowed new types of investigations at unprecedented levels of resolution. Among the primary goals of scRNA-Seq is the classification of cells into distinct types. Many approaches build on existing clustering literature to develop tools specific to single-cell. However, almost all of these methods rely on heuristics or user-supplied parameters to control the number of clusters. This affects both the resolution of the clusters within the original dataset as well as their replicability across datasets. While many recommendations exist, in general, there is little assurance that any given set of parameters will represent an optimal choice in the trade-off between cluster resolution and replicability. For instance, another set of parameters may result in more clusters that are also more replicable. RESULTS: Here, we propose Dune, a new method for optimizing the trade-off between the resolution of the clusters and their replicability. Our method takes as input a set of clustering results-or partitions-on a single dataset and iteratively merges clusters within each partitions in order to maximize their concordance between partitions. As demonstrated on multiple datasets from different platforms, Dune outperforms existing techniques, that rely on hierarchical merging for reducing the number of clusters, in terms of replicability of the resultant merged clusters as well as concordance with ground truth. Dune is available as an R package on Bioconductor: https://www.bioconductor.org/packages/release/bioc/html/Dune.html . CONCLUSIONS: Cluster refinement by Dune helps improve the robustness of any clustering analysis and reduces the reliance on tuning parameters. This method provides an objective approach for borrowing information across multiple clusterings to generate replicable clusters most likely to represent common biological features across multiple datasets.

Relevance of pyroptosis-associated genes in nasopharyngeal carcinoma diagnosis and subtype classification.

BACKGROUND: Nasopharyngeal carcinoma (NPC) is a highly aggressive and metastatic malignancy originating in the nasopharyngeal tissue. Pyroptosis is a relatively newly discovered, regulated form of necrotic cell death induced by inflammatory caspases that is associated with a variety of diseases. However, the role and mechanism of pyroptosis in NPC are not fully understood. METHODS: We analyzed the differential expression of pyroptosis-related genes (PRGs) between patients with and without NPC from the GSE53819 and GSE64634 datasets of the Gene Expression Omnibus (GEO) database. We mapped receptor operating characteristic profiles for these key PRGs to assess the accuracy of the genes for disease diagnosis and prediction of patient prognosis. In addition, we constructed a nomogram based on these key PRGs and carried out a decision curve analysis. The NPC patients were classified into different pyroptosis gene clusters by the consensus clustering method based on key PRGs, whereas the expression profiles of the key PRGs were analyzed by applying principal component analysis. We also analyzed the differences in key PRGs, immune cell infiltration and NPC-related genes between the clusters. Finally, we performed differential expression analysis for pyroptosis clusters and obtained differentially expressed genes (DEGs) and performed Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. RESULTS: We obtained 14 differentially expressed PRGs from GEO database. Based on these 14 differentially expressed PRGs, we applied least absolute shrinkage and selection operator analysis and the random forest algorithm to obtain four key PRGs (CHMP7, IL1A, TP63 and GSDMB). We completely distinguished the NPC patients into two pyroptosis gene clusters (pyroptosis clusters A and B) based on four key PRGs. Furthermore, we determined the immune cell abundance of each NPC sample, estimated the association between the four PRGs and immune cells, and determined the difference in immune cell infiltration between the two pyroptosis gene clusters. Finally, we obtained and functional enrichment analyses 259 DEGs by differential expression analysis for both pyroptosis clusters. CONCLUSIONS: PRGs are critical in the development of NPC, and our research on the pyroptosis gene cluster may help direct future NPC therapeutic approaches.

Brain Evoked Response Qualification Using Multi-Set Consensus Clustering: Toward Single-Trial EEG Analysis.

In event-related potential (ERP) analysis, it is commonly assumed that individual trials from a subject share similar properties and originate from comparable neural sources, allowing reliable interpretation of group-averages. Nevertheless, traditional group-level ERP analysis methods, including cluster analysis, often overlook critical information about individual subjects' neural processes due to using fixed measurement intervals derived from averaging. We developed a multi-set consensus clustering pipeline to examine cognitive processes at the individual subject level. Initially, consensus clustering from diverse methods was applied to single-trial EEG epochs of individual subjects. Subsequently, a second level of consensus clustering was performed across the trials of each subject. A newly modified time window determination method was then employed to identify individual subjects' ERP(s) of interest. We validated our method with simulated data for ERP components N2 and P3, and real data from a visual oddball task to confirm the P3 component. Our findings revealed that estimated time windows for individual subjects provide precise ERP identification compared to fixed time windows across all subjects. Additionally, Monte Carlo simulations with synthetic single-trial data demonstrated stable scores for the N2 and P3 components, confirming the reliability of our method. The proposed method enhances the examination of brain-evoked responses at the individual subject level by considering single-trial EEG data, thereby extracting mutual information relevant to the neural process. This approach offers a significant improvement over conventional ERP analysis, which relies on the averaging mechanism and fixed measurement interval.

Significance of Oxidative Stress in the Diagnosis and Subtype Classification of Intervertebral Disc Degeneration.

Intervertebral disc degeneration (IVDD) is a common illness of aging, and its pathophysiological process is mainly manifested by cell aging and apoptosis, an imbalance in the production and catabolism of extracellular matrix, and an inflammatory response. Oxidative stress (OS) is an imbalance that decreases the body's intrinsic antioxidant defense system and/or raises the formation of reactive oxygen species and performs multiple biological functions in the body. However, our current knowledge of the effect of OS on the progression and treatment of IVDD is still extremely limited. In this study, we obtained 35 DEGs by differential expression analysis of 437 OS-related genes (OSRGs) between IVDD patients and healthy individuals from GSE124272 and GSE150408. Then, we identified six hub OSRGs (ATP7A, MELK, NCF1, NOX1, RHOB, and SP1) from 35 DEGs, and the high accuracy of these hub genes was confirmed by constructing ROC curves. In addition, to forecast the risk of IVDD patients, we developed a nomogram. We obtained two OSRG clusters (clusters A and B) by consensus clustering based on the six hub genes. Then, 3147 DEGs were obtained by differential expression analysis in the two clusters, and all samples were further divided into two gene clusters (A and B). We investigated differences in immune cell infiltration levels between different clusters and found that most immune cells had higher infiltration levels in OSRG cluster B or gene cluster B. In conclusion, OS is important in the formation and progression of IVDD, and we believe that our work will help guide future research on OS in IVDD.

Distinct phenotyping of critical patients with demand-capacity imbalance initiating acute renal replacement therapy by consensus clustering.

BACKGROUND: Certain patient subpopulations requiring dialysis initiation show varied survival rates and chances of ending renal replacement therapy (RRT). Consensus clustering can help identify these subgroups and their dialysis outcomes. METHODS: The study included patients who were over 18 years old with urine output above 400 ml per day and an estimated glomerular filtration rate over 15 ml/min/1.73 m2. They underwent acute RRT because of systemic demand-capacity imbalance. Using consensus clustering with 33 clinical variables and urea:creatinine ratio (UCR) to the variables to investigate the catabolic demand. Endpoints were all-cause mortality and being dialysis-free at 180-day follow-up after RRT initiation. RESULTS: Of 946 patients (mean 63 ± 17 years and 649 men, 68.6 %) three distinct phenotypes were identified. 509 (53.8%) patients died and 364 (38.5%) patients were weaned off dialysis. Cluster 2 showed better survival (60.23% vs. 53.18% [cluster 1] vs. 45.85% [cluster 3], P < 0.01) and higher possibility to be weaned off RRT (45.24% vs. 38.44% [cluster 1] vs. 31.62% [cluster 3], P < 0.01). High UCR had increased mortality (59.16% vs. 47.75%, P < 0.01) and a lower weaning rates (33.27%; 45.72%, P < .01). UCR with the clustering phenotype improved risk stratification. CONCLUSIONS: Among critical patients undergoing RRT due to systemic demand-capacity imbalance, more than half of the patients died. We identified distinct phenotypes in demand-capacity imbalance in a heterogeneous cohort of patients initializing RRT. Additionally, we found that pre-dialysis UCR as a novel predictor for mortality and the likelihood of being dialysis-free.

PSA-FL-CDM: A Novel Federated Learning-Based Consensus Model for Post-Stroke Assessment.

The rapid development of Internet of Things (IoT) technologies and the potential benefits of employing the vast datasets generated by IoT devices, including wearable sensors and camera systems, has ushered in a new era of opportunities for enhancing smart rehabilitation in various healthcare systems. Maintaining patient privacy is paramount in healthcare while providing smart insights and recommendations. This study proposed the adoption of federated learning to develop a scalable AI model for post-stroke assessment while protecting patients' privacy. This research compares the centralized (PSA-MNMF) model performance with the proposed scalable federated PSA-FL-CDM model for sensor- and camera-based datasets. The computational time indicates that the federated PSA-FL-CDM model significantly reduces the execution time and attains comparable performance while preserving the patient's privacy. Impact Statement-This research introduces groundbreaking contributions to stroke assessment by successfully implementing federated learning for the first time in this domain and applying consensus models in each node. It enables collaborative model training among multiple nodes or clients while ensuring the privacy of raw data. The study explores eight different clustering methods independently on each node, revolutionizing data organization based on similarities in stroke assessment. Additionally, the research applies the centralized PSA-MNMF consensus clustering technique to each client, resulting in more accurate and robust clustering solutions. By utilizing the FedAvg federated learning algorithm strategy, locally trained models are combined to create a global model that captures the collective knowledge of all participants. Comparative performance measurements and computational time analyses are conducted, facilitating a fair evaluation between centralized and federated learning models in stroke assessment. Moreover, the research extends beyond a single type of database by conducting experiments on two distinct datasets, wearable and camera-based, broadening the understanding of the proposed methods across different data modalities. These contributions develop stroke assessment methodologies, enabling efficient collaboration and accurate consensus clustering models and maintaining data privacy.

PWSC: a novel clustering method based on polynomial weight-adjusted sparse clustering for sparse biomedical data and its application in cancer subtyping.

BACKGROUND: Clustering analysis is widely used to interpret biomedical data and uncover new knowledge and patterns. However, conventional clustering methods are not effective when dealing with sparse biomedical data. To overcome this limitation, we propose a hierarchical clustering method called polynomial weight-adjusted sparse clustering (PWSC). RESULTS: The PWSC algorithm adjusts feature weights using a polynomial function, redefines the distances between samples, and performs hierarchical clustering analysis based on these adjusted distances. Additionally, we incorporate a consensus clustering approach to determine the optimal number of classifications. This consensus approach utilizes relative change in the cumulative distribution function to identify the best number of clusters, resulting in more stable clustering results. Leveraging the PWSC algorithm, we successfully classified a cohort of gastric cancer patients, enabling categorization of patients carrying different types of altered genes. Further evaluation using Entropy showed a significant improvement (p = 2.905e-05), while using the Calinski-Harabasz index demonstrates a remarkable 100% improvement in the quality of the best classification compared to conventional algorithms. Similarly, significantly increased entropy (p = 0.0336) and comparable CHI, were observed when classifying another colorectal cancer cohort with microbial abundance. The above attempts in cancer subtyping demonstrate that PWSC is highly applicable to different types of biomedical data. To facilitate its application, we have developed a user-friendly tool that implements the PWSC algorithm, which canbe accessed at http://pwsc.aiyimed.com/ . CONCLUSIONS: PWSC addresses the limitations of conventional approaches when clustering sparse biomedical data. By adjusting feature weights and employing consensus clustering, we achieve improved clustering results compared to conventional methods. The PWSC algorithm provides a valuable tool for researchers in the field, enabling more accurate and stable clustering analysis. Its application can enhance our understanding of complex biological systems and contribute to advancements in various biomedical disciplines.

WormTensor: a clustering method for time-series whole-brain activity data from C. elegans.

BACKGROUND: In the field of neuroscience, neural modules and circuits that control biological functions have been found throughout entire neural networks. Correlations in neural activity can be used to identify such neural modules. Recent technological advances enable us to measure whole-brain neural activity with single-cell resolution in several species including [Formula: see text]. Because current neural activity data in C. elegans contain many missing data points, it is necessary to merge results from as many animals as possible to obtain more reliable functional modules. RESULTS: In this work, we developed a new time-series clustering method, WormTensor, to identify functional modules using whole-brain activity data from C. elegans. WormTensor uses a distance measure, modified shape-based distance to account for the lags and the mutual inhibition of cell-cell interactions and applies the tensor decomposition algorithm multi-view clustering based on matrix integration using the higher orthogonal iteration of tensors (HOOI) algorithm (MC-MI-HOOI), which can estimate both the weight to account for the reliability of data from each animal and the clusters that are common across animals. CONCLUSION: We applied the method to 24 individual C. elegans and successfully found some known functional modules. Compared with a widely used consensus clustering method to aggregate multiple clustering results, WormTensor showed higher silhouette coefficients. Our simulation also showed that WormTensor is robust to contamination from noisy data. WormTensor is freely available as an R/CRAN package https://cran.r-project.org/web/packages/WormTensor .

Pancancer network analysis reveals key master regulators for cancer invasiveness.

BACKGROUND: Tumor invasiveness reflects numerous biological changes, including tumorigenesis, progression, and metastasis. To decipher the role of transcriptional regulators (TR) involved in tumor invasiveness, we performed a systematic network-based pan-cancer assessment of master regulators of cancer invasiveness. MATERIALS AND METHODS: We stratified patients in The Cancer Genome Atlas (TCGA) into invasiveness high (INV-H) and low (INV-L) groups using consensus clustering based on an established robust 24-gene signature to determine the prognostic association of invasiveness with overall survival (OS) across 32 different cancers. We devise a network-based protocol to identify TRs as master regulators (MRs) unique to INV-H and INV-L phenotypes. We validated the activity of MRs coherently associated with INV-H phenotype and worse OS across cancers in TCGA on a series of additional datasets in the Prediction of Clinical Outcomes from the Genomic Profiles (PRECOG) repository. RESULTS: Based on the 24-gene signature, we defined the invasiveness score for each patient sample and stratified patients into INV-H and INV-L clusters. We observed that invasiveness was associated with worse survival outcomes in almost all cancers and had a significant association with OS in ten out of 32 cancers. Our network-based framework identified common invasiveness-associated MRs specific to INV-H and INV-L groups across the ten prognostic cancers, including COL1A1, which is also part of the 24-gene signature, thus acting as a positive control. Downstream pathway analysis of MRs specific to INV-H phenotype resulted in the identification of several enriched pathways, including Epithelial into Mesenchymal Transition, TGF-ß signaling pathway, regulation of Toll-like receptors, cytokines, and inflammatory response, and selective expression of chemokine receptors during T-cell polarization. Most of these pathways have connotations of inflammatory immune response and feasibility for metastasis. CONCLUSION: Our pan-cancer study provides a comprehensive master regulator analysis of tumor invasiveness and can suggest more precise therapeutic strategies by targeting the identified MRs and downstream enriched pathways for patients across multiple cancers.

Unraveling the evolutionary patterns and phylogenomics of coronaviruses: A consensus network approach.

The COVID-19 pandemic emphasizes the significance of studying coronaviruses (CoVs). This study investigates the evolutionary patterns of 350 CoVs using four structural proteins (S, E, M, and N) and introduces a consensus methodology to construct a comprehensive phylogenomic network. Our clustering of CoVs into 4 genera is consistent with the current CoV classification. Additionally, we calculate network centrality measures to identify CoV strains with significant average weighted degree and betweenness centrality values, with a specific focus on RaTG13 in the beta genus and NGA/A116E7/2006 in the gamma genus. We compare the phylogenetics of CoVs using our distance-based approach and the character-based model with IQ-TREE. Both methods yield largely consistent outcomes, indicating the reliability of our consensus approach. However, it is worth mentioning that our consensus method achieves an approximate 5000-fold increase in speed compared to IQ-TREE when analyzing the data set of 350 CoVs. This improved efficiency enhances the feasibility of conducting large-scale phylogenomic studies on CoVs.

Bioinformatics Identification and Validation of Aging­Related Molecular Subtype and Prognostic Signature in Sarcoma.

Aging could regulate many biological processes in malignancies by regulating cell senescence. Consensus cluster analysis was conducted to differentiate TCGA sarcoma cases. LASSO cox regression analysis was performed to construct an aging-related prognostic signature. We identified two categories of TCGA-sarcoma with significant difference in prognosis, immune infiltration and chemotherapy and targeted therapy. Moreover, an aging-related prognostic signature was constructed for sarcoma, which had a good performance in predicting the 3-year and 5-year overall survival of sarcoma patients. We also identified a lncRNA MALAT1/miR-508-3p/CCNA2 regulatory axis for sarcoma. This stratification could provide more evidence for estimating prognosis and immunotherapy of sarcoma.

Diagnostic signature composed of seven genes in HIF-1 signaling pathway for preeclampsia.

PURPOSE: In this study, we explored the relationship of genes in HIF-1 signaling pathway with preeclampsia and establish a logistic regression model for diagnose preeclampsia using bioinformatics analysis. METHOD: Two microarray datasets GSE75010 and GSE35574 were downloaded from the Gene Expression Omnibus database, which was using for differential expression analysis. DEGs were performed the Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and Gene set enrichment analysis (GSEA). Then we performed unsupervised consensus clustering analysis using genes in HIF-1 signaling pathway, and clinical features and immune cell infiltration were compared between these clusters, as well as the least absolute shrinkage and selection operator (LASSO) method to screened out key genes to constructed logistic regression model, and receiver operating characteristic (ROC) curve was plotted to evaluate the accuracy of the model. RESULTS: 57 DEGs were identified, of which GO, KEGG and analysis GSEA showed DEGs were mostly involved in HIF-1 signaling pathway. Two subtypes were identified of preeclampsia and 7 genes in HIF1-signaling pathway were screened out to establish the logistic regression model for discrimination preeclampsia from controls, of which the AUC are 0.923 and 0.845 in training and validation datasets respectively. CONCLUSION: Seven genes (including MKNK1, ARNT, FLT1, SERPINE1, ENO3, LDHA, BCL2) were screen out to build potential diagnostic model of preeclampsia.

Alternations of gene expression in PI3K and AR pathways and DNA methylation features contribute to metastasis of prostate cancer.

OBJECTIVE: The molecular heterogeneity of prostate cancer (PCa) gives rise to distinct tumor subclasses based on epigenetic modification and gene expression signatures. Identification of clinically actionable molecular subtypes of PCa is key to improving patient outcome, and the balance between specific pathways may influence PCa outcome. It is also urgent to identify progression-related markers through cytosine-guanine (CpG) methylation in predicting metastasis for patients with PCa. METHODS: We performed bioinformatics analysis of transcriptomic, and clinical data in an integrated cohort of 551 prostate samples. The datasets included retrospective The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts. Two algorithms, Least Absolute Shrinkage and Selector Operation and Support Vector Machine-Recursive Feature Elimination, were used to select significant CpGs. RESULTS: We found that PCa progression is more likely to occur after the third year through conditional survival (CS) analysis, and prostate-specific antigen (PSA) was a better predictor of Progression-free survival (PFS) than Gleason score (GS). Our study first demonstrated that PCa tumors have distinct expression profiles based on the expression of genes involved in androgen receptor (AR) and PI3K-AKT, which influence disease outcome. Our results also indicated that there are multiple phenotypes relevant to the AR-PI3K axis in PCa, where tumors with mixed phenotype may be more aggressive or have worse outcome than quiescent phenotype. In terms of epigenetics, we obtained CpG sites and their corresponding genes which have a good predictive value of PFS. However, various evidences showed that the predictive value of CpGs corresponding genes was much lower than GpG sites in Overall survival (OS) and PFS. CONCLUSIONS: PCa classification specific to AR and PI3K pathways provides novel biological insight into previously established PCa subtypes and may help develop personalized therapies. Our results support the potential clinical utility of DNA methylation signatures to distinguish tumor metastasis and to predict prognosis and outcomes.

Identifying chronic obstructive pulmonary disease from integrative omics and clustering in lung tissue.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a highly morbid and heterogenous disease. While COPD is defined by spirometry, many COPD characteristics are seen in cigarette smokers with normal spirometry. The extent to which COPD and COPD heterogeneity is captured in omics of lung tissue is not known. METHODS: We clustered gene expression and methylation data in 78 lung tissue samples from former smokers with normal lung function or severe COPD. We applied two integrative omics clustering methods: (1) Similarity Network Fusion (SNF) and (2) Entropy-Based Consensus Clustering (ECC). RESULTS: SNF clusters were not significantly different by the percentage of COPD cases (48.8% vs. 68.6%, p = 0.13), though were different according to median forced expiratory volume in one second (FEV1) % predicted (82 vs. 31, p = 0.017). In contrast, the ECC clusters showed stronger evidence of separation by COPD case status (48.2% vs. 81.8%, p = 0.013) and similar stratification by median FEV1% predicted (82 vs. 30.5, p = 0.0059). ECC clusters using both gene expression and methylation were identical to the ECC clustering solution generated using methylation data alone. Both methods selected clusters with differentially expressed transcripts enriched for interleukin signaling and immunoregulatory interactions between lymphoid and non-lymphoid cells. CONCLUSIONS: Unsupervised clustering analysis from integrated gene expression and methylation data in lung tissue resulted in clusters with modest concordance with COPD, though were enriched in pathways potentially contributing to COPD-related pathology and heterogeneity.

Data-Driven Chronic Allograft Phenotypes: A Novel and Validated Complement for Histologic Assessment of Kidney Transplant Biopsies.

BACKGROUND: No validated system currently exists to realistically characterize the chronic pathology of kidney transplants that represents the dynamic disease process and spectrum of disease severity. We sought to develop and validate a tool to describe chronicity and severity of renal allograft disease and integrate it with the evaluation of disease activity. METHODS: The training cohort included 3549 kidney transplant biopsies from an observational cohort of 937 recipients. We reweighted the chronic histologic lesions according to their time-dependent association with graft failure, and performed consensus k-means clustering analysis. Total chronicity was calculated as the sum of the weighted chronic lesion scores, scaled to the unit interval. RESULTS: We identified four chronic clusters associated with graft outcome, based on the proportion of ambiguous clustering. The two clusters with the worst survival outcome were determined by interstitial fibrosis and tubular atrophy (IFTA) and by transplant glomerulopathy. The chronic clusters partially overlapped with the existing Banff IFTA classification (adjusted Rand index, 0.35) and were distributed independently of the acute lesions. Total chronicity strongly associated with graft failure (hazard ratio [HR], 8.33; 95% confidence interval [CI], 5.94 to 10.88; P<0.001), independent of the total activity scores (HR, 5.01; 95% CI, 2.83 to 7.00; P<0.001). These results were validated on an external cohort of 4031 biopsies from 2054 kidney transplant recipients. CONCLUSIONS: The evaluation of total chronicity provides information on kidney transplant pathology that complements the estimation of disease activity from acute lesion scores. Use of the data-driven algorithm used in this study, called RejectClass, may provide a holistic and quantitative assessment of kidney transplant injury phenotypes and severity.

Automatic Post-Stroke Severity Assessment Using Novel Unsupervised Consensus Learning for Wearable and Camera-Based Sensor Datasets.

Stroke survivors often suffer from movement impairments that significantly affect their daily activities. The advancements in sensor technology and IoT have provided opportunities to automate the assessment and rehabilitation process for stroke survivors. This paper aims to provide a smart post-stroke severity assessment using AI-driven models. With the absence of labelled data and expert assessment, there is a research gap in providing virtual assessment, especially for unlabeled data. Inspired by the advances in consensus learning, in this paper, we propose a consensus clustering algorithm, PSA-NMF, that combines various clusterings into one united clustering, i.e., cluster consensus, to produce more stable and robust results compared to individual clustering. This paper is the first to investigate severity level using unsupervised learning and trunk displacement features in the frequency domain for post-stroke smart assessment. Two different methods of data collection from the U-limb datasets-the camera-based method (Vicon) and wearable sensor-based technology (Xsens)-were used. The trunk displacement method labelled each cluster based on the compensatory movements that stroke survivors employed for their daily activities. The proposed method uses the position and acceleration data in the frequency domain. Experimental results have demonstrated that the proposed clustering method that uses the post-stroke assessment approach increased the evaluation metrics such as accuracy and F-score. These findings can lead to a more effective and automated stroke rehabilitation process that is suitable for clinical settings, thus improving the quality of life for stroke survivors.

C3NA: correlation and consensus-based cross-taxonomy network analysis for compositional microbial data.

BACKGROUND: Studying the co-occurrence network structure of microbial samples is one of the critical approaches to understanding the perplexing and delicate relationship between the microbe, host, and diseases. It is also critical to develop a tool for investigating co-occurrence networks and differential abundance analyses to reveal the disease-related taxa-taxa relationship. In addition, it is also necessary to tighten the co-occurrence network into smaller modules to increase the ability for functional annotation and interpretability of  these taxa-taxa relationships.  Also, it is critical to retain the phylogenetic relationship among the taxa to identify differential abundance patterns, which can be used to resolve contradicting functions reported by different studies. RESULTS: In this article, we present Correlation and Consensus-based Cross-taxonomy Network Analysis (C3NA), a user-friendly R package for investigating compositional microbial sequencing data to identify and compare co-occurrence patterns across different taxonomic levels. C3NA contains two interactive graphic user interfaces (Shiny applications), one of them dedicated to the comparison between two diagnoses, e.g., disease versus control. We used C3NA to analyze two well-studied diseases, colorectal cancer, and Crohn's disease. We discovered clusters of study and disease-dependent taxa that overlap with known functional taxa studied by other discovery studies and differential abundance analyses. CONCLUSION: C3NA offers a new microbial data analyses pipeline for refined and enriched taxa-taxa co-occurrence network analyses, and the usability was further expanded via the built-in Shiny applications for interactive investigation.

Pan-cancer analysis of UBE2T with a focus on prognostic and immunological roles in lung adenocarcinoma.

BACKGROUND: Ubiquitin-conjugating enzyme E2 T (UBE2T) is a potential oncogene. However, Pan-cancer analyses of the functional, prognostic and predictive implications of this gene are lacking. METHODS: We first analyzed UBE2T across 33 tumor types in The Cancer Genome Atlas (TCGA) project. We investigated the expression level of UBE2T and its effect on prognosis using the TCGA database. The correlation between UBE2T and cell cycle in pan-cancer was investigated using the single-cell sequencing data in Cancer Single-cell State Atlas (CancerSEA) database. The Weighted Gene Co-expression Network analysis (WGCNA), Univariate Cox and Least absolute shrinkage and selection operator (LASSO) Cox regression models, and receiver operating characteristic (ROC) were applied to assess the prognostic impact of UBE2T-related cell cycle genes (UrCCGs). Furthermore, the consensus clustering (CC) method was adopted to divide TCGA-lung adenocarcinoma (LUAD) patients into subgroups based on UrCCGs. Prognosis, molecular characteristics, and the immune panorama of subgroups were analyzed using Single-sample Gene Set Enrichment Analysis (ssGSEA). Results derived from TCGA-LUAD patients were validated in International Cancer Genome Consortium (ICGC)-LUAD data. RESULTS: UBE2T is highly expressed and is a prognostic risk factor in most tumors. CancerSEA database analysis revealed that UBE2T was positively associated with the cell cycle in various cancers(r > 0.60, p < 0.001). The risk signature of UrCCGs can reliably predict the prognosis of LUAD (AUC1 year = 0.720, AUC3 year = 0.700, AUC5 year = 0.630). The CC method classified the TCGA-LUAD cohort into 4 UrCCG subtypes (G1-G4). Kaplan-Meier survival analysis demonstrated that G2 and G4 subtypes had worse survival than G3 (Log-rank test PTCGA training set < 0.001, PICGC validation set < 0.001). A comprehensive analysis of immune infiltrates, immune checkpoints, and immunogenic cell death modulators unveiled different immune landscapes for the four subtypes. High immunophenoscore in G3 and G4 tumors suggested that these two subtypes were immunologically "hot," tending to respond to immunotherapy compared to G2 subtypes (p < 0.001). CONCLUSIONS: UBE2T is a critical oncogene in many cancers. Moreover, UrCCG classified the LUAD cohort into four subgroups with significantly different survival, molecular features, immune infiltrates, and immunotherapy responses. UBE2T may be a therapeutic target and predictor of prognosis and immunotherapy sensitivity.