Clustered Data Research Articles

Geessbin: an R package for analyzing small-sample binary data using modified generalized estimating equations with bias-adjusted covariance estimators.

The generalized estimating equation (GEE) method is widely used for analyzing longitudinal and clustered data. Although the GEE estimate for regression coefficients and sandwich covariance estimate are consistent regardless of the choice of covariance structure, they are generally biased for small sample sizes. Various researchers have proposed modified GEE methods and covariance estimators to handle small-sample bias. We briefly present bias-corrected and penalized GEE methods, along with 11 bias-adjusted covariance estimators. In addition, we focus on analyzing longitudinal or clustered data with binary outcomes using the logit link function and introduce package geessbin in R to implement conventional and modified GEE methods with bias-adjusted covariance estimators. Finally, we illustrate the implementation and detail a usage example of the package. The package is available from the Comprehensive R Archive Network (CRAN) at https://cran.r-project.org/web/packages/geessbin/index.html . The geessbin package provides three GEE estimates with numerous covariance estimates. It is useful for analyzing correlated data such as longitudinal and clustered data. Additionally, the geessbin is designed to be user-friendly, making it accessible to non-statisticians.

On the development of theoretical approaches to the analysis of clusters as network spatial-economic entities

On the development of theoretical approaches to the analysis of clusters as network spatial-economic entities

Using Multilevel Models and Generalized Estimating Equation Models to Account for Clustering in Neurology Clinical Research.

In clinical and health services research, clustered data (also known as data with a multilevel or hierarchical structure) are frequently encountered. For example, patients may be clustered or nested within hospitals. Understanding when data have a multilevel structure is important because clustering of individuals can induce a homogeneity in outcomes within clusters, so that, even after adjusting for measured covariates, outcomes for 2 individuals in the same cluster are more likely to be similar than outcomes for 2 individuals from different clusters. Using conventional statistical regression models to analyze clustered data can result in incorrect conclusions being drawn. In particular, estimated CIs may be artificially narrow, and significance levels may be artificially low. As a result, one may conclude that there is a statistically significant association when there is none. To avoid this problem, investigators should ensure that their analyses use techniques that account for clustering of data. Generalized linear models estimated using generalized estimating equation (GEE) methods and multilevel regression models (also known as hierarchical regression models, mixed-effects models, or random-effects models) are two such techniques. We provide an introduction to clustered or multilevel data and describe how GEE models or multilevel models can be used for the analysis of multilevel data.

A single-cell transcriptomic atlas of human stem cells from apical papilla during the committed differentiation.

Human stem cells derived from the apical papilla (SCAPs) are recognized for their multilineage differentiation potential and their capacity for functional tooth root regeneration. However, the molecular mechanisms underlying odonto/osteogenic differentiation remain largely unexplored. In this study, we utilized single-cell RNA sequencing (scRNA-seq) to conduct an in-depth analysis of the transcriptional changes associated with chemically induced osteogenesis in SCAPs. scRNA-seq identified SCAPs as distinct subpopulations. Differentially expressed genes (DEGs) and Gene Ontology (GO) analyses were conducted to evaluate the potential function of each cluster. Pseudotime trajectory analysis was employed to elucidate the potential differentiation processes of the identified SCAP populations. To investigate the osteo/odontogenic potential of Deiodinase Iodothyronine Type 2 (DIO2) on SCAPs, we performed alkaline phosphatase staining, western blot analysis, Alizarin Red S staining and immunofluorescence staining. Additionally, SCAP components were transplanted into mouse calvarial defects to evaluate osteogenesis invivo. The analysis of cell clusters derived from our scRNA-seq data revealed a significant shift in cellular composition when cells were cultured in a mineralization induction medium compared to those cultured in a complete medium. Both groups exhibited heterogeneity, with some cells intrinsically predisposed to osteogenesis and others appearing to be primed for proliferative functions. Notably, we identified a subpopulation characterized by high expression of DIO2, which exhibited pronounced osteogenic activity during differentiation. Our study is the first to reveal a shift in the cellular composition of SCAPs when cultured in a mineralization induction medium compared to a complete medium. Following both invitro and invivo validation, the DIO2+ subpopulation exhibited the highest transcriptional similarity to osteogenic function, suggesting its potential utility in tissue regeneration applications.

CSIG-04. ANTI-TUMOR RESPONSE OF ONCOMAGNETIC MONOTHERAPY IN GLIOBLASTOMA

Abstract BACKGROUND Glioblastoma (GBM) is the most lethal primary malignant brain tumor with a median survival of 15–20 months. Concurrent temozolomide (TMZ) chemotherapy and radiation (XRT) remain the current standard of care (SOC) treatment for newly diagnosed GBM. The addition of tumor treating fields (TTFs) improves median survival for newly diagnosed GBM from 16 to 20.9 months. This modest improvement underscores the urgent need to identify a new treatment modality as a safe and effective therapy. PURPOSE The noninvasive Oncomagnetic device developed in our laboratory selectively kills glioblastoma (GBM) in vitro. We studied the cellular and molecular effects of Oncomagnetic monotherapy (OMT) on GBM cell lines and investigated immune response in a syngeneic mouse model. RESULTS OMT significantly reduces cell proliferation and cell survival in vitro. OMT induces persistent ROS primarily by increasing superoxide level in cancer cells and reduces mitochondrial-membrane potential in GBM cells. It also induces DNA damage and arrests cells in G1-phase of the cell cycle. Whole-body Oncomagnetic stimulation caused a substantial retardation of tumor growth and reduction of the contrast-enhanced tumor volume in 9.4T MRI scans. OMT showed significant increase in overall survival in treated mice. We also observed upregulated immune response indicated by RNA sequencing data obtained from OMT-treated GBM cells as well as in treated GBM mouse tumor sections by imaging mass cytometry analysis. The tSNE analysis of various immune clusters suggests significant increase in immune response by activating CD4+, CD8+, murine-macrophages and M1-macrophages located at tumor site in the treated group. CONCLUSION The obtained data indicate significant anti-tumor response by inhibiting cancer signaling pathways and an increased immune response. Our findings suggest unique mechanism of action for OMT stimulation and provide a strong rationale for standalone OMT for GBM.

A General Framework for the Multiple Nonparametric Behrens-Fisher Problem With Dependent Replicates.

In many trials and experiments, subjects are not only observed once but multiple times, resulting in a cluster of possibly correlated observations (e.g., brain regions per patient). Observations often do not fulfill model assumptions of mixed models and require the use of nonparametric methods. In this article, we develop and present a purely nonparametric rank-based procedure that flexibly allows the unbiased and consistent estimation of the Wilcoxon-Mann-Whitney effect in clustered data designs. Compared with existing methods, we allow flexible weights to be used in effect estimation. Additionally, we develop global and multiple contrast test procedures to test null hypotheses formulated regarding the generalized Mann-Whitney effects and for the computation of range-preserving simultaneous confidence intervals in a unified way. Extensive simulation studies show that these methods control the type-I error rate well and have reasonable power to detect alternatives in various situations.

Whole-Genome Sequencing And Characterization Of Two Bacillus velezensis Strains from Termitarium and A Comprehensive Comparative Genomic Analysis of Biosynthetic Gene Clusters.

The speciesBacillus velezensisis known for its biosynthetic potential and metabolic versatility in producing several secondary metabolites and promoting plant growth. In this study, we isolated twoB. velezensisstrains, WGTg-8 and WGTm-299, from the termite gut and termitarium, which exhibited antimicrobial activity against multiple clinical and phytopathogens. The whole genomes of these strains were sequenced using the Illumina platform and annotated. The genome mining of the draft genome sequences revealed 48 biological gene clusters (BGCs) responsible for synthesizing various secondary metabolites. The construction of the similarity network of the BGCs and the comparative analysis with the genetically related organisms are aided in the identification of metabolites produced by these strains. We identified biosynthetic gene clusters (BGCs) coding for macrolactin H, bacilysin, bacillibactin, amylocyclin, comX4, and LCI, found in both strains with 100% similarity. The difficidin, bacillaene, thusin_alpha, and cericidin BGCs are exclusively found in strain WGTg-8, while the colicin BGC is exclusively present in the WGTm-299 strain. The fengycin and surfactin gene clusters are present in both strains with 80% similarity. Furthermore, 28 putative NRPS BGCs, NRPS-T1PKS hybrid clusters, a T1PKS, and a bacteriocin BGC were identified with very low similarity (≤ 25%) or no similarity with known antibiotics. In addition, we found several genes coding for plant growth-promoting properties, including nitrogen metabolism, hormone synthesis, sulfur metabolism, phosphate metabolism, and a few other properties.

Single-cell RNA sequencing analysis identifies acute changes in the tumor microenvironment induced by interferon α gene therapy in a murine bladder cancer model

IntroductionNadofaragene firadenovec (Ad-IFNα/Syn3) is now approved for BCG-unresponsive bladder cancer (BLCA). IFNα is a pleiotropic cytokine that causes direct tumor cell killing via TRAIL-mediated apoptosis, angiogenesis inhibition, and activation of the innate and adaptive immune system. We established an immunocompetent murine BLCA model to study the effects of murine adenoviral IFNα (muAd-Ifnα) gene therapy on cancer cells and the tumor microenvironment using a novel murine equivalent of Nadofaragene firadenovec (muAd-Ifnα).MethodsTumors were induced by instilling MB49 cells into the bladders of mice; luciferase imaging confirmed tumor development. Mice were treated with adenovirus control (Ad-Ctrl; empty vector), or muAd-Ifnα (3x1011 VP/mL), and survival analysis was performed. For single-cell sequencing (scRNAseq) analysis (72h), bladders were harvested and treated with collagenase/hyaluronidase and TrypLE for cell dissociation. Single cells were suspended in PBS/1% FBS buffer; viability was assessed with Vicell cell counter. scRNAseq analysis was performed using 10X genomics 3’ sequencing. Raw RNAseq data were pre-processed using Cell Ranger single-cell software. Seurat (R package) was used to normalize and cluster the scRNA data. Pooled differential gene expression analysis in specific cell clusters was performed with DESeq2.ResultsWe identified 16 cell clusters based on marker expression which were grouped into epithelial (tumor), uroplakin-enriched, endothelial, T-cells, neutrophils, and macrophage clusters. Top differentially expressed genes between muAd-Ifnα and Ad-Ctrl were identified. Within the specific cell clusters, IPA analysis revealed significant differences between muAd-Ifnα and control. IFNα signaling and hypercytokinemia/chemokinemia were upregulated in all clusters. Cell death pathways were upregulated in tumor and endothelial clusters. T-cells demonstrated upregulation of the immunogenic cell death signaling pathway and a decrease in the Th2 pathway genes. Macrophages showed upregulation of PD1/PD-L1 pathways along with downregulation of macrophage activation pathways (alternate and classical). Multiplex immunofluorescence confirmed increased infiltration with macrophages in muAd-Ifnα treated tumors compared to controls. PD1/PD-L1 expression was reduced at 72h.DiscussionThis single-cell analysis builds upon our understanding of the impact of Ad-IFNα on tumor cells and other compartments of the microenvironment. These data will help identify mechanisms to improve patient selection and therapeutic efficacy of Nadofaragene firadenovec.

Lower uterine segment thickness to predict uterine rupture: a secondary analysis of PRISMA cluster randomized trial

BackgroundThird-trimester lower uterine segment thickness (LUST) is associated with uterine rupture during trial of labor after cesarean (TOLAC) but threshold values vary according to the approach used (lower values with vaginal ultrasound, higher values with abdominal ultrasound). ObjectiveTo estimate the optimal LUST cut-off value combining vaginal and abdominal ultrasound to predict uterine rupture during TOLAC. Study DesignWe performed a secondary analysis of PRISMA cluster randomized trial including women with a single previous cesarean who underwent ultrasound LUST measurement at 34-38 weeks using the thinnest measurement obtained by combining transvaginal and transabdominal measurements. Participants in the intervention group were informed about the risk of uterine rupture according to LUST reported in 3 categories [≥2.5 mm: low risk - TOLAC is safe; 2.0-2.4 mm: intermediate-risk - TOLAC is safe under specific conditions (e.g. Grobman estimate of vaginal delivery of at or above 70 percent and/or having a history of vaginal delivery; estimated fetal weight below 4000 grams; inter-delivery interval ≥18 months); <2.0 mm: high-risk for uterine rupture]. Delivery outcomes including uterine rupture were compared using non-parametric analyses and receiver operating characteristics (ROC) curves. ResultsAmong 3460 participants, 2809 (81%); 385 (11%); and 266 (8%) were identified at low-; intermediate-; and high-risk for uterine rupture, respectively. As expected, low-risk participants were more likely to undergo TOLAC (49% vs 46% vs 13%; p<0.001) and more likely to undergo labor induction (16% vs 12% vs 3%, respectively; p<0.001) than intermediate-risk and high-risk participants. Four (0.3%) cases of uterine rupture during TOLAC occurred among 1382 low-risk participants but none among the intermediate-risk (0/178) and high-risk (0/35) participants (p=0.73). Among low-risk participants, uterine rupture was associated with LUST combining vaginal and abdominal ultrasound (area under the ROC curve : 0.93; 95% confidence interval: 0.86 – 0.99; p<0.001) with all cases occuring among women with LUST between 2.5 and 3.0 mm (4/371 or 1.1%) compared to none (0/1011) among those with LUST ≥3.0 mm (p<0.01). ConclusionsThird-trimester LUST measurement influences the rates of TOLAC and uterine rupture. TOLAC appears to be associated with a low risk of uterine rupture with a LUST between 2.0 and 2.4 mm under specific conditions. However, these conditions should perhaps be extended to patients with a LUST of less than 3.0 mm, as we found an increase in uterine ruptures in this sub-group (between 2.5 and 3.0 mm) who had no special conditions to comply with. LUST ≥3.0 mm combining vaginal and abdominal ultrasound was associated with a very low risk for uterine rupture. A large-scale study using these new parameters would have the potential to demonstrate a reduction in uterine ruptures during TOLAC.

Mass modeling and kinematics of galaxy clusters in modified gravity

The chameleon screening mechanism has been constrained many a time using dynamic and kinematic galaxy cluster observables. Current constraints are, however, insensitive to different mass components within galaxy clusters and have been mainly focused on a single mass density profile, the Navarro-Frenk-White mass density model. In this work, we extend the study of the Chameleon screening mechanism in galaxy clusters by considering a series of mass density models, namely: generalized-Navarro-Frenk-While, b-Navarro-Frenk-While, Burket, Isothermal and Einasto. The coupling strength (β) and asymptotic value of the chameleon field (ϕ ∞) are constrained by using kinematics analyses of simulated galaxy clusters, generated both assuming General Relativity and a strong chameleon scenario. By implementing a Bayesian analysis we comprehensively show that the biases introduced due to an incorrect assumption of the mass model are minimal. Similarly, we also demonstrate that a spurious detection of evidence for modifications to gravity is highly unlikely when utilizing the kinematics of galaxy clusters.

Psychometric Properties of the Generalized Anxiety Disorder Scale (GAD-7) in Older Adults With Advanced Cancer.

Over half of new cancer diagnoses occur in patients aged 65 or older, with up to 40% experiencing anxiety. The American Society of Clinical Oncology recommends using the Generalized Anxiety Disorder Scale (GAD-7) for anxiety screening, but the GAD-7 psychometric properties in this population are unknown. This study examined the GAD-7's reliability, validity, and item parameters, comparing its utility with the GAD-2 in older adults with cancer. This cross-sectional secondary analysis of a nationwide multi-site two-arm cluster randomized trial in older adults (≥70)with advanced cancer. The GAD-7 was administered at baseline. Properties were evaluated with Cronbach's α, Pearson correlation coefficients, and a 2-parameter logistic model. Logistic regression models compared the GAD-2 and GAD-7. The sample included 718 participants (Mean age=77, SD=5) with mild anxiety (M=3.74, SD=4.74). Internal consistency was strong (Cronbach's alpha=0.89) and item-total correlations ranged 0.53 to 0.78. Item 2 (Not being able to stop or control worrying) was the most discriminating and item 5 (Being so restless that it is hard to sit still) was least discriminating. Area Under the Curve (AUC) analyses demonstrated the GAD-2 had a 0.93-0.96 AUC. Establishing the psychometric properties of anxiety screening measures is crucial in the older adults with cancer to maximize referral efficiency and accuracy. This study indicates that the GAD-7 is reliable and valid for older adults with cancer. Analyses suggest the GAD-2 may be as sufficient as the GAD-7 in identifying anxiety in older adults with cancer, thereby reducing assessment burden.

Inclusion of unexposed clusters improves the precision of fixed effects analysis of stepped-wedge cluster randomized trials with binary and count outcomes

BackgroundThe fixed effects model is a useful alternative to the mixed effects model for analyzing stepped-wedge cluster randomized trials (SW-CRTs). It controls for all time-invariant cluster-level confounders and has proper control of type I error when the number of clusters is small. While all clusters in a SW-CRT are typically designed to crossover from the control to receive the intervention, some trials can end with unexposed clusters (clusters that never receive the intervention), such as when a trial is terminated early due to safety concerns. It was previously unclear whether unexposed clusters would contribute to the estimation of the intervention effect in a fixed effects analysis. However, recent work has demonstrated that including an unexposed cluster can improve the precision of the intervention effect estimator in a fixed effects analysis of SW-CRTs with continuous outcomes. Still, SW-CRTs are commonly designed with binary outcomes and it is unknown if those previous results extend to SW-CRTs with non-continuous outcomes.MethodsIn this article, we mathematically prove that the inclusion of unexposed clusters improves the precision of the fixed effects intervention effect estimator for SW-CRTs with binary and count outcomes. We then explore the benefits of including an unexposed cluster in simulated datasets with binary or count outcomes and a real palliative care data example with binary outcomes.ResultsThe simulations show that including unexposed clusters leads to tangible improvements in the precision, power, and root mean square error of the intervention effect estimator. The inclusion of the unexposed cluster in the SW-CRT of a novel palliative care intervention with binary outcomes yielded smaller standard errors and narrower 95% Wald Confidence Intervals.ConclusionsIn this article, we demonstrate that the inclusion of unexposed clusters in the fixed effects analysis can lead to the improvements in precision, power, and RMSE of the fixed effects intervention effect estimator for SW-CRTs with binary or count outcomes.

A longitudinal study of the impacts of area-level social cohesion on multimorbidity in Scotland

Abstract Background Multimorbidity is a growing global public health challenge, commonly referring to the co-occurrence of 2+ conditions but with varying measurements. Prior evidence has linked social cohesion with single health conditions; however, its impact on multimorbidity remains unclear. This study aims to examine the association between area-level social cohesion and multimorbidity and to explore whether this association will vary across multimorbidity measurements and levels of urban-rural classification. Methods Using a dataset that links the Scottish Longitudinal Study (5.3% of the Scottish population) with hospitalisation records (2001-2019) and an index that reflects area-level social cohesion (2012), focused on the cohorts of adults aged 40+, we used 3-level logistic regression to account for the clustered data structure of diagnosed chronic conditions over time within individuals within neighbourhoods. Results Areas with the highest social cohesion were observed to have the lowest risks of multimorbidity (p &amp;lt; 0.001), even after adjusting for area-level income deprivation. Varying by multimorbidity measurements, an inverted U-shaped pattern was observed between reduced area-level social cohesion and increased risks of having 2+ co-occurring chronic conditions; while a linear pattern was found for mental-physical multimorbidity. Moderated by levels of urban-rural classification, as compared to rural residents, urban residents were more sensitive to the impact of social cohesion on the co-occurrence of 2+ conditions; however, for mental-physical multimorbidity, no interaction effects between area-level social cohesion and urban-rural classification were found. Conclusions Although varying patterns were seen for different multimorbidity measurements, people living in areas with the highest area-level social cohesion were less likely to be multimorbid. This study offers new insights for developing effective place-based public health interventions. Key messages • This is the first longitudinal study that analysed the association between area-level social cohesion and multimorbidity using a nationally representative dataset based on secondary care data. • Area-level social cohesion affects multimorbidity, but its impact may vary by multimorbidity measurements and by urban-rural classification.

A Novel Non-Contact Multi-User Online Indoor Positioning Strategy Based on Channel State Information.

The IEEE 802.11bf-based wireless fidelity (WiFi) indoor positioning system has gained significant attention recently. It is important to recognize that multi-user online positioning occurs in real wireless environments. This paper proposes an indoor positioning sensing strategy that includes an optimized preprocessing process and a new machine learning (ML) method called NKCK. The NKCK method can be broken down into three components: neighborhood component analysis (NCA) for dimensionality reduction, K-means clustering, and K-nearest neighbor (KNN) classification with cross-validation (CV). The KNN algorithm is particularly suitable for our dataset since it effectively classifies data based on proximity, relying on the spatial relationships between points. Experimental results indicate that the NKCK method outperforms traditional methods, achieving reductions in error rates of 82.4% compared to naive Bayes (NB), 85.0% compared to random forest (RF), 72.1% compared to support vector machine (SVM), 64.7% compared to multilayer perceptron (MLP), 50.0% compared to density-based spatial clustering of applications with noise (DBSCAN)-based methods, 42.0% compared to linear discriminant analysis (LDA)-based channel state information (CSI) amplitude fingerprinting, and 33.0% compared to principal component analysis (PCA)-based approaches. Due to the sensitivity of CSI, our multi-user online positioning system faces challenges in detecting dynamic human activities, such as human tracking, which requires further investigation in the future.

Joint regression analysis of clustered current status data with latent variables.

Clustered current status data frequently occur in many fields of survival studies. Some potential factors related to the hazards of interest cannot be directly observed but are characterized through multiple correlated observable surrogates. In this article, we propose a joint modeling method for regression analysis of clustered current status data with latent variables and potentially informative cluster sizes. The proposed models consist of a factor analysis model to characterize latent variables through their multiple surrogates and an additive hazards frailty model to investigate covariate effects on the failure time and incorporate intra-cluster correlations. We develop an estimation procedure that combines the expectation-maximization algorithm and the weighted estimating equations. The consistency and asymptotic normality of the proposed estimators are established. The finite-sample performance of the proposed method is assessed via a series of simulation studies. This procedure is applied to analyze clustered current status data from the National Toxicology Program on a tumorigenicity study given by the United States Department of Health and Human Services.

Boosting the evolutionary picture of Cl 0024+17 and MS 0451-03: A case study at intermediate-redshift

Abstract In this work we improve the dynamic-evolutionary framework of two massive clusters at intermediate redshifts: Cl 0024+17 at z ∼ 0.4 and MS 0451-03 at z ∼ 0.5. The spectroscopic galaxy members were selected from Moran et al. (2007a), which combine optical and UV imaging with spectroscopy. Using a set of dynamic estimators with different approaches, our results show that both Cl 0024+17 and MS 0451-03 are non-relaxed systems with distinct dynamical configurations. Cl 0024+17 exhibits a disturbed kinematics, displaying significant gaps and a velocity dispersion profile suggesting a merger. This is confirmed by the presence of previously reported substructures and new ones identified in this study. MS 0451-03 appears less disturbed than Cl 0024+17, indicating by the significant segregation between late and early-type galaxies, with the latter occupying more central regions of the projected phase-space. However, five previously unobserved substructures and non-Gaussianity in the velocity distribution indicate that MS 0451-03 is also out of equilibrium. In both clusters, there are substructures infalling onto the systems, indicating key moments in their assembly histories and potential effects on the pre-processing of galaxies within these subgroups. This is suggested by the high percentage of early-type galaxies outside R200 (approximately $83\%$) in the case of CL 0024+17. This work reinforces the importance of more detailed dynamical analysis of clusters to better characterize their evolutionary picture.

Earthquake analysis of clusters of the most appropriate partition

Earthquake analysis of clusters of the most appropriate partition

Uterine mesenchymal tumours harboring the KAT6B/A::KANSL1 gene fusion represent a distinct type of uterine sarcoma based on DNA methylation profiles.

Uterine mesenchymal tumours harboring KAT6B/A::KANSL1 gene fusions typically exhibit histological and immunophenotypic overlap with endometrial stromal and smooth muscle tumours. To date it remains uncertain whether such neoplasms should be regarded as variants of smooth muscle or endometrial stromal neoplasm, or if they constitute a distinct tumour type. In this study we investigated DNA methylation patterns and copy number variations (CNVs) in a series of uterine tumours harboring KAT6B/A::KANSL1 gene fusions in comparison to other mesenchymal neoplasms of the gynecological tract. Unsupervised hierarchical clustering and t-SNE analysis of DNA methylation data (Illumina EPIC array) identified a distinct cluster for 8/13 KAT6B/A::KANSL1 tumours (herein referred to as core cluster). The other 5 tumours (herein referred to as outliers) did not assign to the core cluster but clustered near various other tumour types. CNV analysis did not identify significant alterations in the core cluster. In contrast, various alterations, including deletions at the CDKN2A/B and NF1 loci were identified in the outlier group. Analysis of the DNA methylation clusters in relation to histological features revealed that while tumours in the core KAT6B/A::KANSL1 cluster were histologically bland, outlier tumours frequently exhibited "high-grade" histologic features in the form of significant nuclear atypia, increased mitotic activity and necrosis. Three of the five patients with outlier tumours died from their disease while clinical follow-up in the remaining two patients was limited (less than 12months). In comparison, none of the 7 out of 8 patients with tumors in the core KAT6B/A::KANSL1 sarcoma cluster, where follow-up was available, died from disease. Furthermore, only 1 out of 7 patients recurred (mean follow-up of 30months). In conclusion, KAT6B/A::KANSL1 uterine sarcoma is a molecularly unique type of uterine tumour that should be recognized as a distinct entity. These tumors typically exhibit low-grade histologic features but are occasionally morphologically high-grade; the latter have a DNA methylation profile different from the typical low-grade neoplasms and may be associated with aggressive behaviour.

A review of current practice in the design and analysis of extremely small stepped-wedge cluster randomized trials.

Stepped-wedge cluster randomized trials tend to require fewer clusters than standard parallel-arm designs due to the switches between control and intervention conditions, but there are no recommendations for the minimum number of clusters. Trials randomizing an extremely small number of clusters are not uncommon, but the justification for small numbers of clusters is often unclear and appropriate analysis is often lacking. In addition, stepped-wedge cluster randomized trials are methodologically more complex due to their longitudinal correlation structure, and ignoring the distinct within- and between-period intracluster correlations can underestimate the sample size in small stepped-wedge cluster randomized trials. We conducted a review of published small stepped-wedge cluster randomized trials to understand how and why they are used, and to characterize approaches used in their design and analysis. Electronic searches were used to identify primary reports of full-scale stepped-wedge cluster randomized trials published during the period 2016-2022; the subset that randomized two to six clusters was identified. Two reviewers independently extracted information from each report and any available protocol. Disagreements were resolved through discussion. We identified 61 stepped-wedge cluster randomized trials that randomized two to six clusters: median sample size (Q1-Q3) 1426 (420-7553) participants. Twelve (19.7%) gave some indication that the evaluation was considered a "preliminary" evaluation and 16 (26.2%) recognized the small number of clusters as a limitation. Sixteen (26.2%) provided an explanation for the limited number of clusters: the need to minimize contamination (e.g. by merging adjacent units), limited availability of clusters, and logistical considerations were common explanations. Majority (51, 83.6%) presented sample size or power calculations, but only one assumed distinct within- and between-period intracluster correlations. Few (10, 16.4%) utilized restricted randomization methods; more than half (34, 55.7%) identified baseline imbalances. The most common statistical method for analysis was the generalized linear mixed model (44, 72.1%). Only four trials (6.6%) reported statistical analyses considering small numbers of clusters: one used generalized estimating equations with small-sample correction, two used generalized linear mixed model with small-sample correction, and one used Bayesian analysis. Another eight (13.1%) used fixed-effects regression, the performance of which requires further evaluation under stepped-wedge cluster randomized trials with small numbers of clusters. None used permutation tests or cluster-period level analysis. Methods appropriate for the design and analysis of small stepped-wedge cluster randomized trials have not been widely adopted in practice. Greater awareness is required that the use of standard sample size calculation methods can provide spuriously low numbers of required clusters. Methods such as generalized estimating equations or generalized linear mixed models with small-sample corrections, Bayesian approaches, and permutation tests may be more appropriate for the analysis of small stepped-wedge cluster randomized trials. Future research is needed to establish best practices for stepped-wedge cluster randomized trials with a small number of clusters.

Multi-site analysis of biosynthetic gene clusters from the periodontitis oral microbiome.

Background. Bacteria significantly influence human health and disease, with bacterial biosynthetic gene clusters (BGCs) being crucial in the microbiome-host and microbe-microbe interactions.Gap statement. Despite extensive research into BGCs within the human gut microbiome, their roles in the oral microbiome are less understood.Aim. This pilot study utilizes high-throughput shotgun metagenomic sequencing to examine the oral microbiota in different niches, particularly focusing on the association of BGCs with periodontitis.Methodology. We analysed saliva, subgingival plaque and supragingival plaque samples from periodontitis patients (n=23) and controls (n=16). DNA was extracted from these samples using standardized protocols. The high-throughput shotgun metagenomic sequencing was then performed to obtain comprehensive genetic information from the microbial communities present in the samples.Results. Our study identified 10 742 BGCs, with certain clusters being niche-specific. Notably, aryl polyenes and bacteriocins were the most prevalent BGCs identified. We discovered several 'novel' BGCs that are widely represented across various bacterial phyla and identified BGCs that had different distributions between periodontitis and control subjects. Our systematic approach unveiled the previously unexplored biosynthetic pathways that may be key players in periodontitis.Conclusions. Our research expands the current metagenomic knowledge of the oral microbiota in both healthy and periodontally diseased states. These findings highlight the presence of novel biosynthetic pathways in the oral cavity and suggest a complex network of host-microbe and microbe-microbe interactions, potentially influencing periodontal disease. The BGCs identified in this study pave the way for future investigations into the role of small-molecule-mediated interactions within the human oral microbiota and their impact on periodontitis.