BACKGROUND:Chronic obstructive pulmonary disease (COPD) is a heterogeneous condition whose clinical severity may be influenced by social factors. We aimed to identify distinct COPD clinical phenotypes and assess variation by social determinants of health. METHODS:In this retrospective cohort study, we identified adults aged 50-80 years with a diagnosis of COPD (n=59 797) at a tertiary academic medical centre in North-Central Florida using codes from International Classification of Diseases. Latent class analysis defined COPD clinical phenotypes using indicators of clinical severity, including frequency of acute care encounters (urgent care, emergency department visits and hospitalisations), presence of COPD as the principal diagnosis, comorbidity burden and use of Global Initiative for Chronic Obstructive Lung Disease Group D medications. Kaplan-Meier survival curves and Cox proportional hazards models assessed mortality across phenotypes. Multinomial logistic regression models estimated associations between phenotype membership and race/ethnicity, income, rurality and smoking status, using the minimal phenotype as reference. RESULTS:Five clinical phenotypes were identified: minimal (20.9%), mild (35.2%), moderate (22.5%), severe (12.2%) and very severe (9.3%). The very severe phenotype had the highest mortality (adjusted HR 2.94; 95% CI 2.72 to 3.18). Odds of very severe COPD were higher among non-Hispanic Black (adjusted OR (aOR) 1.29; 95% CI 1.21 to 1.36) and Hispanic individuals (aOR 1.75; 95% CI 1.63 to 1.87), those in the lowest income communities (aOR 1.25; 95% CI 1.18 to 1.32), rural residents (aOR 1.80; 95% CI 1.68 to 1.92) and individuals who currently smoke (aOR 1.30; 95% CI 1.20 to 1.42). CONCLUSION:Most patients with COPD had mild disease; however, the very severe phenotype, which was associated with higher mortality, was more common among Black and Hispanic individuals, those residing in lower-income and rural areas and those who currently smoke. These clinical phenotypes highlight sociodemographic differences in COPD severity as reflected in healthcare utilisation and outcomes.

Background: Expression quantitative trait loci (eQTL) analysis links genetic variants to gene expression levels, helping to uncover how genetic variation contributes to gene regulation. While traditional eQTL analyses rely on bulk RNA-seq data, recent advances in single-cell RNA sequencing (scRNA-seq) have made it possible to detect cell-type-specific eQTLs. However, the inherent sparsity and heterogeneity of scRNA-seq data present major challenges for standard modeling approaches. Methods: In this paper, we propose a novel statistical framework, Bootstrap Penalized Hurdle regression model (BPHurdle), designed specifically for scRNA-seq data. BPHurdle employs a hurdle modeling framework, where a logistic component accounts for the excess zeros in single-cell expression data, and a Poisson component jointly evaluates the effects of multiple SNPs on positive gene expression levels. Results: Through simulation studies, we show that BPHurdle achieves high accuracy and robustness in identifying regulatory variants. We further demonstrate its utility on a real dataset through a case study focusing on a subset of differentially expressed genes, where it successfully identifies reliable cell-type-specific eQTLs. Conclusions: Overall, BPHurdle offers an advanced and flexible approach for single-cell eQTL mapping, providing deeper insight into the genetic regulation of gene expression at cellular resolution.

Sepsis is a life-threatening dysregulated response to infection, the heterogeneity of which precludes effective targeted therapies. To address this, we created a transcriptomic atlas of publicly available adult sepsis data, on which we performed molecular subtyping and identified potential subtype-specific drug repurposing opportunities. In total, we harmonized data from 3,713 samples across 28 datasets, of which 2,251 were from sepsis patients. Using this data, we identified four molecular subtypes of sepsis (C1 - C4) by clustering the sepsis samples based on expression differences in immune-and lipid-related genes. We next identified gene signatures unique to each molecular subtype. Pathway analysis of these signatures revealed patterns of immune exhaustion and metabolic dysregulation in C1, suggesting potential benefit from corticosteroid treatment. C2 had the youngest patient population and the lowest mortality, and C2 expression patterns were often anti-correlated with those of C1. C3 was enriched for inflammatory and cellular stress pathways, while the highest mortality subtype, C4, showed evidence of immunosuppression and metabolic reprogramming. Gene and pathway-level analysis of our molecular subtypes statistically correlated with results from analysis of 28-day mortality, with the best (C2) and worst subtypes (C4) exhibiting similar molecular dysregulation as survivors and non-survivors, respectively. For each subtype, we then evaluated potential targeted therapies. Using a large-scale pharmacogenomics database, we identified drugs targeting the subtype gene signatures and assessed the potential clinical impacts of these drugs. We identified several potential candidate therapies for each molecular subtype, including possible responsiveness to Methylene Blue therapy for patients from our highest mortality subtype, C4. Notably, our drug repurposing analysis revealed a significant representation of anti-inflammatory monoclonal antibody therapies across molecular subtypes. The anti-correlated signatures in C1 and C2 suggest that monoclonal antibody therapies may not be effective for patients in both subtypes, which may explain why prior clinical trials have been unsuccessful. Altogether, our detailed molecular subtyping and analysis identify potential drug targets within each molecular subtype, with implications for future precision medicine for sepsis.

Understanding the spatiotemporal dynamics of disease progression in relation to transcriptomic profiles provides key insights into complex conditions such as Alzheimer disease. To enable such investigations, STARmap PLUS technology offers joint profiling of high-resolution spatial transcriptomics and protein detection within the same tissue section. Motivated by data from Zeng et al. (2023), we develop a novel kernel-weighted regression framework that models plaque size as a collective effect of the spatial transcriptomics of neighboring cells, automatically integrating across cell types and tissue samples from different disease states. To further strengthen interpretability and efficiency, we incorporate a sparse low-rank factorization that enables gene selection while borrowing strength across genes, cell types, and time points. The proposed approach is implemented in a fully automated manner with data-driven specification of key model components. Through simulation studies, we demonstrate the robustness of the proposed method and its superiority across a range of specification scenarios. Applied to Alzheimer disease data, the proposed framework uncovers biologically meaningful associations, highlighting its potential for advancing the understanding of disease mechanisms.

Background: Obesity is a risk factor for breast cancer mortality in postmenopausal women. However, it remains unclear which specific components of adipose tissue and skeletal muscle are associated with risk. This study assessed the associations between MRI-assessed adiposity, skeletal mass, and breast cancer risk in a population-based cohort. Methods: We analyzed data from 15,669 postmenopausal women in the UK Biobank who underwent MRI for body composition assessment. Age- and multivariable-adjusted hazard ratios (HRs) with 95% confidence intervals (CIs) were estimated using Cox proportional-hazards regression to evaluate the associations between body composition and breast cancer risk, adjusting for relevant confounders. Sensitivity analyses were conducted by excluding breast cancer cases diagnosed within 2 years of the MRI scan. To explore nonlinear relationships, we applied restricted cubic splines with three knots to model associations between visceral adipose tissue (VAT), muscle-fat infiltration (MFI), and breast cancer risk. Results: The mean age of participants was 58.6 years (SD = 5.2; range = 40-69). Higher VAT was significantly associated with increased breast cancer risk (3rd vs. 1st tertile aHR = 1.24, 95% CI: 1.10-1.45). Elevated MFI was also linked with greater risk (3rd vs. 1st tertile aHR = 1.53, 95% CI: 1.25-1.87). These associations persisted after excluding early cancer cases. We observed a J-shaped relationship between VAT, MFI, and breast cancer risk. Conclusions: Higher levels of VAT and MFI are associated with elevated breast cancer risk in postmenopausal women, suggesting that imaging-derived body composition measures may enhance risk prediction and inform prevention strategies.

Background: Cholesterol metabolism is dysregulated in sepsis contributing to patient heterogeneity. Subphenotypes displaying lower lipoprotein levels and higher mortality (previously subphenotyped hypolipoprotein phenotype [HYPO]) or higher lipoprotein levels and lower mortality (previously subphenotyped normolipoprotein phenotype [NORMO]) were described. We developed a simplified clinical algorithm for bedside subphenotype recognition. Methods: We analyzed data from four prospective studies (internal dataset), focusing on HYPO and NORMO subphenotypes. A 1,000-tree random forest classifier and logistic regression models were built, using clinical features to predict subphenotypes. Performance was evaluated by comparing predictions to actual subphenotypes derived from a machine learning model. The model was applied to an external dataset of 281 patients from three French studies. Results: The internal cohort consisted of 386 patients (median age, 63 years; 46% female). Four clinical features (hepatic SOFA, cardiovascular SOFA, low [low-density lipoprotein cholesterol {LDL-C}] and high-density lipoprotein cholesterol [high-density lipoprotein cholesterol {HDL-C}]) predicted HYPO versus NORMO subphenotypes with an area under the receiver operating characteristic curve of 0.86, a sensitivity of 0.771, and a specificity of 0.779. In the internal dataset, 28-day mortality for HYPO versus NORMO patients was 26% versus 15%, and in the external cohort, 30% versus 10%. HYPO internal versus external dataset LDL-C levels were similar (P = 0.99), but HDL-C (P = 0.02) levels were different. Median NORMO internal versus external dataset LDL-C (P = 0.99) and HDL-C (P = 0.12) levels were similar. HYPO patients had lower LDL-C, HDL-C and total cholesterol than NORMO patients in both internal and external datasets. Conclusions: Our simplified clinical data algorithm may allow for bedside recognition of septic patients displaying lipid dysregulation subphenotypes. External validation is needed to verify these results.

Key PointsThe characteristics and roles of circulating fungal DNA signatures (mycobiota) in patients undergoing hemodialysis remain unknown.We found that higher fungal diversity and presence of specific fungal genera in the blood associated with higher cardiovascular mortality.alpha Circulating mycobiota signatures may serve as novel prognostic biomarkers for premature cardiovascular mortality in patients undergoing hemodialysis.BackgroundAlterations of the circulating microbiota have recently been implicated in the pathogenesis of cardiometabolic disease. However, the evidence is based primarily on bacterial DNA signatures, whereas the characteristics and roles of circulating fungal DNA signatures (mycobiota) remain unknown.MethodsIn a nationwide prospective cohort of 960 patients undergoing hemodialysis, we characterized circulating cell-free mycobiota signatures in baseline serum samples using internal transcribed spacer ribosomal DNA (rDNA) sequencing and examined their associations with all-cause and cardiovascular mortality using Cox models with adjustment for potential confounders. The added predictive ability of circulating mycobiota signatures over known risk factors for premature mortality and the mediation effect of inflammation on their association with mortality were also examined.ResultsIn this cohort, the mean age of patients was 60 +/- 13 years, 53% of patients were male, 57% had diabetes mellitus, and the median (interquartile interval) hemodialysis vintage was 3.1 (1.5-5.8) years. After stringent quality controls, internal transcribed spacer rDNA was detected in 80% of these patients. Taxonomic analysis of the detected rDNA demonstrated a total of 397 fungal taxa, including seven phyla, 149 families, and 241 genera. During a median (interquartile interval) follow-up of 2.2 (1.7-2.4) years, 205 and 75 patients experienced all-cause and cardiovascular death, respectively. Although circulating mycobiota signatures were not associated with all-cause mortality, higher alpha diversity (adjusted hazard ratio [95% confidence interval], 1.64 [1.14 to 2.39] per one unit higher) and the presence of specific genera (3.79 [2.20 to 6.51], 2.72 [1.44 to 5.12], and 2.21 [1.28 to 3.81] for Wallemia, Cladosporium, and Fusarium, respectively) were significantly associated with higher cardiovascular mortality, without a significant mediation effect of inflammation. Adding these genera to models with known risk factors improved cardiovascular mortality prediction.ConclusionsCirculating mycobiota signatures were associated with cardiovascular mortality in patients undergoing hemodialysis.

Proper joining techniques of NiTinol, a widely used functional advanced material in many fields, would provide increased design flexibility among design engineers in terms of smart design for multifunctional systems. Laser welding is the widely utilized welding technique of NiTinol which can retain the superior characteristics of the material after welding. The effect of process parameters on the microstructure, mechanical properties, bead geometry, and new phase formation in butt welding configuration using fiber laser for a 2-mm thick NiTinol sheet was ascertained by this study. The scan speed, laser power, duty factor, frequency, and focal positions were used as input process variables, while microhardness and bead area were considered as output variables. Regression analysis was executed so as to create the input–output interactions. The optimization method was used with the goal of obtaining the smallest bead that met the condition of the bead having the least change in microhardness from the base material. The problem was devised as a constrained one and resolved using four metaheuristic techniques, such as Particle swarm optimization, Genetic algorithm, Jaya algorithm, and Bonobo Optimizer. The outcomes of these new procedures were also assessed using a traditional method called desirability function analysis. The results estimated utilizing all the different optimization techniques and the investigative data were established to be in excellent agreement.

This hypothesis-generating study aims to examine the extent to which computed tomography-assessed body composition phenotypes are associated with immune and phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT) signaling pathways in breast tumors. A total of 52 patients with newly diagnosed breast cancer were classified into four body composition types: adequate (lowest two tertiles of total adipose tissue [TAT]) and highest two tertiles of total skeletal muscle [TSM] areas); high adiposity (highest tertile of TAT and highest two tertiles of TSM); low muscle (lowest tertile of TSM and lowest two tertiles of TAT); and high adiposity with low muscle (highest tertile of TAT and lowest tertile of TSM). Immune and PI3K/AKT pathway proteins were profiled in tumor epithelium and the leukocyte-enriched stromal microenvironment using GeoMx (NanoString). Linear mixed models were used to compare log2-transformed protein levels. Compared with the normal type, the low muscle type was associated with higher expression of INPP4B (log2-fold change = 1.14, p = 0.0003, false discovery rate = 0.028). Other significant associations included low muscle type with increased CTLA4 and decreased pan-AKT expression in tumor epithelium, and high adiposity with increased CD3, CD8, CD20, and CD45RO expression in stroma (p < 0.05; false discovery rate > 0.2). With confirmation, body composition can be associated with signaling pathways in distinct components of breast tumors, highlighting the potential utility of body composition in informing tumor biology and therapy efficacies.

AbstractThe purpose of this study was to investigate changes in the lipidome of patients with sepsis to identify signaling lipids associated with poor outcomes that could be linked to future therapies. Adult patients with sepsis were enrolled within 24h of sepsis recognition. Patients meeting Sepsis‐3 criteria were enrolled from the emergency department or intensive care unit and blood samples were obtained. Clinical data were collected and outcomes of rapid recovery, chronic critical illness (CCI), or early death were adjudicated by clinicians. Lipidomic analysis was performed on two platforms, the Sciex™ 5500 device to perform a lipidomic screen of 1450 lipid species and a targeted signaling lipid panel using liquid‐chromatography tandem mass spectrometry. For the lipidomic screen, there were 274 patients with sepsis: 192 with rapid recovery, 47 with CCI, and 35 with early deaths. CCI and early death patients were grouped together for analysis. Fatty acid (FA) 12:0 was decreased in CCI/early death, whereas FA 17:0 and 20:1 were elevated in CCI/early death, compared to rapid recovery patients. For the signaling lipid panel analysis, there were 262 patients with sepsis: 189 with rapid recovery, 45 with CCI, and 28 with early death. Pro‐inflammatory signaling lipids from ω‐6 poly‐unsaturated fatty acids (PUFAs), including 15‐hydroxyeicosatetraenoic (HETE), 12‐HETE, and 11‐HETE (oxidation products of arachidonic acid [AA]) were elevated in CCI/early death patients compared to rapid recovery. The pro‐resolving lipid mediator from ω‐3 PUFAs, 14(S)‐hydroxy docosahexaenoic acid (14S‐HDHA), was also elevated in CCI/early death compared to rapid recovery. Signaling lipids of the AA pathway were elevated in poor‐outcome patients with sepsis and may serve as targets for future therapies.

Lipids play a critical role in defense against sepsis. We sought to investigate gene expression and lipidomic patterns of lipid dysregulation in sepsis. Data from four adult sepsis studies were analyzed and findings were investigated in two external datasets. Previously characterized lipid dysregulation subphenotypes of hypolipoprotein (HYPO; low lipoproteins, increased mortality) and normolipoprotein (NORMO; higher lipoproteins, lower mortality) were studied. Leukocytes collected within 24 h of sepsis underwent RNA sequencing (RNAseq) and shotgun plasma lipidomics was performed. Of 288 included patients, 43

Titanium foam finds its application in different fields like battery electrodes, heat exchangers, biological prosthetics, and lightweight structures for aeronautical parts. However, the inherent property of the foam to fail during mechanical loading conditions poses a challenge in providing final shape to these materials for practical applications. The present work investigates forming of Titanium foam (Ti Foam) using inline process monitoring for achieving controlled foam deformation while maintaining its quality without failure. Infrared (IR) pyrometer and laser-based displacement sensor were used to monitor the foam deformation behavior and thermal signature. The monitoring data were used to understand the process mechanism of laser forming. The bending mechanism within the foam was analysed with respect to solid sheet bending process. The correlation of the achieved bending angles, was attempted with the process parameters for surface, microstructural, elemental, and phase transformation. A critical analysis of Ti foam behavior, like surface melting, in situ oxidation, phase transformation, and formation of nano-structures on foam surface was addressed based on process parameter combinations. Micro-computed tomography (mu CT) was carried out to investigate the pore deformation mechanism. Metallurgical characterisations for foam surface and phase analysis were carried out using X-ray photoelectron spectroscopy (XPS) and tunneling electron microscopy (TEM) to determine the chemical and crystallographic states of the laser-formed surface.

The inference of cell-cell communication is important, as it unveils the intricate cellular behaviors at the molecular level, providing crucial insights essential for understanding complex biological processes and informing targeted interventions in various pathological contexts. Here, we present TWCOM, an R package that implements a Tweedie distribution-based model for accurate cell-cell communication inference. Operating under a generalized additive model framework, TWCOM adeptly handles both single-cell resolution and spot-based spatially resolved transcriptomics data, providing a versatile tool for robust biological sample analysis. Availability and implementation: The R package TWCOM is available at https://github.com/dongyuanwu/TWCOM. Comprehensive documentation is included with the package.

IntroductionWith the advancement of high-throughput studies, an increasing wealth of high-dimensional multi-omics data is being collected from the same patient cohort. However, leveraging this multi-omics data to predict survival outcomes poses a significant challenge due to its complex structure.MethodsIn this article, we present a novel approach, the Adaptive Sparse Multi-Block Partial Least Squares (asmbPLS) Regression model, which introduces a dynamic assignment of penalty factors to distinct blocks within various PLS components, facilitating effective feature selection and prediction.ResultsWe compared the proposed method with several state-of-the-art algorithms encompassing prediction performance, feature selection and computation efficiency. We conducted comprehensive evaluations using both simulated data with various scenarios and a real dataset from the melanoma patients to validate the effectiveness and efficiency of the asmbPLS method. Additionally, we applied the lung squamous cell carcinoma (LUSC) dataset from The Cancer Genome Atlas (TCGA) to further assess the feature selection capability of asmbPLS.DiscussionThe inherent nature of asmbPLS imparts it with higher sensitivity in feature selection compared to other methods. Furthermore, an R package called asmbPLS implementing this method is made publicly available.

Physical activity has been shown to affect the mammalian target of rapamycin (mTOR) signaling pathway and consequently breast carcinogenesis. Given that Black women in the USA are less physically active, it is not well understood whether there are gene–environment interactions between mTOR pathway genes and physical activity in relation to breast cancer risk in Black women. The study included 1398 Black women (567 incident breast cancer cases and 831 controls) from the Women’s Circle of Health Study (WCHS). We examined interactions between 43 candidate single-nucleotide polymorphisms (SNPs) in 20 mTOR pathway genes with levels of vigorous physical activity in relation to breast cancer risk overall and by ER-defined subtypes using Wald test with 2-way interaction term and multivariable logistic regression. AKT1 rs10138227 (C > T) and AKT1 rs1130214 (C > A) were only associated with a decreased risk of ER + breast cancer among women with vigorous physical activity (odds ratio [OR] = 0.15, 95

Bead-on-plate laser welding of 3 mm thick NiTinol sheet was tried with the help of 2.3 kW Yb:Fiber laser. It was found that the laser power was insufficient to get the full penetration with narrow weld-bead by varying the scan speed when shielding gas was provided from the side of the bead and the laser head was kept perpendicular to the plate. It was observed that when gas was supplied from the top of the plate and moving with the laser head, fully penetrated bead was obtained at much lesser value of heat input (P/v) because of the reduction of length of plasma plume. It was also detected that with increase in rotation of laser head angle the depth of penetration and laser bead width decreases because of the increase in laser spot area, reduction of laser intensity and increase in length of plasma plume. The increase in length of plasma plume increased the plasma shielding effect and reduced the absorptivity of the laser in the NiTinol sheet. According to the study, the maximum depth of penetration at a constant laser heat input was obtained for minimum value of plasma plume length and maximum value of laser intensity.

The friction stir welding (FSW) technique is an emerging solid-state welding approach that can weld both similar and dissimilar materials. Initially, FSW was only used for welding softer materials such as aluminum and its alloys. But recently, it is eventually being used to weld high melting-point alloys such as magnesium, copper, titanium, nickel, steel, stainless steel, and polymers, leading to premature tool wear and failure. It is known to find applications in several sectors, including shipbuilding, aerospace, defense, railway, and electronics. This book chapter briefly discusses the effect of varying different process parameters such as tool traverse/welding speed (TS/WS), tool rotational speed (TRS), plunge depth, and tool tilt angle on the output responses such as temperature distribution, heat generation, and axial force variation, etc. These variations in the input responses ultimately change the welded joint's microstructure, hardness, and weld strength, applications in several industrial sectors, and the ability to weld various similar and dissimilar materials. A rigorous literature survey on tool design, heat transfer, material flow behavior, thermo-mechanical effect due to the rotational heat generation effect of pin and shoulder, understanding defect formation due to varying process parameters, etc., was done. Repetitive advancements of the FSW process led to the development of its successors, such as underwater friction stir welding (UFSW), friction stir additive manufacturing (FSAM), friction stir processing (FSP), friction stir spot welding (FSSW), and micro FSW (µFSW).

Introduction: The development of multimodal single-cell omics methods has enabled the collection of data across different omics modalities from the same set of single cells. Each omics modality provides unique information about cell type and function, so the ability to integrate data from different modalities can provide deeper insights into cellular functions. Often, single-cell omics data can prove challenging to model because of high dimensionality, sparsity, and technical noise. Methods: We propose a novel multimodal data analysis method called joint graph-regularized Single-Cell Kullback-Leibler Sparse Non-negative Matrix Factorization (jrSiCKLSNMF, pronounced "junior sickles NMF") that extracts latent factors shared across omics modalities within the same set of single cells. Results: We compare our clustering algorithm to several existing methods on four sets of data simulated from third party software. We also apply our algorithm to a real set of cell line data. Discussion: We show overwhelmingly better clustering performance than several existing methods on the simulated data. On a real multimodal omics dataset, we also find our method to produce scientifically accurate clustering results.

Laser welding of NiTinol is a promising fabrication method. An understanding of the effects of laser heat input on different surface properties and corrosion resistance behaviour is needed for laser-welded sea water-flooded components of NiTinol. Here the effect of laser heat input on various physical, chemical and mechanical surface properties along with the corrosion resistance behaviour of the laser welded samples was investigated. The corrosion resistance performance of the phase-separated samples was found to be inferior to the other laser-welded samples. The top surface showed a superior corrosion resistance performance because of the recirculation of the melt pool and Ti enrichment on top surface.