A hypoxia-derived gene signature to suggest cisplatin-based therapeutic responses in patients with cervical cancer

Cervical cancer remains a significant global public health concern, often exhibits cisplatin resistance in clinical settings. Hypoxia, a characteristic of cervical cancer, substantially contributes to cisplatin resistance. To evaluate the therapeutic efficacy of cisplatin in patients with cervical cancer and to identify potential effective drugs against cisplatin resistance, we established a hypoxia-inducible factor-1 (HIF-1)-related risk score (HRRS) model using clinical data from patients treated with cisplatin. Cox and LASSO regression analyses were used to stratify patient risks and prognosis. Through qRT-PCR, we validated nine potential prognostic HIF-1 genes that successfully predict cisplatin responsiveness in patients and cell lines. Subsequently, we identified fostamatinib, an FDA-approved spleen tyrosine kinase inhibitor, as a promising drug for targeting the HRRS-high group. We observed a positive correlation between the IC50 values of fostamatinib and HRRS in cervical cancer cell lines. Moreover, fostamatinib exhibited potent anticancer effects on high HRRS groups in vitro and in vivo. In summary, we developed a hypoxia-related gene signature that suggests cisplatin response prediction in cervical cancer and identified fostamatinib as a potential novel treatment approach for resistant cases.

Target repositioning using multi-layer networks and machine learning: The case of prostate cancer

The discovery of novel therapeutic targets, defined as proteins which drugs can interact with to induce therapeutic benefits, typically represent the first and most important step of drug discovery. One solution for target discovery is target repositioning, a strategy which relies on the repurposing of known targets for new diseases, leading to new treatments, less side effects and potential drug synergies. Biological networks have emerged as powerful tools for integrating heterogeneous data and facilitating the prediction of biological or therapeutic properties. Consequently, they are widely employed to predict new therapeutic targets by characterizing potential candidates, often based on their interactions within a Protein-Protein Interaction (PPI) network, and their proximity to genes associated with the disease. However, over-reliance on PPI networks and the assumption that potential targets are necessarily near known genes can introduce biases that may limit the effectiveness of these methods. This study addresses these limitations in two ways. First, by exploiting a multi-layer network which incorporates additional information such as gene regulation, metabolite interactions, metabolic pathways, and several disease signatures such as Differentially Expressed Genes, mutated genes, Copy Number Alteration, and structural variants. Second, by extracting relevant features from the network using several approaches including proximity to disease-associated genes, but also unbiased approaches such as propagation-based methods, topological metrics, and module detection algorithms. Using prostate cancer as a case study, the best features were identified and utilized to train machine learning algorithms to predict 5 novel promising therapeutic targets for prostate cancer: IGF2R, C5AR, RAB7, SETD2 and NPBWR1.

Screening biomarkers for spinal cord injury using weighted gene co-expression network analysis and machine learning

JOURNAL/nrgr/04.03/01300535-202412000-00028/figure1/v/2024-04-08T165401Z/r/image-tiff Immune changes and inflammatory responses have been identified as central events in the pathological process of spinal cord injury. They can greatly affect nerve regeneration and functional recovery. However, there is still limited understanding of the peripheral immune inflammatory response in spinal cord injury. In this study, we obtained microRNA expression profiles from the peripheral blood of patients with spinal cord injury using high-throughput sequencing. We also obtained the mRNA expression profile of spinal cord injury patients from the Gene Expression Omnibus (GEO) database (GSE151371). We identified 54 differentially expressed microRNAs and 1656 differentially expressed genes using bioinformatics approaches. Functional enrichment analysis revealed that various common immune and inflammation-related signaling pathways, such as neutrophil extracellular trap formation pathway, T cell receptor signaling pathway, and nuclear factor-κB signal pathway, were abnormally activated or inhibited in spinal cord injury patient samples. We applied an integrated strategy that combines weighted gene co-expression network analysis, LASSO logistic regression, and SVM-RFE algorithm and identified three biomarkers associated with spinal cord injury: ANO10, BST1, and ZFP36L2. We verified the expression levels and diagnostic performance of these three genes in the original training dataset and clinical samples through the receiver operating characteristic curve. Quantitative polymerase chain reaction results showed that ANO10 and BST1 mRNA levels were increased and ZFP36L2 mRNA was decreased in the peripheral blood of spinal cord injury patients. We also constructed a small RNA-mRNA interaction network using Cytoscape. Additionally, we evaluated the proportion of 22 types of immune cells in the peripheral blood of spinal cord injury patients using the CIBERSORT tool. The proportions of naïve B cells, plasma cells, monocytes, and neutrophils were increased while the proportions of memory B cells, CD8+ T cells, resting natural killer cells, resting dendritic cells, and eosinophils were markedly decreased in spinal cord injury patients increased compared with healthy subjects, and ANO10, BST1 and ZFP26L2 were closely related to the proportion of certain immune cell types. The findings from this study provide new directions for the development of treatment strategies related to immune inflammation in spinal cord injury and suggest that ANO10, BST1, and ZFP36L2 are potential biomarkers for spinal cord injury. The study was registered in the Chinese Clinical Trial Registry (registration No. ChiCTR2200066985, December 12, 2022).

Different surface treatments and adhesive monomers for zirconia-resin bonds: A systematic review and network meta-analysis

This review examined the efficacy of surface treatments and adhesive monomers for enhancing zirconia-resin bond strength. A comprehensive literature search in PubMed, Embase, Web of Science, Scopus, and the Cochrane Library yielded relevant in vitro studies. Employing pairwise and Bayesian network meta-analyses, 77 articles meeting inclusion criteria were analyzed. Gas plasma was found to be ineffective, while treatments including air abrasion, silica coating, laser, selective infiltration etching, hot etching showed varied effectiveness. Air abrasion with finer particles (25-53 µm) showed higher immediate bond strength than larger particles (110-150 µm), with no significant difference post-aging. The Rocatec silica coating system outperformed the CoJet system in both immediate and long-term bond strength. Adhesives containing 10-methacryloyloxydecyl dihydrogen phosphate (10-MDP) were superior to other acidic monomers. The application of 2-hydroxyethyl methacrylate and silane did not improve bonding performance. Notably, 91.2 % of bonds weakened after aging, but this effect was less pronounced with air abrasion or silica coating. The findings highlight the effectiveness of air abrasion, silica coating, selective infiltration etching, hot etching, and laser treatment in improving bond strength, with 10-MDP in bonding agents enhancing zirconia bonding efficacy.

Optimal variable identification for accurate detection of causal expression Quantitative Trait Loci with applications in heart-related diseases

Gene expression plays a pivotal role in various diseases, contributing significantly to their mechanisms. Most GWAS risk loci are in non-coding regions, potentially affecting disease risk by altering gene expression in specific tissues. This expression is notably tissue-specific, with genetic variants substantially influencing it. However, accurately detecting the expression Quantitative Trait Loci (eQTL) is challenging due to limited heritability in gene expression, extensive linkage disequilibrium (LD), and multiple causal variants. The single variant association approach in eQTL analysis is limited by its susceptibility to capture the combined effects of multiple variants, and a bias towards common variants, underscoring the need for a more robust method to accurately identify causal eQTL variants. To address this, we developed an algorithm, CausalEQTL, which integrates L 0 +L 1 penalized regression with an ensemble approach to localize eQTL, thereby enhancing prediction performance precisely. Our results demonstrate that CausalEQTL outperforms traditional models, including LASSO, Elastic Net, Ridge, in terms of power and overall performance. Furthermore, analysis of heart tissue data from the GTEx project revealed that eQTL sites identified by our algorithm provide deeper insights into heart-related tissue eQTL detection. This advancement in eQTL mapping promises to improve our understanding of the genetic basis of tissue-specific gene expression and its implications in disease. The source code and identified causal eQTLs for CausalEQTL are available on GitHub: https://github.com/zhc-moushang/CausalEQTL.

Multi-layered knowledge graph neural network reveals pathway-level agreement of three breast cancer multi-gene assays

Multi-gene assays have been widely used to predict the recurrence risk for hormone receptor (HR)-positive breast cancer patients. However, these assays lack explanatory power regarding the underlying mechanisms of the recurrence risk. To address this limitation, we proposed a novel multi-layered knowledge graph neural network for the multi-gene assays. Our model elucidated the regulatory pathways of assay genes and utilized an attention-based graph neural network to predict recurrence risk while interpreting transcriptional subpathways relevant to risk prediction. Evaluation on three multi-gene assays-Oncotype DX, Prosigna, and EndoPredict-using SCAN-B dataset demonstrated the efficacy of our method. Through interpretation of attention weights, we found that all three assays are mainly regulated by signaling pathways driving cancer proliferation especially RTK-ERK-ETS-mediated cell proliferation for breast cancer recurrence. In addition, our analysis highlighted that the important regulatory subpathways remain consistent across different knowledgebases used for constructing the multi-level knowledge graph. Furthermore, through attention analysis, we demonstrated the biological significance and clinical relevance of these subpathways in predicting patient outcomes. The source code is available at http://biohealth.snu.ac.kr/software/ExplainableMLKGNN.

Gene regulatory network analysis identifies MYL1, MDH2, GLS, and TRIM28 as the principal proteins in the response of mesenchymal stem cells to Mg2+ ions

Magnesium (Mg)-based implants have emerged as a promising alternative for orthopedic applications, owing to their bioactive properties and biodegradability. As the implants degrade, Mg2+ ions are released, influencing all surrounding cell types, especially mesenchymal stem cells (MSCs). MSCs are vital for bone tissue regeneration, therefore, it is essential to understand their molecular response to Mg2+ ions in order to maximize the potential of Mg-based biomaterials. In this study, we conducted a gene regulatory network (GRN) analysis to examine the molecular responses of MSCs to Mg2+ ions. We used time-series proteomics data collected at 11 time points across a 21-day period for the GRN construction. We studied the impact of Mg2+ ions on the resulting networks and identified the key proteins and protein interactions affected by the application of Mg2+ ions. Our analysis highlights MYL1, MDH2, GLS, and TRIM28 as the primary targets of Mg2+ ions in the response of MSCs during 1-21 days phase. Our results also identify MDH2-MYL1, MDH2-RPS26, TRIM28-AK1, TRIM28-SOD2, and GLS-AK1 as the critical protein relationships affected by Mg2+ ions. By offering a comprehensive understanding of the regulatory role of Mg2+ ions on MSCs, our study contributes valuable insights into the molecular response of MSCs to Mg-based materials, thereby facilitating the development of innovative therapeutic strategies for orthopedic applications.

MIMOSA: a resource consisting of improved methylome prediction models increases power to identify DNA methylation-phenotype associations

Although DNA methylation (DNAm) has been implicated in the pathogenesis of numerous complex diseases, from cancer to cardiovascular disease to autoimmune disease, the exact methylation sites that play key roles in these processes remain elusive. One strategy to identify putative causal CpG sites and enhance disease etiology understanding is to conduct methylome-wide association studies (MWASs), in which predicted DNA methylation that is associated with complex diseases can be identified. However, current MWAS models are primarily trained using the data from single studies, thereby limiting the methylation prediction accuracy and the power of subsequent association studies. Here, we introduce a new resource, MWAS Imputing Methylome Obliging Summary-level mQTLs and Associated LD matrices (MIMOSA), a set of models that substantially improve the prediction accuracy of DNA methylation and subsequent MWAS power through the use of a large summary-level mQTL dataset provided by the Genetics of DNA Methylation Consortium (GoDMC). Through the analyses of GWAS (genome-wide association study) summary statistics for 28 complex traits and diseases, we demonstrate that MIMOSA considerably increases the accuracy of DNA methylation prediction in whole blood, crafts fruitful prediction models for low heritability CpG sites, and determines markedly more CpG site-phenotype associations than preceding methods. Finally, we use MIMOSA to conduct a case study on high cholesterol, pinpointing 146 putatively causal CpG sites.

VIBES: A consensus subtyping of the vaginal microbiota reveals novel classification criteria

This study aimed to develop a robust classification scheme for stratifying patients based on vaginal microbiome. By employing consensus clustering analysis, we identified four distinct clusters using a cohort that includes individuals diagnosed with Bacterial Vaginosis (BV) as well as control participants, each characterized by unique patterns of microbiome species abundances. Notably, the consistent distribution of these clusters was observed across multiple external cohorts, such as SRA022855, SRA051298, PRJNA208535, PRJNA797778, and PRJNA302078 obtained from public repositories, demonstrating the generalizability of our findings. We further trained an elastic net model to predict these clusters, and its performance was evaluated in various external cohorts. Moreover, we developed VIBES, a user-friendly R package that encapsulates the model for convenient implementation and enables easy predictions on new data. Remarkably, we explored the applicability of this new classification scheme in providing valuable insights into disease progression, treatment response, and potential clinical outcomes in BV patients. Specifically, we demonstrated that the combined output of VIBES and VALENCIA scores could effectively predict the response to metronidazole antibiotic treatment in BV patients. Therefore, this study's outcomes contribute to our understanding of BV heterogeneity and lay the groundwork for personalized approaches to BV management and treatment selection.

Elevated expression of ECT2 as a diagnostic marker and prognostic indicator in endometrial cancer

OBJECTIVES

The study aims to investigate genes associated with endometrial cancer (EC) progression to identify new biomarkers for early detection.

METHODS

Differentially expressed genes (DEGs), Series test of cluster (STC) and protein-protein interaction analyses identified hub genes in EC. Clinical samples were utilized to examine the expression pattern of ECT2, assess its prognostic value, and evaluate its diagnostic potential.

RESULTS

Upregulated DEGs were significantly enriched in cancer-related processes and pathways. Validations across databases identified ASPM, ATAD2, BUB1B, ECT2, KIF14, NUF2, NCAPG, and SPAG5 as potential hub genes, with ECT2 exhibiting the highest diagnostic efficacy. The expression levels of ECT2 varied significantly across different clinical stages, pathological grades, and metastasis statuses in UCEC. Furthermore, ECT2 mRNA was upregulated in the p53abn group, indicating a poorer prognosis, and downregulated in the MMRd and NSMP groups, suggesting a moderate prognosis. In clinical samples, ECT2 expression increased from normal endometria and endometrial hyperplasia without atypia (EH) to atypical endometrial hyperplasia (AH) and EC, effectively distinguishing between benign and malignant endometria. High ECT2 expression was associated with an unfavourable prognosis.

CONCLUSIONS

ECT2 expression significantly rises in AH and EC, showing high accuracy in distinguishing between benign and malignant endometria. ECT2 emerges as a promising biomarker for diagnosing endometrial neoplasia and as a prognostic indicator in EC.

Plasma metals, genetic risk, and rapid kidney function decline among type 2 diabetes

BACKGROUND

Rapid kidney function decline (RKFD) is a main clinical feature of early chronic kidney disease (CKD) in type 2 diabetes (T2D). Environmental and genetic factors influencing RKFD remain inadequately elucidated.

OBJECTIVES

This study aimed to examine the associations of metals with RKFD among T2D and to further investigate the effect of metal mixtures on RKFD with the modifying effect of genetic susceptibility.

METHODS

This study included 2209 people with T2D (1942 had genotyping data) free of CKD at baseline from the Dongfeng-Tongji cohort. We used inductively coupled plasma-mass spectrometry (ICP-MS) to measure 23 metals in baseline plasma. Using elastic net (ENET), multivariate logistic regression, and Bayesian kernel machine regression (BKMR) model, we examined independent associations of multiple metals with RKFD. We calculated the environmental risk score (ERS) to assess the effects of metal mixtures on RKFD and the genetic risk score (GRS) to assess genetic susceptibility. RKFD was defined as estimated glomerular filtration rate (eGFR) loss > 3 mL/min/1.73 m2/year.

RESULTS

During a median of 9.8 years follow-up, 262 participants developed RKFD. Aluminum, vanadium, zinc, selenium, rubidium, tin, barium, and tungsten were screened from ENET. In multivariate logistic models, vanadium, selenium, and tungsten were negatively associated with RKFD, while zinc, tin, and rubidium were positively associated. The BKMR showed a nonlinear association of vanadium and rubidium with RKFD and interactions between metals (barium‑vanadium, barium‑rubidium). The ERS was positive associated with RKFD (per SD increase in ERS, OR = 1.94, 95% CI: 1.66, 2.27). No significant interaction between ERS and GRS was observed on RKFD, however, participants in the highest ERS and GRS group had the highest RKFD risk.

CONCLUSION

Vanadium and rubidium were associated with RKFD in T2D. Metal mixtures was associated with an increased risk of RKFD in T2D, particularly in those at high genetic risk.

Associations between exposure to pesticides mixture and semen quality among the non-occupationally exposed males: Four statistical models

Most epidemiological studies on the associations between pesticides exposure and semen quality have been based on a single pesticide, with inconsistent major results. In contrast, there was limited human evidence on the potential effect of pesticides mixture on semen quality. Our study aimed to investigate the relationship of pesticide profiles with semen quality parameters among 299 non-occupationally exposed males aged 25-50 without any clinical abnormalities. Serum concentrations of 21 pesticides were quantified by gas chromatography-tandem mass spectrometry (GC-MS/MS). Semen quality parameters were abstracted from medical records. Generalized linear regression models (GLMs) and three mixture approaches, including weighted quantile sum regression (WQS), elastic net regression (ENR) and Bayesian kernel machine regression (BKMR), were applied to explore the single and mixed effects of pesticide exposure on semen quality. In GLMs, as the serum levels of Bendiocarb, β-BHC, Clomazone, Dicrotophos, Dimethenamid, Paclobutrazole, Pentachloroaniline and Pyrimethanil increased, the straight-line velocity (VSL), linearity (LIN) and straightness (STR) decreased. This negative association also occurred between the concentration of β-BHC, Pentachloroaniline, Pyrimethanil and progressive motility, total motility. In the WQS models, pesticides mixture was negatively associated with total motility and several sperm motility parameters (β: -3.07∼-1.02 per decile, FDR-P<0.05). After screening the important pesticides derived from the mixture by ENR model, the BKMR models showed that the decreased qualities for VSL, LIN, and STR were also observed when pesticide mixtures were at ≥ 70th percentiles. Clomazone, Dimethenamid, and Pyrimethanil (Posterior inclusion probability, PIP: 0.2850-0.8900) were identified as relatively important contributors. The study provides evidence that exposure to single or mixed pesticide was associated with impaired semen quality.

Neighbourhood benthic configuration reveals hidden co-occurrence social diversity

Ecological interactions among benthic communities are crucial for shaping marine ecosystems. Understanding these interactions is essential for predicting how ecosystems will respond to environmental changes, invasive species, and conservation management. However, determining the prevalence of species interactions at the community scale is challenging. To overcome this challenge, we employ tools from social network analysis, specifically exponential random graph modelling (ERGM). Our approach explores the relationships among animal and plant organisms within their neighbourhoods. Inspired by companion planting in agriculture, we use spatiotemporal co-occurrence as a measure of mixed species interaction. In other words, the variety of community interactions based on co-occurrence defines what we call 'co-occurrence social diversity'. Our objective is to use ERGM to quantify the proportion of interactions at both the simple paired level and the more complex triangle level, enabling us to measure and compare co-occurrence social diversity. Applying our approach to the Spanish coastal zone across eight sites, five depths, and sunlit/shaded aspects, we discover that 80% of sessile communities, consisting of over a hundred species, exhibit co-occurrence social diversity, with 5% of species consistently forming associations with other species. These organism-level interactions probably have a significant impact on the overall character of the site. This article is part of the theme issue 'Connected interactions: enriching food web research by spatial and social interactions'.

Instructional Influencers: Teaching Professors as Potential Departmental Change Agents in Diversity, Equity, and Inclusion

At many research-intensive universities in North America, there is a disproportionate loss of minoritized undergraduate students from Science, Technology, Engineering, and Mathematics (STEM) majors. Efforts to confront this diversity, equity, and inclusion (DEI) challenge, such as faculty adoption of evidenced-based instructional approaches that promote student success, have been slow. Instructional and pedagogical change efforts at the academic department level have been demonstrated to be effective at enacting reform. One potential strategy is to embed change agent individuals within STEM departments that can drive change efforts. This study seeks to assess whether tenure-track, teaching-focused faculty housed in STEM departments are perceived as influential on the instructional and pedagogical domains of their colleagues. To answer this, individuals across five STEM departments at large, research-intensive campuses identified faculty who were influential upon six domains of their instruction and pedagogy. Social network analysis of individuals in these departments revealed heterogeneity across the instructional domains. Some, like the teaching strategies network, are highly connected and involve the majority of the department; while others, like the DEI influence network, comprise a significantly smaller population of faculty. Importantly, we demonstrate that tenure-track, teaching-focused faculty are influential across all domains of instruction, but are disproportionately so in the sparsely populated DEI influence networks.

Unraveling the genetic and molecular landscape of sepsis and acute kidney injury: A comprehensive GWAS and machine learning approach

OBJECTIVES

This study aimed to explore the underlying mechanisms of sepsis and acute kidney injury (AKI), including sepsis-associated AKI (SA-AKI), a frequent complication in critically ill sepsis patients.

METHODS

GWAS data was analyzed for genetic association between AKI and sepsis. Then, we systematically applied three distinct machine learning algorithms (LASSO, SVM-RFE, RF) to rigorously identify and validate signature genes of SA-AKI, assessing their diagnostic and prognostic value through ROC curves and survival analysis. The study also examined the functional and immunological aspects of these genes, potential drug targets, and ceRNA networks. A mouse model of sepsis was created to test the reliability of these signature genes.

RESULTS

LDSC confirmed a positive genetic correlation between AKI and sepsis, although no significant shared loci were found. Bidirectional MR analysis indicated mutual increased risks of AKI and sepsis. Then, 311 key genes common to sepsis and AKI were identified, with 42 significantly linked to sepsis prognosis. Six genes, selected through LASSO, SVM-RFE, and RF algorithms, showed excellent predictive performance for sepsis, AKI, and SA-AKI. The models demonstrated near-perfect AUCs in both training and testing datasets, and a perfect AUC in a sepsis mouse model. Significant differences in immune cells, immune-related pathways, HLA, and checkpoint genes were found between high- and low-risk groups. The study identified 62 potential drug treatments for sepsis and AKI and constructed a ceRNA network.

CONCLUSIONS

The identified signature genes hold potential clinical applications, including prognostic evaluation and targeted therapeutic strategies for sepsis and AKI. However, further research is needed to confirm these findings.

Three-gene signature revealing the dynamics of lymphocyte infiltration in subchondral bone during osteoarthritis progression

Osteoarthritis (OA), a degenerative joint disorder, has an unclear immune infiltration mechanism in subchondral bone (SCB). Thus, this study aims to discern immune infiltration variations in SCB between early- and late-stages of OA and identify pertinent biomarkers. Utilizing the GSE515188 bulk-seq profile from the Gene Expression Omnibus database, we performed single-sample gene-set enrichment analysis alongside weighted gene co-expression network analysis to identify key cells and immune-related genes (IRGs) involved in SCB at both stages. At the meanwhile, differentially expressed genes (DEGs) were identified in the same dataset and intersected with IRGs to find IR-DEGs. Protein-protein interaction network and enrichment analyses and further gene filtering using LASSO regression led to the discovery of potential biomarkers, which were then validated by ROC curve analysis, single-cell RNA sequencing, qRT-PCR, western blot and immunofluorescence. ScRNA-seq analysis using GSE196678, qRT-PCR, western blot and immunofluorescence results confirmed the upregulation of their expression levels in early-stage OA SCB samples. Our comprehensive analysis revealed lymphocytes infiltration as a major feature in early OA SCB. A total of 13 IR-DEGs were identified, showing significant enrichment in T- or B-cell activation pathways. Three of them (CD247, POU2AF1, and TNFRSF13B) were selected via the LASSO regression analysis, and results from the ROC curve analyses indicated the diagnostic efficacy of these 3 genes as biomarkers. These findings may aid in investigating the mechanisms of SCB immune infiltration in OA, stratifying OA progression, and identifying relevant therapeutic targets.

Cellular senescence-Related genes define the immune microenvironment and molecular characteristics in severe asthma patients

Recent studies have shown that cellular senescence is involved in the pathogenesis of severe asthma (SA). The objective of this study was to investigate the role of cellular senescence-related genes (CSGs) in the pathogenesis of SA. Here, 54 differentially expressed CSGs were identified in SA patients compared to healthy control individuals. Among the 54 differentially expressed CSGs, 3 CSGs (ETS2, ETS1 and AURKA) were screened using the LASSO regression analysis and logistic regression analysis to establish the CSG-based prediction model to predict severe asthma. Moreover, we found that the protein expression levels of ETS2, ETS1 and AURKA were increased in the severe asthma mouse model. Then, two distinct senescence subtypes of SA with distinct immune microenvironments and molecular biological characteristics were identified. Cluster 1 was characterized by increased infiltration of immature dendritic cells, regulatory T cells, and other cells. Cluster 2 was characterized by increased infiltration levels of eosinophils, neutrophils, and other cells. The molecular biological characteristics of Cluster 1 included aerobic respiration and oxidative phosphorylation, whereas the molecular biological characteristics of Cluster 2 included activation of the immune response and immune receptor activity. Then, we established an Random Forest model to predict the senescence subtypes of SA to guide treatment. Finally, potential drugs were searched for each senescence subgroup of SA patients via the Connectivity Map database. A peroxisome proliferator-activated receptor agonist may be a potential therapeutic drug for patients in Cluster 1, whereas a tachykinin antagonist may be a potential therapeutic drug for patients in Cluster 2. In summary, CSGs are likely involved in the pathogenesis of SA, which may lead to new therapeutic options for SA patients.

Bayesian hierarchical profile regression for binary covariates

Dysphagia, a common result of other medical conditions, is caused by malfunctions in swallowing physiology resulting in difficulty eating and drinking. The Modified Barium Swallow Study (MBSS), the most commonly used diagnostic tool for evaluating dysphagia, can be assessed using the Modified Barium Swallow Impairment Profile (MBSImP™). The MBSImP assessment tool consists of a hierarchical grouped data structure with multiple domains, a set of components within each domain which characterize specific swallowing physiologies, and a set of tasks scored on a discrete scale within each component. We lack sophisticated approaches to extract patterns of physiologic swallowing impairment from the MBSImP task scores within a component while still recognizing the nested structure of components within a domain. We propose a Bayesian hierarchical profile regression model, which uses a Bayesian profile regression model in conjunction with a hierarchical Dirichlet process mixture model to (1) cluster subjects into impairment profile patterns while respecting the hierarchical grouped data structure of the MBSImP, and (2) simultaneously determine associations between latent profile cluster membership for all components and the outcome of dysphagia severity. We apply our approach to a cohort of patients referred for an MBSS and assessed using the MBSImP. Our research results can be used to inform appropriate intervention strategies, and provide tools for clinicians to make better multidimensional management and treatment decisions for patients with dysphagia.

Bayesian transition models for ordinal longitudinal outcomes

Ordinal longitudinal outcomes are becoming common in clinical research, particularly in the context of COVID-19 clinical trials. These outcomes are information-rich and can increase the statistical efficiency of a study when analyzed in a principled manner. We present Bayesian ordinal transition models as a flexible modeling framework to analyze ordinal longitudinal outcomes. We develop the theory from first principles and provide an application using data from the Adaptive COVID-19 Treatment Trial (ACTT-1) with code examples in R. We advocate that researchers use ordinal transition models to analyze ordinal longitudinal outcomes when appropriate alongside standard methods such as time-to-event modeling.

Rational adaptation in word production: Strong conceptual ability reduces the effect of lexical impairments on verb retrieval in aphasia

Language users rely on both linguistic and conceptual processing abilities to efficiently comprehend or produce language. According to the principle of rational adaptation, the degree to which a cognitive system relies on one process vs. another can change under different conditions or disease states with the goal of optimizing behavior. In this study, we investigated rational adaptation in reliance on linguistic versus conceptual processing in aphasia, an acquired disorder of language. In individuals living with aphasia, verb-retrieval impairments are a pervasive deficit that negatively impacts communicative function. As such, we examined evidence of adaptation in verb production, using parallel measures to index impairment in two of verb naming's critical subcomponents: conceptual and linguistic processing. These component processes were evaluated using a standardized assessment battery designed to contrast non-linguistic (picture input) and linguistic (word input) tasks of conceptual action knowledge. The results indicate that non-linguistic conceptual action processing can be impaired in people with aphasia and contributes to verb-retrieval impairments. Furthermore, relatively unimpaired conceptual action processing can ameliorate the influence of linguistic processing deficits on verb-retrieval impairments. These findings are consistent with rational adaptation accounts, indicating that conceptual processing plays a key role in language function and can be leveraged in rehabilitation to improve verb retrieval in adults with chronic aphasia.

An in vitro-in silico workflow for predicting renal clearance of PFAS

Per- and poly-fluoroalkyl substances (PFAS) have a wide range of elimination half-lives (days to years) in humans, thought to be in part due to variation in proximal tubule reabsorption. While human biomonitoring studies provide important data for some PFAS, renal clearance (CLrenal) predictions for hundreds of PFAS in commerce requires experimental studies with in vitro models and physiologically-based in vitro-to-in vivo extrapolation (IVIVE). Options for studying renal proximal tubule pharmacokinetics include cultures of renal proximal tubule epithelial cells (RPTECs) and/or microphysiological systems. This study aimed to compare CLrenal predictions for PFAS using in vitro models of varying complexity (96-well plates, static 24-well Transwells and a fluidic microphysiological model, all using human telomerase reverse transcriptase-immortalized and OAT1-overexpressing RPTECs combined with in silico physiologically-based IVIVE. Three PFAS were tested: one with a long half-life (PFOS) and two with shorter half-lives (PFHxA and PFBS). PFAS were added either individually (5 μM) or as a mixture (2 μM of each substance) for 48 h. Bayesian methods were used to fit concentrations measured in media and cells to a three-compartmental model to obtain the in vitro permeability rates, which were then used as inputs for a physiologically-based IVIVE model to estimate in vivo CLrenal. Our predictions for human CLrenal of PFAS were highly concordant with available values from in vivo human studies. The relative values of CLrenal between slow- and faster-clearance PFAS were most highly concordant between predictions from 2D culture and corresponding in vivo values. However, the predictions from the more complex model (with or without flow) exhibited greater concordance with absolute CLrenal. Overall, we conclude that a combined in vitro-in silico workflow can predict absolute CLrenal values, and effectively distinguish between PFAS with slow and faster clearance, thereby allowing prioritization of PFAS with a greater potential for bioaccumulation in humans.

Deep learning-based automated detection and segmentation of bone and traumatic bone marrow lesions from MRI following an acute ACL tear

INTRODUCTION

Traumatic bone marrow lesions (BML) are frequently identified on knee MRI scans in patients following an acute full-thickness, complete ACL tear. BMLs coincide with regions of elevated localized bone loss, and studies suggest these may act as a precursor to the development of post-traumatic osteoarthritis. This study addresses the labour-intensive manual assessment of BMLs by using a 3D U-Net for automated identification and segmentation from MRI scans.

METHODS

A multi-task learning approach was used to segment both bone and BML from T2 fat-suppressed (FS) fast spin echo (FSE) MRI sequences for BML assessment. Training and testing utilized datasets from individuals with complete ACL tears, employing a five-fold cross-validation approach and pre-processing involved image intensity normalization and data augmentation. A post-processing algorithm was developed to improve segmentation and remove outliers. Training and testing datasets were acquired from different studies with similar imaging protocol to assess the model's performance robustness across different populations and acquisition conditions.

RESULTS

The 3D U-Net model exhibited effectiveness in semantic segmentation, while post-processing enhanced segmentation accuracy and precision through morphological operations. The trained model with post-processing achieved a Dice similarity coefficient (DSC) of 0.75 ± 0.08 (mean ± std) and a precision of 0.87 ± 0.07 for BML segmentation on testing data. Additionally, the trained model with post-processing achieved a DSC of 0.93 ± 0.02 and a precision of 0.92 ± 0.02 for bone segmentation on testing data. This demonstrates the approach's high accuracy for capturing true positives and effectively minimizing false positives in the identification and segmentation of bone structures.

CONCLUSION

Automated segmentation methods are a valuable tool for clinicians and researchers, streamlining the assessment of BMLs and allowing for longitudinal assessments. This study presents a model with promising clinical efficacy and provides a quantitative approach for bone-related pathology research and diagnostics.

Electrophysiological and behavioral differences of general and food-specific inhibitory control in people with different levels of intuitive eating

Intuitive eating influences health-related behaviors (including calorie and nutritional intake) that are modulated by inhibitory control, producing implications for physical, mental, and emotional health. However, little is known about the relationship between intuitive eating habits and inhibitory control. Therefore, we tested intuitive eating's influence on measures of general and food-related inhibitory control using behavioral and event-related potentials (N2 and P3 components). We included 40 healthy participants: 23 had a higher level of intuitive eating, and 17 had a lower level. They participated in food-specific go/no-go and general go/no-go tasks for which we recorded electroencephalogram data. As expected, in the food-specific go/no-go task, the P3 component amplitude in the lower intuitive eating group was significantly larger than in the higher intuitive eating group; there were no significant between-group differences in the N2 amplitudes or behavioral measures. Moreover, there were no ERP or behavioral difference between groups in the general go/no-go task. Further research is needed to understand the role of positive eating behaviors in food-specific inhibitory control.

What Do Your Neighbors Think About You? How Perceived Neighbor Attitudes Toward Latinos Influence Mental Health Among a Pregnant Latina Cohort

Latina women living in the USA experience disproportionately higher rates of psychological distress compared to their non-Latina White counterparts. Poor maternal mental health during pregnancy can contribute to intergenerational mental health disparities. Through this pathway, mothers' experiences, environments, and exposures (henceforth "exposures") during pregnancy become biologically embodied and can negatively affect the fetus and life-long developmental trajectories of her child. One of the exposures that can affect mother-offspring dyads is the neighborhood. With the goal of integrating anthropological and sociological theories to explain mental health disparities among pregnant Latina women, we explored how perceptions of neighbor attitudes may influence mental health during pregnancy. We analyzed self-reported responses from 239 pregnant Latina women in Southern California (131 foreign-born, 108 US-born) on their mental health and perceived attitudes of their neighbors using multiple linear regression models. Among foreign-born Latina women, living in neighborhoods with more favorable views of Latinos was associated with lower depression scores (pooled β =  - .70, SE = .29, p = .019) and lower pregnancy-related anxiety scores (pooled β =  - .11, SE = .05, p = .021), but greater state anxiety scores (pooled β = .09, SE = .04, p = .021). Among US-born women, there were no associations between neighbor attitudes and mental health. Overall, results suggest that social environments are correlated with mental health and that foreign-born and US-born Latinas have varied mental health experiences in the USA. Our findings highlight the importance of improving aspects of neighborhood cohesion as part of maternal-fetal care management.

Body mass index stratification enables cytokine-based prediction of ACPA status and Power-Doppler disease activity in rheumatoid arthritis

OBJECTIVE

Rheumatoid arthritis can be classified according to ACPA and RF status. ACPA status may be associated with other pathophysiological differences, e.g., the cytokines driving inflammation. Obesity influences the course of RA, likely involving leptin; the exact mechanisms are not completely understood. This study investigates BMI influence on RA cytokine profiles and the possibility of predicting ACPA status and disease activity measured by Power-Doppler sonography (PDS).

METHODS

Patients were examined using a multi-biomarker disease assay and PDS examination of wrists and MCP and PIP joints and stratified according to ACPA status and BMI, using prediction precision to determine BMI cutoff. Analysis was performed using elastic net regularization of logistic and multiple regression. We then attempted to predict ACPA status/PDS activity based on a bootstrap approach.

RESULTS

A total of 120 measurements from 95 patients were performed. ACPA status prediction peaked at BMI 26 kg/m2, with AUC 0.82. PDS activity prediction had a mean average error of < 1.6 PDS points for all groups. In obese patients, cytokine profiles appear to align in ACPA-positive and -negative patients, with leptin playing a greater role in predicting PDS activity, but with some remaining differences.

CONCLUSION

When stratified according to BMI, cytokine patterns can predict ACPA status and PDS activity in RA with a high degree of precision. This indicates that studies into the pathophysiology of RA should take BMI into account, to differentiate between disease- and obesity-associated phenomena. The underlying pathological processes of ACPA-negative and -positive RA appear different. Multi-cytokine evaluations may provide a deeper understanding of disease processes. Key Points • A multi-cytokine approach combined with ultrasonography and modern mathematical methods can contribute to a deeper understanding of the relationship between systemic and joint inflammation. • BMI influences cytokine profiles in rheumatoid arthritis and appears to "override" disease-specific processes. • Using cytokines only, and adjusting for BMI, it is possible to predict the ACPA status and joint inflammation with considerable precision.

Association Between Multiple Metal(loid)s Exposure and Blood Lipid Levels: Evidence from a Cross-Sectional Study of Southeastern China

Exposure to essential and toxic metals occurs simultaneously as a mixture in real-life. However, there is no consensus regarding the effects of co-exposure to multiple metal(loid)s (designated hereafter metals) on blood lipid levels. Thus, blood concentrations of six human essential metals and five toxic metals in 720 general populations from southeastern China were simultaneously determined as a measure of exposure. In addition, quantile g-computation, Bayesian kernel machine regression, elastic net regression, and generalized linear model were used to investigate both the joint and individual effects of exposure to this metal mixture on human blood lipid levels. The significant positive joint effect of exposure to this metal mixture on serum total cholesterol (TC) levels, rather than on serum triglycerides, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol, Castelli risk index I, Castelli risk index II, atherogenic coefficient, and non-HDL-C levels, was found. In addition, the positive effect may be primarily driven by selenium (Se), lead (Pb), and mercury (Hg) exposure. In addition, on the effect of TC levels, the synergistic effect between Pb and Hg and the antagonistic effect between Se and Pb were identified. Our finding suggests that combined exposure to this metal mixture may affect human blood lipid levels. Therefore, reducing exposure to heavy metals, such as Pb and Hg, should be a priority for the general population. In addition, Se supplementation should also be considered with caution.

Genetic network structure of 13 psychiatric disorders in the general population

Psychiatric disorders frequently co-occur and share common symptoms and genetic backgrounds. Previous research has used genome-wide association studies to identify the interrelationships among psychiatric disorders and identify clusters of disorders; however, these methods have limitations in terms of their ability to examine the relationships among disorders as a network structure and their generalizability to the general population. In this study, we explored the network structure of the polygenic risk score (PRS) for 13 psychiatric disorders in a general population (276,249 participants of European ancestry from the UK Biobank) and identified communities and the centrality of the network. In this network, the nodes represented a PRS for each psychiatric disorder and the edges represented the connections between nodes. The psychiatric disorders comprised four robust communities. The first community included attention-deficit hyperactivity disorder, autism spectrum disorder, major depressive disorder, and anxiety disorder. The second community consisted of bipolar I and II disorders, schizophrenia, and anorexia nervosa. The third group included Tourette's syndrome and obsessive-compulsive disorder. Cannabis use disorder, alcohol use disorder, and post-traumatic stress disorder make up the fourth community. The PRS of schizophrenia had the highest values for the three metrics (strength, betweenness, and closeness) in the network. Our findings provide a comprehensive genetic network of psychiatric disorders and biological evidence for the classification of psychiatric disorders.

Time Thresholds for Using Pressure Reactivity Index in Neuroprognostication for Patients With Severe Traumatic Brain Injury

BACKGROUND AND OBJECTIVES

Severe traumatic brain injury (sTBI) represents a diffuse, heterogeneous disease where therapeutic targets for optimizing clinical outcome remain unclear. Mean pressure reactivity index (PRx) values have demonstrated associations with clinical outcome in sTBI. However, the retrospective derivation of a mean value diminishes its bedside significance. We evaluated PRx temporal profiles for patients with sTBI and identified time thresholds suggesting optimal neuroprognostication.

METHODS

Patients with sTBI and continuous bolt intracranial pressure monitoring were identified. Outcomes were dichotomized by disposition status ("good outcome" was denoted by home and acute rehabilitation). PRx values were obtained every minute by taking moving correlation coefficients of intracranial pressures and mean arterial pressures. Average PRx trajectories for good and poor outcome groups were calculated by extending the last daily averaged PRx value to day 18. Each patient also had smoothed PRx trajectories that were used to generate "candidate features." These "candidate features" included daily average PRx's, cumulative first-order changes in PRx and cumulative second-order changes in PRx. Changes in sensitivity over time for predicting poor outcome was then evaluated by generating penalized logistic regression models that were derived from the "candidate features" and maximized specificity.

RESULTS

Among 33 patients with sTBI, 18 patients achieved good outcome and 15 patients had poor outcome. Average PRx trajectories for the good and poor outcome groups started on day 6 and consistently diverged at day 9. When targeting a specificity >83.3%, an 85% maximum sensitivity for determining poor outcome was achieved at hospital day 6. Subsequent days of PRx monitoring showed diminishing sensitivities.

CONCLUSION

Our findings suggest that in a population of sTBI, PRx sensitivities for predicting poor outcome was maximized at hospital day 6. Additional study is warranted to validate this model in larger populations.

Identifying high school risk factors that forecast heavy drinking onset in understudied young adults

Heavy alcohol drinking is a major, preventable problem that adversely impacts the physical and mental health of US young adults. Studies seeking drinking risk factors typically focus on young adults who enrolled in 4-year residential college programs (4YCP) even though most high school graduates join the workforce, military, or community colleges. We examined 106 of these understudied young adults (USYA) and 453 4YCPs from the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) by longitudinally following their drinking patterns for 8 years from adolescence to young adulthood. All participants were no-to-low drinkers during high school. Whereas 4YCP individuals were more likely to initiate heavy drinking during college years, USYA participants did so later. Using mental health metrics recorded during high school, machine learning forecasted individual-level risk for initiating heavy drinking after leaving high school. The risk factors differed between demographically matched USYA and 4YCP individuals and between sexes. Predictors for USYA drinkers were sexual abuse, physical abuse for girls, and extraversion for boys, whereas 4YCP drinkers were predicted by the ability to recognize facial emotion and, for boys, greater openness. Thus, alcohol prevention programs need to give special consideration to those joining the workforce, military, or community colleges, who make up the majority of this age group.

The Role of Leader-Member Exchange Relations and Individual Differences on Counterproductive Work Behavior

Although researchers have documented relations between abusive supervision and subordinate counterproductive work behavior (CWB), might CWB result from non-abusive treatment? We address the question by examining the relation between leader-member relations (LMX) and CWB as well as potential mediators and moderators of that relation. One hundred and eighty subordinates completed surveys assessing their LMX quality, entitlement, negative emotions, self-control, and CWB. Twenty-six supervisors also provided CWB data on 88 of those subordinates. We found that subordinates reporting lower quality relations with their supervisors were more likely to experience CWB than other subordinates and that anger mediated that relation. There was some support for the moderating effect of self-control on the negative emotion - CWB relation. Entitlement did not moderate the LMX - anger relation as hypothesized. One implication is that supervisors do not have to treat their subordinates poorly for the subordinates to react negatively and engage in behavior detrimental to their organization.

Patellar tendon biomechanical and morphologic properties and their relationship to serum clinical variables in persons with prediabetes and type 2 diabetes

Tendon biomechanical properties and fibril organization are altered in patients with diabetes compared to healthy individuals, yet few biomarkers have been associated with in vivo tendon properties. We investigated the relationships between in vivo imaging-based tendon properties, serum variables, and patient characteristics across healthy controls (n = 14, age: 45 ± 5 years, body mass index [BMI]: 24 ± 1, hemoglobin A1c [HbA1c]: 5.3 ± 0.1%), prediabetes (n = 14, age: 54 ± 5 years, BMI: 29 ± 2; HbA1c: 5.7 ± 0.1), and type 2 diabetes (n = 13, age: 55 ± 3 years, BMI: 33 ± 2, HbA1c: 6.7 ± 0.3). We used ultrasound speckle-tracking and measurements from magnetic resonance imaging (MRI) to estimate the patellar tendon in vivo tangent modulus. Analysis of plasma c-peptide, interleukin-1β (IL-1β), IL-6, IL-8, tumor necrosis factor-α (TNF-α), adiponectin, leptin, insulin-like growth factor 1 (IGF-1), and C-reactive protein (CRP) was completed. We built regression models incorporating statistically significant covariates and indicators for the clinically defined groups. We found that tendon cross-sectional area normalized to body weight (BWN CSA) and modulus were lower in patients with type 2 diabetes than in healthy controls (p < 0.05). Our regression analysis revealed that a model that included BMI, leptin, high-density lipoprotein (HDL), low-density lipoprotein (LDL), age, and group explained ~70% of the variability in BWN CSA (R2 = 0.70, p < 0.001). For modulus, including the main effects LDL, groups, HbA1c, age, BMI, cholesterol, IGF-1, c-peptide, leptin, and IL-6, accounted for ~54% of the variability in modulus (R2 = 0.54, p < 0.05). While BWN CSA and modulus were lower in those with diabetes, group was a poor predicter of tendon properties when considering the selected covariates. These data highlight the multifactorial nature of tendon changes with diabetes and suggest that blood variables could be reliable predictors of tendon properties.

Identification of collagen subtypes of gastric cancer for distinguishing patient prognosis and therapeutic response

Background

Gastric cancer is a highly heterogeneous disease, presenting a major obstacle to personalized treatment. Effective markers of the immune checkpoint blockade response are needed for precise patient classification. We, therefore, divided patients with gastric cancer according to collagen gene expression to indicate their prognosis and treatment response.

Methods

We collected data for 1250 patients with gastric cancer from four cohorts. For the TCGA-STAD cohort, we used consensus clustering to stratify patients based on expression levels of 44 collagen genes and compared the prognosis and clinical characteristics between collagen subtypes. We then identified distinct transcriptomic and genetic alteration signatures for the subtypes. We analyzed the associations of collagen subtypes with the responses to chemotherapy, immunotherapy, and targeted therapy. We also established a platform-independent collagen-subtype predictor. We verified the findings in three validation cohorts (GSE84433, GSE62254, and GSE15459) and compared the collagen subtyping method with other molecular subtyping methods.

Results

We identified two subtypes of gastric adenocarcinoma: a high-expression collagen subtype (CS-H) and a low-expression collagen subtype (CS-L). Collagen subtype was an independent prognostic factor, with better overall survival in the CS-L subgroup. The inflammatory response, angiogenesis, and phosphoinositide 3-kinase (PI3K)/Akt pathways were transcriptionally active in the CS-H subtype, while DNA repair activity was significantly greater in the CS-L subtype. PIK3CA was frequently amplified in the CS-H subtype, while PIK3C2A, PIK3C2G, and PIK3R1 were frequently deleted in the CS-L subtype. CS-H subtype tumors were more sensitive to fluorouracil, while CS-L subtype tumors were more sensitive to immune checkpoint blockade. CS-L subtype was predicted to be more sensitive to HER2-targeted drugs, and CS-H subtype was predicted to be more sensitive to vascular endothelial growth factor and PI3K pathway-targeting drugs. Collagen subtyping also has the potential to be combined with existing molecular subtyping methods for better patient classification.

Conclusions

We classified gastric cancers into two subtypes based on collagen gene expression and validated these subtypes in three validation cohorts. The collagen subgroups differed in terms of prognosis, clinical characteristics, transcriptome, and genetic alterations. The subtypes were closely related to patient responses to chemotherapy, immunotherapy, and targeted therapy.

Nomogram established on account of Lasso-logistic regression for predicting hemorrhagic transformation in patients with acute ischemic stroke after endovascular thrombectomy

BACKGROUND

Hemorrhagic transformation (HT) is a common and serious complication in patients with acute ischemic stroke (AIS) after endovascular thrombectomy (EVT). This study was performed to determine the predictive factors associated with HT in stroke patients with EVT and to establish and validate a nomogram that combines with independent predictors to predict the probability of HT after EVT in patients with AIS.

METHODS

All patients were randomly divided into development and validation cohorts at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) regression was used to select the optimal factors, and multivariate logistic regression analysis was used to build a clinical prediction model. Calibration plots, decision curve analysis (DCA) and receiver operating characteristic curve (ROC) were generated to assess predictive performance.

RESULTS

LASSO regression analysis showed that Alberta Stroke Program Early CT Scores (ASPECTS), international normalized ratio (INR), uric acid (UA), neutrophils (NEU) were the influencing factors for AIS with HT after EVT. A novel prognostic nomogram model was established to predict the possibility of HT with AIS after EVT. The calibration curve showed that the model had good consistency. The results of ROC analysis showed that the AUC of the prediction model established in this study for predicting HT was 0.797 in the development cohort and 0.786 in the validation cohort.

CONCLUSION

This study proposes a novel and practical nomogram based on ASPECTS, INR, UA, NEU, which can well predict the probability of HT after EVT in patients with AIS.

Cultural Bias in Parent Reports: The Role of Socialization Goals When Parents Report on Their Child's Problem Behavior

It has been called into question whether widely used screening instruments for child mental health can provide comparable results across countries and cultures. Socialization goals can influence whether and to what extent a parent considers a behavior to be problematic and thus might influence parental reports on their child's behavior. We tested comparability of parental reports between native German (N = 116) and Turkish origin (N = 77) parents in Germany in an online study using a vignette approach. Parents were asked to rate the perceived problem severity of the same behavior depicted in the vignettes. We expected and found that parents of Turkish origin in Germany rate the externalizing problem behaviour depicted in the vignettes as more problematic compared to native German parents. The effect was fully mediated by parental approval of the socialization goals obedience and collectivism. We also controlled for social desirability responding and an extreme response style.

Associations of plasma proteomics and age-related outcomes with brain age in a diverse cohort

Machine learning models are increasingly being used to estimate "brain age" from neuroimaging data. The gap between chronological age and the estimated brain age gap (BAG) is potentially a measure of accelerated and resilient brain aging. Brain age calculated in this fashion has been shown to be associated with mortality, measures of physical function, health, and disease. Here, we estimate the BAG using a voxel-based elastic net regression approach, and then, we investigate its associations with mortality, cognitive status, and measures of health and disease in participants from Atherosclerosis Risk in Communities (ARIC) study who had a brain MRI at visit 5 of the study. Finally, we used the SOMAscan assay containing 4877 proteins to examine the proteomic associations with the MRI-defined BAG. Among N = 1849 participants (age, 76.4 (SD 5.6)), we found that increased values of BAG were strongly associated with increased mortality and increased severity of the cognitive status. Strong associations with mortality persisted when the analyses were performed in cognitively normal participants. In addition, it was strongly associated with BMI, diabetes, measures of physical function, hypertension, prevalent heart disease, and stroke. Finally, we found 33 proteins associated with BAG after a correction for multiple comparisons. The top proteins with positive associations to brain age were growth/differentiation factor 15 (GDF-15), Sushi, von Willebrand factor type A, EGF, and pentraxin domain-containing protein 1 (SEVP 1), matrilysin (MMP7), ADAMTS-like protein 2 (ADAMTS), and heat shock 70 kDa protein 1B (HSPA1B) while EGF-receptor (EGFR), mast/stem-cell-growth-factor-receptor (KIT), coagulation-factor-VII, and cGMP-dependent-protein-kinase-1 (PRKG1) were negatively associated to brain age. Several of these proteins were previously associated with dementia in ARIC. These results suggest that circulating proteins implicated in biological aging, cellular senescence, angiogenesis, and coagulation are associated with a neuroimaging measure of brain aging.

Differences in scanpath pattern and verbal working memory predicts efficient reading in the Cloze gap-filling test

Different tests measure text comprehension, including the cloze gap-filling test, often used for language learning. Different studies hypothesized cognitive strategies in this type of test and their relationship with working memory and performance. However, no study investigated the cloze test, working memory, and possible cognitive strategies, while performing the test. Therefore, this study aimed to identify cognitive visual strategies in the cloze test by applying an unsupervised algorithm and to analyze the relationship between these strategies with working memory and performance in the cloze test. Our sample consisted of 51 university students, the largest sample in studies of cognitive strategies with cloze tests. Participants answered an 11-item cloze test in a computer with eye-tracking, a verbal working memory test, and a visuospatial working memory test. Our analysis of participants' scanpath identified two main strategies: one with fewer toggles between text and word bank and fewer fixations than the other one, indicating the existence of a global strategy. Furthermore, a model predicting the efficiency of participants in the cloze test found that item complexity, using a global strategy, and higher scores of working memory were the most significant predictors. These results confirm the hypothesis of a global strategy being related to successfully achieving higher-order reading processes.

Sleep disturbances and incident risk of major depressive disorder in a population-based cohort

Sleep disturbances are well-known symptoms of major depressive disorder (MDD). However, the prospective risk of MDD in the presence of sleep disturbances in a general population-based cohort is not well known. This study investigated associations between both polysomnography (PSG)-based or subjective sleep features and incident MDD. Participants representative of the general population who had never had MDD completed sleep questionnaires (n = 2000) and/or underwent PSG (n = 717). Over 8 years' follow-up, participants completed psychiatric interviews enabling the diagnosis of MDD. Survival Cox models were used to analyze associations between sleep features and MDD incidence. A higher Epworth Sleepiness Scale and presence of insomnia symptoms were significantly associated with a higher incidence of MDD (hazard ratio [HR] [95 % confidence interval (CI)]: 1.062 [1.022-1.103], p = 0.002 and 1.437 [1.064-1.940], p = 0.018, respectively). Higher density of rapid eye movements in rapid eye movement (REM) sleep was associated with a higher incidence of MDD in men (HR 1.270 [95 % CI 1.064-1.516], p = 0.008). In women, higher delta power spectral density was associated with a lower MDD incidence (HR 0.674 [95 % CI 0.463-0.981], p = 0.039). This study confirmed the associations between subjective and objective sleep features and the incidence of MDD in a large community dwelling cohort.

Disulfidptosis‑related lncRNA prognosis model to predict survival therapeutic response prediction in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the most common pathological type of lung cancer, and disulfidptosis is a newly discovered mechanism of programmed cell death. However, the effects of disulfidptosis-related lncRNAs (DR-lncRNAs) in LUAD have yet to be fully elucidated. The aim of the present study was to identify and validate a novel lncRNA-based prognostic marker that was associated with disulfidptosis. RNA-sequencing and associated clinical data were obtained from The Cancer Genome Atlas database. Univariate Cox regression and lasso algorithm analyses were used to identify DR-lncRNAs and to establish a prognostic model. Kaplan-Meier curves, receiver operating characteristic curves, principal component analysis, Cox regression, nomograms and calibration curves were used to assess the reliability of the prognostic model. Functional enrichment analysis, immune infiltration analysis, somatic mutation analysis, tumor microenvironment and drug predictions were applied to the risk model. Reverse transcription-quantitative PCR was subsequently performed to validate the mRNA expression levels of the lncRNAs in normal cells and tumor cells. These analyses enabled a DR-lncRNA prognosis signature to be constructed, consisting of nine lncRNAs; U91328.1, LINC00426, MIR1915HG, TMPO-AS1, TDRKH-AS1, AL157895.1, AL512363.1, AC010615.2 and GCC2-AS1. This risk model could serve as an independent prognostic tool for patients with LUAD. Numerous immune evaluation algorithms indicated that the low-risk group may exhibit a more robust and active immune response against the tumor. Moreover, the tumor immune dysfunction exclusion algorithm suggested that immunotherapy would be more effective in patients in the low-risk group. The drug-sensitivity results showed that patients in the high-risk group were more sensitive to treatment with crizotinib, erlotinib or savolitinib. Finally, the expression levels of AL157895.1 were found to be lower in A549. In summary, a novel DR-lncRNA signature was constructed, which provided a new index to predict the efficacy of therapeutic interventions and the prognosis of patients with LUAD.

Integrated Bulk and Single-Cell RNA-Sequencing Reveals the Effects of Circadian Rhythm Disruption on the Metabolic Reprogramming of CD4+ T Cells in Alzheimer's Disease

Although growing evidence suggests close correlations between Alzheimer's disease (AD) and circadian rhythm disruption (CRD), few studies have focused on the influence of circadian rhythm on levels of immune cells in AD. We aimed to delineate the mechanism underlying the effects of circadian related genes on T cell immune function in AD. A total of 112 brain samples were used to construct the CRD-related model by performing weighted gene co-expression network analysis and machine learning algorithms (LASSO, SVM-RFE, and RF). The ssGSEA method was used to calculate the CRDscore in order to quantify CRD status. Using single-cell transcriptome data of CSF cells, we investigated the CD4+ T cell metabolism and cell-cell communication in high- and low-risk CRD groups. Connectivity map (CMap) was applied to explore small molecule drugs targeting CRD, and the expression of the signature gene GPR4 was further validated in AD. The CRDscore algorithm, which is based on 23 circadian-related genes, can effectively classify the CRD status in AD datasets. The single-cell analysis revealed that the CD4+ T cells with high CRDscore were characterized by hypometabolism. Cell communication analysis revealed that CD4+ T cells might be involved in promoting CD8+ T cell adhesion under CRD, which may facilitate T cell infiltration into the brain parenchyma. Overall, this study indicates the potential connotation of circadian rhythm in AD, providing insights into understanding T cell metabolic reprogramming under CRD.

Predictive Modeling for One-Year Lower Extremity Endovascular Revascularization Failure in Black Persons

INTRODUCTION

Black persons bear a disproportionate burden of peripheral artery disease (PAD) and experience higher rates of endovascular revascularization failure (ERF) when compared with non-Hispanic White persons. We aimed to identify predictors of ERF in Black persons using predictive modeling.

METHODS

This retrospective study included all persons identifying as Black who underwent an initial endovascular revascularization procedure for PAD between 2011 and 2018 at a midwestern tertiary care center. Three predictive models were developed using (1) logistic regression, (2) penalized logistic regression (least absolute shrinkage and selection operator [LASSO]), and (3) random forest (RF). Predictive performance was evaluated under repeated cross-validation.

RESULTS

Of the 163 individuals included in the study, 113 (63.1%) experienced ERF at 1 y. Those with ERF had significant differences in symptom status (P < 0.001), lesion location (P < 0.001), diabetes status (P = 0.037), and annual procedural volume of the attending surgeon (P < 0.001). Logistic regression and LASSO models identified tissue loss, smoking, femoro-popliteal lesion location, and diabetes control as risk factors for ERF. The RF model identified annual procedural volume, age, PAD symptoms, number of comorbidities, and lesion location as most predictive variables. LASSO and RF models were more sensitive than logistic regression but less specific, although all three methods had an overall accuracy of ≥75%.

CONCLUSIONS

Black persons undergoing endovascular revascularization for PAD are at high risk of ERF, necessitating need for targeted intervention. Predictive models may be clinically useful for identifying high-risk patients, although individual predictors of ERF varied by model. Further exploration into these models may improve limb salvage for this population.

Early-Life Social Determinants of SCA6 Age at Onset, Severity, and Progression

SCA6 patients with the same size CAG repeat allele can vary significantly in age at onset (AAO) and clinical progression. The specific external factors affecting SCA6 have yet to be investigated. We assessed the effect of early life events on AAO, severity, and progression in SCA6 patients using a social determinant of health approach. We performed a survey of biological and social factors in SCA6 patients enrolled in the SCA6 Network at the University of Chicago. AAO of ataxia symptoms and patient-reported outcome measure (PROM) of ataxia were used as primary outcome measures. Least absolute shrinkage and selection operation (LASSO) regressions were used to identify which early life factors are predictive of SCA6 AAO, severity, and progression. Multiple linear regression models were then used to assess the degree to which these determinants influence SCA6 health outcomes. A total of 105 participants with genetically confirmed SCA6 completed the assessments. SCA6 participants with maternal difficulty during pregnancy, active participation in school sports, and/or longer CAG repeats were determined to have earlier AAO. We found a 13.44-year earlier AAO for those with maternal difficulty in pregnancy than those without (p = 0.008) and a 12.31-year earlier AAO for those active in school sports than those who were not (p < 0.001). Higher education attainment was associated with decreased SCA6 severity and slower progression. Early life biological and social factors can have a strong influence on the SCA6 disease course, indicating that non-genetic factors can contribute significantly to SCA6 health outcomes.

Metabolomic and lipidomic fingerprints in inflammatory skin diseases - Systemic illumination of atopic dermatitis, hidradenitis suppurativa and plaque psoriasis

Auto-inflammatory skin diseases place considerable symptomatic and emotional burden on the affected and put pressure on healthcare expenditures. Although most apparent symptoms manifest on the skin, the systemic inflammation merits a deeper analysis beyond the surface. We set out to identify systemic commonalities, as well as differences in the metabolome and lipidome when comparing between diseases and healthy controls. Lipidomic and metabolomic LC-MS profiling was applied, using plasma samples collected from patients suffering from atopic dermatitis, plaque-type psoriasis or hidradenitis suppurativa or healthy controls. Plasma profiles revealed a notable shift in the non-enzymatic anti-oxidant defense in all three inflammatory disorders, placing cysteine metabolism at the center of potential dysregulation. Lipid network enrichment additionally indicated the disease-specific provision of lipid mediators associated with key roles in inflammation signaling. These findings will help to disentangle the systemic components of autoimmune dermatological diseases, paving the way to individualized therapy and improved prognosis.

Network analysis of histopathological image features and genomics data improving prognosis performance in clear cell renal cell carcinoma

INTRODUCTION

Clear cell renal cell carcinoma is the most common type of kidney cancer, but the prediction of prognosis remains a challenge.

METHODS

We collected whole-slide histopathological images, corresponding clinical and genetic information from the The Cancer Imaging Archive and The Cancer Genome Atlas databases and randomly divided patients into training (n = 197) and validation (n = 84) cohorts. After feature extraction by CellProfiler, we used 2 different machine learning techniques (Least Absolute Shrinkage and Selector Operation-regularized Cox and Support Vector Machine-Recursive Feature Elimination) and weighted gene co-expression network analysis to select prognosis-related image features and genes, respectively. These features and genes were integrated into a joint model using random forest and used to create a nomogram that combines other predictive indicators.

RESULTS

A total of 4 overlapped features were identified, represented by the computed histopathological risk score in the random forest model, and showed predictive value for overall survival (test set: 1-year area under the curves (AUC) = 0.726, 3-year AUC = 0.727, and 5-year AUC = 0.764). The histopathological-genetic risk score (HGRS) integrating the genetic information computed performed better than the model that used image features only (test set: 1-year AUC = 0.682, 3-year AUC = 0.734, and 5-year AUC = 0.78). The nomogram (gender, stage, and HGRS) achieved the highest net benefit according to decision curve analysis compared to HGRS or clinical model.

CONCLUSION

This study developed a histopathological-genetic-related nomogram by combining histopathological features and clinical predictors, providing a more comprehensive prognostic assessment for clear cell renal cell carcinoma patients.

Cost-effectiveness of the McGill interactive pediatric oncogenetic guidelines in identifying Li-Fraumeni syndrome in female patients with osteosarcoma

BACKGROUND

Li-Fraumeni syndrome (LFS) is a penetrant cancer predisposition syndrome (CPS) associated with the development of many tumor types in young people including osteosarcoma and breast cancer (BC). The McGill Interactive Pediatric OncoGenetic Guidelines (MIPOGG) decision-support tool provides a standardized approach to identify patients at risk of CPSs.

METHODS

We conducted a cost-utility analysis, from the healthcare payer perspective, to compare MIPOGG-guided, physician-guided, and universal genetic testing strategies to detect LFS in female patients diagnosed at an age of less than 18 years with osteosarcoma. We developed a decision tree and discrete-event simulation model to simulate the clinical and cost outcomes of the three genetic referral strategies on a cohort of female children diagnosed with osteosarcoma, especially focused on BC as subsequent cancer. Outcomes included BC incidence, quality-adjusted life-years (QALYs), healthcare costs, and incremental cost-utility ratios (ICURs). We conducted probabilistic and scenario analyses to assess the uncertainty surrounding model parameters.

RESULTS

Compared to the physician-guided testing, the MIPOGG-guided strategy was marginally more expensive by $105 (-$516; $743), but slightly more effective by 0.003 (-0.04; 0.045) QALYs. Compared to MIPOGG, the universal testing strategy was $1333 ($732; $1953) more costly and associated with 0.011 (-0.043; 0.064) additional QALYs. The ICUR for the MIPOGG strategy was $33,947/QALY when compared to the physician strategy; the ICUR for universal testing strategy was $118,631/QALY when compared to the MIPOGG strategy.

DISCUSSION

This study provides evidence for clinical and policy decision-making on the cost-effectiveness of genetic referral strategies to identify LFS in the setting of osteosarcoma. MIPOGG-guided strategy was most likely to be cost-effective at a willingness-to-pay threshold value of $50,000/QALY.

Diversity in invasive species management networks

Effective management of invasive species requires collaboration across a range of stakeholders. These stakeholders exhibit diverse attributes such as organisation types, operational scale, objectives, and roles within projects. Identifying the diverse attributes of stakeholders is beneficial for increasing collaboration success while minimising potential conflicts among multiple stakeholders when managing invasive species across landscapes. Despite the increasing number of studies on connections among stakeholders, there is little understanding of the diverse attributes of stakeholders involved in invasive species management. This is a notable gap because the diversity of stakeholders is one of the significant factors that can influence collaboration success. To bridge this knowledge gap, we used a social network approach to identify the attributes of stakeholders that influence their participation in collaborations using a case study of invasive wild pig (Sus scrofa) management in Queensland, Australia. Our findings suggest that even though the overall stakeholder network was diverse, the stakeholder network at the project level exhibited a lack of diversity on average, particularly regarding the scale of operation and type of organisation. In other words, stakeholders are highly likely to form ties in projects involving other stakeholders from similar types of organisations or operational scales. We suggest that targeting a greater diversity of stakeholders across types of organisations and scales of operations might enhance the success of collaborative invasive species management.

Longitudinal Relationships Between Depressive Symptoms and Generalized Anxiety Symptoms in Adolescents: A Cross-Lagged Network Analysis

The comorbidity of depression and generalized anxiety have raised questions about the existing classification system. Based on the perspective of network analysis, the developmental relationship between psychological symptoms can be explored more clearly. The current study aimed to explore the longitudinal network association of depressive symptoms and generalized anxiety symptoms in non-clinical adolescents. The sample contained 1,947 Chinese adolescents (Mage = 13.93, SD = 1.71; 48.5% girls). Their depression (PHQ-9) and generalized anxiety (GAD-7) symptoms were assessed at baseline (T1) and 6-month follow-up (T2). Data was analyzed using a cross-lagged network analysis. Depressive symptoms and generalized anxiety symptoms were closely related and did not appear to be distinct categories in adolescence. More generalized anxiety symptoms predicted depressive symptoms rather than vice versa. After controlling for gender, and age, "Difficulty relaxing" presented the highest out-expected influence (out-EI) in the network, while "trouble concentrating", and "suicidal ideation" showed the lowest in expected influence (in-EI). Findings suggested that interventions for generalized anxiety symptoms may help reduce depressive symptoms among Chinese adolescents. Specifically, alleviating "difficulty relaxing" may improve overall mental health.

Systematic evaluation of high-throughput PBK modelling strategies for the prediction of intravenous and oral pharmacokinetics in humans

Physiologically based kinetic (PBK) modelling offers a mechanistic basis for predicting the pharmaco-/toxicokinetics of compounds and thereby provides critical information for integrating toxicity and exposure data to replace animal testing with in vitro or in silico methods. However, traditional PBK modelling depends on animal and human data, which limits its usefulness for non-animal methods. To address this limitation, high-throughput PBK modelling aims to rely exclusively on in vitro and in silico data for model generation. Here, we evaluate a variety of in silico tools and different strategies to parameterise PBK models with input values from various sources in a high-throughput manner. We gather 2000 + publicly available human in vivo concentration-time profiles of 200 + compounds (IV and oral administration), as well as in silico, in vitro and in vivo determined compound-specific parameters required for the PBK modelling of these compounds. Then, we systematically evaluate all possible PBK model parametrisation strategies in PK-Sim and quantify their prediction accuracy against the collected in vivo concentration-time profiles. Our results show that even simple, generic high-throughput PBK modelling can provide accurate predictions of the pharmacokinetics of most compounds (87% of Cmax and 84% of AUC within tenfold). Nevertheless, we also observe major differences in prediction accuracies between the different parameterisation strategies, as well as between different compounds. Finally, we outline a strategy for high-throughput PBK modelling that relies exclusively on freely available tools. Our findings contribute to a more robust understanding of the reliability of high-throughput PBK modelling, which is essential to establish the confidence necessary for its utilisation in Next-Generation Risk Assessment.

The impact of maternal childhood maltreatment on child externalizing behaviour and the mediating factors underlying this association: a three-level meta-analysis and systematic review

Child maltreatment can negatively impact not only survivors but also survivors' children. However, research on the intergenerational effect of maternal childhood maltreatment on child externalizing behaviour has yielded contradictory results and has not yet been systematically synthesised. The current three-level meta-analysis and systematic review aimed to provide a quantitative estimate of the strength of the association between maternal childhood maltreatment and child externalizing behaviour and to summarise research on potential mediating factors of this association. PsycINFO, PubMed, and Embase were searched and 39 studies with 82 effects sizes were included in the meta-analysis. Results revealed a small significant association between maternal childhood maltreatment and child externalizing behaviour (r = 0.16; 95% CI 0.12-0.19; publication bias-adjusted effect size: r = 0.12, 95% CI 0.08-0.16). Maternal mental health, particularly depressive symptoms, maternal parenting and children's maltreatment exposure were the most frequently examined mediators of this association, with relatively robust mediating effects for children's maltreatment exposure and maternal depressive symptoms, but mixed evidence for the mediating role of maternal parenting. This meta-analysis provides evidence for a small but significant association between maternal childhood maltreatment and children's externalizing behaviour, emphasizing the need to develop effective preventive and intervention strategies to minimise the effects of childhood maltreatment on the next generation.

Development of a processing factor prediction model for pesticides in processed tomato foods using elastic net regularization

A novel regularized elastic net regression model was developed to predict processing factor (PF) for pesticide residues, which represents a change in the residue levels during food processing. The PF values for tomato juice, wet pomace and dry pomace in the evaluations and reports published by the Joint FAO/WHO Meeting on Pesticide Residues significantly correlated with the physicochemical properties of pesticides, and subsequently the correlation was observed in the present tomato processing study. The elastic net regression model predicted the PF values using the physicochemical properties as predictor variables for both training and test data within a 2-fold range for 80-100% of the pesticides tested in the tomato processing study while overcoming multicollinearity. These results suggest that the PF values are predictable at a certain degree of accuracy from the unique sets of physicochemical properties of pesticides using the developed model based on a processing study with representative pesticides.

Structured learning in time-dependent Cox models

Cox models with time-dependent coefficients and covariates are widely used in survival analysis. In high-dimensional settings, sparse regularization techniques are employed for variable selection, but existing methods for time-dependent Cox models lack flexibility in enforcing specific sparsity patterns (ie, covariate structures). We propose a flexible framework for variable selection in time-dependent Cox models, accommodating complex selection rules. Our method can adapt to arbitrary grouping structures, including interaction selection, temporal, spatial, tree, and directed acyclic graph structures. It achieves accurate estimation with low false alarm rates. We develop the sox package, implementing a network flow algorithm for efficiently solving models with complex covariate structures. sox offers a user-friendly interface for specifying grouping structures and delivers fast computation. Through examples, including a case study on identifying predictors of time to all-cause death in atrial fibrillation patients, we demonstrate the practical application of our method with specific selection rules.