Five-Year Outcomes among U.S. Bronchiectasis and NTM Research Registry Patients

Rationale: Nontuberculous mycobacteria (NTM) are prevalent among patients with bronchiectasis. However, the long-term natural history of patients with NTM and bronchiectasis is not well described. Objectives: To assess the impact of NTM on 5-year clinical outcomes and mortality in patients with bronchiectasis. Methods: Patients in the Bronchiectasis and NTM Research Registry with ⩾5 years of follow-up were eligible. Data were collected for all-cause mortality, lung function, exacerbations, hospitalizations, and disease severity. Outcomes were compared between patients with and without NTM at baseline. Mortality was assessed using Cox proportional hazards models and the log-rank test. Measurements and Main Results: In total, 2,634 patients were included: 1,549 (58.8%) with and 1,085 (41.2%) without NTM at baseline. All-cause mortality (95% confidence interval) at Year 5 was 12.1% (10.5%, 13.7%) overall, 12.6% (10.5%, 14.8%) in patients with NTM, and 11.5% (9.0%, 13.9%) in patients without NTM. Independent predictors of 5-year mortality were baseline FEV1 percent predicted, age, hospitalization within 2 years before baseline, body mass index, and sex (all P < 0.01). The probabilities of acquiring NTM or Pseudomonas aeruginosa were approximately 4% and 3% per year, respectively. Spirometry, exacerbations, and hospitalizations were similar, regardless of NTM status, except that annual exacerbations were lower in patients with NTM (P < 0.05). Conclusions: Outcomes, including exacerbations, hospitalizations, rate of loss of lung function, and mortality rate, were similar across 5 years in patients with bronchiectasis with or without NTM.

Identification of immune-associated biomarkers of diabetes nephropathy tubulointerstitial injury based on machine learning: a bioinformatics multi-chip integrated analysis

BACKGROUND

Diabetic nephropathy (DN) is a major microvascular complication of diabetes and has become the leading cause of end-stage renal disease worldwide. A considerable number of DN patients have experienced irreversible end-stage renal disease progression due to the inability to diagnose the disease early. Therefore, reliable biomarkers that are helpful for early diagnosis and treatment are identified. The migration of immune cells to the kidney is considered to be a key step in the progression of DN-related vascular injury. Therefore, finding markers in this process may be more helpful for the early diagnosis and progression prediction of DN.

METHODS

The gene chip data were retrieved from the GEO database using the search term ' diabetic nephropathy '. The ' limma ' software package was used to identify differentially expressed genes (DEGs) between DN and control samples. Gene set enrichment analysis (GSEA) was performed on genes obtained from the molecular characteristic database (MSigDB. The R package 'WGCNA' was used to identify gene modules associated with tubulointerstitial injury in DN, and it was crossed with immune-related DEGs to identify target genes. Gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed on differentially expressed genes using the 'ClusterProfiler' software package in R. Three methods, least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE) and random forest (RF), were used to select immune-related biomarkers for diagnosis. We retrieved the tubulointerstitial dataset from the Nephroseq database to construct an external validation dataset. Unsupervised clustering analysis of the expression levels of immune-related biomarkers was performed using the 'ConsensusClusterPlus 'R software package. The urine of patients who visited Dongzhimen Hospital of Beijing University of Chinese Medicine from September 2021 to March 2023 was collected, and Elisa was used to detect the mRNA expression level of immune-related biomarkers in urine. Pearson correlation analysis was used to detect the effect of immune-related biomarker expression on renal function in DN patients.

RESULTS

Four microarray datasets from the GEO database are included in the analysis : GSE30122, GSE47185, GSE99340 and GSE104954. These datasets included 63 DN patients and 55 healthy controls. A total of 9415 genes were detected in the data set. We found 153 differentially expressed immune-related genes, of which 112 genes were up-regulated, 41 genes were down-regulated, and 119 overlapping genes were identified. GO analysis showed that they were involved in various biological processes including leukocyte-mediated immunity. KEGG analysis showed that these target genes were mainly involved in the formation of phagosomes in Staphylococcus aureus infection. Among these 119 overlapping genes, machine learning results identified AGR2, CCR2, CEBPD, CISH, CX3CR1, DEFB1 and FSTL1 as potential tubulointerstitial immune-related biomarkers. External validation suggested that the above markers showed diagnostic efficacy in distinguishing DN patients from healthy controls. Clinical studies have shown that the expression of AGR2, CX3CR1 and FSTL1 in urine samples of DN patients is negatively correlated with GFR, the expression of CX3CR1 and FSTL1 in urine samples of DN is positively correlated with serum creatinine, while the expression of DEFB1 in urine samples of DN is negatively correlated with serum creatinine. In addition, the expression of CX3CR1 in DN urine samples was positively correlated with proteinuria, while the expression of DEFB1 in DN urine samples was negatively correlated with proteinuria. Finally, according to the level of proteinuria, DN patients were divided into nephrotic proteinuria group (n = 24) and subrenal proteinuria group. There were significant differences in urinary AGR2, CCR2 and DEFB1 between the two groups by unpaired t test (P < 0.05).

CONCLUSIONS

Our study provides new insights into the role of immune-related biomarkers in DN tubulointerstitial injury and provides potential targets for early diagnosis and treatment of DN patients. Seven different genes ( AGR2, CCR2, CEBPD, CISH, CX3CR1, DEFB1, FSTL1 ), as promising sensitive biomarkers, may affect the progression of DN by regulating immune inflammatory response. However, further comprehensive studies are needed to fully understand their exact molecular mechanisms and functional pathways in DN.

Preoperative Delirium Risk Screening in Patients Undergoing a Cardiac Surgery: Results from the Prospective Observational FINDERI Study

OBJECTIVE

Postoperative delirium (POD) is a common complication of cardiac surgery that is associated with higher morbidity, longer hospital stay, cognitive decline, and mortality. Preoperative assessments may help to identify patients´ POD risk. However, a standardized screening assessment for POD risk has not been established.

DESIGN

Prospective observational FINd DElirium RIsk factors (FINDERI) study.

PARTICIPANTS

Patients aged ≥50 years undergoing cardiac surgery.

MEASUREMENTS

The primary aim was to analyze the predictive value of the Delirium Risk Screening Questionnaire (DRSQ) prior to cardiac surgery. Secondary aims are to investigate cognitive, frailty, and geriatric assessments, and to use data-driven machine learning (ML) in predicting POD. Predictive properties were assessed using receiver operating characteristics analysis and multivariate approaches (regularized LASSO regression and decision trees).

RESULTS

We analyzed a data set of 504 patients (68.3 ± 8.2 years, 21.4% women) who underwent cardiac surgery. The incidence of POD was 21%. The preoperatively administered DRSQ showed an area under the curve (AUC) of 0.68 (95% CI 0.62, 0.73), and the predictive OR was 1.25 (95% CI 1.15, 1.35, p <0.001). Using a ML approach, a three-rule decision tree prediction model including DRSQ (score>7), Trail Making Test B (time>118), and Montreal Cognitive Assessment (score ≤ 22) was identified. The AUC of the three-rule decision tree on the training set was 0.69 (95% CI 0.63, 0.75) and 0.62 (95% CI 0.51, 0.73) on the validation set.

CONCLUSION

Both the DRSQ and the three-rule decision tree might be helpful in predicting POD risk before cardiac surgery.

A prognostic model built on amino acid metabolism patterns in HPV-associated head and neck squamous cell carcinoma

OBJECTIVES

To compare amino acid metabolism patterns between HPV-positive and HPV-negative head and neck squamous cell carcinoma (HNSCC) patients and identify key genes for a prognostic model.

DESIGN

Utilizing the Cancer Genome Atlas dataset, we analyzed amino acid metabolism genes, differentiated genes between HPV statuses, and selected key genes via LASSO regression for the prognostic model. The model's gene expression was verified through immunohistochemistry in clinical samples. Functional enrichment and CIBERSORTx analyses explored biological functions, molecular mechanisms, and immune cell correlations. The model's prognostic capability was assessed using nomograms, calibration, and decision curve analysis.

RESULTS

We identified 1157 key genes associated with amino acid metabolism in HNSCC and HPV status. The prognostic model, featuring genes like IQCN, SLC22A1, SYT12, and TLX3, highlighted functions in development, metabolism, and pathways related to receptors and enzymes. It significantly correlated with immune cell infiltration and outperformed traditional staging in prognosis prediction, despite immunohistochemistry results showing limited clinical identification of HPV-related HNSCC.

CONCLUSIONS

Distinct amino acid metabolism patterns differentiate HPV-positive from negative HNSCC patients, underscoring the prognostic model's utility in predicting outcomes and guiding therapeutic strategies.

Family living and cooperative breeding in birds are associated with the number of avian predators

Cooperative breeding occurs when individuals contribute parental care to offspring that are not their own. Numerous intra- and interspecific studies have aimed to explain the evolution of this behavior. Recent comparative work suggests that family living (i.e., when offspring remain with their parents beyond independence) is a critical stepping stone in the evolution of cooperative breeding. Thus, it is key to understand the factors that facilitate the evolution of family living. Within-species studies suggest that protection from predators is a critical function of group living, through both passive benefits such as dilution effects and active benefits such as prosocial antipredator behaviors in family groups. However, the association between predation risk and the formation and prevalence of family groups and cooperative breeding remains untested globally. Here, we use phylogenetic comparative analyses including 2,984 bird species to show that family living and cooperative breeding are associated with increased occurrence of avian predators. These cross-species findings lend support to previous suggestions based on intraspecific studies that social benefits of family living, such as protection against predation, could favor the evolution of delayed dispersal and cooperative breeding.

hopsy - a methods marketplace for convex polytope sampling in Python

SUMMARY

Effective collaboration between developers of Bayesian inference methods and users is key to advance our quantitative understanding of biosystems. We here present hopsy, a versatile open-source platform designed to provide convenient access to powerful Markov chain Monte Carlo sampling algorithms tailored to models defined on convex polytopes (CP). Based on the high-performance C++ sampling library HOPS, hopsy inherits its strengths and extends its functionalities with the accessibility of the Python programming language. A versatile plugin-mechanism enables seamless integration with domain-specific models, providing method developers with a framework for testing, benchmarking, and distributing CP samplers to approach real-world inference tasks. We showcase hopsy by solving common and newly composed domain-specific sampling problems, highlighting important design choices. By likening hopsy to a marketplace, we emphasize its role in bringing together users and developers, where users get access to state-of-the-art methods, and developers contribute their own innovative solutions for challenging domain-specific inference problems.

AVAILABILITY AND IMPLEMENTATION

Sources, documentation and a continuously updated list of sampling algorithms are available at https://jugit.fz-juelich.de/IBG-1/ModSim/hopsy, with Linux, Windows and MacOS binaries at https://pypi.org/project/hopsy/.

The oncologic burden of residual disease in incidental gallbladder cancer: An elastic net regression model to profile high-risk features

INTRODUCTION

Incidental Gallbladder Cancer (IGBC) following cholecystectomy constitutes a significant portion of gallbladder cancer diagnoses. Re-exploration is advocated to optimize disease clearance and enhance survival rates. The consistent association of residual disease (RD) with inferior oncologic outcomes prompts a critical examination of re-resection's role as a modifying factor in the natural history of IGBC.

METHODS

All patients diagnosed with gallbladder cancer between 2012 and 2022 were included. An elastic net regularized regression model was employed to profile high-risk predictors of RD within the IGBC group. Survival outcomes were assessed based on resection margins and RD.

RESULTS

Among the 181 patients undergoing re-exploration for IGBC, 133 (73.5 %) harbored RD, while 48 (26.5 %) showed no evidence. The elastic net model, utilizing a selected λ = 0.029, identified six coefficients associated with the risk of RD: aspiration from cholecystectomy (0.141), hepatic tumor origin (1.852), time to re-exploration >8 weeks (1.879), positive margin status (2.575), higher T stage (1.473), and poorly differentiated tumors (2.241). Furthermore, the study revealed a median overall survival of 44 months (CI 38-60) for IGBC patients with no evidence of RD, compared to 31 months (23-42) for those with RD (p < 0.001).

CONCLUSION

Re-resection revealed a high incidence of RD (73.5 %), significantly correlating with poorer survival outcomes. The preoperative identification of high-risk features provides a reliable biological disease profile. This aids in strategic preselection of patients who may benefit from re-resection, underscoring the need to consolidate outcomes with tailored chemotherapy for those with unfavorable characteristics.

Clinical significance and immune landscape of a novel immune cell infiltration-based prognostic model in lung adenocarcinoma

Tumor-infiltrating immune cells (TICs) play a central role in the tumor microenvironment, which can reflect the host anti-tumor immune response. However, few studies have explored TICs in predicting the prognosis of lung adenocarcinoma (LUAD). In our study, we enrolled 2470 LUAD patients from TCGA and GEO databases, and the normalized enrichment scores for 65 immune cell types were quantified for each patient. An immune-related risk score (IRRS) was built on the basis of 17 selected TICs using LASSO regression analysis, and the results showed that high-risk patients were correlated with shorter survival time for the LUAD cohorts. Correlation analyses between IRRS and clinical characteristics were also evaluated to validate the clinical use of IRRS. In addition, we analyzed the differences in the distribution of immune cell infiltration and immunoregulatory gene expression, which may facilitate individual immunotherapy. Based on the above result, we conclude that IRRS can act as a powerful predictor for risk stratification and prognosis prediction, and may facilitate the decision-making process for LUAD patients.

Detection and assessment of immune and stromal related risk genes to predict preeclampsia: A bioinformatics analysis with dataset

This study aimed to investigate immune score and stromal score-related signatures associated with preeclampsia (PE) and identify key genes for diagnosing PE using bioinformatics analysis. Four microarray datasets, GSE75010, GSE25906, GSE44711, and GSE10588 were obtained from the Gene Expression Omnibus database. GSE75010 was utilized for differential expressed gene (DEGs) analysis. Subsequently, bioinformatic tools such as gene ontology, Kyoto Encyclopedia of Genes and Genomes, weighted gene correlation network analysis, and gene set enrichment analysis were employed to functionally characterize candidate target genes involved in the pathogenesis of PE. The least absolute shrinkage and selection operator regression approach was employed to identify crucial genes and develop a predictive model. This method also facilitated the creation of receiver operating characteristic (ROC) curves, enabling the evaluation of the model's precision. Furthermore, the model underwent external validation through the other three datasets. A total of 3286 DEGs were identified between normal and PE tissues. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses revealed enrichments in functions related to cell chemotaxis, cytokine binding, and cytokine-cytokine receptor interaction. weighted gene correlation network analysis identified 2 color modules strongly correlated with immune and stromal scores. After intersecting DEGs with immune and stromal-related genes, 13 genes were selected and added to the least absolute shrinkage and selection operator regression. Ultimately, 7 genes were screened out to establish the risk model for discriminating preeclampsia from controls, with each gene having an area under the ROC curve >0.70. The constructed risk model demonstrated that the area under the ROC curves in internal and the other three external datasets were all greater than 0.80. A 7-gene risk signature was identified to build a potential diagnostic model and performed well in the external validation group for PE patients. These findings illustrated that immune and stromal cells played essential roles in PE during its progression.

Construction of an immune-related prognostic signature and lncRNA-miRNA-mRNA ceRNA network in acute myeloid leukemia

The progression of acute myeloid leukemia (AML) is influenced by the immune microenvironment in the bone marrow and dysregulated intracellular competing endogenous RNA (ceRNA) networks. Our study utilized data from UCSC Xena, The Cancer Genome Atlas Program, the Gene Expression Omnibus, and the Immunology Database and Analysis Portal. Using Cox regression analysis, we identified an immune-related prognostic signature. Genomic analysis of prognostic messenger RNA (mRNA) was conducted through Gene Set Cancer Analysis (GSCA), and a prognostic ceRNA network was constructed using the Encyclopedia of RNA Interactomes. Correlations between signature mRNAs and immune cell infiltration, checkpoints, and drug sensitivity were assessed using R software, gene expression profiling interactive analysis (GEPIA), and CellMiner, respectively. Adhering to the ceRNA hypothesis, we established a potential long noncoding RNA (lncRNA)/microRNA (miRNA)/mRNA regulatory axis. Our findings pinpointed 9 immune-related prognostic mRNAs (KIR2DL1, CSRP1, APOBEC3G, CKLF, PLXNC1, PNOC, ANGPT1, IL1R2, and IL3RA). GSCA analysis revealed the impact of copy number variations and methylation on AML. The ceRNA network comprised 14 prognostic differentially expressed lncRNAs (DE-lncRNAs), 6 prognostic DE-miRNAs, and 3 prognostic immune-related DE-mRNAs. Correlation analyses linked these mRNAs' expression to 22 immune cell types and 6 immune checkpoints, with potential sensitivity to 27 antitumor drugs. Finally, we identified a potential LINC00963/hsa-miR-431-5p/CSRP1 axis. This study offers innovative insights for AML diagnosis and treatment through a novel immune-related signature and ceRNA axis. Identified novel biomarkers, including 2 mRNAs (CKLF, PNOC), 1 miRNA (hsa-miR-323a-3p), and 10 lncRNAs (SNHG25, LINC01857, AL390728.6, AC127024.5, Z83843.1, AP002884.1, AC007038.1, AC112512, AC020659.1, AC005921.3) present promising candidates as potential targets for precision medicine, contributing to the ongoing advancements in the field.

Journey to kidney transplantation: patient dynamics, suspensions, transplantation and deaths in the Australian kidney transplant waitlist

BACKGROUND

People on the kidney waitlist are less informed about potential suspensions. Disparities may exist among those who are suspended and who return to the waitlist. We evaluated the patient journey after entering the waitlist, including suspensions and outcomes, and factors associated with these transitions.

METHODS

We included all incident patients waitlisted for their first transplant from deceased donors in Australia from 2006 to 2019. We described all clinical transitions after entering the waitlist. We predicted the restricted mean survival time (unadjusted and adjusted) until first transplant by the number of prior suspensions. We evaluated factors associated with transitions using flexible survival models and clinical endpoints using Cox models.

RESULTS

Of 8466 patients waitlisted and followed over 45 757.4 person-years (median 4.8 years), 6741 (80%) were transplanted, 381 (5%) died waiting and 1344 (16%) were still waiting. A total of 3127 (37%) people were suspended at least once. Predicted mean time from waitlist to transplant was 3.0 years [95% confidence interval (CI) 2.8-3.2] when suspended versus 1.9 years (95% CI 1.8-1.9) when never suspended. Prior suspension increased the likelihood of further suspensions 4.2-fold (95% CI 3.8-4.6) and returning to the waitlist by 50% (95% CI 36-65) but decreased the likelihood of transplantation by 29% (95% CI 62-82). Death risk while waiting was increased 12-fold (95% CI 8.0-18.3) when currently suspended. Australian non-Indigenous males were 13% [hazard ratio (HR) 1.13 (95% CI 1.04-1.23)] and Asian males 23% [HR 1.23 (95% CI 1.06-1.42)] more likely to return to the waitlist compared with females of the same ethnicity.

CONCLUSION

The waitlist journey was not straightforward. Suspension was common, impacted the chance of transplantation and meant waiting an average of 1 year longer until transplant. We have provided estimates for and factors associated with suspension, relisting and outcomes after waitlisting to support more informed discussions. This evidence is critical to further understand drivers of inequitable access to transplantation.

Proteome-wide association study using cis and trans variants and applied to blood cell and lipid-related traits in the Women's Health Initiative study

In most Proteome-Wide Association Studies (PWAS), variants near the protein-coding gene (±1 Mb), also known as cis single nucleotide polymorphisms (SNPs), are used to predict protein levels, which are then tested for association with phenotypes. However, proteins can be regulated through variants outside of the cis region. An intermediate GWAS step to identify protein quantitative trait loci (pQTL) allows for the inclusion of trans SNPs outside the cis region in protein-level prediction models. Here, we assess the prediction of 540 proteins in 1002 individuals from the Women's Health Initiative (WHI), split equally into a GWAS set, an elastic net training set, and a testing set. We compared the testing r2 between measured and predicted protein levels using this proposed approach, to the testing r2 using only cis SNPs. The two methods usually resulted in similar testing r2, but some proteins showed a significant increase in testing r2 with our method. For example, for cartilage acidic protein 1, the testing r2 increased from 0.101 to 0.351. We also demonstrate reproducible findings for predicted protein association with lipid and blood cell traits in WHI participants without proteomics data and in UK Biobank utilizing our PWAS weights.

Analysis of Clinical Criteria for Discharge Among Patients Hospitalized for COVID-19: Development and Validation of a Risk Prediction Model

BACKGROUND

Patients hospitalized with COVID-19 can clinically deteriorate after a period of initial stability, making optimal timing of discharge a clinical and operational challenge.

OBJECTIVE

To determine risks for post-discharge readmission and death among patients hospitalized with COVID-19.

DESIGN

Multicenter retrospective observational cohort study, 2020-2021, with 30-day follow-up.

PARTICIPANTS

Adults admitted for care of COVID-19 respiratory disease between March 2, 2020, and February 11, 2021, to one of 180 US hospitals affiliated with the HCA Healthcare system.

MAIN MEASURES

Readmission to or death at an HCA hospital within 30 days of discharge was assessed. The area under the receiver operating characteristic curve (AUC) was calculated using an internal validation set (33% of the HCA cohort), and external validation was performed using similar data from six academic centers associated with a hospital medicine research network (HOMERuN).

KEY RESULTS

The final HCA cohort included 62,195 patients (mean age 61.9 years, 51.9% male), of whom 4704 (7.6%) were readmitted or died within 30 days of discharge. Independent risk factors for death or readmission included fever within 72 h of discharge; tachypnea, tachycardia, or lack of improvement in oxygen requirement in the last 24 h; lymphopenia or thrombocytopenia at the time of discharge; being ≤ 7 days since first positive test for SARS-CoV-2; HOSPITAL readmission risk score ≥ 5; and several comorbidities. Inpatient treatment with remdesivir or anticoagulation were associated with lower odds. The model's AUC for the internal validation set was 0.73 (95% CI 0.71-0.74) and 0.66 (95% CI 0.64 to 0.67) for the external validation set.

CONCLUSIONS

This large retrospective study identified several factors associated with post-discharge readmission or death in models which performed with good discrimination. Patients 7 or fewer days since test positivity and who demonstrate potentially reversible risk factors may benefit from delaying discharge until those risk factors resolve.

penalizedclr: an R package for penalized conditional logistic regression for integration of multiple omics layers

BACKGROUND

The matched case-control design, up until recently mostly pertinent to epidemiological studies, is becoming customary in biomedical applications as well. For instance, in omics studies, it is quite common to compare cancer and healthy tissue from the same patient. Furthermore, researchers today routinely collect data from various and variable sources that they wish to relate to the case-control status. This highlights the need to develop and implement statistical methods that can take these tendencies into account.

RESULTS

We present an R package penalizedclr, that provides an implementation of the penalized conditional logistic regression model for analyzing matched case-control studies. It allows for different penalties for different blocks of covariates, and it is therefore particularly useful in the presence of multi-source omics data. Both L1 and L2 penalties are implemented. Additionally, the package implements stability selection for variable selection in the considered regression model.

CONCLUSIONS

The proposed method fills a gap in the available software for fitting high-dimensional conditional logistic regression models accounting for the matched design and block structure of predictors/features. The output consists of a set of selected variables that are significantly associated with case-control status. These variables can then be investigated in terms of functional interpretation or validation in further, more targeted studies.

Identification and validation of cuproptosis-related genes in acetaminophen-induced liver injury using bioinformatics analysis and machine learning

Background

Acetaminophen (APAP) is commonly used as an antipyretic analgesic. However, acetaminophen overdose may contribute to liver injury and even liver failure. Acetaminophen-induced liver injury (AILI) is closely related to mitochondrial oxidative stress and dysfunction, which play critical roles in cuproptosis. Here, we explored the potential role of cuproptosis-related genes (CRGs) in AILI.

Methods

The gene expression profiles were obtained from the Gene Expression Omnibus database. The differential expression of CRGs was determined between the AILI and control samples. Protein protein interaction, correlation, and functional enrichment analyses were performed. Machine learning was used to identify hub genes. Immune infiltration was evaluated. The AILI mouse model was established by intraperitoneal injection of APAP solution. Quantitative real-time PCR and western blotting were used to validate hub gene expression in the AILI mouse model. The copper content in the mouse liver samples and AML12 cells were quantified using a colorimetric assay kit. Ammonium tetrathiomolybdate (ATTM), was administered to mouse models and AML12 cells in order to investigate the effects of copper chelator on AILI.

Results

The analysis identified 7,809 differentially expressed genes, 4,245 of which were downregulated and 3,564 of which were upregulated. Four optimal feature genes (OFGs; SDHB, PDHA1, NDUFB2, and NDUFB6) were identified through the intersection of two machine learning algorithms. Further nomogram, decision curve, and calibration curve analyses confirmed the diagnostic predictive efficacy of the four OFGs. Enrichment analysis indicated that the OFGs were involved in multiple pathways, such as IL-17 pathway and chemokine signaling pathway, that are related to AILI progression. Immune infiltration analysis revealed that macrophages were more abundant in AILI than in control samples, whereas eosinophils and endothelial cells were less abundant. Subsequently, the AILI mouse model was successfully established, and histopathological analysis using hematoxylin-eosin staining along with liver function tests revealed a significant induction of liver injury in the APAP group. Consistent with expectations, both mRNA and protein levels of the four OFGs exhibited a substantial decrease. The administration of ATTAM effectively mitigates copper elevation induced by APAP in both mouse model and AML12 cells. However, systemic administration of ATTM did not significantly alleviate AILI in the mouse model.

Conclusion

This study first revealed the potential role of CRGs in the pathological process of AILI and offered novel insights into its underlying pathogenesis.

Longitudinal fragility phenotyping contributes to the prediction of lifespan and age-associated morbidity in C57BL/6 and Diversity Outbred mice

Aging studies in mammalian models often depend on natural lifespan data as a primary outcome. Tools for lifespan prediction could accelerate these studies and reduce the need for veterinary intervention. Here, we leveraged large-scale longitudinal frailty and lifespan data on two genetically distinct mouse cohorts to evaluate noninvasive strategies to predict life expectancy in mice. We applied a modified frailty assessment, the Fragility Index, derived from existing frailty indices with additional deficits selected by veterinarians. We developed an ensemble machine learning classifier to predict imminent mortality (95% proportion of life lived [95PLL]). Our algorithm represented improvement over previous predictive criteria but fell short of the level of reliability that would be needed to make advanced prediction of lifespan and thus accelerate lifespan studies. Highly sensitive and specific frailty-based predictive endpoint criteria for aged mice remain elusive. While frailty-based prediction falls short as a surrogate for lifespan, it did demonstrate significant predictive power and as such must contain information that could be used to inform the conclusion of aging experiments. We propose a frailty-based measure of healthspan as an alternative target for aging research and demonstrate that lifespan and healthspan criteria reveal distinct aspects of aging in mice.

TimiGP-Response: the pan-cancer immune landscape associated with response to immunotherapy

Accumulating evidence suggests that the tumor immune microenvironment (TIME) significantly influences the response to immunotherapy, yet this complex relationship remains elusive. To address this issue, we developed TimiGP-Response (TIME Illustration based on Gene Pairing designed for immunotherapy Response), a computational framework leveraging single-cell and bulk transcriptomic data, along with response information, to construct cell-cell interaction networks associated with responders and estimate the role of immune cells in treatment response. This framework was showcased in triple-negative breast cancer treated with immune checkpoint inhibitors targeting the PD-1:PD-L1 interaction, and orthogonally validated with imaging mass cytometry. As a result, we identified CD8+ GZMB+ T cells associated with responders and its interaction with regulatory T cells emerged as a potential feature for selecting patients who may benefit from these therapies. Subsequently, we analyzed 3,410 patients with seven cancer types (melanoma, non-small cell lung cancer, renal cell carcinoma, metastatic urothelial carcinoma, hepatocellular carcinoma, breast cancer, and esophageal cancer) treated with various immunotherapies and combination therapies, as well as several chemo- and targeted therapies as controls. Using TimiGP-Response, we depicted the pan-cancer immune landscape associated with immunotherapy response at different resolutions. At the TIME level, CD8 T cells and CD4 memory T cells were associated with responders, while anti-inflammatory (M2) macrophages and mast cells were linked to non-responders across most cancer types and datasets. Given that T cells are the primary targets of these immunotherapies and our TIME analysis highlights their importance in response to treatment, we portrayed the pan-caner landscape on 40 T cell subtypes. Notably, CD8+ and CD4+ GZMK+ effector memory T cells emerged as crucial across all cancer types and treatments, while IL-17-producing CD8+ T cells were top candidates associated with immunotherapy non-responders. In summary, this study provides a computational method to study the association between TIME and response across the pan-cancer immune landscape, offering resources and insights into immune cell interactions and their impact on treatment efficacy.

Mendelian randomization based on genome-wide association studies and expression quantitative trait loci, predicting gene targets for the complexity of osteoarthritis as well as the clinical prognosis of the condition

Background

Osteoarthritis (OA) entails a prevalent chronic ailment, marked by the widespread involvement of entire joints. Prolonged low-grade synovial inflammation serves as the key instigator for a cascade of pathological alterations in the joints.

Objective

The study seeks to explore potential therapeutic targets for OA and investigate the associated mechanistic pathways.

Methods

Summary-level data for OA were downloaded from the genome-wide association studies (GWAS) database, expression quantitative trait loci (eQTL) data were acquired from the eQTLGen consortium, and synovial chip data for OA were obtained from the GEO database. Following the integration of data and subsequent Mendelian randomization analysis, differential analysis, and weighted gene co-expression network analysis (WGCNA) analysis, core genes that exhibit a significant causal relationship with OA traits were pinpointed. Subsequently, by employing three machine learning algorithms, additional identification of gene targets for the complexity of OA was achieved. Additionally, corresponding ROC curves and nomogram models were established for the assessment of clinical prognosis in patients. Finally, western blotting analysis and ELISA methodology were employed for the initial validation of marker genes and their linked pathways.

Results

Twenty-two core genes with a significant causal relationship to OA traits were obtained. Through the application of distinct machine learning algorithms, MAT2A and RBM6 emerged as diagnostic marker genes. ROC curves and nomogram models were utilized for evaluating both the effectiveness of the two identified marker genes associated with OA in diagnosis. MAT2A governs the synthesis of SAM within synovial cells, thereby thwarting synovial fibrosis induced by the TGF-β1-activated Smad3/4 signaling pathway.

Conclusion

The first evidence that MAT2A and RBM6 serve as robust diagnostic for OA is presented in this study. MAT2A, through its involvement in regulating the synthesis of SAM, inhibits the activation of the TGF-β1-induced Smad3/4 signaling pathway, thereby effectively averting the possibility of synovial fibrosis. Concurrently, the development of a prognostic risk model facilitates early OA diagnosis, functional recovery evaluation, and offers direction for further therapy.

Computational single-cell methods for predicting cancer risk

Despite recent biotechnological breakthroughs, cancer risk prediction remains a formidable computational and experimental challenge. Addressing it is critical in order to improve prevention, early detection and survival rates. Here, I briefly summarize some key emerging theoretical and computational challenges as well as recent computational advances that promise to help realize the goals of cancer-risk prediction. The focus is on computational strategies based on single-cell data, in particular on bottom-up network modeling approaches that aim to estimate cancer stemness and dedifferentiation at single-cell resolution from a systems-biological perspective. I will describe two promising methods, a tissue and cell-lineage independent one based on the concept of diffusion network entropy, and a tissue and cell-lineage specific one that uses transcription factor regulons. Application of these tools to single-cell and single-nucleus RNA-seq data from stages prior to invasive cancer reveal that they can successfully delineate the heterogeneous inter-cellular cancer-risk landscape, identifying those cells that are more likely to turn cancerous. Bottom-up systems biological modeling of single-cell omic data is a novel computational analysis paradigm that promises to facilitate the development of preventive, early detection and cancer-risk prediction strategies.

Review of weighted exponential random graph models frameworks applied to neuroimaging

Neuro-imaging data can often be represented as statistical networks, especially for functional magnetic resonance imaging (fMRI) data, where brain regions are defined as nodes and the functional interactions between those regions are taken as edges. Such networks are commonly divided into classes depending on the type of edges, namely binary or weighted. A binary network means edges can either be present or absent. Whereas the edges of a weighted network are associated with weight values, and fMRI networks belong to weighted networks. Statistical methods are often adopted to analyse such networks, among which, the exponential random graph model (ERGM) is an important network analysis approach. Typically ERGMs are applied to binary networks, and weighted networks often need to be binarised by arbitrarily selecting a threshold value to define the presence of the edges, which can lead to non-robustness and loss of valuable edge weight information representing the strength of fMRI interaction in fMRI networks. While it is therefore important to gain deeper insight in adopting ERGM on weighted networks, there only exists a few different ERGM frameworks for weighted networks; some of these are not directly implementable on fMRI networks based on their original proposal. We systematically review, implement, analyse and compare five such frameworks via a simulation study and provide guidelines on each modelling framework as well as conclude the suitability of them on fMRI networks based on a range of criteria. We concluded that Multi-Layered ERGM is currently the most suitable framework.

Elucidating prognosis in cervical squamous cell carcinoma and endocervical adenocarcinoma: a novel anoikis-related gene signature model

Background

Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) are among the most prevalent gynecologic malignancies globally. The prognosis is abysmal once cervical cancer progresses to lymphatic metastasis. Anoikis, a specialized form of apoptosis induced by loss of cell adhesion to the extracellular matrix, plays a critical role. The prediction model based on anoikis-related genes (ARGs) expression and clinical data could greatly aid clinical decision-making. However, the relationship between ARGs and CESC remains unclear.

Methods

ARGs curated from the GeneCards and Harmonizome portals were instrumental in delineating CESC subtypes and in developing a prognostic framework for patients afflicted with this condition. We further delved into the intricacies of the immune microenvironment and pathway enrichment across the identified subtypes. Finally, our efforts culminated in the creation of an innovative nomogram that integrates ARGs. The utility of this prognostic tool was underscored by Decision Curve Analysis (DCA), which illuminate its prospective benefits in guiding clinical interventions.

Results

In our study, We discerned a set of 17 survival-pertinent, anoikis-related differentially expressed genes (DEGs) in CESC, from which nine were meticulously selected for the construction of prognostic models. The derived prognostic risk score was subsequently validated as an autonomous prognostic determinant. Through comprehensive functional analyses, we observed distinct immune profiles and drug response patterns among divergent prognostic stratifications. Further, we integrated the risk scores with the clinicopathological characteristics of CESC to develop a robust nomogram. DCA corroborated the utility of our model, demonstrating its potential to enhance patient outcomes through tailored clinical treatment strategies.

Conclusion

The predictive signature, encompassing nine pivotal genes, alongside the meticulously constructed nomogram developed in this research, furnishes clinicians with a sophisticated tool for tailoring treatment strategies to individual patients diagnosed with CESC.

Parameter grouping and co-estimation in physiologically based kinetic models using genetic algorithms

Physiologically based kinetic (PBK) models are widely used in pharmacology and toxicology for predicting the internal disposition of substances upon exposure, voluntarily or not. Due to their complexity, a large number of model parameters need to be estimated, either through in silico tools, in vitro experiments, or by fitting the model to in vivo data. In the latter case, fitting complex structural models on in vivo data can result in overparameterization and produce unrealistic parameter estimates. To address these issues, we propose a novel parameter grouping approach, which reduces the parametric space by co-estimating groups of parameters across compartments. Grouping of parameters is performed using genetic algorithms and is fully automated, based on a novel goodness-of-fit metric. To illustrate the practical application of the proposed methodology, two case studies were conducted. The first case study demonstrates the development of a new PBK model, while the second focuses on model refinement. In the first case study, a PBK model was developed to elucidate the biodistribution of titanium dioxide (TiO2) nanoparticles in rats following intravenous injection. A variety of parameter estimation schemes were employed. Comparative analysis based on goodness-of-fit metrics demonstrated that the proposed methodology yields models that outperform standard estimation approaches, while utilizing a reduced number of parameters. In the second case study, an existing PBK model for perfluorooctanoic acid (PFOA) in rats was extended to incorporate additional tissues, providing a more comprehensive portrayal of PFOA biodistribution. Both models were validated through independent in vivo studies to ensure their reliability.

CT imaging-derived phenotypes for abdominal muscle and their association with age and sex in a medical biobank

The study of muscle mass as an imaging-derived phenotype (IDP) may yield new insights into determining the normal and pathologic variations in muscle mass in the population. This can be done by determining 3D abdominal muscle mass from 12 distinct abdominal muscle regions and groups using computed tomography (CT) in a racially diverse medical biobank. To develop a fully automatic technique for assessment of CT abdominal muscle IDPs and preliminarily determine abdominal muscle IDP variations with age and sex in a clinically and racially diverse medical biobank. This retrospective study was conducted using the Penn Medicine BioBank (PMBB), a research protocol that recruits adult participants during outpatient visits at hospitals in the Penn Medicine network. We developed a deep residual U-Net (ResUNet) to segment 12 abdominal muscle groups including the left and right psoas, quadratus lumborum, erector spinae, gluteus medius, rectus abdominis, and lateral abdominals. 110 CT studies were randomly selected for training, validation, and testing. 44 of the 110 CT studies were selected to enrich the dataset with representative cases of intra-abdominal and abdominal wall pathology. The studies were divided into non-overlapping training, validation and testing sets. Model performance was evaluated using the Sørensen-Dice coefficient. Volumes of individual muscle groups were plotted to distribution curves. To investigate associations between muscle IDPs, age, and sex, deep learning model segmentations were performed on a larger abdominal CT dataset from PMBB consisting of 295 studies. Multivariable models were used to determine relationships between muscle mass, age and sex. The model's performance (Dice scores) on the test data was the following: psoas: 0.85 ± 0.12, quadratus lumborum: 0.72 ± 0.14, erector spinae: 0.92 ± 0.07, gluteus medius: 0.90 ± 0.08, rectus abdominis: 0.85 ± 0.08, lateral abdominals: 0.85 ± 0.09. The average Dice score across all muscle groups was 0.86 ± 0.11. Average total muscle mass for females was 2041 ± 560.7 g with a high of 2256 ± 560.1 g (41-50 year old cohort) and a change of - 0.96 g/year, declining to an average mass of 1579 ± 408.8 g (81-100 year old cohort). Average total muscle mass for males was 3086 ± 769.1 g with a high of 3385 ± 819.3 g (51-60 year old cohort) and a change of - 1.73 g/year, declining to an average mass of 2629 ± 536.7 g (81-100 year old cohort). Quadratus lumborum was most highly correlated with age for both sexes (correlation coefficient of - 0.5). Gluteus medius mass in females was positively correlated with age with a coefficient of 0.22. These preliminary findings show that our CNN can automate detailed abdominal muscle volume measurement. Unlike prior efforts, this technique provides 3D muscle segmentations of individual muscles. This technique will dramatically impact sarcopenia diagnosis and research, elucidating its clinical and public health implications. Our results suggest a peak age range for muscle mass and an expected rate of decline, both of which vary between genders. Future goals are to investigate genetic variants for sarcopenia and malnutrition, while describing genotype-phenotype associations of muscle mass in healthy humans using imaging-derived phenotypes. It is feasible to obtain 3D abdominal muscle IDPs with high accuracy from patients in a medical biobank using fully automated machine learning methods. Abdominal muscle IDPs showed significant variations in lean mass by age and sex. In the future, this tool can be leveraged to perform a genome-wide association study across the medical biobank and determine genetic variants associated with early or accelerated muscle wasting.

A universal urinary cell gene signature of acute rejection in kidney allografts

INTRODUCTION

Acute rejection (AR) undermines the life-extending benefits of kidney transplantation and is diagnosed using the invasive biopsy procedure. T cell-mediated rejection (TCMR), antibody-mediated rejection (ABMR), or concurrent TCMR + ABMR (Mixed Rejection [MR]) are the three major types of AR. Development of noninvasive biomarkers diagnostic of AR due to any of the three types is a useful addition to the diagnostic armamentarium.

METHODS

We developed customized RT-qPCR assays and measured urinary cell mRNA copy numbers in 145 biopsy-matched urine samples from 126 kidney allograft recipients. We determined whether the urinary cell three-gene signature diagnostic of TCMR (Suthanthiran et al., 2013) discriminates patients with no rejection biopsies (NR, n = 50) from those with ABMR (n = 28) or MR (n = 20) biopsies.

RESULTS

The urinary cell three-gene signature discriminated all three types of rejection biopsies from NR biopsies (P < 0.0001, One-way ANOVA). Dunnett's multiple comparisons test yielded P < 0.0001 for NR vs. TCMR; P < 0.001 for NR vs. ABMR; and P < 0.0001 for NR vs. MR. By bootstrap resampling, optimism-corrected area under the receiver operating characteristic curve (AUC) was 0.749 (bias-corrected 95% confidence interval [CI], 0.638 to 0.840) for NR vs. TCMR (P < 0.0001); 0.780 (95% CI, 0.656 to 0.878) for NR vs. ABMR (P < 0.0001); and 0.857 (95% CI, 0.727 to 0.947) for NR vs. MR (P < 0.0001). All three rejection categories were distinguished from NR biopsies with similar accuracy (all AUC comparisons P > 0.05).

CONCLUSION

The urinary cell three-gene signature score discriminates AR due to TCMR, ABMR or MR from NR biopsies in human kidney allograft recipients.

Pulmonary function test-related prognostic models in non-small cell lung cancer patients receiving neoadjuvant chemoimmunotherapy

Background

This study aimed to establish a comprehensive clinical prognostic risk model based on pulmonary function tests. This model was intended to guide the evaluation and predictive management of patients with resectable stage I-III non-small cell lung cancer (NSCLC) receiving neoadjuvant chemoimmunotherapy.

Methods

Clinical pathological characteristics and prognostic survival data for 175 patients were collected. Univariate and multivariate Cox regression analyses, and least absolute shrinkage and selection operator (LASSO) regression analysis were employed to identify variables and construct corresponding models. These variables were integrated to develop a ridge regression model. The models' discrimination and calibration were evaluated, and the optimal model was chosen following internal validation. Comparative analyses between the risk scores or groups of the optimal model and clinical factors were conducted to explore the potential clinical application value.

Results

Univariate regression analysis identified smoking, complete pathologic response (CPR), and major pathologic response (MPR) as protective factors. Conversely, T staging, D-dimer/white blood cell ratio (DWBCR), D-dimer/fibrinogen ratio (DFR), and D-dimer/minute ventilation volume actual ratio (DMVAR) emerged as risk factors. Evaluation of the models confirmed their capability to accurately predict patient prognosis, exhibiting ideal discrimination and calibration, with the ridge regression model being optimal. Survival analysis demonstrated that the disease-free survival (DFS) in the high-risk group (HRG) was significantly shorter than in the low-risk group (LRG) (P=2.57×10-13). The time-dependent receiver operating characteristic (ROC) curve indicated that the area under the curve (AUC) values at 1 year, 2 years, and 3 years were 0.74, 0.81, and 0.79, respectively. Clinical correlation analysis revealed that men with lung squamous cell carcinoma or comorbid chronic obstructive pulmonary disease (COPD) were predominantly in the LRG, suggesting a better prognosis and potentially identifying a beneficiary population for this treatment combination.

Conclusion

The prognostic model developed in this study effectively predicts the prognosis of patients with NSCLC receiving neoadjuvant chemoimmunotherapy. It offers valuable predictive insights for clinicians, aiding in developing treatment plans and monitoring disease progression.

A novel signature constructed by mitochondrial function and cell death-related gene for the prediction of prognosis in bladder cancer

Bladder urothelial carcinoma (BLCA) presents a persistent challenge in clinical management. Despite recent advancements demonstrating the BLCA efficacy of immune checkpoint inhibitors (ICI) in BLCA patients, there remains a critical need to identify and expand the subset of individuals who benefit from this treatment. Mitochondria, as pivotal regulators of various cell death pathways in eukaryotic cells, exert significant influence over tumor cell fate and survival. In this study, our objective was to investigate biomarkers centered around mitochondrial function and cell death mechanisms to facilitate prognostic prediction and guide therapeutic decision-making in BLCA. Utilizing ssGSEA and LASSO regression, we developed a prognostic signature termed mitochondrial function and cell death (mtPCD). Subsequently, we evaluated the associations between mtPCD score and diverse clinical outcomes, including prognosis, functional pathway enrichment, immune cell infiltration, immunotherapy response analysis and drug sensitivity, within high- and low-risk subgroups. Additionally, we employed single-cell level functional assays, RT-qPCR, and immunohistochemistry to validate the differential expression of genes comprising the mtPCD signature. The mtPCD signature comprises a panel of 10 highly influential genes, strongly correlated with survival outcomes in BLCA patients and exhibiting robust predictive capabilities. Importantly, individuals classified as high-risk according to mtPCD score displayed a subdued overall immune response, characterized by diminished immunotherapeutic efficacy. In summary, our findings highlight the development of a novel prognostic signature, which not only holds promise as a biomarker for BLCA prognosis but also offers insights into the immune landscape of BLCA. This paradigm may pave the way for personalized treatment strategies in BLCA management.

Cross-Sectional and Longitudinal Association of Clinical and Neurocognitive Factors With Apathy in Patients With Parkinson Disease

BACKGROUND AND OBJECTIVES

A robust understanding of the natural history of apathy in Parkinson disease (PD) is foundational for developing effective clinical management tools. However, large longitudinal studies are lacking while the literature is inconsistent about even cross-sectional associations. We aimed to determine the longitudinal predictors of apathy development in a large cohort of people with PD and its cross-sectional associations and trajectories over time, using sophisticated Bayesian modeling techniques.

METHODS

People with PD followed up in the longitudinal New Zealand Parkinson's progression project were included. Apathy was defined using the neuropsychiatric inventory subscale ≥4, and analyses were also repeated using a less stringent cutoff of ≥1. Both MoCA and comprehensive neuropsychological testing were used as appropriate to the model. Depression was assessed using the hospital anxiety and depression scale. Cross-sectional Bayesian regressions were conducted, and a multistate predictive model was used to identify factors that predict the initial onset of apathy in nonapathetic PD, while also accounting for the competing risk of death. The relationship between apathy presence and mortality was also investigated.

RESULTS

Three hundred forty-six people with PD followed up for up to 14 years across a total of 1,392 sessions were included. Apathy occurrence did not vary significantly across the disease course (disease duration odds ratio [OR] = 0.55, [95% CI 0.28-1.12], affecting approximately 11% or 22% of people at any time depending on the NPI cutoff used. Its presence was associated with a significantly higher risk of death after controlling for all other factors (hazard ratio [HR] = 2.92 [1.50-5.66]). Lower cognition, higher depression levels, and greater motor severity predicted apathy development in those without motivational deficits (HR [cognition] = 0.66 [0.48-0.90], HR [depression] = 1.45 [1.04-2.02], HR [motor severity] = 1.37 [1.01-1.86]). Cognition and depression were also associated with apathy cross-sectionally, along with male sex and possibly lower dopaminergic therapy level, but apathy still occurred across the full spectrum of each variable (OR [cognition] = 0.58 [0.44-0.76], OR [depression] = 1.43 [1.04-1.97], OR [female sex] = 0.45 [0.22-0.92], and OR [levodopa equivalent dose] = 0.78 [0.59-1.04].

DISCUSSION

Apathy occurs across the PD time course and is associated with higher mortality. Depressive symptoms and cognitive impairment in particular predict its future development in those with normal motivation.

Strain-specific gut microbial signatures in type 2 diabetes identified in a cross-cohort analysis of 8,117 metagenomes

The association of gut microbial features with type 2 diabetes (T2D) has been inconsistent due in part to the complexity of this disease and variation in study design. Even in cases in which individual microbial species have been associated with T2D, mechanisms have been unable to be attributed to these associations based on specific microbial strains. We conducted a comprehensive study of the T2D microbiome, analyzing 8,117 shotgun metagenomes from 10 cohorts of individuals with T2D, prediabetes, and normoglycemic status in the United States, Europe, Israel and China. Dysbiosis in 19 phylogenetically diverse species was associated with T2D (false discovery rate < 0.10), for example, enriched Clostridium bolteae and depleted Butyrivibrio crossotus. These microorganisms also contributed to community-level functional changes potentially underlying T2D pathogenesis, for example, perturbations in glucose metabolism. Our study identifies within-species phylogenetic diversity for strains of 27 species that explain inter-individual differences in T2D risk, such as Eubacterium rectale. In some cases, these were explained by strain-specific gene carriage, including loci involved in various mechanisms of horizontal gene transfer and novel biological processes underlying metabolic risk, for example, quorum sensing. In summary, our study provides robust cross-cohort microbial signatures in a strain-resolved manner and offers new mechanistic insights into T2D.

Estimating risk of loneliness in adulthood using survey-based prediction models: A cohort study

It is widely accepted that loneliness is associated with health problems, but less is known about the predictors of loneliness. In this study, we constructed a model to predict individual risk of loneliness during adulthood. Data were from the prospective population-based FinHealth cohort study with 3444 participants (mean age 55.5 years, 53.4% women) who responded to a 81-item self-administered questionnaire and reported not to be lonely at baseline in 2017. The outcome was self-reported loneliness at follow-up in 2020. Predictive models were constructed using bootstrap enhanced LASSO regression (bolasso). The C-index from the final model including 11 predictors from the best bolasso -models varied between 0.65 (95% CI 0.61 to 0.70) and 0.71 (95% CI 0.67 to 0.75) the pooled C -index being 0.68 (95% CI 0.61 to 0.75). Although survey-based individualised prediction models for loneliness achieved a reasonable C-index, their predictive value was limited. High detection rates were associated with high false positive rates, while lower false positive rates were associated with low detection rates. These findings suggest that incident loneliness during adulthood. may be difficult to predict with standard survey data.

Integrated analysis of single-cell sequencing and machine learning identifies a signature based on monocyte/macrophage hub genes to analyze the intracranial aneurysm associated immune microenvironment

Monocytes are pivotal immune cells in eliciting specific immune responses and can exert a significant impact on the progression, prognosis, and immunotherapy of intracranial aneurysms (IAs). The objective of this study was to identify monocyte/macrophage (Mo/MΦ)-associated gene signatures to elucidate their correlation with the pathogenesis and immune microenvironment of IAs, thereby offering potential avenues for targeted therapy against IAs. Single-cell RNA-sequencing (scRNA-seq) data of IAs were acquired from the Gene Expression Synthesis (GEO) database. The significant infiltration of monocyte subsets in the parietal tissue of IAs was identified using single-cell RNA sequencing and high-dimensional weighted gene co-expression network analysis (hdWGCNA). The integration of six machine learning algorithms identified four crucial genes linked to these Mo/MΦ. Subsequently, we developed a multilayer perceptron (MLP) neural model for the diagnosis of IAs (independent external test AUC=1.0, sensitivity =100%, specificity =100%). Furthermore, we employed the CIBERSORT method and MCP counter to establish the correlation between monocyte characteristics and immune cell infiltration as well as patient heterogeneity. Our findings offer valuable insights into the molecular characterization of monocyte infiltration in IAs, which plays a pivotal role in shaping the immune microenvironment of IAs. Recognizing this characterization is crucial for comprehending the limitations associated with targeted therapies for IAs. Ultimately, the results were verified by real-time fluorescence quantitative PCR and Immunohistochemistry.

Integrated bulk and single-cell RNA sequencing identifies an aneuploidy-based gene signature to predict sensitivity of lung adenocarcinoma to traditional chemotherapy drugs and patients' prognosis

Background

Patients with lung adenocarcinoma (LUAD) often develop a poor prognosis. Currently, researches on prognostic and immunotherapeutic capacity of aneuploidy-related genes in LUAD are limited.

Methods

Genes related to aneuploidy were screened based on bulk RNA sequencing data from public databases using Spearman method. Next, univariate Cox and Lasso regression analyses were performed to establish an aneuploidy-related riskscore (ARS) model. Results derived from bioinformatics analysis were further validated using cellular experiments. In addition, typical LUAD cells were identified by subtype clustering, followed by SCENIC and intercellular communication analyses. Finally, ESTIMATE, ssGSEA and CIBERSORT algorithms were employed to analyze the potential relationship between ARS and tumor immune environment.

Results

A five-gene ARS signature was developed. These genes were abnormally high-expressed in LUAD cell lines, and in particular the high expression of CKS1B promoted the proliferative, migratory and invasive phenotypes of LUAD cell lines. Low ARS group had longer overall survival time, higher degrees of inflammatory infiltration, and could benefit more from receiving immunotherapy. Patients in low ASR group responded more actively to traditional chemotherapy drugs (Erlotinib and Roscovitine). The scRNA-seq analysis annotated 17 cell subpopulations into seven cell clusters. Core transcription factors (TFs) such as CREB3L1 and CEBPD were enriched in high ARS cell group, while TFs such as BCLAF1 and UQCRB were enriched in low ARS cell group. CellChat analysis revealed that high ARS cell groups communicated with immune cells via SPP1 (ITGA4-ITGB1) and MK (MDK-NCl) signaling pathways.

Conclusion

In this research, integrative analysis based on the ARS model provided a potential direction for improving the diagnosis and treatment of LUAD.

Constructing a prognostic model for colon cancer: insights from immunity-related genes

BACKGROUND

Colon cancer (CC) is a malignancy associated with significant morbidity and mortality within the gastrointestinal tract. Recurrence and metastasis are the main factors affecting the prognosis of CC patients undergoing radical surgery; consequently, we attempted to determine the impact of immunity-related genes.

RESULT

We constructed a CC risk model based on ZG16, MPC1, RBM47, SMOX, CPM and DNASE1L3. Consistently, we found that a significant association was found between the expression of most characteristic genes and tumor mutation burden (TMB), microsatellite instability (MSI) and neoantigen (NEO). Additionally, a notable decrease in RBM47 expression was observed in CC tissues compared with that in normal tissues. Moreover, RBM47 expression was correlated with clinicopathological characteristics and improved disease-free survival (DFS) and overall survival (OS) among patients with CC. Lastly, immunohistochemistry and co-immunofluorescence staining revealed a clear positive correlation between RBM47 and CXCL13 in mature tertiary lymphoid structures (TLS) region.

CONCLUSION

We conclude that RBM47 was identified as a prognostic-related gene, which was of great significance to the prognosis evaluation of patients with CC and was correlated with CXCL13 in the TLS region.

Construction of a pathway-level model for preeclampsia based on gene expression data

Preeclampsia (PE) is a heterogeneous disease that seriously affects the health of mothers and fetuses. Lack of detection assays, its diagnosis and intervention are often delayed when the clinical symptoms are atypical. Using personalized pathway-based analysis and machine learning algorithms, we built a PE diagnosis model consisting of nine core pathways using multiple cohorts from the Gene Expression Omnibus database. The model showed an area under the receiver operating characteristic (AUROC) curve of 0.959 with the data from the placental tissue samples in the development cohort. In the two validation cohorts, the AUROCs were 0.898 and 0.876, respectively. The model also performed well with the maternal plasma data in another validation cohort (AUROC: 0.815). Moreover, we identified tyrosine-protein kinase Lck (LCK) as the hub gene in this model and found that LCK and pLCK proteins were downregulated in placentas from PE patients. The pathway-level model for PE can provide a novel direction to develop molecular diagnostic assay and investigate potential mechanisms of PE in future studies.

Bayesian survival analysis with INLA

This tutorial shows how various Bayesian survival models can be fitted using the integrated nested Laplace approximation in a clear, legible, and comprehensible manner using the INLA and INLAjoint R-packages. Such models include accelerated failure time, proportional hazards, mixture cure, competing risks, multi-state, frailty, and joint models of longitudinal and survival data, originally presented in the article "Bayesian survival analysis with BUGS." In addition, we illustrate the implementation of a new joint model for a longitudinal semicontinuous marker, recurrent events, and a terminal event. Our proposal aims to provide the reader with syntax examples for implementing survival models using a fast and accurate approximate Bayesian inferential approach.

Bayesian Estimation Improves Prediction of Outcomes after Epilepsy Surgery

Low power is a problem in many fields, as underpowered studies that find a statistically significant result will exaggerate the magnitude of the observed effect size. We quantified the statistical power and magnitude error of studies of epilepsy surgery outcomes. The median power across all studies was 14%. Studies with a median sample size or less (n<=56) and a statistically significant result exaggerated the true effect size by a factor of 5.4 (median odds ratio 9.3 vs. median true odds ratio 1.7), while the Bayesian estimate of the odds ratio only exaggerated the true effect size by a factor of 1.6 (2.7 vs. 1.7). We conclude that Bayesian estimation of odds ratio attenuates the exaggeration of significant effect sizes in underpowered studies. This approach could help improve patient counseling about the chance of seizure freedom after epilepsy surgery.

Maternal Depressive Symptoms and Offspring Internalizing Problems: A Cross-Lagged Panel Network Analysis in Late Childhood and Early Adolescence

The present study investigated the relations between maternal depressive symptoms and internalizing problems in offspring during late childhood and early adolescence, examining sex differences using symptom network analysis. A total of 885 Chinese youths in late childhood (n = 497, 38.6% girls; age = 9.58 years, SD = 0.24) and early adolescence (n = 388, 48.5% girls; age = 11.30 years, SD = 0.24) and their mothers (Mage = 37.34 years, SD = 5.42) were recruited. Cross-lagged panel network (CLPN) analysis was used to explore bridge symptoms (i.e., symptoms connecting two or more mental disorders) and identify transmission pathways between maternal depressive symptoms and offspring's internalizing problems at these two developmental stages. The CLPN results revealed that in late childhood, the bridge connections in the network model were boys feeling worried to mothers feeling distractible, and girls feeling worried to mothers feeling powerless. In early adolescence, the bridge connections were boys experiencing depressed mood to mothers feeling powerless, and mothers feeling bad to girls experiencing depressed mood. These findings highlight the network-level relations between maternal depressive symptoms and offspring internalizing problems. They provide insights into the developmental differences and similarities in symptoms during these periods and suggest ways to break the vicious cycle of psychopathology between mothers and their children.

A universal preference for animate agents in hominids

When conversing, humans instantaneously predict meaning from fragmentary and ambiguous mspeech, long before utterance completion. They do this by integrating priors (initial assumptions about the world) with contextual evidence to rapidly decide on the most likely meaning. One powerful prior is attentional preference for agents, which biases sentence processing but universally so only if agents are animate. Here, we investigate the evolutionary origins of this preference, by allowing chimpanzees, gorillas, orangutans, human children, and adults to freely choose between agents and patients in still images, following video clips depicting their dyadic interaction. All participants preferred animate (and occasionally inanimate) agents, although the effect was attenuated if patients were also animate. The findings suggest that a preference for animate agents evolved before language and is not reducible to simple perceptual biases. To conclude, both humans and great apes prefer animate agents in decision tasks, echoing a universal prior in human language processing.

The Neurocomputational Mechanism Underlying Decision-Making on Unfairness to Self and Others

Fairness is a fundamental value in human societies, with individuals concerned about unfairness both to themselves and to others. Nevertheless, an enduring debate focuses on whether self-unfairness and other-unfairness elicit shared or distinct neuropsychological processes. To address this, we combined a three-person ultimatum game with computational modeling and advanced neuroimaging analysis techniques to unravel the behavioral, cognitive, and neural patterns underlying unfairness to self and others. Our behavioral and computational results reveal a heightened concern among participants for self-unfairness over other-unfairness. Moreover, self-unfairness consistently activates brain regions such as the anterior insula, dorsal anterior cingulate cortex, and dorsolateral prefrontal cortex, spanning various spatial scales that encompass univariate activation, local multivariate patterns, and whole-brain multivariate patterns. These regions are well-established in their association with emotional and cognitive processes relevant to fairness-based decision-making. Conversely, other-unfairness primarily engages the middle occipital gyrus. Collectively, our findings robustly support distinct neurocomputational signatures between self-unfairness and other-unfairness.

A dynamic microRNA profile that tracks a chemotherapy resistance phenotype in osteosarcoma. Implications for novel therapeutics

Osteosarcoma is a rare primary bone tumor for which no significant therapeutic advancement has been made since the late 1980s despite ongoing efforts. Overall, the five-year survival rate remains about 65%, and is much lower in patients with tumors unresponsive to methotrexate, doxorubicin, and cisplatin therapy. Genetic studies have not revealed actionable drug targets, but our group, and others, have reported that epigenomic biomarkers, including regulatory RNAs, may be useful prognostic tools for osteosarcoma. We tested if microRNA (miRNA) transcriptional patterns mark the transition from a chemotherapy sensitive to resistant tumor phenotype. Small RNA sequencing was performed using 14 patient matched pre-chemotherapy biopsy and post-chemotherapy resection high-grade osteosarcoma frozen tumor samples. Independently, small RNA sequencing was performed using 14 patient matched biopsy and resection samples from untreated tumors. Separately, miRNA specific Illumina DASL arrays were used to assay an independent cohort of 65 pre-chemotherapy biopsy and 26 patient matched post-chemotherapy resection formalin fixed paraffin embedded (FFPE) tumor samples. mRNA specific Illumina DASL arrays were used to profile 37 pre-chemotherapy biopsy and five post-chemotherapy resection FFPE samples, all of which were also used for Illumina DASL miRNA profiling. The National Cancer Institute Therapeutically Applicable Research to Generate Effective Treatments dataset, including PCR based miRNA profiling and RNA-seq data for 86 and 93 pre-chemotherapy tumor samples, respectively, was also used. Paired differential expression testing revealed a profile of 17 miRNAs with significantly different transcriptional levels following chemotherapy. Genes targeted by the miRNAs were differentially expressed following chemotherapy, suggesting the miRNAs may regulate transcriptional networks. Finally, an in vitro pharmacogenomic screen using miRNAs and their target transcripts predicted response to a set of candidate small molecule therapeutics which potentially reverse the chemotherapy resistance phenotype and synergize with chemotherapy in otherwise treatment resistant tumors. Importantly, these novel therapeutic targets are distinct from targets identified by a similar pharmacogenomic analysis of previously published prognostic miRNA profiles from pre chemotherapy biopsy specimens.

Predicting responses to climate change using a joint species, spatially dependent physiologically guided abundance model

Predicting the effects of warming temperatures on the abundance and distribution of organisms under future climate scenarios often requires extrapolating species-environment correlations to climatic conditions not currently experienced by a species, which can result in unrealistic predictions. For poikilotherms, incorporating species' thermal physiology to inform extrapolations under novel thermal conditions can result in more realistic predictions. Furthermore, models that incorporate species and spatial dependencies may improve predictions by capturing correlations present in ecological data that are not accounted for by predictor variables. Here, we present a joint species, spatially dependent physiologically guided abundance (jsPGA) model for predicting multispecies responses to climate warming. The jsPGA model uses a basis function approach to capture both species and spatial dependencies. We apply the jsPGA model to predict the response of eight fish species to projected climate warming in thousands of lakes in Minnesota, USA. By the end of the century, the cold-adapted species was predicted to have high probabilities of extirpation across its current range-with 10% of lakes currently inhabited by this species having an extirpation probability >0.90. The remaining species had varying levels of predicted changes in abundance, reflecting differences in their thermal physiology. Though the model did not identify many strong species dependencies, the variation in estimated spatial dependence across species suggested that accounting for both dependencies was important for predicting the abundance of these fishes. The jsPGA model provides a new tool for predicting changes in the abundance, distribution, and extirpation probability of poikilotherms under novel thermal conditions.

Robust and consistent biomarker candidates identification by a machine learning approach applied to pancreatic ductal adenocarcinoma metastasis

BACKGROUND

Machine Learning (ML) plays a crucial role in biomedical research. Nevertheless, it still has limitations in data integration and irreproducibility. To address these challenges, robust methods are needed. Pancreatic ductal adenocarcinoma (PDAC), a highly aggressive cancer with low early detection rates and survival rates, is used as a case study. PDAC lacks reliable diagnostic biomarkers, especially metastatic biomarkers, which remains an unmet need. In this study, we propose an ML-based approach for discovering disease biomarkers, apply it to the identification of a PDAC metastatic composite biomarker candidate, and demonstrate the advantages of harnessing data resources.

METHODS

We utilised primary tumour RNAseq data from five public repositories, pooling samples to maximise statistical power and integrating data by correcting for technical variance. Data were split into train and validation sets. The train dataset underwent variable selection via a 10-fold cross-validation process that combined three algorithms in 100 models per fold. Genes found in at least 80% of models and five folds were considered robust to build a consensus multivariate model. A random forest model was constructed using selected genes from the train dataset and tested in the validation set. We also assessed the goodness of prediction by recalibrating a model using only the validation data. The biological context and relevance of signals was explored through enrichment and pathway analyses using QIAGEN Ingenuity Pathway Analysis and GeneMANIA.

RESULTS

We developed a pipeline that can detect robust signatures to build composite biomarkers. We tested the pipeline in PDAC, exploiting transcriptomics data from different sources, proposing a composite biomarker candidate comprised of fifteen genes consistently selected that showed very promising predictive capability. Biological contextualisation revealed links with cancer progression and metastasis, underscoring their potential relevance. All code is available in GitHub.

CONCLUSION

This study establishes a robust framework for identifying composite biomarkers across various disease contexts. We demonstrate its potential by proposing a plausible composite biomarker candidate for PDAC metastasis. By reusing data from public repositories, we highlight the sustainability of our research and the wider applications of our pipeline. The preliminary findings shed light on a promising validation and application path.

Integrating DNA/RNA microbe detection and host response for accurate diagnosis, treatment and prognosis of childhood infectious meningitis and encephalitis

BACKGROUND

Infectious meningitis/encephalitis (IM) is a severe neurological disease that can be caused by bacterial, viral, and fungal pathogens. IM suffers high morbidity, mortality, and sequelae in childhood. Metagenomic next-generation sequencing (mNGS) can potentially improve IM outcomes by sequencing both pathogen and host responses and increasing the diagnosis accuracy.

METHODS

Here we developed an optimized mNGS pipeline named comprehensive mNGS (c-mNGS) to monitor DNA/RNA pathogens and host responses simultaneously and applied it to 142 cerebrospinal fluid samples. According to retrospective diagnosis, these samples were classified into three categories: confirmed infectious meningitis/encephalitis (CIM), suspected infectious meningitis/encephalitis (SIM), and noninfectious controls (CTRL).

RESULTS

Our pipeline outperformed conventional methods and identified RNA viruses such as Echovirus E30 and etiologic pathogens such as HHV-7, which would not be clinically identified via conventional methods. Based on the results of the c-mNGS pipeline, we successfully detected antibiotic resistance genes related to common antibiotics for treating Escherichia coli, Acinetobacter baumannii, and Group B Streptococcus. Further, we identified differentially expressed genes in hosts of bacterial meningitis (BM) and viral meningitis/encephalitis (VM). We used these genes to build a machine-learning model to pinpoint sample contaminations. Similarly, we also built a model to predict poor prognosis in BM.

CONCLUSIONS

This study developed an mNGS-based pipeline for IM which measures both DNA/RNA pathogens and host gene expression in a single assay. The pipeline allows detecting more viruses, predicting antibiotic resistance, pinpointing contaminations, and evaluating prognosis. Given the comparable cost to conventional mNGS, our pipeline can become a routine test for IM.

Network meta-analysis made simple: a composite likelihood approach

Network meta-analysis, also known as mixed treatments comparison meta-analysis or multiple treatments meta-analysis, extends conventional pairwise meta-analysis by simultaneously synthesizing multiple interventions in a single integrated analysis. Despite the growing popularity of network metaanalysis within comparative effectiveness research, it comes with potential challenges. For example, within-study correlations among treatment comparisons are rarely reported in the published literature. Yet, these correlations are pivotal for valid statistical inference. As demonstrated in earlier studies, ignoring these correlations can inflate mean squared errors of the resulting point estimates and lead to inaccurate standard error estimates. This paper introduces a composite likelihood-based approach that ensures accurate statistical inference without requiring knowledge of the within-study correlations. The proposed method is computationally robust and efficient, with substantially reduced computational time compared to the state-of-the-science methods implemented in R packages. The proposed method was evaluated through extensive simulations and applied to two important applications including a network meta-analysis comparing interventions for primary open-angle glaucoma, and another comparing treatments for chronic prostatitis and chronic pelvic pain syndrome.

Renewable risk assessment of heterogeneous streaming time-to-event cohorts

The analysis of streaming time-to-event cohorts has garnered significant research attention. Most existing methods require observed cohorts from a study sequence to be independent and identically sampled from a common model. This assumption may be easily violated in practice. Our methodology operates within the framework of online data updating, where risk estimates for each cohort of interest are continuously refreshed using the latest observations and historical summary statistics. At each streaming stage, we introduce parameters to quantify the potential discrepancy between batch-specific effects from adjacent cohorts. We then employ penalized estimation techniques to identify nonzero discrepancy parameters, allowing us to adaptively adjust risk estimates based on current data and historical trends. We illustrate our proposed method through extensive empirical simulations and a lung cancer data analysis.

Individual Predictors of Response to A Behavioral Activation-Based Digital Smoking Cessation Intervention: A Machine Learning Approach

Background: Depression is prevalent among individuals who smoke cigarettes and increases risk for relapse. A previous clinical trial suggests that Goal2Quit, a behavioral activation-based smoking cessation mobile app, effectively increases smoking abstinence and reduces depressive symptoms. Objective: Secondary analyses were conducted on these trial data to identify predictors of success in depression-specific digitalized cessation interventions. Methods: Adult who smoked cigarettes (age = 38.4 ± 10.3, 53% women) were randomized to either use Goal2Quit for 12 weeks (N = 103), paired with a 2-week sample of nicotine replacement therapy (patch and lozenge) or to a Treatment-As-Usual (TAU) control (N = 47). The least absolute shrinkage and selection operator was utilized to identify a subset of baseline variables predicting either smoking or depression intervention outcomes. The retained predictors were then fitted via linear regression models to determine relations to each intervention outcome. Results: Relative to TAU, only individuals who spent significant time using Goal2Quit (56 ± 46 min) were more likely to reduce cigarette use by at least 50% after 12 weeks, whereas those who spent minimal time using Goal2Quit (10 ± 2 min) did not exhibit significant changes. An interaction between educational attainment and treatment group revealed that, as compared to TAU, only app users with an educational degree beyond high school exhibited significant reductions in depression. Conclusions: The findings highlight the importance of tailoring depression-specific digital cessation interventions to individuals' unique engagement needs and educational level. This study provides a potential methodological template for future research aimed at personalizing technology-based treatments for cigarette users with depressive symptoms.

Multi-modal segmentation with missing image data for automatic delineation of gross tumor volumes in head and neck cancers

BACKGROUND

Head and neck (HN) gross tumor volume (GTV) auto-segmentation is challenging due to the morphological complexity and low image contrast of targets. Multi-modality images, including computed tomography (CT) and positron emission tomography (PET), are used in the routine clinic to assist radiation oncologists for accurate GTV delineation. However, the availability of PET imaging may not always be guaranteed.

PURPOSE

To develop a deep learning segmentation framework for automated GTV delineation of HN cancers using a combination of PET/CT images, while addressing the challenge of missing PET data.

METHODS

Two datasets were included for this study: Dataset I: 524 (training) and 359 (testing) oropharyngeal cancer patients from different institutions with their PET/CT pairs provided by the HECKTOR Challenge; Dataset II: 90 HN patients(testing) from a local institution with their planning CT, PET/CT pairs. To handle potentially missing PET images, a model training strategy named the "Blank Channel" method was implemented. To simulate the absence of a PET image, a blank array with the same dimensions as the CT image was generated to meet the dual-channel input requirement of the deep learning model. During the model training process, the model was randomly presented with either a real PET/CT pair or a blank/CT pair. This allowed the model to learn the relationship between the CT image and the corresponding GTV delineation based on available modalities. As a result, our model had the ability to handle flexible inputs during prediction, making it suitable for cases where PET images are missing. To evaluate the performance of our proposed model, we trained it using training patients from Dataset I and tested it with Dataset II. We compared our model (Model 1) with two other models which were trained for specific modality segmentations: Model 2 trained with only CT images, and Model 3 trained with real PET/CT pairs. The performance of the models was evaluated using quantitative metrics, including Dice similarity coefficient (DSC), mean surface distance (MSD), and 95% Hausdorff Distance (HD95). In addition, we evaluated our Model 1 and Model 3 using the 359 test cases in Dataset I.

RESULTS

Our proposed model(Model 1) achieved promising results for GTV auto-segmentation using PET/CT images, with the flexibility of missing PET images. Specifically, when assessed with only CT images in Dataset II, Model 1 achieved DSC of 0.56 ± 0.16, MSD of 3.4 ± 2.1 mm, and HD95 of 13.9 ± 7.6 mm. When the PET images were included, the performance of our model was improved to DSC of 0.62 ± 0.14, MSD of 2.8 ± 1.7 mm, and HD95 of 10.5 ± 6.5 mm. These results are comparable to those achieved by Model 2 and Model 3, illustrating Model 1's effectiveness in utilizing flexible input modalities. Further analysis using the test dataset from Dataset I showed that Model 1 achieved an average DSC of 0.77, surpassing the overall average DSC of 0.72 among all participants in the HECKTOR Challenge.

CONCLUSIONS

We successfully refined a multi-modal segmentation tool for accurate GTV delineation for HN cancer. Our method addressed the issue of missing PET images by allowing flexible data input, thereby providing a practical solution for clinical settings where access to PET imaging may be limited.

A Bayesian multilevel model for populations of networks using exponential-family random graphs

The collection of data on populations of networks is becoming increasingly common, where each data point can be seen as a realisation of a network-valued random variable. Moreover, each data point may be accompanied by some additional covariate information and one may be interested in assessing the effect of these covariates on network structure within the population. A canonical example is that of brain networks: a typical neuroimaging study collects one or more brain scans across multiple individuals, each of which can be modelled as a network with nodes corresponding to distinct brain regions and edges corresponding to structural or functional connections between these regions. Most statistical network models, however, were originally proposed to describe a single underlying relational structure, although recent years have seen a drive to extend these models to populations of networks. Here, we describe a model for when the outcome of interest is a network-valued random variable whose distribution is given by an exponential random graph model. To perform inference, we implement an exchange-within-Gibbs MCMC algorithm that generates samples from the doubly-intractable posterior. To illustrate this approach, we use it to assess population-level variations in networks derived from fMRI scans, enabling the inference of age- and intelligence-related differences in the topological structure of the brain's functional connectivity.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11222-024-10446-0.

A mitochondria-related genes associated neuroblastoma signature - based on bulk and single-cell transcriptome sequencing data analysis, and experimental validation

Background

Neuroblastoma (NB), characterized by its marked heterogeneity, is the most common extracranial solid tumor in children. The status and functionality of mitochondria are crucial in regulating NB cell behavior. While the significance of mitochondria-related genes (MRGs) in NB is still missing in key knowledge.

Materials and methods

This study leverages consensus clustering and machine learning algorithms to construct and validate an MRGs-related signature in NB. Single-cell data analysis and experimental validation were employed to characterize the pivotal role of FEN1 within NB cells.

Results

MRGs facilitated the classification of NB patients into 2 distinct clusters with considerable differences. The constructed MRGs-related signature and its quantitative indicators, mtScore and mtRisk, effectively characterize the MRGs-related patient clusters. Notably, the MRGs-related signature outperformed MYCN in predicting NB patient prognosis and was adept at representing the tumor microenvironment (TME), tumor cell stemness, and sensitivity to the chemotherapeutic agents Cisplatin, Topotecan, and Irinotecan. FEN1, identified as the most contributory gene within the MRGs-related signature, was found to play a crucial role in the communication between NB cells and the TME, and in the developmental trajectory of NB cells. Experimental validations confirmed FEN1's significant influence on NB cell proliferation, apoptosis, cell cycle, and invasiveness.

Conclusion

The MRGs-related signature developed in this study offers a novel predictive tool for assessing NB patient prognosis, immune infiltration, stemness, and chemotherapeutic sensitivity. Our findings unveil the critical function of FEN1 in NB, suggesting its potential as a therapeutic target.

Principal component-based clinical aging clocks identify signatures of healthy aging and targets for clinical intervention

Clocks that measure biological age should predict all-cause mortality and give rise to actionable insights to promote healthy aging. Here we applied dimensionality reduction by principal component analysis to clinical data to generate a clinical aging clock (PCAge) identifying signatures (principal components) separating healthy and unhealthy aging trajectories. We found signatures of metabolic dysregulation, cardiac and renal dysfunction and inflammation that predict unsuccessful aging, and we demonstrate that these processes can be impacted using well-established drug interventions. Furthermore, we generated a streamlined aging clock (LinAge), based directly on PCAge, which maintains equivalent predictive power but relies on substantially fewer features. Finally, we demonstrate that our approach can be tailored to individual datasets, by re-training a custom clinical clock (CALinAge), for use in the Comprehensive Assessment of Long-term Effects of Reducing Intake of Energy (CALERIE) study of caloric restriction. Our analysis of CALERIE participants suggests that 2 years of mild caloric restriction significantly reduces biological age. Altogether, we demonstrate that this dimensionality reduction approach, through integrating different biological markers, can provide targets for preventative medicine and the promotion of healthy aging.

FPR1, as a Potential Biomarker of Diagnosis and Infliximab Therapy Responses for Crohn's Disease, is Related to Disease Activity, Inflammation and Macrophage Polarization

Purpose

Crohn's disease (CD) represents a multifaceted inflammatory gastrointestinal condition, with a profound significance placed on unraveling its molecular pathways to enhance both diagnostic capabilities and therapeutic interventions. This study focused on identifying a robust macrophage-related signatures (MacroSig) for diagnosing CD, emphasizing the role of FPR1 in macrophage polarization and its implications in CD.

Patients and Methods

Expression profiles from intestinal biopsies and macrophages of 1804 CD patients were retrieved from the Gene Expression Omnibus (GEO). Utilizing CIBERSORTx, differential expression analysis, and weighted correlation network analysis to to identify macrophage-related genes (MRGs). By unsupervised clustering, distinct clusters of CD were identified. Potential biomarkers were identified via using four machine learning algorithms, leading to the establishment of MacroSig which combines insights from 12 machine learning algorithms. Furthermore, the expression of FPR1 was verified in intestinal biopsies of CD patients and two murine experimental colitis models. Finally, we further explored the role of FPR1 in macrophage polarization through single-cell analysis as well as through the study of RAW264.7 cells and peritoneal macrophages.

Results

Two distinct clusters with differential levels of macrophage infiltration and inflammation were identified. The MacroSig, which included FPR1 and LILRB2, exhibited high diagnostic accuracy and outperformed existing biomarkers and signatures. Clinical analysis demonstrated a strong correlation of FPR1 with disease activity, endoscopic inflammation status, and response to infliximab treatment. The expression levels of FPR1 were validated in our CD cohort by immunohistochemistry and confirmed in two colitis mouse models. Single-cell analysis indicated that FPR1 is predominantly expressed in macrophages and monocytes. In vitro studies demonstrated that FPR1 was upregulated in M1 macrophages, and its activation promoted M1 polarization.

Conclusion

We developed a promising diagnostic signature for CD, and targeting FPR1 to modulate macrophage polarization may represent a novel therapeutic strategy.