Identification of SUMOylation-related biomarkers in papillary thyroid carcinoma

BACKGROUND

Small ubiquitin-like modifier (SUMO) modification is increasingly recognized as critical in tumorigenesis and progression. This study identifies biomarkers linked to SUMOylation in papillary thyroid carcinoma (PTC), aiming to advance therapeutic and prognostic strategies.

METHODS

Employing PTC datasets and SUMO related genes (SRGs), we utilized univariate Cox regression for prognosis-related SRGs, conducted differential expression analyses, and integrated findings to pinpoint candidate genes. These genes underwent further validation through survival, gene set enrichment, immune infiltration, and drug sensitivity analyses, including external validation via quantitative RT-qPCR. In our final step, we conducted immunohistochemical staining on tumor samples from PTC patients at our center and integrated this with their clinical data to validate BMP8A's effectiveness in predicting recurrence in PTC.

RESULTS

Three biomarkers-BMP8A, RGS8, and SERPIND1-emerged as significant. Gene Set Enrichment Analysis (GSEA) showed their involvement in immune-related pathways, with differential immune infiltration patterns and drug response correlations observed, underscoring their potential for targeted therapy. Lastly, we validated the efficacy of BMP8A in predicting the recurrence of PTC in patients using clinical and pathological data from our center.

CONCLUSION

The study identifies BMP8A, RGS8, and SERPIND1 as key biomarkers associated with SUMOylation in PTC. Their linkage to immune response and drug sensitivity highlights their importance as targets for therapeutic intervention and prognosis in PTC research.

Reservoir displacement by an invasive rodent reduces Lassa virus zoonotic spillover risk

The black rat (Rattus rattus) is a globally invasive species that has been widely introduced across Africa. Within its invasive range in West Africa, R. rattus may compete with the native rodent Mastomys natalensis, the primary reservoir host of Lassa virus, a zoonotic pathogen that kills thousands annually. Here, we use rodent trapping data from Sierra Leone and Guinea to show that R. rattus presence reduces M. natalensis density within the human dwellings where Lassa virus exposure is most likely to occur. Further, we integrate infection data from M. natalensis to demonstrate that Lassa virus zoonotic spillover risk is lower at sites with R. rattus. While non-native species can have numerous negative effects on ecosystems, our results suggest that R. rattus invasion has the indirect benefit of decreasing zoonotic spillover of an endemic pathogen, with important implications for invasive species control across West Africa.

A novel pyroptosis-related gene signature exhibits distinct immune cells infiltration landscape in Wilms' tumor

BACKGROUND

Wilms' tumor (WT) is the most common renal tumor in childhood. Pyroptosis, a type of inflammation-characterized and immune-related programmed cell death, has been extensively studied in multiple tumors. In the current study, we aim to construct a pyroptosis-related gene signature for predicting the prognosis of Wilms' tumor.

METHODS

We acquired RNA-seq data from TARGET kidney tumor projects for constructing a gene signature, and snRNA-seq data from GEO database for validating signature-constructing genes. Pyroptosis-related genes (PRGs) were collected from three online databases. We constructed the gene signature by Lasso Cox regression and then established a nomogram. Underlying mechanisms by which gene signature is related to overall survival states of patients were explored by immune cell infiltration analysis, differential expression analysis, and functional enrichment analysis.

RESULTS

A pyroptosis-related gene signature was constructed with 14 PRGs, which has a moderate to high predicting capacity with 1-, 3-, and 5-year area under the curve (AUC) values of 0.78, 0.80, and 0.83, respectively. A prognosis-predicting nomogram was established by gender, stage, and risk score. Tumor-infiltrating immune cells were quantified by seven algorithms, and the expression of CD8( +) T cells, B cells, Th2 cells, dendritic cells, and type 2 macrophages are positively or negatively correlated with risk score. Two single nuclear RNA-seq samples of different histology were harnessed for validation. The distribution of signature genes was identified in various cell types.

CONCLUSIONS

We have established a pyroptosis-related 14-gene signature in WT. Moreover, the inherent roles of immune cells (CD8( +) T cells, B cells, Th2 cells, dendritic cells, and type 2 macrophages), functions of differentially expressed genes (tissue/organ development and intercellular communication), and status of signaling pathways (proteoglycans in cancer, signaling pathways regulating pluripotent of stem cells, and Wnt signaling pathway) have been elucidated, which might be employed as therapeutic targets in the future.

An integrated omics approach highlights how epigenetic events can explain and predict response to neoadjuvant chemotherapy and bevacizumab in breast cancer

Treatment with the anti-angiogenic drug bevacizumab in addition to chemotherapy has shown efficacy for breast cancer in some clinical trials, but better biomarkers are needed to optimally select patients for treatment. Here, we present an omics approach where DNA methylation profiles are integrated with gene expression and results from proteomic data in breast cancer patients to predict response to therapy and pinpoint response-related epigenetic events. Fresh-frozen tumor biopsies taken before, during, and after treatment from human epidermal growth factor receptor 2 negative non-metastatic patients receiving neoadjuvant chemotherapy with or without bevacizumab were subjected to molecular profiling. Here, we report that DNA methylation at enhancer CpGs related to cell cycle regulation can predict response to chemotherapy and bevacizumab for the estrogen receptor positive subset of patients (AUC = 0.874), and we validated this observation in an independent patient cohort with a similar treatment regimen (AUC = 0.762). Combining the DNA methylation scores with the scores from a previously published protein signature resulted in a slight increase in the prediction performance (AUC = 0.784). We also show that tumors receiving the combination treatment underwent more extensive epigenetic alterations. Finally, we performed an integrative expression-methylation quantitative trait loci analysis on alterations in DNA methylation and gene expression levels, showing that the epigenetic alterations that occur during treatment are different between responders and non-responders and that these differences may be explained by the proliferation-epithelial-to-mesenchymal transition axis through the activity of grainyhead like transcription factor 2. Using tumor purity computed from copy number data, we developed a method for estimating cancer cell-specific methylation to confirm that the association to response reflects DNA methylation in cancer cells. Taken together, these results support the potential for clinical benefit of the addition of bevacizumab to chemotherapy when administered to the correct patients.

Comparing various AI approaches to traditional quantitative assessment of the myocardial perfusion in [82Rb] PET for MACE prediction

Assessing the individual risk of Major Adverse Cardiac Events (MACE) is of major importance as cardiovascular diseases remain the leading cause of death worldwide. Quantitative Myocardial Perfusion Imaging (MPI) parameters such as stress Myocardial Blood Flow (sMBF) or Myocardial Flow Reserve (MFR) constitutes the gold standard for prognosis assessment. We propose a systematic investigation of the value of Artificial Intelligence (AI) to leverage [82 Rb] Silicon PhotoMultiplier (SiPM) PET MPI for MACE prediction. We establish a general pipeline for AI model validation to assess and compare the performance of global (i.e. average of the entire MPI signal), regional (17 segments), radiomics and Convolutional Neural Network (CNN) models leveraging various MPI signals on a dataset of 234 patients. Results showed that all regional AI models significantly outperformed the global model ( p < 0.001 ), where the best AUC of 73.9% (CI 72.5-75.3) was obtained with a CNN model. A regional AI model based on MBF averages from 17 segments fed to a Logistic Regression (LR) constituted an excellent trade-off between model simplicity and performance, achieving an AUC of 73.4% (CI 72.3-74.7). A radiomics model based on intensity features revealed that the global average was the least important feature when compared to other aggregations of the MPI signal over the myocardium. We conclude that AI models can allow better personalized prognosis assessment for MACE.

Predictors of opioid use for rib fractures in a regional Australian hospital

BACKGROUND

Rib fractures (RFs) are the leading type of single serious injury in New South Wales trauma patients. Uncontrolled pain drives the sequelae of atelectasis, pneumonia, respiratory failure, and death in severe cases. Opioids are the mainstay of management; however, they carry numerous adverse effects. Understanding patient or injury factors which predict opioid requirement is important to tailor management. Existing evidence is limited to metropolitan trauma centres (MTCs).

METHODS

We conducted an observational, retrospective, single-centre cohort study of all admissions to Albury Wodonga Health diagnosed with one or more RFs and discharged between January 1st, 2017, and December 31st, 2022, inclusive. Data collected included demographics, injury characteristics, and management, including analgesia. LASSO regression was performed to determine predictors of average daily opioid use for the first five days of admission in oral morphine equivalents (mg). R2 and root mean square error (RMSE) were calculated to assess model performance.

RESULTS

We included 624 patients. LASSO selected number of RFs, fracture displacement score, pulmonary contusion, new injury severity score, age, chest tube use, chronic pain history, opioid history and upper or middle lateral RF location categories as predictors. Sex, middle anterior, middle posterior, and lower RF location categories were excluded by LASSO. The out of sample R2 was 28.6 %. On the scale of log OME, the RMSE was 1.08.

CONCLUSION

The model is effective at identifying predictors of opioid use in this regional centre, which are similar to those described in evidence from MTCs. However, the low R2 with wide prediction intervals limits its utility on an individual level.

Exploring combinations of dimensionality reduction, transfer learning, and regularization methods for predicting binary phenotypes with transcriptomic data

BACKGROUND

Numerous transcriptomic-based models have been developed to predict or understand the fundamental mechanisms driving biological phenotypes. However, few models have successfully transitioned into clinical practice due to challenges associated with generalizability and interpretability. To address these issues, researchers have turned to dimensionality reduction methods and have begun implementing transfer learning approaches.

METHODS

In this study, we aimed to determine the optimal combination of dimensionality reduction and regularization methods for predictive modeling. We applied seven dimensionality reduction methods to various datasets, including two supervised methods (linear optimal low-rank projection and low-rank canonical correlation analysis), two unsupervised methods [principal component analysis and consensus independent component analysis (c-ICA)], and three methods [autoencoder (AE), adversarial variational autoencoder, and c-ICA] within a transfer learning framework, trained on > 140,000 transcriptomic profiles. To assess the performance of the different combinations, we used a cross-validation setup encapsulated within a permutation testing framework, analyzing 30 different transcriptomic datasets with binary phenotypes. Furthermore, we included datasets with small sample sizes and phenotypes of varying degrees of predictability, and we employed independent datasets for validation.

RESULTS

Our findings revealed that regularized models without dimensionality reduction achieved the highest predictive performance, challenging the necessity of dimensionality reduction when the primary goal is to achieve optimal predictive performance. However, models using AE and c-ICA with transfer learning for dimensionality reduction showed comparable performance, with enhanced interpretability and robustness of predictors, compared to models using non-dimensionality-reduced data.

CONCLUSION

These findings offer valuable insights into the optimal combination of strategies for enhancing the predictive performance, interpretability, and generalizability of transcriptomic-based models.

Tissue and cellular spatiotemporal dynamics in colon aging

Tissue structure and molecular circuitry in the colon can be profoundly impacted by systemic age-related effects, but many of the underlying molecular cues remain unclear. Here, we built a cellular and spatial atlas of the colon across three anatomical regions and 11 age groups, encompassing ∼1,500 mouse gut tissues profiled by spatial transcriptomics and ∼400,000 single nucleus RNA-seq profiles. We developed a new computational framework, cSplotch, which learns a hierarchical Bayesian model of spatially resolved cellular expression associated with age, tissue region, and sex, by leveraging histological features to share information across tissue samples and data modalities. Using this model, we identified cellular and molecular gradients along the adult colonic tract and across the main crypt axis, and multicellular programs associated with aging in the large intestine. Our multi-modal framework for the investigation of cell and tissue organization can aid in the understanding of cellular roles in tissue-level pathology.

Estimating SARS-CoV-2 infection probabilities with serological data and a Bayesian mixture model

The individual results of SARS-CoV-2 serological tests measured after the first pandemic wave of 2020 cannot be directly interpreted as a probability of having been infected. Plus, these results are usually returned as a binary or ternary variable, relying on predefined cut-offs. We propose a Bayesian mixture model to estimate individual infection probabilities, based on 81,797 continuous anti-spike IgG tests from Euroimmun collected in France after the first wave. This approach used serological results as a continuous variable, and was therefore not based on diagnostic cut-offs. Cumulative incidence, which is necessary to compute infection probabilities, was estimated according to age and administrative region. In France, we found that a "negative" or a "positive" test, as classified by the manufacturer, could correspond to a probability of infection as high as 61.8% or as low as 67.7%, respectively. "Indeterminate" tests encompassed probabilities of infection ranging from 10.8 to 96.6%. Our model estimated tailored individual probabilities of SARS-CoV-2 infection based on age, region, and serological result. It can be applied in other contexts, if estimates of cumulative incidence are available.

A Prognostic Methylation-Driven Two-Gene Signature in Medulloblastoma

Medulloblastoma (MB) is one of the most common pediatric brain tumors and it is estimated that one-third of patients will not achieve long-term survival. Conventional prognostic parameters have limited and unreliable correlations with MB outcome, presenting a major challenge for patients' clinical improvement. Acknowledging this issue, our aim was to build a gene signature and evaluate its potential as a new prognostic model for patients with the disease. In this study, we used six datasets totaling 1679 samples including RNA gene expression and DNA methylation data from primary MB as well as control samples from healthy cerebellum. We identified methylation-driven genes (MDGs) in MB, genes whose expression is correlated with their methylation. We employed LASSO regression, incorporating the MDGs as a parameter to develop the prognostic model. Through this approach, we derived a two-gene signature (GS-2) of candidate prognostic biomarkers for MB (CEMIP and NCBP3). Using a risk score model, we confirmed the GS-2 impact on overall survival (OS) with Kaplan-Meier analysis. We evaluated its robustness and accuracy with receiver operating characteristic curves predicting OS at 1, 3, and 5 years in multiple independent datasets. The GS-2 showed highly significant results as an independent prognostic biomarker compared to traditional MB markers. The methylation-regulated GS-2 risk score model can effectively classify patients with MB into high and low-risk, reinforcing the importance of this epigenetic modification in the disease. Such genes stand out as promising prognostic biomarkers with potential application for MB treatment.

Going beyond political ideology: A computational analysis of civic trust in science

Numerous studies have been conducted to identify the factors that predict trust/distrust in science. However, most of these studies are based on closed-ended survey research, which does not allow researchers to gain a more nuanced understanding of the phenomenon. This study integrated survey analysis conducted within the United States with computational text analysis to reveal factors previously obscured by traditional survey methodologies. Even after controlling for political ideology-which has been the most significant explanatory factor in determining trust in science within a survey framework-we found those with concerns over boundary-crossing (i.e. concerns or perceptions that science overlaps with politics, the government, and funding) were less likely to trust science than their counterparts.

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

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

Exploring the diagnostic value of endothelial cell and angiogenesis-related genes in Hashimoto's thyroiditis Based on transcriptomics and single cell RNA sequencing

(1) BACKGROUND: Hashimoto's thyroiditis (HT) can cause angiogenesis in the thyroid gland. However, the molecular mechanism of endothelial cells and angiogenesis related genes (ARGs) has not been extensively studied in HT. (2) METHODS: The HRA001684, GSE29315 and GSE163203 datasets were included in this study. Using single-cell analysis, weighted gene co-expression network analysis (WGCNA), functional enrichment analysis, machine learning algorithms and expression analysis for exploration. And receiver operator characteristic (ROC) curves was draw. Gene set enrichment analysis (GSEA) was utilized to investigate the biological function of the biomarkers. Meanwhile, we investigated into the relationship between biomarkers and different types of immune cells. Additionally, the expression of biomarkers in the TCGA-TC dataset was examined and the mRNA-drug interaction network was constructed. (3) RESULTS: We found 14 cell subtypes were obtained in HT samples after single-cell analysis. A total of 5 biomarkers (CD52, CD74, CD79A, HLA-B and RGS1) were derived, and they had excellent diagnostic performance. Then, 27 drugs targeting biomarkers were predicted. The expression analysis showed that CD74 and HLA-B were significantly up-regulated in HT samples. (4) CONCLUSION: In this study, 5 biomarkers (CD52, CD74, CD79A, HLA-B and RGS1) were screened and their expressions in endothelial cells was compared to offer a new reference for the recognition and management of HT.

Bayesian Mechanistic Inference, Statistical Mechanics, and a New Era for Monte Carlo

On the one hand, much of computational chemistry is concerned with "bottom-up" calculations which elucidate observable behavior starting from exact or approximated physical laws, a paradigm exemplified by typical quantum mechanical calculations and molecular dynamics simulations. On the other hand, "top down" computations aiming to formulate mathematical models consistent with observed data, e.g., parametrizing force fields, binding or kinetic models, have been of interest for decades but recently have grown in sophistication with the use of Bayesian inference (BI). Standard BI provides an estimation of parameter values, uncertainties, and correlations among parameters. Used for "model selection," BI can also distinguish between model structures such as the presence or absence of individual states and transitions. Fortunately for physical scientists, BI can be formulated within a statistical mechanics framework, and indeed, BI has led to a resurgence of interest in Monte Carlo (MC) algorithms, many of which have been directly adapted from or inspired by physical strategies. Certain MC algorithms─notably procedures using an "infinite temperature" reference state─can be successful in a 5-20 parameter BI context which would be unworkable in molecular spaces of 103 coordinates and more. This Review provides a pedagogical introduction to BI and reviews key aspects of BI through a physical lens, setting the computations in terms of energy landscapes and free energy calculations and describing promising sampling algorithms. Statistical mechanics and basic probability theory also provide a reference for understanding intrinsic limitations of Bayesian inference with regard to model selection and the choice of priors.

Predicting prognosis and immunotherapy response in colorectal cancer by pericytes insights from single-cell RNA sequencing

Immunotherapy has revolutionized the treatment of tumors, but there are still a large number of patients who do not benefit from immunotherapy. Pericytes play an important role in remodeling the immune microenvironment. However, how pericytes affect the prognosis and treatment resistance of tumors is still unknown. This study jointly analyzed single-cell RNA sequencing (scRNA-seq) data and bulk RNA sequencing data of multiple cancers to reveal pericyte function in the colorectal cancer microenvironment. Analyzing over 800 000 cells, it was found that colorectal cancer had more pericyte enrichment in tumor tissues than other cancers. We then combined the TCGA database with multiple public datasets and enrolled more than 1000 samples, finding that pericyte may be closely related to poor prognosis due to the higher epithelial-mesenchymal transition (EMT) and hypoxic characteristics. At the same time, patients with more pericytes have higher immune checkpoint molecule expressions and lower immune cell infiltration. Finally, the contributions of pericyte in poor treatment response have been demonstrated in multiple immunotherapy datasets (n = 453). All of these observations suggest that pericyte can be used as a potential biomarker to predict patient disease progression and immunotherapy response.

Development and internal validation of an algorithm for estimating mortality in patients encountered by physician-staffed helicopter emergency medical services

BACKGROUND

Severity of illness scoring systems are used in intensive care units to enable the calculation of adjusted outcomes for audit and benchmarking purposes. Similar tools are lacking for pre-hospital emergency medicine. Therefore, using a national helicopter emergency medical services database, we developed and internally validated a mortality prediction algorithm.

METHODS

We conducted a multicentre retrospective observational register-based cohort study based on the patients treated by five physician-staffed Finnish helicopter emergency medical service units between 2012 and 2019. Only patients aged 16 and over treated by physician-staffed units were included. We analysed the relationship between 30-day mortality and physiological, patient-related and circumstantial variables. The data were imputed using multiple imputations employing chained equations. We used multivariate logistic regression to estimate the variable effects and performed derivation of multiple multivariable models with different combinations of variables. The models were combined into an algorithm to allow a risk estimation tool that accounts for missing variables. Internal validation was assessed by calculating the optimism of each performance estimate using the von Hippel method with four imputed sets.

RESULTS

After exclusions, 30 186 patients were included in the analysis. 8611 (29%) patients died within the first 30 days after the incident. Eleven predictor variables (systolic blood pressure, heart rate, oxygen saturation, Glasgow Coma Scale, sex, age, emergency medical services vehicle type [helicopter vs ground unit], whether the mission was located in a medical facility or nursing home, cardiac rhythm [asystole, pulseless electrical activity, ventricular fibrillation, ventricular tachycardia vs others], time from emergency call to physician arrival and patient category) were included. Adjusted for optimism after internal validation, the algorithm had an area under the receiver operating characteristic curve of 0.921 (95% CI 0.918 to 0.924), Brier score of 0.097, calibration intercept of 0.000 (95% CI -0.040 to 0.040) and slope of 1.000 (95% CI 0.977 to 1.023).

CONCLUSIONS

Based on 11 demographic, mission-specific, and physiologic variables, we developed and internally validated a novel severity of illness algorithm for use with patients encountered by physician-staffed helicopter emergency medical services, which may help in future quality improvement.

Deciphering the heterogeneity dominated by tumor-associated macrophages for survival prognostication and prediction of immunotherapy response in lung adenocarcinoma

Tumor-associated macrophages (TAMs) are a specific subset of macrophages that reside inside the tumor microenvironment. The dynamic interplay between TAMs and tumor cells plays a crucial role in the treatment response and prognosis of lung adenocarcinoma (LUAD). The study aimed to examine the association between TAMs and LUAD to advance the development of targeted strategies and immunotherapeutic approaches for treating this type of lung cancer. The study employed single-cell mRNA sequencing data to characterize the immune cell composition of LUAD and delineate distinct subpopulations of TAMs. The "BayesPrism" and "Seurat" R packages were employed to examine the association between these subgroups and immunotherapy and clinical features to identify novel immunotherapy biomarkers. Furthermore, a predictive signature was generated to forecast patient prognosis by examining the gene expression profile of immunotherapy-associated TAMs subsets and using 104 machine-learning techniques. A comprehensive investigation has shown the existence of a hitherto unidentified subgroup of TAMs known as RGS1 + TAMs, which has been found to have a strong correlation with the efficacy of immunotherapy and the occurrence of tumor metastasis in LUAD patients. CD83 was identified CD83 as a distinct biomarker for the expression of RGS1 + TAMs, showcasing its potential utility as an indicator for immunotherapeutic interventions. Furthermore, the prognostic capacity of the RTMscore signature, encompassing three specific mRNA (NR4A2, MMP14, and NPC2), demonstrated enhanced robustness when contrasted against the comprehensive collection of 104 features outlined in the published study. CD83 has potential as an immunotherapeutic biomarker. Meanwhile, The RTMscore signature established in the present study might be beneficial for survival prognostication.

The power of us: breaking barriers and bridging the gap of ultrasound in rheumatology to empower a new generation

OBJECTIVE

This study assesses musculoskeletal ultrasound (MSUS) knowledge, attitudes, and practices among young rheumatologists in Mexico, aiming to identify barriers and facilitators to its clinical use.

METHODS

An online survey distributed to a network of young rheumatologists captured demographics, institutional, and personal MSUS information. Multivariable analysis identified factors associated with positive MSUS attitudes.

RESULTS

Ninety-six rheumatologists (39.18% national response rate) completed the survey. Of respondents (54.2% females, median age 35.1 years), 81.2% deemed MSUS necessary in clinical rheumatology. The main barriers included limited training access (56.2%) and required training time (54.1%). Lack of scientific evidence was not a major barrier (60.4%). Positive MSUS attitudes were associated with learning from conferences (p = 0.029) and colleagues (p = 0.005), formal (p = 0.043), and in-person training (p = 0.020), MSUS use in practice (p = 0.027), and use by radiologists in their institute (p < 0.001). Interest in learning MSUS (88.5%) was significantly higher in those with positive attitudes (94.4%, p < 0.001). Elastic net analysis identified key drivers, including learning MSUS from conferences, colleagues, and in residency; using MSUS in practice; respondent-performed MSUS; and MSUS use by radiologists. Statistically significant associations were found with using MSUS for synovitis/inflammatory joint disease (OR = 1.43, 95% CI 1.00-2.05) and MSUS use by radiologists in respondent's institutes (OR = 1.70, 95% CI 1.20-2.90).

CONCLUSION

Most young rheumatologists in Mexico recognize the necessity of MSUS in clinical practice. By addressing identified barriers, encouraging rheumatologist-radiologist collaboration, and establishing a regulatory body to certify rheumatologist's MSUS experience, there is an opportunity to empower them with the necessary skills for effective MSUS use, ultimately benefiting patient care.

How much of the outcome improvement after successful recanalization is explained by follow-up infarct volume reduction?

BACKGROUND

Follow-up infarct volume (FIV) is used as surrogate for treatment efficiency in mechanical thrombectomy (MT). However, previous works suggest that MT-related FIV reduction has only limited association with outcome comparing MT independently of recanalization success versus medical care. It remains unclear to what extent the relationship between successful recanalization versus persistent occlusion and functional outcome is explained by FIV reduction.

OBJECTIVE

To determine whether FIV mediates the relationship between successful recanalization and functional outcome.

METHODS

All patients from our institution enrolled in the German Stroke Registry (May 2015-December 2019) with anterior circulation stroke; availability of the relevant clinical data, and follow-up-CT were analyzed. The effect of FIV reduction on functional outcome (90-day modified Rankin Scale (mRS) score ≤2) after successful recanalization (Thrombolysis in Cerebral Infarction ≥2b) was quantified using mediation analysis.

RESULTS

429 patients were included, of whom, 309 (72 %) had successful recanalization and 127 (39%) had good functional outcome. Good outcome was associated with age (OR=0.89, P<0.001), pre-stroke mRS score (OR=0.38, P<0.001), FIV (OR=0.98, P<0.001), hypertension (OR=2.08, P<0.05), and successful recanalization (OR=3.57, P<0.01). Using linear regression in the mediator pathway, FIV was associated with Alberta Stroke program Early CT Score (coefficient (Co)=-26.13, P<0.001), admission National Institutes of Health Stroke Scale score (Co=3.69, P<0.001), age (Co=-1.18, P<0.05), and successful recanalization (Co=-85.22, P<0.001). Successful recanalization increased the probability of good outcome by 23 percentage points (pp) (95% CI 16pp to 29pp). 56% (95% CI 38% to 78%) of the improvement in good outcome was explained by FIV reduction.

CONCLUSION

56% (95% CI 38% to 78%) of outcome improvement after successful recanalization was explained by FIV reduction. Results corroborate pathophysiological assumptions and confirm the value of FIV as an imaging endpoint in clinical trials. 44% (95% CI 22% to 62%) of the improvement in outcome was not explained by FIV reduction and reflects the remaining mismatch between radiological and clinical outcome measures.

Cuproptosis-Related Biomarkers and Characterization of Immune Infiltration in Sepsis

Introduction

Sepsis is a worldwide epidemic, with high morbidity and mortality. Cuproptosis is a form of cell death that is associated with a wide range of diseases. This study aimed to explore genes associated with cuproptosis in sepsis, construct predictive models and screen for potential targets.

Methods

The LASSO algorithm and SVM-RFE model has been analysed the expression of cuproptosis-related genes in sepsis and immune infiltration characteristics and identified the marker genes under a diagnostic model. Gene-drug networks, mRNA-miRNA networks and PPI networks were constructed to screen for potential biological targets. The expression of marker genes was validated based on the GSE57065 dataset. Consensus clustering method was used to classify sepsis samples.

Results

We found 381 genes associated with the development of sepsis and discovered significantly differentially expressed cuproptosis-related genes of 16 cell types in sepsis and immune infiltration with CD8/CD4 T cells being lower. NFE2L2, NLRP3, SLC31A1, DLD, DLAT, PDHB, MTF1, CDKN2A and DLST were identified as marker genes by the LASSO algorithm and the SVM-RFE model. AUC > 0.9 was constructed for PDHB and MTF1 alone respectively. The validation group data for PDHB (P=0.00099) and MTF1 (P=7.2e-14) were statistically significant. Consistent clustering analysis confirmed two subtypes. The C1 subtype may be more relevant to cellular metabolism and the C2 subtype has some relevance to immune molecules.The results of animal experiments showed that the gene expression was consistent with the bioinformatics analysis.

Discussion

Our study systematically explored the relationship between sepsis and cuproptosis and constructed a diagnostic model. And, several cuproptosis-related genes may interfere with the progression of sepsis through immune cell infiltration.

Potential utility of pretreatment serum miRNAs for optimal treatment selection in advanced high-grade serous ovarian cancer

OBJECTIVE

The primary treatment of patients with advanced ovarian cancer is selected from whether primary debulking surgery or neoadjuvant chemotherapy. We investigated whether pretreatment serum microRNA profiles are useful for selecting patients with advanced high-grade serous ovarian cancer who obtain better outcomes from undergoing primary debulking surgery or neoadjuvant chemotherapy.

METHODS

Consecutive patients with clinical stage IIIB-IVB and serum microRNA data were selected. Patients who underwent primary debulking surgery or neoadjuvant chemotherapy were subjected to 1:1 propensity score matching before comparing their progression-free survival using Cox modelling. Progression-free probabilities for the selected microRNA profiles were calculated, and the estimated progression-free survival with the recommended primary treatment was determined and compared with the actual progression-free survival of the patients.

RESULTS

Of the 108 patients with stage IIIB-IVB disease, the data of 24 who underwent primary debulking surgery or neoadjuvant chemotherapy were compared. Eleven and three microRNAs were independent predictors of progression-free survival in patients who underwent primary debulking surgery and neoadjuvant chemotherapy, respectively. Two microRNAs correlated significantly with complete resection of the tumours in primary debulking surgery. No differences were found between the actual and estimated progression-free survival in the primary debulking surgery and neoadjuvant chemotherapy groups (P > 0.05). The recommended and actual primary treatments were identical in 27 (56.3%) of the 48 patients. The median improved survival times between recommended and actual treatment were 11.7 and 32.6 months for patients with actual primary debulking surgery and neoadjuvant chemotherapy, respectively.

CONCLUSIONS

Pretreatment microRNA profiles could be used to select subgroups of patients who benefited more from primary debulking surgery or neoadjuvant chemotherapy and might contribute to selecting the optimal primary treatment modality in advanced high-grade serous ovarian cancer patients.

Chronotype and subjective sleep quality predict white matter integrity in young people with emerging mental disorders

Protecting brain health is a goal of early intervention. We explored whether sleep quality or chronotype could predict white matter (WM) integrity in emerging mental disorders. Young people (N = 364) accessing early-intervention clinics underwent assessments for chronotype, subjective sleep quality, and diffusion tensor imaging. Using machine learning, we examined whether chronotype or sleep quality (alongside diagnostic and demographic factors) could predict four measures of WM integrity: fractional anisotropy (FA), and radial, axial, and mean diffusivities (RD, AD and MD). We prioritised tracts that showed a univariate association with sleep quality or chronotype and considered predictors identified by ≥80% of machine learning (ML) models as 'important'. The most important predictors of WM integrity were demographics (age, sex and education) and diagnosis (depressive and bipolar disorders). Subjective sleep quality only predicted FA in the perihippocampal cingulum tract, whereas chronotype had limited predictive importance for WM integrity. To further examine links with mood disorders, we conducted a subgroup analysis. In youth with depressive and bipolar disorders, chronotype emerged as an important (often top-ranking) feature, predicting FA in the cingulum (cingulate gyrus), AD in the anterior corona radiata and genu of the corpus callosum, and RD in the corona radiata, anterior corona radiata, and genu of corpus callosum. Subjective quality was not important in this subgroup analysis. In summary, chronotype predicted altered WM integrity in the corona radiata and corpus callosum, whereas subjective sleep quality had a less significant role, suggesting that circadian factors may play a more prominent role in WM integrity in emerging mood disorders.

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

INTRODUCTION

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

METHODS

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

RESULTS

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

CONCLUSION

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

Estimating the effects of temperature on transmission of the human malaria parasite, Plasmodium falciparum

Despite concern that climate change could increase the human risk to malaria in certain areas, the temperature dependency of malaria transmission is poorly characterized. Here, we use a mechanistic model fitted to experimental data to describe how Plasmodium falciparum infection of the African malaria vector, Anopheles gambiae, is modulated by temperature, including its influences on parasite establishment, conversion efficiency through parasite developmental stages, parasite development rate, and overall vector competence. We use these data, together with estimates of the survival of infected blood-fed mosquitoes, to explore the theoretical influence of temperature on transmission in four locations in Kenya, considering recent conditions and future climate change. Results provide insights into factors limiting transmission in cooler environments and indicate that increases in malaria transmission due to climate warming in areas like the Kenyan Highlands, might be less than previously predicted.

Modeling children's moral development in postwar Taiwan through naturalistic observations preserved in historical texts

A core issue in the interdisciplinary study of human morality is its ontogeny in diverse cultures, but systematic, naturalistic data in specific cultural contexts are rare to find. This study conducts a novel analysis of 213 children's socio-moral behavior in a historical, non-Western, rural setting, based on a unique dataset of naturalistic observations from the first field research on Han Chinese children. Using multilevel multinomial modeling, we examined a range of proactive behaviors in 0-to-12-year-old children's peer cooperation and conflict in an entire community in postwar Taiwan. We modeled the effects of age, sex, kinship, and behavioral roles, and revealed complex interactions between these four variables in shaping children's moral development. We discovered linkages between coercive and non-coercive behaviors as children strategically negotiated leadership dynamics. We identified connections between prosocial and aggressive behaviors, illuminating the nuances of morality in real life. Our analysis also revealed gendered patterns and age-related trends that deviated from cultural norms and contradicted popular assumptions about Chinese family values. These findings highlight the importance of naturalistic observations in cultural contexts for understanding how we become moral persons. This re-analysis of historically significant fieldnotes also enriches the interdisciplinary study of child development across societies.

Identification of clinical characteristics biomarkers for rheumatoid arthritis through targeted DNA methylation sequencing

OBJECTIVES

Rheumatoid arthritis (RA) is a complex disease with a challenging diagnosis, especially in seronegative patients. The aim of this study is to investigate whether the methylation sites associated with the overall immune response in RA can assist in clinical diagnosis, using targeted methylation sequencing technology on peripheral venous blood samples.

METHODS

The study enrolled 241 RA patients, 30 osteoarthritis patients (OA), and 30 healthy volunteers control (HC). Fifty significant cytosine guanine (CG) sites between undifferentiated arthritis and RA were selected and analyzed using targeted DNA methylation sequencing. Logistic regression models were used to establish diagnostic models for different clinical features of RA, and six machine learning methods (logit model, random forest, support vector machine, adaboost, naive bayes, and learning vector quantization) were used to construct clinical diagnostic models for different subtypes of RA. Least absolute shrinkage and selection operator regression and detrended correspondence analysis were utilized to screen for important CGs. Spearman correlation was used to calculate the correlation coefficient.

RESULTS

The study identified 16 important CG sites, including tumor necrosis factort receptor associated factor 5 (TRAF5) (chr1:211500151), mothers against decapentaplegic homolog 3 (SMAD3) (chr15:67357339), tumor endothelial marker 1 (CD248) (chr11:66083766), lysosomal trafficking regulator (LYST) (chr1:235998714), PR domain zinc finger protein 16 (PRDM16) (chr1:3307069), A-kinase anchoring protein 10 (AKAP10) (chr17:19850460), G protein subunit gamma 7 (GNG7) (chr19:2546620), yes1 associated transcriptional regulator (YAP1) (chr11:101980632), PRDM16 (chr1:3163969), histone deacetylase complex subunit sin3a (SIN3A) (chr15:75747445), prenylated rab acceptor protein 2 (ARL6IP5) (chr3:69134502), mitogen-activated protein kinase kinase kinase 4 (MAP3K4) (chr6:161412392), wnt family member 7A (WNT7A) (chr3:13895991), inhibin subunit beta B (INHBB) (chr2:121107018), deoxyribonucleic acid replication helicase/nuclease 2 (DNA2) (chr10:70231628) and chromosome 14 open reading frame 180 (C14orf180) (chr14:105055171). Seven CG sites showed abnormal changes between the three groups (P < 0.05), and 16 CG sites were significantly correlated with common clinical indicators (P < 0.05). Diagnostic models constructed using different CG sites had an area under the receiver operating characteristic curve (AUC) range of 0.64-0.78 for high-level clinical indicators of high clinical value, with specificity ranging from 0.42 to 0.77 and sensitivity ranging from 0.57 to 0.88. The AUC range for low-level clinical indicators of high clinical value was 0.63-0.72, with specificity ranging from 0.48 to 0.74 and sensitivity ranging from 0.72 to 0.88. Diagnostic models constructed using different CG sites showed good overall diagnostic accuracy for the four subtypes of RA, with an accuracy range of 0.61-0.96, a balanced accuracy range of 0.46-0.94, and an AUC range of 0.46-0.94.

CONCLUSIONS

This study identified potential clinical diagnostic biomarkers for RA and provided novel insights into the diagnosis and subtyping of RA. The use of targeted deoxyribonucleic acid (DNA) methylation sequencing and machine learning methods for establishing diagnostic models for different clinical features and subtypes of RA is innovative and can improve the accuracy and efficiency of RA diagnosis.

A novel network-based method identifies a cuproplasia-related pan-cancer gene signature to predict patient outcome

Copper is a vital micronutrient involved in many biological processes and is an essential component of tumour cell growth and migration. Copper influences tumour growth through a process called cuproplasia, defined as abnormal copper-dependent cell-growth and proliferation. Copper-chelation therapy targeting this process has demonstrated efficacy in several clinical trials against cancer. While the molecular pathways associated with cuproplasia are partially known, genetic heterogeneity across different cancer types has limited the understanding of how cuproplasia impacts patient survival. Utilising RNA-sequencing data from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) datasets, we generated gene regulatory networks to identify the critical cuproplasia-related genes across 23 different cancer types. From this, we identified a novel 8-gene cuproplasia-related gene signature associated with pan-cancer survival, and a 6-gene prognostic risk score model in low grade glioma. These findings highlight the use of gene regulatory networks to identify cuproplasia-related gene signatures that could be used to generate risk score models. This can potentially identify patients who could benefit from copper-chelation therapy and identifies novel targeted therapeutic strategies.

Bayesian Regression Facilitates Quantitative Modeling of Cell Metabolism

This paper presents Maud, a command-line application that implements Bayesian statistical inference for kinetic models of biochemical metabolic reaction networks. Maud takes into account quantitative information from omics experiments and background knowledge as well as structural information about kinetic mechanisms, regulatory interactions, and enzyme knockouts. Our paper reviews the existing options in this area, presents a case study illustrating how Maud can be used to analyze a metabolic network, and explains the biological, statistical, and computational design decisions underpinning Maud.

Machine Learning Applied to Pre-Operative Computed-Tomography-Based Radiomic Features Can Accurately Differentiate Uterine Leiomyoma from Leiomyosarcoma: A Pilot Study

BACKGROUND

The accurate discrimination of uterine leiomyosarcomas and leiomyomas in a pre-operative setting remains a current challenge. To date, the diagnosis is made by a pathologist on the excised tumor. The aim of this study was to develop a machine learning algorithm using radiomic data extracted from contrast-enhanced computed tomography (CECT) images that could accurately distinguish leiomyosarcomas from leiomyomas.

METHODS

Pre-operative CECT images from patients submitted to surgery with a histological diagnosis of leiomyoma or leiomyosarcoma were used for the region of interest identification and radiomic feature extraction. Feature extraction was conducted using the PyRadiomics library, and three feature selection methods combined with the general linear model (GLM), random forest (RF), and support vector machine (SVM) classifiers were built, trained, and tested for the binary classification task (malignant vs. benign). In parallel, radiologists assessed the diagnosis with or without clinical data.

RESULTS

A total of 30 patients with leiomyosarcoma (mean age 59 years) and 35 patients with leiomyoma (mean age 48 years) were included in the study, comprising 30 and 51 lesions, respectively. Out of nine machine learning models, the three feature selection methods combined with the GLM and RF classifiers showed good performances, with predicted area under the curve (AUC), sensitivity, and specificity ranging from 0.78 to 0.97, from 0.78 to 1.00, and from 0.67 to 0.93, respectively, when compared to the results obtained from experienced radiologists when blinded to the clinical profile (AUC = 0.73 95%CI = 0.62-0.84), as well as when the clinical data were consulted (AUC = 0.75 95%CI = 0.65-0.85).

CONCLUSIONS

CECT images integrated with radiomics have great potential in differentiating uterine leiomyomas from leiomyosarcomas. Such a tool can be used to mitigate the risks of eventual surgical spread in the case of leiomyosarcoma and allow for safer fertility-sparing treatment in patients with benign uterine lesions.

Discovery and validation of molecular patterns and immune characteristics in the peripheral blood of ischemic stroke patients

Background

Stroke is a disease with high morbidity, disability, and mortality. Immune factors play a crucial role in the occurrence of ischemic stroke (IS), but their exact mechanism is not clear. This study aims to identify possible immunological mechanisms by recognizing immune-related biomarkers and evaluating the infiltration pattern of immune cells.

Methods

We downloaded datasets of IS patients from GEO, applied R language to discover differentially expressed genes, and elucidated their biological functions using GO, KEGG analysis, and GSEA analysis. The hub genes were then obtained using two machine learning algorithms (least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE)) and the immune cell infiltration pattern was revealed by CIBERSORT. Gene-drug target networks and mRNA-miRNA-lncRNA regulatory networks were constructed using Cytoscape. Finally, we used RT-qPCR to validate the hub genes and applied logistic regression methods to build diagnostic models validated with ROC curves.

Results

We screened 188 differentially expressed genes whose functional analysis was enriched to multiple immune-related pathways. Six hub genes (ANTXR2, BAZ2B, C5AR1, PDK4, PPIH, and STK3) were identified using LASSO and SVM-RFE. ANTXR2, BAZ2B, C5AR1, PDK4, and STK3 were positively correlated with neutrophils and gamma delta T cells, and negatively correlated with T follicular helper cells and CD8, while PPIH showed the exact opposite trend. Immune infiltration indicated increased activity of monocytes, macrophages M0, neutrophils, and mast cells, and decreased infiltration of T follicular helper cells and CD8 in the IS group. The ceRNA network consisted of 306 miRNA-mRNA interacting pairs and 285 miRNA-lncRNA interacting pairs. RT-qPCR results indicated that the expression levels of BAZ2B, C5AR1, PDK4, and STK3 were significantly increased in patients with IS. Finally, we developed a diagnostic model based on these four genes. The AUC value of the model was verified to be 0.999 in the training set and 0.940 in the validation set.

Conclusion

Our research explored the immune-related gene expression modules and provided a specific basis for further study of immunomodulatory therapy of IS.

Revealing Prdx4 as a potential diagnostic and therapeutic target for acute pancreatitis based on machine learning analysis

Acute pancreatitis (AP) is a common systemic inflammatory disease resulting from the activation of trypsinogen by various incentives in ICU. The annual incidence rate is approximately 30 out of 100,000. Some patients may progress to severe acute pancreatitis, with a mortality rate of up to 40%. Therefore, the goal of this article is to explore the key genes for effective diagnosis and treatment of AP. The analysis data for this study were merged from two GEO datasets. 1357 DEGs were used for functional enrichment and cMAP analysis, aiming to reveal the pathogenic genes and potential mechanisms of AP, as well as potential drugs for treating AP. Importantly, the study used LASSO and SVM-RFE machine learning to screen the most likely AP occurrence biomarker for Prdx4 among numerous candidate genes. A receiver operating characteristic of Prdx4 was used to estimate the incidence of AP. The ssGSEA algorithm was employed to investigate immune cell infiltration in AP. The biomarker Prdx4 gene exhibited significant associations with a majority of immune cells and was identified as being expressed in NKT cells, macrophages, granulocytes, and B cells based on single-cell transcriptome data. Finally, we found an increase in Prdx4 expression in the pancreatic tissue of AP mice through immunohistochemistry. After treatment with recombinant Prdx4, the pathological damage to the pancreatic tissue of AP mice was relieved. In conclusion, our study identified Prdx4 as a potential AP hub gene, providing a new target for treatment.

Cultivation and sequencing-free protocol for Serratia marcescens detection and typing

Serratia marcescens is an opportunistic pathogen that survives in inhospitable environments causing large outbreaks, particularly in neonatal intensive care units (NICUs). Genomic studies revealed that most S. marcescens nosocomial infections are caused by a specific clone (here "Infectious clone"). Whole genome sequencing (WGS) is the only portable method able to identify this clone, but it requires days to obtain results. We present a cultivation-free hypervariable-locus melting typing (HLMT) protocol for the fast detection and typing of S. marcescens, with 100% detection capability on mixed samples and a limit of detection that can reach the 10 genome copies. The protocol was able to identify the S. marcescens infectious clone with 97% specificity and 96% sensitivity when compared to WGS, yielding typing results portable among laboratories. The protocol is a cost and time saving method for S. marcescens detection and typing for large environmental/clinical surveillance screenings, also in low-middle income countries.

Applicability and clinical utility of the German rivermead post-concussion symptoms questionnaire in proxies of children after traumatic brain injury: an instrument validation study

BACKGROUND

The German Rivermead Post-Concussion Symptoms Questionnaire (RPQ) can be used to assess post-concussion symptoms (PCS) after traumatic brain injury (TBI) in adults, adolescents, and children.

METHODS

In this study, we examined the psychometric properties of the German RPQ proxy version (N = 146) for children (8-12 years) after TBI at the item, total and scale score level. Construct validity was analyzed using rank correlations with the proxy-assessed Post-Concussion Symptoms Inventory (PCSI-P), the Patient Health Questionnaire 9 (PHQ-9), and the Generalized Anxiety Disorder Scale 7 (GAD-7). Furthermore, sensitivity testing was performed concerning subjects' sociodemographic and injury-related characteristics. Differential item functioning (DIF) was analyzed to assess the comparability of RPQ proxy ratings for children with those for adolescents.

RESULTS

Good internal consistency was demonstrated regarding Cronbach's α (0.81-0.90) and McDonald's ω (0.84-0.92). The factorial validity of a three-factor model was superior to the original one-factor model. Proxy ratings of the RPQ total and scale scores were strongly correlated with the PCSI-P (ϱ = 0.50-0.69), as well as moderately to strongly correlated with the PHQ-9 (ϱ = 0.49-0.65) and the GAD-7 (ϱ = 0.44-0.64). The DIF analysis revealed no relevant differences between the child and adolescent proxy versions.

CONCLUSIONS

The German RPQ proxy is a psychometrically reliable and valid instrument for assessing PCS in children after TBI. Therefore, RPQ self- and proxy-ratings can be used to assess PCS in childhood as well as along the lifespan of an individual after TBI.

Artificial intelligence-based diagnosis in fetal pathology using external ear shapes

OBJECTIVE

Here we trained an automatic phenotype assessment tool to recognize syndromic ears in two syndromes in fetuses-=CHARGE and Mandibulo-Facial Dysostosis Guion Almeida type (MFDGA)-versus controls.

METHOD

We trained an automatic model on all profile pictures of children diagnosed with genetically confirmed MFDGA and CHARGE syndromes, and a cohort of control patients, collected from 1981 to 2023 in Necker Hospital (Paris) with a visible external ear. The model consisted in extracting landmarks from photographs of external ears, in applying geometric morphometry methods, and in a classification step using machine learning. The approach was then tested on photographs of two groups of fetuses: controls and fetuses with CHARGE and MFDGA syndromes.

RESULTS

The training set contained a total of 1489 ear photographs from 526 children. The validation set contained a total of 51 ear photographs from 51 fetuses. The overall accuracy was 72.6% (58.3%-84.1%, p < 0.001), and 76.4%, 74.9%, and 86.2% respectively for CHARGE, control and MFDGA fetuses. The area under the curves were 86.8%, 87.5%, and 90.3% respectively for CHARGE, controls, and MFDGA fetuses.

CONCLUSION

We report the first automatic fetal ear phenotyping model, with satisfactory classification performances. Further validations are required before using this approach as a diagnostic tool.

SARS-CoV-2 booster vaccine dose significantly extends humoral immune response half-life beyond the primary series

SARS-CoV-2 lipid nanoparticle mRNA vaccines continue to be administered as the predominant prophylactic measure to reduce COVID-19 disease pathogenesis. Quantifying the kinetics of the secondary immune response from subsequent doses beyond the primary series and understanding how dose-dependent immune waning kinetics vary as a function of age, sex, and various comorbidities remains an important question. We study anti-spike IgG waning kinetics in 152 individuals who received an mRNA-based primary series (first two doses) and a subset of 137 individuals who then received an mRNA-based booster dose. We find the booster dose elicits a 71-84% increase in the median Anti-S half life over that of the primary series. We find the Anti-S half life for both primary series and booster doses decreases with age. However, we stress that although chronological age continues to be a good proxy for vaccine-induced humoral waning, immunosenescence is likely not the mechanism, rather, more likely the mechanism is related to the presence of noncommunicable diseases, which also accumulate with age, that affect immune regulation. We are able to independently reproduce recent observations that those with pre-existing asthma exhibit a stronger primary series humoral response to vaccination than compared to those that do not, and further, we find this result is sustained for the booster dose. Finally, via a single-variate Kruskal-Wallis test we find no difference between male and female humoral decay kinetics, however, a multivariate approach utilizing  Least Absolute Shrinkage and Selection Operator (LASSO) regression for feature selection reveals a statistically significant (p < 1 × 10 - 3 ), albeit small, bias in favour of longer-lasting humoral immunity amongst males.

Integration of single-cell RNA-seq and bulk RNA-seq to construct liver hepatocellular carcinoma stem cell signatures to explore their impact on patient prognosis and treatment

BACKGROUND

Liver hepatocellular carcinoma (LIHC) is a prevalent form of primary liver cancer. Research has demonstrated the contribution of tumor stem cells in facilitating tumor recurrence, metastasis, and treatment resistance. Despite this, there remains a lack of established cancer stem cells (CSCs)-associated genes signatures for effectively predicting the prognosis and guiding the treatment strategies for patients diagnosed with LIHC.

METHODS

The single-cell RNA sequencing (scRNA-seq) and bulk RNA transcriptome data were obtained based on public datasets and computerized firstly using CytoTRACE package and One Class Linear Regression (OCLR) algorithm to evaluate stemness level, respectively. Then, we explored the association of stemness indicators (CytoTRACE score and stemness index, mRNAsi) with survival outcomes and clinical characteristics by combining clinical information and survival analyses. Subsequently, weighted co-expression network analysis (WGCNA) and Cox were applied to assess mRNAsi-related genes in bulk LIHC data and construct a prognostic model for LIHC patients. Single-sample gene-set enrichment analysis (ssGSEA), Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) and Tumor Immune Estimation Resource (TIMER) analysis were employed for immune infiltration assessment. Finally, the potential immunotherapeutic response was predicted by the Tumor Immune Dysfunction and Exclusion (TIDE), and the tumor mutation burden (TMB). Additionally, pRRophetic package was applied to evaluate the sensitivity of high and low-risk groups to common chemotherapeutic drugs.

RESULTS

A total of four genes (including STIP1, H2AFZ, BRIX1, and TUBB) associated with stemness score (CytoTRACE score and mRNAsi) were identified and constructed a risk model that could predict prognosis in LIHC patients. It was observed that high stemness cells occurred predominantly in the late stages of LIHC and that poor overall survival in LIHC patients was also associated with high mRNAsi scores. In addition, pathway analysis confirmed the biological uniqueness of the two risk groups. Personalized treatment predictions suggest that patients with a low risk benefited more from immunotherapy, while those with a high risk group may be conducive to chemotherapeutic drugs.

CONCLUSION

The current study developed a novel prognostic risk signature with genes related to CSCs, which provides novel ideas for the diagnosis, prognosis and treatment of LIHC.

Correlation Analysis Between Echinocytosis Stages and Blood Viscosity During Oxygenator Perfusion: An In Vitro Study

The study aimed to investigate the effect of red blood cell (RBC) morphology on oxygenator perfusion, focusing on stages of echinocytosis and their correlation with blood viscosity. A test circuit with an oxygenator and human RBC mixtures was used to induce changes in RBC shape by increasing sodium salicylate concentrations (0, 10, 20, 30, 60, and 120 mmol/L), while hematocrit, blood temperature, and anticoagulation were maintained. Blood viscosity was measured using a continuous blood viscosity monitoring system based on pressure-flow characteristics. Under a scanning electron microscope, the percentages of discocytes, echinocytes I-III, spheroechinocytes, and spherocytes were determined from approximately 400 cells per RBC sample. Early echinocytes, mainly discocytes and echinocytes I and II in the range of 0-30 mmol/L were predominant, resulting in a gradual increase in blood viscosity from 1.78 ± 0.12 to 1.94 ± 0.12 mPa s. At 60 mmol/L spherocytes emerged, and at 120 mmol/L, spheroidal RBCs constituted 50% of the population, and blood viscosity sharply rose to 2.50 ± 0.15 mPa s, indicating a 40% overall increase. In conclusion, the presence of spherocytes significantly increases blood viscosity, which may affect oxygenator perfusion.

Boosting or inhibiting - how semantic-pragmatic and syntactic cues affect prosodic prominence relations in German

In this exploratory study, we investigate the influence of several semantic-pragmatic and syntactic factors on prosodic prominence production in German, namely referential and lexical newness/givenness, grammatical role, and position of a referential target word within a sentence. Especially in terms of the probabilistic distribution of accent status (nuclear, prenuclear, deaccentuation) we find evidence for an additive influence of the discourse-related and syntactic cues, with lexical newness and initial sentence position showing the strongest boosting effects on a target word's prosodic prominence. The relative strength of the initial position is found in nearly all prosodic factors investigated, both discrete (such as the choice of accent type) and gradient (e.g., scaling of the Tonal Center of Gravity and intensity). Nevertheless, the differentiation of prominence relations is information-structurally less important in the beginning of an utterance than near the end: The prominence of the final object relative to the surrounding elements, especially the verbal component, is decisive for the interpretation of the sentence. Thus, it seems that a speaker adjusts locally important prominence relations (object vs. verb in sentence-final position) in addition to a more global, rhythmically determined distribution of prosodic prominences across an utterance.

PERCEPTION predicts patient response and resistance to treatment using single-cell transcriptomics of their tumors

Tailoring optimal treatment for individual cancer patients remains a significant challenge. To address this issue, we developed PERCEPTION (PERsonalized Single-Cell Expression-Based Planning for Treatments In ONcology), a precision oncology computational pipeline. Our approach uses publicly available matched bulk and single-cell (sc) expression profiles from large-scale cell-line drug screens. These profiles help build treatment response models based on patients' sc-tumor transcriptomics. PERCEPTION demonstrates success in predicting responses to targeted therapies in cultured and patient-tumor-derived primary cells, as well as in two clinical trials for multiple myeloma and breast cancer. It also captures the resistance development in patients with lung cancer treated with tyrosine kinase inhibitors. PERCEPTION outperforms published state-of-the-art sc-based and bulk-based predictors in all clinical cohorts. PERCEPTION is accessible at https://github.com/ruppinlab/PERCEPTION . Our work, showcasing patient stratification using sc-expression profiles of their tumors, will encourage the adoption of sc-omics profiling in clinical settings, enhancing precision oncology tools based on sc-omics.

Tracing genetic diversity captures the molecular basis of misfolding disease

Genetic variation in human populations can result in the misfolding and aggregation of proteins, giving rise to systemic and neurodegenerative diseases that require management by proteostasis. Here, we define the role of GRP94, the endoplasmic reticulum Hsp90 chaperone paralog, in managing alpha-1-antitrypsin deficiency on a residue-by-residue basis using Gaussian process regression-based machine learning to profile the spatial covariance relationships that dictate protein folding arising from sequence variants in the population. Covariance analysis suggests a role for the ATPase activity of GRP94 in controlling the N- to C-terminal cooperative folding of alpha-1-antitrypsin responsible for the correction of liver aggregation and lung-disease phenotypes of alpha-1-antitrypsin deficiency. Gaussian process-based spatial covariance profiling provides a standard model built on covariant principles to evaluate the role of proteostasis components in guiding information flow from genome to proteome in response to genetic variation, potentially allowing us to intervene in the onset and progression of complex multi-system human diseases.

Exploring natural killer cell-related biomarkers in multiple myeloma: a novel nature killer cell-related model predicting prognosis and immunotherapy response using single-cell study

BACKGROUND

Natural killer cells (NKs) may be involved in multiple myeloma (MM) progression. The present study elucidated the correlation between NKs and the progression of MM using single-cell binding transcriptome probes to identify NK cell-related biomarkers.

METHODS

Single-cell analysis was performed including cell and subtype annotation, cell communication, and pseudotime analysis. Hallmark pathway enrichment analysis of NKs and NKs-related differentially expressed genes (DEGs) were conducted using Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and protein-protein interaction (PPI) networks. Then, a risk model was structured based on biomarkers identified through univariate Cox regression analysis and least absolute shrinkage and selection operator regression analysis and subsequently validated. Additionally, correlation of clinical characteristics, gene set enrichment analysis, immune analysis, regulatory network, and drug forecasting were explored.

RESULTS

A total of 13 cell clusters were obtained and annotated, including 8 cell populations that consisted of NKs. Utilizing 123 PPI network node genes, 8 NK-related DEGs were selected to construct a prognostic model. Immune cell infiltration results suggested that 11 immune cells exhibited marked differences in the high and low-risk groups. Finally, the model was used to screen potential drug targets to enhance immunotherapy efficacy.

CONCLUSION

A new prognostic model for MM associated with NKs was constructed and validated. This model provides a fresh perspective for predicting patient outcomes, immunotherapeutic response, and candidate drugs.

Log odds of positive lymph nodes (LODDS)-based novel nomogram for survival estimation in patients with invasive micropapillary carcinoma of the breast

BACKGROUND

Invasive micropapillary carcinoma (IMPC) of the breast is known for its high propensity for lymph node (LN) invasion. Inadequate LN dissection may compromise the precision of prognostic assessments. This study introduces a log odds of positive lymph nodes (LODDS) method to address this issue and develops a novel LODDS-based nomogram to provide accurate prognostic information.

METHODS

The study analyzed data from 1,901 patients with breast IMPC from the Surveillance, Epidemiology, and End Results database. It assessed the relationships between LODDS and the number of excised LN (eLN), positive LN (pLN), and the pLN ratio (pLNR), identifying an optimal threshold value using a restricted cubic spline method. Predictive factors were identified by the Cox least absolute shrinkage and selection operator (Cox-LASSO) regression and validated through multivariate Cox regression to construct a nomogram. The model's accuracy, discrimination, and utility were assessed. The study also explored the consequences of excluding LODDS from the nomogram and compared its effectiveness with the tumor-node-metastasis (TNM) staging system.

RESULTS

LODDS improved N status classification by identifying heterogeneity in patients with pLN ratios of 0% (pLN =0) or 100% (pLN =eLN) and setting -1.08 as the ideal cutoff. Five independent prognostic factors for breast cancer-specific survival (BCSS) were identified: tumor size, N status, LODDS, progesterone receptor status, and histological grade. The LODDS-based nomogram achieved a strong concordance index of 0.802 (95% CI: 0.741-0.863), surpassing both the version without LODDS and the conventional TNM staging in all tests.

CONCLUSIONS

For breast IMPC, LODDS served as an independent prognostic factor, its effectiveness unaffected by the anatomical LN count, enhancing the accuracy of N staging. The LODDS-based nomogram showed promise in offering more personalized prognostic information.

Endoplasmic reticulum stress-related gene expression causes the progression of dilated cardiomyopathy by inducing apoptosis

Background: Previous studies have shown that endoplasmic reticulum stress (ERS) -induced apoptosis is involved in the pathogenesis of dilated cardiomyopathy (DCM). However, the molecular mechanism involved has not been fully characterized. Results: In total, eight genes were obtained at the intersection of 1,068 differentially expressed genes (DEGs) from differential expression analysis between DCM and healthy control (HC) samples, 320 module genes from weighted gene co-expression network analysis (WGCNA), and 2,009 endoplasmic reticulum stress (ERGs). These eight genes were found to be associated with immunity and angiogenesis. Four of these genes were related to apoptosis. The upregulation of MX1 may represent an autocompensatory response to DCM caused by a virus that inhibits viral RNA and DNA synthesis, while acting as an autoimmune antigen and inducing apoptosis. The upregulation of TESPA1 would lead to the dysfunction of calcium release from the endoplasmic reticulum. The upregulation of THBS4 would affect macrophage differentiation and apoptosis, consistent with inflammation and fibrosis of cardiomyocytes in DCM. The downregulation of MYH6 would lead to dysfunction of the sarcomere, further explaining cardiac remodeling in DCM. Moreover, the expression of genes affecting the immune micro-environment was significantly altered, including TGF-β family member. Analysis of the co-expression and competitive endogenous RNA (ceRNA) network identified XIST, which competitively binds seven target microRNAs (miRNAs) and regulates MX1 and THBS4 expression. Finally, bisphenol A and valproic acid were found to target MX1, MYH6, and THBS4. Conclusion: We have identified four ERS-related genes (MX1, MYH6, TESPA1, and THBS4) that are dysregulated in DCM and related to apoptosis. This finding should help deepen understanding of the role of endoplasmic reticulum stress-induced apoptosis in the development of DCM.

Time to Achieve the Minimal Clinically Important Difference in Primary Total Hip Arthroplasty: Comparison of Anterior and Posterior Surgical Approaches

BACKGROUND

Controversy remains over outcomes between total hip arthroplasty (THA) approaches. This study aimed to compare the time to achieve the minimal clinically important difference (MCID) for the Hip Disability and Osteoarthritis Outcome Score-Physical Function Short Form (HOOS-PS) and the Patient-Reported Outcomes Measurement Information System (PROMIS) Global-Physical for patients who underwent anterior and posterior surgical approaches in primary THA.

METHODS

Patients from 2018 to 2021 with preoperative and postoperative HOOS-PS or PROMIS Global-Physical questionnaires were grouped by approach. Demographic and MCID achievement rates were compared, and survival curves with and without interval-censoring were used to assess the time to achieve the MCID by approach. Log-rank and weighted log-rank tests were used to compare groups, and Weibull regression analyses were performed to assess potential covariates.

RESULTS

A total of 2,725 patients (1,054 anterior and 1,671 posterior) were analyzed. There were no significant differences in median MCID achievement times for either the HOOS-PS (anterior: 5.9 months, 95% confidence interval (CI): 4.6 to 6.4; posterior: 4.4 months, 95% CI: 4.1 to 5.1, P = 0.65) or the PROMIS Global-Physical (anterior: 4.2 months, 95% CI: 3.5 to 5.3; posterior: 3.5 months, 95% CI: 3.4 to 3.8, P = 0.08) between approaches. Interval-censoring revealed earlier times of achieving the MCID for both the HOOS-PS (anterior: 1.509 to 1.511 months; posterior: 1.7 to 2.3 months, P = 0.87) and the PROMIS Global-Physical (anterior: 3.0 to 3.1 weeks; posterior: 2.7 to 3.3 weeks, P = 0.18) for both surgical approaches.

CONCLUSIONS

The time to achieve the MCID did not differ by surgical approach. Most patients will achieve clinically meaningful improvements in physical function much earlier than previously believed.

Imaging the top of the Earth's inner core: a present-day flow model

Despite considerable progress in seismology, mineral physics, geodynamics, paleomagnetism, and mathematical geophysics, Earth's inner core structure and evolution remain enigmatic. One of the most significant issues is its thermal history and the current thermal state. Several hypotheses involving a thermally-convecting inner core have been proposed: a simple, high-viscosity, translational mode, or a classical, lower-viscosity, plume-style convection. Here, we use state-of-the-art seismic imaging to probe the outermost shell of the inner core for its isotropic compressional speed and compare it with recently developed attenuation maps. The pattern emerging in the resulting tomograms is interpreted with recent data on the viscosity of iron as the inner core surface manifestation of a thermally-driven flow, with a positive correlation among compressional speed and attenuation and temperature. Although the outer-core convection controls the heat flux across the inner core boundary, the internally driven inner-core convection is a plausible model that explains a range of observations for the inner core, including distinct anisotropy in the innermost inner core.

Integrating Genome-Wide Association Study, Transcriptome and Metabolome Reveal Novel QTL and Candidate Genes That Control Protein Content in Soybean

Protein content (PC) is crucial to the nutritional quality of soybean [Glycine max (L.) Merrill]. In this study, a total of 266 accessions were used to perform a genome-wide association study (GWAS) in three tested environments. A total of 23,131 high-quality SNP markers (MAF ≥ 0.02, missing data ≤ 10%) were identified. A total of 40 association signals were significantly associated with PC. Among them, five novel quantitative trait nucleotides (QTNs) were discovered, and another 32 QTNs were found to be overlapping with the genomic regions of known quantitative trait loci (QTL) related to soybean PC. Combined with GWAS, metabolome and transcriptome sequencing, 59 differentially expressed genes (DEGs) that might control the change in protein content were identified. Meantime, four commonly upregulated differentially abundant metabolites (DAMs) and 29 commonly downregulated DAMs were found. Remarkably, the soybean gene Glyma.08G136900, which is homologous with Arabidopsis hydroxyproline-rich glycoproteins (HRGPs), may play an important role in improving the PC. Additionally, Glyma.08G136900 was divided into two main haplotype in the tested accessions. The PC of haplotype 1 was significantly lower than that of haplotype 2. The results of this study provided insights into the genetic mechanisms regulating protein content in soybean.

Assessing SARS-CoV-2 Testing Adherence in a University Town: Recurrent Event Modeling Analysis

BACKGROUND

Healthy Davis Together was a program launched in September 2020 in the city of Davis, California, to mitigate the spread of COVID-19 and facilitate the return to normalcy. The program involved multiple interventions, including free saliva-based asymptomatic testing, targeted communication campaigns, education efforts, and distribution of personal protective equipment, community partnerships, and investments in the local economy.

OBJECTIVE

This study identified demographic characteristics of individuals that underwent testing and assessed adherence to testing over time in a community pandemic-response program launched in a college town in California, United States.

METHODS

This study outlines overall testing engagement, identifies demographic characteristics of participants, and evaluates testing participation changes over 4 periods of the COVID-19 pandemic, distinguished by the dominant variants Delta and Omicron. Additionally, a recurrent model is employed to explore testing patterns based on the participants' frequency, timing, and demographic characteristics.

RESULTS

A total of 770,165 tests were performed between November 18, 2020, and June 30, 2022, among 89,924 (41.1% of total population) residents of Yolo County, with significant participation from racially or ethnically diverse participants and across age groups. Most positive cases (6351 of total) and highest daily participation (895 per 100,000 population) were during the Omicron period. There were some gender and age-related differences in the pattern of recurrent COVID-19 testing. Men were slightly less likely (hazard ratio [HR] 0.969, 95% CI 0.943-0.996) to be retested and more likely (HR 1.104, 95% CI 1.075-1.134) to stop testing altogether than women. People aged between 20 and 34 years were less likely to be retested (HR 0.861, 95% CI 0.828-0.895) and more likely to stop testing altogether (HR 2.617, 95% CI 2.538-2.699). However, older age groups were less likely to stop testing, especially those aged between 65-74 years and 75-84 years, than those aged between 0 and 19 years. The likelihood of stopping testing was lower (HR 0.93, 95% CI 0.889-0.976) for the Asian group and higher for the Hispanic or Latino (HR 1.185, 95% CI 1.148-1.223) and Black or African American (HR 1.198, 95% CI 1.054-1.350) groups than the White group.

CONCLUSIONS

The unique features of a pandemic response program that supported community-wide access to free asymptomatic testing provide a unique opportunity to evaluate adherence to testing recommendations and testing trends over time. Identification of individual and group-level factors associated with testing behaviors can provide insights for identifying potential areas of improvement in future testing initiatives.

Investigation of the shared biological mechanisms and common biomarker APTAF1 of sleep deprivation and mild cognitive impairment using integrated bioinformatics analysis

Introduction: The relationship between sleep loss and cognitive impairment has long been widely recognized, but there is still a lack of complete understanding of the underlying mechanisms and potential biomarkers. The purpose of this study is to further explore the shared biological mechanisms and common biomarkers between sleep loss and cognitive impairment. Methods: The mitochondria-related genes and gene expression data were downloaded from the MitoCarta3.0 and Gene Expression Omnibus (GEO) databases. We identified the differentially expressed mitochondrial-related genes by combing the differentially expressed genes (DEGs) in sleep deprivation (SD) and mild cognitive impairment (MCI) datasets with mitochondria-related gene lists. Shared DEGs were then further analyzed for enrichment analysis. Next, the common biomarker was identified using two machine learning techniques and further validated using two independent GEO datasets. Then GSEA and GSVA were conducted to analyze the functional categories and pathways enriched for the common biomarker. Finally, immune infiltration analysis was used to investigate the correlation of immune cell infiltration with the common biomarker in SD and MCI. Results: A total of 32 mitochondrial-related differentially expressed genes were identified in SD and MCI. GO analysis indicated that these genes were significantly enriched for mitochondrial transport, and KEGG analysis showed they were mainly involved in pathways of neurodegenerative diseases. In addition, ATPAF1, which was significantly down-regulated in both SD and MCI, was identified through machine learning algorithms as the common biomarker with favorable diagnostic performance. GSEA and GSVA revealed that ATPAF1 was mainly involved in metabolic pathways, such as oxidative phosphorylation, acetylcholine metabolic process, valine, leucine and isoleucine degradation. Immune infiltration analysis showed that the expression of ATPAF1 was correlated with changes in immune cells, especially those key immune cell types associated with SD and MCI. Discussion: This study firstly revealed that mitochondrial dysfunction may be the common pathogenesis of sleep loss and mild cognitive impairment and identified ATPAF1 as a possible biomarker and therapeutic target involved in SD and MCI.

Development and Validation of an Intracranial Hemorrhage Risk Score in Older Adults with Atrial Fibrillation Treated with Oral Anticoagulant

Background

High risk of intracranial hemorrhage (ICH) is a leading reason for withholding anticoagulation in patients with atrial fibrillation (AF). We aimed to develop a claims-based ICH risk prediction model in older adults with AF initiating oral anticoagulation (OAC).

Methods

We used US Medicare claims data to identify new users of OAC aged ≥65 years with AF in 2010-2017. We used regularized Cox regression to select predictors of ICH. We compared our AF ICH risk score with the HAS-BLED bleed risk and Homer fall risk scores by area under the receiver operating characteristic curve (AUC) and assessed net reclassification improvement (NRI) when predicting 1-year risk of ICH.

Results

Our study cohort comprised 840,020 patients (mean [SD] age 77.5 [7.4] years and female 52.2%) split geographically into training (3963 ICH events [0.6%] in 629,804 patients) and validation (1397 ICH events [0.7%] in 210,216 patients) sets. Our AF ICH risk score, including 50 predictors, had superior AUCs of 0.653 and 0.650 in the training and validation sets than the HAS-BLED score of 0.580 and 0.567 (p<0.001) and the Homer score of 0.624 and 0.623 (p<0.001). In the validation set, our AF ICH risk score reclassified 57.8%, 42.5%, and 43.9% of low, intermediate, and high-risk patients, respectively, by HAS-BLED score (NRI: 15.3%, p<0.001). Similarly, it reclassified 0.0, 44.1, and 19.4% of low, intermediate, and high-risk patients, respectively, by the Homer score (NRI: 21.9%, p<0.001).

Conclusion

Our novel claims-based ICH risk prediction model outperformed the standard HAS-BLED score and can inform OAC prescribing decisions.