Additional Cohorts Research Articles

Development of a spontaneous preterm birth predictive model using a panel of serum protein biomarkers for early pregnant women: A nested case-control study.

To develop a model based on maternal serum liquid chromatography tandem mass spectrometry (LC-MS/MS) proteins to predict spontaneous preterm birth (sPTB). This nested case-control study used the data from a cohort of 2053 women in China from July 1, 2018, to January 31, 2019. In total, 110 singleton pregnancies at 11-13+6 weeks of pregnancy were used for model development and internal validation. A total of 72 pregnancies at 20-32 weeks from an additional cohort of 2167 women were used to evaluate the scalability of the model. Maternal serum samples were analyzed by LC-MS/MS, and a predictive model was developed using machine learning algorithms. A novel predictive panel with four proteins, including soluble fms-like tyrosine kinase-1, matrix metalloproteinase 8, ceruloplasmin, and sex-hormone-binding globulin, was developed. The optimal model of logistic regression had an AUC of 0.934, with additional prediction of sPTB in second and third trimester (AUC = 0.868). First-trimester modeling based on maternal serum LC-MS/MS identifies pregnant women at risk of sPTB, which may provide utility in identifying women at risk at an early stage of pregnancy before clinical presentation to allow for earlier intervention.

Fast intraoperative detection of primary CNS lymphoma and differentiation from common CNS tumors using stimulated Raman histology and deep learning.

Accurate intraoperative diagnosis is crucial for differentiating between primary CNS lymphoma (PCNSL) and other CNS entities, guiding surgical decision-making, but represents significant challenges due to overlapping histomorphological features, time constraints, and differing treatment strategies. We combined stimulated Raman histology (SRH) with deep learning to address this challenge. We imaged unprocessed, label-free tissue samples intraoperatively using a portable Raman scattering microscope, generating virtual H&E-like images within less than three minutes. We developed a deep learning pipeline called RapidLymphoma based on a self-supervised learning strategy to (1) detect PCNSL, (2) differentiate from other CNS entities, and (3) test the diagnostic performance in a prospective international multicenter cohort and two additional independent test cohorts. We trained on 54,000 SRH patch images sourced from surgical resections and stereotactic-guided biopsies, including various CNS tumor/non-tumor lesions. Training and test data were collected from four tertiary international medical centers. The final histopathological diagnosis served as ground-truth. In the prospective test cohort of PCNSL and non-PCNSL entities (n=160), RapidLymphoma achieved an overall balanced accuracy of 97.81% ±0.91, non-inferior to frozen section analysis in detecting PCNSL (100% vs. 78.94%). The additional test cohorts (n=420, n=59) reached balanced accuracy rates of 95.44% ±0.74 and 95.57% ±2.47 in differentiating IDH-wildtype diffuse gliomas and various brain metastasis from PCNSL. Visual heatmaps revealed RapidLymphoma's capabilities to detect class-specific histomorphological key features. RapidLymphoma is valid and reliable in detecting PCNSL and differentiating from other CNS entities within three minutes, as well as visual feedback in an intraoperative setting. This leads to fast clinical decision-making and further treatment strategy planning.

Patient-derived organoid biobank identifies epigenetic dysregulation of intestinal epithelial MHC-I as a novel mechanism in severe Crohn’s Disease

ObjectiveEpigenetic mechanisms, including DNA methylation (DNAm), have been proposed to play a key role in Crohn’s disease (CD) pathogenesis. However, the specific cell types and pathways affected as well as...

A Core NRF2 Gene Set Defined Through Comprehensive Transcriptomic Analysis Predicts Selective Drug Resistance and Poor Multicancer Prognosis.

Aims: The nuclear factor erythroid 2-related factor 2-Kelch-like ECH-associated protein 1 (NRF2-KEAP1) pathway plays an important role in the cellular response to oxidative stress but may also contribute to metabolic changes and drug resistance in cancer. However, despite its pervasiveness and important role, most of nuclear factor erythroid 2-related factor 2 (NRF2) target genes are defined in context-specific experiments and analysis, making it difficult to translate from one situation to another. Our study investigates whether a core NRF2 gene signature can be derived and used to represent NRF2 activation in various contexts, allowing better reproducibility and understanding of NRF2. Results: We define a core set of 14 upregulated NRF2 target genes from 7 RNA-sequencing datasets that we generated and analyzed. This NRF2 gene signature was validated using analyses of published datasets and gene sets. An NRF2 activity score based on expression of these core target genes correlates with resistance to drugs such as PX-12 and necrosulfonamide but not to paclitaxel or bardoxolone methyl. We validated these findings in our Kelch-like ECH-associated protein 1 (KEAP1) knockout cancer cell lines. Finally, our NRF2 score is prognostic for cancer survival and validated in additional independent cohorts for lung adenocarcinoma and also novel cancer types not associated with NRF2-KEAP1 mutations such as clear cell renal carcinoma, hepatocellular carcinoma, and acute myeloid leukemia. Innovation and Conclusions: These analyses define a core NRF2 gene signature that is robust, versatile, and useful for evaluating NRF2 activity and for predicting drug resistance and cancer prognosis. Using this gene signature, we uncovered novel selective drug resistance and cancer prognosis associated with NRF2 activation.

21 Unconventional mechanisms of action and resistance to rapalogs in renal cancer

Abstract Background Clear cell renal cell carcinoma (ccRCC) is a prevalent cancer type in the United States driven by inactivation of the von Hippel-Lindau (VHL) gene and with frequent hyperactivation of mammalian target of rapamycin complex 1 (mTORC1). Multiple drugs have been developed to target VEGF/VEGFR2 and mTORC1 for advanced RCC treatment. Two specific mTORC1 inhibitors, temsirolimus and everolimus (known as rapalogs), are FDA-approved for the treatment of RCC but their clinical efficacy is hindered by the emergence of resistance. Methods To study the development of rapalog resistance in RCC, we utilized patient-derived tumorgrafts (TGs) implanted in immunocompromised mice and treated the mice with rapamycin for prolonged periods to generate resistance. Resistance was studied by transplanting resistant tumors into additional cohorts of mice and establishing primary cultures. The impact of rapamycin on tumor growth and mTORC1 activity in both tumor and stromal cells was assessed using molecular techniques. To dissect the role of the tumor microenvironment (TME), we generated genetically engineered immunocompromised recipient mice with an mTOR inhibitor resistance mutation (S2035T). Coculture experiments were performed to further explore the interplay. Results Prolonged rapamycin treatment resulted in the development of resistance in TGs. Notably, this occurred despite persistent inhibition of mTORC1 in tumor cells. Resistance was transient and lost with subsequent transplantation and in cell culture. Surprisingly, resistance was accompanied by mTORC1 reactivation in the TME, specifically in cancer associated fibroblasts. Introducing an mTOR resistance mutation (S2035T) into recipient mice was sufficient to cause resistance. Co-culture experiments with fibroblasts further demonstrated that rapamycin-resistant mutant fibroblasts conferred resistance in tumor cells even in the absence of mTORC1 activity. Conclusions This study uncovers a critical role of the TME, in mediating the response and resistance to rapalogs in RCC. Our data show that inhibition of mTORC1 in non-tumor cells is essential for the anti-tumor activity of rapalogs. These findings shed light on why mTOR mutations are rarely observed in resistant RCC patients and highlight the significance of the TME in modulating drug response and resistance. Moreover, our study emphasizes the potential of targeting TME cells as a therapeutic strategy in RCC and possibly other cancers. DOD CDMRP Funding: yes

Moderate BMI accumulation modified associations between blood benzene, toluene, ethylbenzene and xylene (BTEX) and phenotypic aging: mediating roles of inflammation and oxidative stress

The associations between blood benzene, toluene, ethylbenzene, and xylenes (BTEX) and biological aging among general adults remain elusive. The present study comprised 5780 participants from the National Health and Nutrition Examination Survey 1999–2010. A novel measure of biological aging, phenotypic age acceleration (PhenoAge.Accel), derived from biochemical markers was calculated. Weighted generalized linear regression and weighted quantile sum regression (WQS) were utilized to assess the associations between BTEX components and mixed exposure, and PhenoAge.Accel. The mediating roles of systemic immune-inflammation index (SII) and oxidative stress indicators (serum bilirubin and gamma-glutamyl transferase), along with the modifying effects of body mass index (BMI) were also examined. In the single-exposure model, the highest quantile of blood benzene (b = 0.89, 95%CI: 0.58 to 1.20), toluene (b = 0.87, 95%CI: 0.52 to 1.20), and ethylbenzene (b = 0.80, 95%CI: 0.46 to 1.10) was positively associated with PhenoAge.Accel compared to quantile 1. Mixed-exposure analyses revealed a consistent positive association between BTEX mixed exposure and PhenoAge.Accel (b = 0.88, 95%CI: 0.56 to 1.20), primarily driven by benzene (92.78%). The association between BTEX and PhenoAge.Accel was found to be partially mediated by inflammation and oxidative stress indicators (ranging from 3.2% to 13.7%). Additionally, BMI negatively modified the association between BTEX mixed exposure and PhenoAge.Accel, with a threshold identified at 36.2 kg/m^2. Furthermore, BMI negatively moderated the direct effect of BTEX mixed exposure on PhenoAge.Accel in moderated mediation models, while positively modified the link between SII and PhenoAge.Accel in the indirect path (binteraction = 0.04, 95%CI: 0.01 to 0.06). Overall, BTEX mixed exposure was associated with PhenoAge.Accel among US adults, with benzene may have reported most contribution, and inflammation and oxidative damage processes may partially explain this underlying mechanism. The study also highlighted the potential benefits of appropriate BMI increased. Additional large-scale cohort studies and experiments were necessary to substantiate these findings.

Biomarkers troponin and procalcitonin in addition to CRB-65 enhance risk stratification in patients with community-acquired pneumonia.

Community-acquired pneumonia (CAP) remains a leading cause of infectious disease mortality globally, necessitating intensive care unit (ICU) admission for ∼10% of hospitalised patients. Accurate prediction of disease severity facilitates timely therapeutic interventions. Our study aimed to enhance the predictive capacity of the clinical CRB-65 score by evaluating eight candidate biomarkers: troponin T high-sensitive (TnT-hs), procalcitonin (PCT), N-terminal pro-brain natriuretic peptide, angiopoietin-2, copeptin, endothelin-1, lipocalin-2 and mid-regional pro-adrenomedullin. We utilised a machine-learning approach on 800 samples from the German CAPNETZ network (competence network for CAP) to refine risk prediction models combining these biomarkers with the CRB-65 score regarding our defined end-point: death or ICU admission during the current CAP episode within 28 days after study inclusion. Elevated levels of biomarkers were associated with the end-point. TnT-hs exhibited the highest predictive performance among individual features (area under the receiver operating characteristic curve, AUC=0.74), followed closely by PCT (AUC=0.73). Combining biomarkers with the CRB-65 score significantly improved prediction accuracy. The combined model of CRB-65, TnT-hs and PCT demonstrated the best balance between high predictive value and parsimony, with an AUC of 0.77 (95% CI: 0.72-0.82), while CRB-65 alone achieved an AUC of 0.67 (95% CI: 0.64-0.73). Our findings suggest that augmenting the CRB-65 score with TnT-hs and PCT enhances the prediction of death or ICU admission in hospitalised CAP patients. Validation of this improved risk score in additional CAP cohorts and prospective clinical studies is warranted to assess its broad clinical utility.

Romiplostim in chemotherapy-induced thrombocytopenia: A review of the literature.

Chemotherapy-induced thrombocytopenia (CIT) is a common challenge of cancer therapy and can lead to chemotherapy dose reduction, delay, and/or discontinuation, affecting relative dose intensity, and possibly adversely impacting cancer care. Besides changing anticancer regimens, standard management of CIT has been limited to platelet transfusions and supportive care. Use of the thrombopoietin receptor agonist romiplostim, already approved for use in immune thrombocytopenia, has shown promising signs of efficacy in CIT. In a phase 2 prospective randomized study of solid tumor patients with platelet counts <100 × 109/L for ≥4 weeks due to CIT, weekly romiplostim corrected the platelet count to >100 × 109/L in 93% (14/15) of patients within 3 weeks versus 12.5% (1/8) of untreated patients (p < 0.001). Including patients treated with romiplostim in an additional single-arm cohort, 85% (44/52) of all romiplostim-treated patients responded with platelet count correction within 3 weeks. Several retrospective studies of CIT have also shown responses to weekly romiplostim, with the largest study finding that poor response to romiplostim was predicted by tumor invasion of the bone marrow (odds ratio, 0.029; 95% CI: 0.0046-0.18; p < 0.001), prior pelvic irradiation (odds ratio, 0.078; 95% CI: 0.0062-0.98; p = 0.048), and prior temozolomide treatment (odds ratio 0.24; 95% CI: 0.061-0.96; p = 0.043). Elsewhere, lower baseline TPO levels were predictive of romiplostim response (p = 0.036). No new safety signals have emerged from romiplostim CIT studies. Recent treatment guidelines, including those from the National Comprehensive Cancer Network, now support consideration of romiplostim use in CIT. Data are expected from two ongoing phase 3 romiplostim CIT trials.

Tear Film Hyperosmolarity is Associated with Increased Variation of Light Scatter Following Cataract Surgery.

To study the association between tear film hyperosmolarity and ocular light scatter in a cataract surgery population. Contiguous, 20-second objective scatter index (OSI) scans were recorded in hyperosmolar (≥320 mOsm/L) and normal subjects (<308 mOsm/L) with cataract nuclear opacity ≥3. OSI was measured at screening, baseline and 90 days following surgery. Along with symptoms of ocular surface disease, slit-lamp examination included corneal staining (0-3), tear film breakup time (TBUT) and evaluation of meibomian gland disease (MGD). An additional cohort of hyperosmolar subjects were measured for OSI at screening, baseline, and 5, 10, 15 and 30 minutes following instillation of 0.18% sodium hyaluronate (HA). Thirty-one eyes of 31 patients were included. There was a significant difference in post-operative OSI variation when comparing hyperosmolar (0.65±0.30, N=11) to normal subjects (0.33±0.11, N=10, p=0.005). Of note, there were no significant differences in OSI variation when subjects were sorted by staining (p=0.9), TBUT (p=0.7), symptoms (p=0.7), or MGD status (p=0.9). Instillation of 0.18% HA (N=10) did not alter OSI at 5 minutes, but significant reductions in OSI of 28.8%, 38.5% and 36.7% (all p < 0.001) were observed at 10, 15 and 30 minutes. Hyperosmolar patients exhibited significantly increased variation in light scatter following cataract surgery that was undifferentiated by staining or TBUT. Elevated osmolarity may be indicative of light scatter equivalent to that of a grade 2-3 cataract.

Spatial transcriptome and single-cell reveal the role of nucleotide metabolism in colorectal cancer progression and tumor microenvironment

BackgroundThe intricacies of nucleotide metabolism within tumor cells specific to colorectal cancer (CRC) remain insufficiently characterized. A nuanced examination of particular tumor clusters and their dynamic interplay with the tumor microenvironment (TME) may yield profound insights into these therapeutically auspicious communicative networks.MethodsBy integrating ten types of single-cell enrichment scoring methods, we carried out enrichment analysis on CRC cell types, which was validated through four additional single-cell cohorts. Groups of tumor cells were determined using the average values of the scores. Using cellphonedb, monocle, inferCNV, SCENIC, and Cytotrace, functional analyses were performed. Utilizing the RCTD approach, single-cell groupings were mapped onto spatial transcriptomics, analyzing cell dependency and pathway activity to distinguish between tumor cell subtypes. Differential expression analysis identified core genes in nucleotide metabolism, with single-cell and spatial transcriptomics analyses elucidating the function of these genes in tumor cells and the immune microenvironment. Prognostic models were developed from bulk transcriptome cohorts to forecast responses to immune therapy. Laboratory experiments were conducted to verify the biological function of the core gene.ResultsNucleotide metabolism is significantly elevated in tumor cells, dividing them into two groups: NUhighepi and NUlowepi. The phenotype NUhighepi was discerned to exhibit pronounced malignant attributes. Utilizing the analytical tool stlearn for cell-to-cell communication assessment, it was ascertained that NUhighepi engages in intimate interactions with fibroblasts. Corroborating this observation, spatial transcriptome cell interaction assessment through MISTy unveiled a particular reliance of NUhighepi on fibroblasts. Subsequently, we pinpointed NME1, a key gene in nucleotide metabolism, affirming its role in thwarting metastasis via in vitro examination. Utilizing multiple machine learning algorithms, a stable prognostic model (NRS) has been developed, capable of predicting survival and responses to immune therapy. In addition, targeted drugs have been identified for both high and low scoring groups. Laboratory experiments have revealed that NME1 can inhibit the proliferation and invasion of CRC tumor cells.ConclusionOur study elucidates the potential pro-tumor mechanism of NUhighepi and the role of NME1 in inhibiting metastasis, further deepening the understanding of the role of nucleotide metabolism in colorectal cancer, and providing valuable targets for disrupting its properties.

Polygenic Risk Score Informed Clinical Model for Improving Abdominal Aortic Aneurysm Screening

Abdominal aortic aneurysm (AAA) is a complex disease with environmental and genetic risk factors. Polygenic risk scores (PRSs) based on disease-specific risk-associated single nucleotide variants (SNVs) have demonstrated effectiveness in stratifying individual-level disease risk for cardiovascular diseases. This prospective cohort study assessed associations of PRS of AAA (PRSAAA) with risk of incident AAA, analyzed the effectiveness of a combined clinical-genetic risk model, and explored the clinical utility of the model in identifying high-risk individuals for AAA screening. PRSAAA was calculated using 911,440 SNVs and PRS of coronary artery disease was calculated using 2,324,683 SNVs derived from mixed ancestry genome-wide association studies. The UK Biobank was used as the study cohort. All individuals with complete genetic data available and no diagnosis of AAA at the time of recruitment were included in the analysis and followed prospectively to assess for incident AAA. A PRS-informed clinical model, Prob-AAA, was developed using clinically significant variables and PRSAAA. Four hundred eighty-one thousand one hundred 5 individuals were included in the analysis with 2,668 incident AAA cases. Incident AAA increased from 0.30 to 0.93% between the lowest and highest decile of PRSAAA; similarly, severe AAA, requiring surgery and/or presenting with rupture, increased from 23 to 39% of incident AAA cases across deciles. PRSAAA was a predictor of incident AAA diagnosis (hazard ratio 2.06 [1.70-2.48]) independent of other clinical risk factors including male sex, older age, and smoking history. Prob-AAA was an independent predictor of incident AAA (hazard ratio 1.92 [1.69-2.20]), and identified 9.6% of cases of incident AAA compared to only 4.2% by PRSAAA. Current screening guidelines captured 5.7% of the overall cohort, with an incident AAA rate of approximately 3.2%. Among males not included by current guidelines, Prob-AAA identified an additional cohort, approximately 2% of the overall cohort, with a similar rate of incident AAA. Prob-AAA, a PRS-informed clinical model for AAA, improved upon the predictive power of current, clinical risk factor-informed, screening guidelines for AAA.

Association analysis of indel variants and gene expression identifies MDM4 as a novel locus for skeletal muscle hypertrophy and power athlete status.

Insertions and deletions (indels) are the second most common type of variation in the human genome. However, limited data on their associations with exercise-related phenotypes have been documented. The aim of the present study was to examine the association between 18,370 indel variants and power athlete status, followed by additional studies in 357,246 individuals. In the discovery phase, the D allele of the MDM4 gene rs35493922 I/D polymorphism was over-represented in power athletes compared with control subjects (P=7.8×10-9) and endurance athletes (P=0.0012). These findings were replicated in independent cohorts, showing a higher D allele frequency in power athletes compared with control subjects (P=0.016) and endurance athletes (P=0.031). Furthermore, the D allele was positively associated (P=0.0013) with greater fat-free mass in the UK Biobank. MDM4 encodes a protein that inhibits the activity of p53, which induces muscle fibre atrophy. Accordingly, we found that MDM4 expression was significantly higher in the vastus lateralis of power athletes compared with endurance athletes (P=0.0009) and was positively correlated with the percentage of fast-twitch muscle fibres (P=0.0062) and the relative area occupied by fast-twitch muscle fibres (P=0.0086). The association between MDM4 gene expression and an increased proportion of fast-twitch muscle fibres was confirmed in two additional cohorts. Finally, we found that the MDM4 DD genotype was associated with increased MDM4 gene expression in vastus lateralis and greater cross-sectional area of fast-twitch muscle fibres. In conclusion, MDM4 is suggested to be a potential regulator of muscle fibre specification and size, with its indel variant being associated with power athlete status. HIGHLIGHTS: What is the central question of this study? Which indel variants are functional and associated with sport- and exercise-related traits? What is the main finding and its importance? Out of 18,370 tested indels, the MDM4 gene rs35493922 I/D polymorphism was found to be the functional variant (affecting gene expression) and the most significant, with the deletion allele showing associations with power athlete status, fat-free mass and cross-sectional area of fast-twitch muscle fibres. Furthermore, the expression of MDM4 was positively correlated with the percentage of fast-twitch muscle fibres and the relative area occupied by fast-twitch muscle fibres.

Identification of tumor-specific T cell signature predicting cancer immunotherapy response in bladder cancer by multi-omics analysis and experimental verification

BackgroundNumerous gene signatures predicting the prognosis of bladder cancer have been identified. However, a tumor-specific T cell signature related to immunotherapy response in bladder cancer remains under investigation.MethodsSingle-cell RNA and TCR sequencing from the Gene expression omnibus (GEO) database were used to identify tumor-specific T cell-related genes in bladder cancer. Subsequently, we constructed a tumor-specific T cell signature (TstcSig) and validated its clinical relevance for predicting immunotherapy response in multiple immunotherapy cohorts. Further analyses explored the immune characteristics of TstcSig in bladder cancer patients from other cohorts in the TCGA and GEO databases. Western blot (WB), multicolor immunofluorescence (MIF), qRT-PCR and flow cytometry assays were performed to validate the results of bioinformatics analysis.ResultsThe established TstcSig, based on five tumor-specific T cell-related genes, could predict outcomes in a bladder cancer immunotherapy cohort. This was verified using two additional immunotherapy cohorts and showed better predictive performance compared to 109 published T cell signatures. TstcSig was strongly correlated with immune characteristics such as immune checkpoint gene expression, tumor mutation burden, and T cell infiltration, as validated by single-cell and spatial transcriptomics datasets. Notably, the positive correlation between TstcSig and T cell infiltration was confirmed in the TCGA cohort. Furthermore, pan-cancer analysis demonstrated the heterogeneity of the prognostic value of TstcSig. Tumor-specific T cells highly expressed CD27, IFNG, GZMB and CXCL13 and secreted more effector cytokines for tumor cell killing, as validated experimentally.ConclusionWe developed a five-gene signature (including VAMP5, TIGIT, LCK, CD27 and CACYBP) based on tumor-specific T cell-related genes to predict the immunotherapy response in bladder cancer patients.

Stanford Network for Advancement and Promotion: The impact of a community building-focused leadership development program on the success of underrepresented groups in academic medicine.

Leadership can be an isolating experience and leaders from underrepresented groups (URGs) may experience even greater isolation and vulnerability because of lack of representation. Given the collaborative nature of medicine, leadership programs for physicians need to address isolation. Social support is one mechanism to combat this isolation; however, most leadership programs focus exclusively on skills building. The Stanford Network for Advancement and Promotion (SNAP) program was developed to reduce isolation among physician leaders from URGs in academic medicine leadership by building a supportive network of peers. Ten women physicians from diverse racial/ethnic backgrounds were invited to participate in SNAP. Annual surveys were administered to participants to assess the effectiveness of SNAP on decreasing feelings of isolation and increasing professional leadership growth. The authors charted the expansion and adaptation of the program model across gender and in additional settings. SNAP effectively created a sense of community among the physician leaders. Participants also reported feeling challenged by the program and that they had grown in terms of critical thinking, organizational knowledge, and empowerment as leaders. Participants found community building to be the most valuable program component. Because of this success, the SNAP model has been adapted to create 10 additional cohorts. Leadership programs like SNAP that focus on reducing isolation are instrumental for retaining and promoting the career advancement of physicians from URGs. Developing a diverse workforce of academic physicians is essential to providing high-quality and equitable clinical care, research, and medical education.

An evidence-based screening tool for heart failure with preserved ejection fraction: the HFpEF-ABA score.

Heart failure with preserved ejection fraction (HFpEF) is under-recognized in clinical practice. Although a previously developed risk score, termed H2FPEF, can be used to estimate HFpEF probability, this score requires imaging data, which is often unavailable. Here we sought to develop an HFpEF screening model that is based exclusively on clinical variables and that can guide the need for echocardiography and further testing. In a derivation cohort (n = 414, 249 women), a clinical model using age, body mass index and history of atrial fibrillation (termed the HFpEF-ABA score) showed good discrimination (area under the curve (AUC) = 0.839 (95% confidence interval (CI) = 0.800-0.877), P < 0.0001). The performance of the model was validated in an international, multicenter cohort (n = 736, 443 women; AUC = 0.813 (95% CI = 0.779-0.847), P < 0.0001) and further validated in two additional cohorts: a cohort including patients with unexplained dyspnea (n = 228, 136 women; AUC = 0.840 (95% CI = 0.782-0.900), P < 0.0001) and a cohort for which HF hospitalization was used instead of hemodynamics to establish an HFpEF diagnosis (n = 456, 272 women; AUC = 0.929 (95% CI = 0.909-0.948), P < 0.0001). Model-based probabilities were also associated with increased risk of HF hospitalization or death among patients from the Mayo Clinic (n = 790) and a US national cohort across the Veteran Affairs health system (n = 3076, 110 women). Using the HFpEF-ABA score, rapid and efficient screening for risk of undiagnosed HFpEF can be performed in patients with dyspnea using only age, body mass index and history of atrial fibrillation.

Examining the hospital costs of children born into relative deprivation in England

ObjectiveTo examine the association between being born into relative deprivation and hospital costs during childhood.DesignRetrospective cohort study.MethodsWe created a birth cohort using Hospital Episode Statistics for children born in NHS...

Tumor Subtype Classification Tool for HPV-associated Head and Neck Cancers.

Molecular subtypes of HPV-associated Head and Neck Squamous Cell Carcinoma (HNSCC), named IMU (immune strong) and KRT (highly keratinized), are well-recognized and have been shown to have distinct mechanisms of carcinogenesis, clinical outcomes, and potentially differing optimal treatment strategies. Currently, no standardized method exists to subtype a new HPV+ HNSCC tumor. Our paper introduces a machine learning-based classifier and webtool to reliably subtype HPV+ HNSCC tumors using the IMU/KRT paradigm and highlights the importance of subtype in HPV+ HNSCC. To develop a robust, accurate machine learning-based classification tool that standardizes the process of subtyping HPV+ HNSCC, and to investigate the clinical, demographic, and molecular features associated with subtype in a meta-analysis of four patient cohorts. We conducted RNA-seq on 67 HNSCC FFPE blocks from University of Michigan hospital. Combining this with three publicly available datasets, we utilized a total of 229 HPV+ HNSCC RNA-seq samples. All participants were HPV+ according to RNA expression. An ensemble machine learning approach with five algorithms and three different input training gene sets were developed, with final subtype determined by majority vote. Several additional steps were taken to ensure rigor and reproducibility throughout. The classifier was trained and tested using 84 subtype-labeled HPV+ RNA-seq samples from two cohorts: University of Michigan (UM; n=18) and TCGA-HNC (n=66). The classifier robustness was validated with two independent cohorts: 83 samples from the HPV Virome Consortium and 62 additional samples from UM. We revealed 24 of 39 tested clinicodemographic and molecular variables significantly associated with subtype. The classifier achieved 100% accuracy in the test set. Validation on two additional cohorts demonstrated successful separation by known features of the subtypes. Investigating the relationship between subtype and 39 molecular and clinicodemographic variables revealed IMU is associated with epithelial-mesenchymal transition (p=2.25×10-4), various immune cell types, and lower radiation resistance (p=0.0050), while KRT is more highly keratinized (p=2.53×10-8), and more likely female than IMU (p=0.0082). This study provides a reliable classifier for subtyping HPV+ HNSCC tumors as either IMU or KRT based on bulk RNA-seq data, and additionally, improves our understanding of the HPV+ HNSCC subtypes.

Subchronic intranasal lipopolysaccharide exposure induces pulmonary autoimmunity and glomerulonephritis in NZBWF1 mice

Lupus, a systemic autoimmune disease shaped by gene-environment interplay, often progresses to endstage renal failure. While subchronic systemic exposure to bacterial lipopolysaccharide (LPS) triggers autoimmunity and glomerulonephritis in lupus-prone mice, it is unknown if inhaling LPS, which is common in certain occupations, can similarly trigger lupus. Here we determined how subchronic intranasal (IN) LPS instillation influences autoimmunity and glomerulonephritis development in lupusprone NZBWF1 female mice. Briefly, mice were IN-instilled with vehicle or E. coli LPS (0.8 μg/g) twice weekly for 5 wk, followed by necropsy. For systemic comparison, additional cohorts of mice were injected with LPS intraperitoneally (IP) using identical doses/timing. Lungs were assessed for inflammatory and autoimmune responses and then related to systemic autoimmunity and glomerulonephritis. IN/LPS exposure induced in the lung: i) leukocyte infiltration, ii)mRNA signatures for cytokines, chemokines, IFN-regulated, and cell death-related genes, iii) ectopic lymphoid tissue formation, and iv)diverse IgM and IgG autoantibodies (AAbs). Pulmonary effects coincided with enlarged spleens, elevated plasma IgG AAbs, and inflamed IgG-containing kidney glomeruli. In contrast, IP/LPS treatment induced systemic autoimmunity and glomerulonephritis without pulmonary manifestations. Taken together, these preclinical findings suggest the lung could serve as a critical nexus for triggering autoimmunity by respirable LPS in genetically predisposed individuals.

Epigenetic signals associated with delirium replicated across four independent cohorts

Delirium is risky and indicates poor outcomes for patients. Therefore, it is crucial to create an effective delirium detection method. However, the epigenetic pathophysiology of delirium remains largely unknown. We aimed to discover reliable and replicable epigenetic (DNA methylation: DNAm) markers that are associated with delirium including post-operative delirium (POD) in blood obtained from patients among four independent cohorts. Blood DNA from four independent cohorts (two inpatient cohorts and two surgery cohorts; 16 to 88 patients each) were analyzed using the Illumina EPIC array platform for genome-wide DNAm analysis. We examined DNAm differences in blood between patients with and without delirium including POD. When we compared top CpG sites previously identified from the initial inpatient cohort with three additional cohorts (one inpatient and two surgery cohorts), 11 of the top 13 CpG sites showed statistically significant differences in DNAm values between the delirium group and non-delirium group in the same directions as found in the initial cohort. This study demonstrated the potential value of epigenetic biomarkers as future diagnostic tools. Furthermore, our findings provide additional evidence of the potential role of epigenetics in the pathophysiology of delirium including POD.

The therapeutic and biomarker significance of ferroptosis in chronic myeloid leukemia.

The relationship between ferroptosis and the progression and treatment of hematological tumors has been extensively studied, although its precise association with chronic myeloid leukemia (CML) remains uncertain. Multi-transcriptome sequencing data were utilized to analyze the ferroptosis level of CML samples and its correlation with the tumor microenvironment, disease progression, and treatment response. Machine learning algorithms were employed to identify diagnostic ferroptosis-related genes (FRGs). The consensus clustering algorithm was applied to identify ferroptosis-related molecular subtypes. Clinical samples were collected for sequencing to validate the results obtained from bioinformatics analysis. Cell experiments were conducted to investigate the therapeutic efficacy of induced ferroptosis in drug-resistant CML. Ferroptosis scores were significantly lower in samples from patients with CML compared to normal samples, and these scores further decreased with disease progression and non-response to treatment. Most FRGs were downregulated in CML samples. A high ferroptosis score was also associated with greater immunosuppression and increased activity of metabolic pathways. Through support vector machine recursive feature elimination (SVM-RFE), least absolute shrinkage selection operator (LASSO), and random forest (RF) algorithms, we identified five FRGs (ACSL6, SLC11A2, HMOX1, SLC38A1, AKR1C3) that have high diagnostic value. The clinical diagnostic value of these five FRGs and their effectiveness in differentiating CML from other hematological malignancies were validated using additional validation cohorts and our real-world cohort. There are significant differences in immune landscape, chemosensitivity, and immunotherapy responsiveness between the two ferroptosis-related molecular subtypes. By conducting cellular experiments, we confirmed that CML-resistant cells are more sensitive to induction of ferroptosis and can enhance the sensitivity of imatinib treatment. Our study unveils the molecular signature of ferroptosis in samples from patients with CML. FRG identified by a variety of machine learning algorithms has reliable clinical diagnostic value. Furthermore, the characterization of different ferroptosis-related molecular subtypes provides valuable insights into individual patient characteristics and can guide clinical treatment strategies. Targeting and inducing ferroptosis holds great promise as a therapeutic approach for drug-resistant CML.