A Novel Digital Twin Strategy to Examine the Implications of Randomized Control Trials for Real-World Populations.

Randomized clinical trials (RCTs) are essential to guide medical practice; however, their generalizability to a given population is often uncertain. We developed a statistically informed Generative Adversarial Network (GAN) model, RCT-Twin-GAN, that leverages relationships between covariates and outcomes and generates a digital twin of an RCT (RCT-Twin) conditioned on covariate distributions from a second patient population. We used RCT-Twin-GAN to reproduce treatment effect outcomes of the Systolic Blood Pressure Intervention Trial (SPRINT) and the Action to Control Cardiovascular Risk in Diabetes (ACCORD) Blood Pressure Trial, which tested the same intervention but had different treatment effect results. To demonstrate treatment effect estimates of each RCT conditioned on the other RCT patient population, we evaluated the cardiovascular event-free survival of SPRINT digital twins conditioned on the ACCORD cohort and vice versa (SPRINT-conditioned ACCORD twins). The conditioned digital twins were balanced by the intervention arm (mean absolute standardized mean difference (MASMD) of covariates between treatment arms 0.019 (SD 0.018), and the conditioned covariates of the SPRINT-Twin on ACCORD were more similar to ACCORD than a sprint (MASMD 0.0082 SD 0.016 vs. 0.46 SD 0.20). Most importantly, across iterations, SPRINT conditioned ACCORD-Twin datasets reproduced the overall non-significant effect size seen in ACCORD (5-year cardiovascular outcome hazard ratio (95% confidence interval) of 0.88 (0.73-1.06) in ACCORD vs median 0.87 (0.68-1.13) in the SPRINT conditioned ACCORD-Twin), while the ACCORD conditioned SPRINT-Twins reproduced the significant effect size seen in SPRINT (0.75 (0.64-0.89) vs median 0.79 (0.72-0.86)) in ACCORD conditioned SPRINT-Twin). Finally, we describe the translation of this approach to real-world populations by conditioning the trials on an electronic health record population. Therefore, RCT-Twin-GAN simulates the direct translation of RCT-derived treatment effects across various patient populations with varying covariate distributions.

Nanoparticle-integrated dissolving microneedles for the co-delivery of R848/aPD-1 to synergistically reverse the immunosuppressive microenvironment of triple-negative breast cancer.

Nowadays, effective immunotherapy against triple-negative breast cancer (TNBC) remains challenging due to the immunosuppressive tumor microenvironment. Immune checkpoint inhibitor is mostly employed to restore the activity of tumor-specific immune cells, which however brings little therapeutic outcome owing to the limited number of tumor-infiltrating CD8+ T cells and the inefficient delivery of immune drugs to the tumor tissue. Aiming to solve these problems, we herein constructed a tailor-made dissolving microneedle co-encapsulating the TLR7/8 agonist R848 and the immune checkpoint inhibitor aPD-1, termed αNP-RNP@DMN, and fabricated it as a transdermal drug delivery system. This well-designed microneedle patch, endowed with efficient tumor drug delivery ability, was able to mature tumor-infiltrating dendritic cells (TIDCs) and further promote the infiltration of CD8+ T cells into the tumor tissue with the aid of R848. Moreover, the introduction of aPD-1 blocked the programmed cell death protein 1/programmed cell death ligand 1(PD-1/PD-L1) immune checkpoints, synergistically reversing the immunosuppressive microenvironment of TNBC. In vivo therapeutic results demonstrated that αNP-RNP@DMN not only significantly prolonged the survival time of 4T1 tumor-bearing mice, but also inhibited tumor recurrence and lung metastasis after surgery, implying the great potential of this effective drug delivery system for enhanced immunotherapy of superficial tumors. STATEMENT OF SIGNIFICANCE: The limited number of tumor-infiltrating CD8+ T cells and the inefficient delivery of immune drugs to the tumor tissue hinder the effective immunotherapy of triple-negative breast cancer (TNBC). Herein, a dissolving microneedle co-encapsulating TLR7/8 agonist R848 and immune checkpoint inhibitor aPD-1 was developed and fabricated as a transdermal drug delivery system. This tailor-made microneedle patch not only promoted drug accumulation in tumor sites in a safe and painless manner, but also lifted the immune-suppressive state of tumor-infiltrating dendritic cells (TIDCs). The activated TIDCs further enhanced T-cell infiltration into the tumor tissue, thus successfully boosting the therapeutic efficacy of aPD-1. This study demonstrated that this well-designed microneedle patch could be served as an effective drug delivery system for enhanced immunotherapy of TNBC.

Automatic epicardial adipose tissue segmentation in pulmonary computed tomography venography using nnU-Net.

Background: Epicardial adipose tissue (EAT) is a key aspect in the investigation of cardiac pathophysiology. We sought to develop a deep learning (DL) model for fully automatic extraction and quantification of EAT through pulmonary computed tomography venography (PCTV) images. Methods: In this retrospective study, we included 128 patients with atrial fibrillation and PCTV from 2 hospitals. A DL model for automated EAT segmentation was developed from a training set of 51 patients and a validation set of 13 patients from hospital A. The algorithm was further validated using an internal test set of 16 patients from hospital A and an external test set of 48 patients from hospital B. The consistency and measurement agreement of EAT quantification were compared between the DL model and the conventional manual protocol using the Dice score coefficient (DSC), Hausdorff distance (HD95), Pearson correlation coefficient, and Bland-Altman plot. Results: In the internal and external test set, automated segmentation with DL was successful in all cases. The total analysis time was shorter for DL than for manual reconstruction (5.43±2.52 vs. 106.20±15.90 min; P<0.001). The EAT segmented with the DL model had good consistency with manual segmentation (the DSC of the internal and external test sets were 0.92±0.02 and 0.88±0.03, respectively). The quantification of EAT evaluated with the 2 methods showed excellent correlation (all correlation coefficients >0.9; all P values <0.001) and minimal measurement difference. Conclusions: The proposed DL model achieved fully automatic quantification of EAT from PCTV images. The yielded results were highly consistent with those of manual quantification.

Construction of pH-Sensitive Nanovaccines Encapsulating Tumor Cell Lysates and Immune Adjuvants for Breast Cancer Therapy.

The current immunotherapy strategies for triple negative breast cancer (TNBC) are greatly limited due to the immunosuppressive tumor microenvironment (TME). Immunization with cancer vaccines composed of tumor cell lysates (TCL) can induce an effective antitumor immune response. However, this approach also has the disadvantages of inefficient antigen delivery to the tumor tissues and the limited immune response elicited by single-antigen vaccines. To overcome these limitations, a pH-sensitive nanocalcium carbonate (CaCO3 ) carrier loaded with TCL and immune adjuvant CpG (CpG oligodeoxynucleotide 1826) is herein constructed for TNBC immunotherapy. This tailor-made nanovaccine, termed CaCO3 @TCL/CpG, not only neutralizes the acidic TME through the consumption of lactate by CaCO3 , which increases the proportion of the M1/M2 macrophages and promotes infiltration of effector immune cells but also activates the dendritic cells in the tumor tissues and recruits cytotoxic T cells to further kill the tumor cells. In vivo fluorescence imaging study shows that the pegylated nanovaccine could stay longer in the blood circulation and extravasate preferentially into tumor site. Besides, the nanovaccine exhibits high cytotoxicity in 4T1 cells and significantly inhibits tumor growth of tumor-bearing mice. Overall, this pH-sensitive nanovaccine is a promising nanoplatform for enhanced immunotherapy of TNBC.

Identifying shared genetic architecture between rheumatoid arthritis and other conditions: a phenome-wide association study with genetic risk scores.

BACKGROUND: Rheumatoid arthritis (RA) shares genetic variants with other autoimmune conditions, but existing studies test the association between RA variants with a pre-defined set of phenotypes. The objective of this study was to perform a large-scale, systemic screen to determine phenotypes that share genetic architecture with RA to inform our understanding of shared pathways. METHODS: In the UK Biobank (UKB), we constructed RA genetic risk scores (GRS) incorporating human leukocyte antigen (HLA) and non-HLA risk alleles. Phenotypes were defined using groupings of International Classification of Diseases (ICD) codes. Patients with an RA code were excluded to mitigate the possibility of associations being driven by the diagnosis or management of RA. We performed a phenome-wide association study, testing the association between the RA GRS with phenotypes using multivariate generalized estimating equations that adjusted for age, sex, and first five principal components. Statistical significance was defined using Bonferroni correction. Results were replicated in an independent cohort and replicated phenotypes were validated using medical record review of patients. FINDINGS: We studied n = 316,166 subjects from UKB without evidence of RA and screened for association between the RA GRS and n = 1317 phenotypes. In the UKB, 20 phenotypes were significantly associated with the RA GRS, of which 13 (65%) were immune mediated conditions including polymyalgia rheumatica, granulomatosis with polyangiitis (GPA), type 1 diabetes, and multiple sclerosis. We further identified a novel association in Celiac disease where the HLA and non-HLA alleles had strong associations in opposite directions. Strikingly, we observed that the non-HLA GRS was exclusively associated with greater risk of the validated conditions, suggesting shared underlying pathways outside the HLA region. INTERPRETATION: This study replicated and identified novel autoimmune phenotypes verified by medical record review that share immune pathways with RA and may inform opportunities for shared treatment targets, as well as risk assessment for conditions with a paucity of genomic data, such as GPA. FUNDING: This research was funded by the US National Institutes of Health (P30AR072577, R21AR078339, R35GM142879, T32AR007530) and the Harold and DuVal Bowen Fund.

The Zinc Transporter SLC39A10 Plays an Essential Role in Embryonic Hematopoiesis.

The role of zinc in hematopoiesis is currently unclear. Here, SLC39A10 (ZIP10) is identified as a key zinc transporter in hematopoiesis. The results show that in zebrafish, Slc39a10 is a key regulator of the response to zinc deficiency. Surprisingly, both slc39a10 mutant zebrafish and hematopoietic Slc39a10-deficient mice develop a more severe form of impaired hematopoiesis than animals lacking transferrin receptor 1, a well-characterized iron gatekeeper, indicating that zinc plays a larger role than iron in hematopoiesis, at least in early hematopoietic stem cells (HSCs). Furthermore, it is shown that loss of Slc39a10 causes zinc deficiency in fetal HSCs, which in turn leads to DNA damage, apoptosis, and G1 cell cycle arrest. Notably, zinc supplementation largely restores colony formation in HSCs derived from hematopoietic Slc39a10-deficient mice. In addition, inhibiting necroptosis partially restores hematopoiesis in mouse HSCs, providing mechanistic insights into the requirement for zinc in mediating hematopoiesis. Together, these findings indicate that SLC39A10 safeguards hematopoiesis by protecting against zinc deficiency-induced necroptosis, thus providing compelling evidence that SLC39A10 and zinc homeostasis promote the development of fetal HSCs. Moreover, these results suggest that SLC39A10 may serve as a novel therapeutic target for treating anemia and zinc deficiency-related disorders.

Poor Prognostic Biomarker KIAA1522 Is Associated with Immune Infiltrates in Hepatocellular Carcinoma.

Background: The prognosis is poor for hepatocellular carcinoma (HCC), a tumor and cancer associated with inflammation that is common. New data showed that significant levels of KIAA1522 were expressed in HCC tissues and cell lines, suggesting that KIAA1522 may be a highly useful prognostic marker for HCC. However, its biochemical processes and impacts on the immune system go deeper. Objective: To verify the significance of KIAA1522 in HCC and investigate its related carcinogenic mechanisms. Methods: Studies examining the relationship between KIAA1522 expression and clinical-pathologic characteristics in HCC have been checked in the Cancer Genome Atlas (TCGA) database. A receiver operating characteristic (ROC) curve was used to assess the diagnostic efficacy of KIAA1522 in HCC. Western blot analysis was used to find the presence of the KIAA1522 protein in the tumor and paraneoplastic tissues of eight randomly chosen HCC patients. The GSVA program in R language was used to evaluate the relationship between KIAA1522 and immune cell infiltration in HCC. We searched the Search Tool for the Retrieval of Interacting Genes (STRING) database for interacting proteins connected to the expression of KIAA1522. Pathways were involved in the enrichment analysis of KIAA1522 to anticipate potential mechanisms through which KIAA1522 may affect immunological infiltration. Results: Our study found that KIAA1522 was commonly elevated in HCC tumor tissues and that it also signaled a bad outcome. We found an inverse link between KIAA1522 and cytotoxic cells and an inverse relationship between KIAA1522 and Th2 cell infiltration. In STRING analysis, the top 5 coexpressed proteins of KIAA1522 were BAIAP2, NCK2, TSNAXIP1, POGK, and KLHL31. The effect of KIAA1522 on HCC may entail cell cycle alteration, an immunological response, and suppression of the PPAR signaling pathway. Conclusion: High expression of KIAA1522 was linked to HCC immune cell infiltration, disease progression, and a poor prognosis.

Semisupervised Calibration of Risk with Noisy Event Times (SCORNET) using electronic health record data.

Leveraging large-scale electronic health record (EHR) data to estimate survival curves for clinical events can enable more powerful risk estimation and comparative effectiveness research. However, use of EHR data is hindered by a lack of direct event time observations. Occurrence times of relevant diagnostic codes or target disease mentions in clinical notes are at best a good approximation of the true disease onset time. On the other hand, extracting precise information on the exact event time requires laborious manual chart review and is sometimes altogether infeasible due to a lack of detailed documentation. Current status labels-binary indicators of phenotype status during follow-up-are significantly more efficient and feasible to compile, enabling more precise survival curve estimation given limited resources. Existing survival analysis methods using current status labels focus almost entirely on supervised estimation, and naive incorporation of unlabeled data into these methods may lead to biased estimates. In this article, we propose Semisupervised Calibration of Risk with Noisy Event Times (SCORNET), which yields a consistent and efficient survival function estimator by leveraging a small set of current status labels and a large set of informative features. In addition to providing theoretical justification of SCORNET, we demonstrate in both simulation and real-world EHR settings that SCORNET achieves efficiency akin to the parametric Weibull regression model, while also exhibiting semi-nonparametric flexibility and relatively low empirical bias in a variety of generative settings.

Association Between Inflammation, Incident Heart Failure, and Heart Failure Subtypes in Patients With Rheumatoid Arthritis.

OBJECTIVE: In rheumatoid arthritis (RA), there are limited data on risk factors for the clinical heart failure (HF) subtypes of HF with reduced ejection fraction (HFrEF) and HF with preserved ejection fraction (HFpEF). This study examined the association between inflammation and incident HF subtypes in RA. Because inflammation changes over time with disease activity, we hypothesized that the effect of inflammation may be stronger at the 5-year follow-up than at the standard 10-year follow-up from general population studies of cardiovascular risk. METHODS: We studied an electronic health record (EHR)-based RA cohort with data pre- and post-RA incidence. We applied a validated approach to identify HF and extract ejection fraction to classify HFrEF and HFpEF. Follow-up started from the RA incidence date (index date) to the earliest occurrence of incident HF, death, last EHR encounter, or 10 years. Baseline inflammation was assessed using erythrocyte sedimentation rate or C-reactive protein values. Covariates included demographic characteristics, established HF risk factors, and RA-related factors. We tested the association between baseline inflammation with incident HF and its subtypes using Cox proportional hazards models. RESULTS: We studied 9,087 patients with RA; 8.2% developed HF during 10 years of follow-up. Elevated inflammation was associated with increased risk for HF at both 5- and 10-year follow-ups (hazard ratio [HR] 1.66, 95% confidence interval [95% CI] 1.12-2.46 and HR 1.46, 95% CI 1.13-1.90, respectively), which is also seen for HFpEF at 5 years (HR 1.72, 95% CI 1.09-2.70) and 10 years (HR 1.45, 95% CI 1.07-1.94). HFrEF was not associated with inflammation for either follow-up time. CONCLUSION: Elevated inflammation early in RA diagnosis was associated with HF; this association was driven by HFpEF and not HFrEF, suggesting a window of opportunity for prevention of HFpEF in RA.

Identification of Diagnostic Markers in Infantile Hemangiomas.

Background: Infantile Hemangiomas (IHs) are common benign vascular tumors of infancy that may have serious consequences. The research on diagnostic markers for IHs is scarce. Methods: The "limma" R package was applied to identify differentially expressed genes (DEGs) in developing IHs. Plugin ClueGO in Cytoscape software performed functional enrichment of DEGs. The Search Tool for Retrieving Interacting Genes (STRING) database was utilized to construct the PPI network. The least absolute shrinkage and selection operator (LASSO) regression model and support vector machine recursive feature elimination (SVM-RFE) analysis were used to identify diagnostic genes for IHs. The receiver operating characteristic (ROC) curve evaluated diagnostic genes' discriminatory ability. Single-gene based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) was conducted by Gene Set Enrichment Analysis (GSEA). The chemicals related to the diagnostic genes were excavated by the Comparative Toxicogenomics Database (CTD). Finally, the online website Network Analyst was used to predict the transcription factors targeting the diagnostic genes. Results: A total of 205 DEGs were singled out from IHs samples of 6-, 12-, and 24-month-old infants. These genes principally participated in vasculogenesis and development-related, endothelial cell-related biological processes. Then we mined 127 interacting proteins and created a network with 127 nodes and 251 edges. Furthermore, LASSO and SVM-RRF algorithms identified five diagnostic genes, namely, TMEM2, GUCY1A2, ISL1, WARS, and STEAP4. ROC curve analysis results indicated that the diagnostic genes had a powerful ability to distinguish IHs samples from normal samples. Next, the results of GSEA for a single gene illustrated that all five diagnostic genes inhibited the "valine, leucine, and isoleucine degradation" pathway in the development of IHs. WARS, TMEM2, and STEAP4 activated the "blood vessel development" and "vasculature development" in IHs. Subsequently, inhibitors targeting TMEM2, GUCY1A2, ISL1, and STEAP4 were mined. Finally, 14 transcription factors regulating GUCY1A2, 14 transcription factors regulating STEAP4, and 26 transcription factors regulating ISL1 were predicted. Conclusion: This study identified five diagnostic markers for IHs and further explored the mechanisms and targeting drugs, providing a basis for diagnosing and treating IHs.

Downregulation of MCF2L Promoted the Ferroptosis of Hepatocellular Carcinoma Cells through PI3K/mTOR Pathway in a RhoA/Rac1 Dependent Manner.

Methods and Results: The levels of MCF2L were detected by PCR and western blotting assay. The effect of MCF2L on ferroptosis was confirmed by MTT, colony formation assay, Brdu, in vivo animal experiment, and the content of Iron, GSH, ROS, and MDA. The underlying mechanisms were explored by PCR, western blotting, and affinity precipitation assay. Our findings demonstrated that MCF2L is remarkedly upregulated in HCC tissues, and sorafenib can induce the levels of MCF2L, suggesting that MCF2L might function in sorafenib resistance of HCC. Further analysis showed that downregulation of MCF2L enhances HCC cell death induced by sorafenib, and ferroptosis inhibitor can reverse this process. Subsequent experiments showed that downregulation of MCF2L elevates the content of Iron, ROS, and MDA, which are all indicators of ferroptosis. Finally, mechanism analysis showed that MCF2L regulates the PI3K/AKT pathway in a RhoA/Rac1 dependent manner. Conclusions: Our study showed that targeting MCF2L may be a hopeful method to overcome sorafenib-resistance through inducing ferroptosis in HCC.

A semi-supervised adaptive Markov Gaussian embedding process (SAMGEP) for prediction of phenotype event times using the electronic health record.

While there exist numerous methods to identify binary phenotypes (i.e. COPD) using electronic health record (EHR) data, few exist to ascertain the timings of phenotype events (i.e. COPD onset or exacerbations). Estimating event times could enable more powerful use of EHR data for longitudinal risk modeling, including survival analysis. Here we introduce Semi-supervised Adaptive Markov Gaussian Embedding Process (SAMGEP), a semi-supervised machine learning algorithm to estimate phenotype event times using EHR data with limited observed labels, which require resource-intensive chart review to obtain. SAMGEP models latent phenotype states as a binary Markov process, and it employs an adaptive weighting strategy to map timestamped EHR features to an embedding function that it models as a state-dependent Gaussian process. SAMGEP's feature weighting achieves meaningful feature selection, and its predictions significantly improve AUCs and F1 scores over existing approaches in diverse simulations and real-world settings. It is particularly adept at predicting cumulative risk and event counting process functions, and is robust to diverse generative model parameters. Moreover, it achieves high accuracy with few (50-100) labels, efficiently leveraging unlabeled EHR data to maximize information gain from costly-to-obtain event time labels. SAMGEP can be used to estimate accurate phenotype state functions for risk modeling research.

Association between macronutrients intake distribution and bone mineral density.

BACKGROUND & AIMS: Although it is well known dietary factors are closely correlated with bone health, the association between macronutrients intake distribution and bone mineral density (BMD) is still unclear. The aims of this study were to investigate how macronutrients distribution was correlated with BMD, and to evaluate how the substitution between macronutrients could be associated with BMD. METHODS: We conducted a cross-sectional study based on data from National Health and Nutrition Examination Survey. Dietary recall method was used to assessed the intake of macronutrients. Macronutrient intake distribution including carbohydrate, protein and fat was calculated as percentages of energy intake from total energy. BMD was converted to T-score and low BMD was defined as T-score less than -1.0. The association between the percentages of energy intake from carbohydrate, protein and fat with T-score and risk of low BMD was evaluated using multivariate regression models. Isocaloric substitution analysis was conducted using the multivariate nutrient density method. RESULTS: Data form 4447 adults aged 20 years and older who underwent BMD examination were included in this study. Higher percentage of energy intake from carbohydrate was associated with lower T-score (-0.03 [95%CI, -0.05 to -0.01]; P = 0.001) and higher risk of low BMD (1.05 [95%CI, 1.02-1.08]; P = 0.003), while higher percentage of energy intake from protein was associated with higher T-score (0.05 [95%CI, 0.01-0.08]; P = 0.009) and lower odds of low BMD (0.92 [95%CI, 0.87-0.98]; P = 0.007). The percentage of energy intake from fat seemed to be positively correlated with T-score, but the correlation became insignificant after adjusting for metabolism related confounders. Isocaloric substitution analysis showed that only the substitution between carbohydrate and protein was significantly and independently associated with T-score (-0.05 [95%CI, -0.08 to -0.01]; P = 0.01) and the risk of low BMD (1.08 [95%CI, 1.02-1.15]; P = 0.008). CONCLUSIONS: Based on the results from this study, we hypothesized that a high-protein diet coupled with low carbohydrate intake would be beneficiary for prevention of bone loss in adults. However, randomized clinical trials or longitudinal studies are needed to further assessed our findings.

Nanodrugs Incorporating LDHA siRNA Inhibit M2-like Polarization of TAMs and Amplify Autophagy to Assist Oxaliplatin Chemotherapy against Colorectal Cancer.

Oxaliplatin (OXA) is a first-line chemotherapeutic agent for treating colorectal cancer (CC). However, the chemotherapeutic effect of OXA on CC is limited by the M2-like polarization of tumor-associated macrophages (TAMs) in the tumor microenvironment (TME) and protective autophagy of tumor cells. Here, a cationic polymer APEG-PAsp(PEI) (PAPEI) was prepared to deliver small-interfering RNA (siRNA) to silence the lactate dehydrogenase A (LDHA) gene (LDHA-siRNA) to enhance the chemotherapeutic effect of OXA on CC. The PAPEI/LDHA-siRNA nanocomplex effectively silenced the LDHA gene to inhibit the secretion of lactic acid from tumor cells, resulting in inhibition of the M2-like polarization of TAMs. In addition, the nanocomplex also amplified OXA-induced autophagy and transformed protective autophagy into autophagic death. Consequently, the combination treatment of OXA and PAPEI/LDHA-siRNA showed a dramatically increased chemotherapeutic effect on CC compared with the OXA-alone treatment, which also suggested its attractive potential for treating CC-like immune "cold" tumors.

Binary acronym disambiguation in clinical notes from electronic health records with an application in computational phenotyping.

OBJECTIVE: The use of electronic health records (EHR) systems has grown over the past decade, and with it, the need to extract information from unstructured clinical narratives. Clinical notes, however, frequently contain acronyms with several potential senses (meanings) and traditional natural language processing (NLP) techniques cannot differentiate between these senses. In this study we introduce a semi-supervised method for binary acronym disambiguation, the task of classifying a target sense for acronyms in the clinical EHR notes. METHODS: We developed a semi-supervised ensemble machine learning (CASEml) algorithm to automatically identify when an acronym means a target sense by leveraging semantic embeddings, visit-level text and billing information. The algorithm was validated using note data from the Veterans Affairs hospital system to classify the meaning of three acronyms: RA, MS, and MI. We compared the performance of CASEml against another standard semi-supervised method and a baseline metric selecting the most frequent acronym sense. Along with evaluating the performance of these methods for specific instances of acronyms, we evaluated the impact of acronym disambiguation on NLP-driven phenotyping of rheumatoid arthritis. RESULTS: CASEml achieved accuracies of 0.947, 0.911, and 0.706 for RA, MS, and MI, respectively, higher than a standard baseline metric and (on average) higher than a state-of-the-art semi-supervised method. As well, we demonstrated that applying CASEml to medical notes improves the AUC of a phenotype algorithm for rheumatoid arthritis. CONCLUSION: CASEml is a novel method that accurately disambiguates acronyms in clinical notes and has advantages over commonly used supervised and semi-supervised machine learning approaches. In addition, CASEml improves the performance of NLP tasks that rely on ambiguous acronyms, such as phenotyping.

Relationship Between Rheumatoid Arthritis and Pulmonary Function Measures on Spirometry in the UK Biobank.

OBJECTIVE: To investigate the independent relationship of rheumatoid arthritis (RA) to the type and severity of pulmonary patterns on spirometry compared to the pulmonary patterns in general population controls. METHODS: In this cross-sectional study, we investigated the association of RA with pulmonary function measures on spirometry among subjects in the UK Biobank who underwent spirometry for research purposes. RA cases were identified based on self-report and current disease-modifying antirheumatic drug/glucocorticoid use. Controls were subjects without RA from the general population. Outcome measures included continuous forced expiratory volume in 1 second percent predicted (FEV1 %) and forced vital capacity percent predicted (FVC%), type of spirometric pattern (restrictive or obstructive), and severity of the restrictive or obstructive pattern. We used multivariable regression to estimate the effects in RA cases compared to the effects in controls, adjusting for age, sex, body mass index, and smoking status/pack-years. RESULTS: Among 350,776 analyzed subjects who underwent spirometry (mean age 56.3 years; 55.8% female; 45.5% ever smokers), we identified 2,008 cases of treated RA. In multivariable analyses, RA was associated with lower FEV1 % (ß = -2.93 [95% confidence interval (95% CI) -3.63, -2.24]), FVC% (ß = -2.08 [95% CI -2.72, -1.45]), and FEV1 /FVC (ß = -0.008 [95% CI -0.010, -0.005]) compared to controls. RA was additionally associated with restrictive patterns (odds ratio [OR] 1.36 [95% CI 1.21, 1.52]) and obstructive patterns (OR 1.21 [95% CI 1.07, 1.37]) independent of confounders, and was most strongly associated with severe restrictive and obstructive patterns. CONCLUSION: RA is associated with increased odds of restrictive and obstructive patterns, and this relationship is not explained by confounders, including smoking status. In addition to restrictive lung disease, clinicians should also be aware that airway obstruction may be a pulmonary manifestation of RA.

Long noncoding RNA GAS8-AS1: A novel biomarker in human diseases.

Long non-coding RNAs (lncRNAs) represent a group of ncRNAs with more than 200 nucleotides. These RNAs can specifically regulate gene expression at both the transcriptional and the post-transcriptional levels, and increasing evidence indicates that they play vital roles in a variety of disease-related cellular processes. The lncRNA GAS8 antisense RNA 1 (GAS8-AS1, also known as C16orf3) is located in the second intron of GAS8 and has been reported to be both abnormally expressed in several diseases and closely correlated with many clinical characteristics. GAS8-AS1 has been shown to affect many biological functions, including cell proliferation, migration, invasiveness, and autophagy using several signaling pathways. In this review, we have summarized current studies on GAS8-AS1 roles in disease and discuss its potential clinical utility. GAS8-AS1 may be a promising biomarker for both diagnoses and prognoses, and a novel target for many disease therapies.

Scoring Model to Predict Functional Outcome in Poor-Grade Aneurysmal Subarachnoid Hemorrhage.

Background: Patients with poor-grade aneurysmal subarachnoid hemorrhage (aSAH), defined as World Federation of Neurosurgical Societies (WFNS) grades IV-V have high rates of disability and mortality. The objective of this study was to accurately prognosticate the outcomes of patients with poor-grade aSAH by developing a new scoring model. Methods: A total of 147 poor-grade aSAH patients in our center were enrolled. Risk variables identified by multivariate logistic regression analysis were used to devise a scoring model (total score, 0-9 points). The scores were estimated on the basis of ß coefficients. A cohort of 68 patients from another institute was used to validate the model. Results: Multivariate logistic regression analysis revealed that modified Fisher grade >2 [odds ratio [OR], 2.972; P = 0.034], age ≥65 years (OR, 3.534; P = 0.006), conservative treatment (OR, 5.078; P = 0.019), WFNS grade V (OR, 2.638; P = 0.029), delayed cerebral ischemia (OR, 3.170; P = 0.016), shunt-dependent hydrocephalus (OR, 3.202; P = 0.032), and cerebral herniation (OR, 7.337; P < 0.001) were significant predictors for poor prognosis [modified Rankin Scale [mRS] ≥3]. A scoring system was constructed by the integration of these factors and divided the poor-grade aSAH patients into three categories: low risk (0-1 points), intermediate risk (2-3 points), and high risk (4-9 points), with predicted risks of poor prognosis of 11, 52, and 87%, respectively (P < 0.001). The area under the curve in the derivation cohort was 0.844 (95% CI, 0.778-0.909). The AUC in the validation cohort was 0.831 (95% CI, 0.732-0.929). Conclusions: The new scoring model can improve prognostication and help decision-making for subsequent complementary treatment in patients with aSAH.