Unweighted Sum Research Articles

On the use of the healthy lifestyle index to investigate specific disease outcomes

The healthy lifestyle index (HLI), defined as the unweighted sum of individual lifestyle components, was used to investigate the combined role of lifestyle factors on health-related outcomes. We introduced weighted outcome-specific versions of the HLI, where individual lifestyle components were weighted according to their associations with disease outcomes. Within the European Prospective Investigation into Cancer and Nutrition (EPIC), we examined the association between the standard and the outcome-specific HLIs and the risk of T2D, CVD, cancer, and all-cause premature mortality. Estimates of the hazard ratios (HRs), the Harrell’s C-index and the population attributable fractions (PAFs) were compared. For T2D, the HR for 1-SD increase of the standard and T2D-specific HLI were 0.66 (95% CI: 0.64, 0.67) and 0.43 (0.42, 0.44), respectively, and the C-index were 0.63 (0.62, 0.64) and 0.72 (0.72, 0.73). Similar, yet less pronounced differences in HR and C-index were observed for standard and outcome-specific estimates for cancer, CVD and all-cause mortality. PAF estimates for mortality before age 80 were 57% (55%, 58%) and 33% (32%, 34%) for standard and mortality-specific HLI, respectively. The use of outcome-specific HLI could improve the assessment of the role of lifestyle factors on disease outcomes, thus enhancing the definition of public health recommendations.

Validation of a scoring algorithm for the clinician-reported outcome tool 'prurigo activity and severity (PAS)' based on clinical studies of dupilumab in adults with prurigo Nodularis.

Prurigo nodularis (PN) also known as chronic prurigo, is a chronic inflammatory skin disease characterized by intensely itchy nodules/lesions which occur due to intensive scratching. PN management is, in part, based on clinician evaluations of PN lesions, which can be supported by clinician-reported outcomes (ClinRO) such as the Prurigo Activity and Severity (PAS) instrument. A 5-item version of PAS was included in recent phase-3 dupilumab PN trials (PRIME [NCT04183335]/PRIME2 [NCT04202679]). The PAS score was derived using the unweighted sum of 3-items of the 5-item PAS (range, 0-11; higher score indicates worse activity and severity): Item 2 (number of lesions), Item 5a (percentage of lesions with excoriations/crusts) and Item 5b (percentage of healed lesions) for use in clinical practice and for communication of treatment benefit to physicians. To evaluate the measurement properties of PAS score and derive within-patient (responder definition) and between-group improvement thresholds for interpreting changes in PAS score in patients with PN. The data source was the pooled treatment group, intention-to-treat (ITT) data from the phase-3 PRIME (NCT04183335) and PRIME2 (NCT04202679) studies evaluating the efficacy of dupilumab in adult patients with PN with ≥20 nodules and severe itch uncontrolled with topical therapies. PAS score reliability, validity and sensitivity to change were evaluated, and anchor- and distribution-based methods were applied to derive meaningful change thresholds. The pooled ITT population included 311 patients (mean age 49.5 years, 65.3% female). Adequate to good psychometric properties were demonstrated for PAS score. The within-patient meaningful improvement threshold was estimated as 3.0 points (absolute change) and 37% (per cent change). A 1.7-point (absolute change) and 20% (per cent change) improvement were estimated to reflect a between-group meaningful change in PAS score. PAS score is a simple, clinically relevant indicator of PN lesion activity and severity supported by suitable psychometric performance.

Averaging or adding domain conductivities to calculate the unsaturated soil hydraulic conductivity

AbstractRecent models of the unsaturated hydraulic conductivity curve (UHCC) are the sum of separate UHCCs for domains of capillary water, film water, and water vapor. This requires parallel, noninteracting domains. A theoretical framework for aggregating domain conductivities to a bulk soil UHCC is presented to identify and possibly relax implicit assumptions about domain configuration. The paper develops arithmetic, harmonic, and geometric averages of the liquid‐water conductivities that can be arithmetically averaged with the vapor conductivity. However, current models for capillary and film conductivities are intrinsic, that is, valid within their respective domain. The vapor conductivity is a bulk conductivity, that is, it gives the conductivity of the gaseous domain as it manifests itself in the soil. Conversion relationships use the domain volume fractions as approximations of the as‐yet unknown weighting factors to convert between intrinsic and bulk conductivities. This facilitates consistent averaging of domain conductivities. Even with consistent averaging, a truly physically accurate model of the UHCC based on domain conductivities is fundamentally elusive. Nevertheless, models based on the three averages and the unweighted sum of the domain conductivities produce good fits to data for two soils but diverge in the dry range. The fitted curves for the capillary and film water depend on the averaging (or adding) method. Hence, they are not strictly characteristic of their respective domains. The true intrinsic domain conductivity functions may be impossible to determine.

A Magnetically Driven Tandem Chip Enables Rapid Isolation and Multiplexed Profiling of Extracellular Vesicles.

The protein phenotypes of extracellular vesicles (EVs) have emerged as promising biomarkers for cancer diagnosis and treatment monitoring. However, the technical challenges in rapid isolation and multiplexed molecular detection of EVs have limited their clinical practice. Herein, we developed a magnetically driven tandem chip to achieve streamlined rapid isolation and multiplexed profiling of surface protein biomarkers of EVs. Driven by magnetic force, the magnetic nanomixers not only act as tiny stir bars to promote mass transfer and enhance reaction efficiency of EVs, but also transport on communicating vessels of the tandem chip continuously and expedite the assay workflow. We designed cyclic surface enhancement of Raman scattering (SERS) tags to bind with target EVs and then release them by exonuclease I, eliminating steric hindrance and amplifying the SERS signal of multiple protein biomarkers on EVs. Due to the excellent assay performance, six breast cancer biomarkers were detected simultaneously on EVs using only 10 μL plasma within 1.5 h. The unweighted SUM signature offers great accuracy in discriminating breast cancer patients from healthy donors. Overall, the dynamic magnetic driving tandem chip offers a new avenue to advance the clinical application of EV-based liquid biopsy.

A Magnetically Driven Tandem Chip Enables Rapid Isolation and Multiplexed Profiling of Extracellular Vesicles

AbstractThe protein phenotypes of extracellular vesicles (EVs) have emerged as promising biomarkers for cancer diagnosis and treatment monitoring. However, the technical challenges in rapid isolation and multiplexed molecular detection of EVs have limited their clinical practice. Herein, we developed a magnetically driven tandem chip to achieve streamlined rapid isolation and multiplexed profiling of surface protein biomarkers of EVs. Driven by magnetic force, the magnetic nanomixers not only act as tiny stir bars to promote mass transfer and enhance reaction efficiency of EVs, but also transport on communicating vessels of the tandem chip continuously and expedite the assay workflow. We designed cyclic surface enhancement of Raman scattering (SERS) tags to bind with target EVs and then release them by exonuclease I, eliminating steric hindrance and amplifying the SERS signal of multiple protein biomarkers on EVs. Due to the excellent assay performance, six breast cancer biomarkers were detected simultaneously on EVs using only 10 μL plasma within 1.5 h. The unweighted SUM signature offers great accuracy in discriminating breast cancer patients from healthy donors. Overall, the dynamic magnetic driving tandem chip offers a new avenue to advance the clinical application of EV‐based liquid biopsy.

Identification of novel potential homologous repair deficiency-associated genes in pancreatic adenocarcinoma via WGCNA coexpression network analysis and machine learning

ABSTRACT Homologous repair deficiency (HRD) impedes double-strand break repair, which is a common driver of carcinogenesis. Positive HRD status can be used as theranostic markers of response to platinum- and PARP inhibitor-based chemotherapies. Here, we aimed to fully investigate the therapeutic and prognostic potential of HRD in pancreatic adenocarcinoma (PAAD) and identify effective biomarkers related to HRD using comprehensive bioinformatics analysis. The HRD score was defined as the unweighted sum of the LOH, TAI, and LST scores, and it was obtained based on the previous literature. To characterize PAAD immune infiltration subtypes, the “ConsensusClusterPlus” package in R was used to conduct unsupervised clustering. A WGCNA was conducted to elucidate the gene coexpression modules and hub genes in the HRD-related gene module of PAAD. The functional enrichment study was performed using Metascape. LASSO analysis was performed using the “glmnet” package in R, while the random forest algorithm was realized using the “randomForest” package in R. The prognostic variables were evaluated using univariate Cox analysis. The prognostic risk model was built using the LASSO approach. ROC curve and KM survival analyses were performed to assess the prognostic potential of the risk model. The half-maximal inhibitory concentration (IC50) of the PARP inhibitors was estimated using the “pRRophetic” package in R and the Genomics of Drug Sensitivity in Cancer database. The “rms” package in R was used to create the nomogram. A high HRD score indicated a poor prognosis and an advanced clinical process in PAAD patients. PAAD tumors with high HRD levels revealed significant T helper lymphocyte depletion, upregulated levels of cancer stem cells, and increased sensitivity to rucaparib, Olaparib, and veliparib. Using WGCNA, 11 coexpression modules were obtained. The red module and 122 hub genes were identified as the most correlated with HRD in PAAD. Functional enrichment analysis revealed that the 122 hub genes were mainly concentrated in cell cycle pathways. One novel HRD-related gene signature consisting of CKS1B, HJURP, and TPX2 were screened via LASSO analysis and a random forest algorithm, and they were validated using independent validation sets. No direct association between HRD and CKS1B, HJURP, or TPX2 has not been reported in the literature so far. Thus, these findings indicated that CKS1B, HJURP, and TPX2 have potential as diagnostic and prognostic biomarkers for PAAD. We constructed a novel HRD-related prognostic model that provides new insights into PAAD prognosis and immunotherapy. Based on bioinformatics analysis, we comprehensively explored the therapeutic and prognostic potential of HRD in PAAD. One novel HRD-related gene signature consisting of CKS1B, HJURP, and TPX2 were identified through the combination of WGCNA, LASSO analysis and a random forest algorithm. A novel HRD-related risk model that can predict clinical prognosis and immunotherapeutic response in PAAD patients was constructed.

Investigating predictors of progression from mild cognitive impairment to Alzheimer's disease based on different time intervals.

Mild cognitive impairment (MCI) is the early stage of AD, and about 10-12% of MCI patients will progress to AD every year. At present, there are no effective markers for the early diagnosis of whether MCI patients will progress to AD. This study aimed to develop machine learning-based models for predicting the progression from MCI to AD within 3 years, to assist in screening and prevention of high-risk populations. Data were collected from the Alzheimer's Disease Neuroimaging Initiative, a representative sample of cognitive impairment population. Machine learning models were applied to predict the progression from MCI to AD, using demographic, neuropsychological test and MRI-related biomarkers. Data were divided into training (56%), validation (14%) and test sets (30%). AUC (area under ROC curve) was used as the main evaluation metric. Key predictors were ranked utilising their importance. The AdaBoost model based on logistic regression achieved the best performance (AUC: 0.98) in 0-6month prediction. Scores from the Functional Activities Questionnaire, Modified Preclinical Alzheimer Cognitive Composite with Trails test and ADAS11 (Unweighted sum of 11 items from The Alzheimer's Disease Assessment Scale-Cognitive Subscale) were key predictors. Through machine learning, neuropsychological tests and MRI-related markers could accurately predict the progression from MCI to AD, especially in a short period time. This is of great significance for clinical staff to screen and diagnose AD, and to intervene and treat high-risk MCI patients early.

Pearson chi-squared and unweighted residual sum of square tests of fit for a probit model

Likewise the logistic model and the probit model is often used to consider the associations between a dichotomous dependent variable and some covariates. The probit model is a model that has a lot of important and engaging applications in practice especially in economics and finance. In the framework paper, we propose the Pearson chi-squared (PSC) and unweighted residual sum of square (URSS) tests of fit for a probit model, to enrich and diversify the topic of goodness of fit (GOF) test for regression models. This is a very interesting topic in theory and has enormous practical applications. Theoretically, we have proved that both the proposed approaches are asymptotic to the standard normal distribution. In terms of numerical research and practical applications, several simulations and a real-life data set a fishing data set are investigated in this work. The obtained results in these parts also help to illustrate numerical that the proposed formulas are very reliable. Besides, the findings in the empirical analysis are very consistent with reality. It will be very meaningful evidence to illustrate to everyone how to fish to get the most amount of fish while fishing. Finally, some discussions, conclusions, and future work are also included in this study.

Abstract 3301: Ultrasensitive compartment-free digital phenotyping of circulating extracellular vesicles for diagnosis of Ewing sarcoma

Abstract Ewing sarcoma (EWS) is the second most common pediatric osseous malignancy in the US. Vague symptoms and high prevalence of metastasis and recurrence present serious obstacles to its clinical management. Current diagnostic and monitoring modalities for EWS, including tissue biopsy, imaging, and cytogenetics have limitations in the early diagnosis and disease monitoring. Thus, EWS patients, as do other solid pediatric cancers, face unique challenges in the lack of non-invasive tests to aid with patient management. Extracellular vesicles (EVs), including exosomes, are emerging as a new paradigm of liquid biopsy for non-invasive cancer diagnosis and monitoring owing to their informative cargoes and imperative biological functions. However, limited progresses have been reported in the field of EWS-related EV diagnostics. To address this gap, we developed a new microfluidics-based digital immunoassay platform, termed μTUNER (microfluidic Topographic modUlation and iNtensification of Enzymatic Reaction), for ultrasensitive single-molecular analysis of EV protein biomarkers and liquid biopsy-based diagnosis of EWS. Through constructing nanoconfinements with tunable sizes, this technology affords programmable patterning and modulation of surface enzymatic reactions in a non-contact manner. Such unique capacity enables us to develop a compartment-free digital immunoassay platform which is conceptually distinct from the existing digital assays. As a proof-of-concept for clinical applications, we adapted the μTUNER digital immunoassay platform to quantitatively evaluate four exo-protein biomarkers (CD99, ENO-2, NGFR, and Ezrin) that we identified by extensive proteomics analysis of EWS cell line-derived EVs (PMID: 32821345). Using protein standards and EWS cell line-derived EVs, we demonstrated that our technology affords sensitive and quantitative detection of exo-protein biomarkers with LODs at the fg/mL level, which warrants subsequent assessment of its adaptability to clinical EWS diagnosis. EV profiling of plasma samples from adult and pediatric EWS patients (n = 16) and matched healthy controls (n = 17) reveals unique expression patterns of the four markers. These exo-protein biomarkers individually afforded excellent diagnostic performance (AUC, CD99: 0.992; ENO-2: 0.985; NGFR: 0.996; Ezrin: 0.978) to differentiate the EWS and control groups. The unweighted SUM signature of the four proteins further improves the diagnostic power to yield an AUC of 1.00. Meanwhile, the distinctive EV signature obtained from the machine learning-based analysis permits further classification of control, adult, and pediatric patients with an overall accuracy of 97%. Based on these results, we envision that our technology combined with the novel exo-protein biomarker signature can be useful clinically to improve the diagnosis of patients with Ewing sarcoma. Citation Format: Yunjie Wen, Vikalp Vishwakarma, Andrew K. Godwin, Yong Zeng. Ultrasensitive compartment-free digital phenotyping of circulating extracellular vesicles for diagnosis of Ewing sarcoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3301.

A Test to Distinguish Monotone Homogeneity from Monotone Multifactor Models

The goodness-of-fit of the unidimensional monotone latent variable model can be assessed using the empirical conditions of nonnegative correlations (Mokken in A theory and procedure of scale-analysis, Mouton, The Hague, 1971), manifest monotonicity (Junker in Ann Stat 21:1359–1378, 1993), multivariate total positivity of order 2 (Bartolucci and Forcina in Ann Stat 28:1206–1218, 2000), and nonnegative partial correlations (Ellis in Psychometrika 79:303–316, 2014). We show that multidimensional monotone factor models with independent factors also imply these empirical conditions; therefore, the conditions are insensitive to multidimensionality. Conditional association (Rosenbaum in Psychometrika 49(3):425–435, 1984) can detect multidimensionality, but tests of it (De Gooijer and Yuan in Comput Stat Data Anal 55:34–44, 2011) are usually not feasible for realistic numbers of items. The only existing feasible test procedures that can reveal multidimensionality are Rosenbaum’s (Psychometrika 49(3):425–435, 1984) Case 2 and Case 5, which test the covariance of two items or two subtests conditionally on the unweighted sum of the other items. We improve this procedure by conditioning on a weighted sum of the other items. The weights are estimated in a training sample from a linear regression analysis. Simulations show that the Type I error rate is under control and that, for large samples, the power is higher if one dimension is more important than the other or if there is a third dimension. In small samples and with two equally important dimensions, using the unweighted sum yields greater power.

Mitochondrial TXNRD2 and ME3 Genetic Risk Scores Are Associated with Specific Primary Open-Angle Glaucoma Phenotypes

Genetic variants in regions that include the mitochondrial genes thioredoxin reductase 2 (TXNRD2) and malic enzyme 3 (ME3) are associated with primary open-angle glaucoma (POAG) in genome-wide association studies (GWASs). To assess their clinical impact, we investigated whether TXNRD2 and ME3 genetic risk scores (GRSs) are associated with specific glaucoma phenotypes. Cross-sectional study. A total of 2617 patients with POAG and 2634 control participants from the National Eye Institute Glaucoma Human Genetics Collaboration Hereditable Overall Operational Database (NEIGHBORHOOD) consortium. All POAG-associated single nucleotide polymorphisms (SNPs) in the TXNRD2 and ME3 loci were identified using GWAS data (P < 0.05). Of these, 20 TXNRD2 and 24 ME3 SNPs were selected after adjusting for linkage disequilibrium. The correlation between SNP effect size and gene expression levels was investigated using the Gene-Tissue Expression database. Genetic risk scores were constructed for each individual using the unweighted sum of TXNRD2, ME3, and TXNRD2 + ME3 combined risk alleles. Age- and sex-adjusted odds ratios (ORs) for POAG diagnosis were calculated per decile for each GRS. Additionally, the clinical features of patients with POAG in the top 1%, 5%, and 10% of each GRS were compared with those in the bottom 1%, 5%, and 10%, respectively. Primary open-angle glaucoma OR per GRS decile, maximum treated intraocular pressure (IOP), and prevalence of paracentral visual field loss among patients with POAG with high versus low GRSs. A larger SNP effect size strongly correlated with higher TXNRD2 and lower ME3 expression levels (r= 0.95 and r= -0.97, respectively; P < 0.05 for both). Individuals in decile 10 of the TXNRD2 + ME3 GRS had the highest odds of POAG diagnosis (OR, 1.79 compared with decile 1; 95% confidence interval, 1.39-2.30; P<0.001). Patients with POAG in the top 1% of the TXNRD2 GRS showed higher mean maximum treated IOP compared with the bottom 1% (19.9 mmHg vs. 15.6 mmHg; adjusted P= 0.03). Patients with POAG in the top 1% of the ME3 and TXNRD2 + ME3 GRS showed a higher prevalence of paracentral field loss than the bottom 1% (72.7% vs. 14.3% for ME3 GRS and 88.9% vs. 33.3% for TXNRD2+ME3 GRS; adjusted P= 0.03 for both). Patients with POAG with higher TXNRD2 and ME3 GRSs showed higher treated IOP and a greater prevalence of paracentral field loss. Functional studies exploring how these variants impact mitochondrial function in patients with glaucoma are warranted. Proprietary or commercial disclosure may be found after the references.

Homologous repair deficiency-associated genes in invasive breast cancer revealed by WGCNA co-expression network analysis and genetic perturbation similarity analysis

ABSTRACT Background: Homologous repair deficiency (HRD) causes double-strand break repair to be impeded, which is a common driver of carcinogenesis. However, the therapeutic and prognostic potential of HRD in invasive breast cancer (BRCA) has not been fully explored using comprehensive bioinformatics analysis. Materials and Methods: HRD score was defined as the unweighted sum of LOH, TAI, and LST scores and obtained from the previous study according to Theo A et al. To characterize BRCA tumor microenvironment (TME) subtypes, “ConsensusClusterPlus” R package was used to conduct unsupervised clustering. The xCell algorithm was utilized for tumor composition analysis to estimate the TME in TCGA-BRCA. A WGCNA analysis was conducted to uncover the gene coexpression modules and hub genes in the HRD-related gene module of BRCA. The functional enrichment study was carried out using Metascape. A novel analysis pipeline, Genetic Perturbation Similarity Analysis (GPSA), was used to explore the single-gene perturbation closely related to HRD based on 3048 stable knockdown/knockout cell lines. The prognostic variables were evaluated using univariate COX analysis. Kaplan-Meier (KM) survival analysis was performed to assess the prognostic potential of HRD score. Receiver operator characteristic (ROC) curve was utilized to judge the diagnostic utility. Drug sensitivity was estimated through the R package “oncoPredict” and Genomics of Drug Sensitivity in Cancer (GDSC) database. XSum algorithm was performed to screen the candidate small molecule drugs based on the connectivity map (CMAP) database. Results: Low HRD score suggested a better prognosis in BRCA patients. The tumor with low HRD score had considerably greater degree of infiltration of stromal cells and infiltration of immunocytes was significantly enhanced in the high HRD score group. Using WGCNA, ten co-expression modules were obtained. The turquoise module and 25 hub genes were identified as the most correlated with HRD in BRCA. Functional enrichment analysis revealed that the turquoise gene module was mainly concentrated in the “cell cycle” pathways. Candidate HRD-related gene signatures (MELK) were screened out through WGCNA and GPSA analysis pipeline and then validated on independent validation sets. A small molecule drug (Clofibrate) that has the potential to reverse the increase of high HRD score was screened out to improve oncological outcomes in BRCA. Molecular docking suggested MELK to be one of possible molecular targets in the Clofibrate treatment of BRCA. Conclusion: Based on bioinformatic analysis, we fully explored the therapeutic and prognostic potential of HRD in BRCA. A novel HRD-related gene signature (MELK) were identified through the combination of WGCNA and GPSA analysis. In addition, we detailed the TME landscape in BRCA and identified four unique TME subtypes in group with high or low HRD score group. Moreover, Clofibrate were screened out to improve oncological outcomes in BRCA by reversing the increase of high HRD score. Thus, our study contributes to the development of personalized clinical management and treatment regimens of BRCA.

Task-dependent Optimal Weight Combinations for Static Embeddings

A variety of NLP applications use word2vec skip-gram, GloVe, and fastText word embeddings. These models learn two sets of embedding vectors, but most practitioners use only one of them, or alternately an unweighted sum of both. This is the first study to systematically explore a range of linear combinations between the first and second embedding sets. We evaluate these combinations on a set of six NLP benchmarks including IR, POS-tagging, and sentence similarity. We show that the default embedding combinations are often suboptimal and demonstrate 1.0-8.0% improvements. Notably, GloVe’s default unweighted sum is its least effective combination across tasks. We provide a theoretical basis for weighting one set of embeddings more than the other according to the algorithm and task. We apply our findings to improve accuracy in applications of cross-lingual alignment and navigational knowledge by up to 15.2%.

Comparison of unweighted and item response theory-based weighted sum scoring for the Nine-Questions Depression-Rating Scale in the Northern Thai Dialect

BackgroundThe Nine-Questions Depression-Rating Scale (9Q) has been developed as an alternative assessment tool for assessing the severity of depressive symptoms in Thai adults. The traditional unweighted sum scoring approach does not account for differences in the loadings of the items on the actual severity. Therefore, we developed an Item Response Theory (IRT)-based weighted sum scoring approach to provide a scoring method that is more precise than the unweighted sum score.MethodsSecondary data from a study on the criterion-related validity of the 9Q in the northern Thai dialect was used in this study. All participants were interviewed to obtain demographic data and screened/evaluated for major depressive disorder and the severity of the associated depressive symptoms, followed by diagnosis by a psychiatrist for major depressive disorder. IRT models were used to estimate the discrimination and threshold parameters. Differential item functioning (DIF) of responses to each item between males and females was compared using likelihood-ratio tests. The IRT-based weighed sum scores of the individual items are defined as the linear combination of individual response weighted with the discrimination and threshold parameters divided by the plausible maximum score based on the graded-response model (GRM) for the 9Q score (9Q-GRM) or the nominal-response model (NRM) for categorical combinations of the intensity and frequency of symptoms from the 9Q responses (9QSF-NRM). The performances of the two scoring procedures were compared using relative precision.ResultsOf the 1,355 participants, 1,000 and 355 participants were randomly selected for the developmental and validation group for the IRT-based weighted scoring, respectively. the gender-related DIF were presented for items 2 and 5 for the 9Q-GRM, while most items (except for items 3 and 6) for the 9QSF-NRM, which could be used to separately estimate the parameters between genders. The 9Q-GRM model accounting for DIF had a higher precision (16.7%) than the unweighted sum-score approach.DiscussionOur findings suggest that weighted sum scoring with the IRT parameters can improve the scoring when using 9Q to measure the severity of the depressive symptoms in Thai adults. Accounting for DIF between the genders resulted in higher precision for IRT-based weighted scoring.

Noninvasive Diagnosis of Nasopharyngeal Carcinoma Based on Phenotypic Profiling of Viral and Tumor Markers on Plasma Extracellular Vesicles.

Nasopharyngeal carcinoma (NPC) is a malignant tumor commonly associated with Epstein-Barr virus (EBV) infection, and its early diagnosis as well as its differentiation from nasopharyngitis (NPG) remains challenging due to the insufficient sensitivity of routine screening methods in clinical practice. To date, circulating extracellular vesicles (EVs, 40-1000 nm) have shown appealing potential in liquid biopsy for cancer diagnosis and prognosis. Herein, nanoflow cytometry (nFCM) capable of single EV analysis was applied to examine the expression of surface proteins with very low copy numbers on individual EVs as small as 40 nm. The particle concentrations of five EV subsets exposing EBV-encoded latent membrane proteins (LMP1 and LMP2A) and tumor markers (PD-L1, EGFR, and EpCAM) in plasma were determined rapidly via single-particle enumeration. We identified a five-marker panel named EVSUM5 (an unweighted sum of the concentration of the five individual EV subsets) that significantly surpassed the traditional VCA-IgA assay in discriminating NPC patients from both healthy donors and NPG patients with accuracies of 96.3 and 83.1%, respectively. Moreover, EVSUM2 (an unweighted sum of virus-specific LMP1- and LMP2A-positive EVs) could achieve the diagnosis of NPG with an accuracy of 82.6%. Collectively, the work presented a rapid, reliable, and noninvasive method as well as two diagnostic markers to help more accurately differentiate NPC from NPG patients and healthy donors in clinical practice.

Two-dimensional Shear-Wave Elastography and US Attenuation Imaging for Nonalcoholic Steatohepatitis Diagnosis: A Cross-sectional, Multicenter Study.

Background Multiparametric US examination may have potential in the comprehensive evaluation of nonalcoholic fatty liver disease (NAFLD), but multicenter studies are lacking. Purpose To evaluate the diagnostic performance of multiparametric US with the attenuation coefficient (AC) from attenuation imaging (ATI) and liver stiffness (LS) and dispersion slope (DS) from two-dimensional (2D) shear-wave elastography (SWE) in a multicenter study of patients with NAFLD. Materials and Methods This prospective study enrolled consecutive participants between December 2019 and June 2021 with suspected nonalcoholic steatohepatitis (NASH) who were scheduled to undergo liver biopsy in five tertiary hospitals. Before the procedure, all participants underwent US with ATI and 2D SWE according to the study protocol. Multivariable linear regression analyses were performed to determine the significant determinant factors for AC, LS, and DS. Diagnostic performance was decided based on the areas under the receiver operating characteristic curve (AUCs). Results A total of 132 participants (median age, 38 years; IQR, 27-54 years; 69 women) were evaluated. Among the participant characteristics, including pathologic findings, demographic characteristics, body mass index, and serum markers, hepatic steatosis for AC (P < .001), lobular inflammatory activity for DS (P = .007), and both fibrosis (P = .01) and lobular inflammatory activity (P = .04) for LS were significant determinant factors. At histopathologic examination, 53 of the 132 participants (40.2%) had NASH. The risk score system obtained using unweighted sum of scores from AC and DS showed the best diagnostic performance in the detection of NASH (AUC = 0.94; 95% CI: 0.89, 0.98; P < .05 for all), as compared with serum markers or other US parameters alone (AUC ≤ 0.88). Conclusion US attenuation imaging and two-dimensional shear-wave elastography were useful for assessing hepatic steatosis, lobular inflammation, and fibrosis. The risk score system obtained using the attenuation coefficient and dispersion slope showed the best diagnostic performance fo r nonalcoholic steatohepatitis. cris.nih.go.kr no. KCT0004326 © RSNA, 2022 Online supplemental material is available for this article.

A multi-ethnic polygenic risk score is associated with hypertension prevalence and progression throughout adulthood

In a multi-stage analysis of 52,436 individuals aged 17-90 across diverse cohorts and biobanks, we train, test, and evaluate a polygenic risk score (PRS) for hypertension risk and progression. The PRS is trained using genome-wide association studies (GWAS) for systolic, diastolic blood pressure, and hypertension, respectively. For each trait, PRS is selected by optimizing the coefficient of variation (CV) across estimated effect sizes from multiple potential PRS using the same GWAS, after which the 3 trait-specific PRSs are combined via an unweighted sum called “PRSsum”, forming the HTN-PRS. The HTN-PRS is associated with both prevalent and incident hypertension at 4-6 years of follow up. This association is further confirmed in age-stratified analysis. In an independent biobank of 40,201 individuals, the HTN-PRS is confirmed to be predictive of increased risk for coronary artery disease, ischemic stroke, type 2 diabetes, and chronic kidney disease.

Metabolomic Scores Mediate Associations between Dietary Patterns and Insulin Homeostasis in The Microbiome and Insulin Longitudinal Evaluation Study (MILES)

ObjectivesTo examine whether the metabolomic correlates of three diet patterns mediate the observed associations between diet and insulin homeostasis.MethodsData were included from 295 adults without diabetes (mean age 59.3 y ± 9.00; 63.46% non-Hispanic White and 36.54% African American) participating in MILES. An oral glucose tolerance test was used to measure fasting glucose, insulin, and C-peptide, as well as insulin secretion, insulin sensitivity, and disposition index. Self-reported dietary intake was used to create three diet scores: a Mediterranean-style diet, the Dietary Approaches to Stop Hypertension, and the USDA's Healthy Eating Index-2015. A global metabolomics panel of 1525 metabolites was measured using LC/MS global metabolomic profiling using validated reference standards in fasting plasma.. Main effects models that controlled for age, sex, ethnicity, socioeconomic status, smoking status, physical activity, and daily energy intake examined associations between the three diet scores, metabolite information and insulin homeostasis. 115 individual diet-metabolite associations across 76 metabolites reached significance after correcting for the effective number of independent tests (P < 3.4 * 10−05), informing the creation of diet-specific unweighted metabolite sum scores (MSS). Where warranted, mediation was assessed using the ‘mediate’ package in R.ResultsInsulin secretion did not show a significant association with any diet nor any MSS (all P > 0.01). All other insulin homeostasis traits were strongly associated with all diet-specific MSSs (P < 0.001-P < 2.5 * 10−09). MSSs significantly mediated any associations between insulin homeostasis and diet scores (P < 0.04-P < 2.0 * 10−16) with no evidence of direct effects of diet on insulin homeostasis in mediation models (all P > 0.05).ConclusionsThese data suggest that the metabolomic correlates of three diet patterns, when aggregated, mark insulin homeostasis and mediate the associations of diet with insulin traits. Such metabolomic diet scores may hold promise for revealing the underlying pathways between a healthy diet and glycemic control.Funding SourcesMILES was supported by NIDDK (R01-DK109588, P30-DK063491) and the National Center for Advancing Translational Sciences (UL1TR001420, UL1TR001881).

Characterization of mutational signatures of homologous recombination deficiency (HRD) in Chinese breast cancer.

e15129 Background: Homologous recombination deficiency (HRD) is a common molecular characteristic of genomic instability and has been proved to be an important biomarker for personalized therapy decisions and stratified management in breast cancer. In this study, we explored the mutational features of HRD tumors identified by HRD score in Chinese breast cancer. Methods: Tumor tissue DNA from 68 breast cancer patients was isolated and sequenced by next-generation sequencing (NGS) technologies with AcornMed 808-genes panel. HRD score was calculated as an unweighted sum of loss of heterozygosity (LOH) score, telomeric allelic imbalance (TAI) score and large-scale state transition (LST) score by AcornHRD algorithm, and mutations in 39 homologous recombination (HR) related genes including BRCA1/2 were identified for each case. Results: Based on the mutation alternation profiles from our cohort, somatic BRCA1/2 mutations were present in 15 patients (22.1%) while 29 patients (42.6%) harbored one or more mutations in HR-related genes excluding BRCA1/2. Germline mutations of BRCA1/2 and other HR-related genes occurred in 11.8% and 4.4% of cases detected, respectively.In our cohort, the median HRD score was 40 (range from 3 to 92). Sixty-eight cases were divided into HRD-high or HRD-low group based on a cut-off value of ≥ 42 or &lt; 42, with HRD-high group accounting for approximately 47% of our cohort, indicating the combination with HRD score yielded an increment of 26.5% in coverage of HRD within BRCA1/2 wildtype cases. The HRD-high group covered 82.4% of the BRCA-mutated cases and also showed higher mutation level of BRCA1/2-excluded HR genes than HRD-low group (p = 0.001). Prevalence of mutations in a total of 12 genes, including BRCA1(31.3% vs 5.6%, p = 0.009) and NBN (25% vs 5.6%, p = 0.038), were significant difference between HRD-high and HRD-low group. Besides, Cosmic Signature 1 (spontaneous deamination of 5-methylcytosine) and 5 (unknown) were existed in both groups, whereas Signature 6 (defective DNA mismatch repair) was only discovered in HRD-high group. Compared with HRD-low ones, copy number variations ratio was higher in HRD-high group (p = 0.031). Meanwhile, the patients with ERBB2 amplification (HER2+) showed a lower level of HRD score than HER2- ones. Conclusions: This study comprehensively delineated the genetic characteristics of HRD tumors, which suggests that HRD status marked by HRD score is strongly associated with BRCA1/2 and HR-related genes mutation as well as other genomic instability signatures, thus indicating its predictive value as a promising biomarker for DNA repair deficiency related drugs or therapies in Chinese breast cancer.

A novel radiomics signature based on T2-weighted imaging accurately predicts hepatic inflammation in individuals with biopsy-proven nonalcoholic fatty liver disease: a derivation and independent validation study.

Currently, there are no effective methods for assessing hepatic inflammation without resorting to histological examination of liver tissue obtained by biopsy. T2-weighted images (T2WI) are routinely obtained from liver magnetic resonance imaging (MRI) scan sequences. We aimed to establish a radiomics signature based on T2WI (T2-RS) for assessment of hepatic inflammation in people with nonalcoholic fatty liver disease (NAFLD). A total of 203 individuals with biopsy-confirmed NAFLD from two independent Chinese cohorts with liver MRI examination were enrolled in this study. The hepatic inflammatory activity score (IAS) was calculated by the unweighted sum of the histologic scores for lobular inflammation and ballooning. One thousand and thirty-two radiomics features were extracted from the localized region of interest (ROI) in the right liver lobe of T2WI and, subsequently, selected by minimum redundancy maximum relevance and least absolute shrinkage and selection operator (LASSO) methods. The T2-RS was calculated by adding the selected features weighted by their coefficients. Eighteen radiomics features from Laplacian of Gaussian, wavelet, and original images were selected for establishing T2-RS. The T2-RS value differed significantly between groups with increasing grades of hepatic inflammation (P<0.01). The T2-RS yielded an area under the receiver operating characteristic (ROC) curve (AUROC) of 0.80 [95% confidence interval (CI): 0.71-0.89] for predicting hepatic inflammation in the training cohort with excellent calibration. The AUROCs of T2-RS in the internal cohort and external validation cohorts were 0.77 (0.61-0.93) and 0.75 (0.63-0.84), respectively. The T2-RS derived from radiomics analysis of T2WI shows promising utility for predicting hepatic inflammation in individuals with NAFLD.