Current Scoring Methods Research Articles

Current update on anaphylaxis: anaphylaxis management in recent guidelines.

Anaphylaxis is a potentially fatal hypersensitivity reaction but frequently underrecognized. Although its incidence rates vary according to geographical location, it seems clear that there has been a general increase in recent years, either because of greater recognition of this entity or because it is progressing proportionally to the presence of allergic diseases in the world. The development of anaphylaxis management guidelines adapted to local or regional needs seems of utmost importance. Furthermore, it is necessary to assess their implementation and their positive effect regarding diagnosing and treating anaphylaxis. In this review we explore the currently existing definitions of anaphylaxis and its epidemiology, the potential triggers of anaphylaxis and guideline recommendations in terms of diagnosis and management, proposing a novel anaphylaxis calculator and reviewing the current scoring methods for anaphylactic episodes.

MAGIC: Malware behaviour analysis and impact quantification through signature co-occurrence and regression

Malware poses risks by compromising both data integrity and system security. Proactive defense efforts have led to the adoption of malware scoring, allowing analysts to assess the severity and develop countermeasures. These scores indicate the degree of malware maliciousness based on triggered signatures. However, current scoring methods do not precisely depict the true extent of malware's maliciousness. This inaccuracy is attributed to an inadequate assessment of the impact of behaviour corresponding to signatures on both system and network resources. To address this limitation, the paper proposes a novel scoring approach that accurately quantifies the impact of signatures triggered by malware through co-occurrence analysis. The method assesses the ensemble behaviour of signatures across two phases. In the first phase of signature scoring, an impact score quantification algorithm initializes each signature to predefined severity score bands based on the studied severity and frequency. The second phase refines initial scores iteratively, considering mutual information among signatures co-occurring in the malware's execution. Experimental results validate the proposed method's ability to accurately reflect signature maliciousness. This novel scoring method enhances malware analysis platforms in generating more precise scores compared to traditional methods, thereby improving resilience against evolving malware threats in the dynamic cybersecurity landscape.

Medical record data-enabled machine learning can enhance prediction of left atrial appendage thrombosis in nonvalvular atrial fibrillation

BackgroundAs a major complication of non-valvular atrial fibrillation (NVAF), left atrial appendage (LAA) thrombosis is associated with cerebral ischemic strokes, as well as high morbidity. Due to insufficient incorporation of risk factors, most current scoring methods are limited to the analysis of relationships between clinical characteristics and LAA thrombosis rather than detecting potential risk. Therefore, this study proposes a clinical data-driven machine learning method to predict LAA thrombosis of NVAF. MethodsPatients with NVAF from January 2014 to June 2022 were enrolled from Southwest Hospital. We selected 40 variables for analysis, including demographic data, medical history records, laboratory results, and the structure of LAA. Three machine learning algorithms were adopted to construct classifiers for the prediction of LAA thrombosis risk. The most important variables related to LAA thrombosis and their influences were recognized by SHapley Addictive exPlanations method. In addition, we compared our model with CHADS2 and CHADS2-VASc scoring methods. ResultsA total of 713 participants were recruited, including 127 patients with LAA thrombosis and 586 patients with no obvious thrombosis. The consensus models based on Random Forest and eXtreme Gradient Boosting LAA thrombosis prediction (RXTP) achieved the best accuracy of 0.865, significantly outperforming CHADS2 score and CHA2DS2-VASc score (0.757 and 0.754, respectively). The SHAP results showed that B-type natriuretic peptide, left atrial appendage width, C-reactive protein, Fibrinogen and estimated glomerular filtration rate are closely related to the risk of LAA thrombosis in nonvalvular atrial fibrillation. ConclusionsThe RXTP-NVAF model is the most effective model with the greatest ROC value and recall rate. The summarized risk factors obtained from SHAP enable the optimization of the treatment strategy, thereby preventing thromboembolism events and the occurrence of cardiogenic ischemic stroke.

AGAP2-AS1 as a prognostic biomarker in low-risk clear cell renal cell carcinoma patients with progressing disease

BackgroundClear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cancer and one of the most common cancers. While survival for localized ccRCC is good, the survival of metastatic disease is not, and thirty percent of patients with ccRCC develop metastases during follow-up. Although current scoring methods accurately identify patients at low progression risk, a small subgroup of those patients still experience metastasis. We therefore aimed to identify ccRCC progression biomarkers in “low-risk” patients who were potentially eligible for adjuvant treatments or more intensive follow-up.MethodsWe assembled a cohort of ccRCC patients (n = 443) and identified all “low-risk” patients who later developed progressing tumors (n = 8). Subsequently, we performed genome-wide expression profiling from formalin-fixed samples obtained at initial surgery from these “low-risk” patients and 16 matched patients not progressing to recurrence with metastasis. The patients were matched for Leibovich sore, creatinine, age, sex, tumor size and tumor stage. Key results were confirmed with qPCR and external data from The Cancer Genome Atlas.ResultsPrincipal component analysis indicated that systematic transcriptomic differences were already detectable at the time of initial surgery. One thousand one hundred sixty-seven genes, mainly associated with cancer and immune-related pathways, were differentially expressed between progressors and nonprogressors. A search for a classifier revealed that overexpression of AGAP2-AS1, an antisense long noncoding RNA, correctly classified 23 of 24 samples, years (4.5 years average) in advance of the discovery of metastasis and without requiring larger gene panels. Subsequently, we confirmed AGAP2-AS1 gene overexpression by qPCR in the same samples (p < 0.05). Additionally, in external data from The Cancer Genome Atlas, overexpression of AGAP2-AS1 is correlated with overall unfavorable survival outcome in ccRCC, irrespective of other prognostic predictors (p = 2.44E−7).ConclusionAGAP2-AS1 may represent a novel biomarker identifying high-risk ccRCC patients currently classified as “low risk” at the time of surgery.

Abstract 401: Designing for success: the right CRISPR design strategies for the right experiment

Abstract CRISPR design is a complex multivariate problem. The weight of each variable differs depending on the experiment under consideration. Current methods of designing CRISPR experiments are based on a single-variable, resulting in poor experimental outcomes. From the numerous experiments enabled by the DESKGEN platform, we can demonstrate key design improvements to maximise experimental outcome. Our current scoring methods were tested and validated using an sgRNA library of over 3,500 sgRNAs in essential genes with a variety of predicted sgRNA activities and specificities. The predicted 'good' sgRNAs achieved significantly higher read depletion for essential genes, with fewer failing sgRNAs, compared to sgRNAs that didn't meet the scoring criteria. Our scoring algorithm incorporates multiple published observations for choosing effective sgRNAs, which differs from other CRISPR tools that tend to focus on one factor such as sgRNA sequence or specificity. We were unable to find correlations for some currently accepted sgRNA selection criteria, such as position within a gene, which demonstrates the need to constantly challenge and improve sgRNA design rules. Citation Format: Leigh Brody, Neil Humphryes-Kirilov, Victor Dillard, Riley Doyle. Designing for success: the right CRISPR design strategies for the right experiment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 401. doi:10.1158/1538-7445.AM2017-401

Extracting high confidence protein interactions from affinity purification data: at the crossroads.

The fast progress in AP-MS techniques has prompted the development of a multitude of scoring methods, which are relied upon to remove contaminants and non-specific binders. Choosing the appropriate scoring scheme for a given AP-MS dataset has become a major challenge. The comparative analysis of 6 of the most popular scoring methods, presented here, reveals that overall these methods do not perform as expected. Evidence is provided that this is due to 3 closely related issues: the high 'noise' levels of the raw AP-MS data, the limited capacity of current scoring methods to deal with such high noise levels, and the biases introduced using Gold Standard datasets to benchmark the scoring functions and threshold the networks. For the field to move forward, all three issues will have to be addressed. This article is part of a Special Issue entitled: Protein dynamics in health and disease. Guest Editors: Pierre Thibault and Anne-Claude Gingras.

How to Score the Sexual Experiences Survey? A Comparison of Nine Methods.

Although assessments of sexual assault victimization and perpetration have greatly improved, current scoring methods do not fully utilize the wealth of information they provide. The present studies assessed new methods for scoring sexual assault severity using the Sexual Experiences Survey (SES; Koss et al., 2007). In two studies of female (n = 436) and male (n = 313) non-problem drinkers who had engaged in unprotected sex within the past year, we compared three severity ranking schemes as well as three scoring methods per severity scheme for a total of nine scoring methods. New severity ranking schemes considered tactic types separately, varied combinations of assault outcomes, and accounted for multiple types and frequencies of assaults. Measures assessing convergent validity were also administered. Seventy-eight percent (n = 340) of the women reported victimization, and 58% (n = 180) of the men reported perpetration. All severity scoring methods were strongly associated with convergent measures. Each scoring method is viable; however especially among samples with greater victimization/perpetration rates, there can be advantages to incorporating multiple types and frequencies of assault experiences into SES scores. Recent refinements of the SES necessitate commensurate improvements in its scoring methods in order to significantly advance the field of sexual assault assessment.

Development and Validation of an Objective Balance Error Scoring System

Limited access to sophisticated technology and the unreliability of simple tools prevent accurate and reliable human standing balance assessments outside research laboratory settings. The goal of this study was to develop and validate a simple objective balance assessment tool that provides an accurate, reliable, and affordable alternative to currently available laboratory and clinical methods. Thirty healthy subjects were filmed performing the Balance Error Scoring System (BESS) while wearing inertial measurement units (IMU) measuring linear accelerations and angular velocities from seven locations of the body: forehead, sternum, waist, right and left wrist, and right and left shin. Each video was scored by four experienced BESS raters, whose mean scores were used to develop an algorithm computing objective BESS (oBESS) scores solely from IMU data. Interrater reliability and accuracy of oBESS scores were assessed using intraclass correlations (ICC). Raters displayed low variability in scoring (ICC3,1 = 0.91). The oBESS was able to produce scores with accurate fit to raters (ICC3,1 = 0.92) and predicted individual BESS scores (ICC3,1 = 0.90) using data from one IMU placed at the forehead. oBESS was unable to produce accurate scores (ICC3,1 = 0.68) when using IMU data from the subset of conditions (firm surface only) used in popular concussion identification protocols. The oBESS can reliably predict total BESS scores in healthy subjects. Pending further validation, oBESS could represent a valid tool to assess balance by offering an objective and reliable alternative to the current scoring methods of the BESS.

Improving energy benchmarking with self-reported data

Energy benchmarking for buildings has become increasingly important in government policy and industry practice for energy efficiency. The questions of how energy benchmarking is currently conducted, and how it might be improved using rapidly growing quantities of self-reported data, are examined. A case study of commercial office buildings in New York City demonstrates how the rapid growth in self-reported data presents both new opportunities and challenges for energy benchmarking for buildings. A critique is presented for the scoring methodology and data sources for Energy Star, one of the largest and most successful benchmarking certification schemes. Findings from recent studies are examined to illustrate how this certification currently works in the marketplace. Self-reported building energy data are rapidly growing in Portfolio Manager (the user interface to Energy Star) due to mandatory energy benchmarking laws, and can be used to improve Energy Star's current scoring methods. These self-reported data are tested and improved for analysis by applying theories and methods of data quality developed in computer science, statistics and data management. These new data constitute a critical building block for the development of energy efficiency policies, and will affect how government, consultants, and owners measure and compare building energy use.

P3-05-10: Standardized Quantitative Methods for Investigating the Intratumor Heterogeneity of HER2 in FFPE Breast Cancer Specimens Utilizing the Vectra System, inForm and AQUA Technology.

Abstract Background: While intratumoral heterogeneity of HER2 gene expression is well documented, the HER2 intratumor protein expression heterogeneity and its clinical relevance is largely unknown but generally believed to be worth of investigation to ascertain its impact on HER2 targeted treatment outcome. Methods for investigating intratumor protein expression heterogeneity must preserve tissue architecture yet provide quantitative protein measurement. Immunohistochemistry (IHC) generally lacks standardization and quantitative capabilities and furthermore, current scoring methods do not account for, or report, heterogeneity. Robust and automated methods for characterizing and quantfication of heterogeneity are needed. Material and Methods: Whole tissue samples stained for HER2 expression by fluorescent quantitative IHC methods were analyzed by digital imaging microscopy (Vectra system with inForm software, Caliper, Hopkinton MA) providing automated pattern-recognition-based selection of area of interest and field of view sampling strategies. Quantitative image analysis (AQUA technology, HistoRx, Branford, CT) for quantitative measurement of HER2 protein expression as well as objective, quantitative determination of heterogeneity were performed. Requirements for percent tumor sampling at high-power to capture heterogeneity were assessed, to support optimization of slide scanning. Results: Heatmap representation of HER2 expression levels, and a statistical heterogeneity score developed based on the Simpson's biodiversity index (Simpson 1949) demonstrate visually as well as quantitatively HER2 heterogeneity in breast cancer tissues. Heterogeneity as a function of % area needed for accurate determination of whole section score along with confidence intervals as a function of percentage tumor analyzed inform appropriate sampling strategies. Conclusion: This study demonstrates the suitability of standardized quantitative methods for measuring heterogeneity and the feasibility of its use in investigations exploring the clinical relevance of HER2 intratumor heterogeneity with respect to both measurement strategies as well as treatment outcome. Through characterization of heterogeneous HER2 expression in tumors and optimized measurement strategies, it should be possible to design optimized treatment strategy aimed at treating the whole tumor as a function of objective quantitative assessment of HER2 expression. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P3-05-10.

OMERACT 10 Sharp Symposium: Important Findings in Examination of Imaging Methods for Measurement of Joint Damage in Rheumatoid Arthritis

The Sharp Symposium was held at the Outcome Measures in Rheumatology Clinical Trials 2010 meeting (OMERACT 10) in honor of the late John Sharp, consummate rheumatologist and researcher. The symposium focused on the status of current scoring methods in radiography, magnetic resonance imaging (MRI), and ultrasound (US) in rheumatoid arthritis (RA), as well as on the use of soluble and tissue biomarkers in RA, with the aim of updating recommendations regarding methods for enhanced detection, monitoring, and prediction of joint damage in clinical trials.

A knowledge-guided strategy for improving the accuracy of scoring functions in binding affinity prediction

BackgroundCurrent scoring functions are not very successful in protein-ligand binding affinity prediction albeit their popularity in structure-based drug designs. Here, we propose a general knowledge-guided scoring (KGS) strategy to tackle this problem. Our KGS strategy computes the binding constant of a given protein-ligand complex based on the known binding constant of an appropriate reference complex. A good training set that includes a sufficient number of protein-ligand complexes with known binding data needs to be supplied for finding the reference complex. The reference complex is required to share a similar pattern of key protein-ligand interactions to that of the complex of interest. Thus, some uncertain factors in protein-ligand binding may cancel out, resulting in a more accurate prediction of absolute binding constants.ResultsIn our study, an automatic algorithm was developed for summarizing key protein-ligand interactions as a pharmacophore model and identifying the reference complex with a maximal similarity to the query complex. Our KGS strategy was evaluated in combination with two scoring functions (X-Score and PLP) on three test sets, containing 112 HIV protease complexes, 44 carbonic anhydrase complexes, and 73 trypsin complexes, respectively. Our results obtained on crystal structures as well as computer-generated docking poses indicated that application of the KGS strategy produced more accurate predictions especially when X-Score or PLP alone did not perform well.ConclusionsCompared to other targeted scoring functions, our KGS strategy does not require any re-parameterization or modification on current scoring methods, and its application is not tied to certain systems. The effectiveness of our KGS strategy is in theory proportional to the ever-increasing knowledge of experimental protein-ligand binding data. Our KGS strategy may serve as a more practical remedy for current scoring functions to improve their accuracy in binding affinity prediction.

Docking and scoring protein complexes: CAPRI 3rd Edition

The performance of methods for predicting protein-protein interactions at the atomic scale is assessed by evaluating blind predictions performed during 2005-2007 as part of Rounds 6-12 of the community-wide experiment on Critical Assessment of PRedicted Interactions (CAPRI). These Rounds also included a new scoring experiment, where a larger set of models contributed by the predictors was made available to groups developing scoring functions. These groups scored the uploaded set and submitted their own best models for assessment. The structures of nine protein complexes including one homodimer were used as targets. These targets represent biologically relevant interactions involved in gene expression, signal transduction, RNA, or protein processing and membrane maintenance. For all the targets except one, predictions started from the experimentally determined structures of the free (unbound) components or from models derived by homology, making it mandatory for docking methods to model the conformational changes that often accompany association. In total, 63 groups and eight automatic servers, a substantial increase from previous years, submitted docking predictions, of which 1994 were evaluated here. Fifteen groups submitted 305 models for five targets in the scoring experiment. Assessment of the predictions reveals that 31 different groups produced models of acceptable and medium accuracy-but only one high accuracy submission-for all the targets, except the homodimer. In the latter, none of the docking procedures reproduced the large conformational adjustment required for correct assembly, underscoring yet again that handling protein flexibility remains a major challenge. In the scoring experiment, a large fraction of the groups attained the set goal of singling out the correct association modes from incorrect solutions in the limited ensembles of contributed models. But in general they seemed unable to identify the best models, indicating that current scoring methods are probably not sensitive enough. With the increased focus on protein assemblies, in particular by structural genomics efforts, the growing community of CAPRI predictors is engaged more actively than ever in the development of better scoring functions and means of modeling conformational flexibility, which hold promise for much progress in the future.

Use of Monofilament in the Detection of Foot Lesions in Older Adults

The purpose of this study was to compare Semmes-Weinstein monofilament testing between elderly and young patients in both a diabetic and nondiabetic population. Monofilament mapping (10 g) was performed on 115 patients: 74 who were greater than 60 years of age (47 with diabetes) and 41 who were younger than 60 years (20 with diabetes). Sensations were assessed at 15 predefined pressure/risk points. Sensations were found to have perfect correlation between the same point on the left and right foot (r = 1.0) for each of the 15 points. The data for the 15 points were divided in 2 subsets: one for 8 points similar to a validated method and another with 7 new points on the dorsum and ankle area. Scores on both subsets differed significantly by age group (P < .0001) and diabetes status (P < .001), with older patients and patients with diabetes having higher scores. Analysis of covariance was used to estimate 15-point scores for the 4 combinations of age group and diabetes status, adjusting for foot ulceration, callus, deformity, and ischemia. In older patients without diabetes, each subset score was used to detect future neuropathy if 4 or more points were insensitive. The detections agreed in 37 of 47 cases. In all 10 cases of disagreement, the 7-point subset detected no neuropathy, suggesting that inclusion of the additional points could improve diagnostic specificity. The significant differences noted between young and old patients suggest the importance of aging-related changes, and raise questions about current scoring methods for detection of neuropathy in older adults with diabetes.

Past, present, and future standards for management of dyslipidemia

Evolution of the National Cholesterol Education Program (NCEP) Adult Treatment Panel (ATP) guidelines for lipid lowering reflects a movement toward global risk assessment, including improved identification of risk in individuals without established coronary heart disease (CHD), and toward more aggressive lipid-lowering targets to reduce CHD risk. The current guidelines, for example, identify a segment of the population without established CHD as being at high risk on the basis of criteria that indicate CHD risk equivalency, recommend a low-density lipoprotein cholesterol (LDL-C) plasma level <100 mg/dL as optimal in all individuals, and establish the metabolic syndrome as a secondary target for therapeutic intervention. Many questions remain for future guidelines to address: To what extent should plasma levels of LDL-C be lowered by therapy to afford optimal risk reduction? Can risk assessment be improved, e.g., by using novel risk measures (such as high-sensitivity C-reactive protein) to indicate patients at higher risk who may benefit from more aggressive interventions? Should the metabolic syndrome be considered a high-risk state warranting aggressive intervention irrespective of risk categorization using current scoring methods? Guidelines for lipid management represent a synthesis of constantly emerging and evolving data: ongoing efforts to improve understanding of the relation between dyslipidemia and cardiovascular disease, to increase knowledge of and ability to measure other CHD risk factors, and to improve therapeutic practices and options will be reflected in future guidelines.

Classifying delinquent customers for credit collections: an application of probabilistic inductive learning

Probabilistic Inductive Learning (PrIL), a methodology that incorporates statistically determined measures of goodness with tree inductive algorithms from machine learning, has been applied to the credit collection operations of a major bank. Rules were induced from past customer account data to predict whether an account that is one month delinquent will be rectified or remain delinquent. Each of the rules has a reliability greater than that prescribed by management. The induced rules outperformed current scoring methods in predicting customer behavior on a holdout dataset. In addition, using the rules will result in matching current performance with 30% fewer contacts with delinquent customers.