Topological Performance Measures as Surrogates for Physical Flow Models for Risk and Vulnerability Analysis for Electric Power Systems

Critical infrastructure systems must be both robust and resilient in order to ensure the functioning of society. To improve the performance of such systems, we often use risk and vulnerability analysis to find and address system weaknesses. A critical component of such analyses is the ability to accurately determine the negative consequences of various types of failures in the system. Numerous mathematical and simulation models exist which can be used to this end. However, there are relatively few studies comparing the implications of using different modeling approaches in the context of comprehensive risk analysis of critical infrastructures. Thus in this paper, we suggest a classification of these models, which span from simple topologically-oriented models to advanced physical flow-based models. Here, we focus on electric power systems and present a study aimed at understanding the tradeoffs between simplicity and fidelity in models used in the context of risk analysis. Specifically, the purpose of this paper is to compare performances measures achieved with a spectrum of approaches typically used for risk and vulnerability analysis of electric power systems and evaluate if more simplified topological measures can be combined using statistical methods to be used as a surrogate for physical flow models. The results of our work provide guidance as to appropriate models or combination of models to use when analyzing large-scale critical infrastructure systems, where simulation times quickly become insurmountable when using more advanced models, severely limiting the extent of analyses that can be performed

A Road to Results: A Performance Measurement Guidebook for the Annie E. Casey Foundation's Education Program

Provides an overview of Casey's performance measurement process: how to select measures, set goals, and report results. Includes a measurement matrix; common measures of impact, influence, and leverage; and examples of highlights from grantee reports

Rapid disease progression on immune checkpoint inhibitors in young patients with stage IV melanoma

BackgroundImmune checkpoint inhibitors (ICIs) are the standard of care for metastatic cutaneous melanoma (mCM) patients, but their efficacy in young adults aged less than 40 years remains unclear.Materials and methodsWe retrospectively analyzed 303 stage IV melanoma patients of different ages treated with nivolumab, pembrolizumab, or ipilimumab plus nivolumab combination therapy. Clinical data and blood values such as LDH, CRP, and absolute immune cell counts were retrieved from the medical records. Pre-treatment serum concentrations of soluble immune checkpoint proteins were measured using ELISA. In addition, information on frequencies of various T cell subsets in the peripheral blood was collected from a previously reported study (ELEKTRA). Patient characteristics and clinical information was correlated with PFS and OS using univariate and multivariate cox regression analysis.ResultsOf 303 patients, 33 (11%) were ≤ 40 years old. The older patients had a median age of 64 (95% CI: 61–66). Concerning prognostic parameters, there was no difference between the age groups, e.g., in gender, LDH, or the existence of brain or liver metastases. Patients aged ≤ 40 years [p = 0.014; HR: 1.6 (95% CI: 1.1–2.4)], presence of liver metastases [p = 0.016; HR: 1.4 (95% CI: 1.0–1.9)], line of ICI treatment [p = 0.009; HR: 1.4 (1.0–1.9)], elevated LDH [p = 0.076; HR: 1.3 (95% CI: 0.97–1.8)], and brain metastasis [p = 0.080; HR: 1.3 (95% CI: 0.97–1.7)], were associated with shorter PFS in univariate analysis. Multivariate analysis revealed that the patient’s age (≤ 40 years) remains a high-risk factor upon adjusting for all potential confounders [p = 0.067; HR: 1.5 (95% CI: 0.97–2.3)]. Blood parameters revealed that patients ≤ 40 years have relatively higher frequencies of activated CD4 T cells (CD4 + Ki67 + CD4 + ICOS +) in the blood, and significantly lower number of basophils and CD45RA- memory T cells, compared to patients above 40 years (p < 0.05). In addition, patients ≤ 40 years experiencing disease progression within 6 months of ICI treatment had increased concentrations of sPDL1 (p = 0.05) and sTIM3 (p = 0.054) at baseline.ConclusionYoung patients with stage IV melanoma may experience shorter progression-free survival upon ICI treatment compared to patients above 40 years and are characterized by fewer basophils and memory T cells in the blood

Atlas of the clinical genetics of human dilated cardiomyopathy

[Abstract] Aim. Numerous genes are known to cause dilated cardiomyopathy (DCM). However, until now technological limitations have hindered elucidation of the contribution of all clinically relevant disease genes to DCM phenotypes in larger cohorts. We now utilized next-generation sequencing to overcome these limitations and screened all DCM disease genes in a large cohort. Methods and results. In this multi-centre, multi-national study, we have enrolled 639 patients with sporadic or familial DCM. To all samples, we applied a standardized protocol for ultra-high coverage next-generation sequencing of 84 genes, leading to 99.1% coverage of the target region with at least 50-fold and a mean read depth of 2415. In this well characterized cohort, we find the highest number of known cardiomyopathy mutations in plakophilin-2, myosin-binding protein C-3, and desmoplakin. When we include yet unknown but predicted disease variants, we find titin, plakophilin-2, myosin-binding protein-C 3, desmoplakin, ryanodine receptor 2, desmocollin-2, desmoglein-2, and SCN5A variants among the most commonly mutated genes. The overlap between DCM, hypertrophic cardiomyopathy (HCM), and channelopathy causing mutations is considerably high. Of note, we find that >38% of patients have compound or combined mutations and 12.8% have three or even more mutations. When comparing patients recruited in the eight participating European countries we find remarkably little differences in mutation frequencies and affected genes. Conclusion. This is to our knowledge, the first study that comprehensively investigated the genetics of DCM in a large-scale cohort and across a broad gene panel of the known DCM genes. Our results underline the high analytical quality and feasibility of Next-Generation Sequencing in clinical genetic diagnostics and provide a sound database of the genetic causes of DCM.Hôpitaux de Paris; PHRC AOM0414

The Effect of Motivational Interviewing on Improving Outcomes in Patients with Chronic Diseases

Based on the evidence from the research reviewed, we recommend including MI as an adjunct to physical therapist and patient interactions to enhance lifestyle changes. Motivational interviewing would be useful in treating patients 18 years or older with a diagnosis within a specific spectrum of chronic diseases. These diagnoses included cancer survivors, pre-diabetes, obesity, Type II diabetes, CAD, and CHF. The research supports delivering a minimum dosage of 120 minutes of contact time focusing on patient generated goals and problem solving to modify risk through lifestyle changes. The addition of MI is of minimal cost and can result in clinically significant increases in physical activity, weight management, quality of life measures, and treatment attendance. Further research is indicated to tailor MI to more ethnically and culturally diverse populations and to determine if MI has equal effectiveness when provided integrated within a physical therapy program

Potential Reasons for Unresponsiveness to Anti-PD1 Immunotherapy in Young Patients with Advanced Melanoma

The impact of age on the clinical benefit of anti-PD1 immunotherapy in advanced melanoma patients has been evolving recently. Due to a reduced immune function in elderly patients, young patients with a robust immune system are theoretically expected to benefit more from the treatment approach. However, in contrast to this hypothesis, recent studies in patients with metastatic melanoma have demonstrated that immunotherapy, especially with anti-PD1 treatment, is less effective in patients below 65 years, on average, with significantly lower responses and reduced overall survival compared to patients above 65 years of age. Besides, data on young patients are even more sparse. Hence, in this review, we will focus on age-dependent differences in the previously described resistance mechanisms to the treatment and discuss the development of potential combination treatment strategies for enhancing the anti-tumor efficacy of anti-PD1 or PDL1 treatment in young melanoma patients

Topological performance measures as surrogates for physical flow models for risk and vulnerability analysis for electric power systems.

Critical infrastructure systems must be both robust and resilient in order to ensure the functioning of society. To improve the performance of such systems, we often use risk and vulnerability analysis to find and address system weaknesses. A critical component of such analyses is the ability to accurately determine the negative consequences of various types of failures in the system. Numerous mathematical and simulation models exist that can be used to this end. However, there are relatively few studies comparing the implications of using different modeling approaches in the context of comprehensive risk analysis of critical infrastructures. In this article, we suggest a classification of these models, which span from simple topologically-oriented models to advanced physical-flow-based models. Here, we focus on electric power systems and present a study aimed at understanding the tradeoffs between simplicity and fidelity in models used in the context of risk analysis. Specifically, the purpose of this article is to compare performance estimates achieved with a spectrum of approaches typically used for risk and vulnerability analysis of electric power systems and evaluate if more simplified topological measures can be combined using statistical methods to be used as a surrogate for physical flow models. The results of our work provide guidance as to appropriate models or combinations of models to use when analyzing large-scale critical infrastructure systems, where simulation times quickly become insurmountable when using more advanced models, severely limiting the extent of analyses that can be performed