Background Prior to implementing predictive models in novel settings, analyses of calibration and clinical usefulness remain as important as discrimination, but they are not frequently discussed. Calibration is a model’s reflection of actual outcome prevalence in its predictions. Clinical usefulness refers to the utilities, costs, and harms of using a predictive model in practice. A decision analytic approach to calibrating and selecting an optimal intervention threshold may help maximize the impact of readmission risk and other preventive interventions. Objectives To select a pragmatic means of calibrating predictive models that requires a minimum amount of validation data and that performs well in practice. To evaluate the impact of miscalibration on utility and cost via clinical usefulness analyses. Materials and Methods Observational, retrospective cohort study with electronic health record data from 120,000 inpatient admissions at an urban, academic center in Manhattan. The primary outcome was thirty-day readmission for three causes: all-cause, congestive heart failure, and chronic coronary atherosclerotic disease. Predictive modeling was performed via L1-regularized logistic regression. Calibration methods were compared including Platt Scaling, Logistic Calibration, and Prevalence Adjustment. Performance of predictive modeling and calibration was assessed via discrimination (c-statistic), calibration (Spiegelhalter Z-statistic, Root Mean Square Error [RMSE] of binned predictions, Sanders and Murphy Resolutions of the Brier Score, Calibration Slope and Intercept), and clinical usefulness (utility terms represented as costs). The amount of validation data necessary to apply each calibration algorithm was also assessed. Results C-statistics by diagnosis ranged from 0.7 for all-cause readmission to 0.86 (0.78–0.93) for congestive heart failure. Logistic Calibration and Platt Scaling performed best and this difference required analyzing multiple metrics of calibration simultaneously, in particular Calibration Slopes and Intercepts. Clinical usefulness analyses provided optimal risk thresholds, which varied by reason for readmission, outcome prevalence, and calibration algorithm. Utility analyses also suggested maximum tolerable intervention costs, e.g. $1,720 for all-cause readmissions based on a published cost of readmission of $11,000. Conclusions Choice of calibration method depends on availability of validation data and on performance. Improperly calibrated models may contribute to higher costs of intervention as measured via clinical usefulness. Decision-makers must understand underlying utilities or costs inherent in the use-case at hand to assess usefulness and will obtain the optimal risk threshold to trigger intervention and intervention cost limits as a result.

Tumor-associated macrophages (TAMs) are critically important in the context of solid tumor progression. Counterintuitively, these host immune cells can often support tumor cells along the path from primary tumor to metastatic colonization and growth. Thus, the ability to transform protumor TAMs into antitumor, immune-reactive macrophages would have significant therapeutic potential. However, in order to achieve these effects, two major hurdles would need to be overcome: development of a methodology to specifically target macrophages and increased knowledge of the optimal targets for cell-signaling modulation. This study addresses both of these obstacles and furthers the development of a therapeutic agent based on this strategy. Using ex vivo macrophages in culture, the efficacy of mannosylated nanoparticles to deliver small interfering RNA specifically to TAMs and modify signaling pathways is characterized. Then, selective small interfering RNA delivery is tested for the ability to inhibit gene targets within the canonical or alternative nuclear factor-kappaB pathways and result in antitumor phenotypes. Results confirm that the mannosylated nanoparticle approach can be used to modulate signaling within macrophages. We also identify appropriate gene targets in critical regulatory pathways. These findings represent an important advance toward the development of a novel cancer therapy that would minimize side effects because of the targeted nature of the intervention and that has rapid translational potential.

Purpose of Review.-Summarize sex-specific contributors to the genetic architecture of Alzheimer's disease (AD). Recent Findings.-There are sex differences in the effects of Apolipoprotein E (APOE), genes along the APOE pathway, and genes along the neurotrophic signaling pathway in predicting AD. Reported sex differences are largely driven by stronger associations among females. Evidence also suggests that genetic predictors of amyloidosis are largely shared across sexes, while sex-specific genetic effects emerge downstream of amyloidosis and drive the clinical manifestation of AD. Summary.-There is a lack of comprehensive assessments of sex differences in genome-wide analyses of AD and a need for more systematic reporting a sex-stratified genetic effects. The emerging emphasis on sex as a biological variable provides an opportunity for transdisciplinary collaborations aimed at addressing major analytical challenges that have hampered advancements in the field. Ultimately, sex-specific genetic association studies represent a logical first step towards precision medicine.

Staphylococcus aureus is a common cause of invasive and life-threatening infections that are often multidrug resistant. To develop novel treatment approaches, a detailed understanding of the complex host−pathogen interactions during infection is essential. This is particularly true for the molecular processes that govern the formation of tissue abscesses, as these heterogeneous structures are important contributors to staphylococcal pathogenicity. To fully characterize the developmental process leading to mature abscesses, temporal and spatial analytical approaches are required. Spatially targeted proteomic technologies such as microliquid extraction surface analysis offer insight into complex biological systems including detection of bacterial proteins and their abundance in the host environment. By analyzing the proteomic constituents of different abscess regions across the course of infection, we defined the immune response and bacterial contribution to abscess development through spatial and temporal proteomic assessment. The information gathered was mapped to biochemical pathways to characterize the metabolic processes and immune strategies employed by the host. These data provide insights into the physiological state of bacteria within abscesses and elucidate pathogenic processes at the host−pathogen interface.

Kidney fibrosis constitutes the shared final pathway of nearly all chronic nephropathies, but biomarkers for the non-invasive assessment of kidney fibrosis are currently not available. To address this, we characterize five candidate biomarkers of kidney fibrosis: Cadherin-11 (CDH11), Sparc-related modular calcium binding protein-2 (SMOC2), Pigment epithelium-derived factor (PEDF), Matrix-Gla protein, and Thrombospondin-2. Gene expression profiles in single-cell and single-nucleus RNA-sequencing (sc/snRNA-seq) datasets from rodent models of fibrosis and human chronic kidney disease (CKD) were explored, and Luminex-based assays for each biomarker were developed. Plasma and urine biomarker levels were measured using independent prospective cohorts of CKD: the Boston Kidney Biopsy Cohort, a cohort of individuals with biopsyconfirmed semiquantitative assessment of kidney fibrosis, and the Seattle Kidney Study, a cohort of patients with common forms of CKD. Ordinal logistic regression and Cox proportional hazards regression models were used to test associations of biomarkers with interstitial fibrosis and tubular atrophy and progression to end-stage kidney disease and death, respectively. Sc/snRNA-seq data confirmed cell-specific expression of biomarker genes in fibroblasts. After multivariable adjustment, higher levels of plasma CDH11, SMOC2, and PEDF and urinary CDH11 and PEDF were significantly associated with increasing severity of interstitial fibrosis and tubular atrophy in the Boston Kidney Biopsy Cohort. In both cohorts, higher levels of plasma and urinary SMOC2 and urinary CDH11 were independently associated with progression to end-stage kidney disease. Higher levels of urinary PEDF associated with end-stage kidney disease in the Seattle Kidney Study, with a similar signal in the Boston Kidney Biopsy Cohort, although the latter narrowly missed statistical significance. Thus, we identified CDH11, SMOC2, and PEDF as promising non-invasive biomarkers of kidney fibrosis.