Dynamic predictive probabilities to monitor rapid cystic fibrosis disease progression.

Cystic fibrosis (CF) is a progressive, genetic disease characterized by frequent, prolonged drops in lung function. Accurately predicting rapid underlying lung-function decline is essential for clinical decision support and timely intervention. Determining whether an individual is experiencing a period of rapid decline is complicated due to its heterogeneous timing and extent, and error component of the measured lung function. We construct individualized predictive probabilities for "nowcasting" rapid decline. We assume each patient's true longitudinal lung function, S(t), follows a nonlinear, nonstationary stochastic process, and accommodate between-patient heterogeneity through random effects. Corresponding lung-function decline at time t is defined as the rate of change, S'(t). We predict S'(t) conditional on observed covariate and measurement history by modeling a measured lung function as a noisy version of S(t). The method is applied to data on 30 879 US CF Registry patients. Results are contrasted with a currently employed decision rule using single-center data on 212 individuals. Rapid decline is identified earlier using predictive probabilities than the center's currently employed decision rule (mean difference: 0.65 years; 95% confidence interval (CI): 0.41, 0.89). We constructed a bootstrapping algorithm to obtain CIs for predictive probabilities. We illustrate real-time implementation with R Shiny. Predictive accuracy is investigated using empirical simulations, which suggest this approach more accurately detects peak decline, compared with a uniform threshold of rapid decline. Median area under the ROC curve estimates (Q1-Q3) were 0.817 (0.814-0.822) and 0.745 (0.741-0.747), respectively, implying reasonable accuracy for both. This article demonstrates how individualized rate of change estimates can be coupled with probabilistic predictive inference and implementation for a useful medical-monitoring approach

SPTAN1/Numb Axis Senses Cell Density To Restrain Cell Growth and Oncogenesis Through Hippo Signaling

The loss of contact inhibition is a key step during carcinogenesis. The Hippo-Yes-associated protein (Hippo/YAP) pathway is an important regulator of cell growth in a cell density-dependent manner. However, how Hippo signaling senses cell density in this context remains elusive. Here, we report that high cell density induced the phosphorylation of spectrin α chain, nonerythrocytic 1 (SPTAN1), a plasma membrane-stabilizing protein, to recruit NUMB endocytic adaptor protein isoforms 1 and 2 (NUMB1/2), which further sequestered microtubule affinity-regulating kinases (MARKs) in the plasma membrane and rendered them inaccessible for phosphorylation and inhibition of the Hippo kinases sterile 20-like kinases MST1 and MST2 (MST1/2). WW45 interaction with MST1/2 was thereby enhanced, resulting in the activation of Hippo signaling to block YAP activity for cell contact inhibition. Importantly, low cell density led to SPTAN1 dephosphorylation and NUMB cytoplasmic location, along with MST1/2 inhibition and, consequently, YAP activation. Moreover, double KO of NUMB and WW45 in the liver led to appreciable organ enlargement and rapid tumorigenesis. Interestingly, NUMB isoforms 3 and 4, which have a truncated phosphotyrosine-binding (PTB) domain and are thus unable to interact with phosphorylated SPTAN1 and activate MST1/2, were selectively upregulated in liver cancer, which correlated with YAP activation. We have thus revealed a SPTAN1/NUMB1/2 axis that acts as a cell density sensor to restrain cell growth and oncogenesis by coupling external cell-cell contact signals to intracellular Hippo signaling

Built environment factors predictive of early rapid lung function decline in cystic fibrosis

Background: The extent to which environmental exposures and community characteristics of the built environment collectively predict rapid lung function decline, during adolescence and early adulthood in cystic fibrosis (CF), has not been examined. Objective: To identify built environment characteristics predictive of rapid CF lung function decline. Methods: We performed a retrospective, single-center, longitudinal cohort study (n = 173 individuals with CF aged 6–20 years, 2012–2017). We used a stochastic model to predict lung function, measured as forced expiratory volume in 1 s (FEV1) of % predicted. Traditional demographic/clinical characteristics were evaluated as predictors. Built environmental predictors included exposure to elemental carbon attributable to traffic sources (ECAT), neighborhood material deprivation (poverty, education, housing, and healthcare access), greenspace near the home, and residential drivetime to the CF center. Measurements and Main Results: The final model, which included ECAT, material deprivation index, and greenspace, alongside traditional demographic/clinical predictors, significantly improved fit and prediction, compared with only demographic/clinical predictors (Likelihood Ratio Test statistic: 26.78, p < 0.0001; the difference in Akaike Information Criterion: 15). An increase of 0.1 μg/m3 of ECAT was associated with 0.104% predicted/yr (95% confidence interval: 0.024, 0.183) more rapid decline. Although not statistically significant, material deprivation was similarly associated (0.1-unit increase corresponded to additional decline of 0.103% predicted/year [−0.113, 0.319]). High-risk regional areas of rapid decline and age-related heterogeneity were identified from prediction mapping. Conclusion: Traffic-related air pollution exposure is an important predictor of rapid pulmonary decline that, coupled with community-level material deprivation and routinely collected demographic/clinical characteristics, enhance CF prognostication and enable personalized environmental health interventions

Rainfall- and Irrigation-Induced Landslide Mechanisms in Loess Slopes: An Experimental Investigation in Lanzhou, China

To reveal the mechanism of rainfall- and irrigation-induced landslides in loess slopes within cold regions, a series of tests on loess samples subjected to different permeability durations were conducted, and the effects of rainfall on several performance indicators, including the permeability coefficient, composition, microstructure, soil–water characteristic curve, and the shear strength of the loess, were investigated. The results show that the permeability coefficient of the loess decreased by 68% after permeability testing. With increased permeability duration, there is a marked decrease in total dissolved solids, sand particles, and clay particles, contrasted with an increase in silt particles. This dynamic alters the original soil structure and impacts the soil–water characteristic curve of the loess. Additionally, rainwater infiltration heightens the effective saturation of the loess, in turn diminishing the shear strength of the loess as effective saturation increases. This reduction in shear strength is further intensified with extended infiltration time (or rainfall duration). A landslide is triggered once the shear strength diminishes to the level of the geostatic stress of the loess slope, and the influence of the rainfall-induced loss of soil shear strength should be taken into account during slope stability analysis. This study enhances the understanding of the initiation mechanisms of rainfall-induced landslides in loess slopes

Fermentation of DaiDai fruit and its biological activity

DaiDai fruit, a medicinal and edible plant fruit, is abundant in biologically active compounds and has a long history of use in traditional Chinese medicine. This research focuses on utilizing fermentation to develop a functional DaiDai fruit fermentation broth. Lactobacillus, Bacillus subtilis and Saccharomyces cerevisiae were employed in the fermentation process. By conducting screenings of bacterial strains, single factor experiments, and response surface methodology, the total flavonoids, polysaccharides, polyphenols, and 1,1-diphenyl-2-trinitrophenylhydrazine (DPPH) free radical scavenging rate were used as the index for selection, ultimately identifying Lactobacillus L-13 as the optimal fermentation strain. The optimal fermentation conditions were determined to be a time of 108 h, a temperature of 43.6°C, and a solid–liquid ratio of 1:15.157 (w/v). Under these conditions, the total flavonoid content reached 412.01 mg/g, representing a 36.71% increase compared to conventional extraction methods. The contents of polysaccharides and polyphenols and the DPPH scavenging rate were also increased. The fermentation broth of DaiDai fruit exhibited inhibitory effects on tyrosinase and melanin production in mouse melanoma cells B16-F10 induced by α-MSH and anti-inflammatory properties in a zebrafish inflammation model. These indicate that the DaiDai fruit fermentation broth possesses anti-melanoma, whitening, and anti-inflammatory properties, showcasing significant potential for applications in medicine, cosmetics, and other industries

Built environment factors predictive of early rapid lung function decline in cystic fibrosis

Background: The extent to which environmental exposures and community characteristics of the built environment collectively predict rapid lung function decline, during adolescence and early adulthood in cystic fibrosis (CF), has not been examined. Objective: To identify built environment characteristics predictive of rapid CF lung function decline. Methods: We performed a retrospective, single-center, longitudinal cohort study (n = 173 individuals with CF aged 6–20 years, 2012–2017). We used a stochastic model to predict lung function, measured as forced expiratory volume in 1 s (FEV1) of % predicted. Traditional demographic/clinical characteristics were evaluated as predictors. Built environmental predictors included exposure to elemental carbon attributable to traffic sources (ECAT), neighborhood material deprivation (poverty, education, housing, and healthcare access), greenspace near the home, and residential drivetime to the CF center. Measurements and Main Results: The final model, which included ECAT, material deprivation index, and greenspace, alongside traditional demographic/clinical predictors, significantly improved fit and prediction, compared with only demographic/clinical predictors (Likelihood Ratio Test statistic: 26.78, p < 0.0001; the difference in Akaike Information Criterion: 15). An increase of 0.1 μg/m3 of ECAT was associated with 0.104% predicted/yr (95% confidence interval: 0.024, 0.183) more rapid decline. Although not statistically significant, material deprivation was similarly associated (0.1-unit increase corresponded to additional decline of 0.103% predicted/year [−0.113, 0.319]). High-risk regional areas of rapid decline and age-related heterogeneity were identified from prediction mapping. Conclusion: Traffic-related air pollution exposure is an important predictor of rapid pulmonary decline that, coupled with community-level material deprivation and routinely collected demographic/clinical characteristics, enhance CF prognostication and enable personalized environmental health interventions

Lung Function Decline in Cystic Fibrosis: Impact of Data Availability and Modeling Strategies on Clinical Interpretations

RATIONALE: Studies estimating rate of lung function decline in cystic fibrosis (CF) have been inconsistent regarding methods used. How the methodology used impacts validity of the results and comparability between studies is unknown. OBJECTIVES: The Cystic Fibrosis Foundation established a workgroup whose tasks were to examine the impact of differing approaches to estimating rate of decline in lung function and to provide analysis guidelines. METHODS: We utilized a natural history cohort of 35,252 individuals with CF aged > 6 years of the Cystic Fibrosis Foundation Patient Registry (CFFPR), 2003-2016. Modeling strategies using linear and nonlinear forms of marginal and mixed-effects models, which have previously quantified rate of forced expiratory volume in 1 second (FEV1) decline (% predicted/year), were evaluated under clinically relevant scenarios of available lung function data. Scenarios varied by sample size (overall CFFPR, medium-sized cohort of 3,000 subjects, and small-sized cohort of 150), data collection/reporting frequency (encounter, quarterly, and annual), inclusion of FEV1 during pulmonary exacerbation, and follow-up length (<2 years, 2-5 years, entire duration). RESULTS: Rate-of-FEV1-decline estimates (% predicted/year) differed between linear marginal and mixed-effects models; overall cohort estimates (95% confidence interval) were 1.26 (1.24-1.29) and 1.40 (1.38-1.42), respectively. Marginal models consistently estimated less rapid lung function decline than mixed-effects models across scenarios except for short-term follow-up (both were ~1.4). Rate-of-decline estimates from nonlinear models diverged by age 30. Among mixed-effects models, nonlinear and stochastic terms fit best except for short-term follow-up (< 2 years). Overall CFFPR analysis from a joint longitudinal-survival model implied that an increase in rate of decline of 1% predicted/year in FEV1 associated with a 1.52-fold (52%) increase in the hazard of death/lung transplantation, but results exhibited immortal cohort bias. CONCLUSIONS: Differences were as high as 0.5% predicted/year between rate-of-decline estimates, but we found estimates were robust to lung function data availability scenarios except short-term follow-up and older age ranges. Inconsistencies among previous study results may be attributable to inherent differences in study design, inclusion criteria, or covariate adjustment. Results-based decision points reported herein will support researchers in selecting a strategy to model lung function decline most reflective of nuanced, study-specific goals