The 241 largest snowstorms over the eastern two-thirds of the United States during 1950-2000 exhibited considerable temporal variability ranging from 1 storm in three winters to 10 storms in 1993/94. The peak decadal frequency was 55 storms (1950s), and the minimum was 45 storms (1970s and 1980s). The eastnorth-central region experienced the greatest number of large snowstorms (148) followed by the westnorth-central (136) and central (133) regions. Regional trends were different. Assessment of surface cyclone tracks associated with the large snowstorms identified three primary tracks: one was located from the leeward side of the south-central Rocky Mountains east-northeast toward the Great Lakes; a second was from the lower Mississippi River basin northeastward toward the Great Lakes; and a third was along the coastal mid-Atlantic region northeast toward Maine. Temporal differences in the frequency of certain surface cyclone tracks were related to the decadal trends in regional large snowstorm occurrence. The minimum surface pressure associated with these storms ranged from 959 to 1013 hPa with more than 67% of all storms having a minimum surface pressure between 980 and 999 hPa. The average orthogonal distance from the storm track to the heavy snow region was 201 km. The average rate of cyclone movement ranged from less than 483 to more than 1930 km day Ϫ1, with more than 57% of storms moving between 805 and 1287 km day Ϫ1.
Aim: To evaluate the role of tobacco use in the development of psychosis in individuals at clinical high risk. Method: The North American Prodrome Longitudinal Study is a 2-year multi-site prospective case control study of persons at clinical high risk that aims to better understand predictors and mechanisms for the development of psychosis. The cohort consisted of 764 clinical high risk and 279 healthy comparison subjects. Clinical assessments included tobacco and substance use and several risk factors associated with smoking in general population studies.Results: Clinical high risk subjects were more likely to smoke cigarettes than unaffected subjects (light smoking odds ratio [OR] = 3.0, 95% confidence interval [CI] = 1.9-5; heavy smoking OR = 4.8, 95% CI = 1.7-13.7). In both groups, smoking was associated with mood, substance use, stress and perceived discrimination and in clinical high risk subjects with childhood emotional neglect and adaption to school. Clinical high risk subjects reported higher rates of several factors previously associated with smoking, including substance use, anxiety, trauma and perceived discrimination. After controlling for these potential factors, the relationship between clinical high risk state and smoking was no longer significant (light smoking OR = 0.9, 95% CI = 0.4-2.2; heavy smoking OR = 0.3, 95% CI = 0.05-2.3). Moreover, baseline smoking status (hazard ratio [HR] = 1.16, 95% CI = 0.82-1.65) and categorization as ever smoked (HR = 1.3, 95% CI = 0.8-2.1) did not predict time to conversion.Conclusion: Persons at high risk for psychosis are more likely to smoke and have more factors associated with smoking than controls. Smoking status in clinical high risk subjects does not predict conversion. These findings do not support a causal relationship between smoking and psychosis. K E Y W O R D Sclinical high risk, psychosis, schizophrenia, substance use disorder, tobacco
Key Points Question Are there subgroups of participants in the Flexible Lifestyles Empowering Change (FLEX) trial for whom the intervention is estimated to be optimal, for whom usual care is estimated to be optimal, and for whom control conditions and intervention are estimated to be equivalent? Findings In this post hoc analysis of the FLEX randomized clinical trial of 258 adolescents with type 1 diabetes, an individualized treatment rule showed that a large proportion of participants had equivalent predicted outcomes under intervention vs usual care settings, regardless of randomization. Meaning The precision medicine approach is a conceptually and analytically novel method for post hoc subgroup analysis of randomized clinical trials, capturing data-driven response subgroups without relying on predefined categories.
The complexity of human cancer often results in significant heterogeneity in response to treatment. Precision medicine offers the potential to improve patient outcomes by leveraging this heterogeneity. Individualized treatment rules (ITRs) formalize precision medicine as maps from the patient covariate space into the space of allowable treatments. The optimal ITR is that which maximizes the mean of a clinical outcome in a population of interest. Patient-derived xenograft (PDX) studies permit the evaluation of multiple treatments within a single tumor, and thus are ideally suited for estimating optimal ITRs. PDX data are characterized by correlated outcomes, a high-dimensional feature space, and a large number of treatments. Here we explore machine learning methods for estimating optimal ITRs from PDX data. We analyze data from a large PDX study to identify biomarkers that are informative for developing personalized treatment recommendations in multiple cancers. We estimate optimal ITRs using regression-based (Q-learning) and direct-search methods (outcome weighted learning). Finally, we implement a superlearner approach to combine multiple estimated ITRs and show that the resulting ITR performs better than any of the input ITRs, mitigating uncertainty regarding user choice. Our results indicate that PDX data are a valuable resource for developing individualized treatment strategies in oncology. Supplementary materials for this article are available online.
BackgroundEndotoxin is a component of particulate matter linked to respiratory disease. Our group has shown that experimental endotoxin inhalation challenge reproducibly triggers neutrophilic inflammation in the airways and in peripheral blood. Sputum induction is currently the only available method for assessing airway neutrophilia but is laborious and time-consuming. This analysis examined the correlation between systemic and airway inflammatory responses to endotoxin to determine if peripheral blood could serve as a surrogate marker for neutrophilic airway inflammation.MethodsWe conducted a retrospective study of 124 inhaled endotoxin challenges conducted at our center using 20,000 endotoxin units (EU) of Clinical Center Reference Endotoxin (CCRE). Venipuncture and induced sputum samples were obtained at baseline and 6 hours after completion of endotoxin challenge. The relationship between change in sputum neutrophils (post-challenge – baseline) and change in peripheral blood neutrophils (post-challenge – baseline) was assessed using Spearman’s correlation analyses.ResultsInhaled endotoxin induced a significant increase in mean sputum percent neutrophils and peripheral blood absolute neutrophil counts in healthy adults with or without mild asthma, but no significant correlation was found between airway and systemic neutrophilia (r = 0.13, p = 0.18). Stratification by degree of airway neutrophil response and by atopic or asthmatic status did not change the results.ConclusionsInhalation challenge with endotoxin safely and effectively induces airway neutrophilic inflammation in most individuals. Increases in endotoxin-induced peripheral blood neutrophils do not correlate well with airway responses and should not be used as a surrogate marker of airway inflammation.
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