An Observational Study of Social and Emotional Support in Smoking Cessation Twitter Accounts: Content Analysis of Tweets

Background

Smoking continues to be the number one preventable cause of premature death in the United States. While evidence for the effectiveness of smoking cessation interventions has increased rapidly, questions remain on how to effectively disseminate these findings. Twitter, the second largest online social network, provides a natural way of disseminating information. Health communicators can use Twitter to inform smokers, provide social support, and attract them to other interventions. A key challenge for health researchers is how to frame their communications to maximize the engagement of smokers.

Objective

Our aim was to examine current Twitter activity for smoking cessation.

Methods

Active smoking cessation related Twitter accounts (N=18) were identified. Their 50 most recent tweets were content coded using a schema adapted from the Roter Interaction Analysis System (RIAS), a theory-based, validated coding method. Using negative binomial regression, the association of number of followers and frequency of individual tweet content at baseline was assessed. The difference in followership at 6 months (compared to baseline) to the frequency of tweet content was compared using linear regression. Both analyses were adjusted by account type (organizational or not organizational).

Results

The 18 accounts had 60,609 followers at baseline and 68,167 at 6 months. A total of 24% of tweets were socioemotional support (mean 11.8, SD 9.8), 14% (mean 7, SD 8.4) were encouraging/engagement, and 62% (mean 31.2, SD 15.2) were informational. At baseline, higher frequency of socioemotional support and encouraging/engaging tweets was significantly associated with higher number of followers (socioemotional: incident rate ratio [IRR] 1.09, 95% CI 1.02-1.20; encouraging/engaging: IRR 1.06, 95% CI 1.00-1.12). Conversely, higher frequency of informational tweets was significantly associated with lower number of followers (IRR 0.95, 95% CI 0.92-0.98). At 6 months, for every increase by 1 in socioemotional tweets, the change in followership significantly increased by 43.94 (P=.027); the association was slightly attenuated after adjusting by account type and was not significant (P=.064).

Conclusions

Smoking cessation activity does exist on Twitter. Preliminary findings suggest that certain content strategies can be used to encourage followership, and this needs to be further investigated.     

M-type Phospholipase A2 Receptor Autoantibodies and Renal Function in Patients with Primary Membranous Nephropathy

Background and objectives

Loss of renal function in patients with primary membranous nephropathy cannot be reliably predicted by laboratory or clinical markers at the time of diagnosis. M-type phospholipase A₂ receptor autoantibodies have been shown to be associated with changes in proteinuria. Their eventual effect on renal function, however, is unclear.

Design, setting, participants, & measurements

In this prospective, open, multicenter study, the potential role of M-type phospholipase A₂ receptor autoantibodies levels on the increase of serum creatinine in 118 consecutive patients with membranous nephropathy and positivity for serum M-type phospholipase A₂ receptor autoantibodies was analyzed. Patients were included in the study between April of 2010 and December of 2012 and observed until December of 2013. The clinical end point was defined as an increase of serum creatinine by ≥25% and serum creatinine reaching ≥1.3 mg/dl.

Results

Patients were divided into tertiles according to their M-type phospholipase A₂ receptor autoantibody levels at the time of inclusion in the study: tertile 1 levels=20–86 units/ml (low), tertile 2 levels=87–201 units/ml (medium), and tertile 3 levels ≥202 units/ml (high). The median follow-up time of all patients in the study was 27 months (interquartile range=18–33 months). The clinical end point was reached in 69% of patients with high M-type phospholipase A₂ receptor autoantibodies levels (tertile 3) but only 25% of patients with low M-type phospholipase A₂ receptor autoantibodies levels. The average time to reach the study end point was 17.7 months in patients with high M-type phospholipase A₂ receptor autoantibodies levels and 30.9 months in patients with low M-type phospholipase A₂ receptor autoantibodies levels. A multivariate Cox regression analysis showed that high M-type phospholipase A₂ receptor autoantibodies levels—in addition to men and older age—are an independent predictor for progressive loss of renal function.

Conclusions

High M-type phospholipase A₂ receptor autoantibodies levels were associated with more rapid loss of renal function in this cohort of patients with primary membranous nephropathy and therefore, could be helpful for treatment decisions.     

Continuous tissue microarray based identification of cancers with homogeneous target expression for successful targeted therapy in clinical routine practice

In cancer therapy, the number of drugs targeting cells with characteristic molecular aberrations is continuously rising. However, application of these new drugs still is limited to a few tumor entities. The aim of this study was to test the concept of routinely identifying all possible cancer patients who might eventually benefit from targeted therapy. Therefore, all malignant tumors routinely submitted to our Institute of Pathology over a period of 4 months were brought into a tissue microarray format. Using “in situ” methods, tumors were analyzed for HER2, EGFR, and KIT status as examples for potential therapeutic target genes. In positive cases, target heterogeneity was excluded by analyzing all available large sections. Outside of tumor entities for which targeted drugs are already approved, the study revealed six tumors with homogeneously distributed HER2 overexpression/amplification (bladder, esophageal and colorectal) and seven tumors with homogeneous EGFR amplification (vulvar, ovarian, breast, esophageal and laryngeal, and adenocarcinoma of unknown primary). A total of 151 tumors showed KIT overexpression but none of seven sequenced cases showed KIT mutations. We furthermore report on a 69‐year‐old patient with homogeneously HER2‐amplified metastatic colorectal cancer who is successfully treated by trastuzumab monotherapy. This study demonstrates that tissue microarray based screening for therapeutic target genes in tumors outside established indications represents a feasible approach suitable for routine application. The successful treatment of one patient with homogeneously HER2 positive metastatic colorectal cancer argues for the clinical utility of this approach at least in carefully selected, homogeneous cancers. © 2013 Wiley Periodicals, Inc.     

Predicting species’ range limits from functional traits for the tree flora of North America

Using functional traits to explain species’ range limits is a promising approach in functional biogeography. It replaces the idiosyncrasy of species-specific climate ranges with a generic trait-based predictive framework. In addition, it has the potential to shed light on specific filter mechanisms creating large-scale vegetation patterns. However, its application to a continental flora, spanning large climate gradients, has been hampered by a lack of trait data. Here, we explore whether five key plant functional traits (seed mass, wood density, specific leaf area (SLA), maximum height, and longevity of a tree)—indicative of life history, mechanical, and physiological adaptations—explain the climate ranges of 250 North American tree species distributed from the boreal to the subtropics. Although the relationship between traits and the median climate across a species range is weak, quantile regressions revealed strong effects on range limits. Wood density and seed mass were strongly related to the lower but not upper temperature range limits of species. Maximum height affects the species range limits in both dry and humid climates, whereas SLA and longevity do not show clear relationships. These results allow the definition and delineation of climatic “no-go areas” for North American tree species based on key traits. As some of these key traits serve as important parameters in recent vegetation models, the implementation of trait-based climatic constraints has the potential to predict both range shifts and ecosystem consequences on a more functional basis. Moreover, for future trait-based vegetation models our results provide a benchmark for model evaluation.In 1895 the Danish plant ecologist Eugen Warming defined for the first time the objectives of a functional plant biogeography, when he expressed the need “to investigate the problems concerning the economy of plants, the demands that they make on their environment, and the means that they use to use the surrounding conditions….” He already envisioned how to tackle this: “This subject leads us into deep morphological, anatomical, and physiological investigations; […] it is very difficult, yet very alluring; but only in few cases can its problems be satisfactorily solved at the present time” (1).Since Warming’s days plant science has progressed beyond the study of just a “few cases.” For more than a century now, botanists and plant ecologists have collected data on morphological, anatomical, and physiological traits (2, 3), and have mapped the distributions of tens of thousands of plant species (e.g., Global Biodiversity Information Facility, www.gbif.org). In addition, climatologists and soil scientists have provided us with high-resolution global maps of the plant’s surrounding condition. With this it has now become feasible to analyze the functional underpinnings of plant distributions for entire regional floras across large-scale environmental gradients (4). It is well established that on regional and global scales, climate determines the distribution not only of plant species but also of form and function (5, 6) because it constitutes the overall physical constraint under which plants must establish and reproduce, before biotic interactions may modulate plant fitness. Plants have evolved a multitude of adaptations to climatic constraints, which are expressed in the diversity of their functional traits. These allow them to tolerate climate extremes such as summer drought or low winter temperatures. In other words, the climate range occupied by plants should be predictable from their functional traits.Current species distribution models (SDMs) (7) use correlations between current climate and species distributions, so-called climate envelopes. Even modern dynamic global vegetation models (DGVMs) (8) capable of representing carbon acquisition, water balance, and competitive interactions of plant functional types (PFTs) in great mechanistic detail, still incorporate empirical climate envelopes to constrain PFT distributions. This obvious lack of mechanism is an important limitation when such models are used to predict vegetation shifts under future climate scenarios, especially under novel combinations of climate variables (8). Here, we introduce a unique approach—the “double quantile” approach (Fig. 1 and see Linking Traits to Climate Ranges)—that allows us to predict species distribution limits from functional plant traits. Although still empirical at heart, this approach has distinct advantages: (i) The very nature of the traits emerging as suitable predictors of species distribution limits sheds light on the biological mechanisms. Accordingly, below we are able to put forward concrete hypotheses of the biological underpinnings of trait–climate limit relationships. (ii) Functional traits serve as a common currency across species and thus provide the basis for assimilating the behavior of many species into a single generic predictive framework. (iii) Because this approach replaces idiosyncrasy by generality, the handshake with process-oriented models is greatly facilitated as will be discussed below.Open in a separate windowFig. 1.(A) Species are distributed along climatic gradients and occupy species-specific climate ranges, which can be characterized by three measures: the upper limit (red squares), the lower limit (blue squares), and the median (black squares) for which the highest species’ occurrence probability is suggested. (B) To explore the response of the climate range measures to traits, we related them separately against the traits using linear quantile regression analysis. We estimated the upper quantiles for the upper limits, the lower quantiles for the lower limits, and the median quantile for the median; a solid line indicates a slope significantly different from zero (increasing or decreasing) and a dotted line represents a nonsignificant slope. The area between the outermost regression lines represents the possible climate range species can occupy across their trait values whereas areas outside these lines describe no-go areas. (C) We distinguish three types of response patterns: (i) one-sided constraint, i.e., significant slope at only one limit (the upper or the lower one); (ii) two-sided constraint with reverse slopes at both limits; and (iii) constant shift with aligned slopes at both limits.Here, we explore the potential of five functional traits—specific leaf area (SLA), wood density, maximum height, seed mass, and tree longevity—to explain the climate range limits and mean climate preferences of 250 North American tree species covering a temperature gradient from the boreal to the subtropics and a gradient from 65 to 3,000 mm of annual precipitation. Although there has been a first attempt to incorporate trait information in SDMs (9), we present here a unique study using plant functional traits to predict their limiting effect on species’ climate ranges at a taxonomic and climatic scale relevant for DGVMs. We chose to present the relationship between traits and species climate range limits from a trait perspective to highlight their potential for predicting species’ climate niches as a holistic measure of plant performance in response to climate. Unlike previous studies, our double quantile approach places an emphasis on the responses of species-specific climate ranges at the potentially stressful ends of climate gradients, where strong effects of functional traits on range limits can be expected.

Functional Traits: Selection and Relevance.

The five traits represent key functions defining plant strategy axes related to the fundamental tradeoffs of resource acquisition and reproduction (10, 11) and are thus indicative of life history, mechanical, and physiological mechanisms. Furthermore, some of these traits are frequently used as parameters in DGVMs (2). Because these traits vary across climatic gradients (12, 13), they are ideally suited to gain insight into processes shaping tree distributions at continental scales and at the same time to improve predictions on ecosystem functions under climate change. SLA is a key trait of the leaf economic spectrum (14) and defines a species’ resource use strategy from acquisitive to conservative. It is related to growth rate under different climatic conditions (15) and reflects tradeoffs in species’ shade and drought tolerances (16). Wood density is related to the efficiency and safety of water transport (17) and represents a tradeoff between mechanical strength and vertical growth. It is strongly correlated with growth and mortality rates (12). Maximum height describes the maximum recorded height of a species and quantifies species’ carbon gain strategy via light capture (18); it is related to successional status, shade tolerance and responds to gradients in precipitation on a global scale (19). Seed mass correlates positively with seedling survival rates under hazardous conditions during seedling establishment (11) and negatively with dispersal distance and the number of seeds produced per unit energy invested (20). Maximum tree longevity determines species responses to disturbance (21), compensates for reduced fecundity or juvenile survival (22), and relates to defensive investment (23).

Linking Traits to Climate Ranges.

We derive a tree species’ climate range from its natural geographic distribution (24). We use a set of eight bioclimatic variables (Methods) which represent dominant climatic gradients over North America and are widely used in climatic niche modeling (7, 25). To define a species’ climate range (Fig. 1A) we estimate for each bioclimatic variable the lower (5th quantile) and upper limits (95th quantile) and the median (50th quantile) across a species’ distribution range. Using linear quantile regression analysis (26), we regress across all species the three species-specific range measures against each of the five traits separately estimating the lower (10th, 5th), the upper (90th, 95th) and median (50th) regression quantiles, respectively (Fig. 1B). Thus, the 50th quantile regression lines fit to the medians (black line and squares in Fig. 1B) and describe how the mean realized climate niche depends on the trait values. The lower and upper quantile regression lines fit to the lower and upper limits (blue line and squares and red line and squares, respectively). In this double quantile approach, the outer regression lines enclose an area corresponding to the climate range the pool of 250 North American tree species can occupy across the range of their trait values (Fig. 1B). At the same time it identifies “no-go areas” which cannot be occupied by trees with a given trait value. The delineated areas can attain three possible shapes: (i) the area is wedge-shaped when there is a one-sided constraint, i.e., only one outer quantile represents a climatic extreme requiring a trait adaptation. (ii) The area has the form of an acute-angled triangle, when there is a two-sided constraint leading to reverse responses of the outer quantiles. Both triangular shapes, i and ii, imply that the possible climate range of the species pool changes with a given trait value (see Fig. 1C for examples). (iii) The area can have a rhomboid shape when the two-sided constraints are aligned. This implies a shift in the mean climate preference, but no change in the potential climate range per trait value.     

Binding and neutralization of vascular endothelial growth factor (VEGF) and related ligands by VEGF Trap, ranibizumab and bevacizumab

Pharmacological inhibition of VEGF-A has proven to be effective in inhibiting angiogenesis and vascular leak associated with cancers and various eye diseases. However, little information is currently available on the binding kinetics and relative biological activity of various VEGF inhibitors. Therefore, we have evaluated the binding kinetics of two anti-VEGF antibodies, ranibizumab and bevacizumab, and VEGF Trap (also known as aflibercept), a novel type of soluble decoy receptor, with substantially higher affinity than conventional soluble VEGF receptors. VEGF Trap bound to all isoforms of human VEGF-A tested with subpicomolar affinity. Ranibizumab and bevacizumab also bound human VEGF-A, but with markedly lower affinity. The association rate for VEGF Trap binding to VEGF-A was orders of magnitude faster than that measured for bevacizumab and ranibizumab. Similarly, in cell-based bioassays, VEGF Trap inhibited the activation of VEGFR1 and VEGFR2, as well as VEGF-A induced calcium mobilization and migration in human endothelial cells more potently than ranibizumab or bevacizumab. Only VEGF Trap bound human PlGF and VEGF-B, and inhibited VEGFR1 activation and HUVEC migration induced by PlGF. These data differentiate VEGF Trap from ranibizumab and bevacizumab in terms of its markedly higher affinity for VEGF-A, as well as its ability to bind VEGF-B and PlGF.