Interleukin-4 enhances the survival of severe combined immunodeficient mice engrafted with human B-cell precursor leukemia

Human interleukin-4 (huIL-4) has been shown to inhibit the growth in vitro of cells from patients with acute lymphoblastic leukemia (ALL). With the aim of determining whether this cytokine might be useful in the treatment of patients with ALL, the effects of huIL-4 on human B- cell precursor ALL engrafted in severe combined immunodeficient (SCID) mice were examined. The inhibition of [3H] thymidine uptake of primary ALL cells by huIL-4 was maintained following engraftment and passage of leukemia in SCID mice. Five of seven xenograft leukemias showed significant inhibition in vitro by huIL-4 at concentrations as low as 0.5 ng/mL; furthermore, huIL-4 counteracted the proliferative effects of IL-7. When used to treat two human leukemias engrafted in SCID mice, huIL-4 200 microgram/kg/d, as a continuous 14-day subcutaneous infusion, suppressed the appearance of circulating lymphoblasts and extended survival of mice by 39% and 108%, respectively, the first demonstration of IL-4 activity against human leukemia in vivo. The mean steady-state huIL-4 level in mouse plasma during the infusion was 1.46 ng/mL (SEM +/- 0.14 ng/mL), which was similar to concentrations found to be effective in vitro. ALL cells obtained from mice relapsing after huIL-4 treatment continued to show inhibition by the cytokine in vitro. These data suggest that IL-4 may be useful in the treatment of patients with ALL.     

Impact of cytogenetics on the outcome of autotransplantation for acute myeloid leukemia in first remission: is the benefit of intensive pretransplant therapy limited to patients with good karyotypes?

A total of 81 adults with acute myeloid leukemia (AML) (47% favorable karyotypes) were autografted in first remission after melphalan-total body irradiation, having received 0 (n=7), 1 (n=19), 2 (n=51), or 3 (n=4) consolidation chemotherapy cycles before harvest. The cumulative 5-year incidences of relapse and transplant-related mortality were 37 and 17%, respectively. The actuarial 5-year probability of disease-free survival (DFS) was 46%. In Cox analysis, favorable karyotypes, increasing numbers of consolidation cycles (0 vs > or =1 or 1 vs >1), and higher nucleated cell doses were associated with lower relapse rates and higher DFS. Patients with favorable karyotypes benefited from every additional cycle of consolidation therapy (0 vs > or =1 as well as 1 vs >1). Among patients with other karyotypes, while the benefit of one cycle of consolidation was clear (0 vs > or =1), there was no obvious beneficial impact of further consolidation therapy (1 vs >1). Administration of consolidation chemotherapy prior to harvest is essential in AML. While it is possible to enhance the benefit of consolidation with favorable karyotypes by delivering two cycles, its usefulness is limited in others. In them, it may be worthwhile exploring alternatives not normally used in AML (eg high-dose cyclophosphamide) that could have antileukemic effects while permitting mobilization of stem cells.     

Glycemia (or, in women, estimated glucose disposal rate) predict lower extremity arterial disease events in type 1 diabetes

The purpose of this study was to determine the predictors of lower extremity arterial disease (LEAD) events in a type 1 diabetes population. Data are from the Pittsburgh Epidemiology of Diabetes Complications Study of childhood onset type 1 diabetes. At baseline, the study population had a mean age 28 (range, 8 to 47) years and duration 19 (range, 7 to 37) years. LEAD events, assessed by questionnaire or clinical examination, were defined as claudication (Rose questionnaire), foot ulceration, or lower extremity amputation. Estimated glucose disposal rate (eGDR), a measure of insulin resistance, was calculated from glycosylated hemoglobin (HbA(1)), waist-to-hip ratio (WHR), and hypertension using an equation previously validated with hyperinsulinemic euglycemic clamp studies. There were incident LEAD events in 70 of 586 subjects during 10 years follow-up, giving an incidence density of 1.3 events/100 person-years. Incidence did not differ by gender. Major predictors of LEAD events were diabetes duration, low-density lipoprotein-cholesterol (LDL-C), heart rate, eGDR, log albumin excretion rate (AER), systolic blood pressure (SBP), hypertension, proliferative retinopathy, distal symmetric polyneuropathy, and overt nephropathy (each P <.001). HbA(1), low ankle brachial index (ABI) (<0.9), and a high ankle brachial difference (ABD) (SBP > or = 75 mm Hg) also predicted LEAD events. Cox modeling suggested that duration (P <.001), HbA(1) (P <.001), hypertension (P =.006), log albumin excretion rate (P =.011), and heart rate (P =.028) predicted events independently. The overall model with HbA(1) and hypertension was significantly better than with eGDR, while the alternate models in men were similar. In women, the model with eGDR showed a significantly better fit. Glycemia, insulin resistance, hypertension and renal disease are powerful predictors of symptomatic lower extremity arterial disease in type 1 diabetes.     

Collection of peripheral blood stem cells in newly diagnosed myeloma patients without any prior cytoreductive therapy: the first step towards an 'operational cure'?

We have shown that primary therapy with non-myeloablative (140 mg/m(2)) high-dose melphalan (HDM) without hematopoietic support results in high response rates in untreated myeloma and very long-term survival of some patients. This study was designed to see if sufficient CD34 (+) cells can be harvested at presentation in newly diagnosed patients to administer myeloablative HDM (200 mg/m(2); HDM200) with autograft as primary therapy. This may improve outcome by rapid achievement of complete remission (CR) and possible avoidance of late myelodysplasia as a consequence of non-transplant induction chemotherapy. Thirty untreated patients received 1 g/m(2) methylprednisolone daily (days 1-6) and 12-16 micro g/kg G-CSF daily (days 3-6), and underwent leukapheresis on days 6 and 7. The median CD34(+) cell yield was 1.31 x10(6)/kg (range, 0.23-5.63), and was > or =1 x10(6)/kg in 73%. Cell yields were significantly lower than in 82 historical controls apheresed after completion of induction chemotherapy (median 2.16 x 10(6)/kg), and improved in patients who were apheresed again after induction chemotherapy. Three patients received primary therapy with HDM200 and autograft using these cells and attained CR. We conclude that it is possible to harvest stem cells in three-quarters of untreated myeloma patients. Increasing the number of apheresis procedures is needed to improve the number of CD34(+) cells collected.     

Impact of previous high-dose therapy on outcome after allografting for multiple myeloma

We describe a single centre experience of 33 patients allografted for multiple myeloma, of which 28 received matched sibling marrow, one haploidentical family donor marrow and four matched but unrelated donor marrow. Median follow-up after transplant is 27 months, and 13 patients are currently alive. One out of four patients with an unrelated donor survives and 12 out of 28 (42.8%) with matched sibling donors. Four patients were unevaluable because of early death (相似文献   

Content-Driven Analysis of an Online Community for Smoking Cessation: Integration of Qualitative Techniques,Automated Text Analysis,and Affiliation Networks

Objectives. We identified content-specific patterns of network diffusion underlying smoking cessation in the context of online platforms, with the aim of generating targeted intervention strategies.Methods. QuitNet is an online social network for smoking cessation. We analyzed 16 492 de-identified peer-to-peer messages from 1423 members, posted between March 1 and April 30, 2007. Our mixed-methods approach comprised qualitative coding, automated text analysis, and affiliation network analysis to identify, visualize, and analyze content-specific communication patterns underlying smoking behavior.Results. Themes we identified in QuitNet messages included relapse, QuitNet-specific traditions, and cravings. QuitNet members who were exposed to other abstinent members by exchanging content related to interpersonal themes (e.g., social support, traditions, progress) tended to abstain. Themes found in other types of content did not show significant correlation with abstinence.Conclusions. Modeling health-related affiliation networks through content-driven methods can enable the identification of specific content related to higher abstinence rates, which facilitates targeted health promotion.Epidemiological evidence indicates that modifiable risky health behaviors place a substantial socioeconomic burden on human health and wellness.1 Understanding human behavior in real-time settings is essential to improving health outcomes related to these behaviors.2,3 Technological advances in connectivity offer the means to obtain potentially valuable data sets in the form of electronic traces of the activities of online social communities. These data may help us to understand the intra- and interindividual intricacies of health-related behaviors. Studies of online and offline social networks provide valuable insight into social influence, information spread, and behavioral diffusion.4–6 Most of these analyses have paid more attention to the frequency of communication between members than to its content. The content, however, is relevant to behavior change theories, which address the use of specialized content to stimulate and support individuals to achieve a desired change.7,8 Contemporary work on social media data rarely addresses this fundamental concern of behavior change theorists.Outside the context of online networks, several theories have been formulated to explain behavior change. Some, such as the Transtheoretical Model,9 belong to the intrapersonal category; others, such as Social Cognitive Theory10 and social network and support models,11 are classified as interpersonal. (Appendix A, available as a supplement to the online version of this article at http://www.ajph.org, provides an overview of the theoretical constructs.) Empirical research on the applicability of these models to behavior change of health consumers in the digital era is minimal.12 Recent research showed that participation in health issue–specific social networking sites significantly influenced social factors such as identification, perceived subjective norms, and social support, which in turn resulted in greater smoking cessation self-efficacy.13 Content inclusion in analytical models of social networks can enable us to examine the content-specific patterns of social factors underlying behavior change. Through mapping of the specific content to theories, such content inclusion can facilitate the development of network interventions for health behavior changes by harnessing the power of social relationships.Studies of QuitNet, an online social network for smoking cessation, have examined the structure of peer-to-peer communication patterns and provided insights into social integrators and opinion leaders.5,14 Previous work showed the applicability of affiliation networks to real-world diffusion networks, enriching our understanding of the affiliation-based sources of influence on individuals’ behavior. Examples include the diffusion of (1) ratification of the World Health Organization Framework Convention on Tobacco Control by comembership with an online forum among countries15; (2) gunshot victimization by co-offending with victims among Chicago, Illinois, gangsters16; (3) substance use by coparticipating in school-sponsored sports or co-identifying with the same crowd types17,18; and (4) sexual behavior by coaffiliating with venues among male sex workers.19We used affiliation networks to analyze messages for content-specific patterns of network diffusion. We took an interdisciplinary approach, integrating methods from sociobehavioral sciences, social network analytics, and biomedical informatics. We employed qualitative techniques derived from grounded theory, automated text analysis, and affiliation network analysis to investigate the communication patterns underlying human behavior in online environments. Our study had 3 major components: (1) a qualitative study of human communication within user-generated data in QuitNet, (2) computational text analysis to further extend this analysis, and (3) identification of communication patterns pertinent to behavior change in affiliation networks. We anticipate that the insights gained from this research will enhance our understanding of behavior change and will have implications for the design of sociobehavioral interventions that draw upon social influence.