Full-length human placental sFlt-1-e15a isoform induces distinct maternal phenotypes of preeclampsia in mice

<div><p>Objective</p><p>Most anti-angiogenic preeclampsia models in rodents utilized the overexpression of a truncated soluble fms-like tyrosine kinase-1 (sFlt-1) not expressed in any species. Other limitations of mouse preeclampsia models included stressful blood pressure measurements and the lack of postpartum monitoring. We aimed to 1) develop a mouse model of preeclampsia by administering the most abundant human placental sFlt-1 isoform (hsFlt-1-e15a) in preeclampsia; 2) determine blood pressures in non-stressed conditions; and 3) develop a survival surgery that enables the collection of fetuses and placentas and postpartum (PP) monitoring.</p><p>Methods</p><p>Pregnancy status of CD-1 mice was evaluated with high-frequency ultrasound on gestational days (GD) 6 and 7. Telemetry catheters were implanted in the carotid artery on GD7, and their positions were verified by ultrasound on GD13. Mice were injected through tail-vein with adenoviruses expressing hsFlt-1-e15a (n = 11) or green fluorescent protein (GFP; n = 9) on GD8/GD11. Placentas and pups were delivered by cesarean section on GD18 allowing PP monitoring. Urine samples were collected with cystocentesis on GD6/GD7, GD13, GD18, and PPD8, and albumin/creatinine ratios were determined. GFP and hsFlt-1-e15a expression profiles were determined by qRT-PCR. Aortic ring assays were performed to assess the effect of hsFlt-1-e15a on endothelia.</p><p>Results</p><p>Ultrasound predicted pregnancy on GD7 in 97% of cases. Cesarean section survival rate was 100%. Mean arterial blood pressure was higher in hsFlt-1-e15a-treated than in GFP-treated mice (∆MAP = 13.2 mmHg, p = 0.00107; GD18). Focal glomerular changes were found in hsFlt-1-e15a -treated mice, which had higher urine albumin/creatinine ratios than controls (109.3±51.7μg/mg vs. 19.3±5.6μg/mg, p = 4.4x10^-2; GD18). Aortic ring assays showed a 46% lesser microvessel outgrowth in hsFlt-1-e15a-treated than in GFP-treated mice (p = 1.2x10^-2). Placental and fetal weights did not differ between the groups. One mouse with liver disease developed early-onset preeclampsia-like symptoms with intrauterine growth restriction (IUGR).</p><p>Conclusions</p><p>A mouse model of late-onset preeclampsia was developed with the overexpression of hsFlt-1-e15a, verifying the <i>in vivo</i> pathologic effects of this primate-specific, predominant placental sFlt-1 isoform. HsFlt-1-e15a induced early-onset preeclampsia-like symptoms associated with IUGR in a mouse with a liver disease. Our findings support that hsFlt-1-e15a is central to the terminal pathway of preeclampsia, and it can induce the full spectrum of symptoms in this obstetrical syndrome.</p></div

What differences are detected by superiority trials or ruled out by noninferiority trials? A cross-sectional study on a random sample of two-hundred two-arms parallel group randomized clinical trials

<p>Abstract</p> <p>Background</p> <p>The smallest difference to be detected in superiority trials or the largest difference to be ruled out in noninferiority trials is a key determinant of sample size, but little guidance exists to help researchers in their choice. The objectives were to examine the distribution of differences that researchers aim to detect in clinical trials and to verify that those differences are smaller in noninferiority compared to superiority trials.</p> <p>Methods</p> <p>Cross-sectional study based on a random sample of two hundred two-arm, parallel group superiority (100) and noninferiority (100) randomized clinical trials published between 2004 and 2009 in 27 leading medical journals. The main outcome measure was the smallest difference in favor of the new treatment to be detected (superiority trials) or largest unfavorable difference to be ruled out (noninferiority trials) used for sample size computation, expressed as standardized difference in proportions, or standardized difference in means. Student t test and analysis of variance were used.</p> <p>Results</p> <p>The differences to be detected or ruled out varied considerably from one study to the next; e.g., for superiority trials, the standardized difference in means ranged from 0.007 to 0.87, and the standardized difference in proportions from 0.04 to 1.56. On average, superiority trials were designed to detect larger differences than noninferiority trials (standardized difference in proportions: mean 0.37 versus 0.27, <it>P </it>= 0.001; standardized difference in means: 0.56 versus 0.40, <it>P </it>= 0.006). Standardized differences were lower for mortality than for other outcomes, and lower in cardiovascular trials than in other research areas.</p> <p>Conclusions</p> <p>Superiority trials are designed to detect larger differences than noninferiority trials are designed to rule out. The variability between studies is considerable and is partly explained by the type of outcome and the medical context. A more explicit and rational approach to choosing the difference to be detected or to be ruled out in clinical trials may be desirable.</p

What differences are detected by superiority trials or ruled out by noninferiority trials? A cross-sectional study on a random sample of two-hundred two-arms parallel group randomized clinical trials

BACKGROUND: The smallest difference to be detected in superiority trials or the largest difference to be ruled out in noninferiority trials is a key determinant of sample size, but little guidance exists to help researchers in their choice. The objectives were to examine the distribution of differences that researchers aim to detect in clinical trials and to verify that those differences are smaller in noninferiority compared to superiority trials. METHODS: Cross-sectional study based on a random sample of two hundred two-arm, parallel group superiority (100) and noninferiority (100) randomized clinical trials published between 2004 and 2009 in 27 leading medical journals. The main outcome measure was the smallest difference in favor of the new treatment to be detected (superiority trials) or largest unfavorable difference to be ruled out (noninferiority trials) used for sample size computation, expressed as standardized difference in proportions, or standardized difference in means. Student t test and analysis of variance were used. RESULTS: The differences to be detected or ruled out varied considerably from one study to the next; e.g., for superiority trials, the standardized difference in means ranged from 0.007 to 0.87, and the standardized difference in proportions from 0.04 to 1.56. On average, superiority trials were designed to detect larger differences than noninferiority trials (standardized difference in proportions: mean 0.37 versus 0.27, P = 0.001; standardized difference in means: 0.56 versus 0.40, P = 0.006). Standardized differences were lower for mortality than for other outcomes, and lower in cardiovascular trials than in other research areas. CONCLUSIONS: Superiority trials are designed to detect larger differences than noninferiority trials are designed to rule out. The variability between studies is considerable and is partly explained by the type of outcome and the medical context. A more explicit and rational approach to choosing the difference to be detected or to be ruled out in clinical trials may be desirable

Psychosocial interventions for patients with advanced cancer – a systematic review of the literature

Advanced cancer is associated with emotional distress, especially depression and feelings of sadness. To date, it is unclear which is the most effective way to address these problems. This review focuses on the effects of psychosocial interventions on the quality of life (QoL) of patients with advanced cancer. It was hypothesised that patients will benefit from psychosocial interventions by improving QoL, especially in the domain of emotional functioning. The review was conducted using systematic review methodology involving a systematic search of the literature published between 1990 and 2002, quality assessment of included studies, systematic data extraction and narrative data synthesis. In all, 10 randomised controlled studies involving 13 trials were included. Overall interventions and outcome measures across studies were heterogeneous. Outcome measures, pertaining to the QoL dimension of emotional functioning, were most frequently measured. A total of 12 trials evaluating behaviour therapy found positive effects on one or more indicators of QoL, for example, depression. The results of the review support recommendation of behaviour therapy in the care of patients with advanced cancer

Psychosocial interventions for patients with advanced cancer – a systematic review of the literature

Advanced cancer is associated with emotional distress, especially depression and feelings of sadness. To date, it is unclear which is the most effective way to address these problems. This review focuses on the effects of psychosocial interventions on the quality of life (QoL) of patients with advanced cancer. It was hypothesised that patients will benefit from psychosocial interventions by improving QoL, especially in the domain of emotional functioning. The review was conducted using systematic review methodology involving a systematic search of the literature published between 1990 and 2002, quality assessment of included studies, systematic data extraction and narrative data synthesis. In all, 10 randomised controlled studies involving 13 trials were included. Overall interventions and outcome measures across studies were heterogeneous. Outcome measures, pertaining to the QoL dimension of emotional functioning, were most frequently measured. A total of 12 trials evaluating behaviour therapy found positive effects on one or more indicators of QoL, for example, depression. The results of the review support recommendation of behaviour therapy in the care of patients with advanced cancer

Interrogating host immunity to predict treatment response in inflammatory bowel disease

IBD treatment is undergoing a transformation with an expanding repertoire of drugs targeting different aspects of the immune response. Three novel classes of drugs have emerged in the past decade that target leukocyte trafficking to the gut (vedolizumab), neutralize key cytokines with antibodies (ustekinumab) and inhibit cytokine signalling pathways (tofacitinib). In advanced development are other drugs for IBD, including therapies targeting other cytokines such as IL-23 and IL-6. However, all agents tested so far are hampered by primary and secondary loss of response, so it is desirable to develop personalized strategies to identify which patients should be treated with which drugs. Stratification of patients with IBD by clinical parameters alone lacks sensitivity, and alternative modalities are now needed to deliver precision medicine in IBD. High-resolution profiling of immune response networks in individual patients is a promising approach and different technical platforms, including in vivo real-time molecular endoscopy, tissue transcriptomics and germline genetics, are promising tools to help predict responses to specific therapies. However, important challenges remain regarding the clinical utility of these technologies, including their scalability and accessibility. This Review focuses on unravelling some of the complexity of mucosal immune responses in IBD pathogenesis and how current and emerging analytical platforms might be harnessed to effectively stratify and individualise IBD therapy