Practical guidance for applying the ADNEX model from the IOTA group to discriminate between different subtypes of adnexal tumors.

All gynecologists are faced with ovarian tumors on a regular basis, and the accurate preoperative diagnosis of these masses is important because appropriate management depends on the type of tumor. Recently, the International Ovarian Tumor Analysis (IOTA) consortium published the Assessment of Different NEoplasias in the adneXa (ADNEX) model, the first risk model that differentiates between benign and four types of malignant ovarian tumors: borderline, stage I cancer, stage II-IV cancer, and secondary metastatic cancer. This approach is novel compared to existing tools that only differentiate between benign and malignant tumors, and therefore questions may arise on how ADNEX can be used in clinical practice. In the present paper, we first provide an in-depth discussion about the predictors used in ADNEX and the ability for risk prediction with different tumor histologies. Furthermore, we formulate suggestions about the selection and interpretation of risk cut-offs for patient stratification and choice of appropriate clinical management. This is illustrated with a few example patients. We cannot propose a generally applicable algorithm with fixed cut-offs, because (as with any risk model) this depends on the specific clinical setting in which the model will be used. Nevertheless, this paper provides a guidance on how the ADNEX model may be adopted into clinical practice

Soils, Science and Community ActioN (SoilSCAN): a citizen science tool to empower community-led land management change in East Africa

Pastoralist communities worldwide face complex challenges regarding food and feed productivity. Primary production systems are under stress, nutritional choices are changing and the relationship between development and agriculture is undergoing profound transformation. Under increasing pressure from climate and land use change, East African agro-pastoral systems are approaching a tipping point in terms of land degradation. There is an urgent need for evidence-led sustainable land management interventions to reverse degradation of natural resources that support food and water security. A key barrier, however, is a lack of high spatial resolution soil health data wherein collecting such information for each individual community is beyond their means. In this context, we tested whether bridging such data gaps could be achieved through a coordinated programme at the boundary between participation and citizen science. Key outputs included a community-led trial of a hand-held soil scanner, which highlighted a range of positive benefits and practical challenges in using this technology in this context, with identification of some potential solutions; and a targeted soil organic matter and nutrient status dataset in a small catchment-based community setting. The results show that if the practical challenges can be resolved, use of portable soil scanner technology has the potential to fill key knowledge gaps and thereby improve resilience to the threat of land degradation through locally responsive farmer and community decision-making

Monotone return to steady nonequilibrium

We propose and analyze a new candidate Lyapunov function for relaxation towards general nonequilibrium steady states. The proposed functional is obtained from the large time asymptotics of time-symmetric fluctuations. For driven Markov jump or diffusion processes it measures an excess in dynamical activity rates. We present numerical evidence and we report on a rigorous argument for its monotonous time-dependence close to the steady nonequilibrium or in general after a long enough time. This is in contrast with the behavior of approximate Lyapunov functions based on entropy production that when driven far from equilibrium often keep exhibiting temporal oscillations even close to stationarity.Comment: Accepted for publication in Phys. Rev. Let

A meaningful expansion around detailed balance

We consider Markovian dynamics modeling open mesoscopic systems which are driven away from detailed balance by a nonconservative force. A systematic expansion is obtained of the stationary distribution around an equilibrium reference, in orders of the nonequilibrium forcing. The first order around equilibrium has been known since the work of McLennan (1959), and involves the transient irreversible entropy flux. The expansion generalizes the McLennan formula to higher orders, complementing the entropy flux with the dynamical activity. The latter is more kinetic than thermodynamic and is a possible realization of Landauer's insight (1975) that, for nonequilibrium, the relative occupation of states also depends on the noise along possible escape routes. In that way nonlinear response around equilibrium can be meaningfully discussed in terms of two main quantities only, the entropy flux and the dynamical activity. The expansion makes mathematical sense as shown in the simplest cases from exponential ergodicity.Comment: 19 page

Random-effects meta-analysis of the clinical utility of tests and prediction models

The use of data from multiple studies or centers for the validation of a clinical test or a multivariable prediction model allows researchers to investigate the test's/model's performance in multiple settings and populations. Recently, meta-analytic techniques have been proposed to summarize discrimination and calibration across study populations. Here, we rather consider performance in terms of net benefit, which is a measure of clinical utility that weighs the benefits of true positive classifications against the harms of false positives. We posit that it is important to examine clinical utility across multiple settings of interest. This requires a suitable meta-analysis method, and we propose a Bayesian trivariate random-effects meta-analysis of sensitivity, specificity, and prevalence. Across a range of chosen harm-to-benefit ratios, this provides a summary measure of net benefit, a prediction interval, and an estimate of the probability that the test/model is clinically useful in a new setting. In addition, the prediction interval and probability of usefulness can be calculated conditional on the known prevalence in a new setting. The proposed methods are illustrated by 2 case studies: one on the meta-analysis of published studies on ear thermometry to diagnose fever in children and one on the validation of a multivariable clinical risk prediction model for the diagnosis of ovarian cancer in a multicenter dataset. Crucially, in both case studies the clinical utility of the test/model was heterogeneous across settings, limiting its usefulness in practice. This emphasizes that heterogeneity in clinical utility should be assessed before a test/model is routinely implemented

Reconstructing the Changes in Sedimentation and Source Provenance in East African Hydropower Reservoirs: A Case Study of Nyumba ya Mungu in Tanzania

This study aimed to reconstruct the sedimentation rates over time and identify the changing sources of sediment in a major hydropower reservoir in Tanzania, the Nyumba ya Mungu (NYM). Fallout 210Pb measurements were used to estimate age of sediment deposits and broad changes in sedimentation rates were reconstructed. Sedimentation peaks were cross referenced to geochemical profiles of allogenic and autogenic elemental constituents of the sediment column to confirm a causal link. Finally, geochemical fingerprinting of the sediment cores and potential sources were compared using a Bayesian mixing model (MixSIAR) to attribute the dominant riverine and land use sources to the reservoir together with changes through recent decades. Reservoir sedimentation generally increased from 0.1 g cm−2 yr−1 in the lower sediment column to 1.7 g cm−2 yr−1 in the most recent deposits. These results correlated to changes in allogenic and autogenic tracers. The model output pointed to one of two major tributaries, the Kikuletwa River with 60.3%, as the dominant source of sediment to the entire reservoir, while the other tributary, Ruvu River, contributed approximately 39.7%. However, downcore unmixing results indicated that the latest increases in sedimentation seem to be mainly driven by an increased contribution from the Ruvu River. Cultivated land (CU) was shown to be the main land use source of riverine sediment, accounting for 38.4% and 44.6% in Kikuletwa and Ruvu rivers respectively. This study explicitly demonstrated that the integration of sediment tracing and dating tools can be used for quantifying the dominant source of sediment infilling in East African hydropower reservoirs. The results underscore the necessity for catchment-wide management plans that target the reduction of both hillslope erosion reduction and the sediment connectivity from hillslope source areas to rivers and reservoirs, which will help to maintain and enhance food, water and energy security in Eastern Africa.</jats:p

Predicting the risk of malignancy in adnexal masses based on the Simple Rules from the International Ovarian Tumor Analysis group

BACKGROUND: Accurate methods to preoperatively characterize adnexal tumors are pivotal for optimal patient management. A recent metaanalysis concluded that the International Ovarian Tumor Analysis algorithms such as the Simple Rules are the best approaches to preoperatively classify adnexal masses as benign or malignant. OBJECTIVE: We sought to develop and validate a model to predict the risk of malignancy in adnexal masses using the ultrasound features in the Simple Rules. STUDY DESIGN: This was an international cross-sectional cohort study involving 22 oncology centers, referral centers for ultrasonography, and general hospitals. We included consecutive patients with an adnexal tumor who underwent a standardized transvaginal ultrasound examination and were selected for surgery. Data on 5020 patients were recorded in 3 phases from 2002 through 2012. The 5 Simple Rules features indicative of a benign tumor (B-features) and the 5 features indicative of malignancy (M-features) are based on the presence of ascites, tumor morphology, and degree of vascularity at ultrasonography. Gold standard was the histopathologic diagnosis of the adnexal mass (pathologist blinded to ultrasound findings). Logistic regression analysis was used to estimate the risk of malignancy based on the 10 ultrasound features and type of center. The diagnostic performance was evaluated by area under the receiver operating characteristic curve, sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-), positive predictive value (PPV), negative predictive value (NPV), and calibration curves. RESULTS: Data on 4848 patients were analyzed. The malignancy rate was 43% (1402/3263) in oncology centers and 17% (263/1585) in other centers. The area under the receiver operating characteristic curve on validation data was very similar in oncology centers (0.917; 95% confidence interval, 0.901-0.931) and other centers (0.916; 95% confidence interval, 0.873-0.945). Risk estimates showed good calibration. In all, 23% of patients in the validation data set had a very low estimated risk (<1%) and 48% had a high estimated risk (≥30%). For the 1% risk cutoff, sensitivity was 99.7%, specificity 33.7%, LR+ 1.5, LR- 0.010, PPV 44.8%, and NPV 98.9%. For the 30% risk cutoff, sensitivity was 89.0%, specificity 84.7%, LR+ 5.8, LR- 0.13, PPV 75.4%, and NPV 93.9%. CONCLUSION: Quantification of the risk of malignancy based on the Simple Rules has good diagnostic performance both in oncology centers and other centers. A simple classification based on these risk estimates may form the basis of a clinical management system. Patients with a high risk may benefit from surgery by a gynecological oncologist, while patients with a lower risk may be managed locally

Changing predictor measurement procedures affected the performance of prediction models in clinical examples

Objectives: The aim of this study was to quantify the impact of predictor measurement heterogeneity on prediction model performance. Predictor measurement heterogeneity refers to variation in the measurement of predictor(s) between the derivation of a prediction model and its validation or application. It arises, for instance, when predictors are measured using different measurement instruments or protocols. Study Design and Setting: We examined the effects of various scenarios of predictor measurement heterogeneity in r

Does ignoring clustering in multicenter data influence the performance of prediction models? A simulation study

Clinical risk prediction models are increasingly being developed and validated on multicenter datasets. In this article, we present a comprehensive framework for the evaluation of the predictive performance of prediction models at the center level and the population level, considering population-averaged predictions, center-specific predictions, and predictions assuming an average random center effect. We demonstrated in a simulation study that calibration slopes do not only deviate from one because of over- or underfitting of patterns in the development dataset, but also as a result of the choice of the model (standard versus mixed effects logistic regression), the type of predictions (marginal versus conditional versus assuming an average random effect), and the level of model validation (center versus population). In particular, when data is heavily clustered (ICC 20%), center-specific predictions offer the best predictive performance at the population level and the center level. We recommend that models should reflect the data structure, while the level of model validation should reflect the research question

Somatostatin subtype-2 receptor-targeted metal-based anticancer complexes

Conjugates of a dicarba analogue of octreotide, a potent somatostatin agonist whose receptors are overexpressed on tumor cells, with [PtCl 2(dap)] (dap = 1-(carboxylic acid)-1,2-diaminoethane) (3), [(η 6-bip)Os(4-CO 2-pico)Cl] (bip = biphenyl, pico = picolinate) (4), [(η 6-p-cym)RuCl(dap)] + (p-cym = p-cymene) (5), and [(η 6-p-cym)RuCl(imidazole-CO 2H)(PPh 3)] + (6), were synthesized by using a solid-phase approach. Conjugates 3-5 readily underwent hydrolysis and DNA binding, whereas conjugate 6 was inert to ligand substitution. NMR spectroscopy and molecular dynamics calculations showed that conjugate formation does not perturb the overall peptide structure. Only 6 exhibited antiproliferative activity in human tumor cells (IC 50 = 63 ± 2 μ in MCF-7 cells and IC 50 = 26 ± 3 μ in DU-145 cells) with active participation of somatostatin receptors in cellular uptake. Similar cytotoxic activity was found in a normal cell line (IC 50 = 45 ± 2.6 μ in CHO cells), which can be attributed to a similar level of expression of somatostatin subtype-2 receptor. These studies provide new insights into the effect of receptor-binding peptide conjugation on the activity of metal-based anticancer drugs, and demonstrate the potential of such hybrid compounds to target tumor cells specifically. © 2012 American Chemical Society