1,590 research outputs found
A single chemosensor for multiple analytes: fluorogenic and ratiometric absorbance detection of ZnĀ²āŗ, MgĀ²āŗ and Fā», and its cell imaging
A simple coumarin based sensor 1 has been synthesized from the condensation reaction of 7-hydroxycoumarin and ethylenediamine via the intermediate 7-hydroxy-8-aldehyde-coumarin. As a multiple analysis sensor, 1 can monitor ZnĀ²āŗ with the fluorescence enhanced at 457 nm, and ratiometric detection at 290 nm, 350 nm and 420 nm in DMF/HāO (1/4, v/v) medium. Sensor 1 can also monitor MgĀ²āŗ with the fluorescence enhanced at 430 nm, and ratiometric detection at 290 nm, 370 nm and 430 nm in DMF medium through the interaction of chelation enhance fluorescence (CHEF) with metal ions. Furthermore, 1 also can monitor Fā» with the fluorescence enhanced at 460 nm, and ratiometric detection at 290 nm and 390 nm in DMF medium simultaneously via hydrogen bonding and deprotonation with Fā anion. Spectral titration, isothermal titration calorimetry and mass spectrometry revealed that the sensor formed a 1:1 complex with MgĀ²āŗ, ZnĀ²āŗ or Fā», with stability constants of 4.5 Ć 10ā¶, 3.4 Ć 10ā¶, 8.0 Ć 10ā“ Mā»1 respectively. The complexation of the ions by 1 was an exothermic reaction driven by entropy processes. Furthermore, the sensor exhibits good membrane-permeability and was capable of monitoring at the intracellular ZnĀ²āŗ level in living cells
De-confounding Representation Learning for Counterfactual Inference on Continuous Treatment via Generative Adversarial Network
Counterfactual inference for continuous rather than binary treatment
variables is more common in real-world causal inference tasks. While there are
already some sample reweighting methods based on Marginal Structural Model for
eliminating the confounding bias, they generally focus on removing the
treatment's linear dependence on confounders and rely on the accuracy of the
assumed parametric models, which are usually unverifiable. In this paper, we
propose a de-confounding representation learning (DRL) framework for
counterfactual outcome estimation of continuous treatment by generating the
representations of covariates disentangled with the treatment variables. The
DRL is a non-parametric model that eliminates both linear and nonlinear
dependence between treatment and covariates. Specifically, we train the
correlations between the de-confounded representations and the treatment
variables against the correlations between the covariate representations and
the treatment variables to eliminate confounding bias. Further, a
counterfactual inference network is embedded into the framework to make the
learned representations serve both de-confounding and trusted inference.
Extensive experiments on synthetic datasets show that the DRL model performs
superiorly in learning de-confounding representations and outperforms
state-of-the-art counterfactual inference models for continuous treatment
variables. In addition, we apply the DRL model to a real-world medical dataset
MIMIC and demonstrate a detailed causal relationship between red cell width
distribution and mortality.Comment: 15 pages,4 figure
Prediction of the Lymph Node Status in Patients with Intrahepatic Cholangiocarcinoma: Analysis of 320 Surgical Cases
Purpose: This study was conducted to identify factors involved in lymph node metastasis (LNM) and evaluate their role in predicting LNM in clinically lymph node negative (clinical stage IāIII) intrahepatic cholangiocarcinoma (ICC). Materials and Methods: We selected 320 patients who were diagnosed with ICC with no apparent clinical LNM (T1ā3N0M0). Age, gender, tumor boundary, histological differentiation, tumor size, and carbohydrate antigen 19-9 value were the studied factors. Univariate and multivariate logistic analysis were conducted. Receiver operating characteristics curve analysis was used to test the predicting value of each factor and a test which combined the associated factors was used to predict LNM. Results: LNM was observed in 76 cases (76/320, 23.8%). Univariate and multivariate analysis showed that histological differentiation as well as tumor boundary and tumor size significantly correlated with LNM. The sensitivity and negative predictive value for LNM for the three factors when combined was 96.1 and 95% respectively. This means that 5% of the patients who did not have the risk factors mentioned above developed LNM. Conclusion: This model used the combination of three factors (low-graded histological differentiation, distinct tumor boundary, small tumor size) and they proved to be useful in predicting LNM in ICC with clinically lymph node negative cases. In patients with these criteria, lymph node dissection or lymph node irradiation may be omitted and such cases may also be good candidates for stereotactic body radiotherapy (SBRT)
- ā¦