A single chemosensor for multiple analytes: fluorogenic and ratiometric absorbance detection of Zn²⁺, Mg²⁺ and F⁻, 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 Zn²⁺ with the fluorescence enhanced at 457 nm, and ratiometric detection at 290 nm, 350 nm and 420 nm in DMF/H₂O (1/4, v/v) medium. Sensor 1 can also monitor Mg²⁺ 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 F⁻ 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 F− anion. Spectral titration, isothermal titration calorimetry and mass spectrometry revealed that the sensor formed a 1:1 complex with Mg²⁺, Zn²⁺ or F⁻, with stability constants of 4.5 × 10⁶, 3.4 × 10⁶, 8.0 × 10⁴ M⁻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 Zn²⁺ 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 I–III) intrahepatic cholangiocarcinoma (ICC). Materials and Methods: We selected 320 patients who were diagnosed with ICC with no apparent clinical LNM (T1–3N0M0). 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)