Comparative efficacy and acceptability of seven augmentation agents for treatment-resistant depression: A multiple-treatments meta-analysis

Background. Treatment-resistant depression (TRD) is a therapeutic challenge for clinicians. Augmentation pharmacotherapy is effective for TRD, but it is still unclear which augmentation agent is most efficacious.  Objective. To assess the effects of seven augmentation agents on TRD.  Methods. We did a multiple-treatments meta-analysis, accounting for both direct and indirect comparisons. PubMed, the Center for Clinical and Translational Research, Web of Science, Embase, CBM-disc, the Chinese National Knowledge Infrastructure and relevant websites (up to August 2013) were searched for randomised controlled trials (RCTs) about augmentation agents. The following terms were used: ‘potentiation’, ‘augmentation’, and ‘adjunct’ paired with ‘depression’ and ‘resistant depression’. No language limitation was imposed. Results. We systematically reviewed 12 RCTs (1 936 participants), which included seven augmentation agents: lithium, tricyclic antidepressant (TCA), atypical antipsychotics (AAPs), antiepileptic drugs (AEDs), buspirone, cognitive behaviour therapy (CBT) and tri-iodothyronine (T3). The results revealed that T3 was more efficacious than lithium, TCA, AAPs, AEDs, buspirone and CBT with odds ratios (ORs) of 1.58, 1.56, 1.51, 1.47, 1.77 and 1.25, respectively. ORs favoured CBT compared with lithium, TCA, AAPs, AEDs and buspirone. Buspirone was the least efficacious of all the other augmentation agents tested. AAPs were significantly more acceptable than lithium, and CBT more than buspirone. T3 was slightly more acceptable than lithium, and CBT more than AAPs. Conclusion. T3 as an augmentation agent should be a clinician’s first consideration instead of lithium in acute treatment for TRD. CBT might be a good augmentation agent in some communities. Buspirone should be a final option as an augmentation agent. Further research is needed, such as a well-designed, large-scale controlled trial, to support and draw definite conclusions

Transcribing Content from Structural Images with Spotlight Mechanism

Transcribing content from structural images, e.g., writing notes from music scores, is a challenging task as not only the content objects should be recognized, but the internal structure should also be preserved. Existing image recognition methods mainly work on images with simple content (e.g., text lines with characters), but are not capable to identify ones with more complex content (e.g., structured symbols), which often follow a fine-grained grammar. To this end, in this paper, we propose a hierarchical Spotlight Transcribing Network (STN) framework followed by a two-stage "where-to-what" solution. Specifically, we first decide "where-to-look" through a novel spotlight mechanism to focus on different areas of the original image following its structure. Then, we decide "what-to-write" by developing a GRU based network with the spotlight areas for transcribing the content accordingly. Moreover, we propose two implementations on the basis of STN, i.e., STNM and STNR, where the spotlight movement follows the Markov property and Recurrent modeling, respectively. We also design a reinforcement method to refine the framework by self-improving the spotlight mechanism. We conduct extensive experiments on many structural image datasets, where the results clearly demonstrate the effectiveness of STN framework.Comment: Accepted by KDD2018 Research Track. In proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'18

Recycle-GAN: Unsupervised Video Retargeting

We introduce a data-driven approach for unsupervised video retargeting that translates content from one domain to another while preserving the style native to a domain, i.e., if contents of John Oliver's speech were to be transferred to Stephen Colbert, then the generated content/speech should be in Stephen Colbert's style. Our approach combines both spatial and temporal information along with adversarial losses for content translation and style preservation. In this work, we first study the advantages of using spatiotemporal constraints over spatial constraints for effective retargeting. We then demonstrate the proposed approach for the problems where information in both space and time matters such as face-to-face translation, flower-to-flower, wind and cloud synthesis, sunrise and sunset.Comment: ECCV 2018; Please refer to project webpage for videos - http://www.cs.cmu.edu/~aayushb/Recycle-GA

Higher-Order Orthogonal Causal Learning for Treatment Effect

Most existing studies on the double/debiased machine learning method concentrate on the causal parameter estimation recovering from the first-order orthogonal score function. In this paper, we will construct the $k^{\mathrm{th}}$-order orthogonal score function for estimating the average treatment effect (ATE) and present an algorithm that enables us to obtain the debiased estimator recovered from the score function. Such a higher-order orthogonal estimator is more robust to the misspecification of the propensity score than the first-order one does. Besides, it has the merit of being applicable with many machine learning methodologies such as Lasso, Random Forests, Neural Nets, etc. We also undergo comprehensive experiments to test the power of the estimator we construct from the score function using both the simulated datasets and the real datasets

Expression and role of fibroblast activation protein-alpha in microinvasive breast carcinoma

<p>Abstract</p> <p>Background</p> <p>Diagnosis of ductal carcinoma in situ (DCIS) in breast cancer cases is challenging for pathologist due to a variety of in situ patterns and artefacts, which could be misinterpreted as stromal invasion. Microinvasion is detected by the presence of cytologically malignant cells outside the confines of the basement membrane and myoepithelium. When malignant cells invade the stroma, there is tissue remodeling induced by perturbed stromal-epithelial interactions. Carcinoma-associated fibroblasts (CAFs) are main cells in the microenvironment of the remodeled tumor-host interface. They are characterized by the expression of the specific fibroblast activation protein-alpha (FAP-α), and differ from that of normal fibroblasts exhibiting an immunophenotype of CD34. We hypothesized that staining for FAP-α may be helpful in determining whether DCIS has microinvasion.</p> <p>Methods</p> <p>349 excised breast specimens were immunostained for smooth muscle actin SMA, CD34, FAP-α, and Calponin. Study material was divided into 5 groups: group 1: normal mammary tissues of healthy women after plastic surgery; group 2: usual ductal hyperplasia (UDH); group 3: DCIS without microinvasion on H & E stain; group 4: DCIS with microinvasion on H & E stain (DCIS-MI), and group 5: invasive ductal carcinoma (IDC). A comparative evaluation of the four immunostains was conducted.</p> <p>Results</p> <p>Our results demonstrated that using FAP-α and Calponin adjunctively improved the sensitivity of pathological diagnosis of DCIS-MI by 11.29%, whereas the adjunctive use of FAP-α and Calponin improved the sensitivity of pathological diagnosis of DCIS by 13.6%.</p> <p>Conclusions</p> <p>This study provides the first evidence that immunostaining with FAP-α and Calponin can serve as a novel marker for pathologically diagnosing whether DCIS has microinvasion.</p

The Causal Learning of Retail Delinquency

This paper focuses on the expected difference in borrower's repayment when there is a change in the lender's credit decisions. Classical estimators overlook the confounding effects and hence the estimation error can be magnificent. As such, we propose another approach to construct the estimators such that the error can be greatly reduced. The proposed estimators are shown to be unbiased, consistent, and robust through a combination of theoretical analysis and numerical testing. Moreover, we compare the power of estimating the causal quantities between the classical estimators and the proposed estimators. The comparison is tested across a wide range of models, including linear regression models, tree-based models, and neural network-based models, under different simulated datasets that exhibit different levels of causality, different degrees of nonlinearity, and different distributional properties. Most importantly, we apply our approaches to a large observational dataset provided by a global technology firm that operates in both the e-commerce and the lending business. We find that the relative reduction of estimation error is strikingly substantial if the causal effects are accounted for correctly.Comment: This paper was accepted and will be published in the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21