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Objective: Patients with bipolar disorder spend most of their clinical lifetime in the depressive phase of their illness. However, antidepressants are discouraged in the treatment of bipolar depression due to concerns over manic induction and drug ineffectiveness. Some reports suggest that monoamine oxidase inhibitors (MAOIs) may be safe and effective compared to other antidepressants in treating bipolar depression. The present study compared the safety and effectiveness of MAOI therapy in patients with bipolar versus unipolar depression. Methods: Data were collected from approximately 2500 clinical research charts of patients treated with MAOI therapy at a university mood disorder clinic between 1983 and 2015. A mixed‐effects model was created with patient entered as the random effect. The model included the primary diagnosis (i.e., either unipolar or bipolar depression) and other clinical covariates as fixed‐effect predictors. Results: Patients with bipolar depression demonstrated lower post‐treatment clinical global impressions/severity scores versus patients with unipolar depression (p = 0.04). Neither group demonstrated a full syndromal manic or hypomanic episode. A higher proportion of patients with bipolar depression reported myoclonic tics and tremors, which may have resulted from concomitant lithium use. Amongst the covariates, only the number of prior antidepressant trials predicted poorer outcomes from MAOI therapy. Conclusion: MAOIs may be more effective—and as safe—for patients with bipolar depression versus unipolar depression. Future studies should explore this possible advantage using a larger sample size. [ABSTRACT FROM AUTHOR]

The highly strained bowl‐shaped pentacyclic structure of herquline A has rendered it one of the most difficult problems in organic synthesis yet to be solved. The challenges associated with the synthesis of herquline A have been well documented in four Ph.D. dissertations and in multiple reports regarding syntheses of its structurally simpler congeners. Herein, we report the construction of the pentacyclic core of herquline A that contains both N10−C2 and C3−C3′ bonds. The key for success was the development of the tandem aza‐Michael addition/enolate capture protocol that set the stage for subsequent palladium catalyzed C3(sp2)−C3′(sp2) coupling reaction. Ensuing oxidative dearomatization of the left aryl ring allowed the formation of the pentacyclic diketone core of herquline A. [ABSTRACT FROM AUTHOR]

Objectives: Lithium and quetiapine are known to be effective treatments for bipolar disorder. However, little information is available to inform prediction of response to these medications. Machine‐learning methods can identify predictors of response by examining variables simultaneously. Further evaluation of models on a test sample can estimate how well these models would generalize to other samples. Methods: Data (N = 482) were drawn from a randomized clinical trial of outpatients with bipolar I or II disorder who received adjunctive personalized treatment plus either lithium or quetiapine. Elastic net regularization (ENR) was used to generate models for lithium and quetiapine; these models were evaluated on a test set. Results: Predictions from the lithium model explained 17.4% of the variance in actual observed scores of patients who received lithium in the test set, while predictions from the quetiapine model explained 32.1% of the variance of patients that received quetiapine. Of the baseline variables selected, those with the largest parameter estimates were: severity of mania; attention‐deficit/hyperactivity disorder (ADHD) comorbidity; nonsuicidal self‐injurious behavior; employment; and comorbidity with each of two anxiety disorders (social phobia/society anxiety and agoraphobia). Predictive accuracy of the ENR model outperformed the simple and basic theoretical models. Conclusion: ENR is an effective approach for building optimal and generalizable models. Variables identified through this methodology can inform future research on predictors of response to lithium and quetiapine, as well as future modeling efforts of treatment choice in bipolar disorder. [ABSTRACT FROM AUTHOR]

Examines explanation that emphasize voter intention. Ability of the House candidates to separate themselves in the eyes of constituents from the national party image; Role of political elites in influencing the behavior of split-ticket voters; Analysis of the personality of voters.

Purpose: Measurements of breast arterial calcifications (BAC) can offer a personalized, non‐invasive approach to risk‐stratify women for cardiovascular diseases such as heart attack and stroke. We aim to detect and segment breast arterial calcifications in mammograms accurately and suggest novel measurements to quantify detected BAC for future clinical applications. Methods: To separate BAC in mammograms, we propose a lightweight fine vessel segmentation method Simple Context U‐Net (SCU‐Net). Due to the large image size of mammograms, we adopt a patch‐based way to train SCU‐Net and obtain the final whole‐image‐size results by stitching patchwise results together. To further quantify calcifications, we test five quantitative metrics to inspect the progression of BAC for subjects: sum of mask probability metric (PM), sum of mask area metric (AM), sum of mask intensity metric (SIM), sum of mask area with threshold intensity metric TAMX, and sum of mask intensity with threshold X metric TSIMX. Finally, we demonstrate the ability of the metrics to longitudinally measure calcifications in a group of 26 subjects and evaluate our quantification metrics compared with calcified voxels and calcium mass on breast CT for 10 subjects. Results: Our segmentation results are compared with state‐of‐the‐art network architectures based on recall, precision, accuracy, F1 score/Dice score, and Jaccard index evaluation metrics and achieve corresponding values of 0.789, 0.708, 0.997, 0.729, and 0.581 for whole‐image‐size results. The quantification results all show >95% correlation between quantification measures on predicted masks of SCU‐Net as compared to the groundtruth and measurement of calcification on breast CT. For the calcification quantification measurement, our calcification volume (voxels) results yield R2‐correlation values of 0.834, 0.843, 0.832, 0.798, and 0.800 for the PM,AM,SIM,TAM100,TSIM100 metrics, respectively; our calcium mass results yield comparable R2‐correlation values of 0.866, 0.873, 0.840, 0.774, and 0.798 for the same metrics. Conclusions: Simple Context U‐Net is a simple method to accurately segment arterial calcification retrospectively on routine mammograms. Quantification of the calcifications based on this segmentation in the retrospective cohort study has sufficient sensitivity to detect the normal progression over time and should be useful for future research and clinical applications. [ABSTRACT FROM AUTHOR]