Laura Ación

University of Buenos Aires

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Stephan Arndt

University of Iowa

Ricardo E. Jorge

Michael E. DeBakey VA Medical Center

Grace Chan

University of Connecticut

Victor Hesselbrock

University of Connecticut

Samuel Kuperman

University of Iowa

Kathleen K. Bucholz

Washington University in St. Louis

Howard J. Edenberg

Indiana University – Purdue University Indianapolis

Marc A. Schuckit

University of California, San Diego

John R. Kramer

University of Iowa

Bernice Porjesz

SUNY Downstate Health Sciences University

Cooperative Institutions

University of Iowa

Washington University in St. Louis

Indiana University – Purdue University Indianapolis

Consejo Nacional de Investigaciones Científicas y Técnicas

Heidelberg University

SUNY Downstate Health Sciences University

University of Buenos Aires

State University of New York

University of California, San Diego

University of Iowa Hospitals and Clinics

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Density and Dichotomous Family History Measures of Alcohol Use Disorder as Predictors of Behavioral and Neural Phenotypes: A Comparative Study Across Gender and Race/Ethnicity

Alcoholism Clinical and Experimental Research (2020)

Gayathri Pandey Michael J. Seay Jacquelyn L. Meyers David B. Chorlian Ashwini K. Pandey

Family history (FH) is an important risk factor for the development of alcohol use disorder (AUD). A variety of dichotomous and density measures of FH have been used to predict alcohol outcomes; yet, a systematic comparison of these FH measures is lacking. We compared 4 density and 4 commonly used dichotomous FH measures and examined variations by gender and race/ethnicity in their associations with age of onset of regular drinking, parietal P3 amplitude to visual target, and likelihood of developing AUD.

Alcohol use disorder

Alcohol Dependence

10.1111/acer.14280

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Citations (22)

Escitalopram and Enhancement of Cognitive Recovery Following Stroke

Archives of General Psychiatry (2010)

Ricardo E. Jorge Laura Ación David J. Moser Harold P. Adams Robert G. Robinson

Adjunctive restorative therapies administered during the first few months after stroke, the period with the greatest degree of spontaneous recovery, reduce the number of stroke patients with significant disability.To examine the effect of escitalopram on cognitive outcome. We hypothesized that patients who received escitalopram would show improved performance in neuropsychological tests assessing memory and executive functions than patients who received placebo or underwent Problem Solving Therapy.Randomized trial.Stroke center.One hundred twenty-nine patients were treated within 3 months following stroke. The 12-month trial included 3 arms: a double-blind placebo-controlled comparison of escitalopram (n = 43) with placebo (n = 45), and a nonblinded arm of Problem Solving Therapy (n = 41).Change in scores from baseline to the end of treatment for the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) and Trail-Making, Controlled Oral Word Association, Wechsler Adult Intelligence Scale-III Similarities, and Stroop tests.We found a difference among the 3 treatment groups in change in RBANS total score (P < .01) and RBANS delayed memory score (P < .01). After adjusting for possible confounders, there was a significant effect of escitalopram treatment on the change in RBANS total score (P < .01, adjusted mean change in score: escitalopram group, 10.0; nonescitalopram group, 3.1) and the change in RBANS delayed memory score (P < .01, adjusted mean change in score: escitalopram group, 11.3; nonescitalopram group, 2.5). We did not observe treatment effects in other neuropsychological measures.When compared with patients who received placebo or underwent Problem Solving Therapy, stroke patients who received escitalopram showed improvement in global cognitive functioning, specifically in verbal and visual memory functions. This beneficial effect of escitalopram was independent of its effect on depression. The utility of antidepressants in the process of poststroke recovery should be further investigated. Trial Registration clinicaltrials.gov Identifier: NCT00071643.

Escitalopram

Stroke

10.1001/archgenpsychiatry.2009.185

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Citations (205)

Clinical, genomic, and neurophysiological correlates of lifetime suicide attempts among individuals with alcohol dependence

Research Square (Research Square) (2024)

Peter B. Barr Zoë Neale Chris Chatzinakos Jessica Schulman Niamh Mullins

Abstract Research has identified clinical, genomic, and neurophysiological markers associated with suicide attempts (SA) among individuals with psychiatric illness. However, there is limited research among those with an alcohol use disorder (AUD), despite their disproportionately higher rates of SA. We examined lifetime SA in 4,068 individuals with DSM-IV alcohol dependence from the Collaborative Study on the Genetics of Alcoholism (23% lifetime suicide attempt; 53% female; 17% Admixed African American ancestries; mean age: 38). We 1) conducted a genome-wide association study (GWAS) of SA and performed downstream analyses to determine whether we could identify specific biological pathways of risk, and 2) explored risk in aggregate across other clinical conditions, polygenic scores (PGS) for comorbid psychiatric problems, and neurocognitive functioning between those with AD who have and have not reported a lifetime suicide attempt. The GWAS and downstream analyses did not produce any significant associations. Participants with an AUD who had attempted suicide had greater rates of trauma exposure, major depressive disorder, post-traumatic stress disorder, and other substance use disorders compared to those who had not attempted suicide. Polygenic scores for suicide attempt, depression, and PTSD were associated with reporting a suicide attempt (ORs = 1.22–1.44). Participants who reported a SA also had decreased right hemispheric frontal-parietal theta and decreased interhemispheric temporal-parietal alpha electroencephalogram resting-state coherences relative to those who did not, but differences were small. Overall, individuals with alcohol dependence who report SA appear to experience a variety of severe comorbidities and elevated polygenic risk for SA. Our results demonstrate the need to further investigate suicide attempts in the presence of substance use disorders.

Neurocognitive

Alcohol Dependence

Genome-wide Association Study

Alcohol use disorder

Depression

10.21203/rs.3.rs-3894892/v1

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Metrics reloaded: Recommendations for image analysis validation

arXiv (Cornell University) (2022)

Lena Maier‐Hein Annika Reinke Patrick Godau Minu D. Tizabi Florian Buettner

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen performance metrics often do not reflect the domain interest, thus failing to adequately measure scientific progress and hindering translation of ML techniques into practice. To overcome this, our large international expert consortium created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. The framework was developed in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint - a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output. Based on the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as a classification task at image, object or pixel level, namely image-level classification, object detection, semantic segmentation, and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool, which also provides a point of access to explore weaknesses, strengths and specific recommendations for the most common validation metrics. The broad applicability of our framework across domains is demonstrated by an instantiation for various biological and medical image analysis use cases.

Representation

10.48550/arxiv.2206.01653

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Citations (29)

Learning Deep Features for Stain-free Live-dead Human Breast Cancer Cell Classification

Research Square (Research Square) (2020)

Gisela Pattarone Laura Ación Marina Simian Emmanuel Iarussi

Abstract Automated cell classification in cancer biology is a challenging topic in computer vision and machine learning research. Breast cancer is the most common malignancy in women that usually involves phenotypically diverse populations of breast cancer cells and an heterogeneous stroma. In recent years, automated microscopy technologies are allowing the study of live cells over extended periods of time, simplifying the task of compiling large image databases. For instance, there have been several studies oriented towards building machine learning systems capable of automatically classifying images of different cell types (i.e. motor neurons, stem cells). In this work we were interested in classifying breast cancer cells as live or dead, based on a set of automatically retrieved morphological characteristics using image processing techniques. Our hypothesis is that live-dead classification can be performed without any staining and using only bright-field images as input. To our knowledge, there is no previous work attempting this task on in vitro studies of breast cancer cells, nor is there a dataset available to explore solutions related to this issue. We tackled this problem using the JIMT-1 breast cancer cell line that grows as an adherent monolayer. First, a vast image set composed by JIMT-1 human breast cancer cells that had been exposed to a chemotherapeutic drug treatment (doxorubicin and paclitaxel) or vehicle control was compiled. Next, several state-of-the-art classifiers were trained based on convolutional neural networks (CNN) to perform supervised classification using labels obtained from fluorescence microscopy images associated with each bright-field image. Model performances were evaluated and compared on a large number of bright-field images. The best model reached an AUC = 0.941 for classifying breast cancer cells without treatment. Furthermore, it reached AUC = 0.978 when classifying breast cancer cells under drug treatment. Our results highlight the potential of machine learning and computational image analysis to build new diagnosis tools that benefit the biomedical field by reducing cost, time, and stimulating work reproducibility. More importantly, we analyzed the way our classifiers clusterize bright-field images in the learned high-dimensional embedding and linked these groups to salient visual characteristics in live-dead cell biology observed by trained experts.

Contextual image classification

10.21203/rs.3.rs-109542/v1

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Citations (1)

High Polygenic Risk Scores Are Associated With Early Age of Onset of Alcohol Use Disorder in Adolescents and Young Adults at Risk

Biological Psychiatry Global Open Science (2021)

John I. Nürnberger Yumin Wang Yong Zang Dongbing Lai Leah Wetherill

Genome-wide association studies have been conducted in alcohol use disorder (AUD), and they permit the use of polygenic risk scores (PRSs), in combination with clinical variables, to predict the onset of AUD in vulnerable populations. A total of 2794 adolescent/young adult subjects from the Collaborative Study on the Genetics of Alcoholism were followed, with clinical assessments every 2 years. Subjects were genotyped using a genome-wide chip. Separate PRS analyses were performed for subjects of European ancestry and African ancestry. Age of onset of DSM-5 AUD was evaluated using the Cox proportional hazard model. Predictive power was assessed using receiver operating characteristic curves and by analysis of the distribution of PRS. European ancestry subjects with higher than median PRSs were at greater risk for onset of AUD than subjects with lower than median PRSs (p = 3 × 10–7). Area under the curve for the receiver operating characteristic analysis peaked at 0.88 to 0.95 using PRS plus sex, family history, comorbid disorders, age at first drink, and peer drinking; predictive power was primarily driven by clinical variables. In this high-risk sample, European ancestry subjects with a PRS score in the highest quartile showed a 72% risk for developing AUD and a 35% risk of developing severe AUD (compared with risks of 54% and 16%, respectively, in the lowest quartile). Predictive power for PRSs in the extremes of the distribution suggests that these may have future clinical utility. Uncertainties in interpretation at the individual level still preclude current application.

Polygenic risk score

Alcohol use disorder

10.1016/j.bpsgos.2021.10.007

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Citations (13)

Dataset of reported synthetic conditions for ZIF-8 since 2006 and final characteristics and properties of the particles obtained

Zenodo (CERN European Organization for Nuclear Research) (2023)

Juan A. Allegretto Diego Onna Laura Ación Sara A. Bilmes Omar Azzaroni

This is a dataset generated by mining available literature between 2006 and August 2021. Initial literature search was done using Scopus database, employing a combination of keywords such: "ZIF-8", "Zeolitic+ZIF-8", "Framework+ZIF-8", "Zeolitic+MOF". First, removal of duplicates and review articles was done and the remaining documents were chosen by title+abstract analysis. The dataset contains a total of 254 entries, i.e., 254 individual reported synthesis. Throughout this dataset, the following parameters can be found: -About synthesis conditions: Zinc source and the amount employed for the synthesis in mmol (milli-moles); 2-methylimidazole in mmol (HmIm); solvent and quantity employed (in mmol). Modulator and quantity employed (in mmol). Please note that quantities in milli-moles were calculated by hand in most of the cases, since reported data was expressed in different units. Reaction temperature (in °C), reaction time (min) and stirring condition (YES-NO-Initial-time). Finally, reports were classified as "systematic" (or not) based on wether the scope of the work was to explore different synthetic conditions. -About ZIF-8 characteristics: information is mainly focusing on structure-related characteristic, namely the particle morphology (classified as Faceted, poor-faceted, quasispherical, aggregated) and the particle´s size. Reported sizes where classified by the different techniques employed. Finally, Surface area determined by BET formalism was also included.

10.5281/zenodo.8153450

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Author Correction: Multivariate analysis of 1.5 million people identifies genetic associations with traits related to self-regulation and addiction

Nature Neuroscience (2024)

Richard Karlsson Linnér Travis T. Mallard Peter B. Barr Sandra Sanchez‐Roige James W. Madole

10.1038/s41593-024-01853-7

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Hands-on-Data: Artificial intelligence for the design of public policy in Latin America

Zenodo (CERN European Organization for Nuclear Research) (2019)

Lucila Berniell Laura Ación Leandro Lombardi Edgar Altszyler Carlos Sarraute

10.5281/zenodo.2800128

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Citations (1)

Clinical, genomic, and neurophysiological correlates of lifetime suicide attempts among individuals with an alcohol use disorder

Complex Psychiatry (2025)

Peter B. Barr Zoë Neale Chris Chatzinakos Jessica Schulman Niamh Mullins

Introduction: Research has identified multiple risk factors associated with suicide attempt (SA) among individuals with psychiatric illness. However, there is limited research among those with an alcohol use disorder (AUD), despite their disproportionately higher rates of SA. Methods: We examined lifetime suicide attempt in 4,068 individuals with an AUD from the Collaborative Study on the Genetics of Alcoholism (23% lifetime SA; 53% female; mean age: 38). We explored risk for lifetime SA across other clinical conditions ascertained from a clinical interview, polygenic scores (PGS) for comorbid psychiatric problems, and neurocognitive functioning. Results: Participants with an AUD who attempted suicide had greater rates of trauma exposure, major depressive disorder, post-traumatic stress disorder, other substance use disorders (SUD), and suicidal ideation. Polygenic scores for suicide attempt, depression, and PTSD were associated with increased odds of reporting a suicide attempt (ORs = 1.22 – 1.44). Participants who reported a SA also had decreased right hemispheric frontal-parietal theta and decreased interhemispheric temporal-parietal alpha electroencephalogram resting-state coherences relative to those who did not, but differences were small. Conclusions: Overall, individuals with an AUD who report lifetime SA experience greater levels of trauma, have more severe comorbidities, and carry increased polygenic risk for other psychiatric problems. Our results demonstrate the need to further investigate suicide attempts in the presence of SUDs.

Alcohol use disorder

Neurophysiology

10.1159/000543222

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