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HCN channels in the hippocampus regulate active coping behavior.
Fisher DW, Han Y, Lyman KA, Heuermann RJ, Bean LA, Ybarra N, Foote KM, Dong H, Nicholson DA, Chetkovich DM
(2018) J Neurochem 146: 753-766
MeSH Terms: Adaptation, Psychological, Animals, Avoidance Learning, Depression, Disease Models, Animal, Exploratory Behavior, Hippocampus, Hyperpolarization-Activated Cyclic Nucleotide-Gated Channels, Male, Maze Learning, Membrane Proteins, Mice, Mice, Inbred C57BL, Mice, Transgenic, Microscopy, Electron, Peroxins, Pyramidal Cells, Swimming
Show Abstract · Added April 2, 2019
Active coping is an adaptive stress response that improves outcomes in medical and neuropsychiatric diseases. To date, most research into coping style has focused on neurotransmitter activity and little is known about the intrinsic excitability of neurons in the associated brain regions that facilitate coping. Previous studies have shown that HCN channels regulate neuronal excitability in pyramidal cells and that HCN channel current (I ) in the CA1 area increases with chronic mild stress. Reduction of I in the CA1 area leads to antidepressant-like behavior, and this region has been implicated in the regulation of coping style. We hypothesized that the antidepressant-like behavior achieved with CA1 knockdown of I is accompanied by increases in active coping. In this report, we found that global loss of TRIP8b, a necessary subunit for proper HCN channel localization in pyramidal cells, led to active coping behavior in numerous assays specific to coping style. We next employed a viral strategy using a dominant negative TRIP8b isoform to alter coping behavior by reducing HCN channel expression. This approach led to a robust reduction in I in CA1 pyramidal neurons and an increase in active coping. Together, these results establish that changes in HCN channel function in CA1 influences coping style.
© 2018 International Society for Neurochemistry.
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18 MeSH Terms
Therapeutic endocannabinoid augmentation for mood and anxiety disorders: comparative profiling of FAAH, MAGL and dual inhibitors.
Bedse G, Bluett RJ, Patrick TA, Romness NK, Gaulden AD, Kingsley PJ, Plath N, Marnett LJ, Patel S
(2018) Transl Psychiatry 8: 92
MeSH Terms: Amidohydrolases, Animals, Anti-Anxiety Agents, Anxiety Disorders, Behavior, Animal, Benzodioxoles, Body Temperature, Brain, Carbamates, Endocannabinoids, Female, Locomotion, Male, Maze Learning, Mice, Inbred C57BL, Mice, Inbred ICR, Monoacylglycerol Lipases, Piperazines, Piperidines, Pyridines, Stress, Psychological
Show Abstract · Added April 12, 2019
Recent studies have demonstrated anxiolytic potential of pharmacological endocannabinoid (eCB) augmentation approaches in a variety of preclinical models. Pharmacological inhibition of endocannabinoid-degrading enzymes, such as fatty acid amide hydrolase (FAAH) and monoacylglycerol lipase (MAGL), elicit promising anxiolytic effects in rodent models with limited adverse behavioral effects, however, the efficacy of dual FAAH/MAGL inhibition has not been investigated. In the present study, we compared the effects of FAAH (PF-3845), MAGL (JZL184) and dual FAAH/MAGL (JZL195) inhibitors on (1) anxiety-like behaviors under non-stressed and stressed conditions, (2) locomotor activity and body temperature, (3) lipid levels in the brain and (4) cognitive functions. Behavioral analysis showed that PF-3845 or JZL184, but not JZL195, was able to prevent restraint stress-induced anxiety in the light-dark box assay when administered before stress exposure. Moreover, JZL195 treatment was not able to reverse foot shock-induced anxiety-like behavior in the elevated zero maze or light-dark box. JZL195, but not PF-3845 or JZL184, decreased body temperature and increased anxiety-like behavior in the open-field test. Overall, JZL195 did not show anxiolytic efficacy and the effects of JZL184 were more robust than that of PF-3845 in the models examined. These results showed that increasing either endogenous AEA or 2-AG separately produces anti-anxiety effects under stressful conditions but the same effects are not obtained from simultaneously increasing both AEA and 2-AG.
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MeSH Terms
Integrating linear optimization with structural modeling to increase HIV neutralization breadth.
Sevy AM, Panda S, Crowe JE, Meiler J, Vorobeychik Y
(2018) PLoS Comput Biol 14: e1005999
MeSH Terms: Algorithms, Amino Acid Motifs, Antibodies, Neutralizing, Computational Biology, Epitopes, HIV Antibodies, HIV Infections, HIV-1, Humans, Linear Models, Machine Learning, Regression Analysis, Software, Support Vector Machine
Show Abstract · Added March 14, 2018
Computational protein design has been successful in modeling fixed backbone proteins in a single conformation. However, when modeling large ensembles of flexible proteins, current methods in protein design have been insufficient. Large barriers in the energy landscape are difficult to traverse while redesigning a protein sequence, and as a result current design methods only sample a fraction of available sequence space. We propose a new computational approach that combines traditional structure-based modeling using the Rosetta software suite with machine learning and integer linear programming to overcome limitations in the Rosetta sampling methods. We demonstrate the effectiveness of this method, which we call BROAD, by benchmarking the performance on increasing predicted breadth of anti-HIV antibodies. We use this novel method to increase predicted breadth of naturally-occurring antibody VRC23 against a panel of 180 divergent HIV viral strains and achieve 100% predicted binding against the panel. In addition, we compare the performance of this method to state-of-the-art multistate design in Rosetta and show that we can outperform the existing method significantly. We further demonstrate that sequences recovered by this method recover known binding motifs of broadly neutralizing anti-HIV antibodies. Finally, our approach is general and can be extended easily to other protein systems. Although our modeled antibodies were not tested in vitro, we predict that these variants would have greatly increased breadth compared to the wild-type antibody.
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14 MeSH Terms
Generating Quantitative Cell Identity Labels with Marker Enrichment Modeling (MEM).
Diggins KE, Gandelman JS, Roe CE, Irish JM
(2018) Curr Protoc Cytom 83: 10.21.1-10.21.28
MeSH Terms: Animals, Electronic Data Processing, Flow Cytometry, Humans, Machine Learning, Models, Theoretical
Show Abstract · Added January 19, 2018
Multiplexed single-cell experimental techniques like mass cytometry measure 40 or more features and enable deep characterization of well-known and novel cell populations. However, traditional data analysis techniques rely extensively on human experts or prior knowledge, and novel machine learning algorithms may generate unexpected population groupings. Marker enrichment modeling (MEM) creates quantitative identity labels based on features enriched in a population relative to a reference. While developed for cell type analysis, MEM labels can be generated for a wide range of multidimensional data types, and MEM works effectively with output from expert analysis and diverse machine learning algorithms. MEM is implemented as an R package and includes three steps: (1) calculation of MEM values that quantify each feature's relative enrichment in the population, (2) reporting of MEM labels as a heatmap or as a text label, and (3) quantification of MEM label similarity between populations. The protocols here show MEM analysis using datasets from immunology and oncology. These MEM implementations provide a way to characterize population identity and novelty in the context of computational and expert analyses. © 2018 by John Wiley & Sons, Inc.
Copyright © 2018 John Wiley & Sons, Inc.
2 Communities
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6 MeSH Terms
Benefits and Risks of Machine Learning Decision Support Systems.
Lasko TA, Walsh CG, Malin B
(2017) JAMA 318: 2355
MeSH Terms: Decision Support Systems, Clinical, Expert Systems, Humans, Machine Learning, Risk Assessment
Added March 14, 2018
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5 MeSH Terms
Assessing Working Memory in Mild Cognitive Impairment with Serial Order Recall.
Emrani S, Libon DJ, Lamar M, Price CC, Jefferson AL, Gifford KA, Hohman TJ, Nation DA, Delano-Wood L, Jak A, Bangen KJ, Bondi MW, Brickman AM, Manly J, Swenson R, Au R, Consortium for Clinical and Epidemiological Neuropsychological Data Analysis (CENDA)
(2018) J Alzheimers Dis 61: 917-928
MeSH Terms: Aged, Aged, 80 and over, Cognitive Dysfunction, Executive Function, Female, Humans, Male, Memory Disorders, Memory, Short-Term, Mental Recall, Neuropsychological Tests, Regression Analysis, Serial Learning
Show Abstract · Added March 16, 2018
BACKGROUND - Working memory (WM) is often assessed with serial order tests such as repeating digits backward. In prior dementia research using the Backward Digit Span Test (BDT), only aggregate test performance was examined.
OBJECTIVE - The current research tallied primacy/recency effects, out-of-sequence transposition errors, perseverations, and omissions to assess WM deficits in patients with mild cognitive impairment (MCI).
METHODS - Memory clinic patients (n = 66) were classified into three groups: single domain amnestic MCI (aMCI), combined mixed domain/dysexecutive MCI (mixed/dys MCI), and non-MCI where patients did not meet criteria for MCI. Serial order/WM ability was assessed by asking participants to repeat 7 trials of five digits backwards. Serial order position accuracy, transposition errors, perseverations, and omission errors were tallied.
RESULTS - A 3 (group)×5 (serial position) repeated measures ANOVA yielded a significant group×trial interaction. Follow-up analyses found attenuation of the recency effect for mixed/dys MCI patients. Mixed/dys MCI patients scored lower than non-MCI patients for serial position 3 (p < 0.003) serial position 4 (p < 0.002); and lower than both group for serial position 5 (recency; p < 0.002). Mixed/dys MCI patients also produced more transposition errors than both groups (p < 0.010); and more omissions (p < 0.020), and perseverations errors (p < 0.018) than non-MCI patients.
CONCLUSIONS - The attenuation of a recency effect using serial order parameters obtained from the BDT may provide a useful operational definition as well as additional diagnostic information regarding working memory deficits in MCI.
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13 MeSH Terms
Learning bundled care opportunities from electronic medical records.
Chen Y, Kho AN, Liebovitz D, Ivory C, Osmundson S, Bian J, Malin BA
(2018) J Biomed Inform 77: 1-10
MeSH Terms: Comorbidity, Data Mining, Delivery of Health Care, Electronic Health Records, Humans, Machine Learning, Medical Informatics, Patient Care Bundles, Patient Care Management, Phenotype, Workflow
Show Abstract · Added April 10, 2018
OBJECTIVE - The traditional fee-for-service approach to healthcare can lead to the management of a patient's conditions in a siloed manner, inducing various negative consequences. It has been recognized that a bundled approach to healthcare - one that manages a collection of health conditions together - may enable greater efficacy and cost savings. However, it is not always evident which sets of conditions should be managed in a bundled manner. In this study, we investigate if a data-driven approach can automatically learn potential bundles.
METHODS - We designed a framework to infer health condition collections (HCCs) based on the similarity of their clinical workflows, according to electronic medical record (EMR) utilization. We evaluated the framework with data from over 16,500 inpatient stays from Northwestern Memorial Hospital in Chicago, Illinois. The plausibility of the inferred HCCs for bundled care was assessed through an online survey of a panel of five experts, whose responses were analyzed via an analysis of variance (ANOVA) at a 95% confidence level. We further assessed the face validity of the HCCs using evidence in the published literature.
RESULTS - The framework inferred four HCCs, indicative of (1) fetal abnormalities, (2) late pregnancies, (3) prostate problems, and (4) chronic diseases, with congestive heart failure featuring prominently. Each HCC was substantiated with evidence in the literature and was deemed plausible for bundled care by the experts at a statistically significant level.
CONCLUSIONS - The findings suggest that an automated EMR data-driven framework conducted can provide a basis for discovering bundled care opportunities. Still, translating such findings into actual care management will require further refinement, implementation, and evaluation.
Copyright © 2017 Elsevier Inc. All rights reserved.
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11 MeSH Terms
A novel mechanism for Ca/calmodulin-dependent protein kinase II targeting to L-type Ca channels that initiates long-range signaling to the nucleus.
Wang X, Marks CR, Perfitt TL, Nakagawa T, Lee A, Jacobson DA, Colbran RJ
(2017) J Biol Chem 292: 17324-17336
MeSH Terms: Animals, Calcium Channels, Calcium-Calmodulin-Dependent Protein Kinase Type 2, Cell Nucleus, Female, Hippocampus, Learning, Memory, Neurons, Protein Domains, Rats, Rats, Sprague-Dawley, Signal Transduction
Show Abstract · Added November 13, 2017
Neuronal excitation can induce new mRNA transcription, a phenomenon called excitation-transcription (E-T) coupling. Among several pathways implicated in E-T coupling, activation of voltage-gated L-type Ca channels (LTCCs) in the plasma membrane can initiate a signaling pathway that ultimately increases nuclear CREB phosphorylation and, in most cases, expression of immediate early genes. Initiation of this long-range pathway has been shown to require recruitment of Ca-sensitive enzymes to a nanodomain in the immediate vicinity of the LTCC by an unknown mechanism. Here, we show that activated Ca/calmodulin-dependent protein kinase II (CaMKII) strongly interacts with a novel binding motif in the N-terminal domain of Ca1 LTCC α1 subunits that is not conserved in Ca2 or Ca3 voltage-gated Ca channel subunits. Mutations in the Ca1.3 α1 subunit N-terminal domain or in the CaMKII catalytic domain that largely prevent the interaction also disrupt CaMKII association with intact LTCC complexes isolated by immunoprecipitation. Furthermore, these same mutations interfere with E-T coupling in cultured hippocampal neurons. Taken together, our findings define a novel molecular interaction with the neuronal LTCC that is required for the initiation of a long-range signal to the nucleus that is critical for learning and memory.
© 2017 by The American Society for Biochemistry and Molecular Biology, Inc.
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13 MeSH Terms
A comparison of rule-based and machine learning approaches for classifying patient portal messages.
Cronin RM, Fabbri D, Denny JC, Rosenbloom ST, Jackson GP
(2017) Int J Med Inform 105: 110-120
MeSH Terms: Bayes Theorem, Communication, Electronic Health Records, Health Personnel, Health Services Needs and Demand, Humans, Machine Learning, Natural Language Processing, Patient Portals
Show Abstract · Added March 14, 2018
OBJECTIVE - Secure messaging through patient portals is an increasingly popular way that consumers interact with healthcare providers. The increasing burden of secure messaging can affect clinic staffing and workflows. Manual management of portal messages is costly and time consuming. Automated classification of portal messages could potentially expedite message triage and delivery of care.
MATERIALS AND METHODS - We developed automated patient portal message classifiers with rule-based and machine learning techniques using bag of words and natural language processing (NLP) approaches. To evaluate classifier performance, we used a gold standard of 3253 portal messages manually categorized using a taxonomy of communication types (i.e., main categories of informational, medical, logistical, social, and other communications, and subcategories including prescriptions, appointments, problems, tests, follow-up, contact information, and acknowledgement). We evaluated our classifiers' accuracies in identifying individual communication types within portal messages with area under the receiver-operator curve (AUC). Portal messages often contain more than one type of communication. To predict all communication types within single messages, we used the Jaccard Index. We extracted the variables of importance for the random forest classifiers.
RESULTS - The best performing approaches to classification for the major communication types were: logistic regression for medical communications (AUC: 0.899); basic (rule-based) for informational communications (AUC: 0.842); and random forests for social communications and logistical communications (AUCs: 0.875 and 0.925, respectively). The best performing classification approach of classifiers for individual communication subtypes was random forests for Logistical-Contact Information (AUC: 0.963). The Jaccard Indices by approach were: basic classifier, Jaccard Index: 0.674; Naïve Bayes, Jaccard Index: 0.799; random forests, Jaccard Index: 0.859; and logistic regression, Jaccard Index: 0.861. For medical communications, the most predictive variables were NLP concepts (e.g., Temporal_Concept, which maps to 'morning', 'evening' and Idea_or_Concept which maps to 'appointment' and 'refill'). For logistical communications, the most predictive variables contained similar numbers of NLP variables and words (e.g., Telephone mapping to 'phone', 'insurance'). For social and informational communications, the most predictive variables were words (e.g., social: 'thanks', 'much', informational: 'question', 'mean').
CONCLUSIONS - This study applies automated classification methods to the content of patient portal messages and evaluates the application of NLP techniques on consumer communications in patient portal messages. We demonstrated that random forest and logistic regression approaches accurately classified the content of portal messages, although the best approach to classification varied by communication type. Words were the most predictive variables for classification of most communication types, although NLP variables were most predictive for medical communication types. As adoption of patient portals increases, automated techniques could assist in understanding and managing growing volumes of messages. Further work is needed to improve classification performance to potentially support message triage and answering.
Copyright © 2017 Elsevier B.V. All rights reserved.
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9 MeSH Terms
An active learning-enabled annotation system for clinical named entity recognition.
Chen Y, Lask TA, Mei Q, Chen Q, Moon S, Wang J, Nguyen K, Dawodu T, Cohen T, Denny JC, Xu H
(2017) BMC Med Inform Decis Mak 17: 82
MeSH Terms: Computer Simulation, Humans, Medical Informatics, Natural Language Processing, Problem-Based Learning
Show Abstract · Added March 14, 2018
BACKGROUND - Active learning (AL) has shown the promising potential to minimize the annotation cost while maximizing the performance in building statistical natural language processing (NLP) models. However, very few studies have investigated AL in a real-life setting in medical domain.
METHODS - In this study, we developed the first AL-enabled annotation system for clinical named entity recognition (NER) with a novel AL algorithm. Besides the simulation study to evaluate the novel AL algorithm, we further conducted user studies with two nurses using this system to assess the performance of AL in real world annotation processes for building clinical NER models.
RESULTS - The simulation results show that the novel AL algorithm outperformed traditional AL algorithm and random sampling. However, the user study tells a different story that AL methods did not always perform better than random sampling for different users.
CONCLUSIONS - We found that the increased information content of actively selected sentences is strongly offset by the increased time required to annotate them. Moreover, the annotation time was not considered in the querying algorithms. Our future work includes developing better AL algorithms with the estimation of annotation time and evaluating the system with larger number of users.
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5 MeSH Terms