Preventing posttraumatic stress disorder following childbirth: a systematic review and meta-analysis.

OBJECTIVE: Women can develop posttraumatic stress disorder in response to experienced or perceived traumatic, often medically complicated, childbirth; the prevalence of these events remains high in the United States. Currently, no recommended treatment exists in routine care to prevent or mitigate maternal childbirth-related posttraumatic stress disorder. We conducted a systematic review and meta-analysis of clinical trials that evaluated any therapy to prevent or treat childbirth-related posttraumatic stress disorder. DATA SOURCES: PsycInfo, PsycArticles, PubMed (MEDLINE), ClinicalTrials.gov, CINAHL, ProQuest, Sociological Abstracts, Google Scholar, Embase, Web of Science, ScienceDirect, Scopus, and the Cochrane Central Register of Controlled Trials (CENTRAL) were searched for eligible trials published through September 2023. STUDY ELIGIBILITY CRITERIA: Trials were included if they were interventional, if they evaluated any therapy for childbirth-related posttraumatic stress disorder for the indication of symptoms or before posttraumatic stress disorder onset, and if they were written in English. METHODS: Independent coders extracted the sample characteristics and intervention information of the eligible studies and evaluated the trials using the Downs and Black's quality checklist and Cochrane's method for risk of bias evaluation. Meta-analysis was conducted to evaluate pooled effect sizes of secondary and tertiary prevention trials. RESULTS: A total of 41 studies (32 randomized controlled trials, 9 nonrandomized trials) were reviewed. They evaluated brief psychological therapies including debriefing, trauma-focused therapies (including cognitive behavioral therapy and expressive writing), memory consolidation and reconsolidation blockage, mother-infant-focused therapies, and educational interventions. The trials targeted secondary preventions aimed at buffering childbirth-related posttraumatic stress disorder usually after traumatic childbirth (n=24), tertiary preventions among women with probable childbirth-related posttraumatic stress disorder (n=14), and primary prevention during pregnancy (n=3). A meta-analysis of the combined randomized secondary preventions showed moderate effects in reducing childbirth-related posttraumatic stress disorder symptoms when compared with usual treatment (standardized mean difference, -0.67; 95% confidence interval, -0.92 to -0.42). Single-session therapy within 96 hours of birth was helpful (standardized mean difference, -0.55). Brief, structured, trauma-focused therapies and semi-structured, midwife-led, dialogue-based psychological counseling showed the largest effects (standardized mean difference, -0.95 and -0.91, respectively). Other treatment approaches (eg, the Tetris game, mindfulness, mother-infant-focused treatment) warrant more research. Tertiary preventions produced smaller effects than secondary prevention but are potentially clinically meaningful (standardized mean difference, -0.37; -0.60 to -0.14). Antepartum educational approaches may help, but insufficient empirical evidence exists. CONCLUSION: Brief trauma-focused and non-trauma-focused psychological therapies delivered early in the period following traumatic childbirth offer a critical and feasible opportunity to buffer the symptoms of childbirth-related posttraumatic stress disorder. Future research that integrates diagnostic and biological measures can inform treatment use and the mechanisms at work.

SigCom LINCS: data and metadata search engine for a million gene expression signatures.

Millions of transcriptome samples were generated by the Library of Integrated Network-based Cellular Signatures (LINCS) program. When these data are processed into searchable signatures along with signatures extracted from Genotype-Tissue Expression (GTEx) and Gene Expression Omnibus (GEO), connections between drugs, genes, pathways and diseases can be illuminated. SigCom LINCS is a webserver that serves over a million gene expression signatures processed, analyzed, and visualized from LINCS, GTEx, and GEO. SigCom LINCS is built with Signature Commons, a cloud-agnostic skeleton Data Commons with a focus on serving searchable signatures. SigCom LINCS provides a rapid signature similarity search for mimickers and reversers given sets of up and down genes, a gene set, a single gene, or any search term. Additionally, users of SigCom LINCS can perform a metadata search to find and analyze subsets of signatures and find information about genes and drugs. SigCom LINCS is findable, accessible, interoperable, and reusable (FAIR) with metadata linked to standard ontologies and vocabularies. In addition, all the data and signatures within SigCom LINCS are available via a well-documented API. In summary, SigCom LINCS, available at https://maayanlab.cloud/sigcom-lincs, is a rich webserver resource for accelerating drug and target discovery in systems pharmacology.

Establishing the validity of a diagnostic questionnaire for childbirth-related posttraumatic stress disorder.

BACKGROUND: Labor and delivery can entail complications and severe maternal morbidities that threaten a woman's life or cause her to believe that her life is in danger. Women with these experiences are at risk for developing posttraumatic stress disorder. Postpartum posttraumatic stress disorder, or childbirth-related posttraumatic stress disorder, can become an enduring and debilitating condition. At present, validated tools for a rapid and efficient screen for childbirth-related posttraumatic stress disorder are lacking. OBJECTIVE: We examined the diagnostic validity of the Posttraumatic Stress Disorder Checklist for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, for detecting posttraumatic stress disorder among women who have had a traumatic childbirth. This Checklist assesses the 20 Diagnostic and Statistical Manual of Mental Disorders, posttraumatic stress disorder symptoms and is a commonly used patient-administrated screening instrument. Its diagnostic accuracy for detecting childbirth-related posttraumatic stress disorder is unknown. STUDY DESIGN: The sample included 59 patients who reported a traumatic childbirth experience determined in accordance with the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, posttraumatic stress disorder criterion A for exposure involving a threat or potential threat to the life of the mother or infant, experienced or perceived, or physical injury. The majority (66%) of the participants were less than 1 year postpartum (for full sample: median, 4.67 months; mean, 1.5 years) and were recruited via the Mass General Brigham's online platform, during the postpartum unit hospitalization or after discharge. Patients were instructed to complete the Posttraumatic Stress Disorder Checklist for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, concerning posttraumatic stress disorder symptoms related to childbirth. Other comorbid conditions (ie, depression and anxiety) were also assessed. They also underwent a clinician interview for posttraumatic stress disorder using the gold-standard Clinician-Administered PTSD Scale for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. A second administration of the checklist was performed in a subgroup (n=43), altogether allowing an assessment of internal consistency, test-retest reliability, and convergent and diagnostic validity of the Checklist. The diagnostic accuracy of the Posttraumatic Stress Disorder Checklist for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, in reference to the Clinician-Administered PTSD Scale for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, was determined using the area under the receiver operating characteristic curve; an optimal cutoff score was identified using the Youden's J index. RESULTS: One-third of the sample (35.59%) met the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, criteria for a posttraumatic stress disorder diagnosis stemming from childbirth. The Posttraumatic Stress Disorder Checklist for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, symptom severity score was strongly correlated with the Clinician-Administered PTSD Scale for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, total score (ρ=0.82; P<.001). The area under the receiver operating characteristic curve was 0.93 (95% confidence interval, 0.87-0.99), indicating excellent diagnostic performance of the Posttraumatic Stress Disorder Checklist for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. A cutoff value of 28 maximized the sensitivity (0.81) and specificity (0.90) and correctly diagnosed 86% of women. A higher value (32) identified individuals with more severe posttraumatic stress disorder symptoms (specificity, 0.95), but with lower sensitivity (0.62). Checklist scores were also stable over time (intraclass correlation coefficient, 0.73), indicating good test-retest reliability. Posttraumatic Stress Disorder Checklist for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, scores were moderately correlated with the depression and anxiety symptom scores (Edinburgh Postnatal Depression Scale: ρ=0.58; P<.001 and the Brief Symptom Inventory, anxiety subscale: ρ=0.51; P<.001). CONCLUSION: This study demonstrates the validity of the Posttraumatic Stress Disorder Checklist for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, as a screening tool for posttraumatic stress disorder among women who had a traumatic childbirth experience. The instrument may facilitate screening for childbirth-related posttraumatic stress disorder on a large scale and help identify women who might benefit from further diagnostics and services. Replication of the findings in larger, postpartum samples is needed.

DrugShot: querying biomedical search terms to retrieve prioritized lists of small molecules.

BACKGROUND: PubMed contains millions of abstracts that co-mention terms that describe drugs with other biomedical terms such as genes or diseases. Unique opportunities exist for leveraging these co-mentions by integrating them with other drug-drug similarity resources such as the Library of Integrated Network-based Cellular Signatures (LINCS) L1000 signatures to develop novel hypotheses. RESULTS: DrugShot is a web-based server application and an Appyter that enables users to enter any biomedical search term into a simple input form to receive ranked lists of drugs and other small molecules based on their relevance to the search term. To produce ranked lists of small molecules, DrugShot cross-references returned PubMed identifiers (PMIDs) with DrugRIF or AutoRIF, which are curated resources of drug-PMID associations, to produce an associated small molecule list where each small molecule is ranked according to total co-mentions with the search term from shared PubMed IDs. Additionally, using two types of drug-drug similarity matrices, lists of small molecules are predicted to be associated with the search term. Such predictions are based on literature co-mentions and signature similarity from LINCS L1000 drug-induced gene expression profiles. CONCLUSIONS: DrugShot prioritizes drugs and small molecules associated with biomedical search terms. In addition to listing known associations, DrugShot predicts additional drugs and small molecules related to any search term. Hence, DrugShot can be used to prioritize drugs and preclinical compounds for drug repurposing and suggest indications and adverse events for preclinical compounds. DrugShot is freely and openly available at: https://maayanlab.cloud/drugshot and https://appyters.maayanlab.cloud/#/DrugShot .

Progress in and Opportunities for Applying Information Theory to Computational Biology and Bioinformatics.

This editorial is intended to provide a brief history of the application of Information Theory to the fields of Computational Biology and Bioinformatics; to succinctly summarize the current state of associated research, and open challenges; and to describe the scope of the invited content for this Special Issue of the journal Entropy with the theme of "Information Theory in Computational Biology" [...].

Prioritizing Pain-Associated Targets with Machine Learning.

While hundreds of genes have been associated with pain, much of the molecular mechanisms of pain remain unknown. As a result, current analgesics are limited to few clinically validated targets. Here, we trained a machine learning (ML) ensemble model to predict new targets for 17 categories of pain. The model utilizes features from transcriptomics, proteomics, and gene ontology to prioritize targets for modulating pain. We focused on identifying novel G-protein-coupled receptors (GPCRs), ion channels, and protein kinases because these proteins represent the most successful drug target families. The performance of the model to predict novel pain targets is 0.839 on average based on AUROC, while the predictions for arthritis had the highest accuracy (AUROC = 0.929). The model predicts hundreds of novel targets for pain; for example, GPR132 and GPR109B are highly ranked GPCRs for rheumatoid arthritis. Overall, gene-pain association predictions cluster into three groups that are enriched for cytokine, calcium, and GABA-related cell signaling pathways. These predictions can serve as a foundation for future experimental exploration to advance the development of safer and more effective analgesics.

EnrichrBot: Twitter bot tracking tweets about human genes.

MOTIVATION: Micro-blogging with Twitter to communicate new results, discuss ideas and share techniques is becoming central. While most Twitter users are real people, the Twitter API provides the opportunity to develop Twitter bots and to analyze global trends in tweets. RESULTS: EnrichrBot is a bot that tracks and tweets information about human genes implementing six principal functions: (i) tweeting information about under-studied genes including non-coding lncRNAs, (ii) replying to requests for information about genes, (iii) responding to GWASbot, another bot that tweets Manhattan plots from genome-wide association study analysis of the UK Biobank, (iv) tweeting randomly selected gene sets from the Enrichr database for analysis with Enrichr, (v) responding to mentions of human genes in tweets with additional information about these genes and (vi) tweeting a weekly report about the most trending genes on Twitter. AVAILABILITY AND IMPLEMENTATION: https://twitter.com/botenrichr; source code: https://github.com/MaayanLab/EnrichrBot. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

ChEA3: transcription factor enrichment analysis by orthogonal omics integration.

Identifying the transcription factors (TFs) responsible for observed changes in gene expression is an important step in understanding gene regulatory networks. ChIP-X Enrichment Analysis 3 (ChEA3) is a transcription factor enrichment analysis tool that ranks TFs associated with user-submitted gene sets. The ChEA3 background database contains a collection of gene set libraries generated from multiple sources including TF-gene co-expression from RNA-seq studies, TF-target associations from ChIP-seq experiments, and TF-gene co-occurrence computed from crowd-submitted gene lists. Enrichment results from these distinct sources are integrated to generate a composite rank that improves the prediction of the correct upstream TF compared to ranks produced by individual libraries. We compare ChEA3 with existing TF prediction tools and show that ChEA3 performs better. By integrating the ChEA3 libraries, we illuminate general transcription factor properties such as whether the TF behaves as an activator or a repressor. The ChEA3 web-server is available from https://amp.pharm.mssm.edu/ChEA3.

Role-Aware Information Spread in Online Social Networks.

Understanding the complex process of information spread in online social networks (OSNs) enables the efficient maximization/minimization of the spread of useful/harmful information. Users assume various roles based on their behaviors while engaging with information in these OSNs. Recent reviews on information spread in OSNs have focused on algorithms and challenges for modeling the local node-to-node cascading paths of viral information. However, they neglected to analyze non-viral information with low reach size that can also spread globally beyond OSN edges (links) via non-neighbors through, for example, pushed information via content recommendation algorithms. Previous reviews have also not fully considered user roles in the spread of information. To address these gaps, we: (i) provide a comprehensive survey of the latest studies on role-aware information spread in OSNs, also addressing the different temporal spreading patterns of viral and non-viral information; (ii) survey modeling approaches that consider structural, non-structural, and hybrid features, and provide a taxonomy of these approaches; (iii) review software platforms for the analysis and visualization of role-aware information spread in OSNs; and (iv) describe how information spread models enable useful applications in OSNs such as detecting influential users. We conclude by highlighting future research directions for studying information spread in OSNs, accounting for dynamic user roles.

Drug Gene Budger (DGB): an application for ranking drugs to modulate a specific gene based on transcriptomic signatures.

SUMMARY: Mechanistic molecular studies in biomedical research often discover important genes that are aberrantly over- or under-expressed in disease. However, manipulating these genes in an attempt to improve the disease state is challenging. Herein, we reveal Drug Gene Budger (DGB), a web-based and mobile application developed to assist investigators in order to prioritize small molecules that are predicted to maximally influence the expression of their target gene of interest. With DGB, users can enter a gene symbol along with the wish to up-regulate or down-regulate its expression. The output of the application is a ranked list of small molecules that have been experimentally determined to produce the desired expression effect. The table includes log-transformed fold change, P-value and q-value for each small molecule, reporting the significance of differential expression as determined by the limma method. Relevant links are provided to further explore knowledge about the target gene, the small molecule and the source of evidence from which the relationship between the small molecule and the target gene was derived. The experimental data contained within DGB is compiled from signatures extracted from the LINCS L1000 dataset, the original Connectivity Map (CMap) dataset and the Gene Expression Omnibus (GEO). DGB also presents a specificity measure for a drug-gene connection based on the number of genes a drug modulates. DGB provides a useful preliminary technique for identifying small molecules that can target the expression of a single gene in human cells and tissues. AVAILABILITY AND IMPLEMENTATION: The application is freely available on the web at http://DGB.cloud and as a mobile phone application on iTunes https://itunes.apple.com/us/app/drug-gene-budger/id1243580241? mt=8 and Google Play https://play.google.com/store/apps/details? id=com.drgenebudger. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Data Portal for the Library of Integrated Network-based Cellular Signatures (LINCS) program: integrated access to diverse large-scale cellular perturbation response data.

The Library of Integrated Network-based Cellular Signatures (LINCS) program is a national consortium funded by the NIH to generate a diverse and extensive reference library of cell-based perturbation-response signatures, along with novel data analytics tools to improve our understanding of human diseases at the systems level. In contrast to other large-scale data generation efforts, LINCS Data and Signature Generation Centers (DSGCs) employ a wide range of assay technologies cataloging diverse cellular responses. Integration of, and unified access to LINCS data has therefore been particularly challenging. The Big Data to Knowledge (BD2K) LINCS Data Coordination and Integration Center (DCIC) has developed data standards specifications, data processing pipelines, and a suite of end-user software tools to integrate and annotate LINCS-generated data, to make LINCS signatures searchable and usable for different types of users. Here, we describe the LINCS Data Portal (LDP) (http://lincsportal.ccs.miami.edu/), a unified web interface to access datasets generated by the LINCS DSGCs, and its underlying database, LINCS Data Registry (LDR). LINCS data served on the LDP contains extensive metadata and curated annotations. We highlight the features of the LDP user interface that is designed to enable search, browsing, exploration, download and analysis of LINCS data and related curated content.

Enrichr: a comprehensive gene set enrichment analysis web server 2016 update.

Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr.

Developing a framework for digital objects in the Big Data to Knowledge (BD2K) commons: Report from the Commons Framework Pilots workshop.

The volume and diversity of data in biomedical research have been rapidly increasing in recent years. While such data hold significant promise for accelerating discovery, their use entails many challenges including: the need for adequate computational infrastructure, secure processes for data sharing and access, tools that allow researchers to find and integrate diverse datasets, and standardized methods of analysis. These are just some elements of a complex ecosystem that needs to be built to support the rapid accumulation of these data. The NIH Big Data to Knowledge (BD2K) initiative aims to facilitate digitally enabled biomedical research. Within the BD2K framework, the Commons initiative is intended to establish a virtual environment that will facilitate the use, interoperability, and discoverability of shared digital objects used for research. The BD2K Commons Framework Pilots Working Group (CFPWG) was established to clarify goals and work on pilot projects that address existing gaps toward realizing the vision of the BD2K Commons. This report reviews highlights from a two-day meeting involving the BD2K CFPWG to provide insights on trends and considerations in advancing Big Data science for biomedical research in the United States.

GEN3VA: aggregation and analysis of gene expression signatures from related studies.

BACKGROUND: Genome-wide gene expression profiling of mammalian cells is becoming a staple of many published biomedical and biological research studies. Such data is deposited into data repositories such as the Gene Expression Omnibus (GEO) for potential reuse. However, these repositories currently do not provide simple interfaces to systematically analyze collections of related studies. RESULTS: Here we present GENE Expression and Enrichment Vector Analyzer (GEN3VA), a web-based system that enables the integrative analysis of aggregated collections of tagged gene expression signatures identified and extracted from GEO. Each tagged collection of signatures is presented in a report that consists of heatmaps of the differentially expressed genes; principal component analysis of all signatures; enrichment analysis with several gene set libraries across all signatures, which we term enrichment vector analysis; and global mapping of small molecules that are predicted to reverse or mimic each signature in the aggregate. We demonstrate how GEN3VA can be used to identify common molecular mechanisms of aging by analyzing tagged signatures from 244 studies that compared young vs. old tissues in mammalian systems. In a second case study, we collected 86 signatures from treatment of human cells with dexamethasone, a glucocorticoid receptor (GR) agonist. Our analysis confirms consensus GR target genes and predicts potential drug mimickers. CONCLUSIONS: GEN3VA can be used to identify, aggregate, and analyze themed collections of gene expression signatures from diverse but related studies. Such integrative analyses can be used to address concerns about data reproducibility, confirm results across labs, and discover new collective knowledge by data reuse. GEN3VA is an open-source web-based system that is freely available at: http://amp.pharm.mssm.edu/gen3va .

Persistence of collective memory of corporate bankruptcy events discussed on X (Twitter) is influenced by pre-bankruptcy public attention.

Collective attention and memory involving significant events can be quantitatively studied via social media data. Previous studies analyzed user attention to discrete events that do not change post-event, and assume universal public attention patterns. However, dynamic events with ongoing updates are common, yielding varied individual attention patterns. We explore memory of U.S. companies filing Chapter 11 bankruptcy and being mentioned on X (formerly Twitter). Unlike discrete events, Chapter 11 entails ongoing financial changes as the company typically remains operational, influencing post-event attention dynamics. We collected 248,936 X mentions for 74 companies before and after each bankruptcy. Attention surged after bankruptcy, with distinct Low and High persistence levels compared with pre-bankruptcy attention. The two tweeting patterns were modeled using biexponential models, successfully predicting (F1-score: 0.81) post-bankruptcy attention persistence. Studying bankruptcy events on social media reveals diverse attention patterns, demonstrates how pre-bankruptcy attention affects post-bankruptcy recollection, and provides insights into memory of dynamic events.

OpenAI's Narrative Embeddings Can Be Used for Detecting Post-Traumatic Stress Following Childbirth Via Birth Stories.

Free-text analysis using Machine Learning (ML)-based Natural Language Processing (NLP) shows promise for diagnosing psychiatric conditions. Chat Generative Pre-trained Transformer (ChatGPT) has demonstrated preliminary initial feasibility for this purpose; however, whether it can accurately assess mental illness remains to be determined. This study evaluates the effectiveness of ChatGPT and the text-embedding-ada-002 (ADA) model in detecting post-traumatic stress disorder following childbirth (CB-PTSD), a maternal postpartum mental illness affecting millions of women annually, with no standard screening protocol. Using a sample of 1,295 women who gave birth in the last six months and were 18+ years old, recruited through hospital announcements, social media, and professional organizations, we explore ChatGPT's and ADA's potential to screen for CB-PTSD by analyzing maternal childbirth narratives. The PTSD Checklist for DSM-5 (PCL-5; cutoff 31) was used to assess CB-PTSD. By developing an ML model that utilizes numerical vector representation of the ADA model, we identify CB-PTSD via narrative classification. Our model outperformed (F1 score: 0.82) ChatGPT and six previously published large language models (LLMs) trained on mental health or clinical domains data, suggesting that the ADA model can be harnessed to identify CB-PTSD. Our modeling approach could be generalized to assess other mental health disorders.

AI and narrative embeddings detect PTSD following childbirth via birth stories.

Free-text analysis using machine learning (ML)-based natural language processing (NLP) shows promise for diagnosing psychiatric conditions. Chat Generative Pre-trained Transformer (ChatGPT) has demonstrated preliminary initial feasibility for this purpose; however, whether it can accurately assess mental illness remains to be determined. This study evaluates the effectiveness of ChatGPT and the text-embedding-ada-002 (ADA) model in detecting post-traumatic stress disorder following childbirth (CB-PTSD), a maternal postpartum mental illness affecting millions of women annually, with no standard screening protocol. Using a sample of 1295 women who gave birth in the last six months and were 18+ years old, recruited through hospital announcements, social media, and professional organizations, we explore ChatGPT's and ADA's potential to screen for CB-PTSD by analyzing maternal childbirth narratives. The PTSD Checklist for DSM-5 (PCL-5; cutoff 31) was used to assess CB-PTSD. By developing an ML model that utilizes numerical vector representation of the ADA model, we identify CB-PTSD via narrative classification. Our model outperformed (F1 score: 0.81) ChatGPT and six previously published large text-embedding models trained on mental health or clinical domains data, suggesting that the ADA model can be harnessed to identify CB-PTSD. Our modeling approach could be generalized to assess other mental health disorders.

Screening for post-traumatic stress disorder following childbirth using the Peritraumatic Distress Inventory.

BACKGROUND: Post-traumatic stress disorder (PTSD) following traumatic childbirth may undermine maternal and infant health, but screening for maternal childbirth-related PTSD (CB-PTSD) remains lacking. Acute emotional distress in response to a traumatic experience strongly associates with PTSD. The Peritraumatic Distress Inventory (PDI) assesses acute distress in non-postpartum individuals, but its use to classify women likely to endorse CB-PTSD is unknown. METHODS: 3039 women provided information about their mental health and childbirth experience. They completed the PDI regarding their recent childbirth event, and a PTSD symptom screen to determine CB-PTSD. We employed Exploratory Graph Analysis and bootstrapping to reveal the PDI's factorial structure and optimal cutoff value for CB-PTSD classification. RESULTS: Factor analysis revealed two strongly correlated stable factors based on a modified version of the PDI: (1) negative emotions and (2) bodily arousal and threat appraisal. A score of 15+ on the modified PDI produced high sensitivity and specificity: 88 % with a positive CB-PTSD screen in the first postpartum months and 93 % with a negative screen. LIMITATIONS: In this cross-sectional study, the PDI was administered at different timepoints postpartum. Future work should examine the PDI's predictive utility for screening women as closely as possible to the time of childbirth, and establish clinical cutoffs in populations after complicated deliveries. CONCLUSIONS: Brief self-report screening concerning a woman's emotional reactions to childbirth using our modified PDI tool can detect those likely to endorse CB-PTSD in the early postpartum. This may serve as the initial step of managing symptoms to ultimately prevent chronic manifestations.

The Psychedelic Future of Post-Traumatic Stress Disorder Treatment.

Post-traumatic stress disorder (PTSD) is a mental health condition that can occur following exposure to a traumatic experience. An estimated 12 million U.S. adults are presently affected by this disorder. Current treatments include psychological therapies (e.g., exposure-based interventions) and pharmacological treatments (e.g., selective serotonin reuptake inhibitors (SSRIs)). However, a significant proportion of patients receiving standard-of-care therapies for PTSD remain symptomatic, and new approaches for this and other trauma-related mental health conditions are greatly needed. Psychedelic compounds that alter cognition, perception, and mood are currently being examined for their efficacy in treating PTSD despite their current status as Drug Enforcement Administration (DEA)- scheduled substances. Initial clinical trials have demonstrated the potential value of psychedelicassisted therapy to treat PTSD and other psychiatric disorders. In this comprehensive review, we summarize the state of the science of PTSD clinical care, including current treatments and their shortcomings. We review clinical studies of psychedelic interventions to treat PTSD, trauma-related disorders, and common comorbidities. The classic psychedelics psilocybin, lysergic acid diethylamide (LSD), and N,N-dimethyltryptamine (DMT) and DMT-containing ayahuasca, as well as the entactogen 3,4-methylenedioxymethamphetamine (MDMA) and the dissociative anesthetic ketamine, are reviewed. For each drug, we present the history of use, psychological and somatic effects, pharmacology, and safety profile. The rationale and proposed mechanisms for use in treating PTSD and traumarelated disorders are discussed. This review concludes with an in-depth consideration of future directions for the psychiatric applications of psychedelics to maximize therapeutic benefit and minimize risk in individuals and communities impacted by trauma-related conditions.

HetIG-PreDiG: A Heterogeneous Integrated Graph Model for Predicting Human Disease Genes based on gene expression.

Graph analytical approaches permit identifying novel genes involved in complex diseases, but are limited by (i) inferring structural network similarity of connected gene nodes, ignoring potentially relevant unconnected nodes; (ii) using homogeneous graphs, missing gene-disease associations' complexity; (iii) relying on disease/gene-phenotype associations' similarities, involving highly incomplete data; (iv) using binary classification, with gene-disease edges as positive training samples, and non-associated gene and disease nodes as negative samples that may include currently unknown disease genes; or (v) reporting predicted novel associations without systematically evaluating their accuracy. Addressing these limitations, we develop the Heterogeneous Integrated Graph for Predicting Disease Genes (HetIG-PreDiG) model that includes gene-gene, gene-disease, and gene-tissue associations. We predict novel disease genes using low-dimensional representation of nodes accounting for network structure, and extending beyond network structure using the developed Gene-Disease Prioritization Score (GDPS) reflecting the degree of gene-disease association via gene co-expression data. For negative training samples, we select non-associated gene and disease nodes with lower GDPS that are less likely to be affiliated. We evaluate the developed model's success in predicting novel disease genes by analyzing the prediction probabilities of gene-disease associations. HetIG-PreDiG successfully predicts (Micro-F1 = 0.95) gene-disease associations, outperforming baseline models, and is validated using published literature, thus advancing our understanding of complex genetic diseases.