The early course and treatment of posttraumatic stress disorder in very young children: diagnostic prevalence and predictors in hospital-attending children and a randomized controlled proof-of-concept trial of trauma-focused cognitive therapy, for 3- to 8-year-olds.

BACKGROUND: The introduction of developmentally adapted criteria for posttraumatic stress disorder (PTSD) has improved the identification of ≤6-year-old children with clinical needs. Across two studies, we assess predictors of the development of PTSD in young children (PTSD-YC), including the adult-led acute stress disorder (ASD) diagnosis, and provide proof of principle for cognitive-focused therapy for this age range, with the aim of increasing treatment options for children diagnosed with PTSD-YC. METHOD: Study 1 (N = 105) assessed ASD and PTSD-YC diagnosis in 3- to 8-year-old children within one month and at around three months following attendance at an emergency room. Study 2 (N = 37) was a preregistered (www.isrctn.com/ISRCTN35018680) randomized controlled early-phase trial comparing CBT-3M, a cognitive-focused intervention, to treatment-as-usual (TAU) delivered within the UK NHS to 3- to 8-year-olds diagnosed with PTSD-YC. RESULTS: In Study 1, the ASD diagnosis failed to identify any young children. In contrast, prevalence of acute PTSD-YC (minus the duration requirement) was 8.6% in the first month post-trauma and 10.1% at 3 months. Length of hospital stay, but no other demographic or trauma-related characteristics, predicted development of later PTSD-YC. Early (within one month) diagnosis of acute PTSD-YC had a positive predictive value of 50% for later PTSD-YC. In Study 2, most children lost their PTSD-YC diagnosis following completion of CBT-3M (84.6%) relative to TAU (6.7%) and CBT-3M was acceptable to recipient families. Effect sizes were also in favor of CBT-3M for secondary outcome measures. CONCLUSIONS: The ASD diagnosis is not fit for purpose in this age-group. There was a strong and encouraging signal of putative efficacy for young children treated using a cognitive-focused treatment for PTSD, and a larger trial of CBT-3M is now warranted

The early course and treatment of posttraumatic stress disorder in very young children: diagnostic prevalence and predictors in hospital‐attending children and a randomized controlled proof‐of‐concept trial of trauma‐focused cognitive therapy, for 3‐ to 8‐year‐olds

Background: The introduction of developmentally adapted criteria for posttraumatic stress disorder (PTSD) has improved the identification of ≤6‐year‐old children with clinical needs. Across two studies, we assess predictors of the development of PTSD in young children (PTSD‐YC), including the adult‐led acute stress disorder (ASD) diagnosis, and provide proof of principle for cognitive‐focused therapy for this age range, with the aim of increasing treatment options for children diagnosed with PTSD‐YC. Method: Study 1 (N = 105) assessed ASD and PTSD‐YC diagnosis in 3‐ to 8‐year‐old children within one month and at around three months following attendance at an emergency room. Study 2 (N = 37) was a preregistered (www.isrctn.com/ISRCTN35018680) randomized controlled early‐phase trial comparing CBT‐3M, a cognitive‐focused intervention, to treatment‐as‐usual (TAU) delivered within the UK NHS to 3‐ to 8‐year‐olds diagnosed with PTSD‐YC. Results: In Study 1, the ASD diagnosis failed to identify any young children. In contrast, prevalence of acute PTSD‐YC (minus the duration requirement) was 8.6% in the first month post‐trauma and 10.1% at 3 months. Length of hospital stay, but no other demographic or trauma‐related characteristics, predicted development of later PTSD‐YC. Early (within one month) diagnosis of acute PTSD‐YC had a positive predictive value of 50% for later PTSD‐YC. In Study 2, most children lost their PTSD‐YC diagnosis following completion of CBT‐3M (84.6%) relative to TAU (6.7%) and CBT‐3M was acceptable to recipient families. Effect sizes were also in favor of CBT‐3M for secondary outcome measures. Conclusions: The ASD diagnosis is not fit for purpose in this age‐group. There was a strong and encouraging signal of putative efficacy for young children treated using a cognitive‐focused treatment for PTSD, and a larger trial of CBT‐3M is now warranted

A Library of Phosphoproteomic and Chromatin Signatures for Characterizing Cellular Responses to Drug Perturbations

Although the value of proteomics has been demonstrated, cost and scale are typically prohibitive, and gene expression profiling remains dominant for characterizing cellular responses to perturbations. However, high-throughput sentinel assays provide an opportunity for proteomics to contribute at a meaningful scale. We present a systematic library resource (90 drugs × 6 cell lines) of proteomic signatures that measure changes in the reduced-representation phosphoproteome (P100) and changes in epigenetic marks on histones (GCP). A majority of these drugs elicited reproducible signatures, but notable cell line- and assay-specific differences were observed. Using the “connectivity” framework, we compared signatures across cell types and integrated data across assays, including a transcriptional assay (L1000). Consistent connectivity among cell types revealed cellular responses that transcended lineage, and consistent connectivity among assays revealed unexpected associations between drugs. We further leveraged the resource against public data to formulate hypotheses for treatment of multiple myeloma and acute lymphocytic leukemia. This resource is publicly available at https://clue.io/proteomics. A large compendium of cellular responses to drugs as profiled through proteomic assays of phosphosignaling and histone modifications reveals cellular responses that transcend lineage, discovers unexpected associations between drugs, and recognizes therapeutic hypotheses for treatment of multiple myeloma and acute lymphocytic leukemia. Keywords: mass spectrometry; proteomics; drug discovery; signaling; epigenetics; mechanism of action; LINCS project; GCP; P100; L1000NIH Common Fund's Library of Integrated Network-based Cellular Signatures (LINCS) program (Grant U54HG008097)NIH Common Fund's Library of Integrated Network-based Cellular Signatures (LINCS) program (Grant U54HG008699

Next-generation characterization of the Cancer Cell Line Encyclopedia

Large panels of comprehensively characterized human cancer models, including the Cancer Cell Line Encyclopedia (CCLE), have provided a rigorous framework with which to study genetic variants, candidate targets, and small-molecule and biological therapeutics and to identify new marker-driven cancer dependencies. To improve our understanding of the molecular features that contribute to cancer phenotypes, including drug responses, here we have expanded the characterizations of cancer cell lines to include genetic, RNA splicing, DNA methylation, histone H3 modification, microRNA expression and reverse-phase protein array data for 1,072 cell lines from individuals of various lineages and ethnicities. Integration of these data with functional characterizations such as drug-sensitivity, short hairpin RNA knockdown and CRISPR-Cas9 knockout data reveals potential targets for cancer drugs and associated biomarkers. Together, this dataset and an accompanying public data portal provide a resource for the acceleration of cancer research using model cancer cell lines

Same data, different analysts: variation in effect sizes due to analytical decisions in ecology and evolutionary biology

Gould E, Fraser H, Parker T, et al. Same data, different analysts: variation in effect sizes due to analytical decisions in ecology and evolutionary biology. 2023.Although variation in effect sizes and predicted values among studies of similar phenomena is inevitable, such variation far exceeds what might be produced by sampling error alone. One possible explanation for variation among results is differences among researchers in the decisions they make regarding statistical analyses. A growing array of studies has explored this analytical variability in different (mostly social science) fields, and has found substantial variability among results, despite analysts having the same data and research question. We implemented an analogous study in ecology and evolutionary biology, fields in which there have been no empirical exploration of the variation in effect sizes or model predictions generated by the analytical decisions of different researchers. We used two unpublished datasets, one from evolutionary ecology (blue tit, Cyanistes caeruleus, to compare sibling number and nestling growth) and one from conservation ecology (Eucalyptus, to compare grass cover and tree seedling recruitment), and the project leaders recruited 174 analyst teams, comprising 246 analysts, to investigate the answers to prespecified research questions. Analyses conducted by these teams yielded 141 usable effects for the blue tit dataset, and 85 usable effects for the Eucalyptus dataset. We found substantial heterogeneity among results for both datasets, although the patterns of variation differed between them. For the blue tit analyses, the average effect was convincingly negative, with less growth for nestlings living with more siblings, but there was near continuous variation in effect size from large negative effects to effects near zero, and even effects crossing the traditional threshold of statistical significance in the opposite direction. In contrast, the average relationship between grass cover and Eucalyptus seedling number was only slightly negative and not convincingly different from zero, and most effects ranged from weakly negative to weakly positive, with about a third of effects crossing the traditional threshold of significance in one direction or the other. However, there were also several striking outliers in the Eucalyptus dataset, with effects far from zero. For both datasets, we found substantial variation in the variable selection and random effects structures among analyses, as well as in the ratings of the analytical methods by peer reviewers, but we found no strong relationship between any of these and deviation from the meta-analytic mean. In other words, analyses with results that were far from the mean were no more or less likely to have dissimilar variable sets, use random effects in their models, or receive poor peer reviews than those analyses that found results that were close to the mean. The existence of substantial variability among analysis outcomes raises important questions about how ecologists and evolutionary biologists should interpret published results, and how they should conduct analyses in the future