The effectiveness of the behavioural components of cognitive behavioural therapy for insomnia in older adults:A systematic review

Insomnia is more prevalent in older adults (< 60 years) than in the general population. Cognitive behavioural therapy for insomnia is the gold-standard treatment; however, it may prove too cognitively taxing for some. This systematic review aimed to critically examine the literature exploring the effectiveness of explicitly behavioural interventions for insomnia in older adults, with secondary aims of investigating their effect on mood and daytime functioning. Four electronic databases (MEDLINE – Ovid, Embase – Ovid, CINAHL, and PsycINFO) were searched. All experimental, quasi-experimental and pre-experimental studies were included, provided they: (a) were published in English; (b) recruited older adults with insomnia; (c) used sleep restriction and/or stimulus control; (d) reported outcomes pre-and-post intervention. Database searches returned 1689 articles; 15 studies, summarising the results of 498 older adults, were included – three focused on stimulus control, four on sleep restriction, and eight adopted multicomponent treatments comprised of both interventions. All interventions brought about significant improvements in one or more subjectively measured facets of sleep although, overall, multicomponent therapies demonstrated larger effects (median Hedge's g = 0.55). Actigraphic or polysomnographic outcomes demonstrated smaller or no effects. Improvements in measures of depression were seen in multicomponent interventions, but no intervention demonstrated any statistically significant improvement in measures of anxiety. This corroborates with the existing consensus that multicomponent approaches confer the most benefit, and adds to the literature by demonstrating this to be the case in brief, explicitly behavioural interventions. This review guides future study of treatments for insomnia in populations where cognitive behavioural therapy for insomnia is not appropriate

Assessing insomnia after stroke: a diagnostic validation of the Sleep Condition Indicator in self-reported stroke survivors

Background: Insomnia is common after stroke and is associated with poorer recovery and greater risk of subsequent strokes. Yet, no insomnia measures have been validated in English-speaking individuals affected by stroke. Aims: This prospective diagnostic validation study investigated the discriminatory validity and optimal diagnostic cut-off of the Sleep Condition Indicator when screening for Diagnostic and Statistical Manual of Mental Disorders—fifth edition (DSM-5) insomnia disorder post-stroke. Methods: A convenience sample of 180 (60.0% women, mean age=49.61 ± 12.41 years) community-based, adult (≥18 years) self-reported stroke survivors completed an online questionnaire. Diagnosis of DSM-5 insomnia disorder was based on analysis of a detailed sleep history questionnaire. Statistical analyses explored discriminant validity, convergent validity, relationships with demographic and mood variables, and internal consistency. Receiver operating characteristic curves were plotted to assess diagnostic accuracy. Results: Data from the sleep history questionnaire suggested that 75 participants (41.67%) met criteria for DSM-5 insomnia disorder, 33 (18.33%) exhibited symptoms of insomnia but did not meet diagnostic criteria, and 72 (40.0%) had no insomnia symptoms at the time of assessment. The Sleep Condition Indicator (SCI) demonstrated ‘excellent’ diagnostic accuracy in the detection of insomnia post-stroke, with an area under the curve of 0.86 (95% CI (0.81, 0.91)). The optimal cut-off was determined as being ≤13, yielding a sensitivity of 88.0% and a specificity of 71.43%. Conclusions: The findings of this study demonstrate the SCI to be a valid and reliable method with which to diagnose DSM-5 insomnia disorder and symptoms post-stroke. However, a lower threshold than is used in the general population may be necessary after stroke

Characterising neutrophil subtypes in cancer using scRNA sequencing demonstrates the importance of IL-1β/CXCR2 axis in generation of metastasis specific neutrophils

Neutrophils are a highly heterogeneous cellular population. However, a thorough examination of the different transcriptional neutrophil states between health and malignancy has not been performed. We utilized single-cell RNA sequencing of human and murine datasets, both publicly available and independently generated, to identify neutrophil transcriptomic subtypes and developmental lineages in health and malignancy. Datasets of lung, breast, and colorectal cancer were integrated to establish and validate neutrophil gene signatures. Pseudotime analysis was used to identify genes driving neutrophil development from health to cancer. Finally, ligand–receptor interactions and signaling pathways between neutrophils and other immune cell populations in primary colorectal cancer and metastatic colorectal cancer were investigated. We define two main neutrophil subtypes in primary tumors: an activated subtype sharing the transcriptomic signatures of healthy neutrophils; and a tumor-specific subtype. This signature is conserved in murine and human cancer, across different tumor types. In colorectal cancer metastases, neutrophils are more heterogeneous, exhibiting additional transcriptomic subtypes. Pseudotime analysis implicates IL1β/CXCL8/CXCR2 axis in the progression of neutrophils from health to cancer and metastasis, with effects on T-cell effector function. Functional analysis of neutrophil-tumoroid cocultures and T-cell proliferation assays using orthotopic metastatic mouse models lacking Cxcr2 in neutrophils support our transcriptional analysis. We propose that the emergence of metastatic-specific neutrophil subtypes is driven by the IL1β/CXCL8/CXCR2 axis, with the evolution of different transcriptomic signals that impair T-cell function at the metastatic site. Thus, a better understanding of neutrophil transcriptomic programming could optimize immunotherapeutic interventions into early and late interventions, targeting different neutrophil states

Sequencing of prostate cancers identifies new cancer genes, routes of progression and drug targets

Prostate cancer represents a substantial clinical challenge because it is difficult to predict outcome and advanced disease is often fatal. We sequenced the whole genomes of 112 primary and metastatic prostate cancer samples. From joint analysis of these cancers with those from previous studies (930 cancers in total), we found evidence for 22 previously unidentified putative driver genes harboring coding mutations, as well as evidence for NEAT1 and FOXA1 acting as drivers through noncoding mutations. Through the temporal dissection of aberrations, we identified driver mutations specifically associated with steps in the progression of prostate cancer, establishing, for example, loss of CHD1 and BRCA2 as early events in cancer development of ETS fusion-negative cancers. Computational chemogenomic (canSAR) analysis of prostate cancer mutations identified 11 targets of approved drugs, 7 targets of investigational drugs, and 62 targets of compounds that may be active and should be considered candidates for future clinical trials

The sensitivity and specificity of the Sleep Condition Indicator when screening for insomnia post-stroke: A diagnostic validation

Background: Post-stroke insomnia is common and associated with poorer outcomes and greater risk of recurrent stroke. Highlighting the importance of identifying individuals who require targeted sleep interventions. However, validations of existing insomnia screening tools in English are lacking. Aims: This prospective diagnostic tool validation investigated the discriminatory validity and optimal diagnostic cut-off of the Sleep Condition Indicator (SCI), and a shorter 2-item version (SCI-2) when screening for DSM-V insomnia disorder post-stroke. Methods: A convenience sample of 180 (60.0% female) UK community based, adult (≥18) stroke survivors completed an online questionnaire. Exclusion criteria included: being jetlagged, working nightshifts, and undergoing treatment that may interfere with sleep. The accuracy of the SCI, and a shorter 2-item version (SCI-2), were validated against diagnoses made via a comprehensive sleep history questionnaire. Statistical analyses explored the discriminant validity, convergent validity, and internal consistency. Receiver operating characteristic curves were plotted to assess the diagnostic accuracy of the SCI and SCI-2. General demographic information was also compared between classifications. The pre-registered protocol for this study can be found at: https://doi.org/10.17605/OSF.IO/4DGXW. Results: The mean age of participants was 49.61 years (SD = 12.41, range = 20 - 79). Seventy-five (41.67%) met criteria for DSM-V insomnia disorder, 33 (18.33%) exhibited symptoms of insomnia but did not meet diagnostic criteria, and 72 (40.0%) had no insomnia symptoms. When detecting DSM-V insomnia post-stroke, the SCI demonstrated ‘excellent’ diagnostic accuracy with an AUC of 0.86 (95% CI [0.81, 0.91]). The optimal cut off was ≤13, yielding a sensitivity of 88.0%, a specificity of 71.43%. A robust, negative relationship existed between the SCI and the ISI (r = -0.86, p<.001). The SCI demonstrated ‘good’ internal consistency, with a Cronbach’s α of 0.84 (95% CI [0.80, 0.87]). Conclusions: This study confirms the SCI’s validity and reliability when detecting DSM-V insomnia in English speaking stroke survivors. The SCI demonstrates ‘excellent’ diagnostic accuracy post-stroke; however, lower thresholds may be necessary compared to the general population. The scale’s accuracy, reliability, and brevity, make it an attractive and cost-effective means of screening for DSM-V insomnia disorder post-stroke, in both clinical and research settings

Supplementary Figure S2 from Characterizing Neutrophil Subtypes in Cancer Using scRNA Sequencing Demonstrates the Importance of IL1β/CXCR2 Axis in Generation of Metastasis-specific Neutrophils

Figure S2. Gene expression and neutrophil signatures in neutrophils from healthy and tumour tissue.</p

Supplementary Figure S5 from Characterizing Neutrophil Subtypes in Cancer Using scRNA Sequencing Demonstrates the Importance of IL1β/CXCR2 Axis in Generation of Metastasis-specific Neutrophils

Figure S5. L-R interactions from the significant signalling pathways that mediate neutrophil and T-cell communication in CRCLM.</p

Supplementary Tables from Characterizing Neutrophil Subtypes in Cancer Using scRNA Sequencing Demonstrates the Importance of IL1β/CXCR2 Axis in Generation of Metastasis-specific Neutrophils

Table S1: Mouse data for ScRNAsequencingTable S2: Primer sequences for qPCRTable S3. Top 5 markers in the integrated mouse dataset neutrophil clustersTable S4: Pseudotime Lineages of the Neutrophil datasetsTable S5: Lineage-specific differentialy expressed genes (Start Vs End) in PYMT dataset.Table S6: Lineage-specific differentialy expressed genes (Start Vs End) in CRC dataset.Table S7: Lineage-specific differentialy expressed genes (Start Vs End) in human NSCLC dataset.Table S8. Top 10 markers in the human CRCLM dataset neutrophil clustersTable S9: Top 10 Differentially expressed genes in neutrophils derived from primary tumors and metastatic tissue.Table S10: Differentially expressed genes in CRCLM neutrophilsTable S11: Differentially expressed genes in CD4 T cells in LM vs PTTable S12: Differentially expressed genes in CD8 T cells in LM vs PTTable S13: Differentially expressed genes in CD4 T cells in LM vs PT used in KEGG analysis(Threshold >3)Table S14: Description of the different neutrophil subtypes identified in CRCLM</p

FIGURE 2 from Characterizing Neutrophil Subtypes in Cancer Using scRNA Sequencing Demonstrates the Importance of IL1β/CXCR2 Axis in Generation of Metastasis-specific Neutrophils

Characterization of neutrophils in metastasis. A, UMAP of neutrophils in CRCLM. B, Coexpression of H_enriched and T_enriched signatures. One cluster is not enriched for either signature (blue arrow). C and D, Unsupervised pseudotime analysis and estimated smoothers for TXNIP expression over the different numbered pseudotime lineages. E, Coexpression of TXNIP and CXCR2. F and G, Expression and estimated smoothers for CXCL8 over pseudotime. H, Coexpression of CXCL8 and IL1β. I, IHC staining of TXNIP in a patient CRCLM sample at 4x (left) and 10x (right). Scale bars = 50 µm. J and K, IHC staining of ITGAM (Neutrophils) and CD3 (T cells) in a patient CRCLM sample at 4x, scale bars = 50 µm. Dashed squares indicate regions where immune cells cluster. L, Differentially expressed genes in metastasis-specific neutrophil cluster. M, H_enriched, T_enriched, and M_enriched gene signatures in mouse bulk-RNA-seq neutrophil dataset. L, Healthy liver tissue, PT: Primary tumor, LMET: Liver metastasis. N, Averaged expression of the individual genes of the three signatures. O, UMAP plot of integrated human neutrophils from PT and metastatic (M) datasets of different cancers. P, Coexpression of CXCR2 with IL1β (top) and TXNIP (bottom). Q, Differential gene expression between neutrophils in malignancy compared with PT. R and S, GO and KEGG analysis of M_CRC neutrophils.</p