"I Just Assumed This Was Already Being Done": Canadian Patient Preferences for Enhanced Data Sharing for Precision Oncology.

PURPOSE: In Canada, health data are siloed, slowing bioinnovation and evidence generation for personalized cancer care. Secured data-sharing platforms (SDSPs) can enable data analysis across silos through rapid concatenation across trial and real-world settings and timely researcher access. To motivate patient participation and trust in research, it is critical to ensure that SDSP design and oversight align with patients' values and address their concerns. We sought to qualitatively characterize patient preferences for the design of a pan-Canadian SDSP. METHODS: Between January 2022 and July 2023, we conducted pan-Canadian virtual focus groups with individuals who had a personal history of cancer. Following each focus group, participants were invited to provide feedback on early-phase analysis results via a member-checking survey. Three trained qualitative researchers analyzed data using thematic analysis. RESULTS: Twenty-eight individuals participated across five focus groups. Four focus groups were conducted in English and one in French. Thematic analysis generated two major and five minor themes. Analytic themes spanned personal and population implications of data sharing and willingness to manage perceived risks. Participants were supportive of increasing access to health data for precision oncology research, while voicing concerns about unintended data use, reidentification, and inequitable access to costly therapeutics. To mitigate perceived risks, participants highlighted the value of data access oversight and governance and informational transparency. CONCLUSION: Strategies for secured data sharing should anticipate and mitigate the risks that patients perceive. Participants supported enhancing timely research capability while ensuring safeguards to protect patient autonomy and privacy. Our study informs the development of data-governance and data-sharing frameworks that integrate real-world and trial data, informed by evidence from direct patient input.

Lived experience at the core: A classification system for risk-taking behaviours in bipolar.

Objective: Clinical observations suggest that individuals with a diagnosis of bipolar face difficulties regulating emotions and impairments to their cognitive processing, which can contribute to high-risk behaviours. However, there are few studies which explore the types of risk-taking behaviour that manifest in reality and evidence suggests that there is currently not enough support for the management of these behaviours. This study examined the types of risk-taking behaviours described by people who live with bipolar and their access to support for these behaviours. Methods: Semi-structured interviews were conducted with n = 18 participants with a lived experience of bipolar and n = 5 healthcare professionals. The interviews comprised open-ended questions and a Likert-item questionnaire. The responses to the interview questions were analysed using content analysis and corpus linguistic methods to develop a classification system of risk-taking behaviours. The Likert-item questionnaire was analysed statistically and insights from the questionnaire were incorporated into the classification system. Results: Our classification system includes 39 reported risk-taking behaviours which we manually inferred into six domains of risk-taking. Corpus linguistic and qualitative analysis of the interview data demonstrate that people need more support for risk-taking behaviours and that aside from suicide, self-harm and excessive spending, many behaviours are not routinely monitored. Conclusion: This study shows that people living with bipolar report the need for improved access to psychologically informed care, and that a standardised classification system or risk-taking questionnaire could act as a useful elicitation tool for guiding conversations around risk-taking to ensure that opportunities for intervention are not missed. We have also presented a novel methodological framework which demonstrates the utility of computational linguistic methods for the analysis of health research data.

Matricellular Protein CCN1 Promotes Collagen Alignment and Scar Integrity After Myocardial Infarction.

BACKGROUND: Members of the cellular communication network family (CCN) of matricellular proteins, like CCN1, have long been implicated in the regulation of cellular processes underlying wound healing, tissue fibrogenesis, and collagen dynamics. While many studies suggest antifibrotic actions for CCN1 in the adult heart through the promotion of myofibroblast senescence, they largely relied on exogenous supplementation strategies in in vivo models of cardiac injury where its expression is already induced-which may confound interpretation of its function in this process. The objective of this study was to interrogate the role of the endogenous protein on fibroblast function, collagen structural dynamics, and its associated impact on cardiac fibrosis after myocardial infarction (MI). METHODS/RESULTS: Here, we employed CCN1 loss-of-function methodologies, including both in vitro siRNA-mediated depletion and in vivo fibroblast-specific knockout mice to assess the role of the endogenous protein on cardiac fibroblast fibrotic signaling, and its involvement in acute scar formation after MI. In vitro depletion of CCN1 reduced cardiac fibroblast senescence and proliferation. Although depletion of CCN1 decreased the expression of collagen processing and stabilization enzymes (i.e., P4HA1, PLOD1, and PLOD2), it did not inhibit myofibroblast induction or type I collagen synthesis. Alone, fibroblast-specific removal of CCN1 did not negatively impact ventricular performance or myocardial collagen content but did contribute to disorganization of collagen fibrils and increased matrix compliance. Similarly, Ccn1 ablated animals subjected to MI showed no discernible alterations in cardiac structure or function one week after permanent coronary artery ligation, but exhibited marked increases in incidence of mortality and cardiac rupture. Consistent with our findings that CCN1 depletion does not assuage myofibroblast conversion or type I collagen synthesis in vitro, Ccn1 knockout animals revealed no measurable differences in collagen scar width or mass compared to controls; however, detailed structural analyses via SHG and TEM of scar regions revealed marked alterations in their scar collagen topography-exhibiting changes in numerous macro- and micro-level collagen architectural attributes. Specifically, Ccn1 knockout mice displayed heightened ECM structural complexity in post-MI scar regions, including diminished local alignment and heightened tortuosity of collagen fibers, as well as reduced organizational coherency, packing, and size of collagen fibrils. Associated with these changes in ECM topography with the loss of CCN1 were reductions in fibroblast-matrix interactions, as evidenced by reduced fibroblast nuclear and cellular deformation in vivo and reduced focal-adhesion formation in vitro; findings that ultimately suggest CCN1's ability to influence fibroblast-led collagen alignment may in part be credited to its capacity to augment fibroblast-matrix interactions. CONCLUSIONS: These findings underscore the pivotal role of endogenous CCN1 in the scar formation process occurring after MI, directing the appropriate arrangement of the extracellular matrix's collagenous components in the maturing scar-shaping the mechanical properties that support its structural stability. While this suggests an adaptive role for CCN1 in regulating collagen structural attributes crucial for supporting scar integrity post MI, the long-term protracted expression of CCN1 holds maladaptive implications, potentially diminishing collagen structural complexity and compliance in non-infarct regions. ABSTRACT (SHORT) BACKGROUND: The cellular communication network (CCN) family of matricellular proteins, including CCN1, plays a critical role in regulating cellular processes essential for wound healing, tissue fibrogenesis, and collagen dynamics. However, previous studies predominantly relied on exogenous supplementation strategies in in vivo models of cardiac injury, potentially confounding interpretations of CCN1's function in these processes. This study aimed to investigate the endogenous protein's role in fibroblast function, collagen structural dynamics, and its impact on cardiac fibrosis following myocardial infarction (MI). METHODS/RESULTS: Employing CCN1 loss-of-function approaches, including in vitro siRNA-mediated depletion and in vivo fibroblast-specific knockout mice, we assessed CCN1's influence on cardiac fibroblast fibrotic signaling and acute scar formation post-MI. In vitro CCN1 depletion reduced cardiac fibroblast senescence and proliferation, as well as decreased the expression of enzymes crucial for collagen processing and stabilization. In vivo fibroblast-specific CCN1 removal did not impair ventricular performance or alter myocardial collagen content but led to collagen fibril disorganization and increased matrix compliance. Ccn1 knockout animals exhibited elevated mortality and cardiac rupture post-MI, with no significant differences in collagen scar width or mass compared to wildtype controls. Yet, detailed structural analyses revealed alterations in scar collagen topography, including increased ECM structural complexity and diminished collagen alignment. These changes correlated with reduced fibroblast-matrix interactions, suggesting CCN1's role in influencing collagen alignment through augmenting these interactions. CONCLUSIONS: Endogenous CCN1 plays a pivotal role in scar formation post-MI by orchestrating the arrangement of collagenous components in the maturing scar, thereby shaping its mechanical properties and structural stability. While CCN1's adaptive role in regulating collagen structural attributes crucial for scar integrity is evident, prolonged expression may lead to diminished collagen structural complexity and compliance in non-infarct regions, highlighting potential maladaptive implications in the long-term.

Exploring spatial heterogeneity in factors associated with injury severity in speeding-related crashes: An integrated machine learning and spatial modeling approach.

Speeding, a risky act of driving a vehicle at a speed exceeding the posted limit, has consistently emerged as a leading contributor to traffic fatalities. Identifying the risk factors associated with injury severity in speeding-related crashes is essential for implementing countermeasures aimed at preventing severe injury incidents and achieving Vision Zero goals. With the wealth of traffic crash data collected by various agencies, researchers have a valuable opportunity to conduct data-driven studies and employ various modeling methods to gain insights into the correlated factors affecting injury severity in traffic crashes. Machine learning models, owing to their superior predictive power compared to statistical models, are increasingly being adopted by researchers. These models, in conjunction with interpretation techniques, can reveal potential relationships between crash injury severity and contributing factors. Traffic crashes are inherently tied to geographic locations, distributed across road networks influenced by diverse socioeconomic and geographical factors. Recognizing spatial heterogeneity in traffic safety is crucial for tailored safety measures to address speeding-related crashes, as a one-size-fits-all approach may not work effectively everywhere. However, most existing machine learning models are unable to incorporate the spatial dependency among observations, such as traffic crashes, which hinders their ability to uncover spatial heterogeneity in traffic safety. To address this gap, this study introduces the Geographically Weighted Neural Network (GWNN) model, a spatial machine-learning model that integrates neural network (NN) and geographically weighted modeling approaches to investigate spatial heterogeneity in speeding-related crashes. Unlike the traditional NN model, which trains a single set of model parameters for all observations, the GWNN trains a local NN model for each crash location using a spatially weighted subsample of nearby crashes, allowing for the quantification of corresponding local effects of features through calculating local marginal effects. To understand the spatial heterogeneity in speeding-related crashes, this study extracted two years (2020 and 2021) of speeding-related crash data from Alabama for the development of the GWNN local models. The modeling results show significant spatial variability among several factors contributing to injury severity in speeding-related crashes. These factors include driver condition, vehicle type, crash type, speed limit, weather, crash time and location, roadway alignment, and traffic volume. Based on the GWNN modeling results, this study identified three types of spatial variations in relationships between contributing factors and crash injury severity: consistent positive associations, consistent negative associations, and inverse associations (i.e., marginal effects can vary between positive and negative depending on the location). This study contributes by integrating advanced machine learning and spatial modeling approaches to uncover intricate spatial patterns and factors influencing injury severity in speeding-related crashes, thereby facilitating the development of targeted policy implementations and safety interventions.

A sequel to "Comprehensive analysis of single- and multi-vehicle large truck at-fault crashes on rural and urban roadways in Alabama": Accounting for temporal instability in crash factors.

This exploratory study is a follow-up to a 2014 study that investigated factors associated with large truck at-fault crash outcomes in Alabama. To assess unobserved temporal changes in the effects of the crash factors, this study re-creates the original crash models developed in the 2014 study using crash data from 2017 to 2019. Four mixed logit models were re-created using the same variables used in the previous study to analyze contributing crash factors to injury severity of single-vehicle (SV) and multi-vehicle-involved (MV) large truck at-fault crashes in urban and rural settings. It was found that there have been temporal changes in how many of the factors influenced crash severity with some of them no longer showing any significant association with crash outcomes, while others remained significant. Further, it was observed that some of the variables that remained significant had different relationships with crash injury severity in the newer severity models. For instance, while factors such as fatigued driver (in rural crashes), clear weather (in urban crashes), single-unit truck (in rural SV crashes), truck rollover (in urban SV crashes) maintained consistent significance over time, the effects of variables such as at-fault male drivers (in urban MV crashes), at-fault female drivers (in urban MV crashes), and hitting fixed object (in rural MV crashes) have changed. One such notable difference is the variable for absence of traffic control which increased the probability of major injury in rural SV crashes by 49.50% in the 2014 model but decreased the probability of recording major injuries by 108.90% using the 2017-2019 data. Considering the temporal changes that were observed in the recreated models, newer models were developed, revealing the emergence of new variables such as truck age that are significantly associated with truck crash severity. The findings of this study provide evidence to suggest that some crash severity factors for at-fault large truck collisions vary over time, with newer ones also emerging over time. These findings can also help trucking companies, transportation engineers, and other industry experts in developing measures to reduce large truck crashes.

It is time to reconsider the use of naturally-occurring endotoxins in endotoxin recovery studies: Part 2 of a BioPhorum harmonized endotoxin recovery study.

The evaluation of Naturally Occurring Endotoxins (NOEs) for Low Endotoxin Recovery (LER) studies has been a topic in the industry and regulatory agencies have been hesitant to endorse NOE use in LER studies over purified Lipopolysaccharide (LPS) standards such as Control Standard Endotoxin (CSE) or Reference Standard Endotoxin (RSE). In a recent study involving 11 BioPhorum member companies across 13 sites, NOEs prepared in high and low nutrient conditions were evaluated in two common monoclonal antibody buffer formulations: 10 mM Sodium Citrate, 0.05 % Polysorbate 80, pH 6.0 and 20 mM Histidine, 0.05 % Polysorbate 80, pH 6.0. 12 g-negative bacterial isolates were used to prepare NOE analytes, which were spiked into the formulation buffers. Additionally, the NOEs were spiked into Limulus Amebocyte Lysate (LAL) reagent water as controls and purified LPS into the citrate/polysorbate buffer as the LER control. Results showed the average of three runs per organism was >50 % recovery, at the conclusion of the 7-day period, regardless of nutrient culture preparation conditions. Furthermore, purified LPS controls became undetectable (<50 % recovery) in the citrate/polysorbate buffer, highlighting the presence of LER. These findings highlight the potential value of using NOEs from relevant manufacturing facilities to assess overall risk when purified LPS recovery is insufficient.

Expanded genetic testing in familial hypercholesterolemia-A single center's experience.

Objective: Assess the yield of genetic testing for pathogenic variants in ABCG5, ABCG8, LIPA, and APOE in individuals with personal and family histories suggestive of familial hypercholesterolemia. Methods: Retrospective review of patients seen in the Advanced Lipid Disorders Clinic at Johns Hopkins. Results: In the lipid clinic at a single center during the years 2015-2023, 607 patients underwent genetic testing for familial hypercholesterolemia, of which 263 underwent the expanded genetic testing for sitosterolemia. Eighty-eight patients had genetic testing which included APOE, and 22 patients had testing which included LIPA. Among these, one patient was identified to have a pathogenic variant in APOE and another patient with a pathogenic variant in ABCG5 (0.7 % yield). The frequency of a positive result was double that of a variant of uncertain significance. Conclusion: These data suggest in rare cases expanded testing can provide answers for patients and families with a minimal likelihood of a variant of uncertain significance.

Strangulated Indirect Inguinal Hernia-Containing Bladder: A Case Report.

Inguinal hernias involving the bladder are exceedingly rare and pose a diagnostic challenge. Identifying bladder involvement within an inguinal hernia is imperative to avoid iatrogenic bladder injuries and subsequent complications. Here we discuss a case of inguinal bladder herniation and bladder visualization using methylene blue dye intraoperatively. We present a case of a 45-year-old male who presented with a six-hour history of dysuria and a painful non-reducible right-sided groin mass that had previously been reducible for 17 years. Computed tomography demonstrated an irreducible indirect inguinal hernia-containing bladder. Open Lichtenstein repair was performed, and intraoperative methylene blue-dyed saline successfully identified the herniated bladder, preventing iatrogenic bladder injury. This case report demonstrates the importance of preoperative imaging and intraoperative visualization for the prevention of complications in a rare occurrence of a strangulated indirect inguinal hernia-containing bladder.

AI-based histopathology image analysis reveals a distinct subset of endometrial cancers.

Endometrial cancer (EC) has four molecular subtypes with strong prognostic value and therapeutic implications. The most common subtype (NSMP; No Specific Molecular Profile) is assigned after exclusion of the defining features of the other three molecular subtypes and includes patients with heterogeneous clinical outcomes. In this study, we employ artificial intelligence (AI)-powered histopathology image analysis to differentiate between p53abn and NSMP EC subtypes and consequently identify a sub-group of NSMP EC patients that has markedly inferior progression-free and disease-specific survival (termed 'p53abn-like NSMP'), in a discovery cohort of 368 patients and two independent validation cohorts of 290 and 614 from other centers. Shallow whole genome sequencing reveals a higher burden of copy number abnormalities in the 'p53abn-like NSMP' group compared to NSMP, suggesting that this group is biologically distinct compared to other NSMP ECs. Our work demonstrates the power of AI to detect prognostically different and otherwise unrecognizable subsets of EC where conventional and standard molecular or pathologic criteria fall short, refining image-based tumor classification. This study's findings are applicable exclusively to females.

Supplementation with soluble or insoluble rice-bran fibers increases short-chain fatty acid producing bacteria in the gut microbiota in vitro.

Introduction: Studies have shown that a diet high in fiber and prebiotics has a positive impact on human health due largely to the fermentation of these compounds by the gut microbiota. One underutilized source of fiber may be rice bran, a waste product of rice processing that is used most frequently as an additive to livestock feed but may be a good source of fibers and other phenolic compounds as a human diet supplement. Previous studies focused on specific compounds extracted from rice bran showed that soluble fibers extracted from rice bran can improve glucose response and reduce weight gain in mouse models. However, less is known about changes in the human gut microbiota in response to regular rice bran consumption. Methods: In this study, we used a Simulator of the Human Intestinal Microbial Ecology (SHIME®) to cultivate the human gut microbiota of 3 different donors in conditions containing either soluble or insoluble fiber fractions from rice bran. Using 16S rRNA amplicon sequencing and targeted metabolomics via Gas Chromatography-Mass Spectrometry, we explored how gut microbial communities developed provided different supplemental fiber sources. Results: We found that insoluble and soluble fiber fractions increased short-chain fatty acid production, indicating that both fractions were fermented. However, there were differences in response between donors, for example the gut microbiota from donor 1 increased acetic acid production with both fiber types compared with control; whereas for donors 2 and 3, butanoic acid production increased with ISF and SF supplementation. Both soluble and insoluble rice bran fractions increased the abundance of Bifidobacterium and Lachnospiraceae taxa. Discussion: Overall, analysis of the effect of soluble and insoluble rice bran fractions on the human in vitro gut microbiota and the metabolites produced revealed individually variant responses to these prebiotics.

Understanding the Impacts of Online Mental Health Peer Support Forums: Realist Synthesis.

BACKGROUND: Online forums are widely used for mental health peer support. However, evidence of their safety and effectiveness is mixed. Further research focused on articulating the contexts in which positive and negative impacts emerge from forum use is required to inform innovations in implementation. OBJECTIVE: This study aimed to develop a realist program theory to explain the impacts of online mental health peer support forums on users. METHODS: We conducted a realist synthesis of literature published between 2019 and 2023 and 18 stakeholder interviews with forum staff. RESULTS: Synthesis of 102 evidence sources and 18 interviews produced an overarching program theory comprising 22 context-mechanism-outcome configurations. Findings indicate that users' perceptions of psychological safety and the personal relevance of forum content are foundational to ongoing engagement. Safe and active forums that provide convenient access to information and advice can lead to improvements in mental health self-efficacy. Within the context of welcoming and nonjudgmental communities, users may benefit from the opportunity to explore personal difficulties with peers, experience reduced isolation and normalization of mental health experiences, and engage in mutual encouragement. The program theory highlights the vital role of moderators in creating facilitative online spaces, stimulating community engagement, and limiting access to distressing content. A key challenge for organizations that host mental health forums lies in balancing forum openness and anonymity with the need to enforce rules, such as restrictions on what users can discuss, to promote community safety. CONCLUSIONS: This is the first realist synthesis of online mental health peer support forums. The novel program theory highlights how successful implementation depends on establishing protocols for enhancing safety and strategies for maintaining user engagement to promote forum sustainability. TRIAL REGISTRATION: PROSPERO CRD42022352528; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=352528.

The Types of Psychosocial Factors Associated with Suicidality Outcomes for People Living with Bipolar Disorder: A Scoping Review.

Bipolar Disorder is associated with high rates of suicidal thoughts, behaviors, and outcomes, yet the lived experience of suicidality and Bipolar Disorder is not particularly well understood. Understanding the role of psychosocial aetiologies in suicidality outcomes for those living with Bipolar Disorder is key for developing appropriately targeted interventions focusing on factors that are amenable to change. In line with PRISMA guidance, we conducted a scoping review to identify the types of psychosocial factors studied in relation to the experience of suicidality for people living with Bipolar Disorder diagnoses. Systematic literature searches identified a sample of 166 articles from which key study data were extracted and charted. A narrative synthesis of the reviewed literature is presented ordered by the factors investigated across studies, a frequency count of the types of psychological/social aetiologies studied, and a brief overview of the key findings for each aetiology. Most of the identified literature took the form of quantitative cross-sectional studies, with only one qualitative study and 18 quantitative prospective studies. The most studied aetiologies were trauma (specifically early adverse experiences and childhood traumas) and stressful life events, impulsivity (primarily subjective self-reported trait impulsivity), social support and functioning, and personality/temperament factors. Only six studies in the final sample reported basing their research questions and/or hypotheses on an explicit theoretical model of suicide. The literature was primarily focused on using self-report measurements of key aetiologies and on factors which lead to worsened suicidality rather than focusing on potentially protective or buffering factors. Future research needs to better justify the aetiologies investigated in relation to suicidality outcomes for people living with Bipolar Disorder, including a firmer basis in theory and hypothesis testing, more prospective designs, and the use of alternative assessments of psychosocial aetiologies in addition to self-report questionnaires.

Exploring the lived experiences and care challenges of formal paid caregivers for people with intellectual disability and dementia.

A greater number of people with intellectual disability are living into older age and are at increased risk of developing conditions such as dementia. Caring for a person with dementia presents several challenges for formal caregivers due to the progressive nature of the disease. An interpretive phenomenological analysis was used to understand the lived experiences of a purposive sample of formal caregivers in caring for people with intellectual disability and dementia. Discussions from 14 individual interviews generated data were analysed. Four key super-ordinate themes emerged which were: (1) recognising early indicators and diagnosis, (2) post diagnostic support, (3) coping with change and (4) need for future development. Themes reflected the experiences, barriers to dementia diagnosis and provide a valuable insight into the challenges faced by formal caregivers in providing aged care services.

Whole genome and transcriptome integrated analyses guide clinical care of pediatric poor prognosis cancers.

The role for routine whole genome and transcriptome analysis (WGTA) for poor prognosis pediatric cancers remains undetermined. Here, we characterize somatic mutations, structural rearrangements, copy number variants, gene expression, immuno-profiles and germline cancer predisposition variants in children and adolescents with relapsed, refractory or poor prognosis malignancies who underwent somatic WGTA and matched germline sequencing. Seventy-nine participants with a median age at enrollment of 8.8 y (range 6 months to 21.2 y) are included. Germline pathogenic/likely pathogenic variants are identified in 12% of participants, of which 60% were not known prior. Therapeutically actionable variants are identified by targeted gene report and whole genome in 32% and 62% of participants, respectively, and increase to 96% after integrating transcriptome analyses. Thirty-two molecularly informed therapies are pursued in 28 participants with 54% achieving a clinical benefit rate; objective response or stable disease ≥6 months. Integrated WGTA identifies therapeutically actionable variants in almost all tumors and are directly translatable to clinical care of children with poor prognosis cancers.

VOLTA: an enVironment-aware cOntrastive ceLl represenTation leArning for histopathology.

In clinical oncology, many diagnostic tasks rely on the identification of cells in histopathology images. While supervised machine learning techniques necessitate the need for labels, providing manual cell annotations is time-consuming. In this paper, we propose a self-supervised framework (enVironment-aware cOntrastive cell represenTation learning: VOLTA) for cell representation learning in histopathology images using a technique that accounts for the cell's mutual relationship with its environment. We subject our model to extensive experiments on data collected from multiple institutions comprising over 800,000 cells and six cancer types. To showcase the potential of our proposed framework, we apply VOLTA to ovarian and endometrial cancers and demonstrate that our cell representations can be utilized to identify the known histotypes of ovarian cancer and provide insights that link histopathology and molecular subtypes of endometrial cancer. Unlike supervised models, we provide a framework that can empower discoveries without any annotation data, even in situations where sample sizes are limited.

Substance Use Within Trials of Psychological Interventions for Psychosis: Sample Inclusion, Secondary Measures, and Intervention Effectiveness.

INTRODUCTION: Current clinical guidelines recommend that patients with co-occurring psychosis and alcohol or substance use disorders (A/SUD) receive evidenced-based treatment for both disorders, including psychological intervention for psychosis. However, the efficacy of such treatments for individuals with co-occurring psychosis and A/SUD is unclear. STUDY DESIGN: Randomized controlled trials (RCTs) of psychological interventions for psychosis were systematically reviewed, to investigate how alcohol and substance use has been accounted for across sample inclusion and secondary measures. Findings from trials including individuals with co-occurring alcohol or substance use issues were then narratively summarized using the Synthesis Without Meta-Analysis guidelines, to indicate the overall efficacy of psychological interventions for psychosis, for this comorbid population. STUDY RESULTS: Across the 131 trials identified, 60.3% of trials excluded individuals with alcohol or substance use issues. Additionally, only 6.1% measured alcohol or substance use at baseline, while only 2.3% measured alcohol or substance use as a secondary outcome. Across trials explicitly including individuals with alcohol or substance use issues, insufficient evidence was available to conclude the efficacy of any individual psychological intervention. However, preliminary findings suggest that psychoeducation (PE) and metacognitive therapy (MCT) may be proposed for further investigation. CONCLUSION: Overall, co-occurring alcohol and substance use issues have been largely neglected across the recent RCTs of psychological interventions for psychosis; highlighting the challenges of making treatment decisions for these individuals using the current evidence base.

GlycA Levels Independently Predict Coronary Artery Calcium Incidence and Progression in the ELSA-Brasil Cohort (Brazilian Longitudinal Study of Adult Health).

Atherosclerosis is an inflammatory disease. Coronary artery calcium (CAC) is a marker of atherosclerotic disease events and mortality risk. Increased GlycA, an emerging marker of inflammation, is associated with a higher risk for coronary artery disease (CAD). However, there is conflicting evidence on whether GlycA predicts subclinical CAD progression. We hypothesized that GlycA can predict subclinical CAC incidence/progression in healthy participants. We included 2,690 ELSA-Brasil cohort participants without cardiovascular/chronic inflammatory disease not receiving statin therapy who had GlycA levels measured and 2 interval CAC assessments between 2010 and 2018. Multivariable logistic and linear regression models were computed to evaluate GlycA as a predictor of CAC incidence and progression. CAC incidence required a baseline CAC of 0. CAC progression required a baseline CAC >0. The mean age of participants was 48.6 ± 7.7 years, 56.7% were women, and 54.6% and 16.1% (429 of 2,690) were White and Black, respectively. The mean CAC interscan period was 5.1 ± 0.9 years, the mean GlycA level was 414.7 ± 65 µmol/L, and the incidence of CAC was 13.1% (280 of 2,129). The GlycA level odds ratio for CAC incidence was 1.002 (95% confidence interval 1.0005 to 1.005, p = 0.016), adjusted for demographics, lifestyle, a family history of early CAD (≤60 years), lipids, and co-morbidities. The GlycA (≤p25 vs ≥p75) odds ratio for CAC progression (Berry definition) was 1.77 (95% confidence interval 1.07 to 2.96, p = 0.03) in a similar multivariable-adjusted model. Higher GlycA levels were associated with CAC incidence and progression in a healthy Brazilian cohort.

Exploration of Germline Correlates and Risk of Immune-Related Adverse Events in Advanced Cancer Patients Treated with Immune Checkpoint Inhibitors.

Immune checkpoint inhibitors (ICIs) are increasingly used in the treatment of many tumor types, and durable responses can be observed in select populations. However, patients may exhibit significant immune-related adverse events (irAEs) that may lead to morbidity. There is limited information on whether the presence of specific germline mutations may highlight those at elevated risk of irAEs. We evaluated 117 patients with metastatic solid tumors or hematologic malignancies who underwent genomic analysis through the ongoing Personalized OncoGenomics (POG) program at BC Cancer and received an ICI during their treatment history. Charts were reviewed for irAEs. Whole genome sequencing of a fresh biopsy and matched normal specimens (blood) was performed at the time of POG enrollment. Notably, we found that MHC class I alleles in the HLA-B27 family, which have been previously associated with autoimmune conditions, were associated with grade 3 hepatitis and pneumonitis (q = 0.007) in patients treated with combination PD-1/PD-L1 and CTLA-4 inhibitors, and PD-1 inhibitors in combination with IDO-1 inhibitors. These data highlight that some patients may have a genetic predisposition to developing irAEs.

The benefit of a complete reference genome for cancer structural variant analysis.

The complexities of cancer genomes are becoming more easily interpreted due to advancements in sequencing technologies and improved bioinformatic analysis. Structural variants (SVs) represent an important subset of somatic events in tumors. While detection of SVs has been markedly improved by the development of long-read sequencing, somatic variant identification and annotation remains challenging. We hypothesized that use of a completed human reference genome (CHM13-T2T) would improve somatic SV calling. Our findings in a tumour/normal matched benchmark sample and two patient samples show that the CHM13-T2T improves SV detection and prioritization accuracy compared to GRCh38, with a notable reduction in false positive calls. We also overcame the lack of annotation resources for CHM13-T2T by lifting over CHM13-T2T-aligned reads to the GRCh38 genome, therefore combining both improved alignment and advanced annotations. In this process, we assessed the current SV benchmark set for COLO829/COLO829BL across four replicates sequenced at different centers with different long-read technologies. We discovered instability of this cell line across these replicates; 346 SVs (1.13%) were only discoverable in a single replicate. We identify 49 somatic SVs, which appear to be stable as they are consistently present across the four replicates. As such, we propose this consensus set as an updated benchmark for somatic SV calling and include both GRCh38 and CHM13-T2T coordinates in our benchmark. The benchmark is available at: 10.5281/zenodo.10819636 Our work demonstrates new approaches to optimize somatic SV prioritization in cancer with potential improvements in other genetic diseases.

Injury-severity analysis of crashes involving defective vehicles and accounting for the underlying socioeconomic mediators.

Crashes occur from a combination of factors related to the driver, roadway, and vehicle factors. The impact of vehicles on road crashes is a critical consideration within road safety analysis, even though not much studies have been conducted in this area. This study assessed how various vehicle and other crash factors are significantly associated with crash outcomes. To do this, historical vehicle defect-related crashes were obtained for the state of Alabama from 2016 to 2020. After data cleaning, a crash injury severity model was developed using the random parameters multinomial logit with heterogeneity in means approach to account for possible unobserved heterogeneity in the data. A spatial analysis was further conducted to better understand vehicle defect crashes as a broader societal issue and potentially explore their connection with the socio-demographic characteristics of the drivers of these vehicles. The preliminary data analysis showed that brake and tire defects accounted for about 65% of the vehicle defects associated with the crashes. The model estimation results revealed that improper tread depth and headlight defects were associated with major injury outcomes, while brake defects were more associated with minor injuries. Also, crashes associated with speeding, drunk driving, failure to use seatbelts, and those that occurred on curved roads left with downgrades were likely to result in major injuries. Findings from the spatial analysis showed that postal codes with higher median incomes are more likely to record lower vehicle defect-related crashes, unlike those that have higher proportions of females and African Americans. The study's findings provide data-driven evidence for sustained safety campaigns, workshops, and training on basic vehicle maintenance practices in the low-income communities in the state.