Trajectories Of Progression Research Articles

Characterising illness stages and recovery trajectories of eating disorders in young people via remote measurement technology (STORY): a multi-centre prospective cohort study protocol

BackgroundEating disorders (EDs) are serious, often chronic, conditions associated with pronounced morbidity, mortality, and dysfunction increasingly affecting young people worldwide. Illness progression, stages and recovery trajectories of EDs are still poorly characterised. The STORY study dynamically and longitudinally assesses young people with different EDs (restricting; bingeing/bulimic presentations) and illness durations (earlier; later stages) compared to healthy controls. Remote measurement technology (RMT) with active and passive sensing is used to advance understanding of the heterogeneity of earlier and more progressed clinical presentations and predictors of recovery or relapse.MethodsSTORY follows 720 young people aged 16–25 with EDs and 120 healthy controls for 12 months. Online self-report questionnaires regularly assess ED symptoms, psychiatric comorbidities, quality of life, and socioeconomic environment. Additional ongoing monitoring using multi-parametric RMT via smartphones and wearable smart rings (‘Ōura ring’) unobtrusively measures individuals’ daily behaviour and physiology (e.g., Bluetooth connections, sleep, autonomic arousal). A subgroup of participants completes additional in-person cognitive and neuroimaging assessments at study-baseline and after 12 months.DiscussionBy leveraging these large-scale longitudinal data from participants across ED diagnoses and illness durations, the STORY study seeks to elucidate potential biopsychosocial predictors of outcome, their interplay with developmental and socioemotional changes, and barriers and facilitators of recovery. STORY holds the promise of providing actionable findings that can be translated into clinical practice by informing the development of both early intervention and personalised treatment that is tailored to illness stage and individual circumstances, ultimately disrupting the long-term burden of EDs on individuals and their families.

Unraveling progression subtypes in people with Huntington’s disease

BackgroundHuntington’s disease (HD) is a progressive neurodegenerative disease caused by a CAG trinucleotide expansion in the huntingtin gene. The length of the CAG repeat is inversely correlated with disease onset. HD is characterized by hyperkinetic movement disorder, psychiatric symptoms, and cognitive deficits, which greatly impact patient’s quality of life. Despite this clear genetic course, high variability of HD patients’ symptoms can be observed. Current clinical diagnosis of HD solely relies on the presence of motor signs, disregarding the other important aspects of the disease. By incorporating a broader approach that encompasses motor as well as non-motor aspects of HD, predictive, preventive, and personalized (3P) medicine can enhance diagnostic accuracy and improve patient care.MethodsMultisymptom disease trajectories of HD patients collected from the Enroll-HD study were first aligned on a common disease timescale to account for heterogeneity in disease symptom onset and diagnosis. Following this, the aligned disease trajectories were clustered using the previously published Variational Deep Embedding with Recurrence (VaDER) algorithm and resulting progression subtypes were clinically characterized. Lastly, an AI/ML model was learned to predict the progression subtype from only first visit data or with data from additional follow-up visits.ResultsResults demonstrate two distinct subtypes, one large cluster (n = 7122) showing a relative stable disease progression and a second, smaller cluster (n = 411) showing a dramatically more progressive disease trajectory. Clinical characterization of the two subtypes correlates with CAG repeat length, as well as several neurobehavioral, psychiatric, and cognitive scores. In fact, cognitive impairment was found to be the major difference between the two subtypes. Additionally, a prognostic model shows the ability to predict HD subtypes from patients’ first visit only.ConclusionIn summary, this study aims towards the paradigm shift from reactive to preventive and personalized medicine by showing that non-motor symptoms are of vital importance for predicting and categorizing each patients’ disease progression pattern, as cognitive decline is oftentimes more reflective of HD progression than its motor aspects. Considering these aspects while counseling and therapy definition will personalize each individuals’ treatment. The ability to provide patients with an objective assessment of their disease progression and thus a perspective for their life with HD is the key to improving their quality of life. By conducting additional analysis on biological data from both subtypes, it is possible to gain a deeper understanding of these subtypes and uncover the underlying biological factors of the disease. This greatly aligns with the goal of shifting towards 3P medicine.

Recommendations on Methods for Assessing Multimorbidity Changes Over Time: Aligning the Method to the Purpose.

The rapidly growing field of multimorbidity research demonstrates that changes in multimorbidity in mid- and late-life have far reaching effects on important person-centered outcomes, such as health-related quality of life. However, there are few organizing frameworks and comparatively little work weighing the merits and limitations of various quantitative methods applied to the longitudinal study of multimorbidity. We identify and discuss methods aligned to specific research objectives with the goals of (i) establishing a common language for assessing longitudinal changes in multimorbidity, (ii) illuminating gaps in our knowledge regarding multimorbidity progression and critical periods of change, and (iii) informing research to identify groups that experience different rates and divergent etiological pathways of disease progression linked to deterioration in important health-related outcomes. We review practical issues in the measurement of multimorbidity, longitudinal analysis of health-related data, operationalizing change over time, and discuss methods that align with 4 general typologies for research objectives in the longitudinal study of multimorbidity: (i) examine individual change in multimorbidity, (ii) identify subgroups that follow similar trajectories of multimorbidity progression, (iii) understand when, how, and why individuals or groups shift to more advanced stages of multimorbidity, and (iv) examine the coprogression of multimorbidity with key health domains. This work encourages a systematic approach to the quantitative study of change in multimorbidity and provides a valuable resource for researchers working to measure and minimize the deleterious effects of multimorbidity on aging populations.

Impact of healthy lifestyle on the incidence and progression trajectory of mental disorders: A prospective study in the UK Biobank

BackgroundHealthier lifestyle decreased the risk of mental disorders (MDs) such as depression and anxiety. However, research on the effects of a comprehensive healthy lifestyle on their progression is lacking. Methods385,704 individuals without baseline MDs from the UK Biobank cohort were included. A composite healthy lifestyle score was computed by assessing alcohol intake, smoking status, television viewing time, physical activity, sleep duration, fruit and vegetable intake, oily fish intake, red meat intake, and processed meat intake. Follow-up utilized hospital and death register records. Multistate model was used to examine the role of healthy lifestyle on the progression of specific MDs, while a piecewise Cox regression model was utilized to assess the influence of healthy lifestyle across various phases of disease progression. ResultsHigher lifestyle score reduced risks of transitions from baseline to anxiety and depression, as well as from anxiety and depression to comorbidity, with corresponding hazard ratios (HR) and 95 % confidence intervals (CI) of 0.94 (0.93, 0.95), 0.90 (0.89, 0.91), 0.94 (0.91, 0.98), and 0.95 (0.92, 0.98), respectively. Healthier lifestyle decreased the risk of transitioning from anxiety to comorbidity within 2 years post-diagnosis, with HR 0.93 (0.88, 0.98). Higher lifestyle scores at 2–4 years and 4–6 years post-depression onset were associated with reduced risk of comorbidity, with HR 0.93 (0.87, 0.99) and 0.92 (0.86, 0.99), respectively. LimitationThe generalizability to other ethnic groups is limited. ConclusionThis study observed a protective role of holistic healthy lifestyle in the trajectory of MDs and contributed to identifying critical progression windows.

The Dependence of Gamma-Ray Burst Jet Collimation on Black Hole Spin

Gamma-ray bursts (GRBs) are the most luminous events in the Universe and are excellent laboratories to study extreme physical phenomena in the cosmos. Despite a long trajectory of progress in understanding these highly energetic events, there are still many observed features that are yet to be fully explained. Observations of the jet opening angle of long gamma-ray bursts (LGRBs) suggest that LGRB jets are narrower for those GRBs at higher redshift. This phenomenon has been explained in the context of collimation by the stellar envelope, with denser (lower metallicity) stars at higher redshifts able to collimate the jet more effectively. However, until now, the dependence of the jet opening angle on the properties of the central engine has not been explored. We investigate the effect of black hole spin on the jet collimation angle for a magnetically launched jet, using the general relativistic radiation magnetohydrodynamical code ν bhlight. We present 3D results for a range of spin values. The simulations show that higher-spinning black holes tend to create narrower jets. If indeed LGRB progenitors in the early Universe are able to produce black hole central engines with higher spin, this could account for at least some of the observed jet opening angle-redshift correlation.

Evolving Minds and Riding Waves: Development and Progress in ChatGPT

This research paper delves into the dynamic evolution of ChatGPT, a foremost conversational Artificial Intelligence (AI) model, with a focus on its developmental journey and progressive advancements. Termed "Evolving Minds" and "Riding Waves," we investigate the intricate interplay between cognitive evolution and the dynamic trajectory of progress. Through a comprehensive methodological framework encompassing historical analysis, comparative studies, and trend assessment, we meticulously trace ChatGPT's developmental milestones and transformative shifts. Our inquiry extends into the underlying mechanisms propelling its advancement within the landscape of conversational AI. By unravelling these mechanisms, our research aims to provide insights into the fundamental drivers of AI-driven conversation evolution. By shedding light on ChatGPT's evolution, we seek to deepen the understanding of the dynamics shaping progress in this swiftly evolving domain. Ultimately, this study contributes to the ongoing discourse on the future trajectory of conversational AI and its potential implications across various sectors and applications. Through a nuanced examination of ChatGPT's evolution, we aim to inform future developments and pave the way for innovative advancements in AI-driven conversation technologies. Key Words: ChatGPT, Artificial Intelligence, progress, innovative.

Application progress of latent class growth models in dynamic prevention and control strategies for acquired immunodeficiency syndrome.

The prevention and control requirements for HIV/AIDS vary significantly among different populations, posing substantial challenges to the formulation and implementation of intervention strategies. Dynamically assessing the heterogeneity and disease progression trajectories of various groups is crucial. Latent class growth model (LCGM) serves as a statistical approach that fits a longitudinal data into N subgroups of individual development trajectories, identifying and analyzing the progression paths of different subgroups, thereby offering a novel perspective for disease control strategies. LCGM has shown significant advantages in the application of HIV/AIDS prevention and control, especially in gaining a deeper understanding and analysis of epidemiological characteristics, risk behaviors, psychological research, heterogeneity in testing, and dynamic changes. Summarizing the advantages and limitations of applying LCGM can provide a reliable basis for precise prevention and control of HIV/AIDS.

The multiple trajectories of the allergic march.

The allergic march comprises the sequential appearance of a series of allergic comorbidities. However, variability in the onset and progression of allergic diseases generates a heterogeneous scenario that does not follow a linear and single trajectory. Almost half of the pediatric population presents at least 1 allergy symptom. However, only 4%-6% present multimorbidity, with several allergic diseases co-occurring. It has recently been shown that although they share etiological mechanisms and risk factors, allergic diseases arise independently. In most cases, progression is not consecutive, or at least not the same in all patients. TH2- mediated inflammation, epithelial barrier dysfunction, and genetic predisposition play a fundamental role in the etiology of allergic diseases, on which the interaction with the exposome acts decisively. Therefore, studying diseases from an omics point of view is essential when attempting to describe the various trajectories of allergic progression and to propose effective interventions to prevent multimorbidity. In this narrative review, we provide an overview of the current perception of the allergic march, including clinical observations, omics data, risk factors, and measures aimed at modifying its course or even preventing its onset.

A Critique of the Discursive Turn of Australian Multicultural Policy in the Globalisation Era

This article examines the changing discourses of Australian multicultural policy from the late 1990s until now and discusses their ideological significance under the twin-frames of globalization and Australian political culture. By systematically analyzing the series of policy papers launched by successive governments, it becomes clear that the multicultural policy has completed a discursive turn, deviating from its progressive trajectory of cultural diversity and substantive equality to suit the neo-conservative agenda of national unity and economic productivity. While the strengthened national identity based on the civic model corresponds with the surging tide of social conservatism, the shrinking government funding in this policy area and the utilitarian perception of cultural diversity are tied up with market-oriented reforms, indicating the government's shifting interests from minimizing the existing cultural barriers to maximizing cultural dividends through diversity management strategies.

Abstract LB217: Novel organoid models of lineage plasticity in castration resistant prostate cancer

Abstract The development of next-generation AR-targeted drugs, such as abiraterone and enzalutamide, has improved survival of patients with castration-resistant prostate cancer (CRPC). However, many patients eventually progress to therapy-resistant lethal prostate cancer. CRPC exhibits variant cellular states, including AR-pathway active prostate cancer (ARPC), AR activity-low prostate cancer (ARLPC), neuroendocrine prostate cancer (NEPC), amphicrine prostate cancer co-expressing AR and neuroendocrine genes (AMPC), and double-negative prostate cancer (DNPC) lacking both AR expression and neuroendocrine differentiation, indicating that lineage plasticity facilitates therapeutic resistance. While molecular characteristics of ARPC and NEPC are well described, other common forms of CRPC, such as DNPC which occurs in 20-25% of CRPC patients, remain poorly understood. Therefore, characterization of pre-clinical models of DNPC and identification of novel regulators of lineage plasticity in DNPC are urgent necessities for the development of effective treatment for lethal prostate cancer.In our studies, we have generated a new mouse model of CRPC in which Pten and Smad4 are deleted in distal luminal cells upon tamoxifen administration (Nkx3.1CreERT2/+; Ptenfl/fl; Smad4fl/fl; R26R-eYFP/+, NPS). We observed a molecular transition from ARPC to ARLPC and DNPC in the prostate tumors derived from NPS mice during tumor progression. Moreover, a combination of castration and enzalutamide treatment promoted a partial epithelial-mesenchymal transition (EMT) in DNPC. To identify the evolutionary trajectories of cancer progression and drivers of lineage plasticity in CRPC, we have established organoid lines from the prostate tumor cells of NPS mice. Single-cell RNA-sequencing (scRNA-seq) and marker validation analyses shown that these organoid lines display overt lineage plasticity, corresponding to ARPC, ARLPC, partial EMT-absent DNPC, and partial EMT-present DNPC. Orthotopic grafts derived from the established NPS organoid lines show consistent molecular phenotypes and confirm that AR-targeted therapies facilitate partial EMT in vivo. Furthermore, cross-species bioinformatic analyses using scRNA-seq datasets indicate that the established NPS organoid lines partially recapitulate the spectrum of lineage plasticity in human CRPC.In summary, we have established prostate cancer organoid lines from a novel mouse model of CRPC, which not only provide large and reproducible sources of two distinct forms of DNPC (partial EMT-absent DNPC and partial EMT-present DNPC) for molecular analyses, but also can capture the regulatory complexity of lineage plasticity in human CRPC. Therefore, NPS mice provide an excellent pre-clinical model of CRPC, especially DNPC, to identify novel regulators that drive lineage plasticity and to pursue assays for development of candidate drugs that can inhibit or even possibly convert therapy-resistant forms of CRPC to therapy-sensitive ones. Citation Format: Jinqiu Lu, Michael M. Shen. Novel organoid models of lineage plasticity in castration resistant prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB217.

Pathways to a Shiny Future: Building the Foundation for Computational Physical Chemistry and Biophysics in 2050.

In the last quarter-century, the field of molecular dynamics (MD) has undergone a remarkable transformation, propelled by substantial enhancements in software, hardware, and underlying methodologies. In this Perspective, we contemplate the future trajectory of MD simulations and their possible look at the year 2050. We spotlight the pivotal role of artificial intelligence (AI) in shaping the future of MD and the broader field of computational physical chemistry. We outline critical strategies and initiatives that are essential for the seamless integration of such technologies. Our discussion delves into topics like multiscale modeling, adept management of ever-increasing data deluge, the establishment of centralized simulation databases, and the autonomous refinement, cross-validation, and self-expansion of these repositories. The successful implementation of these advancements requires scientific transparency, a cautiously optimistic approach to interpreting AI-driven simulations and their analysis, and a mindset that prioritizes knowledge-motivated research alongside AI-enhanced big data exploration. While history reminds us that the trajectory of technological progress can be unpredictable, this Perspective offers guidance on preparedness and proactive measures, aiming to steer future advancements in the most beneficial and successful direction.

Multiple sclerosis endophenotypes identified by high-dimensional blood signatures are associated with distinct disease trajectories.

One of the biggest challenges in managing multiple sclerosis is the heterogeneity of clinical manifestations and progression trajectories. It still remains to be elucidated whether this heterogeneity is reflected by discrete immune signatures in the blood as a surrogate of disease pathophysiology. Accordingly, individualized treatment selection based on immunobiological principles is still not feasible. Using two independent multicentric longitudinal cohorts of patients with early multiple sclerosis (n = 309 discovery and n = 232 validation), we were able to identify three distinct peripheral blood immunological endophenotypes by a combination of high-dimensional flow cytometry and serum proteomics, followed by unsupervised clustering. Longitudinal clinical and paraclinical follow-up data collected for the cohorts revealed that these endophenotypes were associated with disease trajectories of inflammation versus early structural damage. Investigating the capacity of immunotherapies to normalize endophenotype-specific immune signatures revealed discrete effect sizes as illustrated by the limited effect of interferon-β on endophenotype 3-related immune signatures. Accordingly, patients who fell into endophenotype 3 subsequently treated with interferon-β exhibited higher disease progression and MRI activity over a 4-year follow-up compared with treatment with other therapies. We therefore propose that ascertaining a patient's blood immune signature before immunomodulatory treatment initiation may facilitate prediction of clinical disease trajectories and enable personalized treatment decisions based on pathobiological principles.

Fresh fruit, dried fruit, raw vegetables, and cooked vegetables consumption associated with progression trajectory of type 2 diabetes: a multi-state analysis of a prospective cohort.

To examine the effects of fresh fruit, dried fruit, raw vegetables, and cooked vegetables on type 2 diabetes (T2D) progression trajectory. We included 429,886 participants in the UK Biobank who were free of diabetes and diabetes complications at baseline. Food groups were determined using a validated food frequency questionnaire. Outcomes were T2D incidence, complications, and mortality. Multi-state model was used to analyze the effects of food groups on T2D progression. During a follow-up of 12.6 years, 10,333 incident T2D cases were identified, of whom, 3961 (38.3%) developed T2D complications and 1169 (29.5%) died. We found that impacts of four food groups on T2D progression varied depending on disease stage. For example, compared to participants who ate less than one piece of dried fruit per day, the hazard ratios and 95% confidence intervals for those who ate ≥ 2 pieces of dried fruit per day were 0.82 (0.77, 0.87), 0.88 (0.85, 0.92), and 0.86 (0.78, 0.95) for transitions from diabetes-free state to incident T2D, from diabetes-free state to total death, and from incident T2D to T2D complications, respectively. Higher intake of fresh fruit was significantly associated with lower risk of disease progression from diabetes-free state to all-cause death. Higher intake of raw and cooked vegetables was significantly associated with lower risks of disease progression from diabetes-free state to incident T2D and to total death. These findings indicate that higher intake of fresh fruit, dried fruit, raw vegetables, and cooked vegetables could be beneficial for primary and secondary prevention of T2D.

Single cell deciphering of progression trajectories of the tumor ecosystem in head and neck cancer

Head and neck squamous cell carcinoma is the sixth most common cancer worldwide and has high heterogeneity and unsatisfactory outcomes. To better characterize the tumor progression trajectory, we perform single-cell RNA sequencing of normal tissue, precancerous tissue, early-stage, advanced-stage cancer tissue, lymph node, and recurrent tumors tissue samples. We identify the transcriptional development trajectory of malignant epithelial cells and a tumorigenic epithelial subcluster regulated by TFDP1. Furthermore, we find that the infiltration of POSTN+ fibroblasts and SPP1+ macrophages gradually increases with tumor progression; their interaction or interaction with malignant cells also gradually increase to shape the desmoplastic microenvironment and reprogram malignant cells to promote tumor progression. Additionally, we demonstrate that during lymph node metastasis, exhausted CD8+ T cells with high CXCL13 expression strongly interact with tumor cells to acquire more aggressive phenotypes of extranodal expansion. Finally, we delineate the distinct features of malignant epithelial cells in primary and recurrent tumors, providing a theoretical foundation for the precise selection of targeted therapy for tumors at different stages. In summary, the current study offers a comprehensive landscape and deep insight into epithelial and microenvironmental reprogramming throughout initiation, progression, lymph node metastasis and recurrence of head and neck squamous cell carcinoma.

Abstract 1251: Tobacco smoke alters the adaptive landscape of lung adenocarcinoma and influences the strength of epistatic interactions

Abstract Tobacco smoke produces both mutagenic and physiological effects, altering the mutation rate of cells and the selection acting on them in the damaged lung environment, and facilitating its role in driving cancer progression. In addition to these gene-by-environment interactions, gene-by-gene epistatic interactions also shape the adaptive landscape, influencing the evolutionary trajectories of cancer. Understanding the influence of tobacco smoke on the epistatic trajectories of lung cancer progression would help guide the development of targeted therapies that are more effective for smoker or never-smoker populations. To address this need, we constructed a continuous-time Markov chain model for the evolutionary trajectories of lung adenocarcinoma (LUAD), the most common subtype of lung cancer and the most frequent subtype among never-smokers. Using thousands of tumor sample sequences aggregated across studies, we profiled the trajectories of LUAD in smokers and never-smokers and estimated the rates at which mutations are gained from each possible set of pre-existing mutations, or genetic states. We then accounted for differences in baseline mutation rates to quantify the selective benefit of mutations. We find that epistasis is prevalent in LUAD, with several strong synergistic interactions—such as between RB1 and EGFR mutations- and antagonistic interactions-such as between EGFR and KEAP1 or KRAS mutations. Additionally, we identify non-additive epistatic effects: STK11 mutations are synergistic with KEAP1 and KRAS mutations but do not experience additional selection when both are mutated. In contrast, GNAS mutations are not synergistic with KEAP1 or RBM10 mutations alone but are synergistic with their co-mutation. Smoking has a large influence on the adaptive landscape, with several common mutations being preferentially selected in never-smoker cancers, including EGFR, SMAD4, GNAS, and PIK3CA mutations. KEAP1, STK11, and KRAS mutations are preferentially selected in smoker cancers. Smoking’s physiological influence also affects the strength of certain epistatic interactions, such as between STK11 and KEAP1 mutations. Overall, we find that smoking not only increases the mutation rate of lung cells but also substantially alters the adaptive landscape of lung adenocarcinoma, leading to clinically relevant differences in selection on mutations between smoker and never-smoker cancers. We additionally detect frequent pairwise and higher-order epistatic effects in LUAD that may inform the personalized application of targeted therapies. Citation Format: Krishna Dasari, Jorge Alfaro-Murillo, Jeffrey Townsend. Tobacco smoke alters the adaptive landscape of lung adenocarcinoma and influences the strength of epistatic interactions [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1251.

Abstract 2446: Genomic landscape of longitudinally collected preinvasive airway lesions

Abstract Lung carcinoma in situ (CIS) lesions, as precursors to squamous cell carcinoma, exhibit microscopic uniformity while displaying diverse clinical trajectories. Half of these lesions progress to invasive cancer, while the remaining half either regress or remain indolent. Previous genomic profiling efforts have elucidated progression-specific genomic changes against a backdrop of inherent heterogeneity and notable chromosomal instability signatures. Here, we present a novel approach by analyzing longitudinally collected samples of preinvasive airway lesions, encompassing both progressive and indolent trajectories. Our findings reveal that genomic integrity and cell cycle dynamics constitute key distinctions between progressive and indolent lesions. Notably, a decrease in clonal diversity and an inverse correlation between TCRA-Tcell fraction and genomic instability are observed as lesions approach invasion. Somatic mutations in cancer driver genes BRAC2, CSMD3, and MUC16 are found significantly associated with lesion progression. Furthermore, progressive lesions exhibit a higher frequency of copy number changes compared to indolent lesions. Copy number loss in 1p, 3p, 9p, and 17p are found to be significantly associated with lesion progression. Phylogenetic analysis further confirms positive selection for copy number loss in these specific genomic regions, providing insights into the evolutionary trajectory of alterations in progressive lesions. The comprehensive understanding derived from these genomic analyses is anticipated to advance early detection strategies, mitigate overtreatment, and facilitate the development of preventative therapies targeting early clonal events in lung cancer. Citation Format: Ahmed Alhendi, Lukas Kalinke, Adam Pennycuick, Kate Davies, Pascal Durrenberger, Kate Gowers, Kristiana Grigoriadis, Nicholas McGranahan, Sam Janes. Genomic landscape of longitudinally collected preinvasive airway lesions [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2446.

Sustainable Gender Equality: A Comparative Perspective on STEM Education and Employment in Jordan

Gender equality is a concept that is synonymous with debates towards economic and societal advancement, as manifested through the United Nation’s Sustainable Development Goal 5. This study sets out to identify the key indicators driving gender equality in Science, Technology, Engineering, and Mathematics (STEM) education and employment in Jordan. This area is both critical and timely in light of emerging challenges facing technological advancement, progressive economies, and wider societal inclusion strategies within the professional environment. This study predominantly employs a quantitative methodology, utilising a survey to evaluate and rank a comprehensive set of indicators/challenges associated with gender equality in the published literature. Feedback is gathered from both male and female participants. The findings of this study reveal certain categories and indicators that are highly ranked compared with others, with practical aspects such as workplace conditions and professional treatment, societal norms and gender stereotypes, and professional perceptions and underlying gender bias being more predominant in Jordan. This study also revealed differing challenges facing gender equality in employment compared with the ones faced in education. This finding resonates with the historical trajectory of academic progress in STEM fields in Jordan, and its divergence emphasises the need for a nuanced exploration to advance gender equality in STEM effectively.

Identification of Genetic Risk Factors Based on Disease Progression Derived From Longitudinal Brain Imaging Phenotypes.

Neurodegenerative disorders usually happen stage-by-stage rather than overnight. Thus, cross-sectional brain imaging genetic methods could be insufficient to identify genetic risk factors. Repeatedly collecting imaging data over time appears to solve the problem. But most existing imaging genetic methods only use longitudinal imaging phenotypes straightforwardly, ignoring the disease progression trajectory which might be a more stable disease signature. In this paper, we propose a novel sparse multi-task mixed-effects longitudinal imaging genetic method (SMMLING). In our model, disease progression fitting and genetic risk factors identification are conducted jointly. Specifically, SMMLING models the disease progression using longitudinal imaging phenotypes, and then associates fitted disease progression with genetic variations. The baseline status and changing rate, i.e., the intercept and slope, of the progression trajectory thus shoulder the responsibility to discover loci of interest, which would have superior and stable performance. To facilitate the interpretation and stability, we employ l2,1 -norm and the fused group lasso (FGL) penalty to identify loci at both the individual level and group level. SMMLING can be solved by an efficient optimization algorithm which is guaranteed to converge to the global optimum. We evaluate SMMLING on synthetic data and real longitudinal neuroimaging genetic data. Both results show that, compared to existing longitudinal methods, SMMLING can not only decrease the modeling error but also identify more accurate and relevant genetic factors. Most risk loci reported by SMMLING are missed by comparison methods, implicating its superiority in genetic risk factors identification. Consequently, SMMLING could be a promising computational method for longitudinal imaging genetics.

Progression analysis versus traditional methods to quantify slowing of disease progression in Alzheimer’s disease

BackgroundThe clinical meaningfulness of the effects of recently approved disease-modifying treatments (DMT) in Alzheimer’s disease is under debate. Available evidence is limited to short-term effects on clinical rating scales which may be difficult to interpret and have limited intrinsic meaning to patients. The main value of DMTs accrues over the long term as they are expected to cause a delay or slowing of disease progression. While awaiting such evidence, the translation of short-term effects to time delays or slowing of progression could offer a powerful and readily interpretable representation of clinical outcomes.MethodsWe simulated disease progression trajectories representing two arms, active and placebo, of a hypothetical clinical trial of a DMT. The placebo arm was simulated based on estimated mean trajectories of clinical dementia rating scale–sum of boxes (CDR-SB) recordings from amyloid-positive subjects with mild cognitive impairment (MCI) from Alzheimer’s Disease Neuroimaging Initiative (ADNI). The active arm was simulated to show an average slowing of disease progression versus placebo of 20% at each visit. The treatment effects in the simulated trials were estimated with a progression model for repeated measures (PMRM) and a mixed model for repeated measures (MMRM) for comparison. For PMRM, the treatment effect is expressed in units of time (e.g., days) and for MMRM in units of the outcome (e.g., CDR-SB points). PMRM results were implemented in a health economics Markov model extrapolating disease progression and death over 15 years.ResultsThe PMRM model estimated a 19% delay in disease progression at 18 months and 20% (~ 7 months delay) at 36 months, while the MMRM model estimated a 25% reduction in CDR-SB (~ 0.5 points) at 36 months. The PMRM model had slightly greater power compared to MMRM. The health economic model based on the estimated time delay suggested an increase in life expectancy (10 months) without extending time in severe stages of disease.ConclusionPMRM methods can be used to estimate treatment effects in terms of slowing of progression which translates to time metrics that can be readily interpreted and appreciated as meaningful outcomes for patients, care partners, and health care practitioners.

Quantifying the clusterness and trajectoriness of single-cell RNA-seq data.

Among existing computational algorithms for single-cell RNA-seq analysis, clustering and trajectory inference are two major types of analysis that are routinely applied. For a given dataset, clustering and trajectory inference can generate vastly different visualizations that lead to very different interpretations of the data. To address this issue, we propose multiple scores to quantify the "clusterness" and "trajectoriness" of single-cell RNA-seq data, in other words, whether the data looks like a collection of distinct clusters or a continuum of progression trajectory. The scores we introduce are based on pairwise distance distribution, persistent homology, vector magnitude, Ripley's K, and degrees of connectivity. Using simulated datasets, we demonstrate that the proposed scores are able to effectively differentiate between cluster-like data and trajectory-like data. Using real single-cell RNA-seq datasets, we demonstrate the scores can serve as indicators of whether clustering analysis or trajectory inference is a more appropriate choice for biological interpretation of the data.