PCR87 Collecting Exploratory Patient Preference Data Using an Efficient Approach: An Example in Rare Diseases

The study of rare diseases has long been an area of challenge for medical researchers, with agonizingly slow movement towards improved understanding of pathophysiology and treatments compared with more common illnesses. The push towards evidence-based medicine (EBM), which prioritizes certain types of evidence over others, poses a particular issue when mapped onto rare diseases, which may not be feasibly investigated using the methodologies endorsed by EBM, due to a number of constraints. While other trial designs have been suggested to overcome these limitations (with varying success), perhaps the most recent and enthusiastically adopted is the application of artificial intelligence to rare disease data. This paper critically examines the pitfalls of EBM (and its trial design offshoots) as it pertains to rare diseases, exploring the current landscape of AI as a potential solution to these challenges. This discussion is also taken a step further, providing philosophical commentary on the weaknesses and dangers of AI algorithms applied to rare disease research. While not proposing a singular solution, this article does provide a thoughtful reminder that no 'one-size-fits-all' approach exists in the complex world of rare diseases. We must balance cautious optimism with critical evaluation of new research paradigms and technology, while at the same time not neglecting the ever-important aspect of patient values and preferences, which may be challenging to incorporate into computer-driven models.

BackgroundIn families segregating a monogenic genetic disorder with a single disease gene introduction, patients share a mutation-carrying chromosomal interval with identity-by-descent (IBD). Such a shared chromosomal interval or haplotype, surrounding the actual pathogenic mutation, is typically detected and defined by multipoint linkage and phased haplotype analysis using microsatellite or SNP genotype data. High-density SNP genotype data presents a computational challenge for conventional genetic analyses. A novel non-parametric method termed Homozygosity Haplotype (HH) was recently proposed for the genome-wide search of the autosomal segments shared among patients using high density SNP genotype data.Methodology/Principal FindingsThe applicability and the effectiveness of HH in identifying the potential linkage of disease causative gene with high-density SNP genotype data were studied with a series of monogenic disorders ascertained in eastern Canadian populations. The HH approach was validated using the genotypes of patients from a family affected with a rare autosomal dominant disease Schnyder crystalline corneal dystrophy. HH accurately detected the ∼1 Mb genomic interval encompassing the causative gene UBIAD1 using the genotypes of only four affected subjects. The successful application of HH to identify the potential linkage for a family with pericentral retinal disorder indicates that HH can be applied to perform family-based association analysis by treating affected and unaffected family members as cases and controls respectively. A new strategy for the genome-wide screening of known causative genes or loci with HH was proposed, as shown the applications to a myoclonus dystonia and a renal failure cohort.Conclusions/SignificanceOur study of the HH approach demonstrates that HH is very efficient and effective in identifying potential disease linked region. HH has the potential to be used as an efficient alternative approach to sequencing or microsatellite-based fine mapping for screening the known causative genes in genetic disease study.

Multiomics also described as integrative omics is an analytical approach that combines data from multiple ‘omics’ approaches including genomics, transcriptomics, proteomics, metabolomics, epigenomics, metagenomics and Meta transcriptomics to answer the complex biological processes involved in rare genetic disorders. This omics approach is particularly helpful since it identifies biomarkers of disease progression and treatment progress by collective characterization and quantification of pools of biological molecules within and among the various types of cells to better understand and categorize the Mendelian and non- Mendelian forms of rare diseases. As compared to studies of a single omics type, multi-omics offers the opportunity to understand the flow of information that underlies the disease. A range of omics software and databases, for example WikiPathways, MixOmics, MONGKIE, GalaxyP, GalaxyM, CrossPlatform Commander, and iCluster are used for multi-omics data exploration and integration in rare disease analysis. Recent advances in the field of genetics and translational research have opened new treatment avenues for patients. The innovation in the next generation sequencing and RNA sequencing has improved the ability from diagnostics to detection of molecular alterations like gene mutations in specific disease types. In this chapter, we provide an overview of such omics technologies and focus on methods for their integration across multiple omics layers. The scrupulous understanding of rare genetic disorders and their treatment at the molecular level led to the concept of a personalized approach, which is one of the most significant advancements in modern research which enable researchers to better comprehend the flow of knowledge which underpins genetic diseases.

SummarySingle-cell technologies generate large, high-dimensional datasets encompassing a diversity of omics. Dimensionality reduction captures the structure and heterogeneity of the original dataset, creating low-dimensional visualizations that contribute to the human understanding of data. Existing algorithms are typically unsupervised, using measured features to generate manifolds, disregarding known biological labels such as cell type or experimental time point. We repurpose the classification algorithm, linear discriminant analysis (LDA), for supervised dimensionality reduction of single-cell data. LDA identifies linear combinations of predictors that optimally separate a priori classes, enabling the study of specific aspects of cellular heterogeneity. We implement feature selection by hybrid subset selection (HSS) and demonstrate that this computationally efficient approach generates non-stochastic, interpretable axes amenable to diverse biological processes such as differentiation over time and cell cycle. We benchmark HSS-LDA against several popular dimensionality-reduction algorithms and illustrate its utility and versatility for the exploration of single-cell mass cytometry, transcriptomics, and chromatin accessibility data.

Determining Patient Preference for Treatment of Extracranial Carotid Artery Stenosis: Carotid Angioplasty and Stenting versus Carotid Endarterectomy

Ophthalmic nonsteroidal anti-inflammatory drugs (NSAIDs) are commonly used by clinicians to manage ocular inflammation and pain following cataract surgery. Over the past decade, the US Food and Drug Administration has approved multiple topical NSAIDs for these purposes, including several reformulated products. One of these medications, bromfenac ophthalmic solution, has a long and extensive history, with proven efficacy and safety in patients following cataract surgery. The evolution of bromfenac ophthalmic solution over the years has involved either lowering the concentration of the active ingredient or extending the dosing interval to improve patient adherence/compliance. This review will focus on the history and progression of bromfenac ophthalmic solution and report the available patient preference and adherence data regarding this ocular NSAID throughout its evolution.

A new method of applying randomised control study data to the individual patient: A novel quantitative patient-centred approach to interpreting composite end points

Salvage Chemotherapy in Follicular Non-Hodgkin's Lymphoma: Focus on Tolerability

SUBGLOTTIC STENOSIS: A CASE SERIES

Background. Commuting poses a considerable burden and costs for patients with rare rheumatic diseases such as systemic sclerosis (SSc), who require care from tertiary medical centres. In a universal healthcare system, the selection of healthcare services is possibly influenced by geographical distribution, referral networks from primary care, and patient preferences. This study aims to evaluate whether longer commuting distances are associated with clinical variables and differences in medication access among Italian SSc patients receiving follow-up care at tertiary centres. Materials and Methods. The driving distance from the city of residence to the care centre was calculated using the Google Maps Distance Matrix API for anonymized patients with SSc from the Systemic Sclerosis Progression INvestiGation (SPRING) Italian registry. This distance was then correlated with clinical and drug history in an ordinal logistic regression analysis. Results. The characteristics of 1,782 patients were obtained from 38 centres across the country (Figure 1 and Table 1): 74.2% were categorized as short-distance commuters (traveling less than 100 km), 20.1% as intermediate-distance commuters (traveling 100-500 km), and 5.6% as long-distance commuters (traveling more than 500 km). Notably, 24.6% of the patients received care outside their residential administrative region. Patients with interstitial lung disease (ILD), pulmonary arterial hypertension, lower Forced Vital Capacity (FVC), reduced Diffusion Capacity for Carbon Monoxide (DLCO), anti-Scl70 positivity, and anti-centromere negativity had higher probability of traveling longer distances. Similarly, access to multiple immunosuppressants (cyclophosphamide, azathioprine, methotrexate, tocilizumab) and vasoactive/vasodilator drugs (Endothelin Receptor Antagonists (ERA), Phosphodiesterase Type 5 Inhibitors (PDE5i), iloprost) was associated with increased probability of long travel distances (Table 1). No significant differences were correlated to demographics or comorbidity burden. Conclusions. SSc patients with pulmonary complications are more likely to undertake longer commuting distances and have a higher likelihood of receiving advanced treatments. Public health strategies should aim to enhance accessibility to specialized care and advanced therapies for these patients, recognizing the impact of travel burden on healthcare access.

MotivationHierarchical data structures are prevalent across several research fields, as they represent an organized and efficient approach to study complex interconnected systems. Their significance is particularly evident in microbiome analysis, where microbial communities are classified at various taxonomic levels using phylogenetic trees. In light of this trend, the R/Bioconductor community has established a reproducible analytical framework for hierarchical data, which relies on the generic and optimized TreeSummarizedExperiment data container. However, this framework requires basic programming skills.ResultsTo reduce the entry requirements, we developed iSEEtree, an R package, which provides a visual interface for the analysis and exploration of TreeSummarizedExperiment objects, thereby expanding the interactive graphics capabilities of related work to hierarchical structures. This way, users can interactively explore several aspects of their data without the need for an extensive knowledge of R programming. We describe how iSEEtree enables the exploration of hierarchical multi-table data and demonstrate its functionality with applications to microbiome analysis.Availability and implementationiSEEtree was implemented in the R programming language and is available on Bioconductor at https://bioconductor.org/packages/iSEEtree under an Artistic 2.0 license.

Rationale: Existing data processing applications for SP-ICP-ToF-MS, while valuable, have some limitations including restricted multi-sample analysis capabilities, vendor-specific constraints and the lack of efficient visualization approaches. Researchers need flexible, interactive visualization tools that can reveal compositional patterns and enable side-by-side sample comparisons for rapid trend identification and comprehensive data exploration across large datasets. Methodology: we developed IsotopeTrack, an open-source Python-based platform designed specifically for SP-ICP-ToF-MS data processing with cross-platform compatibility (Windows and MacOS). The application implements complete calibration methodologies including transport rate and sensitivity calibrations, three distinct peak detection algorithms (Currie method, Formula C, and compound Poisson log-normal) and element specific parameter optimization. Performance was validated using diverse engineered nanoparticles including TiO₂, CeO₂, metallic alloys (Ni-Fe-Co, Ni-Fe-Cr-Mn, Ni-Fe-Mo), and Au/Ag core-shell particles. The platform features an interactive results canvas with drag and drop capabilities for constructing customized analysis pipelines and it supports multiple file formats. Results: IsotopeTrack successfully analyzed multi-element alloy compositions with a high accuracy. For Ni-Fe-Co alloys, measured mass compositions of ⁶⁰Ni (29.3%), ⁵⁷Fe (55.1%), and ⁵⁹Co (17.7%) closely matched known values of 28% Ni, 55% Fe, and 17% Co. Ultra-uniform gold nanoparticles yielded mean diameters of 52.0±5.0 nm, 29.5±4.4 nm, and 21.1±4.3 nm for nominal 50, 30, and 20 nm particles. The platform generated comprehensive visualizations including elemental correlations, isotopic ratio distributions, ternary diagrams, and composition heatmaps. Processing time was reduced from hours to minutes through parallel processing. The comparison of environmental samples consisting of hundreds of thousands of particles was greatly facilitated. Discussion: IsotopeTrack addressed critical limitations in SP-ICP-ToF-MS data analysis by providing batch processing and interactive visualization tools. Element specific optimization ensured analytical rigor, while dramatically reducing processing time. This open-source framework represents a significant advance in single particle analysis, enabling efficient processing of large datasets essential for nanoparticle characterization in complex systems.

Mitochondrial neurogastrointestinal encephalomyopathy (MNGIE) is a rare autosomal recessive disease caused by TYMP mutations and thymidine phosphorylase (TP) deficiency. Thymidine and deoxyuridine accumulate impairing the mitochondrial DNA maintenance and integrity. Clinically, patients show severe and progressive gastrointestinal and neurological manifestations. The onset typically occurs in the second decade of life and mean age at death is 37 years. Signs and symptoms of MNGIE are heterogeneous and confirmatory diagnostic tests are not routinely performed by most laboratories, accounting for common misdiagnosis. Factors predictive of progression and appropriate tests for monitoring are still undefined. Several treatment options showed promising results in restoring the biochemical imbalance of MNGIE. The lack of controlled studies with appropriate follow-up accounts for the limited evidence informing diagnostic and therapeutic choices. The International Consensus Conference (ICC) on MNGIE, held in Bologna, Italy, on 30 March to 31 March 2019, aimed at an evidence-based consensus on diagnosis, prognosis, and treatment of MNGIE among experts, patients, caregivers and other stakeholders involved in caring the condition. The conference was conducted according to the National Institute of Health Consensus Conference methodology. A consensus development panel formulated a set of statements and proposed a research agenda. Specifically, the ICC produced recommendations on: (a) diagnostic pathway; (b) prognosis and the main predictors of disease progression; (c) efficacy and safety of treatments; and (f) research priorities on diagnosis, prognosis, and treatment. The Bologna ICC on diagnosis, management and treatment of MNGIE provided evidence-based guidance for clinicians incorporating patients' values and preferences.

The traditional development model of high consumption and low efficiency approaches the threshold of resource and environmental carrying capacity, bringing a series of severe social and ecological environmental problems, and it is urgent to realize green development transformation. Based on a multidimensional perspective, this paper constructed a comprehensive evaluation system for urban green development along the Yellow River Basin (YRB). Entropy method, exploratory spatial data analysis (ESDA) model, and trend analysis were used to measure and characterize the spatiotemporal evolution of urban green development index (UGDI) along the YRB in 2008, 2013, and 2018, and geographically weighted regression (GWR) model was used to explore the influencing factors of urban green development. The results are as follows: (1) the UGDI along the YRB showed a slow upward trend, but the absolute value was relatively low, mainly concentrated in 0.2699–0.3799. (2) The UGDI had obvious regional differences, and cities such as Baotou and Zibo showed a “high‐high” agglomeration, while most cities in the midstream showed a “low‐low” agglomeration, and in terms of space, they were “high in the east and west, low in the middle” and “high in the north and low in the south.” (3) Influencing factors had different degrees of impact on the UGDI. Economic, industrial, urbanization, and green infrastructure factors played a positive role in promoting the urban green development, while the relationship between governance, technological factors, and green development varies from city to city.

<h3>Objective:</h3> The objective of RESTORE (NCT04451668) is to assess the long-term safety and tolerability of once-nightly sodium oxybate (ON-SXB; FT218), an investigational extended-release formulation. Interim safety data are reported; dose titration and patient preference data are presented separately. <h3>Background:</h3> In the pivotal phase 3 REST-ON trial (NCT02720744), ON-SXB met its 3 coprimary efficacy endpoints for treatment of adults with narcolepsy (<i>P</i>&lt;0.001 vs placebo) and had a safety profile consistent with that of immediate-release (IR) SXB. RESTORE is an ongoing open-label extension/switch study. <h3>Design/Methods:</h3> Participants aged ≥16 years with narcolepsy type 1/2 who completed the REST-ON trial, were on stable-dose IR oxybate for ≥1 month, or were oxybate-naive were eligible to enroll in RESTORE. Initial ON-SXB doses are equivalent/closest to previous total nightly dose of IR oxybate (switch participants) or 4.5 g/night (other participants); weekly ON-SXB dose adjustments were allowed at 1.5 g/week increments (maximum, 9 g/night). Safety data for participants who received ≥1 dose of ON-SXB as of 01July2022 are reported. <h3>Results:</h3> This analysis includes 180 individuals (REST-ON participants, n=15 [8.3%]; oxybate-naive, n=35 [19.4%]; switch, n=130 [72.2%]; white, n=150 [83.3%]; female, n=122 [67.8%]; mean age, 35 y [range, 16–84]). Adverse events (AEs) were reported by 105 (58.3%) participants. Serious AEs were recorded in 3 participants (abscess; deep vein thrombosis; rib fracture and pneumothorax); all were deemed unrelated to ON-SXB and did not result in study discontinuation. Seventy-six (42.2%) participants reported an adverse drug reaction (ADR), ie, AE related/possibly related to ON-SXB. The most common ADRs (≥3%) were nausea (11.7%), somnolence (6.7%), headache (5%), enuresis (5%), somnambulism (3.9%), dizziness (3.9%), tremor (3.9%), and vomiting (3.3%). Six (3.3%) participants discontinued due to ADRs. <h3>Conclusions:</h3> Thus far in RESTORE, ON-SXB is generally well tolerated, and no new safety signals have been observed. If approved, ON-SXB will offer a once-nightly oxybate treatment option for adults with narcolepsy. <b>Disclosure:</b> Dr. Stern has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Avadel Pharmaceutical. Dr. Stern has received intellectual property interests from a discovery or technology relating to health care. Dr. Roy has received personal compensation in the range of $5,000-$9,999 for serving on a Speakers Bureau for Jazz Pharma. Dr. Roy has received personal compensation in the range of $5,000-$9,999 for serving on a Speakers Bureau for Harmony biosciences. Dr. Roy has received personal compensation in the range of $5,000-$9,999 for serving on a Speakers Bureau for Eisai . Dr. Roy has received personal compensation in the range of $5,000-$9,999 for serving on a Speakers Bureau for Inspire. The institution of Dr. Roy has received research support from Jazz . The institution of Dr. Roy has received research support from Inspire. The institution of Dr. Roy has received research support from Avadel. Colin Shapiro has nothing to disclose. Dr. Harsh has nothing to disclose. Akinyemi Ajayi has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Jazz Pharmaceutical. Akinyemi Ajayi has received personal compensation in the range of $5,000-$9,999 for serving on a Speakers Bureau for Jazz Pharmaceutical. The institution of Dr. Ibrahim has received research support from Avadel. The institution of Dr. Ibrahim has received research support from NIH. Dr. Seiden has received personal compensation for serving as an employee of Avadel Pharmaceuticals. Dr. Seiden has received stock or an ownership interest from Avadel Pharmaceuticals. Dr. Dubow has received personal compensation in the range of $100,000-$499,999 for serving as a Consultant for Paragon Biosciences. Dr. Dubow has stock in Avadel Pharmaceuticals. Miss Gudeman has received personal compensation for serving as an employee of Avadel Pharmaceuticals. Miss Gudeman has stock in Avadel Pharmaceuticals.