Can structure predict function at individual level in the human connectome?

Several studies predicting Functional Connectivity (FC) from Structural Connectivity (SC) at individual level have been published in recent years, each promising increased performance and utility. We investigated three of these studies, analyzing whether the results truly represent a meaningful individual-level mapping from SC to FC. Using data from the Human Connectome Project shared accross the three studies, we constructed a predictor by averaging FC of training data and analyzed its performance in the same way. In each case, we found that group average FC is an equivalent or better predictor of individual FC than the predictive models in terms of raw prediction performance. Furthermore, we showed that additional analyses performed by the authors of the three studies, in which they attempt to show that their predicted FC has value beyond raw prediction performance, could also be reproduced using the group average FC predictor. This makes it unclear whether any of the three methods represent a meaningful individual-level predictive model. We conclude that either the methods are not appropriate for the data, that the sample size is too small, or that the data does not contain sufficient information to learn a mapping from SC to FC. We advise future individual-level studies to explicitly report results in comparison to the performance of the group average, and carefully demonstrate that their predictions contain meaningful individual-level information. Finally, we believe that investigating alternatives for the construction of SC and FC may improve the chances of developing a meaningful individual-level mapping from SC to FC.

Post-selection Inference in Multiverse Analysis (PIMA): An Inferential Framework Based on the Sign Flipping Score Test.

When analyzing data, researchers make some choices that are either arbitrary, based on subjective beliefs about the data-generating process, or for which equally justifiable alternative choices could have been made. This wide range of data-analytic choices can be abused and has been one of the underlying causes of the replication crisis in several fields. Recently, the introduction of multiverse analysis provides researchers with a method to evaluate the stability of the results across reasonable choices that could be made when analyzing data. Multiverse analysis is confined to a descriptive role, lacking a proper and comprehensive inferential procedure. Recently, specification curve analysis adds an inferential procedure to multiverse analysis, but this approach is limited to simple cases related to the linear model, and only allows researchers to infer whether at least one specification rejects the null hypothesis, but not which specifications should be selected. In this paper, we present a Post-selection Inference approach to Multiverse Analysis (PIMA) which is a flexible and general inferential approach that considers for all possible models, i.e., the multiverse of reasonable analyses. The approach allows for a wide range of data specifications (i.e., preprocessing) and any generalized linear model; it allows testing the null hypothesis that a given predictor is not associated with the outcome, by combining information from all reasonable models of multiverse analysis, and provides strong control of the family-wise error rate allowing researchers to claim that the null hypothesis can be rejected for any specification that shows a significant effect. The inferential proposal is based on a conditional resampling procedure. We formally prove that the Type I error rate is controlled, and compute the statistical power of the test through a simulation study. Finally, we apply the PIMA procedure to the analysis of a real dataset on the self-reported hesitancy for the COronaVIrus Disease 2019 (COVID-19) vaccine before and after the 2020 lockdown in Italy. We conclude with practical recommendations to be considered when implementing the proposed procedure.

No evidence that a transmissible cancer has shifted from emergence to endemism in Tasmanian devils.

Tasmanian devils are endangered by a transmissible cancer known as Tasmanian devil facial tumour 1 (DFT1). A 2020 study by Patton et al. (Science 370, eabb9772 (doi:10.1126/science.abb9772)) used genome data from DFT1 tumours to produce a dated phylogenetic tree for this transmissible cancer lineage, and thence, using phylodynamics models, to estimate its epidemiological parameters and predict its future trajectory. It concluded that the effective reproduction number for DFT1 had declined to a value of one, and that the disease had shifted from emergence to endemism. We show that the study is based on erroneous mutation calls and flawed methodology, and that its conclusions cannot be substantiated.

Reproducing RECIST lesion selection via machine learning: Insights into intra and inter-radiologist variation.

Background: The Response Evaluation Criteria in Solid Tumors (RECIST) aims to provide a standardized approach to assess treatment response in solid tumors. However, discrepancies in the selection of measurable and target lesions among radiologists using these criteria pose a significant limitation to their reproducibility and accuracy. This study aimed to understand the factors contributing to this variability. Methods: Machine learning models were used to replicate, in parallel, the selection process of measurable and target lesions by two radiologists in a cohort of 40 patients from an internal pan-cancer dataset. The models were trained on lesion characteristics such as size, shape, texture, rank, and proximity to other lesions. Ablation experiments were conducted to evaluate the impact of lesion diameter, volume, and rank on the selection process. Results: The models successfully reproduced the selection of measurable lesions, relying primarily on size-related features. Similarly, the models reproduced target lesion selection, relying mostly on lesion rank. Beyond these features, the importance placed by different radiologists on different visual characteristics can vary, specifically when choosing target lesions. Worth noting that substantial variability was still observed between radiologists in both measurable and target lesion selection. Conclusions: Despite the successful replication of lesion selection, our results still revealed significant inter-radiologist disagreement. This underscores the necessity for more precise guidelines to standardize lesion selection processes and minimize reliance on individual interpretation and experience as a means to bridge existing ambiguities.

Reproducible machine learning research in mental workload classification using EEG.

This study addresses concerns about reproducibility in scientific research, focusing on the use of electroencephalography (EEG) and machine learning to estimate mental workload. We established guidelines for reproducible machine learning research using EEG and used these to assess the current state of reproducibility in mental workload modeling. We first started by summarizing the current state of reproducibility efforts in machine learning and in EEG. Next, we performed a systematic literature review on Scopus, Web of Science, ACM Digital Library, and Pubmed databases to find studies about reproducibility in mental workload prediction using EEG. All of this previous work was used to formulate guidelines, which we structured along the widely recognized Cross-Industry Standard Process for Data Mining (CRISP-DM) framework. By using these guidelines, researchers can ensure transparency and comprehensiveness of their methodologies, therewith enhancing collaboration and knowledge-sharing within the scientific community, and enhancing the reliability, usability and significance of EEG and machine learning techniques in general. A second systematic literature review extracted machine learning studies that used EEG to estimate mental workload. We evaluated the reproducibility status of these studies using our guidelines. We highlight areas studied and overlooked and identify current challenges for reproducibility. Our main findings include limitations on reporting performance on unseen test data, open sharing of data and code, and reporting of resources essential for training and inference processes.

Blind measurements did not confirm effects of forest fragmentation on fluctuating asymmetry of a tropical butterfly Morpho helenor.

Re-evaluation of photographs of the tropical butterfly Morpho helenor from a previous study (Pignataro et al. 2023) revealed that its conclusion regarding increased wing fluctuating asymmetry in forest edge habitats compared to forest interior habitats could not be replicated. This discrepancy likely arises from (i) original measurements not being conducted blindly, (ii) insufficient photograph quality hindering accurate landmark selection, and (iii) a lack of detailed description of the measurement protocol. The likelihood of false positive discoveries within the published data concerning the impacts of environmental stress on the fluctuating asymmetry of plants and animals is probably higher than previously assumed.

Test-Retest Reproducibility of Reduced-Field-of-View Density-Weighted CRT MRSI at 3T.

Quantifying an imaging modality's ability to reproduce results is important for establishing its utility. In magnetic resonance spectroscopic imaging (MRSI), new acquisition protocols are regularly introduced which improve upon their precursors with respect to signal-to-noise ratio (SNR), total acquisition duration, and nominal voxel resolution. This study has quantified the within-subject and between-subject reproducibility of one such new protocol (reduced-field-of-view density-weighted concentric ring trajectory (rFOV-DW-CRT) MRSI) by calculating the coefficient of variance of data acquired from a test-retest experiment. The posterior cingulate cortex (PCC) and the right superior corona radiata (SCR) were selected as the regions of interest (ROIs) for grey matter (GM) and white matter (WM), respectively. CVs for between-subject and within-subject were consistently around or below 15% for Glx, tCho, and Myo-Ins, and below 5% for tNAA and tCr.

A Multidisciplinary Assessment of ChatGPT's Knowledge of Amyloidosis: Observational Study.

BACKGROUND: Amyloidosis, a rare multisystem condition, often requires complex, multidisciplinary care. Its low prevalence underscores the importance of efforts to ensure the availability of high-quality patient education materials for better outcomes. ChatGPT (OpenAI) is a large language model powered by artificial intelligence that offers a potential avenue for disseminating accurate, reliable, and accessible educational resources for both patients and providers. Its user-friendly interface, engaging conversational responses, and the capability for users to ask follow-up questions make it a promising future tool in delivering accurate and tailored information to patients. OBJECTIVE: We performed a multidisciplinary assessment of the accuracy, reproducibility, and readability of ChatGPT in answering questions related to amyloidosis. METHODS: In total, 98 amyloidosis questions related to cardiology, gastroenterology, and neurology were curated from medical societies, institutions, and amyloidosis Facebook support groups and inputted into ChatGPT-3.5 and ChatGPT-4. Cardiology- and gastroenterology-related responses were independently graded by a board-certified cardiologist and gastroenterologist, respectively, who specialize in amyloidosis. These 2 reviewers (RG and DCK) also graded general questions for which disagreements were resolved with discussion. Neurology-related responses were graded by a board-certified neurologist (AAH) who specializes in amyloidosis. Reviewers used the following grading scale: (1) comprehensive, (2) correct but inadequate, (3) some correct and some incorrect, and (4) completely incorrect. Questions were stratified by categories for further analysis. Reproducibility was assessed by inputting each question twice into each model. The readability of ChatGPT-4 responses was also evaluated using the Textstat library in Python (Python Software Foundation) and the Textstat readability package in R software (R Foundation for Statistical Computing). RESULTS: ChatGPT-4 (n=98) provided 93 (95%) responses with accurate information, and 82 (84%) were comprehensive. ChatGPT-3.5 (n=83) provided 74 (89%) responses with accurate information, and 66 (79%) were comprehensive. When examined by question category, ChatGTP-4 and ChatGPT-3.5 provided 53 (95%) and 48 (86%) comprehensive responses, respectively, to "general questions" (n=56). When examined by subject, ChatGPT-4 and ChatGPT-3.5 performed best in response to cardiology questions (n=12) with both models producing 10 (83%) comprehensive responses. For gastroenterology (n=15), ChatGPT-4 received comprehensive grades for 9 (60%) responses, and ChatGPT-3.5 provided 8 (53%) responses. Overall, 96 of 98 (98%) responses for ChatGPT-4 and 73 of 83 (88%) for ChatGPT-3.5 were reproducible. The readability of ChatGPT-4's responses ranged from 10th to beyond graduate US grade levels with an average of 15.5 (SD 1.9). CONCLUSIONS: Large language models are a promising tool for accurate and reliable health information for patients living with amyloidosis. However, ChatGPT's responses exceeded the American Medical Association's recommended fifth- to sixth-grade reading level. Future studies focusing on improving response accuracy and readability are warranted. Prior to widespread implementation, the technology's limitations and ethical implications must be further explored to ensure patient safety and equitable implementation.

Assessment of management reasoning: Design considerations drawn from analysis of simulated outpatient encounters.

PURPOSE: Management reasoning is a distinct subset of clinical reasoning. We sought to explore features to be considered when designing assessments of management reasoning. METHODS: This is a hybrid empirical research study, narrative review, and expert perspective. In 2021, we reviewed and discussed 10 videos of simulated (staged) physician-patient encounters, actively seeking actions that offered insights into assessment of management reasoning. We analyzed our own observations in conjunction with literature on clinical reasoning assessment, using a constant comparative qualitative approach. RESULTS: Distinguishing features of management reasoning that will influence its assessment include management scripts, shared decision-making, process knowledge, illness-specific knowledge, and tailoring of the encounter and management plan. Performance domains that merit special consideration include communication, integration of patient preferences, adherence to the management script, and prognostication. Additional facets of encounter variation include the clinical problem, clinical and nonclinical patient characteristics (including preferences, values, and resources), team/system characteristics, and encounter features. We cataloged several relevant assessment approaches including written/computer-based, simulation-based, and workplace-based modalities, and a variety of novel response formats. CONCLUSIONS: Assessment of management reasoning could be improved with attention to the performance domains, facets of variation, and variety of approaches herein identified.

A survey of experimental stimulus presentation code sharing in major areas of psychology.

Computer code plays a vital role in modern science, from the conception and design of experiments through to final data analyses. Open sharing of code has been widely discussed as being advantageous to the scientific process, allowing experiments to be more easily replicated, helping with error detection, and reducing wasted effort and resources. In the case of psychology, the code used to present stimuli is a fundamental component of many experiments. It is not known, however, the degree to which researchers are sharing this type of code. To estimate this, we conducted a survey of 400 psychology papers published between 2016 and 2021, identifying those working with the open-source tools Psychtoolbox and PsychoPy that openly share stimulus presentation code. For those that did, we established if it would run following download and also appraised the code's usability in terms of style and documentation. It was found that only 8.4% of papers shared stimulus code, compared to 17.9% sharing analysis code and 31.7% sharing data. Of shared code, 70% ran directly or after minor corrections. For code that did not run, the main error was missing dependencies (66.7%). The usability of the code was moderate, with low levels of code annotation and minimal documentation provided. These results suggest that stimulus presentation code sharing lags behind other forms of code and data sharing, potentially due to less emphasis on such code in open-science discussions and in journal policies. The results also highlight a need for improved documentation to maximize code utility.

Tutorial review of error evaluation in experimental water research at the example of membrane filtration.

Experimental water research lacks clear methodology to estimate experimental error. Especially when natural waters are involved, the characterization tools bear method-specific artifacts while the varying environmental conditions prevent regular repeats. This tutorial review identifies common mistakes, and proposes a practical procedure to determine experimental errors at the example of membrane filtration. Statistical analysis is often applied to an insufficient number of repeated measurements, while not all error sources and contributions are considered. This results in an underestimation of the experimental error. Variations in relevant experimental parameters need to be investigated systematically, and the related errors are quantified as a half of the variation between the max and min values when standard deviation is not applicable. Error of calculated parameters (e.g. flux, pollutant removal and mass loss) is estimated by applying error propagation, where weighing contributions of the experimental parameters are considered. Appropriate judgment and five-time repetition of a selected experiment under identical conditions are proposed to validate the propagated experimental error. For validation, the five repeated data points should lie within the estimated error range of the error bar. The proposed error evaluation procedure is adaptable in experimental water research and intended for researchers to identify the contributing factors of an experimental error and carry out appropriate error quantification and validation. The most important aim is to raise awareness of the necessity to question error methodology and reproducibility of experimental data, to produce and publish high quality research.

Seeing beyond the blot: A critical look at assumptions and raw data interpretation in Western blotting.

Rapid advancements in technology refine our understanding of intricate biological processes, but a crucial emphasis remains on understanding the assumptions and sources of uncertainty underlying biological measurements. This is particularly critical in cell signaling research, where a quantitative understanding of the fundamental mechanisms governing these transient events is essential for drug development, given their importance in both homeostatic and pathogenic processes. Western blotting, a technique developed decades ago, remains an indispensable tool for investigating cell signaling, protein expression, and protein-protein interactions. While improvements in statistical analysis and methodology reporting have undoubtedly enhanced data quality, understanding the underlying assumptions and limitations of visual inspection in Western blotting can provide valuable additional information for evaluating experimental conclusions. Using the example of agonist-induced receptor post-translational modification, we highlight the theoretical and experimental assumptions associated with Western blotting and demonstrate how raw blot data can offer clues to experimental variability that may not be fully captured by statistical analyses and reported methodologies. This article is not intended as a comprehensive technical review of Western blotting. Instead, we leverage an illustrative example to demonstrate how assumptions about experimental design and data normalization can be revealed within raw data and subsequently influence data interpretation.

Early Childhood Oral Health Impact Scale: Psychometric Evaluation in Portuguese Preschoolers.

Objective: to contribute to the validation of the Early Childhood Oral Impact Scale (ECOHIS) by studying its psychometric properties when applied to a Portuguese preschool population. Methods: Cross-sectional study conducted with children aged between three and five-years-old. The non-probabilistic sample included two preschools in the municipality of Lisbon. Children who agreed to participate and whose guardians signed the informed consent were included. Data collection included a questionnaire, administered to the parents, and an intraoral examination of the children. The questionnaire included the Portuguese version of ECOHIS. The intraoral examination included the caries diagnosis according to the World Health Organization criteria. Discriminant validity compared the ECOHIS score between children with and without caries experience (Mann-Whitney U-test). Cohen's d was calculated to estimate the magnitude of the difference. Reliability analysis included Cronbach's α and test-retest. Construct validity was analyzed by the correlation between the ECOHIS score and dmft (Spearman's correlation). A significance level of 5% was used. Results: The sample included 104 children (mean age 4.1 years). ECOHIS values were significantly different between children with and without caries (p=0.004). The Cohen's d was 0.84. The Cronbach's was 0.78, with no significant increase in value when eliminating any of the items. The test-retest showed significant correlation (r=0.76; p=0.01). There was a significant correlation between the ECOHIS score and caries experience (r=0.28; p=0.004). Conclusion: The Portuguese version of the ECOHIS showed good psychometric properties, indicating that it is a reliable and valid tool to measure the impact of oral health in preschool children.

Effect of anti-VEGF on retinal blood flow in diabetic mice using laser speckle flowgraphy.

PURPOSE: To assess intra- (repeatability) and inter-observer (reproducibility) variability of laser speckle flowgraphy (LSFG) for retinal blood flow (RBF) measurement in 20 eyes of wild type (C57BL/6J) mice and effect of intravitreal Aflibercept on RBF in optic nerve head (ONH) region of 10 eyes of Ins2 (Akita) diabetic mice. METHODS: 'Mean blur rate (MBR)' was measured for all quadrants of tissue area (MT), vessel (MV) and total area (MA) of ONH region. Changes in MT were analysed at each timepoint. Repeatability was evaluated by measuring MBR variability without changing mouse head position, and reproducibility after resetting mouse head position by another operator. Coefficient of repeatability (CR) through Bland-Altman plot method coefficient of variation (COV) and Intraclass correlation coefficient (ICC) was calculated. Intravitreal Aflibercept (1 µg) was administered to Akita eyes and intraocular pressure (IOP) was measured using a tonometer at baseline, day 7, 14, 21 and 28 post-injection. Hurvich and Tsai's criterion was used. RESULTS: Coefficient of repeatability values of repeatability and reproducibility for all quadrants were within limits of agreement. Reliability was excellent (ICC 0.98-0.99) and reproducibility was moderate to excellent (ICC 0.64-0.96). There was a non-significant IOP increase in all Akita eyes at Day 28 (p > 0.05), and significant increase in MT in all quadrants at Day 21 and superior, inferior and temporal quadrants at Day 28 (p < 0.05). CONCLUSION: Laser speckle flowgraphy demonstrates excellent repeatability and moderate to excellent reproducibility in measuring RBF. Intravitreal Aflibercept injection results in a significant increase in MT up to 28 days post-injection without significant increase in IOP.

Psychometric properties of the Disability of Arm Shoulder and Hand (DASH) in subjects with frozen shoulder: a reliability and validity study.

BACKGROUND: Frozen Shoulder (FS) is a painful condition characterized by severe pain and progressive restriction of shoulder movement, leading to functional impairment and reduced quality of life. While different Patient Reported Outcome Measurements (PROMs) tools exist for assessing shoulder diseases, few specific PROMs are validated for FS patients. PURPOSE: This study aims to assess the psychometric properties of the Disability of Arm, Shoulder, and Hand (DASH) questionnaire in FS patients. METHODS: One hundred and twenty-four subjects (mean ± SD age = 55.4 ± 7.9 years; 55.6% female) diagnosed with FS were included and completed the DASH questionnaire, the Numerical Pain Rating Scale (NPRS), the Shoulder Pain and Disability Index (SPADI), and the Short-Form Health Survey 36 (SF-36). Floor or ceiling effects were investigated. Structural validity was analysed through a unidimensional Confirmatory Factor Analysis (CFA), internal consistency through Cronbach's alpha, test-retest reliability through the Intraclass Correlation coefficient (ICC), measurement error through the Standard Error of Measurement (SEM), and the Minimum Detectable Change (MDC), and construct validity through the hypothesis testing with the correlation with the other outcome measures used. RESULTS: No floor or ceiling effects were observed. CFA confirmed a one-factor structure after addressing local item dependency (Root Mean Square Error of Approximation = 0.055; Standardized Root Mean Square Residual = 0.077; Comparative Fit Index = 0.970; Tucker-Lewis Index = 0.968). Cronbach's alpha was high (= 0.951), and test-retest reliability was excellent (ICC = 0.999; 95% CI: 0.998-1.000). SEM was equal to 0.5 points, and MDC to 1.5 points. Construct validity was considered satisfactory as 80% of the a-priori hypotheses were met. CONCLUSION: The DASH questionnaire demonstrated good psychometric properties in FS patients, supporting its use as a valuable tool for assessing the impact of FS in clinical and research settings.

Toward reproducible tumor organoid culture: focusing on primary liver cancer.

Organoids present substantial potential for pushing forward preclinical research and personalized medicine by accurately recapitulating tissue and tumor heterogeneity in vitro. However, the lack of standardized protocols for cancer organoid culture has hindered reproducibility. This paper comprehensively reviews the current challenges associated with cancer organoid culture and highlights recent multidisciplinary advancements in the field with a specific focus on standardizing liver cancer organoid culture. We discuss the non-standardized aspects, including tissue sources, processing techniques, medium formulations, and matrix materials, that contribute to technical variability. Furthermore, we emphasize the need to establish reproducible platforms that accurately preserve the genetic, proteomic, morphological, and pharmacotypic features of the parent tumor. At the end of each section, our focus shifts to organoid culture standardization in primary liver cancer. By addressing these challenges, we can enhance the reproducibility and clinical translation of cancer organoid systems, enabling their potential applications in precision medicine, drug screening, and preclinical research.

ISIT-QA: In Silico Imaging Trial to Evaluate a Low-Count Quantitative SPECT Method Across Multiple Scanner-Collimator Configurations for 223Ra-Based Radiopharmaceutical Therapies.

Personalized dose-based treatment planning requires accurate and reproducible noninvasive measurements to ensure safety and effectiveness. Dose estimation using SPECT is possible but challenging for alpha (α)-particle-emitting radiopharmaceutical therapy (α-RPT) because of complex γ-emission spectra, extremely low counts, and various image-degrading artifacts across a plethora of scanner-collimator configurations. Through the incorporation of physics-based considerations and skipping of the potentially lossy voxel-based reconstruction step, a recently developed projection-domain low-count quantitative SPECT (LC-QSPECT) method has the potential to provide reproducible, accurate, and precise activity concentration and dose measures across multiple scanners, as is typically the case in multicenter settings. To assess this potential, we conducted an in silico imaging trial to evaluate the LC-QSPECT method for a 223Ra-based α-RPT, with the trial recapitulating patient and imaging system variabilities. Methods: A virtual imaging trial titled In Silico Imaging Trial for Quantitation Accuracy (ISIT-QA) was designed with the objectives of evaluating the performance of the LC-QSPECT method across multiple scanner-collimator configurations and comparing performance with a conventional reconstruction-based quantification method. In this trial, we simulated 280 realistic virtual patients with bone-metastatic castration-resistant prostate cancer treated with 223Ra-based α-RPT. The trial was conducted with 9 simulated SPECT scanner-collimator configurations. The primary objective of this trial was to evaluate the reproducibility of dose estimates across multiple scanner-collimator configurations using LC-QSPECT by calculating the intraclass correlation coefficient. Additionally, we compared the reproducibility and evaluated the accuracy of both considered quantification methods across multiple scanner-collimator configurations. Finally, the repeatability of the methods was evaluated in a test-retest study. Results: In this trial, data from 268 223RaCl2 treated virtual prostate cancer patients, with a total of 2,903 lesions, were used to evaluate LC-QSPECT. LC-QSPECT provided dose estimates with good reproducibility across the 9 scanner-collimator configurations (intraclass correlation coefficient > 0.75) and high accuracy (ensemble average values of recovery coefficients ranged from 1.00 to 1.02). Compared with conventional reconstruction-based quantification, LC-QSPECT yielded significantly improved reproducibility across scanner-collimator configurations, accuracy, and test-retest repeatability ([Formula: see text] Conclusion: LC-QSPECT provides reproducible, accurate, and repeatable dose estimations in 223Ra-based α-RPT as evaluated in ISIT-QA. These findings provide a strong impetus for multicenter clinical evaluations of LC-QSPECT in dose quantification for α-RPTs.

Solid-state NMR spectra of amino acid enantiomers and their relative intensities.

Under normal experimental conditions in an achiral environment, NMR spectra of enantiomers have chemical shifts and J couplings which are not differentiable. In this work, the reproducibility of spectral intensities for pairs of amino acid enantiomers, as well as factors influencing these intensities, is assessed using 13C and 15N cross-polarization magic-angle spinning (CP/MAS) NMR spectroscopy. Prompted by a recent literature debate over a possible influence of the chirality-induced spin selectivity (CISS) effect on spectral intensities obtained in CP/MAS NMR experiments carried out on enantiomers, a number of control experiments were performed with recycle delays of at least 5T1. These included the analysis of proton-decoupled Bloch decay solid-state NMR spectra as well as solution NMR spectra where the cross polarization process is absent. Bloch decay and CP/MAS NMR spectra yield the same relative intensities for pairs of enantiomers while solution NMR spectra provide relative intensities closest to unity. Differences of plus-or-minus a few percent in the D/L spectral intensity ratios observed in all solid-state NMR experiments are due to sample preparation (i.e., grinding, particle size, partial amorphization) and limitations on sample purity. As previously described in the literature, more drastic intensity differences on the order of 50% are easily created by ball milling the samples. Finally, apodization is shown to invert the apparent D/L ratio in low signal-to-noise 15N CP/MAS NMR spectra of aspartic acid enantiomers. In summary, no spectral intensity differences attributable to enantiomerism are identified.

Increase transparency and reproducibility of real-world evidence in rare diseases through disease-specific Federated Data Networks.

PURPOSE: In rare diseases, real-world evidence (RWE) generation is often restricted due to small patient numbers and global geographic distribution. A federated data network (FDN) approach brings together multiple data sources harmonized for collaboration to increase the power of observational research. In this paper, we review how to increase reproducibility and transparency of RWE studies in rare diseases through disease-specific FDNs. METHOD: To be successful, a multiple stakeholder scientific FDN collaboration requires a strong governance model in place. In such a model, each database owner remains in full control regarding the use of and access to patient-level data and is responsible for data privacy, ethical, and legal compliance. Provided that all this is well documented and good database descriptions are in place, such a governance model results in increased transparency, while reproducibility is achieved through data curation and harmonization, and distributed analytical methods. RESULTS: Leveraging the OHDSI community set of methods and tools, two rare disease-specific FDNs are discussed in more detail. For multiple myeloma, HONEUR-the Haematology Outcomes Network in Europe-has built a strong community among the data partners dedicated to scientific exchange and research. To advance scientific knowledge in pulmonary hypertension (PH) an FDN, called PHederation, was established to form a partnership of research institutions with PH databases coming from diverse origins.