Automatic cell-type harmonization and integration across Human Cell Atlas datasets.

Harmonizing cell types across the single-cell community and assembling them into a common framework is central to building a standardized Human Cell Atlas. Here, we present CellHint, a predictive clustering tree-based tool to resolve cell-type differences in annotation resolution and technical biases across datasets. CellHint accurately quantifies cell-cell transcriptomic similarities and places cell types into a relationship graph that hierarchically defines shared and unique cell subtypes. Application to multiple immune datasets recapitulates expert-curated annotations. CellHint also reveals underexplored relationships between healthy and diseased lung cell states in eight diseases. Furthermore, we present a workflow for fast cross-dataset integration guided by harmonized cell types and cell hierarchy, which uncovers underappreciated cell types in adult human hippocampus. Finally, we apply CellHint to 12 tissues from 38 datasets, providing a deeply curated cross-tissue database with ∼3.7 million cells and various machine learning models for automatic cell annotation across human tissues.

SiMix: A domain generalization method for cross-site brain MRI harmonization via site mixing.

Brain magnetic resonance imaging (MRI) is widely used in clinical practice for disease diagnosis. However, MRI scans acquired at different sites can have different appearances due to the difference in the hardware, pulse sequence, and imaging parameter. It is important to reduce or eliminate such cross-site variations with brain MRI harmonization so that downstream image processing and analysis is performed consistently. Previous works on the harmonization problem require the data acquired from the sites of interest for model training. But in real-world scenarios there can be test data from a new site of interest after the model is trained, and training data from the new site is unavailable when the model is trained. In this case, previous methods cannot optimally handle the test data from the new unseen site. To address the problem, in this work we explore domain generalization for brain MRI harmonization and propose Site Mix (SiMix). We assume that images of travelling subjects are acquired at a few existing sites for model training. To allow the training data to better represent the test data from unseen sites, we first propose to mix the training images belonging to different sites stochastically, which substantially increases the diversity of the training data while preserving the authenticity of the mixed training images. Second, at test time, when a test image from an unseen site is given, we propose a multiview strategy that perturbs the test image with preserved authenticity and ensembles the harmonization results of the perturbed images for improved harmonization quality. To validate SiMix, we performed experiments on the publicly available SRPBS dataset and MUSHAC dataset that comprised brain MRI acquired at nine and two different sites, respectively. The results indicate that SiMix improves brain MRI harmonization for unseen sites, and it is also beneficial to the harmonization of existing sites.

Harmonizing three-dimensional MRI using pseudo-warping field guided GAN.

In pursuit of cultivating automated models for magnetic resonance imaging (MRI) to aid in diagnostics, an escalating demand for extensive, multisite, and heterogeneous brain imaging datasets has emerged. This potentially introduces biased outcomes when directly applied for subsequent analysis. Researchers have endeavored to address this issue by pursuing the harmonization of MRIs. However, most existing image-based harmonization methods for MRI are tailored for 2D slices, which may introduce inter-slice variations when they are combined into a 3D volume. In this study, we aim to resolve inconsistencies between slices by introducing a pseudo-warping field. This field is created randomly and utilized to transform a slice into an artificially warped subsequent slice. The objective of this pseudo-warping field is to ensure that generators can consistently harmonize adjacent slices to another domain, without being affected by the varying content present in different slices. Furthermore, we construct unsupervised spatial and recycle loss to enhance the spatial accuracy and slice-wise consistency across the 3D images. The results demonstrate that our model effectively mitigates inter-slice variations and successfully preserves the anatomical details of the images during the harmonization process. Compared to generative harmonization models that employ 3D operators, our model exhibits greater computational efficiency and flexibility.

Harmonization of CSF and imaging biomarkers in Alzheimer's disease: Need and practical applications for genetics studies and preclinical classification.

In Alzheimer's disease (AD) research, cerebrospinal fluid (CSF) Amyloid beta (Aß), Tau and pTau are the most accepted and well validated biomarkers. Several methods and platforms exist to measure those biomarkers, leading to challenges in combining data across studies. Thus, there is a need to identify methods that harmonize and standardize these values. We used a Z-score based approach to harmonize CSF and amyloid imaging data from multiple cohorts and compared GWAS results using this approach with currently accepted methods. We also used a generalized mixture model to calculate the threshold for biomarker-positivity. Based on our findings, our normalization approach performed as well as meta-analysis and did not lead to any spurious results. In terms of dichotomization, cutoffs calculated with this approach were very similar to those reported previously. These findings show that the Z-score based harmonization approach can be applied to heterogeneous platforms and provides biomarker cut-offs consistent with the classical approaches without requiring any additional data.

Comparison of histological delineations of medial temporal lobe cortices by four independent neuroanatomy laboratories.

The medial temporal lobe (MTL) cortex, located adjacent to the hippocampus, is crucial for memory and prone to the accumulation of certain neuropathologies such as Alzheimer's disease neurofibrillary tau tangles. The MTL cortex is composed of several subregions which differ in their functional and cytoarchitectonic features. As neuroanatomical schools rely on different cytoarchitectonic definitions of these subregions, it is unclear to what extent their delineations of MTL cortex subregions overlap. Here, we provide an overview of cytoarchitectonic definitions of the entorhinal and parahippocampal cortices as well as Brodmann areas (BA) 35 and 36, as provided by four neuroanatomists from different laboratories, aiming to identify the rationale for overlapping and diverging delineations. Nissl-stained series were acquired from the temporal lobes of three human specimens (two right and one left hemisphere). Slices (50 µm thick) were prepared perpendicular to the long axis of the hippocampus spanning the entire longitudinal extent of the MTL cortex. Four neuroanatomists annotated MTL cortex subregions on digitized slices spaced 5 mm apart (pixel size 0.4 µm at 20× magnification). Parcellations, terminology, and border placement were compared among neuroanatomists. Cytoarchitectonic features of each subregion are described in detail. Qualitative analysis of the annotations showed higher agreement in the definitions of the entorhinal cortex and BA35, while the definitions of BA36 and the parahippocampal cortex exhibited less overlap among neuroanatomists. The degree of overlap of cytoarchitectonic definitions was partially reflected in the neuroanatomists' agreement on the respective delineations. Lower agreement in annotations was observed in transitional zones between structures where seminal cytoarchitectonic features are expressed less saliently. The results highlight that definitions and parcellations of the MTL cortex differ among neuroanatomical schools and thereby increase understanding of why these differences may arise. This work sets a crucial foundation to further advance anatomically-informed neuroimaging research on the human MTL cortex.

Experience and Reporting of Postnatal Depression Across Cultures: A Comparison Using Anchoring Vignettes of Mothers in the United Kingdom and India.

Postnatal mental health is often assessed using self-assessment questionnaires in epidemiologic research. Differences in response style, influenced by language, culture, and experience, may mean that the same response may not have the same meaning in different settings. These differences need to be identified and accounted for in cross-cultural comparisons. Here we describe the development and application of anchoring vignettes to investigate the cross-cultural functioning of the Edinburgh Postnatal Depression Scale (EPDS) in urban community samples in India (n = 549) and the United Kingdom (n = 828), alongside a UK calibration sample (n = 226). Participants completed the EPDS and anchoring vignettes when their children were 12-24 months old. In an unadjusted item-response theory model, UK mothers reported higher depressive symptoms than Indian mothers (d = 0.48, 95% confidence interval: 0.358, 0.599). Following adjustment for differences in response style, these positions were reversed (d = -0.25, 95% confidence interval: -0.391, -0.103). Response styles vary between India and the United Kingdom, indicating a need to take these differences into account when making cross-cultural comparisons. Anchoring vignettes offer a valid and feasible method for global data harmonization.

Global Governance of Human Genome Editing: What Are the Rules?

Human gene editing, particularly using the new CRISPR/Cas9 technology, will greatly increase the capability to make precise changes to human genomes. Human gene editing can be broken into four major categories: somatic therapy, heritable gene editing, genetic enhancement, and basic and applied research. Somatic therapy is generally well governed by national regulatory systems, so the need for global governance is less urgent. All nations are in agreement that heritable gene editing should not proceed at this time, but there is likely to be divergence if and when such procedures are shown to be safe and effective. Gene editing for enhancement purposes is not feasible today but is more controversial with the public, and many nations do not have well-developed regulatory systems for addressing genetic enhancement. Finally, different nations treat research with human embryos very differently based on deeply embedded social, cultural, ethical, and legal traditions. Several international governance mechanisms are currently in operation for human gene editing, and several other governance mechanisms have been proposed. It is unlikely that any single mechanism will alone be effective for governing human gene editing; rather, a polycentric or ecosystem approach that includes several overlapping and interacting components is likely to be necessary.

Acute-on-chronic liver failure - steps towards harmonization of the definition!

Acute-on-chronic liver failure (ACLF), usually precipitated by alcohol misuse or viral reactivation, is characterised by rapid onset and usually reversible liver failure. Various definitions of ACLF have been proposed and widely used across the globe, including those by APASL, COSSH, EASL-CLIF, Japanese experts, and NACSELD. Although all the definitions have several similarities and connote high short-term mortality, a clear and standardised definition is still lacking, hampering research in this key area. In this review, we discuss the similarities and differences among various definitions and propose steps to harmonise EASL-CLIF, APASL, NACSELD, Japanese, and Chinese definitions of ACLF.

Diffusion MRI harmonization via personalized template mapping.

One fundamental challenge in diffusion magnetic resonance imaging (dMRI) harmonization is to disentangle the contributions of scanner-related effects from the variable brain anatomy for the observed imaging signals. Conventional harmonization methods rely on establishing an atlas space to resolve anatomical variability and generate a unified inter-site mapping function. However, this approach is limited in accounting for the misalignment of neuroanatomy that still widely persists even after registration, especially in regions close to cortical boundaries. To overcome this challenge, we propose a personalized framework in this paper to more effectively address the confounding from the misalignment of neuroanatomy in dMRI harmonization. Instead of using a common template representing site-effects for all subjects, the main novelty of our method is the adaptive computation of personalized templates for both source and target scanning sites to estimate the inter-site mapping function. We integrate our method with the rotation invariant spherical harmonics (RISH) features to achieve the harmonization of dMRI signals between sites. In our experiments, the proposed approach is applied to harmonize the dMRI data acquired from two scanning platforms: Siemens Prisma and GE MR750 from the Adolescent Brain Cognitive Development dataset and compared with a state-of-the-art method based on RISH features. Our results indicate that the proposed harmonization framework achieves superior performance not only in reducing inter-site variations due to scanner differences but also in preserving sex-related biological variability in original cohorts. Moreover, we assess the impact of harmonization on the estimation of fiber orientation distributions and show the robustness of the personalized harmonization procedure in preserving the fiber orientation of original dMRI signals.

DeepComBat: A statistically motivated, hyperparameter-robust, deep learning approach to harmonization of neuroimaging data.

Neuroimaging data acquired using multiple scanners or protocols are increasingly available. However, such data exhibit technical artifacts across batches which introduce confounding and decrease reproducibility. This is especially true when multi-batch data are analyzed using complex downstream models which are more likely to pick up on and implicitly incorporate batch-related information. Previously proposed image harmonization methods have sought to remove these batch effects; however, batch effects remain detectable in the data after applying these methods. We present DeepComBat, a deep learning harmonization method based on a conditional variational autoencoder and the ComBat method. DeepComBat combines the strengths of statistical and deep learning methods in order to account for the multivariate relationships between features while simultaneously relaxing strong assumptions made by previous deep learning harmonization methods. As a result, DeepComBat can perform multivariate harmonization while preserving data structure and avoiding the introduction of synthetic artifacts. We apply this method to cortical thickness measurements from a cognitive-aging cohort and show DeepComBat qualitatively and quantitatively outperforms existing methods in removing batch effects while preserving biological heterogeneity. Additionally, DeepComBat provides a new perspective for statistically motivated deep learning harmonization methods.

A roadmap for multi-omics data integration using deep learning.

High-throughput next-generation sequencing now makes it possible to generate a vast amount of multi-omics data for various applications. These data have revolutionized biomedical research by providing a more comprehensive understanding of the biological systems and molecular mechanisms of disease development. Recently, deep learning (DL) algorithms have become one of the most promising methods in multi-omics data analysis, due to their predictive performance and capability of capturing nonlinear and hierarchical features. While integrating and translating multi-omics data into useful functional insights remain the biggest bottleneck, there is a clear trend towards incorporating multi-omics analysis in biomedical research to help explain the complex relationships between molecular layers. Multi-omics data have a role to improve prevention, early detection and prediction; monitor progression; interpret patterns and endotyping; and design personalized treatments. In this review, we outline a roadmap of multi-omics integration using DL and offer a practical perspective into the advantages, challenges and barriers to the implementation of DL in multi-omics data.

Reduced cross-scanner variability using vendor-agnostic sequences for single-shell diffusion MRI.

PURPOSE: To reduce the inter-scanner variability of diffusion MRI (dMRI) measures between scanners from different vendors by developing a vendor-neutral dMRI pulse sequence using the open-source vendor-agnostic Pulseq platform. METHODS: We implemented a standard EPI based dMRI sequence in Pulseq. We tested it on two clinical scanners from different vendors (Siemens Prisma and GE Premier), systematically evaluating and comparing the within- and inter-scanner variability across the vendors, using both the vendor-provided and Pulseq dMRI sequences. Assessments covered both a diffusion phantom and three human subjects, using standard error (SE) and Lin's concordance correlation to measure the repeatability and reproducibility of standard DTI metrics including fractional anisotropy (FA) and mean diffusivity (MD). RESULTS: Identical dMRI sequences were executed on both scanners using Pulseq. On the phantom, the Pulseq sequence showed more than a 2.5× reduction in SE (variability) across Siemens and GE scanners. Furthermore, Pulseq sequences exhibited markedly reduced SE in-vivo, maintaining scan-rescan repeatability while delivering lower variability in FA and MD (more than 50% reduction in cortical/subcortical regions) compared to vendor-provided sequences. CONCLUSION: The Pulseq diffusion sequence reduces the cross-scanner variability for both phantom and in-vivo data, which will benefit multi-center neuroimaging studies and improve the reproducibility of neuroimaging studies.

Agreement between routinely used immunoassays for thyroid function testing in non-pregnant and pregnant adults.

OBJECTIVE: Thyroid function tests are common biochemical analyses, and agreement between the routinely used immunoassays is important for diagnosis and monitoring of thyroid disease. Efforts are continuously made to align the biochemical assays, and we aimed to evaluate the agreement between immunoassays used in a clinical laboratory setting among non-pregnant and pregnant adults. DESIGN: Cross-sectional study. PARTICIPANTS: Serum samples were obtained from 192 blood donors (non-pregnant adults) and from 86 pregnant women in the North Denmark Region with no known thyroid disease. MEASUREMENTS: Each sample was used for measurement of thyroid-stimulating hormone (TSH) with the routinely used automatic immunoassays in the regional Departments of Clinical Biochemistry (Alinity, Abbott Laboratories, Cobas, Roche Diagnostics, and Atellica, Siemens Healthineers) and reported as the median with 95% confidence interval (95% CI). RESULTS: In nonpregnant adults, the level of TSH was higher with Cobas and Atellica than with Alinity as reflected by median (Alinity: 1.39 mIU/L (95% CI: 1.30-1.51 mIU/L); Cobas: 1.57 mIU/L (95% CI: 1.48-1.75 mIU/L); Atellica: 1.74 mIU/L (95% CI: 1.61-1.83 mIU/L)). Similarly, a trend was seen towards higher median TSH with Cobas than with Alinity among pregnant women (Alinity: 1.90 mIU/L (95% CI: 1.37-2.82 mIU/L); Cobas: 2.33 mIU/L (95% CI: 1.69-3.62 mIU/L)). CONCLUSION: Results of thyroid function tests obtained with different immunoassays were not interchangeable when evaluated among pregnant and non-pregnant adults. The distinct differences are relevant for clinical decision making and emphasize the necessity of clinical laboratory information when different assays are used for diagnosis and monitoring of patients with thyroid disease.

Multicenter PET image harmonization using generative adversarial networks.

PURPOSE: To improve reproducibility and predictive performance of PET radiomic features in multicentric studies by cycle-consistent generative adversarial network (GAN) harmonization approaches. METHODS: GAN-harmonization was developed to harmonize whole-body PET scans to perform image style and texture translation between different centers and scanners. GAN-harmonization was evaluated by application to two retrospectively collected open datasets and different tasks. First, GAN-harmonization was performed on a dual-center lung cancer cohort (127 female, 138 male) where the reproducibility of radiomic features in healthy liver tissue was evaluated. Second, GAN-harmonization was applied to a head and neck cancer cohort (43 female, 154 male) acquired from three centers. Here, the clinical impact of GAN-harmonization was analyzed by predicting the development of distant metastases using a logistic regression model incorporating first-order statistics and texture features from baseline 18F-FDG PET before and after harmonization. RESULTS: Image quality remained high (structural similarity: left kidney ≥ 0.800, right kidney ≥ 0.806, liver ≥ 0.780, lung ≥ 0.838, spleen ≥ 0.793, whole-body ≥ 0.832) after image harmonization across all utilized datasets. Using GAN-harmonization, inter-site reproducibility of radiomic features in healthy liver tissue increased at least by ≥ 5 ± 14% (first-order), ≥ 16 ± 7% (GLCM), ≥ 19 ± 5% (GLRLM), ≥ 16 ± 8% (GLSZM), ≥ 17 ± 6% (GLDM), and ≥ 23 ± 14% (NGTDM). In the head and neck cancer cohort, the outcome prediction improved from AUC 0.68 (95% CI 0.66-0.71) to AUC 0.73 (0.71-0.75) by application of GAN-harmonization. CONCLUSIONS: GANs are capable of performing image harmonization and increase reproducibility and predictive performance of radiomic features derived from different centers and scanners.

Harmonizing T1-Weighted Images to Improve Consistency of Brain Morphology Among Different Scanner Manufacturers in Alzheimer's disease.

BACKGROUND: Brain MRI scanner variability can introduce bias in measurements. Harmonizing scanner variability is crucial. PURPOSE: To develop a harmonization method aimed at removing scanner variability, and to evaluate the consistency of results in multicenter studies. STUDY TYPE: Retrospective. POPULATION: Multicenter data from 170 healthy participants (males/females = 98/72; age = 73.8 ± 7.3) and 170 Alzheimer's disease patients (males/females = 98/72; age = 76.2 ± 8.5) were compared with reference data from another 340 participants. FIELD STRENGTH/SEQUENCE: 3-T, magnetization prepared rapid gradient echo and turbo field echo; 1.5-T, inversion recovery prepared fast spoiled gradient echo T1-weighted sequences. ASSESSMENT: Gray matter (GM) brain images, obtained through segmentation of T1-weighted images, were utilized to evaluate the performance of the harmonization method using common orthogonal basis extraction (HCOBE) and four other methods (removal of artificial voxel effect by linear regression, RAVEL; Z_score; general linear model, GLM; ComBat). Linear discriminant analysis (LDA) was used to access the effectiveness of different methods in reducing scanner variability. The performance of harmonization methods in preserving GM volumes heterogeneity was evaluated by the similarity of the relationship between GM proportion and age in the reference and multicenter data. Furthermore, the consistency of the harmonized multicenter data with the reference data were evaluated based on classification results (train/test = 7/3) and brain atrophy. STATISTICAL TESTS: Two-sample t-tests, area under the curve (AUC), and Dice coefficients were used to analyze the consistency of results from the reference and harmonized multicenter data. A P-value <0.01 was considered statistically significant. RESULTS: HCOBE reduced the scanner variability from 0.09 before harmonization to 0.003 (ideal: 0, RAVEL/Z_score/GLM/ComBat = 0.087/0.003/0.006/0.013). GM volumes showed no significant difference (P = 0.52) between the reference and HCOBE-harmonized multicenter data. Consistency evaluation showed that AUC values of 0.95 for both reference and HCOBE-harmonized multicenter data (RAVEL/Z_score/GLM/ComBat = 0.86/0.86/0.84/0.89), and the Dice coefficient increased from 0.73 before harmonization to 0.82 (ideal: 1, RAVEL/Z_score/GLM/ComBat = 0.39/0.64/0.59/0.74). DATA CONCLUSION: HCOBE may help to remove scanner variability and could improve the consistency of results in multicenter studies. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 1.

Improvements in Between-Vendor MRI Harmonization of Renal T2 Mapping using Stimulated Echo Compensation.

BACKGROUND: T2 mapping is valuable to evaluate pathophysiology in kidney disease. However, variations in T2 relaxation time measurements across MR scanners and vendors may occur requiring additional correction. PURPOSE: To harmonize renal T2 measurements between MR vendor platforms, and use an extended-phase-graph-based fitting method ("StimFit") to correct stimulated echoes and reduce between-vendor variations. STUDY TYPE: Prospective. SUBJECTS: 8 healthy "travelling" volunteers (37.5% female, 32 ± 6 years) imaged on four MRI systems across three vendors at four sites, 10 healthy volunteers (50% female, 32 ± 8 years) scanned multiple times on a given MR scanner for repeatability evaluation. ISMRM/NIST system phantom scanned for evaluation of T2 accuracy. FIELD STRENGTH/SEQUENCE: 3T, multiecho spin-echo sequence. ASSESSMENT: T2 images fit using conventional monoexponential fitting and "StimFit." Mean absolute percentage error (MAPE) of phantom measurements with reference T2 values. Average cortex and medulla T2 values compared between MR vendors, with masks obtained from T2-weighted images and T1 maps. Full-width-at-half-maximum (FWHM) T2 distributions to evaluate local homogeneity of measurements. STATISTICAL TESTS: Coefficient of variation (CV), linear mixed-effects model, analysis of variance, student's t-tests, Bland-Altman plots, P-value <0.05 considered statistically significant. RESULTS: In the ISMRM/NIST phantom, "StimFit" reduced the MAPE from 4.9%, 9.1%, 24.4%, and 18.1% for the four sites (three vendors) to 3.3%, 3.0%, 6.6%, and 4.1%, respectively. In vivo, there was a significant difference in kidney T2 measurements between vendors using a monoexponential fit, but not with "StimFit" (P = 0.86 and 0.92, cortex and medulla, respectively). The intervendor CVs of T2 measures were reduced from 8.0% to 2.6% (cortex) and 7.1% to 2.8% (medulla) with StimFit, resulting in no significant differences for the CVs of intravendor repeat acquisitions (P = 0.13 and 0.05). "StimFit" significantly reduced the FWHM of T2 distributions in the cortex and whole kidney. DATA CONCLUSION: Stimulated-echo correction reduces renal T2 variation across MR vendor platforms. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 1.

Epilepsy phenotype and its reproducibility after lateral fluid percussion-induced traumatic brain injury in rats: Multicenter EpiBioS4Rx study project 1.

OBJECTIVE: This study was undertaken to assess reproducibility of the epilepsy outcome and phenotype in a lateral fluid percussion model of posttraumatic epilepsy (PTE) across three study sites. METHODS: A total of 525 adult male Sprague Dawley rats were randomized to lateral fluid percussion-induced brain injury (FPI) or sham operation. Of these, 264 were assigned to magnetic resonance imaging (MRI cohort, 43 sham, 221 traumatic brain injury [TBI]) and 261 to electrophysiological follow-up (EEG cohort, 41 sham, 220 TBI). A major effort was made to harmonize the rats, materials, equipment, procedures, and monitoring systems. On the 7th post-TBI month, rats were video-EEG monitored for epilepsy diagnosis. RESULTS: A total of 245 rats were video-EEG phenotyped for epilepsy on the 7th postinjury month (121 in MRI cohort, 124 in EEG cohort). In the whole cohort (n = 245), the prevalence of PTE in rats with TBI was 22%, being 27% in the MRI and 18% in the EEG cohort (p > .05). Prevalence of PTE did not differ between the three study sites (p > .05). The average seizure frequency was .317 ± .725 seizures/day at University of Eastern Finland (UEF; Finland), .085 ± .067 at Monash University (Monash; Australia), and .299 ± .266 at University of California, Los Angeles (UCLA; USA; p < .01 as compared to Monash). The average seizure duration did not differ between UEF (104 ± 48 s), Monash (90 ± 33 s), and UCLA (105 ± 473 s; p > .05). Of the 219 seizures, 53% occurred as part of a seizure cluster (≥3 seizures/24 h; p >.05 between the study sites). Of the 209 seizures, 56% occurred during lights-on period and 44% during lights-off period (p > .05 between the study sites). SIGNIFICANCE: The PTE phenotype induced by lateral FPI is reproducible in a multicenter design. Our study supports the feasibility of performing preclinical multicenter trials in PTE to increase statistical power and experimental rigor to produce clinically translatable data to combat epileptogenesis after TBI.

Cross-cultural application of the International Classification of Cognitive Disorders in Epilepsy (IC-CoDE): Cognitive phenotypes in people with temporal lobe epilepsy in India.

OBJECTIVE: Efforts to understand the global variability in cognitive profiles in patients with epilepsy have been stymied by the lack of a standardized diagnostic system. This study examined the cross-cultural applicability of the International Classification of Cognitive Disorders in Epilepsy (IC-CoDE) in a cohort of patients with temporal lobe epilepsy (TLE) in India that was diverse in language, education, and cultural background. METHODS: A cohort of 548 adults with TLE from Mumbai completed a presurgical comprehensive neuropsychological evaluation. The IC-CoDE taxonomy was applied to derive cognitive phenotypes in the sample. Analyses of variance were conducted to examine differences in demographic and clinical characteristics across the phenotypes, and chi-squared tests were used to determine whether the phenotype distribution differed between the Mumbai sample and published data from a multicenter US sample. RESULTS: Using the IC-CoDE criteria, 47% of our cohort showed an intact cognitive profile, 31% a single-domain impairment, 16% a bidomain impairment, and 6% a generalized impairment profile. The distribution of cognitive phenotypes was similar between the Indian and US cohorts for the intact and bidomain phenotypes, but differed for the single and generalized domains. There was a larger proportion of patients with single-domain impairment in the Indian cohort and a larger proportion with generalized impairment in the US cohort. Among patients with single-domain impairment, a greater proportion exhibited memory impairment in the Indian cohort, whereas a greater proportion showed language impairment in the US sample, likely reflecting differences in language administration procedures and sample characteristics including a higher rate of mesial temporal sclerosis in the Indian sample. SIGNIFICANCE: Our results demonstrate the applicability of IC-CoDE in a group of culturally and linguistically diverse patients from India. This approach enhances our understanding of cognitive variability across cultures and enables harmonized and inclusive research into the neuropsychological aspects of epilepsy.

Advancing the Harmonization of Biopredictive Methodologies through the Product Quality Research Institute (PQRI) Consortium: Biopredictive Dissolution of Dipyridamole Tablets.

Biorelevant dissolution and its concept have been widely accepted and further developed to meaningfully predict the bioperformance of oral drug products. Biorelevant methodologies have been applied to design and optimize oral formulations, to facilitate formulation bridging, and to predict the outcome of bioperformance by coupling the results with modeling. Yet, those methodologies have often been independently customized to align with specific aspects of the oral drug products being developed. Therefore, the evolution of biorelevant dissolution methodologies has taken slightly diverse pathways rather than being standardized like compendial quality control (QC) methodologies. This manuscript presents an effort through the Product Quality Research Institute (PQRI, https://pqri.org) consortium entitled: the standardization of "in vivo predictive dissolution methodologies and in silico bioequivalent study working group" to find the key parameters for biorelevant dissolution, to identify the best practices, and to move toward standardization of biorelevant dissolution methodologies. This working group is composed of members from 10 pharmaceutical companies and academic institutes. The consortium project will be accomplished in five phases, whereby the first two phases have already been completed and published. In this paper, the next two phases are addressed by reporting the biorelevant dissolution profiles of dipyridamole, a weak base model drug, then incorporating the dissolution results into physiologically based biopharmaceutics modeling (PBBM) to determine whether they would lead to bioequivalence (BE) or non-BE.

Comparative analysis of gene and disease selection in genomic newborn screening studies.

Genomic newborn screening (gNBS) is on the horizon given the decreasing costs of sequencing and the advanced understanding of the impact of genetic variants on health and diseases. Key to ongoing gNBS pilot studies is the selection of target diseases and associated genes to be included. In this study, we present a comprehensive analysis of seven published gene-disease lists from gNBS studies, evaluating gene-disease count, composition, group proportions, and ClinGen curations of individual disorders. Despite shared selection criteria, we observe substantial variation in total gene count (median 480, range 237-889) and disease group composition. An intersection was identified for 53 genes, primarily inherited metabolic diseases (83%, 44/53). Each study investigated a subset of exclusive gene-disease pairs, and the total number of exclusive gene-disease pairs was positively correlated with the total number of genes included per study. While most pairs receive "Definitive" or "Strong" ClinGen classifications, some are labeled as "Refuted" (n = 5) or "Disputed" (n = 28), particularly in genetic cardiac diseases. Importantly, 17%-48% of genes lack ClinGen curation. This study underscores the current absence of consensus recommendations for selection criteria for target diseases for gNBS resulting in diversity in proposed gene-disease pairs, their coupling with gene variations and the use of ClinGen curation. Our findings provide crucial insights into the selection of target diseases and accompanying gene variations for future gNBS program, emphasizing the necessity for ongoing collaboration and discussion about criteria harmonization for panel selection to ensure the screening's objectivity, integrity, and broad acceptance.