bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cell-type-specific mapping of enhancers and target genes from single-cell multimodal data
Authors: Su Chang , Lee Dongsoo , Jin Peng , Zhang Jingfei 
Published: September 2024

AbstractMapping enhancers and target genes in disease-related cell types has provided critical insights into the functional mechanisms of genetic variants identified by genomewide association studies (GWAS). However, most existing analyses rely on bulk data or cultured cell lines, which may fail to identify cell-type-specific enhancers and target genes. Recently, single-cell multimodal data measuring both gene expression and chromatin accessibility within the same cells have enabled the inference of enhancer-gene pairs in a cell-type-specific and context-specific manner. However, this task is challenged by the data’s high sparsity, sequencing depth variation, and the computational burden of analyzing a large number of enhancer-gene pairs. To address these challenges, we propose scMultiMap, a statistical method that infers enhancer-gene association from sparse multimodal counts using a joint latent-variable model. It adjusts for technical confounding, permits fast moment-based estimation and provides analytically derivedp-values. In systematic analyses of blood and brain data, scMultiMap shows appropriate type I error control, high statistical power with greater reproducibility across independent datasets and stronger consistency with orthogonal data modalities. Meanwhile, its computational cost is less than 1% of existing methods. When applied to single-cell multimodal data from postmortem brain samples from Alzheimer’s disease (AD) patients and controls, scMultiMap gave the highest heritability enrichment in microglia and revealed new insights into the regulatory mechanisms of AD GWAS variants in microglia.
OPUS-DSD2: Disentangling Dynamics and Compositional Heterogeneity for Cryo-EM/ET
Authors: Luo Zhenwei , Chen Xiangru , Zhang Yiqiu , Wang Qinghua , Ma Jianpeng 
Published: September 2024

AbstractCryo-electron microscopy and tomography (cryo-EM/ET) capture structural heterogeneities in macromolecules, ranging from dynamic motions to compositional changes— key to understanding biological mechanisms. While the deep learning framework, OPUS-DSD, advanced heterogeneity analysis for cryo-EM, it conflates different types of heterogeneities, and remains incompatible with cryo-ET which can elucidate macromolecular functions in their native cellular environments. Here, we present OPUS-DSD2, a unified framework for disentangling structural heterogeneity in both cryo-EM and cryo-ET data. OPUS-DSD2 augments a 3D convolutional neural network with a multi-layer perceptron based rigid-body dynamics model, advocating the separation of subunit level rigid-body dynamics from other heterogeneities. Tests on real datasets demonstrate that OPUS-DSD2 effectively captures large-scale subunit motions while isolating spatially localized structural variations into distinct principal components of the composition latent space. Critically, OPUS-DSD2 enables direct analysis of noisy cryo-ET template-matching results, bypassing labor-intensive subtomogram classification and averaging and unlocking high-throughput visual proteomics. Backward-compatible with OPUS-DSD, OPUS-DSD2 is available athttps://github.com/alncat/opusDSD.
Neurocognitive and resting-state functional MRI changes in patients with diffuse gliomas after chemoradiotherapy
Authors: Liu Zhihua , Mitchell Timothy J. , Luo Chongliang , Park Ki Yun , Shimony Joshua S. , Fucetola Robert , Leuthardt Eric C. , Perkins Stephanie M. , Snyder Abraham Z. , Zhu Tong , Huang Jiayi 
Published: September 2024

AbstractBackgroundThis prospective observational study employed resting-state functional MRI (rs-fMRI) to investigate network-level disturbances associated with neurocognitive function (NCF) changes in patients with gliomas following radiation therapy (RT).MethodsAdult patients with either IDH-wildtype or IDH-mutant gliomas underwent computerized NCF testing and rs-fMRI before and 6 months after RT. NCF changes were quantified by the percent change in age-normalized composite scores from baseline (ΔNCFcomp). rs-fMRI data underwent seed-based functional connectivity (FC) analysis. Whole-brain connectivity regression analysis assessed the association between network FC changes and NCF changes, using a split-sample approach with a 26-patient training set and a 6-patient validation set, iterated 200 times. Permutation tests evaluated the significance of network selection.ResultsBetween September 2020 and December 2023, 43 patients were enrolled, with 32 completing both initial and follow-up evaluations. The mean ΔNCFcompwas 2.9% (SD: 13.7%), with 38% experiencing a decline. Intra-hemispheric FC remained similar between ipsilateral and contralateral hemispheres for most patients at both time points. FC changes accounted for a moderate amount of variance in NCF changes (mean R2: 0.301, SD: 0.249), with intra-network FC of the Parietal Memory Network (PMN-PMN,P=0.001) and inter-network FC between the PMN and the Visual Network (PMN-VN,P=0.002) as the most significant factors. Similar findings were obtained by sensitivity analyses using only the FC data from the hemisphere contralateral to the tumor.ConclusionsPost-RT rs-fMRI changes significantly predicted NCF decline, highlighting rs-fMRI as a promising imaging biomarker for neurocognitive decline after RT.
A scoping review of remote and unsupervised digital cognitive assessments in preclinical Alzheimer’s disease
Authors: Polk S. E. , Öhman F. , Hassenstab J. , König A. , Papp K. V. , Schöll M. , Berron D. 
Published: September 2024

AbstractSubtle cognitive changes in preclinical Alzheimer’s disease (AD) are difficult to detect using traditional pen-and-paper neuropsychological assessments. Remote and unsupervised digital assessments can improve scalability, measurement reliability, and ecological validity, enabling the detection and monitoring of subtle cognitive change. Here, we evaluate such tools deployed in preclinical AD samples, defined as cognitively unimpaired individuals with abnormal levels of amyloid-β (Aβ), or Aβ and tau.In this scoping review, we screened 1,680 unique reports for studies using remote and unsupervised cognitive assessment tools in preclinical AD samples; 23 tools were found. We describe each tool’s usability, validity, and reported metrics of reliability.Construct and criterion validity according to associations with established neuropsychological assessments and measures of Aβ and tau are reported.With this review, we aim to present a necessary update to a rapidly evolving field, following a previous review by Öhman and colleagues (2021;Alzheimers Dement. Diagn. Assess. Dis. Monit) and addressing the open questions of feasibility and reliability of remote testing in the target population. We discuss future directions for using remote and unsupervised digital cognitive assessments in preclinical AD and how such tools may be used for longitudinal monitoring of cognitive function, scalable case finding, and individualized prognostics in both clinical trials and healthcare contexts.
Trajectories of mHealth-tracked mental health symptoms and their predictors in chronic pelvic pain
Authors: Leventhal Emily L. , Nukavarapu Nivedita , Elhadad Noemie , Bakken Suzanne , Elovitz Michal , Hirten Robert , Rodrigues Jovita , Danieletto Matteo , Landell Kyle , Ensari Ipek 
Published: September 2024

AbstractBackground.Female chronic pelvic pain disorders (CPPDs) affect 1 in 7 women worldwide and are characterized by psychosocial comorbidities, including reduced quality of life and 2-10 fold increased risk of depression and anxiety. Despite its prevalence and morbidity, CPPDs are often inadequately managed with few patients experiencing relief from any medical intervention. Characterizing mental health symptom trajectories and lifestyle predictors of mental health is a starting point to enhancing patient self-efficacy in managing symptoms. Here, we investigate the association between mental health, pain, and physical activity (PA) in females with CPPD and demonstrate a method for handling multi-modal mobile health (mHealth) data.Method.The study sample included 4,270 person-level days and 799 person-level weeks of data from CPPD participants (N=76). Participants recorded PROMIS global mental health (GMH) and physical functioning, and pain weekly for 14 weeks using a research mHealth app, and moderate-to-vigorous PA (MVPA) was passively collected via activity trackers.Data analysis.We used penalized functional regression (PFR) to regress weekly GMH-T (GMH-T) on MVPA and weekly pain outcomes, while adjusting for baseline measures, time in study, and the random intercept of the individual. We converted 7-day MVPA data into a single smooth using spline basis functions to model the potential non-linear relationship.ResultsMVPA was a significant, curvilinear predictor of GMH-T (p<0.001), independent of pain measures and prior psychiatric diagnosis. Physical functioning was positively associated with GMH-T, while pain was negatively associated with GMH-T (β=2.24, β=-1.16, respectively; p<0.05).ConclusionThese findings suggest that engaging in MVPA is beneficial to the mental health of females with CPPD. Additionally, this study demonstrates the potential of ambulatory mHealth-based data combined with functional models for delineating inter-individual and temporal variability.
Low Responsiveness of Machine Learning Models to Critical or Deteriorating Health Conditions
Authors: Pias Tanmoy Sarkar , Afrose Sharmin , Tuli Moon Das , Trisha Ipsita Hamid , Deng Xinwei , Nemeroff Charles B. , Yao Danfeng (Daphne) 
Published: September 2024

AbstractBackgroundMachine learning (ML) based mortality prediction models can be immensely useful in intensive care units. Such a model should generate warnings to alert physicians when a patient’s condition rapidly deteriorates, or their vitals are in highly abnormal ranges. Before clinical deployment, it is important to comprehensively assess models’ ability to recognize critical patient conditions.MethodsWe develop testing approaches to systematically assess machine learning models’ ability to respond to serious medical emergencies by generating such conditions. We evaluated multiple machine learning models trained on four different datasets from two different clinical prediction tasks and evaluated ten different machine learning models including three resampling techniques.ResultsWe identified serious deficiencies in the models’ responsiveness, i.e., the inability to recognize severely impaired medical conditions or rapidly deteriorating health. For in-hospital mortality prediction, the models tested using our synthesized cases fail to recognize 66% of the test cases involving injuries. In some instances, the models fail to generate adequate mortality risk scores for all test cases. Our testing methods identified similar kinds of deficiencies in the responsiveness of 5-year breast and lung cancer prediction models.ConclusionUsing generated test cases, we found that statistical machine-learning models trained solely from patient data are grossly insufficient and have many dangerous blind spots. Despite their overall performance metrics, most ML models we tested failed to respond adequately to critically ill patients. Our proposed testing framework acts as a critical bridge between machine learning model development and clinical deployment, ensuring reliability and safety through rigorous evaluation.Plain Language SummaryMachine learning models are increasingly used in healthcare to predict patients’ death risk or cancer survivability. These models could help doctors identify patients with worsening condition and take action to save lives. We developed a new method to test how well these models respond to severe health emergencies by creating life-threatening scenarios. We found most models failed to respond adequately to critical health events. For example, models missed 66% of cases involving serious injuries. This research emphasizes that current machine learning models have major limitations and could be dangerous if used in hospitals without thorough testing. Our testing framework can help improve these models to ensure they are safe and reliable before being used in real-life medical settings.
Combination of Imipridone ONC201 or ONC206 with Temozolomide and Radiotherapy in triple ITR therapy reduces intracranial tumor burden and prolongs survival in an orthotopic wild-type IDH GBM mouse model
Authors: Zhou Lanlan , Zhang Leiqing , Zhang Jun , Wu Laura Jinxuan , Zhang Shengliang , George Andrew , Hahn Marina , Safran Howard P. , Chen Clark C. , Seyhan Attila A. , Wong Eric T. , El-Deiry Wafik S. 
Published: September 2024

AbstractGlioblastoma remains the most lethal common primary brain tumor in adults with limited therapeutic options. TIC10/ONC201, a first-in-class imipridone we discovered, achieved meaningful therapeutic effects in phase I/II trials in patients with diffuse gliomas harboring H3K27M mutations, and currently the drug is in randomized phase III testing (ACTION trial;NCT05580562). ONC201 targets mitochondrial protease ClpP to disrupt oxidative phosphorylation and trigger the integrated stress response (ISR), TRAIL/DR5, and tumor cell death. We hypothesized that ONC201 and its analogue ONC206 synergize with temozolomide (TMZ) and ionizing radiation (IR), standard-of-care glioblastoma therapies. ONC201 enhances TMZ or IR-induced apoptosis, and cytotoxicity. ClpP-silencing suppresses ONC201-induced cytotoxicity but not TMZ or RT. Both ONC201 and ONC206 reduce expression of TMZ-resistance mediator MGMT. Suppression of MGMT protein was observed in H3K27M-mutated DIPG cell lines following treatment with ONC201 or ONC206 with or without TMZ. Cytokine profiling indicates distinct ONC201 alterations relative to TMZ suggesting distinct anti-tumor immune mechanisms. Triple IR+TMZ+ONC201 (ITR) therapy prolongs median survival to 123 days with a tail on survival curve (3-of-7 mice alive beyond 200-days) in an orthotopic U251 GBM model versus ONC201 (44-days; p=0.000197), IR (63-days; p=0.0012), TMZ (78-days; p=0.0354), ONC201+IR (55-days; p=0.0004), ONC201+TMZ (80-days; p=0.0041) and IR+TMZ (103-days; p>0.05). By 231-days, the only surviving mice were in IRT group. Our results support investigation of ONC201/ONC206 in combination with TMZ and IR (ITR) in GBM or H3K27M mutated diffuse glioma therapy.
Multi-behavioral phenotyping in zebrafish identifies a novel disruptor of non-associative learning with environmental and human relevance
Authors: Leuthold David , Herold Nadia K. , Nerlich Jana , Bartmann Kristina , Scharkin Ilka , Hallermann Stefan J. , Schweiger Nicole , Fritsche Ellen , Tal Tamara 
Published: September 2024

AbstractThe vertebrate nervous system is vulnerable to chemical toxicity and the widespread release of chemicals into the environment outstrips the capacity to assess their safety. We devised a battery of automated behavior assays in larval zebrafish (Danio rerio), a 3R-compliant model amenable to higher-throughput chemical screens. The battery captures stereotypical visual and acoustic behaviors including habituation, a form of non-associative learning. Known pharmacological modulators of habituation in zebrafish evoked distinct behavioral patterns. By screening chemicals positive forex vivoN-methyl-D-aspartate receptor (NMDAR) modulation, we identified chlorophene, a biocide that caused sedation, paradoxical excitation, and reduced habituation in zebrafish. Usingin silicotarget predictions and pharmacological interventions, we discovered that chlorophene acts via gamma-aminobutyric acid A receptors (GABAARs), a previously unknown target site. Orthogonal validation in cultured mouse cortical neurons and human stem cell-derived BrainSpheres confirmed chlorophene’s interaction with GABAARs. Together, multi-behavioral phenotyping in zebrafish can accelerate the identification of neurotoxicants and their underlying mode of action.
Expression-based selection identifies a microglia-tropic AAV capsid for direct and CSF routes of administration in mice
Authors: Santoscoy Miguel C. , Espinoza Paula , Hanlon Killian S. , Yang Luna , Nieland Lisa , Ng Carrie , Badr Christian E. , Hickman Suzanne , de la Cruz Demitri , Griciuc Ana , Elkhoury Joseph , Bennett Rachel E. , Shen Shiqian , Maguire Casey A. 
Published: September 2024

AbstractMicroglia are critical innate immune cells of the brain.In vivotargeting of microglia using gene-delivery systems is crucial for studying brain physiology and developing gene therapies for neurodegenerative diseases and other brain disorders such as NeuroAIDS. Historically, microglia have been extremely resistant to transduction by viral vectors, including adeno-associated virus (AAV) vectors. Recently, there has been some progress demonstrating the feasibility and potential of using AAV to transduce microglia after direct intraparenchymal vector injection. Data suggests that combining specific AAV capsids with microglia-specific gene expression cassettes to reduce neuron off-targeting will be key. However, no groups have developed AAV capsids for microglia transduction after intracerebroventricular (ICV) injection. The ICV route of administration has advantages such as increased brain biodistribution while avoiding issues related to systemic injection. Here, we performed anin vivoselection using an AAV peptide display library that enables recovery of capsids that mediate transgene expression in microglia. Using this approach, we identified a capsid, MC5, which mediated enhanced transduction of microglia after ICV injection compared to AAV9. Furthermore, MC5 enhanced both the efficiency (85%) and specificity (93%) of transduction compared to a recently described evolved AAV9 capsid for microglia targeting after direct injection into the brain parenchyma. Exploration of the use of MC5 in a mouse models of Alzheimer’s disease revealed transduced microglia surrounding and within plaques. Overall, our results demonstrate that the MC5 capsid is a useful gene transfer tool to target microgliain vivoby direct and ICV routes of administration.
Mapping the aggregate g-ratio of white matter tracts using multi-modal MRI
Authors: Lu Wen Da , Nelson Mark C. , Leppert Ilana R. , Campbell Jennifer S.W. , Schiavi Simona , Pike G. Bruce , Rowley Christopher D. , Daducci Alessandro , Tardif Christine L. 
Published: September 2024

AbstractThe g-ratio of a myelinated axon is defined as the ratio of the inner-to-outer diameter of the myelin sheath and modulates conduction speed of action potentials along axons. This g-ratio can be mappedin vivoat the macroscopic scale across the entire human brain using multi-modal MRI and sampled along white matter streamlines reconstructed from diffusion-weighted images to derive the g-ratio of a white matter tract. This tractometry approach has shown spatiotemporal variations in myelin g-ratio across white matter tracts and networks. However, tractometry is biased by partial volume effects where voxels contain multiple fiber populations. To address this limitation, we used the Convex Optimization Modeling for Microstructure-Informed Tractography (COMMIT) framework to derive tract-specific axonal and myelin volumes, which are used to compute the tract-specific aggregate g-ratio. We compare our novel COMMIT-based tract-specific g-ratio mapping approach to conventional tractometry in a group of 10 healthy adults. Our findings demonstrate that the tract-specific g-ratio mapping approach preserves the overall spatial distribution observed in tractometry and enhances contrast between tracts. Additionally, our scan-rescan data shows high repeatability for medium to large caliber tracts. We show that short and large caliber tracts have a lower g-ratio, whereas tractometry results show the opposite trends. This technique advances tract-specific analysis by reducing biases introduced by the complex network of crossing white matter fibers.
Exploring Breast Cancer-Related Biochemical Changes in Circulating Extracellular Vesicles Using Raman Spectroscopy
Authors: Bonizzi Arianna , Signati Lorena , Grimaldi Maria , Truffi Marta , Piccotti Francesca , Gagliardi Stella , Dotti Giulia , Mazzucchelli Serena , Albasini Sara , Cazzola Roberta , Bhowmik Debanjan , Narayana Chandrabhas , Corsi Fabio , Morasso Carlo 
Published: September 2024

AbstractExtracellular vesicles (EVs) are a subgroup of the circulating particles, released by cells in both normal and diseased states, carrying active biomolecules. They have gained significant attention as potential cancer biomarkers, particularly in breast cancer (BC). Previous research showed variations in EVs content and quantity between BC patients and healthy controls (HC). However, studying EVs biochemical profile remains challenging due to their low abundance and complex composition. Additionally, EVs may interact with other plasma components, like lipoproteins (LPs), forming a so called “biomolecular corona” that further complicates their analysis. Here, Raman spectroscopy (RS) is proposed as a fast tool to obtain the biochemical profile of circulating EVs in the context of BC. RS was employed to differentiate various extracellular particles (EPs) in blood, including LPs and EVs. The study also evaluated RS’s capability to quantify major classes of biomolecules and compared these results with those obtained by traditional biochemical assays. Finally, compositional differences in large EVs (lEVs) and small EVs (sEVs) were assessed between 30 HC and 34 BC patients. RS revealed the existence of distinct biochemical signatures associated with BC, highlighting increased levels of nucleic acids and lipids in the BC group.
Multi-View Integrative Approach For Imputing Short-Chain Fatty Acids and Identifying Key factors predicting Blood SCFA
Authors: Liu Anqi , Tian Bo , Qiu Chuan , Su Kuan-Jui , Jiang Lindong , Zhao Chen , Song Meng , Liu Yong , Qu Gang , Zhou Ziyu , Zhang Xiao , Mungasavalli Gnanesh Shashank Sajjan , Thumbigere-Math Vivek , Luo Zhe , Tian Qing , Zhang Li-Shu , Wu Chong , Ding Zhengming , Shen Hui , Deng Hong-Wen 
Published: September 2024

AbstractShort-chain fatty acids (SCFAs) are the main metabolites produced by bacterial fermentation of dietary fiber within gastrointestinal tract. SCFAs produced by gut microbiotas (GMs) are absorbed by host, reach bloodstream, and are distributed to different organs, thus influencing host physiology. However, due to the limited budget or the poor sensitivity of instruments, most studies on GMs have incomplete blood SCFA data, limiting our understanding of the metabolic processes within the host. To address this gap, we developed an innovative multi-task multi-view integrative approach (M2AE, Multi-task Multi-View Attentive Encoders), to impute blood SCFA levels using gut metagenomic sequencing (MGS) data, while taking into account the intricate interplay among the gut microbiome, dietary features, and host characteristics, as well as the nuanced nature of SCFA dynamics within the body. Here, each view represents a distinct type of data input (i.e., gut microbiome compositions, dietary features, or host characteristics). Our method jointly explores both view-specific representations and cross-view correlations for effective predictions of SCFAs. We applied M2AE to two in-house datasets, which both include MGS and blood SCFAs profiles, host characteristics, and dietary features from 964 subjects and 171 subjects, respectively. Results from both of two datasets demonstrated that M2AE outperforms traditional regression-based and neural-network based approaches in imputing blood SCFAs. Furthermore, a series of gut bacterial species (e.g.,Bacteroides thetaiotaomicronandClostridium asparagiforme), host characteristics (e.g., race, gender), as well as dietary features (e.g., intake of fruits, pickles) were shown to contribute greatly to imputation of blood SCFAs. These findings demonstrated that GMs, dietary features and host characteristics might contribute to the complex biological processes involved in blood SCFA productions. These might pave the way for a deeper and more nuanced comprehension of how these factors impact human health.