bluuebunny/crossref_metadata_2025_split huggingface dataset:
Large-Scale Mendelian Randomization Study Reveals Circulating Blood-based Proteomic Biomarkers for Psychopathology and Cognitive Task Performance
Authors: Bhattacharyya Upasana , John Jibin , Lam Max , Fisher Jonah , Sun Benjamin , Baird Denis , Chen Chia-Yen , Lencz Todd 
Published: January 2024

AbstractBackgroundResearch on peripheral (e.g., blood-based) biomarkers for psychiatric illness has typically been low-throughput in terms of both the number of subjects and the range of assays performed. Moreover, traditional case-control studies examining blood-based biomarkers are subject to potential confounds of treatment and other exposures common to patients with psychiatric illnesses. Our research addresses these challenges by leveraging large-scale, high-throughput proteomics data and Mendelian Randomization (MR) to examine the causal impact of circulating proteins on psychiatric phenotypes and cognitive task performance.MethodsWe utilized plasma proteomics data from the UK Biobank (3,072 proteins assayed in 34,557 European-ancestry individuals) and deCODE Genetics (4,719 proteins measured across 35,559 Icelandic individuals). Significant proteomic quantitative trait loci (both cis-pQTLs and trans-pQTLs) served as MR instruments, with the most recent GWAS for schizophrenia, bipolar disorder, major depressive disorder, and cognitive task performance (all excluding overlapping UK Biobank participants) as phenotypic outcomes.ResultsMR revealed 109 Bonferroni-corrected causal associations (44 novel) involving 88 proteins across the four phenotypes. Several immune-related proteins, including interleukins and complement factors, stood out as pleiotropic across multiple outcome phenotypes. Drug target enrichment analysis identified several novel potential pharmacologic repurposing opportunities, including anti-inflammatory agents for schizophrenia and bipolar disorder and duloxetine for cognitive performance.ConclusionsIdentification of causal effects for these circulating proteins suggests potential biomarkers for these conditions and offers insights for developing innovative therapeutic strategies. The findings also indicate substantial evidence for the pleiotropic effects of many proteins across different phenotypes, shedding light on the shared etiology among psychiatric conditions and cognitive ability.
GWAS of ∼30,000 samples with bone mineral density at multiple skeletal sites and its clinical relevance on fracture prediction, genetic correlations and prioritization of drug targets
Authors: Qian Yu , Xia Jiangwei , Wang Pingyu , Xie Chao , Lin Hong-Li , Li Gloria Hoi-Yee , Yuan Cheng-Da , Qiu Mo-Chang , Fang Yi-Hu , Yu Chun-Fu , Cai Xiang-Chun , Khederzadeh Saber , Zhao Pian-Pian , Yang Meng-Yuan , Zhong Jia-Dong , Li Xin , Guan Peng-Lin , Gu Jia-Xuan , Gai Si-Rui , Yi Xiang-Jiao , Tao Jian-Guo , Chen Xiang , Miao Mao-Mao , Chen Guo-Bo , Xu Lin , Xie Shu-Yang , Tian Geng , Yue Hua , Li Guangfei , Xiao Wenjin , Karasik David , Xu Youjia , Yang Liu , Cheung Ching-Lung , Huang Fei , Zhang Zhenlin , Zheng Hou-Feng 
Published: January 2024

AbstractWe conducted genome-wide association studies (GWAS) of dual-energy X-ray absorptiometry (DXA)-derived bone mineral density (BMD) traits at 11 skeletal sites, within over 30,000 European individuals from the UK Biobank. A total of 92 unique and independent loci were identified for 11 DXA-derived BMD traits and fracture, including five novel loci (harboring genes such asABCA1,CHSY1,CYP24A1,SWAP70, andPAX1) for six BMD traits. These loci exhibited evidence of association in both males and females, which could serve as independent replication. We demonstrated that polygenic risk scores (PRSs) were independently associated with fracture risk. Although incorporating multiple PRSs (metaPRS) with the clinical risk factors (i.e., the FRAX model) exhibited the highest predictive performance, the improvement was marginal in fracture prediction. The metaPRS were capable of stratifying individuals into different trajectories of fracture risk, but clinical risk factors played a more significant role in the stratification. Additionally, we uncovered genetic correlation and shared polygenicity between head BMD and intracranial aneurysm. Finally, by integrating gene expression and GWAS datasets, we prioritized genes (e.g.ESR1andSREBF1) encoding druggable human proteins along with their respective inhibitors/antagonists. In conclusion, this comprehensive investigation revealed a new genetic basis for BMD and its clinical relevance on fracture prediction. More importantly, it was suggested that head BMD was genetically correlated with intracranial aneurysm. The prioritization of genetically supported targets implied the potential repurposing drugs (e.g. the n-3 PUFA supplement targeting SREBF1) for the prevention of osteoporosis.
Connecting genomic results for psychiatric disorders to human brain cell types and regions reveals convergence with functional connectivity
Authors: Yao Shuyang , Harder Arvid , Darki Fahimeh , Chang Yu-Wei , Li Ang , Nikouei Kasra , Volpe Giovanni , Lundström Johan N , Zeng Jian , Wray Naomi , Lu Yi , Sullivan Patrick F , Hjerling-Leffler Jens 
Published: January 2024

AbstractUnderstanding the temporal and spatial brain locations etiological for psychiatric disorders is essential for targeted neurobiological research. Integration of genomic insights from genome-wide association studies with single-cell transcriptomics is a powerful approach although past efforts have necessarily relied on mouse atlases. Leveraging a comprehensive atlas of the adult human brain, we prioritized cell types via the enrichment of SNP-heritabilities for brain diseases, disorders, and traits, progressing from individual cell types to brain regions. Our findings highlight specific neuronal clusters significantly enriched for the SNP-heritabilities for schizophrenia, bipolar disorder, and major depressive disorder along with intelligence, education, and neuroticism. Extrapolation of cell-type results to brain regions reveals important patterns for schizophrenia with distinct subregions in the hippocampus and amygdala exhibiting the highest significance. Cerebral cortical regions display similar enrichments despite the known prefrontal dysfunction in those with schizophrenia highlighting the importance of subcortical connectivity. Using functional MRI connectivity from cases with schizophrenia and neurotypical controls, we identified brain networks that distinguished cases from controls that also confirmed involvement of the central and lateral amygdala, hippocampal body, and prefrontal cortex. Our findings underscore the value of single-cell transcriptomics in decoding the polygenicity of psychiatric disorders and offer a promising convergence of genomic, transcriptomic, and brain imaging modalities toward common biological targets.
Restoration of upper-extremity function after task-oriented, intention-driven functional electrical stimulation therapy using a wearable sleeve in adults with chronic stroke: a case series
Authors: Baumgart Ian W. , Darrow Michael J. , Tacca Nicholas J. , Dunlap Collin F. , Colachis Samuel C. , Kamath Ashwini , Schlink Bryan R. , Putnam Philip T. , Branch Joshua , Friedenberg David A. , Wengerd Lauren R. , Meyers Eric C. 
Published: January 2024

AbstractBackgroundFunctional electrical stimulation (FES) has been recognized for decades as a method to retrain the motor system after stroke. Benefits of FES rehabilitation can be enhanced by combining task-oriented therapy, dubbed FES therapy (FEST). Furthermore, by synchronizing FES with the user’s volitional motor intention and incorporating multiple trained tasks FES can be better integrated into common task-oriented rehabilitation practice. Using wearable FES technology, we tested therapy incorporating these elements in two chronic stroke survivors.MethodsOur group has developed the NeuroLife® Sleeve, a wearable forearm sleeve that contains a high-density grid of embedded FES electrodes, that may be controlled by an operator or by the wearer’s own electromyographic (EMG) signals. During eight weeks of FEST, intention-driven FES enabling multiple movements was delivered via operator control twice weekly and EMG control once weekly.ResultsAt the end of the therapy period, subjects A and B had both improved their scores: Box and Blocks Test (A: +5, B: +7), the Action Arm Research Test (A: +7, B: +12), the Fugl Meyer Upper Extremity section (A: +11, B: +9), and the 9-Hole Peg Test (A: 158 sec, B: 54 sec, both previously unable). All score improvements persisted over the 10-week follow-up period despite greatly reduced (>80%) effective dose of FES.ConclusionsThis case series provides additional evidence that intention-driven FEST drives long-lasting motor recovery in chronic stroke survivors. The NeuroLife Sleeve enabled this therapy through the easily donned wearable sleeve interface, control schemes for pairing FES with motor intention, and efficient transitions between tasks with programmable FES placement and parameters.
SEETrials: Leveraging Large Language Models for Safety and Efficacy Extraction in Oncology Clinical Trials
Authors: Lee Kyeryoung , Paek Hunki , Huang Liang-Chin , Hilton C Beau , Datta Surabhi , Higashi Josh , Ofoegbu Nneka , Wang Jingqi , Rubinstein Samuel M , Cowan Andrew J , Kwok Mary , Warner Jeremy L. , Xu Hua , Wang Xiaoyan 
Published: January 2024

ABSTRACTBackgroundInitial insights into oncology clinical trial outcomes are often gleaned manually from conference abstracts. We aimed to develop an automated system to extract safety and efficacy information from study abstracts with high precision and fine granularity, transforming them into computable data for timely clinical decision-making.MethodsWe collected clinical trial abstracts from key conferences and PubMed (2012-2023). The SEETrials system was developed with four modules: preprocessing, prompt modeling, knowledge ingestion and postprocessing. We evaluated the system’s performance qualitatively and quantitatively and assessed its generalizability across different cancer types— multiple myeloma (MM), breast, lung, lymphoma, and leukemia. Furthermore, the efficacy and safety of innovative therapies, including CAR-T, bispecific antibodies, and antibody-drug conjugates (ADC), in MM were analyzed across a large scale of clinical trial studies.ResultsSEETrials achieved high precision (0.958), recall (sensitivity) (0.944), and F1 score (0.951) across 70 data elements present in the MM trial studies Generalizability tests on four additional cancers yielded precision, recall, and F1 scores within the 0.966-0.986 range. Variation in the distribution of safety and efficacy-related entities was observed across diverse therapies, with certain adverse events more common in specific treatments. Comparative performance analysis using overall response rate (ORR) and complete response (CR) highlighted differences among therapies: CAR-T (ORR: 88%, 95% CI: 84-92%; CR: 95%, 95% CI: 53-66%), bispecific antibodies (ORR: 64%, 95% CI: 55-73%; CR: 27%, 95% CI: 16-37%), and ADC (ORR: 51%, 95% CI: 37-65%; CR: 26%, 95% CI: 1-51%). Notable study heterogeneity was identified (>75%I2heterogeneity index scores) across several outcome entities analyzed within therapy subgroups.ConclusionSEETrials demonstrated highly accurate data extraction and versatility across different therapeutics and various cancer domains. Its automated processing of large datasets facilitates nuanced data comparisons, promoting the swift and effective dissemination of clinical insights.
Evaluating Large Language Models in Echocardiography Reporting: Opportunities and Challenges
Authors: Chao Chieh-Ju , Banerjee Imon , Arsanjani Reza , Ayoub Chadi , Tseng Andrew , Delbrouck Jean-Benoit , Kane Garvan C. , Lopez-Jimenez Francisco , Attia Zachi , Oh Jae K , Erickson Bradley , Fei-Fei Li , Adeli Ehsan , Langlotz Curtis 
Published: January 2024

AbstractBackgroundThe increasing need for diagnostic echocardiography (echo) tests presents challenges in preserving the quality and promptness of reports. While Large Language Models (LLMs) have proven effective in summarizing clinical texts, their application in echo remains underexplored.AimsTo evaluate open-source LLMs in echo report summarization.MethodsAdult echo studies conducted at the Mayo Clinic from January 1, 2017, to December 31, 2017, were categorized into two groups: development (all Mayo locations except Arizona) and Arizona validation sets. We adapted open-source LLMs (Llama-2, MedAlpaca, Zephyr, and Flan-T5) using In-Context Learning (ICL) and Quantized Low-Rank Adaptation (QLoRA) fine-tuning for echo report summarization from “Findings” to “Impressions.” Against cardiologist-generated Impressions, the models’ performance was assessed both quantitatively with automatic metrics and qualitatively by cardiologists.ResultsThe development dataset included 97,506 reports from 71,717 unique patients, predominantly male (55.4%), with an average age of 64.3±15.8 years. EchoGPT, a QLoRA fine-tuned Llama-2 model, outperformed other LLMs with win rates ranging from 87% to 99% in various automatic metrics, and produced reports comparable to cardiologists in qualitative review (significantly preferred in conciseness (p< 0.001), with no significant preference in completeness, correctness, and clinical utility). Correlations between automatic and human metrics were fair to modest, with the best being RadGraph F1 scores versus clinical utility (r=0.42) and automatic metrics showed insensitivity (0-5% drop) to changes in measurement numbers.ConclusionsEchoGPT can generate draft reports for human review and approval, helping to streamline the workflow. However, scalable evaluation approaches dedicated to echo reports remains necessary.Clinical Perspectives1. What is new?This study evaluated multiple open-source LLMs and different model adaptation methods in echocardiography report summarization.The resulting system, EchoGPT, can generate echo reports comparable in quality to cardiologists.Future metrics for echo report quality should emphasize factual correctness, especially on numerical measurements.2. What are the clinical implications?EchoGPT system demonstrated the potential of introducing LLMs into echocardiography practice to generate draft reports for human review and approval.
A framework for detecting causal effects of risk factors at an individual level based on principles of Mendelian randomization: Applications to modelling individualized effects of lipids on coronary artery disease
Authors: Shi Yujia , Xiang Yong , Ye Yuxin , He Tingwei , Sham Pak-Chung , So Hon-Cheong 
Published: January 2024

AbstractMendelian Randomization (MR), a method that employs genetic variants as instruments for causal inference, has gained popularity in assessing the causal effects of risk factors. However, almost all MR studies primarily concentrate on the population’saveragecausal effects. With the advent of precision medicine, theindividualizedtreatment effect (ITE) is often of greater interest. For instance, certain risk factors may pose a higher risk to some individuals compared to others, and the benefits of a treatment may vary among individuals. This highlights the importance of considering individual differences in risk and treatment response.We propose a new framework that expands the concept of MR to investigateindividualizedcausal effects. We presented several approaches for estimating Individualized Treatment Effects (ITEs) within this MR framework, primarily grounded on the principles of the”R-learner”. To evaluate the existence of causal effect heterogeneity, we proposed two permutation testing methods. We employed Polygenic Risk Scores (PRS) as the instrument and demonstrated that the removal of potentially pleiotropic SNPs could enhance the accuracy of ITE estimates. The validity of our approach was substantiated through comprehensive simulations.We applied our framework to study the individualized causal effect of various lipid traits, including Low-Density Lipoprotein Cholesterol (LDL-C), High-Density Lipoprotein Cholesterol (HDL-C), Triglycerides (TG), and Total Cholesterol (TC), on the risk of Coronary Artery Disease (CAD) using data from the UK Biobank. Our findings indicate that an elevated level of LDL-C is causally linked to increased CAD risks, with the effect demonstrating significant heterogeneity. Similar results were observed for TC. We also revealed clinical factors contributing to the heterogeneity of ITE based on Shapley value analysis. Furthermore, we identified clinical factors contributing to the heterogeneity of ITEs through Shapley value analysis. This underscores the importance of individualized treatment plans in managing CAD risks.
Procedural Volume and Outcomes with Atrial Fibrillation Ablation: A Report from the NCDR AFib Ablation Registry
Authors: Kattel Sharma , Tan Zhen , Lin Zhenqiu , Mszar Reed , Sanders Prashanthan , Zeitler Emily P. , Zei Paul C. , Bunch T Jared , Mansour Moussa , Akar Joseph , Curtis Jeptha P. , Friedman Daniel J. , Freeman James V 
Published: January 2024

AbstractBackgroundHospital and physician procedure volumes have been associated with outcomes for several cardiac procedures; however, this has not been well defined for atrial fibrillation (AF) ablation in the contemporary practice.ObjectiveTo determine the association between hospital and physician procedural volume and success for AF ablation and the risk of major adverse events (MAE).MethodsProcedures reported to the NCDR AFib Ablation Registry between July 2019 and June 2022 were included. Physician and hospital procedural volumes were annualized and stratified into quartiles (Q) to compare success and MAE. Three level hierarchical (patient, hospital and physician) generalized linear models were used to assess the adjusted relationship between procedural volume and the likelihood of procedural outcomes.ResultsA total of 70,296 first time AF ablation at 186 hospitals across the United States were included. The mean age was 66.1 ± 10.3 years, 36% were women, the mean CHA2DS2-VASc score was 2.7±1.6, and 57.4% of patients had paroxysmal AF. The median annual procedural volume for hospitals and physicians were 230 (IQR:144.7-307.2) and 66 (IQR: 44.7-97.5), respectively. Overall, acute procedural success was 98.5 % and the MAE rate was 1.0%. With Q4 (highest) hospital volume as a reference, the adjusted likelihood of procedural success was significantly lower for Q1 (OR: 0.44, CI: 0.29-0.68), Q2 (OR: 0.50, CI: 0.33-0.75) and Q3 (OR: 0.60, CI: 0.40-0.89). Similarly, for physician, procedural success was less among Q1 (OR: 0.38, CI: 0.28-0.51), Q2 (OR: 0.51, CI: 0.38-0.69) and Q3 (OR: 0.55, CI: 0.42-0.72). With MAE, compared with Q4, there was an inverse relationship between procedural volume for hospitals in Q1 (OR: 1.78, CI: 1.26-2.51) but not Q2 (OR: 1.06, CI: 0.77-1.46) or Q3 (OR: 1.19, CI: 0.89-1.58) and for physicians in Q1 (OR: 1.93, CI: 1.44-2.58) and Q2 (OR: 1.49, CI: 1.13-1.97) but not in Q3 (OR: 1.22, CI: 0.94-1.58). An adjusted MAE ≤ 1% was predicted by an annual volume of approximately 190 for hospitals and 60 for physicians.ConclusionIn this national cohort, hospital and physician AF ablation procedural volumes were directly related to acute procedural success and inversely related to rates of MAE.Condensed AbstractThe relationship between atrial fibrillation (AF) ablation procedure volume and outcomes is not well defined in contemporary practice. In this national cohort of 70,296 patients undergoing first time AF ablation, acute procedural success was relatively high (98.5%) and risk of major adverse event remained low (1.0%). Greater hospital and physician procedure volume was associated with higher procedural success. There was an inverse relationship between hospital and physician procedural volume and the risk of major adverse events.
DeepResBat: deep residual batch harmonization accounting for covariate distribution differences
Authors: An Lijun , Zhang Chen , Wulan Naren , Zhang Shaoshi , Chen Pansheng , Ji Fang , Ng Kwun Kei , Chen Christopher , Zhou Juan Helen , Yeo B.T. Thomas ,   Alzheimer’s Disease Neuroimaging Initiative,   Australian Imaging Biomarkers and Lifestyle Study of Aging
Published: January 2024

AbstractPooling MRI data from multiple datasets requires harmonization to reduce undesired inter-site variabilities, while preserving effects of biological variables (or covariates). The popular harmonization approach ComBat uses a mixed effect regression framework that explicitly accounts for covariate distribution differences across datasets. There is also significant interest in developing harmonization approaches based on deep neural networks (DNNs), such as conditional variational autoencoder (cVAE). However, current DNN approaches do not explicitly account for covariate distribution differences across datasets. Here, we provide mathematical results, suggesting that not accounting for covariates can lead to suboptimal harmonization. We propose two DNN-based covariate-aware harmonization approaches: covariate VAE (coVAE) and DeepResBat. The coVAE approach is a natural extension of cVAE by concatenating covariates and site information with site- and covariate-invariant latent representations. DeepResBat adopts a residual framework inspired by ComBat. DeepResBat first removes the effects of covariates with nonlinear regression trees, followed by eliminating site differences with cVAE. Finally, covariate effects are added back to the harmonized residuals. Using three datasets from three continents with a total of 2787 participants and 10085 anatomical T1 scans, we find that DeepResBat and coVAE outperformed ComBat, CovBat and cVAE in terms of removing dataset differences, while enhancing biological effects of interest. However, coVAE hallucinates spurious associations between anatomical MRI and covariates even when no association exists. Future studies proposing DNN-based harmonization approaches should be aware of this false positive pitfall. Overall, our results suggest that DeepResBat is an effective deep learning alternative to ComBat. Code for DeepResBat can be found here:https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/harmonization/An2024_DeepResBat.
Single and Multi-Site Cortical Stimulation Related to Human Sensorimotor Function
Authors: Ryun Seokyun , Chung Chun Kee 
Published: January 2024

AbstractSomatosensory feedback is crucial for precise control of our body and thereby affects various sensorimotor-related brain areas for movement control. Electrical stimulation on the primary somatosensory cortex (S1) elicits various artificial somatosensations. However, replicating the spatiotemporal dynamics of somatosensory feedback and fine control of elicited somatosensation are still challenging. Furthermore, how and where the somatosensory feedback interacts with neural activity for sensorimotor processing is unclear. Here, we replicate the spatiotemporal dynamics of somatosensory feedback and control the quality of elicited somatosensation using multi-site direct cortical stimulation (DCS). We also investigate how and where the neural feedback activity interacts with neural activity for motor processing by stimulating the downstream areas of the S1. We found that multi-site DCS on the S1 elicits different sensations simultaneously. Using the artificial feedback, blindfolded patients could efficiently perform a DCS-guided reach-and-grasp task successfully. Interestingly, we also found that multi-site DCS close to each other elicits different qualities of somatosensation in the same body part. Additionally, we found that DCS on the ventral premotor area (vPM) can affect hand grasping with eliciting artificial sensation of the hand. Throughout this study, we showed that semi-invasive, macro-level, and multi-site DCS can precisely elicit/modulate somatosensations in human. We suggest that activation of multiple cortical areas elicits simultaneous and independent somatosensations and that interplay among the stimulated sites can change the somatosensation quality. Finally, the results of vPM stimulation indicate that vPM has a critical role in function-specific sensorimotor interactions, such as hand grasping.