bluuebunny/crossref_metadata_2025_split huggingface dataset:
Leveraging Longitudinal Patient-Reported Outcomes Trajectories to Predict Survival in Non-Small-Cell Lung Cancer
Authors: Zhou Jiawei , Muluneh Benyam , Wang Zhaoyang , Yao Huaxiu , Hughes Jim H. 
Published: January 2025

AbstractPurposeDespite their potential, patient-reported outcomes (PROs) are often underutilized in clinical decision-making, especially when improvements in PROs do not align with clinical outcomes. This misalignment may result from insufficient analytical methods that overlook the temporal dynamics and substantial variability of PROs data. To address these gaps, we developed a novel approach to investigate the prognostic value of longitudinal PRO dynamics in non-small-cell lung cancer (NSCLC) using Lung Cancer Symptom Scale (LCSS) data.MethodsLongitudinal patient-reported LCSS data from 481 NSCLC participants in the placebo arm of a Phase III trial were analyzed. A population modeling approach was applied to describe PRO progression trajectories while accounting for substantial variability in the data. Associations between PRO model parameters and survival outcomes were assessed using Cox proportional hazards models. Model-informed PRO parameters were further used to predict survival via machine learning.ResultsA PRO progression model described LCSS dynamics and predicted a median time to symptom progression of 229 days (95% CI: 15-583). Faster PRO progression rates were significantly associated with poorer survival (HR 1.13, 95% CI: 1.076-1.18), while greater placebo/prior treatment effects correlated with improved survival (HR 0.93, 95% CI: 0.883-0.99). A machine learning model using PRO parameters achieved an AUC-ROC of 0.78, demonstrating their potential to predict overall survival.ConclusionsThis study demonstrates that longitudinal PRO data can provide prognostic insights into survival in NSCLC. The findings support the use of PRO dynamics to improve clinical decision-making and optimize patient-centered treatment strategies.Translational RelevancePatient-reported outcomes (PROs) offer a patient-centered, non-invasive approach to assessing health status and symptoms, making them a valuable tool for understanding the patient experience. However, PROs are often underutilized in clinical decision-making, especially when improvements in PROs do not align with clinical outcomes. This misalignment is often driven by analytical methods that fail to account for the temporal dynamics and inherent variability of PROs data. To address these limitations, we have developed a novel computational framework that integrates population modeling with machine learning to leverage longitudinal PRO dynamics for predicting survival in non-small-cell lung cancer patients. This innovative approach can be generalized to other PRO datasets, providing personalized insights into PRO trajectories for each patient. By enhancing the utility of PROs data, our framework holds the potential to significantly improve clinical decision-making, refine patient care strategies, and optimize the design and evaluation of clinical trials, ultimately advancing precision oncology.
Association of Oral Triptans and Other Oral Migraine Treatments With Cardiovascular Outcomes in Patients With a History of Cardiovascular Conditions
Authors: Dodick David W. , Fisher Matt , DerSarkissian Maral , Murphy Shawn N. , Duh Mei Sheng , Gao Chi , Weiner Rory B. , Yu Louise H. , Herrick Christopher , Hsieh Yichuan Grace , Wang Travis , Belsky Greg , Banatwala Azeem , Boyle-Kelly Janet , Costello Jennifer , Murphy Marykate , Wong Amy K. , Ailani Jessica 
Published: January 2025

ABSTRACTOBJECTIVETo assess risk of major adverse cardiovascular events associated with acute antimigraine treatments in patients with preexisting cardiovascular conditions.PATIENTS AND METHODSIn this retrospective, longitudinal, observational cohort study, we examined data from the Mass General Brigham Research Patient Data Registry on adults who received one or more prescriptions for acute migraine (index date) between January 2006 and December 2020 and who had one or more diagnoses of a cardiovascular condition during 12 months prior to index date. Study endpoints were nonfatal myocardial infarction, nonfatal stroke, and in-facility all-cause mortality, assessed separately and together as a composite major adverse cardiovascular events–proxy endpoint.RESULTSPrescriptions were identified for 4016 oral triptans, 6084 opioids/butalbital, and 2021 NSAIDs. Hazard ratios (HRs) for major adverse cardiovascular events were 0.38 (95% CI: 0.22, 0.67;P=.001) comparing oral triptans with opioids/butalbital and 0.46 (95% CI: 0.29, 0.71;P<.001) with NSAIDs. There was no increased risk of nonfatal stroke comparing oral triptans with opioids/butalbital (HR 0.39; 95% CI 0.22, 0.69;P=.001) and with NSAIDs (HR 0.44; 95% CI 0.26, 0.76;P=.003). In a subgroup analysis of patients taking sumatriptan, HRs for major adverse cardiovascular events were 0.34 (95% CI: 0.21, 0.56;P<.001) when comparing oral sumatriptan with opioids/butalbital and 0.47 (95% CI: 0.28, 0.79;P=.004) with NSAIDs.CONCLUSIONSThe risk of adverse cardiovascular events observed with oral triptans is not greater than opioids/butalbital and NSAIDs in patients with migraine and preexisting cardiovascular conditions.
[Re] Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL
Authors: Gitau Antony M. , Adeyemi Ayoade , Tavashi Belal , Singstad Bjørn-Jostein 
Published: January 2025

Reproducibility SummaryScope of ReproducibilityThe authors of the original paper present six benchmark tasks on the previously published PTB-XL dataset, containing, 21837 12-lead ECGs from 18885 patients. They evaluate seven different neural network architectures on the six bench-mark tasks. The authors have published all code and claim full reproducibility. In addition, they published code for easy implementation of new models. To validate the claim of reproducibility we implemented a new model and tested it, and the seven models presented by the authors of the original paper, on the six benchmark tasks.MethodologyWe used the publicly available code, published by the authors of the original paper, as a starting point for our experiment. Furthermore, we modified the code slightly in order to make it compatible with a cloud-hosted Jupyter Notebook, Google Colab. We ran the experiments using Google Colab Pro, using 32 GB RAM and either 1 x NVIDIA P100 or 1 x NVIDIA T4 GPU.ResultsWe successfully managed to reproduce the original work and also verified the validity of the main claims of the original paper. In addition, we showed how robust the models were to noise and finally implemented a new model that showed comparable performance with the models proposed in the original paper.What was easyThe publicly available code published by the authors made it easy to reproduce and obtain the same results as reported in their paper.What was difficultWe faced two main issues in this work. (1) running the code in a cloudhosted jupyter notebook. This was done in order to get access to free or cheap GPUs. (2) Implement own models using the provided template. The description on how to use the base class and the configuration file could have been more detailed.Communication with original authorsCommunication with the authors of the original paper was established early in the project and helped us by clarifying some aspects of the work. In the final stage of this project the authors of the original paper were given this manuscript in order to read it and provide feedback.
Cingulate and Frontopolar Cortical Projections to the Cerebellar Vermis Support Prolonged Reaction Time in Identifying Negative Emotional Scenes in Women
Authors: Wong Hak Kei , Chaudhary Shefali , Chen Yu , Ide Jaime S. , Zhang Sheng , Li Chiang-Shan R. 
Published: January 2025

ABSTRACTWe previously observed sex differences in the association of individual anxiety and reaction time (RT) during identification of negative emotional scenes in a Hariri task. Prolonged RT in identifying negative (vs. neutral) images represents a behavioral marker of individual anxiety in women but not in men. However, the neural circuit that supports this behavioral observation remains unclear. Here, with a larger sample (64 men and 62 women), we employed whole-brain regression on individual differences in RT during matching negative vs. neutral images or RT (negative – neutral) and evaluated the results at a corrected threshold. Women but not men showed a significant correlation between individual anxiety and RT (negative – neutral), with a slope test confirming the sex difference. In women alone the cerebellar vermis showed activity in positive correlation with RT (negative – neutral). Further, Granger causality mapping (GCM) showed multiple brain regions, including the anterior cingulate cortex/frontopolar cortex (ACC/FPC), that provide inputs to the cerebellar vermis in women. Amongst these regions, only the ACC/FPC cluster showed activity (β) in significant correlation with both STAI State score and RT (negative – neutral) in women. GCM also identified a small cluster in the pons, suggesting that the cortical pontine cerebellar circuit may support prolonged RT during identification of negative emotions. Path analyses further characterized the inter-relationships amongst the neural markers, RT, and anxiety. These findings highlight a behavioral and circuit marker of anxiety state in neurotypical women. Studies with different behavioral paradigms are needed to characterize the behavioral and neural mechanisms of male anxiety.
Impact of Electronic Immunization Registries and Electronic Logistics Management Information Systems in four Low-and Middle-Income Countries: Guinea, Honduras, Rwanda, and Tanzania
Authors: Mantel C , Hugo C , Federici C , Sano N , Camara S , Rodriguez E , Castillo L , Condo J , Irakiza P , Sabi I , Nyanda E , Olomi W , Cavazza M , Mangiaterra V , Verykiou M , Ferenchick E , Torbica A , Cherian T , Malvolti S 
Published: January 2025

AbstractBackgroundThere is increasing interest in low-and middle-income countries (LMICs) to introduce and scale-up digital health tools like electronic immunization registries (eIR), and electronic logistics management information systems (eLMIS) to support immunization services. An evaluation of the use of these tools was conducted in four countries to inform decisions about their further expansion and investments.MethodsPurposive sampling of regions, districts, and health facilities was done in each country based on predefined criteria. Primary data were collected between October 2021 and September 2022 in 50 health facilities in Guinea, 88 in Honduras, 36 in Rwanda, and 101 in Tanzania using semi- structured questionnaires, standardized competency assessments and data accuracy checks. Data focused on electronic tool usage, user experience, infrastructure, workforce needs, and decision- making, as well as immunization data quality and perceptions of health workers and vaccine recipients. Data analysis combined both quantitative and qualitative methods.FindingsThe implementation of eIR and eLMIS was associated with improvements in National Immunization Programme (NIP) processes and outcomes. Users were satisfied with the tools (87% satisfaction rate), and 95% of users in Africa valued the accessibility of information, with 91% finding it accurate and complete. Some caregivers reported better organization and shorter waiting times in health facilities using the tools. Most eIR users noted improvements in process efficiencies (81%) and immunization service delivery (89%). In Rwanda and Tanzania data accuracy was higher in exclusively paper or electronic settings (60%) compared to dual paper-electronic systems (45%). eLMIS use was associated with improvements in vaccine stock data quality and reduced stock-outs. While 77% of health workers were digitally literate, inadequate digital infrastructure was a key barrier to tool use. Interoperability with the Civil Registration and Vital Statistics system (CRVS) was limited, hindering the tracking of unimmunized children.ConclusionsTo fully realize the potential of electronic tools in LMICs, full government ownership, targeted infrastructure investments, migration to fully electronic systems, and integration of eIR with the CRVS are essential.
Methylphenidate stabilizes dynamic brain network organization during tasks probing attention and reward processing in stimulant-naïve children with ADHD
Authors: Nugiel Tehila , Fogleman Nicholas D. , Sheridan Margaret A. , Cohen Jessica R. 
Published: January 2025

AbstractChildren with ADHD often exhibit fluctuations in attention and heightened reward sensitivity. Psychostimulants, such as methylphenidate (MPH), improve these behaviors in many, but not all, children with ADHD. Given the extent to which psychostimulants are prescribed for children, coupled with variable efficacy on an individual level, a better understanding of the mechanisms through which MPH changes brain function and behavior is necessary. MPH’s primary action is on catecholamines, including dopamine and norepinephrine. Catecholaminergic signaling can influence the tradeoff between flexibility and stability of brain function, which is one candidate mechanism through which MPH may alter brain function and behavior. Time-varying functional connectivity, which models how functional brain networks reconfigure on short timescales, can be used to examine brain flexibility versus stability, and is thus well-suited to test how MPH impacts brain function. Here, we scanned stimulant-naïve children with ADHD (8-12 years) on and off a single dose of MPH. In the MRI machine, participants completed two attention-demanding tasks: 1) a standard go/no-go task and 2) a rewarded go/no-go task. For both tasks, using a within-subjects design, we compared the degree to which brain organization changed throughout the course of the MRI scan, termed whole brain flexibility, on and off MPH. We found that whole brain flexibility decreased on MPH. Further, individuals with greater decreases in whole brain flexibility on MPH exhibited greater improvements in task performance. Together, these results provide novel insights into the neurobiological mechanisms underlying the effectiveness of MPH administration for children with ADHD.
The Association of Demographic Factors to Health Policy Subscriptions in Saudi Arabia: insight from National Survey
Authors: Alasiri Ahmed Ali , Zhang Qi , Sahabi Mohammed 
Published: January 2025

ABSTRACTBackgroundSaudi approaches to delivering health services seem not sustainable, and it may limit access to the necessary healthcare services. Therefore, the health system is undergoing several paradigms shifts with the investment of the private sector and insurance. The subscriptions to the health policy are affected by several variables, while demographic indicators of Health policy subscription among Saudi and expatriate populations remain underexplored.AimThe study aims to test the association of the age, gender and nationality on the subscription of health policy by utilizing national data.MethodThe study retrieved secondary data from Saudi Data & AI Authority (SDAIA), collected between 2021 -2022 with a nationality sample that cover all Saudi regions (n=14.780). The project utilized descriptive and inferential statistical tests to analyze the data and understand the relationship between the demographic variables and how they affect the subscription to health insurance.ResultWhile age, gender, and nationality have impacted the subscription of insurance to varying degrees, however, these factors revealed a small effect on the policy subscription, indicating that other social-economic variables may have a stronger prediction of the subscription rate.
Exploring booster session attendance, prescription, and outcomes in adults with chronic low back pain: Secondary analysis of a randomized clinical trial
Authors: Lanier Vanessa M. , Lohse Keith R. , Hooker Quenten L. , Civello Jesse M. , van Dillen Linda R. 
Published: January 2025

ABSTRACTIntroductionBooster sessions are a potential method for maintaining self-management behaviors and treatment effects in people with chronic low back pain (LBP). However, few studies have examined booster prescription or outcomes in people with LBP.Objective(1) Compare booster prescription for two exercise-based treatments for low back pain (LBP) in a randomized clinical trial (RCT) where the number of boosters prescribed was based on self-management program independence, (2) Determine if there are variables that predict who will require >1 booster, (3) Explore the effects of boosters on pain and function in people who required >1 booster.DesignSecondary analysis of a RCT in which participants were randomized to motor skill training (MST), MST+Boosters (MST+B), strength and flexibility exercise (SFE), or SFE+B.SettingAcademic research setting.Participants76 participants with chronic LBP assigned to receive boosters.InterventionsThis secondary analysis focuses only on the MST+B and SFE+B groups. Both groups received 6 visits of MST or SFE and six months later received up to 3 boosters. The number of boosters was based on self-management program independence at the first booster. Those who required >1 booster were not able to independently perform their program at the first booster.Main Outcome MeasuresBooster attendance and prescription, pain (Numeric Pain Rating Scale), function (modified Oswestry Disability Questionnaire)ResultsThere was not a significant difference between MST+B and SFE+B in returning for the initial booster, χ2(1)=1.76, p=0.185. SFE+B were more likely to require >1 booster than MST+B;β=2.39,p<0.001. No participant-specific factors we examined were statistically related to needing >1 booster.ConclusionMST+B participants were less likely to require additional boosters. No additional participant-specific factors we examined were associated with needing additional boosters. Qualitatively, attending additional booster sessions did not appear to change pain or function in the current sample.
Examining the cognitive phenotype in Neurofibromatosis 1: a systematic review of functional imaging studies
Authors: Wild Anna , Tziraki Maria , Lea-Carnall Caroline , Garg Shruti 
Published: January 2025

AbstractNeurofibromatosis 1 (NF1) is a rare genetic condition, frequently associated with learning difficulties. Functional neuroimaging techniques have been used to investigate brain mechanisms underlying learning difficulties in NF1, but these remain poorly understood. We conducted a systematic review of functional neuroimaging studies in NF1 from 2000-2024 aiming to develop a neurobiological framework for understanding the NF1 cognitive phenotype. A total of 44 studies were identified as relevant across functional magnetic resonance imaging (fMRI), electroencephalography (EEG), magnetic resonance spectroscopy (MRS), positron emission topography (PET), transcranial magnetic stimulation (TMS) and arterial spin labelling (ASL) techniques. Findings suggest mechanisms underlying learning in NF1 may be characterised as excitation/ inhibition imbalance, dysconnectivity between brain regions and perturbations in neural activation patterns. We suggest, disruptions in GABAergic signalling, beginning in early development, may alter the whole brain function, explaining the differential patterns of brain activity, connectivity, and brain chemistry. As most of the reviewed studies were cross-sectional in nature, future research should focus on longitudinal designs to better understand developmental changes in brain mechanisms and how these processes evolve over time. Finally, it is essential to explore environmental modifiers in shaping the cognitive phenotypes associated with NF1.
Immediate postoperative minimal residual disease detection with MAESTRO predicts recurrence and survival in head and neck cancer patients treated with surgery
Authors: Sim Edward S. , Rhoades Justin , Xiong Kan , Walsh Laurel , Crnjac Andjela , Blewett Timothy , Al-Inaya Yana , Mendel Julia , Ruiz-Torres Daniel A. , Efthymiou Vasileios , Lumaj Gjystina , Benjamin William J. , Makrigiorgos G. Mike , Tabrizi Shervin , Adalsteinsson Viktor A. , Faden Daniel L. 
Published: January 2025

AbstractPurposeWhile circulating tumor DNA (ctDNA) is a promising biomarker for minimal residual disease (MRD) detection in head and neck squamous cell carcinoma (HNSCC), more sensitive assays are needed for accurate MRD detection at clinically-relevant timepoints. Ultrasensitive MRD detection immediately after surgery could guide adjuvant therapy decisions, but early ctDNA dynamics are poorly understood.Experimental DesignWe applied MAESTRO, a whole-genome, tumor-informed, mutation-enrichment sequencing assay, in a pooled testing format called MAESTRO-Pool, to plasma samples from HNSCC patients collected immediately after surgery and during surveillance. We evaluated whether early MRD detection could predict outcomes.ResultsAmong 24 predominantly HPV-independent (95.8%) HNSCC patients, rapid ctDNA clearance occurred by the first postoperative sample (1-3 days postoperatively) in 9 patients without an event (recurrence or death). 13/15 patients with an event were MRD+ (PPV = 92.9%; NPV = 80%) with a median tumor fraction (TFx) of 54 ppm (range 6-1,177 ppm). In the first and last sample of the immediate postoperative window, 8/13 and 10/13 patients had TFx below 100 ppm, respectively, the detection limit of leading commercial assays. Early MRD detection correlated with worse overall survival (HR = 8.3; 95% CI: 1.1-66.1;P= 0.02) and event-free survival (HR = 27.4; 95% CI: 3.5-214.5;P< 0.0001) independent of high-risk pathology.ConclusionsImmediate postoperative MRD detection by MAESTRO was predictive of recurrence and death. Given the ultralow TFxs observed, ultrasensitive assays will be essential for reliable MRD detection during early postoperative timepoints to enable personalized adjuvant therapy decision-making in HNSCC.
Transformers Enhance the Predictive Power of Network Medicine
Authors: Spector Jonah , Aldana Andrés , Sebek Michael , Ehlert Joseph , DeFrondeville Christian , Ghiassian Susan Dina , Barabási Albert-László 
Published: January 2025

AbstractBackgroundSelf-attention mechanisms and token embeddings behind transformers allow the extraction of complex patterns from large datasets, and enhance the predictive power over traditional machine learning models. Yet, being trained to make predictions about individual cells or genes, it is not clear if transformers can learn the inherent interaction patterns between genes, ultimately responsible for their mechanism of action. We use Geneformer, pretrained on single-cell transcriptomes, to ask if transformers can implicitly capture molecular dependencies, including protein-protein interactions (PPIs), allowing us to explore the use of transformers to improve network medicine tasks such as disease gene identification and drug repurposing.MethodsWe extracted the cosine similarity of gene embeddings and the attention weights contained in Geneformer, allowing us to test if these weights capture experimentally validated protein interactions. Using dilated cardiomyopathy as a case study, we evaluated the effectiveness of the resulting weighted networks in disease module detection and drug repurposing.ResultsWe found that Geneformer displays awareness of experimentally documented Protein-Protein Interactions, exhibiting higher cosine similarity and attention weights for gene pairs with physical interactions. Weighting PPI networks with the cosine similarity and attention weights improved the detection of disease-associated genes and the accuracy of drug repurposing predictions for dilated cardiomyopathy, surpassing the accuracy of unweighted networks. Finally, we find that combining attention weights and cosine similarities with ranking methods enhances drug candidate prioritization for drug repurposing.ConclusionsWe find that transformers, by implicitly learning the interactions between genes, offer a promising pathway for advancing medicine and drug discovery when integrated with the graph theoretic algorithms used in network medicine.