bluuebunny/crossref_metadata_2025_split huggingface dataset:
Predicting Motor Vigor from EEG Phase Amplitude Coupling (PAC) in Parkinson’s Disease: Effects of Dopaminergic Medication and Non-invasive Modulation
Authors: Kazemi Alireza , Nouri Salar , Mirian Maryam S. , Lee Soojin , McKeown Martin J. 
Published: December 2024

AbstractImpaired motor vigor is a defining characteristic of Parkinson’s disease (PD), yet the underlying brain mechanisms governing motor vigor (MV) remain unclear. Recent studies have suggested beta-gamma Phase-Amplitude Coupling (PAC) derived from the resting-state electroencephalogram (EEG) is a potential biomarker for PD that is modulated by Deep Brain Stimulation (DBS) and L-dopa treatment. Specifically, PAC has been suggested to be a marker of transitions between motor movements, as opposed to encoding the vigor of the current movement. Here, we comprehensively investigate the potential of various PAC interactions—across different frequency pairs—beyond the linear approaches typically employed to predict MV during motor tasks in PD and study the effects of dopaminergic medication and non-invasive Galvanic Vestibular Stimulation (GVS). We recorded EEG data from 20 PD patients and 22 healthy controls executing a simple, overlearned handgrip task. Subjects were tested on and off L-dopa medication and with and without GVS (multi-sine either 50-100Hz, 100-150Hz). In a preliminary linear (LASSO-based) analysis comparing various PACs and a broad range of commonly used EEG features, PAC features were found to be crucial for predicting MV approximately equally in PD and HC. Initial findings from the linear analysis showed PAC as a significant indicator for MV in both groups, although with variability in cross-validation that implied a complex, non-linear relationship between PAC and MV. To extensively investigate the PAC-MV relation, we used a deep convolutional neural network (PACNET)—developed based on pre-trained VGG-16 architecture—to estimate MV from PAC values. In both PD and HCs, delta-beta, theta-, alpha-, and beta-gamma PACs were important for MV prediction. In PD subjects, GVS affected delta-beta, theta-gamma-, and beta-gamma PACs role in MV prediction, which was sensitive to different GVS stimulation parameters. These PACs were also relevant for PD patients’ MV prediction after L-dopa medication. This study supports the hypothesis that EEG PAC across multiple frequency pairs, not just beta-gamma, predicts MV and not just motor transitions and can be a biomarker for assessing the impact of electrical stimulation and dopaminergic medication in PD. Our results suggest that PAC is involved in MV, in addition to a range of previously reported cognitive processes, including working and long-term memory, attention, language, and fluid intelligence. Non-linear approaches appear important for examining EEG PAC and behavior relations.
A multi-center investigation on the effect of decompressive surgery on Balance and physical ActiviTy Levels in patients with lumbar Spinal stenosis (B-ATLAS): Protocol for a prospective cohort study
Authors: Zielinski OB. , Hallager DW. , Jensen KY. , Carreon LY. , Andersen M.Ø. , Diederichsen LP. , Bech RD. 
Published: December 2024

ABSTRACTBackgroundPatients with lumbar spinal stenosis may complain of poor balance, decreased physical function and problems maintaining physical activity levels due to radiculopathy. Decompressive surgery is often indicated if conservative management fails to achieve a satisfactory clinical outcome. While surgical management has proven effective at treating radiculopathy, and patients report increased physical function post-operatively, objective measures of postural control and physical activity remains sparse. This study aims to investigate the effects of decompressive surgery on balance and activity levels of elderly patients with lumbar spinal stenosis using objective measurements.Methods and analysisThis is a 24-month, multi-centre, prospective cohort study. Patients ≥ 65 years of age with MRI-verified symptomatic lumbar spinal stenosis will be recruited from two separate inclusion centres, and all participants will undergo decompressive surgery for lumbar spinal stenosis. Pre-operative data is collected up to 3 months before surgery, with follow-up data collected at 3, 6, 12 and 24-months post-operatively. Balance measurements are performed using the Wii Balance Board, mini–Balance Evaluation Systems Test and Tandem test, and data concerning physical activity levels are collected using ActiGraph wGT3X-BT accelerometers. Patient reported outcomes regarding quality of life and physical function are collected from the EuroQol-5D, 36-Item Short Form Health Survey and Zurich Claudication Questionnaire. Primary outcomes are the change in sway area of centre of pressure and total activity counts per day from baseline to follow-up at 24-months. A sample size of 80 participants has been calculated.Ethics and disseminationThe study has been approved by the Regional Ethics Committee of Region Zealand (ID EMN-2022-08110) and the Danish Data Protection Agency (ID REG-100-2022). All results from the study will be published in international peer-reviewed journals and presented at national and international conferences. Study findings will be disseminated through national patient associations.Trial registration numberNCT06075862 &NCT06057428STRENGTHS AND LIMITATIONSThis study will provide new knowledge concerning the effects of lumbar spinal decompression surgery on postural control and physical activity levels.A follow-up period of two years ensures that changes in the primary outcomes are sufficiently documented.This will be the first study to correlate changes in postural control with changes in physical activity levels amongst patients with lumbar spinal stenosis.A pre-post prospective cohort design provides the necessary comparative control group.
Pulmonary Hypertension and the Risk of 30-day Postoperative Pulmonary Complications after Gastrointestinal Surgical or Endoscopic Procedures: A Retrospective Propensity Score Weighted Cohort Analysis
Authors: Tatsuoka Yoshio , Carr Zyad J. , Jayakumar Sachidhanand , Lin Hung-Mo , He Zili , Farroukh Adham , Heerdt Paul 
Published: December 2024

AbstractBackgroundPulmonary hypertension (PH) patients are at higher risk of postoperative complications. We analyzed the association of PH with 30-day postoperative pulmonary complications (PPC).MethodsA single-center propensity score overlap weighting (OW) retrospective cohort study of 164 patients with mean pulmonary artery pressure (mPAP) of >20 mmHg within 24 months of procedure and a control cohort (N=1981), undergoing elective inpatient abdominal surgery or endoscopic procedures under general anesthesia. The primary outcome was PPC, and the secondary outcomes were PPC sub-composites; respiratory failure (RF), pneumonia (PNA), aspiration pneumonia/pneumonitis (ASP), pulmonary embolism (PE), length of stay (LOS), and 30-day mortality.ResultsPPC was higher in the PH cohort (29.9% vs. 11.2%, p<0.001). When sub-composites were analyzed, higher rates of RF (19.3% vs. 6.6%, p<0.001) and PNA (11.2% vs. 5.7%, p=0.01) were observed. After OW, PH was still associated with higher PPC [RR 1.66, 95% CI (1.05 – 2.71), p=0.036] and increased LOS (median 8.0 days vs. 4.9 days) but not 30-day mortality. Sub-cohort analysis showed no difference in PPC between pre- and post-capillary PH patients.ConclusionsAfter covariate balancing, PH was associated with a higher risk for PPC and prolonged LOS. This elevated PPC risk should be considered during preoperative risk assessment.
High diagnostic rate of whole genome sequencing in primary ciliary dyskinesia
Authors: Black Holly A , Marion de Proce Sophie , Campos Jose L , Meynert Alison , Halachev Mihail , Marsh Joseph A , Hirst Robert A , O’Callaghan Chris , Genomes Partnership Scottish , Santoyo-Lopez Javier , Murray Jennie , Macleod Kenneth , Urquhart Don S , Unger Stefan , Aitman Timothy J , Mill Pleasantine 
Published: December 2024

AbstractAimPrimary ciliary dyskinesia (PCD) is a genetic disorder affecting motile cilia. Most cases are inherited recessively, due to variants in more than 50 genes that result in abnormal or absent motile cilia. This leads to chronic upper and lower airway disease, sub-fertility and laterality defects in some cases. Given overlapping clinical features and genetic heterogeneity, diagnosis can be difficult and often occurs late. Of those tested, an estimated 30% of genetically screened PCD patients still lack a molecular diagnosis. Here, we aimed to identify how readily a genetic diagnosis could be made in a clinically diagnosed population using whole genome sequencing (WGS) to facilitate identification of pathogenic variants in known genes as well as identify novel PCD candidate genes.MaethodsWGS was used to screen for variants causing PCD in 8 clinically diagnosed PCD patients, sequenced as trios where parental samples were available.ResultsSeven of the eight cases (87.5%) had homozygous or biallelic variants inDNAH5,DNAAF4orDNAH11that were classified as pathogenic or likely pathogenic. Three of the variants were deletions, ranging from 3kb to 13kb, for which WGS identified precise breakpoints, permitting confirmation by Sanger sequencing. WGS yielded a high genetic diagnostic rate from this clinically diagnosed population, in part through detection of structural variants as well as identification of ade novovariant in a novel PCD geneTUBB4B.ConclusionA molecular diagnosis allows for appropriate clinical management for cases and their families, including prediction of phenotypic features correlated to genotype. Here, WGS uplifted genetic diagnosis in cases of clinically diagnosed PCD by identifying structural variants and novel modes of inheritance in new candidate genes. Our study suggests that WGS could be a powerful part of the PCD diagnostic toolkit to increase the current molecular diagnostic yield from 70%. It provides important new insight into our understanding of fundamental biology of motile cilia as well as of variation in the non-coding genome in PCD.SummaryWhole genome sequencing (WGS) yielded a high genetic diagnostic rate (100%) in eight Scottish patients with clinically diagnosed primary ciliary dyskinesia (PCD) by detection of large structural variants, homology modelling and identification of a novel disease gene with a dominant mode of inheritance. Prioritised WGS may facilitate early genetic diagnosis in PCD.
Discrimination of Primary and Chronic CMV Infection based on Humoral Immune Profiles in Pregnancy
Authors: Hederman Andrew P. , Remmel Christopher J. , Sharma Shilpee , Natarajan Harini , Weiner Joshua A. , Wrapp Daniel , Donner Catherine , Delforge Marie-Luce , d’Angelo Piera , Furione Milena , Fornara Chiara , McLellan Jason S. , Lilleri Daniele , Marchant Arnaud , Ackerman Margaret E. 
Published: December 2024

AbstractMost humans have been infected by Cytomegalovirus (CMV) by the time they reach forty years of age. Whereas most of these CMV infections are well controlled by the immune system, congenital infection can lead to serious health effects and death for the fetus and neonate. With clear evidence that risk to the fetus is lower during chronic maternal infection, and varies in association with gestational age at the time of primary maternal infection, further research on humoral immune responses to primary CMV infection during pregnancy is needed. Here, systems serology tools were applied to characterize antibody responses to CMV infection among pregnant and non-pregnant women experiencing either primary or chronic infection. Whereas strikingly different antibody profiles were observed depending on infection status, more limited differences were associated with pregnancy status. Beyond known differences in IgM responses that are used clinically for identification of primary infection, distinctions observed in IgA and FcγR binding antibody responses and among viral antigen specificities accurately predicted infection status in a cross-sectional cohort. Leveraging machine learning, longitudinal samples were also used to define an immunological clock of CMV infection from primary to chronic states and predict time since primary infection with high accuracy. Humoral responses diverged over time in an antigen-specific manner, with IgG3 responses toward tegument decreasing over time as is typical of viral infections, while those directed to pentamer and glycoprotein B were lower during acute and greatest during chronic infection. In sum, this work provides new insights into the antibody response associated with CMV infection status in the context of pregnancy, revealing aspects of humoral immunity that have the potential to improve CMV diagnostics and to support clinical trials of interventions to reduce mother-to-fetus transmission of CMV.
Determining Line of Therapy from Real-World Data in Non-Small Cell Lung Cancer
Authors: Grady Connor B. , Hwang Wei-Ting , Reuss Joshua E. , Iams Wade , Cass Amanda , Liu Geoffrey , Patel Devalben , Liu Stephen V. , Bravo Montenegro Gabriela Liliana , Patil Tejas , Nieva Jorge J. , Herrmann Amanda , Marrone Kristen A. , Lam Vincent K. , Schwartzman William , Dowell Jonathan , Villaruz Liza C , Miller Kelsey Leigh , Weiss Jared , Sun Fangdi , Velcheti Vamsidhar , Camidge D. Ross , Aggarwal Charu , Sun Lova , Marmarelis Melina E. 
Published: December 2024

AbstractIntroductionDetermining lines of therapy (LOT) using real-world data is crucial to inform clinical decisions and support clinical research. Existing rules for determining LOT in patients with metastatic non-small cell lung cancer (mNSCLC) do not incorporate the growing number of targeted therapies used in treatment today. Therefore, we propose rules for determining LOT from real-world data of patients with mNSCLC treated with targeted therapies.MethodsLOT rules were developed through expert consensus using a real-world cohort of 550 patients withALK+ orROS1+ mNSCLC in the multi-institutional, electronic medical record-based Academic Thoracic Oncology Medical Investigators Consortium’s (ATOMIC) Driver Mutation Registry. Rules were subsequently modified based on a review of appropriate LOT determination. These resulting rules were then applied to an independent cohort of patients withEGFR+ mNSCLC to illustrate their use.ResultsSix rules for determining LOTs were developed. Among 1133 patients withEGFRmutations and mNSCLC, a total of 3168 regimens were recorded with a median of 2 regimens per patient (IQR, 1-4; range, 1-13). After applying our rules, there were 2834 total LOTs with a median of 2 LOTs per patient (IQR, 1-3; range, 1-11). Rules 1-3 kept 11% of regimen changes from advancing the LOT. When compared to previously published rules, LOT assignments differed 5.7% of the time, mostly in LOTs with targeted therapy.ConclusionThese rules provide an updated framework to evaluate current treatment patterns, accounting for the increased use of targeted therapies in patients with mNSCLC and promote standardization of methods for determining LOT from real-world data.Key PointsUse of targeted therapy to treat patients with mNSCLC is growingDetermining lines of therapy from real-world data is crucial for clinical researchOur rules aim to advance the line of therapy to reflect changes in clinical statusUsing these rules can lead to better method harmonization in mNSCLC research
Variation in health visiting for the under 5s: A cross-sectional analysis of administrative data in England for 2018-2020
Authors: Liu Mengyun , Woodman Jenny , Grath-Lone Louise Mc , Clery Amanda , Bunting Catherine , Bennett Samantha , Kendall Sally , Kirman Jennifer , Weatherly Helen , Barlow Jane , Bedford Helen , Harron Katie 
Published: December 2024

AbstractIntroductionThe health visiting service in England leads the delivery of the government’s Healthy Child Programme (HCP) for children under five. However, local authorities and their provider partners deliver this service differently in respond to the challenges of funding cuts, low numbers of health visitors and high levels of family needs.ObjectiveWe aimed to describe local authority variation in the delivery of health visiting services to children under 5 in England between 2018 and 2020.MethodsWe used nationally published statistics on mandated health visiting contacts, and administrative data from the Community Services Dataset (CSDS) on duration, location, and medium of delivery of contacts. We mapped the population coverage of mandated contacts (new birth visit, 6-8-week review, one-year review and 2-2 ½year review), and additional contacts, and described the frequency and characteristics of contacts across local authorities.ResultsAccording to the published statistics, 99% of eligible children received their new birth visit, 89% received the 6-8-week review and the one-year review, and 82% received the 2-2½-year review. There was substantial variation across local authorities: coverage for the 2-2½-year review ranged between 33%-97%. Based on CSDS, 80% of local authorities (n=46/57) delivered more additional contacts than mandated contacts: on average, 1.6 additional contacts (range: 0.1-8.5) were delivered for each mandated contact. There was also significant variation in the duration of contacts and the percentage of contacts delivered face-to-face and at home.ConclusionsOur study demonstrates substantial variation in the delivery of health visiting services across England, particularly in the delivery of additional contacts. Further research is needed to explore the extent to which this trend has continued and the reasons for this variation. There is also a need to exploit this data to further explore the impact of these different models of service delivery on family outcomes.
Advancing primary care for childhood pneumonia: a machine learning-based approach to prognosis and case management
Authors: Serin Oguzhan , Akbasli Izzet Turkalp , Bocutcu Cetin Sena , Koseoglu Busra , Deveci Ahmet Fatih , Ugur Muhsin Zahid , Ozsurekci Yasemin 
Published: December 2024

AbstractBackgroundPneumonia is the leading cause of preventable mortality under five years of age. Appropriate case management is as essential as disease prevention interventions, especially in primary care settings. Computer science has been used accurately and widely for pneumonia diagnosis; however, prognosis studies are relatively low. Herein, we developed a machine learning-based clinical decision support system tool for childhood pneumonia to provide prognostic support for case management.MethodsWe analyzed data from 437 children admitted to our clinic with a pneumonia diagnosis between 2014 and 2020. Pediatricians encoded the raw dataset according to candidate features. Before the experimental study of the machine learning algorithms of Pycaret, SMOTE-Tomek was utilized for managing imbalanced datasets. The feature selection was made by examining the SHAP values of the algorithm with the highest performance and re-modeled with the most important clinical features. We optimized hyperparameters and employed ensemble methods to develop a robust predictive model.ResultsOptimized models predicted pneumonia prognosis with %77-88 accuracy. It was shown that severity could be determined over %84 by five clinical features: hypoxia, respiratory distress, age, Z score of weight for age, and antibiotic usage before admission.ConclusionsIn this experimental study, we demonstrated that contemporary data science methods, such as oversampling, feature selection, and machine learning tools, are promising in predicting the critical care need of patients. Even in small-size samples like our study, ML methods can reach current wisdom.HighlightsPneumonia accounts for 14% of mortality in children under 5, with over 740,000 deaths in 2019 alone.WHO and UNICEF’s GAPPD aims to cut mortality rates by focusing on vaccinations, sanitation, breastfeeding, and addressing pediatric HIV.Accurate diagnosis and timely treatment can reduce pneumonia mortality by up to 28%, yet diagnosing can be challenging.Many in underdeveloped regions lack access to essential equipment and trained staff, exacerbating mortality rates.Data science and machine learning offer promising solutions for pneumonia management, especially in LMICs, with a focus on prognostic support.
seg-metrics: a Python package to compute segmentation metrics
Authors: Jia Jingnan , Staring Marius , Stoel Berend C. 
Published: December 2024

AbstractMedical image segmentation (MIS) is an important task in medical image processing. Unfortunately, there is not a out-of-the-box python package for the evaluation metrics of MIS. Therefore, we developedseg-metrics, an open-source Python package for MIS model evaluation. Unlike existing packages,seg-metricsoffers user-friendly interfaces for various overlap-based and distance-based metrics, providing a comprehensive solution.seg-metricssupports multiple file formats and is easily installable through the Python Package Index (PyPI). With a focus on speed and convenience,seg-metricsstands as a valuable tool for efficient MIS model assessment.
Divergent treatment responses in chronic pain: Identifying subgroups of patients through cluster analysis
Authors: Rijsdijk Mienke , Smits Hidde M. , Azizoglu Hazal R. , Brugman Sylvia , van de Burgt Yoeri , van Charldorp Tessa C. , van Gelder Dewi J. , de Grauw Janny C. , van Lange Eline A. , Meye Frank J. , Strick Madelijn , Walravens Hedi , Winkens Laura H.H. , Huygen Frank , Drylewicz Julia , Willemen Hanneke L.D.M. 
Published: December 2024

AbstractBackgroundChronic pain is an ill-defined disease with complex biopsychosocial aspects, posing treatment challenges. We hypothesize that treatment failure results, at least partly, from limited understanding of diverse patient subgroups. We aim to identify subgroups through psychometric data, allowing for more tailored interventions.MethodsFor this retrospective cohort study, we extracted patient-reported data from two Dutch tertiary multidisciplinary outpatient pain clinics (2018-2023) for unsupervised hierarchical clustering. Clusters were defined by anxiety, depression, pain catastrophizing, and kinesiophobia. Sociodemographics, pain characteristics, diagnosis, lifestyle, health-related quality of life (HRQoL) and treatment efficacy were compared among clusters. A prediction model was built utilizing a minimum set of questions to reliably assess cluster allocation.ResultsAmong 5,454 patients with chronic pain, three clusters emerged. Cluster 1 (n=750) was characterized by high psychological burden, low HRQoL, lower educational levels and employment rates, and more smoking. Cluster 2 (n=1,795) showed low psychological burden, intermediate HRQoL, higher educational levels and employment rates, and more alcohol consumption. Cluster 3 (n=2,909) showed intermediate features. Pain reduction following treatment was least in cluster 1 (28.6% after capsaicin patch, 18.2% after multidisciplinary treatment), compared to >50% in clusters 2 and 3. A model incorporating 15 psychometric questions reliably predicted cluster allocation.In conclusion,our study identifies distinct chronic pain patient clusters through 15 psychometric questions, revealing one cluster with notably poorer response to conventional treatment. Our prediction model may help clinicians improve treatment by allowing patient-subgroup targeted therapy according to cluster allocation.In briefHierarchical clustering of chronic pain patients revealed three clusters based on pain experience and psychological welfare, with diverse sociodemographics and treatment effects suggesting potential for tailored interventions.