bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Struggle to Vaccinate: Unveiling the Reality of the first year of Covid-19 Vaccination in the Democratic Republic of Congo
Authors: Adidja Amani , Aimé Cikomola Mwana Bene , Lungoyo Luhata Christophe , Kabwaya Mukoka Arsène , Lekeumo Cheuyem Fabrice Zobel , Mpinganjira Samuel , Sibongile Nkosi Dumisile , Chido Konono Kimberly Cheryl , Ngigi Michael , Ongolo-Zogo Pierre 
Published: January 2024

AbstractIntroductionThe emergence of COVID-19 as a global pandemic has affected countries worldwide, including the Democratic Republic of Congo (DRC). The DRC has experienced four waves of COVID-19, each associated with a new variant of the virus. To control the spread of the virus, the government of the DRC implemented various measures, including vaccination. The country developed a COVID-19 vaccine deployment plan, targeting high-risk groups, and launched a vaccination campaign in April 2021. This study aims to comprehensively assess the COVID-19 vaccination program in the DRC during its first year of implementation, including progress, coverage, types of vaccines administered, and a comparison with other neighboring countries.MethodsThis study was an analysis of the COVID-19 vaccination data during its first year of implementation in DRC. Data were collected from multiple sources, including the Ministry of Health and the WHO, and analyzed using descriptive statistics. The study received clearance and used de-identified and aggregated data.ResultsOut of the 26 provinces in the country, only 15 began immunization activities with varying levels of coverage, ranging from 0.02% to 6.91%. The number of functional vaccination sites remained patchy across the country. By March 2022, 5.7% of the population had received at least one vaccine dose, with 1.03% fully vaccinated. In most provinces, men were more compliant with vaccination than women. More than half of the vaccinated individuals preferred the Janssen vaccine. Compared to neighboring countries, the DRC has lagged behind in its vaccination efforts, having administered only 1.1 million of doses received (8%) and has vaccinated only 2% of its population with at least one dose, the lowest among the countries analyzed.ConclusionDespite the challenges faced in the first year of the COVID-19 vaccination, DRC has made significant progress in vaccinating its population. The slow progress highlights the need for continued investment in health systems. These insights can inform future Covid-19 vaccination campaigns in DRC and other low-income countries.
Multi-omics profiling with untargeted proteomics for blood-based early detection of lung cancer
Authors: Koh Brian , Liu Manway , Almonte Rebecca , Ariad Daniel , Bundalian Ghristine , Chan Jessica , Choi Jinlyung , Chou Wan-Fang , Cuaresma Rea , Hoedt Esthelle , Hopper Lexie , Hu Yuntao , Jain Anisha , Khaledian Ehdieh , Khin Thidar , Kokate Ajinkya , Lee Joon-Yong , Leung Stephanie , Lin Chi-Hung , Marispini Mark , Malekpour Hoda , Mora Megan , Mudaliar Nithya , Golmaei Sara Nouri , Qian Hao , Ramaiah Madhuvanthi , Ramaswamy Saividya , Ranjan Purva , Shu Guanhua , Spiro Peter , Ta Benjamin , Vitko Dijana , Waiss Jacob , Yanagihara Zachary , Zawada Robert , Zeng Jimmy Yi , Zhang Susan , Yee James , Blume John E. , Belthangady Chinmay , Wilcox Bruce , Ma Philip 
Published: January 2024

AbstractBlood-based approaches to detect early-stage cancer provide an opportunity to improve survival rates for lung cancer, the most lethal cancer world-wide. Multiple approaches for blood-based cancer detection using molecular analytes derived from individual ‘omics (cell-free DNA, RNA transcripts, proteins, metabolites) have been developed and tested, generally showing significantly lower sensitivity for early-stage versus late-stage cancer. We hypothesized that an approach using multiple types of molecular analytes, including broad and untargeted coverage of proteins, could identify biomarkers that more directly reveal changes in gene expression and molecular phenotype in response to carcinogenesis to potentially improve detection of early-stage lung cancer. To that end, we designed and conducted one of the largest multi-omics, observational studies to date, enrolling 2513 case and control subjects. Multi-omics profiling detected 113,671 peptides corresponding to 8385 protein groups, 219,729 RNA transcripts, 71,756 RNA introns, and 1801 metabolites across all subject samples. We then developed a machine learning-based classifier for lung cancer detection comprising 682 of these multi-omics analytes. This multi-omics classifier demonstrated 89%, 80%, and 98-100% sensitivity for all-stage, stage I, and stage III-IV lung cancer, respectively, at 89% specificity in a validation set. The application of a multi-omics platform for discovery of blood-based disease biomarkers, including proteins and complementary molecular analytes, enables the noninvasive detection of early-stage lung cancer with the potential for downstaging at initial diagnosis and the improvement of clinical outcomes.
Are socio-demographic factors associated with electronic Health Information System utilization?A cross sectional study of the District Health Information System -2 (DHIS-2) in Burundi
Authors: Yandemye Innocent , Nimubona Alexandre 
Published: January 2024

AbstractThe utilization of Electronic Health Information System is crucial in informing credible decision-making process, especially in developing countries. We aimed to investigate the socio-demographic factors of Health workers that are associated with their utilization of the DHIS-2 platform in Burundi. We conducted a cross-sectional study amongst health workers involved in collecting and validating data in health facilities. The male gender was more associated with the DHIS-2 effect on the enhancement of health information access (χ2 = 7.995, P=.005) than the female gender. Marital status was associated with quality information (χ2 = 6.437, P= .011) and health information management (χ2= 5.053, P= .025) improvement. The lower time travel to workplace was associated with the improvement of quality information (χ2= 11.224, P= .001) and health information management (χ2= 6.568, P= .010), training on DHIS-2 (χ2= 16.374, P= .000), DHIS-2 compatibility with health facility management tools (χ2 = 4.726, P= .030), national health information system design (χ2= 8.023, P= .005), compatibility with primary health information tools (χ2= 9.339, P= .002) and the national health policy objective (χ2= 9.699, P= .002), required security elements (χ2= 14.205, P= .000), data transfer security (χ2= 6.288, P= .012), data protection against modification (χ2= 6.497, P= .011), and assurance of personal privacy (χ2= 7.650, P= .006). The lower educational level was associated with information error minimization (χ2= 7.243, P= .007) and learning ease (χ2 = 4.175, P= .041). The health worker function was associated with DHIS-2 compatibility with national health policy objective (χ2 =4.496, P= .034). The geographical area of work was associated with DHIS-2 ease of use and interoperability assurance (χ2 = 10.287, P= .006; χ2 = 16.086, P= .000). There is a need to make the use of the DHIS-2 platform a global public health concern in fragile health systems for robust decision-making process.
Racial Differences in the Relationship Between Blood Pressure and Cognitive Decline
Authors: Oliver Michael D. , Morrison Cassandra , El-Hulu Sondos , Harvey Marquinta , Barnes Lisa L. 
Published: January 2024

AbstractBackgroundCognition may be influenced by health-related factors such as blood pressure (BP). However, variations in BP may differentially affect cognition as a function of race. This study investigates the relationship between normal, high, and variable BP and cognitive decline in older Black and White adults.Methods2284 participants (1139 Blacks, 1145 Whites,MAge=73.4,SD=6.6) from 3 harmonized cohorts of older adults from the Rush Alzheimer’s Disease Center, were divided into 3 groups (normal, high, variable) based on systolic BP mean and standard deviation. Cognitive scores were computed from multiple assessments in 5 domains (i.e., episodic memory, semantic memory, working memory, processing speed, visuospatial ability). Performance across 19 tests were averaged to create a measure of global cognition. Linear mixed-effects models examined racial differences between BP and cognitive change over an average of 6.7 years.ResultsWhite adults with high or variable BP had faster rates of decline in global cognition compared to Black adults. White adults with high BP declined faster in perceptual speed, semantic memory, and working memory compared to Black adults with high BP, whereas White adults with variable BP had faster rates of decline in all cognitive domains compared to Black adults with variable BP. No racial differences were observed in individuals with normal BP.ConclusionsWhite older adults with elevated or fluctuating BP show faster rates of cognitive decline compared to older Black adults. Findings highlight the complex interplay between BP and cognitive health, emphasizing the need for targeted interventions to address racial disparities in cognitive well-being.
Digital gait outcomes for ARSACS: discriminative, convergent and ecological validity in a multi-center study (PROSPAX)
Authors: Beichert Lukas , Ilg Winfried , Kessler Christoph , Traschütz Andreas , Reich Selina , Santorelli Filippo M. , Başak Ayşe Nazli , Gagnon Cynthia ,   PROSPAX consortium, Schüle Rebecca , Synofzik Matthis 
Published: January 2024

AbstractBackgroundWith treatment trials on the horizon, this study aimed to identify candidate digital-motor gait outcomes for Autosomal Recessive Spastic Ataxia of Charlevoix-Saguenay (ARSACS), capturable by wearable sensors with multi-center validity, and ideally also ecological validity during free walking outside laboratory settings.MethodsCross-sectional multi-center study (4 centers), with gait assessments in 36 subjects (18 ARSACS patients; 18 controls) using three body-worn sensors (Opal, APDM) in laboratory settings and free walking in public space. Sensor gait measures were analyzed for discriminative validity from controls, and for convergent (i.e. clinical and patient-relevance) validity by correlations with SPRSmobility(primary outcome) and SARA, SPRS and FARS-ADL (exploratory outcomes).ResultsOf 30 hypothesis-based digital gait measures, 14 measures discriminated ARSACS patients from controls with large effect sizes (|Cliff’s δ| > 0.8) in laboratory settings, with strongest discrimination by measures of spatiotemporal variability Lateral Step Deviation (δ=0.98), SPcmp (δ=0.94) and Swing CV (δ=0.93). Large correlations with the SPRSmobilitywere observed for Swing CV (Spearman’s ρ = 0.84), Speed (ρ=-0.63) and Harmonic Ratio V (ρ=-0.62). During supervised free walking in public space, 11/30 gait measures discriminated ARSACS from controls with large effect sizes. Large correlations with SPRSmobilitywere here observed for Swing CV (ρ=0.78) and Speed (ρ=-0.69), without reductions in effect sizes compared to lab settings.ConclusionWe identified a promising set of digital-motor candidate gait outcomes for ARSACS, applicable in multi-center settings, correlating with patient-relevant health aspects, and with high validity also outside lab settings, thus simulating real-life walking with higher ecological validity.
Coronary Artery Calcium Scans Powered by Artificial Intelligence Predicts Atrial Fibrillation Comparably to Cardiac Magnetic Resonance Imaging: The Multi-Ethnic Study of Atherosclerosis (MESA)
Authors: Naghavi Morteza , Reeves Anthony P. , Atlas Kyle , Li Dong , Goodarzynejad Hamidreza , Zhang Chenyu , Atlas Thomas , Henschke Claudia , Budoff Matthew J. , Yankelevitz David 
Published: January 2024

AbstractBackgroundApplying artificial intelligence to coronary artery calcium computed tomography scan (AI-CAC) provides more actionable information beyond the Agatston coronary artery calcium (CAC) score. We have recently shown that AI-CAC automated left atrial (LA) volumetry enabled prediction of atrial fibrillation (AF) in as early as one year. In this study we evaluated the performance of AI-CAC automated LA volumetry versus LA volume measured by human experts using cardiac magnetic resonance imaging (CMRI) for predicting AF, and compared them with CHARGE-AF risk score, Agatston score, and NT-proBNP (BNP).MethodsWe used 15-year outcome data from 3552 asymptomatic individuals (52.2% women, ages 45-84 years) who underwent both CAC scans and CMRI in the baseline examination (2000-2002) of the Multi-Ethnic Study of Atherosclerosis (MESA). AI-CAC took on average 21 seconds per scan. CMRI LA volume was previously measured by human experts. Data on BNP, CHARGE-AF risk score and the Agatston score were obtained from MESA.ResultsOver 15 years follow-up, 562 cases of AF accrued. The ROC AUC for AI-CAC versus CMRI and CHARGE-AF were not significantly different (AUC 0.807, 0.808, 0.800 respectively, p=0.60). The AUC for BNP (0.707) and Agatston score (0.694) were significantly lower than the rest (p<.0001). AI-CAC and CMRI significantly improved the continuous Net Reclassification Index (NRI) for prediction of AF when added to CHARGE-AF risk score (0.28, 0.31), BNP (0.43, 0.32), and Agatston score (0.69, 0.41) respectively (p for all<0.0001).ConclusionAI-CAC automated LA volumetry and CMRI LA volume measured by human experts similarly predicted incident AF over 15 years.
Functional connectivity of the nucleus accumbens predicts clinical course in treated and non-responder adult ADHD
Authors: Zaher Ahmed , Leonards Jan , Reif Andreas , Grimm Oliver 
Published: January 2024

AbstractAttention deficit hyperactivity disorder (ADHD) is a neurodevelopmental condition that often persists into adulthood, contributing to a challenging trajectory characterised by increased rates of psychiatric comorbidity. Contrary to previous assumptions, the clinical course of ADHD in adulthood is characterised by dynamic changes influenced by stimulant treatment. To improve the precision of treatment, the identification of a predictive neuroimaging biomarker would be of great clinical importance.In this study, we explore the potential of seed-based functional connectivity (FC) as a predictive tool for assessing the clinical course of ADHD symptoms. We conducted a longitudinal follow-up study of 54 adult ADHD patients who underwent magnetic resonance imaging (MRI) using a 3 Tesla scanner. All patients received stimulant treatment during an initial run-in period. After an average of three years, only subjective responders continued treatment (n=34), while subjective non-responders discontinued medication (n=20). We reassessed patients after three years of treatment to 1) evaluate the prediction of individual outcome by baseline fMRI and 2) to investigate differences in prediction by baseline fMRI depending on long-term treatment vs. discontinuation.Our investigation focused on determining whether a relationship could be established between resting-state FC of the nucleus accumbens (NAc) and the trajectory of symptom development, as well as identifying possible differences in FC between individuals who had been on long-term stimulant treatment and those who had not used stimulants at the time of follow-up.In general, reduced FC between the NAc and the default mode network (DMN) was associated with initially higher symptom burden, whereas patterns of improvement correlated with reduced FC between the NAc and the salience network (SN).Comparatively, higher FC between the NAc and the SN was associated with better symptom outcomes in patients receiving long-term stimulant medication, whereas lower FC between the NAc and the SN was correlated with a more favourable prognosis in non-responders.Our prospective fMRI study suggests that functional connectivity between the dopaminergic reward circuitry and the SN, especially the insula, is predictive of better ADHD symptom outcomes. This work highlights the potential of dopaminergic functional connectivity as a prognostic factor in ADHD. While future research with larger samples and longer follow-up is warranted, our study highlights the role of the NAc in the prognosis of ADHD.
Association between antinuclear antibodies (ANA) patterns and extractable nuclear antigens (ENA) in patients with autoimmune diseases
Authors: Haris Abdul Rehman , Tipu Hamid Nawaz , Ahmed Dawood , Nabeel Muhammad Abdullah , Akhtar Muhammad Shoaib 
Published: January 2024

AbstractBackgroundAntinuclear antibodies (ANA) and extractable nuclear antigens (ENA) are crucial biomarkers for the diagnosis of autoimmune diseases (AID) such as systemic lupus erythematosus (SLE), Sjogren’s syndrome, systemic sclerosis, and polymyositis. In the present study, we assessed the most frequent ANA patterns associated with the most detectable ENA antigen (Ag) that could be used as a diagnostic and efficient prognostic marker of AID.Materials and MethodsThe primary objective of this study was to investigate the association between immunofluorescence (IF) ANA and ENA in patients with AIDs. This was a retrospective cross-sectional study. The study was performed at the Immunology Department of the Armed Forces Institute of Pathology, Rawalpindi. Retrospective data from 76 patients were tested for ANA and ENA from June 2020 to Nov 2020.ResultsA total of 76 patients comprising 14 (18.4%) males and 62(81.6%) females were tested for AIDs. The most frequent pattern among AID patients was coarse speckled, followed by the peripheral ANA pattern. The most frequent ENA Ags were Sjogren’s syndrome A (SSA) and B (SSB). SSA was significantly associated with coarse speckled and peripheral ANA patterns, whereas SSB was associated with coarse speckled ANA patterns. These associations are relevant for accurate diagnosis of autoimmune diseases.ConclusionSSA was associated with coarse speckled and peripheral ANA patterns, whereas SSB was associated with coarse speckled ANA patterns. The ANA patterns were significantly associated with ENA antigens.
Dual-energy X-ray absorptiometry derived knee shape may provide a useful imaging biomarker for predicting total knee replacement: findings from a study of 37,843 people in UK Biobank
Authors: Beynon Rhona A , Saunders Fiona R , Ebsim Raja , Frysz Monika , Faber Benjamin G , Gregory Jennifer S , Lindner Claudia , Sarmanova Aliya , Aspden Richard M , Harvey Nicholas C , Cootes Timothy , Tobias Jonathan H 
Published: January 2024

AbstractObjectiveWe developed a novel imaging biomarker derived from knee dual-energy x-ray absorptiometry (DXA) to predict subsequent total knee replacement (TKR). The biomarker is based on knee shape, determined through statistical shape modelling. It was developed and evaluated using data and scans from the UK Biobank cohort.MethodsUsing a 129-point statistical shape model (SSM), knee shape (B-score) and minimum joint space width (mJSW) of the medial joint compartment (binarized as above or below the first quartile) were derived. Osteophytes were manually graded in a subset of DXA images. Cox proportional hazards models were used to examine the associations of B-score, mJSW and osteophyte score with the risk of TKR, adjusted for age, sex, height and weight.ResultsThe analysis included 37,843 individuals (mean 63.7 years). In adjusted models, B-score and mJSW were associated with TKR: a standard deviation increase in B-score was associated with a hazard ratio (HR) of 2.32 (2.13, 2.54), and a lower mJSW with a HR of 2.21 (1.76, 2.76). In the 6,719 images scored for osteophytes, mJSW was replaced by osteophyte score in the most strongly predictive model for TKR. In subsequent ROC analyses, a model combining B-score, osteophyte score, and demographic variables had superior discrimination (AUC=0.87) in predicting TKR at five years compared with a model with demographic variables alone (AUC=0.73).ConclusionsAn imaging biomarker derived from knee DXA scans reflecting knee shape and osteophytes, in conjunction with demographic factors, could help identify those at high risk of TKR, in whom preventative strategies should be targeted.
Contrast-enhanced ultrasound for assessing tissue perfusion and predicting functional recovery following acute traumatic spinal injury: translation from rat model to humans
Authors: Khaing Zin Z. , Leyendecker Jannik , Harmon Jennifer N. , Sivakanthan Sananthan , Cates Lindsay N. , Hyde Jeffrey E. , Krueger Melissa , Glenny Robb W. , Bruce Matthew , Hofstetter Christoph P. 
Published: January 2024

AbstractTraumatic spinal cord injury (tSCI) leads to an immediate loss of neurological function, with its recovery being difficult to predict in the acute phase. Here, we developed a contrast-enhanced ultrasound (CEUS) imaging biomarker to quantify the intraspinal vascular disruption after tSCI. In rodent thoracic tSCI, CEUS revealed a perfusion area deficit (PAD) which increased with injury severity (p = 0.001). The PAD size significantly correlated with hindlimb locomotor function at 8 weeks post injury (R2= 0.82, p < 0.001). Additionally, we calculated a spinal perfusion index (SPI) comparing the amount of perfused tissue at the injury center to that in injury periphery. Our experiments demonstrated that SPI decreased in more severe injuries and correlated significantly with hindlimb locomotor function at 8 weeks post injury (R2= 0.83, p < 0.001). Subsequently, we demonstrated the feasibility of intraoperative CEUS imaging in 20 patients with acute tSCI. A hyper-perfusion pattern was commonly seen in cervical motor-incomplete tSCI, while necrosis penumbra pattern was associated with motor-complete cervical or thoracic tSCI. We measured both PAD and SPI and detected statistically significant differences between motor-complete and motor-incomplete patients. In our patient cohort, SPI exhibited a strong predictive capacity for functional recovery at 6 months (R2= 0.79, p < .001) compared to PAD. In conclusion, our study suggests that an intraoperative CEUS-derived biomarker holds promise for predicting injury severity and chronic functional outcome after tSCI. Larger clinical studies are needed to better assess the reliability of the proposed CEUS-derived biomarker and its prognostic capacity.One Sentence SummaryThis paper introduces a novel biomarker utilizing contrast-enhanced ultrasound in humans and rats that accurately predicts injury severity after traumatic spinal cord injury based on tissue perfusion.