bluuebunny/crossref_metadata_2025_split huggingface dataset:
Characterization of the murine spine for spaceflight studies
Authors: Lim Shiyin , Veres Joanna E. , Almeida Eduardo A.C. , O’Connell Grace D. 
Published: March 2024

AbstractRodents provide a useful analog for understanding the effects of spaceflight on the human body, offering opportunities for investigations into the relationship between microgravity and the musculoskeletal system. In particular, rodents have often been utilized to improve our understanding of the effects of spaceflight on the spine, including intervertebral disc and vertebral body health. However, there are a number of experimental factors that differ between existing works, including mission duration, animal housing, and anatomical location of interest, making it difficult to draw holistic conclusions. Additionally, the quadrupedal nature of the murine spine results in different biomechanical loading than in a bipedal organism. Thus, the objective of this study was to more fully define the bulk properties of the murine lumbar spine model after 28 days of spaceflight. Additionally, the proximal tibia was analyzed to provide insight into the skeletal site-specificity of gravitational unloading in space. Results indicated that the effects of spaceflight on vertebral body bone microarchitecture, intervertebral disc biochemistry, and intervertebral disc joint mechanics were statistically insignificant, while large and significant bone loss was observed in the proximal tibia of the same animals. We hypothesize that this may be due to site-specific loading changes in space. Specifically, vigorous ambulatory behaviors observed in this experiment after initial acclimation to spaceflight may increase axial load-bearing in the lumbar spine, while maintaining microgravity induced mechanical unloading in the tibia. In total, this work shows that the rodent spine, unlike the weight bearing tibia in the same mice, is not affected by gravitational unloading, suggesting the tissue degenerative effects of spaceflight are site- and load-specific and not systemic. This study also highlights the importance of considering experimental variables such as habitat acclimation, physical activity, and experiment duration as key factors in determining musculoskeletal and spine health outcomes during spaceflight.
Reconstructed influenza A/H3N2 infection histories reveal variation in incidence and antibody dynamics over the life course
Authors: Hay James A. , Zhu Huachen , Jiang Chao Qiang , Kwok Kin On , Shen Ruiyin , Kucharski Adam , Yang Bingyi , Read Jonathan M. , Lessler Justin , Cummings Derek A. T. , Riley Steven 
Published: March 2024

AbstractHumans experience many influenza infections over their lives, resulting in complex and varied immunological histories. Although experimental and quantitative analyses have improved our understanding of the immunological processes defining an individual’s antibody repertoire, how these within-host processes are linked to population-level influenza epidemiology remains unclear. Here, we used a multi-level mathematical model to jointly infer antibody dynamics and individual-level lifetime influenza A/H3N2 infection histories for 1,130 individuals in Guangzhou, China, using 67,683 haemagglutination inhibition (HI) assay measurements against 20 A/H3N2 strains from repeat serum samples collected between 2009 and 2015. These estimated infection histories allowed us to reconstruct historical seasonal influenza patterns and to investigate how influenza incidence varies over time, space and age in this population. We estimated median annual influenza infection rates to be approximately 18% from 1968 to 2015, but with substantial variation between years. 88% of individuals were estimated to have been infected at least once during the study period (2009-2015), and 20% were estimated to have three or more infections in that time. We inferred decreasing infection rates with increasing age, and found that annual attack rates were highly correlated across all locations, regardless of their distance, suggesting that age has a stronger impact than fine-scale spatial effects in determining an individual’s antibody profile. Finally, we reconstructed each individual’s expected antibody profile over their lifetime and inferred an age-stratified relationship between probability of infection and HI titre. Our analyses show how multi-strain serological panels provide rich information on long term, epidemiological trends, within-host processes and immunity when analyzed using appropriate inference methods, and adds to our understanding of the life course epidemiology of influenza A/H3N2.
Integrated multiomics implicates dysregulation of ECM and cell adhesion pathways as drivers of severe COVID-associated kidney injury
Authors: Anandakrishnan Nanditha , Yi Zhengzi , Sun Zeguo , Liu Tong , Haydak Jonathan , Eddy Sean , Jayaraman Pushkala , DeFronzo Stefanie , Saha Aparna , Sun Qian , Yang Dai , Mendoza Anthony , Mosoyan Gohar , Wen Huei Hsun , Schaub Jennifer A. , Fu Jia , Kehrer Thomas , Menon Rajasree , Otto Edgar A. , Godfrey Bradley , Suarez-Farinas Mayte , Leffters Sean , Twumasi Akosua , Meliambro Kristin , Charney Alexander W. , García-Sastre Adolfo , Campbell Kirk N. , Gusella G. Luca , He John Cijiang , Miorin Lisa , Nadkarni Girish N. , Wisnivesky Juan , Li Hong , Kretzler Matthias , Coca Steve G. , Chan Lili , Zhang Weijia , Azeloglu Evren U. 
Published: March 2024

AbstractCOVID-19 has been a significant public health concern for the last four years; however, little is known about the mechanisms that lead to severe COVID-associated kidney injury. In this multicenter study, we combined quantitative deep urinary proteomics and machine learning to predict severe acute outcomes in hospitalized COVID-19 patients. Using a 10-fold cross-validated random forest algorithm, we identified a set of urinary proteins that demonstrated predictive power for both discovery and validation set with 87% and 79% accuracy, respectively. These predictive urinary biomarkers were recapitulated in non-COVID acute kidney injury revealing overlapping injury mechanisms. We further combined orthogonal multiomics datasets to understand the mechanisms that drive severe COVID-associated kidney injury. Functional overlap and network analysis of urinary proteomics, plasma proteomics and urine sediment single-cell RNA sequencing showed that extracellular matrix and autophagy-associated pathways were uniquely impacted in severe COVID-19. Differentially abundant proteins associated with these pathways exhibited high expression in cells in the juxtamedullary nephron, endothelial cells, and podocytes, indicating that these kidney cell types could be potential targets. Further, single-cell transcriptomic analysis of kidney organoids infected with SARS-CoV-2 revealed dysregulation of extracellular matrix organization in multiple nephron segments, recapitulating the clinically observed fibrotic response across multiomics datasets. Ligand-receptor interaction analysis of the podocyte and tubule organoid clusters showed significant reduction and loss of interaction between integrins and basement membrane receptors in the infected kidney organoids. Collectively, these data suggest that extracellular matrix degradation and adhesion-associated mechanisms could be a main driver of COVID-associated kidney injury and severe outcomes.
Diagnostic performance of multiplex lateral flow tests in ambulatory patients with acute respiratory illness
Authors: Murphy Caitriona , Mak Loretta , Cheng Samuel M. S. , Liu Gigi Y. Z. , Chun Alan M. C. , Leung Katy K. Y. , Sum Natalie Y. W. , Poukka Eero , Peiris Malik , Cowling Benjamin J. 
Published: March 2024

ABSTRACTBackgroundWe assessed the performance of three different multiplex lateral flow assays which provide results for influenza, respiratory syncytial virus (RSV) and SARS-CoV-2.MethodsAmbulatory patients 6 months and older presenting with two or more symptoms or signs of an acute respiratory illness were enrolled in an outpatient clinic in Hong Kong. Multiplex lateral flow tests manufactured by SureScreen, Microprofit and Goldsite were performed by trained research staff using the nasal swabs from each test kit, and separate swabs were collected for RT-PCR testing.ResultsBetween 4 April and 20 October 2023, 1646 patients were enrolled and tested by at least one lateral flow test. The point estimates for all three multiplex tests had high sensitivity above 80% for influenza A and SARS-CoV-2, and the tests manufactured by Microprofit and Goldsite had sensitivity exceeding 84% to detect RSV. Test sensitivity increased with viral load. Specificity was higher than 97% for all three tests except for the SureScreen test which had specificity 86.2% (95% CI: 83.9% to 88.3%) for influenza A.ConclusionsThe multiplex lateral flow tests provided timely diagnosis of influenza, RSV and SARS-CoV-2 infection and can be used to inform clinical management and infection control such as isolation behaviours.
DentalSegmentator: robust deep learning-based CBCT image segmentation
Authors: Dot Gauthier , Chaurasia Akhilanand , Dubois Guillaume , Savoldelli Charles , Haghighat Sara , Azimian Sarina , Rahbar Taramsari Ali , Sivaramakrishnan Gowri , Issa Julien , Dubey Abhishek , Schouman Thomas , Gajny Laurent 
Published: March 2024

AbstractObjectivesSegmentation of anatomical structures on dento-maxillo-facial (DMF) computed tomography (CT) or cone beam computed tomography (CBCT) scans is increasingly needed in digital dentistry. The main aim of this research was to propose and evaluate a novel open source tool called DentalSegmentator for fully automatic segmentation of five anatomic structures on DMF CT and CBCT scans: maxilla/upper skull, mandible, upper teeth, lower teeth, and the mandibular canal.MethodsA retrospective sample of 470 CT and CBCT scans was used as a training/validation set. The performance and generalizability of the tool was evaluated by comparing segmentations provided by experts and automatic segmentations in two hold-out test datasets: an internal dataset of 133 CT and CBCT scans acquired before orthognathic surgery and an external dataset of 123 CBCT scans randomly sampled from routine examinations in 5 institutions.ResultsThe mean overall results in the internal test dataset (n= 133) were a Dice similarity coefficient (DSC) of 92.2 ± 6.3% and a normalised surface distance (NSD) of 98.2 ± 2.2%. The mean overall results on the external test dataset (n= 123) were a DSC of 94.2 ± 7.4% and a NSD of 98.4 ± 3.6%.ConclusionsThe results obtained from this highly diverse dataset demonstrate that this tool can provide fully automatic and robust multiclass segmentation for DMF CT and CBCT scans. To encourage the clinical deployment of DentalSegmentator, the pre-trained nnU-Net model has been made publicly available along with an extension for the 3D Slicer software.Clinical SignificanceDentalSegmentator open source 3D Slicer extension provides a free, robust, and easy-to-use approach to obtaining patient-specific three-dimensional models from CT and CBCT scans. These models serve various purposes in a digital dentistry workflow, such as visualization, treatment planning, intervention, and follow-up.
Unraveling the factors associated with digital health intervention uptake
Authors: Ruotsalainen Ilona , Valtanen Mikko , Kärsämä Riikka , Umer Adil , Liedes Hilkka , Parikka Suvi , Lundqvist Annamari , Aittola Kirsikka , Manninen Suvi , Pihlajamäki Jussi , Vuorinen Anna-Leena , Lindström Jaana 
Published: March 2024

AbstractFor preventing chronic diseases, reducing modifiable lifestyle risk factors is crucial. Digital health interventions (DHIs) hold promise for reaching large population groups, but raise health equity concerns. A subgroup (n=6978) of 20 to 74-year-old participants of the population-based Healthy Finland survey were offered an opportunity to start using a web-based DHI, aiming to support adoption of healthy lifestyle habits. We used adjusted logistic regression models to identify significant predictors of DHI uptake. Women (adjusted odds ratio [aOR] 1.69, 95% CI 1.49–1.93), middle-aged individuals (1.47, 1.21–1.79), and those with higher income (aORs between 1.76–1.97) and education (1.10, 1.08–1.12) had higher odds of DHI uptake. Moreover, healthier lifestyle indicated by better diet quality (1.07, 1.04– 1.10), less frequent or non-smoking (aORs between 1.59–2.29), sleep (0.58, 0.37–0.86), higher functional capacity (1.06, 1.02–1.11), and good overall current health (1.46, 1.15– 1.89) associated with increased likelihood of DHI uptake. Lastly, individuals with better internet connections, higher competence to use e-services (aORs between 2.00–4.10), low concerns about data security (aORs between 1.37–1.76), stronger belief in benefits of digital services (1.04, 1.02–1.05), and reporting to use e-services (aORs between 2.48–6.08) had higher odds of uptake. Our findings indicate that those with higher socioeconomic status, better health and healthier lifestyle are more likely to take up DHI. Our research also highlights the impact of digital literacy and positive attitudes towards online services in adopting DHIs. These insights will be valuable in shaping strategies for future DHI initiatives.
Digital Health Technologies for Alzheimer’s Disease and Related Dementias: Initial Results from a Landscape Analysis and Community Collaborative Effort
Authors: Lott Sarah Averill , Streel Emmanuel , Bachman Shelby L. , Bode Kai , Dyer John , Fitzer-Attas Cheryl , Goldsack Jennifer C. , Hake Ann , Jannati Ali , Fuertes Ricardo Sainz , Fromy Piper 
Published: March 2024

AbstractDigital health technologies offer valuable advantages to dementia researchers and clinicians as screening tools, diagnostic aids, and monitoring instruments. To support the use and advancement of these resources, a comprehensive overview of the current technological landscape is essential.A multi-stakeholder working group, convened by the Digital Medicine Society (DiMe), conducted a landscape review to identify digital health technologies for Alzheimer’s disease and related dementia populations. We searched studies indexed in PubMed, Embase, and APA PsycInfo to identify manuscripts published between May 2003 to May 2023 reporting analytical validation, clinical validation, or usability/feasibility results for relevant digital health technologies. Additional technologies were identified through community outreach. We collated peer-reviewed manuscripts, poster presentations, or regulatory documents for 106 different technologies for Alzheimer’s disease and related dementia assessment covering diverse populations such as Lewy Body, vascular dementias, frontotemporal dementias, and all severities of Alzheimer’s disease. Wearable sensors represent 32% of included technologies, non-wearables 61%, and technologies with components of both account for the remaining 7%. Neurocognition is the most prevalent concept of interest, followed by physical activity and sleep. Clinical validation is reported in 69% of evidence, analytical validation in 34%, and usability/feasibility in 20% (not mutually exclusive).These findings provide clinicians and researchers a landscape overview describing the range of technologies for assessing Alzheimer’s disease and related dementias. A living library of technologies is presented for the clinical and research communities which will keep findings up-to-date as the field develops.
Quantitative transport mapping of multi-delay arterial spin labeling MRI detects early blood perfusion alteration in Alzheimer’s disease
Authors: Guo Yihao , Zhou Liangdong , Li Yi , Chiang Gloria C. , Liu Tao , Chen Huijuan , Huang Weiyuan , de Leon Mony J. , Wang Yi , Chen Feng 
Published: March 2024

AbstractBackgroundQuantitative transport mapping (QTM) of blood velocity, based on the transport equation has been demonstrated higher accuracy and sensitivity of perfusion quantification than the traditional Kety’s method-based blood flow (Kety flow). This study aimed to investigate the associations between QTM velocity and cognitive function in Alzheimer’s disease (AD) using multiple post-labeling delay arterial spin labeling (ASL) MRI.MethodsA total of 128 subjects (21 normal controls (NC), 80 patients with mild cognitive impairment (MCI), and 27 AD) were recruited prospectively. All participants underwent MRI examination and neuropsychological evaluation. QTM velocity and traditional Kety flow maps were computed from multiple delay ASL. Regional quantitative perfusion measurements were performed and compared to study group differences. We tested the hypothesis that cognition declines with reduced cerebral blood flow with consideration of age and gender effects.ResultsIn cortical gray matter (GM) and the hippocampus, QTM velocity and Kety flow showed decreased values in AD group compared to NC and MCI groups; QTM velocity, but not Kety flow, showed a significant difference between MCI and NC groups. QTM velocity and Kety flow showed values decreasing with age; QTM velocity, but not Kety flow, showed a significant gender difference between male and female. QTM velocity and Kety flow in the hippocampus were positively correlated with cognition, including global cognition, memory, executive function, and language function.ConclusionThis study demonstrated an increased sensitivity of QTM velocity as compared with the traditional Kety flow. Specifically, we observed only in QTM velocity, reduced perfusion velocity in GM and the hippocampus in MCI compared with NC. Both QTM velocity and Kety flow demonstrated reduction in AD vs controls. Decreased QTM velocity and Kety flow in the hippocampus were correlated with cognitive measures. These findings suggest QTM velocity as an improved biomarker for early AD blood flow alterations.
Time analysis of dengue deaths that occurred in two regions of Peru during the climatic-atmospheric phenomena El Niño Costero and Cyclone Yaku
Authors: Ramos Willy , Oyola-García Alfredo Enrique , Aguirre Aida , De la Cruz-Vargas Jhony A. , Luna Miguel , Alarcón Tania , Meléndez Mónica , Huaccho-Rojas Juan , Cóndor-Rojas Yudy Cley 
Published: March 2024

ABSTRACTOBJECTIVETo perform a dengue-related death time analysis that occurred in Piura and Ica (Peru) during the atmospheric phenomena of El Niño Costero and Cyclone Yaku in 2023.MATERIAL AND METHODSA case-series study was performed. We included the deaths between January 1 and June 3, 2023. We reviewed the research data base of the deaths as well as the clinical epidemiology records, death certificates and laboratory test results. A time analysis was performed from the date of onset of symptoms, date of first visit at a health establishment, date that dengue diagnosis was first registered and date of death.RESULTSWe included 40 deaths by dengue in the study. 70% were females, the median age of death was 51 years of age, and 70% came from Piura. 51.2% presented some comorbidity and 14% had self-medicated with metamizole, other NSAIDS or corticoids before the initial medical consultation. 37.5% presented an unconventional clinical pattern at admission and dengue was not suspected in 40% during the initial assessment. The median time until the initial assessment, the diagnosis time, and the time from diagnosis until death were 3 days, 0 days, and 1 day, respectively. Adults presented a time until the initial assessment significantly greater than the other age groups (p=0.048), the diagnosis time was significantly less than those seen at the Health Department establishments and regional government establishments (MINSA/GORE) than in private establishments (p=0.014) and greater in pediatrics (p=0.018). The time from diagnosis until death was significantly less in those that self-medicated before the initial assessment (p=0.041).CONCLUSIONThe adults presented a significantly greater time until the initial assessment; the diagnosis time was less than those seen in MINSA/GORE establishments and greater in pediatrics, furthermore, the time from diagnosis until death was significantly less in those who self-medicated before the initial assessment. An important fraction of deaths presented comorbidities and an unconventional clinical pattern at admission, with dengue frequently not suspected during the initial assessment.
Fecal microbial load is a major determinant of gut microbiome variation and a confounder for disease associations
Authors: Nishijima Suguru , Stankevic Evelina , Aasmets Oliver , Schmidt Thomas S. B. , Nagata Naoyoshi , Keller Marisa Isabell , Ferretti Pamela , Juel Helene Bæk , Fullam Anthony , Robbani Shahriyar Mahdi , Schudoma Christian , Hansen Johanne Kragh , Holm Louise Aas , Israelsen Mads , Schierwagen Robert , Torp Nikolaj , Arumugam Manimozhiyan , Bendtsen Flemming , Brøns Charlotte , Fonvig Cilius Esmann , Holm Jens-Christian , Nielsen Trine , Pedersen Julie Steen , Thiele Maja Sofie , Trebicka Jonel , Org Elin , Krag Aleksander , Hansen Torben , Kuhn Michael , Bork Peer ,   MicrobLiver Consortia
Published: March 2024

AbstractThe microbiota in individual habitats differ both in relative composition and absolute abundance. While sequencing approaches determine only the relative abundances of taxa and genes, experimental techniques for absolute abundance determination are rarely applied to large-scale microbiome studies. Here, we developed a machine learning approach to predict fecal microbial loads (microbial cells per gram) solely from relative abundance data. Applied to large-scale datasets (n = 34,539), we demonstrate that microbial load is the major determinant of gut microbiome variation and associated with numerous host factors. We found that for several diseases, the altered microbial load, not the disease itself, was the main driver of the gut microbiome changes. Adjusting for this effect substantially reduced the significance of more than half of the disease-associated species. Our analysis reveals that the fecal microbial load is a major confounder in microbiome studies, highlighting its importance for understanding microbiome variation in health and disease.