bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effect of Caffeine on Colonic Manometry in Children
Authors: Mehta Vijay , Hopson Puanani , Irastorza Laura , Rizvi Syed Ahsan , Fernandez Jenelle , Thomas Jessina , Nabar Shruti , Safder Shaista 
Published: December 2022

Objectives:Coffee and caffeinated products have been widely consumed for many centuries. Previous adult studies have suggested that both coffee and decaffeinated beverages induce colonic motility. However, no study has been conducted in pediatrics, and the role of caffeine alone in pediatric colonic motility needs to be explored.Methods:A prospective study of pediatric patients undergoing standard colonic motility testing that were able to consume caffeinated coffee, decaffeinated coffee, and caffeine tablet during colonic manometry. Patients who had a gastrocolonic reflex and high amplitude propagated contractions (HAPCs) in response to intraluminal administration of bisacodyl in the colon were included in the final analyses.Results:Thirty‐eight patients were recruited, 22 of which were excluded, 11 due to abnormal studies (no HAPC seen in response to intraluminal response to bisacodyl), and 11 due to inability to consume all study agents or complete the study. Sixteen patients met criteria for final analyses. Intracolonic bisacodyl produced a larger area under the curve (AUC) compared to all other agents. Caffeinated coffee resulted in a higher AUC, motility index (MI), and time to HAPC compared with decaffeinated coffee (P < 0.05). There was no significant difference between caffeinated coffee and caffeine tablet, or caffeine tablet and decaffeinated coffee.Conclusions:Caffeine is indeed a colonic stimulant; however, other components of caffeinated and non‐caffeinated beverages likely induce colonic response and require further evaluation for possible use as a colonic stimulant.
The Association of Corticosteroids and Pediatric Sepsis Biomarker Risk Model (PERSEVERE)-II Biomarker Risk Stratification With Mortality in Pediatric Septic Shock*
Authors: Klowak Jennifer A. , Bijelić Vid , Barrowman Nick , Menon Kusum ,   for the Genomics of Pediatric Septic Shock Investigators
Published: December 2022

OBJECTIVES:
Mortality risk stratification may identify a subset of children who benefit from or are harmed by corticosteroid administration. The Pediatric Sepsis Biomarker Risk Model (PERSEVERE)-II score is a biomarker-based mortality risk stratification tool for pediatric sepsis. Our objective was to assess the association of corticosteroid administration with 28-day mortality within different levels of baseline mortality risk (PERSEVERE-II) in a cohort of children with septic shock.

DESIGN:
We performed a secondary analysis using prospectively collected data (January 2015 to December 2018).

SETTING:
PICUs in 13 tertiary care, academic centers in the United States.

PATIENTS:
Children with septic shock.

INTERVENTIONS:
None.

MEASUREMENTS AND MAIN RESULTS:
We assessed the association of corticosteroid administration within PERSEVERE-II risk score categories and 28-day mortality, ICU-free days, and maximum failed organs in children with septic shock. We analyzed a total of 461 patients (215 with corticosteroids exposure, 246 without corticosteroid exposure) with an average age of 7.1 years (interquartile range, 2.2–13.6 yr). In the subgroup of patients with a high PERSEVERE-II score, corticosteroid administration was associated with an increased adjusted risk of 28-day mortality (odds ratio [OR] 4.10 [95% CI 1.70–9.86]; p = 0.002), but not in the low risk group (OR 0.20 [95% CI 0.02–1.73]; p = 0.15). A significant interaction between PERSEVERE-II score and corticosteroids was seen for both secondary outcomes complicated course (p = 0.01) and maximum failed organs (p < 0.001). Corticosteroid exposure was associated with fewer ICU-free days (p < 0.0001).

CONCLUSIONS:
In our multicenter observational study, corticosteroid administration was associated with increased mortality in a subgroup of children with a high PERSEVERE-II risk score.
Liver T1 Relaxation Quantification Using a 3-Dimensional Interleaved Look-Locker Acquisition With T2 Preparation Pulse Sequence (3D-QALAS): Comparison With Conventional 2-Dimensional MOLLI
Authors: Gandhi Deep B. , Pednekar Amol , Wang Hui , Tkach Jean A. , Dudley Jonathan A. , Manhard Mary Kate , Trout Andrew T. , Dillman Jonathan R. 
Published: December 2022

Background
Changes in liver magnetic resonance imaging T1 relaxation times are associated with histologic inflammation and fibrosis.

Objective
To compare liver T1 measurements obtained using a novel single-breath-hold 3-dimensional (3D) whole-liver T1 estimation method (3D-QALAS) to standard-of-care 2-dimensional (2D) modified Look-Locker (2D-MOLLI) measurements.

Methods
With institutional review board approval, research magnetic resonance imaging examinations were performed in 19 participants at 1.5 T. T1 relaxometry of the liver was performed using a novel 3D whole-liver T1 estimation method (3D-QALAS) as well as a 2D modified Look-Locker (2D-MOLLI) method. The 3D method covered the entire liver in a single breath hold, whereas 2D imaging was performed at 4 anatomic levels in 4 consecutive breath holds. T1 measurements from parametric maps were obtained by a single operator, and region-of-interest area-weighted mean T1 values were calculated. Pearson correlation (r) was used to assess correlation between T1 estimation methods, and the paired t test and Bland-Altman analysis were used to compare agreement in T1 measurements.

Results
In 18 participants (1 participant was excluded from analysis because of respiratory motion artifacts on 3D-QALAS images), 2D-MOLLI and 3D-QALAS mean T1 measurements were strongly correlated (r = 0.95, [95% CI: 0.87–0.98]; P < 0.0001). 2D-MOLLI T1 values were significantly longer than 3D-QALAS values (647.2 ± 87.3 milliseconds vs. 554.7 ± 75.8 milliseconds; P < 0.0001) with mean bias = 92.5 milliseconds (95% limits of agreement, 36.8, 148.2 milliseconds).

Conclusion
Whole-liver T1 measurements obtained using a novel single-breath-hold 3D T1 estimation method correlate with a standard-of-care multiple consecutive-breath-hold 2D single-slice method but demonstrate systematic bias that should be considered or corrected when used in a clinical or research setting.
Contribution of quality improvement initiative in strengthening laboratory services in Primary Healthcare Facilities in Tanzania: findings from 2017/2018 Star Rating Assessment
Authors: Kinyenje Erick , Ngowi Ruth R. , Msigwa Yohanes S. , Hokororo Joseph C. , Yahya Talhiya A. , German Chrisogone C. , Mawazo Akili , Mohamed Mohamed A. , Nassoro Omary A. , Degeh Mbwana M. , Bahegwa Radenta P. , Marandu Laura E. , Mwaisengela Syabo M. , Mwanginde Lutengano W. , Makala Robert , Eliakimu Eliudi S. 
Published: December 2022

ABSTRACTBackgroundAccurate and reliable diagnosis is the cornerstone of disease management and control. A reliable and properly organized laboratory system not only generates information critical to individual case management but also to disease surveillance, control, and outbreak management. This study presents the status of quality of laboratory services in Tanzania after a nationwide quality assessment, Star Rating Assessment (SRA) of PHC facilities conducted in 2017/18.MethodsThis was a cross-sectional study using secondary data from SRA dataset. Eight indicators were used to measure quality of laboratory services; of which together with facility characteristics are considered independent variables. Dependent variable is the general scores for laboratory services during SRA. Firstly, proportions of facilities for the indicators was calculated. Then, multiple linear regression was employed to determine impact of each variable on quality of laboratory services. P-value of < 0.05 was considered significant.ResultsApproximately one-quarter of 6,663 PHC facilities included, i.e., 1,773(26.6%) had appropriate staffing level for qualified health laboratory personnel. The situation was better in private facilities compared to public facilities (63% vs 19%, p<0.001); and in urban-based facilities compared to rural-based facilities (62% vs 16%, p<0.001). None of the indicators was complied with at least half of the facilities. Three indicators were the strongest positive predictor of laboratory quality scores: having a laboratory safety system (Beta = 3.403), availability of essential laboratory tests with SOPs available and adhered (Beta = 2.739), and appropriate staffing level for laboratory personnel (Beta = 1.498). The scores were likely to be low if the facility was a dispensary (Beta = -1.325), located in a rural area (Beta = -0.068) or publicly owned (Beta = -0.048).ConclusionThere is a critical shortage of qualified laboratory personnel in PHC facilities, especially in public facilities that are based in rural areas. There is a need to further strengthen laboratory services in PHC facilities to ensure quality of laboratory test results, since none of the indicators was complied with at least half of the facilities.
STrack: A tool to Simply Track bacterial cells in microscopy time-lapse images
Authors: Todorov Helena , Miguel Trabajo Tania , van der Meer Jan Roelof 
Published: December 2022

AbstractBacterial growth can be studied at the single cell-level through time-lapse microscopy imaging. Technical advances in microscopy lead to increasing image quality, which in turn allows to visualize larger areas of growth, containing more and more cells. In this context, the use of automated computational tools becomes essential.In this paper, we present STrack, a tool that allows to track cells in time-lapse images in a fast and efficient way. We compared it to three recently published tracking tools on images ranging over six different bacterial strains, and STrack showed to be the most consistent tracking tool, returning more than 80% of correct cell lineages on average.The python implementation of STrack, a docker structure, and a tutorial on how to download and use the tool can be found on the following github page:https://github.com/Helena-todd/STrack
Clinical validation of novel second-trimester preterm preeclampsia risk predictors combining clinical variables and serum protein biomarkers
Authors: Laurent Louise C. , Saade George R. , Markenson Glenn R. , Heyborne Kent D. , Schoen Corina N. , Baxter Jason K. , Longo Sherri A. , Pereira Leonardo M. , Su Emily J. , Hoffman Matthew K. , Lam Garrett K. , Fox Angela C. , Polpitiya Ashoka D. , Badsha Md. Bahadur , Fleischer Tracey C. , Garite Thomas J. , Boniface J. Jay , Kearney Paul E. 
Published: December 2022

AbstractObjectiveTo develop and validate mid-trimester preterm preeclampsia (PE) risk predictors combining clinical factors and serum protein analytes, and to compare their performance with those of widely used clinical and risk assessment algorithms endorsed by professional societies.MethodsThis was a secondary analysis of data from two large, multicenter studies in pregnant individuals (PAPR,NCT01371019; TREETOP,NCT02787213), originally conducted to discover, verify, and validate a serum proteomic predictor of preterm birth risk. Serum protein abundances were determined by mass spectrometry. Classifier models combined one or two novel protein ratio(s) with a composite clinical variable, denoted as ClinRisk3, which included prior PE, pre-existing hypertension, or pregestational diabetes. Predictive performance was assessed for the full validation cohort and for a subset that had early gestational age (GA) dating via ultrasound. Classifier performance was compared directly to the U.S. Preventive Services Task Force (USPSTF) algorithm for identification of pregnancies that should receive low-dose aspirin (LDASA) for PE prevention.ResultsNine of nine prespecified classifier models were validated for risk of preterm PE with delivery <37 weeks’ gestation. Areas under the receiver operating characteristic curve ranged from 0.72-0.78 in the full validation cohort, compared to 0.68 for both ClinRisk3 alone and for the USPSTF algorithm. In the early GA dating subcohort, an exemplar predictor, ClinRisk3 + inhibin subunit beta C chain/sex hormone binding globulin (ClinRisk3+INHBC/SHBG) showed a markedly lower screen positive rate (11.1% vs 43.3%) and higher positive predictive value (13.0% vs 5.0%) and odds ratio (9.93 vs 5.24) than USPSTF. Its performance was similar in nulliparas and all parities.ConclusionNine preterm PE risk predictors were identified, validated in an independent cohort, and shown to be more predictive than the USPSTF-endorsed algorithm. Our results indicate that a single blood test performed in the first half of pregnancy can be used for personalized PE risk assessment, particularly for pregnancies with minimal or no identified clinical risk factors, including nulliparas. Results can be used to guide personalized pregnancy management, including but not restricted to LDASA for PE prophylaxis, and serve as a basis for developing new prevention strategies.Funding SourceSera Prognostics, Inc.PrécisCombining novel serum protein biomarkers and selected clinical variables for preterm preeclampsia prediction outperforms widely used clinical risk assessment algorithms currently recommended by practice guidelines.
Immunomics analysis of rheumatoid arthritis identified precursor dendritic cells as a key cell subset of treatment resistance
Authors: Yamada Saeko , Nagafuchi Yasuo , Wang Min , Ota Mineto , Hatano Hiroaki , Takeshima Yusuke , Okubo Mai , Kobayashi Satomi , Sugimori Yusuke , Nakano Masahiro , Yoshida Ryochi , Hanata Norio , Suwa Yuichi , Tsuchida Yumi , Iwasaki Yukiko , Sumitomo Shuji , Kubo Kanae , Shimane Kenichi , Setoguchi Keigo , Azuma Takanori , Kanda Hiroko , Shoda Hirofumi , Zhang Xuan , Yamamoto Kazuhiko , Ishigaki Kazuyoshi , Okamura Tomohisa , Fujio Keishi 
Published: December 2022

AbstractObjectivesLittle is known about the immunology underlying variable treatment response in rheumatoid arthritis (RA). We performed large scale transcriptome analyses of peripheral blood immune cell subsets to identify immune cells that predict treatment resistance.MethodsWe isolated 18 peripheral blood immune cell subsets of 55 pre-treatment RA patients and 39 healthy controls, and performed RNA sequencing. Transcriptome changes in RA and treatment effects were systematically characterized. Association between immune cell gene modules and treatment resistance was evaluated. We validated predictive value of identified parameters for treatment resistance using quantitative polymerase chain reaction (qPCR) and mass cytometric analysis cohorts. We also characterized the identified population by synovial single cell RNA-seq analysis.ResultsImmune cells of RA patients were characterized by enhanced interferon and IL6-JAK-STAT3 signaling that demonstrate partial normalization after treatment. A gene expression module of plasmacytoid dendritic cells (pDC) reflecting the expansion of pre-dendritic cells (pre-DC) exhibited strongest association with treatment resistance. Type I interferon signaling was negatively correlated to pre-DC gene expression. qPCR and mass cytometric analysis in independent cohorts validated that the pre-DC associated gene expression and the proportion of pre-DC were significantly higher before treatment in treatment-resistant patients. A cluster of synovial DCs showed both features of pre-DC and proinflammatory conventional DC2s.ConclusionsAn increase in pre-DC in peripheral blood predicted RA treatment resistance. Pre-DC could have pathophysiological relevance to RA treatment response.Key messagesWhat is already known about this subject?Limited information is available about the immune cells that are associated with RA treatment resistance.What does this study add?RA treatment resistance can be predicted by an increase in pre-DC in peripheral blood prior to treatment.The expression of genes reflecting an increase in pre-DC is negatively correlated to the type I interferon signature, which is associated with good therapeutic response.Synovial pre-DC-like cells are proinflammatory cDC2s.How might this impact on clinical practice or future developments?Stratified treatment of RA is possible using pre-DC as a biomarker, and it might be possible to develop new therapies for treatment-resistant RA by targeting pre-DC.
Unbiased single cell spatial analysis localises inflammatory clusters of immature neutrophils-CD8 T cells to alveolar progenitor cells in fatal COVID-19 lungs
Authors:   COSMIC – COVID-19 Lung Single Cell Mass Cytometry Imaging Consortium STUDY, Weeratunga Praveen , Denney Laura , Bull Joshua A. , Repapi Emmanouela , Sergeant Martin , Etherington Rachel , Vuppussetty Chaitanya , Turner Gareth D.H. , Clelland Colin , Cross Amy , Issa Fadi , de Andrea Carlos Eduardo , Bermejo Ignacio Melero , Sims David , McGowan Simon , Zurke Yasemin-Xiomara , Ahern David J. , Gamez Eddie C , Whalley Justin , Richards Duncan , Klenerman Paul , Monaco Claudia , Udalova Irina A. , Dong Tao , Ogg Graham , Knight Julian C. , Byrne Helen M. , Taylor Stephen , Ho Ling-Pei 
Published: December 2022

AbstractSingle cell spatial interrogation of the immune-structural interactions in COVID -19 lungs is challenging, mainly because of the marked cellular infiltrate and architecturally distorted microstructure. To address this, we developed a suite of mathematical tools to search for statistically significant co-locations amongst immune and structural cells identified using 37-plex imaging mass cytometry. This unbiased method revealed a cellular map interleaved with an inflammatory network of immature neutrophils, cytotoxic CD8 T cells, megakaryocytes and monocytes co-located with regenerating alveolar progenitors and endothelium. Of note, a highly active cluster of immature neutrophils and cytotoxic CD8 T cells, was found spatially linked with alveolar progenitor cells, and temporally with the diffuse alveolar damage stage. These findings provide new insights into how immune cells interact in the lungs of severe COVID-19 disease. We provide our pipeline [Spatial Omics Oxford Pipeline (SpOOx)] and visual-analytical tool, Multi-Dimensional Viewer (MDV) software, as a resource for spatial analysis.Graphical abstract
Development and validation of the Behavioral Health Acuity Risk model: a predictive model for suicide prevention through clinical interventions
Authors: Digumarthi Varun , Strange Heather E , Norman Heather B , Ayers Derek , Patel Raj , Hegarty Karen E 
Published: December 2022

AbstractCommon suicidal ideation screening tools used in healthcare settings rely on the willingness of the patient to express having suicidal thoughts. We present an automatic and data-driven risk model that examines information available in the medical record captured during the normal course of care. This model uses random forests to assess the likelihood of suicidal behavior in patients aged seven or older presenting at any healthcare setting. The Behavioral Health Acuity Risk (BHAR) model achieves an area under the receiver operating curve (AUC) of 0.84 and may be used on its own or as a component of a comprehensive suicidal behavior risk assessment.
Genome-wide association study of obstructive sleep apnea in the Million Veteran Program uncovers genetic heterogeneity by sex
Authors: Sofer Tamar , Kurniansyah Nuzulul , Murray Michael , Ho Yuk-Lam , Huffman Jennifer E. , Cho Kelly , Wilson Peter W.F. , Gottlieb Daniel J 
Published: December 2022

AbstractBackgroundGenome-wide association studies (GWAS) for obstructive sleep apnea (OSA) are limited due to the underdiagnosis of OSA, leading to misclassification of OSA, which consequently reduces statistical power. We performed a GWAS of OSA in the Million Veteran Program (MVP) of the U.S. Department of Veterans Affairs (VA) healthcare system, where OSA prevalence is close to its true population prevalence.MethodsWe performed GWAS of 568,576 MVP participants, stratified by biological sex and by harmonized race/ethnicity and genetic ancestry (HARE) groups of White, Black, Hispanic, and Asian individuals. We considered both BMI adjusted (BMI-adj) and unadjusted (BMI-unadj) models. We replicated associations in independent datasets, and analyzed the heterogeneity of OSA genetic associations across HARE and sex groups. We finally performed a larger meta-analysis GWAS of MVP, FinnGen, and the MGB Biobank, totaling 916,696 individuals.FindingsMVP participants are 91% male. OSA prevalence is 21%. In MVP there were 18 and 6 genome-wide significant loci in BMI-unadj and BMI-adj analyses, respectively, corresponding to 21 association regions. Of these, 17 were not previously reported in association with OSA, and 13 replicated in FinnGen (False Discovery Rate p-value<0.05). There were widespread significant differences in genetic effects between men and women, but less so across HARE groups. Meta-analysis of MVP, FinnGen, and MGB biobank revealed 17 additional, novel, genome-wide significant regions.InterpretationSex differences in genetic associations with OSA are widespread, likely associated with multiple OSA risk factors. OSA shares genetic underpinnings with several sleep phenotypes, suggesting shared etiology and causal pathways.FundingDescribed in acknowledgements.
Fingerprint: An AI-based method for detection of mislabeled CT studies in clinical trials
Authors: Edenbrandt Lars , Enqvist Olof , Gillberg Tony 
Published: December 2022

AbstractThe manual step of replacing the identification number of each subject in clinical trials with the correct trial identification number is critical. Mislabeling studies may lead to excluding subjects or visits or to false study results. This article presents an automated AI-based method to detect if a pair of de-identified CT studies has been obtained from the same subject.ApproachAn automated segmentation of bones in CT is performed using Organ Finder (SliceVault AB, Malmö, Sweden). Corresponding bones from the two images are aligned using Iterative closest point. The percentage of vertices in the smaller mesh with residuals below 1.5 mm is used as a measure of similarity, theanatomical match. If the anatomical match is less than 80% for the left and right hip bone and the left and right scapula, the two CT studies are classified as being obtained from different subjects. The Fingerprint method was tested on a group of 58 patients, who each had two CT studies obtained at different occasions. From the 116 CT studies 6,612 unique pairs of CT from different patients and 58 pairs from the same patient could be selected.ResultsThe Fingerprint method classified all 6,612 pairs of CT studies from different patients correctly (sensitivity 100%; 95% confidence interval 99-100%) and all 58 pairs of CT studies from the same patients correctly (specificity 100%; 95% confidence interval 94-100%).ConclusionThis study shows how an AI-based method can be used to accurately detect if a pair of de-identified CT studies has been obtained from two different subjects.
Structure and regulation of full-length human leucine-rich repeat kinase 1
Authors: Metcalfe R.D. , Fiesco J.A. Martinez , Zhang P. 
Published: December 2022

AbstractThe two human leucine-rich repeat kinases (LRRKs), LRRK1 and LRRK2 are large and unusually complex multi-domain kinases which serve to regulate fundamental cellular processes, with both implicated in human disease. Despite recent experimentally determined structures of LRRK2, the structure and exact molecular mechanisms regulating the activity of the LRRK1, and differences in the regulation of LRRK1 and LRRK2 remain unclear. Here, we report a cryo-EM structure of the LRRK1 monomer, and a lower-resolution cryo-EM map of the LRRK1 dimer. The monomer structure, in which the kinase is in an inactive conformation, reveals key interdomain interfaces which serve to control kinase activity, which are further validated experimentally. Both the LRRK1 monomer and dimer are structurally distinct compared to LRRK2, implying functional differences in both proteins. Overall, our results provide new structural insights into the activation of the human LRRKs for understanding the physiology and pathology of these proteins.