bluuebunny/crossref_metadata_2025_split huggingface dataset:
Dissecting the multi-omics atlas of the exosomes released by human lung adenocarcinoma stem-like cells
Authors: Luo Hai-Tao , Zheng Yuan-Yuan , Tang Jun , Shao Li-Juan , Mao Yi-Heng , Yang Wei , Yang Xiao-Fei , Li Yang , Tian Rui-Jun , Li Fu-Rong 
Published: June 2021

AbstractLung adenocarcinoma is heterogeneous and hierarchically organized, with a subpopulation of stem-like cells (CSCs) that reside at the apex of the hierarchy, in which exosomes act as important mediators by transporting specific molecules among different cell populations. Although there have been numerous studies on tumor exosomes, the constituents and functional properties of CSC-derived exosomes are still poorly characterized. Here we present a detail transcriptome and proteome atlas of the exosomes released by human lung adenocarcinoma stem-like cells (LSLCs). The transcriptome analysis indicates the specific patterns of exosomal constituents, including the fragmentation of transcripts and the low-level presence of circular RNAs, and identifies multiple exosomal-enriched mRNAs and lncRNAs. Integrative analysis of transcriptome and proteome data reveals the diverse functions of exosomal-enriched RNAs and proteins, many of which are associated with tumorigenesis. Importantly, several LSLC markers we identified are highly expressed in LSLC-derived exosomes and associate with poor survival, which may serve as promising liquid biopsy biomarkers for lung adenocarcinoma diagnosis. Our study provides a resource for the future elucidation of the functions of tumor-derived exosomes and their regulatory mechanisms in mediating lung cancer development.
The progression of disorder-specific brain pattern expression in schizophrenia over 9 years
Authors: Lieslehto Johannes , Jääskeläinen Erika , Kiviniemi Vesa , Haapea Marianne , Jones Peter B. , Murray Graham K. , Veijola Juha , Dannlowski Udo , Grotegerd Dominik , Meinert Susanne , Hahn Tim , Ruef Anne , Isohanni Matti , Falkai Peter , Miettunen Jouko , Dwyer Dominic B. , Koutsouleris Nikolaos 
Published: June 2021

AbstractAge plays a crucial role in the performance of schizophrenia vs. controls (SZ-HC) neuroimaging-based machine learning (ML) models as the accuracy of identifying first-episode psychosis from controls is poor compared to chronic patients. Resolving whether this finding reflects longitudinal progression in a disorder-specific brain pattern or a systematic but non-disorder-specific deviation from a normal brain aging (BA) trajectory in schizophrenia would help the clinical translation of diagnostic ML models. We trained two ML models on structural MRI data: an SZ-HC model based on 70 schizophrenia patients and 74 controls and a BA model (based on 561 healthy individuals, age range = 66 years). We then investigated the two models’ predictions in the naturalistic longitudinal Northern Finland Birth Cohort 1966 (NFBC1966) following 29 schizophrenia and 61 controls for nine years. The SZ-HC model’s schizophrenia-specificity was further assessed by utilizing independent validation (62 schizophrenia, 95 controls) and depression samples (203 depression, 203 controls). We found better performance at the NFBC1966 follow-up (sensitivity = 75.9%, specificity = 83.6%) compared to the baseline (sensitivity = 58.6%, specificity = 86.9%). This finding resulted from progression in disorder-specific pattern expression in schizophrenia and was not explained by concomitant acceleration of brain aging. The disorder-specific pattern’s progression reflected longitudinal changes in cognition, outcomes, and local brain changes, while BA captured treatment-related and global brain alterations. The SZ-HC model was also generalizable to independent schizophrenia validation samples but classified depression as control subjects. Our research underlines the importance of taking account of longitudinal progression in a disorder-specific pattern in schizophrenia when developing ML classifiers for different age groups.
Corticotropin-releasing factor neurons in the bed nucleus of the stria terminalis exhibit sex-specific pain encoding in mice
Authors: Yu Waylin , Caira Christina M. , del R. Rivera Sanchez Natalia , Moseley Garrett A. , Kash Thomas L. 
Published: June 2021

AbstractThe bed nucleus of the stria terminalis (BNST) plays an emerging role in pain regulation. Pharmacological studies have found that inhibiting corticotropin-releasing factor (CRF) signaling in the BNST can selectively mitigate the sensory and affective-motivational components of pain. However, mechanistic insight on the source of CRF that drives BNST responses to these harmful experiences remains unknown. In the present study, we used a series of genetic approaches to show that CRF in the BNST is engaged in the processing and modulation of pain. We conducted cell-type specific in vivo calcium imaging in CRF-Cre mice and found robust and synchronized recruitment of BNSTCRF neurons during acute exposures to noxious heat. Distinct patterns of recruitment were observed by sex, as the magnitude and timing of heat responsive activity in BNSTCRF neurons differed for male and female mice. We then used a viral approach in Floxed-CRF mice to selectively reduce CRF expression in the BNST and found it decreased nociceptive sensitivity for both sexes and increased paw attending for females. Together, these findings reveal that CRF in the BNST influences multiple facets of the pain experience to impact the sex-specific expression of pain-related behaviors.
Cross sectional study of the clinical characteristics of French primary care patients with COVID-19
Authors: Sebo Paul , Tudrej Benoit , Lourdaux Julie , Cuzin Clara , Floquet Martin , Haller Dagmar M. , Maisonneuve Hubert 
Published: June 2021

AbstractThe early identification of patients suffering from SARS-CoV-2 infection in primary care is of outmost importance in the current pandemic. The objective of this study was to describe the clinical characteristics of primary care patients who tested positive for SARS-CoV-2. We conducted a cross-sectional study between March 24 and May 7, 2020, involving consecutive patients undergoing RT-PCR testing in two community-based laboratories in Lyon (France) for a suspicion of COVID-19. We examined the association between symptoms and a positive test using univariable and multivariable logistic regression, adjusted for clustering within laboratories, and calculated the diagnostic performance of these symptoms. Of the 1561 patients tested, 1543 patients (99%) agreed to participate. Among them, 253 were positive for SARS-CoV-2 (16%). The three most frequently reported ‘ear-nose-throat’ and non-‘ear-nose-throat’ symptoms in patients who tested positive were dry throat (42%), loss of smell (36%) and loss of taste (31%), respectively fever (58%), cough (52%) and headache (45%). In multivariable analyses, loss of taste (OR 3.8 [95% CI 3.3–4.4], p-value < 0.001), loss of smell (OR 3.0 [95% CI 1.9–4.8], p < 0.001), muscle pain (OR 1.6 [95% CI 1.2–2.0], p = 0.001) and dry nose (OR 1.3 [95% CI 1.1–1.6], p = 0.01) were significantly associated with a positive result. In contrast, sore throat (OR 0.6 [95% CI 0.4–0.8], p = 0.003), stuffy nose (OR 0.6 [95% CI 0.6–0.7], p < 0.001), diarrhea (OR 0.6 [95% CI 0.5–0.6], p < 0.001) and dyspnea (OR 0.5 [95% CI 0.3–0.7], p < 0.001) were inversely associated with a positive test. The combination of loss of taste or smell had the highest diagnostic performance (OR 6.7 [95% CI 5.9–7.5], sensitivity 44.7% [95% CI 38.4–51.0], specificity 90.8% [95% CI 89.1–92.3]). No other combination of symptoms had a higher performance. Our data could contribute to the triage and early identification of new clusters of cases.
Increases in adipose tissue and muscle function are longitudinally associated with better quality of life in colorectal cancer survivors
Authors: Kenkhuis Marlou-Floor , van Roekel Eline H. , Koole Janna L. , Breedveld-Peters José J. L. , Breukink Stéphanie O. , Janssen-Heijnen Maryska L. G. , Keulen Eric T. P. , van Duijnhoven Fränzel J. B. , Mols Floortje , Weijenberg Matty P. , Bours Martijn J. L. 
Published: June 2021

AbstractColorectal cancer (CRC) survivors need evidence-based guidelines pertaining to post-treatment body composition, which could benefit health-related quality of life (HRQoL). We aimed to describe the course of several body composition measures, and to assess longitudinal associations of these measures with HRQoL, fatigue and chemotherapy-induced peripheral neuropathy (CIPN). In a prospective cohort among stage I–III CRC survivors (n = 459), five repeated home visits from diagnosis up to 24 months post-treatment were executed. Body mass index (BMI), waist circumference and fat percentage were assessed as measures of adiposity, and muscle arm circumference and handgrip strength as measures of muscle mass and function. We applied linear mixed-models to describe changes in body composition over time and to analyze overall longitudinal associations. Of included participants, 44% was overweight and 31% was obese at diagnosis. All body composition measures followed similar trends, decreasing from diagnosis to 6 weeks and then increasing up to 24 months post-treatment. In confounder-adjusted mixed models, increases in adipose tissue and muscle function were longitudinally associated with better HRQoL and less fatigue, regardless of pre-treatment body composition. With regards to improving HRQoL, decreasing fatigue and CIPN, clinical practice should also focus on restoring body tissues after CRC treatment.Trial registration: NTR7099.
Deletion of the lactoperoxidase gene causes multisystem inflammation and tumors in mice
Authors: Yamakaze Jayden , Lu Zhe 
Published: June 2021

AbstractStrongly oxidative H2O2 is biologically important, but if uncontrolled, would lead to tissue injuries. Lactoperoxidase (LPO) catalyzes the redox reaction of reducing highly reactive H2O2 to H2O while oxidizing thiocyanate (SCN−) to relatively tissue-innocuous hypothiocyanite (OSCN−). SCN− is the only known natural, effective reducing-substrate of LPO; humans normally derive SCN− solely from food. While its enzymatic mechanism is understood, the actual biological role of the LPO-SCN− system in mammals remains unestablished. Our group previously showed that this system protected cultured human cells from H2O2-caused injuries, a basis for the hypothesis that general deficiency of such an antioxidative mechanism would lead to multisystem inflammation and tumors. To test this hypothesis, we globally deleted the Lpo gene in mice. The mutant mice exhibited inflammation and lesions in the cardiovascular, respiratory, digestive or excretory systems, neuropathology, and tumors, with high incidence. Thus, this understudied LPO-SCN− system is an essential protective mechanism in vivo.
Time-domain based evaluation of detection efficiency in liquid scintillation counting
Authors: Mitev Krasimir , Dutsov Chavdar , Cassette Philippe , Sabot Benoît 
Published: June 2021

AbstractThis work explores the distribution of time intervals between signals from the photomultiplier tubes (PMTs) of a liquid scintillation counting (LSC) system when a scintillation burst caused by an ionizing particle is detected. This distribution is termed the cross-correlation distribution and it is shown that it contains information about the probability to detect a scintillation event. A theoretical model that describes the cross-correlation distribution is derived. The model can be used to estimate the mean number of detected photons in a LSC measurement, which allows the calculation of the detection efficiency. The theoretical findings are validated by Monte Carlo simulations and by experiments with low-energy beta-emitting and electron-capture radionuclides ($$^3\hbox {H}$$

3

H

, $$^{14}\hbox {C}$$

14

C

, $$^{63}\hbox {Ni}$$

63

Ni

and $$^{55}\hbox {Fe}$$

55

Fe

), with dedicated LSC systems and several commercial LSC cocktails. The results show that some of the parameters of the cross-correlation distribution such as the peak height or the kurtosis can be used as detection efficiency estimators or quenching indicators in LSC. Thus, although the time domain and the cross-correlation distribution have received little to no attention in the practice of LSC, they have the capacity to bring significant improvements in almost all LSC applications related to activity determination of low-energy beta-emitting and electron-capture radionuclides. The results also suggest concepts for the development of innovative LSC systems.
Critical assessment of coiled-coil predictions based on protein structure data
Authors: Simm Dominic , Hatje Klas , Waack Stephan , Kollmar Martin 
Published: June 2021

AbstractCoiled-coil regions were among the first protein motifs described structurally and theoretically. The simplicity of the motif promises that coiled-coil regions can be detected with reasonable accuracy and precision in any protein sequence. Here, we re-evaluated the most commonly used coiled-coil prediction tools with respect to the most comprehensive reference data set available, the entire Protein Data Bank, down to each amino acid and its secondary structure. Apart from the 30-fold difference in minimum and maximum number of coiled coils predicted the tools strongly vary in where they predict coiled-coil regions. Accordingly, there is a high number of false predictions and missed, true coiled-coil regions. The evaluation of the binary classification metrics in comparison with naïve coin-flip models and the calculation of the Matthews correlation coefficient, the most reliable performance metric for imbalanced data sets, suggests that the tested tools’ performance is close to random. This implicates that the tools’ predictions have only limited informative value. Coiled-coil predictions are often used to interpret biochemical data and are part of in-silico functional genome annotation. Our results indicate that these predictions should be treated very cautiously and need to be supported and validated by experimental evidence.
Characteristics of left atrial remodeling in patients with atrial fibrillation and hypertrophic cardiomyopathy in comparison to patients without hypertrophy
Authors: Nedios Sotirios , Dinov Borislav , Seewöster Timm , Lindemann Frank , Richter Sergio , Arya Arash , Dagres Nikolaos , Husser Daniela , Bollmann Andreas , Hindricks Gerhard , Müssigbrodt Andreas 
Published: June 2021

AbstractAtrial fibrillation (AF) leads to remodeling characterized by changes in both size and shape of the left atrium (LA). Here we aimed to study the effect of hypertrophic cardiomyopathy (HCM) on the pattern of LA remodeling in AF-patients. HCM-patients (n = 23) undergoing AF ablation (2009–2012) were matched and compared with 125 Non-HCM patients from our prospective registry. Pre-procedural CT data were analyzed (EnSite Verismo, SJM, MN) to determine the maximal sagittal (anterior–posterior, AP), coronal (superior-inferior, SI and transversal, TV) dimensions and the sphericity index (LAS). Volume (LAV) was rendered after appendage (LAA) and pulmonary vein (PV) exclusion. A cutting plane, between PV ostia/LAA and parallel to the posterior wall, divided LAV into anterior- (LA­A) and posterior-LA (LA­P) parts. The ratio LA-A/LAV was defined as asymmetry index (ASI). HCM patients had a wider inter-ventricular septum and a smaller LV than Non-HCM patients. LA volume (LAV 166 ± 72 vs. 130 ± 36 ml, p = 0.03) and LA diameters were significantly larger in HCM patients. Anterior volume (LA-A: 112 ± 48 vs. 83 ± 26 ml, p < 0.001) differed significantly between groups, whereas the posterior volume LA-P (55 ± 28 vs. 47 ± 13 ml, p = 0.23) and LAS (75% vs. 78%, p = 0.089) was similar in both groups. As a result, ASI was significantly higher (67 ± 6 vs. 63 ± 6%, p = 0.01) in HCM than in Non-HCM patients. In conclusion, LA remodeling in patients with AF and HCM is characterized by asymmetric dilatation, driven by an anterior rather than a posterior dilatation. This can be characterized by three-dimensional imaging and could be used as surrogate of advanced atrial remodeling.
In vivo multi-parametric manganese-enhanced MRI for detecting amyloid plaques in rodent models of Alzheimer’s disease
Authors: Kim Eugene , Di Censo Davide , Baraldo Mattia , Simmons Camilla , Rosa Ilaria , Randall Karen , Ballard Clive , Dickie Ben R. , Williams Steven C. R. , Killick Richard , Cash Diana 
Published: June 2021

AbstractAmyloid plaques are a hallmark of Alzheimer’s disease (AD) that develop in its earliest stages. Thus, non-invasive detection of these plaques would be invaluable for diagnosis and the development and monitoring of treatments, but this remains a challenge due to their small size. Here, we investigated the utility of manganese-enhanced MRI (MEMRI) for visualizing plaques in transgenic rodent models of AD across two species: 5xFAD mice and TgF344-AD rats. Animals were given subcutaneous injections of MnCl2 and imaged in vivo using a 9.4 T Bruker scanner. MnCl2 improved signal-to-noise ratio but was not necessary to detect plaques in high-resolution images. Plaques were visible in all transgenic animals and no wild-types, and quantitative susceptibility mapping showed that they were more paramagnetic than the surrounding tissue. This, combined with beta-amyloid and iron staining, indicate that plaque MR visibility in both animal models was driven by plaque size and iron load. Longitudinal relaxation rate mapping revealed increased manganese uptake in brain regions of high plaque burden in transgenic animals compared to their wild-type littermates. This was limited to the rhinencephalon in the TgF344-AD rats, while it was most significantly increased in the cortex of the 5xFAD mice. Alizarin Red staining suggests that manganese bound to plaques in 5xFAD mice but not in TgF344-AD rats. Multi-parametric MEMRI is a simple, viable method for detecting amyloid plaques in rodent models of AD. Manganese-induced signal enhancement can enable higher-resolution imaging, which is key to visualizing these small amyloid deposits. We also present the first in vivo evidence of manganese as a potential targeted contrast agent for imaging plaques in the 5xFAD model of AD.
Exploring antimicrobial resistance to beta-lactams, aminoglycosides and fluoroquinolones in E. coli and K. pneumoniae using proteogenomics
Authors: Foudraine Dimard E. , Strepis Nikolaos , Stingl Christoph , ten Kate Marian T. , Verbon Annelies , Klaassen Corné H. W. , Goessens Wil H. F. , Luider Theo M. , Dekker Lennard J. M. 
Published: June 2021

AbstractAntimicrobial resistance is mostly studied by means of phenotypic growth inhibition determinations, in combination with PCR confirmations or further characterization by means of whole genome sequencing (WGS). However, the actual proteins that cause resistance such as enzymes and a lack of porins cannot be detected by these methods. Improvements in liquid chromatography (LC) and mass spectrometry (MS) enabled easier and more comprehensive proteome analysis. In the current study, susceptibility testing, WGS and MS are combined into a multi-omics approach to analyze resistance against frequently used antibiotics within the beta-lactam, aminoglycoside and fluoroquinolone group in E. coli and K. pneumoniae. Our aim was to study which currently known mechanisms of resistance can be detected at the protein level using liquid chromatography–mass spectrometry (LC–MS/MS) and to assess whether these could explain beta-lactam, aminoglycoside, and fluoroquinolone resistance in the studied isolates. Furthermore, we aimed to identify significant protein to resistance correlations which have not yet been described before and to correlate the abundance of different porins in relation to resistance to different classes of antibiotics. Whole genome sequencing, high-resolution LC–MS/MS and antimicrobial susceptibility testing by broth microdilution were performed for 187 clinical E. coli and K. pneumoniae isolates. Resistance genes and proteins were identified using the Comprehensive Antibiotic Resistance Database (CARD). All proteins were annotated using the NCBI RefSeq database and Prokka. Proteins of small spectrum beta-lactamases, extended spectrum beta-lactamases, AmpC beta-lactamases, carbapenemases, and proteins of 16S ribosomal RNA methyltransferases and aminoglycoside acetyltransferases can be detected in E. coli and K. pneumoniae by LC–MS/MS. The detected mechanisms matched with the phenotype in the majority of isolates. Differences in the abundance and the primary structure of other proteins such as porins also correlated with resistance. LC–MS/MS is a different and complementary method which can be used to characterize antimicrobial resistance in detail as not only the primary resistance causing mechanisms are detected, but also secondary enhancing resistance mechanisms.
Reproductive risk factors of pterygium in postmenopausal women: a nationwide study in the Republic of Korea
Authors: Shin Eunhae , Lim Dong Hui , Chung Tae-Young , Han Gyule , Yoo Jung Eun , Jeon Keun Hye , Han Kyungdo , Shin Dong Wook 
Published: June 2021

AbstractThis study is to elucidate the associations between female reproductive factors and pterygium. A total of 1,339,969 postmenopausal women in a retrospective cohort of Korean National Health Insurance Service data on ages 40 and above in 2009 was included. Cox proportional hazards regression was conducted to assess the hazard ratio (HR) for pterygium according to reproductive factors. Late menarche, early menopause, short reproductive period, increasing parity (≥ 2 children), breastfeeding (≥ 6 months), and no use of hormone replacement therapy (HRT) or oral contraceptive (OC) were significantly associated with risk of pterygium. In multivariate analysis, the HR for pterygium was 1.764 (95% confidence interval [CI], 1.529–2.035) for menarche age ≥ 17 years (reference: menarche age < 12 years). The HR of menopause age ≥ 55 years was 0.782 (95% CI, 0.724–0.845) (reference: menopause age < 40 years). The HR of parity ≥ 2 was 1.261 (95% CI, 1.148–1.385) (reference: nulliparity). The HR of breastfeeding ≥ 1 year was 1.663 (95% CI, 1.564–1.768) (reference: no breastfeeding). The HRs of HRT and OC use for any length of time were lower than those for the non-user groups (reference). Reproductive factors that increase estrogen exposure have protective effects against pterygium in females.
Development and external validation of a nomogram for predicting preterm birth at < 32 weeks in twin pregnancy
Authors: Zhang Jun , Zhan Wenqiang , Lin Yanling , Yang Danlin , Li Li , Xue Xiaoying , Lin Zhi , Pan Mian 
Published: June 2021

AbstractThe purpose of this study was to develop a dynamic model to predict the risk of spontaneous preterm birth at < 32 weeks in twin pregnancy. A retrospective clinical study of consecutively asymptomatic women with twin pregnancies from January 2017 to December 2019 in two tertiary medical centres was performed. Data from one centre were used to construct the model, and data from the other were used to evaluate the model. Data on maternal demographic characteristics, transvaginal cervical length and funnelling during 20–24 weeks were extracted. The prediction model was constructed with independent variables determined by multivariate logistic regression analyses. After applying specified exclusion criteria, an algorithm with maternal and biophysical factors was developed based on 88 twin pregnancies with a preterm birth < 32 weeks and 639 twin pregnancies with a delivery ≥ 32 weeks. It was then evaluated among 34 pregnancies with a preterm birth < 32 weeks and 252 pregnancies with a delivery ≥ 32 weeks in a second tertiary centre without specific training. The model reached a sensitivity of 80.00%, specificity of 88.17%, positive predictive value of 50.33% and negative predictive value of 96.71%; ROC characteristics proved that the model was superior to any single parameter with an AUC of 0.848 (all P < 0.005). We developed and validated a dynamic nomogram model to predict the individual probability of early preterm birth to better represent the complex aetiology of twin pregnancies and hopefully improve the prediction and indication of interventions.