bluuebunny/crossref_metadata_2025_split huggingface dataset:
Determinants and mediating mechanisms of quality of life and disease-specific symptoms among thyroid cancer patients: the design of the WaTCh study
Authors: Mols Floortje , Schoormans Dounya , Netea-Maier Romana , Husson Olga , Beijer Sandra , Van Deun Katrijn , Zandee Wouter , Kars Marleen , Wouters van Poppel Pleun C. M. , Simsek Suat , van Battum Patrick , Kisters Jérôme M. H. , de Boer Jan Paul , Massolt Elske , van Leeuwaarde Rachel , Oranje Wilma , Roerink Sean , Vermeulen Mechteld , van de Poll-Franse Lonneke 
Published: July 2023

AbstractBackgroundThyroid cancer (TC) patients are understudied but appear to be at risk for poor physical and psychosocial outcomes. Knowledge of the course and determinants of these deteriorated outcomes is lacking. Furthermore, little is known about mediating biological mechanisms.ObjectivesThe WaTCh-study aims to;Examine the course of physical and psychosocial outcomes.Examine the association of demographic, environmental, clinical, physiological, and personality characteristics to those outcomes. In other words,whois at risk?Reveal the association of mediating biological mechanisms (inflammation, kynurenine pathway) with poor physical and psychological outcomes. In other words,whyis a person at risk?Design and methodsNewly diagnosed TC patients from 13 Dutch hospitals will be invited. Data collection will take place before treatment, and at 6, 12 and 24 months after diagnosis. Sociodemographic and clinical information is available from the Netherlands Cancer Registry. Patients fill-out validated questionnaires at each time-point to assess quality of life, TC-specific symptoms, physical activity, anxiety, depression, health care use, and employment. Patients are asked to donate blood three times to assess inflammation and kynurenine pathway. Optionally, at each occasion, patients can use a weighing scale with bioelectrical impedance analysis (BIA) system to assess body composition; can register food intake using an online food diary; and can wear an activity tracker to assess physical activity and sleep duration/quality. Representative Dutch normative data on the studied physical and psychosocial outcomes is already available.ImpactWaTCh will reveal the course of physical and psychosocial outcomes among TC patients over time and answers the questionwhois at risk for poor outcomes, andwhy.This knowledge can be used to provide personalized information, to improve screening, to develop and provide tailored treatment strategies and supportive care, to optimize outcomes, and ultimately increase the number of TC survivors that live in good health.
Exploring breast tissue microbial composition and the association with breast cancer risk factors
Authors: German Rana , Marino Natascia , Hemmerich Chris , Podicheti Ram , Rusch Douglas B. , Stiemsma Leah T. , Gao Hongyu , Xuei Xiaoling , Rockey Pam , Storniolo Anna Maria 
Published: July 2023

Abstract
Background
Microbial dysbiosis has emerged as an important element in the development and progression of various cancers, including breast cancer. However, the microbial composition of the breast from healthy individuals, even relative to risk of developing breast cancer, remains unclear. Here, we performed a comprehensive analysis of the microbiota of the normal breast tissue, which was analyzed in relation to the microbial composition of the tumor and adjacent normal tissue.

Methods
The study cohorts included 403 cancer-free women (who donated normal breast tissue cores) and 76 breast cancer patients (who donated tumor and/or adjacent normal tissue samples). Microbiome profiling was obtained by sequencing the nine hypervariable regions of the 16S rRNA gene (V1V2, V2V3, V3V4, V4V5, V5V7, and V7V9). Transcriptome analysis was also performed on 190 normal breast tissue samples. Breast cancer risk score was assessed using the Tyrer-Cuzick risk model.

Results
The V1V2 amplicon sequencing resulted more suitable for the analysis of the normal breast microbiome and identified Lactobacillaceae (Firmicutes phylum), Acetobacterraceae, and Xanthomonadaceae (both Proteobacteria phylum) as the most abundant families in the normal breast. However, Ralstonia (Proteobacteria phylum) was more abundant in both breast tumors and histologically normal tissues adjacent to malignant tumors. We also conducted a correlation analysis between the microbiome and known breast cancer risk factors. Abundances of the bacterial taxa Acetotobacter aceti, Lactobacillus vini, Lactobacillus paracasei, and Xanthonomas sp. were associated with age (p < 0.0001), racial background (p < 0.0001), and parity (p < 0.0001). Finally, transcriptome analysis of normal breast tissues showed an enrichment in metabolism- and immune-related genes in the tissues with abundant Acetotobacter aceti, Lactobacillus vini, Lactobacillus paracasei, and Xanthonomas sp., whereas the presence of Ralstonia in the normal tissue was linked to dysregulation of genes involved in the carbohydrate metabolic pathway.

Conclusions
This study defines the microbial features of normal breast tissue, thus providing a basis to understand cancer-related dysbiosis. Moreover, the findings reveal that lifestyle factors can significantly affect the normal breast microbial composition.
Genomic sequencing of fourteen Bacillus thuringiensis isolates: insights into geographic variation and phylogenetic implications
Authors: Blackburn Michael B. , Sparks Michael E. , Mishra Ruchir , Bonning Bryony C. 
Published: July 2023

Abstract
Objective
This work was performed in support of a separate study investigating the activity of pesticidal proteins produced by Bacillus thuringiensis against the Asian citrus psyllid, Diaphorina citri. The fourteen Bacillus isolates chosen were selected from a large, geographically diverse collection that was characterized only by biochemical phenotype and morphology of the parasporal crystal, hence, for each isolate it was desired to determine the specific pesticidal proteins produced, assign each to a Bacillus cereus multilocus sequence type (ST), and predict their placement within the classical Bt serotyping system. In addition, phylogenetic distances between the isolates and Bacillus thuringiensis serovar type strains were determined by calculating digital DNA-DNA hybridization (dDDH) values among the isolates.

Results
Based on the assembled sequence data, the isolates were found to be likely representatives of the Bt serovars kurstaki (ST 8), pakistani (ST 550), toumanoffi (ST 240), israelensis (ST 16), thuringiensis (ST 10), entomocidus (ST 239), and finitimus (ST 171). In cases where multiple isolates occurred within a predicted serovar, pesticidal protein profiles were found to be identical, despite the geographic diversity of the isolates. As expected, the dDDH values calculated for pairwise comparisons of the isolates and their apparent corresponding Bt serovar type strains were quite high (> 98%), however dDDH comparisons of the isolates with other serovar type strains were often surprisingly low (< 70%) and suggest unrecognized taxa within Bt and the Bacillus cereus sensu lato.
Roxadustat alleviates the inflammatory status in patients receiving maintenance hemodialysis with erythropoiesis-stimulating agent resistance by increasing the short-chain fatty acids producing gut bacteria
Authors: Zhao Xiu-Nan , Liu Shu-Xin , Wang Zhen-Zhen , Zhang Shuang , You Lian-Lian 
Published: July 2023

AbstractBackgroundHypoxia-inducible factor-prolyl hydroxylase inhibitors (HIF-PHIs) have improved the treatment of renal anemia, especially in patients resistant to erythropoiesis-stimulating agents (ESAs). HIF facilitates maintain gut microbiota homeostasis, which plays an important role in inflammation and iron metabolism, which are in turn key factors affecting ESA resistance. The current study aimed to investigate the effects of roxadustat on inflammation and iron metabolism and on the gut microbiota in patients with ESA resistance.MethodsWe conducted a self-controlled, single-center study including 30 patients with ESA resistance undergoing maintenance hemodialysis. All patients received roxadustat without iron agents for renal anemia. Hemoglobin and inflammatory factors were monitored. Fecal samples were collected before and after 3 months’ administration and the gut microbiota were analyzed by 16S ribosomal RNA gene sequencing.ResultsHemoglobin levels increased after treatment with roxadustat for 3 months (P < 0.05). Gut microbiota diversity and abundance also changed, with increases in short-chain fatty acid (SCFA)-producing bacteria (Acidaminococcaceae, Butyricicoccus,Ruminococcus bicirculans,Ruminococcus bromii,Bifidobacterium dentium,Eubacterium hallii) (P < 0.05). Serum SCFA levels also increased (P < 0.05). Inflammatory factors, including interleukin (IL)-1, IL-6, tumor necrosis factor (TNF)-α, interferon-γ, and endotoxin gradually decreased (P < 0.05). Serum hepcidin, ferritin, and total and unsaturated iron-binding capacities decreased (P < 0.05), while soluble transferrin receptor levels increased at each time point (P < 0.05). There were no significant differences in serum iron and transferrin saturation at each time point. The abundance ofAlistipes shahiiwas significantly negatively correlated with IL-6 and TNF-α (P < 0.05).ConclusionsRoxadustat alleviated renal anemia in patients with ESA resistance by decreasing inflammatory factors and hepcidin levels and improving iron utilization. These effects were at least partly mediated by improved diversity and abundance of SCFA-producing gut bacteria, probably via activation of HIF.
Smoking-related dysregulation of plasma circulating microRNAs: the Rotterdam study
Authors: Karabegović Irma , Maas Silvana C. E. , Shuai Yu , Ikram M. Arfan , Stricker Bruno , Aerts Joachim , Brusselle Guy , Lahousse Lies , Voortman Trudy , Ghanbari Mohsen 
Published: July 2023

AbstractBackgroundMicroRNAs (miRNAs) are post-transcriptional regulators of gene expression. Differential miRNA expression, which is widely shown to be associated with the pathogenesis of various diseases, can be influenced by lifestyle factors, including smoking. This study aimed to investigate the plasma miRNA signature of smoking habits, the potential effect of smoking cessation on miRNA levels, and relate the findings with lung cancer incidence.ResultsA targeted RNA-sequencing approach measured plasma miRNA levels in 2686 participants from the population-based Rotterdam study cohort. The association between cigarette smoking (current versus never) and 591 well-expressed miRNAs was assessed via adjusted linear regression models, identifying 41 smoking-associated miRNAs that passed the Bonferroni-corrected threshold (P < 0.05/591 = 8.46 × 10–5). Moreover, we found 42 miRNAs with a significant association (P < 8.46 × 10–5) between current (reference group) and former smokers. Then, we used adjusted linear regression models to explore the effect of smoking cessation time on miRNA expression levels. The expression levels of two miRNAs were significantly different within 5 years of cessation (P < 0.05/41 = 1.22 × 10–3) from current smokers, while for cessation time between 5 and 15 years we found 19 miRNAs to be significantly different from current smokers, and finally, 38 miRNAs were significantly different after more than 15 years of cessation time (P < 1.22 × 10–3). These results imply the reversibility of the smoking effect on plasma levels of at least 38 out of the 41 smoking-miRNAs following smoking cessation. Next, we found 8 out of the 41 smoking-related miRNAs to be nominally associated (P < 0.05) with the incidence of lung cancer.ConclusionsThis study demonstrates smoking-related dysregulation of plasma miRNAs, which might have a potential for reversibility when comparing different smoking cessation groups. The identified miRNAs are involved in several cancer-related pathways and include 8 miRNAs associated with lung cancer incidence. Our results may lay the groundwork for further investigation of miRNAs as potential mechanism linking smoking, gene expression and cancer.
Towards improving the visual explainability of artificial intelligence in the clinical setting
Authors: Rao Adrit , Aalami Oliver 
Published: July 2023

AbstractImproving the visual explainability of medical artificial intelligence (AI) is fundamental to enabling reliable and transparent clinical decision-making. Medical image analysis systems are becoming increasingly prominent in the clinical setting as algorithms are learning to accurately classify diseases in various imaging modalities. Saliency heat-maps are commonly leveraged in the clinical setting and allow clinicians to visually interpret regions of an image that the model is focusing on. However, studies have shown that in certain scenarios, models do not attend to clinically significant regions of an image and perform inference using insignificant visual features. Here, we discuss the importance of focusing on visual explainability and an effective strategy that has the potential to improve a model's ability to focus more on clinically relevant regions of a given medical image using attention mechanisms.
Machine Learning–Assisted Recurrence Prediction for Patients With Early-Stage Non–Small-Cell Lung Cancer
Authors: Janik Adrianna , Torrente Maria , Costabello Luca , Calvo Virginia , Walsh Brian , Camps Carlos , Mohamed Sameh K. , Ortega Ana L. , Nováček Vít , Massutí Bartomeu , Minervini Pasquale , Campelo M. Rosario Garcia , del Barco Edel , Bosch-Barrera Joaquim , Menasalvas Ernestina , Timilsina Mohan , Provencio Mariano 
Published: July 2023

PURPOSE Stratifying patients with cancer according to risk of relapse can personalize their care. In this work, we provide an answer to the following research question: How to use machine learning to estimate probability of relapse in patients with early-stage non–small-cell lung cancer (NSCLC)? MATERIALS AND METHODS For predicting relapse in 1,387 patients with early-stage (I-II) NSCLC from the Spanish Lung Cancer Group data (average age 65.7 years, female 24.8%, male 75.2%), we train tabular and graph machine learning models. We generate automatic explanations for the predictions of such models. For models trained on tabular data, we adopt SHapley Additive exPlanations local explanations to gauge how each patient feature contributes to the predicted outcome. We explain graph machine learning predictions with an example-based method that highlights influential past patients. RESULTS Machine learning models trained on tabular data exhibit a 76% accuracy for the random forest model at predicting relapse evaluated with a 10-fold cross-validation (the model was trained 10 times with different independent sets of patients in test, train, and validation sets, and the reported metrics are averaged over these 10 test sets). Graph machine learning reaches 68% accuracy over a held-out test set of 200 patients, calibrated on a held-out set of 100 patients. CONCLUSION Our results show that machine learning models trained on tabular and graph data can enable objective, personalized, and reproducible prediction of relapse and, therefore, disease outcome in patients with early-stage NSCLC. With further prospective and multisite validation, and additional radiological and molecular data, this prognostic model could potentially serve as a predictive decision support tool for deciding the use of adjuvant treatments in early-stage lung cancer.
Decision Rule Approaches for Pessimistic Bilevel Linear Programs Under Moment Ambiguity with Facility Location Applications
Authors: Goyal Akshit , Zhang Yiling , He Chuan 
Published: July 2023

We study a pessimistic stochastic bilevel program in the context of sequential two-player games, where the leader makes a binary here-and-now decision, and the follower responds with a continuous wait-and-see decision after observing the leader’s action and revelation of uncertainty. We assume that only the information regarding the mean, covariance, and support is known. We formulate the problem as a distributionally robust (DR) two-stage problem. The pessimistic DR bilevel program is shown to be equivalent to a generic two-stage distributionally robust stochastic (nonlinear) program with both a random objective and random constraints under proper conditions of ambiguity sets. Under continuous distributions, using linear decision rule approaches, we construct upper bounds on the pessimistic DR bilevel program based on (1) a 0-1 semidefinite programming (SDP) approximation and (2) an exact 0-1 copositive programming reformulation. When the ambiguity set is restricted to discrete distributions, an exact 0-1 SDP reformulation is developed, and explicit construction of the worst-case distribution is derived. To further improve the computation of the proposed 0-1 SDPs, a cutting-plane framework is developed. Moreover, based on a mixed-integer linear programming approximation, another cutting-plane algorithm is proposed. Extensive numerical studies are conducted to demonstrate the effectiveness of the proposed approaches on a facility location problem. History: Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0168 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0168 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .
Comparative evaluation of glomerular morphometric techniques reveals differential technical artifacts between focal segmental glomerulosclerosis and normal glomeruli
Authors: Reghuvaran Anand C. , Lin Qisheng , Basgen John M. , Banu Khadija , Shi Hongmei , Vashist Anushree , Pell John , Perinchery Sudhir , He John C. , Moledina Dennis , Wilson F. Perry , Menon Madhav C. 
Published: July 2023

AbstractMorphometric estimates of mean or individual glomerular volume (MGV, IGV) have biological implications, over and above qualitative histologic data. However, morphometry is time‐consuming and requires expertise limiting its utility in clinical cases. We evaluated MGV and IGV using plastic‐ and paraffin‐embedded tissue from 10 control and 10 focal segmental glomerulosclerosis (FSGS) mice (aging and 5/6th nephrectomy models) using the gold standard Cavalieri (Cav) method versus the 2‐profile and Weibel–Gomez (WG) methods and a novel 3‐profile method. We compared accuracy, bias and precision, and quantified results obtained when sampling differing numbers of glomeruli. In both FSGS and controls, we identified an acceptable precision for MGV of 10‐glomerular sampling versus 20‐glomerular sampling using the Cav method, while 5‐glomerular sampling was less precise. In plastic tissue, 2‐ or 3‐profile MGVs showed greater concordance with MGV when using Cav, versus MGV with WG. IGV comparisons using the same glomeruli reported a consistent underestimation bias with both 2‐ or 3‐profile methods versus the Cav method. FSGS glomeruli showed wider variations in bias estimation than controls. Our 3‐profile method offered incremental benefit to the 2‐profile method in both IGV and MGV estimation (improved correlation coefficient, Lin's concordance and reduced bias). In our control animals, we quantified a shrinkage artifact of 52% from tissue processed for paraffin‐embedded versus plastic‐embedded tissue. FSGS glomeruli showed overall reduced shrinkage albeit with variable artifact signifying periglomerular/glomerular fibrosis. A novel 3‐profile method offers slightly improved concordance with reduced bias versus 2‐profile. Our findings have implications for future studies using glomerular morphometry.
Poor subjective sleep reported by people living with HIV is associated with impaired working memory
Authors: Zahr Natalie M. , Sullivan Edith V. , Pfefferbaum Adolf 
Published: July 2023

Abstract
Poor sleep can undermine health and may be especially disruptive to those with chronic conditions including HIV infection. Here, clinically well-described people living with HIV [PLWH] (74 men, 35 women) and healthy control (38 men, 35 women) participants were administered the Pittsburgh Sleep Quality Index (PSQI), a validated measure of subjective sleep with a global score ≥5 able to distinguish good from poor sleepers. In addition, participants completed a battery of neuropsychological tests. PLWH (6.8 ± 3.7) had higher global PSQI scores than healthy controls (4.1 ± 2.8): 39.7 % of uninfected controls and 68.8 % of PLWH had a PSQI≥5 indicative of poor sleep. There were no relations between the global PSQI score and any evaluated variables among uninfected individuals or with demographic or HIV-related variables in PLWH. Instead, a higher global PSQI score among PLWH was associated with worse “Quality of Life” scores [Global Assessment of Functioning (GAF, p=0.0007), Medical Outcomes Study survey (21-item short form, SF-21, p<0.0001), and Activities of Daily Living-Instrumental (ADL-I, p=0.0041)] and higher Beck Depression Index (BDI, p<0.0001) depressive symptoms. Further, in PLWH, higher global PSQI scores were associated with poor performance on a working memory task, the digit backward span (p=0.0036). In PLWH, the 5 variables together explained 32.3 % of the global PSQI score variance; only 3 variables – the SF-21, BDI, and digit backward scores – explained 30.6 % of the variance. To the extent that poor subjective sleep contributes to impaired working memory in HIV, we speculate that this impairment may be ameliorated by improved sleep health.
Local coordination of mRNA storage and degradation near mitochondria modulates C. elegans ageing
Authors: Daskalaki Ioanna , Markaki Maria , Gkikas Ilias , Tavernarakis Nektarios 
Published: July 2023

AbstractMitochondria are central regulators of healthspan and lifespan, yet the intricate choreography of multiple, tightly controlled steps regulating mitochondrial biogenesis remains poorly understood. Here, we uncover a pivotal role for specific elements of the 5′‐3′ mRNA degradation pathway in the regulation of mitochondrial abundance and function. We find that the mRNA degradation and the poly‐A tail deadenylase CCR4‐NOT complexes form distinct foci in somatic Caenorhabditis elegans cells that physically and functionally associate with mitochondria. Components of these two multi‐subunit complexes bind transcripts of nuclear‐encoded mitochondria‐targeted proteins to regulate mitochondrial biogenesis during ageing in an opposite manner. In addition, we show that balanced degradation and storage of mitochondria‐targeted protein mRNAs are critical for mitochondrial homeostasis, stress resistance and longevity. Our findings reveal a multifaceted role of mRNA metabolism in mitochondrial biogenesis and show that fine‐tuning of mRNA turnover and local translation control mitochondrial abundance and promote longevity in response to stress and during ageing.