bluuebunny/crossref_metadata_2025_split huggingface dataset:
An in vitro model of human hematopoiesis identifies a regulatory role for the aryl hydrocarbon receptor
Authors: Khan D M Isha Olive , Karmaus Peer W. F. , Bach Anthony , Crawford Robert B. , Kaminski Norbert E. 
Published: July 2023

Abstract
In vitro models to study simultaneous development of different human immune cells and hematopoietic lineages are lacking. We identified and characterized, using single-cell methods, an in vitro stromal cell–free culture system of human hematopoietic stem and progenitor cell (HSPC) differentiation that allows concurrent development of multiple immune cell lineages. The aryl hydrocarbon receptor (AHR) is a ligand–activated transcription factor influencing many biological processes in diverse cell types. Using this in vitro model, we found that AHR activation by the highly specific AHR ligand, 2,3,7,8-tetrachlorodibenzo-p-dioxin, drives differentiation of human umbilical cord blood–derived CD34+ HSPCs toward monocytes and granulocytes with a significant decrease in lymphoid and megakaryocyte lineage specification that may lead to reduced immune competence. To our knowledge, we also discovered for the first time, using single-cell modalities, that AHR activation decreased the expression of BCL11A and IRF8 in progenitor cells, which are critical genes involved in hematopoietic lineage specification processes at both transcriptomic and protein levels. Our in vitro model of hematopoiesis, coupled with single-cell tools, therefore allows for a better understanding of the role played by AHR in modulating hematopoietic differentiation.
Psychometric evaluation of the postpartum specific anxiety scale – research short-form among Iranian women (PSAS-IR-RSF)
Authors: Mashayekh-Amiri Sepideh , Jafarabadi Mohammad Asghari , Davies Siân M , Silverio Sergio A. , Fallon Victoria , Montazeri Maryam , Mirghafourvand Mojgan 
Published: July 2023

AbstractBackgroundThe increasing prevalence of postpartum anxiety as a common psychological problem affects a large part of women’s lives. Despite the existence of tools in this field, but due to the lack of specificity in reflecting postpartum anxiety, it is necessary to have a specific tool to screen it. Since the psychometric evaluation of the Postpartum Specific Anxiety Scale-Research Short-Form (PSAS-RSF) among Iranian women has not been assessed in Iran until now, so we decided to conduct this study with the aim of psychometric evaluation of the PSAS-IR-RSF.MethodsWe included 180 women (six weeks to six months postpartum) in the study by random sampling during the period from December 2021 to June 2022. We examined the validity of the PSAS-IR-RSF tool in terms of face, content and construct (through exploratory and confirmatory factor analyses). We used internal consistency and test-retest reliability to determine the reliability of the scale.ResultsIn the present study, content validity index (CVI) and content validity ratio (CVR) of the PSAS-IR-RSF tool were equal to 0.91 and 0.97, respectively. We extracted a four-factor structure through the process of exploratory factor analysis. The values of fitting indices confirmed the validity of the model. Cronbach’s alpha coefficient was equal to 0.72 and intra-class correlation coefficient (with 95% confidence interval) was 0.97 (0.98 to 0.93).ConclusionsThe Persian version of the PSAS-IR-RSF is a valid and reliable tool for the specific evaluation of postpartum anxiety among Iranian women.
The short and long-term effects of a lifestyle intervention in children with mental illnesses: a randomized controlled trial (Movementss study)
Authors: van Tetering Emilie M. A. , Muskens Jet B. , Deenik Jeroen , Pillen Sigrid , Cahn Wiepke , von Rosenstiel Inès , Oomen Mieke , Rommelse Nanda N. , Staal Wouter G. , Klip Helen 
Published: July 2023

Abstract
Background
A lifestyle including poor diet, physical inactivity, excessive gaming and inadequate sleep hygiene is frequently seen among Dutch children. These lifestyle behaviors can cause long-term health problems later in life. Unhealthy lifestyle and poor physical health are even more prevalent among children with mental illness (MI) such as autism, attention-deficit/hyperactivity disorder, depression, and anxiety. However, research on lifestyle interventions among children with MI is lacking. As a result, there are currently no guidelines, or treatment programs where children with MI and poor lifestyle can receive effective support. To address these issues and to provide insight into the effectiveness of lifestyle interventions in children with MI and their families, the Movementss study was designed. This paper describes the rationale, study design, and methods of an ongoing randomized controlled trial (RCT) comparing the short-term (12 weeks) and long-term (1 year) effects of a lifestyle intervention with care as usual (CAU) in children with MI and an unhealthy lifestyle.

Methods
A total of 80 children (6–12 years) with MI according to DSM-V and an unhealthy lifestyle are randomized to the lifestyle intervention group or CAU at a specialized child and adolescent mental hospital. The primary outcome measure is quality of life measured with the KIDSCREEN. Secondary outcomes include emotional and behavior symptoms, lifestyle parameters regarding diet, physical activity, sleep, and screen time, cognitive assessment (intelligence and executive functions), physical measurements (e.g., BMI), parenting styles, and family functioning, prior beliefs, adherence, satisfaction, and cost-effectiveness. Assessments will take place at the start of the study (T0), after 12 weeks (T1), six months (T2), and 12 months of baseline (T3) to measure long-term effects.

Discussion
This RCT will likely contribute to the currently lacking knowledge on lifestyle interventions in children with MI.

Trial registration
trialsearch.who.int/ NL9822. Registered at November 2nd, 2021.
Stapler port position determination using 3-dimensional virtual simulation software in robot-assisted thoracic surgery
Authors: Hayashi Satoshi , Kawada Masaya , Mori Yuki 
Published: July 2023

Abstract
Background
In robot-assisted thoracic surgery (RATS) lobectomy using a robotic stapler, stapling is difficult when the stapler port place is close to the resection target vessel. We examined whether three-dimensional computed tomography (3D-CT) software enables stapler port place determination for stapling.

Methods
Seventy-three patients who underwent RATS lobectomy were enrolled. The SureForm 45 Curved-Tip stapler (136 mm from the remote center to the anvil tip) was used. The virtual distance between the resection target vessel and stapler port place (VD) was preoperatively measured with 3D-CT software. The stapler port place was the most cranial intercostal space with a VD ≥ 136 mm. The actual distance between the resection target vessel and anvil tip (AD) was measured intraoperatively. We examined the associations of the difficulty in stapling with VD, AD, chest wall damage, and clinical features.

Results
Stapling was easier with a larger anteroposterior thoracic diameter and AD. The cut-off VD and AD for smooth stapling were 142 mm and 6 mm. Chest wall damage was frequently observed at the caudal and dorsal side ports.

Conclusions
As the stapler port place is located more caudally, stapling becomes easier. However, chest wall damage increases. If the stapler port place is positioned at a site ensuring VD ≥ 142 mm by 3D-CT software, smooth stapling may be possible with a decreased incidence of chest wall damage.
Machine learning-based mortality prediction models for smoker COVID-19 patients
Authors: Sharifi-Kia Ali , Nahvijou Azin , Sheikhtaheri Abbas 
Published: July 2023

Abstract
Background
The large number of SARS-Cov-2 cases during the COVID-19 global pandemic has burdened healthcare systems and created a shortage of resources and services. In recent years, mortality prediction models have shown a potential in alleviating this issue; however, these models are susceptible to biases in specific subpopulations with different risks of mortality, such as patients with prior history of smoking. The current study aims to develop a machine learning-based mortality prediction model for COVID-19 patients that have a history of smoking in the Iranian population.

Methods
A retrospective study was conducted across six medical centers between 18 and 2020 and 15 March 2022, comprised of 678 CT scans and laboratory-confirmed COVID-19 patients that had a history of smoking. Multiple machine learning models were developed using 10-fold cross-validation. The target variable was in-hospital mortality and input features included patient demographics, levels of care, vital signs, medications, and comorbidities. Two sets of models were developed for at-admission and post-admission predictions. Subsequently, the top five prediction models were selected from at-admission models and post-admission models and their probabilities were calibrated.

Results
The in-hospital mortality rate for smoker COVID-19 patients was 20.1%. For “at admission” models, the best-calibrated model was XGBoost which yielded an accuracy of 87.5% and F1 score of 86.2%. For the “post-admission” models, XGBoost also outperformed the rest with an accuracy of 90.5% and F1 score of 89.9%. Active smoking was among the most important features in patients’ mortality prediction.

Conclusion
Our machine learning-based mortality prediction models have the potential to be adapted for improving the management of smoker COVID-19 patients and predicting patients’ chance of survival.
Differential subgenome expression underlies biomass accumulation in allotetraploid Pennisetum giganteum
Authors: Xing Longsheng , Wang Meijia , He Qiang , Zhang Hongyu , Liang Hanfei , Zhou Qinghong , Liu Yu , Liu Ze , Wang Yu , Du Cailian , Xiao Yao , Liu Jianan , Li Wei , Liu Guixia , Du Huilong 
Published: July 2023

Abstract
Background
Pennisetum giganteum (AABB, 2n = 4x = 28) is a C4 plant in the genus Pennisetum with origin in Africa but currently also grown in Asia and America. It is a crucial forage and potential energy grass with significant advantages in yield, stress resistance, and environmental adaptation. However, the mechanisms underlying these advantageous traits remain largely unexplored. Here, we present a high-quality genome assembly of the allotetraploid P. giganteum aiming at providing insights into biomass accumulation.

Results
Our assembly has a genome size 2.03 Gb and contig N50 of 88.47 Mb that was further divided into A and B subgenomes. Genome evolution analysis revealed the evolutionary relationships across the Panicoideae subfamily lineages and identified numerous genome rearrangements that had occurred in P. giganteum. Comparative genomic analysis showed functional differentiation between the subgenomes. Transcriptome analysis found no subgenome dominance at the overall gene expression level; however, differentially expressed homoeologous genes and homoeolog-specific expressed genes between the two subgenomes were identified, suggesting that complementary effects between the A and B subgenomes contributed to biomass accumulation of P. giganteum. Besides, C4 photosynthesis-related genes were significantly expanded in P. giganteum and their sequences and expression patterns were highly conserved between the two subgenomes, implying that both subgenomes contributed greatly and almost equally to the highly efficient C4 photosynthesis in P. giganteum. We also identified key candidate genes in the C4 photosynthesis pathway that showed sustained high expression across all developmental stages of P. giganteum.

Conclusions
Our study provides important genomic resources for elucidating the genetic basis of advantageous traits in polyploid species, and facilitates further functional genomics research and genetic improvement of P. giganteum.
Flavour by design: food-grade lactic acid bacteria improve the volatile aroma spectrum of oat milk, sunflower seed milk, pea milk, and faba milk towards improved flavour and sensory perception
Authors: Tangyu Muzi , Fritz Michel , Tan Jan Patrick , Ye Lijuan , Bolten Christoph J. , Bogicevic Biljana , Wittmann Christoph 
Published: July 2023

Abstract
Background
The global market of plant-based milk alternatives is continually growing. Flavour and taste have a key impact on consumers’ selection of plant-based beverages. Unfortunately, natural plant milks have only limited acceptance. Their typically bean-like and grassy notes are perceived as “off-flavours” by consumers, while preferred fruity, buttery, and cheesy notes are missing. In this regard, fermentation of plant milk by lactic acid bacteria (LAB) appears to be an appealing option to improve aroma and taste.

Results
In this work, we systematically studied LAB fermentation of plant milk. For this purpose, we evaluated 15 food-approved LAB strains to ferment 4 different plant milks: oat milk (representing cereal-based milk), sunflower seed milk (representing seed-based milk), and pea and faba milk (representing legume-based milk). Using GC‒MS analysis, flavour changes during anaerobic fermentations were studied in detail. These revealed species-related and plant milk-related differences and highlighted several well-performing strains delivered a range of beneficial flavour changes. A developed data model estimated the impact of individual flavour compounds using sensory scores and predicted the overall flavour note of fermented and nonfermented samples. Selected sensory perception tests validated the model and allowed us to bridge compositional changes in the flavour profile with consumer response.

Conclusion
Specific strain-milk combinations provided quite different flavour notes. This opens further developments towards plant-based products with improved flavour, including cheesy and buttery notes, as well as other innovative products in the future. S. thermophilus emerged as a well-performing strain that delivered preferred buttery notes in all tested plant milks. The GC‒MS-based data model was found to be helpful in predicting sensory perception, and its further refinement and application promise enhanced potential to upgrade fermentation approaches to flavour-by-design strategies.
Environmental exposures in early-life and general health in childhood
Authors: Amine Ines , Guillien Alicia , Philippat Claire , Anguita-Ruiz Augusto , Casas Maribel , de Castro Montserrat , Dedele Audrius , Garcia-Aymerich Judith , Granum Berit , Grazuleviciene Regina , Heude Barbara , Haug Line Småstuen , Julvez Jordi , López-Vicente Mónica , Maitre Léa , McEachan Rosemary , Nieuwenhuijsen Mark , Stratakis Nikos , Vafeiadi Marina , Wright John , Yang Tiffany , Yuan Wen Lun , Basagaña Xavier , Slama Rémy , Vrijheid Martine , Siroux Valérie 
Published: July 2023

AbstractBackgroundEarly-life environmental exposures are suspected to be involved in the development of chronic diseases later in life. Most studies conducted so far considered single or few exposures and single-health parameter. Our study aimed to identify a childhood general health score and assess its association with a wide range of pre- and post-natal environmental exposures.MethodsThe analysis is based on 870 children (6–12 years) from six European birth cohorts participating in the Human Early-Life Exposome project. A total of 53 prenatal and 105 childhood environmental factors were considered, including lifestyle, social, urban and chemical exposures. We built a general health score by averaging three sub-scores (cardiometabolic, respiratory/allergy and mental) built from 15 health parameters. By construct, a child with a low score has a low general health status. Penalized multivariable regression through Least Absolute Shrinkage and Selection Operator (LASSO) was fitted in order to identify exposures associated with the general health score.FindingsThe results of LASSO show that a lower general health score was associated with maternal passive and active smoking during pregnancy and postnatal exposure to methylparaben, copper, indoor air pollutants, high intake of caffeinated drinks and few contacts with friends and family. Higher child’s general health score was associated with prenatal exposure to a bluespace near residency and postnatal exposures to pets, cobalt, high intakes of vegetables and more physical activity. Against our hypotheses, postnatal exposure to organochlorine compounds and perfluorooctanoate were associated with a higher child’s general health score.ConclusionBy using a general health score summarizing the child cardiometabolic, respiratory/allergy and mental health, this study reinforced previously suspected environmental factors associated with various child health parameters (e.g. tobacco, air pollutants) and identified new factors (e.g. pets, bluespace) warranting further investigations.
GDF-11 downregulates placental human chorionic gonadotropin expression by activating SMAD2/3 signaling
Authors: Wu Ze , Zhang Lingling , Jia Yuanyuan , Bi Beibei , Fang Lanlan , Cheng Jung-Chien 
Published: July 2023

Abstract
Background
The production of human chorionic gonadotropin (hCG) by the placental trophoblast cells is essential for maintaining a normal pregnancy. Aberrant hCG levels are associated with reproductive disorders. The protein of hCG is a dimer consisting of an α subunit and a β subunit. The β subunit is encoded by the CGB gene and is unique to hCG. Growth differentiation factor-11 (GDF-11), a member of the transforming growth factor-β (TGF-β) superfamily, is expressed in the human placenta and can stimulate trophoblast cell invasion. However, whether the expression of CGB and the production of hCG are regulated by GDF-11 remains undetermined.

Methods
Two human choriocarcinoma cell lines, BeWo and JEG-3, and primary cultures of human cytotrophoblast (CTB) cells were used as experimental models. The effects of GDF-11 on CGB expression and hCG production, as well as the underlying mechanisms, were explored by a series of in vitro experiments.

Results
Our results show that treatment of GDF-11 downregulates the expression of CGB and the production of hCG in both BeWo and JEG-3 cells as well as in primary CTB cells. Using a pharmacological inhibitor and siRNA-mediated approach, we reveal that both ALK4 and ALK5 are required for the GDF-11-induced downregulation of CGB expression. In addition, treatment of GDF-11 activates SMAD2/3 but not SMAD1/5/8 signaling pathways. Moreover, both SMAD2 and SMAD3 are involved in the GDF-11-downregulated CGB expression. ELISA results show that the GDF-11-suppressed hCG production requires the ALK4/5-mediated activation of SMAD2/3 signaling pathways.

Conclusions
This study not only discovers the biological function of GDF-11 in the human placenta but also provides important insights into the regulation of the expression of hCG.
Elucidating shared biomarkers and pathways in kidney stones and diabetes: insights into novel therapeutic targets and the role of resveratrol
Authors: Shen Shanlin , Wei Jiafeng , Kang Weiting , Wang Tengteng 
Published: July 2023

AbstractBackgroundThe pathogenic mechanisms shared between kidney stones and diabetes at the transcriptional level remain elusive, and the molecular mechanisms by which resveratrol exerts its protective effects against these conditions require further investigation.MethodsTo address these gaps in knowledge, we conducted a comprehensive analysis of microarray and RNA-seq datasets to elucidate shared biomarkers and biological pathways involved in the pathogenesis of kidney stones and diabetes. An assortment of bioinformatic approaches was employed to illuminate the common molecular markers and associated pathways, thereby contributing to the identification of innovative therapeutic targets. Further investigation into the molecular mechanisms of resveratrol in preventing these conditions was conducted using molecular docking simulation and first-principles calculations.ResultsThe study identified 11 potential target genes associated with kidney stones and diabetes through the intersection of genes from weighted gene co-expression network analysis (WGCNA) and differentially expressed genes (DEGs) screening. Among these, Interleukin 11 (IL11) emerged as a pivotal hub gene and a potential diagnostic biomarker for both conditions, particularly in males. Expression analysis of IL11 demonstrated elevated levels in kidney stones and diabetes groups compared to controls. Additionally, IL11 exhibited correlations with specific cell types and differential expression in normal and pathological conditions. Gene set enrichment analysis (GSEA) highlighted significant disparities in biological processes, pathways, and immune signatures associated with IL11. Moreover, molecular docking simulation of resveratrol towards IL11 and a first-principles investigation of Ca adsorption on the resveratrol surface provided structural evidence for the development of resveratrol-based drugs for these conditions.ConclusionsOverall, this investigation illuminates the discovery of common molecular mechanisms underlying kidney stones and diabetes, unveils potential diagnostic biomarkers, and elucidates the significance of IL11 in these conditions. It also provides insights into IL11 as a promising therapeutic target and highlights the role of resveratrol. Nonetheless, further research is warranted to enhance our understanding of IL11 targeting mechanisms and address any limitations in the study.
Artificial intelligence in cardiovascular diseases: diagnostic and therapeutic perspectives
Authors: Sun Xiaoyu , Yin Yuzhe , Yang Qiwei , Huo Tianqi 
Published: July 2023

AbstractArtificial intelligence (AI), the technique of extracting information from complex database using sophisticated computer algorithms, has incorporated itself in medical field. AI techniques have shown the potential to accelerate the progression of diagnosis and treatment of cardiovascular diseases (CVDs), including heart failure, atrial fibrillation, valvular heart disease, hypertrophic cardiomyopathy, congenital heart disease and so on. In clinical scenario, AI have been proved to apply well in CVD diagnosis, enhance effectiveness of auxiliary tools, disease stratification and typing, and outcome prediction. Deeply developed to capture subtle connections from massive amounts of healthcare data, recent AI algorithms are expected to handle even more complex tasks than traditional methods. The aim of this review is to introduce current applications of AI in CVDs, which may allow clinicians who have limited expertise of computer science to better understand the frontier of the subject and put AI algorithms into clinical practice.