bluuebunny/crossref_metadata_2025_split huggingface dataset:
(Pro)renin receptor inhibited ABCA1/G1 expression associated with low HDL-c and promoted atherosclerosis
Authors: Dong Yubing , Zhang Wei , Liu Wanyu , Bai Cuiping , Zhang Ying , Jiang Yinong , Song Wei 
Published: April 2024

AbstractBackground(Pro)renin receptors [(P)RR] binding both renin and its inactive proenzyme for prorenin induce increasing the efficiency of angiotensin I conversion and the production of angiotensin II (Ang II). Recently, (P)RR was reported to played a pathological role in diabetic renal disease independent of Ang II. Ang II is involved in lipid metabolism and atherosclerosis, but the (P)RR participation in atherosclerosis remains unclear. Thus, the objective of this study was to examine the relative importance of Ang II-independent actions of (P)RR and its mechanism in the development of atherosclerosis.MethodsClinical demographic characteristics and laboratory profile data of 1189 essential hypertensive were collected. Patients were divided into four groups based on the quartiles of plasma renin activity (PRA). The plasma lipoprotein levels of four groups were compared. Furthermore, we examined serum and liver lipoprotein levels, as well as ATP-binding cassette transporter A1 (ABCA1) and ATP-binding cassette transporter G1 (ABCG1) expression in ApoE-/-mice treated with prorenin. ABCA1 and ABCG1 expression and their function in mediating cholesterol efflux were evaluated in THP-1 cells by prorenin treatment. Agonists of the peroxisome proliferator-activated receptor-γ (PPAR-γ) and liver X receptor α (LXRα) were used to investigate the mechanism involved in vitro.ResultsPlasma TC, TG, LDL-c, and apoB were significantly increased, and HDL-c and apoA were dramatically decreased with a trend of higher plasma renin activity (PRA) ( allP< 0.05) in hypertensive patients. Prorenin accelerated atherosclerosis by inhibiting mRNA and protein expression of ABCA1/ABCG1 in the liver of ApoE-/-mice. Prorenin binding to (P)RR inhibited the functions of ABCA1- and ABCG1-mediated cholesterol efflux. Furthermore, Prorenin reduced ABCA1/G1 expression of THP-1 cells through PPARγ/LXRα pathway.Conclusions(P)RR activated by prorenin inhibited the expression of ABCA1/ABCG1 through the PPARγ/LXRα pathway and the functions of ABCA1- and ABCG1-mediated cholesterol efflux, resulting in dyslipidemia and eventually atherosclerosis.HighlightsWith RPA increases in hypertension, plasma TC, TG, LDL-c, and apoB were higher, while HDL-c and apoA were lower.Prorenin binding to (P)RR accelerated atherosclerosis by inhibiting ABCA1/G1 expression and function.Prorenin reduced ABCA1/G1 expression of THP-1 cells through PPARγ/LXRα pathway.
Human iPSC-derived cell grafts promote functional recovery by molecular interaction with stroke-injured brain
Authors: Weber Rebecca Z. , Achón Buil Beatriz , Rentsch Nora H. , Perron Patrick , Bosworth Allison , Zhang Mingzi , Kisler Kassandra , Bodenmann Chantal , Zürcher Kathrin J. , Uhr Daniela , Meier Debora , Peter Siri L. , Generali Melanie , Nitsch Roger M. , Zlokovic Berislav V. , Tackenberg Christian , Rust Ruslan 
Published: April 2024

AbstractStroke is a leading cause of disability and death due to the brain’s limited ability to regenerate damaged neural circuits. To date, stroke patients have only few therapeutic options and are often left with considerable disabilities. Induced pluripotent stem cell (iPSC)-based therapies are emerging as a promising therapeutic approach for stroke recovery. In this study, we demonstrate that local transplantation of good manufacturing practice (GMP)-compatible iPSC-derived neural progenitor cells (NPCs) improve long-term recovery-associated brain tissue responses and reduce neurological deficits after cerebral ischemia in mice. Usingin vivobioluminescence imaging andpost-mortemhistology, we showed long-term graft survival over the course of five weeks and preferential graft differentiation into mature neurons without signs of pluripotent residuals. Transplantation of NPCs led to a set of recovery-associated tissue responses including increased vascular sprouting and repair, improved blood-brain barrier integrity, reduced microglial activation, and increased neurogenesis compared to littermate control animals receiving sham transplantation. Employing deep learning-assisted behavior analysis, we found that NPC-treated mice displayed improved gait performance and complete fine-motor recovery in the horizontal ladder rung walk, five weeks post-injury. To dissect the molecular graft composition and identify graft-host interactions, single nucleus profiling of the cell transplants and host stroke tissue was performed. We identified graft differentiation preferentially towards GABAergic cells with remaining cells acquiring glutamatergic neuron, astrocyte, and NPC-like phenotypes. Interaction between graft and host transcriptome indicated that GABAergic cell grafts were primarily involved in graft-host communication through the regeneration-associated NRXN, NRG, NCAM and SLIT signalling pathways. In conclusion, our study reveals that transplanted iPSC-derived NPCs primarily differentiate into GABAergic neurons contributing to long-term recovery, and further delineates the regenerative interactions between the graft and the stroke-injured host tissue.
Future shapes present: autonomous goal-directed and sensory-focused mode switching in a Bayesian allostatic network model
Authors: Idei Hayato , Tani Jun , Ogata Tetsuya , Yamashita Yuichi 
Published: April 2024

AbstractTrade-offs between moving to achieve goals and perceiving the surrounding environment highlight the complexity of continually adapting behaviors. The need to switch between goal-directed and sensory-focused modes, along with the goal emergence phenomenon, challenges conventional optimization frameworks, necessitating heuristic solutions. In this study, we propose a Bayesian recurrent neural network framework for homeostatic behavior adaptation via hierarchical multimodal integration. In it, the meta-goal of “minimizing predicted future sensory entropy” underpins the dynamic self-organization of future sensorimotor goals and their precision regarding the increasing sensory uncertainty due to unusual physiological conditions. We demonstrated that after learning a hierarchical predictive model of a dynamic environment through random exploration, our Bayesian agent autonomously switched self-organized behavior between goal-directed feeding and sensory-focused resting. It increased feeding before anticipated food shortages, explaining predictive energy regulation (allostasis) in animals. Our modeling framework opens new avenues for studying brain information processing and anchoring continual behavioral adaptations.
PsiPartition: Improved Site Partitioning for Genomic Data by Parameterized Sorting Indices and Bayesian Optimization
Authors: Xu Shijie , Onoda Akira 
Published: April 2024

ABSTRACTPhylogenetics has been widely used in molecular biology to infer the evolutionary relationships among species. With the rapid development of sequencing technology, genomic data with thousands of sites becomes increasingly common in phylogenetic analysis, while heterogeneity among sites arises as one of the major challenges. A single homogeneous model is not sufficient to describe the evolution of all sites and partitioned models are often employed to model the evolution of heterogeneous sites by partitioning them into distinct groups and utilizing distinct evolutionary models for each group. It is crucial to determine the best partitioning, which greatly affects the reconstruction correctness of phylogeny. However, the best partitioning is usually intractable to obtain in practice. Traditional partitioning methods rely on heuristic algorithms or greedy search to determine the best ones in their solution space, are usually time-consuming, and with no guarantee of optimality. In this study, we propose a novel partitioning approach, termed PsiPartition, based on the parameterized sorting indices of sites and Bayesian optimization. We apply our method to empirical data sets and it performs significantly better compared to existing methods, in terms of Bayesian information criterion (BIC) and the corrected Akaike information criterion (AICc). We test PsiPartition on the simulated data sets with different site heterogeneity, alignment lengths, and number of loci. It is demonstrated that PsiPartition evidently and stably outperforms other methods in terms of the Robinson-Foulds (RF) distance between the true simulated trees and the reconstructed trees, especially on the data with more site heterogeneity. More importantly, our proposed Bayesian optimization-based method, for the first time, provides a new general framework to efficiently determine the optimal number of partitions. The corresponding reproducible source code and data are available athttp://github.com/xu-shi-jie/PsiPartition.
Is ChatGPT detrimental to innovation? A field experiment among university students
Authors: Hassan Mazen , Amin Engi , Mansour Sarah , Kelani Zeyad 
Published: April 2024

AbstractChatGPT represents a momentous technological breakthrough whose implications – along with other AI innovations – are yet to fully materialize. This paper is among the first attempts to experimentally test the effect of AI applications (in the form of ChatGPT) on three dependent variables usually assumed to be AI-collaterals: innovation, readiness to exert effort, and risk behaviour. We took advantage of the delayed introduction of ChatGPT in Egypt and conducted a pre-registered field experiment with nearly 100 senior university students at a public university. Over one month during term time, participants were asked to submit three graded essay assignments. In the treatment group, students were asked to write the essays using ChatGPT whereas in the control group, such option was neither mentioned nor allowed (the experiment was fielded before ChatGPT was legally operable in Egypt). One week after all assignments were submitted, the two groups were invited to the lab to play an innovation game (deploying multiple strategies to increase the sales of a hypothetical lemonade stand), a risk game (bomb risk elicitation task), and do a real effort task. The ChatGPT group was significantly less innovative, significantly less risk averse, and exerted less effort (however not statistically significant). Our results point to possible negative effects of AI applications but need further testing and larger samples to be confirmed.
Adaptive trials in stroke: Current use & future directions
Authors: Hayward Kathryn S , Dalton Emily J , Campbell Bruce CV , Khatri Pooja , Dukelow Sean P , Johns Hannah , Walter Silke , Yogendrakumar Vignan , Pandian Jeyaraj , Sacco Simona , Bernhardt Julie , Parsons Mark , Saver Jeffrey L , Churilov Leonid 
Published: April 2024

AbstractInclusion of adaptive design features in a clinical trial provides pre-planned flexibility to dynamically modify a trial during its conduct, while preserving validity and integrity. Adaptive trials are needed to accelerate the conduct of more efficient, informative, and ethical clinical research in the field of neurology as compared to traditional fixed designs. Stroke is a natural candidate for adoption of these innovative approaches to trial design. This Research Methods in Neurology paper is informed by scoping review that identified 45 completed and ongoing adaptive clinical trials in stroke that were appraised: 14 trials had published results with or without a published protocol, 15 trials had a published protocol, and 16 trials were registered only. Treatments spanned acute (n=28), rehabilitation (n=8), prevention (n=8), and rehabilitation and prevention (n=1) domains. A subsample of these trials were selected to illustrate the utility of adaptive design features and discuss why each adaptive feature(s) were incorporated in the design to best achieve the aim, whether each individual feature was used and if it resulted in expected efficiencies, and any learnings during preparation, conduct or reporting. We then discuss the operational, ethical, and regulatory considerations that warrant careful consideration during adaptive trial planning and reflect on the workforce readiness to deliver adaptive trials in practice. We conclude that adaptive trials can be designed, funded, conducted, and published for a wide range of research questions and offer future directions to support adoption of adaptive trial designs in stroke and neurological research more broadly.
White matter lesion volumes on 3-T MRI in people with MS who had followed a diet- and lifestyle program for more than 10 years
Authors: Jaftha Mariaan , Robertson Frances , van Rensburg Susan J , Kidd Martin , van Toorn Ronald , Kemp Merlisa C. , Johannes Clint , Moremi Kelebogile E. , Whati Lindiwe , Kotze Maritha J , Engel-Hills Penelope 
Published: April 2024

AbstractBackgroundCerebral white matter lesions (WMLs) in people with multiple sclerosis (pwMS) are associated with the death of myelin-producing oligodendrocytes. MS treatment strategies aim to limit WML accumulation and disability progression. It is commonly accepted that nutrition is one of the possible environmental factors involved in the pathogenesis of MS, but its role as a complementary MS treatment is unclear and, to a large extent, ignored.ObjectiveA pilot case control study over a 10 year period to ascertain whether a dietary- and lifestyle modification Program in pwMS reduces or prevents WML formation.MethodsMRI was performed at baseline and after an interval period of at least 10 years or longer in 22 pwMS. WML volumes were determined using Sequence Adaptive Multimodal SEGmentation (SAMSEG) software, part of FreeSurfer 7.2. Other variables include age at MRI, disease duration, disability status and medication.ResultsPwMS (n=13) who had followed the Program for more than 10 years, had significantly smaller lesion volumes (mm3) compared to pwMS who did not adhere to the Program (n=9) (4950 ± 5303 vs 17934 ± 11139; p=0.002). WML volumes were significantly associated (p=0.02) with disability (EDSS) but not with age (p=0.350), disease duration (p=0.709), or Interferon-β treatment (p=0.70).ConclusionDietary- and lifestyle changes may lower the risk of developing cerebral WMLs in pwMS and potentially slow down disease progression. Larger studies are required to confirm the effectiveness of such interventions in pwMS.
Optimizing delivery strategies for 3HP TB preventive treatment in Tanzania: A qualitative study on acceptability of family approach in HIV care and treatment centers
Authors: Pamba Doreen , Sanga Erica , Mlalama Killian , Maganga Lucas , Mangu Chacha , Lwilla Anange , Olomi Willyhelmina , Minja Lilian Tina , Sabi Issa , Kisonga Riziki , Matechi Emmanuel , Jelly Isaya , Neema Peter , Rwebembera Anath , Aboud Said , Ntinginya Nyanda Elias 
Published: April 2024

AbstractIntroductionTanzania rolled-out a 12-dose, weekly regimen of isoniazid plus rifapentine (3HP) TB preventive treatment in January, 2024. Although 3HP completion rate is generally ≥ 80%, variations exist depending on type of delivery strategy and programmatic setting. Prior to the roll-out, a mixed methods study was conducted to assess whether a family approach involving family member support, SMS reminders and three health education sessions, was acceptable and optimized 3HP uptake and completion. This paper describes acceptability of the family approach among people living with HIV (PLHIV), treatment supporters (TS) and community health workers (CHWs).MethodsThis was a qualitative descriptive study in 12 HIV care and treatment centers across six administrative regions. We purposively sampled 20 PLHIV, 12 CHWs for in-depth interviews and 23 TS for three focus group discussions held between September to December, 2023. The theoretical framework of acceptability guided thematic-content analysis using a framework approach.ResultsParticipants understood that PLHIV have high risk for active TB and that 3HP provides shortened treatment for TB disease prevention. They reported gaining TB and 3HP knowledge from health education sessions. However, participation of TS in health education sessions was low and many reported expensive transportation costs to clinics. Receiving support from someone close and SMS were perceived as good adherence reminders. The majority reported mild self-limiting side effects but expressed positive attitudes because of the shortened treatment, TB counselling, satisfaction from helping others, alignment with lifestyle and work responsibilities and reduced work burden. Some PLHIV reported difficulties in identifying family members for support thus, chose other close friends or CHWs.ConclusionsDelivery of 3HP with support from family members and SMS reminders is widely accepted by CHWs, PLHIVs and TS. Restricting support from only family members was unacceptable and attendance of all three health education sessions by TS may not be feasible.
Adults prenatally exposed to the Dutch Famine exhibit a metabolic signature associated with a broad spectrum of common diseases
Authors: Taeubert M. Jazmin , Kuipers Thomas B. , Zhou Jiayi , Li Chihua , Wang Shuang , Wang Tian , Tobi Elmar W. ,   BBMRI-NL Metabolomics consortium, Belsky Daniel W. , Lumey L. H. , Heijmans Bastiaan T. 
Published: April 2024

AbstractBackgroundExposure to famine in the prenatal period is associated with an increased risk of metabolic disease, including obesity and type-2 diabetes. We employed nuclear magnetic resonance (NMR) metabolomic profiling to provide a deeper insight into the metabolic changes associated with survival of prenatal famine exposure during the Dutch Famine at the end of World War II and explore their link to disease.MethodsNMR metabolomics data were generated from serum in 480 individuals prenatally exposed to famine (mean 58.8 years, 0.5 SD) and 464 controls (mean 57.9 years, 5.4 SD). We tested associations of prenatal famine exposure with levels of 168 individual metabolic biomarkers and compared the metabolic biomarker signature of famine exposure with those of 154 common diseases.ResultsPrenatal famine exposure was associated with higher concentrations of branched-chain amino acids ((iso)-leucine), aromatic amino acid (tyrosine), and glucose in later life (0.2-0.3 SD, p < 3×10-3). The metabolic biomarker signature of prenatal famine exposure was positively correlated to that of incident type-2 diabetes (r = 0.77, p = 3×10-27), also when re-estimating the signature of prenatal famine exposure among individuals without diabetes (r = 0.67, p = 1×10-18). Remarkably, this association extended to 115 common diseases for which signatures were available (0.3 :< r :< 0.9, p < 3.2×10-4). Correlations among metabolic signatures of famine exposure and disease outcomes were attenuated when the famine signature was adjusted for body mass index.ConclusionsPrenatal famine exposure is associated with a metabolic biomarker signature that strongly resembles signatures of a diverse set of diseases, an observation that can in part be attributed to a shared involvement of obesity.
Evaluating biomedical feature fusion on machine learning’s predictability and interpretability of COVID-19 severity types
Authors: West-Page Haleigh , McGoff Kevin , Latimer Harrison , Olufadewa Isaac , Chen Shi 
Published: April 2024

AbstractBackgroundAccurately differentiating severe from non-severe COVID-19 clinical types is critical for the healthcare system to optimize workflow, as severe patients require intensive care. Current techniques lack the ability to accurately predict COVID-19 patients’ clinical type, especially as SARS-CoV-2 continues to mutate.ObjectiveIn this work, we explore both predictability and interpretability of multiple state-of-the-art machine learning (ML) techniques trained and tested under different biomedical data types and COVID-19 variants.MethodsComprehensive patient-level data were collected from 362 patients (214 severe, 148 non-severe) with the original SARS-CoV-2 variant in 2020 and 1000 patients (500 severe, 500 non-severe) with the Omicron variant in 2022-2023. The data included 26 biochemical features from blood testing and 26 clinical features from each patient’s clinical characteristics and medical history. Different types of ML techniques, including penalized logistic regression (LR), random forest (RF),k-nearest neighbors (kNN), and support vector machines (SVM) were applied to build predictive models based on each data modality separately and together for each variant set.ResultsAll ML models performed similarly under different testing scenarios. The fused characteristic modality yielded the highest area under the curve (AUC) score achieving 0.914 on average. The second highest AUC was 0.876 achieved by the biochemical modality alone, followed by 0.825 achieved by clinical modality alone. All ML models were robust when cross-tested with original and Omicron variant patient data. Upon model interpretation, our models ranked elevated d-dimer (biochemical feature), elevated high sensitivity troponin I (biochemical feature), and age greater than 55 years (clinical feature) as the most predictive features of severe COVID-19.ConclusionsWe found ML to be a powerful tool for predicting severe COVID-19 based on comprehensive individual patient-level data. Further, ML models trained on the biochemical and clinical modalities together witness enhanced predictive power. The improved performance of these ML models when trained and cross-tested with Omicron variant data supports the robustness of ML as a tool for clinical decision support.
“It’s not about the money, money” - Well, actually it is. Divergent views on drivers of early phase clinical trial participation among ethnically diverse potential trial participants in the United Kingdom: A Mixed Methods Study
Authors: Hortelano Pilar Artiach , Morton Neil , Wicks Paul , Young Michael , Burdell Rebecca , Richards Duncan 
Published: April 2024

AbstractBackgroundNovel therapeutics should always be tested in a sample representative of the population in need of treatment. Initial efforts of drug development take place in early phase trials (phase-I and -II), setting the direction for late-stage studies (phase-III and -IV). However, study samples in early phase trials typically fail to recruit Black, Asian and minority ethnic groups, which might produce results which don’t generalise to a broader population in later trials, and ultimately, clinical practice. Focusing on early phase clinical trials the present study (1) explored the barriers and incentives that determine participation of ethnic minorities in clinical research, and (2) proposes strategies that mitigate such barriers.MethodsA systematic literature review explored barriers affecting participation rates from individuals from diverse ethnic backgrounds. An exploratory phase involved two online surveys (researchers and general population) and focus groups (general population) analysed using thematic analysis.ResultsThe systematic review found little published evidence, with most studies undertaken in the USA and focused on specific clinical areas. The exploratory phase showed a discordance between researchers’ and general public’s perspectives on both drivers and barriers to early phase trial participation.DiscussionThese findings were synthesised into aClinical Trials Participatory Framework, which contextualises reasons for reduced trial participation, while providing mechanisms/strategies to increase uptake among minority ethnic participants. This may guide researchers when implementing strategies to aid under-representation in their samples. Further research should evaluate the framework by actively implementing, testing, and iterating upon the strategies.