bluuebunny/crossref_metadata_2025_split huggingface dataset:
A new and simple score to predict adequate and deep response to ursodeoxycholic acid in patients with primary biliary cholangitis: the ALP-A score
Authors: Cançado Guilherme Grossi Lopes , Gomes Nathalia Mota de Faria , Couto Cláudia Alves , Cançado Eduardo Luiz Rachid , Terrabuio Debora Raquel Benedita , Villela-Nogueira Cristiane Alves , Braga Michelle Harriz , Nardelli Mateus Jorge , Faria Luciana Costa , Oliveira Elze Maria Gomes , Rotman Vivian , Oliveira Maria Beatriz , Cunha Simone Muniz Carvalho Fernandes da , Mazo Daniel Ferraz de Campos , Mendes Liliana Sampaio Costa , Ivantes Claudia Alexandra Pontes , Codes Liana , Borges Valéria Ferreira de Almeida e , Pace Fabio Heleno de Lima , Pessôa Mário Guimarães , Signorelli Izabelle Venturini , Coral Gabriela Perdomo , Bittencourt Paulo Lisboa , Fucuta Patrícia , Filho Roberto José de Carvalho , Ferraz Maria Lucia Gomes 
Published: March 2024

Background
Ursodeoxycholic acid (UDCA) is the standard treatment for primary biliary cholangitis (PBC), but a significant proportion of patients do not respond adequately, leading to increased risk of adverse outcomes. This study aims to develop a new and straightforward predictive score to identify PBC patients likely to achieve a complete response to UDCA.

Methods
A logistic regression analysis was conducted using a derivation cohort of PBC patients to identify pre-treatment variables associated with response to UDCA. This analysis led to the development of the ALP-A score, calculated as: Age at diagnosis divided by (alkaline phosphatase at diagnosis/upper limit of normal). ALP-A score accuracy was evaluated using the area under the ROC curve, validated with a large external cohort from Brazil. Additionally, the correlation between the ALP-A score and the previously validated UDCA response score (URS) was assessed.

Results
ALP-A score had good predictive power for adequate (AUC 0.794; 95% CI, 0.737–0.852) and deep (0.76; 95% CI, 0.69–0.83) UDCA response at 1 year of treatment. A cutoff score of 17 and 23 points was determined to be the optimal threshold for distinguishing adequate and deep responders, respectively, from non-responders. ALP-A score demonstrated a sensitivity of 73%, specificity of 71%, positive predictive value of 65%, negative predictive value of 78%, and overall accuracy of 72% for biochemical response. The URS displayed similar discriminative ability (AUC 0.798; 95% CI, 0.741–0.855).

Conclusion
ALP-A score performs comparably to URS but offers the great advantage of simplicity for routine clinical use. It serves as a valuable tool to identify PBC patients less likely to respond to UDCA treatment, facilitating early consideration of alternative therapeutic approaches.
Replay of Interictal Sequential Activity Shapes the Epileptic Network Dynamics
Authors: Wang Kang , Wang Haixiang , Yan Yuxiang , Li Wenzheng , Cai Fang , Zhou Wenjing , Hong Bo 
Published: March 2024

AbstractBoth the imbalance of neuronal excitation and inhibition, and the network disorganization may lead to hyperactivity in epilepsy. However, the insufficiency of seizure data poses the challenge of elucidating the network mechanisms behind the frequent and recurrent abnormal discharges. Our study of two extensive intracranial EEG datasets revealed that the seizure onset zone exhibits recurrent synchronous activation of interictal events. These synchronized discharges formed repetitive sequential patterns, indicative of a stable and intricate network structure within the seizure onset zone (SOZ). We hypothesized that the frequent replay of interictal sequential activity shapes the structure of the epileptic network, which in turn supports the occurrence of these discharges. The Hopfield-Kuramoto oscillator network model was employed to characterize the formation and evolution of the epileptic network, encoding the interictal sequential patterns into the network structure using the Hebbian rule. This model successfully replicated patient-specific interictal sequential activity. Dynamic change of the network connections was further introduced to build an adaptive Kuramoto model to simulate the interictal to ictal transition. The Kuramoto oscillator network with adaptive connections (KONWAC) model we proposed essentially combines two scales of Hebbian plasticity, shaping both the stereotyped propagation and the ictal transition in epileptic networks through the interplay of regularity and uncertainty in interictal discharges.
Effectiveness of an Impella versus intra-aortic balloon pump in patients who received extracorporeal membrane oxygenation
Authors: Nishimoto Yuji , Ohbe Hiroyuki , Nakata Jun , Takiguchi Toru , Nakajima Mikio , Sasabuchi Yusuke , Isogai Toshiaki , Matsui Hiroki , Sato Yukihito , Watanabe Tetsuya , Yamada Takahisa , Fukunami Masatake , Yasunaga Hideo 
Published: March 2024

AbstractBackgroundPrevious studies have suggested that left ventricular (LV) unloading with an intra-aortic balloon pump (IABP) or percutaneous ventricular assist device (Impella) in combination with extracorporeal membrane oxygenation (ECMO) is associated with lower mortality; however, it is unclear which is better. This study aimed to evaluate the effectiveness of LV unloading with an Impella versus IABP on in-hospital mortality and other clinical outcomes.MethodsUsing the Japanese Diagnosis Procedure Combination database from September 28, 2016, to March 31, 2022, we identified inpatients who received an Impella or IABP in combination with ECMO (ECPella or ECMO+IABP group, respectively). The primary outcome was in-hospital mortality and the secondary outcomes were the length of hospital stay, length of ECMO, total hospitalization cost, and complications. Propensity score matching was performed to compare the outcomes between the groups.ResultsOf 14,525 eligible patients, 603 (4.2%) received ECPella and 13,922 (96%) received ECMO+IABP. After propensity score matching, there was no significant difference in in-hospital mortality between the two groups (58.9% versus 56.6%; risk difference, 2.3%; 95% confidence interval, −3.9% to 8.5%). The ECPella group had a longer hospital stay, higher total hospitalization cost, and more frequent major bleeding, vascular complications, and renal replacement therapy during hospitalization than the ECMO+IABP group.ConclusionsThis nationwide inpatient database study showed that ECPella was not associated with a survival benefit but was associated with a longer hospital stay, higher total hospitalization cost, and more complications than ECMO+IABP.
Proteome-wide association studies using summary pQTL data of three tissues identified 30 risk genes of Alzheimer’s disease dementia
Authors: Hu Tingyang , Liu Qiang , Dai Qile , Parrish Randy L. , Buchman Aron S. , Tasaki Shinya , Seyfried Nicholas T. , Wang Yanling , Bennett David A. , De Jager Philip L. , Epstein Michael P. , Yang Jingjing 
Published: March 2024

AbstractBackgroundProteome-wide association study (PWAS) integrating proteomic data with genome-wide association study (GWAS) summary data is a powerful tool for studying Alzheimer’s disease (AD) dementia. Existing PWAS analyses of AD often rely on the availability of individual-level proteomic and genetic data of a reference panel. Leveraging summary protein quantitative trait loci (pQTL) reference data of multiple AD-relevant tissues is expected to improve PWAS findings of AD dementia.MethodsWe conducted PWAS of AD dementia by integrating publicly available summary pQTL data of brain, cerebrospinal fluid (CSF), and plasma tissues, with the latest GWAS summary data of AD dementia. For each target protein per tissue, we employed our recently published OTTERS tool to obtain omnibus PWAS p-value, to test whether the genetically regulated protein abundance in the corresponding tissue is associated with AD dementia. Protein-protein interactions and enriched pathways of identified significant PWAS risk genes were analyzed by STRING. The potential causal effects of these PWAS risk genes were assessed by probabilistic Mendelian randomization analyses.ResultsWe identified 30 unique significant PWAS risk genes for AD dementia, including 11 for brain, 9 for CSF, and 16 for plasma tissues. Four of these were shared by at least two tissues, and geneMAPK3was found in all three tissues. We found that 11 of these PWAS risk genes were associated with AD or AD pathological hall marks as shown in GWAS Catalog; 18 of these were detected by transcriptome-wide association studies (TWAS); and 25 of these, including 8 out of 9 novel genes, were interconnected within a protein-protein interaction network involving the well-known AD risk geneAPOE. Especially, these PWAS risk genes were enriched in immune response, glial cell proliferation, and high-density lipoprotein particle clearance pathways. Mediated causal effects were validated for 13 PWAS risk genes (43.3%).ConclusionsOur findings provide novel insights into the genetic mechanisms of AD dementia in brain, CSF, and plasma tissues, and targets for developing therapeutic interventions. We also demonstrated the effectiveness of integrating summary pQTL and GWAS data for mapping risk genes of complex human diseases.
Prediction of Severe Obstructive Coronary Artery Disease Using Radiomic Features from Resting Cardiac Ultrasound Images: A Pilot Study
Authors: Aziz Daniel , Jamthikar Ankush D. , Bhattaru Abhijit , Desingu Karthik , Rajiv Nivedita , Maganti Kameswari , Hamirani Yasmin , Bokhari Sabahat , Yanamala Naveena , Sengupta Partho P. 
Published: March 2024

AbstractBackgroundAlthough cardiac ultrasound is frequently performed in patients with chest pain, the probability of obstructive coronary artery disease (CAD) cannot be quantified. We investigated the potential of cardiac ultrasound radiomics (ultrasomics) to identify obstructive CAD using limited echocardiography frames, suitable for cardiac point-of-care ultrasound evaluation.MethodsIn total, 333 patients who were either healthy controls (n=30), undergoing invasive coronary procedures (n=113), or coronary CT angiography (n=190) were divided into two temporally distinct training (n=271) and testing (n=62) cohorts. Machine learning models were developed using ultrasomics for predicting severe CAD (stenosis >70%) and compared with regional LV wall motion abnormalities (RWMA).ResultsIn total, 94 (28.2%) patients had severe CAD with 50 (15.0%) having high-risk CAD defined as left main stenosis >50% (n=11), multivessel CAD (n=43), or 100% occlusion (n=20). The ultrasomics model was superior to RWMA for predicting severe CAD [area under the receiver operating curve (AUC) of 0.80 (95% confidence interval [CI]: 0.74 to 0.86) vs. 0.67 (95% CI: 0.61-0.72), p=0.0014] in the training set and [0.77 (95% CI: 0.64-0.90) vs. 0.70 (95% CI: 0.56-0.81), p=0.24] in the test set, respectively. The model also predicted high-risk CAD with an AUC of 0.84 (95% CI: 0.77-0.90) in the training set and 0.70 (95% CI: 0.48-0.88) in the test set. A combination of ultrasomics with RWMA showed incremental value over RWMA alone for predicting severe CAD.ConclusionsCardiac ultrasomic features extracted from limited echocardiography views can aid the development of machine learning models to predict the presence of severe obstructive CAD.
The metabolic cofactor Coenzyme A enhances alternative macrophage activation via MyD88-linked signaling
Authors: Jones Anthony E. , Rios Amy , Ibrahimovic Neira , Chavez Carolina , Bayley Nicholas A. , Ball Andréa B. , Hsieh Wei Yuan , Sammarco Alessandro , Bianchi Amber R. , Cortez Angel A. , Graeber Thomas G. , Hoffmann Alexander , Bensinger Steven J. , Divakaruni Ajit S. 
Published: March 2024

ABSTRACTMetabolites and metabolic co-factors can shape the innate immune response, though the pathways by which these molecules adjust inflammation remain incompletely understood. Here we show that the metabolic cofactor Coenzyme A (CoA) enhances IL-4 driven alternative macrophage activation [m(IL-4)]in vitroandin vivo. Unexpectedly, we found that perturbations in intracellular CoA metabolism did not influence m(IL-4) differentiation. Rather, we discovered that exogenous CoA provides a weak TLR4 signal which primes macrophages for increased receptivity to IL-4 signals and resolution of inflammation via MyD88. Mechanistic studies revealed MyD88-linked signals prime for IL-4 responsiveness, in part, by reshaping chromatin accessibility to enhance transcription of IL-4-linked genes. The results identify CoA as a host metabolic co-factor that influences macrophage function through an extrinsic TLR4-dependent mechanism, and suggests that damage-associated molecular patterns (DAMPs) can prime macrophages for alternative activation and resolution of inflammation.
Unveiling Frequency-Specific Microstate Correlates of Anxiety and Depression Symptoms
Authors: Xue Siyang , Shen Xinke , Zhang Dan , Sang Zhenhua , Long Qiting , Song Sen , Wu Jian 
Published: March 2024

AbstractElectroencephalography (EEG) microstates are canonical voltage topographies that reflect the temporal dynamics of resting-state brain networks on a millisecond time scale. Changes in microstate parameters have been described in patients with psychiatric disorders, indicating their potential as clinical biomarkers with broadband EEG signals (e.g., 1–30 Hz). Considering the distinct information provided by specific frequency bands, we hypothesized that microstates in decomposed frequency band could provide a more detailed depiction of the underlying psychological mechanism. In this study, with a large open access resting-state dataset (n = 203), we examined the properties of frequency-specific microstates and their relationship with emotional disorders. We conducted clustering on EEG topographies in decomposed frequency band (delta, theta, alpha and beta), and determined the number of clusters with a meta-criterion. Microstate parameters, including global explained variance (GEV), duration, coverage, occurrence and transition probability, were calculated for eyes-open and eyes-closed states, respectively. Their predictive power for the scores of depression and anxiety symptoms were identified by correlation and regression analysis. Distinct microstate patterns were observed across different frequency bands. Microstate parameters in the alpha band held the best predictive power for emotional symptoms. Microstates B (GEV, coverage) and parieto-central maximum microstate C’ (coverage, occurrence, transitions from B to C’) in the alpha band exhibited significant correlations with depression and anxiety, respectively. Microstate parameters of the alpha band achieved predictive R-square of 0.100 for anxiety scores, which is much higher than those of broadband (R-square = -0.026, p < .01). These results suggested the value of frequency-specific microstates in predicting emotional symptoms.
TraitProtNet: Deciphering the Genome for Trait Prediction with Interpretable Deep Learning
Authors: Wang Sijun 
Published: March 2024

AbstractGenome data is far from fully explored. We present TraitProtNet, an innovative deep learning framework for predictive trait profiling in fungi, leveraging genome data and pretrained language models. The use of Integrated Gradients and bioinformatic analysis provides insights into the model’s interpretability, complementing traditional omics by highlighting the difference between protein importance and expression levels. This framework offers significant potential for future applications in both agriculture and medicine.
Human stem cell transplantation for Parkinson’s disease: A systematic review ofin situsurvival and maturation of progenitors derived from human embryonic or induced stem cells in Parkinsonian models
Authors: Comini Giulia , Dowd Eilís 
Published: March 2024

AbstractStem cell-based brain repair is a promising emergent therapy for Parkinson’s which is based on years of foundational research using human fetal donors as a cell source. Unlike current therapeutic options for patients, this approach has the potential to provide long-term stem cell-derived reconstruction and restoration of the dopaminergic input to denervated regions of the brain allowing for restoration of certain functions to patients. The ultimate clinical success of stem cell-derived brain repair will depend on both the safety and efficacy of the approach, and the latter is dependent on the ability of the transplanted cells to survive and differentiate into functional dopaminergic neurons in the Parkinsonian brain. Because the pre-clinical literature suggests that there is a considerable variability in survival and differentiation between studies, the aim of this systematic review was to assess these parameters in human stem-derived dopaminergic progenitor transplant studies in animal models of Parkinson’s. To do so, a defined systematic search of the PubMed database was completed to identify relevant studies published up to March 2024. After screening, 76 articles were included in the analysis from which 178 separate transplant studies were identified. From these, graft survival could be assessed in 52 studies and differentiation in 129 studies. Overall, we found that graft survival ranged from <1% to 500% of cells transplanted, with a median of 51% of transplanted cells surviving in the brain; while dopaminergic differentiation of the cells ranged from 0% to 46% of cells transplanted with a median of 3%. This systematic review suggests that there is considerable scope for improvement in the differentiation of stem cell-derived dopaminergic progenitors in order to maximize the therapeutic potential of this approach for patients.
CDHu40: a novel marker gene set of neuroendocrine prostate cancer (NEPC)
Authors: Liu Sheng , Nam Hye Seung , Zeng Ziyu , Deng Xuehong , Pashaei Elnaz , Zang Yong , Yang Lei , Li Chenglong , Huang Jiaoti , Wendt Michael K , Lu Xin , Huang Rong , Wan Jun 
Published: March 2024

AbstractProstate cancer (PCa) is the most prevalent cancer affecting American men. Castration-resistant prostate cancer (CRPC) can emerge during hormone therapy for PCa, manifesting with elevated serum prostate-specific antigen (PSA) levels, continued disease progression, and/or metastasis to the new sites, resulting in a poor prognosis. A subset of CRPC patients shows a neuroendocrine (NE) phenotype, signifying reduced or no reliance on androgen receptor (AR) signaling and a particularly unfavorable prognosis. In this study, we incorporated computational approaches based on both gene expression profiles and protein-protein interaction (PPI) networks. We identified 500 potential marker genes, which are significantly enriched in cell cycle and neuronal processes. The top 40 candidates, collectively named as CDHu40, demonstrated superior performance in distinguishing NE prostate cancer (NEPC) and non-NEPC samples based on gene expression profiles compared to other published marker sets. Notably, some novel marker genes in CDHu40, absent in the other marker sets, have been reported to be associated with NEPC in the literature, such as DDC, FOLH1, BEX1, MAST1, and CACNA1A. Importantly, elevated CDHu40 scores derived from our predictive model showed a robust correlation with unfavorable survival outcomes in patients, indicating the potential of the CDHu40 score as a promising indicator for predicting the survival prognosis of those patients with the NE phenotype. Motif enrichment analysis on the top candidates suggests that REST and E2F6 may serve as key regulators in the NEPC progression.Significanceour study integrates gene expression variances in multiple NEPC studies and protein-protein interaction network to pinpoint a specific set of NEPC maker genes namely CDHu40. These genes and scores based on their gene expression levels effectively distinguish NEPC samples and underscore the clinical prognostic significance and potential mechanism.
The Colorado potato beetle gene expression atlas
Authors: Wilhelm Léonore , Wang Yangzi , Xu Shuqing 
Published: March 2024

AbstractThe Colorado potato beetle (CPB) is a major pest of potato crops that has evolved resistance to more than 50 pesticides. For decades, CPB has been a model species for research on insecticide resistance, insect physiology, diapause, reproduction and evolution. Yet, the research progress in CPB is constrained by the lack of comprehensive genomic and transcriptomic information. Here, building on the recently established chromosome-level genome assembly, we built a gene expression atlas of the CPB using the transcriptomes of 61 samples representing major organs and developmental stages. By using both short and long reads, we improved the genome annotation and identified 6,658 more genes that were missed in previous annotations. We then established a web portal allowing the search and visualization of the gene expression for the research community. The CPB atlas provides useful tools and comprehensive gene expression data, which will accelerate future research in both pest control and insect biology fields.
Cis-vaccenic acid is a key product of stearoyl-CoA desaturase 1 and a critical oncogenic factor in prostate cancer
Authors: Scott Julia S , Young Reuben SE , Quek Lake-Ee , Miller Deanna C , Evergren Emma , Dehairs Jonas , Johnson Ian RD , Brooks Doug A , Loda Massimo , Hoy Andrew J , Blanksby Stephen J , Swinnen Johannes V , Nassar Zeyad D , Butler Lisa M 
Published: March 2024

SummaryAltered monounsaturated fatty acid (MUFA) metabolism is a hallmark of oncogenic transformation. Recently, the MUFAcis-vaccenic acid (cVA) was identified as a putative regulator of prostate cancer cell viability. cVA production requires the activity of stearoyl CoA desaturase 1 (SCD1), an enzyme frequently dysregulated in cancer, but the role of cVA in regulating cancer cell phenotypes has not been extensively explored, compared with the more commonly studied product and structural isomer of cVA, oleic acid. Here, we utilised SCD1 inhibition to study the effects of cVA in prostate cancer cells. We found that cVA consistently rescues reductions in cell viability due to SCD1 inhibition and promotes cell growth under normal conditions, thereby identifying cVA as an important and previously unrecognised product of SCD1 in prostate cancer. More broadly, we demonstrate that individual MUFA species exert a diverse range of influence on oncogenic phenotypes, highlighting a need to more precisely characterise the lipidome of cancer cells to understand the molecular pathology of the disease.
Positional information modulates transient regeneration-activated cell states during vertebrate appendage regeneration
Authors: Granillo Augusto Ortega , Zamora Daniel , Schnittker Robert R. , Scott Allison R. , Russell Jonathon , Brewster Carolyn E. , Ross Eric J. , Acheampong Daniel A. , Ferro Kevin , Morrison Jason A. , Rubinstein Boris Y. , Perera Anoja G. , Wang Wei , Alvarado Alejandro Sánchez 
Published: March 2024

SummaryInjury is a common occurrence in the life of organisms. Because the extent of damage cannot be predicted, injured organisms must determine how much tissue needs to be restored. It is known that amputation position determines the regeneration speed of amputated appendages in regeneration-competent animals. Yet, it is not clear how positional information is conveyed during regeneration. Here, we investigated tissue dynamics in regenerating caudal fins in the African killifish (Nothobranchius furzeri). We report position-specific, differential modulation of the spatial distribution, duration, and magnitude of proliferation. Regenerating fins profiled by single cell RNA sequencing identified a Transient Regeneration-Activated Cell State (TRACS) that is amplified to match a given amputation position. We located this TRACS to the basal epidermis and found them to express components and modifiers of the extracellular matrix (ECM). We propose a role for these cells in transducing positional information to the regenerating blastema by remodeling the ECM.HighlightsAmputation position changes tissue-wide proliferation responseTranscriptional compartmentalization is relative to injury typeRegeneration deploys Transient Regeneration-Activated Cell StatesPrediction: positional information is transduced by ECM changes during regeneration