bluuebunny/crossref_metadata_2025_split huggingface dataset:
Resistance to African swine fever virus among African domestic pigs appears to be associated with a distinct polymorphic signature in the RelA gene and upregulation of RelA transcription
Authors: Bisimwa Patrick N. , Ongus Juliette R. , Tonui Ronald , Bisimwa Espoir B. , Steinaa Lucilla 
Published: April 2024

AbstractAfrican swine fever virus (ASFV) is a highly contagious and fatal hemorrhagic disease of domestic pigs, which poses a major threat to the swine industry worldwide. Studies have shown that indigenous African pigs tolerate ASFV infection better than European pigs. The porcine v-rel avian reticuloendotheliosis viral oncogene homolog A (RelA) encoding a p65 kD protein, a major subunit of the NF-kB transcription factor, plays important roles in controlling both innate and adaptive immunity during infection with ASFV. In the present study, RelA genes from ASFV-surviving and symptomatic pigs were sequenced and found to contain polymorphisms revealing two discrete RelA amino acid sequences. One was found in the surviving pigs, and the other in symptomatic pigs. In total, 16 nonsynonymous SNPs (nsSNPs) resulting in codon changes were identified using bioinformatics software (SIFT and Polyphen v2) and web-based tools (MutPre and PredictSNP). Seven nsSNPs (P374-S, T448-S, P462-R, V464-P, Q478-H, L495-E, and P499-Q) were predicted to alter RelA protein function and stability, while 5 of these (P374-S, T448-S, P462-R, L495-E, and Q499-P) were predicted as disease-related SNPs.Additionally, the inflammatory cytokine levels of IFN-α, IL-10, and TNF-α at both the protein and the mRNA transcript levels were measured using ELISA and Real-Time PCR, respectively. The resulting data was used in correlation analysis to assess the association between cytokine levels and the RelA gene expression. Higher levels of IFN-α and detectable levels of IL-10 protein and RelA mRNA were observed in surviving pigs compared to healthy (non-infected). A positive correlation of IFN-α cytokine levels with RelA mRNA expression was also obtained. In conclusion, 7 polymorphic events in the coding region of the RelA gene may contribute to the tolerance of ASFV in pigs.
Spexin ameliorated obesity-related metabolic disorders through promoting white adipose browning mediated by JAK2-STAT3 pathway
Authors: Zeng Bihe , Shen Qin , Wang Bo , Tang Xuan , Jiang Jiaqi , Zheng Yiming , Huang Hongbiao , Zhuo Wenyu , Wang Wang , Gao Yang , Li Xuan , Wang Shuhui , Li Wenjie , Qian Guanghui , Qin Jie , Hou Miao , Lv Haitao 
Published: April 2024

Abstract
Background
Spexin, a 14 amino acid peptide, has been reported to regulate obesity and its associated complications. However, little is known about the underlying molecular mechanism. Therefore, this study aimed to investigate the effects of spexin on obesity and explore the detailed molecular mechanisms in vivo and in vitro.

Methods
Male C57BL/6J mice were fed a high-fat diet (HFD) for 12 weeks to induce obesity, and mice fed a standard fat diet were used as controls. Then, these mice were treated with SPX or Vehicle by intraperitoneal injection for an additional 12 weeks, respectively. The metabolic profile, fat-browning specific markers and mitochondrial contents were detected. In vitro, 3T3-L1 cells were used to investigate the molecular mechanisms.

Results
After 12 weeks of treatment, SPX significantly decreased body weight, serum lipid levels, and improved insulin sensitivity in HFD-induced obese mice. Moreover, SPX was found to promote oxygen consumption in HFD mice, and it increased mitochondrial content as well as the expression of brown-specific markers in white adipose tissue (WAT) of HFD mice. These results were consistent with the increase in mitochondrial content and the expression of brown-specific markers in 3T3-L1 mature adipocytes. Of note, the spexin-mediated beneficial pro-browning actions were abolished by the JAK2/STAT3 pathway antagonists in mature 3T3-L1 cells.

Conclusions
These data indicate that spexin ameliorates obesity-induced metabolic disorders by improving WAT browning via activation of the JAK2/STAT3 signaling pathway. Therefore, SPX may serve as a new therapeutic candidate for treating obesity.
Adaptive immune changes associate with clinical progression of Alzheimer’s disease
Authors: van Olst Lynn , Kamermans Alwin , Halters Sem , van der Pol Susanne M. A. , Rodriguez Ernesto , Verberk Inge M. W. , Verberk Sanne G. S. , Wessels Danielle W. R. , Rodriguez-Mogeda Carla , Verhoeff Jan , Wouters Dorine , Van den Bossche Jan , Garcia-Vallejo Juan J. , Lemstra Afina W. , Witte Maarten E. , van der Flier Wiesje M. , Teunissen Charlotte E. , de Vries Helga E. 
Published: April 2024

Abstract
Background
Alzheimer’s disease (AD) is the most frequent cause of dementia. Recent evidence suggests the involvement of peripheral immune cells in the disease, but the underlying mechanisms remain unclear.

Methods
We comprehensively mapped peripheral immune changes in AD patients with mild cognitive impairment (MCI) or dementia compared to controls, using cytometry by time-of-flight (CyTOF).

Results
We found an adaptive immune signature in AD, and specifically highlight the accumulation of PD1+ CD57+ CD8+ T effector memory cells re-expressing CD45RA in the MCI stage of AD. In addition, several innate and adaptive immune cell subsets correlated to cerebrospinal fluid (CSF) biomarkers of AD neuropathology and measures for cognitive decline. Intriguingly, subsets of memory T and B cells were negatively associated with CSF biomarkers for tau pathology, neurodegeneration and neuroinflammation in AD patients. Lastly, we established the influence of the APOE ε4 allele on peripheral immunity.

Conclusions
Our findings illustrate significant peripheral immune alterations associated with both early and late clinical stages of AD, emphasizing the necessity for further investigation into how these changes influence underlying brain pathology.
Exploring barriers to access to care following the 2021 socio-political changes in Afghanistan: a qualitative study
Authors: Lamberti-Castronuovo Alessandro , Valente Martina , Bocchini Francesca , Trentin Monica , Paschetto Michela , Bahdori Ghulam Ali , Khadem Jan Agha , Nadeem Mirza Sayed , Patmal Mohammad Hanif , Alizai Mohammad Tawoos , Miccio Rossella , Ragazzoni Luca 
Published: April 2024

Abstract
Background
Following the change of government in August 2021, the social and economic landscape of Afghanistan deteriorated into an economic and humanitarian crisis. Afghans continue to struggle to access basic healthcare services, making Universal Health Coverage (UHC) in the country a major challenge. The aim of this study was to perform a qualitative investigation into the main access to care challenges in Afghanistan and whether these challenges have been influenced by the recent socio-political developments, by examining the perspectives of health professionals and hospital directors working in the country.

Methods
Health professionals working in facilities run by an international non-government organisation, which has maintained continuous operations since 1999 and has become a key health reference point for the population, alongside the public health system, and hospital directors working in government hospitals were recruited to participate in an in-depth qualitative study using semi-structured interviews.

Results
A total of 43 participants from ten provinces were interviewed in this study. Four issues were identified as critical barriers to achieving UHC in Afghanistan: (1) the lack of quality human resources; (2) the suboptimal management of chronic diseases and trauma; (3) the inaccessibility of necessary health services due to financial hardship; (4) the unequal accessibility of care for different demographic groups.

Conclusions
Health professionals and hospital directors shed light on weaknesses in the Afghan health system highlighting chronic issues and issues that have deteriorated as a result of the 2021 socio-political changes. In order to improve access to care, future healthcare system reforms should consider the perspectives of Afghan professionals working in the country, who are in close contact with Afghan patients and communities.
Assessment of the measurement properties of the Peabody Developmental Motor Scales-2 by applying the COSMIN methodology
Authors: Zhu Yuanye , Hu Jiahui , Ye Weibing , Korivi Mallikarjuna , Qian Yongdong 
Published: April 2024

AbstractThe Peabody Developmental Motor Scales-2 (PDMS-2) has been used to assess the gross and fine motor skills of children (0–6 years); however, the measurement properties of the PDMS-2 are inconclusive. Here, we aimed to systematically review the measurement properties of PDMS-2, and synthesize the quality of evidence using the Consensus-based Standards for the Selection of Health Measurements Instruments (COSMIN) methodology. Electronic databases, including PubMed, EMBASE, Web of Science, CINAHL and MEDLINE, were searched for relevant studies through January 2023; these studies used PDMS-2. The methodological quality of each study was assessed by the COSMIN risk-of-bias checklist, and the measurement properties of PDMS-2 were evaluated by the COSMIN quality criteria. Modified GRADE was used to evaluate the quality of the evidence. We included a total of 22 articles in the assessment. Among the assessed measurement properties, the content validity of PDMS-2 was found to be sufficient with moderate-quality evidence. The structural validity, internal consistency, test-retest reliability and interrater reliability of the PDMS-2 were sufficient for high-quality evidence, while the intrarater reliability was sufficient for moderate-quality evidence. Sufficient high-quality evidence was also found for the measurement error of PDMS-2. The overall construct validity of the PDMS-2 was sufficient but showed inconsistent quality of evidence. The responsiveness of PDMS-2 appears to be sufficient with low-quality evidence. Our findings demonstrate that the PDMS-2 has sufficient content validity, structural validity, internal consistency, reliability and measurement error with moderate to high-quality evidence. Therefore, PDMS-2 is graded as ‘A’ and can be used in motor development research and clinical settings.
Subcutaneous injection of adipose stromal cell-secretome improves renal function and reduces inflammation in established acute kidney injury
Authors: Ullah Md Mahbub , Collett Jason A. , Monroe Jacob C. , Traktuev Dmitry , Coleman Michael , March Keith L. , Basile David P. 
Published: April 2024

Abstract
Background
Adipose stromal cells (ASC) are a form of mesenchymal stromal cells that elicit effects primarily via secreted factors, which may have advantages for the treatment of injury or disease. Several previous studies have demonstrated a protective role for MSC/ASC on mitigating acute kidney injury but whether ASC derived factors could hasten recovery from established injury has not been evaluated.

Methods
We generated a concentrated secretome (CS) of human ASC under well-defined conditions and evaluated its ability to improve the recovery of renal function in a preclinical model of acute kidney injury (AKI) in rats. 24 h following bilateral ischemia/reperfusion (I/R), rats were randomized following determination of plasma creatinine into groups receiving vehicle -control or ASC-CS treatment by subcutaneous injection (2 mg protein/kg) and monitored for evaluation of renal function, structure and inflammation.

Results
Renal function, assessed by plasma creatinine levels, recovered faster in ASC-CS treated rats vs vehicle. The most prominent difference between the ASC-CS treated vs vehicle was observed in rats with the most severe degree of initial injury (Pcr > 3.0 mg/dl 24 h post I/R), whereas rats with less severe injury (Pcr < 2.9 mg/dl) recovered quickly regardless of treatment. The quicker recovery of ASC-treated rats with severe injury was associated with less tissue damage, inflammation, and lower plasma angiopoietin 2. In vitro, ASC-CS attenuated the activation of the Th17 phenotype in lymphocytes isolated from injured kidneys.

Conclusions
Taken together, these data suggest that ASC-CS represents a potent therapeutic option to improve established AKI.
Construction and validation of machine learning algorithm for predicting depression among home-quarantined individuals during the large-scale COVID-19 outbreak: based on Adaboost model
Authors: Zhou Yiwei , Zhang Zejie , Li Qin , Mao Guangyun , Zhou Zumu 
Published: April 2024

AbstractObjectivesCOVID-19 epidemics often lead to elevated levels of depression. To accurately identify and predict depression levels in home-quarantined individuals during a COVID-19 epidemic, this study constructed a depression prediction model based on multiple machine learning algorithms and validated its effectiveness.MethodsA cross-sectional method was used to examine the depression status of individuals quarantined at home during the epidemic via the network. Characteristics included variables on sociodemographics, COVID-19 and its prevention and control measures, impact on life, work, health and economy after the city was sealed off, and PHQ-9 scale scores. The home-quarantined subjects were randomly divided into training set and validation set according to the ratio of 7:3, and the performance of different machine learning models were compared by 10-fold cross-validation, and the model algorithm with the best performance was selected from 15 models to construct and validate the depression prediction model for home-quarantined subjects. The validity of different models was compared based on accuracy, precision, receiver operating characteristic (ROC) curve, and area under the ROC curve (AUC), and the best model suitable for the data framework of this study was identified.ResultsThe prevalence of depression among home-quarantined individuals during the epidemic was 31.66% (202/638), and the constructed Adaboost depression prediction model had an ACC of 0.7917, an accuracy of 0.7180, and an AUC of 0.7803, which was better than the other 15 models on the combination of various performance measures. In the validation sets, the AUC was greater than 0.83.ConclusionsThe Adaboost machine learning algorithm developed in this study can be used to construct a depression prediction model for home-quarantined individuals that has better machine learning performance, as well as high effectiveness, robustness, and generalizability.
Immune cell trafficking: a novel perspective on the gut-skin axis
Authors: Zhang Jiayan , Yao Zhirong 
Published: April 2024

AbstractImmune cell trafficking, an essential mechanism for maintaining immunological homeostasis and mounting effective responses to infections, operates under a stringent regulatory framework. Recent advances have shed light on the perturbation of cell migration patterns, highlighting how such disturbances can propagate inflammatory diseases from their origin to distal organs. This review collates and discusses current evidence that demonstrates atypical communication between the gut and skin, which are conventionally viewed as distinct immunological spheres, in the milieu of inflammation. We focus on the aberrant, reciprocal translocation of immune cells along the gut-skin axis as a pivotal factor linking intestinal and dermatological inflammatory conditions. Recognizing that the translation of these findings into clinical practices is nascent, we suggest that therapeutic strategies aimed at modulating the axis may offer substantial benefits in mitigating the widespread impact of inflammatory diseases.
Self-operation and low-carbon scheduling optimization of solar thermal power plants with thermal storage systems
Authors: Sun Jing 
Published: April 2024

AbstractPhoto thermal power generation, as a renewable energy technology, has broad development prospects. However, the operation and scheduling of photo thermal power plants rarely consider their internal structure and energy flow characteristics. Therefore, this study explains the structure of a solar thermal power plant with a thermal storage system and analyzes its main energy flow modes to establish a self-operation and low-carbon scheduling optimization model for the solar thermal power plant. The simulation results of the example showed that for the self-operating model oriented towards power generation planning and peak valley electricity prices, the existence of a thermal storage system could improve the power generation capacity and revenue of the photovoltaic power plant. For example, when the capacity of the thermal storage system was greater than 6 h, the penalty for insufficient power generation in the simulation result was 0 $, and the maximum increase in revenue reached 84.9% as the capacity of the thermal storage system increased. In addition, when the capacity of the thermal storage system increased from 0 to 8 h, the comprehensive operating cost decreased from 1635.2 k $ to 1224.6 k $, and the carbon emissions decreased from 26.4 × 103 ton to 22.1 × 103 ton. Compared with the existing literature, this study provides a more comprehensive and systematic solution through detailed energy flow analysis and optimization model. The research has practical and far-reaching significance for promoting the development of clean energy technology, improving the sustainable utilization of renewable energy, and optimizing the overall performance of the energy system.
Principles of fire ecology
Authors: Kobziar Leda N. , Hiers J. Kevin , Belcher Claire M. , Bond William J. , Enquist Carolyn A. , Loudermilk E. L. , Miesel Jessica R. , O’Brien Joseph J. , Pausas Juli G. , Hood Sharon , Keane Robert , Morgan Penelope , Pingree Melissa R. A. , Riley Karin , Safford Hugh , Seijo Francisco , Varner J. Morgan , Wall Tamara , Watts Adam C. 
Published: April 2024

AbstractFire ecology is a complex discipline that can only be understood by integrating biological, physical, and social sciences. The science of fire ecology explores wildland fire’s mechanisms and effects across all scales of time and space. However, the lack of defined, organizing concepts in fire ecology dilutes its collective impact on knowledge and management decision-making and makes the discipline vulnerable to misunderstanding and misappropriation. Fire ecology has matured as a discipline and deserves an enunciation of its unique emergent principles of organization. Most scientific disciplines have established theories, laws, and principles that have been tested, debated, and adopted by the discipline’s practitioners. Such principles reflect the consensus of current knowledge, guide methodology and interpretation, and expose knowledge gaps in a coherent and structured way. In this manuscript, we introduce five comprehensive principles to define the knowledge fire ecology has produced and provide a framework to support the continued development and impact of the fire ecology discipline.
Uncertainty assessment in unsupervised machine-learning methods for deepwater channel seismic facies using outcrop-derived 3D models and synthetic seismic data
Authors: La Marca Karelia , Bedle Heather , Stright Lisa , Marfurt Kurt 
Published: April 2024

Unsupervised machine-learning (ML) techniques have been widely applied to analyze seismic reflection data, including the identification of seismic facies and structural features. However, interpreting the resulting clusters often relies on geoscientists’ expertise, necessitating a robustness assessment of these methods. To evaluate their reliability, synthetic data generated from an actual outcrop model are used to demonstrate how two unsupervised methods, self-organizing maps (SOMs) and generative topographic maps (GTMs), cluster deepwater channel-related seismic facies and then measure the associated error. Six seismic attributes, comprising root-mean-square amplitude, instantaneous envelope, peak magnitude, and spectral decomposition frequencies at 20, 40, and 55 Hz, served as input variables. Geobodies are assigned to each cluster formed, and the error in facies clustering is quantified by comparing the actual 3D model with the facies grouped by ML methods on a voxel-by-voxel basis. This allowed for error quantification and the computation of metrics such as F1 score and accuracy through correlation matrices. Key findings reveal that (1) GTM and SOM exhibit similar performance, with a clustering configuration of 81 for GTM slightly outperforming others. (2) Error rates are approximately 2% for the predominant facies (background shale) but significantly higher for individual channel-related facies, suggesting that channel clusters might represent multiple facies. (3) Resolution and imbalanced data distribution impact seismic facies predictability, resulting in nonuniqueness in cluster generation. (4) Using synthetic seismic data proved valuable for experimenting with different unsupervised MLs, highlighting the need for assessing uncertainty in these methods, given their implications for crucial economic decisions reliant on reservoir interpretation, modeling, and volumetric estimations.
NECTIN4 Amplification Is Frequent in Solid Tumors and Predicts Enfortumab Vedotin Response in Metastatic Urothelial Cancer
Authors: Klümper Niklas , Tran Ngoc Khanh , Zschäbitz Stefanie , Hahn Oliver , Büttner Thomas , Roghmann Florian , Bolenz Christian , Zengerling Friedemann , Schwab Constantin , Nagy Dora , Toma Marieta , Kristiansen Glen , Heers Hendrik , Ivanyi Philipp , Niegisch Günter , Grunewald Camilla Marisa , Darr Christopher , Farid Arian , Schlack Katrin , Abbas Mahmoud , Aydogdu Can , Casuscelli Jozefina , Mokry Theresa , Mayr Michael , Niedersüß-Beke Dora , Rausch Steffen , Dietrich Dimo , Saal Jonas , Ellinger Jörg , Ritter Manuel , Alajati Abdullah , Kuppe Christoph , Meeks Joshua , Vera Badillo Francisco E. , Nakauma-González J. Alberto , Boormans Joost , Junker Kerstin , Hartmann Arndt , Grünwald Viktor , Hölzel Michael , Eckstein Markus 
Published: April 2024

PURPOSE The anti-NECTIN4 antibody-drug conjugate enfortumab vedotin (EV) is approved for patients with metastatic urothelial cancer (mUC). However, durable benefit is only achieved in a small, yet uncharacterized patient subset. NECTIN4 is located on chromosome 1q23.3, and 1q23.3 gains represent frequent copy number variations (CNVs) in urothelial cancer. Here, we aimed to evaluate NECTIN4 amplifications as a genomic biomarker to predict EV response in patients with mUC. MATERIALS AND METHODS We established a NECTIN4-specific fluorescence in situ hybridization (FISH) assay to assess the predictive value of NECTIN4 CNVs in a multicenter EV-treated mUC patient cohort (mUC-EV, n = 108). CNVs were correlated with membranous NECTIN4 protein expression, EV treatment responses, and outcomes. We also assessed the prognostic value of NECTIN4 CNVs measured in metastatic biopsies of non–EV-treated mUC (mUC-non-EV, n = 103). Furthermore, we queried The Cancer Genome Atlas (TCGA) data sets (10,712 patients across 32 cancer types) for NECTIN4 CNVs. RESULTS NECTIN4 amplifications are frequent genomic events in muscle-invasive bladder cancer (TCGA bladder cancer data set: approximately 17%) and mUC (approximately 26% in our mUC cohorts). In mUC-EV, NECTIN4 amplification represents a stable genomic alteration during metastatic progression and associates with enhanced membranous NECTIN4 protein expression. Ninety-six percent (27 of 28) of patients with NECTIN4 amplifications demonstrated objective responses to EV compared with 32% (24 of 74) in the nonamplified subgroup ( P < .001). In multivariable Cox analysis adjusted for age, sex, and Bellmunt risk factors, NECTIN4 amplifications led to a 92% risk reduction for death (hazard ratio, 0.08 [95% CI, 0.02 to 0.34]; P < .001). In the mUC-non-EV, NECTIN4 amplifications were not associated with outcomes. TCGA Pan-Cancer analysis demonstrated that NECTIN4 amplifications occur frequently in other cancers, for example, in 5%-10% of breast and lung cancers. CONCLUSION NECTIN4 amplifications are genomic predictors of EV responses and long-term survival in patients with mUC.