bluuebunny/crossref_metadata_2025_split huggingface dataset:
The suitability and acceptability of a co-designed prototype psychoeducational activity book for autistic children aged five-eleven years
Authors: Powell Lauren , Wheeler Gemma , Redford Chris , Stott Jonathan 
Published: February 2024

Background and aims Evidence suggests that autistic children and young people (CAYP) can benefit from age-appropriate psychoeducation. Co-design is a methodology that iteratively involves end users and stakeholders in producing an intervention which may increase engagement and impact. Few age-appropriate co-designed psychoeducation resources for autistic CAYP exist. Therefore, a paper-based resource was co-designed for autistic CAYP who attend mainstream primary education. The resource aims to educate CAYP about their autism and provide strategies to support them to live well with their autism. Methods This paper describes the evaluation of the prototype resource through online workshops with 12 families and input from four specialist clinicians. The suitability and acceptability of the resource was explored, and sketch notes were taken for respondent validity and engagement purposes. Results A reflexive thematic analysis identified six themes and two subthemes: (1) content appropriateness (subtheme: strategies and unpredictability); (2) relating to content, (3) feelings and emotions, (4) terminology (subtheme: literal thinking); (5) positivity, and (6) communication aid. Suggested improvements were also identified. Results suggest the resource is suitable and acceptable. Conclusions Future intervention development research may consider individual differences of autistic CAYP and the co-design of resources for other age groups of neurodiverse populations.
USF1 transcriptionally activates USP14 to drive atherosclerosis by promoting EndMT through NLRC5/Smad2/3 axis
Authors: Zhang Zhiwen , Guo Quan , Ma Chao , Zhao Zhenzhou , Shi Qingbo , Yu Haosen , Rao Lixin , Li Muwei 
Published: February 2024

Abstract
Background
Endothelial-to-Mesenchymal Transformation (EndMT) plays key roles in endothelial dysfunction during the pathological progression of atherosclerosis; however, its detailed mechanism remains unclear. Herein, we explored the biological function and mechanisms of upstream stimulating factor 1 (USF1) in EndMT during atherosclerosis.

Methods
The in vivo and in vitro atherosclerotic models were established in high fat diet-fed ApoE−/− mice and ox-LDL-exposed human umbilical vein endothelial cells (HUVECs). The plaque formation, collagen and lipid deposition, and morphological changes in the aortic tissues were evaluated by hematoxylin and eosin (HE), Masson, Oil red O and Verhoeff-Van Gieson (EVG) staining, respectively. EndMT was determined by expression levels of EndMT-related proteins. Target molecule expression was detected by RT-qPCR and Western blotting. The release of pro-inflammatory cytokines was measured by ELISA. Migration of HUVECs was detected by transwell and scratch assays. Molecular mechanism was investigated by dual-luciferase reporter assay, ChIP, and Co-IP assays.

Results
USF1 was up-regulated in atherosclerosis patients. USF1 knockdown inhibited EndMT by up-regulating CD31 and VE-Cadherin, while down-regulating α-SMA and vimentin, thereby repressing inflammation, and migration in ox-LDL-exposed HUVECs. In addition, USF1 transcriptionally activated ubiquitin-specific protease 14 (USP14), which promoted de-ubiquitination and up-regulation of NLR Family CARD Domain Containing 5 (NLRC5) and subsequent Smad2/3 pathway activation. The inhibitory effect of sh-USF1 or sh-USP14 on EndMT was partly reversed by USP14 or NLRC5 overexpression. Finally, USF1 knockdown delayed atherosclerosis progression via inhibiting EndMT in mice.

Conclusion
Our findings indicate the contribution of the USF1/USP14/NLRC5 axis to atherosclerosis development via promoting EndMT, which provide effective therapeutic targets.
Bio-functionalized carbon dots for signaling immuno-reaction of carcinoembryonic antigen in an electrochemical biosensor for cancer biomarker detection
Authors: Chellachamy Anbalagan Amarnath , Korram Jyoti , Doble Mukesh , Sawant Shilpa N. 
Published: February 2024

AbstractEarly diagnosis of cancer demands sensitive and accurate detection of cancer biomarkers in blood. Carbon dots (CDs) bio-functionalization with antibodies, peptides or aptamers have played significant role in cancer diagnosis and targeted cancer therapy. Herein, a biosensor for detection of cancer biomarker carcinoembryonic antigen (CEA) in blood serum has been designed using CDs bio-functionalized with HRP-conjugated CEA antibody (CUCDs@CEAAb2) as detection probe. CDs were synthesized by upscaling of cow urine, a nitrogen rich biomass waste, by hydrothermal method. Detection probe based on CDs resulted in 3.5 times higher sensitivity as compared to conventional electrochemical sandwich immunoassay. To further improve the sensor performance, hyper-branched polyethylenimine grafted poly amino aniline (PEI-g-PAANI) was used as the sensing interface, which enabled immobilization of higher amount of capture antibody. Detection of CEA in human blood serum coupled with wide linear range (0.5–50 ng/ml), good specificity, stability, reproducibility and low detection limit (10 pg/ml) signified the excellence of CUCDs based CEA immunosensor. CUCDs exhibited excitation wavelength dependent fluorescence property and showed strong blue emission under UV irradiation. MTT assay indicated that the material is not toxic towards human dental pulp stem cells (hDPSCs) and MG63 osteosarcoma cells (cell viability > 90%). The present study demonstrates a methodology for valorization of animal waste to a cost-effective carbon based functional nanomaterial for clinical detection of cancer biomarkers.
COVID-19 shock, fiscal subsidies, and consumption resilience of new energy vehicles: evidence from Shanghai, China
Authors: Li Xin , Sun Lei , Li Guangqin 
Published: February 2024

AbstractNew energy vehicles are regarded as an important measure for improving air quality, reducing oil dependence, fostering emerging industries, and promoting high-quality development. Since 2009, the Chinese government has been committed to promoting new energy vehicles through various policy measures, such as fiscal subsidies. Meanwhile, the outbreak of the COVID-19 pandemic at the end of 2019 has brought great impact on the economy and society. Thus, it is a topic of research to determine if the fiscal subsidy policy can assist new energy vehicles to resist the pandemic’s impact and build consumption resilience. Current researches lack enough attention to the role of fiscal subsidy policy in enhancing consumption resilience under the pandemic impact. By utilizing monthly sales data of new energy vehicles in Shanghai from January 2018 to February 2021, this study employs regression discontinuity (RD) analysis and the combination of the RD with the differences-in-differences (DID) model, to comprehensively evaluate the impact of the COVID-19 pandemic on new energy vehicle consumption and the effectiveness of fiscal subsidy policies in enhancing consumption resilience. The findings reveal that the COVID-19 pandemic shock, with a bandwidth of 3 months, led to a 23% decrease in new energy vehicles sales. In comparison with unsubsidized vehicles, sales of subsidized new energy vehicles significantly increased by 15–80% during the pandemic shock, indicating that fiscal subsidies are crucial in enhancing the consumption resilience of new energy vehicles. Compared with other country-specific new energy vehicles, the subsidy effect of domestic new energy vehicles is relatively inadequate during the pandemic shock. The retreat from subsidy policies has contributed to a decline in the consumption of new energy vehicles in response to the pandemic shock. It is necessary to further improve the consumption resilience of domestic new energy vehicles and pure electric vehicles.
Expected values for the accuracy of predicted breeding values accounting for genetic differences between reference and target populations
Authors: Cuyabano Beatriz C. D. , Boichard Didier , Gondro Cedric 
Published: February 2024

Abstract
Background
Genetic merit, or breeding values as referred to in livestock and crop breeding programs, is one of the keys to the successful selection of animals in commercial farming systems. The developments in statistical methods during the twentieth century and single nucleotide polymorphism (SNP) chip technologies in the twenty-first century have revolutionized agricultural production, by allowing highly accurate predictions of breeding values for selection candidates at a very early age. Nonetheless, for many breeding populations, realized accuracies of predicted breeding values (PBV) remain below the theoretical maximum, even when the reference population is sufficiently large, and SNPs included in the model are in sufficient linkage disequilibrium (LD) with the quantitative trait locus (QTL). This is particularly noticeable over generations, as we observe the so-called erosion of the effects of SNPs due to recombinations, accompanied by the erosion of the accuracy of prediction. While accurately quantifying the erosion at the individual SNP level is a difficult and unresolved task, quantifying the erosion of the accuracy of prediction is a more tractable problem. In this paper, we describe a method that uses the relationship between reference and target populations to calculate expected values for the accuracies of predicted breeding values for non-phenotyped individuals accounting for erosion. The accuracy of the expected values was evaluated through simulations, and a further evaluation was performed on real data.

Results
Using simulations, we empirically confirmed that our expected values for the accuracy of PBV accounting for erosion were able to correctly determine the prediction accuracy of breeding values for non-phenotyped individuals. When comparing the expected to the realized accuracies of PBV with real data, only one out of the four traits evaluated presented accuracies that were significantly higher than the expected, approaching $$\sqrt{{{\text{h}}}^{2}}$$

h

2

.

Conclusions
We defined an index of genetic correlation between reference and target populations, which summarizes the expected overall erosion due to differences in allele frequencies and LD patterns between populations. We used this correlation along with a trait’s heritability to derive expected values for the accuracy ($${\text{R}}$$
R
) of PBV accounting for the erosion, and demonstrated that our derived $${\text{E}}\left[{\text{R}}|{\text{erosion}}\right]$$

E

R
|
erosion

is a reliable metric.
An uncertainty-based interpretable deep learning framework for predicting breast cancer outcome
Authors: Chai Hua , Lin Siyin , Lin Junqi , He Minfan , Yang Yuedong , OuYang Yongzhong , Zhao Huiying 
Published: February 2024

Abstract
Background
Predicting outcome of breast cancer is important for selecting appropriate treatments and prolonging the survival periods of patients. Recently, different deep learning-based methods have been carefully designed for cancer outcome prediction. However, the application of these methods is still challenged by interpretability. In this study, we proposed a novel multitask deep neural network called UISNet to predict the outcome of breast cancer. The UISNet is able to interpret the importance of features for the prediction model via an uncertainty-based integrated gradients algorithm. UISNet improved the prediction by introducing prior biological pathway knowledge and utilizing patient heterogeneity information.

Results
The model was tested in seven public datasets of breast cancer, and showed better performance (average C-index = 0.691) than the state-of-the-art methods (average C-index = 0.650, ranged from 0.619 to 0.677). Importantly, the UISNet identified 20 genes as associated with breast cancer, among which 11 have been proven to be associated with breast cancer by previous studies, and others are novel findings of this study.

Conclusions
Our proposed method is accurate and robust in predicting breast cancer outcomes, and it is an effective way to identify breast cancer-associated genes. The method codes are available at: https://github.com/chh171/UISNet.
Annotated genome and transcriptome of the endangered Caribbean mountainous star coral (Orbicella faveolata) using PacBio long-read sequencing
Authors: Young Benjamin D. , Williamson Olivia M. , Kron Nicholas S. , Andrade Rodriguez Natalia , Isma Lys M. , MacKnight Nicholas J. , Muller Erinn M. , Rosales Stephanie M. , Sirotzke Stephanie M. , Traylor-Knowles Nikki , Williams Sara D. , Studivan Michael S. 
Published: February 2024

AbstractLong-read sequencing is revolutionizingde-novogenome assemblies, with continued advancements making it more readily available for previously understudied, non-model organisms. Stony corals are one such example, with long-readde-novogenome assemblies now starting to be publicly available, opening the door for a wide array of ‘omics-based research. Here we present a newde-novogenome assembly for the endangered Caribbean star coral,Orbicella faveolata, using PacBio circular consensus reads. Our genome assembly improved the contiguity (51 versus 1,933 contigs) and complete and single copy BUSCO orthologs (93.6% versus 85.3%, database metazoa_odb10), compared to the currently available reference genome generated using short-read methodologies. Our newde-novoassembled genome also showed comparable quality metrics to other coral long-read genomes. Telomeric repeat analysis identified putative chromosomes in our scaffolded assembly, with these repeats at either one, or both ends, of scaffolded contigs. We identified 32,172 protein coding genes in our assembly through use of long-read RNA sequencing (ISO-seq) of additionalO. faveolatafragments exposed to a range of abiotic and biotic treatments, and publicly available short-read RNA-seq data. With anthropogenic influences heavily affectingO. faveolata, as well as itsincreasing incorporation into reef restoration activities, this updated genome resource can be used for population genomics and other ‘omics analyses to aid in the conservation of this species.
Fine-scale geographic difference of the endangered Big-headed Turtle (Platysternon megacephalum) fecal microbiota, and comparison with the syntopic Beale’s Eyed Turtle (Sacalia bealei)
Authors: Fong Jonathan J. , Sung Yik-Hei , Ding Li 
Published: February 2024

Abstract
Background
Studies have elucidated the importance of gut microbiota for an organism, but we are still learning about the important influencing factors. Several factors have been identified in helping shape the microbiome of a host, and in this study we focus on two factors—geography and host. We characterize the fecal microbiota of the Big-headed Turtle (Platysternon megacephalum) and compare across a relatively fine geographic scale (three populations within an 8-km radius) and between two syntopic hosts (P. megacephalum and Sacalia bealei). Both species are endangered, which limits the number of samples we include in the study. Despite this limitation, these data serve as baseline data for healthy, wild fecal microbiotas of two endangered turtle species to aid in conservation management.

Results
For geography, the beta diversity of fecal microbiota differed between the most distant sites. The genus Citrobacter significantly differs between sites, which may indicate a difference in food availability, environmental microbiota, or both. Also, we identify the common core microbiome for Platysternon across Hong Kong as the shared taxa across the three sites. Additionally, beta diversity differs between host species. Since the two species are from the same site and encounter the same environmental microbiota, we infer that there is a host effect on the fecal microbiota, such as diet or the recruitment of host-adapted bacteria. Lastly, functional analyses found metabolism pathways (KEGG level 1) to be the most common, and pathways (KEGG level 3) to be statistically significant between sites, but statistically indistinguishable between species at the same site.

Conclusions
We find that fecal microbiota can significantly differ at a fine geographic scale and between syntopic hosts. Also, the function of fecal microbiota seems to be strongly affected by geographic site, rather than species. This study characterizes the identity and function of the fecal microbiota of two endangered turtle species, from what is likely their last remaining wild populations. These data of healthy, wild fecal microbiota will serve as a baseline for comparison and contribute to the conservation of these two endangered species.
Current state of neuroprotective therapy using antibiotics in human traumatic brain injury and animal models
Authors: Ritter Katharina , Somnuke Pawit , Hu Lingjiao , Griemert Eva-Verena , Schäfer Michael K.E. 
Published: February 2024

AbstractTBI is a leading cause of death and disability in young people and older adults worldwide. There is no gold standard treatment for TBI besides surgical interventions and symptomatic relief. Post-injury infections, such as lower respiratory tract and surgical site infections or meningitis are frequent complications following TBI. Whether the use of preventive and/or symptomatic antibiotic therapy improves patient mortality and outcome is an ongoing matter of debate. In contrast, results from animal models of TBI suggest translational perspectives and support the hypothesis that antibiotics, independent of their anti-microbial activity, alleviate secondary injury and improve neurological outcomes. These beneficial effects were largely attributed to the inhibition of neuroinflammation and neuronal cell death. In this review, we briefly outline current treatment options, including antibiotic therapy, for patients with TBI. We then summarize the therapeutic effects of the most commonly tested antibiotics in TBI animal models, highlight studies identifying molecular targets of antibiotics, and discuss similarities and differences in their mechanistic modes of action.
Clinical nomogram prediction model to assess the risk of prolonged ICU length of stay in patients with diabetic ketoacidosis: a retrospective analysis based on the MIMIC-IV database
Authors: Shi Jincun , Chen Fujin , Zheng Kaihui , Su Tong , Wang Xiaobo , Wu Jianhua , Ni Bukao , Pan Yujie 
Published: February 2024

AbstractBackgroundThe duration of hospitalization, especially in the intensive care unit (ICU), for patients with diabetic ketoacidosis (DKA) is influenced by patient prognosis and treatment costs. Reducing ICU length of stay (LOS) in patients with DKA is crucial for optimising healthcare resources utilization. This study aimed to establish a nomogram prediction model to identify the risk factors influencing prolonged LOS in ICU-managed patients with DKA, which will serve as a basis for clinical treatment, healthcare safety, and quality management research.MethodsIn this single-centre retrospective cohort study, we performed a retrospective analysis using relevant data extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Clinical data from 669 patients with DKA requiring ICU treatment were included. Variables were selected using the Least Absolute Shrinkage and Selection Operator (LASSO) binary logistic regression model. Subsequently, the selected variables were subjected to a multifactorial logistic regression analysis to determine independent risk factors for prolonged ICU LOS in patients with DKA. A nomogram prediction model was constructed based on the identified predictors. The multivariate variables included in this nomogram prediction model were the Oxford acute severity of illness score (OASIS), Glasgow coma scale (GCS), acute kidney injury (AKI) stage, vasoactive agents, and myocardial infarction.ResultsThe prediction model had a high predictive efficacy, with an area under the curve value of 0.870 (95% confidence interval [CI], 0.831–0.908) in the training cohort and 0.858 (95% CI, 0.799–0.916) in the validation cohort. A highly accurate predictive model was depicted in both cohorts using the Hosmer–Lemeshow (H-L) test and calibration plots.ConclusionThe nomogram prediction model proposed in this study has a high clinical application value for predicting prolonged ICU LOS in patients with DKA. This model can help clinicians identify patients with DKA at risk of prolonged ICU LOS, thereby enhancing prompt intervention and improving prognosis.
Effect of neutrophil to lymphocyte ratio on prognosis of elderly patients with severe sepsis combined with diabetes mellitus
Authors: Jin Shan , Yin Jun-bin , Li Wei , Zang Li-li 
Published: February 2024

Abstract
Background
To investigate the predictive value of neutrophil-to-lymphocyte ratio (NLR) in the short-term prognosis of elderly patients with severe sepsis combined with diabetes mellitus (DM).

Methods
The clinical data of 162 elderly patients with severe sepsis combined with DM from January 2018 to December 2022 were retrospectively collected. These patients were divided into a survival group (n = 104) and a death group (n = 58) according to 90-day prognosis. The number of neutrophils, lymphocytes, and NLR were compared. The optimal cut-off value for NLR to predict 90-day prognosis in elderly patients with severe sepsis combined with DM was determined using Receiver Operator Characteristic (ROC) curves, and the patients were divided into high and low NLR groups depending on the optimal cut-off value. The Kaplan-Meier method was used to plot the survival curves of the high and low NLR groups. Risk factors for the 90-day death in elderly patients with severe sepsis combined with DM were analyzed by a multivariate cox regression model.

Results
There were no significant differences in gender, age, history of hypertension and hyperlipidemia, intensive care unit (ICU) stay, duration of mechanical ventilation, and oxygenation index between the survival group and death group (p > 0.05). However, acute physiological and chronic health evaluation II (APACHE II) scores, and sepsis-related organ failure assessment (SOFA) scores were significantly lower in the survival group compared with the death group (p < 0.05). In the survival group, neutrophils counts and NLR were much lower than those in the death group, while lymphocytes counts were much higher (p < 0.05). ROC curves showed that the optimal cut-off value for NLR to predict 90-day mortality in elderly patients with severe sepsis combined with DM was 3.482. Patients were divided into high NLR and low NLR groups based on whether NLR was ≥ 3.482. In terms of the log-rank test results, patients in the low NLR group had a significantly higher 90-day survival rate than those in the high NLR group (Logrank χ2 = 8.635, p = 0.003). The multivariate cox regression model showed that the length of ICU stay longer than 15 days and NLR ≥ 3.482 were independent risk factors for 90-day prognosis in elderly patients with severe sepsis combined with DM.

Conclusion
NLR ≥ 3.482 can be used to predict whether poor prognosis occurs in the short term after illness in elderly patients with severe sepsis combined with DM, and has good assessment value.