bluuebunny/crossref_metadata_2025_split huggingface dataset:
Early continuous glucose monitoring-derived glycemic patterns are associated with subsequent insulin resistance and gestational diabetes mellitus development during pregnancy
Authors: Ku Chee Wai , Zheng Ruther Teo , Tan Hong Ying , Lim Jamie Yong Qi , Chen Ling-Wei , Cheung Yin Bun , Godfrey Keith M. , Chan Jerry Kok Yen , Yap Fabian , Lek Ngee , Loy See Ling 
Published: November 2024

Abstract
Background
Gestational diabetes mellitus (GDM) and insulin resistance (IR) increase the risk of adverse pregnancy outcomes. We aimed to examine the relationship of interstitial glucose assessed by continuous glucose monitoring (CGM) at early gestation, and the subsequent development of IR and GDM, and to determine 24-h interstitial glucose centile distributions in women with normal (non-IR and non-GDM) and suboptimal glycemic status (IR and/or GDM).

Methods
CGM measurements were taken for 3–10 days at 18–24 weeks’ gestation, followed by fasting serum insulin and oral glucose tolerance testing at 24–28 weeks’ gestation. IR and GDM were determined by the updated Homeostasis Model Assessment of IR score of ≥ 1.22 and 2013 World Health Organization criteria, respectively. Risks of IR and GDM were estimated using modified Poisson models, and hourly interstitial glucose centiles determined using Generalized Additive Models for Location, Scale and Shape.

Results
This prospective cohort study involved 167 pregnant women in Singapore, with a mean age of 31.7 years, body mass index of 22.9 kg/m2, and gestation of 20.3 weeks. 25% of women exhibited IR and 18% developed GDM. After confounders adjustment, women with suboptimal glycemic control, indicated by higher mean daily glucose (risk ratio 1.42; 95% confidence interval 1.16, 1.73), glucose management indicator (1.08; 1.03, 1.12), and J-index (1.04; 1.02, 1.06), as well as those with greater glycemic variability, indicated by higher standard deviation (1.69; 1.37, 2.09), coefficient of variation (1.03; 1.00, 1.06), and mean amplitude of glycemic excursions (1.4; 1.14, 1.35) derived from CGM in early gestation were associated with higher risks of developing IR in later gestation. These associations were similarly observed for the development of GDM. Centile curves showed that, compared to those with normal glycemic status, women with suboptimal glycemic status had higher glucose levels, with greater fluctuations throughout 24 h.

Conclusions
In pregnant women who subsequently developed IR and GDM, interstitial glucose levels assessed by CGM were elevated and varied greatly. This supports the potential use of CGM to screen for glycemic changes early in pregnancy.
Accelerated hit identification with target evaluation, deep learning and automated labs: prospective validation in IRAK1
Authors: Kamuntavičius Gintautas , Prat Alvaro , Paquet Tanya , Bastas Orestis , Aty Hisham Abdel , Sun Qing , Andersen Carsten B. , Harman John , Siladi Marc E. , Rines Daniel R. , Flatters Sarah J. L. , Tal Roy , Norvaišas Povilas 
Published: November 2024

Abstract
Background
Target identification and hit identification can be transformed through the application of biomedical knowledge analysis, AI-driven virtual screening and robotic cloud lab systems. However there are few prospective studies that evaluate the efficacy of such integrated approaches.

Results
We synergistically integrate our in-house-developed target evaluation (SpectraView) and deep-learning-driven virtual screening (HydraScreen) tools with an automated robotic cloud lab designed explicitly for ultra-high-throughput screening, enabling us to validate these platforms experimentally. By employing our target evaluation tool to select IRAK1 as the focal point of our investigation, we prospectively validate our structure-based deep learning model. We can identify 23.8% of all IRAK1 hits within the top 1% of ranked compounds. The model outperforms traditional virtual screening techniques and offers advanced features such as ligand pose confidence scoring. Simultaneously, we identify three potent (nanomolar) scaffolds from our compound library, 2 of which represent novel candidates for IRAK1 and hold promise for future development.

Conclusion
This study provides compelling evidence for SpectraView and HydraScreen to provide a significant acceleration in the processes of target identification and hit discovery. By leveraging Ro5’s HydraScreen and Strateos’ automated labs in hit identification for IRAK1, we show how AI-driven virtual screening with HydraScreen could offer high hit discovery rates and reduce experimental costs.

Scientific contribution
We present an innovative platform that leverages Knowledge graph-based biomedical data analytics and AI-driven virtual screening integrated with robotic cloud labs. Through an unbiased, prospective evaluation we show the reliability and robustness of HydraScreen in virtual and high-throughput screening for hit identification in IRAK1. Our platforms and innovative tools can expedite the early stages of drug discovery.
Bactericidal activities and biochemical features of 16 antimicrobial peptides against bovine-mastitis causative pathogens
Authors: Cho Hye-sun , Kim Dohun , Jeon Hyoim , Somasundaram Prathap , Soundrarajan Nagasundarapandian , Park Chankyu 
Published: November 2024

AbstractMastitis, often caused by bacterial infection, is an inflammatory condition affecting the mammary glands. The condition is particularly prevalent in dairy cattle. Current treatment of bovine mastitis heavily relies on the use of antibiotics. To identify alternative solutions to antibiotic use, we evaluated the antimicrobial activity of 14 cathelicidins reported from 10 animal species. In conjunction, we assessed two bacteriocins against the bovine-mastitis causative bacterial panel, consisting of Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, Bacillus cereus, Enterococcus faecalis, Streptococcus agalactiae, Streptococcus dysgalactiae, and Streptococcus equi. Among the antimicrobial peptides (AMPs), cc-CATH3, ML-CATH, and PD-CATH proved to be highly active (minimum inhibitory concentration of 2–41 μg/mL, 0.2–10.3 μM) against all bacterial strains in the panel and field isolates from milk, with elevated somatic cell counts (≥ 500,000 cells/mL). Of the AMPs tested in this study, ML-CATH presented the highest level of effectiveness in controlling mastitis-associated bacterial strains while also possessing minimal cytotoxicity and functional stability against pH change and a high salt condition. The results of in silico analyses on the biochemical features of 12 helical cathelicidins revealed that the charge of AMPs appears to be a major determinant in killing Gram-negative bacteria. Furthermore, we observed a unique motif, “N(n≥3)-P(n≥1)-N(n≥3)”, from the sequences of PMAP-36, cc-CATH3, ML-CATH, and PD-CATH that exhibits potent antimicrobial activity against a broad spectrum of bacteria compared to others. Our findings support the proposition that AMPs could serve as effective antimicrobial alternatives to conventional antibiotics in treating complex animal diseases caused by microbial infection, such as bovine mastitis.
Stearoyl-CoA desaturase in CD4+ T cells suppresses tumor growth through activation of the CXCR3/CXCL11 axis in CD8+ T cells
Authors: Hwang Sung-Hyun , Yang Yeseul , Jung Jae-Ha , Kim Jin Won , Kim Yongbaek 
Published: November 2024

Abstract
Background
Within the tumor microenvironment, altered lipid metabolism promotes cancer cell malignancy by activating oncogenic cascades; however, impact of lipid metabolism in CD4+ tumor-infiltrating lymphocytes (TILs) remains poorly understood. Here, we elucidated that role of stearoyl-CoA desaturase (SCD) increased by treatment with cancer-associated fibroblast (CAF) supernatant in CD4+ T cells on their subset differentiation and activity of CD8+ T cells.

Results
In our study, we observed that CD4+ TILs had higher lipid droplet content than CD4+ splenic T cells. In tumor tissue, CAF-derived supernatant provided fatty acids to CD4+ TILs, which increased the expression of SCD and oleic acid (OA) content. Increased SCD expression by OA treatment enhanced the levels of Th1 cell markers TBX21, interleukin-2, and interferon-γ. However, SCD inhibition upregulated the expression of regulatory T (Treg) cell markers, FOXP3 and transforming growth factor-β. Comparative fatty acid analysis of genetically engineered Jurkat cells revealed that OA level was significantly higher in SCD-overexpressing cells. Overexpression of SCD increased expression of Th1 cell markers, while treatment with OA enhanced the transcriptional level of TBX21 in Jurkat cells. In contrast, palmitic acid which is higher in SCD-KO cells than other subclones enhanced the expression of Treg cell markers through upregulation of mitochondrial superoxide. Furthermore, SCD increased the secretion of the C–X–C motif chemokine ligand 11 (CXCL11) from CD4+ T cells. The binding of CXCL11 to CXCR3 on CD8+ T cells augmented their cytotoxic activity. In a mouse tumor model, the suppressive effect of CD8+ T cells on tumor growth was dependent on CXCR3 expression.

Conclusion
These findings illustrate that SCD not only orchestrates the differentiation of T helper cells, but also promotes the antitumor activity of CD8+ T cells, suggesting its function in adverse tumor microenvironments.

Graphical abstract
A novel function for α-synuclein as a regulator of NCK2 in olfactory bulb: implications for its role in olfaction
Authors: Ren Jing , Wu Chao , Zeng Mengxia , Qu Mingqin , Gao Ge , Chen Ning , Yue Jingjing , Jiang Yuwen , Zhao Tongfei , Xiang Na , Meng Fangang , Lu Ling-ling 
Published: November 2024

AbstractTo investigate physiological function of α-synuclein is important for understanding its pathophysiological mechanism in synucleinopathies including Parkinson’s disease. Employing knockout mice, we found that Snac/α-synuclein deletion induced aberrant projection of olfactory sensory neurons and hyposmia. We identified 9 axon guidance associated differentially expressed proteins using iTRAQ based Liquid Chromatograph Mass Spectrometer. NCK2 is most significantly down-regulated protein among them. We further found that either α-synuclein deletion or NCK2 deficiency induced Eph A4 inactivation. Re-expressing Snac/α-synuclein in its knockout neurons reversed the down-regulation of NCK2, as well as the inactivation of EphA4. Overexpression of Snac/α-synuclein in α-synuclein deleted mice reversed the down-regulation of NCK2 and pEphA4, and improved the olfactory impairment of mice. Correlation analysis showed that there is a significant correlation between the protein level of α-synuclein, NCK2, and pEphA4, respectively. Nonetheless, immunoprecipitation analysis showed that NCK2 was associated with both EphA4 and Rho A, suggesting that NCK2 as a scaffolding protein to modulate Eph A4/Rho A pathway. Moreover, Rho A activity was significantly lower in α-synuclein deficient mice. Thus, α-synuclein regulates olfactory neurons projection through NCK2 dependent EphA4/Rho A pathway. Malfunction of α-synuclein because of deletion may cause aberrant olfactory neurons projection. This extended our knowledge of α-synuclein functions, which may explain why olfaction is usually impaired in some synucleinopathies.
Learned scalable video coding for humans and machines
Authors: Hadizadeh Hadi , Bajić Ivan V. 
Published: November 2024

AbstractVideo coding has traditionally been developed to support services such as video streaming, videoconferencing, digital TV, and so on. The main intent was to enable human viewing of the encoded content. However, with the advances in deep neural networks (DNNs), encoded video is increasingly being used for automatic video analytics performed by machines. In applications such as automatic traffic monitoring, analytics such as vehicle detection, tracking and counting, would run continuously, while human viewing could be required occasionally to review potential incidents. To support such applications, a new paradigm for video coding is needed that will facilitate efficient representation and compression of video for both machine and human use in a scalable manner. In this manuscript, we introduce an end-to-end learnable video codec that supports a machine vision task in its base layer, while its enhancement layer, together with the base layer, supports input reconstruction for human viewing. The proposed system is constructed based on the concept of conditional coding to achieve better compression gains. Comprehensive experimental evaluations conducted on four standard video datasets demonstrate that our framework outperforms both state-of-the-art learned and conventional video codecs in its base layer, while maintaining comparable performance on the human vision task in its enhancement layer.
Review of the target trial methodological approach on treatment effect estimates in kidney failure: protocol for a systematic assessment
Authors: Pinter Jule , Tunnicliffe David J. , Karunikaikumar Pooshwikaa , Anastasiadis Anastasios , Hills Robert K. 
Published: November 2024

Abstract

Background
Patients with kidney failure often lack robust evidence because they are excluded from randomized trials. Trial emulation provides an alternative approach to derive treatment effect estimates when randomized trials cannot be conducted. Critical questions about the comparative efficacy and safety of interventions in kidney failure are now being answered using this approach or parts of it. However, variations and inconsistencies in reporting cast doubt on the reliability and validity of effect estimates not derived from randomized trials. The aim of this methodological systematic review is to understand the extent to which the target study approach is used in kidney failure and the appropriateness of this approach. By identifying and evaluating studies that qualify as emulating a target trial, compared with studies that did not apply the principles. We aim to provide more specific methodological guidance to increase the clarity and reliability of reporting treatment effect estimates when running a trial in kidney failure is not feasible.

Methods
This protocol is developed in accordance with Preferred Reporting Items for Systematic reviews and Meta-Analyses Protocols (PRISMA-P) statement. MEDLINE, Embase, and reference lists (backwards citation chasing) will be searched up until 1st July 2023 and the search updated prior to publication to identify all studies evaluating patient outcomes in late-stage kidney disease and failure that use target trial emulation as the primary approach for analysis. Two authors (A. A., P. K.) will select articles based on title and abstract and then full text, with a third reviewer settling disagreements (J. P.). The prespecified variables will be extracted, and the risk of bias will be assessed by at least two authors (A. A., P. K., A. N.) using prespecified data forms. This will enable the determination of the robustness of the methodological quality of observational studies in using the whole or elements of the target trial approach. We will thereby assess their ability to reliably report treatment effect estimates.

Discussion
We will provide specific methodological recommendations on how to design target trials and model assumptions for emulation to get reliable treatment effect estimates for therapeutic interventions in kidney failure.

Methodological systematic review registration
Open Science Framework: Identifier https://doi.org/10.17605/OSF.IO/Z4Y29.
Exploring the impacts of agricultural emissions from natural gas on ecological footprint
Authors: Luo Biao , Rauf Aisha , Osei-Kusi Frank , Sundas Rauf , Mehmood Rashid 
Published: November 2024

Abstract
Background
This study investigates the long-term effects of agricultural natural gas emissions on ecological footprints across 19 European countries from 2006 to 2020. Employing Cross-Sectional Distributed Lag and Cross-Sectional Autoregressive Distributed Lag models, the research aims to deepen the understanding of agricultural emissions’ dynamics and their impact on ecological sustainability.

Results
The study reveals that reductions in renewable energy consumption negatively affect ecological footprints, indicating the crucial role of renewable energy adoption in environmental sustainability. The findings emphasize the need for policies that promote renewable energy and address barriers to its adoption. Additionally, the research identifies significant correlations between population growth and ecological footprints, demonstrating the influence of demographic factors on environment. The analysis highlights significant correlations between population growth and ecological footprints, underscoring the importance of demographic trends in shaping environmental policy.

Conclusions
The policy implications of this study include advocating for sustainable urban planning and incentivizing eco-friendly agricultural practices to mitigate emissions and promote environmental sustainability. By enhancing our understanding of the relationship between agricultural emissions and ecological footprints, this research provides valuable insights for evidence-based environmental policymaking in European countries.
Integration of UAV LiDAR and WorldView-2 images for modeling mangrove aboveground biomass with GA-ANN wrapper
Authors: Zhu Yuanhui , Myint Soe W. , Liu Kai , Liu Lin , Cao Jingjing 
Published: November 2024

Abstract
Background
Integrating optical and LiDAR data is crucial for accurately predicting aboveground biomass (AGB) due to their complementarily essential characteristics. It can be anticipated that this integration approach needs to deal with an expanded set of variables and scale-related challenges. To achieve satisfactory accuracy in real-world applications, further exploration is needed to optimize AGB models by selecting appropriate scales and variables.

Methods
This study examined the impact of LiDAR point cloud-derived metrics on estimation accuracies at different scales, ranging from 2 to 16 m cell sizes. We integrated WorldView-2 imagery with LiDAR data to construct biomass models and developed a genetic algorithm-based wrapper for variable selection and parameter tuning in artificial neural networks (GA-ANN wrapper).

Results
Our findings indicated that the highest accuracies in estimating AGB were yielded by 4 m and 6 m cell sizes, followed by 8 m and 10 m, associated with the dimensions of vegetation canopies and sampling plots. Models integrating WorldView-2 and LiDAR data outperformed those using each data source individually, reducing RMSEr by 5.80% and 3.89%, respectively. Combining these data sources can capture the canopy spectral responses and vertical vegetation structure. The GA-ANN wrapper model decreased RMSEr by 1.69% over the ANN model and dwindled the number of variables from 38 to 9. The selected variables included vegetation density, height, species, and vegetation indices.

Conclusions
The appropriate cell size for AGB estimation should consider the sizes of vegetation canopies, tree densities, and sampling plots. The GA-ANN wrapper effectively reduced variables and achieved the highest accuracy. Additionally, canopy spectral and vertical structure information are vital for accurate AGB estimation. Our study offered insights into optimizing mangrove AGB models by integrating optical and LiDAR data. The approach, data, model, and indices employed in this research can effectively predict AGB estimates of any other forest types or vegetation cover types in different climate regions.
Assessment of the dietary amino acid profiles and the relative biomarkers for amino acid balance in the low-protein diets for broiler chickens
Authors: Wang Bin , Zhang Xiaodan , Liu Yongfa , Gao Mingkun , Wang Mi , Wang Yuan , Wang Xinzhi , Guo Yuming 
Published: November 2024

Abstract
Background
Research on low-protein-level diets has indicated that even though the profiles of essential amino acids (EAAs) follow the recommendation for a normal-protein-level diet, broilers fed low-protein diets failed to achieve productive performance compared to those fed normal diets. Therefore, it is imperative to reassess the optimum profile of EAAs in low-protein diets and establish a new ideal pattern for amino acid balance. Furthermore, identifying novel sensitive biomarkers for assessing amino acid balance will greatly facilitate the development of amino acid nutrition and application technology. In this study, 12 dietary treatments [Con(+), Con(-), L&A(-), L&A(+), M&C(-), M&C(+), BCAA (-), BCAA(+), Thr(-), Thr(+), Trp(-) and Trp(+)] were established by combining different EAAs including lysine and arginine, methionine and cysteine, branched-chain amino acid (BCAA), threonine, and tryptophan to observe the growth and development of the broiler chickens fed with low-protein-level diets. Based on the biochemical parameters and untargeted metabolomic analysis of animals subjected to different treatments, biomarkers associated with optimal and suboptimal amino acid balance were identified.

Results
Growth performance, carcass characteristics, hepatic enzyme activity, serum biochemical parameters, and breast muscle mRNA expression differed significantly between male and female broilers under different dietary amino acid patterns. Male broilers exhibited higher sensitivity to the adjustment of amino acid patterns than female broilers. For the low-protein diet, the dietary concentrations of lysine, arginine, and tryptophan, but not of methionine, cystine, or threonine, needed to be increased. Therefore, further research on individual BCAA is required. For untargeted metabolomic analysis, Con(+) was selected as a normal diet (NP) while Con(-) represented a low-protein diet (LP). L&A(+) denotes a low-protein amino acid balanced diet (LPAB) and Thr(+) represents a low-protein amino acid imbalance diet (LPAI). The metabolites oxypurinol, pantothenic acid, and D-octopine in birds were significantly influenced by different dietary amino acid patterns.

Conclusion
Adjusting the amino acid profile of low-protein diets is required to achieve normal growth performance in broiler chickens fed normal-protein diets. Oxypurinol, pantothenic acid, and D-octopine have been identified as potentially sensitive biomarkers for assessing amino acid balance.
F. prausnitzii potentially modulates the association between citrus intake and depression
Authors: Samuthpongtorn Chatpol , Chan Allison A. , Ma Wenjie , Wang Fenglei , Nguyen Long H. , Wang Dong D. , Okereke Olivia I. , Huttenhower Curtis , Chan Andrew T. , Mehta Raaj S. 
Published: November 2024

Abstract
Background
The gut microbiome modulates the effects of diet on host health, but it remains unclear which specific foods and microbial features interact to influence risk of depression. To understand this interplay, we leveraged decades of dietary and depression data from a longitudinal cohort of women (n = 32,427), along with fecal metagenomics and plasma metabolomics from a substudy (n = 207) nested in this cohort, as well as an independent validation cohort of men (n = 307).

Results
We report that citrus intake and its components are prospectively associated with a lower risk of depression and altered abundance of 15 gut microbial species, including enriched Faecalibacterium prausnitzii. In turn, we found a lower abundance of F. prausnitzii and its metabolic pathway, S-adenosyl-L-methionine (SAM) cycle I in participants with depression. To explore causality, we found that lower SAM production by F. prausnitzii may decrease intestinal monoamine oxidase A gene expression implicated in serotonin and dopamine synthesis.

Conclusions
These data underscore the role of diet in the prevention of depression and offer a plausible explanation for how the intestinal microbiome modulates the influence of citrus on mental health.