bluuebunny/crossref_metadata_2025_split huggingface dataset:
Missed postoperative metabolic acidosis associated with sodium-glucose transporter 2 inhibitors in cardiac surgery patients: a retrospective analysis
Authors: Kim Hyeon A , Kim Joo Yeon , Kim Young Hwan , Lee Young Tak , Park Pyo Won 
Published: April 2024

AbstractThe increasing use of sodium glucose transporter 2 inhibitors (SGLT2i) for treating cardiovascular (CV) diseases and type 2 diabetes (T2D) is accompanied by a rise in euglycemic diabetic ketoacidosis occurrences in cardiac surgery patients. Patients undergoing cardiac surgery, due to their pre-existing CV disease which often requires SGLT2i prescriptions, face an increased risk of postoperative metabolic acidosis (MA) or ketoacidosis (KA) associated with SGLT2i, compounded by fasting and surgical stress. The primary aim of this study is to quantify the incidence of SGLT2i-related postoperative MA or KA and to identify related risk factors. We analyzed data retrospectively of 823 cardiac surgery patients, including 46 treated with SGLT2i from November 2019 to October 2022. Among 46 final cohorts treated preoperatively with SGLT2i, 29 (63%) developed postoperative metabolic complications. Of these 46 patients, stratified into two categories based on postoperative laboratory findings, risk factor analysis were conducted and compared. Analysis indicated a prescription duration over one week significantly elevated the risk of complications (Unadjusted OR, 11.7; p = 0.032*; Adjusted OR, 31.58; p = 0.014*). A subgroup analysis showed that a cardiopulmonary bypass duration of 60 min or less significantly raises the risk of SGLT2i-related postoperative MA in patients with a sufficient prescription duration. We omitted the term "diabetes" in describing complications related to SGLT2i, as these issues are not exclusive to T2D patients. Awareness of SGLT2i-related postoperative MA or KA can help clinicians distinguish between non-life-threatening conditions and severe causes, thereby preventing unnecessary tests and ensuring best practice.
Experimental validation and comprehensive analysis of m6A methylation regulators in intervertebral disc degeneration subpopulation classification
Authors: Xu Xiaoqian , Shen Lianwei , Qu Yujuan , Li Danyang , Zhao Xiaojing , Wei Hui , Yue Shouwei 
Published: April 2024

AbstractIntervertebral disc degeneration (IVDD) is one of the most prevalent causes of chronic low back pain. The role of m6A methylation modification in disc degeneration (IVDD) remains unclear. We investigated immune-related m6A methylation regulators as IVDD biomarkers through comprehensive analysis and experimental validation of m6A methylation regulators in disc degeneration. The training dataset was downloaded from the GEO database and analysed for differentially expressed m6A methylation regulators and immunological features, the differentially regulators were subsequently validated by a rat IVDD model and RT-qPCR. Further screening of key m6A methylation regulators based on machine learning and LASSO regression analysis. Thereafter, a predictive model based on key m6A methylation regulators was constructed for training sets, which was validated by validation set. IVDD patients were then clustered based on the expression of key m6A regulators, and the expression of key m6A regulators and immune infiltrates between clusters was investigated to determine immune markers in IVDD. Finally, we investigated the potential role of the immune marker in IVDD through enrichment analysis, protein-to-protein network analysis, and molecular prediction. By analysising of the training set, we revealed significant differences in gene expression of five methylation regulators including RBM15, YTHDC1, YTHDF3, HNRNPA2B1 and ALKBH5, while finding characteristic immune infiltration of differentially expressed genes, the result was validated by PCR. We then screen the differential m6A regulators in the training set and identified RBM15 and YTHDC1 as key m6A regulators. We then used RBM15 and YTHDC1 to construct a predictive model for IVDD and successfully validated it in the training set. Next, we clustered IVDD patients based on the expression of RBM15 and YTHDC1 and explored the immune infiltration characteristics between clusters as well as the expression of RBM15 and YTHDC1 in the clusters. YTHDC1 was finally identified as an immune biomarker for IVDD. We finally found that YTHDC1 may influence the immune microenvironment of IVDD through ABL1 and TXK. In summary, our results suggest that YTHDC1 is a potential biomarker for the development of IVDD and may provide new insights for the precise prevention and treatment of IVDD.
Development and validation of the relational behavior interactions scale for couples
Authors: Harel Tal , Koslowsky Meni 
Published: April 2024

AbstractIn this research, we developed and validated a measure of couple-based reported behavior interactions (RBI). Specifically, Study 1 was designed to describe the development of the scale and to examine its reliability; Study 2 (N = 222), was designed to examine factors that could differentiate men and women. Additionally, we tested if women's behaviors could predict their partner's behavior. Results from the exploratory factor analysis (EFA) revealed a three-factor structure for couples' RBI which were labelled: Social Companionship and Affective Behavior Interactions (SAI) (Factor 1), Fulfilling Obligations and Duties of the Partner (FOD) (Factor 2) and Openness in the Relationship (OR) (Factor 3). In linear regression analyses, there was a significant difference between men and women in the second factor, which represents behaviors associated with fulfilling the responsibilities of a partner. On the other hand, neither the SAI factor nor the OR factor showed any distinct gender differences. The SPSS PROCESS analysis revealed that women's Social Companionship and Affective Behavior Interactions (Factor 1), and Openness in the Relationship (Factor 3) significantly predicted their male partner's behaviors. The relationship duration significantly moderated the association between women's and men's behaviors for both factors. Results are discussed in light of the need for a broader understanding of romantic behavioral interactions.
Determinants associated with low dietary diversity among migrants to Morocco: a cross sectional study
Authors: Essayagh Firdaous , Essayagh Meriem , Lambaki Abdellah , Naji Ahmed Anouar , Essayagh Sanah , Essayagh Touria 
Published: April 2024

AbstractLow dietary diversity (LDD) is prevalent among vulnerable populations, posing a morbidity risk. Few studies have been conducted on the dietary diversity of migrants. The objectives of this study are to determine the prevalence of LDD among migrants in Morocco and the risk factors associated with it. In the Oriental region, we conducted a cross-sectional study with migrants between November and December 2021. The sampling method used was convenience sampling. A face-to-face, structured questionnaire was used to collect sociodemographic, behavioral, and clinical data. We calculated a dietary diversity score based on a 24-h food recall and assessed food intake. The risk factors associated with LDD were identified using multivariate logistic regression. A total of 445 migrants was enrolled. The prevalence of LDD was 31.7%. Risk factors associated with LDD were: being homeless (adjusted Odds Ratio (AOR) of 6.32; CI% [3.55–11.25]), a lack of social support (AOR of 2.30; CI% [1.33–03.98]), and low monthly income (AOR of 8.21; CI% [3.39–19.85]). Public policies must focus on social and environmental determinants. Nutrition training programs should be set up for the migrant population.
Fetal and neonatal outcomes of posterior fossa anomalies: a retrospective cohort study
Authors: Alsehli Hanan , Alshahrani Saeed Mastour , Alzahrani Shatha , Ababneh Farouq , Alharbi Nawal Mashni , Alarfaj Nassebah , Baarmah Duaa 
Published: April 2024

AbstractThe primary aim of this study was to estimate the incidence of posterior fossa anomalies (PFA) and assess the associated outcomes in King Abdulaziz Medical City (KAMC), Riyadh. All fetuses diagnosed by prenatal ultrasound with PFA from 2017 to 2021 in KAMC were analyzed retrospectively. PFA included Dandy–Walker malformation (DWM), mega cisterna magna (MCM), Blake's pouch cyst (BPC), and isolated vermian hypoplasia (VH). The 65 cases of PFA were 41.5% DWM, 46.2% MCM, 10.8% VH, and 1.5% BPC. The annual incidence rates were 2.48, 2.64, 4.41, 8.75, and 1.71 per 1000 anatomy scans for 2017, 2018, 2019, 2020, and 2021, respectively. Infants with DWM appeared to have a higher proportion of associated central nervous system (CNS) abnormalities (70.4% vs. 39.5%; p-value = 0.014) and seizures than others (45% vs. 17.9%; p-value = 0.041). Ten patients with abnormal genetic testing showed a single gene mutation causing CNS abnormalities, including a pathogenic variant in MPL, C5orf42, ISPD, PDHA1, PNPLA8, JAM3, COL18A1, and a variant of uncertain significance in the PNPLA8 gene. Our result showed that the most common PFA is DWM and MCM. The autosomal recessive pathogenic mutation is the major cause of genetic disease in Saudi patients diagnosed with PFA.
Comparative molecular profiling of pancreatic ductal adenocarcinoma of the head versus body and tail
Authors: Abdelrahim Maen , Esmail Abdullah , Kasi Anup , Esnaola Nestor F. , Xiu Joanne , Baca Yasmine , Weinberg Benjamin A. 
Published: April 2024

AbstractPancreatic ductal adenocarcinoma (PDAC) of the head (H) and body/tail (B/T) differ in embryonic origin, cell composition, blood supply, lymphatic and venous drainage, and innervation. We aimed to compare the molecular and tumor immune microenvironment (TIME) profiles of PDAC of the H vs. B/T. A total of 3499 PDAC samples were analyzed via next-generation sequencing (NGS) of RNA (whole transcriptome, NovaSeq), DNA (NextSeq, 592 genes or NovaSeq, whole exome sequencing), and immunohistochemistry (Caris Life Sciences, Phoenix, AZ). Significance was determined as p values adjusted for multiple corrections (q) of <0.05. Anatomic subsites of PDAC tumors were grouped by primary tumor sites into H (N = 2058) or B/T (N = 1384). There were significantly more metastatic tumors profiled from B/T vs. H (57% vs. 44%, p < 0.001). KRAS mutations (93.8% vs. 90.2%), genomic loss of heterozygosity (12.7% vs. 9.1%), and several copy number alterations (FGF3, FGF4, FGF19, CCND1, ZNF703, FLT4, MUTYH, TNFRS14) trended higher in B/T when compared to H (p < 0.05 but q > 0.05). Expression analysis of immuno-oncology (IO)-related genes showed significantly higher expression of CTLA4 and PDCD1 in H (q < 0.05, fold change 1.2 and 1.3) and IDO1 and PDCD1LG2 expression trended higher in B/T (p < 0.05, fold change 0.95). To our knowledge, this is one of the largest cohorts of PDAC tumors subjected to broad molecular profiling. Differences in IO-related gene expression and TIME cell distribution suggest that response to IO therapies may differ in PDAC arising from H vs. B/T. Subtle differences in the genomic profiles of H vs. B/T tumors were observed.
Distinct forms of structural plasticity of adult-born interneuron spines in the mouse olfactory bulb induced by different odor learning paradigms
Authors: Ferreira Aymeric , Constantinescu Vlad-Stefan , Malvaut Sarah , Saghatelyan Armen , Hardy Simon V. 
Published: April 2024

AbstractThe morpho-functional properties of neural networks constantly adapt in response to environmental stimuli. The olfactory bulb is particularly prone to constant reshaping of neural networks because of ongoing neurogenesis. It remains unclear whether the complexity of distinct odor-induced learning paradigms and sensory stimulation induces different forms of structural plasticity. In the present study, we automatically reconstructed spines in 3D from confocal images and performed unsupervised clustering based on morphometric features. We show that while sensory deprivation decreased the spine density of adult-born neurons without affecting the morphometric properties of these spines, simple and complex odor learning paradigms triggered distinct forms of structural plasticity. A simple odor learning task affected the morphometric properties of the spines, whereas a complex odor learning task induced changes in spine density. Our work reveals distinct forms of structural plasticity in the olfactory bulb tailored to the complexity of odor-learning paradigms and sensory inputs.
Stereocontrolled Synthesis of a Heptose- and Kdo-Containing Common Inner-Core Trisaccharide of Lipopolysaccharides from Multiple Pathogenic Bacteria
Authors: Li Jiawei , Gao Runing , Li Tiehai , Hao Tianhui 
Published: April 2024

AbstractLipopolysaccharides (LPSs) are major virulence determinants in Gram-negative bacteria and are responsible for many pathophysiological processes during bacterial infections. However, the accessibility of LPS-associated oligosaccharides for infectious mechanism study and vaccine development remains challenging. We report an efficient stereocontrolled approach for the synthesis of a common inner-core trisaccharide containing difficult-to-access, rare, higher-carbon sugars: a heptose (Hep) and 3-deoxy-α-d-manno-oct-2-ulosonic acid (Kdo). Key features include comprehensive elaboration of a practical synthesis of versatile Hep and Kdo building blocks, and stereoselective assembly of an inner-core trisaccharide from multiple pathogenic bacteria.
Advances in the Synthesis of Sulfur-Containing Cyclic Architectures via Insertion of SO2
Authors: Shu Chao , Lu Xi , Huang Yuyang , Yang Yuxing 
Published: April 2024

AbstractSulfur-containing heterocycles, where the S(R) moiety is located within the cyclic structure, have found tremendous applications in the fields of chemical, pharmaceutical, and materials sciences due to their unique chemical, biological and pharmaceutical activities. Recent years have witnessed increasing interest in sulfur-containing heterocycles, and new methods for their synthesis have been reported by adopting modern methodologies and technologies through insertion of sulfur dioxide. The main objective of this Account is to overview the latest major developments on the synthesis of sulfur-containing heterocyclic systems, mainly covering thermo-, photo- and electron-induced cyclization through the insertion of sulfur dioxide (SO2). We aim to provide the readership with a comprehensive understanding of this topic and offer a positive outlook on the promising future of this field.1	Introduction2	Thermal-Induced Cyclization3	Photoinduced Radical Cyclization4	Electron-Induced Radical Cyclization5	Conclusion
Divisions within the British Parliamentary Labour Party under Keir Starmer: Results of a Cluster Analysis
Authors: Jeffery David , Roe-Crines Andrew S. , Heppell Timothy 
Published: April 2024

AbstractThis paper offers a methodologically innovative two-stage approach for studying divisions amongst parliamentary representatives. Using the Parliamentary Labour Party (PLP) as our case study, we construct a dataset of all Labour MPs elected in the 2019 general election, along with their nominations in the 2020 Labour Party leadership and deputy leadership elections and their membership of, or affiliation with, various party-aligned organisations. We then conduct a cluster analysis based on this dataset, which reveals the existence of a two-cluster model—comprised of the Mainstream (N = 162) and the Left (N = 33)—and a five-cluster model—in which the Left exists alongside the Tribune Soft Left, the Labour Friends of Palestine and the Middle East Soft Left, the Unaligned Centrists, and the Right. Finally, we test the robustness of our clusters via a canonical correspondence analysis of the language used by MPs on social media (Twitter/X) and their contributions to parliamentary debates (Hansard). We show that the MPs from different clusters do use different languages to one another in both fora. We also find that the main divide within the PLP is between the left of the party and the rest of the party, and that the deputy leader, Angela Rayner, has a broader base of support amongst the PLP than its current leader Keir Starmer.
How business environment of countries along the Belt and Road impacts China’s OFDI efficiency: a stochastic frontier gravity model approach
Authors: Gao Qi , Wang Qiang , Zhang Wei 
Published: April 2024

AbstractThe Belt and Road Initiative (BRI) provides a new opportunity for the continued deepening of economic and trade cooperation between China and countries along the BRI route. However, different business environments among countries have led to the problem of a low and uneven distribution of China’s overall trade and investment efficiency in the countries along the route. Using the stochastic frontier gravity model, this study evaluates the efficiency of China’s outward foreign direct investment (OFDI) based on the data of 47 countries along the Belt and Road route from 2013 to 2019. The empirical results indicate that the efficiency level of China’s OFDI in countries located along the Belt and Road route is 43.39%, which suggests a regional imbalance. In terms of business environment factors, regulatory governance, civic discourse, government accountability, and regulatory quality in host countries have a positive impact on China’s OFDI. The positive effects of the BRI have enabled Chinese enterprises to better face factors such as political instability, corruption and imperfections in the legal system when investing abroad. The findings and suggestions could help the governments and enterprises of the countries along the route improve the business environment in a more targeted manner, enhance the space for economic and trade cooperation, and promote the common development of the countries along the route.
Random beta-transformations on fat Sierpinski gaskets
Authors: Dajani Karma , Li Wenxia , Zhang Tingyu 
Published: April 2024

We consider the iterated function system (IFS)

f

q
→

(

z
→

)
=

z
→

+

q
→

β

,

q
→

∈
{
(
0
,
0
)
,
(
1
,
0
)
,
(
0
,
1
)
}
.

\begin{equation*} f_{\vec {q}}(\vec {z})=\frac {\vec {z}+\vec {q}}{\beta },\,\,\vec {q}\in \{(0,0),(1,0),(0,1)\}. \end{equation*}

As is well known, for

β
=
2

\beta = 2

the attractor,

S
β

S_\beta

, is a fractal called the Sierpiński gasket (or sieve) and for

β
>
2

\beta >2

it is also a fractal. Our goal is to study random

β
\beta

-transformations on the attractor for this IFS with

1
>
β
≤
3

/

2

1>\beta \leq 3/2

. In this case,

S
β

S_\beta

is a triangle. We show that all

β
\beta

-expansions of a point

z
→

\vec {z}

in

S
β

S_\beta

can be generated by a random map

K
β

K_\beta

defined on

{
0
,
1

}

N

×
{
0
,
1
,
2

}

N

×

S
β

\{0,1\}^\mathbb {N}\times \{0,1,2\}^\mathbb {N}\times S_\beta

and

K
β

K_\beta

has a unique invariant measure of maximal entropy. Furthermore, we show the existence of a

K
β

K_\beta

-invariant probability measure of the form

m
1

⊗

m
2

⊗

μ

β

m_1\otimes m_2 \otimes \mu _{\beta }

, where

m
1

,

m
2

m_1, m_2

are product measures on

{
0
,
1

}

N

,
{
0
,
1
,
2

}

N

\{0,1\}^\mathbb {N},\{0,1,2\}^\mathbb {N}

, respectively, and

μ

β

\mu _{\beta }

is absolutely continuous with respect to the two-dimensional Lebesgue measure

λ
2

\lambda _2

.
DeepFGRN: inference of gene regulatory network with regulation type based on directed graph embedding
Authors: Gao Zhen , Su Yansen , Xia Junfeng , Cao Rui-Fen , Ding Yun , Zheng Chun-Hou , Wei Pi-Jing 
Published: April 2024

AbstractThe inference of gene regulatory networks (GRNs) from gene expression profiles has been a key issue in systems biology, prompting many researchers to develop diverse computational methods. However, most of these methods do not reconstruct directed GRNs with regulatory types because of the lack of benchmark datasets or defects in the computational methods. Here, we collect benchmark datasets and propose a deep learning-based model, DeepFGRN, for reconstructing fine gene regulatory networks (FGRNs) with both regulation types and directions. In addition, the GRNs of real species are always large graphs with direction and high sparsity, which impede the advancement of GRN inference. Therefore, DeepFGRN builds a node bidirectional representation module to capture the directed graph embedding representation of the GRN. Specifically, the source and target generators are designed to learn the low-dimensional dense embedding of the source and target neighbors of a gene, respectively. An adversarial learning strategy is applied to iteratively learn the real neighbors of each gene. In addition, because the expression profiles of genes with regulatory associations are correlative, a correlation analysis module is designed. Specifically, this module not only fully extracts gene expression features, but also captures the correlation between regulators and target genes. Experimental results show that DeepFGRN has a competitive capability for both GRN and FGRN inference. Potential biomarkers and therapeutic drugs for breast cancer, liver cancer, lung cancer and coronavirus disease 2019 are identified based on the candidate FGRNs, providing a possible opportunity to advance our knowledge of disease treatments.
Emerging Victims in Contemporary Drugs Policing
Authors: Marshall Hannah , Bacon Matthew , Spicer Jack 
Published: April 2024

Abstract
Recent shifts in UK policy have seen certain populations, who were predominantly viewed as offenders due to their involvement in drug offences, increasingly recognized as victims of exploitation. Drawing on qualitative data from three studies, this article interrogates how this trend is playing out within contemporary drugs policing, focussing on officers’ responses to people affected by: drug addiction, child criminal exploitation and ‘cuckooing’. It examines the tensions that arise as these three categories of emergent drug victims conflict with officers’ normative expectations of victimhood, perceptions of their role as police and existing operational strategies. It also reveals officers’ attempts to (re)gain ontological security by policing the boundaries of victimhood and re-focussing on new categories of offender.
The origin of return correlation networks
Authors: Banerjee Anirban , Chakrabarti Arnab , Chakrabarti Anindya S 
Published: April 2024

Abstract
Financial networks are constructed from asset price comovements. There is a large literature that takes these networks as given, for example, for portfolio optimization. But what exactly is the origin of these networks? We exploit a unique database with matched asset price and order imbalance data, allowing us to observe the trade orders placed and reveal excess demands along with the resulting prices. Empirically, we find that order imbalance comovement has a positive and statistically significant effect on return comovement. Filtering out the latent market factors from both order imbalance and return leads to a drastic drop in explanatory power. We infer that the market factor of order imbalance is the primary driver of return comovement—robust to model specifications as well as fixed effects. We present complementary results with market volatility and the decomposition of traders in terms of strategic heterogeneity. Our work brings forth the role of order imbalance networks in explaining asset return networks.