bluuebunny/crossref_metadata_2025_split huggingface dataset:
Triglyceride glucose index for the detection of diabetic kidney disease and diabetic peripheral neuropathy in hospitalized patients with Type 2 Diabetes
Authors: Tu Zhihui , Du Juan , Ge Xiaoxu , Peng Wengfang , Shen Lisha , Xia Lili , Jiang Xiaohong , Hu Fan , Huang Shan 
Published: March 2024

Abstract

Purpose: The objective of this study is to investigate the potential of TyG in forecasting the onset of diabetic kidney disease (DKD) and diabetic peripheral neuropathy (DPN).
Methods: A total of 2048 patients were enrolled in this study. The primiary outcomes are DKD and DPN. Quantile regression analysis was employed to investigate the implicit factors of TyG quartiles. Subsequently, based on the implicit factors, logistic regression models were constructed to further examine the relationship between TyG and DKD and DPN.
Results:In the baseline, TyG exhibited higher values across patients with DKD, DPN, and co-existence of DKD and DPN (DKD+DPN) in T2DM. Univariate logistic regressions demonstrated a significant association between an elevated TyG and an increased risk of DKD (OR = 1.842, [95% CI] 1.317–2.578, P for trend < 0.01), DPN (OR = 1.516, [95% CI] 1.114–2.288, P for trend < 0.05), DKD+DPN (OR = 2.088, [95% CI] 1.429–3.052, P for trend < 0.05) . Multivariable logistic regression models suggested a statistically significant increase in the risk of DKD (OR = 1.581, [95% CI] 1.031–2.424, p<0.05) DKD+DPN (OR = 1.779, [95% CI] 1.091–2.903, p<0.05 ) after adjusting the implicit factors of TyG quartiles. However, no significant relationship was observed between TyG and DPN in the multivariable regression analysis.
Conclusion: Elevated TyG was significantly associated with an increased risk of DKD in T2DM, but no significant relationship was showed with DPN. This finding provided further evidence for the clinical significance of integrating the TyG index into the initial assessment of diabetic microvascular complications.
Identification of the Molecular Subtypes of Acute Ischemic Stroke Using Bioinformatics and Machine Learning
Authors: Wu Zongkai , Fan Hongzhen , Qin Lu , Niu Xiaoli , Chu Bao , Zhang Kaihua , Gao Yaran , Wang Hebo 
Published: March 2024

Abstract

Acute ischemic stroke (AIS) is a severe disorder characterized by complex pathophysiological processes, which can lead to disability and death. This study aimed to determine necroptosis-associated genes in Acute ischemic stroke (AIS) and to investigate their potential as diagnostic and therapeutic targets for AIS. Expression profiling data were acquired from the Gene Expression Omnibus database, and necroptosis-associated genes were retrieved from GeneCards. The differentially expressed genes (DEGs) and necroptosis-related genes were intersected to obtain the necroptosis-related DEGs (NRDEGs) in AIS. In AIS, a total of 76 genes associated with necroptosis (referred to as NRDEGs) were identified. Enrichment analysis of these genes revealed that they were primarily enriched in pathways known to induce necroptosis. Using Weighted gene co-expression network analysis (WGCNA), five co-expression modules consisting of NRDEGs were identified, along with two modules that exhibited a strong correlation with AIS. Protein-protein interaction (PPI) analysis resulted in the identification of 20 hub genes. The Least absolute shrinkage and selection operator (LASSO) regression model demonstrated promising potential for diagnostic prediction. The receiver operating characteristic (ROC) curve validated the diagnostic model and selected nine characteristic genes that exhibited statistically significant differences (p < 0.05). By employing consensus clustering, distinct patterns of necroptosis were identified using these nine signature genes. The results were verified by quantitative PCR (qPCR) in HT22 cells and an external data set. Furthermore, the analyzed ceRNA network included nine lncRNAs, six miRNAs, and three mRNAs. Overall, this study offers novel insights into the molecular mechanisms underlying NRDEGs in AIS. The findings provide valuable evidence and contribute to our understanding of the disease.
Assessment of the Color Effect on Cadmium and Lead Contents in the Most Popular Cosmetics in the Saudi Market
Authors: Albugami Mashael M. , Buzaid Alyah , Shah Faheem , Ahmed Amel Y. 
Published: March 2024

Abstract

This study focused on determining the concentrations of lead (Pb) and cadmium (Cd) in four types of coloured cosmetic products (lipstick, blusher, eyeshadow, and hair dye) available at local markets in Saudi Arabia. Forty-eight cosmetic samples were analysed via inductively coupled plasma‒optical emission spectrometry (ICP‒OES) to measure the contents of Pb and Cd.Quality control of the data was achieved by the analysis of Hay Powder, an IAEA-V-10 standard reference material from IAEA. The concentrations of Pb in the lipstick, blusher, eyeshadow, and hair dye samples ranged from 0.052–2.823, 0.204–1.097, 1.100-2.519 and 0.142–0.187 ppm, respectively, while the concentrations of Cd ranged from 0.001–0.071, 0.003–0.019, 0.022 − 0.0061 and 0.000-0.001 ppm, respectively. The effect of color on concentration was also investigated. The results of this study were compared with those of previous studies. According to the Saudi Standards, Metrology and Quality Organization (SASO) and the United States Food and Drug Administration (US FDA), the concentrations of Pb and Cd in all samples that were examined were within permissible limits. The results derived from the utilization of hazard quotient (HQ) indices show that Pb had the highest HQ, while Cd had the lowest HQ in all cosmetics studied. In addition, the hazard indices (HIs) for lipstick, blusher, eyeshadow, and hair days ranged from 0.02–1.179, 0.08–0.457, 0.459–1.375, and 0.059–0.077, respectively.
An exploratory analysis on the association between suicidal ideation and the microbiome in patients with or without major depressive disorder
Authors: Chen Vincent Chin-Hung , Wu Shu-I 
Published: March 2024

Abstract
Background
Scarce research has investigated associations between suicidal ideation and the gut microbiota. We aimed to explore variations in the gut microbiome associated with suicidal ideation and major depressive disorder (MDD).
Results
Among the 140 participants, significant differences in Beta diversity were found between MDD patients with (n=43) or without suicidal ideation (n=34), and healthy volunteers (n=42) (all p<0.001). The strain of g-Phascolarctobacteriumwas found to have significant positive associations with scores of BSSI and BSSI Part 1 (suicidal ideation), particularly in MDD patients with suicidal ideation, after controlling for demographic and mood covariates.
Conclusion
We found different compositions and diversities of the gut microbiome associated with suicidal ideations. Potential mechanisms need further investigation to establish whether this reflects a biological process that might be the focus for intervention development.
Taxonomic triage of the genus Thrissina from Indian waters: a step towards diversity assessment
Authors: Gopinath Ashly , Abdussamad E. M. , S Jeena N. 
Published: March 2024

Abstract
Anchovies of the genus Thrissina (family Engraulidae) are common pelagic fishes that constitute a high-volume fishery resource along the Indian coast. Their accurate identification is essential for the fishery and management of resources. 314 specimens of six species of the genus Thrissina were collected to generate data for exact field identification and species-specific molecular signatures. The identification of the specimens was confirmed using morphometric and meristic parameters, along with COI-based DNA barcodes. This integrative study also aimed to reconstruct a phylogeny to clarify the evolutionary relationships among the species. The morpho-meristic results indicated that maxillary length, number of scutes, number of lower gill rakers, and anal fin ray count were the main characteristics for identifying Thrissina species. The species barcoded included T. setirostris, T. malabarica, T. mystax, T. baelama, T. polybranchialis, and T. vitrirostris. The nucleotide content of each species was examined and the genetic distance was calculated. The interspecific genetic distance (K2P) ranged from 13–22% with the highest distance observed between T. baelama and T. malabarica (22%) and the lowest between T. mystax and T. polybranchialis (13%). The average genetic distance within the genus was 19%. The topology of the phylogenetic tree showed that all the species in this study formed separate clades with a common ancestor. This work provides the first COI-barcode of the least investigated T. polybranchialis from the Indian coast, and establishes the relationship between T. mystax and T. polybranchialis. The integrative data generated here will aid in the correct identification and global studies on the genus. More morphological and molecular studies are recommended to clarify the relationship between all available species within this genus.
Extracorporeal shock wave lithotripsy: Retrospective Study on Possible Predictors of Treatment Success and Revisiting the Role of Non-contrast-enhanced Computer Tomography in Kidney and Ureteral Stone Disease
Authors: Oliveira Beatriz , Teixeira Bernardo , Magalhães Martinha , Vinagre Nuno , Cavadas Vítor , Fraga Avelino 
Published: March 2024

Abstract
Background
Extracorporeal shock wave lithotripsy (ESWL) is a safe and efficient treatment option for urinary stone disease. The overall stone-free rate (SFR) varies significantly. This study aimed to assess the influence of stone size, location, stone density, and skin-to-stone distance (SSD), on the outcome of ESWL. We assessed whether pre-treatment non-contrast-enhanced CT scan (NCCT) confers significant advantages compared to kidney-ureter-bladder film (KUB) only.
Methods
We reviewed the medical records of 307 cases (165 men, 142 women) with renal and ureteral stones treated consecutively at our institution with ESWL between 2020 and 2023. 44 of these underwent a NCCT. The outcome of ESWL was defined in two ways: visible stone fragmentation on KUB, and the need for further treatment.
Results
Overall success of fragmentation was 85% (261 patients). 61% of patients (n = 184) didn’t need any further treatment. Stone size and location correlated significantly with treatment outcomes regarding the need for further treatment (p = 0,004) and stone fragmentation (p = 0,016), respectively. Unlike mean SSD (p = 0,462), the mean attenuation value (MAV) significantly correlated with the need for retreatment (p = 0,016). MAV seems to be a better predictor of treatment success (AUC of the ROC curve: 0,729), compared to stone size (AUC: 0,613). The difference between groups (with and without NCCT) in both treatment outcomes did not reach statistical significance.
Conclusion
During decision-making, information regarding SSD and MAV can be useful in more dubious scenarios. However, it appears that their inclusion doesn’t provide substantial advantages when compared to relying solely on KUB.
Microbe-binding antibodies in the female genital tract: associations with the vaginal microbiome and genital immunology
Authors: Liu Rachel , Pollock James , Huibner Sanja , Udayakumar Suji , Irungu Erastus , Ngurukiri Pauline , Muthoga Peter , Adhiambo Wendy , Kimani Joshua , Beattie Tara , Coburn Bryan , Kaul Rupert 
Published: March 2024

Abstract
Background
Mucosal antibodies in the gut maintain homeostasis between the host and the local microbiome through the clearance of pathogenic bacteria and the development of immune tolerance to inflammatory bacteria. Whether similar bacteria-immunoglobulin interactions modulate cervicovaginal inflammation and/or bacterial colonization in the female genital tract (FGT) is not well understood. Here, we used a flow cytometry-based assay to quantify microbe-binding IgA and IgG in the cervicovaginal secretions of 200 HIV-uninfected women from Nairobi, Kenya that were enriched for bacterial vaginosis (BV) and evaluated the associations of cervicovaginal IgA and IgG with the vaginal microbiome composition and local soluble immune factors.
Results
Total IgA and IgG were abundant in cervicovaginal secretions and frequently demonstrated ex vivo binding to key vaginal bacteria species Gardnerella vaginalis, Prevotella bivia, Lactobacillus iners, and Lactobacillus crispatus. Microbe-binding antibodies were generally not associated with the presence/absence of the corresponding bacteria. Total and microbe-binding IgA and IgG were inversely correlated with total bacterial abundance and positively correlated with several pro-inflammatory cytokines (IL-6, TNF) and chemotactic chemokines (IP-10, MIG, MIP-1α, MIP-1β, MIP-3α, MCP-1, IL-8), independent of total bacterial abundance.
Conclusions
Flow cytometry-based quantification of microbe-binding antibodies provides a platform to investigate host-microbiota interactions in the FGT of human observational and interventional studies. In contrast to the gut, cervicovaginal microbe-binding IgA and IgG do not appear to be immunoregulatory but may indirectly mitigate bacteria-induced inflammation by reducing total bacterial abundance.
Enhancing industrial decision-making through ML-integrated frameworks and multi-criteria decision-making approach
Authors: Hala Eng. Mellouli , Anwar Meddaoui , Abdelhamid Zaki 
Published: March 2024

Abstract

Decision-making in contemporary industrial settings has evolved from intuition to data-driven methodologies, necessitating efficient handling of vast datasets. Conventional Multi-Criteria Decision Making (MCDM) approaches struggle with the complexities of big data. This study introduces an innovative decision-support system integrating multi-criteria methods with machine learning techniques as artificial neural network. The proposed six-step framework aims to optimize operational decisions by analyzing real-time performance data. The research contributes to the advancement of decision-making methodologies in the industrial field, offering dynamic responsiveness and enhanced recommendations compared to traditional MCDM methods. While promising, future work must focus on robustness testing, particularly in real-time data dependencies, to ensure sustained efficacy and mitigate potential biases in recommendations over time.
Distribution, Habitats and Behavior of the Non-Indigenous Brachyuran Percnon gibbesi (Percnidae) of Southwestern Mediterranean (Algeria)
Authors: Bada Neila , Derbal Farid , Nouacer Sabah , Rachedi Mounira 
Published: March 2024

Abstract
Some ecological aspects of the alien crab Percnon gibbesi (H. Milne Edwards, 1853) have been approached by visual surveys in the southwestern Mediterranean waters (East, Algeria). This sampling method, applied on a coastal line of 16.2 km and between 0 and − 3 m depth, allowed us to collect information on abundances and densities by size category, nature of the habitat and behavior of this species. Overall 22 diurnal dives (3 observations/dive) were carried out in 2 different adjacent costal sectors, the Bay of Annaba (Sector 1: Lever de l’Aurore-Amphore), located at the east, and Cap de Garde (Sector 2: Cap de Garde - Djenen El Bey beach), located at the west of the Bay. We recorded an average total of 5325 individuals (± 15.62) with numerical dominance in sector 2 (4472 ± 9 ind.). Medium-sized individuals dominated numerically in sector 1 (412 ± 10 ind.), while large individuals were better represented in sector 2 (1980 ± 12.77 ind.). Average densities in two sectors varied to according on measurement unit (surface, dive time). Whatever the sector, the rock cracks sheltered the greatest number of individuals. No individual was observed on homogeneous bottoms. Overall, crabs are indifferent in the presence of an observer.
Optimizing Hybrid Renewable Energy Systems:Novel Models and Heuristic Algorithms
Authors: Hosseini Eghbal , Al-Ghaili Abbas M. , Deveci Muhammet , Gunasekaran Saraswathy Shamini , Jamil Norziana 
Published: March 2024

Abstract
In practical applications, real-world challenges ofteninvolve the integration of multiple renewable energy sources,including solar, wind, hydro, biomass, geothermal, and energystorage solutions like batteries. The primary objective ofthese integrated systems is to optimize energy production,enhance system reliability, and simultaneously minimize costssuch as those associated with fossil fuels and greenhouse gasemissions. Therefore, it is crucial to model these complexproblems as all objectives are covered. In this paper, wepropose multi-objective and multi-level mathematical modelsfor Hybrid Renewable Energy Systems (HRESs). These modelsallow us to simultaneously address various objectives andlevels of decision-making in the context of renewable energyintegration. Then, to effectively tackle these complex models,the paper introduces two efficient hybrid algorithms for bothmulti-level and multi-objective models. First algorithm is acombined smoothing approach to address multi-level problem.The algorithm uses Karush-Kuhn-Tucker (KKT) conditions,some mathematical principles and proofs, and a heuristicfunction to smooth the multi-level model. Finally, the algorithmemploys Taylor approximation to further refine the smoothedproblem. The second algorithm, focuses on multi-objective model,which involves two phases: first, a heuristic algorithm simplifiesobjective functions through interpolation; second, the populationis enhanced using the Laying Chicken Algorithm (LCA); and aneural network refines the best LCA generation to identify thePareto front in multi-objective problems.
Identifying Psychosis Episodes in Psychiatric Admission Notes via Rule-based Methods, Machine Learning, and Pre-Trained Language Models
Authors: hua yining , Blackley Suzanne , Shinn Ann , Skinner Joseph , Moran Lauren , Zhou Li 
Published: March 2024

Abstract
Early and accurate diagnosis is crucial for effective treatment and improved outcomes, yet identifying psychotic episodes presents significant challenges due to its complex nature and the varied presentation of symptoms among individuals. One of the primary difficulties lies in the underreporting and underdiagnosis of psychosis, compounded by the stigma surrounding mental health and the individuals' often diminished insight into their condition. Existing efforts leveraging Electronic Health Records (EHRs) to retrospectively identify psychosis typically rely on structured data, such as medical codes and patient demographics, which frequently lack essential information. Addressing these challenges, our study leverages Natural Language Processing (NLP) algorithms to analyze psychiatric admission notes for the diagnosis of psychosis, providing a detailed evaluation of rule-based algorithms, machine learning models, and pre-trained language models. Additionally, the study investigates the effectiveness of employing keywords to streamline extensive note data before training and evaluating the models. Analyzing 4,617 initial psychiatric admission notes (1,196 cases of psychosis versus 3,433 controls) from 2005 to 2019, we discovered that the XGBoost classifier employing Term Frequency-Inverse Document Frequency (TF-IDF) features derived from notes pre-selected by expert-curated keywords, attained the highest performance with an F1 score of 0.8881 (AUROC [95% CI]: 0.9725 [0.9717, 0.9733]). BlueBERT demonstrated comparable efficacy an F1 score of 0.8841 (AUROC [95% CI]: 0.97 [0.9580, 0.9820]) on the same set of notes. Both models markedly outperformed traditional International Classification of Diseases (ICD) code-based detection methods from discharge summaries, which had an F1 score of 0.7608, thus improving the margin by 0.12. Furthermore, our findings indicate that keyword pre-selection markedly enhances the performance of both machine learning and pre-trained language models. This study illustrates the potential of NLP techniques to improve psychosis detection within admission notes and aims to serve as a foundational reference for future research on applying NLP for psychosis identification in EHR notes.
Synovial transcriptome-wide association study implicates novel genes underlying rheumatoid arthritis risk
Authors: Hu Shou-ye , Song Hui-Miao , Jiang Feng , Wang Ya-kang , Tian Wen , Wu Hao , Yao Shi , He Chang-Yi , Gao Hui-Wu , Yang Tie-Lin , Yang Zhi , Guo Yan 
Published: March 2024

Abstract
Background
To identify rheumatoid arthritis (RA)-associated susceptibility genes and pathways through integrating genome-wide association study (GWAS) and self-collected gene expression profile data.
Methods
A gene expression prediction model was built for synovium in 202 arthritic patients with matched genotype and gene expression data. Using the FUSION software performed a transcriptome-wide association study (TWAS). GWAS summary data was driven from the largest RA GWAS meta-analysis (n = 276,020). Further analyses (conditional and joint analysis, two types of causal analysis, differential expression analysis and gene-set enrichment analysis) were used to deepen our understanding of genetic architecture and comorbidity aetiology of RA.
Results
We identified eight conditionally independent genes associated with RA after Bonferroni corrections, of which three genes were novel, such as TPRA1 (PTWAS = 9.59 × 10− 6) and HIP1 (PTWAS = 1.47 × 10− 5). We identified four genes that showed strong causal evidence, four genes differentially expressed in RA, and explored the possibility of new uses for known drugs.
Conclusions
By using relevant tissues in synovium, our TWAS analysis led to the identification of previously unknown RA-associated genes, shedding new light on the underlying genetic architecture of RA.
A multi-stage time-backtracking grey wolf optimizer introducing a new hierarchy mechanism
Authors: Yang Jiajie , Gu Wei 
Published: March 2024

Abstract
The Grey Wolf Optimizer (GWO) is a swarm intelligence algorithm that mimics natural creatures. It is characterized by fast convergence, few parameters and high accuracy. However, after extensive research, it is found that GWO is easily converged too early in the process of the algorithm, which leads to the reduction of population diversity and the results are not accurate enough, in addition, GWO has a problem that the results tend to converge to 0 easily, which means that the algorithm obtains exceptionally good results on the test function with the optimal point of 0, but not in other cases. To solve the main problems of GWO, this paper proposes to introduce a new hierarchical multi-stage adaptive search grey wolf optimization algorithm called MTBGWO. In this paper, we compare MTBGWO with other excellent algorithms on 23 benchmark standard test sets before and after panning and CEC2013 conference test sets, and the results show that MTBGWO can largely improve the above mentioned problems and improve the performance of the algorithm. To further test the usability of the algorithm on real-world problems, the algorithm is used in this paper on three classical engineering problems and the results show that the algorithm has good applicability and accuracy on real-world problems.