bluuebunny/crossref_metadata_2025_split huggingface dataset:
Predicting patients with septic shock and sepsis through analyzing whole-blood expression of NK cell-related hub genes using an advanced machine learning framework
Authors: Du Chao , Tan Stephanie C. , Bu Heng-Fu , Subramanian Saravanan , Geng Hua , Wang Xiao , Xie Hehuang , Wu Xiaowei , Zhou Tingfa , Liu Ruijin , Xu Zhen , Liu Bing , Tan Xiao-Di 
Published: November 2024

BackgroundSepsis is a life-threatening condition that causes millions of deaths globally each year. The need for biomarkers to predict the progression of sepsis to septic shock remains critical, with rapid, reliable methods still lacking. Transcriptomics data has recently emerged as a valuable resource for disease phenotyping and endotyping, making it a promising tool for predicting disease stages. Therefore, we aimed to establish an advanced machine learning framework to predict sepsis and septic shock using transcriptomics datasets with rapid turnaround methods.MethodsWe retrieved four NCBI GEO transcriptomics datasets previously generated from peripheral blood samples of healthy individuals and patients with sepsis and septic shock. The datasets were processed for bioinformatic analysis and supplemented with a series of bench experiments, leading to the identification of a hub gene panel relevant to sepsis and septic shock. The hub gene panel was used to establish a novel prediction model to distinguish sepsis from septic shock through a multistage machine learning pipeline, incorporating linear discriminant analysis, risk score analysis, and ensemble method combined with Least Absolute Shrinkage and Selection Operator analysis. Finally, we validated the prediction model with the hub gene dataset generated by RT-qPCR using peripheral blood samples from newly recruited patients.ResultsOur analysis led to identify six hub genes (GZMB, PRF1, KLRD1, SH2D1A, LCK, and CD247) which are related to NK cell cytotoxicity and septic shock, collectively termed 6-HubGss. Using this panel, we created SepxFindeR, a machine learning model that demonstrated high accuracy in predicting sepsis and septic shock and distinguishing septic shock from sepsis in a cross-database context. Remarkably, the SepxFindeR model proved compatible with RT-qPCR datasets based on the 6-HubGss panel, facilitating the identification of newly recruited patients with sepsis and septic shock.ConclusionsOur bioinformatic approach led to the discovery of the 6-HubGss biomarker panel and the development of the SepxFindeR machine learning model, enabling accurate prediction of septic shock and distinction from sepsis with rapid processing capabilities.
Scaling of bite force corresponds with ontogenetic niche shifts in coastal elasmobranchs
Authors: Cullen Joshua A. , Marshall Christopher D. 
Published: November 2024

IntroductionPredators, such as sharks, play important ecological roles in coastal ecosystems when abundant and these roles change over ontogeny. However, these shifts in ecological role are often not evaluated in light of feeding performance. Bite force is an ecologically relevant metric of feeding performance that has been associated with a species’ foraging niche, which may serve as a key factor that constrains prey selection both within and among sympatric species.MethodsThis study applies an interdisciplinary approach to discern the ecomechanical relationships of sympatric bull (Carcharhinus leucas), blacktip (Carcharhinus limbatus), and bonnethead sharks (Sphyrna tiburo) using feeding biomechanics and bulk stable isotopes (δ13C, δ15N). We sought to (1) estimate theoretical bite force and scaling patterns over ontogeny of each species, (2) quantify niche breadth and overlap among species, and (3) characterize the relationship between ecological niche shifts and changes in bite force over ontogeny.ResultsImportantly, we found that smaller conspecifics exhibited positive allometric scaling of bite force, whereas larger conspecifics exhibited isometric scaling. Associations between bite force and ontogenetic niche shifts in habitat and diet were found in bull and bonnethead sharks, but not in blacktip sharks. Additionally, isotopic niche breadth was greatest in generalist bull sharks, followed by specialist bonnethead and blacktip sharks.DiscussionThese findings highlight animal performance measures as critical drivers ofecological relationships both within and among species. Size-based differences in bite force scaling should be considered when evaluating feeding performance in other taxa, which may be an important determinant of ontogenetic dietary shifts.
Analysis of factors affecting prognosis of the visual acuity and baseline risk factors for subretinal fibrosis in neovascular age-related macular degeneration patients
Authors: Liu Shihan , Zheng Minming , Sun Huixin , Pan Chunxing , Li Danting , Zhou Xiyuan , Zheng Zheng 
Published: November 2024

BackgroundTo evaluate factors affecting visual acuity prognosis in patients with neovascular age-related macular degeneration (nAMD) following anti-vascular endothelial growth factor (anti-VEGF) therapy via intravitreal injection and to identify baseline risk factors for subretinal fibrosis (SF).MethodsA retrospective study of 64 nAMD eyes treated with intravitreal anti-VEGF treatment over 12 months of follow-up was conducted. Demographic and optical coherence tomography characteristics at baseline were recorded to explore the relevant factors affecting visual acuity outcome. Find baseline risk factors for SF development. The primary baseline measures included OCT qualitative and quantitative indicators, and optical coherence tomography angiography (OCTA) quantitative features.ResultsBCVA (logMAR) at 12 months was positively correlated with age (r = 0.258, p = 0.040), baseline BCVA (r = 0.749, p &lt; 0.001), central macular thickness (CMT) (r = 0.413, p &lt; 0.001), subretinal hyperreflective material (SHRM) (r = 0.304, p = 0.014), intraretinal fluid (IRF) (r = 0.423, p &lt; 0.001), type 2 macular neovascularization (MNV) (r = 0.272, p = 0.029), and ellipsoidal zone breaks (r = 0.299, p = 0.016), and hyperreflective foci (HF) (r = 0.264, p = 0.035). Eyes with SF had worse baseline BCVA (p &lt; 0.001), greater CMT (p = 0.009), and a higher prevalence of IRF (p = 0.005), type 2 MNV (p = 0.001), SHRM (p = 0.012), and HF (p = 0.028). Logistic binary regression analysis showed that baseline BCVA (logMAR) (OR = 0.02, 95% CI: 0.00–0.45, p = 0.013), HF (OR = 0.11, 95% CI: 0.01–0.95, p = 0.045), and type 2 MNV (OR = 0.08, 95% CI: 0.01–0.88, p = 0.039) were independent risk factors of subretinal fibrosis. As for quantitative OCTA parameters, eyes with subretinal fibrosis had a larger microvascular lesion size (p = 0.003), larger vessels area (p = 0.002), higher number of vessel junctions (p = 0.042) and endpoints (p = 0.024), longer total vessel length (p = 0.005), and lower vessel length density (p = 0.042).ConclusionThis study enplores baseline OCT and OCTA characteristics associated with subretinal fibrosis in nAMD patients. This information can help predict the occurrence and progression of subretinal fibrosis, potentially leading to more personalized treatment approaches for nAMD patients.
Association of different inflammatory indices with risk of early natural menopause: a cross-sectional analysis of the NHANES 2013–2018
Authors: Zheng Mengyu , Li Junying , Cao Yushan , Bao Zhuo , Dong Xing , Zhang Pei , Yan Jinxiang , Liu Yixuan , Guo Yongzhen , Zeng Xianxu 
Published: November 2024

BackgroundEarly natural menopause, characterized by the cessation of ovarian function before the age of 45, has been a subject of prior research indicating that inflammation may predict the onset of menopause. However, the specific relationship between peripheral blood inflammatory parameters and early natural menopause remains ambiguous.MethodsThis observational study utilized data from the National Health and Nutrition Examination Survey (NHANES) spanning the years 2013–2018. The age at menopause was ascertained through the Reproductive Health Questionnaire (RHQ), with early natural menopause defined as menopause occurring before the age of 45 years. Complete blood counts were derived from laboratory test data, and seven indices of inflammation were calculated, including lymphocyte count (LC), neutrophil count (NC), systemic immune inflammation index (SII), product of platelet and neutrophil count (PPN), platelet-lymphocyte ratio (PLR), neutrophil-lymphocyte ratio (NLR), and lymphocyte-monocyte ratio (LMR). A multivariate weighted logistic regression analysis was employed to estimate the association between these inflammatory indices and early natural menopause.ResultsA total of 2,034 participants were included in the analysis, of whom 460 reported experiencing menopause before the age of 45. Both Log2-NC and Log2-PPN were found to be positively correlated with early menopause, with odds ratios (OR) of 1.56 (95% CI: 1.16, 2.09; p = 0.005) and 1.36 (95% CI: 1.07, 1.72; p = 0.015), respectively. The results from models 1 and 2 were consistent with those from model 3. In the trend test, participants in the fourth quartile (Q4) of log2-LC exhibited a positive correlation with early menopause compared to those in the lowest quartile (Q1), with an OR of 1.41 (95% CI: 1.03, 1.93; p = 0.033). Similarly, the fourth quartile (Q4) of log2-NC and log2-PPN demonstrated a positive correlation with early menopause, with odds ratios (OR) of 1.76 (95% CI: 1.27–2.45; p &lt; 0.001) and 1.66 (95% CI: 1.21–2.29; p = 0.002), respectively. In Model 3, log2-SII, log2-PLR, log2-NLR, and log2-LMR were not significantly associated with early menopause.ConclusionOur findings indicate that elevated levels of Log2-LC, Log2-NC, and Log2-PPN are positively correlated with an increased risk of early menopause among women in the United States.
Association between dietary niacin intake and abdominal aortic calcification among the US adults: the NHANES 2013–2014
Authors: Zhang Jiqian , Li Ming , Wang Xinyi , Wang Tongxin , Tian Wende , Xu Hao 
Published: November 2024

BackgroundAbdominal aortic calcification (AAC) serves as a reliable predictor of future cardiovascular incidents. This study investigated the association between dietary niacin intake and AAC in US adults.MethodsIn this study, we conducted a cross-sectional study of 2,238 individuals aged 40 years and older using data from the National Health and Nutrition Examination Survey (NHANES) 2013–2014. AAC was evaluated using the Kauppila scoring system through dual-energy X-ray absorptiometry. Daily niacin intake was calculated by averaging the two dietary recalls and classified in tertiles for analysis. In this study, multiple regression analyses and smoothed curve fitting were used to examine the relationship between dietary niacin intake and AAC, subgroup analyses and interaction tests were used to assess the stability of this relationship across different segments of the population, and forest plots were used to present the results. In addition, we validated the predictive performance of dietary niacin intake on the risk of severe AAC through Receiver Operating Characteristic (ROC) curve analysis.ResultsAmong 2,238 participants aged &gt;40 years, the results showed that the higher dietary niacin intake group was associated with lower AAC score (β = −0.02, 95% CI: −0.04 – −0.01), and a lower risk of severe AAC (OR = 0.97, 95% CI: 0.96–0.99). In the fully adjusted model, the higher tertile group was associated with lower AAC score (β = −0.37, 95% CI: −0.73 – −0.02; P for trend = 0.0461) and a lower risk of severe AAC (OR = 0.60, 95% CI: 0.38–0.93; P for trend = 0.0234). The relationship between dietary niacin intake and AAC differed significantly between diabetic and non-diabetic population. The ROC curve analysis revealed that the area under the curve (AUC) for predicting severe AAC risk based on dietary niacin intake was 0.862, indicating good predictive performance.ConclusionHigher dietary niacin intake group was associated with lower AAC score and a lower risk of severe AAC. Our findings suggest that dietary niacin intake has the potential to offer benefits in preventing AAC in the general population.
Risk factors of esophagojejunal anastomotic leakage after total gastrectomy for gastric and Siewert type II/III esophagogastric cancer: a retrospective analysis from a tertiary hospital
Authors: Liu Junjie , Hu Jinzhi , Fang Jiaming , Chen Yingliang , Chen Yonghe , He Jiasheng , Deng Zijian , Peng Junsheng , Lian Lei , Chen Shi 
Published: November 2024

Background and objectivesTo detect the risk factors associated with esophagojejunal anastomotic leakage (EJAL) after total gastrectomy for gastric and Siewert type II/III esophagogastric cancer.MethodsThe data for 609 patients underwent Roux-en-Y esophagojejunostomy after total gastrectomy between March 2015 and March 2021 were reviewed. Univariate and multivariate analyses were performed to evaluate the risk factors.ResultsEJAL was observed in 48 (7.9%) of 609 patients. Univariate analysis revealed that gender, the number of comorbidities (hypertension, diabetes mellitus, coronary heart disease and chronic obstructive pulmonary disease), postoperative serum albumin, tumor location, duration of operation were risk factors associated with EJAL. Patients who had the following factors including male, the number of comorbidities ≥2, postoperative serum albumin &lt;35 g/L, tumor location was esophagogastric junction, duration of operation ≥260 min were more likely to develop EJAL than those who had not. Multivariate analysis revealed that the number of comorbidities (OR 3.464, 95% CI 1.178 – 10.189, p = 0.024) and duration of operation (OR 2.657, 95% CI 1.242 – 5.685, p = 0.012) were independent risk factors associated with EJAL.ConclusionsMore morbidities and prolonged operative duration were independently associated with EJAL after total gastrectomy for gastric and Siewert type II/III esophagogastric cancer. This study indicated the necessity for careful management of these high-risk patients.
Trajectories of adherence to intravenous biological treatment in patients with inflammatory bowel disease: a longitudinal analysis
Authors: Dong Xiuli , Zhu Suyan , Jin Yiyi , Ren Chaoqun , Chen Chunyan 
Published: November 2024

BackgroundLong-term biological therapies for inflammatory bowel disease (IBD) include infliximab and vedolizumab, which are administered intravenously. Although highly effective, non-adherence to these biologics is common and is associated with adverse sequelae and loss of response.ObjectiveIn this study, we aim to characterize long-term intravenous biologic adherence trajectories among IBD patients and identify the factors linked with these trajectories.MethodsWe conducted a retrospective multicenter study of IBD patients over 2 years to assess their adherence to infliximab and vedolizumab. The date of infusion was determined based on medical and pharmacy records. Using group-based trajectory modeling (GBTM), adherence trajectories were identified based on patients’ 90-day coverage of days over time. The effect of relevant variables on adherence behavior was assessed using multinomial regression analysis.Results374 patients with IBD were included in the study, 68.2% males with a median age of 34.3 (IQR 28.0–44.4) years old. Three distinct adherence trajectories were identified for intravenous biologics: “consistent adherence” (n = 136, 36.4%), “slow decline” (n = 137, 36.6%) and “rapid decline” (n = 101, 27.0%). Compared with consistent adherence, concomitant use of aminosalicylates (OR 3.49, 95% CI 1.34–9.05) was associated with a significantly greater risk of rapid decline. Conversely, being married at the initiation of biologics (OR 0.43, 95% CI 0.19–0.95) and having been hospitalized within preceding years (OR 0.44, 95% CI 0.23–0.88) appeared to have a protective effect against rapid decline. Additionally, being male (OR 0.57, 95% CI 0.32–1.01) was found to be protective against slow decline.ConclusionDistinct adherence patterns for infusion biologics among IBD patients have been identified, offering valuable insights to refine the design and timing of adherence interventions. However, only limited factors were found to be associated with specific adherence trajectories, revealing the complex nature of adherence behavior.
Exercise combined with artificial gravity and exercise only countermeasures prevent organ and blood vessel morpholgical changes induced by 55 days HDT bedrest
Authors: Arbeille P. , Zuj K. , Guillon L. 
Published: November 2024

BackgroundChanges in blood vessel properties have been identified with confinement, spaceflight, bedrest, and dry immersion. Subsequently, it was suspected that other organs may also be affected in these extreme environments. The purposes of the current study were to determine the effects of head-down bedrest (HDT) on cardiovascular and organ measurements made using ultrasound imaging similar to that currently available on the International Space Station, and to evaluate the efficacy of two different countermeasure protocols in preventing any observed changes in the ultrasound measurements with HDT.MethodsUltrasound measures were conducted on 24 individuals (3 groups of 8) pre HDT and on day 55 of the HDT. The control group (C°) remained in passive HDT for the 55 days, the C1 group performed aerobic exercise daily (EX), and the C2 group practiced aerobic exercise under artificial gravity conditions (EX-AG). Fifteen parameters were measured on 10 different organs and blood vessels including the right common carotid artery, abdominal aorta, right tibial artery, left ventricle, right jugular vein, portal vein, right kidney, cervical and lumbar vertebra, and the vastus intermedius muscle.ResultsHDT resulted in changes for many of the parameters investigated. Observed changes in carotid IMT and distensibility, cardiac ejection fraction, portal vein diameter, and vastus intermedius muscle thickness were attenuated with EX and EX-AG, with EX-AG having a greater effect than exercise alone on measures of carotid distensibility.ConclusionResults from this study indicate changes in many structures assessed with ultrasound imaging after 55 days of HDT bedrest with some changes being attenuated with the two investigated countermeasure protocols.
Phyllosphere bacterial community and metabolomic analysis revealed the mechanism of Cd tolerance in the bryophyte Tortella tortuosa (Hedw.) Limpr
Authors: Zhu Yongqi , Li Qiuge , Feng Lanlan , Dong Yijie , Zhang Yuanpei , Nurmaimaiti Nigati , Mamut Reyim 
Published: November 2024

IntroductionPhytoremediation is a safe and green technology for the remediation of heavy metal pollution, a global environmental problem. Bryophytes are well known for their special physiological properties, but there is little research on the use of bryophytes for phytoremediation.MethodsIn this indoor experiment, the impacts of 40 days of Cd pollution (1 (T1), 5 (T2), 10 (T3) mg·L-1) on Cd absorption, growth and physiological characteristics, and phyllosphere bacterial diversity of Tortella tortuosa were explored.ResultsThe results showed that the maximum Cd absorption capacity of T. tortuosa was 5.0135 mg·kg-1. The contents of leaf chlorophyll a (Chl a) and chlorophyll b (Chl b) in T. tortuosa decreased (p &lt; 0.05) with the increase of Cd concentration. Especially, the Chl a and Chl b contents of the T3 treatment reduced by 88% and 91%, respectively compared with those of the CK (Cd: 0 mg·L-1). The catalase (CAT) and peroxidase (POD) activities of the T3 treatment reduced by 55% and 85%, respectively (p &lt; 0.05), and the malondialdehyde (MDA) content increased by 167%, compared with those of the CK. Under Cd exposure, Cyanobacteria (63.49%) and Proteobacteria (26.62%) were the dominant bacterial phyla. The highly abundant phyllosphere bacteria were negatively correlated with the Cd concentration, antioxidant enzyme activity, and chlorophyll content in T. tortuosa, and positively correlated with the relative abundances of Neomycin and N-Acetyl-L-Glutamic acid.DiscussionAlthough the severe Cd pollution could affect the physiological and metabolic characteristics of T. tortuosa, T. tortuosa had a strong absorption capacity for Cd. Therefore, it could be used for phytoremediation of heavy metal pollution. This study will provide a reference for the remediation of soil heavy metal pollution.
Explainable light-weight deep learning pipeline for improved drought stress identification
Authors: Patra Aswini Kumar , Sahoo Lingaraj 
Published: November 2024

IntroductionEarly identification of drought stress in crops is vital for implementing effective mitigation measures and reducing yield loss. Non-invasive imaging techniques hold immense potential by capturing subtle physiological changes in plants under water deficit. Sensor-based imaging data serves as a rich source of information for machine learning and deep learning algorithms, facilitating further analysis that aims to identify drought stress. While these approaches yield favorable results, real-time field applications require algorithms specifically designed for the complexities of natural agricultural conditions.MethodsOur work proposes a novel deep learning framework for classifying drought stress in potato crops captured by unmanned aerial vehicles (UAV) in natural settings. The novelty lies in the synergistic combination of a pre-trained network with carefully designed custom layers. This architecture leverages the pre-trained network’s feature extraction capabilities while the custom layers enable targeted dimensionality reduction and enhanced regularization, ultimately leading to improved performance. A key innovation of our work is the integration of gradient-based visualization inspired by Gradient-Class Activation Mapping (Grad-CAM), an explainability technique. This visualization approach sheds light on the internal workings of the deep learning model, often regarded as a ”black box”. By revealing the model’s focus areas within the images, it enhances interpretability and fosters trust in the model’s decision-making process.Results and discussionOur proposed framework achieves superior performance, particularly with the DenseNet121 pre-trained network, reaching a precision of 97% to identify the stressed class with an overall accuracy of 91%. Comparative analysis of existing state-of-the-art object detection algorithms reveals the superiority of our approach in achieving higher precision and accuracy. Thus, our explainable deep learning framework offers a powerful approach to drought stress identification with high accuracy and actionable insights.