bluuebunny/crossref_metadata_2025_split huggingface dataset:
Memory, Patriarchy and Economy in Turkey
Authors: Uğur-Çinar Meral 
Published: October 2024

This book brings a dynamic approach to Turkish politics by showing how political struggles operate via narratives and how ideas, institutions and narratives interact. By analysing narratives of collective memory, patriarchy and economic development, it shows the ways in which narratives shape politics in both intended and unintended ways. Meral Uğur-Çınar combines rich theoretical discussion with textual and ethnographic data, looking at how narratives serve the reproduction of consent as well as the mobilisation of resistance. Memory, Patriarchy and Economy in Turkey examines how narratives are utilised for the continuation of institutional structures as well as at critical junctures of institutional change and at the gradual erosion of institutions. Through the narrative analysis of texts, ranging from political speeches to museum documents as well as graffiti and posters from protests, the author sheds light on contemporary Turkish politics as well as offering a glance into how narratives operate in the political realm. Through this careful analysis, the book helps make sense of current political discussions in Turkey and elsewhere, particularly on issues such as democratic backsliding, institutional erosion, populism, polarisation and social movements.
Antifungal susceptibility, molecular epidemiology, and clinical risk factors of Candida glabrata in intensive care unit in a Chinese Tertiary Hospital
Authors: Huang Si-Jia , Lv Geng , Song Yi-Hui , Zhao Jun-Tao , Liu Jin-Yan , Wang Lu-Ling , Xiang Ming-Jie 
Published: October 2024

BackgroundThe increasing incidence and high mortality rate of Candida glabrata infection in ICU patients is an important issue. Therefore, it is imperative to investigate the antifungal susceptibility profiles and epidemiological characteristics in local regions.MethodsHerein, antifungal susceptibility testing was conducted to determine the minimum inhibitory concentrations (MICs) of eight antifungal drugs. Multilocus sequence typing (MLST) was used to study the strain genotype, geographical distribution, and susceptibility to antifungal agents among C. glabrata isolates. The mechanism of echinocandin resistance was explored by sequencing the FKS1 and FKS2 genes (encoding 1,3-β-D-glucan synthases) of echinocandin-resistant C. glabrata strains. Moreover, we further investigated the clinical manifestations and the various risk factors of patients infected with C. glabrata in the ICU.ResultsWe selected 234 C. glabrata isolates from 234 patients in the ICU randomly for the follow-up study. Cross-resistance was found among the ICU C. glabrata isolates. Analysis using MLST showed that the genetic diversity among the C. glabrata isolates was low. Furthermore, sequence type showed no correlation with the antifungal resistance profiles, but was associated with geographical distribution. We also revealed novel mutations in FKS1 (S629P) and FKS2 (W1497stop) that mediated high-level echinocandin resistance (MIC &gt;8 µg/mL). More than 14 days’ stay in ICU (P=0.007), Acute Physiology and Chronic Health Evaluation II (APACHE-II) score (P=0.024), prior antifungal exposure (P=0.039) and lung disease (P=0.036) were significantly associated with antifungal resistant/non-wild-type C. glabrata infection.ConclusionOur study shed light on the antifungal susceptibility, molecular epidemiology, and clinical risk factors of C. glabrata in the ICU of a Chinese Tertiary Hospital. Importantly, we revealed the molecular mechanism of echinocandin resistance. These results highlight the significance of continued surveillance in ICUs and provide data support for the treatment of C. glabrata in clinics.
The clinical and genetic aspects of six individuals with GH1 variants and isolated growth hormone deficiency type II
Authors: Huang Xiaozhen , Chen Hong , Shangguan Huakun , Wu Wenyong , Ai Zhuanzhuan , Chen Zhifeng , Chen Ruimin 
Published: October 2024

BackgroundIsolated growth hormone deficiency type II (IGHD II) is an autosomal dominant disorder characterized by a GH1 gene variant resulting in a significant reduction in growth hormone (GH) secretion and a subsequent decrease of plasma insulin-like growth factor 1 (IGF-1) levels and eventual growth impairment.ObjectiveThis study aimed to identify causative variants in six Chinese families with IGHD II, exploring both clinical and genetic characteristics.MethodsDetailed clinical data, including clinical presentations, physical charateristics, medical and family histories, as well as genetic test results, were systematically examined.ResultsSix children, comprising four males and two females, with a mean age of 4.64 ± 1.15 years, exhibited short stature with a mean height of -3.95 ± 1.41 SDS. Four of them had a family history of short stature, while one patient presented with pulmonary hypertension. All children demonstrated GH deficiency in growth hormone stimulation tests (mean peak GH value: 2.83 ± 2.46 ng/mL). Exome sequencing for the six patients and targeted gene sequencing for their family members revealed heterozygous variants in the GH1 gene, including Exon2-5del, c.334T&gt;C, c.291 + 1G&gt;A, c.291 + 2T&gt;A, 1.5 kb deletion, and 1.7 kb deletion, with four variants being novel. Four patients underwent human recombinant growth hormone (rhGH) replacement therapy, initiating treatment at a mean age of 4.6 ± 0.7 years. The mean height increase in patients was 1.21 ± 0.3 SDS in the first six months of treatment and 1.79 ± 0.15 SDS in the first year.ConclusionOur findings contribute to expanding the genotypic and phenotypic spectra of individuals with IGHD II.
A disulfidptosis-related lncRNA signature for analyzing tumor microenvironment and clinical prognosis in hepatocellular carcinoma
Authors: Zheng Haishui , Cheng Jigan , Zhuang Ziyun , Li Duguang , Yang Jing , Yuan Fan , Fan Xiaoxiao , Liu Xiaolong 
Published: October 2024

IntroductionDisulfidptosis is a recently identified form of non-apoptotic programmed cell death which distinguishes itself from classical cell death pathways. However, the prognostic implications of disulfidptosis-related long non-coding RNAs (DRLs) and their underlying mechanisms in hepatocellular carcinoma (HCC) remain largely unexplored.MethodsIn this study, we leveraged RNA-sequencing data and clinical information of HCC patients from the TCGA database. Through expression correlation and prognostic correlation analyses, we identified a set of top-performing long non-coding RNAs. Subsequently, a 5-DRLs predictive signature was established by conducting a Lasso regression analysis.ResultsThis signature effectively stratified patients into high- and low-risk groups, revealing notable differences in survival outcomes. Further validation through univariate and multivariate Cox regression analyses confirmed that the risk score derived from our signature independently predicted the prognosis of HCC patients. Moreover, we observed significant disparities in immune cell infiltration and tumor mutation burden (TMB) between the two risk groups, shedding light on the potential connection between immune-related mechanisms and disulfidptosis. Notably, the signature also exhibited predictive value in the context of chemotherapeutic drug sensitivity and immunotherapy efficacy for HCC patients. Finally, we performed experimental validation at both cellular and patient levels and successfully induced a disulfidptosis phenotype in HCC cells.DiscussionIn general, this multifaceted approach provides a comprehensive overview of DRLs profiles in HCC, culminating in the establishment of a novel risk signature that holds promise for predicting prognosis and therapy outcomes of HCC patients.
The impact of cryopreservation on cytokine secretion and polyfunctionality in human PBMCs: a comparative study
Authors: Linder Aline , Portmann Kevin , Eyer Klaus 
Published: October 2024

IntroductionHuman peripheral blood mononuclear cells (hPBMCs) are widely used in fundamental research and clinical applications as studying their responses to in vitro activation is an effective way to uncover functional alterations and disease associated phenotypes. However, the availability of samples in large numbers at a specific time and location remains challenging, hence they often might preferably be collected and cryopreserved for later analysis. While the effect of cryopreservation on viability and cell surface expression is well established, changes in activity and cytokine secretion still lead to conflicting results as it is often measured in bulk or within the cells.MethodsHere, we used our platform for dynamic single-cell multiplexed cytokine secretion measurement and compared it to a traditional intracellular cytokine staining to quantify the effect of cryopreservation on cytokine secretion and expression of individual hPBMCs.ResultsFollowing stimulation with LPS or anti-CD3/CD28 antibodies for up to 36 or 72 h incubation, we observed distinct alterations in cytokine responses due to cryopreservation when comparing to fresh samples, but also remarkable consistencies for some cytokines and parameters. In short, the frequencies of cytokine-secreting cells in cryopreserved samples were lower for IL-6 (LPS), IL1-β (CD3/CD28) and IFN-γ (CD3/CD28), while the frequency and dynamics of IL-8 secretion were strongly impacted in all cases. We observed a large disconnect between cytokine expression and secretion for TNF-α, where the expression dramatically increased after cryopreservation, but actual secretion was, in comparison, remarkably stable. The polyfunctionality of single cells was altered by cryopreservation in specific co-secreting populations led by the effects on IL-6 or IL-8 secretion. Among immune cells, cryopreservation seemed to affect lymphocytes and monocytes differently as effects appeared early on in lymphocytes while generally observed in later time points in monocytes.ConclusionTogether, this study offers an in-depth quantitative insight into the biological behavior of immune cells in response to cryopreservation and stimulation, further providing some insights into conflicting results in the literature as well as guidelines for researchers planning to assess cytokine-secreting from frozen hPBMCs in immunological research or clinical applications.
A bibliometric analysis of cerebral small vessel disease
Authors: Yan Xiaoxiao , Zhang Yongyin , He Ruqian , Chen Xiachan , Lin Mian 
Published: October 2024

BackgroundCerebral small vessel disease (CSVD) is a significant contributor to both stroke and dementia. While numerous studies on CSVD have been published, herein, we have conducted a bibliometric examination of the literature on CSVD, revealing its hot spots and emerging patterns.MethodsWe used the Web of Science Core Collection as our primary database and conducted a literature search from January 2008 to January 2023. CiteSpace, VOSviewer, online bibliometric platform, and R-bibliometrix were employed to conduct bibliometric analysis and network visualization, including the number of publications, countries, institutions, journals, citations, authors, references, and keywords.ResultsA total of 4891 publications on CSVD were published in 790 journals by 19,066 authors at 3,862 institutions from 84 countries. The United States produced the most written works and had a significant impact in this field of study. The University of Edinburgh had the highest publication count overall. The journal with the most publications and co-citations was Stroke. Wardlaw, Joanna was the most prolific author and commonly cited in the field. The current areas of research interest revolved around “MRI segmentation” and “Enlarged perivascular spaces in the basal ganglia.”ConclusionWe conducted a bibliometric analysis to examine the advancements, focal points, and cutting-edge areas in the field of CSVD to reveal potential future research opportunities. Research on CSVD is currently rapidly advancing, with a consistent rise in publications on the topic since 2008. At the same time, we identified leading countries, institutions, and leading scholars in the field and analyzed journals and representative literature. Keyword co-occurrence analysis and burst graph emergence detection identified MRI segmentation and Basal ganglia enlarged perivascular spaces as the most recent areas of research interest.
Creating an interactive database for nasopharyngeal carcinoma management: applying machine learning to evaluate metastasis and survival
Authors: Sun Yanbo , Tan Jian , Li Cheng , Yu Di , Chen Wei 
Published: October 2024

ObjectiveNasopharyngeal carcinoma (NPC) patients frequently present with distant metastasis (DM), which is typically associated with poor prognosis. This study aims to develop and apply machine learning models to predict DM, overall survival (OS), and cancer-specific survival (CSS) in NPC patients to provide optimal tools for improved predictive accuracy and performance.MethodsWe retrieved over 8,000 NPC patient samples with associated clinical information from the Surveillance, Epidemiology, and End Results (SEER) database. Utilizing two methods for handling missing values—imputation or deletion—we created various cohorts: DM-all, DM-slim, OS-all, OS-slim, CSS-all, and CSS-slim. Five machine learning models were deployed for the binary classification task of DM, and their performance was evaluated using the area under the curve (AUC). For the survival prediction tasks of OS and CSS, we constructed 45 combinations using nine survival machine learning algorithms. The Concordance Index (C-index), 5-year AUC, and Brier score assessed model accuracy. Patients were stratified into two risk groups for survival analysis, and the survival curves were presented.ResultsThis study examines the relationships between clinical factors and survival in NPC patients. The analysis, visualized through forest plots, indicates that demographic and clinical variables like gender, marital status, tumor grade, and stage significantly affect metastatic risks and survival. Specifically, factors such as advanced stages increase metastasis and survival risks, while enhanced treatments improve survival rates. In the cohort for DM prediction, results revealed that the random forest model was the most effective, with an AUC of 0.687. In contrast, when predicting overall survival (OS), the random survival forest (RSF) model consistently showed superior performance with the highest mean C-index of 0.802, a 5-year AUC of 0.857, and a Brier score of 0.167. Similarly, for cancer-specific survival (CSS) prediction, the RSF model demonstrated a mean C-index of 0.822, a 5-year AUC of 0.884, and a Brier score of 0.165. An online Shiny server was developed to allow the models to be used freely and efficiently via http://npcml.shinyapps.io/NPCpre.ConclusionThis study successfully established an online tool by machine learning models for NPC metastasis and survival prediction, providing valuable references for clinicians.
Construction and evaluation of a risk model for adverse outcomes of necrotizing enterocolitis based on LASSO-Cox regression
Authors: Zhang HaiJin , Yang RongWei , Yao Yuan 
Published: October 2024

ObjectiveThis study aimed to develop a nomogram to predict adverse outcomes in neonates with necrotizing enterocolitis (NEC).MethodsIn this retrospective study on neonates with NEC, data on perinatal characteristics, clinical features, laboratory findings, and x-ray examinations were collected for the included patients. A risk model and its nomogram were developed using the least absolute shrinkage and selection operator (LASSO) Cox regression analyses.ResultsA total of 182 cases of NEC were included and divided into a training set (148 cases) and a temporal validation set (34 cases). Eight features, including weight [p = 0.471, HR = 0.99 (95% CI: 0.98–1.00)], history of congenital heart disease [p &lt; 0.001, HR = 3.13 (95% CI:1.75–5.61)], blood transfusion before onset [p = 0.757, HR = 0.85 (95%CI:0.29–2.45)], antibiotic exposure before onset [p = 0.003, HR = 5.52 (95% CI:1.81–16.83)], C-reactive protein (CRP) at onset [p = 0.757, HR = 1.01 (95%CI:1.00–1.02)], plasma sodium at onset [p &lt; 0.001, HR = 4.73 (95%CI:2.61–8.59)], dynamic abdominal x-ray score change [p = 0.001, HR = 4.90 (95%CI:2.69–8.93)], and antibiotic treatment regimen [p = 0.250, HR = 1.83 (0.65–5.15)], were ultimately selected for model building. The C-index for the predictive model was 0.850 (95% CI: 0.804–0.897) for the training set and 0.7880.788 (95% CI: 0.656–0.921) for the validation set. The area under the ROC curve (AUC) at 8-, 10-, and 12-days were 0.889 (95% CI: 0.822–0.956), 0.891 (95% CI: 0.829–0.953), and 0.893 (95% CI:0.832–0.954) in the training group, and 0.812 (95% CI: 0.633–0.991), 0.846 (95% CI: 0.695–0.998), and 0.798 (95%CI: 0.623–0.973) in the validation group, respectively. Calibration curves showed good concordance between the predicted and observed outcomes, and DCA demonstrated adequate clinical benefit.ConclusionsThe LASSO-Cox model effectively identifies NEC neonates at high risk of adverse outcomes across all time points. Notably, at earlier time points (such as the 8-day mark), the model also demonstrates strong predictive performance, facilitating the early prediction of adverse outcomes in infants with NEC. This early prediction can contribute to timely clinical decision-making and ultimately improve patient prognosis.
Questionnaire-based subjective evaluation and analysis of factors influencing the effectiveness of treatment with the vacuum bell in children with pectus excavatum: a cross-sectional observational study
Authors: Zhou Lihuan , Deng Fang , Tian Yunfei , Lin Jiayi , Yang Guihong , Li Yunfei , Latour Jos M. , Deng Xicheng 
Published: October 2024

BackgroundVacuum bell (VB) is a technique to treat pectus excavatum in children. Its effectiveness and influencing factors, however, remain under investigated. The aim of this study was to examine the therapeutic effect and its influencing factors of VB in children with pectus excavatum.MethodsA cross-sectional observational study was conducted. Parents of children with pectus excavatum who underwent treatment with a VB between January 2018 and December 2019 were recruited. A survey was designed based on previously related studies and delivered to the parents in September–October 2021. The therapeutic effect was analyzed using subjective experiences by parents. Factors related to effectiveness were analyzed through univariate analysis and multivariate logistic regression.ResultsOf the 77 surveys distributed, 65 (84%) were returned. The mean duration of VB was 23.20 (SD 9.86) months. Caregivers rated the effect of VB treatment as moderate (41.5%), good (46.2%), excellent (12.3%). There were 39 children (60%) who had at least one pause of using VB for more than two weeks. Univariate analysis showed no significant difference between age, height, weight, duration of VB (months) and the effectiveness of VB therapy as defined by caregivers (p &gt; 0.05) and significant difference were observed of the variables “complication of petechiae” (p = 0.034) and “device returned to manufacturer for repair” (p = 0.011). The multivariate logistic regression showed that the occurrence of complication petechiae (p = 0.046) was an influential factor for the effectiveness of VB.ConclusionsThe evaluations reported by the parents suggested that the VB treatment was effective, although with varying degrees. The complication of petechiae seems an influencing factor to successful VB treatment in children with pectus excavatum. Further studies are needed to assess the long-term outcome and effect of VB and to improve the device and to reduce complications in order to enhance compliance and improve effectiveness.
The art of selecting the ECG input in neural networks to classify heart diseases: a dual focus on maximizing information and reducing redundancy
Authors: Ramirez Elisa , Ruiperez-Campillo Samuel , Casado-Arroyo Ruben , Merino José Luis , Vogt Julia E. , Castells Francisco , Millet José 
Published: October 2024

Background and ObjectivesAccurate diagnosis of cardiovascular diseases often relies on the electrocardiogram (ECG). Since the cardiac vector is located within a three-dimensional space and the standard ECG comprises 12 projections or leads derived from it, redundant information is inherently present. This study aims to quantify this redundancy and its impact on classification tasks using Convolutional Neural Networks (CNNs) in cardiovascular diseases.MethodsWe employed signal theory and mutual information to introduce a novel redundancy metric and explored techniques for redundancy augmentation and reduction. This involved lead selection and transformation to evaluate the effects on neural network performance.ResultsOur findings indicate that optimizing input configurations through redundancy reduction techniques can enhance the performance of deep learning models in cardiovascular diagnostics, provided that the information is preserved and minimally distorted.ConclusionFor the first time, this research has quantified the redundancy present in the input by validating various redundancy reduction techniques using a CNN. This discovery paves the way for advancing biomedical signal processing research, simplifying model complexity, and enhancing diagnostic performance in cardiovascular medicine within reduced lead systems, such as Holter monitors or wearables.
ECG data analysis to determine ST-segment elevation myocardial infarction and infarction territory type: an integrative approach of artificial intelligence and clinical guidelines
Authors: Kim Jongkwang , Shon Byungeun , Kim Sangwook , Cho Jungrae , Seo Jung-Ju , Jang Se Yong , Jeong Sungmoon 
Published: October 2024

IntroductionAcute coronary syndrome (ACS) is one of the leading causes of death from cardiovascular diseases worldwide, with ST-segment elevation myocardial infarction (STEMI) representing a severe form of ACS that exhibits high prevalence and mortality rates. This study proposes a new method for accurately diagnosing STEMI and categorizing the infarction area in detail, based on 12-lead electrocardiogram (ECG) data using a deep learning-based artificial intelligence (AI) algorithm.MethodsUtilizing an ECG database consisting of 888 myocardial infarction (MI) patients, this study enhanced the generalization ability of the AI model through five-fold cross-validation. The developed ST-segment elevation (STE) detector accurately identified STE across all 12 leads, which is a crucial indicator for the clinical ECG diagnosis of STEMI. This detector was employed in the AI model to differentiate between STEMI and non-ST-segment elevation myocardial infarction (NSTEMI).ResultsIn the process of distinguishing between STEMI and NSTEMI, the average area under the receiver operating characteristic curve (AUROC) was 0.939, and the area under the precision-recall curve (AUPRC) was 0.977, demonstrating significant results. Furthermore, this detector exhibited the ability to accurately differentiate between various infarction territories in the ECG, including anterior myocardial infarction (AMI), inferior myocardial infarction (IMI), lateral myocardial infarction (LMI), and suspected left main disease.DiscussionThese results suggest that integrating clinical domains into AI technology for ECG diagnosis can play a crucial role in the rapid treatment and improved prognosis of STEMI patients. This study provides an innovative approach for the diagnosis of cardiovascular diseases and contributes to enhancing the practical applicability of AI-based diagnostic tools in clinical settings.