bluuebunny/crossref_metadata_2025_split huggingface dataset:
Blood urea nitrogen to albumin ratio is associated with cerebral small vessel diseases
Authors: Nam Ki-Woong , Kwon Hyung-Min , Jeong Han-Yeong , Park Jin-Ho , Min Kyungha 
Published: February 2024

AbstractBlood urea nitrogen (BUN) to albumin ratio (BAR) is a comprehensive parameter that reflects renal, inflammatory, nutritional, and endothelial functions. BAR has been shown to be associated with various cancers, pneumonia, sepsis, and pulmonary and cardiovascular diseases; however, few studies have been conducted on its association with cerebrovascular diseases. In this study, we evaluated the association between BAR and cerebral small vessel disease (cSVD) in health check-up participants. We assessed consecutive health check-up participants between January 2006 and December 2013. For the cSVD subtype, we quantitatively measured the volume of white matter hyperintensity (WMH) and qualitatively measured the presence of lacune and cerebral microbleeds (CMBs). The BAR was calculated by dividing BUN by albumin as follows: BAR = BUN (mg/dl)/albumin (g/dl). A total of 3012 participants were evaluated. In multivariable linear regression analysis, BAR showed a statistically significant association with WMH volume after adjusting for confounders [β = 0.076, 95% confidence interval (CI): 0.027–0.125]. In multivariable logistic regression analyses, BAR was significantly associated with lacunes [adjusted odds ratio (aOR) = 1.20, 95% CI: 1.00–1.44] and CMBs (aOR = 1.28, 95% CI: 1.06–1.55). BAR was associated with all types of cSVD in the health check-up participants.
Correlation between surrogate endpoints and overall survival in unresectable hepatocellular carcinoma patients treated with immune checkpoint inhibitors: a systematic review and meta-analysis
Authors: Huang Litao , Kang Deying , Zhao Chongyang , Liu Xueting 
Published: February 2024

AbstractThis study aimed to assess the therapeutic effect of immune checkpoint inhibitors (ICIs) in patients with unresectable hepatocellular carcinoma (uHCC) and investigate the correlation between surrogate endpoints and overall survival (OS). A systematic literature search included phase I, II, and III clinical trials comparing ICIs to placebo or other therapies for uHCC treatment. Correlations between OS and surrogate endpoints were evaluated using meta-regression analyses and calculating the surrogate threshold effect (STE). The correlation analysis showed a weak association between OS and progression-free survival (PFS), with an R2 value of 0.352 (95% CI: 0.000–0.967). However, complete response (CR) exhibited a strong correlation with OS (R2 = 0.905, 95% CI: 0.728–1.000). Subgroup analyses revealed high correlations between OS and PFS, CR, stable disease (SD), and DC in phase III trials (R2: 0.827–0.922). For the ICI + IA group, significant correlations were observed between OS and SD, progressive disease (PD), and grade 3–5 immune-related adverse events (irAEs) (R2: 0.713–0.969). Analyses of the correlation between survival benefit and risk of mortality across various time points showed a strong association within the first year (R2: 0.724–0.868) but a weak association beyond one year (R2: 0.406–0.499). In ICI trials for uHCC, PFS has limited utility as a surrogate endpoint for OS, while CR exhibits a strong correlation with OS. Subgroup analyses highlight high correlations between OS and PFS, SD, and DC in phase III trials. Notably, the ICI + IA group shows significant associations between OS and SD, PD, and grade 3–5 irAEs. These findings offer valuable insights for interpreting trial outcomes and selecting appropriate endpoints in future clinical studies involving ICIs for uHCC patients.
Rheological properties of blood in multiple myeloma patients
Authors: Ptaszek Bartłomiej , Podsiadło Szymon , Jandziś Zuzanna , Teległów Aneta , Piotrowska Anna , Jurczyszyn Artur , Czerwińska-Ledwig Olga 
Published: February 2024

AbstractMultiple myeloma (MM) is considered to be one of the hematological malignancies formed by excessive and abnormal proliferation of plasmocytes. Among other parameters, several blood tests are used to diagnose multiple myeloma. The hemorheological profile in multiple myeloma is not widely studied. Hemorheology includes the study of measuring the deformability and aggregation of erythrocytes, blood viscosity, and sedimentation rate. The degree of deformability of blood cells is necessary to maintain proper vital functions. Proper deformability of red blood cells ensures proper blood circulation, tissue oxidation and carbon dioxide uptake. The aim of the study was to compare morphology and blood rheology parameters in patients with MM and healthy individuals. The study included 33 patients with MM, and 33 healthy subjects of the same age. The hematological blood parameters were evaluated using ABX MICROS 60 hematology analyzer. The LORCA Analyzer to study erythrocyte aggregation and deformability. Patients with MM had lower red blood cells count (RBC) (9.11%) (p < 0.001) and half time of total aggregation (T1/2) (94.29%) (p < 0.001) values and higher mean corpuscular volume (MCV) (5.50%) (p < 0.001), aggregation index (AI) (68.60%) (p < 0.001), total extent of aggregation (AMP) (87.92%) (p < 0.001) values than the healthy control group. Aggregation in patients with MM is different compared to healthy individuals. It was observed that the percentage of cell aggregation is almost 50% higher than in the control group. The study of morphology, aggregation and deformability of erythrocytes in patients with suspected MM may be helpful in making clinical decisions.
Simulation and experimental evaluation of laser-induced graphene on the cellulose and lignin substrates
Authors: Ghavipanjeh Ali , Sadeghzadeh Sadegh 
Published: February 2024

AbstractIn this article, the formation of laser-induced graphene on the two natural polymers, cellulose, and lignin, as precursors was investigated with molecular dynamics simulations and some experiments. These eco-friendly polymers provide significant industrial advantages due to their low cost, biodegradability, and recyclable aspects. It was discovered during the simulation that LIG has numerous defects and a porous structure. Carbon monoxide, H2, and water vapor are gases released by cellulose and lignin substrates. H2O and CO are released when the polymer transforms into an amorphous structure. Later on, as the amorphous structure changes into an ordered graphitic structure, H2 is released continuously. Since cellulose monomer has a higher mass proportion of oxygen (49%) than lignin monomer (29%), it emits more CO. The LIG structure contains many 5- and 7-carbon rings, which cause the structure to have bends and undulations that go out of the plane. In addition, to verify the molecular dynamics simulation results with experimental tests, we used a carbon dioxide laser to transform filter paper, as a cellulose material, and coconut shell, as a lignin material, into graphene. Surprisingly, empirical experiments confirmed the simulation results.
Multidimensional assessment of the biological effects of electronic cigarettes on lung bronchial epithelial cells
Authors: Wang Meng , Cheng Qing , Wu Zehong , Fan Longjiang , Zeng Linghui , Chen Hongyu 
Published: February 2024

AbstractCigarette smoke (CS) exposure is known to cause injury to respiratory tract epithelial cells and is a contributing factor in the development of chronic obstructive pulmonary disease and lung cancer. Electronic cigarettes (e-cigarettes) are gaining popularity as a potential substitute for conventional cigarettes due to their potential for aiding smoking cessation. However, the safety of e-cigarettes remains uncertain, and scientific evidence on this topic is still limited. In this study, we aimed to investigate the effects of CS and e-cigarette smoke (ECS) of different flavors on human lung bronchial epithelial cells. Real-time smoke exposure was carried out using an air–liquid interface system, and cell viability was assessed. RNA-Seq transcriptome analysis was performed to compare the differences between CS and ECS. The transcriptome analysis revealed a significantly higher number of differentially expressed genes in CS than in ECS. Moreover, the impact of mint-flavored e-cigarettes on cells was found to be greater than that of tobacco-flavored e-cigarettes, as evidenced by the greater number of differentially expressed genes. These findings provide a reference for future safety research on traditional cigarettes and e-cigarettes, particularly those of different flavors. The use of omics-scale methodologies has improved our ability to understand the biological effects of CS and ECS on human respiratory tract epithelial cells, which can aid in the development of novel approaches for smoking cessation and lung disease prevention.
A whirl of radiomics-based biomarkers in cancer immunotherapy, why is large scale validation still lacking?
Authors: Ligero Marta , Gielen Bente , Navarro Victor , Cresta Morgado Pablo , Prior Olivia , Dienstmann Rodrigo , Nuciforo Paolo , Trebeschi Stefano , Beets-Tan Regina , Sala Evis , Garralda Elena , Perez-Lopez Raquel 
Published: February 2024

AbstractThe search for understanding immunotherapy response has sparked interest in diverse areas of oncology, with artificial intelligence (AI) and radiomics emerging as promising tools, capable of gathering large amounts of information to identify suitable patients for treatment. The application of AI in radiology has grown, driven by the hypothesis that radiology images capture tumor phenotypes and thus could provide valuable insights into immunotherapy response likelihood. However, despite the rapid growth of studies, no algorithms in the field have reached clinical implementation, mainly due to the lack of standardized methods, hampering study comparisons and reproducibility across different datasets. In this review, we performed a comprehensive assessment of published data to identify sources of variability in radiomics study design that hinder the comparison of the different model performance and, therefore, clinical implementation. Subsequently, we conducted a use-case meta-analysis using homogenous studies to assess the overall performance of radiomics in estimating programmed death-ligand 1 (PD-L1) expression. Our findings indicate that, despite numerous attempts to predict immunotherapy response, only a limited number of studies share comparable methodologies and report sufficient data about cohorts and methods to be suitable for meta-analysis. Nevertheless, although only a few studies meet these criteria, their promising results underscore the importance of ongoing standardization and benchmarking efforts. This review highlights the importance of uniformity in study design and reporting. Such standardization is crucial to enable meaningful comparisons and demonstrate the validity of biomarkers across diverse populations, facilitating their implementation into the immunotherapy patient selection process.
Stroma-specific gene expression signature identifies prostate cancer subtype with high recurrence risk
Authors: Rasmussen Martin , Fredsøe Jacob , Salachan Paul Vinu , Blanke Marcus Pii Lunau , Larsen Stine Hesselby , Ulhøi Benedicte Parm , Jensen Jørgen Bjerggaard , Borre Michael , Sørensen Karina Dalsgaard 
Published: February 2024

AbstractCurrent prognostic tools cannot clearly distinguish indolent and aggressive prostate cancer (PC). We hypothesized that analyzing individual contributions of epithelial and stromal components in localized PC (LPC) could improve risk stratification, as stromal subtypes may have been overlooked due to the emphasis on malignant epithelial cells. Hence, we derived molecular subtypes of PC using gene expression analysis of LPC samples from prostatectomy patients (cohort 1, n = 127) and validated these subtypes in two independent prostatectomy cohorts (cohort 2, n = 406, cohort 3, n = 126). Stroma and epithelium-specific signatures were established from laser-capture microdissection data and non-negative matrix factorization was used to identify subtypes based on these signatures. Subtypes were functionally characterized by gene set and cell type enrichment analyses, and survival analysis was conducted. Three epithelial (E1-E3) and three stromal (S1-S3) PC subtypes were identified. While subtyping based on epithelial signatures showed inconsistent associations to biochemical recurrence (BCR), subtyping by stromal signatures was significantly associated with BCR in all three cohorts, with subtype S3 indicating high BCR risk. Subtype S3 exhibited distinct features, including significantly decreased cell-polarity and myogenesis, significantly increased infiltration of M2-polarized macrophages and CD8 + T-cells compared to subtype S1. For patients clinically classified as CAPRA-S intermediate risk, S3 improved prediction of BCR. This study demonstrates the potential of stromal signatures in identification of clinically relevant PC subtypes, and further indicated that stromal characterization may enhance risk stratification in LPC and may be particularly promising in cases with high prognostic ambiguity based on clinical parameters.
m6A-methylated KCTD21-AS1 regulates macrophage phagocytosis through CD47 and cell autophagy through TIPR
Authors: Liang Dong-Min , Li You-Jie , Zhang Jia-Xiang , Shen Huan-Huan , Wu Chun-Xia , Xie Ning , Liang Yan , Li Yan-Mei , Xue Jiang-Nan , Sun Hong-Fang , Wang Qin , Yang Jian , Li Xiao-Hua , Wang Ping-Yu , Xie Shu-Yang 
Published: February 2024

AbstractBlocking immune checkpoint CD47/SIRPα is a useful strategy to engineer macrophages for cancer immunotherapy. However, the roles of CD47-related noncoding RNA in regulating macrophage phagocytosis for lung cancer therapy remain unclear. This study aims to investigate the effects of long noncoding RNA (lncRNA) on the phagocytosis of macrophage via CD47 and the proliferation of non-small cell lung cancer (NSCLC) via TIPRL. Our results demonstrate that lncRNA KCTD21-AS1 increases in NSCLC tissues and is associated with poor survival of patients. KCTD21-AS1 and its m6A modification by Mettl14 promote NSCLC cell proliferation. miR-519d-5p gain suppresses the proliferation and metastasis of NSCLC cells by regulating CD47 and TIPRL. Through ceRNA with miR-519d-5p, KCTD21-AS1 regulates the expression of CD47 and TIPRL, which further regulates macrophage phagocytosis and cancer cell autophagy. Low miR-519d-5p in patients with NSCLC corresponds with poor survival. High TIPRL or CD47 levels in patients with NSCLC corresponds with poor survival. In conclusion, we demonstrate that KCTD21-AS1 and its m6A modification promote NSCLC cell proliferation, whereas miR-519d-5p inhibits this process by regulating CD47 and TIPRL expression, which further affects macrophage phagocytosis and cell autophagy. This study provides a strategy through miR-519-5p gain or KCTD21-AS1 depletion for NSCLC therapy by regulating CD47 and TIPRL.
Nutrient adequacy for poor households in Africa would improve with higher income but not necessarily with lower food prices
Authors: McCullough Ellen B. , Lu Meichen , Nouve Yawotse , Arsenault Joanne , Zhen Chen 
Published: February 2024

AbstractHealthy diets are not affordable to all in Africa due to a combination of high food prices and low incomes. However, how African consumers might change demand patterns if prices or incomes were to change remains poorly understood. Using nationally representative household panel survey data from five sub-Saharan African countries, we model consumer preferences and examine how nutrient intake responds to changing food prices, total expenditures and other demand determinants. Here we find a stronger positive relationship between growth in poor consumers’ total expenditures and their nutrient intake adequacy than has been previously documented. We also find that poor consumers’ intake adequacy is especially sensitive to food staple prices in countries where one food staple dominates poor consumers’ diets. In countries with multiple food staples, no single staple’s price is a strong determinant of poor consumers’ dietary intake adequacy.
Geospatial investigations in Colombia reveal variations in the distribution of mood and psychotic disorders
Authors: Song Janet , Ramírez Mauricio Castaño , Okano Justin T. , Service Susan K. , de la Hoz Juan , Díaz-Zuluaga Ana M. , Upegui Cristian Vargas , Gallago Cristian , Arias Alejandro , Sánchez Alexandra Valderrama , Teshiba Terri , Sabatti Chiara , Gur Ruben C. , Bearden Carrie E. , Escobar Javier I. , Reus Victor I. , Jaramillo Carlos Lopez , Freimer Nelson B. , Olde Loohuis Loes M. , Blower Sally 
Published: February 2024

Abstract
Background
Geographical variations in mood and psychotic disorders have been found in upper-income countries. We looked for geographic variation in these disorders in Colombia, a middle-income country. We analyzed electronic health records from the Clínica San Juan de Dios Manizales (CSJDM), which provides comprehensive mental healthcare for the one million inhabitants of Caldas.

Methods
We constructed a friction surface map of Caldas and used it to calculate the travel-time to the CSJDM for 16,295 patients who had received an initial diagnosis of mood or psychotic disorder. Using a zero-inflated negative binomial regression model, we determined the relationship between travel-time and incidence, stratified by disease severity. We employed spatial scan statistics to look for patient clusters.

Results
We show that travel-times (for driving) to the CSJDM are less than 1 h for ~50% of the population and more than 4 h for ~10%. We find a distance-decay relationship for outpatients, but not for inpatients: for every hour increase in travel-time, the number of expected outpatient cases decreases by 20% (RR = 0.80, 95% confidence interval [0.71, 0.89], p = 5.67E-05). We find nine clusters/hotspots of inpatients.

Conclusions
Our results reveal inequities in access to healthcare: many individuals requiring only outpatient treatment may live too far from the CSJDM to access healthcare. Targeting of resources to comprehensively identify severely ill individuals living in the observed hotspots could further address treatment inequities and enable investigations to determine factors generating these hotspots.
LSTM‐based post‐processing for noise reduction in SVD‐based particulate matter digital twinning on lightweight embedded devices
Authors: Lee Seungmin , Park Daejin 
Published: February 2024

AbstractAmong various applications of digital twins, measuring particulate matter in the context of air pollution has become increasingly important due to growing interest in atmospheric environments. By obtaining particle count values from analogue‐to‐digital converter raw data through photodiode and calibrating them, particulate matter values can be acquired. The singular‐value–decomposition is efficient transfer function learning method. However, due to the linear nature of the transfer function of singular‐value–decomposition, it also conveys noise information, necessitating post‐processing to suppress noise. This study proposes a method that utilizes long short‐term memory neural networks to effectively stabilize noise based on the output of continuous dust sensor particulate matter distributions. Using the singular value decomposition based particulate matter shows relative root‐mean‐square error (4.4761, 100%) as a reference, the proposed long short‐term memory post‐processing demonstrates an improved result (2.9328, 65.52%) compared to other post‐processing method: the mean filter (3.6704, 82.00%), low‐pass filter (3.7719, 84.27%) and Kalman filter (3.5550, 79.42%). Furthermore, to address the limitation of initial delay in long short‐term memory to achieve stable data output, a method of iteratively training the initial input data sample is proposed. For the initial input data, the non‐initialization approach exhibited a relative error of 87.57% and it took around 5 samples to predict stable output. In contrast, the proposed iterative training method, applied five times to the initial data sample, achieved a relative error of 9.51% and immediately obtained stable data.
TMNIO:Triplet merged network with involution operators for improved few‐shot image classification
Authors: Lulu Qi , Ranhui Xu , Shaojie Zhao , Mingming Zheng , Weiqin Yu 
Published: February 2024

AbstractFew‐shot learning enables machines to learn efficiently from limited labelled data. However, existing few‐shot learning methods may perform poorly when there is a lack of sufficient samples, and may encounter problems such as domain shift or overfitting when applied to new domains or tasks. To address the issues of poor fitting and insufficient generalization ability in new domains, a new method called triplet merged network with involution operators (TMNIO) is proposed. This method employs dual encoders that extract common and distinctive features from the prototype network, thereby enhancing the model's feature extraction capability. To further improve this ability, the traditional convolutional kernels are replaced with involution operators, which not only reduce the parameter count but also enlarge the receptive field to better extract local feature information. Additionally, this method employs a two‐stage training strategy, where triplet loss is used in the first stage to train the model and enhance its robustness and generalization ability. Extensive experiments on the miniImageNet, Omniglot, and Caltech‐UCSD Birds‐200 (CUB) datasets have shown that our proposed method achieved significant improvements in both training speed and accuracy, particularly on the miniImageNet dataset, where it achieved an outstanding 10% performance improvement.
Data‐driven robust look‐ahead power dispatch with preidentification of the worst‐case scenario using combined multilayer perceptron
Authors: Li Zhigang , Zhang Zijing , Zheng J. H. 
Published: February 2024

AbstractTo address the uncertainty caused by renewable energy generation, two‐stage robust optimization (RO) has been proposed for intraday look‐ahead power dispatch (LAPD) at multiple time scales. However, the intensive computational burden of solving RO problems limits its online application. To address this issue, we developed a novel data‐driven robust LAPD method that exploits information transfer in the worst‐case scenario (WCS) involving uncertain parameters between the two stages of RO. By employing the multilayer perceptron (MLP), the WCSs of uncertain parameters are tentatively preidentified as preliminary inputs of the column‐and‐constraint generation (C&CG) algorithm to reduce the stress of the WCS search during the subsequent iterative procedure. In addition, a combined multilayer perceptron is proposed to address correlations between uncertain parameters in the WCS. The MLP model is trained in distinct time segments to reduce the model training complexity and improve the precision of WCS preidentification. Case studies with various system scales and rolling window sizes indicate that the proposed method is effective at accelerating the C&CG process.