bluuebunny/crossref_metadata_2025_split huggingface dataset:
The influence of domestic fire on blood pressure and heart rate: A replication study
Authors: Clark Lindsey , Landgraf Lauren , Lynn Christopher 
Published: December 2024

Abstract

In previous research, fire’s sensory components were disaggregated into separate experimental conditions using a Yule log video to test the hypothesis that domestic fire can induce a relaxation response. The study recorded blood pressure and skin conductance before and after participants watched the 15-minute fire video with and without sound and found a relaxation effect for the audiovisual condition. The current study replicates this with an electric fireplace by measuring pre- and posttest blood pressure (and calculated mean arterial pressure [MAP]) and heart rate (HR) using a randomized crossover design with 101 adults aged 18–60. We duplicated the three original experimental conditions and added an additional condition of fire sounds only. We predicted that the multisensory condition would produce the most relaxation and among those highest in absorption and prosociality. We found a relaxation response for all conditions and more consistent relaxation (systolic, MAP, and HR) and an absorption effect for the multisensory effect; however, the absorption effect was the opposite of our prediction (pre-trial MAP = 92.19 ± 10.95; post-trial = 90.61 ± 10.05, P = .02, df = 97). Our data partially replicate the original study, but the roles of absorption and prosociality need further exploration.
A Machine Learning Approach to Aids Clinical Trials Group (Actg) Study
Authors: Malyala Lakshminarayana Rao , G Mohana Krishna , Thiyagarajan Sneha 
Published: December 2024

Abstract

AIDS - Acquired Immunodeficiency Syndrome, is a major health concern, with an estimated 39 million people living with HIV worldwide. [1] Predicting the risk of mortality in AIDS patients is important for optimizing treatment strategies and improving the outcomes. The choice of Antiretroviral Therapy (ART), whether monotherapy or combined therapy, plays a crucial role in optimizing the treatment strategies. This study aims to apply machine learning techniques to predict patient mortality within a certain window of time using the AIDS Clinical Trials Group (ACTG) Study 175 Dataset. The results demonstrate the role of Data Science and the potential of machine learning models to forecast mortality, providing valuable insights for improving the treatment.
A study on the nutritional status and body composition of children with Hutchinson–Gilford progeria syndrome
Authors: qinmei yu , Wang Jingjing , Fu Haidong , Mao Jianhua 
Published: December 2024

Abstract

Objective: Patients with Hutchinson‒Gilford progeria syndrome (HGPS) often have severe growth retardation, generalized fatty malnutrition, and cardiovascular complications. There are different cardiovascular risks due to the different distributions of fat and muscle tissues in different regions. Therefore, it is necessary to investigate nutritional status and analyze the distribution of muscle and fat tissues in HGPS.
Method: A cross-sectional survey was conducted to select HGPS patients aged 3 years and above who received treatment at Children’s Hospital, Zhejiang University School of Medicine. The medical history, physical measurements, laboratory examination data, dietary data, body composition data, and bone density data of the subjects were collected and analyzed.
Results: A total of eight HGPS patients were included. The weight and height of all the subjectsat birth were normal but decreased to below the 3rd percentile by 2 months of age. The rates of increase in weight and height were relatively slow. A dietary survey was conducted on all patients, and the results revealed that 87.5% (7 out of 8) of the subjects were exposed to insufficient energy intake. Dual-energy X-ray absorptiometry (DXA) was employed to investigate bone density, and the results revealed a decrease in bone density (-2.82±1.46) in the subjects at the chronological age; the Z score of the subjects at height was -0.72±0.99. The fat mass results revealed that 62.5% of the subjects’ total body fat percentage and 100% of the subjects’ fat mass index (FMI) were within the normal range. However, 100% of the subjects’ android to gynoid A/G fat ratios and 75% of the subjects’ torso-leg (T/L) fat ratios were higher than normal. The muscle mass results revealed a decrease in the total and appendicular muscle masses of all the subjects, with lower limb muscles showing a more severe decline than upper limb muscles did.
Conclusions: HGPS patients presented severe growth retardation, with abnormal fat metabolism mainly manifested as abnormal fat distribution, which may increase the risk of cardiovascular events in these patients.
TP53 Mediated Transcriptional Regulation of PHKG2 Promotes Ferroptosis in Head and Neck Squamous Cell Carcinoma by Inhibiting the NRF2/GPX4 Pathway
Authors: Wang Hongbo , Yu Yalian , Luan Meng , Wang Yan , Zang Jian , Luo Le , Wang Tianyi , Wang Tianci 
Published: December 2024

Abstract

Ferroptosis has emerged as a promising therapeutic target in cancer treatment, yet its regulatory mechanisms in head and neck squamous cell carcinoma (HNSCC) remain poorly understood. This study investigates the role of phosphorylase kinase gamma 2 (PHKG2) in regulating ferroptosis through the TP53/NRF2/GPX4 axis in HNSCC. Using bioinformatics analysis and clinical data evaluation, we identified PHKG2 as a ferroptosis driver gene and a favorable prognostic marker in HNSCC patients. PHKG2 expression was found to negatively correlate with the redox regulator NRF2 and the ferroptosis marker GPX4, suggesting a role in modulating oxidative stress. Mechanistic investigations revealed that TP53 activation induces PHKG2 expression, which subsequently activates protein phosphatase 1 (PP1), leading to the nuclear export of NRF2. This cascade results in the downregulation of GPX4, depletion of intracellular reduced glutathione (GSH), and increased levels of reactive oxygen species (ROS), ultimately promoting ferroptosis characterized by mitochondrial damage. These findings indicate that the TP53/PHKG2 axis plays a critical role in ferroptosis regulation by disrupting redox homeostasis in HNSCC. Overall, this study highlights the potential of targeting the TP53-PHKG2-NRF2/GPX4 pathway as a therapeutic strategy to enhance ferroptosis and improve treatment efficacy in HNSCC. Our insights provide a strong foundation for developing novel approaches aimed at sensitizing HNSCC to ferroptosis-inducing agents, offering new opportunities for effective cancer treatment.
Universal Patterns in the Long-term Growth of Urban Infrastructure in U.S. Cities from 1900 to 2015
Authors: Burghardt Keith , Uhl Johannes , Lerman Kristina , Leyk Stefan 
Published: December 2024

Abstract

Despite the rapid growth of cities in the past century, our quantitative, in-depth understanding of how cities grow remains limited due to a consistent lack of historical data. Thus, the scaling laws between a city's features and its population as they evolve over time, known as temporal city scaling, is under-explored, especially for time periods spanning multiple decades. In this paper, we leverage novel data sources such as the Historical Settlement Data Compilation for the U.S. (HISDAC-US), and analyze the temporal scaling laws of developed area, building indoor area, building footprint area, and road length and other road network statistics for nearly all metropolitan areas in the U.S. from 1900 to 2015. We find that scaling exponents vary dramatically between cities as a function of their size and location. Three notable patterns emerge. First, scaling law exponents imply many, but not all, metropolitan areas are becoming less dense and indoor area per capita increases as cities grow, in contrast to expectations. Second, larger cities tend to have a smaller scaling exponent than smaller cities. Third, scaling exponents (and growth patterns) are similar between nearby cities. These results show a long-term trend that could harm urban sustainability as previously dense populations are rapidly spreading out into undeveloped land. Moreover, the regional similarity of long-term urban growth patterns implies that city evolution and sustainability patterns are more interconnected than prior research has suggested. These results help urban planners and scientists understand universal, long-term patterns of city growth across the US.
Explainable SHAP-XGBoost models for pressure ulcers among patients requiring with mechanical ventilation in intensive care unit
Authors: Xue Yu-juan , Zheng Li , Yuan Zhen-nan , Xing Xue-zhong 
Published: December 2024

Abstract

Background
Pressure ulcers are significant concern for ICU patients on mechanical ventilation. Early prediction is crucial for enhancing patient outcomes and reducing healthcare costs. This study aims to develop a predictive model using machine learning techniques, specifically XGBoost combined with SHAP, to identify key risk factors of pressure ulcers in this population.
Methods
Utilizing the MIMIC-IV 2.2 database, we included a cohort of 29,448 mechanically ventilated patients in ICU intensive unit. These patients were divided into a training set (20,614 patients, 70%) and an internal validation set (8,834 patients, 30%). Of these, 2,052 patients developed pressure ulcers. We applied the XGBoost algorithm to build the predictive model and used SHAP analysis to identify the top ten factors influencing pressure ulcer development: 'sepsis', 'age', 'the count of platelet', 'length of ICU stay', 'PaO2/FiO2 ratio', 'hemoglobin concentration', 'admission type', 'renal disease', 'albumin concentration', and 'ethnicity'.
Results
The predictive model achieved an area under the ROC curve (AUC) of 0.797 (95% CI: 0.786–0.808) in the training set and 0.739 (95% CI: 0.721–0.758) in the validation set. Calibration curves demonstrated good fit, and the decision curve analysis indicated clinical utility.
Conclusion
This study successfully developed a machine learning model that accurately predicts the risk of pressure ulcers in ICU patients with mechanical ventilation. This model could serve as a valuable tool for guiding early interventions, ultimately reducing the incidence of pressure ulcers in this vulnerable population. The integration of SHAP analysis offers insights into the most critical factors.
Evaluation by M-charts and microperimetry after broad internal limiting membrane peeling for idiopathic epiretinal membrane
Authors: Uchiyama Naoto , Jujo Tatsuya , Sato Keiji , Sekine Reio , Kakehashi Kota , Endo Akiko , Kitaoka Yasushi 
Published: December 2024

Abstract

Purpose: To assess the impact of broad-internal limiting membrane (ILM) peeling on retinal sensitivity (RS) in patients with idiopathic epiretinal membrane (ERM).
Design: Retrospective observational case series.
Methods: The study analyzed 22 eyes treated with broad-ILM peeling within the vascular arcades area for ERM, over a 6-month follow-up. Outcomes measured included best-corrected visual acuity (BCVA), M-CARTS scores (vertical MV and horizontal MH), central macular thickness (CMT), and RS. RS was evaluated using Microperimetry-3 at specified central degrees, and points below 10 dB were considered microscotomas (MS).
Results: Postoperative BCVA improved significantly at 1, 3, and 6 months (P < 0.01). No significant changes in CMT were noted. MH score improvements were significant at 6 months (P = 0.02), whereas MV scores remained unchanged. RS notably improved within the central 4° at all postoperative points (P < 0.05) but remained unchanged in the central 10° and 20° regions. MS increased transiently at 1 month (P = 0.04) but stabilized at 3 and 6 months.
Conclusion: MS increased temporarily but improved over time. Broad ILM peeling suggests potential for improving visual acuity and metamorphopsia in ERM patients without causing long-term detrimental effects on retinal sensitivity.
Advanced Predictive Modeling of Type 2 Diabetes Using XGBoost and Explainable AI
Authors: Rafie Zahra , Talab Moslem Sedaghat , Koor Behrooz Ebrahim Zadeh , Garavand Ali , Salehnasab Cirruse 
Published: December 2024

Abstract

The increasing prevalence of Type 2 diabetes (D.M. II) globally poses significant public health challenges, necessitating the development of effective predictive models for accurate prediction. This study aims to apply machine learning (ML) algorithms and explainable artificial intelligence (XAI) techniques to predict the risk of D.M. II using health data from the Dena Cohort in Yasuj, Iran. Data was collected from 3,203 individuals aged 35 to 70, incorporating various demographic, clinical, and lifestyle features. Two ML models, XGBoost and CatBoost, were developed and evaluated for their predictive accuracy. Data preprocessing involved handling missing values, normalization of continuous variables, and addressing class imbalance through the Synthetic Minority Over-sampling Technique (SMOTE). The performance of the models was assessed using accuracy, F1-score, and area under the receiver operating characteristic curve (AUC). The values of SHAP (Shapley Additive Explanations) were utilized to explain model predictions and enhance interpretability. The XGBoost model demonstrated superior performance, achieving an accuracy of 96.07% and an AUC of 99.29%. Key predictive factors identified included fasting blood sugar, fatty liver, urolithiasis, age, and lifestyle factors like energy drink consumption and television watching. The application of SHAP provided valuable insights into the contribution of individual features to the model’s predictions, enhancing transparency and understanding for healthcare professionals. This study's findings highlight the potential of ML and XAI in improving the prediction of D.M. II. By identifying critical risk factors, the developed predictive models can support personalized healthcare interventions, improving patient outcomes and reducing healthcare burdens associated with diabetes. This research advocates integrating advanced predictive analytics into clinical practice to enhance diabetes prediction strategies.
The predictive value of radiomics and deep learning for synchronous distant metastasis in clear cell renal cell carcinoma
Authors: He Wanbin , Zhang Winbo , Zhang Yunfeng , Yang Zhijun , He Han , Wang Jia , Zhou Fenghai 
Published: December 2024

Abstract

Objective
The objective of this research was to devise and authenticate a predictive model that employs CT radiomics and deep learning methodologies for the accurate prediction of synchronous distant metastasis (SDM) in clear cell renal cell carcinoma (ccRCC).
Methods
A total of 143 ccRCC patients were included in the training cohort, and 62 ccRCC patients were included in the validation cohort. The CT images from all patients were normalized, and the tumor regions were manually segmented via ITK-SNAP software. Radiomic features were extracted via the FAE toolkit. The least absolute shrinkage and selection operator (LASSO) algorithm was employed to select features and build various machine learning models. Additionally, the largest cross-section of the tumor was cropped to train the deep learning model. Multiple deep learning models were trained to predict SDM in ccRCC patients. The results of the best machine learning model were then fused with those of the deep learning model to create a combined model.
Results
Of the 944 radiomic features identified, 15 were closely associated with SDM. With these 15 features, the support vector machine (SVM) model emerged as the most effective, demonstrating areas under the curve (AUC) of 0.860 and 0.813 in the training and validation cohort, respectively. Among the deep learning models, ResNet101 performed optimally, achieving AUC of 0.815 and 0.743 in the training and validation cohort, respectively. The combined model yielded an AUC of 0.863. Decision curve analysis suggested that the combined model offers superior clinical applicability.
Conclusion
This model, which combines radiomics and deep learning, has substantial potential the prediction of SDM in patients with ccRCC. This study is anticipated to bolster clinical decision-making processes and enhance the prognostic outcomes for individuals diagnosed with ccRCC.
A Comprehensive Analysis of the Cuproptosis-Related Gene GCSH in Pan-Cancer with a Focus on Colorectal Cancer
Authors: Guo Xian-wen , Lei Rong-e , Li Jiao , Shen Liqi , Ding Zhen 
Published: December 2024

Abstract

Background
GCSH, a gene associated with cuproptosis, has been implicated in various cancers, although its role remains incompletely understood. This study aims to conduct a comprehensive analysis of GCSH across multiple cancer types to elucidate its role in tumorigenesis.
Methods
GCSH expression was analyzed in 33 cancer types using data from TCGA database. Associations with the tumor microenvironment and prognostic value were evaluated. scRNA-seq data from colorectal cancer (CRC) was used to assess GCSH expression in different cell types. Clinical CRC tissues, blood samples, and cell lines were utilized for validation. Functional assays and drug sensitivity tests were performed to further elucidate the role of GCSH.
Results
GCSH expression varied among different cancers, with notably higher levels in CRC. GCSH demonstrated significant correlations with 22 types of immune cells across the 33 cancers. Generally, GCSH showed a negative correlation with immune scores and immune checkpoint genes. Prognostic analysis revealed that GCSH was associated with outcomes in adrenocortical carcinoma, hepatocellular carcinoma, and stomach adenocarcinoma, although external cohort results did not consistently support these findings. Validation in clinical samples and cell lines confirmed elevated GCSH in CRC. scRNA-seq data indicated higher GCSH expression in both cancerous and immune cells within tumor tissues compared to normal tissues. Functional and pathway analyses in CRC identified key biological roles for GCSH, and a drug sensitivity to GCSH was identified.
Conclusions
GCSH exerts multifaceted roles in specific cancers and is significantly associated with immune cells and immune checkpoint genes. The study identifies the biological functions of GCSH in CRC and suggests potential drug sensitivities.
Antimicrobial photodynamic therapy (aPDT) as an important adjunctive approach in periapical healing after endodontic retreatment: a case report
Authors: Barbosa-Ribeiro Marlos , Magalhães Vinícius Souto , Cordeiro Joselma Erundina de Lima , Silva Vanderson Menezes , Barros Lívia Maria Mateus de Lucena , Nogueira Ícaro Ruy Coelho , Arruda-Vasconcelos Rodrigo , Filgueira Pedro Tardelly Diniz 
Published: December 2024

Abstract

Antimicrobial photodynamic therapy (aPDT) has been suggested as a complementary approach to endodontic treatment as it promoted greater microbial reduction. This case report describes the effectiveness of aPDT in bacterial reduction and in clinical management in a lower molar presenting previous poorly conducted endodontic treatment, post-treatment apical periodontitis and coronal-root perforation. A 20-year-old female patient with radiographic evidence of post-treatment apical periodontitis and coronal-root perforation was submitted to endodontic retreatment. In addition to the endodontic procedures, aPDT with 0.05% methylene blue was used in a 3-minutes irradiation period. A Ga-Al-Ar diode laser attached to an optical fiber with a diameter of 365 μm was applied by using a 90 s irradiation time, 100 mW of power and 9 J of energy. In conclusion, aPDT associated with endodontic treatment performed with technological devices represents a positive impact in periapical tissues healing in case of teeth with post-treatment apical periodontitis a furcation perforation.
Gut microbiome-meta-transcriptome analysis reveals that pyruvate and amino acid metabolism bacterial genes are involved in hyperuricemia and gout in humans
Authors: Martínez-Nava Gabriela Angélica , Altamirano-Molina Efren , Vázquez-Mellado Janitzia , Casimiro-Soriguer Carlos , Dopazo Joaquín , Lozada-Pérez Carlos , Herrera-López Brígida , Martínez-Gómez Laura E. , Martínez-Armenta Carlos , Guido-Gómora Dafne Lissete , Valle-Gutiérrez Sarahí , Suarez-Ahedo Carlos , Camacho-Rea María del Carmen , Martínez-García Mireya , Gutiérrez-Esparza Guadalupe , Amezcua-Guerra Luis M. , Zamudio-Cuevas Yessica , Martínez-Flores Karina , Fernández-Torres Javier , Burguete-García Ana , Orbe-Orihuela Yaneth Citlalli , Lagunas-Martínez Alfredo , Méndez-Salazar Eder Orlando , Francisco-Balderas Adriana , Palacios-González Berenice , Pineda Carlos , López-Reyes Alberto 
Published: December 2024

Abstract

Background
Several pathologies with metabolic origin, such as hyperuricemia and gout, have been associated with the gut microbiota taxonomic profile. However, there is no evidence of which bacterial genes are being expressed in the gut microbiome, and of their potential effects on hyperuricemia and gout.
Materials and Methods
We sequenced the RNA of 26 fecal samples from 10 healthy normouricemic controls, 10 with asymptomatic hyperuricemia (AH), and six gout patients. The coding sequences were mapped to KEGG orthologues (KO). We compared the expression levels using generalized linear models and validated the expression of three KO in a larger sample by qRT-PCR.
Results
A distinct genetic expression pattern was identified among groups. AH individuals and gout patients showed an over-expression of KOs mainly related to pyruvate metabolism (Log2foldchange > 23, p-adj ≤ 3.56x10− 9), the pentose pathway (Log2foldchange > 24, p-adj < 1.10x10− 12) and purine metabolism (Log2foldchange > 22, p-adj < 1.25x10− 7). AH subjects had lower expression of KO related to glycine metabolism (Log2foldchange=-18, p-adj < 1.72x10− 6) than controls. Gout patients had lower expression (Log2foldchange=-22.42, p-adj < 3.31x10− 16) of a KO involved in phenylalanine biosynthesis, in comparison to controls and AH subjects. The over-expression seen for the KO related to pyruvate metabolism and the pentose pathway in gout patients´ microbiome was validated.
Conclusions
There is a differential gene expression pattern in the gut microbiome of normouricemic individuals, AH subjects and gout patients. These differences are mainly located in metabolic pathways involved in acetate precursors and bioavailability of amino acids.
Molecular characterization of a novel narnavirus from the plant pathogenic fungus Ustilaginoidea virens
Authors: Zhao Wenhua , He Zhenrui , Liao Sisi , Yang Mei , Yang Yingqing , Cheng Baoping , Zhou Erxun 
Published: December 2024

Abstract

Mycoviruses are a class of viruses that take fungi, yeasts and oomycetes as hosts and can replicate and multiply within them. They are widely distributed in plant and animal pathogenic fungi. In this study, we identified a novel positive-sense single-stranded RNA (+ssRNA) mycovirus from Ustilaginoidea virens strain Uv339, the causal agent of rice false smut (RSF), which was named Ustilaginoidea virens narnavirus 5 (UvNV5). Sequence analysis revealed that UvNV5 has a complete genome length of 2091 nt and contains an open reading frame (ORF) (131-2038 nt), which encodes a 635-amino-acid (aa) RNA-dependent RNA polymerase (RdRp), with a molecular weight of 71.8 kDa. BLASTp analysis revealed that the UvNV5 genome shares similarities with the RdRp sequence of Ustilaginoidea virens narnavirus virus 13, but the highest identity is only 38.22%. Phylogenetic analyses indicated that UvNV5 clustered within a branch alongside members of the Narnaviridae family. In conclusion, UvNV5 is a novel mycovirus identified as a member of the family Narnaviridae that infects U. virens.