bluuebunny/crossref_metadata_2025 huggingface dataset:
Quantification of Free Circulating DNA and Differential Methylation Profiling of Selected Genes as Novel Non-Invasive Biomarkers for Endometriosis Diagnosis
Authors: Benkhalifa Moncef , Menoud Pierre Alain , Piquemal David , Hazout Jack Y. , Mahjoub Sami , Zarquaoui Mohammed , Louanjli Noureddine , Cabry Rosalie , Hazout Andre
Published: January 2025

Endometriosis is a chronic, estrogen-dependent disorder associated with the presence of endometrial cells mainly in the pelvic cavity, causing systemic immune inflammation, infertility, epigenetic dysregulation of differential DNA methylation, coelomic metaplasia, and pain. It affects approximately 10–12% of women. Despite decades of research, full pathophysiology, a diagnostic roadmap, and clinical management strategies for endometriosis are not yet fully elucidated. Cell-free DNA (Cf-DNA) in the peripheral blood of diseased and healthy individuals was discovered in the 1950s. Quantifying peripheral Cf-DNA and the specific differential methylation of a group of genes have been proposed as potential non-invasive diagnostic biomarkers for somatic and constitutional genetics and for various other pathological disorders. In this study, we investigated the Cf-DNA levels of 78 young women, 38 of whom had endometriosis confirmed via laparoscopy and 40 of whom were healthy. We found a significant difference between the two groups when Cf-DNA was quantified, with 3.9 times more Cf-DNA in the serum of women with endometriosis. We also identified nine target genes potentially involved in the pathogenesis of endometriosis, with a different methylation profile between the two groups. Our data suggest that the combination of cell-free DNA quantification and the assessment of the epigenetic signature of differential methylation of nine genes can be proposed as a non-invasive predictive and diagnostic test for endometriosis.
Circulating Isthmin-1 Levels and Their Relationship with Diabetes and Metabolic Diseases in Kuwaiti Adults
Authors: Alshawaf Eman , Marafie Sulaiman K. , Abu-Farha Mohamed , Albatineh Ahmed N. , Alramah Tahani , Albuhairi Aldana , Al Qassar Yafa , Zinoun Reem , Shalabi Rawan , Behbehani Sarah , Mohammed Dalal , Alajmi Fahad , Abdalla Mohammed A. , Al-Ozairi Ebaa , Shehab Mohammad , Abdul-Ghani Muhammad , Al-Mulla Fahd , Abubaker Jehad
Published: January 2025

Background/Objectives: Obesity and type 2 diabetes (T2D) are associated with significant alterations in various metabolic biomarkers. Isthmin-1 (Ism1) has recently emerged as a potential marker of metabolic health and was shown in animal studies to associate with metabolic-associated fatty liver disease (MAFLD). In this study, we aimed to investigate the circulatory levels of Ism1 in individuals with obesity compared to non-obese individuals and evaluate their association with insulin resistance, MAFLD, and T2D. The primary outcomes of this study are obesity, insulin resistance, MAFLD, and T2D, while the secondary outcome is hypertension; Methods: This is a cross-sectional study involving 450 participants, who were divided based on their obesity status into people with obesity (n = 182) and those without obesity (n = 265). Circulating Ism1 levels were measured by ELISA and were compared between the groups. Insulin resistance was assessed using the homeostasis model assessment of insulin resistance (HOMA-IR), and fatty liver was evaluated using Fibroscan; Results: Our results showed a significant reduction in circulating Ism1 levels in individuals with obesity (p-value = 0.002). Ism1 levels were negatively associated with the odds of T2D, possibly suggesting a protective role. Additionally, individuals with higher CAP scores demonstrated significantly lower Ism1 levels, and the Spearman’s rank correlation revealed a negative association between Ism1 and both CAP scores (r = −0.109, p-value = 0.025) and insulin resistance (r = −0.141, p-value = 0.004). Logistic regression analysis further supported Ism1 as an independent significant protective factor against obesity-related metabolic dysfunction. This significance persisted after adjusting for several confounders. Furthermore, our ROC results indicate that circulatory Ism1 levels possess significant diagnostic capability for identifying individuals with obesity-related metabolic imbalances with an area under the curve of 0.764 (95% CI = 0.718, 0.811). Finally, the adjusted multinomial analysis suggested that higher levels of Ism1 may play a protective role against pre-diabetes (AOR = 0.88, 95% CI = 0.838, 0.925) and T2D (AOR = 0.87, 95% CI = 0.814, 0.934); Conclusions: This study suggests that reduced Ism1 levels are linked to increased insulin resistance, MAFLD, and T2D in obese individuals. Our findings further corroborate the protective role of Ism1 and highlight its potential utility as a biomarker for monitoring obesity-related metabolic diseases.
A Network Pharmacology Study and Experimental Validation to Identify the Potential Mechanism of Heparan Sulfate on Alzheimer’s Disease-Related Neuroinflammation
Authors: Kim Dong-Uk , Kweon Bitna , Oh Jinyoung , Lim Yebin , Noh Gyeongran , Yu Jihyun , Kang Hyang-Rin , Kwon Tackmin , Lee Kwang youll , Bae Gi-Sang
Published: January 2025

Background/Objectives: Heparan sulfate (HS) is a polysaccharide that is found on the surface of cells and has various biological functions in the body. Methods: The purpose of this study was to predict the pharmacological effects and molecular mechanisms of HS on Alzheimer’s disease (AD) and neuroinflammation (NI) through a network pharmacology analysis and to experimentally verify them. Results: We performed functional enrichment analysis of common genes between HS target genes and AD-NI gene sets and obtained items such as the “Cytokine-Mediated Signaling Pathway”, “Positive Regulation Of MAPK Cascade”, and “MAPK signaling pathway”. To confirm the predicted results, the anti-inflammatory effect of HS was investigated using lipopolysaccharide (LPS)-stimulated BV2 microglia cells. HS inhibited the production of nittic oxide, interleukin (IL)-6, and tumor necrosis factor-α in LPS-stimulated BV2 cells, but not IL-1β. In addition, HS inactivated P38 in the MAPK signaling pathway. Conclusions: These findings suggest the potential for HS to become a new treatment for AD and NI.
Evaluation of Inflammatory Markers and Clinical Outcomes in COVID-19 Patients with Concurrent Clostridioides difficile Infection: A Comparative Cohort Analysis
Authors: Ignuta Flavia , Vlad Adrian , Cerbulescu Teodor , Loredana Stana , Bratosin Felix , Rosca Ovidiu , Stelea Lavinia , Nistor Daciana
Published: January 2025

Background and Objectives: Co-infection with Clostridioides difficile (C. difficile) in COVID-19 patients has emerged as a clinical challenge associated with increased morbidity and mortality. While both infections elicit systemic inflammation, the interplay between inflammatory markers, disease severity, and outcomes in patients with COVID-19 and concurrent C. difficile infection remains poorly characterized. This study aimed to evaluate the inflammatory status and clinical outcomes of patients hospitalized with COVID-19, with and without C. difficile co-infection, and to identify the inflammatory markers most predictive of severe disease. Methods: We conducted a retrospective cohort study of 200 hospitalized adults with confirmed COVID-19, of whom 92 had laboratory-confirmed C. difficile infection. Baseline demographic data, comorbidities, inflammatory markers (C-reactive protein [CRP], interleukin-6 [IL-6], ferritin, neutrophil-to-lymphocyte ratio [NLR], platelet count, albumin, and derived indices such as the CRP-to-Albumin Ratio [CAR] and Prognostic Nutritional Index [PNI]) were recorded. Clinical outcomes included ICU admission, need for mechanical ventilation, length of stay, and in-hospital mortality. Results: Patients with COVID-19 and C. difficile co-infection had significantly elevated inflammatory markers (CRP, IL-6, NLR) and higher CAR, alongside lower PNI, compared to those with COVID-19 alone (p < 0.001). Inflammatory indices correlated strongly with disease severity: elevated CAR and low PNI were associated with higher odds of ICU admission and mortality (p < 0.001). Multivariate analysis identified co-infection status, increased IL-6, and elevated CAR as independent predictors of severe outcomes. Conclusions: C. difficile co-infection in COVID-19 patients is associated with an intensified inflammatory response and worse clinical outcomes. Among the evaluated markers, CAR and PNI emerged as robust predictors of severe disease. Timely recognition of C. difficile co-infection and use of targeted anti-inflammatory and supportive therapies may improve patient management. Future studies should expand on these findings to optimize care and guide therapeutic strategies.
Machine Learning-Based Quantification of Lateral Flow Assay Using Smartphone-Captured Images
Authors: Davis Anne M. , Tomitaka Asahi
Published: January 2025

Lateral flow assay has been extensively used for at-home testing and point-of-care diagnostics in rural areas. Despite its advantages as convenient and low-cost testing, it suffers from poor quantification capacity where only yes/no or positive/negative diagnostics are achieved. In this study, machine learning and deep learning models were developed to quantify the analyte load from smartphone-captured images of the lateral flow assay test. The comparative analysis identified that random forest and convolutional neural network (CNN) models performed well in classifying the lateral flow assay results compared to other well-established machine learning models. When trained on small-size images, random forest models excelled CNN models in image classification. Contrarily, CNN models outperformed random forest models in classifying noisy images.
Comparing the Rates of Further Resection After Intraoperative MRI Visualisation of Residual Tumour Between Brain Tumour Subtypes: A 17-Year Single-Centre Experience
Authors: Madani Daniel , Fonseka R. Dineth , Kim Sihyong Jake , Tang Patrick , Muralidharan Krishna , Chang Nicholas , Wong Johnny
Published: January 2025

BACKGROUND: Maximal safe resection is the objective of most neuro-oncological operations. Intraoperative magnetic resonance imaging (iMRI) may guide the surgeon to improve the extent of safe resection. There is limited evidence comparing the impact of iMRI on the rates of further resection between tumour types. AIM: To investigate the impact of iMRI on the rate of further resection following visualisation of residual tumour. METHODS: A retrospective cohort study identified all intracranial tumour operations performed in the 1.5 T iMRI machine of a single centre (2007–2023). Patients were identified using SurgiNet and were grouped according to their histopathological diagnosis in accordance with the WHO 2021 classification. The primary outcome was the rate of reoperation due to iMRI visualisation of residual tumours. RESULTS: A total of 574 cases were identified, including 152 low-grade gliomas (LGG), 108 high-grade gliomas (HGG), 194 pituitary neuroendocrine tumours (PitNETs), 15 metastases, and 6 meningiomas. Further resection following iMRI visualisation occurred in 45% of LGG cases, 47% of HGG cases, 29% of PitNET cases, and no meningioma or metastasis cases. Chi-square analysis showed that the rate of further resection after iMRI use across 2018–2023 was significantly higher than that across 2007–2012 (46% versus 33%, p = 0.036). CONCLUSION: Intraoperative MRI for guiding further resection was most useful in cases of LGG and HGG, possibly reflecting the difficulty of differentiating these tumour types from normal brain tissue. In addition, there was increased reliance on iMRI over time, which may represent our surgeons becoming accustomed to its use.
Study on the Vibration Propagation Law and Stress Distribution Characteristics in Double-Arch Tunnels During Blasting
Authors: Sun Xiaofei , Su Ying , Liu Dunwen , Tang Yu , Zhang Pei , Hu Jishuang , Sun Xianghao
Published: January 2025

Highway tunnel construction in mountainous areas of China has been developing rapidly. The influence of drilling and blasting on the existing tunnel structure has become a key factor affecting the safety and stability of tunnel construction. The double-arch tunnel has unique structural characteristics. The propagation characteristics of blasting vibrations and the resulting stress responses exhibit a certain level of complexity. There is little research on the influence of single-line blasting excavation of double-arch tunnel on the other line tunnel. This paper analyzes the blasting vibration of a double-arch tunnel by ANSYS/LS-DYNA. The propagation law of blasting vibration velocity and stress distribution law of blasting vibration in different sections of the tunnel is revealed. At the same time, the relationship between the peak particle velocity (PPV) and tensile stress is established, and the threshold vibration velocity is proposed. It provides a scientific basis for tunnel design and construction. The propagation of blasting vibration in the adjacent roadway is affected by the middle pilot tunnel. The peak vibration velocity of different parts decreases with the increase in distance. The monitoring of vibration velocity and stress in section A of the right line of the adjacent tunnel should be strengthened, especially in the tunnel vault, blast-facing side wall, and arch foot. The difference in vibration strength across different tunnel parts provides a basis for optimizing the structure. It helps strengthen the parts susceptible to vibration during the design stage of the multi-arch tunnel, improving the tunnel’s safety and stability.
Empirical Study of the Relationship of Architectural Form Details to the State of Conservation of Modern Heritage Through Damage Maps
Authors: Kaminski Matheus Gregorio , Aciole Paulo Henrique de Sá , Zanoni Vanda Alice Garcia
Published: January 2025

The Sustainable Development Center of the University of Brasilia is one of the modernist buildings that make up the Darcy Ribeiro campus. The architectural project contains several recommendations for the execution of a flat roof waterproofing system, as well as details for rainwater runoff and drainage, which reveals the architect’s concern with watertightness. This research seeks to identify the relationship between the pathological manifestations recognized on the roof and the details of the semicircular shape of the building, assessing the state of conservation using damage maps as an auxiliary analysis tool. This study is based on a field survey using aerophotogrammetry with a drone, the application of vector drawing software for graphic representation and discussion of the possible causes, and the agents and mechanisms of degradation at work. The results show the importance of mechanical protection for the good performance of the waterproofing system, as well as the need for correct sizing of expansion joints to absorb and relieve the stresses caused by hygrothermal variations. The incorporated methodology proved to be effective and economical in diagnosing and monitoring pathological manifestations, making it possible to plan maintenance actions that extend the useful life and preserve the intrinsic characteristics of building systems.
Life Cycle Assessment of a Structural Insulated Panel Modular House in New Zealand
Authors: Dani Aflah Alamsah , Feng Ran , Fang Zhiyuan , Roy Krishanu
Published: January 2025

Innovative solutions are essential to meet the increasing demand for housing in New Zealand. These innovations must also be sustainable, given the significant contribution of the building and construction sectors to global carbon emissions (25–40%) and, specifically, to New Zealand’s gross carbon emissions (20%). This research aims to analyse the environmental impacts of a structural insulated panel (SIP) modular house and evaluate this innovative approach as a sustainable solution to the current housing issue. A life cycle assessment (LCA) was conducted using the New Zealand-specific tool LCAQuick V3.6. The analysis considered seven environmental impact indicators, namely, global warming potential (GWP), ozone depletion potential (ODP), acidification potential (AP), eutrophication potential (EP), photochemical ozone creation potential (POCP), abiotic depletion potential for elements (ADPE), and abiotic depletion potential for fossil fuels (ADPF), with a cradle-to-cradle system boundary. Focusing on the embodied carbon of the SIP modular house, the study revealed that the whole-of-life embodied carbon was 347.15 kg CO2 eq/m2, including Module D, and the upfront carbon was 285.08 kg CO2 eq/m2. The production stage (Modules A1–A3) was identified as the most significant source of carbon emissions due to substantial energy consumption in activities such as sourcing raw materials, transportation, and final product manufacturing. Specifically, the study found that SIP wall and roof panels were the most significant contributors to the house’s overall embodied carbon, with SIP roof panels contributing 25% and SIP wall panels contributing 19%, collectively accounting for 44%. Hence, the study underscored the SIP modular house as a promising sustainable solution to the housing crisis while emphasising the inclusion of operational carbon in further research to fully understand its potential.
Hysteretic Behavior Study on the RBS Connection of H-Shape Columns with Middle-Flanges or Wide-Flange H-Shape Beams
Authors: Al-Saeedi Saleem Mohammed Ali Ahmed , Lu Linfeng , Al-Ansi Osama Zaid Yahya , Ali Saddam
Published: January 2025

Existing research on reduced beam section (RBS) connections in steel frames rarely addresses H-shaped beams with middle and wide flanges. Therefore, this study investigates the hysteretic behavior of RBS connections in H-shaped columns connected to H-shaped beams with middle and wide flanges. Using finite element analysis, the influence of key parameters (a, b, and c, where “a” represents the unweakened beam flange extension length, “b” represents the weakened beam flange length, and “c” represents the weakened beam flange depth, respectively) on structural performance was evaluated, focusing on rotational stiffness, load-carrying capacity, plastic rotation capacity, and ductility. The results indicate that increasing a enhances initial rotational stiffness and load capacity but reduces plastic rotation and ductility, making lower a values (near 0.5bf) optimal for ductile performance. Similarly, higher b values (up to 0.85bf) marginally reduce stiffness and load capacity, improving plastic rotation capacity, with a greater benefit in wide-flange beams. Meanwhile, a lower c value (around 0.20bf) offered balanced performance, with higher c values decreasing stiffness and load capacity but enhancing ductility. Overall, wider flanges improve plastic rotation and ductility but slightly decrease rotational stiffness, providing insights to guide RBS connection designs for seismic resilience.
Menin Inhibitors: New Targeted Therapies for Specific Genetic Subtypes of Difficult-to-Treat Acute Leukemias
Authors: Niscola Pasquale , Gianfelici Valentina , Giovannini Marco , Piccioni Daniela , Mazzone Carla , Fabritiis Paolo de
Published: January 2025

Menin (MEN1) is a well-recognized powerful tumor promoter in acute leukemias (AL) with KMT2A rearrangements (KMT2Ar, also known as MLL) and mutant nucleophosmin 1 (NPM1m) acute myeloid leukemia (AML). MEN1 is essential for sustaining leukemic transformation due to its interaction with wild-type KMT2A and KMT2A fusion proteins, leading to the dysregulation of KMT2A target genes. MEN1 inhibitors (MIs), such as revumenib, ziftomenib, and other active small molecules, represent a promising new class of therapies currently under clinical development. By disrupting the MEN1-KMT2Ar complex, a group of proteins involved in chromatin remodeling, MIs induce apoptosis and differentiation AL expressing KMT2Ar or NPM1m AML. Phase I and II clinical trials have evaluated MIs as standalone treatments and combined them with other synergistic drugs, yielding promising results. These trials have demonstrated notable response rates with manageable toxicities. Among MIs, ziftomenib received orphan drug and breakthrough therapy designations from the European Medicines Agency in January 2024 and the Food and Drug Administration (FDA) in April 2024, respectively, for treating R/R patients with NPM1m AML. Additionally, in November 2024, the FDA approved revumenib for treating R/R patients with KMT2Ar-AL. This review focuses on the pathophysiology of MI-sensitive AL, primarily AML. It illustrates data from clinical trials and discusses the emergence of resistance mechanisms. In addition, we outline future directions for the use of MIs and emphasize the need for further research to fully realize the potential of these novel compounds, especially in the context of specific genetic subtypes of challenging AL.
Correction: Häyrinen et al. The Transcription Factor Twist1 Has a Significant Role in Mycosis Fungoides (MF) Cell Biology: An RNA Sequencing Study of 40 MF Cases. Cancers 2023, 15, 1527
Authors: Häyrinen Marjaana J. , Kiiskilä Jenni , Ranki Annamari , Väkevä Liisa , Barton Henry J. , Kuusisto Milla E. L. , Porvari Katja , Kuitunen Hanne , Haapasaari Kirsi-Maria , Teppo Hanna-Riikka , Kuittinen Outi
Published: January 2025

In the original publication [...]
Metabolic Signaling in the Tumor Microenvironment
Authors: Clay Ryan , Li Kunyang , Jin Lingtao
Published: January 2025

Cancer cells must reprogram their metabolism to sustain rapid growth. This is accomplished in part by switching to aerobic glycolysis, uncoupling glucose from mitochondrial metabolism, and performing anaplerosis via alternative carbon sources to replenish intermediates of the tricarboxylic acid (TCA) cycle and sustain oxidative phosphorylation (OXPHOS). While this metabolic program produces adequate biosynthetic intermediates, reducing agents, ATP, and epigenetic remodeling cofactors necessary to sustain growth, it also produces large amounts of byproducts that can generate a hostile tumor microenvironment (TME) characterized by low pH, redox stress, and poor oxygenation. In recent years, the focus of cancer metabolic research has shifted from the regulation and utilization of cancer cell-intrinsic pathways to studying how the metabolic landscape of the tumor affects the anti-tumor immune response. Recent discoveries point to the role that secreted metabolites within the TME play in crosstalk between tumor cell types to promote tumorigenesis and hinder the anti-tumor immune response. In this review, we will explore how crosstalk between metabolites of cancer cells, immune cells, and stromal cells drives tumorigenesis and what effects the competition for resources and metabolic crosstalk has on immune cell function.
N Simultaneously Doped TiO2@Carbon Hollow Spheres with Enhanced Photocatalytic CO2 Reduction Activity
Authors: Fu Weiwei , Wang Ziyun , Liu Xinjie , Li Tianjiao
Published: January 2025

Converting carbon dioxide (CO2) into solar fuels through photocatalysis represents an appealing approach to tackling the escalating energy crisis and mitigating the greenhouse effect. In this study, using melamine–formaldehyde (MF) nanospheres as a nitrogen source, a N element was simultaneously doped into the TiO2 nanoparticle structure supported by carbon hollow spheres using a one-step carbonization method to form a heterojunction N-CHS@N-TiO2 (marked as (N-(CHS@TiO2)). The composite showed superior photocatalytic activity in reducing CO2 compared with TiO2 and N-CHS: after 6 h of visible light irradiation, the CO yield was 4.3 times that of N-CHS and TiO2; 6 h of UV irradiation later, the CO yield reached 2.6 times that of TiO2 and 7 times that of N-CHS. The substantial enhancement in photocatalytic activity was attributed to the nitrogen simultaneously doped carbon hollow spheres and TiO2, mesoporous structure, small average TiO2 crystal size, large surface areas, and the heterostructure formed by N-CHS and N-TiO2. The UV-vis diffuse reflectance spectra (DRS) exhibit a significant improvement in light absorption, attributed to the visible-light-active carbon hollow sphere and the N element doping, thereby enhancing solar energy utilization.
Textile Wastewater Coupled Treatment Implementing Enhanced Ozonation with Fenton-like Processes and Phytoremediation
Authors: Reyes-Pérez Jazmin A. , Amaya-Chávez Araceli , Roa-Morales Gabriela , Balderas-Hernández Patricia , Pavón Silva Thelma B. , Torres-Blancas Teresa , Barrera-Díaz Carlos E.
Published: January 2025

In this contribution, we described how phytoremediation using M. aquaticum is feasible with coupled ozonation/Fenton-like processes in real wastewater from the denim textile industry, with the purpose of removing color and, therefore, highly polluting particles. For the ozonation/Fenton-like process, pHs of 3 and 9 were evaluated using a copper-enriched pumice to activate the catalytic processes carried out in the Fenton-like reactions. Subsequently, phytoremediation was carried out using M. aquaticum to completely degrade the by-products generated from the ozone/Fenton-like process. Plant health was controlled through the determination of chlorophylls and carotenes. All the analyses were monitored by COD, UV–Vis spectrophotometry for the determinations of color quantification in the wastewater and oxidizable organic matter, and SEM/EDS for the characterization of the material and XPS to corroborate the oxidation state of the copper that gives rise to radical species. The results indicate that, at pH 3.0, the ozonation process with the PMPCu catalyst at 1 g/L is the most efficient, achieving a percentage of color removal of 86.79 ± 1.3% and COD of 76.19%, which is consistent with the optimization analysis of the experimental design. The residual color and its degradation by-products were eliminated by phytoremediation.
Circulating T Cell Subsets in Type 1 Diabetes
Authors: Ferreira-Hermosillo Aldo , Santana-Sánchez Paola , Vaquero-García Ricardo , García-Sáenz Manuel R. , Castro-Ríos Angélica , Chávez-Rueda Adriana K. , Gómez-Díaz Rita A. , Chávez-Sánchez Luis , Legorreta-Haquet María V.
Published: January 2025

Type 1 diabetes (T1D) is a complex disease driven by the immune system attacking the insulin-producing beta cells in the pancreas. Understanding the role of different T cell subpopulations in the development and progression of T1D is crucial. By employing flow cytometry to compare the characteristics of T cells, we can pinpoint potential indicators of treatment response or therapeutic inefficacy. Our study reveals elevated prolactin (PRL) levels in T1D patients, along with a decreased production of key cytokines. Additionally, PD1 appears to play a significant role in T1D. Notably, PRL levels correlate with an earlier disease onset and a specific T cell phenotype, hinting at the potential influence of PRL. These findings highlight the need for further research to identify promising cellular targets for more effective and tailored therapies.
The Effect of Autologous Dendritic Cell Immunotherapy on Kidney Function and Endothelial Dysfunction of Patients with Diabetic Kidney Disease (DKD): An Open Label Clinical Trial
Authors: Yana Martina Lily , Sitepu Enda Cindylosa , Jonny  , Chiuman Linda , Lister I Nyoman Ehrich , Putranto Terawan Agus
Published: January 2025

This study aimed to evaluate the effects of autologous dendritic cell (DC) immunotherapy on clinical outcomes (glomerular filtration rate/GFR and urine creatinine albumin ratio/UACR) and endothelial dysfunction (ICAM, VCAM, VEGF) in patients with diabetic kidney disease (DKD). Endothelial dysfunction induced by inflammation is one of the key factors in the pathogenesis of DKD. In this one-group pretest–posttest quasi-experimental study, 69 subjects with DKD were administered a single dose of autologous DC immunotherapy ex vivo. UACR was measured at baseline and at weeks 1, 2, 3, and 4, while ICAM, VCAM, VEGF, and GFR were measured at baseline and at week 4 post-immunotherapy. The results showed a significant reduction in median UACR from 250 (IQR 71–668) mg/g at baseline to 164 (IQR 49–576) mg/g at week 4 (p < 0.05). GFR did not show any significant changes after immunotherapy. HbA1c (B = −33.270, p = 0.021) and baseline UACR (B = −0.185, p < 0.001) were identified as significant predictors of UACR change. Although there were no significant changes in ICAM, VCAM, and VEGF, subgroup analysis revealed a decrease in VCAM in macroalbuminuria patients and an increase in those with good glycemic control, suggesting differing endothelial responses. In conclusion, autologous DC immunotherapy effectively reduced UACR in DKD patients, and significant VCAM changes were found in macroalbuminuria and good glycemic control subjects. Further research is needed to understand the mechanisms behind UACR reduction and the long-term impact of this therapy.
Microstructural and Mechanical Characterization of Nb-Doped MoS2 Coatings Deposited on H13 Tool Steel Using Nb-Based Interlayers
Authors: Danelon Miguel R. , Fukumasu Newton K. , Carvalho Angelo A. , Rego Ronnie R. , Machado Izabel F. , Souza Roberto M. , Tschiptschin André P.
Published: January 2025

Molybdenum disulfide is a 2D material with excellent lubricant properties, resulting from weak van der Waals forces between lattice layers and shear-induced crystal orientation. The low forces needed to shear the MoS2 crystal layers grant the tribological system low coefficients of friction (COF). However, film oxidation harms its efficacy in humid atmospheres, leading to an increased COF and poor surface adhesion, making its use preferable in dry or vacuum conditions. To overcome these challenges, doping MoS2 with elements such as Nb, Ti, C, and N emerges as a promising solution. Nevertheless, the adhesion of these coatings to a steel substrate presents challenges and strategies involving the reduction in residual stresses and increased chemical affinity to the substrate by using niobium-based materials as interlayers. In this study, Nb-doped MoS2 films were deposited on H13 steel and silicon wafers using the pulsed direct current balanced magnetron sputtering technique. Different niobium-based interlayers (pure Nb and NbN) were deposited to evaluate the adhesion properties of Nb-doped MoS2 coatings. Unlubricated scratch tests, conducted at room temperature and relative humidity under a progressive load, were performed to analyze the COF and adhesion of the coating. Instrumented indentation tests were conducted to assess the hardness and elastic modulus of the coatings. The microstructure of the coatings was obtained by Scanning Electron Microscopy (SEM), Scanning Transmission Electron Microscopy (STEM), and Transmission Electron Microscopy (TEM), with Energy-Dispersive X-Ray Spectroscopy (EDS). Results indicated that niobium doping on MoS2 coatings changes the structure from crystalline to amorphous. Additionally, the Nb concentration of the Nb:MoS2 coating changed the mechanical properties, leading to different cohesive failures by different loads during the scratch tests. Results have also indicated that an NbN interlayer optimally promoted the adhesion of the film. This result is justified by the increase in hardness led by higher Nb concentrations, enhancing the load-bearing capacity of the coating. It is concluded that niobium-based materials can be used to enhance the adhesion properties of Nb-doped MoS2 films and improve their tribological performance.
An Authentication Approach in a Distributed System Through Synergetic Computing
Authors: Wang Jia-Jen , Chen Yaw-Chung , Chen Meng-Chang
Published: January 2025

A synergetic computing mechanism is proposed to authenticate the validity of event data in merchandise exchange applications. The events are handled by the proposed synergetic computing system which is composed of edge devices. Asteroid_Node_on_Duty (ANOD) acts like a supernode to take the duty of coordination. The computation performed by nodes in local area can reduce round-trip data propagation delay to distant data centers. Events with different risk levels are processed in parallel through different flows by using Chief chain (CC) and Telstar chain (TC) methods. Low-risk events are computed in edge nodes to form TC, which can be periodically integrated into CC that contains data of high-risk events. New authentication methods are proposed. The difficulty of authentication tasks is adjusted for different scenarios where lower difficulty in low-risk tasks may accelerate the process of validation. Authentication by a certain number of nodes is required so that the system may ensure the consistency of data. Participants in the system may need to register as members. The transaction processing speed on low-risk events may reach 25,000 TPS based on the assumption of certain member classes given that all of ANOD, and Asteroid_Node_of_Backup (ANB), Edge Cloud, and Core Cloud function normally.
Long-Term Oncological Outcomes Related to Lymphadenectomy in Clinical Stage I NSCLC: A Multicenter Retrospective Experience
Authors: Manfredini Beatrice , Zirafa Carmelina Cristina , Stefani Alessandro , Romano Gaetano , Alì Greta , Morganti Riccardo , Ceccarelli Ilaria , Davini Federico , Filosso Pier Luigi , Melfi Franca
Published: January 2025

Background: Lymphadenectomy is considered a key part of the radical treatment of resectable lung cancer, although its appropriate extension in early stages is a debated topic due to the great heterogeneity of studies in the literature. This study aims to evaluate the impact of lymphadenectomy extent on survival and recurrence in the treatment of early-stage NSCLC patients undergoing lobectomy and lymph node dissection. Methods: Data from clinical stage I NSCLC patients undergoing lobectomy and hilar-mediastinal lymphadenectomy at two thoracic surgery centers from 2016 to 2019 were retrospectively evaluated. Information regarding perioperative outcomes and lymphadenectomy details was collected and analyzed, and their impact on OS, CSS, and DFS was assessed. Results: During the period under review, 323 patients with stage cI lung cancer underwent lobectomy with lymphadenectomy. Statistical analysis showed that the evaluated lymph nodal factors (mean number of lymph nodes removed and number and type of lymph node station explored) did not statistically significantly impact OS, CSS, and DFS at a median follow-up of 59 months (IQR 45–71). Conclusions: The results of this study suggest that a less invasive procedure than systematic lymphadenectomy could be performed in early-stage cases with adequate preoperative staging.
Automatic Characterization of Prostate Suspect Lesions on T2-Weighted Image Acquisitions Using Texture Features and Machine-Learning Methods: A Pilot Study
Authors: Telecan Teodora , Caraiani Cosmin , Boca Bianca , Sipos-Lascu Roxana , Diosan Laura , Balint Zoltan , Hendea Raluca Maria , Andras Iulia , Crisan Nicolae , Lupsor-Platon Monica
Published: January 2025

Background: Prostate cancer (PCa) is the most frequent neoplasia in the male population. According to the International Society of Urological Pathology (ISUP), PCa can be divided into two major groups, based on their prognosis and treatment options. Multiparametric magnetic resonance imaging (mpMRI) holds a central role in PCa assessment; however, it does not have a one-to-one correspondence with the histopathological grading of tumors. Recently, artificial intelligence (AI)-based algorithms and textural analysis, a subdivision of radiomics, have shown potential in bridging this gap. Objectives: We aimed to develop a machine-learning algorithm that predicts the ISUP grade of manually contoured prostate nodules on T2-weighted images and classifies them into clinically significant and indolent ones. Materials and Methods: We included 55 patients with 76 lesions. All patients were examined on the same 1.5 Tesla mpMRI scanner. Each nodule was manually segmented using the open-source 3D Slicer platform, and textural features were extracted using the PyRadiomics (version 3.0.1) library. The software was based on machine-learning classifiers. The accuracy was calculated based on precision, recall, and F1 scores. Results: The median age of the study group was 64 years (IQR 61–68), and the mean PSA value was 11.14 ng/mL. A total of 85.52% of the nodules were graded PI-RADS 4 or higher. Overall, the algorithm classified indolent and clinically significant PCas with an accuracy of 87.2%. Further, when trained to differentiate each ISUP group, the accuracy was 80.3%. Conclusions: We developed an AI-based decision-support system that accurately differentiates between the two PCa prognostic groups using only T2 MRI acquisitions by employing radiomics with a robust machine-learning architecture.
Optimal Coverage Path Planning for UAV-Assisted Multiple USVs: Map Modeling and Solutions
Authors: Pan Shaohua , Xu Xiaosu , Cao Yi , Zhang Liang
Published: January 2025

With the increasing demand for marine monitoring, the use of coverage path planning based on unmanned aerial vehicle (UAV) aerial images to assist multiple unmanned surface vehicles (USVs) has shown great potential in marine applications. However, achieving accurate map modeling and optimal path planning are still key challenges that restrict its widespread application. To this end, an innovative coverage path planning algorithm for UAV-assisted multiple USVs is proposed. First, a semantic segmentation algorithm based on the YOLOv5-assisted prompting segment anything model (SAM) is designed to establish an accurate map model. By refining the axial, length, width, and coordinate information of obstacles, the algorithm enables YOLOv5 to generate accurate object bounding box prompts and then assists SAM in automatically and accurately extracting obstacles and coastlines in complex scenes. Based on this accurate map model, a multi-objective stepwise optimization coverage path planning algorithm is further proposed. The algorithm divides the complete path into two parts, the straight paths and the turning paths, and both the path length and the number of turns is designed, respectively, to optimize each type of path step by step, which significantly improves the coverage effect. Experiments prove that in various complex marine coverage scenarios, the proposed algorithm achieves 100% coverage, the redundancy rate is less than 2%, and it is superior to existing advanced algorithms in path length and number of turns. This research provides a feasible technical solution for efficient and accurate marine coverage tasks and lays the foundation for unmanned marine supervision.
Goal Achievement and Academic Dropout Among Italian University Students: The Mediating Role of Academic Burnout
Authors: Nicita Arianna , Fumia Angelo , Caparello Concettina , Meduri Carmelo Francesco , Filippello Pina , Sorrenti Luana
Published: January 2025

As stated by the Goal Orientation Theory, students want to achieve a goal for multiple reasons, with each having a different impact on academic performance. This framework encompasses a three-factor model of goal achievement: a mastery goal, a performance-avoidance (PAv) goal, and a performance-approach (PAp) goal. Students may experience elevated stress levels and burnout due to adopting an ineffective approach to goal achievement. This can lead to a loss of interest in studies and even physical and psychological exhaustion. In severe cases, this may result in students abandoning their studies early. This study aims to integrate these factors into a comprehensive model. A cross-sectional study comprising 1497 Italian university students examined the mediating role of academic burnout (professional efficacy, cynicism, and emotional exhaustion) in the association between achievement goals (mastery, PAv, and PAp goals) and the intention to drop out (ID). The questionnaires were administered from October 2022 to September 2023. Structural equation modeling was employed to evaluate the association between variables. The results of the mediation analysis indicate that cynicism and professional efficacy fully mediate the association between mastery and dropout. Cynicism (β = −0.28, p < 0.001) and professional efficacy (β = −0.17, p < 0.001) were both negatively associated with ID, while they partially mediate the association between PAv goals and ID (cynicism: β = 0.21, p ≤ 0.001; professional efficacy: β = 0.05, p ≤ 0.001), and between PAp goals and ID via professional efficacy (β = −0.04, p ≤ 0.001). This study contributes to the currently limited literature on the relationship between achievement goals, burnout, and ID in a sample of university students. The findings of this study may have useful implications for the application of interventions that impact students’ well-being and academic success, potentially limiting their possible dropout.
A Feature Map Fusion Self-Distillation Scheme for Image Classification Networks
Authors: Qin Zhenkai , Ni Shuiping , Zhu Mingfu , Jia Yue , Liu Shangxin , Chen Yawei
Published: January 2025

Self-distillation has been widely applied in the field of deep learning. However, the lack of interaction between the multiple shallow branches in the self-distillation framework reduces the effectiveness of self-distillation methods. To address this issue, a feature map fusion self-distillation scheme is proposed. According to the depth of the teacher model, multiple shallow branches as student models are constructed to build a self-distillation framework. Then, the feature map fusion module fuses the intermediate feature maps of each branch to enhance the interaction between the branches. Specifically, this fusion module employs a spatial enhancement module to generate attention masks for multiple feature maps, which are averaged and applied to create intermediate maps. The mean of these intermediate maps results in the final fusion feature map. The experimental findings on the CIFAR10 and CIFAR100 datasets illustrate that our proposed technique has clear advantages in increasing the classification accuracy of the deep learning models. On average, 0.7% and 2.5% accuracy boosts are observed on the CIFAR10 and CIFAR100.
Autonomous Medical Robot Trajectory Planning with Local Planner Time Elastic Band Algorithm
Authors: Turnip Arjon , Faridhan Muhamad Arsyad , Wibawa Bambang Mukti , Anggriani Nursanti
Published: January 2025

Robots have made significant contributions across various industries due to their efficiency and effectiveness. However, indoor navigation remains challenging due to complex environments and sensor signal interference. Changes in indoor conditions and the limited range of GPS signals necessitate the development of an accurate and efficient indoor robot navigation system. This study aims to create an autonomous indoor navigation system for medical robots using sensors such as Marvelmind, LiDAR, IMU, and an odometer, along with the Time Elastic Band (TEB) local planning algorithm to detect dynamic obstacles. The algorithm’s performance is evaluated using metrics like path length, duration, speed smoothness, path smoothness, Mean Squared Error (MSE), and positional error. In the test arena, TEB demonstrated superior efficiency with a path length of 155.55 m, 9.83 m shorter than the Dynamic Window Approach (DWA), which covered 165.38 m, and had a lower yaw error of 0.012 radians. TEB outperformed DWA in terms of speed smoothness, path smoothness, and MSE. In the Sterile Room Arena, TEB had an average path length of 14.84 m, slightly longer than DWA’s 14.32 m, but TEB navigated 2.82 s faster. Additionally, TEB showed better speed and path smoothness. In the Obstacle Room Arena, TEB recorded an average path length of 21.96 m in 57.3 s, outperforming DWA, which covered 23.44 m in 61 s, with better results in MSE, speed smoothness, and path smoothness, highlighting superior path consistency. These findings indicate that the TEB algorithm is an effective choice as a local planner in dynamic hospital environments.
Multi-Label Text Classification Based on Label-Sentence Bi-Attention Fusion Network with Multi-Level Feature Extraction
Authors: Li Anqi , Zhang Lin
Published: January 2025

Multi-label text classification (MLTC) aims to assign the most appropriate label or labels to each input text. Previous studies have focused on mining textual information, ignoring the interdependence of labels and texts, thus leading to the loss of information about labels. In addition, previous studies have tended to focus on the single granularity of information in documents, ignoring the degree of inclination towards labels in different sentences in multi-labeled texts. In order to solve the above problems, this paper proposes a Label-Sentence Bi-Attention Fusion Network (LSBAFN) with multi-level feature extraction for mining multi-granularity information and label information in documents. Specifically, document-level and sentence-level word embeddings are first obtained. Then, the textual relevance of the labels to these two levels is utilized to construct sentence-level textual representations. Next, a multi-level feature extraction mechanism is utilized to acquire a sentence-level textual representation that incorporates contextual information and a document-level textual representation that reflects label features. Subsequently, the label-sentence bi-attention fusion mechanism is used to learn the feature relationships in the two text representations and fuse them. Label attention identifies text features related to labels from the document-level text representation, while sentence attention focuses on the tendency of sentences towards labels. Finally, the effective portion of the fused features is extracted for classification by a multi-layer perceptron. The experimental findings indicate that the LSBAFN can improve the effectiveness of the MLTC task. Compared with the baseline models, the LSBAFN obtains a significant improvement of 0.6% and 7.81% in Micro-F1 and Macro-F1 on the Article Topic dataset and improvements of 1.03% and 0.47% in P@k and 1.02% and 0.38% in nDCG@k on the Software Category dataset and RCV1 dataset.
Integrating Emotional Features for Stance Detection Aimed at Social Network Security: A Multi-Task Learning Approach
Authors: Pu Qiumei , Huang Fangli , Li Fude , Wei Jieyao , Jiang Shan
Published: January 2025

Stance detection seeks to identify the public’s position on a specific topic, providing critical insights for applications such as recommendation systems and rumor detection, which are essential for maintaining a secure social media environment. As one of China’s most influential social media platforms, Weibo significantly shapes public discourse within its complex social network structure. Despite recent advancements in stance detection research on Weibo, many studies fail to adequately address the nuanced emotional features present in text, limiting detection accuracy and effectiveness, and potentially compromising online security. This paper proposes a stance detection approach based on multi-task learning that considers the influence of emotional features to tackle these challenges. Our method utilizes a RoBERTa pre-trained model in the shared layer to extract textual features for both stance detection and sentiment analysis. In the stance detection module, a BiLSTM model captures deeper temporal information, followed by three independent modules dedicated to extracting semantic features for specific stances. Concurrently, the sentiment analysis module employs a BiLSTM model to predict emotional polarity. The experimental results on the NLPCC2016-task4 dataset demonstrate that our approach outperforms existing methods, highlighting the effectiveness of integrating sentiment analysis with stance detection to enhance both accuracy and reliability, ultimately contributing to the security of social networks.