bluuebunny/crossref_metadata_2025 huggingface dataset:
Wearable Fabric System for Sarcopenia Detection
Authors: Huang Zhenhe , Ou Qiuqian , Li Dan , Feng Yuanyi , Cai Liangling , Hu Yue , Chu Hongwei
Published: December 2024

Sarcopenia has been a serious concern in the context of an increasingly aging global population. Existing detection methods for sarcopenia are severely constrained by cumbersome devices, the necessity for specialized personnel, and controlled experimental environments. In this study, we developed an innovative wearable fabric system based on conductive fabric and flexible sensor array. This fabric system demonstrates remarkable pressure-sensing capabilities, with a high sensitivity of 18.8 kPa−1 and extraordinary stability. It also exhibits excellent flexibility for wearable applications. By interacting with different parts of the human body, it facilitates the monitoring of various physiological activities, such as pulse dynamics, finger movements, speaking, and ambulation. Moreover, this fabric system can be seamlessly integrated into sole to track critical indicators of sarcopenia patients, such as walking speed and gait. Clinical evaluations have shown that this fabric system can effectively detect variations in indicators relevant to sarcopenia patients, proving that it offers a straightforward and promising approach for the diagnosis and assessment of sarcopenia.
Portable Amperometric Biosensor Enhanced with Enzyme-Ternary Nanocomposites for Prostate Cancer Biomarker Detection
Authors: Rajarathinam Thenmozhi , Jayaraman Sivaguru , Kim Chang-Seok , Lee Jaewon , Chang Seung-Cheol
Published: December 2024

Enzyme-based portable amperometric biosensors are precise and low-cost medical devices used for rapid cancer biomarker screening. Sarcosine (Sar) is an ideal biomarker for prostate cancer (PCa). Because human serum and urine contain complex interfering substances that can directly oxidize at the electrode surface, rapid Sar screening biosensors are relatively challenging and have rarely been reported. Therefore, highly sensitive and selective amperometric biosensors that enable real-time measurements within <1.0 min are needed. To achieve this, a chitosan–polyaniline polymer nanocomposite (CS–PANI NC), a carrier for dispersing mesoporous carbon (MC), was synthesized and modified on a screen-printed carbon electrode (SPCE) to detect hydrogen peroxide (H2O2). The sarcosine oxidase (SOx) enzyme-immobilized CS–PANI–MC-2 ternary NCs were referred to as supramolecular architectures (SMAs). The excellent electron transfer ability of the SMA-modified SPCE (SMA/SPCE) sensor enabled highly sensitive H2O2 detection for immediate trace Sar biomarker detection. Therefore, the system included an SMA/SPCE coupled to a portable potentiostat linked to a smartphone for data acquisition. The high catalytic activity, porous architecture, and sufficient biocompatibility of CS–PANI–MC ternary NCs enabled bioactivity retention and immobilized SOx stability. The fabricated biosensor exhibited a detection limit of 0.077 μM and sensitivity of 8.09 μA mM−1 cm−2 toward Sar, demonstrating great potential for use in rapid PCa screening.
Impedimetric Biosensors for the Quantification of Serum Biomarkers for Early Detection of Lung Cancer
Authors: Arabnejad Mahdi , Tothill Ibtisam E. , Chianella Iva
Published: December 2024

Lung cancer is the most common type of cancer diagnosed worldwide and is also among the most fatal. Early detection, before symptoms become evident, is fundamental for patients’ survival. Therefore, several lung cancer biomarkers have been proposed to enable a prompt diagnosis, including neuron-specific enolase (NSE) and carcinoembryonic antigen (CEA). NSE and CEA are two serum proteins whose elevated levels have been associated with lung cancer. Hence, in this study, impedimetric biosensors (immunosensors) able to quantify NSE and CEA were developed as proof-of-concept devices for lung cancer diagnosis. The sensing platform exploited for the immunosensors comprises a novel combination of a magnetic platform, screen-printed gold electrode (SPGE), and magnetic nanobeads (MB). The MB were functionalized with antibodies to capture the analyte from the sample and to move it over the sensing area. The immunosensors were then developed by immobilizing another set of antibodies for either CEA or NSE on the SPGE through formation of self-assembled monolayer (SAM). The second set of antibodies enabled a sandwich assay to be formed on the surface of the sensor, while MB manipulation was applied during the sensor performance to depict a microfluidic system and increase antigen–antibody complex formation prior to CEA or NSE detection and quantification. The optimized immunosensors were successfully tested to measure various concentrations of CEA and NSE (0–100 ng/mL) in both phosphate buffer and 100% human serum samples. Clinically relevant detection limits of 0.26 ng/mL and 0.18 ng/mL in buffer and 0.76 ng/mL and 0.52 ng/mL in 100% serum for CEA and NSE, respectively, were achieved via electrochemical impedance spectroscopy with the use of potassium ferri/ferrocyanide as a redox probe. Hence, the two immunosensors demonstrated great potential as tools to be implemented for the early detection of lung cancer.
The Effect of Processing Techniques on the Classification Accuracy of Brain-Computer Interface Systems
Authors: Adolf András , Köllőd Csaba Márton , Márton Gergely , Fadel Ward , Ulbert István
Published: December 2024

Background/Objectives: Accurately classifying Electroencephalography (EEG) signals is essential for the effective operation of Brain-Computer Interfaces (BCI), which is needed for reliable neurorehabilitation applications. However, many factors in the processing pipeline can influence classification performance. The objective of this study is to assess the effects of different processing steps on classification accuracy in EEG-based BCI systems. Methods: This study explores the impact of various processing techniques and stages, including the FASTER algorithm for artifact rejection (AR), frequency filtering, transfer learning, and cropped training. The Physionet dataset, consisting of four motor imagery classes, was used as input due to its relatively large number of subjects. The raw EEG was tested with EEGNet and Shallow ConvNet. To examine the impact of adding a spatial dimension to the input data, we also used the Multi-branch Conv3D Net and developed two new models, Conv2D Net and Conv3D Net. Results: Our analysis showed that classification accuracy can be affected by many factors at every stage. Applying the AR method, for instance, can either enhance or degrade classification performance, depending on the subject and the specific network architecture. Transfer learning was effective in improving the performance of all networks for both raw and artifact-rejected data. However, the improvement in classification accuracy for artifact-rejected data was less pronounced compared to unfiltered data, resulting in reduced precision. For instance, the best classifier achieved 46.1% accuracy on unfiltered data, which increased to 63.5% with transfer learning. In the filtered case, accuracy rose from 45.5% to only 55.9% when transfer learning was applied. An unexpected outcome regarding frequency filtering was observed: networks demonstrated better classification performance when focusing on lower-frequency components. Higher frequency ranges were more discriminative for EEGNet and Shallow ConvNet, but only when cropped training was applied. Conclusions: The findings of this study highlight the complex interaction between processing techniques and neural network performance, emphasizing the necessity for customized processing approaches tailored to specific subjects and network architectures.
Health-Related Quality of Life and Bereavement in the 2019 Georgia Behavioral Risk Factor Surveillance System
Authors: Li Changle , Miles Toni P.
Published: December 2024

Poor Self-Rated Health (SRHp) is part of a four-item scale for self-assessment. SRH from the 2019 Behavioral Risk Factor Surveillance Survey (BRFSS) is used to test hypotheses linking population-level well-being influenced by bereavement due to the death of a close friend or relative. By linking the prevalence rates of population-level well-being with exposure to bereavement, we extend our knowledge of this exposure beyond single-person studies. SRHp and bereavement were asked about in the 2019 field survey of 7354 adults aged 18 years and older. Multiple imputation was applied to handle missing values. Data modeling included adjusted logistic regression and mediation analyses. In the total sample, the prevalence rate of SRHp was 23.1% and the rate of bereavement was 45.5%. The SRHp subgroup had a significantly greater prevalence of bereavement (24.9% versus 21.6% compared to all other SRH categories combined). Elevated bereavement rates were also observed for the group with poor physical health (18.3% versus 14.9%) and poor mental health (17.5% versus 12.8%). Multiple losses (three or more deaths) increased the risk for SRHp by 42% in adjusted logistic models. Although these are cross-sectional data, the analyses provide evidence that bereavement is more common among people engaged in negative health behaviors. In mediation analyses, bereavement increases the prevalence of SRHp by 56.8% through an indirect effect on smoking. Bereavement also increased the rate of SRHp by 40.7% through an indirect effect on obesity. More research is needed to evaluate the association between bereavement and health behavior.
Emotion Regulation Difficulties as a Mediator Between Relationship Satisfaction Predicting Depressive Symptom Trajectories Among Couples in Couple Therapy
Authors: Morgan Preston C. , Wittenborn Andrea K. , Morlan Garrin L. , Snyder Ryan
Published: December 2024

Although adults with depression struggle to effectively emotionally regulate themselves, these findings are limited to one partner in a romantic relationship, community samples, and cross-sectional designs. Hence, we aimed to address these gaps in the literature by investigating emotion regulation difficulties as a predictor of change in depression among couples in couple therapy. Additionally, we aimed to investigate whether emotion regulation difficulties mediated the well-established association between relationship satisfaction and changes in depression of couples in couple therapy. We examined 484 different-sex couples in couple therapy from the Marriage and Family Therapy Practice Research Network—a clinical dataset from clinics across the United States. Dyadic latent growth models revealed the actor and partner effects of emotion regulation difficulties at session 1, predicting decreases in depression trajectories across 16 sessions of couple therapy. Additionally, dyadic latent growth mediation models revealed that emotion regulation difficulties at session four did not mediate the associations between relationship satisfaction at session 1 with depression trajectories of sessions eight through 16. For couple therapy clinicians, emotion regulation difficulties can play a direct role in the treatment of depression. However, our results did not support emotion regulation difficulties as a mediator for the well-established association between relationship satisfaction and depression trajectories.
Factors Influencing Consumer Willingness to Use AI-Driven Autonomous Taxis
Authors: Liu Tingyu , Zhang Yizhou , Zhang Mengze , Chen Min , Yu Shangchun
Published: December 2024

The advancement of autonomous driving technology, particularly Tesla’s launch of its new Robotaxi, marks a transformation in transportation. Understanding the theoretical mechanisms that drive consumers’ intention to use autonomous taxis is essential. This study develops a structural equation model (SEM), extending the applicability of the TAM and TPB model, and incorporates external factors like attitudes, subjective norms, traffic efficiency, and perceived cost–benefit into the model to analyze their impact on consumers’ perceived characteristics (perceived usefulness and perceived ease of use). A survey of 427 valid responses revealed that attitudes, subjective norms, and perceived cost–benefit all have significant positive impacts on perceived usefulness and ease of use, which, in turn, are the primary drivers of consumers’ intention to use. Additionally, perceived risk significantly weakens the positive effects of perceived usefulness and ease of use on the intention to use, underscoring its critical moderating role in the technology acceptance process. This paper suggests strategies to enhance consumer acceptance, including strengthening user perception through marketing and public experience activities, optimizing technology to improve user experience, reinforcing safety and privacy measures to reduce perceived risk, and highlighting the insurance mechanism, convenience, and economic benefits of autonomous taxis in marketing.
Encourage or Reject Employee Involvement: Value Creation in Human Resource Management in the AI Era—An Evolutionary Game Analysis of Enterprises and Employees
Authors: Yang Xiaowei , Yan Shumin
Published: December 2024

In the context of globalization and rapid technological advancement, the introduction of Artificial Intelligence (AI) has brought new opportunities and challenges to Human Resource Management (HRM). This study constructs an evolutionary game model to explore the strategy choices and evolutionary paths of enterprises and employees in HRM value co-creation with AI involvement. We numerically simulated the dynamic evolution of strategies under different scenarios, revealing the equilibrium characteristics of strategic interactions between enterprises and employees in the AI context. The study finds that, first, the evolutionary game system between enterprises and employees converges to two equilibrium points: {cooperation, active} and {non-cooperation, passive}. Overall, the probability of the former is 2.39 times greater than that of the latter. Second, higher initial probabilities of cooperation and active involvement, along with lower costs for cooperation and active involvement, facilitate the system’s evolution towards the {cooperation, active} equilibrium. Third, enterprises are more sensitive to the benefit distribution ratio than employees. This study provides theoretical support for effectively conducting HRM practices in the AI era through systematic analysis of HRM value co-creation behavior, along with practical policy recommendations.
Growth Mindset and Job Crafting: A Trait Activation Perspective with Job Autonomy as Moderator
Authors: Yu Tao , He Lidong , Ying Hu , Liu Jie , Wu Yuzhen , Wang Yun , Pan Xiaofu
Published: December 2024

Job crafting benefits both employees and organizations by enhancing employees’ health, well-being, and performance. Therefore, it is crucial to investigate the individual factors that encourage job crafting and the conditions under which they operate. Based on Trait Activation Theory, this study examined the relationship between employees’ growth mindset and job crafting, as well as the moderating effect of job autonomy on this relationship. In Study 1, we conducted a situational experiment with 180 participants, manipulating growth vs. fixed mindset and high vs. low job autonomy. In Study 2, we surveyed 236 participants over three waves, collecting demographic data and growth mindset at T1, job autonomy at T2 (one month later), and job crafting at T3 (two months later). Results from Study 1 indicated that growth mindset significantly impacted job crafting, moderated by job autonomy. Study 2 confirmed this positive effect of growth mindset on job crafting and revealed a significant positive interaction between growth mindset and job autonomy. These findings suggest that employees with a growth mindset engage more in job crafting, with this relationship strengthened under conditions of high job autonomy. This study highlights job autonomy as a situational cue that activates employees’ growth mindset, enhancing proactive job crafting behaviors. This research advances the literature on positive work behaviors by establishing growth mindset as a direct antecedent and illustrating the moderating role of job autonomy, thereby enriching the understanding of conditions that foster a positive workplace environment.
Bioaugmentation of Industrial Wastewater and Formation of Bacterial–CaCO3 Coupled System for Self-Healing Cement
Authors: Šovljanski Olja , Bulatović Vesna , Milović Tiana , Grahovac Jovana , Erceg Tamara , Dramićanin Miroslav , Tomić Ana
Published: December 2024

This study investigates the potential of bioaugmentation with Bacillus species to enhance wastewater treatment and develop a bacterial–CaCO3 system for self-healing cement applications. Utilizing microbiologically induced calcium carbonate precipitation (MICP), this study evaluates the dual functionality of Bacillus licheniformis and B. muralis strains. For wastewater treatment, the bioaugmentation process achieved significant pollutant reductions, including a 99.52% decrease in biochemical oxygen demand (BOD5), a 92.13% reduction in chemical oxygen demand (COD), and a substantial removal of heavy metals and nutrients. This process also produced high concentrations of CaCO3 precipitate enriched with viable bacterial cells, demonstrating an eco-friendly approach to improving water quality. For self-healing cement applications, bioaugmented CaCO3 crystals were coated with nutrient and sodium silicate layers to form a bacterial–CaCO3 coupled system. This system demonstrated a 92% recovery in compressive strength after 180 days, highlighting its ability to autonomously repair microcracks in cement-based materials. The layered encapsulation strategy ensured bacterial viability and a controlled activation mechanism, offering a scalable and sustainable solution for infrastructure resilience. This dual-function approach addresses critical environmental and construction challenges by linking efficient wastewater treatment with innovative self-healing material development, contributing to global sustainability and circular economy goals.
Data-Driven Modeling of Lateral and Cracking Loads in Confined Masonry Walls Using Machine Learning
Authors: Bile Hamza Mahamad , Güler Kadir
Published: December 2024

Confined masonry (CM) is becoming a widely adopted construction building method even in earthquake-prone regions due to its economic viability, construction simplicity, and material availability. However, existing empirical models for predicting lateral and cracking loads often fall short due to varied material properties, detailing of confining elements and construction practices. In this study, machine learning (ML) algorithms, such as Extreme Gradient Boosting (XGB), Random Forest (RF), and Extremely Randomized Tree (ERT), were employed to predict the seismic performance of CM walls, focusing on maximum lateral load capacity and cracking load based on an experimental dataset from 84 published studies, with 59 samples for training and 25 for testing. Different material, load, geometrical, and reinforcement detailing, related to the lateral load capacity of CM, were considered. This study also compares the performance of the existing empirical equations against the proposed ML models. The ML models demonstrated strong predictive capabilities, outperforming empirical equations in both maximum lateral load and cracking load predictions, with XGBoost yielding the highest accuracy, reflected by R2 values of 0.903 for lateral load and 0.876 for cracking load predictions, and lowest the RMSE (28.742 for lateral and 23.982 for cracking load). Additionally, a comparative analysis shows that while some empirical equations produce reasonably accurate predictions, most exhibit significant deviations from experimental results. This study finally employs Partial Dependence Plot (PDP) analysis to explain the importance and contribution of the factors that influence the lateral strength, and concludes that ML models, especially XGBoost, are highly effective in capturing the complex behavior of CM walls under vertical and lateral loads, making them valuable tools for enhancing the accuracy of seismic performance evaluations.
A Study on the Exploration of the Development Process of Regenerative Applications of Energy Technologies in Industrial Warehouse Buildings: Bibliometric Research from 2004 to 2024
Authors: Chen Hongyu , Zhao Difei , Li Jiayi , Zhang Lanxi , Shen Ting , Yin Yinghao
Published: December 2024

Due to the high energy consumption characteristics of industrial warehouse buildings, the demand for energy regeneration technology is increasingly urgent. In recent years, with the rapid development of building energy technology, warehouse building energy regeneration technology has made remarkable progress in energy conservation and sustainable development. A deep understanding of the previous research progress and trends can provide the scientific basis for guiding subsequent in-depth research. Through the bibliometric analysis of 145 journal articles collected from the Web of Science (WoS) database between 2004 and 2024, this research has studied the research trends and progress on the application of energy regeneration in industrial warehouse buildings. This study first revealed the overall development trend of energy regeneration technology in warehouse buildings through quantitative analysis, indicating that related research is growing rapidly. Core scholars in the field such as Lund H. and Mathiesen B.V., as well as major journals such as Energy and Sustainability, have been identified through the analysis of the literature. Five core research themes, including energy efficiency improvement and regeneration technology, renewable energy system design, life cycle sustainable technology, renewable energy utility assessment, and policy support and energy consumption simulation, were identified through cluster analysis. Through evolutionary analysis, this study demonstrates the development process of energy regeneration in warehouse buildings and the critical role played by advances in new energy technologies in the field of warehouse construction. On this basis, this study proposes current key research directions, including energy life cycle assessment, energy regeneration environment optimization, and energy system management. The research on the energy regeneration of warehouse buildings has gradually become an important cross-subject of architecture and energy technology, providing technical support for the transformation of low-carbon storage buildings. The analysis of the current research status, evolutionary logic, and research trends can provide scientific references for further in-depth research and technological applications in this field.
Enhancing Energy Efficiency in Mediterranean Coastal Buildings Through PCM Integration
Authors: Lahoud Chawki , Chahwan Antoine , Rishmany Jihad , Yehia Chafic , Daaboul Michel
Published: December 2024

In this paper, the impact of phase change material (PCM) integration into building envelopes is evaluated, considering a coastal mediterranean climate. PCM integration represents an innovative technology to lower construction’s heating and cooling loads. A case study was conducted on PCM integration into a Lebanese structure’s envelope where the thermal performance and energy effectiveness were assessed. A significant reduction was observed for both cooling and heating loads. Simulations revealed that the optimal PCMs were those whose melting temperatures were between 21 °C and 24 °C under the Mediterranean weather conditions, showing the importance of selecting the appropriate PCM to enhance energy savings. The annual energy savings calculated were between 11% and 13.4%. The study also emphasized the impact of the specified heating and cooling temperatures within the comfort range on the effectiveness of PCM integration. The results show that optimal PCM selection is a function of the weather conditions and indoor temperature settings.
Urban Built Environment as a Predictor for Coronary Heart Disease—A Cross-Sectional Study Based on Machine Learning
Authors: Jiang Dan , Guo Fei , Zhang Ziteng , Yu Xiaoqing , Dong Jing , Zhang Hongchi , Zhang Zhen
Published: December 2024

The relationship between coronary heart disease (CHD) and complex urban built environments remains a subject of considerable uncertainty. The development of predictive models via machine learning to explore the underlying mechanisms of this association, as well as the formulation of intervention policies and planning strategies, has emerged as a pivotal area of research. A cross-sectional dataset of hospital admissions for CHD over the course of a year from a hospital in Dalian City, China, was assembled and matched with multi-source built environment data via residential addresses. This study evaluates five machine learning models, including decision tree (DT), random forest (RF), eXtreme gradient boosting (XGBoost), multi-layer perceptron (MLP), and support vector machine (SVM), and compares them with multiple linear regression models. The results show that DT, RF, and XGBoost exhibit superior predictive capabilities, with all R2 values exceeding 0.70. The DT model performed the best, with an R2 value of 0.818, and the best performance was based on metrics such as MAE and MSE. Additionally, using explainable AI techniques, this study reveals the contribution of different built environment factors to CHD and identifies the significant factors influencing CHD in cold regions, ranked as age, Digital Elevation Model (DEM), house price (HP), sky view factor (SVF), and interaction factors. Stratified analyses by age and gender show variations in the influencing factors for different groups: for those under 60 years old, Road Density is the most influential factor; for the 61–70 age group, house price is the top factor; for the 71–80 age group, age is the most significant factor; for those over 81 years old, building height is the leading factor; in males, GDP is the most influential factor; and in females, age is the most influential factor. This study explores the feasibility and performance of machine learning in predicting CHD risk in the built environment of cold regions and provides a comprehensive methodology and workflow for predicting cardiovascular disease risk based on refined neighborhood-level built environment factors, offering scientific support for the construction of sustainable healthy cities.
Cooling Energy Challenges in Residential Buildings During Heat Waves: Urban Heat Island Impacts in a Hot-Humid City
Authors: Zou Yukai , Wu Zhuotong , Li Binbin , Jia Yudong
Published: December 2024

Ignoring Urban Heat Island (UHI) effects may lead to an underestimation of the building cooling demand. This study investigates the impact of the UHI on the cooling demand in hot-humid cities, employing the Local Climate Zones (LCZs) classification framework combined with the Urban Weather Generator (UWG) model to simulate UHI effects and improve building performance simulations. The primary aim of this research is to quantify the influence of different LCZs within urban environments on variations in the cooling energy demand, particularly during heat waves, and to explore how these effects can be incorporated into building energy models. The findings reveal significant discrepancies in both the average and peak cooling demand when UHI effects are ignored, especially during nighttime. The most intense UHI effect was observed in LCZ 2.1, characterized by compact mid-rise and high-rise buildings, leading to a cooling demand increase of more than 20% compared to suburban data during the heat waves. Additionally, building envelope thermal performance was found to influence cooling demand variability, with improved thermal properties reducing energy consumption and stabilizing demand. This research contributes to the theoretical understanding of how urban microclimates affect building energy consumption by integrating LCZ classification with UHI simulation, offering a more accurate approach for building energy predictions. Practically, it highlights the importance of incorporating LCZs into building energy simulations and provides a framework that can be adapted to cities with different climatic conditions, urban forms, and development patterns. This methodology can be generalized to regions other than hot-humid areas, offering insights for improving energy efficiency, mitigating UHI effects, and guiding urban planning strategies to reduce the building energy demand in diverse environments.
Risk Factors for Venous Thromboembolism in Acute Promyelocytic Leukemia
Authors: Sabljic Nikica , Pantic Nikola , Virijevic Marijana , Rajic Jovan , Cvetkovic Mirjana , Trajkovic Lazar , Pravdic Zlatko , Bukumiric Zoran , Suvajdzic Vukovic Nada , Bogdanovic Andrija , Vidovic Ana , Todorovic Balint Milena , Bila Jelena , Lekovic Danijela , Djunic Irena , Antic Darko , Mitrovic Mirjana
Published: December 2024

Background: Acute promyelocytic leukemia (APL) is frequently associated with disseminated intravascular coagulation (DIC), leading to potentially life-threatening bleeding. Compared to bleeding, thromboses are a less commonly encountered problem. Objective: The objective of our study was to identify the incidence and predictive value of demographic data, clinical–laboratory parameters, and thrombosis risk assessment models (RAMs) for venous thromboembolism (VTE) in patients with APL. Methods: This study was a retrospective study conducted on adult patients with APL who were treated between 2006 and 2024 at the Clinic of Hematology UCCS with all-trans retinoic acid (ATRA) and anthracycline. The demographic and clinical–laboratory data related to VTE were collected and analyzed alongside the predictive value of two RAMs proposed by Al-Ani and Paterno and colleagues. Results: Among the one-hundred-fifty-five adult patients with APL, VTE was diagnosed in twenty-eight cases (18.1%). The most common location for thrombosis was in the central venous catheter (CVC), which affected twelve (42.8%) patients. A total of six (21.4%) patients had deep vein thrombosis (DVT), one patient (3.6%) showed a pulmonary embolism (PE), and thrombosis at unusual sites was present in nine (32.1%) patients. Our analyses showed that neither Al-Ani’s RAM nor the RAM proposed by Paterno and colleagues were predictive for VTE in patients with APL. The C statistics value for the Al-Ani model was ROC = 0.514, and, for Paterno’s RAM, it was ROC = 0.521. The independent risk factors for VTE, identified via multivariate analysis, were CD114 expression (p = 0.005, OR = 6.4 IC 95%: [1.8–23.2]) and the absence of bleeding at presentation (p = 0.013, OR = 0.086 IC 95%: [0.01–0.59]). Conclusions: To the best of our knowledge, this is the first study showing that a higher expression of CD114 increases the risk of VTE. The absence of bleeding at presentation in patients with APL correlates with thrombosis. Further analyses are needed to confirm these findings and help to develop therapeutic strategies to prevent VTE complications. So far, no risk assessment model has been sufficient to stratify patients with APL according to their risk of VTE.
The Functional Interaction Between Epstein–Barr Virus and MYC in the Pathogenesis of Burkitt Lymphoma
Authors: Solares Sandra , León Javier , García-Gutiérrez Lucía
Published: December 2024

The Epstein–Barr virus (EBV) is associated with a wide range of diseases, malignant and non-malignant. EBV was, in fact, the first virus described with cell transformation capacity, discovered by Epstein in 1964 in lymphoma samples from African children. Since then, EBV has been associated with several human tumors including nasopharyngeal carcinoma, gastric carcinoma, T-cell lymphoma, Hodgkin lymphoma, diffuse large B cell lymphoma, and Burkitt lymphoma among others. The molecular hallmark of Burkitt lymphoma (BL) is a chromosomal translocation that involves the MYC gene and immunoglobulin loci, resulting in the deregulated expression of MYC, an oncogenic transcription factor that appears deregulated in about half of human tumors. The role of MYC in lymphoma is well established, as MYC overexpression drives B cell proliferation through multiple mechanisms, foremost, the stimulation of the cell cycle. Indeed, MYC is found overexpressed or deregulated in several non-Hodgkin lymphomas. Most endemic and many sporadic BLs are associated with EBV infection. While some mechanisms by which EBV can contribute to BL have been reported, the mechanism that links MYC translocation and EBV infection in BL is still under debate. Here, we review the main EBV-associated diseases, with a special focus on BL, and we discuss the interaction of EBV and MYC translocation during B cell malignant transformation in BL.
Description of a New miRNA Signature for the Surgical Management of Thyroid Nodules
Authors: Quiriny Marie , Rodrigues Vitόria Joel , Saiselet Manuel , Dom Geneviève , De Saint Aubain Nicolas , Willemse Esther , Digonnet Antoine , Dequanter Didier , Rodriguez Alexandra , Andry Guy , Detours Vincent , Maenhaut Carine
Published: December 2024

Background: The diagnosis of malignant thyroid nodules is mainly based on the fine-needle aspiration biopsy (FNAB). To improve the detection of malignant nodules, different molecular tests have been developed. We present a new molecular signature based on altered miRNA expressions and specific mutations. Methods: This is a prospective non-interventional study, including all Bethesda categories, carried out on an FNAB sampled in suspicious nodule(s) during thyroidectomy. miRNA quantification and mutations detection were performed. The reference diagnosis was the pathological assessment of the surgical specimen. Different classification algorithms were trained with molecular data to correctly classify the samples. Results: A total of 294 samples were recorded and randomly divided in two equal groups. The random forest algorithm showed the highest accuracy and used mostly miRNAs to classify the nodules. The sensitivity and the specificity of our signature were, respectively, 76% and 96%, and the positive and negative predictive values were both 90% (disease prevalence of 30%). Conclusions: We have identified a molecular classifier that combines miRNA expressions with mutations detection. This signature could potentially help clinicians, as complementary to the Bethesda classification, to discriminate indeterminate FNABs.
DNA Damage Response Network and Intracellular Redox Status in the Clinical Outcome of Patients with Lung Cancer
Authors: Mavroeidi Dimitra , Georganta Anastasia , Stefanou Dimitra T. , Papanikolaou Christina , Syrigos Konstantinos N. , Souliotis Vassilis L.
Published: December 2024

Background/Objectives: DNA damage response (DDR) is a network of molecular pathways associated with the pathogenesis and progression of several diseases, as well as the outcome of chemotherapy. Moreover, the intracellular redox status is essential for maintaining cell viability and controlling cellular signaling. Herein, we analyzed DDR signals and redox status in peripheral blood mononuclear cells (PBMCs) from patients with lung cancer with different response rates to platinum-based chemotherapy. Methods: Several DDR-associated signals and redox status, expressed as the GSH/GSSG ratio, were measured in two lung cancer cell lines (A549, H1299), two normal fibroblast cell lines (WS1, 1BR3hT), and PBMCs from 20 healthy controls and 32 patients with lung cancer at baseline (17 responders and 15 non-responders to subsequent platinum-based chemotherapy). Results: Higher levels of endogenous/baseline DNA damage, decreased GSH/GSSG ratios, and augmented apurinic/apyrimidinic sites, as well as lower nucleotide excision repair (NER) and increased interstrand cross-links (ICLs) repair efficiencies, were observed in lung cancer cell lines compared with normal ones (all p < 0.05). Moreover, PBMCs from patients with lung cancer showed reduced GSH/GSSG ratios, augmented apurinic/apyrimidinic sites, decreased NER and ICL repair capacities, and lower apoptosis rates, compared with healthy controls (all p < 0.001). Interestingly, PBMCs from patients who are responders are characterized by reduced GSH/GSSG ratios, augmented apurinic/apyrimidinic sites, decreased NER and ICL repair capacities, and higher apoptosis rates compared with patients who are non-responders (all p < 0.01). Conclusions: Together, DDR-associated parameters and redox status measured in PBMCs from patients with lung cancer at baseline are associated with the therapeutic benefit of platinum-based chemotherapy.
Melanoma’s New Frontier: Exploring the Latest Advances in Blood-Based Biomarkers for Melanoma
Authors: Prkačin Ivana , Mokos Mislav , Ferara Nikola , Šitum Mirna
Published: December 2024

Melanoma is one of the most malignant cancers, and the global incidence of cutaneous melanoma is increasing. While melanomas are highly prone to metastasize if diagnosed late, early detection and treatment significantly reduce the risk of mortality. Identifying patients at higher risk of metastasis, who might benefit from early adjuvant therapies, is particularly important, especially with the advent of new melanoma treatments. Therefore, there is a pressing need to develop additional prognostic biomarkers for melanoma to improve early stratification of patients and accurately identify high-risk subgroups, ultimately enabling more effective personalized treatments. Recent advances in melanoma therapy, including targeted treatments and immunotherapy, have underscored the importance of biomarkers in determining prognosis and predicting treatment response. The clinical application of these markers holds the potential for significant advancements in melanoma management. Various tumor-derived genetic, proteomic, and cellular components are continuously released into the bloodstream of cancer patients. These molecules, including circulating tumor DNA and RNA, proteins, tumor cells, and immune cells, are emerging as practical and precise liquid biomarkers for cancer. In the current era of effective molecular-targeted therapies and immunotherapies, there is an urgent need to integrate these circulating biomarkers into clinical practice to facilitate personalized treatment. This review highlights recent discoveries in circulating melanoma biomarkers, explores the challenges and potentials of emerging technologies for liquid biomarker discovery, and discusses future directions in melanoma biomarker research.
CT-Scan-Assessed Body Composition and Its Association with Tumor Protein Expression in Endometrial Cancer: The Role of Muscle and Adiposity Quantities
Authors: Mahenge Cuthbert Mario , Akasheh Rand Talal , Kinder Ben , Nguyen Xuan Viet , Kalam Faiza , Cheng Ting-Yuan David
Published: December 2024

Background: Endometrial cancer is strongly associated with obesity, and tumors often harbor mutations in major cancer signaling pathways. To inform the integration of body composition into targeted therapy paradigms, this hypothesis-generating study explores the association between muscle mass, body fat, and tumor proteomics. Methods: We analyzed data from 113 patients in The Cancer Genome Atlas (TCGA) and Cancer Proteomic Tumor Analysis Consortium (CPTAC) cohorts and their corresponding abdominal CT scans. Among these patients, tumor proteomics data were available for 45 patients, and 133 proteins were analyzed. Adiposity and muscle components were assessed at the L3 vertebral level on the CT scans. Patients were stratified into tertiles of muscle and fat mass and categorized into three groups: high muscle/low adiposity, high muscle/high adiposity, and low muscle/all adiposities. Linear and Cox regression models were adjusted for study cohort, stage, histology type, age, race, and ethnicity. Results: Compared with the high-muscle/low-adiposity group, both the high-muscle/high-adiposity (HR = 4.3, 95% CI = 1.0–29.0) and low-muscle (HR = 4.4, 95% CI = 1.3–14.9) groups experienced higher mortality. Low muscle was associated with higher expression of phospho-4EBP1(T37 and S65), phospho-GYS(S641) and phospho-MAPK(T202/Y204) but lower expression of ARID1A, CHK2, SYK, LCK, EEF2, CYCLIN B1, and FOXO3A. High muscle/high adiposity was associated with higher expression of phospho-4EBP1 (T37), phospho-GYS (S641), CHK1, PEA15, SMAD3, BAX, DJ1, GYS, PKM2, COMPLEX II Subunit 30, and phospho-P70S6K (T389) but with lower expression of CHK2, CRAF, MSH6, TUBERIN, PR, ERK2, beta-CATENIN, AKT, and S6. Conclusions: These findings demonstrate an association between body composition and proteins involved in key cancer signaling pathways, notably the PI3K/AKT/MTOR, MAPK/ERK, cell cycle regulation, DNA damage response, and mismatch repair pathways. These findings warrant further validation and assessment in relation to prognosis and outcomes in these patients.
The Intersection of Psoriasis and Neoplasia: Risk Factors, Therapeutic Approaches, and Management Strategies
Authors: Mateescu Larisa-Alexandra , Savu Alexandra-Petruța , Mutu Costina-Cristiana , Vaida Cezara-Diana , Șerban Elena-Daniela , Bucur Ștefana , Poenaru Elena , Nicolescu Alin-Codruț , Constantin Maria-Magdalena
Published: December 2024

The association between psoriasis and increased cancer risk is gaining recognition as studies reveal shared inflammatory and immune pathways. This review examines the relationship between psoriasis and neoplasia, focusing on cancer risk factors in psoriasis patients, the biological pathways underlying this connection, and the impact of various psoriasis treatments on cancer development. Psoriasis patients have a heightened incidence of certain cancers, such as lymphomas, skin cancers, and urological malignancies, potentially linked to immune dysregulation and chronic inflammation. Immunomodulatory treatments for psoriasis, including conventional systemic therapies and biologics, present varied cancer risks, with others, such as phototherapy, associated with an elevated risk of skin cancers. For oncologic patients with psoriasis, management necessitates a tailored approach, balancing effective psoriasis control with minimizing cancer progression risks. The emergence of IL-17 inhibitors, IL-23 inhibitors, and small-molecule therapies offers promising therapeutic alternatives with favorable safety profiles for these patients. This review underscores the need for interdisciplinary collaboration to optimize care for patients managing both psoriasis and malignancy.
Integrative Stacking Machine Learning Model for Small Cell Lung Cancer Prediction Using Metabolomics Profiling
Authors: Sumon Md. Shaheenur Islam , Malluhi Marwan , Anan Noushin , AbuHaweeleh Mohannad Natheef , Krzyslak Hubert , Vranic Semir , Chowdhury Muhammad E. H. , Pedersen Shona
Published: December 2024

Background: Small cell lung cancer (SCLC) is an extremely aggressive form of lung cancer, characterized by rapid progression and poor survival rates. Despite the importance of early diagnosis, the current diagnostic techniques are invasive and restricted. Methods: This study presents a novel stacking-based ensemble machine learning approach for classifying small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) using metabolomics data. The analysis included 191 SCLC cases, 173 NSCLC cases, and 97 healthy controls. Feature selection techniques identified significant metabolites, with positive ions proving more relevant. Results: For multi-class classification (control, SCLC, NSCLC), the stacking ensemble achieved 85.03% accuracy and 92.47 AUC using Support Vector Machine (SVM). Binary classification (SCLC vs. NSCLC) further improved performance, with ExtraTreesClassifier reaching 88.19% accuracy and 92.65 AUC. SHapley Additive exPlanations (SHAP) analysis revealed key metabolites like benzoic acid, DL-lactate, and L-arginine as significant predictors. Conclusions: The stacking ensemble approach effectively leverages multiple classifiers to enhance overall predictive performance. The proposed model effectively captures the complementary strengths of different classifiers, enhancing the detection of SCLC and NSCLC. This work accentuates the potential of combining metabolomics with advanced machine learning for non-invasive early lung cancer subtype detection, offering an alternative to conventional biopsy methods.
Higher Catalytic Activity of Pd Monolayers Versus Single PD Atoms at Different Anatase Facets
Authors: Rybakov Andrey A. , Larin Alexander V. , Vercauteren Daniel P. , Todorova Silviya
Published: December 2024

The structure and catalytic activity of Pd monolayers versus single Pd atoms were studied for the reverse water–gas shift reaction (rWGSR) at the anatase (101) and (001) facets for which Pd flat fragments have been observed experimentally. Thermodynamic and partial kinetic analyses of five steps of the rWGSR scheme were considered on the two facets. The projected density of states for the d-orbitals of single Pd atoms of the (101) facet of a-TiO2 are compared to the ones for Pd atoms in both monolayers at (101) and (001) facets to interpret the different activity of Pd. The low activity of single Pd atoms is probably related to the (001) facet, while a Pd monolayer participates at the (101) facet due to its heterogeneity induced by the support.
Updates on Inflammatory Molecular Pathways Mediated by ADAM17 in Autoimmunity
Authors: Sisto Margherita , Lisi Sabrina
Published: December 2024

ADAM17 is a member of the disintegrin and metalloproteinase (ADAM) family of transmembrane proteases with immunoregulatory activity in multiple signaling pathways. The functional ADAM17 is involved in the shedding of the ectodomain characterizing many substrates belonging to growth factors, cytokines, receptors, and adhesion molecules. The ADAM17-dependent pathways are known to be crucial in tumor development and progression and in the modulation of many pathological and physiological processes. In the last decade, ADAM17 was considered the driver of several autoimmune pathologies, and numerous substrate-mediated signal transduction pathways were identified. However, the discoveries made to date have led researchers to try to clarify the multiple mechanisms in which ADAM17 is involved and to identify any molecular gaps between the different transductional cascades. In this review, we summarize the most recent updates on the multiple regulatory activities of ADAM17, focusing on reported data in the field of autoimmunity.
Methuosis, Alkaliptosis, and Oxeiptosis and Their Significance in Anticancer Therapy
Authors: Bartoszewska Elżbieta , Florek Kamila , Zagórski Karol , Gachowska Martyna , Wietrzyk Anna , Hutny Agata , Nowakowska-Toporowska Agnieszka , Kulbacka Julita
Published: December 2024

Understanding morphological, biochemical, and functional aspects of cell death is essential for targeting new cancer therapies. Even though many different mechanisms of cell death are identified, it is crucial to highlight the role of new and lesser-known pathways, including methuosis, alkaliptosis, and oxeiptosis. The aim of this review was to summarize the data about cell death mechanisms—methuosis, alkaliptosis, and oxeiptosis—and their role in cancer treatment. Unique molecular mechanisms and cellular outcomes characterize each of these forms of cell death. This research on methuosis, alkaliptosis, and oxeiptosis provides a better understating of cell death biology and creates novel opportunities for neoplasm management.