bluuebunny/crossref_metadata_2025_split huggingface dataset:
Impact of LKB1 status on radiation outcome in patients with stage III non-small-cell lung cancer
Authors: Sitthideatphaiboon Piyada , Nantavithya Chonnipa , Chantranuwat Poonchavist , Vinayanuwattikun Chanida , Sriuranpong Virote 
Published: March 2024

AbstractPreclinical studies suggest that loss of LKB1 expression renders cancer cells less responsive to radiation partly through NRF2-mediated upregulation of antioxidant enzymes protecting against radiation-induced DNA damage. Here we investigated the association of an alteration in this pathway with radio-resistance in lung cancer patients. Patients with locally advanced non-small cell lung cancer (LA-NSCLC) who were treated with chemoradiotherapy (CRT) and analyzed for LKB1 expression using semiquantitative immunohistochemistry. Clinical characteristics and expression of LKB1 were analyzed for association with radiotherapy outcomes. We analyzed 74 available tumor specimens from 178 patients. After a median follow-up of 40.7 months, 2-year cumulative incidence of locoregional recurrence (LRR) in patients who had LKB1Low expression was significantly higher than those with LKB1High expression (68.8% vs. 31.3%, P = 0.0001). LKB1Low expression was found significantly associated with a higher incidence of distant metastases (DM) (P = 0.0008), shorter disease-free survival (DFS) (P = 0.006), and worse overall survival (OS) (P = 0.02) compared to LKB1High expression. Moreover, patients with LKB1Low expression showed a significantly higher 2-year cumulative incidence of LRR (77.6% vs. 21%; P = 0.02), higher DM recurrence (P = 0.002), and shorter OS (P < 0.0001) compared with the EGFR-mutant group. For all patients with LKB1Low who had LRR, these recurrences occurred within the field of radiation, in contrast to those with LKB1High expression having both in-field, marginal, and out-of-field failures. LKB1 expression may serve as a potential biomarker for poor outcomes after receiving radiation in LA-NSCLC patients. Further studies to confirm the association and application are warranted.
Microfluidic production of amiodarone loaded nanoparticles and application in drug repositioning in ovarian cancer
Authors: Saorin Asia , Saorin Gloria , Duzagac Fahriye , Parisse Pietro , Cao Ni , Corona Giuseppe , Cavarzerani Enrico , Rizzolio Flavio 
Published: March 2024

AbstractAmiodarone repositioning in cancer treatment is promising, however toxicity limits seem to arise, constraining its exploitability. Notably, amiodarone has been investigated for the treatment of ovarian cancer, a tumour known for metastasizing within the peritoneal cavity. This is associated with an increase of fatty acid oxidation, which strongly depends on CPT1A, a transport protein which has been found overexpressed in ovarian cancer. Amiodarone is an inhibitor of CPT1A but its role still has to be explored. Therefore, in the present study, amiodarone was tested on ovarian cancer cell lines with a focus on lipid alteration, confirming its activity. Moreover, considering that drug delivery systems could lower drug side effects, microfluidics was employed for the development of drug delivery systems of amiodarone obtaining simultaneously liposomes with a high payload and amiodarone particles. Prior to amiodarone loading, microfluidics production was optimized in term of temperature and flow rate ratio. Moreover, stability over time of particles was evaluated. In vitro tests confirmed the efficacy of the drug delivery systems.
Improving hand hygiene of young children with a digital intervention: a cluster-randomised controlled field trial
Authors: Graichen Joanna , Stingl Carlo , Pakarinen Anni , Rosio Riitta , Terho Kirsi , Günther Sebastian A. , Salanterä Sanna , Staake Thorsten 
Published: March 2024

AbstractContagious diseases that affect young children place a great burden on them and their families. Proper hand hygiene is an important measure to reduce the disease burden, however, its implementation in day care centres is challenging. This paper introduces a digital intervention to support independent and good handwashing among young children. The intervention leverages animated instructions triggered by water and soap use, together with a symbolic reward shown to children on a screen during and immediately after handwashing. We tested the intervention in a pre-registered, cluster-randomised controlled field trial in 4 day care centres in Finland and Germany with 162 children over 42 days. The intervention increased soaping time, used as a proxy for handwashing quality, by 5.30 s (+ 62%, p < 0.001). The effect occurs immediately at the onset of the intervention and is maintained throughout the intervention phase.
The study on the appearance of deformation defects in the yacht lamination process using an AI algorithm and expert knowledge
Authors: Szalewski Paweł , Niksa-Rynkiewicz Tacjana , Deja Mariusz 
Published: March 2024

AbstractThis article describes the application of the A-priori algorithm for defining the rule-based relationships between individual defects caused during the lamination process, affecting the deformation defect of the yacht shell. The data from 542 yachts were collected and evaluated. For the proper development of the algorithm, a technological process of the yacht lamination supported by expert decisions was described. The laminating technology is a complex process of a sequential application of individual laminates according to a special strategy. The A-priori algorithm allowed for obtaining the set of association rules defining the relationships between the defects resulting from the lamination process and influencing the deformation defect of the yacht shell, which is one of the most common errors in yacht production. The obtained aggregated rules were compared with the expert knowledge of the employees of the production, quality control, mould regeneration, and technology departments of the yacht yard. The use of the proposed A-priori algorithm allowed for the generation of relationship rules consistent with the general opinion of experts. Associative rules additionally took into account detailed causes of a specific error, which were not always noticed by employees of specific departments. The assessment of the lamination process using an artificial intelligence algorithm turned out to be more objective, which made it possible to gradually reduce the total number of errors occurring in the yacht shell lamination process, and thus shorten the time needed to repair errors and the total time of producing the yacht.
Exploring trajectories of functional decline and recovery among older adults: a data-driven approach
Authors: Khalili Ghazal , Zargoush Manaf , Huang Kai , Ghazalbash Somayeh 
Published: March 2024

AbstractIndependently performing activities of daily living (ADLs) is vital for maintaining one’s quality of life. Losing this ability can significantly impact an individual’s overall health status, including their mental health and social well-being. Aging is an important factor contributing to the loss of ADL abilities, and our study focuses on investigating the trajectories of functional decline and recovery in older adults. Employing trajectory analytics methodologies, this research delves into the intricate dynamics of ADL pathways, unveiling their complexity, diversity, and inherent characteristics. The study leverages a substantial dataset encompassing ADL assessments of nursing home residents with diverse disability profiles in the United States. The investigation begins by transforming these assessments into sequences of disability combinations, followed by applying various statistical measures, indicators, and visual analytics. Valuable insights are gained into the typical disability states, transitions, and patterns over time. The results also indicate that while predicting the progression of ADL disabilities presents manageable challenges, the duration of these states proves more complicated. Our findings hold significant potential for improving healthcare decision-making by enabling clinicians to anticipate possible patterns, develop targeted and effective interventions that support older patients in preserving their independence, and enhance overall care quality.
An efficient breast cancer classification model using bilateral filtering and fuzzy convolutional neural network
Authors: Hayum A. Abdul , Jaya J. , Sivakumar R. , Paulchamy B. 
Published: March 2024

AbstractBC (Breast cancer) is the second most common reason for women to die from cancer. Recent workintroduced a model for BC classifications where input breast images were pre-processed using median filters for reducing noises. Weighed KMC (K-Means clustering) is used to segment the ROI (Region of Interest) after the input image has been cleaned of noise. Block-based CDF (Centre Distance Function) and CDTM (Diagonal Texture Matrix)-based texture and shape descriptors are utilized for feature extraction. The collected features are reduced in counts using KPCA (Kernel Principal Component Analysis). The appropriate feature selection is computed using ICSO (Improved Cuckoo Search Optimization). The MRNN ((Modified Recurrent Neural Network)) values are then improved through optimization before being utilized to divide British Columbia into benign and malignant types. However, ICSO has many disadvantages, such as slow search speed and low convergence accuracy and training an MRNN is a completely tough task. To avoid those problems in this work preprocessing is done by bilateral filtering to remove the noise from the input image. Bilateral filter using linear Gaussian for smoothing. Contrast stretching is applied to improve the image quality. ROI segmentation is calculated based on MFCM (modified fuzzy C means) clustering. CDTM-based, CDF-based color histogram and shape description methods are applied for feature extraction. It summarizes two important pieces of information about an object such as the colors present in the image, and the relative proportion of each color in the given image. After the features are extracted, KPCA is used to reduce the size. Feature selection was performed using MCSO (Mutational Chicken Flock Optimization). Finally, BC detection and classification were performed using FCNN (Fuzzy Convolutional Neural Network) and its parameters were optimized using MCSO. The proposed model is evaluated for accuracy, recall, f-measure and accuracy. This work’s experimental results achieve high values of accuracy when compared to other existing models.
A study on the influencing factors of corporate digital transformation: empirical evidence from Chinese listed companies
Authors: Zhao Xu , Chen Qi-an , Zhang Haitao , Chen Pengyu , Chen Shen 
Published: March 2024

AbstractIn an era where digital technology is reshaping business landscapes, understanding the factors that drive corporate digital transformation is essential. In this paper we explore these influencing factors, focusing on Chinese A-share listed companies from 2007 to 2021. Our approach involved a comprehensive analysis of multiple variables through regression techniques to determine their impact on digital transformation. The findings reveal the drive for reform in the digital transformation endeavours of enterprises. Notably, companies with higher gearing, overhead, and accounts receivable ratios exhibit a stronger inclination towards digital transformation. Conversely, enterprises in monopolistic industries and those at the inception stage of their life cycle show less propensity for such transformation. The findings of this research not only shed light on the strategic decisions behind digital transformation in response to financial and competitive challenges but also provide actionable insights for policymakers and business strategists. This study underscores the importance of contextualizing digital transformation efforts within the unique framework of industry characteristics and company development phases.
CD4+CD25+ regulatory T cells ex vivo generated from autologous naïve CD4+ T cells suppress EAE progression
Authors: Yang Ting-Ting , Liu Pen-Ju , Sun Qing-Yu , Wang Ze-Yi , Yuan Guo-Bin , Fan Ze-Xin , Ma Lin , Lu Jian-Feng , Yuan Bo-Yi , Zou Wen-Long , Zhao Li-Min , Li Qian , Liu Guang-Zhi 
Published: March 2024

AbstractCD4+CD25+ regulatory T cells (Tregs) play an important role in maintaining immune homeostasis in multiple sclerosis (MS). Hence, we aimed to explore the therapeutic efficacy and safety of adoptive cell therapy (ACT) utilizing induced antigen-specific Tregs in an animal model of MS, that is, in an experimental autoimmune encephalomyelitis (EAE) model. B cells from EAE model that were activated with soluble CD40L were used as antigen-presenting cells (APCs) to induce the differentiation of antigen-specific Tregs from naïve CD4 precursors, and then, a stepwise isolation of CD4+CD25highCD127low Tregs was performed using a flow sorter. All EAE mice were divided into Treg-treated group (2 × 104 cells in 0.2 mL per mouse, n = 14) and sham-treated group (0.2 mL normal saline (NS), n = 20), which were observed daily for clinical assessment, and for abnormal appearance for 6 weeks. Afterward, histological analysis, immunofluorescence and real-time PCR were performed. Compared to sham-treated mice, Treg-treated mice exhibited a significant decrease in disease severity scores and reduced inflammatory infiltration and demyelination in the spinal cord. Additionally, Tregs-treated mice demonstrated higher CCN3 protein and mRNA levels than sham-treated mice. The results of this preclinical study further support the therapeutic potential of this ACT approach in the treatment of MS.
Effect of radiotherapy on head and neck cancer tissues in patients receiving radiotherapy: a bioinformatics analysis-based study
Authors: Guan Zhenjie , Liu Jie , Zheng Lian 
Published: March 2024

AbstractRadiotherapy is pivotal in treating head and neck cancers including nasopharyngeal, tongue, hypopharyngeal, larynx, maxillary sinus, parotid gland, and oral cancers. It holds the potential for curative effects and finds application in conjunction with chemotherapy, either as a radical method to preserve organ function or as an adjuvant postoperative treatment. We used bioinformatics analysis to investigate the effects of radiotherapy on head and neck cancer tissues in patients who had received radiotherapy. In this study, the expression and mutation profiles of The Cancer Genome Atlas–Head-Neck Squamous Cell Carcinoma were downloaded from the UCSC-Xena database, categorizing patients into two groups—those receiving radiotherapy and those not receiving radiotherapy. Subsequently, differential expression analysis and gene set enrichment analysis (GSEA) were performed. Following this, single-sample GSEA (ssGSEA) scores related to glucose and lipid metabolism were compared between the two groups. Additionally, immune cell infiltration analysis and single-cell verification were performed. Finally, the mutation profiles of the two groups were compared. The analyses revealed that patients receiving radiotherapy exhibited prolonged survival, enhanced apoptosis in head and neck cancer tissue, and diminished keratinocyte proliferation and migration. A comparison of ssGSEA scores related to glucose and lipid metabolism between the two groups indicated a reduction in glycolysis, tricarboxylic acid cycle activity, and fat synthesis in tissues treated with radiotherapy, suggesting that radiotherapy can effectively inhibit tumour cell energy metabolism. Analyses of immune cell infiltration and single-cell verification suggested decreased infiltration of immune cells post-radiotherapy in head and neck cancer tissues. A comparison of mutation profiles revealed a higher frequency of TP53, TTN, and CDKN2A mutations in patients receiving radiotherapy for head and neck cancer. In conclusion, the bioinformatics analyses delved into the effect of radiotherapy on patients with head and neck carcinoma. This study provides a theoretical framework elucidating the molecular mechanisms underlying radiotherapy's efficacy in treating head and neck cancer and presents scientific recommendations for drug therapy following radiotherapy.
Prognostic factors of renal cell cancer in elderly patients: a population-based cohort study
Authors: Pajunen Heini , Veitonmäki Thea , Huhtala Heini , Nikkola Jussi , Pöyhönen Antti , Murtola Teemu 
Published: March 2024

AbstractMortality in renal cell cancer (RCC) is high in the elderly population. Comorbidities have a greater impact on overall prognosis of RCC among elderly patients than in younger patients. All new RCC cases were collected in people over 74 years of age between 1995 and 2018 from the Finnish cancer registry. The comorbidities were identified from the Care Registry for Healthcare. Charlson Comorbidity Index (CCI) was used to evaluate the risk of death based on comorbidities. The overall risk of death was analyzed using the Cox regression model. The risk for RCC death was analyzed using Fine and Gray regression analysis. Individual prognostic role of CCI components was evaluated by adding each component separately into the multivariable Fine and Gray regression model. Using the most prognostic comorbidities we constructed a nomogram to predict RCC mortality. Statistically significant prognostic factors of RCC death were tumor morphology (clear cell, papillary and chromophobe), sex, operative treatment, age, primary tumor extent and CCI. The strongest prognostic factors for overall mortality were tumor extent, tumor morphology and operative treatment. Among the components of CCI, the most important comorbidities predicting mortality were dementia, heart failure and kidney disease. The limitation of this study is that the comorbidities have only been recorded at inpatient and outpatient hospital contacts, which is why the prevalence of comorbidities is probably underestimated. In addition, physical performance status was not available from registry data, but it significantly affects the treatment decisions. RCC mortality is high in the elderly population. Among comorbidities, dementia and heart failure have the greatest impact on the prognosis. Curative treatment in selected elderly patients is efficient and should be considered in patients who can tolerate it and have only limited comorbidities.
Exploring the utility of Geometric Morphometrics to analyse prehistoric hand stencils
Authors: Fernández Navarro V. , Godinho R. M. , García Martínez D. , Garate Maidagan D. 
Published: March 2024

AbstractHand stencils are a remarkable graphic expression in Prehistoric rock art, dating back to 42 ka BP. Although these stencils provide direct impressions of the artists’ hands, the characterization of their biological profile (i.e., biological sex and age) is very challenging. Previous studies have attempted this analysis with traditional morphometrics (TM), whereas little research has been undertaken using Geometric Morphometrics (GM), a method widely used in other disciplines but only tentatively employed in rock art studies. However, the large variation in relative finger position in archaeological hands poses the question of whether these representations can be examined through GM, or, in contrast, if this creates an unmanageable error in the results. To address this issue, a 2D hand scans sample of 70 living individuals (F = 35; M = 35) has been collected in three standardized positions (n = 210) and digitized with 32 2D conventional landmarks. Results show that the intra-individual distance (mean Procrustes distance between Pos. 1–2 = 0.132; 2–3 = 0.191; 1–3 = 0.292) is larger than the inter-individual distance (mean in 1 = 0.122; 2 = 0.142; 3 = 0.165). Finally, it has been demonstrated that the relative finger positions, as well as the inclusion of all hand parts in the analysis, have an overshadowing effect on other variables potentially involved in the morphometric variability of the hand, such as biological sex.
VirtuousPocketome: a computational tool for screening protein–ligand complexes to identify similar binding sites
Authors: Pallante Lorenzo , Cannariato Marco , Androutsos Lampros , Zizzi Eric A. , Bompotas Agorakis , Hada Xhesika , Grasso Gianvito , Kalogeras Athanasios , Mavroudi Seferina , Di Benedetto Giacomo , Theofilatos Konstantinos , Deriu Marco A. 
Published: March 2024

AbstractProtein residues within binding pockets play a critical role in determining the range of ligands that can interact with a protein, influencing its structure and function. Identifying structural similarities in proteins offers valuable insights into their function and activation mechanisms, aiding in predicting protein–ligand interactions, anticipating off-target effects, and facilitating the development of therapeutic agents. Numerous computational methods assessing global or local similarity in protein cavities have emerged, but their utilization is impeded by complexity, impractical automation for amino acid pattern searches, and an inability to evaluate the dynamics of scrutinized protein–ligand systems. Here, we present a general, automatic and unbiased computational pipeline, named VirtuousPocketome, aimed at screening huge databases of proteins for similar binding pockets starting from an interested protein–ligand complex. We demonstrate the pipeline's potential by exploring a recently-solved human bitter taste receptor, i.e. the TAS2R46, complexed with strychnine. We pinpointed 145 proteins sharing similar binding sites compared to the analysed bitter taste receptor and the enrichment analysis highlighted the related biological processes, molecular functions and cellular components. This work represents the foundation for future studies aimed at understanding the effective role of tastants outside the gustatory system: this could pave the way towards the rationalization of the diet as a supplement to standard pharmacological treatments and the design of novel tastants-inspired compounds to target other proteins involved in specific diseases or disorders. The proposed pipeline is publicly accessible, can be applied to any protein–ligand complex, and could be expanded to screen any database of protein structures.
Construction of environmental vibration prediction model for subway transportation based on machine learning algorithm and database technology
Authors: Zhou Xilong 
Published: March 2024

AbstractVibrations generated in the metro transport environment are mainly caused by, vibrations generated by the interaction between the metro and the track during operation. and the change of vibration factors will affect the normal operation of the subway. However, it is difficult to have a model that can achieve the characteristics of high accuracy, fast computing speed and wide range of use in the traditional metro rail transportation environment prediction. Therefore, this research uses database theory and machine learning algorithms to predict the vibration of subway transportation environment. The experimental results show that the average difference between the whole prediction value and the real value is 1.4 dB, of which the maximum difference error value is 0.29%, the maximum error difference is 8.2%, and the approximate value is 6.2 dB, and the four averages predicted in 40 m are relatively small as 1.6 dB, and the average error value of prediction ability between 40 and 100 m is 1.72 dB, and the experimental prediction value and real value are in good agreement. The agreement between the experimental prediction and the real value is very good. Therefore, the model is able to predict the vibration model of the subway transportation environment with a high degree of agreement and accuracy.