bluuebunny/crossref_metadata_2025_split huggingface dataset:
DDX19A promotes gastric cancer cell proliferation and migration by activating the PI3K/AKT pathway
Authors: Cheng Yu , Lu Yanjie , Xue Jing , Wang Xuemei , Zhou Lili , Luo Yu , Li Yuhong 
Published: August 2024

Abstract
Background
DEAD-box RNA helicase 19 A (DDX19A) is overexpressed in cervical squamous cell carcinoma. However, its role in gastric cancer remains unclear. The present study aimed to explore the role and underlying mechanism of DDX19A in the development of gastric cancer.

Methods
The expression of DDX19A in gastric cancer and paracancerous tissues was evaluated through quantitative polymerase chain reaction, western blotting, and immunohistochemical staining. The biological functions of DDX19A in gastric cancer were determined using CCK8, plate colony-forming, and Transwell migration assays. The specific mechanism of DDX19A in gastric cancer cells was studied using western blotting, RNA-binding protein immunoprecipitation, mRNA half-life detection, and nuclear and cytoplasmic RNA isolation.

Results
DDX19A was highly expressed in gastric cancer and positively associated with malignant clinicopathological features and poor prognosis. Additionally, DDX19A promoted gastric cancer cell proliferation, migration, and epithelial–mesenchymal transition phenotypes. Mechanistically, DDX19A activated the PI3K/AKT pathway by upregulating phosphatidylinositol-3-kinase (PIK3CA) expression. Furthermore, DDX19A interacted with PIK3CA mRNA, stabilized it, and facilitated its export from the nucleus.

Conclusions
Our study reveals a novel mechanism whereby DDX19A promotes the proliferation and migration of gastric cancer cells by enhancing the stability and nuclear export of PIK3CA mRNA, thereby activating the PI3K/AKT pathway.
Utilizing deep learning model for assessing melanocytic density in resection margins of lentigo maligna
Authors: Siarov Jan , Kumar Darshan , Paoli John , Mölne Johan , Gillstedt Martin , Neittaanmäki Noora 
Published: August 2024

Abstract
Background
Surgical excision with clear histopathological margins is the preferred treatment to prevent progression of lentigo maligna (LM) to invasive melanoma. However, the assessment of resection margins on sun-damaged skin is challenging. We developed a deep learning model for detection of melanocytes in resection margins of LM.

Methods
In total, 353 whole slide images (WSIs) were included. 295 WSIs were used for training and 58 for validation and testing. The algorithm was trained with 3,973 manual pixel-wise annotations. The AI analyses were compared to those of three blinded dermatopathologists and two pathology residents, who performed their evaluations without AI and AI-assisted. Immunohistochemistry (SOX10) served as the reference standard. We used a dichotomized cutoff for low and high risk of recurrence (≤ 25 melanocytes in an area of 0.5 mm for low risk and > 25 for high risk).

Results
The AI model achieved an area under the receiver operating characteristic curve (AUC) of 0.84 in discriminating margins with low and high recurrence risk. In comparison, the AUC for dermatopathologists ranged from 0.72 to 0.90 and for the residents in pathology, 0.68 to 0.80. Additionally, with aid of the AI model the performance of two pathologists significantly improved.

Conclusions
The deep learning showed notable accuracy in detecting resection margins of LM with a high versus low risk of recurrence. Furthermore, the use of AI improved the performance of 2/5 pathologists. This automated tool could aid pathologists in the assessment or pre-screening of LM margins.
The interplay of transition metals in ferroptosis and pyroptosis
Authors: Vana Frantisek , Szabo Zoltan , Masarik Michal , Kratochvilova Monika 
Published: August 2024

AbstractCell death is one of the most important mechanisms of maintaining homeostasis in our body. Ferroptosis and pyroptosis are forms of necrosis-like cell death. These cell death modalities play key roles in the pathophysiology of cancer, cardiovascular, neurological diseases, and other pathologies. Transition metals are abundant group of elements in all living organisms. This paper presents a summary of ferroptosis and pyroptosis pathways and their connection to significant transition metals, namely zinc (Zn), copper (Cu), molybdenum (Mo), lead (Pb), cobalt (Co), iron (Fe), cadmium (Cd), nickel (Ni), mercury (Hg), uranium (U), platinum (Pt), and one crucial element, selenium (Se). Authors aim to summarize the up-to-date knowledge of this topic.In this review, there are categorized and highlighted the most common patterns in the alterations of ferroptosis and pyroptosis by transition metals. Special attention is given to zinc since collected data support its dual nature of action in both ferroptosis and pyroptosis. All findings are presented together with a brief description of major biochemical pathways involving mentioned metals and are visualized in attached comprehensive figures.This work concludes that the majority of disruptions in the studied metals’ homeostasis impacts cell fate, influencing both death and survival of cells in the complex system of altered pathways. Therefore, this summary opens up the space for further research.
Graphical abstract
Examining the evidence for mutual modulation between m6A modification and circular RNAs: current knowledge and future prospects
Authors: Tang Xiaozhu , Guo Mengjie , Zhang Yuanjiao , Lv Junxian , Gu Chunyan , Yang Ye 
Published: August 2024

AbstractThe resistance of cancer cells to treatment significantly impedes the success of therapy, leading to the recurrence of various types of cancers. Understanding the specific mechanisms of therapy resistance may offer novel approaches for alleviating drug resistance in cancer. Recent research has shown a reciprocal relationship between circular RNAs (circRNAs) and N6-methyladenosine (m6A) modification, and their interaction can affect the resistance and sensitivity of cancer therapy. This review aims to summarize the latest developments in the m6A modification of circRNAs and their importance in regulating therapy resistance in cancer. Furthermore, we explore their mutual interaction and exact mechanisms and provide insights into potential future approaches for reversing cancer resistance.
The landscape of circulating tumor HPV DNA and TTMV-HPVDNA for surveillance of HPV-oropharyngeal carcinoma: systematic review and meta-analysis
Authors: Campo Flaminia , Iocca Oreste , Paolini Francesca , Manciocco Valentina , Moretto Silvia , De Virgilio Armando , Moretti Claudio , Vidiri Antonello , Venuti Aldo , Bossi Paolo , Blandino Giovanni , Pellini Raul 
Published: August 2024

Abstract
Background
Human papilloma virus (HPV) related cancers of the oropharynx are rapidly increasing in incidence and may soon represent the majority of all head and neck cancers. Improved monitoring and surveillance methods are thus an urgent need in public health.

Main text
The goal is to highlight the current potential and limitations of liquid biopsy through a meta analytic study on ctHPVDNA and TTMV-HPVDNA. It was performed a Literature search on articles published until December 2023 using three different databases: MEDLINE, Embase, and Cochrane Library. Studies that evaluated post-treatment ctHPVDNA and TTMV-HPVDNA in patients with HPV + OPSCC, studies reporting complete data on the diagnostic accuracy in recurrence, or in which the number of true positives, false positives, true negatives, and false negatives was extractable, and methods of detection of viral DNA clearly defined.
The meta-analysis was conducted following the Meta-analysis Of Observational Studies in Epidemiology (MOOSE) reporting guidelines.
The aim of this meta-analysis was to evaluate the sensitivity, specificity, and accuracy of ctHPVDNA and TTMV by ddPCR to define its efficacy in clinical setting for the follow up of HPV-OPSCC.

Conclusion
The 12 studies included in the meta-analysis provided a total of 1311 patients for the analysis (398 valuated with ctHPVDNA and 913 with TTMV-HPVDNA). Pooled sensitivity and specificity were 86% (95% CI: 78%-91%) and 96% (95% CI: 91%-99%), respectively; negative and positive likelihood ratios were 0.072 (95% CI: 0.057–0.093) and 24.7 (95% CI: 6.5–93.2), respectively; pooled DOR was 371.66 (95% CI: 179.1–918). The area under the curve (AUC) was 0.81 (95% CI, 0.67–0.91).
Liquid biopsy for the identification of cell free DNA might identify earlier recurrence in HPV + OPSCC patients. At the present time, liquid biopsy protocol needs to be standardized and liquid biopsy cannot yet be used in clinical setting. In the future, a multidimensional integrated approach which links multiple clinical, radiological, and laboratory data will contribute to obtain the best follow-up strategies for the follow-up of HPV-OPSCC.
The influence of Akkermansia muciniphila on intestinal barrier function
Authors: Mo Chunyan , Lou Xiran , Xue Jinfang , Shi Zhuange , Zhao Yifang , Wang Fuping , Chen Guobing 
Published: August 2024

AbstractIntestinal barriers play a crucial role in human physiology, both in homeostatic and pathological conditions. Disruption of the intestinal barrier is a significant factor in the pathogenesis of gastrointestinal inflammatory diseases, such as inflammatory bowel disease. The profound influence of the gut microbiota on intestinal diseases has sparked considerable interest in manipulating it through dietary interventions, probiotics, and fecal microbiota transplantation as potential approaches to enhance the integrity of the intestinal barrier. Numerous studies have underscored the protective effects of specific microbiota and their associated metabolites. In recent years, an increasing body of research has demonstrated that Akkermansia muciniphila (A. muciniphila, Am) plays a beneficial role in various diseases, including diabetes, obesity, aging, cancer, and metabolic syndrome. It is gaining popularity as a regulator that influences the intestinal flora and intestinal barrier and is recognized as a ‘new generation of probiotics’. Consequently, it may represent a potential target and promising therapy option for intestinal diseases. This article systematically summarizes the role of Am in the gut. Specifically, we carefully discuss key scientific issues that need resolution in the future regarding beneficial bacteria represented by Am, which may provide insights for the application of drugs targeting Am in clinical treatment.
An adaptive composite time series forecasting model for short-term traffic flow
Authors: Shao Qitan , Piao Xinglin , Yao Xiangyu , Kong Yuqiu , Hu Yongli , Yin Baocai , Zhang Yong 
Published: August 2024

AbstractShort-term traffic flow forecasting is a hot issue in the field of intelligent transportation. The research field of traffic forecasting has evolved greatly in past decades. With the rapid development of deep learning and neural networks, a series of effective methods have been proposed to address the short-term traffic flow forecasting problem, which makes it possible to examine and forecast traffic situations more accurately than ever. Different from linear based methods, deep learning based methods achieve traffic flow forecasting by exploring the complex nonlinear relationships in traffic flow. Most existing methods always use a single framework for feature extraction and forecasting only. These approaches treat all traffic flow equally and consider them contain same attribute. However, the traffic flow from different time spots or roads may contain distinct attributes information (such as congested and uncongested). A simple single framework usually ignore the different attributes embedded in different distributions of data. This would decrease the accuracy of traffic forecasting. To tackle these issues, we propose an adaptive composite framework, named Long-Short-Combination (LSC). In the proposed method, two data forecasting modules(L and S) are designed for short-term traffic flow with different attributes respectively. Furthermore, we also integrate an attribute forecasting module (C) to forecast the traffic attributes for each time point in future time series. The proposed framework has been assessed on real-world datasets. The experimental results demonstrate that the proposed model has excellent forecasting performance.
The inoculum dose of Zika virus can affect the viral replication dynamics, cytokine responses and survival rate in immunocompromised AG129 mice
Authors: Yan Yuhuan , Yang Hao , Yang Yun , Wang Junbin , Zhou Yanan , Tang Cong , Li Bai , Huang Qing , An Ran , Liang Xiaoming , Lin Dongdong , Yu Wenhai , Fan Changfa , Lu Shuaiyao 
Published: August 2024

AbstractZika virus, a mosquito-borne arbovirus, has repeatedly caused large pandemics with symptoms worsening from mild and self-limiting diseases to Guillain–Barré syndrome in adults and fetal microcephaly in newborns. In recent years, Zika virus diseases have posed a serious threat to human health. The shortage of susceptible small animal models makes it difficult to study pathogenic mechanisms and evaluate potential therapies for Zika virus infection. Therefore, we chose immunocompromised mice (AG129 mice) deficient in IFN-α/β and IFN-γ receptors, which can abolish the innate immune system that prevents Zika virus infection early. AG129 mice were infected with the Zika virus, and this mouse model exhibited replication dynamics, tissue tropism, pathological lesion and immune activation of the Zika virus. Our results suggest that the inoculum dose of Zika virus can affect the viral replication dynamics, cytokine responses and survival rate in AG129 mice. By testing the potential antiviral drug favipiravir, several critical indicators, including replication dynamics and survival rates, were identified in AG129 mice after Zika virus infection. It is suggested that the model is reliable for drug evaluation. In brief, this model provides a potential platform for studies of the infectivity, virulence, and pathogenesis of the Zika virus. Moreover, the development of an accessible mouse model of Zika virus infection will expedite the research and deployment of therapeutics and vaccines.
Shortening a mixed salt and mobile shale system: A case study from East Breaks, northwest Gulf of Mexico
Authors: Soto Juan I. , Dooley Tim P. , Hudec Michael R. , Peel Frank J. , Apps Gillian M. 
Published: August 2024

The coexistence of two low-shear strength layers in a continental margin, such as salt and shales, conditions the resulting structural style and also constitutes a challenge for seismic imaging and energy resource exploration. We have analyzed the 3D structure of a mixed salt-shale system in the western Gulf of Mexico, in the East Breaks foldbelt, by interpreting a depth-migrated seismic data set. Overlying the allochthonous Sigsbee canopy and pierced by isolated salt diapirs, the Oligocene shale-prone sequences were shortened during the Miocene through pervasive deformations, developing a major suprasalt detachment system associated with duplexes below and different fold and thrust systems above. Oligocene shales locally reach a critical state and flow, resulting in: (1) inflated shale-cored detachment anticlines, with the stacking of allochthonous mobile shale domains; (2) detached lift-off anticlines with shale bulbs and fishtail thrusted welds; (3) allochthonous shale sheets at the upper tip of thrusts, fed by fluidized material migrating along associated fault zones; (4) hourglass shale diapirs; and (5) Christmas trees and vertical mud pipes connected to small mud volcanoes. The salt diapirs deformed simultaneously, as did the shales. Salt accommodated the shortening through localized subvertical welds that isolated them from the allochthonous Sigsbee canopy and through weld thrusts formed by fault-bounded blocks with highly sheared salt and mobile shales. Mobile shale structures wrap around the squeezed salt diapirs, presenting a unique pattern that has not yet been documented in experimental models with precursor salt diapirs deformed under contraction. Mixed salt-shale systems are a new challenge for structural analysis and experimental models, as well as for seismic model building in situations where there is a high dispersion of seismic energy due to the unique properties of mobile shales.
Exercise‐induced changes in intramuscular total creatine concentration measured with 1H magnetic resonance spectroscopy: A pilot study
Authors: Lin Xi , Zhang Jiaying , Kong Xiangwei , Li Yanbin , Xu Xueqin , Du Lianjun , Zhang Jeff L. 
Published: August 2024

AbstractTotal amount of creatine (Cr) and phosphocreatine, or total creatine (tCr), may have a significant impact on the performance of skeletal muscles. In sports such as bodybuilding, it is popular to take Cr supplements to maintain tCr level. However, no study has explored the quantitative relationship between exercise intensity and the induced change in muscle's tCr. In this well‐controlled study, straight‐leg plantar flexion with specific load and duration was performed by 10 healthy subjects inside an MRI scanner, immediately followed by 1H MR spectroscopy (MRS) for measuring tCr concentration in gastrocnemius. For repeatability assessment, the experiment was repeated for each subject on two different days. Across all the subjects, baseline tCr was 46.6 ± 2.4 mM, ranging from 40.6 to 50.1 mM; with exercise, tCr significantly decreased by 10.9% ± 1.0% with 6‐lb load and 21.0% ± 1.3% with 12‐lb load (p < 0.0001). Between two different days, baseline tCr, percentage decrease induced by exercise with a 6‐lb and 12‐lb load differed by 2.2% ± 2.3%, 11.7% ± 6.0% and 4.9% ± 3.2%, respectively. In conclusion, the proposed protocol of controlled exercise stimulation and MRS acquisition can reproducibly monitor tCr level and its exercise‐induced change in skeletal muscles. The measured tCr level is sensitive to exercise intensity, so can be used to quantitatively assess muscle performance or fatigue.
Accelerating regional weather forecasting by super-resolution and data-driven methods
Authors: Mikhaylov Artem , Meshchaninov Fedor , Ivanov Vasily , Labutin Igor , Stulov Nikolai , Burnaev Evgeny , Vanovskiy Vladimir 
Published: August 2024

Abstract
At present, computationally intensive numerical weather prediction systems based on physics equations are widely used for short-term weather forecasting.
In this paper, we investigate the potential of accelerating the Weather Research and Forecasting (WRF-ARW) model using machine learning techniques.
Two main approaches are considered. First, we assess the viability of complete replacing the numerical weather model with deep learning models, capable of predicting the full range forecast directly from basic initial data.
Second, we consider a “super-resolution” technique involving low-resolution WRF computation and a machine learning based downscaling using coarse-grid forecast for conditioning. The process of downscaling is intrinsically an ill-posed problem.
In both categories, several prominent and promising machine learning methods are evaluated and compared on real data from a variety of sources. for the Moscow region
Namely, in addition to the ground truth WRF forecasts that were utilized for training, we compare the model predictions against ERA5 reanalysis and measurements from local weather stations. We show that deep learning approaches can be successfully applied to accelerate a numerical model and even produce more realistic forecasts in other aspects.
As a practical outcome, this study offers empirically validated guidance for the selection and application of deep learning methods to accelerate the computation of detailed short-term atmospheric forecasts tailored to specific needs.
A Comparative Study of the Impact of Social Media Advertisements on Working Women and Homemakers in Jaipur
Authors: Prajapat Sunita 
Published: August 2024

The evolution of social media has transformed advertising strategies, providing businesses with innovative tools to target distinct audience segments effectively. This study investigates the impact of social media advertisements on working women and homemakers, examining their consumption patterns, engagement levels, and overall response to marketing strategies on platforms such as Facebook, Instagram, Twitter, and Pinterest. With social media use reaching approximately 4.9 billion people globally in 2023, advertisers have a unique opportunity to target specific demographics with personalized content. Online advertisements have significantly impacted the purchasing habits of the general public due to the increasing internet use. The online retail sector in India is transitioning, as many brands are shifting away from traditional brick-and-mortar stores. Various parameters of online advertising influence consumer behavior. As the online retail market in India continues to expand, new brands and shopping websites are emerging daily. Women consumers play a crucial role in this growth, as their buying behavior significantly influences the online shopping landscape across different platforms and applications. Social media advertising is rapidly evolving, with platforms like Instagram, Facebook, YouTube, and shopping apps such as Nykaa, Meesho, Amazon, and Myntra offering numerous shopping opportunities for women, featuring a wide range of national and international cosmetic brands. This research focuses on the influence of advertisements on homemakers, who have shifted from traditional media to social media. This also explores how working women use the knowledge gained through online advertisements. The study utilizes a quantitative research questionnaire survey method, gathering data from 100 women (50 working women and 50 homemakers) in Jaipur City to understand their behavior toward social media advertisements. The present research aims to explore a comparative study of the impact of online advertisements on working women and homemakers in Jaipur.
Exploring health knowledge: An extensive review of information seeking behaviors, health literacy, and the relevance of libraries and community resources
Authors: Halder Sambhu Nath , Tiwari V. R. , Ali Sazzad 
Published: August 2024

The complicated interactions between health literacy, health information-seeking behaviors, and the vital roles that libraries and community resources play are all explored in detail in this comprehensive examination. Underscoring the importance of well-informed decision-making and enhanced health outcomes, the research delves into many models, such as the "Health Belief Model" and the "Information-Motivation-Behavioral Skills Model". Factors impacting health information seeking, such as psychological variables and socioeconomic status, highlight the challenges in meeting the requirements of a varied community.The multifaceted character of health literacy is investigated, including aspects such as the quality of educational resources and the influence on patient outcomes. Libraries, which have evolved into vital health information centers, serve critical roles in disseminating trustworthy information. Collaboration between libraries and community services has been shown to dramatically advance health literacy levels.The analysis finds links between health information-seeking behavior, health literacy, and the functions of libraries and community resources. Budget limits coexist with the potential opportunities for leveraging technology and collaborative approaches for enhanced health information services. Practical implications emphasize recommendations for policymakers, librarians, and community organizations to improve health literacy programs.The findings emphasize the constant adaptation required to satisfy the changing needs of the healthcare sector. Future studies should focus on evaluating the influence of innovative library programs, community initiatives, and emerging technology on health literacy outcomes. This study serves as a basic resource for policy and strategy development to promote health literacy on a larger scale.