bluuebunny/crossref_metadata_2025_split huggingface dataset:
Molecular Mechanism of hsa-circKLK3-25 in Regulating Prostate Cancer Progression via the JNK/ERK Signal Transduction Pathway
Authors: Zheng Wei , Yang kebing , Zhang Pu , yao Cenchao , Wang Zhida , Zhang Dahong 
Published: December 2024

Abstract

Background: PCa (representing prostate cancer) ranks among the most prevalent malignancies in males. This disease begins insidiously. Most patients have reached an advanced stage when being diagnosed for the first time. Moreover, this condition is prone to distant metastasis, leading to an unfavorable prognosis. Therefore, the paramount thing is to find the biomarkers underlying this disease to facilitate the early diagnosis of PCa.
Methodology: A high-throughput sequencing method was taken to identify the circular RNA, hsa-circKLK3-25, in PCa tissues. Next, the expression and stability of hsa-circKLK3-25 were assessed by qRT-PCR and Actinomycin D testing. Followed by, PCa cells were transfected with hsa-circKLK3-25 or sh-hsa-circKLK3-25 and tested through CCK-8, Edu, scratch healing and transwell experiments to make clear the influences of heightening or silencing hsa-circKLK3-25 on the aggressive evolution of PCa cells. Afterward, western blot experiments were carried out to determine the expressions of proteins associated with the JNK/ERK signal transduction pathway and judge whether hsa-circKLK3-25 effected by virtue of the JNK/ERK pathway. Finally, subcutaneous xenograft tumor models were formed in nuke mice to uncover the interference of hsa-circKLK3-25 with the evolution of PCa cells in vivo.
Results: The high-throughput sequencing results confirmed the presence of hsa-circKLK3-25 in PCa tissues. In PCa cells, hsa-circKLK3-25 was at a notably and stably heightened level. Excessive hsa-circKLK3-25 propelled PCa cells to multiply, penetrate, and migrate; conversely, silenced hsa-circKLK3-25 impaired the aggressive evolution of such cells. Overexpression of hsa-circKLK3-25 led to an upregulation in the expressions of proteins associated with the JNK/ERK pathway, while silencing hsa-circKLK3-25 lead to the opposite trend. The cancer-promoting effect of excessive hsa-circKLK3-25 was retarded following the administration of the inhibitors for this pathway. Besides, in vivo experiments demonstrated that silencing hsa-circKLK3-25 leveled down the expressions of proteins associated with the said pathway and could suppress the expansion of tumors.
Conclusion: hsa-circKLK3-25 is at a notably over-high level in PCa cells and promotes the aggressive evolution of PCa in vitro/vivo through the JNK/ERK signal transduction pathway.
Nanosecond-level time-domain coding metasurface for radar signal generation
Authors: Chu Hui , Qian Boyang , Zhao Hanjun , Zhu Xiaohua , Li Peng , Guo Yong-Xin 
Published: December 2024

Abstract

The time-domain coding metasurface (TDCM) offers a rapid and efficient approach to manipulate frequency spectra of electromagnetic waves within their transmission channels. To date, not only finite-order harmonics can be generated and coded in a discrete manner, the method of frequency modulation for continuous temporal waves is also investigated. However, due to the achievable speed of phase tuning, the modulation bandwidth and applicable scope of the current methods are still limited with respect to the requirement of practical systems. Here, we provide theoretical evaluations of the quality of generated frequency-modulated continuous wave (FMCW) radar signals with respect to the phase tuning speed, as well as a time-coding metasurface that can achieve a phase tuning speed smaller than 20 ns to support the generation of high quality FMCW signals with a bandwidth larger than 10 MHz. Different from varactors used in conventional approaches, the phase tuning of the proposed TDCM is based on the reconfiguration of a PIN-diode array. With this measure, the phase transition time between adjacent phase states can be reduced to a nano-second level, which is crucial for ultrahigh-speed coding. Furthermore, this design approach is also convenient to be rolled out to X- and even higher frequency bands, which overcomes the frequency spectra constraint when varactors are used. To validate its effectiveness, we have built a C-band radar prototype in which the metasurface operates as a transmitter to measure the range and speed of a flying drone.
Double chambered right ventricle in adulthood: A case report and review of the literature
Authors: Khazzar Taha El , Benjaout Kaoutar , Madani Mouhcine 
Published: December 2024

Abstract

Background: The double chambered right ventricle (DCRV) is a rare congenital malformation, in which a dislocated hypertrophic muscular band causes the subdivision of the right ventricle in two chambers, proximal or high-pressure chamber and distal or low-pressure chamber. It is recommended to use echocardiography and cardiac catheterization for diagnosis. This case report seeks to provide additional data for comparison with existing literature, thereby contributing to a more comprehensive understanding of this condition.
Case presentation: We are reporting the case of DCRV in an adult female, who was evaluated for shortness of breath. Diagnosis was confirmed by transthoracic echocardiography, with a maximal gradient of 97mmHg. The patient underwent a successful surgery.
Conclusions: Most cases of classic DCRV are diagnosed in infant, and are associated with ventricular septal defect (VSD). Surgery is the most effective treatment by removing the muscular band.
Aftershock characteristics of the 2024 Noto Peninsula earthquake (Mw7.5) through centroid moment tensor analysis using a 3-D seismic velocity structure model
Authors: Yamaya Lina , Kubo Hisahiko , Shiomi Katsuhiko , Kimura Takeshi 
Published: December 2024

Abstract

A crustal earthquake of the 2024 Noto Peninsula earthquake with a moment magnitude of 7.5 occurred on January 1, 2024, and was followed by many aftershocks distributed in both onshore and offshore regions. The mainshock was characterized as a reverse fault with NW-SE pressure- (P-) axes. We conducted centroid moment tensor (CMT) inversions for the aftershocks using a three-dimensional seismic velocity structure model to capture the detailed stress state and fault geometries around the source region. CMT solutions were obtained for 221 aftershocks with moment magnitudes of 3.2–6.1 at depths shallower than 15 km. Our approach showed substantial improvement in depth determination of CMT solutions, compared to that of the hypocenter determination using P- and S-wave arrival times, even for the early aftershock period when no seismic station was available close to the earthquake source region. Our CMT solutions were characterized as follows: (1) reverse faults with an NW-SE P-axis, which is consistent with that of the mainshock mechanism; (2) strike-slip faults in predominantly shallower regions compared to type 1; (3) reverse faults with ENE-WSW P-axes, possibly activated following the mainshock in the shallow southwestern aftershock region; and (4) earthquakes predominantly featuring normal and strike-slip faults localizing at a depth of approximately 5 km around the dip transition zone in geologically constructed fault models. Additionally, we conducted the same CMT inversion for earthquakes that occurred between 2007 and 2023 to further understand the effect of the mainshock on the aftershock dynamics. We confirmed that CMT solutions of types 3 and 4 appeared as new earthquake categories after the mainshock, suggesting that the mainshock could have triggered them. Our results provide a deeper understanding of complex stress fields and fault geometries in the source region.
Cardiac Echocardiographic Analysis with Multi-Scale Effective Fusion Module: A Novel Stroke Prediction Approach
Authors: xie Jiachun , Tan Dianhuan , Zheng Tingting , Wei Liya 
Published: December 2024

Abstract

Purpose: To develop a stroke risk prediction model by integrating echocardiographic images (long-axis, short-axis, four-chamber views) and clinical indicators using a novel multi-scale effective fusion (MSEF) module.
Methods: A total of 712 patients with 10,992 images and 27 clinical indicators were included. The MSEF module enhances multi-scale feature fusion by combining deep semantic and shallow high-resolution features. It consists of four components: Global Feature Fusion (GFF), Multi-Feature Reconstruction (MFR), Channel Attention, and Positional Attention, effectively improving small-target feature representation. The fused features and clinical indicators were used to train the stroke prediction model.
Results: The proposed MSEF-based model achieved the highest performance, with an Accuracy of 76.8% and F1 Score of 64.7% on the test set. Ablation studies confirmed the importance of Channel Attention and Position Attention in enhancing feature representation. When integrating echocardiographic features with clinical indicators, the model achieved an Accuracy of 80.2% and F1 Score of 72.1% on the test set.
Conclusion: The proposed MSEF-based approach effectively integrates imaging and clinical data, improving stroke risk prediction accuracy and offering a promising tool for clinical decision-making.
Deep Learning methods for reducing metal artifact in Non-Contrast Brain CT Images with Neurovascular Coils
Authors: Lu Mingjun , Guo Yi , Li Yuxin , Yan Xinran , Zhu Jianbin , Qu Yaoming , Ma Andong , Yu Zhijian , Huang Chengyan , Yu Zhujuan , Ma Jianhua , Wen Zhibo 
Published: December 2024

Abstract

Background
Metal artifacts from neurovascular coil effect image quality in computed tomography (CT), we aim to use two deep learning (DL) methods -- convolutional neural network based metal artifact reduction (CNN-MAR)and dual domain network (DuDoNet) to reduce metal artifacts, and compare these methods with two traditional metal artifact reduction (MAR) methods -- Metal Artifact Reduction for Orthopedic Implants (O-MAR) and sinogram completion fused physical model (SCFPM).
Methods
40 consecutive patients with Neurovascular Coil who underwent brain CT between July 2021 to December 2022 were included. Above methods were compared quantitatively by calculating the Hounsfield Units (HU), standard deviation (SD), contrast to noise ratio (CNR) and artifact index (AI). Two radiologists who were blinded to these MAR methods assessed qualitatively the Neurovascular Coil datas for metal artifact and diagnose confidence, resolution, new artifacts and the contours of different soft tissue interfaces utilizing a five-point scale. Friedman M test was used for quantitative and qualitative evaluation.
Results
The HU, SD and AI were significantly lower in DuDoNet images when compared with Filtered back projection (FBP), O-MAR, CNN-MAR images (p＜0.001), although no statistically significant when compared with SCFPM images. The CNR was not significantly different among FBP, O-MAR, SCFPM, CNN-MAR and DuDoNet methods (p=0.596). For artifact and diagnosis confidence score, SCFPM method was significantly higher than FBP, O-MAR, DuDoNet methods (p＜0.001, p=0.010, p=0.015, respectively) although no statistically significant when compared with CNN-MAR method. The resolution and contours of different soft tissue interfaces score were lower in DuDoNet images when compared with other images (all p<0.001). The SCFPM method introduces the least new artifacts among these MAR methods. In addition, using tradional or deep learning methods, we found new lesion obscured by metal artifacts in two cases.
Conclusions
For Quantitative Image Analysis, DuDoNet achieved the best image quality. For qualitative Image Analysis SCFPM achieved the best image quality. DuDonet method can cause over-smoothing and blurring effect. SCFPM method introduce least new artifact.
Analysis of Risk Factors for Hypoxemia in PACU for Patients Undergoing Thoracoscopic Lung Cancer Resection Based on Logistic Regression Model
Authors: Luo Xi , Ying Yanmei , Yin Lu , Chang Pan 
Published: December 2024

Abstract

Objective
This study aims to analyze the prevalence and influencing factors of hypoxemia in patients following thoracoscopic lung surgery in the post-anesthetic care unit (PACU).
Methods
We conducted a prospective analysis involving 398 patients who underwent elective thoracoscopic lung surgery in West China Hospital, Sichuan University, from April to July 2024. Patients were classified into hypoxemia and non-hypoxemia groups based on the presence of hypoxemia in the PACU. We compared clinical data between the two groups to identify factors influencing hypoxemia. Variables with statistical significance (P < 0.05) in univariate analysis were included in logistic regression to identify independent risk factors for hypoxemia.
Results
Among the 398 patients studied, 149 (37.4%) experienced hypoxemia. Univariate analysis indicated significant differences in age, BMI, height, ASA classification, hypertension, diabetes, lung function test with Forced Expiratory Volume at 1 Second / Forced Vital Capacity (FEV1/FVC), and awakening time between the groups. Logistic regression revealed that age, BMI, ASA classification, hypertension, diabetes, and awakening time were independent risk factors for hypoxemia during anesthesia recovery, while FEV1/FVC was a protective factor.
Conclusion
Effective management of elderly patients and those with high BMI, ASA classification, and attention to preoperative hypertension and diabetes may help reduce hypoxemia incidence in the PACU following thoracoscopic lung surgery. Emphasizing preoperative lung function assessment and enhanced monitoring can facilitate timely interventions, improving post-anesthesia recovery and patient outcomes.
Lipidomics reveals biomarkers of the efficacy of first-line ICIs therapy combined with chemotherapy in NSCLC
Authors: Yu Jia , Xu Hanyan , Xiong Fen , Meng Lingfei , Liu Xiling , Gao Hongchang , Li Yuping 
Published: December 2024

Abstract

Immune checkpoint inhibitors (ICIs) plus chemotherapy have become the first-line standard therapy for patients with the gene-negative advanced non-small cell lung cancer (NSCLC). There is a lack of reliable biomarkers to predict treatment outcomes. This study aimed to identify relevant lipids that can predict treatment outcomes in NSCLC patients receiving first-line ICIs plus chemotherapy via lipidomics. Plasma samples were collected from Forty-nine patients with stage IIIB/IV NSCLC before the start of treatment, and patients were categorized into a long-term benefit group (progression-free survival [PFS] < 12 months) and a short-term benefit group (PFS ≥ 12 months). We identified13 lipids (FA-18:0, FA-18:2, FA-15:0, PS-36:3, PE-P-34:2, LPC-18:0, FA-20:3, LPI-18:1, LPC-14:0, PI-40:4, PE-O-34:2, FA-20:4, FAHFA-37:5) to predict the therapeutic efficacy of ICIs plus chemotherapy with high specificity and sensitivity We further investigated the role of linoleic acid (LA) (FA-18:2), a pivotal lipid involved in immune regulation, in animal and cellular models and explored its potential in enhancing NSCLC immunotherapy. Our results showed that LA can assist PD-1 inhibitors in exerting immune anti-tumor effects, slowing down tumor growth in mouse models, and suppressing the expression of PD-L1 proteins in both Lewis cells and tumor tissues. This study found that lipids were important biomarkers for predicting the efficacy of first-line ICIs plus chemotherapy in NSCLC patients, of which LA is an important adjuvant therapy for immune response.
Assessing the Impact of Triglyceride-Glucose-Body Mass Index (TyG-BMI) on Overall Survival in Autoimmune Hepatitis
Authors: Zhang Lili , Fan Ni , Huang Yaqin , Lin Huiying , Zhou Min , Yin Tingting , Yang Wenting , Zheng Mengyao , Lei Hongtao , Yang Jinhui 
Published: December 2024

Abstract

Background
Autoimmune hepatitis (AIH) is a chronic progressive liver disease. Research indicates a high prevalence of insulin resistance among patients with chronic liver diseases. The triglyceride glucose-body mass index (TyG-BMI index) is significantly correlated with insulin resistance and serves as a reliable surrogate marker for insulin resistance assessment. This study aimed to investigate the relationship between TyG-BMI index and prognosis in patients with AIH.
Methods
A total of 193 patients diagnosed with AIH at the Second Affiliated Hospital of Kunming Medical University between January 1, 2014, and March 15, 2024, were stratified into high and low groups based on TyG-BMI. Demographic characteristics, clinical features, laboratory parameters, and prognosis were retrospectively collected. Cox regression analysis was performed to investigate the relationship between TyG-BMI and prognosis in AIH patients.
Results
This study included a cohort of 193 AIH patients with a median age of 54.55 years and a majority of 158 females. The median follow-up period was 27 months, during which 28 (14.51%) patients died. Based on restricted cubic spline plots, patients were divided into high-score group (n = 43, 22.28%) and low-score group (n = 150, 77.72%). Significant differences were observed between the two groups in BMI, fatty liver, albumin, triglycerides, fasting glucose, prothrombin time, activated partial thromboplastin time, and international normalized ratio (all P < 0.05). Cox regression analysis identified TyG-BMI as an independent risk factor for mortality in AIH patients (HR = 3.230, 95%CI: 1.446–7.213, P = 0.004). Kaplan-Meier curves demonstrated significantly longer survival time in the low-score group compared to the high-score group (P = 0.004).
Conclusions
TyG-BMI index is an significant prognostic indicator for AIH patients, offering valuable insights for risk stratification and individualized treatment in these patients.
Uniform Regularization and Constrative Learning to Mitigate the Long-Tail Effect of Recommendation Algorithms
Authors: Xie Xin , Zheng Hanxin , Liu Kexuan , Zheng Xingpeng , Cheng Xuebo , Wan Tao 
Published: December 2024

Abstract

In recommender systems, Graph Collaborative Filtering (GCF) is widely used for its ability to effectively model the interaction between users and items. However, in practical scenarios, GCF faces a significant problem: the representation of popular items tends to be over-concentrated, while cold items are marginalized, leading to recommendation results biased towards popular items and making it difficult to address the issue of long-tail distribution. To alleviate data sparsity, existing GCF methods typically incorporate Contrastive Learning (CL) to assist in updating node representations. However, inappropriate CL methods can introduce extra noise. For this reason, this paper proposes an Enhanced Contrastive Learning-based Graph Collaborative Filtering (ECL-GCF). The model improves the traditional GCF approach by: 1. capturing explicit interaction information between users and items by exploiting structural neighborhood contrastive learning; 2. introducing semantic neighborhood contrastive learning to construct potential similarity relationships by capturing implicit semantic information of users and items, thereby providing more meaningful representations for cold items; and 3. optimizing the embedding representation by regularizing chi-square and homogeneous embedding representations, ensuring that the embeddings are both close to positive sample pairs and uniformly distributed in the space, thus preventing the marginalization of cold items. Experimental results indicate that the model improves recommendation performance by approximately 5% on the Yelp2018 and iFashion datasets, and especially performs well on cold item recommendation.
Evaluation of glypican-1 in extracellular vesicles from serum and pancreatic tissue as a biomarker for pancreatic cancer
Authors: Ren Rongrong , Cheng Lu , Zhang Huan , Zeng Linggong , Shan Lvhu , Wang Qiang 
Published: December 2024

Abstract

Purpose Pancreatic cancer (PC) is a lethal disease, and early detection is crucial for reducing mortality. Blood exosome glypican-1 (GPC1) has been reported as a powerful diagnostic and screening tool for detecting pancreatic ductal adenocarcinomas (PDAC) even at early stages; however, results from subsequent studies on extracellular vesicles (EVs) or exosome GPC1 have been conflicting. We hypothesized that if EVs GPC1 serves as a biomarker for PC, it may be enriched in pancreatic tumor tissues compared to adjacent non-tumor tissues.
Methods Dissociated tumor tissues and paratumor tissues were treated with collagenase D and DNase I to release EVs from the extracellular matrix. Both serum-derived EVs and tissue-derived EVs were isolated by ultracentrifugation, and EVs GPC1 levels were analyzed by flow cytometry. The expression of EV GPC1 was compared between patients and controls, pre- and post-surgery, and between tumor tissues and adjacent non-tumor tissues.
Results EVs were successfully isolated from pancreatic tissue. Serum EVs GPC1 levels showed no significant difference between PC patients and healthy controls, nor between pre-operative and post-operative samples. EVs GPC1 derived from tumor tissue showed no significant difference compared to matched paratumor tissue.
Conclusion Although EVs GPC1 was found not to be a reliable biomarker for pancreatic cancer, we successfully isolated EVs from pancreatic tissue. Further research is needed to explore the potential of tissue-derived EVs as sources of screening biomarkers and to standardize methods for isolating and detecting EVs biomarkers.
Affordable Clean Energy Through Optimized Hybrid Microgrid Design in Yemen
Authors: Albasheri Mohammed Abdulelah , Mujammal Mujammal Ahmed Hasan , Bouchhida Ouahid , Soufi Youcef , Cherifi Abderrezzak 
Published: December 2024

Abstract

The global pursuit of sustainable development increasingly emphasizes the adoption of renewable energy sources to deliver affordable, clean energy while addressing climate change challenges. This study proposes a comprehensive, three-phase framework for designing a microgrid-based hybrid renewable energy system tailored for a remote area in Yemen. The framework encompasses initial evaluation, design optimization, results assessment, and power quality analysis. The phases conducted using HOMER Pro® software, evaluate and compare six hybrid system configurations based on life-cycle costs, renewable energy fraction, unmet load, and system reliability to identify the optimal design for the target community. The findings reveal that the optimal system configuration consists of photovoltaic panels, battery energy storage, and a power converter, achieving the lowest net present cost of $22,647.71 and a cost of energy (COE) of $0.084912 per kWh. This design exhibits an almost negligible unmet load of 0.017463%, a renewable energy fraction of 100%, and the lowest associated carbon emissions among all configurations. These results underscore the proposed framework's potential for achieving reliable, efficient, and sustainable energy solutions in remote communities.