bluuebunny/crossref_metadata_2025_split huggingface dataset:
Changes in fluctuating head asymmetry in natural populations of Triatoma infestans (Hemiptera, Reduviidae): How does a vector control treatment influence the phenotype of nymphs and adults?
Authors: Hernandez Mari­a Laura , Gleiser Raquel Miranda 
Published: December 2024

Abstract

In triatomines, the measurement of fluctuating asymmetry (FA) might help us infer the (non-lethal) effects of insecticides on morphological characters and how these changes can impact activities of utmost importance in vector management. Insects that survive chemical control present morphological alterations that impact their final development. Our work measures for the first time the changes in FA in nymphs and their comparison with the occurrence of these changes in adults before and after a chemical control. We determined the occurrence and quantified the FA of the head as an indicator of stress during development in adults and fifth instar nymphs of Triatoma infestans - one of the main vectors of Trypanosoma cruzi in the southern cone of South America -, to estimate the effect of exposure to insecticide in the different stages. We measured the FA of the heads of peridomestic populations of the Rioja Llanos (Argentina), pre-treatment with insecticide (15 years without chemical treatment) and post-treatment (3–4 months after the application). Our results show lower FA values in nymphs compared with adults. Furthermore, shape FA was higher post-treatment, although with some exceptions depending on sex. According to what was observed, we conclude that the post-treatment population would have differences over the pre-treatment population (FA increases), at least for fifth instar nymphs and adult males. The results are discussed considering the stage of nymphal development where the insecticide was applied, the possible repellence caused by the insecticide, the influence of insects from neighboring habitats, seasonality and the compensatory mechanisms of development, among others. Working with field insects allows us to understand how populations react to an insecticide application, but without leaving aside characteristics of T. infestans populations under natural conditions.
Dynamic Modelling of a Semi-Active Suspension System for a Half-Car Roll Model Utilizing PID Control Techniques
Authors: Ferhath Aadil Arshad , Kasi Kamalakkannan 
Published: December 2024

Abstract

Vehicle suspension systems are essential elements that ensure passenger comfort and handling efficiency. This study examines the dynamic modelling of a semi-active suspension system for a half-car roll model equipped with a passenger seat, utilising Proportional-Integral-Derivative (PID) control techniques. This study employs PID control methodologies within a half-car roll model to determine the optimal control approach for enhanced ride comfort and vehicle handling. The Semi-Active Suspension System utilising PID controller methods is evaluated against passive suspension systems based on peak value, rise time, settling time, overshoot, and RMS parameters concerning sprung mass acceleration, sprung mass displacement, sprung mass velocity, passenger seat acceleration, and passenger seat velocity. The simulation results indicate that the PID-controlled system significantly improves ride comfort and stability, outperforming conventional passive suspension systems regarding settling time and RMS values. The research findings suggest that the PID-CC approach outperformed the PID-ZN and traditional PID methods across all parameters.
Oracle Character Recognition Using Universal Inverted Bottleneck and Inverse Image Frequency
Authors: Zhou Junwei , Tu Qingpeng , Xu Guojun 
Published: December 2024

Abstract

The oracle bone script, one of the most well-known ancient writing systems, plays a key role in the study of ancient Chinese characters. To speed up the digitization of oracle bone documents through automatic recognition, we propose a recognition model called QROB (Quick Response Oracle Bone). Due to the unique characteristics of oracle bone characters, only a small portion has been translated, leading to an imbalance in the sample distribution across different characters. To address this issue, we use the Inverse Image Frequency De-biasing method and incorporate the UIB (Universal Inverted Bottleneck) module into a lightweight model structure to improve training performance. Additionally, the limited number of translated oracle bone samples often results in sparse data in character datasets. To overcome this, we apply the FFD (Free-Form Deformation) method for data augmentation. We also introduce a new dataset, OBC-V, which better integrates oracle bone characters and words compared to existing datasets. Experimental results on three different datasets (OBC-V, HWOBV, and OBC306) demonstrate the effectiveness of our approach. This study advances oracle bone character recognition and contributes to a more efficient and accurate interpretation of ancient scripts. The code has been published at https://github.com/alphazzv/DnUse
Vestibular and balance functions in Attention Deficit Hyperactivity Disorder and Cognitive Disengagement Syndrome
Authors: Apaydin Zuhal Koç , Soylemez Emre , Demir Nefise 
Published: December 2024

Abstract

Background: In the past, cognitive disengagement syndrome (CDS) and attention deficit hyperactivity disorder (ADHD) were considered similar concepts. However, many differences have recently been identified between the two disorders, and they have been separated from each other. This study aims to investigate balance and vestibular function in individuals at high risk for ADHD and CDS.
Materials and Methods: Sixty university students were included in this study. Adult Concentration Inventory and Adult ADHD Self-Report Scale were administered to the participants. Participants with high and low risk for ADHD and CDS were determined according to the applied indices. Static posturography, cervical vestibular myogenic evoked potentials (cVEMP) and video head impulse test (vHIT) were applied to all participants.
Results: There was no difference in terms of static posturography and cVEMP between individuals with low and high risk for CDS (p>0.05). Similarly, no differences were found in ADHD (p>0.05). While right ear lateral vHIT responses were worse in individuals with high-risk CDS (p>0.05), bilateral posterior vHIT rates were worse in individuals with high-risk ADHD (p>0.05).
Conclusion: The fact that individuals at high risk for CDS and ADHD show different vestibular involvement strengthens the fact that there are different clinical pictures.
SET binding protein 1 (SETBP1) suppresses cell proliferation in estrogen receptor-positive breast cancer.
Authors: Ando Yuki , Masuda Takaaki , Hayashi Naoki , Kosai Keisuke , Shibuta Shohei , Ono Yuya , Ohtsu Hajime , Hisamatsu Yuichi , Yoshizumi Tomoharu , Mimori Koshi 
Published: December 2024

Abstract

Background The single nucleotide polymorphism rs6507583 at the promoter of SET binding protein 1 (SETBP1) was implicated in estrogen receptor (ER)-positive breast carcinogenesis. Here, we evaluated the clinical and biological relevance of SETBP1 expression in ER-positive breast cancer (BC).Methods The associations between SETBP1 expression and clinical outcomes in BC patients were analyzed in independent cohorts. The localizations of SETBP1 expression in BC tissues were observed by immunohistochemical staining. Pathway analyses were conducted using TCGA dataset. An in vitro proliferation assay, protein phosphatase 2A (PP2A) activity assay, and gene expression analysis were performed in SETBP1-knockdown ER-positive BC cells. We investigated the factors influencing SETBP1 mRNA expression using TCGA dataset. rs6507583 presence and SETBP1 mRNA expression in 11 mammary cell lines and 56 BC tissue samples were examined by target sequencing and RT-qPCR, respectively.Results SETBP1 was downregulated in BC cells compared with normal ductal epithelial cells. Low SETBP1 mRNA expression was an independent prognostic factor for poor recurrence-free survival. Pathway analyses revealed an inverse relationship between decreased SETBP1 expression and the expression of E2F, MYC, and G2M checkpoint target genes in BC tissues. SETBP1 knockdown promoted proliferation, inhibition of PP2A activity, and phosphorylation of MAPK in ER-positive BC. Low SETBP1 expression was influenced by high SETBP1 promoter methylation and DNA copy number SETBP1 deletion. SETBP1 expression with rs6507583 was lower than without rs6507583 in BC.Conclusions We demonstrated that low SETBP1 expression could be a poor prognostic biomarker that promotes ER-positive BC proliferation, possibly via phosphorylation of MAPK.
Machine learning method based on radiomics help differentiate posterior pituitary tumors from pituitary adenomas and craniopharyngioma
Authors: Liu Yukun , Zhou Yanpeng , Zhou Chunyao , Wang Zhenmin , Fan Ziwen , Tang Kai , Chen Siyuan 
Published: December 2024

Abstract

Objectives
Posterior pituitary tumors (PPTs) are rare neoplasms, but easily misdiagnosed as pituitary adenoma and craniopharyngioma. This study aimed to differentiate PPTs from pituitary adenoma and craniopharyngioma using a machine learning method based on radiomics.
Design:
Single center cohort study.
Materials and Methods
The cohort used for training and testing contained 33 PPTs and 99 non-posterior pituitary tumors (NPPTs). The validation cohort consisted of prospectively included patients (9 PPTs and 33 NPPTs). Radiomics features based on T1-weighted images and contrast-enhanced (CE) T1-weighted images were extracted, or both. Data of training and testing cohort were input to a nested 10-fold to build models, which were independently validated in the validation cohort. A least absolute shrinkage and selection operator (LASSO) was used for dimensionality reduction and random forest was used as classifier.
Result
Predictive models were successfully established, and models based on CE features had the best performance with an accuracy of 0.786, precision of 0.929, specificity of 0.778, sensitivity of 0.788, and area under the curve of 0.818 in validation. Nine features selected by more than 75% of the models based on CE features were identified as the most predictive features.
Conclusion
We established a group of machine learning models to noninvasively differentiate PPTs from NPPTs before surgery, which may improve the surgical plan of PPTs to better complete resection of the tumors and protection of important structures around the tumors.
A Clinical Risk Score to Predict the Critical Illness in Respiratory Syncytial Virus Co- detected With Streptococcus Pneumoniae Hospitalized Children
Authors: Li Lu , Huang Xiaohan , Xu Ximing , Liu Enmei , Deng Yu 
Published: December 2024

Abstract

Purpose To develop and validate a clinical score at hospital admission for predicting which patients will develop critical illness.
Methods A retrospective study was conducted in the Children’s Hospital of Chongqing Medical University from January 2012 to December 2021. Epidemiological, clinical, laboratory, and imaging variables ascertained at hospital admission were screened using Least Absolute Shrinkage and Selection Operator and logistic regression to construct a predictive risk score. Accuracy of the score was measured by the area under the receiver operating characteristic curve (AUC). Data from our hospital and another hospital were used to validate the score.
Results The study included 1516 patients totally, including training cohort 1039 patients，validation cohort 477 patients, and external cohort 122 patients. From 35 potential predictors, 11 variables were independent predictive factors and were included in the risk score: neonate hospitalization，chronic pulmonary disease，congenital heart disease，immune deficiency disease，anhelation，disorders of consciousness，assisted respiration，lymph decreased，RBC decreased，CRP increased，pulmonary atelectasis. The mean AUC in the training cohort was 0.853, the AUC in the validation cohort was 0.848，and the external cohort was 0.967.
Conclusion In this study, the risk score based on characteristics of RSV-infected patients co-detected with S.pn at the time of admission to the hospital was developed that may help predict a patient’s risk of developing critical illness.
Computed Tomography (CT)-Guided Medical Sodium Hyaluronate Gel for Paravascular Microwave Ablation of Rabbit Liver Large Vessels
Authors: Li Xiaolan , He Shan , Hui Yani , Li Bing , Du Yong 
Published: December 2024

Abstract

Objective The objective of this study is to investigate the effect of medical sodium hyaluronate gel on large blood vessels during microwave ablation (MWA) of rabbit hepatic paravalves.
Methods Forty-eight New Zealand large white rabbits were selected and randomly divided into a sodium hyaluronate gel MWA immediate group (group A, n = 12), sodium hyaluronate gel MWA one-week postoperative group (group B, n = 12), conventional MWA immediate group (group C, n = 12), and conventional MWA one-week postoperative group (group D, n = 12). Groups A and B were both injected with 1 mL of sodium hyaluronate gel and then underwent MWA, and groups C and D underwent direct MWA. Histopathological examinations were performed to observe the damage of large vessels within the ablation area in every group.
Results The difference in the degree of damage to large vessels after MWA in the four groups was statistically significant (P<0.05). The rates of vascular injury after MWA in groups A–D were 0.00%, 58.3%, 100.0%, and 100.0%, respectively . During MWA and postoperative follow-up in the four groups, one rabbit in group D died, while another developed fatty liver, cirrhosis, and ascites in group B. The maximum diameters of the ablation zones in groups B and D were 2.86±0.48 cm and 2.58±0.50 cm, respectively, one week after surgery and 2.87±0.49cm and 2.58±0.50 cm, respectively, immediately after surgery, exhibiting statistically significant differences (P<0.001).
Conclusion Medical sodium hyaluronate gel has a protective effect on large vessels during hepatic paravascular MWA.
Exploring the key role of neuroinflammation in the formation of delayed encephalopathy after acute carbon monoxide poisoning based on multi-omics and snRNA-seq
Authors: Li Jinlan , Li Jun , Fu Junming , Huang Chuying , Lv Wenqi , Zhang Chunlong , Tian Tianjie , Liu Qunhui , Yang Shijun , Tan Yong , Sun Guogen , Xiang Ying , Huang Guoquan , Wang Ning 
Published: December 2024

Abstract

Despite the high incidence and disability rates of delayed encephalopathy after acute carbon monoxide poisoning (DEACMP), its pathogenesis remains enigmatic, and specific predictive markers are lacking. This study aimed to elucidate the molecular underpinnings and identify predictive biomarkers of DEACMP through multi-omics and single-nucleus RNA sequencing (snRNA-seq). We collected clinical data and blood samples from 105 participants, including healthy controls (HCs), acute carbon monoxide poisoning patients (ACOP), and those receiving comprehensive treatment for ACOP (ACOP-CT). Untargeted metabolomics sequencing was employed to profile serum metabolites across these groups. Additionally, individuals from the HCs, ACOP, non-delayed encephalopathy after ACOP (DEACMP-N), and DEACMP groups (n = 3 each) were randomly selected for transcriptome sequencing to identify potential predictive targets and pivotal signaling pathways associated with DEACMP. Furthermore, we established severe DEACMP and Control Sprague-Dawley rat models and assessed neurocognitive function using the Morris water maze on the 28th day. Subsequently, three rats from the Control, DEACMP, and DEACMP + Dexamethasone + Selenomethionine groups were selected for snRNA-seq to analyze hippocampal single-cell transcriptional profiles. Immunofluorescence multiplexing was then performed to validate the identified predictive targets. Our analysis of clinical data from 105 participants highlights the pivotal role of inflammation in influencing the prognosis of carbon monoxide poisoning. Metabolomics analysis identified 19 metabolites that significantly differed between the DEACMP-N and DEACMP groups compared to the ACOP-CT follow-up results. Transcriptomics analysis of 12 participants indicated that DEACMP is primarily associated with six signaling pathways, including lysosome and tuberculosis. Given that microglia are central nervous system immune effectors, our snRNA-seq analysis revealed altered genes expression and signaling pathways in microglia during DEACMP, with KEGG analysis highlighting phagosome, neutrophil extracellular trap formation, lysosome, and tuberculosis as the predominant pathways. Differential gene analysis from transcriptome and snRNA-seq identified 28 genes differentially expressed in DEACMP. The STRING database and immunomultiplexing confirmed the pivotal role of the IFNGR1/STAT1/CTSS axis in DEACMP. This study provides a comprehensive overview of serum metabolite expression, differential genes expression, and signaling pathways in DEACMP patients, offering a robust theoretical foundation for understanding the pathogenesis for DEACMP.
Preoperative CT-based radiomics combined with AFP, PIVKA-II and other laboratory tests can be used to predict the pathological grade of hepatocellular carcinoma
Authors: Wu Meng , Yu Haijia , Pang Siwen , Liu Aie , Liu Jianhua 
Published: December 2024

Abstract

Background To investigate how effectively clinical features and CT-based radiomics predict the pathological grade of hepatocellular carcinoma（HCC）.
Methods We retrospectively analyzed 108 patients diagnosed with hepatocellular carcinoma who underwent pathological examination between May 2020 and May 2024 at the Second Hospital of Jilin University. All patients underwent laboratory tests and contrast-enhanced computed tomography（CECT）scanning of the liver within one month prior to pathological examination. In the first step, we analyzed laboratory tests such as Alpha fetoprotein (AFP) and des-γ-carboxy prothrombin (PIVKA-II) to derive risk factors associated with pathological grading of HCC, and then built and evaluated the performance of the clinical model. After that, we imported the arterial-phase and venous-phase images of the CECT into the uAI Research Portal research platform for ‘one-stop’ processing, which included semi-automatic ROI outlining, feature extraction, dimensionality reduction, model construction and evaluation. To evaluate the model's diagnostic effectiveness, receiver operating characteristic (ROC) curves were produced, and the related accuracy, sensitivity, specificity, and area under the curve (AUC) were computed. The models were compared using the Delong test, and the clinical value of the predictive model was assessed by plotting calibration curves and decision curve analysis (DCA) to quantify the agreement between the model and the actual outcomes.
Results Poorly differentiated hepatocellular carcinoma (pHCC) is associated with risk variables such as HCVAb, PIVKA-Ⅱ, and sex. In the training and validation cohorts, the clinical model's AUC values were 0.719 and 0.692, respectively; the AP model's were 0.843 and 0.773; the VP model's were 0.806 and 0.804; the AP + VP model's AUC values were 0.953 and 0.844 ; and the AP+VP+clinical model's were 0.926 (95% CI: 0.88-0.995) and 0.863 (95% CI: 0.711-1). The DCA curves show that the overall net benefit of the AP + VP + clinical model is higher than that of the other models and that it has the best diagnostic results.
Conclusions CT-based radiomics modeling combined with clinical features (sex) and laboratory tests (e.g., AFP and PIVKA-II, etc.) can reliably predict the pathologic grading of HCC patients prior to surgery.
Identification and validation of an explainable transformer-based model for predicting the prognosis of patients with Non-small cell lung cancer
Authors: Zhang Peng , Ren Ruizi , Ren Zhanliang , Wang Bo , Chen Hang , Wang Yanqiu , Liu Yanzhen , Qi Ruihua , Zhang Yong , Liu Yunhao 
Published: December 2024

Abstract

Background
Currently, there is a lack of practical and explainable prognostic models for NSCLC in clinical settings. This study aims to construct an explainable prognostic model for NSCLC using the Transformer framework in deep learning.
Methods
119751 patients from the Surveillance, Epidemiology and End Results (SEER) database were used to train a Transformer-based model to predict the overall survival (OS) at 12, 24, and 60 months. Additionally, the SHapley Additive exPlanation (SHAP) method was employed to interpret the constructed model, show casing the importance of various clinical indicators on patient survival at different time points.
Result
The time dependent AUC values of 12 months, 24 months and 60 months were 0.853, 0.860 and 0.871 i Currently, there is a lack of practical and explainable prognostic models for NSCLC in clinical settings. This study aims to construct an explainable prognostic model for NSCLC using the Transformer framework in deep learning.
n the training cohort, 0.863, 0.881, 0.899 in the validation cohort, and 0.850, 0.851 and 0.869 in the testing cohort, respectively. Moreover, a risk scoring system based on the Kaplan Meier (KM) survival curves can accurately divide patients into three risk groups. Ultimately, the explainable model demonstrates the differences in the importance of various patient indicators for short-term and long-term survival.
Conclusion
The distant metastasis of tumor has a significant effect on the short-term survival of patients. The effect of surgery on long term survival is more significant than short term survival.
The mitogenome mutation repertoire affects progression of Parkinson´s Disease
Authors: Matos Gustavo Barra , Santos Camille Sena , Sousa Tatiane Piedade , Cavalcante Giovanna Chaves , Silva Caio Santos , Cruz Rebecca Lais Silva , Moura Dafne Dalledone , Ribeiro-dos-Santos Andrea , Lobato Bruno Lopes Santos , Araújo Gilderlanio Santana 
Published: December 2024

Abstract

Background
Mitochondrial genome variation is a risk factor for Parkinson's disease, but its role in levodopa-induced dyskinesia remains incompletely understood. This study investigates the mitochondrial mutation repertoire as potential biomarkers for levodopa-induced dyskinesia in Parkinson's disease patients.
Methods
We analyzed the mitogenome by next generation sequencing data from 42 controls and 45 people with Parkinson's (25 without dyskinesia and 20 with dyskinesia). The mtDNA-server2 workflow were applied for variant calling analysis.
Results
Transition and transversion rates vary during disease progression, especially in patients without levodopa-induced dyskinesia. Although the occurrence of these mutations does not follow a linear pattern, transitions modestly increase with age. Specific coding regions (MT-CO1, MT-CO3, MT-ND4, MT-ND5) and the regulatory region (MT-RNR2) showed enrichment of transitions and transversions in patients without dyskinesia.
Conclusion
The study highlights dynamic shifts in the mitochondrial mutation repertoire with clinical implications in underrepresented populations, emphasizing the importance of considering genetic characteristics in diverse groups.