bluuebunny/crossref_metadata_2025_split huggingface dataset:
Analysis of player speed and angle toward the ball in soccer
Authors: Novillo Álvaro , Cordón-Carmona Antonio , García-Aliaga Abraham , Roman Ignacio Refoyo , del Campo Roberto López , Resta Ricardo , Buldú Javier M. 
Published: May 2024

AbstractThe study analyzes how the magnitude and angle of the speed of soccer players change according to the distance to the ball and the phases of the game, namely the defensive and attacking phases. We observed how the role played in the team (goalkeeper, defender, midfielder, or forward) strongly determines the speed pattern of players. As a general trend, the speed’s modulus is incremented as their position is closer to the ball, however, it is slightly decreased when arriving at it. Next, we studied how the angle of the speed with the direction to the ball is related to the distance to the ball and the game phases. We observed that, during the defensive phase, goalkeepers are the players that run more parallel to the ball, while forwards are the ones running more directly to the ball position. Importantly, this behavior changes dramatically during the attacking phase. Finally, we show how the proposed methodology can be used to analyze the speed-angle patterns of specific players to understand better how they move on the pitch according to the distance to the ball.
A combinatorial MRI sequence-based radiomics model for preoperative prediction of microsatellite instability status in rectal cancer
Authors: Xing Xiaowei , Li Dongxue , Peng Jiaxuan , Shu Zhenyu , Zhang Yang , Song Qiaowei 
Published: May 2024

AbstractThis study aimed to develop an optimal radiomics model for preoperatively predicting microsatellite instability (MSI) in patients with rectal cancer (RC) based on multiparametric magnetic resonance imaging. The retrospective study included 308 RC patients who did not receive preoperative antitumor therapy, among whom 51 had MSI. Radiomics features were extracted and dimensionally reduced from T2-weighted imaging (T2WI), T1-weighted imaging (T1WI), diffusion-weighted imaging (DWI), and T1-weighted contrast enhanced (T1CE) images for each patient, and the features of each sequence were combined. Multifactor logistic regression was used to screen the optimal feature set for each combination. Different machine learning methods were applied to construct predictive MSI status models. Relative standard deviation values were determined to evaluate model performance and select the optimal model. Receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses were performed to evaluate model performance. The model constructed using the k-nearest neighbor (KNN) method combined with T2WI and T1CE images performed best. The area under the curve values for prediction of MSI with this model were 0.849 (0.804–0.887), with a sensitivity of 0.784 and specificity of 0.805. The Delong test showed no significant difference in diagnostic efficacy between the KNN-derived model and the traditional logistic regression model constructed using T1WI + DWI + T1CE and T2WI + T1WI + DWI + T1CE data (P > 0.05) and the diagnostic efficiency of the KNN-derived model was slightly better than that of the traditional model. From ROC curve analysis, the KNN-derived model significantly distinguished patients at low- and high-risk of MSI with the optimal threshold of 0.2, supporting the clinical applicability of the model. The model constructed using the KNN method can be applied to noninvasively predict MSI status in RC patients before surgery based on radiomics features from T2WI and T1CE images. Thus, this method may provide a convenient and practical tool for formulating treatment strategies and optimizing individual clinical decision-making for patients with RC.
Seasonal variability of lesions distribution in acute ischemic stroke: A retrospective study
Authors: Sun Xiao , Xia Xiaoshuang , Xue Juanjuan , Gu Yumeng , Chen Zhuangzhuang , Liu Peilin , Wang Fuyin , Zhou Xiao , Liu Jiaming , Wang Lin , Li Xin 
Published: May 2024

AbstractSeasonal variability could have an impact on the incidence and outcome of stroke. However, little is known about the correlation between seasonal variability and location of acute cerebral infarction. This study aimed to explore the relationship between onset season and the lesions distribution of acute ischemic stroke (AIS). We retrospectively analysis data from 1488 AIS patients admitted to the Second Hospital of Tianjin Medical University from 2018 to 2022. All subjects completed head magnetic resonance imaging examination (MRI) and were divided into four groups according to the onset seasons. The lesions distribution of AIS was evaluated for anterior/posterior/double circulation infarction (DCI), unilateral/bilateral infarctions, and single/multiple cerebral infarctions based on MRI. Logistic regression models were employed to assess the association of season with lesions distribution of AIS. Subgroup analysis was performed in different stroke subtypes. Of 1488 patients, 387 (26.0%) AIS occurred in spring, 425 (28.6%) in summer, 331 (22.2%) in autumn and 345 (23.2%) in winter. Multivariate logistic regression demonstrated that the winter group had 2.15 times (95% CI:1.44–3.21) risk of multiple infarctions, 2.69 times (95% CI:1.80–4.02) of bilateral infarctions and 1.54 times (95% CI:1.05–2.26) of DCI compared with summer group, respectively. Subgroup analysis showed an increased risk of multiple (p < 0.01) or bilateral infarctions (p < 0.01) in small-artery occlusion (SAO) subtype, and higher risk of bilateral infarctions (p < 0.01) or DCI (p < 0.05) in large artery atherosclerosis (LAA) subtype during winter. No significant associations of season with lesions distribution in cardioembolism subtype. Our study highlighted a prominent seasonal variability in the lesions distribution of AIS, particularly in LAA and SAO subtypes. The findings could help to formulating meteorological risk warning strategies for different subtypes.
Heavy metal poisoning caused by Chinese folk remedies in psoriasis patients: a retrospective analysis
Authors: Xue Changjiang , Du Xuqin , Zhu Xiaoli , Wu Na , Ye Qiao 
Published: May 2024

AbstractPsoriasis, characterized as a chronic relapsing disease with a protracted course, often drives patients to seek relief through Chinese folk remedies (CFR). Nonetheless, the complex compositions of these remedies frequently result in unintended adverse effects, notably various types of heavy metal poisoning. Our study involved an exhaustive collection and analysis of clinical data from psoriasis patients who developed heavy metal poisoning due to CFR usage, admitted to Beijing Chao-Yang Hospital from January 2011 to October 2023. Our analysis identified 44 cases of mercury poisoning, 17 of lead poisoning, 21 of arsenic poisoning, and 4 instances of mixed heavy metal poisoning. The folk remedies used ranged from fumigation and inhalation to skin application and oral administration. Distinct pathogenic characteristics were observed in each poisoning type. After treatment with metal chelating agents, all patients experienced a reduction in heavy metal levels in their bodies, accompanied by varying degrees of symptom alleviation. This study underscores the vital necessity of opting for formal, medically approved treatments for psoriasis, thereby avoiding the hazardous consequences of unregulated folk remedies that may lead to severe heavy metal poisoning.
Tactile memory impairments in younger and older adults
Authors: Lange Lilith-Sophie , Chrysidou Anastasia , Liu Peng , Kuehn Esther 
Published: May 2024

AbstractHuman tactile memory allows us to remember and retrieve the multitude of somatosensory experiences we undergo in everyday life. An unsolved question is how tactile memory mechanisms change with increasing age. We here use the ability to remember fine-grained tactile patterns passively presented to the fingertip to investigate age-related changes in tactile memory performance. In experiment 1, we varied the degree of similarity between one learned and several new tactile patterns to test on age-related changes in the “uniqueness” of a stored tactile memory trace. In experiment 2, we varied the degree of stimulus completeness of both known and new tactile patterns to test on age-related changes in the weighting between known and novel tactile information. Results reveal that older adults show only weak impairments in both precision and bias of tactile memories, however, they show specific deficits in reaching peak performance > 85% in both experiments. In addition, both younger and older adults show a pattern completion bias for touch, indicating a higher weighting of known compared to new information. These results allow us to develop new models on how younger and older adults store and recall tactile experiences of the past, and how this influences their everyday behavior.
Convolutional MLP orthogonal fusion of multiscale features for visual place recognition
Authors: Gan Wenjian , Zhou Yang , Hu Xiaofei , Zhao Luying , Huang Gaoshuang , Zhang Chenglong 
Published: May 2024

AbstractVisual place recognition (VPR) involves obtaining robust image descriptors to cope with differences in camera viewpoints and drastic external environment changes. Utilizing multiscale features improves the robustness of image descriptors; however, existing methods neither exploit the multiscale features generated during feature extraction nor consider the feature redundancy problem when fusing multiscale information when image descriptors are enhanced. We propose a novel encoding strategy—convolutional multilayer perceptron orthogonal fusion of multiscale features (ConvMLP-OFMS)—for VPR. A ConvMLP is used to obtain robust and generalized global image descriptors and the multiscale features generated during feature extraction are used to enhance the global descriptors to cope with changes in the environment and viewpoints. Additionally, an attention mechanism is used to eliminate noise and redundant information. Compared to traditional methods that use tensor splicing for feature fusion, we introduced matrix orthogonal decomposition to eliminate redundant information. Experiments demonstrated that the proposed architecture outperformed NetVLAD, CosPlace, ConvAP, and other methods. On the Pittsburgh and MSLS datasets, which contained significant viewpoint and illumination variations, our method achieved 92.5% and 86.5% Recall@1, respectively. We also achieved good performances—80.6% and 43.2%—on the SPED and NordLand datasets, respectively, which have more extreme illumination and appearance variations.
Utilizing machine learning to predict post-treatment outcomes in chronic non-specific neck pain patients undergoing cervical extension traction
Authors: Moustafa Ibrahim M. , Ozsahin Dilber Uzun , Mustapha Mubarak Taiwo , Ahbouch Amal , Oakley Paul A. , Harrison Deed E. 
Published: May 2024

AbstractThis study explored the application of machine learning in predicting post-treatment outcomes for chronic neck pain patients undergoing a multimodal program featuring cervical extension traction (CET). Pre-treatment demographic and clinical variables were used to develop predictive models capable of anticipating modifications in cervical lordotic angle (CLA), pain and disability of 570 patients treated between 2014 and 2020. Linear regression models used pre-treatment variables of age, body mass index, CLA, anterior head translation, disability index, pain score, treatment frequency, duration and compliance. These models used the sci-kit-learn machine learning library within Python for implementing linear regression algorithms. The linear regression models demonstrated high precision and accuracy, and effectively explained 30–55% of the variability in post-treatment outcomes, the highest for the CLA. This pioneering study integrates machine learning into spinal rehabilitation. The developed models offer valuable information to customize interventions, set realistic expectations, and optimize treatment strategies based on individual patient characteristics as treated conservatively with rehabilitation programs using CET as part of multimodal care.
Balanced opioid-free anesthesia with lidocaine and esketamine versus balanced anesthesia with sufentanil for gynecological endoscopic surgery: a randomized controlled trial
Authors: Hu Yang , Zhang Qing-yun , Qin Guan-chao , Zhu Guo-hong , Long Xiang , Xu Jin-fei , Gong Yuan 
Published: May 2024

AbstractIn this randomized controlled trial, 74 patients scheduled for gynecological laparoscopic surgery (American Society of Anesthesiologists grade I/II) were enrolled and randomly divided into two study groups: (i) Group C (control), received sufentanil (0.3 μg/kg) and saline, followed by sufentanil (0.1 μg/kg∙h) and saline; and (ii) Group F (OFA), received esketamine (0.15 mg/kg) and lidocaine (2 mg/kg), followed by esketamine (0.1 mg/kg∙h) and lidocaine (1.5 mg/kg∙h). The primary outcome was the 48-h time-weighted average (TWA) of postoperative pain scores. Secondary outcomes included time to extubation, adverse effects, and postoperative sedation score, pain scores at different time points, analgesic consumption at 48 h, and gastrointestinal functional recovery. The 48-h TWAs of pain scores were 1.32 (0.78) (95% CI 1.06–1.58) and 1.09 (0.70) (95% CI 0.87–1.33) for Groups F and C, respectively. The estimated difference between Groups F and C was − 0.23 (95% CI − 0.58 − 0.12; P = 0.195). No differences were found in any of the secondary outcomes and no severe adverse effects were observed in either group. Balanced OFA with lidocaine and esketamine achieved similar effects to balanced anesthesia with sufentanil in patients undergoing elective gynecological laparoscopic surgery, without severe adverse effects.Clinical Trial Registration: ChiCTR2300067951, www.chictr.org.cn 01 February, 2023.
Metabolic pathway-based subtypes associate glycan biosynthesis and treatment response in head and neck cancer
Authors: Dankó Benedek , Hess Julia , Unger Kristian , Samaga Daniel , Walz Christoph , Walch Axel , Sun Na , Baumeister Philipp , Zeng Peter Y. F. , Walter Franziska , Marschner Sebastian , Späth Richard , Gires Olivier , Herkommer Timm , Dazeh Ramin , Matos Thaina , Kreutzer Lisa , Matschke Johann , Eul Katharina , Klauschen Frederick , Pflugradt Ulrike , Canis Martin , Ganswindt Ute , Mymryk Joe S. , Wollenberg Barbara , Nichols Anthony C. , Belka Claus , Zitzelsberger Horst , Lauber Kirsten , Selmansberger Martin 
Published: May 2024

AbstractHead and Neck Squamous Cell Carcinoma (HNSCC) is a heterogeneous malignancy that remains a significant challenge in clinical management due to frequent treatment failures and pronounced therapy resistance. While metabolic dysregulation appears to be a critical factor in this scenario, comprehensive analyses of the metabolic HNSCC landscape and its impact on clinical outcomes are lacking. This study utilized transcriptomic data from four independent clinical cohorts to investigate metabolic heterogeneity in HNSCC and define metabolic pathway-based subtypes (MPS). In HPV-negative HNSCCs, MPS1 and MPS2 were identified, while MPS3 was enriched in HPV-positive cases. MPS classification was associated with clinical outcome post adjuvant radio(chemo)therapy, with MPS1 consistently exhibiting the highest risk of therapeutic failure. MPS1 was uniquely characterized by upregulation of glycan (particularly chondroitin/dermatan sulfate) metabolism genes. Immunohistochemistry and pilot mass spectrometry imaging analyses confirmed this at metabolite level. The histological context and single-cell RNA sequencing data identified the malignant cells as key contributors. Globally, MPS1 was distinguished by a unique transcriptomic landscape associated with increased disease aggressiveness, featuring motifs related to epithelial-mesenchymal transition, immune signaling, cancer stemness, tumor microenvironment assembly, and oncogenic signaling. This translated into a distinct histological appearance marked by extensive extracellular matrix remodeling, abundant spindle-shaped cancer-associated fibroblasts, and intimately intertwined populations of malignant and stromal cells. Proof-of-concept data from orthotopic xenotransplants replicated the MPS phenotypes on the histological and transcriptome levels. In summary, this study introduces a metabolic pathway-based classification of HNSCC, pinpointing glycan metabolism-enriched MPS1 as the most challenging subgroup that necessitates alternative therapeutic strategies.
Acquired resistance to immunotherapy and chemoradiation in MYC amplified head and neck cancer
Authors: Cyberski Thomas F. , Singh Alka , Korzinkin Michael , Mishra Vasudha , Pun Frank , Shen Le , Wing Claudia , Cheng Xiangying , Baird Brandon , Miao Yuxuan , Elkabets Moshe , Kochanny Sara , Guo Wenji , Dyer Emma , Pearson Alexander T. , Juloori Aditya , Lingen Mark , Cole Grayson , Zhavoronkov Alex , Agrawal Nishant , Izumchenko Evgeny , Rosenberg Ari J. 
Published: May 2024

AbstractThe proto-oncogene MYC encodes a nuclear transcription factor that has an important role in a variety of cellular processes, such as cell cycle progression, proliferation, metabolism, adhesion, apoptosis, and therapeutic resistance. MYC amplification is consistently observed in aggressive forms of several solid malignancies and correlates with poor prognosis and distant metastases. While the tumorigenic effects of MYC in patients with head and neck squamous cell carcinoma (HNSCC) are well known, the molecular mechanisms by which the amplification of this gene may confer treatment resistance, especially to immune checkpoint inhibitors, remains under-investigated. Here we present a unique case of a patient with recurrent/metastatic (R/M) HNSCC who, despite initial response to nivolumab-based treatment, developed rapidly progressive metastatic disease after the acquisition of MYC amplification. We conducted comparative transcriptomic analysis of this patient’s tumor at baseline and upon progression to interrogate potential molecular processes through which MYC may confer resistance to immunotherapy and/or chemoradiation and used TCGA-HNSC dataset and an institutional cohort to further explore clinicopathologic features and key molecular networks associated with MYC amplification in HNSCC. This study highlights MYC amplification as a potential mechanism of immune checkpoint inhibitor resistance and suggest its use as a predictive biomarker and potential therapeutic target in R/M HNSCC.
Evaluation of stenoses using AI video models applied to coronary angiography
Authors: Labrecque Langlais Élodie , Corbin Denis , Tastet Olivier , Hayek Ahmad , Doolub Gemina , Mrad Sebastián , Tardif Jean-Claude , Tanguay Jean-François , Marquis-Gravel Guillaume , Tison Geoffrey H. , Kadoury Samuel , Le William , Gallo Richard , Lesage Frederic , Avram Robert 
Published: May 2024

AbstractThe coronary angiogram is the gold standard for evaluating the severity of coronary artery disease stenoses. Presently, the assessment is conducted visually by cardiologists, a method that lacks standardization. This study introduces DeepCoro, a ground-breaking AI-driven pipeline that integrates advanced vessel tracking and a video-based Swin3D model that was trained and validated on a dataset comprised of 182,418 coronary angiography videos spanning 5 years. DeepCoro achieved a notable precision of 71.89% in identifying coronary artery segments and demonstrated a mean absolute error of 20.15% (95% CI: 19.88–20.40) and a classification AUROC of 0.8294 (95% CI: 0.8215–0.8373) in stenosis percentage prediction compared to traditional cardiologist assessments. When compared to two expert interventional cardiologists, DeepCoro achieved lower variability than the clinical reports (19.09%; 95% CI: 18.55–19.58 vs 21.00%; 95% CI: 20.20–21.76, respectively). In addition, DeepCoro can be fine-tuned to a different modality type. When fine-tuned on quantitative coronary angiography assessments, DeepCoro attained an even lower mean absolute error of 7.75% (95% CI: 7.37–8.07), underscoring the reduced variability inherent to this method. This study establishes DeepCoro as an innovative video-based, adaptable tool in coronary artery disease analysis, significantly enhancing the precision and reliability of stenosis assessment.
Association of physical activity pattern and risk of Parkinson’s disease
Authors: Lin Fabin , Lin Yixiang , Chen Lina , Huang Tingting , Lin Tianxin , He Jiarui , Lu Xiaoyang , Chen Xiaochun , Wang Yingqing , Ye Qinyong , Cai Guoen 
Published: May 2024

AbstractIncreasing evidence suggests an association between exercise duration and Parkinson’s disease. However, no high-quality prospective evidence exists confirming whether differences exist between the two modes of exercise, weekend warrior and equal distribution of exercise duration, and Parkinson’s risk. Hence, this study aimed to explore the association between different exercise patterns and Parkinson’s risk using exercise data from the UK Biobank. The study analyzed data from 89,400 UK Biobank participants without Parkinson’s disease. Exercise data were collected using the Axivity AX3 wrist-worn triaxial accelerometer. Participants were categorized into three groups: inactive, regularly active, and engaged in the weekend warrior (WW) pattern. The relationship between these exercise patterns and Parkinson’s risk was assessed using a multifactorial Cox model. During a mean follow-up of 12.32 years, 329 individuals developed Parkinson’s disease. In a multifactorial Cox model, using the World Health Organization–recommended threshold of 150 min of moderate-to-vigorous physical activity per week, both the active WW group [hazard ratio (HR) = 0.58; 95% confidence interval (CI) = 0.43–0.78; P < 0.001] and the active regular group (HR = 0.44; 95% CI = 0.34–0.57; P < 0.001) exhibited a lower risk of developing Parkinson’s disease compared with the inactive group. Further, no statistically significant difference was observed between the active WW and the active regular groups (HR = 0.77; 95% CI = 0.56–1.05; P = 0.099). In conclusion, in this cohort study, both the WW exercise pattern and an equal distribution of exercise hours were equally effective in reducing Parkinson’s risk.
The gluconeogenesis enzyme PCK2 has a non-enzymatic role in proteostasis in endothelial cells
Authors: de Zeeuw Pauline , Treps Lucas , García-Caballero Melissa , Harjes Ulrike , Kalucka Joanna , De Legher Carla , Brepoels Katleen , Peeters Kristel , Vinckier Stefan , Souffreau Joris , Bouché Ann , Taverna Federico , Dehairs Jonas , Talebi Ali , Ghesquière Bart , Swinnen Johan , Schoonjans Luc , Eelen Guy , Dewerchin Mieke , Carmeliet Peter 
Published: May 2024

AbstractEndothelial cells (ECs) are highly glycolytic, but whether they generate glycolytic intermediates via gluconeogenesis (GNG) in glucose-deprived conditions remains unknown. Here, we report that glucose-deprived ECs upregulate the GNG enzyme PCK2 and rely on a PCK2-dependent truncated GNG, whereby lactate and glutamine are used for the synthesis of lower glycolytic intermediates that enter the serine and glycerophospholipid biosynthesis pathways, which can play key roles in redox homeostasis and phospholipid synthesis, respectively. Unexpectedly, however, even in normal glucose conditions, and independent of its enzymatic activity, PCK2 silencing perturbs proteostasis, beyond its traditional GNG role. Indeed, PCK2-silenced ECs have an impaired unfolded protein response, leading to accumulation of misfolded proteins, which due to defective proteasomes and impaired autophagy, results in the accumulation of protein aggregates in lysosomes and EC demise. Ultimately, loss of PCK2 in ECs impaired vessel sprouting. This study identifies a role for PCK2 in proteostasis beyond GNG.