bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effects of four-week mindfulness training on job pressure, burnout, and coping strategies in sports coaches: A randomised controlled trial
Authors: Tian Qianqian , Cheng Liang 
Published: February 2025

Background This study addresses the gap in evidence-based interventions for work stress, burnout, and coping strategies among sports coaches, which are critical for their psychological health and performance. Objective This study aimed to evaluate the novel application of a mindfulness training program to alleviate stress, burnout, and enhance coping strategies among sports coaches. Methods Sixty-one coaches were randomly assigned to an experimental (n = 30) and control group (n = 31). The experimental group completed a four-week, twice-weekly 90-min mindfulness training. Assessments included the five facets mindfulness questionnaire (FFMQ) and measures of job pressure, burnout, and coping strategies, with analyses of correlations and linear regressions between mindfulness and these factors. Results The FFMQ correlated negatively with various stressors (R ranging from −0.331 to −0.943, p < 0.001) and burnout indicators (R = −0.414 to −0.480, p < 0.001). Job pressure predicted mindfulness levels (β = −0.973), with a predictive model: Y = 206.275–0.973 * job pressure. The experimental group's FFMQ scores rose by 13.9% at week four (p = 0.003), with reductions in job pressure, external competition, and negative coping (7.4%, 20.3%, and 18.1% respectively, p < 0.05). They also outperformed the control group in FFMQ scores and stress measures at week four (p < 0.05). Conclusion A four-week mindfulness program significantly enhances mindfulness, reduces job stress, burnout, and negative coping among sports coaches, underscoring its potential as an evidence-based intervention for improving work performance and mental health.
Multi-path neural network based on mp-MRI for predicting muscle-invasive bladder cancer
Authors: Yu Jie , Cai Lingkai , Chen Chunxiao , Fu Xue , Xiao Yueyue , Wang Liang , Zeng Qinghua , Sun Xueying , Wang Gongcheng , Shao Qiang , Yang Xiao , Lu Qiang 
Published: February 2025

Purpose To assist doctors in clinical diagnosis, we propose a multipath deep learning (MP-DL) model to distinguish between muscle-invasive bladder cancer (MIBC) and non-muscle-invasive bladder cancer (NMIBC) using multiparametric magnetic resonance imaging (mp-MRI) and synthesized samples. Methods The proposed MP-DL model integrates T2-weighted image (T2WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced imaging (DCE) branches, combining high-level features from these sequences. Multichannel inputs, including the original image, segmented bladder, and region of interest from the T2WI branch, enhance the focus on the tumor region. InceptionV3 served as the model backbone for feature extraction with a multitask framework in the DWI and DCE branches. Synthesized samples were generated to supplement missing DWI and DCE sequences, enlarging the sample size and boosting model performance. Evaluation through five-fold cross-validation and internal-external testing demonstrated the model's robustness and generalization capabilities. Results The dataset consisted of 401 cases (287 NMIBC and 114 MIBC cases), which included a training set of 313 cases (containing partly synthesized DWI and DCE samples), validation of 26 cases, internal testing of 34 cases, and external testing of 28 cases. In the internal testing, the model achieved area under curve, accuracy, sensitivity, specificity, and F1 scores of 0.914, 0.835, 0.880, 0.817, and 0.761, respectively. In the external testing, the results were 0.821, 0.814, 0.730, 0.845, and 0.619, respectively. The model performance surpasses that of medical professionals in internal testing while outperforming urologists and approaching senior radiologists in external testing. Conclusions The MP-DL model, incorporating synthesized samples, shows promise in preoperative MIBC prediction, potentially aiding primary urologists and radiologists.
Comparative Analysis for Classification of Rice Genotype Using Statistical and Artificial Neural Network Models on the Basis of Maturity
Authors: Gupta Shavi , Sharma Manish , Jasrotia Nishant , Mahajan Sunali 
Published: February 2025

Classification is that arena of science which deals with grouping of objects on the basis of information available about objects. It plays a significant role for planning purposes in agriculture system. This study aims at comparing classification techniques through statistical as well as artificial neural network models for the primary data related to 140 rice genotypes from the trial laid in SKUAST, Jammu on the basis of maturity. And, the characters like yield per plant, number of days for 50% flowering, number of days for full flowering, plant height, number of effective tillers per plant, panicle length, grain length, grain width and ratio of grain length & grain width acts as supporting variables for classification. The statistical model used for classification technique was ordinal logistic regression model whereas in case of artificial neural network, multilayered perceptron neural network was used. The class variable number of days for 50% flowering was categorized into three classes and was considered as dependent variable and all other characters as independent variables. The ability measures of classification such as Accuracy Rate, Kappa Statistics, Average precision and Average Recall were used for testing samples. It is observed that Multilayered Perceptron Neural Network performed better than Ordinal Logistic Regression for classifying rice genotypes for different classes of number of days for 50% flowering on the basis of classification ability measures. Number of days for full flowering was found to be important attributing character for classification on the basis of maturity.
A novel photocleavable amino-modified graphene for covalent purification of N-glycans from hepatocellular carcinoma patients’ serum for potential biomarkers discovery
Authors: Chao Xuyuan , Yang Shengjie , Zhang Baoying , Zang Xin , Zhang Jingyi , Liu Xizi , Chen Lu , Qi Lu , Xue Xiaofeng , Hu Han , Wang Xinghe 
Published: February 2025

Abstract
Glycosylation alterations have emerged as significant indicators in the landscape of cancer diagnosis, with aberrant N-glycans standing out as potential biomarkers. The existing methods for purifying N-glycans mainly rely on their hydrophilicity, affinity, and size. These methods exhibit relatively weak binding forces and low specificity based on non-covalent interactions. Moreover, the current liquid chromatography-based N-glycan purification and detection techniques must be bettered for clinical settings' rapid diagnostic requirements. Here, we have developed a method involving the covalent binding of graphene modified with photocleavable amino groups to N-glycans in hepatocellular carcinoma (HCC) patients' serum glycoproteins. The N-glycans are identified using MALDI-TOF/TOF MS, catering to the requirements of biological research and the diagnosis of liver cancer disease. Specifically, amino-functionalized graphene is linked with photocleavable groups bearing hydrazide functional group, then undergoes acyl hydrazone covalent reaction with aldehyde groups on the side chains of oxidized monosaccharides to capture the N-glycans. Finally, under UV irradiation at a wavelength of 356 nm for 15 s, the ortho-nitrobenzyl photocleavable groups undergo cleavage, completing the release of captured N-glycans, which are then subjected to MALDI-TOF/TOF MS mass spectrometry for detection. Our findings demonstrate significant differences in the N-glycan profiles between serum samples of liver cancer patients and healthy individuals. These differences are characterized by increased abundance and molecular weight of N-glycans. Through biomarker screening, one N-glycan with an m/z ratio of 1847.01 shows the potential to serve as a diagnostic biomarker for liver cancer (P < 0.05). Our research underscores the potential of photocleavable amino-modified graphene for detecting aberrant N-glycans in HCC patients' serum to elucidate the intricate between aberrant N-glycan alterations and HCC. This research paves the way for developing novel biomarkers, diagnostic strategies, and therapeutic interventions to combat this complex disease.

Graphical Abstract
Report on the complete organelle genomes of Orobanche Filicicola Nakai ex Hyun, Y. S. Lim & H. C. Shin (Orobanchaceae): insights from comparison with Orobanchaceae plant genomes
Authors: Kim Sang-Chul , Kang Eun Su , Kim Tae-Hee , Choi Ye-Rim , Kim Hyuk-Jin 
Published: February 2025

Abstract

Background

Orobanche is a parasitic plant distributed in the temperate zone of Northern Hemisphere, with approximately 200 species found worldwide. In the Republic of Korea, two species of Orobanche, namely O. coerulescens Stephan ex Willd. and O. filicicola Nakai ex Hyun, Y. S. Lim & H. C. Shin, are present, with O. filicicola being endemic. Genome analysis of this species has not yet been performed, and characterizing its complete organelle genome will provide valuable insights into the phylogeny and genome evolution of parasitic plants.

Results
The chloroplast and mitochondrial genomes were analyzed, revealing distinct characteristics. The chloroplast genome is 91,529 bp long with a GC content of 33.6%, containing 33 protein-coding, 30 tRNA, and 4 rRNA genes. In contrast, the mitochondrial genome is 1,058,991 bp long with a GC content of 45.5%, featuring 31 protein-coding, 16 tRNA, and 3 rRNA genes. The mitochondrial genome has over three times more simple sequence repeats and longer long repeats than the chloroplast genome. Analysis of synonymous codon usage in protein-coding genes from nine Orobanchaceae species revealed significant differences between chloroplasts and mitochondria, with codons ending in A or T exhibiting higher coding rates. Ka/Ks ratio calculations indicated that psbI and atpB had the smallest and largest ratios in chloroplasts, respectively, while ccmFC was identified as the only gene under positive selection in mitochondria genomes. Sequence alignment identified 30 homologous fragments between the two genomes, totaling 7,247 bp. Comparison of O. filicicola’s chloroplast genome with related species showed gene loss and conserved inverted repeat sequences. Numerous homologous collinear blocks were found in mitochondrial genomes of related species, but some regions lacked homology. Phylogenetic analysis indicated identical topologies for chloroplasts and mitochondria, with Orobanchaceae forming a strong monophyletic group.

Conclusions
Characterizing the complete organelle genome of O. filicicola enabled a comprehensive analysis of the Orobanchaceae organelle genome, providing important baseline data for its structure and evolution.
Realizing the promise of machine learning in precision oncology: expert perspectives on opportunities and challenges
Authors: Nittas Vasileios , Ormond Kelly E. , Vayena Effy , Blasimme Alessandro 
Published: February 2025

Abstract

Background
The ability of machine learning (ML) to process and learn from large quantities of heterogeneous patient data is gaining attention in the precision oncology community. Some remarkable developments have taken place in the domain of image classification tasks in areas such as digital pathology and diagnostic radiology. The application of ML approaches to the analysis of DNA data, including tumor-derived genomic profiles, microRNAs, and cancer epigenetic signatures, while relatively more recent, has demonstrated some utility in identifying driver variants and molecular signatures with possible prognostic and therapeutic applications.

Methods
We conducted semi-structured interviews with academic and clinical experts to capture the status quo, challenges, opportunities, ethical implications, and future directions.

Results
Our participants agreed that machine learning in precision oncology is in infant stages, with clinical integration still rare. Overall, participants equated ongoing developments with better clinical workflows and improved treatment decisions for more cancer patients. They underscored the ability of machine learning to tackle the dynamic nature of cancer, break down the complexity of molecular data, and support decision-making. Our participants emphasized obstacles related to molecular data access, clinical utility, and guidelines. The availability of reliable and well-curated data to train and validate machine learning algorithms and integrate multiple data sources were described as constraints yet necessary for future clinical implementation. Frequently mentioned ethical challenges included privacy risks, equity, explainability, trust, and incidental findings, with privacy being the most polarizing. While participants recognized the issue of hype surrounding machine learning in precision oncology, they agreed that, in an assistive role, it represents the future of precision oncology.

Conclusions
Given the unique nature of medical AI, our findings highlight the field’s potential and remaining challenges. ML will continue to advance cancer research and provide opportunities for patient-centric, personalized, and efficient precision oncology. Yet, the field must move beyond hype and toward concrete efforts to overcome key obstacles, such as ensuring access to molecular data, establishing clinical utility, developing guidelines and regulations, and meaningfully addressing ethical challenges.
Environmental and health effects of the Barcelona superblocks
Authors: Pérez Katherine , Palència Laia , López Maria José , León-Gómez Brenda Biaani , Puig-Ribera Anna , Gómez-Gutiérrez Anna , Nieuwenhuijsen Mark , Carrasco-Turigas Glòria , Borrell Carme 
Published: February 2025

Abstract

Background
The superblocks model of Barcelona (Spain) seeks to reorganize the city based on reversing the distribution of public space between vehicles and people by prioritizing citizens, thus improving their environmental conditions and quality of life. The objective of this paper was to describe the effects on environmental, health and quality of life of the first three superblocks implemented, discuss the lessons learned, and provide recommendations for the future.

Methods
The evaluation included different approaches depending on the superblock analyzed: A pre-post-intervention health survey, environmental measures of air quality, the Microscale Audit of Urban Landscapes for Pedestrians (MAPS), an observational study on target areas of physical activity (SOPARC), ethnographic guerrilla studies, and focus groups.

Results
Residents and workers in all three of the evaluated superblocks reported a perceived improvement in well-being, tranquillity and quality of sleep, some reduction in noise and pollution and an increase in social interaction. The built environment of the superblocks clearly influenced walkability, and the lower car traffic volume improved air quality measures. In one of the superblocks, the same intervention calmed traffic in one of the areas but not in another.

Conclusions
Although on a small scale, this evaluation of the environmental, and health effects of the superblocks provides support for expanding the model to other areas of the city.
Providers’ insight into quality mental health services – Context-Mechanism-Outcome (CMO) approach
Authors: Badu Eric , O’Brien Anthony Paul , Mitchell Rebecca , Osei Akwasi 
Published: February 2025

Abstract

Background
Evaluation frameworks are relevant to understanding health service providers’ views regarding existing services and possible improvements, but their application to mental health services is limited, particularly in Low Middle-Income Countries.

Aim/question
To identify a program theory that explains the contextual factors and mechanisms that could enhance mental health service outcomes in Ghana.

Method
A three-phase approach was followed: initial theory and assumption, analysis, and CMO configuration. Systematic reviews were used to develop a middle-range theory and assumptions in phase 1. A purposive sample of 30 mental health professionals was recruited to participate in in-depth interviews in phase 2. Thematic analysis was used to analyze the qualitative data and further configure the CMO in phase 3.

Results
The analysis identified five CMO configurations: ripple effects and financing source sustainability; unavailability of modern equipment and logistics to support holistic services; promoting inclusivity and geographical proximity of services; information, sensitization, and awareness encourage mental health quality; and monitoring and evaluation improve mental health service quality.

Conclusion
This study concludes that government stakeholders should integrate mental health services into the ongoing insurance policy and provide adequate modern equipment and logistics. Moreover, mechanisms and priorities given to vulnerable consumers should be integrated into policies.
Assessing germline TP53 mutations in cancer patients: insights into Li-Fraumeni syndrome and genetic testing guidelines
Authors: Danishevich Anastasiia , Fedorova Daria , Bodunova Natalia , Makarova Maria , Byakhova Maria , Semenova Anna , Galkin Vsevolod , Litvinova Maria , Nikolaev Sergey , Efimova Irina , Osinin Pavel , Lisitsa Tatyana , Khakhina Anastasiya , Shipulin German , Nasedkina Tatiana , Shumilova Syuykum , Gusev Oleg , Bilyalov Airat , Shagimardanova Elena , Shigapova Leyla , Nemtsova Marina , Sagaydak Olesya , Woroncow Mary , Gadzhieva Saida , Khatkov Igor 
Published: February 2025

Abstract

Background
Germline TP53 gene variants are intricately linked to Li-Fraumeni syndrome, a rare and aggressive hereditary cancer syndrome. This study investigated the frequency and spectrum of TP53 pathogenic variants associated with Li-Fraumeni syndrome in a large cohort of mainly breast cancer patients from Russia.

Methods
The study analyzed 3,455 genomic DNA samples from cancer patients using next-generation sequencing panels and whole-genome sequencing. Clinically significant TP53 variants were identified and validated using Sanger sequencing. The clinical and family history characteristics of patients with TP53 variants were analyzed.

Results
The analysis identified 13 (0.4%) individuals with clinically significant germline TP53 variants, all of whom were females with either unilateral breast cancer or breast cancer as part of multiple primary malignant neoplasms. The average age of breast cancer manifestation was 39.9 years, with a median of 36 years. Only 38.5% of the TP53 mutation carriers met the modified Chompret criteria for TP53 testing.

Conclusions
The findings underscore the necessity of thorough phenotype and family history analysis in genetic counseling to effectively diagnose LFS, and emphasize the importance of identifying TP53 variant carriers for developing treatment strategies, prognosis, and monitoring, as well as for identifying high-risk family members. The study also highlights that the current guidelines fail to identify over half of the TP53 mutation carriers, suggesting the need for a more comprehensive approach to genetic testing in suspected hereditary cancer cases.
Super-resolution synthetic MRI using deep learning reconstruction for accurate diagnosis of knee osteoarthritis
Authors: Wang Kejun , Liu Weiyin Vivian , Yang Renjie , Li Liang , Lu Xuefang , Lei Haoran , Jiang Jiawei , Zha Yunfei 
Published: February 2025

Abstract

Objective
To assess the accuracy of deep learning reconstruction (DLR) technique on synthetic MRI (SyMRI) including T2 measurements and diagnostic performance of DLR synthetic MRI (SyMRIDL) in patients with knee osteoarthritis (KOA) using conventional MRI as standard reference.

Materials and methods
This prospective study recruited 36 volunteers and 70 patients with suspected KOA from May to October 2023. DLR and non-DLR synthetic T2 measurements (T2-SyMRIDL, T2-SyMRI) for phantom and in vivo knee cartilage were compared with multi-echo fast-spin-echo (MESE) sequence acquired standard T2 values (T2MESE). The inter-reader agreement on qualitative evaluation of SyMRIDL and the positive percent agreement (PPA) and negative percentage agreement (NPA) were analyzed using routine images as standard diagnosis.

Results
DLR significantly narrowed the quantitative differences between T2-SyMRIDL and T2MESE for 0.8 ms with 95% LOA [−5.5, 7.1]. The subjective assessment between DLR synthetic MR images and conventional MRI was comparable (all p > 0.05); Inter-reader agreement for SyMRIDL and conventional MRI was substantial to almost perfect with values between 0.62 and 0.88. SyMRIDL MOAKS had substantial inter-reader agreement and high PPA/NPA values (95%/99%) using conventional MRI as standard reference. Moreover, T2-SyMRIDL measurements instead of non-DLR ones significantly differentiated normal-appearing from injury-visible cartilages.

Conclusion
DLR synthetic knee MRI provided both weighted images for clinical diagnosis and accurate T2 measurements for more confidently identifying early cartilage degeneration from normal-appearing cartilages.

Critical relevance statement
One-acquisition synthetic MRI based on deep learning reconstruction provided an accurate quantitative T2 map and morphologic images in relatively short scan time for more confidently identifying early cartilage degeneration from normal-appearing cartilages compared to the conventional morphologic knee sequences.

Key Points

Deep learning reconstruction (DLR) synthetic knee cartilage T2 values showed no difference from conventional ones.

DLR synthetic T1-, proton density-, STIR-weighted images had high positive percent agreement and negative percentage agreement using MRI OA Knee Score features.

DLR synthetic T2 measurements could identify early cartilage degeneration from normal-appearing ones.

Graphical Abstract