bluuebunny/crossref_metadata_2025_split huggingface dataset:
A nomogram prognostic model for early hepatocellular carcinoma with diabetes mellitus after primary liver resection based on the admission characteristics
Authors: Zhang Menghan , Wang Qi , Zhang Gongming , Li Guangming , Jin Ronghua , Xing Huichun 
Published: February 2024

Background: Patients diagnosed with early-stage hepatocellular carcinoma (HCC) and diabetes mellitus (DM) are at a higher risk of experiencing complications and facing increased mortality rates. Hence, it is crucial to develop personalized clinical strategies for this particular subgroup upon their admission. The objective of this study is to determine the key prognostic factors in early HCC patients who received liver resection combined with DM and develop a practical personalized model for precise prediction of overall survival in these individuals.Method: A total of 1496 patients diagnosed hepatitis B virus (HBV) - related liver cancer from Beijing You’an Hospital were retrospectively enrolled, spanning from 1 January 2014, to 31 December 2019, and ultimately, 622 eligible patients of hepatocellular carcinoma (HCC) patients with diabetes were included in this present investigation. A multivariate COX regression analysis was conducted to identify prognostic factors that are independent of each other and develop a nomogram. The performance of the nomogram was evaluated using various statistical measures such as the C-index, receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) in both the training and validation groups. Survival rates were estimated using the Kaplan-Meier method.Results: The study included a total of 622 early HCC patients who underwent liver resection combined with DM. Random Forrest model and Multivariate Cox regression analysis revealed that drinking, tumor number, monocyte-to-lymphocyte ratio, white blood cell count and international normalized ratio at admission were identified as independent prognostic factors for early HCC patients who underwent liver resection combined with DM. The nomogram demonstrated good predictive performance in the training and validation cohorts based on the C-index values of 0 .756 and 0 .739 respectively, as well as the area under the curve values for 3-, 5-, and 8-year overall survival (0.797, 0.807, 0.840, and 0.725, 0.791, 0.855). Calibration curves and decision curve analysis indicated high accuracy and net clinical benefit rates. Furthermore, the nomogram successfully stratified enrolled patients into low-risk and high-risk groups based on their risk of overall survival. The difference in overall survival between these two groups was statistically significant in both the training and validation cohorts (p &lt; 0.0001 and p = 0.0064).Conclusion: Our results indicate that the admission characteristics demonstrate a highly effective ability to predict the overall survival of early HCC patients who have undergone liver resection in combination with DM. The developed model has the potential to support healthcare professionals in making more informed initial clinical judgments for this particular subgroup of patients.
Establishing the function relationship between time spectrum and proton range in proton therapy through Monte Carlo simulation
Authors: He Yibo , Tong Xin , Li Yuhan , Cheng Jingyi , Zhou Rong 
Published: February 2024

To validate range shifts in proton therapy, we investigated the potential of using the temporal information of prompt gamma rays as an indicator. We simulated the proton transport process using Monte Carlo simulations and used a geometric scorer to obtain the location and timing of prompt gamma ray production. By using a homogeneous target material in the simulation model, we established a fitted relationship between the range of 90–210 MeV protons and the corresponding temporal spectral width. Additionally, by introducing air cavities of 2–20 mm in simulations of inhomogeneous target materials, we observed significant correlations between the range offsets and the temporal spectral widths. These correlations were fitted to derive a functional relationship between the two variables.
Assessment of intracranial aneurysm rupture risk using a point cloud-based deep learning model
Authors: Cao Heshan , Zeng Hui , Lv Lei , Wang Qi , Ouyang Hua , Gui Long , Hua Ping , Yang Songran 
Published: February 2024

Background and Purpose: Precisely assessing the likelihood of an intracranial aneurysm rupturing is critical for guiding clinical decision-making. The objective of this study is to construct and validate a deep learning framework utilizing point clouds to forecast the likelihood of aneurysm rupturing.Methods: The dataset included in this study consisted of a total of 623 aneurysms, with 211 of them classified as ruptured and 412 as unruptured, which were obtained from two separate projects within the AneuX morphology database. The HUG project, which included 124 ruptured aneurysms and 340 unruptured aneurysms, was used to train and internally validate the model. For external validation, another project named @neurIST was used, which included 87 ruptured and 72 unruptured aneurysms. A standardized method was employed to isolate aneurysms and a segment of their parent vessels from the original 3D vessel models. These models were then converted into a point cloud format using open3d package to facilitate training of the deep learning network. The PointNet++ architecture was utilized to process the models and generate risk scores through a softmax layer. Finally, two models, the dome and cut1 model, were established and then subjected to a comprehensive comparison of statistical indices with the LASSO regression model built by the dataset authors.Results: The cut1 model outperformed the dome model in the 5-fold cross-validation, with the mean AUC values of 0.85 and 0.81, respectively. Furthermore, the cut1 model beat the morphology-based LASSO regression model with an AUC of 0.82. However, as the original dataset authors stated, we observed potential generalizability concerns when applying trained models to datasets with different selection biases. Nevertheless, our method outperformed the LASSO regression model in terms of generalizability, with an AUC of 0.71 versus 0.67.Conclusion: The point cloud, as a 3D visualization technique for intracranial aneurysms, can effectively capture the spatial contour and morphological aspects of aneurysms. More structural features between the aneurysm and its parent vessels can be exposed by keeping a portion of the parent vessels, enhancing the model’s performance. The point cloud-based deep learning model exhibited good performance in predicting rupture risk while also facing challenges in generalizability.
An artificial intelligence-based bone age assessment model for Han and Tibetan children
Authors: Liu Qixing , Wang Huogen , Wangjiu Cidan , Awang Tudan , Yang Meijie , Qiongda Puqiong , Yang Xiao , Pan Hui , Wang Fengdan 
Published: February 2024

Background: Manual bone age assessment (BAA) is associated with longer interpretation time and higher cost and variability, thus posing challenges in areas with restricted medical facilities, such as the high-altitude Tibetan Plateau. The application of artificial intelligence (AI) for automating BAA could facilitate resolving this issue. This study aimed to develop an AI-based BAA model for Han and Tibetan children.Methods: A model named “EVG-BANet” was trained using three datasets, including the Radiology Society of North America (RSNA) dataset (training set n = 12611, validation set n = 1425, and test set n = 200), the Radiological Hand Pose Estimation (RHPE) dataset (training set n = 5491, validation set n = 713, and test set n = 79), and a self-established local dataset [training set n = 825 and test set n = 351 (Han n = 216 and Tibetan n = 135)]. An open-access state-of-the-art model BoNet was used for comparison. The accuracy and generalizability of the two models were evaluated using the abovementioned three test sets and an external test set (n = 256, all were Tibetan). Mean absolute difference (MAD) and accuracy within 1 year were used as indicators. Bias was evaluated by comparing the MAD between the demographic groups.Results: EVG-BANet outperformed BoNet in the MAD on the RHPE test set (0.52 vs. 0.63 years, p &lt; 0.001), the local test set (0.47 vs. 0.62 years, p &lt; 0.001), and the external test set (0.53 vs. 0.66 years, p &lt; 0.001) and exhibited a comparable MAD on the RSNA test set (0.34 vs. 0.35 years, p = 0.934). EVG-BANet achieved accuracy within 1 year of 97.7% on the local test set (BoNet 90%, p &lt; 0.001) and 89.5% on the external test set (BoNet 85.5%, p = 0.066). EVG-BANet showed no bias in the local test set but exhibited a bias related to chronological age in the external test set.Conclusion: EVG-BANet can accurately predict the bone age (BA) for both Han children and Tibetan children living in the Tibetan Plateau with limited healthcare facilities.
Language access differentially alters functional connectivity during emotion perception across cultures
Authors: Leshin Joseph , Carter Maleah J. , Doyle Cameron M. , Lindquist Kristen A. 
Published: February 2024

IntroductionIt is often assumed that the ability to recognize the emotions of others is reflexive and automatic, driven only by observable facial muscle configurations. However, research suggests that accumulated emotion concept knowledge shapes the way people perceive the emotional meaning of others’ facial muscle movements. Cultural upbringing can shape an individual’s concept knowledge, such as expectations about which facial muscle configurations convey anger, disgust, or sadness. Additionally, growing evidence suggests that access to emotion category words, such as “anger,” facilitates access to such emotion concept knowledge and in turn facilitates emotion perception.MethodsTo investigate the impact of cultural influence and emotion concept accessibility on emotion perception, participants from two cultural groups (Chinese and White Americans) completed a functional magnetic resonance imaging scanning session to assess functional connectivity between brain regions during emotion perception. Across four blocks, participants were primed with either English emotion category words (“anger,” “disgust”) or control text (XXXXXX) before viewing images of White American actors posing facial muscle configurations that are stereotypical of anger and disgust in the United States.ResultsWe found that when primed with “disgust” versus control text prior to seeing disgusted facial expressions, Chinese participants showed a significant decrease in functional connectivity between a region associated with semantic retrieval (the inferior frontal gyrus) and regions associated with semantic processing, visual perception, and social cognition. Priming the word “anger” did not impact functional connectivity for Chinese participants relative to control text, and priming neither “disgust” nor “anger” impacted functional connectivity for White American participants.DiscussionThese findings provide preliminary evidence that emotion concept accessibility differentially impacts perception based on participants’ cultural background.
Explainable fuzzy clustering framework reveals divergent default mode network connectivity dynamics in schizophrenia
Authors: Ellis Charles A. , Miller Robyn L. , Calhoun Vince D. 
Published: February 2024

IntroductionDynamic functional network connectivity (dFNC) analysis of resting state functional magnetic resonance imaging data has yielded insights into many neurological and neuropsychiatric disorders. A common dFNC analysis approach uses hard clustering methods like k-means clustering to assign samples to states that summarize network dynamics. However, hard clustering methods obscure network dynamics by assuming (1) that all samples within a cluster are equally like their assigned centroids and (2) that samples closer to one another in the data space than to their centroids are well-represented by their centroids. In addition, it can be hard to compare subjects, as in some cases an individual may not manifest a state strongly enough to enter a hard cluster. Approaches that allow a dimensional approach to connectivity patterns (e.g., fuzzy clustering) can mitigate these issues. In this study, we present an explainable fuzzy clustering framework by combining fuzzy c-means clustering with several explainability metrics and novel summary features.MethodsWe apply our framework for schizophrenia (SZ) default mode network analysis. Namely, we extract dFNC from individuals with SZ and controls, identify 5 dFNC states, and characterize the dFNC features most crucial to those states with a new perturbation-based clustering explainability approach. We then extract several features typically used in hard clustering and further present a variety of unique features specially designed for use with fuzzy clustering to quantify state dynamics. We examine differences in those features between individuals with SZ and controls and further search for relationships between those features and SZ symptom severity.ResultsImportantly, we find that individuals with SZ spend more time in states of moderate anticorrelation between the anterior and posterior cingulate cortices and strong anticorrelation between the precuneus and anterior cingulate cortex. We further find that individuals with SZ tend to transition more rapidly than controls between low-magnitude and high-magnitude dFNC states.ConclusionWe present a novel dFNC analysis framework and use it to identify effects of SZ upon network dynamics. Given the ease of implementing our framework and its enhanced insight into network dynamics, it has great potential for use in future dFNC studies.
Prevalence and influencing factors of post-traumatic stress disorder among Chinese healthcare workers during the COVID-19 epidemic: a systematic review and meta-analysis
Authors: Zhang Min , Bo Mingyu , Wang Huixin , Fan Wenyi , Kong Lingling , Zhou Chunjie , Zhang Zhenxing 
Published: February 2024

BackgroundPost-traumatic stress disorder is an important psychological problem affecting the physical mental health of Chinese healthcare workers during the COVID-19 pandemic.AimsTo estimate the prevalence and influencing factors of post-traumatic stress disorder (PTSD) among Chinese healthcare workers during COVID-19.MethodsSearch of Chinese and English literature in PubMed, EMbase, Web of Science, Medline, Elsevier, SpringerLink, China Biomedical Literature Database, CNKI, Wan-fang, and CQVIP for the period from December 2019 to August 2023. Stata 14.0 software was used for data analysis. The methodological quality of each study was scored, and data were extracted from the published reports. Pooled prevalence was estimated using the Random-effects model. Publication bias was evaluated using Egger’s test and Begg’s test.ResultsTwenty-one studies included 11841 Chinese healthcare workers in this review. First, the overall prevalence of Post-traumatic stress disorder among Chinese healthcare workers during the COVID-19 epidemic was 29.2% (95% CI: 20.7% to 33.7%). Twelve factors included in the meta-analysis were found to be protective against PTSD among Chinese healthcare workers: female, nurse, married, front-line work, less work experience, family or friend diagnosed with COVID-19, history of chronic disease and fear of COVID-19. Conversely, outside Hubei, higher education, social support and psychological resilience are protective factors.ConclusionThese recent findings increase our understanding of the psychological status of Chinese healthcare workers and encourage that long-term monitoring and long-term interventions should be implemented to improve the mental health of Chinese healthcare workers in the aftermath of the COVID-19.
Using implementation mapping to refine strategies to improve implementation of an evidence-based mobile market intervention: a study protocol
Authors: Kasprzak Christina M. , Canizares Andy , Lally Anne , Tirabassi Jill N. , Vermont Leah N. , Lev Samuel , Ammerman Alice S. , Leone Lucia A. 
Published: February 2024

ObjectivesThe Veggie Van model is a mobile market model that is efficacious in increasing fruit and vegetable consumption for lower-income participants. The model is currently being evaluated for its effectiveness in a multi-state trial. Preliminary implementation data, collected through process measures surveys and implementation interviews, indicate that there are several barriers to implementation among partner organizations and implementation fidelity to the Veggie Van model was low. Consideration and planning for implementation ought to occur early and often throughout the research process order to ensure Veggie Van model effectiveness. This paper describes the step-by-step process for creating strategies to enhance implementation of Veggie Van model components.MethodsImplementation mapping is a systematic process to develop implementation strategies through engagement with key stakeholders. We conducted a series of interviews (n = 31 representatives) with partner organizations (n = 8) to identify facilitators and barriers to Veggie Van model implementation. We then applied interview findings to an Implementation Mapping process to develop theory and practice-driven strategies to be integrated into existing implementation tools and technical assistance.ResultsWe identified implementation outcomes (e.g., staff implement the Veggie Van model component of nutrition education with fidelity) and performance objectives (e.g., offer nutrition education, in the form of food lessons and/or food demonstrations, at least bi-weekly) to achieve them. We conducted a secondary qualitative analysis of the findings from implementation interviews with partner organizations to identify behavioral determinants (e.g., attitudinal beliefs, social support) which were combined with the performance objectives to generate change objectives (e.g., view the Veggie Van model as advantageous to an organization and communities served). To achieve the change objectives, we developed implementation strategies that would be integrated into existing Veggie Van training resources including an online toolkit, webinars and trainings, an annual mobile market conference, and technical assistance.ConclusionThe development of theory and practice-driven implementation strategies will enable us to improve our implementation tools, thereby improving fidelity to the Veggie Van model among organizations and increasing the likelihood of its effectiveness. Detailing the design of a multifaceted implementation strategy using Implementation Mapping also provides a model to design similar strategies for other community-based interventions.
Linking electromagnetic induction data to soil properties at field scale aided by neural network clustering
Authors: O’Leary Dave , Brogi Cosimo , Brown Colin , Tuohy Pat , Daly Eve 
Published: February 2024

IntroductionThe mapping of soil properties, such as soil texture, at the field scale is important Q6 in the context of national agricultural planning/policy and precision agriculture. Electromagnetic Induction (EMI) surveys are commonly used to measure soil apparent electrical conductivity and can provide valuable insights into such subsurface properties. MethodsMulti-receiver or multi-frequency instruments provide a vertical distribution of apparent conductivity beneath the instrument, while the mobility of such instruments allows for spatial coverage. Clustering is the grouping together of similar multi-dimensional data, such as the processed EMI data over a field. A neural network clustering process, where the number of clusters can be objectively determined, results in a set of one-dimensional apparent electrical conductivity cluster centers, which are representative of the entire three-dimensional dataset. These cluster centers are used to guide inversions of apparent conductivity data to give an estimate of the true electrical conductivity distribution at a site.Results and discussionThe method is applied to two sites and the results demonstrate a correlation between (true) electrical conductivity with soil texture (sampled prior to the EMI surveys) which is superior to correlations where no clustering is included. The method has the potential to be developed further, with the aim of improving the prediction of soil properties at cluster scale, such as texture, from EMI data. A particularly important conclusion from this initial study is that EMI data should be acquired prior to a focused soil sampling campaign to calibrate the electrical conductivity – soil property correlations.
Joint Exposure to Multiple Air Pollutants, Genetic Susceptibility, and Incident Dementia: A Prospective Analysis in the UK Biobank Cohort
Authors: Zhang Shuo , Cao Hongyan , Chen Keying , Gao Tongyu , Zhao Huashuo , Zheng Chu , Wang Ting , Zeng Ping , Wang Ke 
Published: February 2024

Objectives: This study aimed to evaluate the joint effects of multiple air pollutants including PM2.5, PM10, NO2, and NOx with dementia and examined the modifying effects of genetic susceptibility.Methods: This study included 220,963 UK Biobank participants without dementia at baseline. Weighted air pollution score reflecting the joint exposure to multiple air pollutants were constructed by cross-validation analyses, and inverse-variance weighted meta-analyses were performed to create a pooled effect. The modifying effect of genetic susceptibility on air pollution score was assessed by genetic risk score and APOE ε4 genotype.Results: The HR (95% CI) of dementia for per interquartile range increase of air pollution score was 1.13 (1.07∼1.18). Compared with the lowest quartile (Q1) of air pollution score, the HR (95% CI) of Q4 was 1.26 (1.13∼1.40) (Ptrend = 2.17 × 10−5). Participants with high air pollution score and high genetic susceptibility had higher risk of dementia compared to those with low air pollution score and low genetic susceptibility.Conclusion: Our study provides evidence that joint exposure to multiple air pollutants substantially increases the risk of dementia, especially among individuals with high genetic susceptibility.
Xenopus Sox11 Partner Proteins and Functional Domains in Neurogenesis
Authors: Singleton Kaela S. , Silva-Rodriguez Pablo , Cunningham Doreen D. , Silva Elena M. 
Published: February 2024

Sox11, a member of the SoxC family of transcription factors, has distinct functions at different times in neural development. Studies in mouse, frog, chick, and zebrafish show that Sox11 promotes neural fate, neural differentiation, and neuron maturation in the central nervous system. These diverse roles are controlled in part by spatial and temporal-specific protein interactions. However, the partner proteins and Sox11-interaction domains underlying these diverse functions are not well defined. Here, we identify partner proteins and the domains of Xenopus laevis Sox11 required for protein interaction and function during neurogenesis. Our data show that Sox11 co-localizes and interacts with Pou3f2 and Neurog2 in the anterior neural plate and in early neurons, respectively. We also demonstrate that Sox11 does not interact with Neurog1, a high-affinity partner of Sox11 in the mouse cortex, suggesting that Sox11 has species-specific partner proteins. Additionally, we determined that the N-terminus including the HMG domain of Sox11 is necessary for interaction with Pou3f2 and Neurog2, and we established a novel role for the N-terminal 46 amino acids in the specification of placodal progenitors. This is the first identification of partner proteins for Sox11 and of domains required for partner-protein interactions and distinct roles in neurogenesis.