bluuebunny/crossref_metadata_2025_split huggingface dataset:
Interpretability-based machine learning for predicting the risk of death from pulmonary inflammation in Chinese intensive care unit patients
Authors: Zhai Yihai , Lan Danxiu , Lv Siying , Mo Liqin 
Published: June 2024

ObjectiveThe objective of this research was to create a machine learning predictive model that could be easily interpreted in order to precisely determine the risk of premature death in patients receiving intensive care after pulmonary inflammation.MethodsIn this study, information from the China intensive care units (ICU) Open Source database was used to examine data from 2790 patients who had infections between January 2019 and December 2020. A 7:3 ratio was used to randomly assign the whole patient population to training and validation groups. This study used six machine learning techniques: logistic regression, random forest, gradient boosting tree, extreme gradient boosting tree (XGBoost), multilayer perceptron, and K-nearest neighbor. A cross-validation grid search method was used to search the parameters in each model. Eight metrics were used to assess the models’ performance: accuracy, precision, recall, F1 score, area under the curve (AUC) value, Brier score, Jordon’s index, and calibration slope. The machine methods were ranked based on how well they performed in each of these metrics. The best-performing models were selected for interpretation using both the Shapley Additive exPlanations (SHAP) and Local interpretable model-agnostic explanations (LIME) interpretable techniques.ResultsA subset of the study cohort’s patients (120/1668, or 7.19%) died in the hospital following screening for inclusion and exclusion criteria. Using a cross-validated grid search to evaluate the six machine learning techniques, XGBoost showed good discriminative ability, achieving an accuracy score of 0.889 (0.874–0.904), precision score of 0.871 (0.849–0.893), recall score of 0.913 (0.890–0.936), F1 score of 0.891 (0.876–0.906), and AUC of 0.956 (0.939–0.973). Additionally, XGBoost exhibited excellent performance with a Brier score of 0.050, Jordon index of 0.947, and calibration slope of 1.074. It was also possible to create an interactive internet page using the XGBoost model.ConclusionBy identifying patients at higher risk of early mortality, machine learning-based mortality risk prediction models have the potential to significantly improve patient care by directing clinical decision making and enabling early detection of survival and mortality issues in patients with pulmonary inflammation disease.
Research areas and trends in family-centered care in the 21st century: a bibliometric review
Authors: Hriberšek Mojca , Eibensteiner Fabian , Bukowski Nils , Yeung Andy Wai Kan , Atanasov Atanas G. , Schaden Eva 
Published: June 2024

IntroductionFamily-centered care (FCC) is a model of care provision that sees a patient’s loved ones as essential partners to the health care team and positively influences the psychological safety of patients and loved ones.ObjectivesThis review aims to present an overview of impactful publications, authors, institutions, journals, countries, fields of application and trends of FCC in the 21st century as well as suggestions on further research.MethodsThe Web of Science Database was searched for publications on FCC between January 2000 and Dezember 2023. After screening for duplicates, VOS Viewer and CiteSpace were used to analyze and visualize the data.ResultsScientific interest in FCC has grown and resulted in the scientific output of 4,836 publications originating from 103 different countries. Based on the frequent author keywords, FCC was of greatest interest in neonatology and pediatrics, nursing, critical and intensive care, end-of-life and palliative care, and patient-related outcomes. The recent research hotspots are “patient engagement,” “qualitative study,” and “health literacy.”ConclusionFCC has gained recognition and spread from the pediatric to the adult palliative, intensive, end-of-life and geriatric care settings. This is a very reassuring development since adults, especially when older, want and need the assistance of their social support systems. Recent research directions include the involvement of patients in the development of FCC strategies, health literacy interventions and the uptake of telemedicine solutions.
Dementia and metabolic syndrome: a bibliometric analysis
Authors: Peng Tao , Yang Yifan , Ma Jingying , Xu Peili , Xie Xinchun , Hu Nan , Yan Yongmei 
Published: June 2024

BackgroundDementia is a progressive neurodegenerative condition, while metabolic syndrome (MetS) is characterized by a combination of metabolic abnormalities such as hypertension, high blood sugar, and obesity. There exists a connection and overlap between the two conditions in certain aspects, and both are influenced to varying degrees by the process of aging. This study presents an overview of the current research landscape regarding dementia and MetS through bibliometric analysis.MethodsA systematic search was conducted to retrieve relevant literature on dementia and MetS published between 1 January 2000, and 30 November 2023, from the Web of Science Core Collection database. Various bibliometric tools, including VOSviewer, CiteSpace, and the R software package “bibliometrix,” were utilized for analysis.ResultsA total of 717 articles were identified, showing an upward trend in annual publications. Leading contributors included the United States, Italy, and China, with institutions such as the University of California System at the forefront. The Journal of Alzheimer’s Disease emerged as the top publisher, while research published in Neurology garnered significant citations. Noteworthy authors encompassed Panza, Francesco; Frisardi, Vincenza; and Feldman, Eva L, with Kristine Yaffe being the most cited author (280 citations). Recent studies have focused on themes like “gut microbiota,” “neuroinflammation,” “fatty acids,” and “microglia.”ConclusionThis bibliometric analysis summarizes the foundational knowledge structure in the realm of dementia and MetS from 2000 to 2023. By highlighting current research frontiers and trending topics, this analysis serves as a valuable reference for researchers in the field.
Serum TRPA1 mediates the association between olfactory function and cognitive function
Authors: Liang Xiaoniu , Xiao Zhenxu , Wu Jie , Ma Xiaoxi , Zhao Qianhua , Ding Ding 
Published: June 2024

BackgroundOlfactory dysfunction was associated with poorer cognition. However, the association between transient receptor potential cation channel subfamily A member 1 (TRPA1) and cognitive function have not been studied. This study aimed to evaluate the mediation effect of TRPA1 on the association between olfactory and cognitive function among Chinese older adults.MethodsWe recruited 121 participants with cognitive impairment (CI) and 135 participants with normal cognition (NC) from a memory clinic and the “Shanghai Aging Study.” Olfactory identification of each participant was measured by the Sniffin’ Sticks Screening Test 12 (SSST-12). Serum TRPA1 were quantified using the Enzyme-Linked Immunosorbent Assay. The mediation effects of TRPA1 on the association between olfactory function and cognitive function were explored using mediation analysis.ResultsThe CI group had a significantly higher proportion of the high level of serum TRPA1 (58.7%) than the NC group (42.2%) (p = 0.0086). After adjusted for gender, age, and years of education, mediation analysis verified that TRPA1 partially mediated the association between SSST-12 and Mini Mental State Examination (MMSE). It also verified that TRPA1 partially mediated the association between the identification of peppermint and MMSE.ConclusionOur study emphasizes the mediation role of TRPA1 in the relationship between olfactory and cognitive function among older adults. Further research is necessary to explore the mechanism of TRPA1 on the relationship between olfactory and cognitive decline.
The association of cognitive function and its changes with all-cause mortality among community-dwelling older adults
Authors: Li Shangjie , He Xiuping , Wu Liang , Tang Xinming , Ouyang Yijiang , Jing Wenyuan , Yang Ya , Yang Jiacheng , Che Kechun , Pan Congcong , Chen Xiaoting , Zhang Xiaoxia , Zheng Xueting , Xu Jiahao , Liao Shaobin , Yin Mingjuan , Ni Jindong 
Published: June 2024

BackgroundThe association of cognitive function, its changes, and all-cause mortality has not reached a consensus, and the independence of the association between changes in cognitive function and mortality remains unclear. The purpose of this study was to evaluate the longitudinal association between baseline cognitive function and cognitive changes over 1 year with subsequent all-cause mortality among the older adults aged 60 and above.MethodsA prospective cohort study utilizing the Community Older Adults Health Survey data. Initiated in 2018, the study annually assessed all individuals aged 60+ in Dalang Town, Dongguan City. Cognitive function was assessed using the Chinese version of the Mini-Mental State Examination (MMSE). A total of 6,042 older adults individuals were included, and multivariate Cox proportional hazard models were used to examine cognitive function’s impact on mortality.ResultsParticipants’ median age was 70 years, with 39% men. Over a median 3.08-year follow-up, 525 died. Mortality risk increased by 6% per MMSE score decrease (adjusted HR = 1.06, 95%CI: 1.05–1.08). Compared to those with normal cognitive function at baseline, participants with mild cognitive impairment and moderate to severe cognitive impairment had significantly higher mortality risks (adjusted HR = 1.40, 95%CI: 1.07–1.82; HR = 2.49, 95%CI: 1.91–3.24, respectively). The risk of death was 5% higher for each one-point per year decrease in cognitive function change rate (HR = 1.05, 95%CI: 1.02–1.08). Compared with participants with stable cognitive function, those with rapid cognitive decline had a 79% increased risk of death (adjusted HR = 1.79, 95% CI: 1.11–2.87), with baseline cognitive function influencing this relationship significantly (P for interaction = 0.002).ConclusionBaseline cognitive impairment and rapid cognitive decline are associated with higher all-cause mortality risks in Chinese older adults. Baseline function influences the mortality impact of cognitive changes.
Pre-school children single inhalation anesthetic exposure and neuro-psychological development: a prospective study and Mendelian randomization analysis
Authors: Zhang Jinghong , Deng Haixia , Huang Xilu , Wang Lan , Zhou Pinping , Zeng Jie , Yu Cong 
Published: June 2024

BackgroundFor children who are unable to cooperate due to severe dental anxiety (DA), dental treatment of childhood caries under Dental General Anesthesia (DGA) is a safe and high-quality treatment method. This study aims to evaluate the impact on neurocognitive functions and the growth and development of children 2 years after dental procedure based on previous research, and further establish a causal relationship between general anesthesia (GA) and changes in children’s neurocognitive functions by incorporating Mendelian Randomization (MR) analysis.MethodsData were collected and analyzed from 340 cases of S-ECC procedures of preschool children conducted in 2019. This involved comparing the neurocognitive outcomes 2 years post-operation of preschool children receiving dental procedures under general anesthesia or local anesthesia. Physical development indicators such as height, weight, and body mass index (BMI) of children were also compared at baseline, half a year post-operation, and 2 years post-operation. We performed a Mendelian randomization analysis on the causal relationship between children’s cognitive development and general anesthesia, drawing on a large-scale meta-analysis of GWAS for anesthesia, including multiple general anesthesia datasets.ResultsOutcome data were obtained for 111 children in the general anesthesia group and 121 children in the local anesthesia group. The mean FSIQ score for the general anesthesia group was 106.77 (SD 6.96), while the mean score for the local anesthesia group was 106.36 (SD 5.88). FSIQ scores were equivalent between the two groups. The incidence of malnutrition in children in the general anesthesia group was 27.93% (p &lt; 0.001) before surgery and decreased to 15.32% (p &gt; 0.05) after 2 years, which was not different from the general population. The IVW method suggested that the causal estimate (p = 0.99 &gt; 0.05, OR = 1.04, 95% CI = 5.98 × 10−4–1.82 × 103) was not statistically significant for disease prevalence. This indicates no genetic cause-and-effect relationship between anesthesia and childhood intelligence.ConclusionThere were no adverse outcomes in neurocognitive development in 2 years after severe early childhood caries (S-ECC) procedure under total sevoflurane-inhalation in preschool children. The malnutrition condition in children can be improved after S-ECC procedure under general anesthesia. Limited MR evidence does not support a correlation between genetic susceptibility to anesthesia and an increased risk for intelligence in children.
The effect of mobile application-based technology on post-stroke aphasia: a systematic review
Authors: Jiang Zihui , He Mingping , Zhang Chenchen , Chen Xiuen 
Published: June 2024

BackgroundEnhancing speech-language therapy remains the most effective strategy for improving post-stroke aphasia, However, conventional face-to-face interventions often lack the necessary therapeutic intensity. In recent years, mobile application-based speech-language therapy has emerged progressively, offering new opportunities for independent rehabilitation among aphasic patients. This review aims to evaluate the impact of mobile application-based interventions on post-stroke aphasic.MethodsBy conducting a systematic search across five databases (PubMed, Web of Science, EMBASE, CINAHL, and Scopus), we identified and included studies that investigated the utilization of mobile application-based technologies (such as computers, iPads, etc.) for treating post-stroke aphasia.ResultsThis study included 15 research investigations, including 10 randomized controlled trials (RCTs), four self-controlled studies and one cross-over experimental design study. Among these, eight studies demonstrated the efficacy of mobile application-based therapy in enhancing overall language functionality for post-stroke aphasia patients, three studies highlighted its potential for improving communication skills, three studies observed its positive impact on spontaneous speech expression. Moreover, four studies indicated its effectiveness in enhancing naming abilities, two studies underscored the positive influence of mobile application-based interventions on the quality of life for individuals with aphasia. Six studies noted that speech improvement effects were maintained during the follow-up period.ConclusionThe results of this review demonstrate the potential of mobile application-based interventions for improving speech-language function in individuals with aphasia. However, further high-quality research is needed to establish their effects across different domains and to delve into the comparative advantages of various treatment approaches.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?RecordID=405248
A deep neural network and transfer learning combined method for cross-task classification of error-related potentials
Authors: Ren Guihong , Kumar Akshay , Mahmoud Seedahmed S. , Fang Qiang 
Published: June 2024

BackgroundError-related potentials (ErrPs) are electrophysiological responses that naturally occur when humans perceive wrongdoing or encounter unexpected events. It offers a distinctive means of comprehending the error-processing mechanisms within the brain. A method for detecting ErrPs with high accuracy holds significant importance for various ErrPs-based applications, such as human-in-the-loop Brain-Computer Interface (BCI) systems. Nevertheless, current methods fail to fulfill the generalization requirements for detecting such ErrPs due to the high non-stationarity of EEG signals across different tasks and the limited availability of ErrPs datasets.MethodsThis study introduces a deep learning-based model that integrates convolutional layers and transformer encoders for the classification of ErrPs. Subsequently, a model training strategy, grounded in transfer learning, is proposed for the effective training of the model. The datasets utilized in this study are available for download from the publicly accessible databases.ResultsIn cross-task classification, an average accuracy of about 78% was achieved, exceeding the baseline. Furthermore, in the leave-one-subject-out, within-session, and cross-session classification scenarios, the proposed model outperformed the existing techniques with an average accuracy of 71.81, 78.74, and 77.01%, respectively.ConclusionsOur approach contributes to mitigating the challenge posed by limited datasets in the ErrPs field, achieving this by reducing the requirement for extensive training data for specific target tasks. This may serve as inspiration for future studies that concentrate on ErrPs and their applications.
Tractometry of the Human Connectome Project: resources and insights
Authors: Kruper John , Hagen McKenzie P. , Rheault François , Crane Isaac , Gilmore Asa , Narayan Manjari , Motwani Keshav , Lila Eardi , Rorden Chris , Yeatman Jason D. , Rokem Ariel 
Published: June 2024

IntroductionThe Human Connectome Project (HCP) has become a keystone dataset in human neuroscience, with a plethora of important applications in advancing brain imaging methods and an understanding of the human brain. We focused on tractometry of HCP diffusion-weighted MRI (dMRI) data.MethodsWe used an open-source software library (pyAFQ; https://yeatmanlab.github.io/pyAFQ) to perform probabilistic tractography and delineate the major white matter pathways in the HCP subjects that have a complete dMRI acquisition (n = 1,041). We used diffusion kurtosis imaging (DKI) to model white matter microstructure in each voxel of the white matter, and extracted tract profiles of DKI-derived tissue properties along the length of the tracts. We explored the empirical properties of the data: first, we assessed the heritability of DKI tissue properties using the known genetic linkage of the large number of twin pairs sampled in HCP. Second, we tested the ability of tractometry to serve as the basis for predictive models of individual characteristics (e.g., age, crystallized/fluid intelligence, reading ability, etc.), compared to local connectome features. To facilitate the exploration of the dataset we created a new web-based visualization tool and use this tool to visualize the data in the HCP tractometry dataset. Finally, we used the HCP dataset as a test-bed for a new technological innovation: the TRX file-format for representation of dMRI-based streamlines.ResultsWe released the processing outputs and tract profiles as a publicly available data resource through the AWS Open Data program's Open Neurodata repository. We found heritability as high as 0.9 for DKI-based metrics in some brain pathways. We also found that tractometry extracts as much useful information about individual differences as the local connectome method. We released a new web-based visualization tool for tractometry—“Tractoscope” (https://nrdg.github.io/tractoscope). We found that the TRX files require considerably less disk space-a crucial attribute for large datasets like HCP. In addition, TRX incorporates a specification for grouping streamlines, further simplifying tractometry analysis.
Recognition of regions of stroke injury using multi-modal frequency features of electroencephalogram
Authors: Jin Yan , Li Jing , Fan Zhuyao , Hua Xian , Wang Ting , Du Shunlan , Xi Xugang , Li Lihua 
Published: June 2024

ObjectiveNowadays, increasingly studies are attempting to analyze strokes in advance. The identification of brain damage areas is essential for stroke rehabilitation.ApproachWe proposed Electroencephalogram (EEG) multi-modal frequency features to classify the regions of stroke injury. The EEG signals were obtained from stroke patients and healthy subjects, who were divided into right-sided brain injury group, left-sided brain injury group, bilateral brain injury group, and healthy controls. First, the wavelet packet transform was used to perform a time-frequency analysis of the EEG signal and extracted a set of features (denoted as WPT features). Then, to explore the nonlinear phase coupling information of the EEG signal, phase-locked values (PLV) and partial directed correlations (PDC) were extracted from the brain network, and the brain network produced a second set of features noted as functional connectivity (FC) features. Furthermore, we fused the extracted multiple features and used the resnet50 convolutional neural network to classify the fused multi-modal (WPT + FC) features.ResultsThe classification accuracy of our proposed methods was up to 99.75%.SignificanceThe proposed multi-modal frequency features can be used as a potential indicator to distinguish regions of brain injury in stroke patients, and are potentially useful for the optimization of decoding algorithms for brain-computer interfaces.
Deletion of a core APC/C component reveals APC/C function in regulating neuronal USP1 levels and morphology
Authors: Day Jennifer L. , Tirard Marilyn , Brose Nils 
Published: June 2024

IntroductionThe Anaphase Promoting Complex (APC/C), an E3 ubiquitin ligase, plays a key role in cell cycle control, but it is also thought to operate in postmitotic neurons. Most studies linking APC/C function to neuron biology employed perturbations of the APC/C activators, cell division cycle protein 20 (Cdc20) and Cdc20 homologue 1 (Cdh1). However, multiple lines of evidence indicate that Cdh1 and Cdc20 can function in APC/C-independent contexts, so that the effects of their perturbation cannot strictly be linked to APC/C function.MethodsWe therefore deleted the gene encoding Anaphase Promoting Complex 4 (APC4), a core APC/C component, in neurons cultured from conditional knockout (cKO) mice.ResultsOur data indicate that several previously published substrates are actually not APC/C substrates, whereas ubiquitin specific peptidase 1 (USP1) protein levels are altered in APC4 knockout (KO) neurons. We propose a model where the APC/C ubiquitylates USP1 early in development, but later ubiquitylates a substrate that directly or indirectly stabilizes USP1. We further discovered a novel role of the APC/C in regulating the number of neurites exiting somata, but we were unable to confirm prior data indicating that the APC/C regulates neurite length, neurite complexity, and synaptogenesis. Finally, we show that APC4 SUMOylation does not impact the ability of the APC/C to control the number of primary neurites or USP1 protein levels.DiscussionOur data indicate that perturbation studies aimed at dissecting APC/C biology must focus on core APC/C components rather than the APC/C activators, Cdh20 and Cdh1.