bluuebunny/crossref_metadata_2025_split huggingface dataset:
Predicting attention deficits and functional recovery after glioma resection through functional executive networks: insights from dynamic properties
Authors: Saviola Francesca , Zigiotto Luca , Jovicich Jorge , Sarubbo Silvio 
Published: May 2024

AbstractBackgroundPostoperative short-term attentional and executive dysfunctions are common after brain tumor resection, significantly impacting patients’ quality of life and functional recovery. The current study investigated whether presurgical functional dynamics of key brain networks supporting executive functioning could predict postoperative neuropsychological outcomes.MethodsTwenty-two patients with gliomas underwent longitudinal resting-state fMRI scans (before and three-months after surgery), along with neuropsychological assessments (before, one-week and three-months after surgery). Co-activation patterns analysis (CAPs) characterized the functional dynamic properties of executive networks, including the Fronto-parietal (FPN) and Dorsal Attention networks (DAN). Temporal network properties were examined for stability, integration, and centrality over timepoints. Partial least squares analyses and linear models explored associations between network dynamics and cognitive functioning.ResultsImmediate post-surgical attentional deficits were linked to pre-surgical FPN properties revealing associated dynamic patterns of network activation. Pre-surgical FPN temporal properties predicted not only immediate appearance or persistence of post-resection deficits, but also the longitudinal progression of attentional performance otherwise neglected. However, regardless of the severity of attentional deficit, at three months post-surgery, temporal properties and neuropsychological profiles did not significantly differ from the pre-surgical ones, indicating recovery to baseline beyond treatment strategies.ConclusionsOur study demonstrates that presurgical dynamic properties of intrinsic executive networks alone can predict short-term postoperative neuropsychological outcomes, highlighting the clinical utility of temporal functional connectivity. These findings emphasize the potential for using intrinsic brain activity dynamics as predictive markers for postoperative recovery and planning tailored rehabilitation interventions for cognitive deficits.Key messagesWhat is already known on this topic:Attentive deficits are common in short-term post-glioma neurosurgery, yet their anticipation remains a challenge.What this study adds:Integrating presurgical intrinsic fMRI dynamic connectivity properties within executive networks with pre-surgical attention scores enables the prediction of the severity of attentive deficits post-surgery.How this study might affect research, practice or policy:The proposed prognostic framework has the potential to significantly impact patient outcome in neuro oncological treatments by informing the planning of tailored interventions, both at the level of pre-treatment and for post-treatment rehabilitation planning.
Diffusion Tensor Phenomapping of the Healthy and Pressure-Overloaded Human Heart
Authors: Rock Christopher A. , Chen Y. Iris , Wang Ruopeng , Philip Anne L. , Keil Boris , Weiner Rory B. , Elmariah Sammy , Mekkaoui Choukri , Nguyen Christopher T. , Sosnovik David E. 
Published: May 2024

ABSTRACTCurrent techniques to image the microstructure of the heart with diffusion tensor MRI (DTI) are highly under-resolved. We present a technique to improve the spatial resolution of cardiac DTI by almost 10-fold and leverage this to measure local gradients in cardiomyocyte alignment or helix angle (HA). We further introduce a phenomapping approach based on voxel-wise hierarchical clustering of these gradients to identify distinct microstructural microenvironments in the heart. Initial development was performed in healthy volunteers (n=8). Thereader, subjects with severe but well-compensated aortic stenosis (AS, n=10) were compared to age-matched controls (CTL, n=10). Radial HA gradient was significantly reduced in AS (8.0±0.8°/mm vs. 10.2±1.8°/mm, p=0.001) but the other HA gradients did not change significantly. Four distinct microstructural clusters could be idenJfied in both the CTL and AS subjects and did not differ significantly in their properties or distribution. Despite marked hypertrophy, our data suggest that the myocardium in well-compensated AS can maintain its microstructural coherence. The described phenomapping approach can be used to characterize microstructural plasticity and perturbation in any organ system and disease.
Associations between perinatal biomarkers of maternal dairy fat intake and child cognitive development: results from the EDEN mother-child cohort
Authors: Yuan Wen Lun , Armand Martine , Peyre Hugo , Sarté Catherine , Charles Marie Aline , Heude Barbara , Bernard Jonathan Y. 
Published: May 2024

AbstractBackground/ObjectiveOffspring’s optimal neurodevelopment depends on maternal dietary lipids supply during the perinatal period. Dairy products are an important source of fat in Western diets. This study aimed at examining the associations between maternal dairy fat intake during pregnancy, evaluated through biomarkers in perinatal biofluids (C15:0 and C17:0) and child’s cognitive outcomes.Subjects/MethodsParticipants (N up to 1200) were French mother-child pairs from the EDEN cohort study. Biomarkers were assessed in the following biofluids: maternal red blood cells (RBC) membrane at 24 weeks’ gestation, cord RBC membrane and colostrum. Cognitive outcomes were language abilities scores at ages 2 and 3 years and overall cognitive development scores at ages 3 and 5-6 years. Associations were assessed using multiple linear regression models adjusted for factors related to family socioeconomic context, maternal lifestyle and healthy diet score. Interaction of any breastfeeding duration on the associations with dairy fat biomarkers in colostrum was studied.ResultsPositive associations were observed between levels of C17:0 in cord RBC and C15:0 in maternal RBC with language abilities. In children breastfed for at least 6 months, C15:0 level in colostrum was positively associated with language abilities at age 3 years and overall cognitive development at ages 3 and 5-6 years whereas C17:0 level was only associated with overall cognitive development at age 3 years.ConclusionOur results suggest that maternal higher intake of dairy fat during perinatal period could be potentially associated with offspring’s better cognitive development. These pioneering results call for external validation to challenge their causality.
Interactive Segmentation of Lung Tissue and Lung Excursion in Thoracic Dynamic MRI Based on Shape-guided Convolutional Neural Networks
Authors: Xie Lipeng , Udupa Jayaram K. , Tong Yubing , McDonough Joseph M. , Cahill Patrick J. , Anari Jason B. , Torigian Drew A. 
Published: May 2024

AbstractPurposeLung tissue and lung excursion segmentation in thoracic dynamic magnetic resonance imaging (dMRI) is a critical step for quantitative analysis of thoracic structure and function in patients with respiratory disorders such as Thoracic Insufficiency Syndrome (TIS). However, the complex variability of intensity and shape of anatomical structures and the low contrast between the lung and surrounding tissue in MR images seriously hamper the accuracy and robustness of automatic segmentation methods. In this paper, we develop an interactive deep-learning based segmentation system to solve this problem.Material & MethodsConsidering the significant difference in lung morphological characteristics between normal subjects and TIS subjects, we utilized two independent data sets of normal subjects and TIS subjects to train and test our model. 202 dMRI scans from 101 normal pediatric subjects and 92 dMRI scans from 46 TIS pediatric subjects were acquired for this study and were randomly divided into training, validation, and test sets by an approximate ratio of 5:1:4. First, we designed an interactive region of interest (ROI) strategy to detect the lung ROI in dMRI for accelerating the training speed and reducing the negative influence of tissue located far away from the lung on lung segmentation. Second, we utilized a modified 2D U-Net to segment the lung tissue in lung ROIs, in which the adjacent slices are utilized as the input data to take advantage of the spatial information of the lungs. Third, we extracted the lung shell from the lung segmentation results as the shape feature and inputted the lung ROIs with shape feature into another modified 2D U-Net to segment the lung excursion in dMRI. To evaluate the performance of our approach, we computed the Dice coefficient (DC) and max-mean Hausdorff distance (MM-HD) between manual and automatic segmentations. In addition, we utilized Coefficient of Variation (CV) to assess the variability of our method on repeated dMRI scans and the differences of lung tidal volumes computed from the manual and automatic segmentation results.ResultsThe proposed system yielded mean Dice coefficients of 0.96±0.02 and 0.89±0.05 for lung segmentation in dMRI of normal subjects and TIS subjects, respectively, demonstrating excellent agreement with manual delineation results. The Coefficient of Variation and p-values show that the estimated lung tidal volumes of our approach are statistically indistinguishable from those derived by manual segmentations.ConclusionsThe proposed approach can be applied to lung tissue and lung excursion segmentation from dynamic MR images with high accuracy and efficiency. The proposed approach has the potential to be utilized in the assessment of patients with TIS via dMRI routinely.
Interpretable machine learning model for data driven classification of Oral Health Related Quality of Life in Patients with Type 2 Diabetes Mellitus
Authors: Srivastava Roomani , Murali R , Jain Meena , Jadhav Kshitij 
Published: May 2024

AbstractType 2 Diabetes Mellitus(T2DM) is a debilitating condition with a number of complications including those of the oral cavity which can further deteriorate patient’s general and oral health related quality of life (OHRQoL). Machine Learning (ML) can help assign an individual’s propensity to develop poor OHRQoL, given a set of variables, and at the same time identify the most important features contributing to this outcome. Previously inferential statistical methods have attempted to explain this, albeit with limited success. The aim of this cross sectional study is to determine the impact on OHRQoL in T2DM patients, and identify features most likely to be associated with this outcome and to compare ML and DL analytical methods with inferential statistics. Twelve-hundred T2DM patients were subjected to OHRQoL and demographic data questionnaires and WHO Oral Health Assessment form. K-means Clustering was performed to label individuals as having or not having an impact on OHRQoL. Class imbalance was addressed by undersampling of the majority class using informed subset selection. Further, using the collected data as input features we developed ML algorithms (Naive Bayes(NB), Random Forest(RF), Logistic Regression(LR), Kernel Support Vector Machine(SVM) and Artificial Neural Network(ANN)), to accurately classify individuals with or with-out poor oral health related quality of life (OHRQoL) and utilized SHapley Additive exPlanations (SHAP) analysis for feature importance. The best performing model was SVM (AUC=0.983; Sensitivity=1) for classifying the patients into into poor OHRQoL. SHAP values were highest for Age, Prosthetic Need, Tobacco use and years since onset of diabetes. Features closely related to diabetes, that is, periodontal pockets and loss of attachment were not identified as relevant by inferential statistics, but were deemed as important features associated with poor OHRQoL by SHAP analysis.
A qualitative and quantitative analysis of changes in prostate MRI T2WI signals with different abstinence durations
Authors: Ma Wenjun , Ren Baoming , Gao Yanjun , Bai Weixian 
Published: May 2024

PURPOSETo investigate the effect of abstinence duration on image quality of prostate with high-field magnetic resonance imaging(MRI);MATERIALS AND METHODSThis study included males who underwent prostate MRI in Xi’an No.3 Hospital from November 2021 to November 2022.And these males were divided into two groups according to whether they had ejaculation before MRI examination within 3 days. Two radiologists blinded evaluated the boundary sharpness of the peripheral zone (PZ) and central gland (CG) of prostate using a three-point Likert-scale. The region of interests (ROIs) of the PZ and CG at both sides on the sagittal T2W image were drawn and the signal intensity (SI) of ROIs was measured, then the SI ratio (SIR) was calculated. The Wilcoxon’s test and the independent-samples T test was utilized to compare the differences of the two groups;RESULTSA total of fifty young males were included in the study. There were twenty males abstinence more than 3-days, and thirty males were abstinence within 3-days. The qualitative analysis showed that the different structure of prostate in abstinence group was easier to identify than in ejaculation group(P≤0.05). The quantitative analysis showed that the SIR of PZ and CG in the ejaculation group were lower than in the abstinence group (allP≤0.05), and the peripheral zone was significantly lower;CONCLUSIONIn order to make the results more accurate, we suggested that the abstinence should more than 3 days prior to the examination.
The use of artificial intelligence in the diagnosis of carious lesions: Systematic review and meta-analysis
Authors: Arias Pecorari Vanessa Gallego , Almeida Cezário Laís Renata , de Barros Arato Caio Vieira , de Lima Costa Tainá , Cortellazzi Karine Laura , Pecorari Roberto Fiório , Silva José Erasmo 
Published: May 2024

AbstractBackgroundThe use of Artificial Intelligence (AI) has many applications in the healthcare field. Dental caries is a disease with a prevalence rate of over 50% in Brazil. The diagnosis of caries is usually based on a clinical examination and supplementary tests such as X-rays. The accuracy of a diagnostic test is evaluated by its sensitivity, specificity, and accuracy. Various algorithms and neural network configurations are being used for caries diagnosis.ObjectiveThis systematic review evaluated the sensitivity, specificity, and accuracy of using deep machine learning through a convolutional neural network in diagnosing dental caries.MethodsThis systematic review was conducted in accordance with the Preferred Reporting Items for Systematic review and Meta-Analyses (PRISMA) 2020 guidelines and registered with Prospero (ID CRD42024411477). We used the PubMed, MEDLINE, and LILACS databases and MeSH and DECs descriptors in the search.ResultsAfter analyzing the eligibility of the articles, we selected 33 for full-text reading and included 13 in the meta-analysis. We used the sensitivity, specificity, accuracy data, and the number of positive and negative tests to generate a 2x2 table with TP, FP, FN, TN rates, and accuracy. We evaluated the heterogeneity of the SROC curve using the Zhou & Dendurkuri I 2 approach. The results showed that the sensitivity and specificity of the machine learning for detecting dental caries were 0.79 and 0.87, respectively, and the AUC of the SROC curve was 0.885.ConclusionThe literature presented a variety of convolutional neural networks [CNN] architecture, image acquisition methods, and training volumes, which could lead to heterogeneity. However, the accuracy of using artificial intelligence for caries diagnosis was high, making it an essential tool for dentistry.
Characteristics and needs of Vietnamese technical intern trainees (care workers) in Japan: A qualitative study
Authors: Hara Koji , Yamamura Tomokazu , Ningyi Li , Huong Pham Thu 
Published: May 2024

AbstractBackgroundForeign caregivers are assuming increasing importance in Japan due to a significant shortage of caregivers. To secure and train stable human resources, it is important to understand the needs and challenges of foreign caregivers.ObjectiveTo identify the needs and challenges of Vietnamese technical interns, the largest group of foreign caregivers in Japan, to facilitate system reforms for foreign caregivers and a more efficient recruitment process.MethodsA semi-structured interview survey was conducted with 27 Vietnamese technical interns (caregivers). Interview items included reasons for choosing technical internship in Japan, desired length of stay, and expectations in Japan, as well as career advancement after returning home. Descriptive statistics and k-means clustering were used to analyze the data.ResultsSurvey results showed that all respondents had made their own decision to come to Japan and wanted to pursue caregiver certification; 44% had also considered other countries apart from Japan; most wanted to stay in Japan for as long as possible; 37% wanted to live in Japan permanently. The k-means method revealed three clusters: Japanophile cluster or those who preferred Japan for its landscape, culture, and national character; Word-of-mouth cluster or those influenced by personal referrals; and Intellectual cluster or those influenced by Japan’s economic development and care levels.ConclusionsVietnamese technical interns hoped to study advanced Japanese, become certified care workers, and work in the nursing field after returning to their home country. They also tended to want to stay in Japan for a longer period than that assumed for technical internship programs. Our findings indicate that support for obtaining qualifications, Japanese language skills, and caregiving skills are important to secure stable foreign caregivers. Since each cluster had different backgrounds and reasons, motives, and assumptions for coming to Japan, it is necessary to tailor recruitment, training, and support for each cluster.
An Anthropomorphic Diagnosis System of Pulmonary Nodules using Weak Annotation-Based Deep Learning
Authors: Xie Lipeng , Xu Yongrui , Zheng Mingfeng , Chen Yundi , Sun Min , Archer Michael A. , Wan Yuan , Mao Wenjun , Tong Yubing 
Published: May 2024

AbstractPurposeTo develop an anthropomorphic diagnosis system of pulmonary nodules (PN) based on Deep learning (DL) that is trained by weak annotation data and has comparable performance to full-annotation based diagnosis systems.MethodsThe proposed system uses deep learning (DL) models to classify PNs (benign vs. malignant) with weak annotations, which eliminates the need for time-consuming and labor-intensive manual annotations of PNs. Moreover, the PN classification networks, augmented with handcrafted shape features acquired through the ball-scale transform technique, demonstrate capability to differentiate PNs with diverse labels, including pure ground-glass opacities, part-solid nodules, and solid nodules.ResultsThe experiments were conducted on two lung CT datasets: (1) public LIDC-IDRI dataset with 1,018 subjects, (2) In-house dataset with 2740 subjects. Through 5-fold cross-validation on two datasets, the system achieved the following results: (1) an Area Under Curve (AUC) of 0.938 for PN localization and an AUC of 0.912 for PN differential diagnosis on the LIDC-IDRI dataset of 814 testing cases, (2) an AUC of 0.943 for PN localization and an AUC of 0.815 for PN differential diagnosis on the in-house dataset of 822 testing cases. These results demonstrate comparable performance to full annotation-based diagnosis systems.ConclusionsOur system can efficiently localize and differentially diagnose PNs even in resource-limited environments with good robustness across different grade and morphology sub-groups in the presence of deviations due to the size, shape, and texture of the nodule, indicating its potential for future clinical translation.SummaryAn anthropomorphic diagnosis system of pulmonary nodules (PN) based on deep learning and weak annotation was found to achieve comparable performance to full-annotation dataset-based diagnosis systems, significantly reducing the time and the cost associated with the annotation.Key PointsA fully automatic system for the diagnosis of PN in CT scans using a suitable deep learning model and weak annotations was developed to achieve comparable performance (AUC = 0.938 for PN localization, AUC = 0.912 for PN differential diagnosis) with the full-annotation based deep learning models, reducing around 30%∼80% of annotation time for the experts.The integration of the hand-crafted feature acquired from human experts (natural intelligence) into the deep learning networks and the fusion of the classification results of multi-scale networks can efficiently improve the PN classification performance across different diameters and sub-groups of the nodule.
A β-Galactosidase acting on unique galactosides: the structure and function of a β-1,2-galactosidase fromBacteroides xylanisolvens, an intestinal bacterium
Authors: Nakazawa Yutaka , Kageyama Masumi , Matsuzawa Tomohiko , Liang Ziqin , Kobayashi Kaito , Shimizu Hisaka , Masuhiro Miho , Motouchi Sei , Kumano Saika , Tanaka Nobukiyo , Kuramochi Kouji , Nakai Hiroyuki , Taguchi Hayao , Nakajima Masahiro 
Published: May 2024

AbstractGalactosides are major carbohydrates that are found in plant cell walls and various prebiotic oligosaccharides. Studying the detailed biochemical functions of β-galactosidases in degrading these carbohydrates is important. In particular, identifying β-galactosidases with new substrate specificities could help in the production of potentially beneficial oligosaccharides. In this study, we identified a β-galactosidase with novel substrate specificity fromBacteroides xylanisolvens, an intestinal bacterium. The enzyme did not show hydrolytic activity toward natural β-galactosides during the first screening. However, when α-D-galactosyl fluoride (α-GalF) as a donor substrate and galactose or D-fucose as an acceptor substrate were incubated with a nucleophile mutant, reaction products were detected. The galactobiose produced from the α-GalF and galactose was identified as β-1,2-galactobiose using NMR. Kinetic analysis revealed that this enzyme effectively hydrolyzed β-1,2-galactobiose and β-1,2-galactotriose. In the complex structure with methyl β-galactopyranose as a ligand, the ligand is only located at subsite +1. The 2-hydroxy group and the anomeric methyl group of methyl β-galactopyranose faces in the direction of subsite −1 and the solvent, respectively. This observation is consistent with the substrate specificity of the enzyme regarding linkage position and chain length. Overall, we concluded that the enzyme is a β-galactosidase acting on β-1,2-galactooligosaccharides.SynopsisThe structural and functional analysis of β-galactosidase from an intestinal bacterium led to the discovery of a new β-galactosidase hydrolyzing unique β-1,2-galactooligosaccharides.
Full factorial construction of synthetic microbial communities
Authors: Diaz-Colunga Juan , Catalan Pablo , San Roman Magdalena , Arrabal Andrea , Sanchez Alvaro 
Published: May 2024

AbstractConstructing combinatorially complete species assemblages is often necessary to dissect the complexity of microbial interactions and to find optimal microbial consortia. At the moment, this is accomplished through either painstaking, labor intensive liquid handling procedures, or through the use of state-of-the-art microfluidic devices. Here we present a simple, rapid, low-cost, and highly accessible liquid handling methodology for assembling all possible combinations of a library of microbial strains, which can be implemented with basic laboratory equipment. To demonstrate the usefulness of this methodology, we construct a combinatorially complete set of consortia from a library of eightPseudomonas aeruginosastrains, and empirically measure the community-function landscape of biomass productivity, identify the highest yield community, and dissect the interactions that lead to its optimal function. This easy to implement, inexpensive methodology will make the assembly of combinatorially complete microbial consortia easily accessible for all laboratories.