Attention Scores Research Articles

Prenatal Cannabis Exposure and Executive Function and Aggressive Behavior at Age 5 Years.

Cannabis use during pregnancy is increasingly common in the US and may affect the development and behavior of preschool-aged children. To prospectively investigate the association of prenatal cannabis exposure with executive function and aggressive behavior at age 5 years. This cohort study was conducted from 2016 through 2020. Data analysis was conducted from 2022 through 2024. Patients aged 16 through 50 years who were able to communicate in English and intended to deliver at the Ohio State University Wexner Medical Center were enrolled in a pregnancy research repository. Participants (n = 355) who gave consent for future contact and whose child(ren) was presumed to be alive were invited to participate again at child age 5 years (children born May 25, 2010, through February 7, 2016) and 250 children participated (70%). Cannabis exposure was measured prospectively by urine toxicology, maternal self-report, and obstetric record abstraction. Executive function and aggressive behavior, measured via multimodal assessment by staff masked to exposure status. Outcomes were chosen a priori. Of 250 children, 80 were exposed to cannabis (32%). Use of tobacco, other drugs, and alcohol during pregnancy was common (22% to 39% each). Most families were living in poverty. There were 155 children who were Black/African American, non-Hispanic (62%); 11 were Hispanic (4%); 34 indicated more than one race or another race, non-Hispanic (14%); and 50 were White, non-Hispanic (20%). Age-corrected standard scores for children's attention and inhibitory control (National Institutes of Health Toolbox) were about 0.4 SDs lower for those exposed to cannabis compared with unexposed (β = -6.1 points; 95% CI, -10.8 to -1.4), following propensity score weighting and adjustment for confounders. Exposed children also exhibited poorer task-based planning ability and more observed aggression. Caregiver ratings of executive function and behavior and laboratory assessments of other aspects of executive functioning showed no differences by exposure status. Results from this contemporary US cohort where prenatal cannabis exposure was common and indicated that exposed children exhibited some differences in aspects of executive function and behavior relevant to long-term academic success and adaptive functioning. These results may be considered in refining clinical recommendations regarding cannabis use during pregnancy.

How ADHD Self-Report Scale Scores Predict Vigilance Task Performance

As technology advances, the integration of automation into human-machine systems is increasing and humans are more frequently assuming the role of monitor. Unfortunately, humans are poor monitors and struggle to sustain attention. This struggle is often greater for individuals with ADHD. As a result, they tend to exhibit impaired performance on vigilance tasks compared to their counterparts. Prior research has proposed that ADHD is more prevalent in the general population, with many going undiagnosed. The present study conducted several exploratory analyses to determine how differences in ADHD-like symptom manifestation predict vigilance task performance. The results provide evidence for a predictive relationship between impulsive scores and detection accuracy as well as inattention scores and response time on a cognitive vigil. This suggests that various dimensions of attention may influence performance on different measures. Such differences should be considered when assigning monitoring roles to operators.

A simulation study towards sparse-view spectral X-ray CT segmentation using spectrum-aware deep neural networks

ABSTRACT Spectral X-ray computed tomography (SXCT) is able to provide richer spectral information than conventional X-ray CT, thereby improving the capability of discriminating materials in segmentation tasks. However, segmentation algorithms for conventional CT images often lose their performance in material segmentation with SXCT images due to the spectrum-dependent attenuation properties of X-rays. Recently, deep neural networks (DNNs) have demonstrated their effectiveness in segmentation tasks. However, processing SXCT with DNNs requires significant memory resources and may take significant computation time. To address these issues, this paper introduces a simulation study towards realising deep-learning-based SXCT image segmentation. The proposed method employs a convolutional neural network (CNN) that selectively utilises the significant spectrum channels for accurate material segmentation. The proposed method builds upon a prior study on adaptive CT reconstruction, but to further enhance the performance, we introduce a self-attention-based spectrum-selection module. During training, the selection module assigns importance weights to spectrum channels based on the attention scores. Once the network has been trained, the proposed method can obtain satisfactory segmentation results from projection images at one or a combination of several spectrum channels recommended by the selection module. The experimental results demonstrate that the proposed method achieves a significant reduction in computation time while maintaining the segmentation quality.

Cryptocurrency Transaction Fraud Detection Based on Imbalanced Classification With Interpretable Analysis

This study introduces an interpretable imbalanced data classification method for detecting cryptocurrency transaction fraud. We address data imbalance using SMOTE oversampling and data augmentation through contrastive learning. Next, we introduce a Transformer-based deep learning model that learns sample relevance. The model undergoes pre-training with a contrastive loss and fine-tuning through Bayesian optimization to effectively extract high-dimensional, higher-order, and fraud-related features. We employ a SHAP-based interpreter along with attention scores to elucidate the role of various transaction features in fraud detection. Comparative results demonstrate the model's remarkable recall performance in identifying cryptocurrency transaction fraud. Furthermore, it achieves an excellent F1 value, striking a balance between accuracy and recall. This research not only enriches financial fraud detection but also enhances cryptocurrency transaction security, promotes market development, and contributes to economic stability and social security.

How far do oral lectures at European Orthodontic Society congresses reach? A comparison of abstract publication rates, article citations, and social media mentions.

This study investigated the scientific impact of oral abstracts presented in five consecutive European Orthodontic Society (EOS) congresses in terms of full-text publication rates as well as citations and social media metrics (altmetrics) of the resulting articles. PubMed (http://www.ncbi.nlm.nih.gov/pubmed) and Google Scholar (http://scholar.google.nl/) were screened to identify published articles originally presented as lectures at EOS 2015-2019. EOS date, abstract title, subject, number of authors, authors' names, first author's country of origin, and type of affiliation were extracted from congress abstract books. Altmetric Attention Scores (AASs), number of X (formerly Twitter) posts, Mendeley reads, and citations of the articles were retrieved from Altmetric Explorer and Web of Science (WoS) databases, respectively. Abstract details, full publication rates and Altmetric mentions and WoS citations of the related articles were analysed using univariable and multivariable logistic regression. In total, 51.79 to 73.91% of the lectures presented at EOS 2015-2019 were converted into peer-reviewed articles. There was a median interval of 10.95 months (interquartile range 20.5 months) between conference and publication dates. EOS congress year (overall Wald test P-value = .04) and number of authors (overall Wald test P-value < .01) were significant predictors for full-text publication of oral lectures. There was no significant effect of EOS presentation on AASs, citations, X posts, and Mendeley readers (P > .05). Journal impact factors and quartile rankings were not considered to determine the impact of the journals that published articles originally presented as EOS oral abstracts. Overall, 61.30% of EOS 2015-2019 lectures were published as full-length articles. Multi-authored abstracts presented higher odds of publication, whereas oral abstracts presented at EOS 2015 and 2018 had the lowest probabilities to reach full publication. EOS abstract-based articles were assigned similar numbers of citations and AASs to articles not presented at EOS congresses.

Effect of an early music intervention on emotional and neurodevelopmental outcomes of preterm infants at 12 and 24 months

BackgroundFew studies have found long-term effects of early musical environmental enrichment in the NICU on preterm infant’s development. This study examines how early music enrichment affects emotional development and effortful control abilities in 12- and 24-month-old very preterm (VPT) infants.MethodsOne hundred nineteen newborns were recruited, including 83 VPTs and 36 full-term (FT) infants. The VPT infants were randomly assigned to the music intervention (44 VPT-Music) or control (39 VPT-control) groups. VPT-Music infants listened specifically designed music intervention from the 33rd week of gestation until hospital discharge. At 12 and 24 months, children were clinically evaluated using the Bayley-III Scales of Infant and Toddler Development and the Laboratory Temperament Assessment Battery, and at 24 months, with 3 additional episodes of the Effortful Control Battery.Results and discussionOur analysis showed that during a fear eliciting task, the VPT-Music group expressed lower level of fear reactivity and higher positive motor actions than VPT-controls and FT infants. At 24 months, the VPT-music group had lower scores for negative motor actions in the joy task, compared to both VPT-control and FT groups. In addition, both FT and VPT-music had higher scores of sustained attention compared to VPT-controls, but the contrasts were not significant. No significant effects on mental, language and motor outcomes were identified and for all three dimensions of the ECBQ.ConclusionThe present study suggests that an early music intervention in the NICU might influence preterm children’s emotional processing at 12 and 24 months. Limitations and suggestions for future research are highlighted.

Analysis of the effect of CICARE communication nursing on the improvement of serum IL-10, IL-6, IL-4, IL-2 and lung function in outpatients with acute bronchitis

Abstract: OBJECTIVE: The objective of the study was to investigate the impact of CICARE communication nursing on the improvement of serum interleukin-10 (IL-10), interleukin-6 (IL-6), interleukin-4 (IL-4), and interleukin-2 (IL-2) and lung function in outpatients with acute bronchitis (AB). METHODS: One hundred and twenty suffers with AB who are treated in the outpatient department during Revised to 2021 to March 2023 are chosen and grouped according to the time of admission and different nursing practices. The control group consists of 60 patients from March 2021 to March 2022 who receive routine outpatient care; the experimental group consists of 60 patients from April 2022 to March 2023, and CICARE communication nursing is implemented additionally. Before and after intervention, the cognitive level of self-care knowledge, the level of peripheral blood inflammatory factors, and the improvement of lung function are compared. RESULTS: Before intervention, there were no remarkable differences in self-care knowledge cognition scores, peripheral blood inflammatory factors, and lung function levels between them (P &gt; 0.05). After intervention, the cognitive scores of treatment coordination, medication method, daily attention, diet, and activity in experimental groups are superior to control group (P &lt; 0.05). The levels of IL-10, IL-6, IL-4, and IL-2 in test groups are inferior to control group (P &lt; 0.05). Forced vital capacity, forced expiratory volume in 1 s, maximum air volume, and peak flow rate in test groups are superior to control group (P &lt; 0.05). CONCLUSION: CICARE communication nursing can improve the cognition level of self-care knowledge of patients with AB, reduce the inflammatory response of the body, and promote the recovery of lung function.

Efficacy of an Animal-Assisted Therapy Program in the Development of Children With Down Syndrome

ABSTRACT This study aimed to evaluate the efficacy of a dog-assisted intervention program in occupational therapy (OT) sessions in the treatment of children with Down syndrome (DS). A randomized clinical trial was conducted with 14 children with DS (9–12 years old) randomly distributed into two groups: control and experimental. Both received 12 OT sessions, one per week. The experimental group also received dog-assisted therapy. To evaluate the effects of the program, the Beery-Buktenica Developmental Test of Visual-Motor Integration (Beery VMI), the Test of Playfulness (ToP), an ethogram of positive and negative social behaviors, and comparisons of vasopressin levels in the urine of children upon awakening and after sessions were used. The results showed that after the conventional intervention, the control group had a significant increase in visual perception and attention scores. The experimental group showed a significantly higher frequency of satisfaction and interactivity behaviors. We found that the presence of the dog maximizes the social skills of children with DS and increases their engagement in positive social interactions; however, conventional OT sessions were more effective with visual-motor performance. No physiological changes were observed in either group after the intervention.

What Are the Most Popular Articles in Urogynecology and Who Is Disseminating Them

Importance This research describes which articles published in Urogynecology are garnering the most attention online. Understanding which articles are having the largest impact in the online community has become increasingly important due to the exponential increase in the use of social media on the internet. Objective The Altmetric Attention Score (AAS) is a quantitative and qualitative measure of the articles’ online attention in social media and news outlets, blogs, and reference managers. The aim of this study was to provide an analysis of those accessing and sharing the 100 most popular articles published in Urogynecology. Study Design This was a retrospective descriptive analysis. Using Dimensions, a “linked research knowledge system,” data about each article’s demographics on Twitter and other media sources was extracted from each of the articles within Urogynecology with the highest AAS. No articles were excluded. The articles were categorized by topic and metrics of online sharing were compared among categories. Results Fifteen categories were created. Sexual health and education articles had the highest average Altmetric scores per article and reached the highest impressions per article and had the highest average follower count per Twitter user. On average, social justice in medicine had the highest number of tweets per article. Tweets were most often composed by members of the public (58%) compared with health care practitioners (14%), researchers (13%), and science communicators (12%). Conclusions Sexual health and education articles had the highest average AAS and impressions on Twitter. We observed a difference in which articles are being shared most among the general public and health care professionals. The general public comprised a majority of those sharing articles online regardless of category. Additionally, there may be a disparity in research, its funding, and online scientific communications within the field of urogynecology.

Are open science practices in dentistry associated with higher Altmetric scores and citation rates?

AimOpen science, a set of principles and practices, aims to make scientific research more accessible and accountable, benefiting scientists and society. This study evaluated whether adopting open science practices (OSPs) correlates with higher citation rates and Altmetric scores. MethodsA random sample of randomised clinical trials (RCTs) on dental caries published between 2000 and 2022 was selected. A systematic PubMed search identified relevant RCTs, and data on OSPs - study registration, open methodology, open software, open scripts, open analysis plan, open data, open peer review, and open access (OA) - were manually collected by two independent assessors. The Robot Reviewer tool automatically evaluated the risk of bias (RoB). Outcomes included the total number of citations and the Altmetric Attention Score. Associations between OSPs, RoB, and other explanatory variables with the outcomes were assessed using binomial negative regression analysis, and expressed as Incidence Rate Ratio (IRR; α =0.05). ResultsIn total, 323 papers were analysed. At least one OSP was adopted in 57.5 % (n = 186) of the articles, dropping to 39.6 % (n = 128) without OA. Papers with protocol registration (IRR: 1.45; 95 % CI: 1.15, 1.82) and OA publication (IRR: 1.24; 95 % CI: 1.01, 1.53) had higher citation rates. Conversely, papers in full OA journals had fewer citations (IRR: 0.67; 95 % CI: 0.52, 0.87). After adjusting for RoB, low-risk studies showed higher citation rates (IRR: 1.48; 95 % CI: 1.14, 1.91), while OA lost significance. For Altmetric scores, registered and OA manuscripts showed higher scores (IRR: 3.74; 95 % CI: 2.00, 7.01; IRR: 1.69; 95 % CI: 1.04, 2.75), with registration remaining significant after adjusting for RoB and impact factor (IRR: 3.71; 95 % CI: 1.97–6.99). ConclusionThe adoption of OSPs demonstrated a partial correlation with citation rates and Altmetric scores in RCTs on dental caries; however, these effects are complex and seem more related to the journal's impact factor. Clinical significanceThe citations and the attention to clinical trials in dentistry, which could drive clinical decision-making and the elaboration of policies and recommendations, seem to be driven more by the journal's prestige than by the adoption of OSPs.

Exploring the pathways linking prenatal and early childhood greenness exposure to attention-deficit/hyperactivity disorder symptoms during childhood: An approach based on robust causal inference

BackgroundEpidemiological studies suggest that exposure to greenness during childhood may protect children from developing attention-deficit hyperactivity disorder (ADHD). ObjectiveWe analyzed the effect of both prenatal (pregnancy) and early childhood (4-5-year follow-up) residential greenness exposure and green space availability on ADHD symptoms during childhood (up to the age of 12 years) and further explored the potential mediating role of PM2.5 and physical activity in the association. MethodsThe study population included participants from the INfancia y Medio Ambiente (INMA) prospective birth cohort (Gipuzkoa, Sabadell, and Valencia). Average Normalized Difference Vegetation Index (NDVI) in buffers of 100-, 300- and 500-m around the residential addresses was used as an indicator of greenness, while green space availability was determined based on the presence of a major green space within 150-m from the residence. Childhood ADHD symptoms were assessed at the 6-8- and 10-12-year follow-ups using Conners Parents Rating Scale-Revised: Short Form. ResultsAlthough no association was found for the prenatal exposure period, increased early childhood NDVI inversely associated with the OR of clinically significant ADHD symptoms during the 6-8-year follow-up at the 100-m (OR 0.03, 95% CI: 0.003 to 0.44), 300-m (OR 0.04, 95% CI: 0.003 to 0.42) and 500-m (OR 0.08, 95% CI: 0.01 to 0.76) buffers, but exclusively in the context of direct effects. Additionally, the 10-12-year follow-up analysis found moderate to weak evidence of potential total and direct effects of NDVI at both 100- and 300-m buffers on inattention scores, as well as for NDVI at the 300-m buffer on ADHD index scores. The analysis did not reveal evidence of mediation through PM2.5 or physical activity. ConclusionsThe evidence suggests that early childhood greenness exposure may reduce the risk of developing ADHD symptoms later in childhood, and that this association is not mediated through PM2.5 and physical activity.

Dual-model approach for one-shot lithium-ion battery state of health sequence prediction

Lithium-ion batteries play a crucial role in powering various applications, including Electric Vehicles (EVs), underscoring the importance of accurately estimating their State Of Health (SOH) throughout their operational lifespan. This paper introduces two novel models: a Transformer (TOPS-SoH) and a Long Short-Term Memory based (LSTM-OSoH) for One-shot Prediction of SOH. The LSTM-OSoHexcels in accuracy, achieving a Masked Mean Absolute Error (MMAE) of less than 0.01 for precise SOH estimation, while the TOPS-SoHdemonstrates simplicity and efficiency, with accuracy comparable to state-of-the-art models. The TOPS-SoHmodel also offers additional interpretability by providing insights into the attention scores between inputs and outputs, highlighting the cycles used for estimation. These models were trained using the MIT battery dataset, with auto-encoders employed to reduce the dimensionality of the input data. Additionally, the models’ effectiveness was validated against a Bidirectional LSTM (BiLSTM) baseline, demonstrating superior performance in terms of lower MMAE, MMSE, and MAPE values, making them highly suitable for integration into Battery Management Systems (BMS). These findings contribute to advancing SOH estimation up to the End Of Life (EOL), which is crucial for ensuring the reliability and longevity of lithium-ion batteries in diverse applications.

Effects of cognitive training under hypoxia on cognitive proficiency and neuroplasticity in remitted patients with mood disorders and healthy individuals: ALTIBRAIN study protocol for a randomized controlled trial

BackgroundCognitive impairment is prevalent across neuropsychiatric disorders but there is a lack of treatment strategies with robust, enduring effects. Emerging evidence indicates that altitude-like hypoxia cognition training may induce long-lasting neuroplasticity and improve cognition. We will investigate whether repeated cognition training under normobaric hypoxia can improve cognitive functions in healthy individuals and patients with affective disorders and the neurobiological underpinnings of such effects.MethodsIn sub-study 1, 120 healthy participants are randomized to one of four treatment arms in a double-blind manner, allowing for examination of separate and combined effects of three-week repeated moderate hypoxia and cognitive training, respectively. In sub-study 2, 60 remitted patients with major depressive disorder or bipolar disorder are randomized to hypoxia with cognition training or treatment as usual. Assessments of cognition, psychosocial functioning, and quality of life are performed at baseline, end-of-treatment, and at 1-month follow-up. Functional magnetic resonance imaging (fMRI) scans are conducted at baseline and 1-month follow-up, and [11C]UCB-J positron emission tomography (PET) scans are performed at end-of-treatment to quantify the synaptic vesicle glycoprotein 2A (SV2A). The primary outcome is a cognitive composite score of attention, verbal memory, and executive functions. Statistical power of ≥ 80% is reached to detect a clinically relevant between-group difference with minimum n = 26 per treatment arm. Behavioral data are analyzed with an intention-to-treat approach using mixed models. fMRI data is analyzed with the FMRIB Software Library, while PET data is quantified using the simplified reference tissue model (SRTM) with centrum semiovale as reference region.DiscussionThe results will provide novel insights into whether repeated hypoxia cognition training increases cognition and brain plasticity, which can aid future treatment development strategies.Trial registrationClinicalTrials.gov, NCT06121206. Registered on 31 October 2023.

Analyzing hockey publications through scientometric and altmetric perspectives

Purpose This study aims to present scientometric mapping and altmetric analysis of publications related to “Hockey” in the past three decades. By using the advanced analytical techniques of mapping coupled with altmetric analysis, this paper aims to reveal the complex network of collaborations, the dispersion of expertise worldwide and prevailing thematic trends in the field of hockey. Design/methodology/approach The data was extracted from the Web of Science (WoS) database and Altmetric Explorer for articles related to hockey over the past three decades. VOSviewer was used to conduct network analysis whereas MS-Excel was used for altmetric data analysis. The study focused on the articles retrieved using the key term “Hockey” in English language publications. The altmetric attention scores (AAS) were used to measure the level of online attention on different platforms, complementing traditional bibliometric analysis. Findings The study reveals a notable increase in the productivity of hockey research over the past 30 years, with a specific focus on major surges in publication output and altmetric attention in recent times. Coauthorship and country-wise mapping analysis highlight global research collaboration trends, while keyword analysis underscores thematic concentrations. Key journals such as British Journal of Sports Medicine and American Journal of Sports Medicine emerge as crucial dissemination platforms. The importance of X posts (Formerly Twitter) and Mendeley in the diffusion of hockey literature is highlighted by altmetric research. Originality/value The study provides a concise overview of research conducted on the game of hockey. This research will be advantageous for researchers and individuals involved in the hockey community, as it offers bibliographic insights and aids in identifying suitable media for disseminating their findings.

The clinical and neuropsychological profiles of Alzheimer's disease with white matter hyperintensity in North China.

Patients with Alzheimer's disease (AD) often exhibit characteristic clinical manifestations, particularly neuropsychiatric symptoms. Previous studies have shown that white matter hyperintensity (WMH) is strongly associated with AD progression, as well as neuropsychiatric symptoms. The purpose of this study was to investigate the clinical and neuropsychological characteristics of AD patients with WMH. This retrospective study involved 104 18-fluorodeoxyglucose-positron emission computed tomography (18FDG-PET-CT)-defined AD patients treated at Tianjin Huanhu Hospital from January 2010 to December 2022. Cranial magnetic resonance imaging (MRI) provided semi-quantitative data on brain structure and WMH. Collect and analyze patient clinical data. Neuropsychological assessments were used to evaluate cognitive function and psychobehavioral traits. Among the 104 patients, 66 were in the WMH group (63.5%) and 38 in the non-white matter hyperintensity (non-WMH) group (36.5%). There were no significant differences in gender, age, age of onset, education, BMI, smoking, drinking, diabetes, coronary heart disease, dementia family history, fasting blood glucose, total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C) between the two groups. The WMH group showed higher rates of hypertension, homocysteine (Hcy) levels, NPI, and CDR scores as compared to the non-WMH group (p < 0.05). MMSE and MoCA scores were significantly lower in the WMH group (p < 0.05). In the MMSE subitem analysis, patients in the WMH group showed a decrease in attention, recall, and language scores. In the MOCA subitem analysis, WMH patients had lower scores in executive function, naming, attention, language, abstraction, and orientation (p < 0.05). Furthermore, subgroup analysis of NPI showed a higher incidence of delusions, depression, and apathy in the WMH group (p < 0.05). According to the hierarchical analysis of mild, moderate and severe dementia groups, the hypertension, leukoencephalopathy, Hcy level, Fazekas total score, PWMH and DWMH scores in the severe dementia group were significantly higher than those in the mild and moderate dementia groups (p < 0.05). As the disease progresses, more and more patients show increased white matter hyperintensity. White matter lesions are closely correlated with cognitive decline and psychobehavioral symptoms in AD patients, and may be used as an indicator of disease progression. Priority should be given to early screening and prevention of WMH-related risk factors.

PMformer: A novel informer-based model for accurate long-term time series prediction

When applied to long-term time series forecasting, Informer struggles to capture temporal dependencies effectively, leading to suboptimal forecasting accuracy. To address this issue, we propose PMformer, a novel model based on Informer for long-term time series prediction. First, we introduce a probabilistic patch sampling attention mechanism that utilizes a patch-based strategy to compute attention scores within randomly selected sequence patches. This localized approach enhances the model's capability to capture local temporal dependencies, allowing it to better understand and process critical local features in time series while reducing computational complexity. Additionally, we propose a multi-scale scaling sparse attention technique that balances attention distribution by combining coarse- and fine-grained attention scores, thereby improving the model's ability to capture global sequence information. Finally, we design a dilated causal pooling layer and a multilayer perceptual cross self-attention decoder to further enhance the model's prediction accuracy by capturing key information in long-term correlations and precisely focusing on sequences. We conducted experiments on both multivariate and univariate time series forecasting tasks. The results show that PMformer outperforms six baseline models, including PatchTST and FEDformer, in terms of MAE and MSE metrics. This demonstrates its superior ability to capture temporal dependencies, achieving more accurate predictions.

S2MRI-ADNet: an interpretable deep learning framework integrating Euclidean-graph representations of Alzheimer's disease solely from structural MRI.

To establish a multi-dimensional representation solely on structural MRI (sMRI) for early diagnosis of AD. A total of 3377 participants' sMRI from four independent databases were retrospectively identified to construct an interpretable deep learning model that integrated multi-dimensional representations of AD solely on sMRI (called s2MRI-ADNet) by a dual-channel learning strategy of gray matter volume (GMV) from Euclidean space and the regional radiomics similarity network (R2SN) from graph space. Specifically, the GMV feature map learning channel (called GMV-Channel) was to take into consideration spatial information of both long-range spatial relations and detailed localization information, while the node feature and connectivity strength learning channel (called NFCS-Channel) was to characterize the graph-structured R2SN network by a separable learning strategy. The s2MRI-ADNet achieved a superior classification accuracy of 92.1% and 91.4% under intra-database and inter-database cross-validation. The GMV-Channel and NFCS-Channel captured complementary group-discriminative brain regions, revealing a complementary interpretation of the multi-dimensional representation of brain structure in Euclidean and graph spaces respectively. Besides, the generalizable and reproducible interpretation of the multi-dimensional representation in capturing complementary group-discriminative brain regions revealed a significant correlation between the four independent databases (p < 0.05). Significant associations (p < 0.05) between attention scores and brain abnormality, between classification scores and clinical measure of cognitive ability, CSF biomarker, metabolism, and genetic risk score also provided solid neurobiological interpretation. The s2MRI-ADNet solely on sMRI could leverage the complementary multi-dimensional representations of AD in Euclidean and graph spaces, and achieved superior performance in the early diagnosis of AD, facilitating its potential in both clinical translation and popularization.

Altmetric and impact analysis of randomized clinical trials in dentistry

ObjectivesThis study investigated altmetrics, citations, and field-normalized impact of dental articles reporting randomized clinical trials (RCTs) published within a one-year period. MethodsData were collected in 2024 from PubMed-indexed RCTs published in 2019. Dependent variables included Altmetric Attention Scores (AAS), PlumX citations, and Field-Weighted Citation Impact (FWCI). Independent variables encompassed article-, author-, and journal-related variables. Adjusted quasi-Poisson regression models were used to assess associations. Point-biserial correlation evaluated the relationship between Journal Impact Factor (JIF) and selected reporting variables. ResultsA total of 653 RCTs were included, with periodontology, implantology, and oral and maxillofacial surgery comprising 50.4% of the sample. Only 28.6% of the articles reported CONSORT use, 49.6% pre-registered their protocol, and 68.8% reported a sample size calculation. Most articles (63.6%) reported no conflicts of interest, with unclear sponsorship being the most frequent (34.6%). Regression analyses revealed significant associations for AAS, PlumX citations, and FWCI with various factors. JIF increased AAS by 17% per unit, PlumX citations by 13%, and FWCI by 6%. Protocol pre-registration boosted AAS by 132%, while mixed or no sponsorship increased PlumX citations by up to 47%. First author H-index increased PlumX citations and FWCI by 1% per unit, while first author continent impacted AAS, citations, and FWCI. Weak positive correlations between JIF and both protocol pre-registration and CONSORT use were observed. No significant differences were observed across different dental fields for any metric. ConclusionAn interplay among article-, author-, and journal-related variables collectively influenced the online attention, citations, and impact of dental RCT articles. Clinical significanceUnderstanding the factors that influence the visibility and impact of dental RCTs can guide researchers in improving the design, reporting, and dissemination of their studies, ultimately enhancing the quality and reach of dental research.

Research Study Characteristics Associated with Media Engagement in Hip and Knee Arthroplasty Database Studies

Objectives: This study identified associations between a study’s Altmetric Attention Score (AAS) and the number of times it was posted on X (formerly Twitter) with article characteristics such as the topic or publishing journal. Design: Retrospective study. Setting: Articles published in the orthopedic literature. Article Selection: A total of 116 eligible articles using administrative databases or clinical registries published in 2020 in four orthopaedic journals were studied. Intervention: None. Main Outcome Measurements: Each of the 116 selected articles was assigned one of the following topics: complications and outcomes, technology, medications and/or anesthesia, logistics, and surgical techniques. AAS and X posts for each article were retrieved. Kruskal-Wallis tests were used to identify differences in the AAS and X posts across journals and topics. Results: Kruskal-Wallis tests revealed a difference between an article’s publishing journal and its AAS (p = 0.034) and number of X posts (p = 0.032), with articles published in the Bone &amp; Joint Journal (BJJ) having the greatest mean AAS (16.27) and X posts (25.73). Kruskal-Wallis tests showed a difference between an article’s topic and its AAS (p = 0.001) and number of X posts (p= 0.027), with articles focused on surgical techniques having the greatest mean AAS (26.50) and X posts (37.00). Conclusions: There is significant variation in the degree of media engagement for hip and knee arthroplasty database studies. The topic of the article (surgical techniques) and journal of publication (BJJ) were associated with a greater level of engagement. Level of Evidence: Level V.

The impact of scholarly podcasts on research distribution and uptake in oncology.

423 Background: Scholarly podcasts have grown in number and popularity in the post-COVID era. While such podcasts aim to increase research readership and understanding, their impact on these areas has not been quantitatively established. We assessed the relationship between Oncology research podcasts and various research distribution metrics. Methods: All research articles published in the Journal of Clinical Oncology (JCO) from January 2023 to December 2023 were reviewed. Published research articles in the JCO not discussed on the JCO or ASCO Clinical Guidelines podcast were used as controls. Google Scholar citations, Dimensions citations, Mendeley readership, News and Blog shares, Altmetric Attention Score (AAS), social media uptake (i.e., twitter shares), and article downloads were gathered, as well as podcast release dates and the presence of editorial(s) associated with the research article. Mann-Whitney U-tests were used to compare the medians of distribution metrics across podcast and control groups. A multivariate regression analysis incorporating confounding variables (accompanying editorial, subject of research, type of research) was completed. All non-research articles published during the inclusion period were removed from both control and podcast groups. Results: 421 research articles were published during the inclusion period, 57 of which were featured on featured in JCO or ASCO Clinical Guidelines Podcast. The median downloads (p &lt; 0.02), AAS (p &lt; 0.001), and Twitter shares (p &lt;0.001) were significantly higher in the podcast group. Median Twitter shares were 2.3x greater in the podcast group, the largest difference for any metric between podcast and control groups. Median Google Scholar citations, Mendeley readership, News shares, and Dimensions citations were not significantly different between podcast and non-podcast groups. Upon multivariable regression analysis, only Twitter shares were significantly greater in the podcast group (β =22.0; 95% CI: 0.4 – 43.7; p=0.046). Similarly, only Twitter shares were significantly affected by the delay (in days) between podcast release and online publishing of the article (r = -0.284, p = 0.03). Conclusions: Of all distribution metrics, Twitter shares were most positively affected by the association of scholarly podcasts with research published in the JCO, while academic measures (i.e., citations) were unaffected by an association with a podcast. These findings can inform the application of podcasts to increase research uptake amongst target audiences.