bluuebunny/crossref_metadata_2025_split huggingface dataset:
In vitro characterization of [125I]HY-3-24, a selective ligand for the dopamine D3 receptor
Authors: Lee Ji Youn , Kim Ho Young , Martorano Paul , Riad Aladdin , Taylor Michelle , Luedtke Robert R. , Mach Robert H. 
Published: April 2024

IntroductionDopamine D3 receptor (D3R) ligands have been studied for the possible treatment of neurological and neuropsychiatric disorders. However, selective D3R radioligands for in vitro binding studies have been challenging to identify due to the high structural similarity between the D2R and D3R. In a prior study, we reported a new conformationally-flexible benzamide scaffold having a high affinity for D3R and excellent selectivity vs. D2R. In the current study, we characterized the in vitro binding properties of a new radioiodinated ligand, [125I]HY-3-24.MethodsIn vitro binding studies were conducted in cell lines expressing D3 receptors, rat striatal homogenates, and rat and non-human primate (NHP) brain tissues to measure regional brain distribution of this radioligand.ResultsHY-3-24 showed high potency at D3R (Ki = 0.67 ± 0.11 nM, IC50 = 1.5 ± 0.58 nM) compared to other D2-like dopamine receptor subtypes (D2R Ki = 86.7 ± 11.9 nM and D4R Ki &gt; 1,000). The Kd (0.34 ± 0.22 nM) and Bmax (38.91 ± 2.39 fmol/mg) values of [125I]HY-3-24 were determined. In vitro binding studies in rat striatal homogenates using selective D2R and D3R antagonists confirmed the D3R selectivity of [125I]HY-3-24. Autoradiography results demonstrated that [125I]HY-3-24 specifically binds to D3Rs in the nucleus accumbens, islands of Calleja, and caudate putamen in rat and NHP brain sections.ConclusionThese results suggest that [125I]HY-3-24 appears to be a novel radioligand that exhibits high affinity binding at D3R, with low binding to other D2-like dopamine receptors. It is anticipated that [125I]HY-3-24 can be used as the specific D3R radioligand.
The effect of sampling window size on topographical maps of foveal cone density
Authors: Warr Emma , Grieshop Jenna , Cooper Robert F. , Carroll Joseph 
Published: April 2024

PurposeTo characterize the effect of sampling window size on maps of foveal cone density derived from adaptive optics scanning light ophthalmoscope (AOSLO) images of the cone mosaic.MethodsForty-four AOSLO-derived montages of the foveal cone mosaic (300 x 300µm) were used for this study (from 44 individuals with normal vision). Cone photoreceptor coordinates were semi-automatically identified by one experienced grader. From these coordinates, cone density matrices across each foveal montage were derived using 10 different sampling window sizes containing 5, 10, 15, 20, 40, 60, 80, 100, 150, or 200 cones. For all 440 density matrices, we extracted the location and value of peak cone density (PCD), the cone density centroid (CDC) location, and cone density at the CDC.ResultsAcross all window sizes, PCD values were larger than those extracted at the CDC location, though the difference between these density values decreased as the sampling window size increased (p&lt;0.0001). Overall, both PCD (r=-0.8099, p=0.0045) and density at the CDC (r=-0.7596, p=0.0108) decreased with increasing sampling window size. This reduction was more pronounced for PCD, with a 27.8% lower PCD value on average when using the 200-cone versus the 5-cone window (compared to only a 3.5% reduction for density at the CDC between these same window sizes). While the PCD and CDC locations did not occur at the same location within a given montage, there was no significant relationship between this PCD-CDC offset and sampling window size (p=0.8919). The CDC location was less variable across sampling windows, with an average per-participant 95% confidence ellipse area across the 10 window sizes of 47.56µm² (compared to 844.10µm² for the PCD location, p&lt;0.0001).ConclusionCDC metrics appear more stable across varying sampling window sizes than PCD metrics. Understanding how density values change according to the method used to sample the cone mosaic may facilitate comparing cone density data across different studies.
Automated identification of atrial fibrillation from single-lead ECGs using multi-branching ResNet
Authors: Xie Jianxin , Stavrakis Stavros , Yao Bing 
Published: April 2024

Introduction: Atrial fibrillation (AF) is the most common cardiac arrhythmia, which is clinically identified with irregular and rapid heartbeat rhythm. AF puts a patient at risk of forming blood clots, which can eventually lead to heart failure, stroke, or even sudden death. Electrocardiography (ECG), which involves acquiring bioelectrical signals from the body surface to reflect heart activity, is a standard procedure for detecting AF. However, the occurrence of AF is often intermittent, costing a significant amount of time and effort from medical doctors to identify AF episodes. Moreover, human error is inevitable, as even experienced medical professionals can overlook or misinterpret subtle signs of AF. As such, it is of critical importance to develop an advanced analytical model that can automatically interpret ECG signals and provide decision support for AF diagnostics.Methods: In this paper, we propose an innovative deep-learning method for automated AF identification using single-lead ECGs. We first extract time-frequency features from ECG signals using continuous wavelet transform (CWT). Second, the convolutional neural networks enhanced with residual learning (ReNet) are employed as the functional approximator to interpret the time-frequency features extracted by CWT. Third, we propose to incorporate a multi-branching structure into the ResNet to address the issue of class imbalance, where normal ECGs significantly outnumber instances of AF in ECG datasets.Results and Discussion: We evaluate the proposed Multi-branching Resnet with CWT (CWT-MB-Resnet) with two ECG datasets, i.e., PhysioNet/CinC challenge 2017 and ECGs obtained from the University of Oklahoma Health Sciences Center (OUHSC). The proposed CWT-MB-Resnet demonstrates robust prediction performance, achieving an F1 score of 0.8865 for the PhysioNet dataset and 0.7369 for the OUHSC dataset. The experimental results signify the model’s superior capability in balancing precision and recall, which is a desired attribute for ensuring reliable medical diagnoses.
Psychosocial factors contributing to value creation in value-based healthcare: a scoping review
Authors: Marino Leda , Capone Vincenza 
Published: April 2024

BackgroundHealthcare systems constantly evolve to improve care quality and resource utilization. One way is implementing Value-Based Healthcare (VBHC) an economic approach. This scoping review aims to identify and describe the literature on VBHC, particularly its psychosocial aspects, to uncover research gaps.MethodThe review followed the PRISMA guidelines for Scoping Reviews. We took the following 14 steps: (a) defining the research question; (b) identifying relevant studies; (c) selecting studies; (d) 15 mapping data; (e) collecting, synthesizing and reporting results. A detailed Boolean search was conducted from January 2021 to August 31, 2021, across APA PsycINFO and PubMed databases using keywords such as “Value-Based Healthcare” and “psychosocial perspective.” Initially, three reviewers screened 70 e-records independently, assessing titles, abstracts, and full-text against the inclusion criteria. Discrepancies regarding the evaluation of the articles were resolved through consensus sessions between the reviewers.ResultsThe final review included 14 relevant e-records in English from peer-reviewed sources, focusing on quantitative and qualitative research. From the analysis, four areas emerged: (1) Value chains in Healthcare; (2) Styles, activities, and practices of value co-creation in Healthcare; (3) Value co-creation in the encounter process; (4) Value co-creation in preventive health services.ConclusionThe scoping review findings suggest several potential key aspects, including the interdependence between patients and healthcare organizations, organizational culture in healthcare, and the role of patient-centered approaches that focus on relationships, communication, and social support in healthcare. This can be achieved through patient engagement, patient-centered care and communication, health literacy, psychosocial support services, comprehensive psychosocial assessments, care coordination, and continuity of care. Integrating psychosocial elements in VHBC enhances quality and optimizes resource use. Findings highlight the need to develop practical guidance on how to implement a culture of value in care that takes into account the psychosocial aspects that have emerged, but not fully addressed. The pandemic teaches that the workforce poorly receives sudden and unsystematic changes. This review could provide an initial basis for the redesign of value in healthcare and a paradigm shift that has already begun with patient-centered medicine and patient engagement.
Human olfactory neural progenitor cells reveal differences in IL-6, IL-8, thrombospondin-1, and MCP-1 in major depression disorder and borderline personality disorder
Authors: Davalos-Guzman Alan Patrick , Vegas-Rodriguez Francisco Javier , Ramirez-Rodriguez Gerardo Bernabe , Flores-Ramos Monica , Romero-Luevano Perla Vanessa , Gonzalez-Olvera Jorge Julio , Saracco-Alvarez Ricardo Arturo 
Published: April 2024

BackgroundDiscovering biological markers is essential for understanding and treating mental disorders. Despite the limitations of current non-invasive methods, neural progenitor cells from the olfactory epithelium (hNPCs-OE) have been emphasized as potential biomarker sources. This study measured soluble factors in these cells in Major Depressive Disorder (MDD), Borderline Personality Disorder (BPD), and healthy controls (HC).MethodsWe assessed thirty-five participants divided into MDD (n=14), BPD (n=14), and HC (n=7). MDD was assessed using the Hamilton Depression Rating Scale. BPD was evaluated using the DSM-5 criteria and the Structured Clinical Interview for Personality Disorders. We isolated hNPCs-OE, collected intracellular proteins and conditioned medium, and quantified markers and soluble factors, including Interleukin-6, interleukin-8, and others. Analysis was conducted using one-way ANOVA or Kruskal-Wallis test and linear regression.ResultsWe found that hNPCs-OE of MDD and BPD decreased Sox2 and laminin receptor-67 kDa levels. MASH-1 decreased in BPD, while tubulin beta-III decreased in MDD compared to controls and BPD. Also, we found significant differences in IL-6, IL-8, MCP-1, and thrombospondin-1 levels between controls and MDD, or BPD, but not between MDD and BPD.ConclusionsAltered protein markers are evident in the nhNPCs-OE in MDD and BPD patients. These cells also secrete higher concentrations of inflammatory cytokines than HC cells. The results suggest the potential utility of hNPCs-OE as an in vitro model for researching biological protein markers in psychiatric disorders. However, more extensive validation studies are needed to confirm their effectiveness and specificity in neuropsychiatric disorders.
Reduced prevalence of drug-related problems in psychiatric inpatients after implementation of a pharmacist-supported computerized physician order entry system - a retrospective cohort study
Authors: Wien Katharina , Thern Julia , Neubert Anika , Matthiessen Britta-Lena , Borgwardt Stefan 
Published: April 2024

IntroductionIn 2021, a computerized physician order entry (CPOE) system with an integrated clinical decision support system (CDSS) was implemented at a tertiary care center for the treatment of mental health conditions in Lübeck, Germany. To date, no study has been reported on the types and prevalence of drug-related problems (DRPs) before and after CPOE implementation in a psychiatric inpatient setting. The aim of this retrospective before-and-after cohort study was to investigate whether the implementation of a CPOE system with CDSS accompanied by the introduction of regular medication plausibility checks by a pharmacist led to a decrease of DRPs during hospitalization and unsolved DRPs at discharge in psychiatric inpatients.MethodsMedication charts and electronic patient records of 54 patients before (cohort I) and 65 patients after (cohort II) CPOE implementation were reviewed retrospectively by a clinical pharmacist. All identified DRPs were collected and classified based on ‘The PCNE Classification V9.1’, the German database DokuPIK, and the ‘NCC MERP Taxonomy of Medication Errors’.Results325 DRPs were identified in 54 patients with a mean of 6 DRPs per patient and 151.9 DRPs per 1000 patient days in cohort I. In cohort II, 214 DRPs were identified in 65 patients with a mean of 3.3 DRPs per patient and 81.3 DRPs per 1000 patient days. The odds of having a DRP were significantly lower in cohort II (OR=0.545, 95% CI 0.412-0.721, p&lt;0.001). The most frequent DRP in cohort I was an erroneous prescription (n=113, 34.8%), which was significantly reduced in cohort II (n=12, 5.6%, p&lt;0.001). During the retrospective in-depth review, more DRPs were identified than during the daily plausibility analyses. At hospital discharge, patients had significantly less unsolved DRPs in cohort II than in cohort I.DiscussionThe implementation of a CPOE system with an integrated CDSS reduced the overall prevalence of DRPs, especially of prescription errors, and led to a smaller rate of unsolved DRPs in psychiatric inpatients at hospital discharge. Not all DRPs were found by plausibility analyses based on the medication charts. A more interactive and interdisciplinary patient-oriented approach might result in the resolution of more DRPs.
Existential anxiety about artificial intelligence (AI)- is it the end of humanity era or a new chapter in the human revolution: questionnaire-based observational study
Authors: Alkhalifah Joud Mohammed , Bedaiwi Abdulrahman Mohammed , Shaikh Narmeen , Seddiq Waleed , Meo Sultan Ayoub 
Published: April 2024

BackgroundExistential anxiety can profoundly affect an individual, influencing their perceptions, behaviours, sense of well-being, academic performance, and decisions. Integrating artificial intelligence into society has elicited complex public reactions, marked by appreciation and concern, with its acceptance varying across demographics and influenced by factors such as age, gender, and prior AI experiences. This study aimed to investigate the existential anxiety about artificial intelligence (AI) in public in Saudi Arabia.MethodsThe present questionnaire-based observational, analytical cross-sectional study with a structured, self-administered survey was conducted via Google Forms, using a scale to assess the existential anxiety levels induced by the recent development of AI. The study encompassed a diverse population with a sample size of 300 participants.ResultsThis study’s findings revealed a high prevalence of existential anxieties related to the rapid advancements in AI. Key concerns included the fear of death (96% of participants), fate’s unpredictability (86.3%), a sense of emptiness (79%), anxiety about meaninglessness (92.7%), guilt over potential AI-related catastrophes (87.7%), and fear of condemnation due to ethical dilemmas in AI (93%), highlighting widespread apprehensions about humanity’s future in an AI-dominated era.ConclusionThe public has concerns including unpredictability, a sense of emptiness, anxiety, guilt over potential AI-related catastrophes, and fear of condemnation due to ethical dilemmas in AI, highlighting widespread apprehensions about humanity’s future in an AI-dominated era. The results indicate that there is a need for a multidisciplinary strategy to address the existential anxieties in the AI era. The strategic approach must blend technological advancements with psychological, philosophical, and ethical insights, underscoring the significance of human values in an increasingly technology-driven world.
The effect of intergenerational support from children on loneliness among older adults-the moderating effect of internet usage and intergenerational distance
Authors: Huang Ruyi , Gong Rengui , Deng Qiong , Hu Yangming 
Published: April 2024

ObjectiveLoneliness is a key social and public health issue, mainly affecting the mental health of older adults. The article aimed to explore the influence of intergenerational support from children on loneliness among older adults. Meanwhile, the article also analyzed the moderating effects of internet usage and intergenerational distance in this process.MethodsBased on the data received from 2018 China Longitudinal Aging Social Survey (CLASS), the ordinary least square (OLS) regression model was used to analyze the influence of intergenerational support from children on loneliness among older adults. Furthermore, the Bootstrap method was used to test the moderating effect of internet usage and intergenerational distance on the relationship between intergenerational support from children on loneliness among older adults.ResultsBaseline regression showed that economic support (β = −0.059, p &lt; 0.001), caregiving support (β = −0.070, p &lt; 0.001), and emotional support (β = −0.108, p &lt; 0.001) from children can positively influence loneliness among older adults. Meanwhile, the results of the moderated effects analysis showed that internet usage and intergenerational distance moderates the relationship between caregiving support, emotional support from children and loneliness among older adults.ConclusionThe article demonstrates that family support, particularly intergenerational support from children plays a pivotal role in alleviating loneliness among older adults, so the government should further regulate the behavior of children’s alimony support, improve the digital infrastructure, these measures help to reduce loneliness among older adults and expand the depth and breadth of family care of older adults.
A systematic review of artificial intelligence tools for chronic pulmonary embolism on CT pulmonary angiography
Authors: Abdulaal Lojain , Maiter Ahmed , Salehi Mahan , Sharkey Michael , Alnasser Turki , Garg Pankaj , Rajaram Smitha , Hill Catherine , Johns Christopher , Rothman Alex Matthew Knox , Dwivedi Krit , Kiely David G. , Alabed Samer , Swift Andrew James 
Published: April 2024

BackgroundChronic pulmonary embolism (PE) may result in pulmonary hypertension (CTEPH). Automated CT pulmonary angiography (CTPA) interpretation using artificial intelligence (AI) tools has the potential for improving diagnostic accuracy, reducing delays to diagnosis and yielding novel information of clinical value in CTEPH. This systematic review aimed to identify and appraise existing studies presenting AI tools for CTPA in the context of chronic PE and CTEPH.MethodsMEDLINE and EMBASE databases were searched on 11 September 2023. Journal publications presenting AI tools for CTPA in patients with chronic PE or CTEPH were eligible for inclusion. Information about model design, training and testing was extracted. Study quality was assessed using compliance with the Checklist for Artificial Intelligence in Medical Imaging (CLAIM).ResultsFive studies were eligible for inclusion, all of which presented deep learning AI models to evaluate PE. First study evaluated the lung parenchymal changes in chronic PE and two studies used an AI model to classify PE, with none directly assessing the pulmonary arteries. In addition, a separate study developed a CNN tool to distinguish chronic PE using 2D maximum intensity projection reconstructions. While another study assessed a novel automated approach to quantify hypoperfusion to help in the severity assessment of CTEPH. While descriptions of model design and training were reliable, descriptions of the datasets used in training and testing were more inconsistent.ConclusionIn contrast to AI tools for evaluation of acute PE, there has been limited investigation of AI-based approaches to characterising chronic PE and CTEPH on CTPA. Existing studies are limited by inconsistent reporting of the data used to train and test their models. This systematic review highlights an area of potential expansion for the field of AI in medical image interpretation.There is limited knowledge of A systematic review of artificial intelligence tools for chronic pulmonary embolism in CT. This systematic review provides an assessment on research that examined deep learning algorithms in detecting CTEPH on CTPA images, the number of studies assessing the utility of deep learning on CTPA in CTEPH was unclear and should be highlighted.
Hyperdimensional computing with holographic and adaptive encoder
Authors: Hernández-Cano Alejandro , Ni Yang , Zou Zhuowen , Zakeri Ali , Imani Mohsen 
Published: April 2024

IntroductionBrain-inspired computing has become an emerging field, where a growing number of works focus on developing algorithms that bring machine learning closer to human brains at the functional level. As one of the promising directions, Hyperdimensional Computing (HDC) is centered around the idea of having holographic and high-dimensional representation as the neural activities in our brains. Such representation is the fundamental enabler for the efficiency and robustness of HDC. However, existing HDC-based algorithms suffer from limitations within the encoder. To some extent, they all rely on manually selected encoders, meaning that the resulting representation is never adapted to the tasks at hand.MethodsIn this paper, we propose FLASH, a novel hyperdimensional learning method that incorporates an adaptive and learnable encoder design, aiming at better overall learning performance while maintaining good properties of HDC representation. Current HDC encoders leverage Random Fourier Features (RFF) for kernel correspondence and enable locality-preserving encoding. We propose to learn the encoder matrix distribution via gradient descent and effectively adapt the kernel for a more suitable HDC encoding.ResultsOur experiments on various regression datasets show that tuning the HDC encoder can significantly boost the accuracy, surpassing the current HDC-based algorithm and providing faster inference than other baselines, including RFF-based kernel ridge regression.DiscussionThe results indicate the importance of an adaptive encoder and customized high-dimensional representation in HDC.