bluuebunny/crossref_metadata_2025_split huggingface dataset:
Eco-epidemiology of Rickettsia amblyommatis and Rickettsia parkeri in naturally infected ticks (Acari: Ixodida) from South Carolina
Authors: Gual-Gonzalez Lídia , Self Stella C. W. , Zellars Kia , Meyer Madeleine , Dye-Braumuller Kyndall C. , Evans Chris L. , Cantillo-Barraza Omar , Torres Myriam W. , Nolan Melissa S. 
Published: January 2024

Abstract
Background
Spotted fever group Rickettsia (SFGR) is the largest group of Rickettsia species of clinical and veterinary importance emerging worldwide. Historically, SFGR cases were linked to Rickettsia rickettsii, the causal agent of Rocky Mountain spotted fever; however, recently discovered species Rickettsia parkeri and Rickettsia amblyommatis have been shown to cause a wide range of clinical symptoms. The role of R. amblyommatis in SFGR eco-epidemiology and the possible public health implications remain unknown.

Methods
This study evaluated statewide tick surveillance and land-use classification data to define the eco-epidemiological relationships between R. amblyommatis and R. parkeri among questing and feeding ticks collected across South Carolina between 2021 and 2022. Questing ticks from state parks and feeding ticks from animal shelters were evaluated for R. parkeri and R. amblyommatis using reverse transcriptase quantitative polymerase chain reaction (RT-qPCR) on pooled samples. A Bayesian multivariable logistic regression model for pool testing data was used to assess associations between R. parkeri or R. amblyommatis infection and land-use classification variables among questing ticks. The Spearman correlation was used to evaluate the relationship between the two tested pathogens.

Results
The infection prevalence for R. amblyommatis was 24.8% (23.4–26.3%) among questing ticks, and 39.5% (37.4–42.0%) among feeding ticks; conversely, for R. parkeri it was 19.0% (17.6–20.5%) among questing ticks and 22.4% (20.3–24.5%) among feeding ticks. A negative, refractory correlation was found between the species, with ticks significantly more likely to contain one or the other pathogen, but not both simultaneously. The Bayesian analysis revealed that R. amblyommatis infection was positively associated with deciduous, evergreen, and mixed forests, and negatively associated with hay and pasture fields, and emergent herbaceous wetlands. Rickettsia parkeri infection was positively associated with deciduous, mixed, and evergreen forests, herbaceous vegetation, cultivated cropland, woody wetlands, and emergent herbaceous wetlands, and negatively associated with hay and pasture fields.

Conclusions
This is the first study to evaluate the eco-epidemiological factors driving tick pathogenicity in South Carolina. The negative interactions between SFGR species suggest the possible inhibition between the two pathogens tested, which could have important public health implications. Moreover, land-use classification factors revealed environments associated with tick pathogenicity, highlighting the need for tick vector control in these areas.

Graphical Abstract
mRNA-based precision targeting of neoantigens and tumor-associated antigens in malignant brain tumors
Authors: Trivedi Vrunda , Yang Changlin , Klippel Kelena , Yegorov Oleg , von Roemeling Christina , Hoang-Minh Lan , Fenton Graeme , Ogando-Rivas Elizabeth , Castillo Paul , Moore Ginger , Long-James Kaytora , Dyson Kyle , Doonan Bently , Flores Catherine , Mitchell Duane A. 
Published: January 2024

Abstract
Background
Despite advancements in the successful use of immunotherapy in treating a variety of solid tumors, applications in treating brain tumors have lagged considerably. This is due, at least in part, to the lack of well-characterized antigens expressed within brain tumors that can mediate tumor rejection; the low mutational burden of these tumors that limits the abundance of targetable neoantigens; and the immunologically “cold” tumor microenvironment that hampers the generation of sustained and productive immunologic responses. The field of mRNA-based therapeutics has experienced a boon following the universal approval of COVID-19 mRNA vaccines. mRNA-based immunotherapeutics have also garnered widespread interest for their potential to revolutionize cancer treatment. In this study, we developed a novel and scalable approach for the production of personalized mRNA-based therapeutics that target multiple tumor rejection antigens in a single therapy for the treatment of refractory brain tumors.

Methods
Tumor-specific neoantigens and aberrantly overexpressed tumor-associated antigens were identified for glioblastoma and medulloblastoma tumors using our cancer immunogenomics pipeline called Open Reading Frame Antigen Network (O.R.A.N). Personalized tumor antigen-specific mRNA vaccine was developed for each individual tumor model using selective gene capture and enrichment strategy. The immunogenicity and efficacy of the personalized mRNA vaccines was evaluated in combination with anti-PD-1 immune checkpoint blockade therapy or adoptive cellular therapy with ex vivo expanded tumor antigen-specific lymphocytes in highly aggressive murine GBM models.

Results
Our results demonstrate the effectiveness of the antigen-specific mRNA vaccines in eliciting robust anti-tumor immune responses in GBM hosts. Our findings substantiate an increase in tumor-infiltrating lymphocytes characterized by enhanced effector function, both intratumorally and systemically, after antigen-specific mRNA-directed immunotherapy, resulting in a favorable shift in the tumor microenvironment from immunologically cold to hot. Capacity to generate personalized mRNA vaccines targeting human GBM antigens was also demonstrated.

Conclusions
We have established a personalized and customizable mRNA-therapeutic approach that effectively targets a plurality of tumor antigens and demonstrated potent anti-tumor response in preclinical brain tumor models. This platform mRNA technology uniquely addresses the challenge of tumor heterogeneity and low antigen burden, two key deficiencies in targeting the classically immunotherapy-resistant CNS malignancies, and possibly other cold tumor types.
Interactive training workshop to improve prostate mpMRI knowledge: results from the ESOR Nicholas Gourtsoyiannis teaching fellowship
Authors: Barrett Tristan , Lee Kang-Lung , Illerstam Fredrik , Thomsen Henrik S. , Jhaveri Kartik S. , Løgager Vibeke 
Published: January 2024

Abstract

Purpose
Prostate MRI is established for the investigation of patients presenting with suspected early prostate cancer. Outcomes are dependent on both image quality and interpretation. This study assessed the impact of an educational intervention on participants’ theoretical knowledge of the technique.

Methods
Eighty-one clinicians from two centers with varying experience in prostate MRI participated. Baseline knowledge was assessed with 10 written and image-based multiple-choice questions (MCQs) prior to a course including didactic lectures and hands-on interactive workshops on prostate MRI interpretation. Post-course, participants completed a second 10-question MCQ test, matched by format, themes, and difficulty, to assess for any improvement in knowledge and performance. Results were assessed using the Wilcoxon rank sum test, and the Wilcoxon signed-rank test for paired data.

Results
Thirty-nine participants, including 25/49 (51.0%) and 14/32 (43.8%) at each center completed both assessments, with their results used for subsequent evaluation. Overall, there was a significant improvement from pre- (4.92 ± 2.41) to post-course scores (6.77 ± 1.46), p < 0.001 and at both Copenhagen (5.92 ± 2.25 to 7.36 ± 1.25) and Toronto (3.14 ± 1.51 to 5.71 ± 1.20); p = 0.005 and p = 0.002, respectively. Participants with no prostate MRI experience showed the greatest improvement (3.77 ± 1.97 to 6.18 ± 1.5, p < 0.001), followed by intermediate level (< 500 MRIs reported) experience (6.18 ± 1.99 to 7.46 ± 1.13, p = 0.058), then advanced (> 500 MRIs reported) experience (6.83 ± 2.48 to 7.67 ± 0.82, p = 0.339).

Conclusions
A dedicated prostate MRI teaching course combining didactic lectures and hands-on workshops significantly improved short-term theoretical knowledge of the technique for clinicians with differing levels of experience.

Critical relevance statement
A dedicated teaching course significantly improved theoretical knowledge of the technique particularly for clinicians with less reporting experience and a lower baseline knowledge. The multiple-choice questions format mapped improved performance and may be considered as part of future MRI certification initiatives.

Key points
• Prostate MRI knowledge is important for image interpretation and optimizing acquisition sequences.
• A dedicated teaching course significantly improved theoretical knowledge of the technique.
• Improved performance was more apparent in clinicians with less reporting experience and a lower baseline knowledge.

Graphical Abstract
A holistic approach to implementing artificial intelligence in radiology
Authors: Kim Bomi , Romeijn Stephan , van Buchem Mark , Mehrizi Mohammad Hosein Rezazade , Grootjans Willem 
Published: January 2024

Abstract
Objective
Despite the widespread recognition of the importance of artificial intelligence (AI) in healthcare, its implementation is often limited. This article aims to address this implementation gap by presenting insights from an in-depth case study of an organisation that approached AI implementation with a holistic approach.

Materials and methods
We conducted a longitudinal, qualitative case study of the implementation of AI in radiology at a large academic medical centre in the Netherlands for three years. Collected data consists of 43 days of work observations, 30 meeting observations, 18 interviews and 41 relevant documents. Abductive reasoning was used for systematic data analysis, which revealed three change initiative themes responding to specific AI implementation challenges.

Results
This study identifies challenges of implementing AI in radiology at different levels and proposes a holistic approach to tackle those challenges. At the technology level, there is the issue of multiple narrow AI applications with no standard use interface; at the workflow level, AI results allow limited interaction with radiologists; at the people and organisational level, there are divergent expectations and limited experience with AI. The case of Southern illustrates that organisations can reap more benefits from AI implementation by investing in long-term initiatives that holistically align both social and technological aspects of clinical practice.

Conclusion
This study highlights the importance of a holistic approach to AI implementation that addresses challenges spanning technology, workflow, and organisational levels. Aligning change initiatives between these different levels has proven to be important to facilitate wide-scale implementation of AI in clinical practice.

Critical relevance statement
Adoption of artificial intelligence is crucial for future-ready radiological care. This case study highlights the importance of a holistic approach that addresses technological, workflow, and organisational aspects, offering practical insights and solutions to facilitate successful AI adoption in clinical practice.

Key points
1. Practical and actionable insights into successful AI implementation in radiology are lacking.
2. Aligning technology, workflow, organisational aspects is crucial for a successful AI implementation
3. Holistic approach aids organisations to create sustainable value through AI implementation.

Graphical Abstract
Convolutional neural networks for the differentiation between benign and malignant renal tumors with a multicenter international computed tomography dataset
Authors: Klontzas Michail E. , Kalarakis Georgios , Koltsakis Emmanouil , Papathomas Thomas , Karantanas Apostolos H. , Tzortzakakis Antonios 
Published: January 2024

Abstract
Objectives
To use convolutional neural networks (CNNs) for the differentiation between benign and malignant renal tumors using contrast-enhanced CT images of a multi-institutional, multi-vendor, and multicenter CT dataset.

Methods
A total of 264 histologically confirmed renal tumors were included, from US and Swedish centers. Images were augmented and divided randomly 70%:30% for algorithm training and testing. Three CNNs (InceptionV3, Inception-ResNetV2, VGG-16) were pretrained with transfer learning and fine-tuned with our dataset to distinguish between malignant and benign tumors. The ensemble consensus decision of the three networks was also recorded. Performance of each network was assessed with receiver operating characteristics (ROC) curves and their area under the curve (AUC-ROC). Saliency maps were created to demonstrate the attention of the highest performing CNN.

Results
Inception-ResNetV2 achieved the highest AUC of 0.918 (95% CI 0.873–0.963), whereas VGG-16 achieved an AUC of 0.813 (95% CI 0.752–0.874). InceptionV3 and ensemble achieved the same performance with an AUC of 0.894 (95% CI 0.844–0.943). Saliency maps indicated that Inception-ResNetV2 decisions are based on the characteristics of the tumor while in most tumors considering the characteristics of the interface between the tumor and the surrounding renal parenchyma.

Conclusion
Deep learning based on a diverse multicenter international dataset can enable accurate differentiation between benign and malignant renal tumors.

Critical relevance statement
Convolutional neural networks trained on a diverse CT dataset can accurately differentiate between benign and malignant renal tumors.

Key points
• Differentiation between benign and malignant tumors based on CT is extremely challenging.
• Inception-ResNetV2 trained on a diverse dataset achieved excellent differentiation between tumor types.
• Deep learning can be used to distinguish between benign and malignant renal tumors.

Graphical Abstract
Novel insight into the lipid network of plasma extracellular vesicles reveal sex-based differences in the lipidomic profile of alcohol use disorder patients
Authors: Perpiñá-Clérigues Carla , Mellado Susana , Galiana-Roselló Cristina , Fernández-Regueras María , Marcos Miguel , García-García Francisco , Pascual María 
Published: January 2024

Abstract
Background
Alcohol use disorder (AUD) is one of the most common psychiatric disorders, with the consumption of alcohol considered a leading cause of preventable deaths worldwide. Lipids play a crucial functional role in cell membranes; however, we know little about the role of lipids in extracellular vesicles (EVs) as regulatory molecules and disease biomarkers.

Methods
We employed a sensitive lipidomic strategy to characterize lipid species from the plasma EVs of AUD patients to evaluate functional roles and enzymatic activity networks to improve the knowledge of lipid metabolism after alcohol consumption. We analyzed plasma EV lipids from AUD females and males and healthy individuals to highlight lipids with differential abundance and biologically interpreted lipidomics data using LINEX2, which evaluates enzymatic dysregulation using an enrichment algorithm.

Results
Our results show, for the first time, that AUD females exhibited more significant substrate-product changes in lysophosphatidylcholine/phosphatidylcholine lipids and phospholipase/acyltransferase activity, which are potentially linked to cancer progression and neuroinflammation. Conversely, AUD males suffer from dysregulated ceramide and sphingomyelin lipids involving sphingomyelinase, sphingomyelin phosphodiesterase, and sphingomyelin synthase activity, which relates to hepatotoxicity. Notably, the analysis of plasma EVs from AUD females and males demonstrates enrichment of lipid ontology terms associated with “negative intrinsic curvature” and “positive intrinsic curvature”, respectively.

Conclusions
Our methodological developments support an improved understanding of lipid metabolism and regulatory mechanisms, which contribute to the identification of novel lipid targets and the discovery of sex-specific clinical biomarkers in AUD.
Comparison of 18F-FDG PET and arterial spin labeling MRI in evaluating Alzheimer’s disease and amnestic mild cognitive impairment using integrated PET/MR
Authors: Bi Sheng , Yan Shaozhen , Chen Zhigeng , Cui Bixiao , Shan Yi , Yang Hongwei , Qi Zhigang , Zhao Zhilian , Han Ying , Lu Jie 
Published: January 2024

Abstract
Background
Developing biomarkers for early stage AD patients is crucial. Glucose metabolism measured by 18F-FDG PET is the most common biomarker for evaluating cellular energy metabolism to diagnose AD. Arterial spin labeling (ASL) MRI can potentially provide comparable diagnostic information to 18F-FDG PET in patients with neurodegenerative disorders. However, the conclusions about the diagnostic performance of AD are still controversial between 18F-FDG PET and ASL. This study aims to compare quantitative cerebral blood flow (CBF) and glucose metabolism measured by 18F-FDG PET diagnostic values in patients with Alzheimer’s disease (AD) and amnestic mild cognitive impairment (aMCI) using integrated PET/MR.

Results
Analyses revealed overlapping between decreased regional rCBF and 18F-FDG PET SUVR in patients with AD compared with NC participants in the bilateral parietotemporal regions, frontal cortex, and cingulate cortex. Compared with NC participants, patients with aMCI exclusively demonstrated lower 18F-FDG PET SUVR in the bilateral temporal cortex, insula cortex, and inferior frontal cortex. Comparison of the rCBF in patients with aMCI and NC participants revealed no significant difference (P > 0.05). The ROC analysis of rCBF in the meta-ROI could diagnose patients with AD (AUC, 0.87) but not aMCI (AUC, 0.61). The specificity of diagnosing aMCI has been improved to 75.56% when combining rCBF and 18F-FDG PET SUVR.

Conclusion
ASL could detect similar aberrant patterns of abnormalities compared to 18F-FDG PET in patients with AD compared with NC participants but not in aMCI. The diagnostic efficiency of 18F-FDG-PET for AD and aMCI patients remained higher to ASL. Our findings support that applying 18F-FDG PET may be preferable for diagnosing AD and aMCI.
Predictive mobility and cost-aware flow placement in SDN-based IoT networks: a Q-learning approach
Authors: Huang Gan , Ullah Ihsan , Huang Hanyao , Kim Kyung Tae 
Published: January 2024

AbstractSoftware-Defined Networking (SDN) has emerged as an innovative networking method that offers effective management and remarkable flexibility. However, current SDN-based solutions primarily focus on static networks or concentrate on backbone networks, where network dynamics have minimal impact. The existing methods for placing flow entries in Software-Defined Networking (SDN)-based Internet of Things (IoT) systems exhibit shortcomings in accurately predicting outcomes and efficiently reducing table misses and associated performance metrics. This research introduces a new approach, specifically the mobility-aware adaptive flow entry placement scheme for SDN-based Internet of Things (IoT) environments, to address the mobility aspect of networks. The proposed scheme utilizes the Q-learning algorithm to predict the next possible location of end devices, while the cost-sensitive AdaBoost algorithm is employed to select heavy and active flows. As a result, efficient flow rules for incoming flows can be dynamically implemented without controller intervention. Extensive computer simulations demonstrate that this approach significantly enhances match probability and prediction accuracy while concurrently reducing the number of table misses and resource expenditure compared to existing schemes.
Health system barriers to the implementation of the national action plan to combat antimicrobial resistance in Vietnam: a scoping review
Authors: Pham Giang N. , Dang Tho T. H. , Nguyen Thu-Anh , Zawahir Shukry , Le Hien T. T. , Negin Joel , Schneider Carmen Huckel , Fox Greg J. 
Published: January 2024

Abstract
Background
Vietnam is among 11 countries in the Western Pacific region that has developed a National Action Plan for Antimicrobial Resistance (NAPCA).

Methods
This scoping review characterises health system barriers to the implementation of the Vietnam NAPCA, with reference to the WHO Health Systems Framework.

Results
Over 7 years, between 2013 and 2020, the Ministry of Health (MOH) of Vietnam has been implementing activities to achieve the six NAPCA objectives. They include revision of regulations needed for antimicrobial resistance (AMR) prevention programs; formation and operation of national management bodies; improvement of antimicrobial stewardship (AMS) in hospitals; maintenance of surveillance systems for AMR; provision of trainings on AMR and antibiotics use to doctors and pharmacists; and organization of nation-wide educational campaigns. Limited cooperation between MOH management bodies, shortages of human resource at all health system levels, a low degree of agreement between national and hospital guidelines on antibiotic use, low capability in the domestic supply of standardised drugs, and unequal training opportunities for lower-level health professionals present ongoing challenges. Actions suggested for the next period of the NAPCA include a final review of what has been achieved by the plan so far and evaluating the effectiveness of the different components of the plan. Different options on how to improve coordination across sectors in the development of a new NAPCA should be put forward.

Conclusions
The 6-year implementation of the Vietnam NAPCA has yielded valuable lessons for AMS in Vietnam, guiding the development of future national plans, with a central focus on scaling up AMS in hospitals and promoting community AMS programs to combat AMR.
Exploring the inner hygro-mechanical behaviour of historical panel paintings: a novel approach using digital twins
Authors: Lorenzo Riparbelli , Paola Mazzanti , Thomas Helfer , Chiara Manfriani , Luca Uzielli , Ciro Castelli , Andrea Santacesaria , Luciano Ricciardi , Sandra Rossi , Joseph Gril , Marco Fioravanti 
Published: January 2024

Abstract
Wooden Panel Paintings (WPPs) stand as invaluable cultural artefacts from the past. These works present an intriguing challenge in understanding their complex mechanical behaviour and ensuring their long-term preservation. The present study assumes as founding paradigm the unicity of each WPP, in terms of its material composition, historical background, physical dimensions, and the specific environmental conditions it has been subjected to over time, and their complex behaviour, which requires the knowledge of both mechanical and materials specificity. These characteristics need to be considered, and studied in-depth for each individual WPP, particularly if the aim is to develop a comprehensive understanding of its individual mechanical behaviour. The study provides new computational models calibrated to reproduce the physical and mechanical behaviour of artworks and acting as their 'digital twins'. The models developed contribute significantly to the understanding of the mechanics of these artworks, including the impact of environmental thermo-hygrometric fluctuations and the role of structural elements such as crossbeams. The results, corroborated by experimental analyses, indicate that environmental variations, both short and long term, exert specific and quantifiable effects on the WPPs, and that the presence of crossbeams significantly influences the distribution of stresses within the panel, particularly affecting the pictorial layers that are often the most susceptible to damage. In this context the generated digital twins serve as an invaluable tool, offering the potential to simulate various risk scenarios or to evaluate the effectiveness of engineered conservation interventions on the WPPs.