bluuebunny/crossref_metadata_2025_split huggingface dataset:
Tele-education in point-of-care ultrasound training
Authors: Suzuki Reina , Riley William J. , Bushman Matthew S. , Dong Yue , Sekiguchi Hiroshi 
Published: October 2024

Abstract
Background
Traditionally, ultrasound skills have been taught through a one-on-one approach, where instructors physically guide learners’ hands at the bedside or in the workshop. However, this method is frequently challenged by scheduling and cost limitations. Our objective was to create a tele-education model for point-of-care ultrasound training and evaluate its effectiveness and its impact on learners’ perceived workload compared to conventional education and self-directed learning methods.

Methods
We conducted a 3-arm randomized trial, comparing tele-education (TE), conventional education (CE) and self-directed learning (SL) methods. All subjects underwent online didactic lectures prior to a hands-on ultrasound workshop. The TE group utilized an ultrasound machine equipped with a speakerphone, a webcam for direct visualization of learner’s hand maneuvers, and an analog-to-video converter for the real-time streaming of ultrasound images. This configuration enabled remote instructors to provide immediate verbal feedback to learners. In contrast, the CE group received in-person coaching, while the SL group had no instructors present. Following the coaching session, subjects completed a scenario-based skill test and a survey on the National Aeronautics and Space Administration task load index (NASA-TLX) to measure their ultrasound competency and perceived workload, respectively.

Results
Twenty-seven ultrasound novices were randomly allocated into 3 groups. The median skill test score of TE, CE, and SL was 22 [interquartile range (IQR): 18–28], 24 [IQR: 21–31], and 16 [IQR: 15–18], respectively (p < 0.01). Pairwise comparisons of median test scores of 3 groups demonstrated a statistical significance in comparisons of TE vs. SL (22 vs. 16, p = 0.01) and CE vs. SL (24 vs. 16, p < 0.01), but not in TE vs. CE (22 vs. 24, p = 0.56). There was no statistical significance observed in the median NASA-TLX scores among the 3 groups; 54 [IQR:47–61] in TE, 57 [IQR:22–64] in CE, and 66 [IQR: 66–72] in SL (p = 0.05).

Conclusions
Our tele-education model was more effective than self-directed learning. There was no statistically significant difference in effectiveness between the tele-education and the conventional education groups. Importantly, tele-education did not impose a significantly higher workload on learners compared to conventional education or self-directed learning. Tele-education has a substantial potential as an alternative to conventional ultrasound training.
Prediction of microvascular invasion in hepatocellular carcinoma with conventional ultrasound, Sonazoid-enhanced ultrasound, and biochemical indicator: a multicenter study
Authors: Lu Dan , Wang Li-Fan , Han Hong , Li Lin-Lin , Kong Wen-Tao , Zhou Qian , Zhou Bo-Yang , Sun Yi-Kang , Yin Hao-Hao , Zhu Ming-Rui , Hu Xin-Yuan , Lu Qing , Xia Han-Sheng , Wang Xi , Zhao Chong-Ke , Zhou Jian-Hua , Xu Hui-Xiong 
Published: October 2024

Abstract
Purpose
To develop and validate a preoperative prediction model based on multimodal ultrasound and biochemical indicator for identifying microvascular invasion (MVI) in patients with a single hepatocellular carcinoma (HCC) ≤ 5 cm.

Methods
From May 2022 to November 2023, a total of 318 patients with pathologically confirmed single HCC ≤ 5 cm from three institutions were enrolled. All of them underwent preoperative biochemical, conventional ultrasound (US), and contrast-enhanced ultrasound (CEUS) (Sonazoid, 0.6 mL, bolus injection) examinations. Univariate and multivariate logistic regression analyses on clinical information, biochemical indicator, and US imaging features were performed in the training set to seek independent predictors for MVI-positive. The models were constructed and evaluated using the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis in both validation and test sets. Subgroup analyses in patients with different liver background and tumor sizes were conducted to further investigate the model’s performance.

Results
Logistic regression analyses showed that obscure tumor boundary in B-mode US, intra-tumoral artery in pulsed-wave Doppler US, complete Kupffer-phase agent clearance in Sonazoid-CEUS, and biomedical indicator PIVKA-II were independently correlated with MVI-positive. The combined model comprising all predictors showed the highest AUC, which were 0.937 and 0.893 in the validation and test sets. Good calibration and prominent net benefit were achieved in both sets. No significant difference was found in subgroup analyses.

Conclusions
The combination of biochemical indicator, conventional US, and Sonazoid-CEUS features could help preoperative MVI prediction in patients with a single HCC ≤ 5 cm.

Critical relevance statement
Investigation of imaging features in conventional US, Sonazoid-CEUS, and biochemical indicators showed a significant relation with MVI-positivity in patients with a single HCC ≤ 5 cm, allowing the construction of a model for preoperative prediction of MVI status to help treatment decision making.

Key Points

MVI status is important for patients with a single HCC ≤ 5 cm.

The model based on conventional US, Sonazoid-CEUS and PIVKA-II performs best for MVI prediction.

The combined model has potential for preoperative prediction of MVI status.

Graphical Abstract
Comparison of image quality and lesion conspicuity between conventional and deep learning reconstruction in gadoxetic acid-enhanced liver MRI
Authors: Yoon Jeong Hee , Lee Jeong Eun , Park So Hyun , Park Jin Young , Kim Jae Hyun , Lee Jeong Min 
Published: October 2024

Abstract
Objective
To compare the image quality and lesion conspicuity of conventional vs deep learning (DL)-based reconstructed three-dimensional T1-weighted images in gadoxetic acid-enhanced liver magnetic resonance imaging (MRI).

Methods
This prospective study (NCT05182099) enrolled participants scheduled for gadoxetic acid-enhanced liver MRI due to suspected focal liver lesions (FLLs) who provided signed informed consent. A liver MRI was conducted using a 3-T scanner. T1-weighted images were reconstructed using both conventional and DL-based (AIRTM Recon DL 3D) reconstruction algorithms. Three radiologists independently reviewed the image quality and lesion conspicuity on a 5-point scale.

Results
Fifty participants (male = 36, mean age 62 ± 11 years) were included for image analysis. The DL-based reconstruction showed significantly higher image quality than conventional images in all phases (3.71–4.40 vs 3.37–3.99, p < 0.001 for all), as well as significantly less noise and ringing artifacts than conventional images (p < 0.05 for all), while also showing significantly altered image texture (p < 0.001 for all). Lesion conspicuity was significantly higher in DL-reconstructed images than in conventional images in the arterial phase (2.15 [95% confidence interval: 1.78, 2.52] vs 2.03 [1.65, 2.40], p = 0.036), but no significant difference was observed in the portal venous phase and hepatobiliary phase (p > 0.05 for all). There was no significant difference in the figure-of-merit (0.728 in DL vs 0.709 in conventional image, p = 0.474).

Conclusion
DL reconstruction provided higher-quality three-dimensional T1-weighted imaging than conventional reconstruction in gadoxetic acid-enhanced liver MRI.

Critical relevance statement
DL reconstruction of 3D T1-weighted images improves image quality and arterial phase lesion conspicuity in gadoxetic acid-enhanced liver MRI compared to conventional reconstruction.

Key Points

DL reconstruction is feasible for 3D T1-weighted images across different spatial resolutions and phases.

DL reconstruction showed superior image quality with reduced noise and ringing artifacts.

Hepatic anatomic structures were more conspicuous on DL-reconstructed images.

Graphical Abstract
A radiomics-based interpretable machine learning model to predict the HER2 status in bladder cancer: a multicenter study
Authors: Wei Zongjie , Bai Xuesong , Xv Yingjie , Chen Shao-Hao , Yin Siwen , Li Yang , Lv Fajin , Xiao Mingzhao , Xie Yongpeng 
Published: October 2024

Abstract
Objective
To develop a computed tomography (CT) radiomics-based interpretable machine learning (ML) model to preoperatively predict human epidermal growth factor receptor 2 (HER2) status in bladder cancer (BCa) with multicenter validation.

Methods
In this retrospective study, 207 patients with pathologically confirmed BCa were enrolled and divided into the training set (n = 154) and test set (n = 53). Least absolute shrinkage and selection operator (LASSO) regression was used to identify the most discriminative features in the training set. Five radiomics-based ML models, namely logistic regression (LR), support vector machine (SVM), k-nearest neighbors (KNN), eXtreme Gradient Boosting (XGBoost) and random forest (RF), were developed. The predictive performance of established ML models was evaluated by the area under the receiver operating characteristic curve (AUC). The Shapley additive explanation (SHAP) was used to analyze the interpretability of ML models.

Results
A total of 1218 radiomics features were extracted from the nephrographic phase CT images, and 11 features were filtered for constructing ML models. In the test set, the AUCs of LR, SVM, KNN, XGBoost, and RF were 0.803, 0.709, 0.679, 0.794, and 0.815, with corresponding accuracies of 71.7%, 69.8%, 60.4%, 75.5%, and 75.5%, respectively. RF was identified as the optimal classifier. SHAP analysis showed that texture features (gray level size zone matrix and gray level co-occurrence matrix) were significant predictors of HER2 status.

Conclusions
The radiomics-based interpretable ML model provides a noninvasive tool to predict the HER2 status of BCa with satisfactory discriminatory performance.

Critical relevance statement
An interpretable radiomics-based machine learning model can preoperatively predict HER2 status in bladder cancer, potentially aiding in the clinical decision-making process.

Key Points

The CT radiomics model could identify HER2 status in bladder cancer.

The random forest model showed a more robust and accurate performance.

The model demonstrated favorable interpretability through SHAP method.

Graphical Abstract
Large vessel vasculitis evaluation by CTA: impact of deep-learning reconstruction and “dark blood” technique
Authors: Ding Ning , Yang Xi-Ao , Xu Min , Wang Yun , Jin Zhengyu , Wang Yining , Xue Huadan , Kong Lingyan , Wang Zhiwei , Zhang Daming 
Published: October 2024

Abstract
Objectives
To assess the performance of the “dark blood” (DB) technique, deep-learning reconstruction (DLR), and their combination on aortic images for large-vessel vasculitis (LVV) patients.

Materials and methods
Fifty patients diagnosed with LVV scheduled for aortic computed tomography angiography (CTA) were prospectively recruited in a single center. Arterial and delayed-phase images of the aorta were reconstructed using the hybrid iterative reconstruction (HIR) and DLR algorithms. HIR or DLR DB image sets were generated using corresponding arterial and delayed-phase image sets based on a “contrast-enhancement-boost” technique. Quantitative parameters of aortic wall image quality were evaluated.

Results
Compared to the arterial phase image sets, decreased image noise and increased signal-noise-ratio (SNR) and CNRouter (all p < 0.05) were obtained for the DB image sets. Compared with delayed-phase image sets, dark-blood image sets combined with the DLR algorithm revealed equivalent noise (p > 0.99) and increased SNR (p < 0.001), CNRouter (p = 0.006), and CNRinner (p < 0.001). For overall image quality, the scores of DB image sets were significantly higher than those of delayed-phase image sets (all p < 0.001). Image sets obtained using the DLR algorithm received significantly better qualitative scores (all p < 0.05) in all three phases. The image quality improvement caused by the DLR algorithm was most prominent for the DB phase image sets.

Conclusion
DB CTA improves image quality and provides better visualization of the aorta for the LVV aorta vessel wall. The DB technique reconstructed by the DLR algorithm achieved the best overall performance compared with the other image sequences.

Critical relevance statement
Deep-learning-based “dark blood” images improve vessel wall image wall quality and boundary visualization.

Key Points

Dark blood CTA improves image quality and provides better aortic wall visualization.

Deep-learning CTA presented higher quality and subjective scores compared to HIR.

Combination of dark blood and deep-learning reconstruction obtained the best overall performance.

Graphical Abstract
Towards the prediction of drug solubility in binary solvent mixtures at various temperatures using machine learning
Authors: Bao Zeqing , Tom Gary , Cheng Austin , Watchorn Jeffrey , Aspuru-Guzik Alán , Allen Christine 
Published: October 2024

Abstract
Drug solubility is an important parameter in the drug development process, yet it is often tedious and challenging to measure, especially for expensive drugs or those available in small quantities. To alleviate these challenges, machine learning (ML) has been applied to predict drug solubility as an alternative approach. However, the majority of existing ML research has focused on the predictions of aqueous solubility and/or solubility at specific temperatures, which restricts the model applicability in pharmaceutical development. To bridge this gap, we compiled a dataset of 27,000 solubility datapoints, including solubility of small molecules measured in a range of binary solvent mixtures under various temperatures. Next, a panel of ML models were trained on this dataset with their hyperparameters tuned using Bayesian optimization. The resulting top-performing models, both gradient boosted decision trees (light gradient boosting machine and extreme gradient boosting), achieved mean absolute errors (MAE) of 0.33 for LogS (S in g/100 g) on the holdout set. These models were further validated through a prospective study, wherein the solubility of four drug molecules were predicted by the models and then validated with in-house solubility experiments. This prospective study demonstrated that the models accurately predicted the solubility of solutes in specific binary solvent mixtures under different temperatures, especially for drugs whose features closely align within the solutes in the dataset (MAE < 0.5 for LogS). To support future research and facilitate advancements in the field, we have made the dataset and code openly available.
Scientific contribution
Our research advances the state-of-the-art in predicting solubility for small molecules by leveraging ML and a uniquely comprehensive dataset. Unlike existing ML studies that predominantly focus on solubility in aqueous solvents at fixed temperatures, our work enables prediction of drug solubility in a variety of binary solvent mixtures over a broad temperature range, providing practical insights on the modeling of solubility for realistic pharmaceutical applications. These advancements along with the open access dataset and code support significant steps in the drug development process including new molecule discovery, drug analysis and formulation.

Graphical Abstract
Trend in serological and molecular diagnostic methods for Toxoplasma gondii infection
Authors: Kim Min-ju , Park Soeun J. , Park Hyunwoo 
Published: October 2024

Abstract
Background
Toxoplasma gondii, an intracellular parasite, is a significant cause of zoonotic disease, with an estimated one-third of the world’s human population believed to be infected. T. gondii is transmitted to humans through the consumption of contaminated water, soil, vegetables, fruits, shellfish or undercooked meat, and can also be passed from human to human through vertical transmission, transplants and blood transfusion. While T. gondii infection typically manifests mild symptoms such as colds among immunocompetent individuals, it can prove lethal for those with weakened immune systems.

Methods
To summarize the diagnostic methods for Toxoplasma gondii infection, we performed a literature search on PubMed from 1948 to 2023 using the keywords “T. gondii serological diagnosis” or “T. gondii molecular diagnosis”.

Results
Rapid and accurate diagnosis of T. gondii infection is imperative. Although a diagnostic kit is currently commercially available, there are a number of disadvantages to the validation principles applied to each diagnostic kit. Consequently, multiple diagnostic methods are concurrently employed to offset these limitations. Serological methods for diagnosing T. gondii infection include the Dye Test (DT), Agglutination Test (AT), Modified Agglutination Test (MAT), Latex Agglutination Test (LAT), Enzyme-Linked Immunosorbent Assay (ELISA), and Western Blot. Meanwhile, molecular methods such as polymerase chain reaction (PCR), nested PCR, real-time PCR, loop-mediated isothermal amplification (LAMP), multiplex PCR, and PCR–restriction fragment length polymorphism (PCR–RFLP) are also utilized. Each of these methods possess its own set of advantages and disadvantages.

Conclusions
By summarizing the advantages and disadvantages of different diagnostic techniques, it is hoped that the epidemiology, prevention, and control of toxoplasmosis will be improved in the future through the use of appropriate technologies.
Manufacturing production as a source of growth: the case of Portugal
Authors: Ferraz Ricardo 
Published: October 2024

AbstractThe aim of this paper is to test the relationship between manufacturing production and economic growth in Portugal from the 1950s to the present day, following Nicholas Kaldor’s (1908–1986) thinking, which assumes that manufacturing is the true engine of the economic activity of a country. By estimating a series of econometric models, including a Vector Autoregressive (VAR) model, it was possible to identify a positive relationship between growth and manufacturing production since the 1950s. The results also show that each increase of 1 percentage point (pp) in real growth rate of the production of basic metallurgical industries and manufacture of metallic products in a certain year t−1 for the period 1956–2019, caused an increase of 0.07 pp in the Portuguese economic growth during a following year t on average. We hope that these findings will stimulate new and interesting research regarding the important issues of manufacturing production and economic growth.
Spatial immunogenomic patterns associated with lymph node metastasis in lung adenocarcinoma
Authors: Meng Fanjie , Li Hao , Jin Ruoyi , Yang Airong , Luo Hao , Li Xiao , Wang Peiyu , Zhao Yaxing , Chervova Olga , Tang Kaicheng , Cheng Sida , Hu Bin , Li Yun , Sheng Jianpeng , Yang Fan , Carbone David , Chen Kezhong , Wang Jun 
Published: October 2024

Abstract
Background
Lung adenocarcinoma (LUAD) with lymph node (LN) metastasis is linked to poor prognosis, yet the underlying mechanisms remain largely undefined. This study aimed to elucidate the immunogenomic landscape associated with LN metastasis in LUAD.

Methods
We employed broad-panel next-generation sequencing (NGS) on a cohort of 257 surgically treated LUAD patients to delineate the molecular landscape of primary tumors and identify actionable driver-gene alterations. Additionally, we used multiplex immunohistochemistry (mIHC) on a propensity score-matched cohort, which enabled us to profile the immune microenvironment of primary tumors in detail while preserving cellular metaclusters, interactions, and neighborhood functional units. By integrating data from NGS and mIHC, we successfully identified spatial immunogenomic patterns and developed a predictive model for LN metastasis, which was subsequently validated independently.

Results
Our analysis revealed distinct immunogenomic alteration patterns associated with LN metastasis stages. Specifically, we observed increased mutation frequencies in genes such as PIK3CG and ATM in LN metastatic primary tumors. Moreover, LN positive primary tumors exhibited a higher presence of macrophage and regulatory T cell metaclusters, along with their enriched neighborhood units (p < 0.05), compared to LN negative tumors. Furthermore, we developed a novel predictive model for LN metastasis likelihood, designed to inform non-surgical treatment strategies, optimize personalized therapy plans, and potentially improve outcomes for patients who are ineligible for surgery.

Conclusions
This study offers a comprehensive analysis of the genetic and immune profiles in LUAD primary tumors with LN metastasis, identifying key immunogenomic patterns linked to metastatic progression. The predictive model derived from these insights marks a substantial advancement in personalized treatment, underscoring its potential to improve patient management.
Time synchronisation for millisecond-precision on bio-loggers
Authors: Wild Timm A. , Wilbs Georg , Dechmann Dina K. N. , Kohles Jenna E. , Linek Nils , Mattingly Sierra , Richter Nina , Sfenthourakis Spyros , Nicolaou Haris , Erotokritou Elena , Wikelski Martin 
Published: October 2024

AbstractTime-synchronised data streams from bio-loggers are becoming increasingly important for analysing and interpreting intricate animal behaviour including split-second decision making, group dynamics, and collective responses to environmental conditions. With the increased use of AI-based approaches for behaviour classification, time synchronisation between recording systems is becoming an essential challenge. Current solutions in bio-logging rely on manually removing time errors during post processing, which is complex and typically does not achieve sub-second timing accuracies.We first introduce an error model to quantify time errors, then optimise three wireless methods for automated onboard time (re)synchronisation on bio-loggers (GPS, WiFi, proximity messages). The methods can be combined as required and, when coupled with a state-of-the-art real time clock, facilitate accurate time annotations for all types of bio-logging data without need for post processing. We analyse time accuracy of our optimised methods in stationary tests and in a case study on 99 Egyptian fruit bats (Rousettus aegyptiacus). Based on the results, we offer recommendations for projects that require high time synchrony.During stationary tests, our low power synchronisation methods achieved median time accuracies of 2.72 / 0.43 ms (GPS / WiFi), compared to UTC time, and relative median time accuracies of 5 ms between tags (wireless proximity messages). In our case study with bats, we achieved a median relative time accuracy of 40 ms between tags throughout the entire 10-day duration of tag deployment. Using only one automated resynchronisation per day, permanent UTC time accuracies of ≤ 185 ms can be guaranteed in 95% of cases over a wide temperature range between 0 and 50 °C. Accurate timekeeping required a minimal battery capacity, operating in the nano- to microwatt range.Time measurements on bio-loggers, similar to other forms of sensor-derived data, are prone to errors and so far received little scientific attention. Our combinable methods offer a means to quantify time errors and autonomously correct them at the source (i.e., on bio-loggers). This approach facilitates sub-second comparisons of simultaneously recorded time series data across multiple individuals and off-animal devices such as cameras or weather stations. Through automated resynchronisations on bio-loggers, long-term sub-second accurate timestamps become feasible, even for life-time studies on animals. We contend that our methods have potential to greatly enhance the quality of ecological data, thereby improving scientific conclusions.