bluuebunny/crossref_metadata_2025_split huggingface dataset:
Die kontinuierliche Optimierung von experimentellen Modellen – Darstellung an einem Beispiel aus der Glaukomforschung
Authors: Verma-Fuehring Raoul , Dakroub Mohamad , Haider Malik Salman , Hillenkamp Jost , Kampik Daniel , Loewen Nils Axel 
Published: November 2023

Zusammenfassung
Hintergrund Es besteht ein stetiger Bedarf an Modellen, die die Prüfung neuer chirurgischer und therapeutischer Ansätze in der Glaukomtherapie ermöglichen. Um diesem Bedarf gerecht
zu werden und Alternativen zu In-vivo-Tiermodellen auszubauen, haben wir ein etabliertes In-vitro-Perfusionsmodell für Schweineaugen modifiziert.
Methoden Wir haben 2 Schwächen eines etablierten In-vitro-Perfusionsmodells für Vordersegmente von Schweineaugen identifiziert: den spontanen Flüssigkeitsaustritt während der
Perfusion und die ungewollte Desintegration einzelner Komponenten aufgrund eines fragilen Versuchsaufbaus. Um diese Schwachstellen zu beheben, haben wir die Perfusionsschalen überarbeitet
und neue Komponenten in den Modellaufbau integriert. Um ein Austreten von Flüssigkeit bei hohen Perfusionsraten zu verhindern, fixierten wir die Vordersegmente mit einem Kompressionsring,
Stahlschrauben und Muttern fester auf den Perfusionsschalen. Individuell gefertigte Haltevorrichtungen dienten der Befestigung der Perfusionsschale und des Drucksensors als eine Einheit. Die
Haltevorrichtungen wurden aus Polylactid (PLA) hergestellt und mit einem 3-D-Drucker gedruckt.
Ergebnisse Durch den Einsatz von Stahlschrauben und Muttern konnten die Vordersegmente fester eingespannt werden und ein Austritt von Medium verhindert werden. Unsere
maßgeschneiderten Halterungen stabilisierten die Konstruktion und erleichterten die Handhabung des Aufbaus während der Experimente, wodurch die Vergleichbarkeit zwischen den getesteten
Schweineaugen verbessert wurde. Zusätzlich verhinderten sie ein unbeabsichtigtes Lösen der Drucksensoren, was zu stabilen Druckkurven führte. Unsere Halterungen aus PLA haben
Inkubationstemperaturen von bis zu 37 °C und die Desinfektion mit enzymatischen Reinigungsmitteln und 70%igem Ethanol gut überstanden, ohne nach bis zu 4 Monaten regelmäßiger Verwendung
Anzeichen von Verformung oder Zersetzung zu zeigen.
Schlussfolgerung Die Modifikationen an einem etablierten Perfusionsmodell haben dessen Effizienz und die Reproduzierbarkeit der Experimente verbessert. Unser Modell ist ein Beispiel
dafür, dass viele Modelle durch kritisches Hinterfragen optimiert werden können, was langfristig Ressourcen spart und bessere Ergebnisse liefert.
Methodological evaluation of original articles on radiomics and machine learning for outcome prediction based on positron emission tomography (PET)
Authors: Rogasch Julian Manuel Michael , Shi Kuangyu , Kersting David , Seifert Robert 
Published: November 2023

Abstract
Aim Despite a vast number of articles on radiomics and machine learning in positron emission tomography (PET) imaging, clinical applicability remains limited, partly owing to poor methodological quality. We therefore systematically investigated the methodology described in publications on radiomics and machine learning for PET-based outcome prediction.
Methods A systematic search for original articles was run on PubMed. All articles were rated according to 17 criteria proposed by the authors. Criteria with >2 rating categories were binarized into “adequate” or “inadequate”. The association between the number of “adequate” criteria per article and the date of publication was examined.
Results One hundred articles were identified (published between 07/2017 and 09/2023). The median proportion of articles per criterion that were rated “adequate” was 65% (range: 23–98%). Nineteen articles (19%) mentioned neither a test cohort nor cross-validation to separate training from testing. The median number of criteria with an “adequate” rating per article was 12.5 out of 17 (range, 4–17), and this did not increase with later dates of publication (Spearman’s rho, 0.094; p = 0.35). In 22 articles (22%), less than half of the items were rated “adequate”. Only 8% of articles published the source code, and 10% made the dataset openly available.
Conclusion Among the articles investigated, methodological weaknesses have been identified, and the degree of compliance with recommendations on methodological quality and reporting shows potential for improvement. Better adherence to established guidelines could increase the clinical significance of radiomics and machine learning for PET-based outcome prediction and finally lead to the widespread use in routine clinical practice.
Artificial Intelligence-powered automatic volume calculation in medical images – available tools, performance and challenges for nuclear medicine
Authors: Wendler Thomas , Kreissl Michael C. , Schemmer Benedikt , Rogasch Julian Manuel Michael , De Benetti Francesca 
Published: November 2023

AbstractVolumetry is crucial in oncology and endocrinology, for diagnosis, treatment planning, and evaluating response to therapy for several diseases. The integration of Artificial Intelligence (AI) and Deep Learning (DL) has significantly accelerated the automatization of volumetric calculations, enhancing accuracy and reducing variability and labor. In this review, we show that a high correlation has been observed between Machine Learning (ML) methods and expert assessments in tumor volumetry; Yet, it is recognized as more challenging than organ volumetry. Liver volumetry has shown progression in accuracy with a decrease in error. If a relative error below 10 % is acceptable, ML-based liver volumetry can be considered reliable for standardized imaging protocols if used in patients without major anomalies. Similarly, ML-supported automatic kidney volumetry has also shown consistency and reliability in volumetric calculations. In contrast, AI-supported thyroid volumetry has not been extensively developed, despite initial works in 3D ultrasound showing promising results in terms of accuracy and reproducibility. Despite the advancements presented in the reviewed literature, the lack of standardization limits the generalizability of ML methods across diverse scenarios. The domain gap, i. e., the difference in probability distribution of training and inference data, is of paramount importance before clinical deployment of AI, to maintain accuracy and reliability in patient care. The increasing availability of improved segmentation tools is expected to further incorporate AI methods into routine workflows where volumetry will play a more prominent role in radionuclide therapy planning and quantitative follow-up of disease evolution.
Using Clinical Decision Support Systems to Decrease Intravenous Acetaminophen Use: Implementation and Lessons Learned
Authors: Tse Gabriel , Algaze Claudia , Pageler Natalie , Wood Matthew , Chadwick Whitney 
Published: November 2023

Abstract
Background Clinical decision support systems (CDSS) can enhance medical decision-making by providing targeted information to providers. While they have the potential to improve quality of care and reduce costs, they are not universally effective and can lead to unintended harm.
Objectives To describe the implementation of an unsuccessful interruptive CDSS that aimed to promote appropriate use of intravenous (IV) acetaminophen at an academic pediatric hospital, with an emphasis on lessons learned.
Methods Quality improvement methodology was used to study the effect of an interruptive CDSS, which set a mandatory expiry time of 24 hours for all IV acetaminophen orders. This CDSS was implemented on April 5, 2021. The primary outcome measure was number of IV acetaminophen administrations per 1,000 patient days, measured pre- and postimplementation. Process measures were the number of IV acetaminophen orders placed per 1,000 patient days. Balancing measures were collected via survey data and included provider and nursing acceptability and unintended consequences of the CDSS.
Results There was no special cause variation in hospital-wide IV acetaminophen administrations and orders after CDSS implementation, nor when the CDSS was removed. A total of 88 participants completed the survey. Nearly half (40/88) of respondents reported negative issues with the CDSS, with the majority stating that this affected patient care (39/40). Respondents cited delays in patient care and reduced efficiency as the most common negative effects.
Conclusion This study underscores the significance of monitoring CDSS implementations and including end user acceptability as an outcome measure. Teams should be prepared to modify or remove CDSS that do not achieve their intended goal or are associated with low end user acceptability. CDSS holds promise for improving clinical practice, but careful implementation and ongoing evaluation are crucial for maximizing their benefits and minimizing potential harm.
A Comparative Evaluation of the Cleaning Efficacy of Five Different Root Canal Irrigation Devices: A Histological Study
Authors: Akçay Alper , Gorduysus Melahat , Gorduysus Mehmet Omer , Annamma Lovely M. , Müftüoglu Sevda 
Published: November 2023

Abstract
Objectives This study is aimed to evaluate the cleaning efficacy of five different irrigation systems as SonicMax, RinsEndo, EndoVac, passive ultrasonic irrigation (PUI), and manual needle irrigation (MNI) to histologically evaluate the presence of organic structures and the penetration of irrigation solution.
Materials and Methods Forty-two single-rooted, extracted human mandibular premolars were used in the study. Each tooth was decoronated at the cementoenamel junction and the root canals were instrumented using ProTaper rotary instruments in a crown-down manner. The specimens were randomly divided into five experimental groups (n = 7) Group (1) SonicMax, group (2) RinsEndo,group (3) EndoVac, group (4) PUI, group (5) MNI, and the control groups (n = 7). Each system used 2.5% sodium hypochlorite (NaOCl), 17% ethylenediamine tetraacetic acid (EDTA), and 2.5% NaOCl, respectively, in the experimental groups. The control group did not receive any final irrigation.The measurements were analyzed by employing two-way analysis of variance multivariate results to show significant differences between the length of the dentin tubules in the apical, middle, and coronal of the six groups. The post-hoc test was used when groups were compared by pairs.
Results The results of this study indicate that among the five groups, the RinsEndo and EndoVac were found to be most effective in the cleaning of root canals. The RinsEndo shows highly significant results in the cleaning efficiency of the coronal and middle parts compared with the other groups. The cleaning efficiency in the apical area was the same for RinsEndo and EndoVac.
Conclusion The result of our study indicates that RinsEndo and EndoVac may be more effective in clinical practice.
Evaluating the Necessity of Screw Replacement in Sacral Bone Loosening: A Minimally Invasive Approach for Treatment with Local Anesthesia
Authors: Baloglu Murat , Millet Hakan , Ercan Serdar 
Published: November 2023

Abstract
Background This study aimed to investigate a minimally invasive approach to address the issue of bone loosening in patients who have undergone posterior spinal fusion surgery. If left untreated, sacral bone loosening can result in nerve damage, reduced mobility, and chronic pain. The standard surgical treatment involves replacing the loosened screw with a larger one, requiring significant surgical intervention and complete instrument disassembly. The use of polymethylmethacrylate (PMMA) to increase the strength of the vertebral body was also described, but the results were contradictory. We aim to evaluate the efficacy of filling just only the gap between bone and screw instead of the vertebral body.
Methods This study included patients who had undergone posterior transpedicular stabilization but showed signs of sacral bone loosening in follow-up. The gap between the screw and the bone was targeted instead of the vertebral body and filled using PMMA. The procedure was performed under local anesthesia and fluoroscopy, and the preoperative and postoperative visual analog scale (VAS) scores were compared at 1, 3, and 12 months after the procedure.
Results The study included 17 patients who underwent 28 procedures, with 11 patients receiving bilateral and 6 receiving unilateral approaches. The results showed a significant decrease in postoperative VAS scores compared to the preoperative scores, indicating reduced pain and discomfort. PMMA, as a bone filler, has been reported to provide good stability and support to the bone-implant interface, thereby reducing the risk of screw loosening and improving the outcome of spinal fusion surgery.
Conclusion In conclusion, the study demonstrates the efficacy of a minimally invasive approach using PMMA to treat sacral bone loosening in patients who have undergone posterior spinal fusion surgery. The procedure is safe, minimally invasive, and provides significant pain relief, making it a viable alternative to traditional surgical methods.
Achievement of One-Year MCID Varies with Preoperative Function of Patients Undergoing Total Hip Arthroplasty
Authors: Albright Patrick , Ormseth Andrew , Huyke-Hernández Fernando A. , Doxey Stephen A. , Banks Evan , Only Arthur J. , Horst Patrick K. , Cunningham Brian P. 
Published: November 2023

AbstractThe minimum clinically important difference (MCID) of an orthopaedic outcome measure is commonly used to assess treatment success including for patients undergoing total hip arthroplasty (THA). Various patient factors may contribute to MCID values in patients undergoing THA. The purpose of this study was to calculate the 1-year MCID for the Hip Disability and Osteoarthritis Outcome Score for Joint Replacement (HOOS-JR) based on patient factors and evaluate if there were any differences between each cohort's MCID.A patient-reported outcome measure (PROM) database was reviewed for patients undergoing THA from 2017 to 2018. Patients were included that underwent primary THA and completed baseline and 1-year follow-up for Patient-Reported Outcome Measurement Information System 10-item global health form (PROMIS10) and HOOS-JR surveys. MCID was calculated using the distribution method and an anchor-based method that used the PROMIS10 Physical Health item. Differences in MCID were assessed between patient cohorts stratified by preoperative characteristics.For the 523 patients that underwent THA, the average 1-year HOOS-JR MCID values were 33.9 ± 16.3 (men), 37.0 ± 13.9 (women), 38.1 ± 17.1 (diagnosis of mental health), and 35.0 ± 14.6 (no diagnosis). MCID values were not significantly different between groups according to sex (p = 0.186) or mental health status (p = 0.302). MCID value did not correlate with body mass index (β = –0.126). MCID achievement had a notable inverse relationship with preoperative HOOS-JR.This study demonstrated that preoperative function of patients undergoing THA affects the ability to achieve HOOS-JR 1-year MCID. Future investigation is necessary to clarify which patient factors may affect MCID.
Enhancing gut health and performance of grower-finisher pigs: assessing the efficacy of the mixture of microencapsulated essential oils and saponins
Authors: Yu Sung J. , Morris Andrew , Kayal Advait , Van Thi Thu Hao , Stanley Dragana , Bajagai Yadav S. 
Published: November 2023

Context The livestock industry demands alternative approaches to maintain animal health and productivity without using antimicrobials. Phytogenic feed additives, consisting of plant-derived compounds, offer promising benefits to grower-finisher pigs’ gut microbiota and overall health. Aims This study investigated the impact of essential oils and saponins as phytogenic feed additives on the gut microbiota and productivity of grower-finisher pigs. Methods Four hundred male Large White × Landrace pigs were randomly allocated into control and treatment groups and provided with a basal diet. The treatment group supplemented commercial phytogenic feed additives. Performance data, general health, cumulative mortality, and bodyweight were monitored. Intestinal samples from different gut locations and rectal swab samples were collected for microbiota analysis. Key results No significant difference in weight was observed between the two groups; however, the phytogenic feed additives notably affected gut microbiota. Greater microbial diversity and changes in genera abundance were evident. Campylobacter and Clostridium increased, while beneficial Lactobacillus increased, and Escherichia decreased. The supplemented group demonstrated more consistent growth, with higher minimum weights. Conclusions Phytogenic feed additives have a significant impact on the gut microbiota of grower-finisher pigs. A diverse microbial community and specific changes in genera abundance were observed, indicating the potential role of these additives in modulating gut health. Implications This study emphasises the potential advantages of using phytogenic feed additives to improve the health and productivity of livestock without the use of antibiotics.
COOMET regional comparison of the national standards of air kerma for x-radiation qualities used for radiation protection and diagnostic radiology (COOMET.RI(I)-S3)
Authors: Saroka Siarhei , Salas Gonzalo Walwyn , Luchian Efimia , Büermann Ludwig , Oborin Alexandr , Sukhishvili Simon , Toroi Paula , Shahverdiyev Elmar , Orobinski Andrey , Mamyrbek Nasir 
Published: November 2023

Main text
The results of the COOMET supplementary comparison of the national measurement standards of air kerma for x-radiation qualities used for radiation protection and diagnostic radiology are presented. Nine National Metrology Institutes from the COOMET organization and the International Atomic Energy Agency participated in this COOMET project no.641. The BelGIM acted as a pilot laboratory. The comparison reference value (CRV) was obtained as the mean result of the PTB and the VNIIM. Almost all participants obtained valid results which were consistent within the relative standard uncertainties of the comparison ranging from 0.28% to 2.6%. Some participants had unacceptable results.
To reach the main text of this paper, click on Final Report. Note that this text is that which appears in Appendix B of the BIPM key comparison database https://www.bipm.org/kcdb/.
The final report has been peer-reviewed and approved for publication by the CCRI, according to the provisions of the CIPM Mutual Recognition Arrangement (CIPM MRA).
Novel multi-layer magnetic Vernier group method for multi-turn encoding*
Authors: Cai Jiongjiong , Jiang Zhaoyu , Wu Shimu , Wen Hui , Li Yang 
Published: November 2023

Abstract
Multi-turn encoders receives great attentions recently, especially the gear-type ones with mechanical structure and permanent magnets, which has the function of multi-turn mechanical memory. However, the current gear-type multi-turn encoders still have many challenges, such as complex structure, large volume, complex calculation method, etc. To provide a more flexible multi-turn magnetic encoder that can encode position information in an analytical way, this paper studies the principle, system model, encoding algorithm, and design policy of a novel multi-layer magnetic Vernier group (MVG) method. The weakness of the conventional multi-turn magnetic Vernier encoder is firstly analyzed. Then, the new principle, system model and general encoding algorithm of the investigated multi-layer MVG method are introduced. The design policy of the novel multi-layer MVG method are carefully discussed: (1) from the tooth number of the minimum common period (TNMCP) perspective, all possible combinations of two lower-layer MVGs to upgrade to a higher-layer MVG are analyzed. (2) The gear and tooth number decision policy for lower layers are investigated when the top-layer TNMCP is decided. A gear-saving scheme is established, and the corresponding parameter selections are analyzed. (3) The general process of upgrading the current MVG into a higher-layer alternative is shown out. Finally, a two-layer MVG encoding system with three gears is designed as an example for experimental verification. The results demonstrate that angle information and multi-turn encoding can be realized according to the proposed multi-layer MVG method. The correctness of the gear-saving scheme is also verified. The turn values decoded from the multi-layer MVG are consistent with those from the reference motor encoder.
A bottom-up method for roof plane extraction from airborne LiDAR point clouds
Authors: Xue Jiaming , Xiong Shun , Liu Yongmei , Men Chaoguang , Tian Zeyu 
Published: November 2023

Abstract
Accurate roof plane extraction is a crucial step in constructing a three-dimensional model for buildings. Due to the significant differences in size and shape of building roofs in airborne light detection and ranging point clouds, many existing plane extraction methods are struggling to achieve good performance. To solve the above problem, a bottom–up method for roof plane extraction is proposed in this paper. Starting with the division of the roof point cloud into voxels, the initial planes are obtained in the voxels. The initial planes are then expanded by a parameter-adaptive region growing algorithm. Then, the grown planes are merged according to predefined constraints. Finally, an energy minimization-based method is applied to optimize the results of roof plane extraction. The performance of our proposed method is evaluated on the Vaihingen dataset and the DALES dataset. Experiments demonstrate that our proposed method achieves a superior roof plane extraction result.
Planetary gearbox fault diagnosis based on FDKNN-DGAT with few labeled data
Authors: Tao Hongfeng , Shi Haojin , Qiu Jier , Jin Guanghu , Stojanovic Vladimir 
Published: November 2023

Abstract
Although data-driven methods have been widely used in planetary gearbox fault diagnosis, the difficulty and high cost of manual labeling leads to little labeled training data, which limits the classification performance of traditional data-driven methods. Therefore, the semi-supervised fault diagnosis method with few labeled samples becomes one of the main research directions. Graph attention network (GAT) is distinguished from traditional classification network by using graph structure for fault node information aggregation and feature extraction, which is an effective semi-supervised learning algorithm. This paper uses fast Fourier transform to process the original vibration signal of gearbox and use it as graph nodes, and propose a KNN graph construction method using pooling for fuzzy distance calculation. In addition, this paper improves the distribution of attention weights by introducing dynamic graph attention networks to correct the problem that classical static GATs cannot clearly distinguish the weights of different categories of nodes. Experiments show that the method proposed in this paper can better extract fault features in complex gearbox vibration signals with an accuracy of more than 99% with very few labeled samples, and has better diagnostic performance compared with other graph neural network architectures and traditional classification networks.