bluuebunny/crossref_metadata_2025_split huggingface dataset:
Validation of a novel, low-fidelity virtual reality simulator and an artificial intelligence assessment approach for peg transfer laparoscopic training
Authors: Bogar Peter Zoltan , Virag Mark , Bene Matyas , Hardi Peter , Matuz Andras , Schlegl Adam Tibor , Toth Luca , Molnar Ferenc , Nagy Balint , Rendeki Szilard , Berner-Juhos Krisztina , Ferencz Andrea , Fischer Krisztina , Maroti Peter 
Published: July 2024

AbstractSimulators are widely used in medical education, but objective and automatic assessment is not feasible with low-fidelity simulators, which can be solved with artificial intelligence (AI) and virtual reality (VR) solutions. The effectiveness of a custom-made VR simulator and an AI-based evaluator of a laparoscopic peg transfer exercise was investigated. Sixty medical students were involved in a single-blinded randomised controlled study to compare the VR simulator with the traditional box trainer. A total of 240 peg transfer exercises from the Fundamentals of Laparoscopic Surgery programme were analysed. The experts and AI-based software used the same criteria for evaluation. The algorithm detected pitfalls and measured exercise duration. Skill improvement showed no significant difference between the VR and control groups. The AI-based evaluator exhibited 95% agreement with the manual assessment. The average difference between the exercise durations measured by the two evaluation methods was 2.61 s. The duration of the algorithmic assessment was 59.47 s faster than the manual assessment. The VR simulator was an effective alternative practice compared with the training box simulator. The AI-based evaluation produced similar results compared with the manual assessment, and it could significantly reduce the evaluation time. AI and VR could improve the effectiveness of basic laparoscopic training.
A modified quality control protocol for infectious disease serology based on the Westgard rules
Authors: Wang Yuanfang , Li Xiaohan , Li Dongdong , Xie Yi 
Published: July 2024

AbstractWhen traditional statistical quality control protocols, represented by the Westgard protocol were applied to infectious disease serology, the rejection limits were questioned because of the high rejection probability. We first define the probability of false rejection (Pfr) and error detection (Ped) for infectious disease serology. QC data in 6 months were collected and the Pfr of each rule in the Westgard protocol and Rilibak protocol was evaluated. Then, as improvements, we chose different rules for negative and positive QC data to constitute an asymmetric protocol, furthermore, while reagent lot changes, the mean value of QC protocol is reset with the first 15 QC results of new lot reagent. QC materials and Standard Reference Materials were tested synchronously in the next 6 months, to verify whether the Pfr and Ped of the asymmetric protocol could meet the requirement. Protocol 1 exhibited the higher level of rejection rate among the two protocols, especially after reagent lot changes; Pfr below the lower control limit (LCL) was 1.39–21.78 times higher than the upper control limit (UCL); false rejections were more likely to occur in negative QC data, with Pfr-total of 27–65%. The asymmetric protocol can significantly reduce the proportion of analytes with Pfr by over 20%. Systematic error due to reagent lot changes and random error due to routine QC data variation were considered potential factors for excessive Pfr. Asymmetric QC protocol that can reduce Pfr by different control limits for negative and positive QC data.
Pre-movement muscle co-contraction associated with motor performance deterioration under high reward conditions
Authors: Senta Naoki , Ushiba Junichi , Takemi Mitsuaki 
Published: July 2024

AbstractReward usually enhances task performance, but exceptionally large rewards can impede performance due to psychological pressure. In this study, we investigated motor activity changes in high-reward situations and identified indicators for performance decline. Fourteen healthy adults practiced a velocity-dependent right-hand motor task for three days, followed by a test day with varying monetary reward for each trial. Participants were divided into low performers (LPs) and high performers (HPs) according to whether success rate decreased or increased, respectively, on the highest reward trials compared to lower reward trials. Both LPs and HPs demonstrated increased hand velocity during higher reward trials, but only LPs exhibited a significant increase in velocity variance. There was also a negative correlation between the pre-movement co-contraction index (CCI) of the biceps and triceps muscles and success rate on the highest reward trials. This correlation was confirmed in a second experiment with 12 newly recruited participants, suggesting that pre-movement CCI is a marker for performance decline caused by high reward. These findings suggest that interventions to reduce pre-movement CCI such as biofeedback training could be useful for preventing the paradoxical decline in motor performance associated with high rewards.
Learning generalizable AI models for multi-center histopathology image classification
Authors: Asadi-Aghbolaghi Maryam , Darbandsari Amirali , Zhang Allen , Contreras-Sanz Alberto , Boschman Jeffrey , Ahmadvand Pouya , Köbel Martin , Farnell David , Huntsman David G. , Churg Andrew , Black Peter C. , Wang Gang , Gilks C. Blake , Farahani Hossein , Bashashati Ali 
Published: July 2024

AbstractInvestigation of histopathology slides by pathologists is an indispensable component of the routine diagnosis of cancer. Artificial intelligence (AI) has the potential to enhance diagnostic accuracy, improve efficiency, and patient outcomes in clinical pathology. However, variations in tissue preparation, staining protocols, and histopathology slide digitization could result in over-fitting of deep learning models when trained on the data from only one center, thereby underscoring the necessity to generalize deep learning networks for multi-center use. Several techniques, including the use of grayscale images, color normalization techniques, and Adversarial Domain Adaptation (ADA) have been suggested to generalize deep learning algorithms, but there are limitations to their effectiveness and discriminability. Convolutional Neural Networks (CNNs) exhibit higher sensitivity to variations in the amplitude spectrum, whereas humans predominantly rely on phase-related components for object recognition. As such, we propose Adversarial fourIer-based Domain Adaptation (AIDA) which applies the advantages of a Fourier transform in adversarial domain adaptation. We conducted a comprehensive examination of subtype classification tasks in four cancers, incorporating cases from multiple medical centers. Specifically, the datasets included multi-center data for 1113 ovarian cancer cases, 247 pleural cancer cases, 422 bladder cancer cases, and 482 breast cancer cases. Our proposed approach significantly improved performance, achieving superior classification results in the target domain, surpassing the baseline, color augmentation and normalization techniques, and ADA. Furthermore, extensive pathologist reviews suggested that our proposed approach, AIDA, successfully identifies known histotype-specific features. This superior performance highlights AIDA’s potential in addressing generalization challenges in deep learning models for multi-center histopathology datasets.
The effect of testosterone on the gut microbiome in mice
Authors: Moadi Lelyan , Turjeman Sondra , Asulin Nofar , Koren Omry 
Published: July 2024

AbstractThe role of hormones in gut–brain crosstalk is largely elusive, but recent research supports specific changes in hormone levels correlated with the gut microbiota. An interesting but unstudied area in microbial endocrinology is the interplay between the microbiota and sex hormones. The aim of this study is to investigate the effect of testosterone and sex on the mouse gut microbiome. We use in vitro experiments to test direct effects of testosterone on bacteria in fecal samples collected from male and female mice pre- and post-puberty. Sex-specific microbial and metabolic differences surrounding puberty are also examined in vivo. We then explore effects of testosterone supplementation in vivo, characterizing microbiota and metabolomes of male and female mice. We detect sex-specific differences in microbiota and associated metabolites of mice post-puberty, but in vitro experiments reveal that testosterone only affects microbiota of fecal samples collected before puberty. Testosterone supplementation in vivo affects gut microbiota and metabolomes in both male and female mice. Taking our results from in vitro and in vivo experiments, we conclude that the shift in the microbiome after puberty is at least partially caused by the higher levels of sex hormones, mainly testosterone, in the host.
Diversity of post-translational modifications and cell signaling revealed by single cell and single organelle mass spectrometry
Authors: Mun Dong-Gi , Bhat Firdous A. , Joshi Neha , Sandoval Leticia , Ding Husheng , Jain Anu , Peterson Jane A. , Kang Taewook , Pujari Ganesh P. , Tomlinson Jennifer L. , Budhraja Rohit , Zenka Roman M. , Kannan Nagarajan , Kipp Benjamin R. , Dasari Surendra , Gaspar-Maia Alexandre , Smoot Rory L. , Kandasamy Richard K. , Pandey Akhilesh 
Published: July 2024

AbstractThe rapid evolution of mass spectrometry-based single-cell proteomics now enables the cataloging of several thousand proteins from single cells. We investigated whether we could discover cellular heterogeneity beyond proteome, encompassing post-translational modifications (PTM), protein-protein interaction, and variants. By optimizing the mass spectrometry data interpretation strategy to enable the detection of PTMs and variants, we have generated a high-definition dataset of single-cell and nuclear proteomic-states. The data demonstrate the heterogeneity of cell-states and signaling dependencies at the single-cell level and reveal epigenetic drug-induced changes in single nuclei. This approach enables the exploration of previously uncharted single-cell and organellar proteomes revealing molecular characteristics that are inaccessible through RNA profiling.
Overcoming data scarcity in biomedical imaging with a foundational multi-task model
Authors: Schäfer Raphael , Nicke Till , Höfener Henning , Lange Annkristin , Merhof Dorit , Feuerhake Friedrich , Schulz Volkmar , Lotz Johannes , Kiessling Fabian 
Published: July 2024

AbstractFoundational models, pretrained on a large scale, have demonstrated substantial success across non-medical domains. However, training these models typically requires large, comprehensive datasets, which contrasts with the smaller and more specialized datasets common in biomedical imaging. Here we propose a multi-task learning strategy that decouples the number of training tasks from memory requirements. We trained a universal biomedical pretrained model (UMedPT) on a multi-task database including tomographic, microscopic and X-ray images, with various labeling strategies such as classification, segmentation and object detection. The UMedPT foundational model outperformed ImageNet pretraining and previous state-of-the-art models. For classification tasks related to the pretraining database, it maintained its performance with only 1% of the original training data and without fine-tuning. For out-of-domain tasks it required only 50% of the original training data. In an external independent validation, imaging features extracted using UMedPT proved to set a new standard for cross-center transferability.
Millimetre‐wave on‐chip SIW filtering crossover using 0.25 µm GaAs pHEMT technology
Authors: Zhou Xin , Lu Siyuan , Li Desen , Ding Daqi , Chio Chi‐Hou , Tam Kam‐Weng 
Published: July 2024

AbstractThis letter presents a novel millimetre‐wave (mm‐wave) on‐chip substrate integrated waveguide (SIW) filtering crossover using 0.25 µm GaAs pHEMT technology. The design methodology of the proposed crossover is thoroughly illustrated. The proposed filtering crossover employs a dual‐mode cavity with TE102 and TE201 degenerate mode resonances at the intersection, leveraging the degenerate modes for in‐band resonance and inter‐channel isolation. Additionally, four TE101 mode resonant half‐mode SIW cavities are coupled around the dual‐mode cavity to achieve two third‐order bandpass response channels and reduce the overall size. A prototype is designed, analysed, and fabricated to validate the proposed approach, with measured results showing good agreement with simulations. The presented on‐chip SIW filtering crossover offers promising potential for mm‐wave applications, demonstrating the effectiveness of the design methodology and GaAs pHEMT technology integration.
Guest Editorial: Quantum industry: Applications in quantum communication (Quantum.Tech Europe 2022)
Authors: De Debashis , Lord Andrew 
Published: July 2024

AbstractQuantum technology harnesses the principles of quantum mechanics to accomplish tasks in a different way as compared to the classical technologies. This includes quantum computing, which uses qubits for parallel information processing, greatly enhancing computation speed and entanglement and empowering problem‐solving abilities. Quantum communication provides secure data transmission through quantum cryptography, while quantum sensing offers improved measurement precision, benefiting areas such as cryptography, material science, and pharmaceuticals. Additionally, the implementation and commercialisation of quantum technology involve transitioning theoretical quantum concepts into practical applications and marketable products. To achieve widespread adoption of quantum industry, significant research efforts are crucial among academia, industry, and government.
Active sonar target recognition method based on multi‐domain transformations and attention‐based fusion network
Authors: Wang Qingcui , Du Shuanping , Zhang Wei , Wang Fangyong 
Published: July 2024

AbstractThe classification and recognition of underwater targets by an active sonar system remain challenging and complex. Traditional methods have limited classification performance in time and spatially varying ocean channels. An active sonar target recognition method is proposed based on multi‐domain transformations and an attention‐based fusion network. Initially, the active target echo undergoes time‐frequency analysis, auditory signal processing, and matched filtering to represent target attributes in joint spatial‐time‐frequency domains. Subsequently, multiple attention‐based fusion models fuse the multi‐domain transformations either early or late in the processing stages. An attention module further enhances significant feature channels through adaptive weight assignment. Experiment results demonstrate that the recognition accuracy of active sonar echoes using multi‐domain transformations improves significantly compared to that of single‐domain methods, with an increase of up to 10.5%. The incorporation of multiple transformation domains provides complementary information about the target, thereby enhancing the network's representation ability, especially with limited data samples. Furthermore, the findings indicate that feature fusion of multiple transformations in a high‐level feature space yields more informative and effective results for active sonar echoes compared to low‐level feature spaces.
Power frequency magnetic field interference suppression method for online frequency response analysis of power transformers
Authors: Cheng Yangchun , Liu Xiangdong , Sha Yufei , Chang Wenzhi , Bi Jiangang 
Published: July 2024

AbstractFrequency response analysis is widely used for the offline diagnosis of winding deformations in power transformers. To apply this approach to a working transformer, the magnitude of the response current needs to be measured by using Rogowski coil sensors across a load current. The saturation of the power frequency magnetic field in these current sensors must be prevented to ensure accurate measurement of such small response currents. Here, a method is presented to suppress the power frequency magnetic field using a sensing system including a special connection of three‐phase current sensors based on the sum of the three‐phase power frequency load currents of the transformer being close to zero. Each sensor comprises two secondary coils: a measuring coil and an anti‐saturation coil. The anti‐saturation coils are connected in parallel with one another through small inductors to eliminate the power frequency magnetic field in the cores of the sensors. Theoretical analysis is used to derive a solution for this system. The experimental results verify the proposed method as enabling the sensors to function with a transformer carrying a load current of 2333 A.
Enhancing offloading with cybersecurity in edge computing for digital twin‐driven patient monitoring
Authors: Jameil Ahmed K. , Al‐Raweshidy Hamed 
Published: July 2024

AbstractIn healthcare, the use of digital twin (DT) technology has been recognised as essential for enhancing patient care through real‐time remote monitoring. However, concerns regarding risk prediction, task offloading, and data security have been raised due to the integration of the Internet of Things (IoT) in remote healthcare. In this study, a new method was introduced, combines edge computing with sophisticated cybersecurity solutions. A vast amount of environmental and physiological data has been gathered, allowing for thorough understanding of patients. The system included hybrid encryption, threat prediction, Merkle Tree verification, certificate‐based authentication, and secure communication. The feasibility of the proposal was evaluated by using an ESP32‐Azure IoT Kit and Azure Cloud to evaluate the system's capacity to securely send patient data to healthcare institutions and stakeholders, while simultaneously upholding data confidentiality. The system demonstrated a notable improvement in encryption speed, with 27.18%, represented as the improved efficiency and achieved storage efficiency ratio 0.673. Furthermore, the evidence from the simulations showed that the system's performance was not affected by encryption since encryption times continuously remained within a narrow range. Moreover, proactive alert of probable security risks would be detected from the predictive analytics, hence strong data integrity assurance. The results suggest the proposed system provided a proactive, personalised care approach for cybersecurity‐protected DT healthcare (DTH) high‐level modelling and simulation, enabled via IoT and cloud computing under improved threat prediction.
Health risks for medical personnel due to magnetic fields in magnetic resonance imaging
Authors: König Alexander Marc , Pöschke Antje , Mahnken Andreas H. 
Published: July 2024

AbstractThe current state of medical and scientific knowledge on the effects of exposure to electromagnetic fields on workers in the field of clinical magnetic resonance imaging (MRI) is summarized here.A systematic literature search was conducted to analyze the health risks to medical personnel from magnetic fields in MRI. A total of 7273 sources were identified, with 7139 being excluded after screening of the title and abstract. After full-text screening, 34 sources remained and were included in this paper.There are a number of scientific publications on the occurrence of short-term sensory effects such as vertigo, metallic taste, phosphenes as well as on the occurrence of neurocognitive and neurobehavioral effects. For example, short-term exposure to clinical magnetic fields has been reported to result in a 4% reduction in speed and precision and a 16% reduction in visual contrast sensitivity at close range. Both eye-hand precision and coordination speed are affected. The long-term studies concern, among other things, the influence of magnetic fields on sleep quality, which could be linked to an increased risk of accidents. The data on the exposure of healthcare workers to magnetic fields during pregnancy is consistently outdated. However, it has been concluded that there are no particular deviations with regard to the duration of pregnancy, premature births, miscarriages, and birth weight. Epidemiological studies are lacking. With a focus on healthcare personnel, there is a considerable need for high-quality data, particularly on the consequences of long-term exposure to electromagnetic fields from clinical MRI and the effects on pregnancy.
Saliva Versus Plasma Therapeutic Drug Monitoring of Valproic Acid in
Jordanian Patients
Authors: Idkaidek Nasir , Al-Tarawneh Aya , Alshoaibi Laith , Tuffaha Haya , Zinati Asma , Abdelqader Majed , Al-Ghazawi Ahmad , Rabayah Ayman , Hamadi Salim 
Published: July 2024

AbstractTherapeutic drug monitoring is used to ensure that medications are prescribed and
administered according to safe doseage advice and for the purpose of achieving
the desired therapeutic effects in patients. Several methods are used to perform
therapeutic drug monitoring. However, there is insufficient evidence to
currently support therapeutic drug monitoring of Valproic acid using salivary
samples. The aim of this paper is to determine the feasibility of using salivary
samples as a substitute for plasma samples for therapeutic drug monitoring of
Valproic acid. In this study a total of 23 patients participated, with the mean
age of 33.39. Salivary and plasma samples were collected and analysed to
determine the peak and trough concentrations of Valproic acid for comparison
between the two methods. Calibrated LC- MS/ MS was used to measure Valproic acid
levels. Statistical analyses were performed using ANOVA test and ethical
approval was obtained prior to sample collection. The results showed that saliva
Valproic acid levels were less than that of plasma levels. There was no
significant correlation between saliva and plasma level of Valproic acid
(P>0.05). However, there was a significant correlation between the area under
the curve for both saliva and plasma Valproic acid (P<0.05). Creatinine
clearance was significantly correlated with peak plasma levels of Valproic acid
(P<0.05). Albumin was significantly correlated with plasma levels of Valproic
acid. There was also a significantly positive and moderate relationship between
Log Saliva Cmax and Log plasma free Valproic acid concentration (r=0.76,
p<0.018). In conclusion, saliva samples can be used as a substitute for
plasma samples in the therapeutic drug monitoring of Valproic acid.
Should we perform regular surveillance capsule endoscopies in patients following small-bowel adenocarcinoma resection? A case report and discussion
Authors: Wiedbrauck Damian , Hollerbach Stephan , Wiedbrauck Felix 
Published: July 2024

AbstractBecause small-bowel tumors are rare, prospective data on the utility of video capsule endoscopy (VCE) for their detection are limited. Current guidelines do not advocate for surveillance VCEs in patients following small-bowel tumor resection, which is mostly due to a lack of data. Here, we report an 81-year-old male patient who had undergone curative segmental ileal adenocarcinoma resection 15 years ago and another segmental jejunal adenocarcinoma resection (TNM-Classification: pT2 pN0 (0/2) G2M0) 7 years ago. He now presents with melena, progressive dyspnea, and decreased hemoglobin levels. VCE revealed local intestinal recurrence of the previously resected jejunal adenocarcinoma, leading to a second segmental jejunal resection (TNM-Classification: pT3 L1 pN0 (0/5) G2 M0). We believe that regular surveillance VCEs after the first jejunal adenocarcinoma resection might have facilitated earlier detection of tumor recurrence in this patient’s case. Therefore, we suggest considering regular surveillance VCEs, at least in patients with recurrent small-bowel malignancies. However, future prospective studies are warranted to validate our findings.