GnRH antagonist treatment of malignant adrenocortical tumors

Doroszko, Milena; Chruściel, Marcin; Stelmaszewska, Joanna; Ślęzak, T.; Anisimowicz, Sławomir; Plöckinger, Ursula; Quinkler, Marcus; Bonomi, Marco; Wołczyński, S; Huhtaniemi, Ilpo; Toppari, Jorma; Na, Rahman

doi:10.1530/ey.16.8.13

Cited by 2 publications

(2 citation statements)

References 33 publications

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“…The benefits of CIJ over DOD are improved image quality, throw distance, and the ability to deflect droplets independent of gravity. [192] Since CIJ printing produces large-diameter droplets (≈40 mm), the print resolution is relatively low. They are able to use volatile solvent-based inks, allowing rapid drying and proper adhesion on many substrates.…”

Section: Continuous Inkjet (Cij) Printingmentioning

confidence: 99%

Advances in Printed Electronic Textiles

Islam,

Afroj,

Yin

et al. 2023

Advanced Science

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Electronic textiles (e‐textiles) have emerged as a revolutionary solution for personalized healthcare, enabling the continuous collection and communication of diverse physiological parameters when seamlessly integrated with the human body. Among various methods employed to create wearable e‐textiles, printing offers unparalleled flexibility and comfort, seamlessly integrating wearables into garments. This has spurred growing research interest in printed e‐textiles, due to their vast design versatility, material options, fabrication techniques, and wide‐ranging applications. Here, a comprehensive overview of the crucial considerations in fabricating printed e‐textiles is provided, encompassing the selection of conductive materials and substrates, as well as the essential pre‐ and post‐treatments involved. Furthermore, the diverse printing techniques and the specific requirements are discussed, highlighting the advantages and limitations of each method. Additionally, the multitude of wearable applications made possible by printed e‐textiles is explored, such as their integration as various sensors, supercapacitors, and heated garments. Finally, a forward‐looking perspective is provided, discussing future prospects and emerging trends in the realm of printed wearable e‐textiles. As advancements in materials science, printing technologies, and design innovation continue to unfold, the transformative potential of printed e‐textiles in healthcare and beyond is poised to revolutionize the way wearable technology interacts and benefits.

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Section: Continuous Inkjet (Cij) Printingmentioning

confidence: 99%

Advances in Printed Electronic Textiles

Islam,

Afroj,

Yin

et al. 2023

Advanced Science

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show abstract

“…The dataset is obtained from kaggle open-source data repository (Fedesoriano, 2021). It comprises of 10 stroke risk factors and target output, which signifies stroke or no stroke (Alberto & Rodríguez, 2021;Emon et al, 2020;Sailasya & Kumari, 2021). Detail description of the stroke risk factors is given in Table 1.…”

Section: Data Collectionmentioning

confidence: 99%

Comparative analysis of resampling algorithms in the prediction of stroke diseases

Abdullahi,

Aliyu,

Musa Abdullahi

2023

USci

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Stroke disease is a serious cause of death globally. Early predictions of the disease will save a lot of lives but most of the clinical datasets are imbalanced in nature including the stroke dataset, making the predictive algorithms biased towards the majority class. The objective of this research is to compare different data resampling algorithms on the stroke dataset to improve the prediction performances of the machine learning models. This paper considered five (5) resampling algorithms namely; Random over Sampling (ROS), Synthetic Minority oversampling Technique (SMOTE), Adaptive Synthetic (ADASYN), hybrid techniques like SMOTE with Edited Nearest Neighbor (SMOTE-ENN), and SMOTE with Tomek Links (SMOTE-TOMEK) and trained on six (6) machine learning classifiers namely; Logistic Regression (LR), Decision Tree (DT), K-nearest Neighbor (KNN), Support Vector Machines (SVM), Random Forest (RF), and XGBoost (XGB). The hybrid technique SMOTE-ENN influences the machine learning classifiers the best followed by the SMOTE technique while the combination of SMOTE and XGB perform better with an accuracy of 97.99% and G-mean score of 0.99, and auc_roc score of 0.99. Resampling algorithms balance the dataset and enhanced the predictive power of machine learning algorithms. Therefore, we recommend resampling stroke dataset in predicting stroke disease than modeling on the imbalanced dataset.

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GnRH antagonist treatment of malignant adrenocortical tumors

Cited by 2 publications

References 33 publications

Advances in Printed Electronic Textiles

Advances in Printed Electronic Textiles

Comparative analysis of resampling algorithms in the prediction of stroke diseases

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