Showing 441 - 460 results of 3,155 for search '(((((((rate OR rate) OR rate) OR rate) OR rate) OR rate) OR rate) OR gate) and patterns', query time: 0.17s Refine Results
  1. 441

    Lichen Planus: A Cross-Sectional Evaluation of US Dermatologists’ Comorbidity Screening and Management Patterns by Savanna I. Vidal, Nikita Menta, Adam Friedman

    Published 2025-04-01
    “…This study aimed to explore dermatology practitioners’ comorbidity screening patterns and treatment practices for management of LP. …”
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    Article
  2. 442
  3. 443

    Prescription patterns of traditional Chinese medications and potential consequences in patients with new-onset cardiac or vascular-related diseases: a nationwide cohort study by Sheng-Shing Lin, Hsin-Hui Tsai, Daniel Hsiang-Te Tsai, Chiu-Lin Tsai, Nanae Itokazu, Jaung-Geng Lin, Edward Chia-Cheng Lai, Hsiang-Wen Lin, Yu-Chang Hou

    Published 2025-07-01
    “…Abstract Background The patterns of Chinese medicine prescriptions, corresponding diagnoses, co-morbidities, and Western medication (WM) use among patients with cardiac or vascular-related diseases are uncertain. …”
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    Article
  4. 444

    Reirradiation practices of Radiation Therapists (RePoRT) study by Neva Pang, Alvin Cuni, Amanda Caissie, Leigh Conroy, Aileen Duffton, Winnie Li, Brian Liszewski, Donna H. Murrell, Andrea Shessel, Fátima Silva, Yat Tsang, Michael Velec

    Published 2025-09-01
    “…The 48-item questionnaire asked RTTs the frequency of performing a range of reirradiation activities, to self-rate their competency levels, and to identify enablers and barriers to reirradiation practice. …”
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    Article
  5. 445

    The pattern of lung function tests in children with sickle cell disease: A case-control study. by Abinaya Kannan, Gaurav Sarnaik, Nikita Agarwal, Atul Jindal

    Published 2025-01-01
    “…<h4>Conclusions</h4>Children with SCD often exhibit restrictive, obstructive, or mixed pulmonary function patterns. FeNO levels donot correlate with PFT severity.…”
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  6. 446
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    Structural background of intraspecific color polymorphism and the driver of geographic patterns in a shining leaf chafer by Yuanyuan Lu, Alexander Kovalev, Lulu Li, Chuchu Li, Xinyi Zhu, Min He, Xingke Yang, Ming Bai, Stanislav N. Gorb

    Published 2025-08-01
    “…Results Here we studied the distribution pattern of color phenotypes in the beetle Popillia mutans (Insecta: Coleoptera: Rutelinae). …”
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    Article
  8. 448
  9. 449

    Patterns and predictors of mental workload in intern nursing students: a latent profile analysis by Yanmei Gan, Tingting Liao, Lingfang Liu, Yao Du, Mingjuan Guo, Gaoye Li

    Published 2025-05-01
    “…Age and monthly income of 3000–5000 RMB were the main predictors of low MWL-high self-rated pattern. In contrast, long internships, passive coping strategies, college degree and monthly income < 3000 RMB were predictors of moderate MWL pattern. …”
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  11. 451

    Five-year evaluation of the antimicrobial susceptibility patterns of bacteria causing bloodstream infections in Iran by Babak Pourakbari, Alireza Sadr, Mohammad Taghi Haghi Ashtiani, Setareh Mamishi, Mahdi Dehghani, Shima Mahmoudi, Ali Salavati, Farhad Asgari

    Published 2011-09-01
    “…The frequency of Gram-positive bacteria isolated was 47.6% (1228 of 2581) and that for Gram-negatives was 52.4% (1353 of 2581). The rates of methicillin (oxacillin) resistance in Staphylococcus aureus and coagulase-negative staphylococci (CoNS) were 79% and 89%, respectively. …”
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    Article
  12. 452

    Cross-User Electromyography Pattern Recognition Based on a Novel Spatial-Temporal Graph Convolutional Network by Mengjuan Xu, Xiang Chen, Yuwen Ruan, Xu Zhang

    Published 2024-01-01
    “…The ablation experiments show that each functional module of the proposed CNN-MSTGCN network has played a more or less positive role in improving the performance of EMG pattern recognition. The user-independent recognition experiments and the transfer learning-based cross-user recognition experiments verify the advantages of the proposed CNN-MSTGCN network in improving recognition rate and reducing user training burden. …”
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  13. 453

    Automatic Identification of Weave Patterns of Checked and Colored Fabrics Using Optical Coherence Tomography by Metin Sabuncu, Hakan Ozdemir, Mete U. Akdogan

    Published 2017-01-01
    “…Identifying the weave pattern of fabrics can be done manually or automatically. …”
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  14. 454

    Different Cytokine and Chemokine Expression Patterns in Malignant Compared to Those in Nonmalignant Renal Cells by Nadine Gelbrich, Hannes Ahrend, Anne Kaul, Lars-Ove Brandenburg, Uwe Zimmermann, Alexander Mustea, Martin Burchardt, Denis Gümbel, Matthias B. Stope

    Published 2017-01-01
    “…Caki-1 and 786-O cells exhibited significantly increased proliferation rates, whereas RCC4 and A498 cells demonstrated attenuated proliferation, compared to nonmalignant RC-124 cells. …”
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  15. 455

    Evaluation of soil quality and analysis of drivers of different vegetation patterns in the loess region of Northern Shaanxi by Youfu Wang, Fangfang Qiang, Guangquan Liu, Changhai Liu, Jie Gao, Ning Ai

    Published 2025-08-01
    “…The results revealed that (1) the maximum water holding capacity (MWHC), capillary water holding capacity (CWHC), total porosity (TCP), soil organic carbon (SOC), quick acting phosphorus (AP), C/P, and C/N of the PTF sample were significantly greater than those of the other vegetation models, and the maximum water holding capacity (MWHC), capillary water holding capacity (CWHC), soil organic carbon (SOC), total nitrogen (TN), and total phosphorus (TP) of the MAF sample site were significantly lower than those of the other sample sites, while soil bulk density (BD) was significantly higher than other sample sites. (2) According to the principal component analysis of the 16 physical and chemical indicators, the eigenvalues of the first four principal components were 1, and the cumulative contribution rate reached 77.482%, which effectively included the information of the original variables. (3) The soil qualities of the different vegetation types in the loess area of northern Shaanxi were ranked as follows: PTF (0.534) > SLP (0.494) > SF (0.462) > MF1 (0.430) > HPF (0.423) > BLF (0.420) MF2 > (0.415) > MAF (0.389). (4) SEM revealed that soil quality drivers varied among vegetation patterns, but soil organic carbon (SOC), as the main influencing factor, positively affected all vegetation. …”
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