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51.
M. Tobias Heinrichs George L. Drusano David L. Brown Michael S. Maynard Sherwin K.B. Sy Kenneth H. Rand Charles A. Peloquin Arnold Louie Hartmut Derendorf 《International journal of antimicrobial agents》2019,53(3):275-283
Introduction
There is an urgent need for new anti-tuberculosis (TB) drugs and optimization of current TB treatment. Moxifloxacin and linezolid are valuable options for the treatment of drug-resistant TB; however, it is crucial to find a dose at which these drugs not only show high efficacy but also suppress the development of further drug resistance.Methods
Activity of moxifloxacin and linezolid against Mycobacterium tuberculosis was studied in the hollow-fiber infection model system in log-phase growth under neutral pH and slow growth in an acidic environment. Doses that achieved maximum bacterial kill while suppressing the emergence of drug resistance were determined. Through Monte Carlo simulations the quantitative output of this in vitro study was bridged to the human patient population to inform optimal dosage regimens while accounting for clinical minimum inhibitory concentration (MIC) distributions.Results and Discussion
Moxifloxacin activity was significantly decreased in an acidified environment. The loss of activity was compensated by accumulation of the drug in TB lung lesions; therefore, moderate efficacy can be expected. Moxifloxacin 800 mg/day is the dose that most likely leads to resistance suppression while exerting maximum bacterial kill. Linezolid demonstrated very good activity even at a reduced pH. Linezolid 900 mg once-daily (QD) is likely to achieve a maximum killing effect and prevent the emergence of drug resistance; 600 mg QD in a robust drug regimen may have similar potential. 相似文献52.
P. Herent B. Schmauch P. Jehanno O. Dehaene C. Saillard C. Balleyguier J. Arfi-Rouche S. Jégou 《Diagnostic and interventional imaging》2019,100(4):219-225
Purpose
The purpose of this study was to assess the potential of a deep learning model to discriminate between benign and malignant breast lesions using magnetic resonance imaging (MRI) and characterize different histological subtypes of breast lesions.Materials and methods
We developed a deep learning model that simultaneously learns to detect lesions and characterize them. We created a lesion-characterization model based on a single two-dimensional T1-weighted fat suppressed MR image obtained after intravenous administration of a gadolinium chelate selected by radiologists. The data included 335 MR images from 335 patients, representing 17 different histological subtypes of breast lesions grouped into four categories (mammary gland, benign lesions, invasive ductal carcinoma and other malignant lesions). Algorithm performance was evaluated on an independent test set of 168 MR images using weighted sums of the area under the curve (AUC) scores.Results
We obtained a cross-validation score of 0.817 weighted average receiver operating characteristic (ROC)-AUC on the training set computed as the mean of three-shuffle three-fold cross-validation. Our model reached a weighted mean AUC of 0.816 on the independent challenge test set.Conclusion
This study shows good performance of a supervised-attention model with deep learning for breast MRI. This method should be validated on a larger and independent cohort. 相似文献53.
V. Jenkins I. Solis-Trapala H. Payne M. Mason L. Fallowfield S. May L. Matthews S. Catt 《Clinical oncology (Royal College of Radiologists (Great Britain))》2019,31(2):99-107
Aims
Delaying progression, ameliorating symptoms and maintaining quality of life (QoL) are primary aims of treatment for metastatic castrate-resistant prostate cancer (mCRPC). Real-world rather than clinical trial data about symptoms and side-effects are sparse. In EXTREQOL, patients' QoL, pain and information needs were recorded during treatment.Material and methods
Men with mCRPC from 20 UK cancer centres starting various systemic mCRPC treatments completed QoL, pain and information needs questionnaires at baseline, 3 and 6 months.Results
In total, 132 patients were recruited. Overall QoL declined significantly by 6 months (Functional Assessment of Cancer Therapy-Prostate [FACT-P] mean = –3.89, 95% confidence interval –6.7 to –1.05, P = 0.007; Trial Outcome Index [TOI] analysis mean = –3.10, 95% confidence interval –5.34 to –0.83, P = 0.007). Those who came off novel therapy and remained on luteinising hormone-releasing hormone agonist therapy alone had worse scores than patients receiving concomitant chemotherapy (Prostate Concerns Subscale mean difference = –4.45, 95% confidence interval –7.06 to –1.83, P = 0.001; TOI mean difference = –5.62, 95% confidence interval –10.97 to –0.26, P = 0.040). At 3 and 6 months, men who reported pain at baseline improved (43%, 40%), but for others pain levels remained the same (45%, 42%) or worsened (13%, 18%). Information regarding supportive care was lacking throughout the period of time on the study.Conclusion
Most mCRPC treated patients experience reduced QoL and inadequate pain control. More help with pain management and better information provision regarding supportive care is warranted. 相似文献54.
55.
Sarah J. Schrauben Haochang Shou Xiaoming Zhang Amanda Hyre Anderson Joseph V. Bonventre Jing Chen Steven Coca Susan L. Furth Jason H. Greenberg Orlando M. Gutierrez Joachim H. Ix James P. Lash Chirag R. Parikh Casey M. Rebholz Venkata Sabbisetti Mark J. Sarnak Michael G. Shlipak Sushrut S. Waikar Paul L. Kimmel Ramachandran S. Vasan Harold I. Feldman Jeffrey R. Schelling 《Journal of the American Society of Nephrology : JASN》2021,32(1):115
56.
Satoru Kudose Dominick Santoriello Hanna Debiec Pietro A. Canetta Andrew S. Bomback M. Barry Stokes Ibrahim Batal Pierre Ronco Vivette D. D’Agati Glen S. Markowitz 《Kidney international》2021,99(1):247-255
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