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41.

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   
42.

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   
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The present study enumerates the attenuating effects of curcumin and α-tocopherol against propoxur induced oxidative DNA damage in human peripheral blood mononuclear cells (PBMC). Cultured cells were isolated from peripheral blood of healthy volunteers, and were exposed to varying concentrations of propoxur (0–21?μg/ml) for 6, 12, and 24?h, and in combination with curcumin (9.2?μg/ml) or α-tocopherol (4.3?μg/ml) or both. Cytotoxic effect of propoxur was examined by MTT assay. The role of oxidative stress beneath the cytotoxicity of propoxur was evaluated by the measurement of reduced glutathione (GSH), malondialdehyde (MDA) and 8-hydroxy-2′-deoxyguanosine (8-OH-dG) levels in cell lysate. A concentration-dependent cell death, depletion of GSH, an increase in the level of both MDA and 8-OH-dG were observed. Co-treatment with curcumin or α-tocopherol significantly attenuates depleted GSH, decrease in MDA and 8-OH-dG levels in propoxur exposed cells (p?相似文献   
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