Combinatorial nanodroplet platform for screening antibiotic combinations

Lab on a Chip ◽  
2022 ◽  
Author(s):  
Hui Li ◽  
Pengfei Zhang ◽  
Kuangwen Hsieh ◽  
Tza-Huei Wang
Keyword(s):  
Drug Response

We have developed a combinatorial nanodroplet platform for screening antibiotic combinations and successfully screened drug response of pairwise antibiotic combinations from selected antibiotics using the platform.

THE DRUG RESPONSE FEATURES OF PATIENTS WITH CORONARY HEART DISEASE AT A STATINOTERAPIYA

2019 ◽  
Vol 4 (3) ◽  
pp. 11-15
Author(s):  
Оплимах К.С. ◽  
◽  
Таранова К.С. ◽  
Keyword(s):  
Coronary Heart Disease ◽  
Heart Disease ◽  
Drug Response

Towards Realizing the Vision of Precision Medicine: AI Based Prediction of Clinical Drug Response

2020 ◽  
Author(s):  
Johann de Jong ◽  
Ioana Cutcutache ◽  
Matthew Page ◽  
Sami Elmoufti ◽  
Cynthia Dilley ◽  
...  
Keyword(s):  
Precision Medicine ◽  
Drug Response ◽  
Clinical Drug

Pharmacoepigenomics: An Interplay of Epigenetic Modulation of Drug Response and Modulation of the Epigenome by Drugs

2014 ◽  
Vol 20 (11) ◽  
pp. 1819-1830 ◽  
Author(s):  
Shweta Mendiratta ◽  
Shruti Jain ◽  
Jayant Maini ◽  
Vani Brahmachari
Keyword(s):  
Drug Response ◽  
Epigenetic Modulation

Using Naïve Bayes Algorithm to Estimate the Response to Drug in Lung Cancer Patients

2019 ◽  
Vol 21 (10) ◽  
pp. 734-748 ◽  
Author(s):  
Baoling Guo ◽  
Qiuxiang Zheng
Keyword(s):  
Lung Cancer ◽  
Side Effects ◽  
Cancer Patients ◽  
Drug Response ◽  
Treatment Options ◽  
Clinical Manifestations ◽  
Treatment Strategies ◽  
Cancer Resistance ◽  
Lung Cancer Patients ◽  
Bayes Algorithm

Aim and Objective: Lung cancer is a highly heterogeneous cancer, due to the significant differences in molecular levels, resulting in different clinical manifestations of lung cancer patients there is a big difference. Including disease characterization, drug response, the risk of recurrence, survival, etc. Method: Clinical patients with lung cancer do not have yet particularly effective treatment options, while patients with lung cancer resistance not only delayed the treatment cycle but also caused strong side effects. Therefore, if we can sum up the abnormalities of functional level from the molecular level, we can scientifically and effectively evaluate the patients' sensitivity to treatment and make the personalized treatment strategies to avoid the side effects caused by over-treatment and improve the prognosis. Result & Conclusion: According to the different sensitivities of lung cancer patients to drug response, this study screened out genes that were significantly associated with drug resistance. The bayes model was used to assess patient resistance.

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