Surface-enhanced Raman spectroscopy of polymerase chain reaction (PCR) products of Rifampin resistant and susceptible tuberculosis patients-Reference-Cited by-同舟云学术

Surface-enhanced Raman spectroscopy of polymerase chain reaction (PCR) products of Rifampin resistant and susceptible tuberculosis patients

Published:2022-06 Issue: Volume:38 Page:102758
ISSN:1572-1000
Container-title:Photodiagnosis and Photodynamic Therapy
language:en
Short-container-title:Photodiagnosis and Photodynamic Therapy

Author:

Dastgir Ghulam,Majeed Muhammad Irfan^ORCID,Nawaz Haq,Rashid Nosheen,Raza Ali,Ali Muhammad Zeeshan,Shakeel Muhammad,Javed Mahrosh,Ehsan Usama,Ishtiaq Shazra,Fatima Rida,Abdulraheem Aliza

Publisher

Elsevier BV

Subject

Pharmacology (medical),Dermatology,Oncology,Biophysics

Reference49 articles.

1. A blood RNA signature for tuberculosis disease risk: a prospective cohort study;Zak;Lancet,2016

2. Genetic diversity of multidrug-resistant Mycobacterium tuberculosis isolates in Punjab, Pakistan;Bakuła;Infect. Genet. Evol.,2019

3. Is Mycobacterium tuberculosis a closer relative to Gram-positive or Gram–negative bacterial pathogens?;Fu;Tuberculosis,2002

4. High incidence of pulmonary tuberculosis in children admitted with severe pneumonia in Uganda;Nantongo;BMC Pediatr.,2013

5. Multidrug resistant typhoid fever: study of an outbreak in Calcutta;Arora;Cough,1992

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