Evidence of the Anomalous Fluctuating Magnetic State by Pressure-Driven 4f Valence Change in EuNiGe3.

In rare-earth compounds with valence fluctuation, the proximity of the 4f level to the Fermi energy leads to instabilities of the charge configuration and the magnetic moment. Here, we provide direct experimental evidence for an induced magnetic polarization of the Eu3+ atomic shell with J = 0, due to intra-atomic exchange and spin-orbital coupling interactions with the Eu2+ atomic shell. By applying external pressure, a transition from antiferromagnetic to a fluctuating behavior in EuNiGe3 single crystals is probed. Magnetic polarization is observed for both valence states of Eu2+ and Eu3+ across the entire pressure range. The anomalous magnetism is discussed in terms of a homogeneous intermediate valence state where frustrated Dzyaloshinskii-Moriya couplings are enhanced by the onset of spin-orbital interaction and engender a chiral spin-liquid-like precursor.

A deep learning approach to detect blood vessels in basal cell carcinoma.

PURPOSE: Blood vessels called telangiectasia are visible in skin lesions with the aid of dermoscopy. Telangiectasia are a pivotal identifying feature of basal cell carcinoma. These vessels appear thready, serpiginous, and may also appear arborizing, that is, wide vessels branch into successively thinner vessels. Due to these intricacies, their detection is not an easy task, neither with manual annotation nor with computerized techniques. In this study, we automate the segmentation of telangiectasia in dermoscopic images with a deep learning U-Net approach. METHODS: We apply a combination of image processing techniques and a deep learning-based U-Net approach to detect telangiectasia in digital basal cell carcinoma skin cancer images. We compare loss functions and optimize the performance by using a combination loss function to manage class imbalance of skin versus vessel pixels. RESULTS: We establish a baseline method for pixel-based telangiectasia detection in skin cancer lesion images. An analysis and comparison for human observer variability in annotation is also presented. CONCLUSION: Our approach yields Jaccard score within the variation of human observers as it addresses a new aspect of the rapidly evolving field of deep learning: automatic identification of cancer-specific structures. Further application of DL techniques to detect dermoscopic structures and handle noisy labels is warranted.

Transition from spin glass to paramagnetism in the magnetic properties of PrAu2Si2.

It is unexpected that a spin-glass (SG) transition, which generally occurs only in systems with some form of disorder, was observed in the ThCr2Si2-type compound PrAu2Si2at a temperature of ∼3 K. This puzzling phenomenon was later explained based on a novel dynamic frustration model that does not involve static disorder. We present the results of re-verification of the reported SG behaviors by measuring the physical properties of three polycrystalline PrAu2Si2samples annealed under different conditions. Indeed, in the sample annealed at 827 °C for one week, a SG transition does occur at a temperature ofTf∼ 2.8 K as that reported previously in the literature. However, it is newly found that the SG effect is actually more pronounced in the as-cast sample, and almost completely disappears in the well-annealed (at 850 °C for four weeks) sample. The annealing effect observed in PrAu2Si2, that is, SG to paramagnetism transition is discussed by comparing with earlier results reported on the same system and other isomorphic compounds.

Development of artificial neural networks software for arsenic adsorption from an aqueous environment.

Arsenic contamination is a global problem, as it affects the health of millions of people. For this study, data-driven artificial neural network (ANN) software was developed to predict and validate the removal of As(V) from an aqueous solution using graphene oxide (GO) under various experimental conditions. A reliable model for wastewater treatment is essential in order to predict its overall performance and to provide an idea of how to control its operation. This model considered the adsorption process parameters (initial concentration, adsorbent dosage, pH, and residence time) as the input variables and arsenic removal as the only output. The ANN model predicted the adsorption efficiency with high accuracy for both training and testing datasets, when compared with the available response surface methodology (RSM) model. Based on the best model synaptic weights, user-friendly ANN software was created to predict and analyze arsenic removal as a function of adsorption process parameters. We developed various graphical user interfaces (GUI) for easy use of the developed model. Thus, a researcher can efficiently operate the software without an understanding of programming or artificial neural networks. Sensitivity analysis and quantitative estimation were carried out to study the function of adsorption process parameter variables on As(V) removal efficiency, using the GUI of the model. The model prediction shows that the adsorbent dosages, initial concentration, and pH are the most influential parameters. The efficiency was increased as the adsorbent dosages increased, decreasing with initial concentration and pH. The result show that the pH 2.0-5.0 is optimal for adsorbent efficiency (%).

Knowledge extraction of sonophotocatalytic treatment for acid blue 113 dye removal by artificial neural networks.

Removing decolorizing acid blue 113 (AB113) dye from textile wastewater is challenging due to its high stability and resistance to removal. In this study, we used an artificial neural network (ANN) model to estimate the effect of five different variables on AB113 dye removal in the sonophotocatalytic process. The five variables considered were reaction time (5-25 min), pH (3-11), ZnO dosage (0.2-1.0 g/L), ultrasonic power (100-300 W/L), and persulphate dosage (0.2-3 mmol/L). The most effective model had a 5-7-1 architecture, with an average deviation of 0.44 and R2 of 0.99. A sensitivity analysis was used to analyze the impact of different process variables on removal efficiency and to identify the most effective variable settings for maximum dye removal. Then, an imaginary sonophotocatalytic system was created to measure the quantitative impact of other process parameters on AB113 dye removal. The optimum process parameters for maximum AB 113 removal were identified as 6.2 pH, 25 min reaction time, 300 W/L ultrasonic power, 1.0 g/L ZnO dosage, and 2.54 mmol/L persulfate dosage. The model created was able to identify trends in dye removal and can contribute to future experiments.

Comparative analysis of the diagnostic performance of five commercial COVID-19 qRT PCR kits used in India.

To meet the unprecedented requirement of diagnostic testing for SARS-CoV-2, a large number of diagnostic kits were authorized by concerned authorities for diagnostic use within a short period of time during the initial phases of the ongoing pandemic. We undertook this study to evaluate the inter-test agreement and other key operational features of 5 such commercial kits that have been extensively used in India for routine diagnostic testing for COVID-19. The five commercial kits were evaluated, using a panel of positive and negative respiratory samples, considering the kit provided by National Institute of Virology, Indian Council of Medical Research (2019-nCoV Kit) as the reference. The positive panel comprised of individuals who fulfilled the 3 criteria of being clinically symptomatic, having history of contact with diagnosed cases and testing positive in the reference kit. The negative panel included both healthy and disease controls, the latter being drawn from individuals diagnosed with other respiratory viral infections. The same protocol of sample collection, same RNA extraction kit and same RT-PCR instrument were used for all the kits. Clinical samples were collected from a panel of 92 cases and 60 control patients, who fulfilled our inclusion criteria. The control group included equal number of healthy individuals and patients infected with other respiratory viruses (n = 30, in each group). We observed varying sensitivity and specificity among the evaluated kits, with LabGun COVID-19 RT-PCR kit showing the highest sensitivity and specificity (94% and 100% respectively), followed by TaqPath COVID-19 Combo and Allplex 2019-nCoV assays. The extent of inter-test agreement was not associated with viral loads of the samples. Poor correlation was observed between Ct values of the same genes amplified using different kits. Our findings reveal the presence of wide heterogeneity and sub-optimal inter-test agreement in the diagnostic performance of the evaluated kits and hint at the need of adopting stringent standards for fulfilling the quality assurance requirements of the COVID-19 diagnostic process.

Modeling and optimization of process parameters of biofilm reactor for wastewater treatment.

The efficiency of heavy metal in biofilm reactors depends on absorption process parameters, and those relationships are complicated. This study explores artificial neural networks (ANNs) feasibility to correlate the biofilm reactor process parameters with absorption efficiency. The heavy metal removal and turbidity were modeled as a function of five process parameters, namely pH, temperature(°C), feed flux(ml/min), substrate flow(ml/min), and hydraulic retention time(h). We developed a standalone ANN software for predicting and analyzing the absorption process in handling industrial wastewater. The model was tested extensively to confirm that the predictions are reasonable in the context of the absorption kinetics principles. The model predictions showed that the temperature and pH values are the most influential parameters affecting absorption efficiency and turbidity.

Artificial neural networks modeling for lead removal from aqueous solutions using iron oxide nanocomposites from bio-waste mass.

Heavy metal ions in aqueous solutions are taken into account as one of the most harmful environmental issues that ominously affect human health. Pb(II) is a common pollutant among heavy metals found in industrial wastewater, and various methods were developed to remove the Pb(II). The adsorption method was more efficient, cheap, and eco-friendly to remove the Pb(II) from aqueous solutions. The removal efficiency depends on the process parameters (initial concentration, the adsorbent dosage of T-Fe3O4 nanocomposites, residence time, and adsorbent pH). The relationship between the process parameters and output is non-linear and complex. The purpose of the present study is to develop an artificial neural networks (ANN) model to estimate and analyze the relationship between Pb(II) removal and adsorption process parameters. The model was trained with the backpropagation algorithm. The model was validated with the unseen datasets. The correlation coefficient adj.R2 values for total datasets is 0.991. The relationship between the parameters and Pb(II) removal was analyzed by sensitivity analysis and creating a virtual adsorption process. The study determined that the ANN modeling was a reliable tool for predicting and optimizing adsorption process parameters for maximum lead removal from aqueous solutions.

Prediction of batch sorption of barium and strontium from saline water.

Celestite and barite formation results in contamination of barium and strontium ions hinder oilfield water purification. Conversion of bio-waste sorbent products deals with a viable, sustainable and clean remediation approach for removing contaminants. Biochar sorbent produced from rice straw was used to remove barium and strontium ions of saline water from petroleum industries. The removal efficiency depends on biochar amount, pH, contact time, temperature, and Ba/Sr concentration ratio. The interactions and effects of these parameters with removal efficiency are multifaceted and nonlinear. We used an artificial neural network (ANN) model to explore the correlation between process variables and sorption responses. The ANN model is more accurate than that of existing kinetic and isotherm equations in assessing barium and strontium removal with adj. R2 values of 0.994 and 0.991, respectively. We developed a standalone user interface to estimate the barium and strontium removal as a function of sorption process parameters. Sensitivity analysis and quantitative estimation were carried out to study individual process variables' impact on removal efficiency.

Magnetic and transport properties of new ternary uranium-based germanide U2Rh3Ge5.

A new ternary uranium germanide U2Rh3Ge5 has been successfully synthesized and investigated by means of magnetic susceptibility χ(T, H), isothermal magnetization M(T, H), electrical resistivity ρ(T), and specific heat C(T, H) measurements. This compound is found to crystallize in the U2Co3Si5-type orthorhombic structure. The low-field χ(T) shows a clear peak at T N = 41.5 K corresponding to an antiferromagnetic transition. The M(H) curve measured up to 70 kOe exhibits an H-linear behavior at 2 K with very small induced magnetic moments, while it shows upward curvature with increasing temperature, implying the possible presence of a metamagnetic transition in high-field region above 70 kOe. As the temperature decreases, ρ(T) increases slowly at T > T N and decreases rapidly at T < T N, which can be understood based on a semiconductor-like narrow band gap model (or the c-f hybridization effect) and an antiferromagnetic spin-wave model, respectively. No evidence of heavy-fermion behavior or superconductivity transition is observed at temperatures as low as 0.4 K. The obtained experimental results are discussed by comparing with those reported for the isomorphic compound U2Ir3Si5 and the quasi-isomorphic compound U2Rh3Si5.

Steady expression of high oleic acid in peanut bred by marker-assisted backcrossing for fatty acid desaturase mutant alleles and its effect on seed germination along with other seedling traits.

Peanut (Arachis hypogaea L.) is an important nutrient-rich food legume and valued for its good quality cooking oil. The fatty acid content is the major determinant of the quality of the edible oil. The oils containing higher monounsaturated fatty acid are preferred for improved shelf life and potential health benefits. Therefore, a high oleic/linoleic fatty acid ratio is the target trait in an advanced breeding program. The two mutant alleles, ahFAD2A (on linkage group a09) and ahFAD2B (on linkage group b09) control fatty acid composition for higher oleic/linoleic ratio in peanut. In the present study, marker-assisted backcrossing was employed for the introgression of two FAD2 mutant alleles from SunOleic95R into the chromosome of ICGV06100, a high oil content peanut breeding line. In the marker-assisted backcrossing-introgression lines, a 97% increase in oleic acid, and a 92% reduction in linoleic acid content was observed in comparison to the recurrent parent. Besides, the oleic/linoleic ratio was increased to 25 with respect to the recurrent parent, which was only 1.2. The most significant outcome was the stable expression of oil-content, oleic acid, linoleic acid, and palmitic acid in the marker-assisted backcrossing-introgression lines over the locations. No significant difference was observed between high oleic and normal oleic in peanuts for seedling traits except germination percentage. In addition, marker-assisted backcrossing-introgression lines exhibited higher yield and resistance to foliar fungal diseases, i.e., late leaf spot and rust.

Should vitamin D be routinely checked for all chronic obstructive pulmonary disease patients?

AIMS AND OBJECTIVES: This study aimed to compare the vitamin D levels between chronic obstructive pulmonary disease (COPD) patients and healthy controls and to describe the correlation between vitamin D levels and lung functions. METHODS: Fifty COPD patients (cases) and 30 healthy volunteers (controls) were recruited and their serum vitamin D level was measured together with lung function (forced vital capacity and forced expiratory volume in 1 s [FEV1]) by spirometry. vitamin D was categorized as ≤20 nmol/l: deficient, 21-50 nmol/l: inadequate, and ≥51 nmol/l as sufficient. RESULTS: In this case-control cross-sectional study, lower vitamin D levels were associated with lower lung function in both cases as well as controls, the effect being more pronounced in cases. Mean FEV1 at vitamin D ≤20 nmol/l (0.98 ± 0.40 vs. controls 1.93 ± 0.24 with P = 0.006), mean FEV1 at vitamin D 21-50 nmol/l (1.55 ± 0.54 vs. 2.20 ± 0.31 with P = 0.000), and mean FEV1 at vitamin D ≥51 nmol/l (2.06 ± 0.54 vs. 2.20 ± 0.31 with P = 0.002). Moreover, the severity of predicted postbronchodilator FEV1% was also much lower among COPD cohort versus healthy volunteers (mean FEV1%: cases 47.88 ± 14.22 vs. controls 58.76 ± 15.05 with P = 0.002). CONCLUSIONS: Importantly, lung function in both the groups was affected by decreased vitamin D level; decrease in FEV1 was more pronounced among COPD patients compared to controls showing more expiratory airflow limitation. Vitamin D levels are associated with changes in lung function in cases of COPD as well as healthy controls. Larger studies to confirm the association in Indian context are required and routine assessment of vitamin D may be undertaken to obviate the effects of low vitmain D level on lung function.

Anticarcinogenic activity of blue fluorescent hexagonal boron nitride quantum dots: as an effective enhancer for DNA cleavage activity of anticancer drug doxorubicin.

Blue fluorescent hexagonal boron nitride quantum dots (h-BNQDs) of ∼10 nm size as an effective enhancer for DNA cleavage activity of anticancer drug doxorubicin (DOX) were synthesized using simple one-step hydrothermal disintegration of exfoliated hexagonal boron nitride at very low temperature âˆ¼ 120 °C. Boron nitride quantum dots (BNQDs) at a concentration of 25 µg/ml enhanced DNA cleavage activity of DOX up to 70% as checked by converting supercoiled fragment into nicked circular PBR322 DNA. The interaction of BNQDs with DOX is proportional to the concentration of BNQDs, with binding constant K b ∼0.07338 µg/ml. In addition, ab initio theoretical results indicate that DOX is absorbed on BNQDs at the N-terminated edge with binding energy -1.075 eV and prevented the normal replication mechanisms in DNA. BNQDs have been shown to kill the breast cancer cell MCF-7 extensively as compared with the normal human keratinocyte cell HaCaT. The cytotoxicity of BNQDs may be correlated with reduced reactive oxygen species level and increased apoptosis in MCF-7 cells, which may be liable to enhance the anticancerous activity of DOX. The results provide a base to develop BNQD-DOX as a more effective anticancer drug.

Bifidobacterium longum-fermented rice bran and rice bran supplementation affects the gut microbiome and metabolome.

This study investigated gut microbiota composition along with food, host, and microbial derived metabolites in the colon and systemic circulation of healthy mice following dietary rice bran and fermented rice bran intake. Adult male BALB/c mice were fed a control diet or one of two experimental diets containing 10% w/w rice bran fermented by Bifidobacterium longum or 10% w/w non-fermented rice bran for 15 weeks. Metabolomics was performed on the study diets (food), the murine colon and whole blood. These were analysed in concert with 16S rRNA amplicon sequencing of faeces, caecum, and colon microbiomes. Principal components analysis of murine microbiota composition displayed marked separation between control and experimental diets, and between faecal and tissue (caecum and colon) microbiomes. Colon and caecal microbiomes in both experimental diet groups showed enrichment of Roseburia, Lachnospiraceae, and Clostridiales related amplicon sequence variants compared to control. Bacterial composition was largely similar between experimental diets. Metabolite profiling revealed 530 small molecules comprising of 39% amino acids and 21% lipids that had differential abundances across food, colon, and blood matrices, and statistically significant between the control, rice bran, and fermented rice bran groups. The amino acid metabolite, N-delta-acetylornithine, was notably increased by B. longum rice bran fermentation when compared to non-fermented rice bran in food, colon, and blood. These findings support that dietary intake of rice bran fermented with B. longum modulates multiple metabolic pathways important to the gut and overall health.

Distribution of Class II integrons and their contribution to antibiotic resistance within Enterobacteriaceae family in India.

BACKGROUND: Integrons are the main contributors to the development of multidrug resistance (MDR) among Gram-negative bacilli. There is a lack of knowledge about the molecular relation between gene cassettes and antibiotic resistance in India. OBJECTIVE: In this study, we have investigated the occurrence of Class II integron and their cassette array among Enterobacteriaceae. MATERIALS AND METHODS: A total of 268 MDR non-duplicate strains of Enterobacteriaceae were collected from Silchar Medical College and Hospital, Silchar, Assam, India, during June 2012 to May 2013. Polymerase chain reaction was performed for detection of the integrase genes and gene cassettes within the Class II integron which were further analysed by sequencing. RESULTS: Class II integron was observed in 47 isolates. Four different gene cassette arrangements were detected: dfrA1-sat2-aadA1; dfrA1-sat2-aadA1-orfX-ybeA-ybfA-ybfB-ybgA; dfrA12-sat2-aadA1; and dfrA1-linF-aadA1. The most prevalent cassette combination was dfrA1-sat2-aadA1. This study has also identified a set of gene cassette associated with linF gene instead of sat2 gene. CONCLUSION: Further investigation is required to determine the current situation and important reservoir of Class II integron for the transmission of drug resistance among Enterobacteriaceae and their contribution to antimicrobial resistance in hospital environment .

Prevalence of nontuberculous mycobacteria among extrapulmonary tuberculosis cases in tertiary care centers in Northern India.

The reports of nontuberculous mycobacteria (NTM) associated with extrapulmonary diseases are increasing in tertiary care hospitals. Despite a significant increase in knowledge about NTM infections, they still represent a diagnostic and therapeutic challenge. The aim of this study is to know the prevalence of NTN among extrapulmonary tuberculosis cases in tertiary care centers in Northern India. A total of 227 culture positive isolates from 756 cases were tested for niacin production and catalase assay. BIO-LINE SD Ag MPT64 TB test and final identification and differentiation between MTBC and different species of NTM were further confirmed by GenoType Mycobacterium CM/AS assay. 71 cases (9.3%) were positive for AFB by ZN staining and 227 cases (30.1%) were positive for mycobacteria by culture. Niacin production and catalase activity were negative in 62/227 (27.4%) strains and after using a panel of different biochemicals and final confirmation by GenoType Mycobacterium CM assay. Out of 227 cultures tested, 165 (72.6%) strains were confirmed as M. tuberculosis complex, and 62 (27.4%) were confirmed as NTM. The most common NTM species identified were M. fortuitum 17 (27.5%) and M. intracellulare 13 (20.9%). The rapid identification of NTM species may help in targeted therapy and management of the diseases.

Contribution of efflux pumps in fluroquinolone resistance in multi-drug resistant nosocomial isolates of Pseudomanas aeruginosa from a tertiary referral hospital in north east India.

BACKGROUND: Pseudomonas aeruginosa is one of the leading opportunistic pathogen and its ability to acquire resistance against series of antimicrobial agents confine treatment option for nosocomial infections. Increasing resistance to fluroquinolone (FQ) agents has further worsened the scenario. The major mechanism of resistance to FQs includes mutation in FQs target genes in bacteria (DNA gyrase and/or topoisomerases) and overexpression of antibiotic efflux pumps. OBJECTIVE: We have investigated the role of efflux pump mediated FQ resistance in nosocomial isolates of P. aeruginosa from a tertiary referral hospital in north eastern part of India. MATERIALS AND METHODS: A total of 234 non-duplicate, consecutive clinical isolates of P. aeruginosa were obtained from a tertiary referral hospital of north-east India. An efflux pump inhibitor (EPI), carbonyl cyanide m-chlorophenylhydrazone (CCCP) based method was used for determination of efflux pump activity and multiplex polymerase chain reaction (PCR) was performed for molecular characterisation of efflux pump. Minimum inhibitory concentration (MIC) reduction assay was also performed for all the isolates. RESULTS AND CONCLUSION: A total number of 56 (23%) have shown efflux mediated FQ resistance. MexAB-OprM efflux system was predominant type. This is the first report of efflux pump mediated FQ resistance from this part of the world and the continued emergence of these mutants with such high MIC range from this part of the world demands serious awareness, diagnostic intervention, and proper therapeutic option.

EuNiGe3, an anisotropic antiferromagnet.

Single crystals of EuNiGe3, crystallizing in the non-centrosymmetric BaNiSn3-type structure, were grown using In flux, enabling us to explore the anisotropic magnetic properties, which was not possible with previously reported polycrystalline samples. The EuNiGe3 single crystalline sample is found to order antiferromagnetically at 13.2 K, as revealed from the magnetic susceptibility, heat capacity and electrical resistivity data. The low temperature magnetization M (H) is distinctly different for the field parallel to the ab-plane and c-axis; the ab-plane magnetization varies almost linearly with the field before the occurrence of an induced ferromagnetic (FM) phase (spin-flip) at 6.2 Tesla. On the other hand M (H) along the c-axis is accompanied by two metamagnetic transitions followed by a spin-flip at 4.1 T. A model including anisotropic exchange and dipole-dipole interactions reproduces the main features of magnetization plots but falls short of full representation. (H,T) phase diagrams have been constructed for the field applied along the principal directions. From the (151)Eu Mössbauer spectra, we determine that the 13.2 K transition leads to an incommensurate antiferromagnetic (AFM) intermediate phase followed by a transition near 10.5 K to a commensurate AFM configuration.

Rapid detection of drug resistance and mutational patterns of extensively drug-resistant strains by a novel GenoType® MTBDRsl assay.

BACKGROUND: The emergence of extensively drug-resistant tuberculosis (XDR-TB) is a major concern in the India. The burden of XDR-TB is increasing due to inadequate monitoring, lack of proper diagnosis, and treatment. The GenoType ® Mycobacterium tuberculosis drug resistance second line (MTBDRsl) assay is a novel line probe assay used for the rapid detection of mutational patterns conferring resistance to XDR-TB. AIM: The aim of this study was to study the rapid detection of drug resistance and mutational patterns of the XDR-TB by a novel GenoType ® MTBDRsl assay. MATERIALS AND METHODS: We evaluated 98 multidrug-resistant (MDR) M. tuberculosis isolates for second line drugs susceptibility testing by 1% proportion method (BacT/ALERT 3D system) and GenoType ® MTBDRsl assay for rapid detection of conferring drug resistance to XDR-TB. RESULTS: A total of seven (17.4%) were identified as XDR-TB by using standard phenotypic method. The concordance between phenotypic and GenoType ® MTBDRsl assay was 91.7-100% for different antibiotics. The sensitivity and specificity of the MTBDRsl assay were 100% and 100% for aminoglycosides; 100% and 100% for fluoroquinolones; 91.7% and 100% for ethambutol. The most frequent mutations and patterns were gyrA MUT1 (A90V) in seven (41.2%) and gyrA + WT1-3 + MUT1 in four (23.5%); rrs MUT1 (A1401G) in 11 (64.7%), and rrs WT1-2 + MUT1 in eight (47.1%); and embB MUT1B (M306V) in 11 (64.7%) strains. CONCLUSIONS: These data suggest that the GenoType ® MTBDRsl assay is rapid, novel test for detection of resistance to second line anti-tubercular drugs. This assay provides additional information about the frequency and mutational patterns responsible for XDR-TB resistance.

Use of GenoType® MTBDRplus assay to assess drug resistance and mutation patterns of multidrug-resistant tuberculosis isolates in northern India.

PURPOSE: The emergence and spread of multidrug-resistant tuberculosis (MDR-TB) is a major public health problem. The diagnosis of MDR-TB is of paramount importance in establishing appropriate clinical management and infection control measures. The aim of this study was to evaluate drug resistance and mutational patterns in clinical isolates MDR-TB by GenoType® MTBDRplus assay. MATERIAL AND METHODS: A total of 350 non-repeated sputum specimens were collected from highly suspected drug-resistant pulmonary tuberculosis (PTB) cases; which were processed by microscopy, culture, differentiation and first line drug susceptibility testing (DST) using BacT/ALERT 3D system. RESULTS: Among a total of 125 mycobacterium tuberculosis complex (MTBC) strains, readable results were obtained from 120 (96%) strains by GenoType® MTBDRplus assay. Only 45 MDR-TB isolates were analysed for the performance, frequency and mutational patterns by GenoType® MTBDRplus assay. The sensitivity of the GenoType® MDRTBplus assay for detecting individual resistance to rifampicin (RIF), isoniazid (INH) and multidrug resistance was found to be 95.8%, 96.3% and 97.7%, respectively. Mutation in codon S531L of the rpoB gene and codon S315T1 of katG genes were dominated in MDR-TB strains, respectively (P < 0.05). CONCLUSIONS: The GenoType® MTBDRplus assay is highly sensitive with short turnaround times and a rapid test for the detection of the most common mutations conferring resistance in MDR-TB strains that can readily be included in a routine laboratory workflow.