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2. Genetic Correlation and Path Coefficient Analysis of Yield Attributing Parameters in Indian Mustard

Author

Aditi Shrivastava, M. K. Tripathi, R. S. Solanki, Sushma Tiwari, Niraj Tripathi, J. Singh, and Rakesh Yadav

Subjects
Psychiatry and Mental health
Abstract

Indian mustard (Brassica juncea L. Czern. & Coss) is a natural amphidiploid which is the greatest pre-dominating crop of oilseed Brassica group. A study was undertaken to estimate the genetic variability, correlation and path coefficient analysis of yield and its contributing traits in 75 mustard genotypes grown in Randomized Block Design with two replications. The analysis of variance was highly significant for all the characters investigated. All thirteen characters were showed higher values of phenotypic coefficients of variation than genotypic coefficients of variation. The higher heritability in broad sense was estimated for all the characters. High value of heritability indicates that it may be due to higher contribution of genotypic components. High heritability coupled with high genetic advance as percent of means were recorded for days to 50% flowering, plant height (cm), number(s) of secondary branches per plant, length of main raceme (cm), siliquae length (cm), seed yield per plant (g), yield per plot (g), harvest index and biological yield that indicated predominance of additive gene action in the inheritance of these traits. The higher direct positive genotypic and phenotypic correlations for the biological yield, numbers of primary branches, numbers of siliquae on main raceme and numbers of secondary branches were documented. Whereas, days to maturity and siliquae length showed direct negative correlations with grain yield. Seventy-five genotypes, included in study were grouped into 6 clusters. The maximum inter cluster D2 value indicated that genotypes of cluster III and IV are not so closely related while the genotypes of cluster I and III are closely related. It is apparent therefore; the genotypes of various clusters differ so significantly with regards to their relative genetic distance as indicated from the high variation of D2 values. This makes it clear that the genotypes included in these clusters have a wide range of genetic diversity and may be used in a mustard hybridization programme to develop higher yielding cultivars.

Published
2023

3. Correlation between visual field and retinal nerve fiber layer thickness in adult north Indian population from Gurugram region with glaucoma suspect

Author

Gaurav Dubey, M D Masihuzzaman, Sunanda Sarkhel, Anshul Pratap Singh, Suneel Kumar Dixit, and Rakesh Yadav

Subjects
General Medicine
Abstract

To find the correlation between visual field and retinal nerve layer thickness in adult North Indian population from Gurugram region with glaucoma suspect. Correlative and quantitative study was carried between the visual field and retinal nerve fiber layer (RNFL) thickness of 400 adult eyes. The age of subjects participating in the study ranged between 18 to 80 years with mean age 45 ± 14years. The standard automated perimetry was acquired by Humphrey visual field analyser using 24-2 SITA standard strategy. Retinal nerve fiber layer thickness was measured by Spectral Domain OCT (3D OCT2000FA). Visual field was performed on the same day or within ± three months of OCT acquisition. Statistical analysis was performed using MS Excel, SPSS (ver.20) and other descriptive statistical tools.: The mean MD and PSD were -2.79 dB ±2.21 and 2.52 dB ±1.49, respectively. The average thickness of RNFL of the four quadrants calculated was 98.40 µm±10.70. RNFL thickness in an inferior and superior quadrant was 122.49µm ± 16.71 and 118.86 µm ±15.21 respectively. The mean cup to disc area ratio (CDAR) in the glaucoma suspect subject was 0.60± 0.10, and the vertical cup to disc ratio (VCDR) was 0.74± 0.074. Correlation of the average RNFL thickness, Inferior RNFL thickness and superior RNFL thickness with GHT was 0.245(P=0.011), 0.19 (P=0.094) and 0.27, (P=0.004), respectively. Superior RNFL thickness showed a more significant Correlation (r= 0.193, P Retinal nerve fiber layer thickness demonstrated a weak to mild and statistically significant correlation with the visual field. The correlation of average RNFL thickness with Visual field global indices and parameters were significant but weaker. Correlation between superior RNFL thicknesses was highest with GHT in adult North India Gurugram subjects with glaucoma suspect. Superior RNFL thickness showed a higher Correlation with Mean deviation (MD) and VFI of the visual field

Published
2023

4. Cerebrospinal fluid adenosine deaminase for the diagnosis of tuberculous meningitis

Author

Ashok K Pannu, Suresh Selvam, Nadim Rahman, Devender Kumar, Atul Saroch, Arun K Sharma, Sunil Sethi, Rakesh Yadav, and Vikas Bhatia

Subjects
Biochemistry (medical), Clinical Biochemistry, Drug Discovery
Abstract

Background: A consensus on the diagnostic utility of cerebrospinal fluid adenosine deaminase (ADA) for tuberculous meningitis (TBM) is lacking. Methods: Patients aged ≥12 years admitted with CNS infections were enrolled prospectively. ADA was measured with spectrophotometry. Results: We enrolled 251 TBM and 131 other CNS infections. The optimal cutoff of ADA was calculated at 5.5 U/l against microbiological reference standard with area under curve 0.743, sensitivity 80.7%, specificity 60.3%, positive likelihood ratio 2.03 and negative likelihood ratio 3.12. The widely used cutoff value 10 U/l had specificity 82% and sensitivity 50%. The discriminating power was higher for TBM versus viral meningoencephalitis than bacterial or cryptococcal meningitis. Conclusion: Cerebrospinal fluid ADA has a low-to-modest diagnostic utility.

Published
2023

7. Non-compliance to the Tobacco Control and Regulatory Act among Vendors in the Vicinities of Schools of a Metropolitan City: A Descriptive Cross-sectional Study

Author

Ankush Kumar Gupta, Dip Narayan Thakur, Mukesh Kumar Sah, Rakesh Yadav, and Prem Lal Basel

Subjects
General Medicine
Abstract

Introduction: Tobacco use is the underlying cause of ill health, preventable deaths, and disabilities worldwide. The Tobacco Product Control and Regulation Act 2011 prohibits the sale of tobacco in public places including educational institutions but non-compliance to the law had not been assessed. This study aimed to find out the prevalence of non-compliance to the Tobacco Product Control and Regulation Act among vendors in the vicinities of schools in a metropolitan city. Methods: This descriptive cross-sectional study was conducted in a metropolitan city in August 2018. Ethical approval was taken from Institutional Review Committee [Reference number: 23(6-11-E)2/075/076]. A convenience sampling method was used to recruit vendors within 100 meters radius of secondary schools. The data were collected through face-to-face interviews using a semi-structured questionnaire. Point estimate and 95% Confidence Interval were calculated. Results: Out of total 217 vendors, non-compliance to the section 3 of section 11 of Tobacco Product Control and Regulation Act was found in 195 (89.86%) (85.84-93.88 at 95% Confidence Interval). Among the non-compliers, 110 (56.41%) were selling both smoked and smokeless tobacco products, 78 (40%) were selling smoked and 7 (3.59%) were selling smokeless tobacco products. Conclusions: The non-compliance with Tobacco Product Control and Regulation Act's prohibition of tobacco sales within 100 m of schools in Kathmandu Metropolitan was similar with other studies conducted in similar settings.

Published
2022

8. Machine learning based model for risk prediction after ST-Elevation myocardial infarction: Insights from the North India ST elevation myocardial infarction (NORIN-STEMI) registry

Author

Manu Kumar Shetty, Shekhar Kunal, M.P. Girish, Arman Qamar, Sameer Arora, Michael Hendrickson, Padhinhare P. Mohanan, Puneet Gupta, S. Ramakrishnan, Rakesh Yadav, Ankit Bansal, Geevar Zachariah, Vishal Batra, Deepak L. Bhatt, Anubha Gupta, and Mohit Gupta

Subjects
Machine Learning, Percutaneous Coronary Intervention, Risk Factors, Humans, ST Elevation Myocardial Infarction, Female, Stroke Volume, Registries, Cardiology and Cardiovascular Medicine, Ventricular Function, Left
Abstract

Risk prediction following ST-Elevation Myocardial Infarction (STEMI) in resource limited countries is critical to identify patients at an increased risk of mortality who might benefit from intensive management.North India ST-Elevation Myocardial Infarction (NORIN-STEMI) is an ongoing registry that has prospectively enrolled 3,635 STEMI patients. Of these, 3191 patients with first STEMI were included. Patients were divided into two groups: development (n=2668) and validation (unseen) dataset (n=523). Various ML strategies were used to train and tune the model based on validation dataset results that included 31 clinical characteristics. These models were compared in sensitivity, specificity, F1-score, receiver operating characteristic area under the curve (AUC), and overall accuracy to predict mortality at 30 days. ML model decision making was analyzed using the Shapley Additive exPlanations (ShAP) summary plot.At 30 days, the mortality was 7.7%. On the validation dataset, Extra Tree ML model had the best predictive ability with sensitivity: 85%, AUC: 79.7%, and Accuracy: 75%. ShAP interpretable summary plot determined delay in time to revascularization, baseline cardiogenic shock, left ventricular ejection fraction30%, age, serum creatinine, heart failure on presentation, female sex, and moderate-severe mitral regurgitation to be major predictors of all-cause mortality at 30 days (P0.001 for all).ML models lead to an improved mortality prediction following STEMI. ShAP summary plot for the interpretability of the AI model helps to understand the model's decision in identifying high-risk individuals who may benefit from intensified follow-up and close monitoring.

Published
2022

11. COVID-19 Infected ST-Elevation Myocardial Infarction in INDIA (COSTA INDIA)

Author

Abdullakutty Jabir, Geevar Zachariah, Padinhare Purayil Mohanan, Mohit Dayal Gupta, Sivasubramanian Ramakrishnan, Chandra Bhan Meena, Sridhar L, Meennahalli Palleda Girish, Dipak Ranjan Das, Anshul Gupta, Praveen Nagula, Tom Devasia, Bhavesh Vajifdar, null Kamlesh Thakkar, Urmil Shah, Tanuj Bhatia, Smit Srivastava, Sanjeev Sharma, Priya Kubendiran, Pathiyil Balagopalan Jayagopal, Sudeep Kumar, Sadanandan Deepthy, Mathew Lincy, Nitish Naik, Anup Banerji, S.M. Ashraf, P.K. Asokan, Bishwa Bhushan Bharti, Biswajit Majumder, Dhiman Kahali, Dhurjati Prasad Sinha, Dipak Sharma, Dipankar Ghosh Dastidar, Dipankar Mukhapdhyay, Gurpreet Sing Wander, Harinder Kumar Bali, Kesavamoorthy B, Manoj Kumar Agarwala, Narendra Nath Khanna, B.H. Natesh, Pravin K. Goel, Rabindra Nath Chakraborty, Rajendra Kumar Jain, Rakesh Yadav, Sameer L. Dani, Satyavan Sharma, Satyendra Tewari, K.K. Sethi, Sharad Chandra, Subrato Mandal, Suman Bhandari, Sundandan Sikdar, Vivek Gupta, Pratap Chandra Rath, Vijay Harikisan Bang, Debabrata Roy, Mrinal Kanti Das, and Partho Sarathi Banerjee

Subjects
Cardiology and Cardiovascular Medicine
Published
2023

13. An efficient generalized fuzzy TOPSIS algorithm for the selection of the hybrid energy resources: A comparative study between single and hybrid energy plant installation in Turkey

Author

Gurpreet Kaur, Rakesh Yadav, and Arunava Majumder

Subjects
Management Science and Operations Research, Computer Science Applications, Theoretical Computer Science
Abstract

This paper develops an efficient algorithm for selecting the most suitable and appropriate hybridized energy power plant using “fuzzy multi-criteria decision-making” (MCDM) in Turkey. This research compares the findings of existing studies with energy hybridization. The study investigated the method of suitable location selection to install renewable energy power plants. Installation of an energy power plant is quite a difficult task as there are many factors such as availability of resources and environmental or social factors that significantly impact determining the best energy resource plant to be implemented. The purpose of this research is to extend the single-resource plant installation policy to multi-resource (hybridized) energy usage. An efficient algorithm is developed with the help of combination theory and combined fuzzy TOPSIS method to choose the best suitable alternative out of all possible single and hybrid energy resources in Turkey. All criteria, alternatives, and numerical values are chosen identically with the previous literature to compare the efficiency of the developed method. The result obtains the decision for the best hybridization along with the most suitable combination of various energy resources and sMAPE analysis. The results also supports the real situation of energy resources in Turkey.

Published
2022

14. Food intake pattern to achieve nutritional goals for Indian adults by linear programming optimization techniques

Author

Rakesh Yadav and Monika Sahu

Subjects
General Nursing, Education
Abstract

This paper discusses about the student diet selection taking some food items to complete the daily needs of nutrition. The objective of the present study was to minimize the cost of per day. To make a mathematical model of diet selection for the students in Indian Institute of Technology, Mumbai. This model is helped in the student’s diet problem with minimum possible cost. This model is the application of ILP of the food selection. The ILP model is prepared to pick suitable food items and minimize the cost of the diet taking by the students which makes all students are healthy. The result obtained from the mathematical model disclosed the minimum cost of a balanced diet is Rs 58.05 per day.

Published
2022

15. Predictive mutagenesis of prolyl endopeptidase from non-pathogenic acidophilic bacteria for gliadin catalysis

Author

Ravi Kant Pathak, Surbhi Badyal, Nitesh Kharga, Joydeep Dutta, Rakesh Yadav, and Umesh Goutam

Abstract

The enzyme prolyl endopeptidase is a member of serine peptidase group belonging to the MEROPS peptidase family S9 of clan SC. It is popularly known for its preferential cleavage of small peptides usually 30 amino acid long at the carboxyl end of proline residues. This characteristic cleavage property makes prolyl endopeptidase a therapeutic in treating gluten allergy which is triggered by 33 amino acid long (gliadin α-2) or 26 amino acid long peptides rich in proline and glutamine residues. Digestion of gliadin peptides to a length lesser than 9 amino acids can impede an autoimmune response and thus gluten sensitivity in genetically susceptible individuals. To address this issue, we have investigated the prolyl endopeptidase interactions with gliadin peptide by docking studies. Based on the docking exercises, interacting residues of endopeptidase can be further subjected to introduction of in silico mutations. A series of favourable mutations sites such as N477, I478, N483 and A682 in human PEP corresponding to which sites A548, G549, A555 and I737 have been identified respectively in Candidatus sulfotelmatobacter sp. sbA7, a non-pathogenic acidophilic human PEP homolog. Simulation of single substitution mutation at site A548 was tested capable to catalyse complete digestion of immunogenic gliadin α-2 peptide.

Published
2023

18. To Study the Effect of Potassium and Sulfur on Growth and Yield of Black Gram (Vigna mungo L.)

Author

Rajesh Singh and Rakesh Yadav

Subjects
General Medicine
Abstract

A field experiment was conducted during Zaid 2021, at Research Farm, Department of Agronomy. The experiment was laid out in Randomized Block Design with nine treatments which are replicated thrice. The treatments details viz., T1: 20 kg K ha-1 + 15 kg S ha-1, T2: 30 kg K ha-1 + 20 kg S ha-1, T3: 40 kg K ha-1 + 25 kg S ha-1, T4: 20 kg K ha-1 + 15 kg S ha-1, T5: 30 kg K ha-1 + 20 kg S ha-1, T6: 40 kg K ha-1 + 25 kg S ha-1, T7: 20 kg K ha-1 + 15 kg S ha-1, T8: 30 kg K ha-1 + 20 kg S ha-1, T9: 40 kg K ha-1 + 25 kg S ha-1 was used. The result showed that higher plant height (50.3 cm), maximum number of branches per plant (5.93), highest number of nodules per plant (15.73), maximum dry weight (8.63), CGR g/m2/day (7.90), maximum number of pods per plant (20.13), highest number of seeds per pod (6.20), test weight (33.60), seed yield (1456.67 kg ha-1), harvest index (32.06) and relative growth rate (0.013) were recorded significantly with the application of 40 kg ha-1 Potassium pulse 25 kg ha-1 Sulphur. Highest crop growth rate (7.90) was recorded with 40 Potassium kg ha-1 + 25 Sulphur kg ha-1.

Published
2022

21. Effect of Core Muscle Stabilisation Exercises on Disability Associated with Non Specific Low Back Pain in Postmenopausal Women: A Prospective Longitudinal Study

Author

Santosh Kumar Singh, Jigyasa Singh, Rahul Shankar, Snehashish Mukherjee, and Rakesh Yadav

Subjects
Clinical Biochemistry, General Medicine
Abstract

Introduction: Non Specific Low Back Pain (NSLBP) is a frequent problem faced by the majority of postmenopausal women at some stage of their lives, resulting in a significant level of disability. Aim: To evaluate the effectiveness of core stabilisation exercises compared to traditional physical treatment in postmenopausal women with NSLBP. Materials and Methods: A prospective longitudinal study was conducted from January 2022 to August 2022, including 50 postmenopausal women aged 45-60 years with NSLBP. They were placed into two groups. Conventional LBP physical therapy methods were administered to group 2. The identical traditional and Core muscle Stabilisation Exercises (CSE) were implemented in the group 1. The Modified Oswestry Disability Index (MODI) was used for the assessment of disability. The Mann-Whitney U test and Friedman Analysis of Variance (ANOVA) were conducted to analyse changes in disability scores across and among groups at the ends of the second, fourth, and sixth weeks of treatment. Results: Of 50 patients initially enrolled in the study, 33 patients were available for the final follow-up. Group 1 consisted of 16 patients, while group 2 had 17 patients. Significant reduction in disability was found across the two groups at the second, fourth, and sixth weeks of treatment (p-value

Published
2023

22. An efficient decision support system for selecting very light business jet using CRITIC-TOPSIS method

Author

Aishwarya Dhara, Gurpreet Kaur, Pon Maa Kishan, Arunava Majumder, and Rakesh Yadav

Subjects
Aerospace Engineering
Abstract

Purpose This paper aims to assure the selection of the most suitable very light business aircraft which is preferred by the passengers based on effectiveness and aesthetic comfort. The proposed approach to determine the light business jet aircraft would provide long-range, less travel time, cozy seating arrangements, on-board lavatory facility, other aesthetic ambiance (audio systems, light systems and temperature-noise control) and appliances at reasonable flight cost. Design/methodology/approach The selection of a light business jet is obtained through multi-criteria decision-making based on the speed limit ranges from 0.57 to 0.70 Mach number and the distance traveled up to 3,000 km with the best aesthetic comfort level. To validate the approach, case studies of five aircrafts such as Honda Jet HA 420, Cessna Citation jet M2, Embraer Phenom 100, Eclipse 550 and Cessna Citation Mustang are performed. To obtain the best suitable business jet, criteria importance through intercriteria correlation (CRITIC) and technique for order performance by similarity to ideal solution (TOPSIS) is used to determine the rankings of listed aircraft. Findings The study concludes that the Cessna Citation jet M2 is chosen as the best Very Light Jet (VLJ) on the basis of speed, range, weight, cost, aesthetic and comfort. Based on the sensitivity, mean absolute percentage error (MAPE) and symmetric mean absolute percentage error analysis (sMAPE), the most and least sensitive criteria for a business jet came out to be cost and speed, respectively. Originality/value A real case study for several parameters of five different jets such as Honda Jet HA 420, Cessna Citation jet M2, Embraer Phenom 100, Eclipse 550 and Cessna Citation Mustang are shown in this paper. Based on the case study numerical values are assigned with speed, range, weight, cost, aesthetic and comfort which are applied with CRITIC and TOPSIS to obtain the most suitable business jet among the five mentioned jets which are rarely found in the literature.

Published
2021

23. COVID 19-related burnout among healthcare workers in India and ECG based predictive machine learning model: Insights from the BRUCEE- Li study

Author

Mohit D. Gupta, Manish Kumar Jha, Ankit Bansal, Rakesh Yadav, Sivasubramanian Ramakrishanan, M.P. Girish, Prattay G. Sarkar, Arman Qamar, Suresh Kumar, Satish Kumar, Ajeet Jain, Rajni Saijpaul, Vandana Gupta, Deepankar Kansal, Sandeep Garg, Sameer Arora, P.S. Biswas, Jamal Yusuf, Rajeev K. Malhotra, Vishal Batra, Sanjeev Kathuria, Vimal Mehta, null Safal, Manu Kumar Shetty, Saibal Mukhopadhyay, Sanjay Tyagi, and Anubha Gupta

Subjects
RD1-811, Coronavirus disease 2019 (COVID-19), Health Personnel, health care facilities, manpower, and services, media_common.quotation_subject, education, Burn out, India, Burnout, Psychological, Burnout, Stress, Machine learning, computer.software_genre, Article, Mental wellbeing, Machine Learning, Electrocardiography, Health care, Diseases of the circulatory (Cardiovascular) system, Humans, Health care worker, Heart rate variability, Medicine, Pandemics, media_common, SARS-CoV-2, business.industry, COVID-19, virus diseases, Feeling stressed, Feeling, RC666-701, Surgery, Artificial intelligence, Cardiology and Cardiovascular Medicine, business, computer, psychological phenomena and processes
Abstract

Objectives: COVID-19 pandemic has led to unprecedented increase in rates of stress and burn out among healthcare workers (HCWs). Heart rate variability (HRV) has been shown to be reflective of stress and burnout. The present study evaluated the prevalence of burnout and attempted to develop a HRV based predictive machine learning (ML) model to detect burnout among HCWs during COVID-19 pandemic. Methods: Mini-Z 1.0 survey was collected from 1615 HCWs, of whom 664, 512 and 439 were frontline, second-line and non-COVID HCWs respectively. Burnout was defined as score ≥3 on Mini-Z-burnout-item. A 12-lead digitized ECG recording was performed and ECG features of HRV were obtained using feature extraction. A ML model comprising demographic and HRV features was developed to detect burnout. Results: Burnout rates were higher among second-line workers 20.5% than frontline 14.9% and non-COVID 13.2% workers. In multivariable analyses, features associated with higher likelihood of burnout were feeling stressed (OR = 6.02), feeling dissatisfied with current job (OR = 5.15), working in a chaotic, hectic environment (OR = 2.09) and feeling that COVID has significantly impacted the mental wellbeing (OR = 6.02). HCWs with burnout had a significantly lower HRV parameters like root mean square of successive RR intervals differences (RMSSD) [p

Published
2021

25. Quinolines and Macrolides Resistance-Associated Mutations in Chlamydia trachomatis in Women Endocervical Samples in the West Region of Cameroon

Author

J. D. D. Tamokou, J.-R. Kuiate, Rajneesh Dadwal, W. J. Takougoum Marbou, Rakesh Yadav, B. E. Djoumessi Gomseu, and Sunil Sethi

Subjects
Mutation rate, Mutation, Antibiotic resistance, 23S ribosomal RNA, medicine.drug_class, Antibiotics, medicine, Biology, medicine.disease_cause, Chlamydia trachomatis, Quinolone, Macrolide Antibiotics, Microbiology
Abstract

Chlamydia trachomatis infection is a public health problem worldwide. Although antibiotic resistance of this strict intracellular bacterium is rare, it is important to monitor the appearance of resistance genes to available efficient antibiotics. This study aimed to screen for mutations in some of these genes in C. trachomatis clinical isolates, which may be associated to resistance to quinolone and macrolide antibiotics. Thirty-five endocervical samples were collected from women aged between 18 and 49 in five district hospitals in the Western Region of Cameroon. The mutations in quinolones (parC and gyrA) and macrolides (L4, L22 and 23S rRNA) resistance domains were detected by PCR followed by sequencing on positive samples to C. trachomatis. The overall mutation rate for the studied genes was 60% in the studied samples. Seven (20%) and twelve (34%) samples presented mutations in the parC and gyrA gene respectively. Mutations in L4 (11.42%) and L22 (60%) were detected in ours samples, while no mutation was found in 23S rRNA gene. Seven clinical samples (20%) presented mutations to both macrolide and quinolone resistance genes. This study revealed a relatively high rate of mutations in the resistance genes to macrolides and quinolones in C. trachomatis in the West Cameroon. This rate of mutation calls for the competent authorities for better surveillance of C. trachomatis infection in West Cameroon to avoid a sudden increase in resistance to antibiotics in the years to come.

Published
2021

26. Evaluation of TB-LAMP assay for detection of Mycobacterium tuberculosis in children

Author

Meenu Singh, Priya Daroch, Joseph L. Mathew, Pankaj C Vaidya, Sunil Sethi, Parakriti Gupta, and Rakesh Yadav

Subjects
Microbiology (medical), medicine.medical_specialty, Tuberculosis, General Immunology and Microbiology, biology, business.industry, Diagnostic accuracy, General Medicine, bacterial infections and mycoses, medicine.disease, biology.organism_classification, Predictive value, Smear microscopy, Mycobacterium tuberculosis, Infectious Diseases, Internal medicine, Medicine, Nucleic Acid Amplification Tests, business, Prospective cohort study, Reference standards
Abstract

Background Paediatric tuberculosis remains a major public health problem in developing countries. The diagnosis of tuberculosis in children is challenging because of the paucibacillary nature of the disease, due to which more sensitive nucleic acid amplification tests are needed. In this study, we determined the accuracy of WHO endorsed TB-LAMP assay for detection of Mycobacterium tuberculosis in children. Methods This was a prospective study conducted between March to July, 2018. A total of 177 samples from consecutive suspected TB children were received for microbiological diagnosis of TB. All tests for Mycobacterium tuberculosis detection were performed in parallel (smear microscopy, mycobacterial culture, Xpert MTB/RIF and TB-LAMP). The diagnostic accuracy of index test i.e. TB LAMP were determined using mycobacterial culture as a reference standard. Results Of the 177 samples, 2 (1.1%) were excluded from the study. Among 175 samples, TB-LAMP and Xpert MTB/RIF were positive in 27 (15.4%) and 25 (14.3%) samples, respectively. The sensitivity of both Xpert MTB/RIF and TB-LAMP was same, i.e. 84% (95%CI: 63.9-95.5%), when culture was considered as the reference standard. The specificity, positive predictive value and negative predictive value of TB-LAMP assay was 96% (95%CI: 91.5-98.5%), 77.8% (95%CI: 61.1-88.6%) and 97.3% (95%CI: 93.6-98.9%), respectively. Conclusion For the detection of M. tuberculosis in paediatric samples, TB-LAMP showed a sensitivity and specificity comparable to Xpert MTB/RIF.

Published
2021

27. Association of Chlamydia trachomatis , Neisseria gonorrhoeae , Mycoplasma genitalium and Ureaplasma species infection and organism load with cervicitis in north Indian population

Author

Amrapali Dasgupta, Anuradha Chakraborti, Rakesh Yadav, Amit Roy, Sunil Sethi, Sivanantham Krishnamoorthi, Rajneesh Dadwal, and Pankaj Kumar Singh

Subjects
biology, business.industry, Cervicitis, urologic and male genital diseases, biology.organism_classification, medicine.disease_cause, medicine.disease, Applied Microbiology and Biotechnology, female genital diseases and pregnancy complications, Microbiology, Ureaplasma parvum, Ureaplasma sp, medicine, Neisseria gonorrhoeae, Dysuria, medicine.symptom, Mycoplasma genitalium, Chlamydia trachomatis, business, Ureaplasma urealyticum
Abstract

Cervicitis is predominantly caused by Neisseria gonorrhoeae and Chlamydia trachomatis, which accounts for almost half of all the cases of cervicitis. The role of newer organisms like Mycoplasma genitalium and Ureaplasma sp. and association of bacterial load with cervicitis are also not well established. So the study aimed to determine the relative frequency of these organisms and their load in association with cervicitis cases from north India. A case-control study involving 300 women was conducted using quantitative real-time PCR from endocervical swabs for identification of organisms and quantification of bacterial load. Among 150 cervicitis cases, C. trachomatis, N. gonorrhoeae, M. genitalium and Ureaplasma parvum were detected in 5 (3·3%), 10 (6·6%), 37(24·6%) and 47 (31·3%) respectively. Old age (

Published
2021

28. Fluoroquinolones: a review on anti-tubercular activity

Author

Rakesh Yadav, Sapna Joshi, and Divya Yadav

Subjects
Drug, medicine.medical_specialty, Tuberculosis, biology, media_common.quotation_subject, General Chemistry, Disease, medicine.disease, biology.organism_classification, Review article, Mycobacterium tuberculosis, chemistry.chemical_compound, chemistry, medicine, Bedaquiline, Delamanid, Intensive care medicine, Rifampicin, medicine.drug, media_common
Abstract

Tuberculosis is among the most calamitous disease, leading to a high number of deaths worldwide. After the discovery of rifampicin, no new anti-tuberculosis drug has been introduced; however, USFDA approved bedaquiline and delamanid which are limited to the treatment of extensive drug resistance tuberculosis. Fluoroquinolones are already categorized as anti-tuberculosis drugs by the WHO, but Mycobacterium tuberculosis has developed resistance to fluoroquinolones. Due to the overwhelming nature of tuberculosis across the globe, urge the scientific community to develop molecules against Mycobacterium tuberculosis with improved affinity and efficacy. In this review article, we have tried to summarize the role, structure–activity relationship, and current developments of fluoroquinolone derivatives in the management of tuberculosis. The present review will provide better insights to work further in this area.

Published
2021

29. Prioritizing the indicators responsible for sustainable municipal solid waste management using SF-AHP and SF-TOPSIS

Author

MANBIR KAUR and Rakesh Yadav

Abstract

Municipal corporations of small cities in India are struggling with the challenges that emerge from the unstructured solid waste management system. For successful implementation of the Municipal solid waste management system, all the basic components of this system need to work effectively and efficiently. Identification of performance indicators is completed with the help of a literature review and experts information. Interviews were conducted with the experts working in the field of solid waste management and three decisions criteria i.e., importance, performance, and understandability were defined to evaluate the selected performance indicators. For uncertainties, criteria weights were established using the spherical fuzzy analytical hierarchy process approach and the combined pairwise comparison matrix was aggregated by applying the spherical fuzzy TOPSIS method. Eventually, the indicators responsible for sustainable waste management were selected and ranked. An assessment of the conceptual framework of MSWM is also proposed for practical implementation of the ranked indicators for the MSWM system in Dinanagar and other cities with similar situations.

Published
2022

30. Implementation analysis of municipal solid waste management in Dinanagar city of Punjab, India

Author

MANBIR KAUR and RAKESH YADAV

Subjects
Waste generation, Sources, Characterization, Treatment techniques
Abstract

Unscientific waste management is increasingly becoming a major reason for environmental issues in Indian cities. Unlike previous municipal solid waste management in cities of Punjab(India ), this study analyzes the implementation of solid waste management. Also, examine the factors responsible for the dysfunction of the municipal corporation of Dinanagar(MCD) city of Punjab(India). To fulfill the research objectives, primary and secondary data are collected from various sources for qualitative and quantitative analysis. However, some drawbacks and flaws were found in the existing practices of municipal solid waste management. Internal consistency and validity are measured using Cronbach’s alpha. The importance-performance analysis and strengths, weaknesses, opportunities, and threats analysis are performed to conclude the present scenario of MCD. This study eventually concluded with some suggestions to waste management authorities and researchers for contributions to the improvement of the current system.

Published
2022

31. Spherical fuzzy programming approach to optimize the transportation problem

Author

MANBIR KAUR and Rakesh Yadav

Abstract

In any real-life problem, decision-making plays a very important role. It is always observable that uncertainty, hesitation, vagueness, etc. are involved in real-life situations. The existence of such factors increases the difficulty for the decision maker(s) to decide the accurate/precise, crisp value of parameters involved in the specific problem. In the present study, the uncertainty included in the transportation problem is dealt with by the proposed method based on the derived accuracy function. The function is derived using the centroid method and is further used to convert the fuzzy number into a crisp value in the proposed approach. The applicability and validity are presented with the numerical illustration. The superiority and comparative study are shown by applying it to the real-life transportation problem as a case study of Dinanagar city, India.

Published
2022

33. Numerical Simulations of a Lifted Hydrogen Jet Flame Using Flamelet Generated Manifold Approach

Author

Yu Xia, Ishan Verma, Pravin Nakod, Rakesh Yadav, Stefano Orsino, and Shaoping Li

Subjects
Fuel Technology, Nuclear Energy and Engineering, Mechanical Engineering, Energy Engineering and Power Technology, Aerospace Engineering
Abstract

A turbulent lifted H2/N2 jet flame in a vitiating co-flow environment is numerically investigated, using the Flamelet Generated Manifold (FGM) combustion model with Large Eddy Simulations (LES). Due to the hot vitiated H2/air co-flow, the primary stabilization mechanism is the autoignition followed by a premixed flame. In addition to using H2 as a fuel, this flame poses two other modeling challenges: (i) the autoignition, which is a transient chemistry-driven phenomenon; (ii) the existence of multiple combustion regimes, e.g., diffusion at autoignition location but premixed in the post flame. A series of LES/FGM simulations are completed in this work by reducing the co-flow temperature from 1045 K to 1000 K. The FGM model can predict the characteristics of the flame by showing a lifted flame. It also accurately predicts the trend in the flame lift-off distance with a change in the co-flow temperature. The current results are compared for mixture fraction, temperature, and OH mass fraction at multiple locations, which have also been correctly captured. It is noted that for a high co-flow temperature (and hence a low lift-off distance), the flame’s lift-off is highly sensitive to the inlet boundary conditions and the mesh resolution near the jet entry. A relatively coarse mesh is used for all the simulations, which is generated using a careful strategy that not only resolves the jet instabilities near the fuel inlet, but also keeps the overall mesh count low and allows for a large computational time step. A systematic sensitivity analysis on the computational speed is also performed. This work provides some useful guidelines in simulating the H2 diluted flames using the FGM model, which may be valuable to the gas turbine industry.

Published
2022

35. Initial Invasive or Conservative Strategy for Stable Coronary Disease

Author

Maron D. J., Hochman J. S., Reynolds H. R., Bangalore S., O'Brien S. M., Boden W. E., Chaitman B. R., Senior R., Lopez-Sendon J., Alexander K. P., Lopes R. D., Shaw L. J., Berger J. S., Newman J. D., Sidhu M. S., Goodman S. G., Ruzyllo W., Gosselin G., Maggioni A. P., White H. D., Bhargava B., Min J. K., John Mancini G. B., Berman D. S., Picard M. H., Kwong R. Y., Ali Z. A., Mark D. B., Spertus J. A., Krishnan M. N., Elghamaz A., Moorthy N., Hueb W. A., Demkow M., Mavromatis K., Bockeria O., Peteiro J., Miller T. D., Szwed H., Doerr R., Keltai M., Selvanayagam J. B., Gabriel Steg P., Held C., Kohsaka S., Mavromichalis S., Kirby R., Jeffries N. O., Harrell F. E., Rockhold F. W., Broderick S., Bruce Ferguson T., Williams D. O., Harrington R. A., Stone G. W., Rosenberg Y, ISCHEMIA Research Group: Joseph Ricci, A Tello Montoliu, A I Robero Aniorte, Abbey Mulder, Abhay A Laddu, Abhinav Goyal, Abhishek Dubey, Abhishek Goyal, Abigail Knighton, Abraham Oomman, Adam J Jaskowiak, Adam Kolodziej, Adam Witkowski, Adnan Hameed, Adriana Anesini, Afshan Hussain, Agne Juceviciene, Agne Urboniene, Agnes Jakal, Agnieszka Szramowska, Ahmad Khairuddin, Ahmed Abdel-Latif, Ahmed Adel, Ahmed Aljzeeri, Ahmed Kamal, Ahmed Talaat, Aimee Mann, Aira Contreras, Ajit Kumar, V K Kumar, Akemi Furukawa, Akshay Bagai, Akvile Smigelskaite, Alain Furber, Alain Rheault, Alaine Melanie Loehr, Alan Rosen, Albert Varga, Albertina Qelaj, Alberto Barioli, Aldo Russo, Alec Moorman, Alejandro Gisbert, Aleksandra Fratczak, Aleksandras Laucevicius, Alena Kuleshova, Alessandro Sionis, Alexander A Sirker, Alexander M Chernyavskiy, Alexandra Craft, Alexandra Vazquez, Alexandre Ciappina Hueb, Alexandre S Colafranseschi, Alexandre Schaan de Quadros, Alexandre Tognon, Ali Alghamdi, Alice Manica Muller, Aline Nogueira Rabaça, Aline Peixoto Deiro, Alison Hallam, Allegra Stone, Allison Schley, Almudena Castro, Alvaro Rabelo Ales, Amanda Germann, Amanda O'Malley, Amar Uxa, Amarachi Ojajuni, Amarino C Oliveira Jr, Amber B Hull, Ambuj Roy, Amer Zarka, Amir Janmohamed, Ammani Brown, Ammy Malinay, Amparo Martinez Monzonis, Amy J Richards, Amy Iskandrian, Amy Ollinger, Ana D Djordjevic-Dikic, Ana Fernández Martínez, Ana Gomes Almeida, Ana Paula Batista, Ana Rita Francisco, Ana S Mladenovic, Ana Santana, Anam Siddiqui, Anastasia M Kuzmina-Krutetskaya, Andras Vertes, Andre S Sousa, Andre Gabriel, André Schmidt, Andrea M Lundeen, Andrea Bartykowszki, Andrea Lorimer, Andrea Mortara, Andrea Pascual, Andreia Coelho, Andreia Rocha, Andrés García-Rincón, Andrew G Howarth, Andrew J Moriarty, Andrew Docherty, Andrew Starovoytov, Andrew Zurick, Andrzej Łabyk, Andrzej Swiatkowski, Andy Lam, Anelise Kawakami, Angela Hoye, Angela Kim, Angelique Smit, Angelo Nobre, Anil V Shah, Anja Ljubez, Anjali Anand, Ankush Sachdeva, Ann Greenberg, Ann Luyten, Ann Ostrander, Anna Di Donato, Anna Cichocka-Radwan, Anna Fojt, Anna Plachcinska, Anna Proietti, Anna Teresinska, Anne Marie Webb, Anne Cartwright, Anne Heath, Anne Mackin, Anong Amaritakomol, Anong Chaiyasri, Anoop Chauhan, Anoop Mathew, Anthony Gemignani, Anto Luigi Andres, Antonia Vega, Antonietta Hansen, Antonino Ginel Iglesias, Antonio Carlos Carvalho, Antonio Di Chiara, Antonio Serra Peñaranda, Antonio Carvalho, Antonio Colombo, Antonio Fiarresga, Anupama Rao, Aquiles Valdespino-Estrada, Araceli Boan, Areef Ishani, Ariel Diaz, Arijit Ghosh, Arintaya Prommintikul, Arline Roberts, Arnold H Seto, Arnold P Good, Arshed Quyyumi, Arthur J Labovitz, Arthur Kerner, Arturo S Campos-Santaolalla, Arunima Misra, Ashok Mukherjee, Ashok Seth, Ashraf Seedhom, Asim N Cheema, Asker Ahmed, Atul Mathur, Atul Verma, Audrey W Leong, Axel Åkerblom, Axelle Fuentes, Aynun Naher, Badhma Valaiyapathi, Baljeet Kaur, Bandula Guruge, Barbara 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Ellis W Lader, Elvin Kedhi, Emanuela Racca, Emilie Tachot, Emily DeRosa, Encarnación Alonso-Álvarez, Eric Nicollet, Eric Peterson, Erick Alexánderson Rosas, Erick Donato Morales, Erin Orvis, Ermina Moga, Estelle Montpetit, Estevao Figueiredo, Eugene Passamani, Eugenia Nikolsky, Eunice Yeoh, Evgeniy I Kretov, Ewa Szczerba, Ewelina Wojtala, Expedito Eustáquio Ribeiro Silva, F Marin Ortuño, Fabio R Farias, Fabio Fimiani, Fabrizio Rolfo, Fa-Chang Yu, Fadi Hage, Fadi Matar, Fahim Haider Jafary, Fang Feng, Fang Liu, Fatima Ranjbaran, Fatima Rodriguez, Fausto J Pinto, Fauzia Rashid, Federica Ramani, Fei Wang, Fernanda Igansi, Filipa Silva, Filippo Ottani, Fiona Haines, Firas Al Solaiman, Flávia Egydio, Flavio Lyra, Florian Egger, Fran Farquharson, Frances Laube, Francesc Carreras Costa, Francesca de Micco, Francesca Bianchini, Francesca Pezzetta, Francesca Pietrucci, Francesco Orso, Francesco Pisano, Francis Burt, Francisca Patuleia Figueiras, Francisco Fernandez-Aviles, Francois Pierre 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Grazyna Anna Szulczyk, Gregor Simonis, Gregory Kumkumian, Gretchen Ann Peichel, Grzegorz Gajos, Gudrun Steinmaurer, Guilherme G Rucatti, Guilherme Portugal, Guilhermina Cantinho Lopes, Guillem Pons Lladó, Gunnar Frostfelt, Gurpreet S Wander, Gurpreet Gulati, Gustavo Pucci, Hafidz Abd Hadi, Haibo Zhang, Haitao Wang, Halina Marciniak, Han Chen, Hanan Kerr, Hani Najm, Hanna Douglas, Hannah Phillips, Hao Dai, Haojian Dong, Haqeel Jamil, Harikrishnan Sivadasanpillai, Harry Suryapranata, Hassan Reda, Hayley Pomeroy, Heather Barrentine, Heather Golden, Heather Hurlburt, Heidi Wilson, Helen C Tucker, Helene Abergel, Hemalata Siddaram, Hermine Osseni, Herwig Schuchlenz, Hesong Zeng, Hicham Skali, Hilda Solomon, Hollie Horton, Holly Hetrick, Holly Little, Holly Park, Hongjie Chi, Hossam Mahrous, Howard A Levite, Hristo Pejkov, Huajun Li, Hugo Bloise-Adames, Hugo Marques, Hui Zhong, Hui-Min Zhang, Humayrah Hashim, Hung-I Yeh, Hussien El Fishawy, Ian Webb, Iftikhar Kullo, Igor O Grazhdankin, Ihab 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Bermudez, Eapen Punnoose, Edgar L Tay, Edgar Karanjah, Edoardo Verna, Eduardo Hernandez-Rangel, Edward D Nicol, Edward O McFalls, Edward T Martin, Edyta Kaczmarska, Ekaterina I Lubinskaya, Elena A Demchenko, Elena Refoyo Salicio, Eli Feen, Elihú Durán-Cortés, Elisabeth M Janzen, Elise L Hannemann, Elise van Dongen, Elissa Restelli Piloto, Eliza Kaplan, Elizabeta Srbinovska Kostovska, Elizabeth Capasso-Gulve, Elizabeth Congdon, Elizabeth Ferguson, Elizaveta V Zbyshevskaya, Ellen Magedanz, Ellie Fridell, Ellis W Lader, Elvin Kedhi, Emanuela Racca, Emilie Tachot, Emily DeRosa, Encarnación Alonso-Álvarez, Eric Nicollet, Eric Peterson, Erick Alexánderson Rosas, Erick Donato Morales, Erin Orvis, Ermina Moga, Estelle Montpetit, Estevao Figueiredo, Eugene Passamani, Eugenia Nikolsky, Eunice Yeoh, Evgeniy I Kretov, Ewa Szczerba, Ewelina Wojtala, Expedito Eustáquio Ribeiro Silva, F Marin Ortuño, Fabio R Farias, Fabio Fimiani, Fabrizio Rolfo, Fa-Chang Yu, Fadi Hage, Fadi Matar, Fahim Haider 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Niedzwiecka, J David Knight, Jacek Kusmierek, Jackie M White, Jackie Chow, Jacob Udell, Jacqueline E Tamis-Holland, Jacqueline Fannon, Jacquelyn A Quin, Jacquelyn Do, Jaekyeong Heo, Jakub Maksym, James E Davies, James H O'Keefe Jr, James J Jang, James Cha, James Harrison, James Hirsch, James Stafford, James Tatoulis, Jamie Rankin, Jan Henzel, Jan Orga, Jana Tancredi, Janaina Oliveira, Jane Burton, Jane Eckstein, Jane Marucci, Janet P Knight, Janet Blount, Janet Halliday, Janetta Kourzenkova, Janitha Raj, Jan-Malte Sinning, Jaqueline Pozzibon, Jaroslaw Drozdz, Jaroslaw Karwowski, Jason D Glover, Jason Loh Kwok, Jason T Call, Jason Linefsky, Jassira Gomes, Jati Anumpa, Javier J Garcia, Javier Courtis, Jay Meisner, K Jayakumar, Jayne Scales, Jean E Denaro, Jean Michel Juliard, Jean Ho, Jeanette K Stansborough, Jean-Michel Juliard, Jeanne Russo, Jeannette J M Schoep, Jeet Thambyrajah, Jeff Leimberger, Jeffery A Breall, Jeffrey A Kohn, Jeffrey C Milliken, Jeffrey Anderson, Jeffrey Blume, Jeffrey Kanters, Jeffrey Lorin, Jeffrey Moses, Jelena J Stepanovic, Jelena Celutkiene, Jelena Djokic, Jelena Stojkovic, Jenne M Jose, Jenne Manchery, Jennifer A Mull, Jennifer H Czerniak, Jennifer L Stanford, Jennifer Gillis, Jennifer Horst, Jennifer Isaacs, Jennifer Langdon, Jennifer Thomson, Jennifer Tomfohr, Jennifer White, Jen-Yuan Kuo, Jeremy Rautureau, Jerome Fleg, Jessica Berg, Jessica Rodriguez, Jessica Waldron, Jhina Patro, Jia Li, Jiajia Mao, Jiamin Liu, Jian'an Wang, Jianhua Li, Jianxin Zhang, Jie Qi, Jihyun Lyo, Jill Marcus, Jim Blankenship, Jing Zhang, Jingjing Liu, Jing-Yao Fan, Jiun-Yi Li, Jiwan Pradhan, Jiyan Chen, J M Rivera Caravaca, Jo Evans, Joan Garcia Picart, Joan Hecht, Joanna Jaroch, Joanna Zalewska, Joanne Kelly, Joanne Taaffe, João Reynaldo Abbud, João V Vitola, Joaquín V Peñafiel, Jocelyne Benatar, Jody Bindeman, Joe Sabik, Joel Klitch, Johann Christopher, Johannes Aspberg, John D Friedman, John F Beltrame, John F Heitner, John Joseph Graham, John R Davies, John Doan, John Kotter, John Kurian, John Mukai, John Pownall, Jolanta Sobolewska, Jon Kobashigawa, Jonathan L Goldberg, Jonathan W Bazeley, Jonathan Byrne, Jonathan Himmelfarb, Jonathan Leipsic, Jonean Thorsen, Jorge F Trejo Gutierrez, Jorge Escobedo, Jorik Timmer, José A Ortega-Ramírez, José Antonio Marin-Neto, Jose D Salas, Jose Enrique Castillo, Jose Francisco Saraiva, José J Cuenca-Castillo, Jose L Diez, José Luis Narro Villanueva, José Luiz da Vieira, José M Flores-Palacios, Jose Ramon Gonzalez, Jose Seijas Amigo, Jose Fragata, Josep Maria Padró, Josheph F X McGarvey Jr, Joseph Hannan, Joseph Sacco, Joseph Sweeny, Joseph Wiesel, Josephine D Abraham, Joshua P Loh, Joy Burkhardt, Joyce R White, Joyce Riestenberg-Smith, Judit Sebo, Judith L Meadows, Judith Wright, Judy Mae Foltz, Judy Hung, Judy Otis, Juergen Stumpf, Jui-Peng Tsai, Julia S Dionne, Julia de Aveiro Morata, Julie Bunke, Julie Morrow, Julio César Figal, Jun Fujita, Jun Jiang, Junhua Li, Junqing Yang, Juntima Euathrongchit, Jyotsna Garg, K Manjula Rani, K Preethi, Kaatje Goetschalckx, Kai Eggers, Kamalakar Surineni, Kanae Hirase, T R Kapilamoorthy, Karen Calfas, Karen Gratrix, Karen Hallett, Karen Hultberg, Karen Nugent, Karen Petrosyan, Karen Swan, Karolina Kryczka, Karolina Wojtczak-Soska, Karolina Wojtera, Karsten Lenk, Karthik Ramasamy, Katarzyna Łuczak, Katarzyna Malinowska, Kate Pointon, Kate Robb, Katherine Martin, Kathleen Claes, Kathryn Carruthers, Kathy E Siegel, Katia Drouin, Katie Fowler-Lehman, Kavita Rawat, Kay Rowe, Keiichi Fukuda, Keith A A Fox, Ken Mahaffey, Kendra Unterbrink, Kenneth Giedd, Kerrie Van Loo, Kerry Lee, Kerstin Bonin, Kevin R Bainey, Kevin T Harley, Kevin Anstrom, Kevin Chan, Kevin Croce, Kevin Landolfo, Kevin Marzo, Keyur Patel, Khaled Abdul-Nour, Khaled Alfakih, Khaled Dajani, Khaled Ziada, Khaula Baloch, Khrystyna Kushniriuk, Kian-Keong Poh, Kim F Ireland, Kim Holland, Kimberly Ann Byrne, Kimberly E Halverson, Kimberly Elmore, Kimberly Miller-Cox, Kiran Reddy, Kirsten J Quiles, Kirsty Abercrombie, Klaus Matschke, Konrad Szymczyk, Koo Hui Chan, Kotiboinna Preethi, Kozhaya Sokhon, Krissada Meemuk, Kristian Thygesen, Kristin M Salmi, Kristin Newby, Kristina Wippler, Kristine Arges, Kristine Teoh, Krystal Etherington, Krystyna Łoboz-Grudzień, Krzysztof W Reczuch, Krzysztof Bury, Krzysztof Drzymalski, Krzysztof Kukuła, Kuo-Tzu Sung, Kurt Huber, Ladda Douangvila, Lance Sullenberger, Larissa Miranda Trama, Laszlone Matics, Laura Drew, Laura Flint, Laura Keinaite, Laura Sarti, Laurel Kolakaluri, Lawrence M Phillips, Lawrence Friedman, Lawrence Phillips, Lazar Velicki, Leah Howell, Leandro C Maranan, Leanne Cox, Ledjalem Daba, Lei Zhang, Lekshmi Dharmarajan, Leo Bockeria, Leonardo Pizzol Caetano, Leonardo Bridi, Leonid L Bershtein, Leszek Sokalski, Li Hai Yan, Li Li, Lia Nijmeijer, Lidia Sousa, Lihong Xu, Lihua Zhang, Lili Zhang, Lilia Schiavi, Lilian Mazza Barbosa, Lillian L Khor, Lina Felix-Stern, Linda L Hall, Linda M Hollenweger, Linda Arcand, Linda Davidson-Ray, Linda Schwarz, Lindsey N Sikora, Lingping Chi, Lino Patricio, Liping Zhang, Lisa Chaytor, Lisa Hatch, Lisa McCloy, Lisa Wong, Liselotte Persson, Lixin Jiang, Liz Low, Ljiljana Pupic, Loïc Bière, Lorenzo Monti, Lori Christensen, Lori Pritchard, Loriane Black, Lori-Ann Desimone, Lori-Ann Larmand, Lorraine McGregor, Louise Morby, Louise Thomson, Luc Harvey, Luciana de Pádua Baptista, Lucilla Garcia, Ludivine Eliahou, Ludmila Helmer, Luis F Smidt, Luis Bernanrdes, Luis Guzman, Luiz A Carvalho, Luyang Xiong, Lynette L Teo, Lynn M Neeson, Lynne Winstanley, M Barbara Srichai-Parsia, M Quintana Giner, M Sowjanya Reddy, M Valdés Chávarri, M Grazia Rossi, Maarten Simoons, Maayan Konigstein, Maciej Lesiak, Maciej Olsowka, Mafalda Selas, Magalie Corfias, Magdalena Madero Rovalo, Magdalena Łanocha, Magdalena Miller, Magdalena Misztal-Teodorczyk, Magdalena Rantinella, Magdy Abdelhamid, Magnolia Jimenez, Mahboob Alam, Mahevamma Mylarappa, Mahfouz El Shahawy, Mahmoud Mohamed, Mahmud Al-Bustami, Majo X Joseph, Malgorzata Frach, Małgorzta Celińska-Spodar, Malte Helm, Manas Chacko, Mandy Murphy, Manitha Vinod, Manjula Rani, Manu Dhawan, Manuela Mombelli, Marcel Weber, Marcello Galvani, Marcelo Jamus Rodrigues, Marcia F Dubin, Marcia F Werner Bayer, Marcin Szkopiak, Marco Antonio Monsalve, Marco Bizzaro Santos, Marco Magnoni, Marco Marini, Marco Sicuro, Marco Zenati, Marcos Valério Coimbra Resende, Marek Roik, Margalit Bentzvi, Margaret Gilsenan, Margaret Iraola, Margot C Quinn, Maria A Alfonso, Maria Antonieta Pereira Moraes, María Dolores Martínez-Ruíz, María Fernanda Canales, Maria Inês Caetano, Maria P Corral, Maria Pérez García, Maria Victoria Actis, Maria Aguirre, Maria Andreasson, Maria Aprile, Maria Colton, Maria Eugenia Martin, Maria Lasala, Maria Lorenzo, Maria Posada, Maria Shier, Maria Thottam, Mariana V Furtado, Mariana Yumi Okada, Marianna D A Dracoulakis, Marianne De Andrade, Mariano Rubio, Marie Essermark, Marielle Scherrer-Crosbie, Marija T Petrovic, Marija Zdravkovic, Marilyn Black, Marina Garcia, Mario J Garcia, Mariola Szulik, Marisa Orgera, Mark A de Belder, Mark Harbinson, Mark Hyun, Mark Peterson, Mark Xavier, Marlowe Mosley, Marta Capinha, Marta Marcinkiewicz-Siemion, Marta Swiderek, Martha Meyer, Martina Ceseri, Martina Tricoli, Marvin Kronenberg, Mary Williams, Mary Ann Champagne, Mary Colleen Rogge, Mary R Soltau, Mary Streif, Massimo Villella, Massoud Leesar, Matei Claudia, Mateusz Solecki, Matías Nicolás Mungo, Matthew Wall Jr, Matthew Budoff, Matthew Jezior, Matthew Luckie, Matthias Friedrich, Mauren P Haeffner, Maximilian Tscharre, Max-Paul Winter, Mayana Almeida, Mayil S Krishnam, Mayuri Patel, Meenakshi Mishra, Megan Manocchia, Meghana Kakade, Melanie J Munro, Melissa D Chaplin, Melissa LeFevre, Mervyn Andiapen, Michael A Gibson, Michael B Rubens, Michael C Turner, Michael D Shapiro, Michael W Lee, Michael Berlowitz, Michael Davidson, Michael Mack, Michael McDaniel, Michael Mumma, Michal Wlodarczyk, Michel G Khouri, Michel S Slama, Michele Rawlins, Michelle M Bonner, Michelle M Seib, Michelle Chang, Michelle Crowder, Michelle Dixon, Michelle Mayon, Michelle McEvoy, Michelle Yee, Miguel M Fernandes, Miguel Nobre Menezes, Miguel Souto Bayarri, Miguel Barrero, Mikhail T Torosoff, Milan R Dobric, Milan Dobric, Milica Nikola Dekleva, Milind Avdhoot Gadkari, Millie Gomez, Min Tun Kyaw, Miriam Brooks, Miroslav Stevo Martinovic, Mitchel B Lustre, Mohammad Tariq Vakani, Mohammad El-Hajjar, Mohammed Al-Amoodi, Mohammed Hussain, Mohammed Saleem, Moisés Blanco-Calvo, Moisés Jiménez-Santos, Mona Bhatia, Monica Rosca, Monika Laukyte, Montserrat Gracida Blanca, Montserrat Vila Perales, Mouaz H Al-Mallah, Moysés de Oliveira Filho, Mpiko Ntsekhe, Muhamed Saric, Mulei Chen, Myriam Brousseau, Myrthes Emy Takiuti, Nada Cemerlic-Adjic, Nadia Asif, Nadia Gakou, Nafisa Hussain, Nana O Katamadze, Nancy L Clapp, Nancy Aedy, Nandita Nataraj, Nanette K Wenger, Naomi Uchida, Nasrul Ismail, Natalia S Oliveira, Natalia de Carvalho Maffei, Natalie Spitzer, Natasha C Putnam, Naved Aslam, Neamat Mowafy, Neeraj Pandit, Neeraj Parakh, Nevena Garcevic, Ngaire Meadows, Nhi N Tran, Nicholas Danchin, Nicki Lakeman, Nicola Johnston, Nicolas W Shammas, Nicole Saint Vrestil, Nicole Deming, Nier Zhong, Niket Patel, Nikola N Boskovic, Nikolaos Karogiannis, Nikos Werner, Nina Johnston, Ning Zhang, Ning Zhou, Niree Hindoyan, Nirmal Kumar, Nitika Chadha, Nitish Naik, Nodira Aripova, Noloyiso Mtana, Nona A Eskelson, Noor Syamira Mokhtar, Noppon Taksaudom, Nor Asiah Basri, Nora Marchelletta, Norma Hogg, Nungshi Jungla, Nuno Ferreira, Oksana A Lubyanaya, Olga B Nikolaeva, Olga Cañavate, Olga Sobrino, Olga Walesiak, Olga Walter, Olga Zdończyk, Olivia J Lim, Olivia Anaya, Olivia Mancilla, Olivier Dubourg, Olugbenga Bello, Omar Almousalli, Omar Thompson, Oni Olurinde, Or Harel, Osama Raheem, Oscar Méndiz, Óscar Prada-Delgado, Oz Shapira, P Christian Schulze, Pachara Panpunuan, Pal Maurovich-Horvat, Pallav Garg, Paloma Moraga, Pam Singh, Pamela Julian, Pamela Ouyang, Pamela Sigel, Pamela Woodard, Panpan Zhou, Paola Emanuela Poggio, Paola Smanio, Paolo Calabro, Paramjit Jeetley, Pascal Goube, Patricia K Nguyen, Patricia Alarie, Patricia Arakelian, Patricia Arsenault, Patricia Blaise, Patricia Brito, Patricia Cowper, Patricia Endsley, Patricia Mieses, Patrick B Alexander, Patrick Donnelly, Patrick Wilmot, Patrycja Lebioda, Paul C Gordon, Paul Der Mesropian, Paul Galiwango, Paul Hauptman, Paul Kennedy, Paula Beardsley, Paula García-González, Paulo Cury Rezende, Paulo Ricardo Caramori, Pavel S Kozlov, Pedro Canas Silva, Pedro Gabriel Melo Barros E Silva, Pedro Píccaro de Oliveira, Pedro Carvalho, Pedro Modas, Pedro Rio, Peeyush Jain, Peiyu He, Peter A McCullough, Peter H Stone, Peter M Pollak, Peter Douglass, Peter Henriksen, Peter OKane, Peter Ong, Philip Jones, Philip Rogal, Philippe Généreux, Philippe Menasche, Philippe Rheault, Phoebe Goold, Pierre Gervais, Pierre Michaud, Pilar Calvillo, Ping Chai, Piotr Jakubowski, Piotr Pruszczyk, Piotr Slomka, Piyamitr Sritara, Poay-Huan Loh, Poonam Sonawane, Pouneh Samadi, Pragnesh P Parikh, Prakash Deedwania, Pranav M Patel, Praneeth Polamuri, Pratiksha Sharma, Precilia Vasquez, Preeti Kamath, Prince Thomas, Priyadarshani Arambam, Puja K Mehta, Purvez Grant, Pushpa Naik, Qi Zhong, Qian Zhao, Qiang Zhou, Qianqian Yuan, Qin Yu, Qingxian Li, Qiulan Xie, Qiutang Zeng, R J Vindhya, R James Gerlach, Rachel King, Rada Vučić, Radmila Lyubarova, Radoslaw Pracon, Raewyn Fisher, Rafael Beyar, Rafael Diaz, Rafael Selgas, Raffaele Bugiardini, Raffaele Fanelli, Raisa Kavalakkat, V S Rajalekshmi, Rajat S Barua, Rajeev Menon, Rajesh Gopalan Nair, Rajesh Francis, Rajiv Narang, Rakesh Yadav, Ralph Alan Huston, T Ramakrishnan, Ramesh de Silva, Rami El Mahmoud, Ramiro Carvalho, Ramon de Jesús-Pérez, Ramona Stevens, Ran Leng, Ranjan Kachru, Ranjit Kumar Nath, Raquel Sanchez, Raven R Dwyer, Raven Lee, Ray Wyman, Raymond C Wong, Raymond W Little, Raymundo Ocaranza Sanchez, Rebecca J Wimmer, Rebecca Bariciano, Rebecca Otis, Rebekah R Herrmann, Reem Yunis, Reinette Hampson, Renato Abdala Karam, Renee C Hessian, Renee Kaneshiro, Reshma Ravindran, Reto Andreas Gamma, Reyna Bhandari, Reza Arsanjani, Ricardo L Lopes, Ricardo Mendes Oliveira, Ricardo Costa, Richa Bhatt, Richard F Davies, Richard H J Trimlett, Richard Goldweit, Rik Hermanides, Rine Nakanishi, Rinu R Sidh, Risha Patel, Rita Coram, Rizwan A Siddiqui, Rob S Beanlands, Robert J Hamburger, Robert K Riezebos, Robert M Donnino, Robert Bojar, Robert Chilton, Robert Guyton, Robert Henderson, Robert Kornberg, Robert Leber, Robert Mao, Robert Stenberg, Roberta P Santos, Roberto René Favaloro, Roberto Amati, Rodolfo G S D Lima, Rodrigo J Cerci, Rogerio Tumelero, Rohit Tandon, Roma Tewari, Romalisa Miranda-Peats, Ron Wald, Ronald A Mastouri, Ronald G Morford, Ronald G Schwartz, Ronald P Pedalino, Rongrong Hu, Ronnell A Hansen, Ronny A Cohen, Rory Hachamovitch, Rosa Homem, Rosa Sandonato, Rosane Laimer, Rosann Gans, Roxanne Yost, Roy Mathew, Rubén Baleón-Espinosa, Ruben Ramos, Rubine Gevorgyan, Rui Ferreira, Rui Jing, Ruth Pérez-Fernández, S K Dwivedi, S Ramakrishnan, Saadat Khan, Sabahat Bokhari, Sabu Thomas, Sadath Lubna, Sajeeda Parveen Khan, Sajeev Chakanalil Govindan, Saket Girotra, Saleem Kassam, Sallie Canada, Salvador Cruz-Flores, Samaa Mohamed, Samantha Ly, Sameh El Kaffas, Samia Massalha, Sampoornima Setty, Samuel Nwosu, Sandeep Seth, Sandeep Singh, Sander R Niehe, Sandra M Rivest, Sandra S Zier, Sandra Ahoud, Sandy Carr, Sanjay Ganapathi, Sanjay Shetty, Sanjeev Sharma, Santa Jimenez, Santhosh Satheesh, Santiago A Garcia, Sara Fernandez, Sara Karlsson, Sara Salkind, Sara Temiyasathit, Sarah Medina Rodriguez, Sarah Beaudry, Sarah Hadjih, Sarah Williams, Sarah Zahrani, Sarju Ralhan, Sasa Hinic, Sasko Kedev, Satinder Singh, Satoshi Yasuda, Satvic Cholenahally Manjunath, Sau Lee, Scott M Kaczkowski, Scott Kinlay, Sean W Hayes, Sebastian Sobczak, Senait Asier, Sergey A Sayganov, Seth I Sokol, Shaheen Pandie, Shaiful Azmi Yahaya, Shamir Mehta, Shao-Ping Nie, Sharad Chandra, Sharder Islam, Sharon Tai, Sheetal Rupesh Karwa, Sheri Ussery, Sheromani Bajaj, Sherron C Crook, Shigeyuki Nishimura, Shintaro Nakano, Shirin Heydari, Shiv Kumar Choudhary, Shivali Patel, Shobana Ganesan, Shruti Pandey, Shuyang Zhang, Shweta Hande, Siddharth Gadage, Sik-Yin V Tan, Silvia Zottis Poletti, Silvia Riera, Silvia Valbuena, Simon Walsh, Simona Maspoli, Simone Savaris, Si-Ting Feng, So Yang Cho, Solomon Yakubov, Songlin Zhu, Songtao Wang, Sonia Guerrero, Sonika Gupta, Sonja Salinger Martinovic, Sonya Brons, Sorin Brener, Sothinathan Gurunathan, Souheil Saba, Soundarya Nayak, Sowjanya Reddy, Srinivasa Potluri, Sriram Sudarshan, Srun Kuanprasert, Stacie Van Oosterhout, Stamatios Lerakis, Stanley E Cobos, Stefan C Bertog, Stefan M Simović, Stefan Weikl, Stefano Di Marco, Stefano Provasoli, Stephanie A Tirado, Stephanie C Boer, Stephanie M Lane, Stephanie Ferket, Stephanie Kelly, Stephanie Wasmiller, Stephen H McKellar, Stephen P Hoole, Stephen Fremes, Stephen Preston, Steve Leung, Steven A Fein, Steven J Lindsay, Steven P Sedlis, Steven Giovannone, Steven Michael, Steven Weitz, Stijn van Vugt, Subhash Banerjee, Sudhir Naik, Suellen Hosino, Sukie Desire, Sukit Yamwong, Suku T Thambar, Sulagna Mookherjee, Suman Singh, Sundeep Mishra, Sunil Kumar Verma, Supap Kulthawong, Supatchara Khwakhong, Surendra Naik, Suresh Babu, Surin Woragidpoonpol, Suryaprakash Narayanappa, Susan Derbyshire, Susan Gent, Susan Mathus, Susan Milbrandt, Susan Moore, Susan Regan, Susan Stinson, Susan Webber, Susana Silva, Susanna Stevens, Susanne Gruensfelder, Suthara Aramcharoen, Suvarna Kolhe, Suzana Tavares, Suzanne Arnold, Suzanne Welsh, Svetlana Apostolovic, Swapna Kunhunny, Ta-Chuan Hung, Taissa Zappernick, Tali Sharir, Talita Silva, Tamara Colaiácovo Soares, Tapan Umesh Pillay, Tarun K Mittal, Tatiana Trifonova, Tauane Bello Duarte, Tauqir Huk, Téodora Dutoiu, Terrance Chua, Terry Weyand, Thabitha Charles, Theodoros Kofidis, Theresa McCreary, Thierry Lefevre, Thippeekaa Arumairajah, Thitipong Tepsuwan, Thomas J Mulhearn, Thomas M Meyer, Thomas P Rocco, Thomas R Downes, Thomas Crain, Thomas Haldis, Thomas Mathew, Thomas Redick, Thounaojam Indira Devi, Thuraia Nageh, Tia Cauthren, Tiago Silva, Tiffany Little, Tijana Andric, Tina Harding, Titus Lau, Tiziana Formisano, Tiziano Moccetti, Tomasz Ciurus, Tomasz Mazurek, Tomasz Tarchalski, Toshiyuki Nagai, Tri Tran, Tricia Youn, Trish Tucker, Trudie Milner, Tuhina Bose, Tushar Kotecha, Udo Sechtem, Uma S Valeti, Umberto Cucchini, Umesh Badami, Upendra Kaul, V K Bahl, V S Narain, Valentina Casali, Valeria Godoy, Valerie Robesyn, Vamshi P Priya, Vandana Yadav, Vera McKinney, Veronica De Lenges, Veronica Tinnirello, Vicente Miro, Victor Navarro, Victoria Gumerova, Victoria Hernandez, Vidya Seeratan, Vijay Kumar, Vikentiy Y Kozulin, Viktoria Bulkley, Vilmar Veiga Jr, Vincent Setang, C P Vineeth, Virginai Pubull Nuñez, Virginia Fernández-Figares, Vitor Gomes, Viviana Gabriel, Viviane Dos Santos, Viviane Almeida, Vlad A Iliescu, Vladan Mudrenovic, Vladimir Dzavik, Vojislav L Giga, Walter Enrique Mogrovejo, Wan Xian Chan, Wanda C Marfori, Wanda Parker, Warangkana Mekara, Wassim Nona, Wayne Old, Wayne Pennachi, Weerachai Nawarawong, Wei Chen, Wei Su, Weibing Xing, Wei-Ren Lan, Wenda Crawford, Wendy L Stewart, Wendy Drewes, Wenhua Lin, William B Abernethy, William D Salerno, William F Fearon, William Vergoni, William Weintraub, Winnie C Sia, Wlodzimierz J Musial, Xacobe Flores-Ríos, Xavier Garcia-Moll Marimon, Xi Su, Xiang Ma, Xiangqiong Gu, Xiao Wang, Xiaomei Li, Xiaowei Yao, Xin Fu, Xin Su, Xin Zeng, Xinchun Yang, Xiuhong Li, Xuehua Fang, Xutong Wang, Yaming Geng, Yan Yan, Yanek Pépin-Dubois, Yanfu Wang, Yang Wang, Yanmeng Tian, Yaping Huang, Yechen Han, Yesenia Zambrano, Yi-Hsuan Yang, Ying Tung Sia, Yining Yang, Yitong Ma, Yolayfi Peralta, Yongjian Wu, Yu Kunwu, Yu Zhao, Yudong Peng, Yueh-Hung Lin, Yulan Zhao, Yumei Dong, Yunhai Zhao, Yutthaphan Wannasopha, Yvonne Taul, Zakir Sahul, Zalina Kudzoeva, Zbigniew Kalarus, Zeljko Z Markovic, Zhen Huang, Zheng Ji, Zhenyu Liu, Zhou Yue, Zhulin Zhang, Zhuxi Li, Zile Singh Meharwal, Ziliang Bai, Zixiang Yu, Zohra Huda, Zoltan Davidovits

Subjects
Male, Cardiac Catheterization, Computed Tomography Angiography, medicine.medical_treatment, Myocardial Ischemia, Coronary Disease, Coronary Artery Disease, Kaplan-Meier Estimate, 030204 cardiovascular system & hematology, Coronary Angiography, ISCHEMIA Research Group, law.invention, Angina, Coronary artery disease, 0302 clinical medicine, Randomized controlled trial, law, Cardiovascular Disease, Myocardial Revascularization, 030212 general & internal medicine, Coronary Artery Bypass, 11 Medical and Health Sciences, Cardiac catheterization, General Medicine, Middle Aged, humanities, Cardiovascular Diseases, Cardiology, Female, Human, medicine.medical_specialty, Ischemia, Article, 03 medical and health sciences, Geriatric cardiology, Percutaneous Coronary Intervention, General & Internal Medicine, Internal medicine, medicine, Humans, Angina, Unstable, Aged, business.industry, Coronary Artery Bypa, Percutaneous coronary intervention, Bayes Theorem, medicine.disease, Heart failure, Quality of Life, business
Abstract

BACKGROUND: Among patients with stable coronary disease and moderate or severe ischemia, whether clinical outcomes are better in those who receive an invasive intervention plus medical therapy than in those who receive medical therapy alone is uncertain. METHODS: We randomly assigned 5179 patients with moderate or severe ischemia to an initial invasive strategy (angiography and revascularization when feasible) and medical therapy or to an initial conservative strategy of medical therapy alone and angiography if medical therapy failed. The primary outcome was a composite of death from cardiovascular causes, myocardial infarction, or hospitalization for unstable angina, heart failure, or resuscitated cardiac arrest. A key secondary outcome was death from cardiovascular causes or myocardial infarction. RESULTS: Over a median of 3.2 years, 318 primary outcome events occurred in the invasive-strategy group and 352 occurred in the conservative-strategy group. At 6 months, the cumulative event rate was 5.3% in the invasive-strategy group and 3.4% in the conservative-strategy group (difference, 1.9 percentage points; 95% confidence interval [CI], 0.8 to 3.0); at 5 years, the cumulative event rate was 16.4% and 18.2%, respectively (difference, -1.8 percentage points; 95% CI, -4.7 to 1.0). Results were similar with respect to the key secondary outcome. The incidence of the primary outcome was sensitive to the definition of myocardial infarction; a secondary analysis yielded more procedural myocardial infarctions of uncertain clinical importance. There were 145 deaths in the invasive-strategy group and 144 deaths in the conservative-strategy group (hazard ratio, 1.05; 95% CI, 0.83 to 1.32). CONCLUSIONS: Among patients with stable coronary disease and moderate or severe ischemia, we did not find evidence that an initial invasive strategy, as compared with an initial conservative strategy, reduced the risk of ischemic cardiovascular events or death from any cause over a median of 3.2 years. The trial findings were sensitive to the definition of myocardial infarction that was used. (Funded by the National Heart, Lung, and Blood Institute and others; ISCHEMIA ClinicalTrials.gov number, NCT01471522.).

Published
2020

36. Surgical Management of Tibial Plateau Fractures with Locking Compression Plate

Author

Kishor Man Shrestha, Bipan Shrestha, Rakesh Yadav, Shreshal Shrestha, and Prakriti Raj Kandel

Subjects
Orthodontics, business.industry, Medicine, business, Compression (physics), Plateau (mathematics)
Abstract

INTRODUCTION Tibial plateau fracture is a common fracture that accounts for 1-2% of all fracture. Various treatment options including proximal tibial plating with locking compression plates are available for the treatment of tibial plateau fracture. This study was done to determine the clinical profile and functional outcome of tibial plateau fracture following locking compression plating. MATERIAL AND METHODS This prospective and observational study was carried out in Orthopedics Department of Universal College of Medical Sciences-Teaching Hospital (UCMS-TH) from December 2018 to July 2020. After Ethical clearance (UCMS/IRC/224/18) from Institutional Review Board (IRB) of UCMS-TH and informed written consent, all patients with tibial plateau fracture (Schatzker II-VI) who fulfilled the inclusion criteria were enrolled in the study and treated with locking compression plate. Post-operatively patients were regularly followed at 6 weeks, 3 months and 6 months for clinical, radiological and functional assessment. Descriptive statistics like frequency, percentage, mean and standard deviation were used to analyze the data. RESULTS In our study of 30 cases, the mean age was 37.77 ±15.65 years. Most of the cases were Schatzker type VI (13 patients) and type II (9 patients). The average duration for fractures union was 23.4 ±2.1 weeks. Superficial wound infection was the common complication seen in five cases. At six months, the mean knee society score (KSS) was 78 ±7.22 and majority of patients (19 patients) had good results. CONCLUSION Locking compression plate has an excellent functional and radiological outcome. It is an effective implant that can be adopted for the treatment of tibial plateau fractures in adults.

Published
2021

37. Tuberculous meningitis in the elderly

Author

Devender Kumar, Ashish Bhalla, Sunil Sethi, Ashok Kumar Pannu, Nadim Rahman, Rakesh Yadav, Mandeep Garg, and Atul Saroch

Subjects
Adult, Pediatrics, medicine.medical_specialty, Tuberculosis, India, Tuberculous meningitis, Young Adult, 03 medical and health sciences, 0302 clinical medicine, Older patients, Humans, Medicine, Prospective Studies, 030212 general & internal medicine, Young adult, Prospective cohort study, Survival rate, Aged, Geriatrics, business.industry, Headache, General Medicine, medicine.disease, Tuberculosis, Meningeal, Vomiting, medicine.symptom, business, 030217 neurology & neurosurgery
Abstract

Summary Objective Although the elderly population remains at high risk for tuberculosis, studies addressing tuberculous meningitis (TBM) in this age group are scarce. The present study aimed to evaluate the spectrum and outcome of geriatric TBM and document differences between older and young patients. Methods A prospective cohort study was conducted in the adult TBM patients admitted at PGIMER, Chandigarh (India). Consecutive older patients aged 60 years and above were enrolled from January 2019 to December 2020, and young adults aged 18–59 years were enrolled from July 2019 to December 2019. Results Fifty-five older patients with a mean age of 66.6 years and 73 young patients with a mean age of 35.1 years were enrolled. At admission, older patients were more likely to have altered mental status (96.4% vs. 78.1%, P = 0.003) and advanced disease with British medical research council staging 2 or 3 (98.2% vs. 89.0%, P = 0.043); however, headache (38.2% vs. 67.1%, P = 0.001), vomiting (18.2% vs. 35.6%, P = 0.030) and fever (80.0% vs. 91.8%, P = 0.052) were less common. Cerebrospinal fluid (CSF) abnormalities were less marked in older patients, with a significant difference in median total cells (70 vs. 110/µl, P = 0.013). Hydrocephalous and infarct were common neuroimaging abnormalities in both groups; however, tuberculomas were significantly less in the elderly (15.1% vs. 35.2%, P = 0.012). Older patients had a significantly low survival rate (56.4% vs. 76.7%, P = 0.021). Conclusion Significant differences in clinical, CSF and radiological characteristics exist between elderly and young TBM patients, with survival remains dismal in the elderly.

Published
2021

38. Solution-Based Mesh Adaption Criteria Development for Accelerating Flame Tracking Simulations

Author

Sourabh Shrivastava, Ishan Verma, Rakesh Yadav, and Pravin Nakod

Subjects
Fuel Technology, Nuclear Energy and Engineering, Mechanical Engineering, Energy Engineering and Power Technology, Aerospace Engineering
Abstract

Accurate flame tracking plays a vital role in predicting the combustion characteristics of a system. This is even more critical for systems that evolve over time. Predicting relight performance of an aero combustor, predicting flame propagation due to gas leakage from a storage tank or during the thermal runaway of batteries, are some examples of such dynamic systems. Predicting accurate flame position also plays an important role in deriving the correct pollutant formation rate from a combustion system. The challenge with flame tracking through a 3D computational fluid dynamics (CFD) simulation comes from the requirement to have a good resolution of gradients along the flame front. This requirement can push the overall mesh count of any industrial cases to a very large value (several million-mesh count). Further, the global drive towards using hydrogen or hydrogen blended fuels for different combustion applications pushes the limits on having even finer cells since hydrogen is a fast-burning fuel and has a much thinner flame front compared to hydrocarbons. Solution-based mesh adaption approaches have been widely studied and tested by different research groups to generate the required finer meshes in the critical regions on the fly while keeping the overall mesh count to a manageable level. However, these approaches are typically applicable for a set of problems, and therefore, there is a need for a generic approach suitable for a broader range of problems. This work explores various parameters and specific weightage factors to predict correct flame-tracking outcomes for different types of flames. The selections of flow quantities (flow-variables, their gradients, curvatures) are performed using simple flames and flow configurations. The functions based on selected flow-quantities derived from these studies are then tested to predict the results for the more complex set of published flames like the Engine Combustion Network (ECN) spray flame and Knowledge for Ignition, Acoustics and Instabilities (KIAI) five-burner configuration (liquid and gas fuel). Derived adaption criteria are found to predict the correct flame tracking behavior in terms of transient evolution of flame front, flame propagation, and ignition timing of burners. The parameters used for the study are identified keeping genericity as the key point, and thus making sure that the derived adaption functions can be applied across different types of fuel blends, combustion systems (gaseous or liquid fuel-based systems) and combustion models, for example species transport or mixture fraction-based models.

Published
2022

39. Turbulent Combustion Modeling of Swirl Stabilized Blended CH4/H2 Flames by Using Flamelet Generated Manifold

Author

Ishan Verma, Rakesh Yadav, Sourabh Shrivastava, and Pravin Nakod

Abstract

Hydrogen has been identified as one of the key elements of the decarbonization initiatives. The level of maturity with different original equipment manufacturers (OEMs) varies significantly for a 100% H2 gas turbine combustor. The typical standard short-term goal is to blend hydrogen with existing fuel as a promising alternative to meet regulatory standards for emission. A typical Dry Low NOx (DLN) combustion system can handle a certain level of hydrogen blending. However, due to fundamental differences between the properties of hydrogen and methane, existing designs of combustion systems are not capable of handling moderate to high levels of hydrogen blending. Therefore, prior knowledge of blend ratios that a given combustion system can handle is essential for the system’s stable operation. Computational Fluid Dynamic (CFD) simulation can help study the effect of different blend ratios on flame stability, peak temperature, pollutants, etc., without affecting the hardware. Thus, helping in reducing the overall cost and time spent deciding the allowable blend ratios. In this work, the accuracy and consistency of Flamelet Generated Manifold (FGM) with Large Eddy Simulation (LES) have been assessed to model swirling turbulent combustion of CH4/H2 blends for gas turbine engine combustors. FGM characterizes the extent of reaction using a reaction progress variable typically defined as a weighted sum of some representative product species of hydrocarbon combustion like CO and CO2. With H2 blending, the mixture now has multiple heat release time scales, and the prevailing choices of reaction progress definition are not optimal. Therefore, the first and foremost task is to correctly describe the reaction rate by choosing a reaction progress variable with validity over a range of H2 blending ratios and equivalence ratios. Additionally, the variation in the laminar properties of the blended mixture, e.g., thermal conductivity and viscosity, is enhanced when H2 is added to the fuel. In this work, we have used kinetic theory to compute these properties accurately as a function of temperature and composition. The flame configurations used to validate FGM in this work are CH4/H2 swirl flame (SMH1) and HM3e. The burner designs belong to a detailed and widely simulated database from Sydney Swirl Burner, with a CH4/H2 blend ratio of 1:1 (by volume). The FGM generates flamelets from opposed flow diffusion flames and freely propagates premix flame configuration. The solution of both the FGM approaches is compared with Finite Rate detailed chemistry solution, and definitive advantages/disadvantages of each approach are identified based on computational speed and accuracy. The results are then compared with experimental data for velocity, temperature, major and minor species distribution to establish the computational accuracy of each approach. Together with the inclusion of modifications in the modeling framework and usage of detailed chemistry with FGM-LES, these results provide important insights into the simulation of hydrogen-blended methane flames.

Published
2022

40. Modeling of Flashback With Different Blends of CH4 and H2 by Using Finite Rate Chemistry With Large Eddy Simulation

Author

Ishan Verma, Rakesh Yadav, Naseem Ansari, Stefano Orsino, Shaoping Li, and Pravin Nakod

Abstract

Due to its clean combustion characteristics, hydrogen fuel is gaining attention in power generation. New designs of engine systems and components are being explored to allow blending with the increasing amount of hydrogen in natural gas. Adding H2 increases the probability of flashback and often is one of the main constraints in using high H2 blends in premixed combustors. There are several mechanisms of flashback like boundary layer flashback, combustion induced vortex break down, turbulence in the flow, fluctuations in equivalence ratio, etc. Semi-empirical models, based on non-dimensional numbers and flame speed, have successfully predicted flashback propensity for a given operating condition. The semi-empirical models are computationally very efficient; however, they lack generality. A typical combustor can have multiple flashback mechanisms. The relative importance of each mechanism can change with a change in the combustor design or even with a difference in the operating conditions for the same combustor. Since prediction of flashback requires accurate modeling of highly transient chemistry phenomena and the impact of heat loss on chemistry, a viable detailed chemistry solution is preferred to model flashback. This paper describes the use of a finite rate chemistry model to predict flashbacks in a turbulent premixed combustor in this work. The configuration used is a swirl stabilized combustor (SimVal) from National Energy Technology Laboratory. The current computations are done with Finite Rate Chemistry (FRC) and Large Eddy Simulations (LES). Simulations are carried out for a varied percentage of CH4/H2 blends, ranging from 0% H2 to 100% H2 at a fixed equivalence ratio and inlet mass flow. As the percentage of H2 is increased in the fuel, flame speed also increases. With this, the propensity for flashbacks also increases. A 28-species reduced mechanism for CH4/H2 blend flames is used to keep the simulations computationally tractable. The simulations with the reduced mechanism are performed by considering non-adiabatic effects from heat loss near the walls and multi-component property considerations. This improves the accuracy of the FRC-LES simulations to capture the onset of boundary layer flashback towards the inlet. The simulations from FRC-LES suggest a fine mesh in the boundary layer for an accurate prediction that makes the simulations expensive. Therefore, an Adaptive Mesh Refinement (AMR) approach has been used for different CH4/H2 blends to accurately model the flashback without any loss in generality as the AMR criteria used here are applicable for a wide range of conditions. The FRC-based solution strategy proposed in this work provides a framework to model flashback for different blends without any case-specific tuning.

Published
2022

41. Predicting NOx Emissions In Gas Turbines Using Finite Rate Approach

Author

Saurabh Patwardhan, Pravin Nakod, Stefano Orsino, Rakesh Yadav, Fang Xu, and Dustin Brandt

Abstract

Emission standard agencies are coming up with more stringent regulations on Nitrogen Oxides (NOx), given their adverse effect on the environment. The aircraft engines operate at varying operating conditions and temperature-dependent emissions like NOx are significantly affected by varying conditions. Computational Fluid Dynamics (CFD) simulations are playing a key role in the design of gas turbine combustors and an accurate NOx model will be an important tool for the designers. The new stringent regulations will require new computational approaches over the traditional methods so that the NOx can be predicted accurately under a wide range of operating conditions. Traditionally, the high temperature NOx is predicted using a three-step Zeldovich mechanism. However, it has been observed that the NO (Nitrogen oxide) mass fraction predicted by the Zeldovich mechanism is not accurate for low power conditions due to its predominantly high-temperature kinetics. A significant amount of NO2 (Nitrogen dioxide) is observed in the experimental data at lower temperatures. This requires the inclusion of NO2 chemistry in the NOx mechanism. With the increase in the available computational power, a detailed chemistry simulation is gaining attention, especially for pollutant prediction. In this work, we explore the finite rate (FR) chemistry approach for the prediction of total NOx (NO + NO2) in a gas turbine combustor designed for Aerospace applications. Two reduced mechanisms are investigated namely, the PERK mechanism with 31 species and the Hychem mechanism with 71 species. Simulations with both mechanisms show good comparison with the experimental data and predict the individual contribution of NO and NO2 reasonably well. Further, it is observed that the spray breakup model has a significant impact on the NOx prediction, and it is important to capture the fuel spray correctly to predict the right amount of NOx.

Published
2022

45. ANTIULCER AND ANTI-INFLAMMATORY ACTIVITIES OF HYDROALCOHOLIC EXTRACTS OF STEM BARK AND LEAF OF ANOGEISSUS PENDULA EDGEW

Author

Amit Nayak, Deeksha Singh, Rakesh Yadav, and Uttam Singh Baghel

Subjects
Pharmacology, Stem bark, Traditional medicine, medicine.drug_class, Drug Discovery, medicine, Pharmaceutical Science, Biology, Anogeissus pendula, complex mixtures, Anti-inflammatory
Abstract

Anogeissus pendula Edgew has been reported to be used in gastric disorder even though no attempt has been made to evaluate the same. The present study was designed to evaluate the hydroalcoholic extracts of stem bark and leaves for in vivo acute antiulcer and anti-inflammatory activities. Antiulcer activity was studied by pylorus ligation induced ulcers while anti-inflammatory activities was studied by carrageenan induced paw edema. The extract of stem bark at 200 and 400 mg/kg b. w., significantly (P

Published
2021

46. Comparison of performances of loop‐mediated isothermal amplification, XPERT MTB/RIF and BACTEC MGIT in the diagnosis of childhood tuberculosis

Author

Rakesh Yadav, Rashmi Ranjan Das, Meenu Singh, Achyudhananda Govindan S, Indu Verma, Nancy Mehra, Aastha Saini, Pankaj C Vaidya, and Sunil Sethi

Subjects
medicine.medical_specialty, Loop-mediated isothermal amplification, India, Sensitivity and Specificity, Mycobacterium tuberculosis, 03 medical and health sciences, 0302 clinical medicine, Pulmonary tuberculosis, 030225 pediatrics, Internal medicine, medicine, Humans, Tuberculosis, Mycobacteria growth indicator tube, 030212 general & internal medicine, Child, Childhood tuberculosis, biology, business.industry, Sputum, Gold standard (test), bacterial infections and mycoses, biology.organism_classification, Cross-Sectional Studies, Molecular Diagnostic Techniques, Pediatrics, Perinatology and Child Health, bacteria, Proper treatment, medicine.symptom, business, Nucleic Acid Amplification Techniques
Abstract

AIM Key to the successful management of paediatric pulmonary tuberculosis (PTB) lies in the early detection and proper treatment. We evaluated the performances of modern diagnostic tests: loop-mediated isothermal amplification (LAMP-IS6110), Xpert MTB/RIF (Cepheid) and mycobacteria growth indicator tube (BACTEC MGIT 960 culture) against a modified version of international consensus diagnostic definition (i.e. composite reference standard (CRS)). METHODS A cross-sectional analytical study was conducted in a tertiary care hospital in North India from July 2016 to December 2017 involving 100 children

Published
2021

47. Prevention of relapse in drug sensitive pulmonary tuberculosis patients with and without vitamin D3 supplementation: A double blinded randomized control clinical trial

Author

Sanjeev Sinha, Himanshu Thukral, Imtiyaz Shareef, Devashish Desai, Binit Singh, Bimal Kumar Das, Sahajal Dhooria, Rohit Sarin, Rupak Singla, Saroj Kumari Meena, R. M. Pandey, Shivam Pandey, Digambar Behera, Sunil Sethi, Ashumeet Kajal, Rakesh Yadav, Ashutosh Nath Aggarwal, and Sanjay Bhadada

Subjects
Multidisciplinary
Abstract

Background The immunomodulatory effects of vitamin D are widely recognized and a few studies have been conducted to determine its utility in the treatment of tuberculosis, with mixed results. This study was conducted to see if vitamin D supplementation in patients with active pulmonary tuberculosis (PTB) in the Indian population contributed to sputum smear and culture conversion as well as the prevention of relapse. Methods This randomized double-blind placebo-controlled trial was conducted in three sites in India. HIV negative participants aged 15–60 years with sputum smear positive PTB were recruited according to the Revised National Tuberculosis Control Program guidelines and were randomly assigned (1:1) to receive standard anti-tubercular treatment (ATT) with either supplemental dose of oral vitamin D3 (60,000 IU/sachet weekly for first two months, fortnightly for next four months followed by monthly for the next 18 months) or placebo with same schedule. The primary outcome was relapse of PTB and secondary outcomes were time to conversion of sputum smear and sputum culture. Results A total of 846 participants were enrolled between February 1, 2017 to February 27, 2021, and randomly assigned to receive either 60,000 IU vitamin D3 (n = 424) or placebo (n = 422) along with standard ATT. Among the 697 who were cured of PTB, relapse occurred in 14 participants from the vitamin D group and 19 participants from the placebo group (hazard risk ratio 0.68, 95%CI 0.34 to 1.37, log rank p value 0.29). Similarly, no statistically significant difference was seen in time to sputum smear and sputum culture conversion between both groups. Five patients died each in vitamin D and placebo groups, but none of the deaths were attributable to the study intervention. Serum levels of vitamin D were significantly raised in the vitamin D group as compared to the placebo group, with other blood parameters not showing any significant difference between groups. Conclusions The study reveals that vitamin D supplementation does not seem to have any beneficial effect in the treatment of PTB in terms to the prevention of relapse and time to sputum smear and culture conversion. Trial registration CTRI/2021/02/030977 (ICMR, Clinical trial registry-India).

Published
2023

49. Air pollution and its impact on cardiovascular health – It's time to act fast!

Author

Rakesh Yadav, Surender Deora, and Geetika Yadav

Subjects
RD1-811, business.industry, Cardiovascular health, Air pollution, medicine.disease_cause, Cardiovascular System, Editorial, Cardiovascular Diseases, RC666-701, Air Pollution, Environmental health, Diseases of the circulatory (Cardiovascular) system, Humans, Medicine, Surgery, Cardiology and Cardiovascular Medicine, business
Published
2021

50. Diagnostic accuracy of Xpert MTB/RIF ultra for detection of Mycobacterium tuberculosis in children: a prospective cohort study

Author

Rajiv Khaneja, Meenu Singh, Priyanka Agarwal, Joseph L. Mathew, Sunil Sethi, Shreya Singh, Rakesh Yadav, and Pankaj C Vaidya

Subjects
Male, 0106 biological sciences, medicine.medical_specialty, Tuberculosis, Diagnostic accuracy, Sensitivity and Specificity, 01 natural sciences, Applied Microbiology and Biotechnology, Mycobacterium tuberculosis, 03 medical and health sciences, 010608 biotechnology, Internal medicine, Drug Resistance, Bacterial, medicine, Humans, Prospective Studies, Overall performance, Child, Prospective cohort study, Antibiotics, Antitubercular, Tuberculosis, Pulmonary, 0303 health sciences, biology, Diagnostic Tests, Routine, 030306 microbiology, business.industry, medicine.disease, biology.organism_classification, Pediatric tuberculosis, Molecular Diagnostic Techniques, Female, Rifampin, Detection rate, business, Nucleic Acid Amplification Techniques
Abstract

The Xpert MTB/RIF Ultra is a recent advancement in molecular diagnostics of tuberculosis (TB) with higher sensitivity compared to its predecessor, the Xpert MTB/RIF assay. Prospective studies evaluating the performance of Xpert MTB/RIF Ultra in children with suspected TB are lacking. In this study, we evaluated the Xpert MTB/RIF Ultra for detection of Mycobacterium tuberculosis in samples from 156 children, of which one was excluded from the analysis. Of the remaining 155 samples, 6·5% (10/155), 21·3% (33/155), 20% (31/155) and 21·9% (34/155) were positive by smear examination, MGIT culture, Xpert MTB/RIF and Xpert MTB/RIF Ultra, respectively. The Xpert MTB/RIF and Xpert MTB/RIF Ultra had a similar overall sensitivity of 81·8% (95% CI: 64·5-93) and 84·8% (95% CI: 68·1-94·9), respectively. In suspected pediatric TB patients, the Xpert MTB/RIF Ultra had higher sensitivity compared to the Xpert MTB/RIF (72·7 vs 63·6). The AUC (area under the curve) of 0·905 for the Xpert MTB/RIF and 0·893 for the Xpert MTB/RIF Ultra indicate similar and good overall performance. Both Xpert assays were found to be equally efficient, however Xpert MTB/RIF Ultra showed better detection rate in suspected TB cases.

Published
2020

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