Reactions of Hafnium Tetrachloride with Benzoyl Hydrazones

Hafnium tetrachloride reacts with monofunctional bidentate (BHyH) and bifunctional tridentate (BHy\u27H2) benzoyl hydrazones (derived from the condensation of benzoyl hydrazine with different aldehydes/ketones) in refluxing dichloromethane to form products of the type, HfC13(BHy), HfC12(BHy)2 and HfCb(BHy\u27). These reaction products have been characterized on the basis of elemental analysis, electrical conductance measurements and spectral (infrared and electronic) data

Reactions of Hafnium Tetrachloride with Benzoyl Hydrazones

Hafnium tetrachloride reacts with monofunctional bidentate (BHyH) and bifunctional tridentate (BHy\u27H2) benzoyl hydrazones (derived from the condensation of benzoyl hydrazine with different aldehydes/ketones) in refluxing dichloromethane to form products of the type, HfC13(BHy), HfC12(BHy)2 and HfCb(BHy\u27). These reaction products have been characterized on the basis of elemental analysis, electrical conductance measurements and spectral (infrared and electronic) data

Q-PAC: Automated Detection of Quantum Bug-Fix Patterns

Context: Bug-fix pattern detection has been investigated in the past in the context of classical software. However, while quantum software is developing rapidly, the literature still lacks automated methods and tools to identify, analyze, and detect bug-fix patterns. To the best of our knowledge, our work previously published in SEKE'23 was the first to leverage classical techniques to detect bug-fix patterns in quantum code. Objective: To extend our previous effort, we present a research agenda (Q-Repair), including a series of testing and debugging methodologies, to improve the quality of quantum software. The ultimate goal is to utilize machine learning techniques to automatically predict fix patterns for existing quantum bugs. Method: As part of the first stage of the agenda, we extend our initial study and propose a more comprehensive automated framework, called Q-PAC, for detecting bug-fix patterns in IBM Qiskit quantum code. In the framework, we develop seven bug-fix pattern detectors using abstract syntax trees, syntactic filters, and semantic checks. Results: To demonstrate our method, we run Q-PAC on a variety of quantum bug-fix patterns using both real-world and handcrafted examples of bugs and fixes. The experimental results show that Q-PAC can effectively identify bug-fix patterns in IBM Qiskit. Conclusion: We hope our initial study on quantum bug-fix detection can bring awareness of quantum software engineering to both researchers and practitioners. Thus, we also publish Q-PAC as an open-source software on GitHub. We would like to encourage other researchers to work on research directions (such as Q-Repair) to improve the quality of the quantum programming.Comment: 16 pages, 2 figure

Pembrolizumab in combination with pomalidomide and low-dose dexamethasone in refractory or relapsed and refractory multiple myeloma (rrMM): Randomized, phase 3 KEYNOTE-183 study

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Burden and predictors of hypertension in India: results of SEEK (Screening and Early Evaluation of Kidney Disease) study

Background: Hypertension (HTN) is one of the major causes of cardiovascular morbidity and mortality. The objective of the study was to investigate the burden and predictors of HTN in India. Methods: 6120 subjects participated in the Screening and Early Evaluation of Kidney disease (SEEK), a community-based screening program in 53 camps in 13 representative geographic locations in India. Of these, 5929 had recorded blood pressure (BP) measurements. Potential predictors of HTN were collected using a structured questionnaire for SEEK study. Results: HTN was observed in 43.5% of our cohort. After adjusting for center variation (p < 0.0001), predictors of a higher prevalence of HTN were older age ≥40 years (p < 0.0001), BMI of ≥ 23 Kg/M2 (p < 0.0004), larger waist circumference (p < 0.0001), working in sedentary occupation (p < 0.0001), having diabetes mellitus (p < 0.0001), having proteinuria (p < 0.0016), and increased serum creatinine (p < 0.0001). High school/some college education (p = 0.0016), versus less than 9th grade education, was related with lower prevalence of HTN. Of note, proteinuria and CKD were observed in 19% and 23.5% of HTN subjects. About half (54%) of the hypertensive subjects were aware of their hypertension status. Conclusions: HTN was common in this cohort from India. Older age, BMI ≥ 23 Kg/M2, waist circumference, sedentary occupation, education less, diabetes mellitus, presence of proteinuria, and raised serum creatinine were significant predictors of hypertension. Our data suggest that HTN is a major public health problem in India with low awareness, and requires aggressive community-based screening and education to improve health

Epidemiology and risk factors of chronic kidney disease in India – results from the SEEK (Screening and Early Evaluation of Kidney Disease) study

Background: There is a rising incidence of chronic kidney disease that is likely to pose major problems for both healthcare and the economy in future years. In India, it has been recently estimated that the age-adjusted incidence rate of ESRD to be 229 per million population (pmp), and >100,000 new patients enter renal replacement programs annually. Methods: We cross-sectionally screened 6120 Indian subjects from 13 academic and private medical centers all over India. We obtained personal and medical history data through a specifically designed questionnaire. Blood and urine samples were collected. Results: The total cohort included in this analysis is 5588 subjects. The mean ± SD age of all participants was 45.22 ± 15.2 years (range 18–98 years) and 55.1% of them were males and 44.9% were females. The overall prevalence of CKD in the SEEK-India cohort was 17.2% with a mean eGFR of 84.27 ± 76.46 versus 116.94 ± 44.65 mL/min/1.73 m2 in non-CKD group while 79.5% in the CKD group had proteinuria. Prevalence of CKD stages 1, 2, 3, 4 and 5 was 7%, 4.3%, 4.3%, 0.8% and 0.8%, respectively. Conclusion: The prevalence of CKD was observed to be 17.2% with ~6% have CKD stage 3 or worse. CKD risk factors were similar to those reported in earlier studies. It should be stressed to all primary care physicians taking care of hypertensive and diabetic patients to screen for early kidney damage. Early intervention may retard the progression of kidney disease. Planning for the preventive health policies and allocation of more resources for the treatment of CKD/ESRD patients are imperative in India