A test strip for the estimation of urea in serum

We have developed a biostrip for determination of urea in serum. The test strip is based on enzymatic assay where urease has been immobilized on the chromatographic paper along with chromogen, phenol red. The chromogen is easily soluble in water and does not require other components for the color change. Serum urea reacts with urease and water to liberate ammonia and carbon dioxide. The liberated ammonia changes the pH of the reaction medium, which is monitored by the chromogen phenol red. A single step working reagent strip has been developed and the reaction is completed within 50 seconds at room temperature. With this test strip urea concentration is measured in serum as low as 0.15 g/L. The speed and convenience of determining urea in serum by this strip instantly makes it well suited for individuals, physicians and emergency centres.     

The origin and direction of industrial R&D in India

This paper surveys the trends in industrial R&D in India over the last two decades. It shows that there has been a rapid rise in R&D expenditure and a shift in its composition towards in-house corporate R&D and away from R&D in government laboratories, which is explained by the laboratories' lack of market orientation and manufacturing experience. According to cross-section studies of corporate R&D, larger companies aim towards larger technological advances and take a longer view; but the overall composition of corporate R&D shows no discernible change. This apparent inconsistency is explained by the development of the technology market. Much R&D was triggered off by the need for import replacement arising from import controls till 1965 and later by the need for product diversification in the recession. But construction of new plants and mechanization for speeding up operations, activities where sustained R&D can yield large firms a steady flow of innovations, were unimportant or infrequent, and the demand for technology they gave rise to was largely met by imports.     

Dynamic Changes in Circulatory Cytokines and Chemokines Levels in Mild to Severe COVID-19 Patients

Immune dysregulation is a key feature of the coronavirus disease-2019 (COVID-19). However, disparities in responses across ethnic groups are underappreciated. This study aimed to determine the relationship between chemokines and cytokines and the severity of COVID-19. Multiplex magnetic bead-based Luminex-100 was used to assess chemokine and cytokine levels in COVID-19 patients at admission (day-1) and after 4 days. The mean age of the patients recruited was 54.3 years, with 19 (63.3%) males. COVID-19 patients had significantly lower lymphocyte, monocyte, hemoglobin and eosinophil levels than controls (p < 0.05). COVID-19 patients showed significantly higher neutrophil levels than controls (p < 0.05). The baseline levels of IL-2, IL-6, IL-8, IL-10, and IFN-α/γ significantly increased in COVID-19 patients (p < 0.05). Chemokine levels (IP-10, MCP-1, MIG, and CCL-5) were significantly in COVID-19 patients. IL-8, IP-10, and MIG levels were significantly higher in the patients with severe COVID-19 (p < 0.05). Individuals with mild COVID-19 showed significantly higher levels of INF-α, IL-2, IL-6, and IL-8, whereas IL-10 levels were significantly lower (p < 0.05). TNF-levels decreased significantly in individuals with severe COVID-19, whereas IL-6, IL-8, and MIG levels increased (p < 0.05). After 4 days, INFα-, IL-2, IL-6, IL-8, IP-10, and MIG levels were significantly higher in patients with mild disease, whereas IL-6, MIG, and TNF-αlevels were significantly higher in patients with severe disease (p < 0.05). Thus, we conclude that COVID-19 is characterized by INF-α/γ, IL-6, IL-10, IP-10, MCP-1, MIG, and CCL5 dysregulation. IL-8, MIG, and IP-10 levels distinguish between moderate and severe COVID-19. Changes in INF-α, IL-2, IL-6, IL-8, IP-10, and MIG levels can be used to monitor disease progression.Supplementary InformationThe online version contains supplementary material available at 10.1007/s12291-022-01108-x.     

Hyperinsulinemia and Hypoadiponectinemia are Associated with Increased Risk for Occurrence of Ovarian Cancer in Non-diabetic Women of North Indian Population

Ovarian cancer has been emerged as a most common and lethal gynecological malignancy in India. High serum insulin and low adiponectin have been associated with increased risk of ovarian cancer. But their role in development of ovarian cancer is conflicting and little evidence is available. We aimed to evaluate blood levels of insulin and adiponectin in epithelial ovarian cancer (EOC) patients and their association with the risk to develop EOC. The study included following three groups; Group 1: fifty cases of cytohistopathologically confirmed cases of EOC, Group 2: fifty age matched cases of benign ovarian conditions and Group 3: fifty ages matched healthy controls with no evidence of any benign or malignant ovarian pathology as ruled out by clinical examination and relevant investigations. Cytohistopathologically confirmed and newly diagnosed cases of EOC and benign ovarian cancer were included in this study. The median value of fasting serum insulin was significantly high (15.0 µlU/ml, P = 0.02) and adiponectin were significantly low (5.1 µg/ml, P < 0.001) in ovarian cancer patients compared to benign ovarian tumors and healthy controls group. A significant increase risk of ovarian cancer was found in high tertile (≥ 18.7 µlU/ml) of serum insulin level (OR = 2.7; 95% CI = 1.00–6.67, P = 0.04) and lower tertile (≤ 5.45 µg/ml) of adiponectin level (OR = 3.2; 95% CI = 1.10–9.71, P = 0.03). High serum insulin level and low adiponectin levels were significantly associated with increased risk for development of ovarian cancer.     

Theorema Aureum — 1

Are there perfect squares which on division by 7 leave remainder 3? Are there perfect squares which on division by 3 leave remainder 7? (A “remainder of 7” on division by 3 is the same as a remainder of 1.) The answers: ‘NO’ and ‘YES’, respectively. These facts are stated by number theorists as follows: 3 is a quadratic non-residue modulo 7; 7 is a quadratic residue modulo 3. The notion of quadratic residue is far reaching, and the key theorem here is the Law of Quadratic Reciprocity, first stated by Euler in 1783, but without proof, and first proved by Gauss, in 1796. The theorem is easy to state but is mysterious, as it reveals a connection between two questions that appear unconnected. Let p, q be distinct odd primes; then the questions are: “Is p a quadratic residue modulo q?” and “Is q a quadratic residue modulo p?” Gauss had a high regard for this result and called it Theorema Aureum, the Golden Theorem. Though it has been proved in many different ways, it retains its mystery. In this two part article we give three proofs of the theorem. The first one, described in this part, is based on group theory.     

Assessment of glycemic potential of<Emphasis Type="Italic">Musa paradisiaca</Emphasis> stem juice

The present study reveals the effect of Musa paradisiaca stem juice on blood glucose level (BGL) of normal & diabetic rats. The dose of 500 mg/kg bodyweight produces a significant rise of 28.3% in blood glucose level after 6h of oral administration in normal rats. Whereas, in sub diabetic rats the same dose produces a rise of 16.4% in blood glucose levels within 1h during glucose tolerance test (GTT) and a rise of 16% after 4 h in fasting blood glucose levels of severe diabetic cases. These results were unexpected and important to report as other species of Musa like Musa sapientum has been reported for its hypoglycemic effect.     

Search result diversification on attributed networks via nonnegative matrix factorization

Search result diversification is an effective way to tackle query ambiguity and enhance result novelty. In the context of large information networks, diversifying search result is also critical for further design of applications such as link prediction and citation recommendation. In previous work, this problem has mainly been tackled in a way of implicit query intent. To further enhance the performance on attributed networks, we propose a novel search result diversification approach via nonnegative matrix factorization. Our approach encodes latent query intents as well as nodes as representation vectors by a novel nonnegative matrix factorization model, and the diversity of the results accounts for the query relevance and the novelty w.r.t. these vectors. To learn the representation vectors of nodes, we derive the multiplicative updating rules to train the nonnegative matrix factorization model. We perform a comprehensive evaluation on our approach with various baselines. The results show the effectiveness of our proposed solution, and verify that attributes do help improve diversification performance.     

Probing the solar interior

Recent developments in solar seismology have enabled us to observe and analyse the vibrations of the sun, and help to probe its hidden interior. Understanding the sun’s internal structure and dynamics promises to test and expand our knowledge of physics, cosmology, and astrophysics. Seismic sounding of the sun has begun to shed light on its hidden internal anatomy.     

Development and validation of Mobile Learning Acceptance Measure

The growth of Smartphone usage, increased acceptance of electronic learning (E-learning), the availability of high reliability mobile networks and need for flexibility in learning have resulted in the growth of mobile learning (M-learning). This has led to a tremendous interest in the acceptance behaviors related to M-learning users among the information systems researchers. Despite a large amount of significant research in the field of M-learning, the measurement of user acceptance of M-learning has not received attention. This research study intends to develop and validate a survey that measures users’ acceptance of M-learning. A total sample of 806 university and higher college students from different institutes in Oman participated in this study. This research study was conducted in two stages. The first stage using a sample size of 388 students initiated a generic questionnaire, and examined factorial validity and reliability. The second stage that was conducted using different sample of 418 students employed confirmatory factor analysis to establish factorial validity and measurement invariance. A correlated six-factor model (Flexibility, Suitability, Enjoyment, Efficiency, Economic and Social) was fit using maximum likelihood estimation. The internal consistency and item reliability of Mobile Learning Acceptance Measure was found to be at acceptable level for both samples.