Anti-bacterial activity of inorganic nanomaterials and their antimicrobial peptide conjugates against resistant and non-resistant pathogens

This review details the antimicrobial applications of inorganic nanomaterials of mostly metallic form, and the augmentation of activity by surface conjugation of peptide ligands. The review is subdivided into three main sections, of which the first describes the antimicrobial activity of inorganic nanomaterials against gram-positive, gram-negative and multidrug-resistant bacterial strains. The second section highlights the range of antimicrobial peptides and the drug resistance strategies employed by bacterial species to counter lethality. The final part discusses the role of antimicrobial peptide-decorated inorganic nanomaterials in the fight against bacterial strains that show resistance. General strategies for the preparation of antimicrobial peptides and their conjugation to nanomaterials are discussed, emphasizing the use of elemental and metallic oxide nanomaterials. Importantly, the permeation of antimicrobial peptides through the bacterial membrane is shown to aid the delivery of nanomaterials into bacterial cells. By judicious use of targeting ligands, the nanomaterial becomes able to differentiate between bacterial and mammalian cells and, thus, reduce side effects. Moreover, peptide conjugation to the surface of a nanomaterial will alter surface chemistry in ways that lead to reduction in toxicity and improvements in biocompatibility

Mathematical modeling of drying kinetic of Bhimkol (Musa balbisiana) Pulp by applying master curve technique and general code developed in MATLAB

Thin layer drying kinetics of Bhimkol pulp was carried out on the basis of a general code developed in MATLAB R2012a software. In this code, single input of the drying parameters helps to provide very precise and petite analysis of batches of drying experiment at different temperatures. Drying experiment was carried out in a dehumidified dryer at 40, 50 and 60  temperatures respectively. Among the five thin layer drying models used for the analysis, Midilli Kucuk showed best fitted result with greatest  value and lowest RMSE and SSE values.For better generalization of the drying model, an attempt of master curve technique was also carried out. Master curve technique gave better fit with a R2 value of 0.9969 than the global drying constant method and normal mathematical modeling technique.From the results it was also observed that the value of drying constant k and effective diffusivity for the drying followed a rising trend with the increasing temperature. The activation energy for the drying was 5.0778 X1004 kJ/mol.

Not Available

Not AvailableNot AvailableNot Availabl

Machine Learning-Enabled Predictions of Condensed Fukui Functions and Designing of Metal Pincer Complexes for Catalytic Hydrogenation of CO₂

This research showcases the machine learning (ML)-enabled homogeneous catalyst discovery to be employed in carbon dioxide hydrogenation. To achieve the desired turnover frequency (TOF), the electrophilicity of the central metal atom is a crucial factor in transition metal pincer complexes. The condensed Fukui function is a direct measure of the catalytic performance of these pincer complexes. Herein, we demonstrate that machine learning is a convenient and effiecient method to calculate condensed Fukui functions of the central metal atom. The electrophilicity values of 202 pincer complexes were calculated by using density functional theory (DFT) to train the ML model. The test data of the experimentally established pincer complexes show a direct linkage between calculated electrophilicity and experimental TOF. Further, this data was used to develop an ML protocol to screen 2,84,062 catalyst complexes to get the electrophilicity values of the Mn, Fe, Co, and Ni transition metals encompassing various permutation combinations of PNP, PNN, NNN, and PCP pincer ligands. These findings validate the efficacy of machine learning in the rapid screening of metal pincer catalysts based on condensed Fukui functions

Assessment of risk factors for overweight and obesity among school going children in Kanpur, Uttar Pradesh

Background: Adolescent obesity is one of the most serious public health challenges of the 21st century. The problem is global and is steadily affecting many low and middle-income countries, particularly in urban settings. Objective: To determine risk factors for overweight and obesity among school going children of age group 12-15 years in Kanpur. Method: A cross-sectional study was conducted from September 2013 to August 2014 among students of age group 12-15 years in four schools of Kanpur that were selected by using multistage random sampling. Sample size was 806. The information about dietary habits and physical activity pattern was obtained by direct interview method. Height and weight were measured using standard techniques for the same and BMI was calculated. Student who had BMI >85th and <95th percentile of reference population were classified as overweight and BMI for age >95th percentile of reference population were classified as obese. Results: The prevalence of obesity and overweight was 3.97% and 9.80%  respectively and consuming fast foods and carbonated drinks regularly, low levels of physical activity, watching television for more than 2 hours per day or playing computer games for more than 2 hours per day were significantly associated with overweight and obesity. Conclusion: Unhealthy dietary habits and sedentary lifestyle are the major risk factors for overweight/ obesity in adolescents. Intervention measures focusing mainly on increasing the physical activity, decreasing consumption of energy dense foods and providing psychological support is essential to fight this new emerging problem of obesity in adolescents

Machine Learning-Enabled Predictions of Condensed Fukui Functions and Designing of Metal Pincer Complexes for Catalytic Hydrogenation of CO₂

This research showcases the machine learning (ML)-enabled homogeneous catalyst discovery to be employed in carbon dioxide hydrogenation. To achieve the desired turnover frequency (TOF), the electrophilicity of the central metal atom is a crucial factor in transition metal pincer complexes. The condensed Fukui function is a direct measure of the catalytic performance of these pincer complexes. Herein, we demonstrate that machine learning is a convenient and effiecient method to calculate condensed Fukui functions of the central metal atom. The electrophilicity values of 202 pincer complexes were calculated by using density functional theory (DFT) to train the ML model. The test data of the experimentally established pincer complexes show a direct linkage between calculated electrophilicity and experimental TOF. Further, this data was used to develop an ML protocol to screen 2,84,062 catalyst complexes to get the electrophilicity values of the Mn, Fe, Co, and Ni transition metals encompassing various permutation combinations of PNP, PNN, NNN, and PCP pincer ligands. These findings validate the efficacy of machine learning in the rapid screening of metal pincer catalysts based on condensed Fukui functions