Granulation of hydrophobic powders

Granulation of hydrophobic powders is frequently required in the pharmaceutical industry. The structural complexity of new drug molecules mean that is increasingly common for entire classes of drug compounds to be highly hydrophobic. This creates considerable difficulty in understanding, controlling and trouble-shooting these industrial granulation processes.There have been many recent advances in granulation theory. Essential to this theory is that wetting and spreading of the fluid through the powder particles is a prerequisite for good granulation. The possibility of a fine, hydrophobic powder spreading over the surface of the liquid during nucleation has been identified theoretically based on surface chemistry and as a potential nucleation mechanism. Recently, investigation confirmed that nucleation can occur by spreading of the sub-micron particles around the template drop to form a “liquid marble”. The hollow granule structure formed by the “solid-spreading” nucleation mechanism suggests the possibility of using the controlled, open granule structure to manufacture designer structured agglomerates with advantageous properties, including controlled granule structure and size and excellent ideal flow and handling properties. This paper describes single drop solid-spreading nucleation experiments where single fluid droplets are placed onto loosely packed powder beds of hydrophobic powders and the formation of the powder shell observed. Experimental results and observations for some model systems are presented, together with a preliminary framework for liquid marble formation.     

Encapsulation of 2-amino-2-methyl-1-propanol with tetraethyl orthosilicate for CO2 capture

Carbon capture is widely recognised as an essential strategy to meet global goals for climate protection. Although various CO₂ capture technologies including absorption, adsorption and membrane exist, they are not yet mature for post-combustion power plants mainly due to high energy penalty. Hence researchers are concentrating on developing non-aqueous solvents like ionic liquids, CO₂-binding organic liquids, nanoparticle hybrid materials and microencapsulated sorbents to minimize the energy consumption for carbon capture. This research aims to develop a novel and efficient approach by encapsulating sorbents to capture CO₂ in a cold environment. The conventional emulsion technique was selected for the microcapsule formulation by using 2-amino-2-methyl-1-propanol (AMP) as the core sorbent and silicon dioxide as the shell. This paper reports the findings on the formulated microcapsules including key formulation parameters, microstructure, size distribution and thermal cycling stability. Furthermore, the effects of microcapsule quality and absorption temperature on the CO₂ loading capacity of the microcapsules were investigated using a self-developed pressure decay method. The preliminary results have shown that the AMP microcapsules are promising to replace conventional sorbents.     

Protein–Energy Malnutrition: A Risk Factor for Various Ailments

The wheel of industrialization that spun throughout the last century resulted in urbanization coupled with modifications in lifestyles and dietary habits. However, the communities living in developing economies are facing many problems related to their diet and health. Amongst, the prevalence of nutritional problems especially protein–energy malnutrition (PEM) and micronutrients deficiencies are the rising issues. Moreover, the immunity or susceptibility to infect-parasitic diseases is also directly linked with the nutritional status of the host. Likewise, disease-related malnutrition that includes an inflammatory component is commonly observed in clinical practice thus affecting the quality of life. The PEM is treatable but early detection is a key for its appropriate management. However, controlling the menace of PEM requires an aggressive partnership between the physician and the dietitian. This review mainly attempts to describe the pathophysiology, prevalence and consequences of PEM and aims to highlight the importance of this clinical syndrome and the recent growth in our understanding of the processes behind its development. Some management strategies/remedies to overcome PEM are also the limelight of the article. In the nutshell, early recognition, prompt management, and robust follow up are critical for best outcomes in preventing and treating PEM.     

Comparison of total ozone column observations from space-borne Ozone Monitoring Instrument with ground-based Dobson Ozone Spectrophotometer measurements at an urban location in Indo-Gangetic Basin

This article presents a comparison analysis of OMIT (Ozone Monitoring Instrument retrieved overpass total ozone column (TOC)), and DOST (Dobson Ozone Spectrophotometer observed TOC) over Delhi during a period from October 2004 to June 2011. Megacity Delhi, located in Indo-Gangetic Basin, is an important site for comparison of ground-based and satellite retrieved TOCs due to significant anthropogenic emissions of ozone precursors, large shift in seasons, and large-scale crop residue burning in the region. DOST and OMIT data show an overall bias of 3.07% and significant correlation with coefficient of determination R² = 0.73. Large seasonal fluctuations in the biases and correlations have been observed ranging from 2.46% (winter) to 3.82% (spring), and R² = 0.84 (winter) to R² = 0.09 (summer), respectively. The large biases are attributed to changes in temperature, cloud cover, pollutants emissions from urban area, and crop-residue burning events. We also find notable variations in correlations between the datasets due to the varying burden of absorbing aerosols from open field crop-residue burning. The R² has changed from 0.67 (for aerosol optical depth, AOD 1.5–3.5) to 0.77 (for AOD 0–0.99). The dependence of the bias on solar zenith angle, cloud fraction, and satellite distance is also discussed. A simple linear regression analysis is applied to check the linkage between DOST and OMIT. The influence of atmospheric air temperature and relative humidity on OMIT at different pressure levels between 1000 and 20 hPa has been discussed.     

Suggestion Mining from Opinionated Text of Big Social Media Data

Social media data are rapidly increasing and constitute a source of user opinions and tips on a wide range of products and services. The increasing availability of such big data on biased reviews and blogs creates challenges for customers and businesses in reviewing all content in their decision-making process. To overcome this challenge, extracting suggestions from opinionated text is a possible solution. In this study, the characteristics of suggestions are analyzed and a suggestion mining extraction process is presented for classifying suggestive sentences from online customers’ reviews. A classification using a word-embedding approach is used via the XGBoost classifier. The two datasets used in this experiment relate to online hotel reviews and Microsoft Windows App Studio discussion reviews. F1, precision, recall, and accuracy scores are calculated. The results demonstrated that the XGBoost classifier outperforms—with an accuracy of more than 80%. Moreover, the results revealed that suggestion keywords and phrases are the predominant features for suggestion extraction. Thus, this study contributes to knowledge and practice by comparing feature extraction classifiers and identifying XGBoost as a better suggestion mining process for identifying online reviews.     

Improving Routine Immunization Coverage Through Optimally Designed Predictive Models

Routine immunization (RI) of children is the most effective and timely public health intervention for decreasing child mortality rates around the globe. Pakistan being a low-and-middle-income-country (LMIC) has one of the highest child mortality rates in the world occurring mainly due to vaccine-preventable diseases (VPDs). For improving RI coverage, a critical need is to establish potential RI defaulters at an early stage, so that appropriate interventions can be targeted towards such population who are identified to be at risk of missing on their scheduled vaccine uptakes. In this paper, a machine learning (ML) based predictive model has been proposed to predict defaulting and non-defaulting children on upcoming immunization visits and examine the effect of its underlying contributing factors. The predictive model uses data obtained from Paigham-e-Sehat study having immunization records of 3,113 children. The design of predictive model is based on obtaining optimal results across accuracy, specificity, and sensitivity, to ensure model outcomes remain practically relevant to the problem addressed. Further optimization of predictive model is obtained through selection of significant features and removing data bias. Nine machine learning algorithms were applied for prediction of defaulting children for the next immunization visit. The results showed that the random forest model achieves the optimal accuracy of 81.9% with 83.6% sensitivity and 80.3% specificity. The main determinants of vaccination coverage were found to be vaccine coverage at birth, parental education, and socio-economic conditions of the defaulting group. This information can assist relevant policy makers to take proactive and effective measures for developing evidence based targeted and timely interventions for defaulting children.     

Biocompatible Material-Based Flexible Biosensors: From Materials Design to Wearable/Implantable Devices and Integrated Sensing Systems

Human beings have a greater need to pursue life and manage personal or family health in the context of the rapid growth of artificial intelligence, big data, the Internet of Things, and 5G/6G technologies. The application of micro biosensing devices is crucial in connecting technology and personalized medicine. Here, the progress and current status from biocompatible inorganic materials to organic materials and composites are reviewed and the material-to-device processing is described. Next, the operating principles of pressure, chemical, optical, and temperature sensors are dissected and the application of these flexible biosensors in wearable/implantable devices is discussed. Different biosensing systems acting in vivo and in vitro, including signal communication and energy supply are then illustrated. The potential of in-sensor computing for applications in sensing systems is also discussed. Finally, some essential needs for commercial translation are highlighted and future opportunities for flexible biosensors are considered.     

Propolis to Curb Lifestyle Related Disorders: An Overview

The natural products are gaining immense importance in the domain of nutrition to prevent various maladies and improve the quality of life. Among these, natural exudates are of significant worth as these biochemical compounds are released by various living entities having pharmacological properties for utilization in various drug developments. These natural exudates are the promising source for the discovery of new medications. Numerous bioactive moieties collected by honeybees from exudates and buds of particular trees and plants, considered to be utilized as defensive barrier with special reference to propolis. It generally contains numerous biochemical components, i.e., polyphenols, steroids, terpenoids, and amino acids. They also contain isoferulic acid, sinapinic acid, caffeic acid, and chrysin responsible for antibacterial perspectives. With special attention to propolis, it has been utilized in folk medicines due to several of its therapeutic activities, i.e., antioxidant, antidiabetic, anti-inflammatory, antiviral, and anticancer properties. In this context, it is extensively used in foodstuffs and beverages to improve health related disorders like inflammation, diabetes, heart disease, protects injured gums, and cancer insurgence. Moreover, it has been used to curtail stomatology, gastroenterology, skin lesions, and otorhinolaryngologic and respiration diseases.