Ultrasound-assisted preparation of novel ibuprofen-loaded excipient with improved compression and dissolution properties

Context: Most of the active pharmaceutical ingredients (APIs) suffer from a drawback of poor aqueous solubility. In addition to the same, some APIs show poor tabletting behavior creating problems in formulation development. Crystal engineering can be an efficient tool in rectification of such problems associated with the APIs. Thus present work deals with crystallization of ibuprofen (a model drug) onto the surface of dicalcium phosphate (DCP) particles using different techniques.

Objective: The objective of the present work was to prepare ibuprofen-loaded DCP particles and further to analyze them for compressibility and dissolution behavior.

Materials and methods: Various crystallization techniques such as solvent evaporation (SE), melt crystallization (MC), melt sonocrystallization (MSC), antisolvent crystallization (AC), and antisolvent sonocrystallization (ASC) were screened for the preparation of ibuprofen-loaded DCP. Products obtained from different techniques were analyzed for physicochemical, micromeritic and compression properties.

Results and discussion: ASC technique was found to be suitable for preparing directly compressible ibuprofen-loaded DCP particles. The change in the crystal habit (needle to plate shape) of ibuprofen and its crystallization in miniscular form onto the surface of DCP particles showed significant improvement in the dissolution rate and compression properties of ibuprofen due to an increase in specific surface area when compared with ibuprofen crystallized by other techniques. Additionally, the tablets prepared from ASC powder did not require binder since ibuprofen acted as melt binder during compression.

Conclusion: Directly compressible ibuprofen-loaded DCP particles can serve as an alternative for conventional ibuprofen tablets prepared by wet granulation technique.     

Predicting How to Distribute Work Between Algorithms and Humans to Segment an Image Batch

International Journal of Computer Vision - Foreground object segmentation is a critical step for many image analysis tasks. While automated methods can produce high-quality results, their failures...     

Solvent extraction separation and spectrophotometric determination of ruthenium(III) with p-methylphenyl thiourea: sequential separation of ruthenium,osmium and iron

ABSTRACT

A selective and sensitive solvent extraction and spectrophotometric study of the ruthenium(III)–p-methylphenyl thiourea (PMPT) system is presented. The optimum conditions were determined by a critical study of acid concentration, reagent concentration, equilibration period, heating time and effect of solvent on the equilibrium. Ruthenium(III) forms 1:1 complex with PMPT in 20% ethanol and extracted into chloroform. Conformity to Beer’s law at 600 nm was observed up to 40 µg mL^–1 of ruthenium. Molar absorptivity and Sandell’s sensitivity were found to be 2.31 × 10³ L mol^?1cm^?1 and 0.044 μg cm^?2, respectively. The detection limits were 0.11 μg mL^?1 of ruthenium. The method is free from interferences from large number of cations and anions. Proposed method was successfully applied to the separation and determination of ruthenium from synthetic alloys, catalyst and water samples. Sequential separation and determination method for ruthenium(III), osmium(VIII) and iron(II) has been developed.     

Click Carving: Interactive Object Segmentation in Images and Videos with Point Clicks

International Journal of Computer Vision - We present a novel form of interactive object segmentation called Click Carving which enables accurate segmentation of objects in images and videos with...     

Analysis of Bearing Cup Assembly Failure in Drive Shaft Assembly

The automobile is a typical industrial product that involves a variety of materials and technologies. The present societal needs necessitate that metallic materials are ideally suited for applications in heavily stressed components that require high durability. In this study, failure analysis of bearing cup in a drive shaft assembly is carried out, and a detail analysis using QC story methodology was done to find out initially the possible root causes and finally the main root cause. Alternative solutions were identified, and these are validated on an endurance test rig. Chemical analysis and microstructure and hardness tests were carried out on optical microscope and Vickers hardness testing machine to verify the results. Investigation reveals that carbonitriding process gives good result to avoid through hardening of bearing cup assembly, and the problem was thus eliminated by means of cost effective solution. This study can be used for similar applications in industry where wear resistance is preferred and wall thickness of material is low.     

运用多相数字电源解决方案解决系统问题

通过将F2806数字信号控制器与UCD7230栅极驱动及电流传感放大器完美结合,设计了一款用于数控多相交错式DC/DC降压系统的完整信号控制解决方案,该方案具有多相同步降压转换器的优点,同时可以运用数字方法关闭电压控制环路,对不同负载和散热条件下的相位进行管理以获得最佳电源性能。     

Predicting Foreground Object Ambiguity and Efficiently Crowdsourcing the Segmentation(s)

We propose the ambiguity problem for the foreground object segmentation task and motivate the importance of estimating and accounting for this ambiguity when designing vision systems. Specifically, we distinguish between images which lead multiple annotators to segment different foreground objects (ambiguous) versus minor inter-annotator differences of the same object. Taking images from eight widely used datasets, we crowdsource labeling the images as “ambiguous” or “not ambiguous” to segment in order to construct a new dataset we call STATIC. Using STATIC, we develop a system that automatically predicts which images are ambiguous. Experiments demonstrate the advantage of our prediction system over existing saliency-based methods on images from vision benchmarks and images taken by blind people who are trying to recognize objects in their environment. Finally, we introduce a crowdsourcing system to achieve cost savings for collecting the diversity of all valid “ground truth” foreground object segmentations by collecting extra segmentations only when ambiguity is expected. Experiments show our system eliminates up to 47% of human effort compared to existing crowdsourcing methods with no loss in capturing the diversity of ground truths.