SELF DOUBLE EMULSIFYING DRUG DELIVERY SYSTEM (SDEDDS): A REVIEW

Self emulsifying drug delivery systems (SEDDS) are well known for its potential to improve the aqueous solubility and oral absorption of lipophilic drugs. Self double-emulsifying drug delivery system (SDEDDS) are basically used for drugs having low solubility in water, but its potential application for the drugs defined as “high solubility low permeability class†or a biopharmaceutical classification system [BCS] class III drug is appreciable, in which gastrointestinal permeation is the rate controlling step in the absorption process. The most important factor affecting the oral absorption of a drug, besides dissolution, is the permeability of the drug across the gastrointestinal lining. Improving permeability may, therefore, potentially improve the bioavailability of a drug. In this review we discuss the preparation, stability, formulation and characterization of SDEDDS.   Key Words: Multiple emulsion, self emulsifying, Protein peptide drug delivery, High soluble poorly permeable drugs

Application of advanced algorithms for enhancement in machining performance of Inconel 718

366-376Inconel 718 is the most promising nickel-based alloy finding wide usage in engineering applications because of its good mechanical properties. However, this alloy is difficult to machine and results in poor surface quality after machining. Optimization of parameters is essential for improving machining performance of this costly and hard to cut material. The research discusses estimation of optimum parameters using teaching-learning based optimization (TLBO) and compares them to those obtained by genetic algorithm (GA) in turning of Inconel 718. The parameters cutting speed, feed rate and depth of cut are selected as independent variables. The experiments are designed using central composite design of response surface methodology for the modelling of turning process. Surface roughness, tool flank wear and cutting temperature are selected as response parameters for minimization. The adequacy of modified models developed by response surface methodology are tested and then utilized for formulation of multi-objective optimization function. The function is solved by GA and TLBO. After comparing optimization results, the best algorithm is used for confirmation test. Convergence of TLBO algorithm is much faster as compared to GA even though there is very little difference in the optimum values of parameters

Deep learning in chest radiography: Detection of findings and presence of change.

BACKGROUND:Deep learning (DL) based solutions have been proposed for interpretation of several imaging modalities including radiography, CT, and MR. For chest radiographs, DL algorithms have found success in the evaluation of abnormalities such as lung nodules, pulmonary tuberculosis, cystic fibrosis, pneumoconiosis, and location of peripherally inserted central catheters. Chest radiography represents the most commonly performed radiological test for a multitude of non-emergent and emergent clinical indications. This study aims to assess accuracy of deep learning (DL) algorithm for detection of abnormalities on routine frontal chest radiographs (CXR), and assessment of stability or change in findings over serial radiographs. METHODS AND FINDINGS:We processed 874 de-identified frontal CXR from 724 adult patients (> 18 years) with DL (Qure AI). Scores and prediction statistics from DL were generated and recorded for the presence of pulmonary opacities, pleural effusions, hilar prominence, and enlarged cardiac silhouette. To establish a standard of reference (SOR), two thoracic radiologists assessed all CXR for these abnormalities. Four other radiologists (test radiologists), unaware of SOR and DL findings, independently assessed the presence of radiographic abnormalities. A total 724 radiographs were assessed for detection of findings. A subset of 150 radiographs with follow up examinations was used to asses change over time. Data were analyzed with receiver operating characteristics analyses and post-hoc power analysis. RESULTS:About 42% (305/ 724) CXR had no findings according to SOR; single and multiple abnormalities were seen in 23% (168/724) and 35% (251/724) of CXR. There was no statistical difference between DL and SOR for all abnormalities (p = 0.2-0.8). The area under the curve (AUC) for DL and test radiologists ranged between 0.837-0.929 and 0.693-0.923, respectively. DL had lowest AUC (0.758) for assessing changes in pulmonary opacities over follow up CXR. Presence of chest wall implanted devices negatively affected the accuracy of DL algorithm for evaluation of pulmonary and hilar abnormalities. CONCLUSIONS:DL algorithm can aid in interpretation of CXR findings and their stability over follow up CXR. However, in its present version, it is unlikely to replace radiologists due to its limited specificity for categorizing specific findings