Revealing the compressive and flow properties of novel bone scaffold structure manufactured by selective laser sintering technique.

Additive manufacturing is revolutionizing the field of medical sciences through its key application in the development of bone scaffolds. During scaffold fabrication, achieving a good level of porosity for enhanced mechanical strength is very challenging. The bone scaffolds should hold both the porosity and load withstanding capacity. In this research, a novel structure was designed with the aim of the evaluation of flexible porosity. A CAD model was generated for the novel structure using specific input parameters, whereas the porosity was controlled by varying the input parameters. Poly Amide (PA 2200) material was used for the fabrication of bone scaffolds, which is a biocompatible material. To fabricate a novel structure for bone scaffolds, a Selective Laser Sintering machine (SLS) was used. The displacement under compression loads was observed using a Universal Testing Machine (UTM). In addition to this, numerical analysis of the components was also carried out. The compressive stiffness found through the analysis enables the verification of the load withstanding capacity of the specific bone scaffold model. The experimental porosity was compared with the theoretical porosity and showed almost 29% to 30% reductions when compared to the theoretical porosity. Structural analysis was carried out using ANSYS by changing the geometry. Computational Fluid Dynamics (CFD) analysis was carried out using ANSYS FLUENT to estimate the blood pressure and Wall Shear Stress (WSS). From the CFD analysis, maximum pressure of 1.799 Pa was observed. Though the porosity was less than 50%, there was not much variation of WSS. The achievement from this study endorses the great potential of the proposed models which can successfully be adapted for the required bone implant applications.

Deep Learning Approach for Discovery of In Silico Drugs for Combating COVID-19.

Early diagnosis of pandemic diseases such as COVID-19 can prove beneficial in dealing with difficult situations and helping radiologists and other experts manage staffing more effectively. The application of deep learning techniques for genetics, microscopy, and drug discovery has created a global impact. It can enhance and speed up the process of medical research and development of vaccines, which is required for pandemics such as COVID-19. However, current drugs such as remdesivir and clinical trials of other chemical compounds have not shown many impressive results. Therefore, it can take more time to provide effective treatment or drugs. In this paper, a deep learning approach based on logistic regression, SVM, Random Forest, and QSAR modeling is suggested. QSAR modeling is done to find the drug targets with protein interaction along with the calculation of binding affinities. Then deep learning models were used for training the molecular descriptor dataset for the robust discovery of drugs and feature extraction for combating COVID-19. Results have shown more significant binding affinities (greater than -18) for many molecules that can be used to block the multiplication of SARS-CoV-2, responsible for COVID-19.

Novel heterocyclic thiosemicarbazones derivatives as colorimetric and "turn on" fluorescent sensors for fluoride anion sensing employing hydrogen bonding.

Two novel heterocyclic thiosemicarbazone derivatives have been synthesized, and characterized, by means of spectroscopic and single crystal X-ray diffraction methods. Their chromophoric-fluorogenic response towards anions in competing solvent dimethyl sulfoxide (DMSO) was studied. The receptor shows selective recognition towards fluoride anion. The binding affinity of the receptors with fluoride anion was calculated using UV-visible and fluorescence spectroscopic techniques.

Identification, isolation and characterization of impurities of clindamycin palmitate hydrochloride.

Clindamycin palmitate hydrochloride is a water soluble hydrochloride salt of the ester of clindamycin and palmitic acid. It is inactive in vitro, rapid in vivo hydrolysis converts this compound to the antibacterially active clindamycin. Total 12 impurities at levels ranging from 0.05% to 0.5% were detected by isocratic reverse-phase high performance liquid chromatography (HPLC) using RI detector. The molecular weights of impurities were determined by LC-MS analysis. Two impurities were starting materials and the remaining impurities were isolated from crude samples/enriched mother liquors using reverse-phase preparative HPLC. Based on the spectral data the structures of these impurities were characterized as, clindamycin palmitate sulphoxides alpha-/beta-isomers (impurity I); clindamycin laurate (impurity II); lincomycin palmitate (impurity III); clindamycin myristate (impurity IV); epiclindamycin palmitate (impurity V); clindamycin palmitate 3-isomer (impurity VI); clindamycin pentadecanoate (impurity VII); clindamycin B-palmitate (impurity VIII); clindamycin heptadecanoate (impurity IX) and clindamycin stearate (impurity X). Structural elucidation of all impurities by spectral data ((1)H NMR, (13)C NMR, MS and IR) and formation of these impurities have been discussed in detail.