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Biogenic Synthesis of Nanoparticulate Materials for Antiviral Applications
Published in Devarajan Thangadurai, Saher Islam, Charles Oluwaseun Adetunji, Viral and Antiviral Nanomaterials, 2022
Kah Hon Leong, Jit Jang Ng, Lan Ching Sim, Pichiah Saravanan, Chaomeng Dai, Bo Tan
Nanoliposomes have been extensively used as an antiviral agent for many applications. The small size nanoparticles synthesis can be divided into two categories resulting from the characteristics of their internal structures: single-ring vesicles (unilamellar) and onion-ring structure (multilamellar). The inner region is formed by the bilayer of phospholipid molecules in each ring and their nonpolar tails, while the outer layers are formed by the hydrophilic head group (Mozafari et al. 2008). Nanoliposomes were first prepared by dissolving the phospholipids in organic solvent and drying them to produce phospholipid bilayers and form nanoliposomes. It can also be incorporated with mechanical dispersion methods, which are appropriate for the large-scale creation of nanoliposomes. Nanoliposomes can distribute both lipophilic and hydrophilic antivirals, mainly due to containing nonpolar and polar areas within the assemblies of the bilayer. The report has found that using the nanoliposome method can overcome the common antiviral agent limitation, such as low permeability thru cell membranes and weak cell internalization.
Granulation of Poorly Water-Soluble Drugs
Published in Dilip M. Parikh, Handbook of Pharmaceutical Granulation Technology, 2021
Albert W. Brzeczko, Firas El Saleh, Hibreniguss Terefe
Having insight into the structures of solid dispersions is critical to clearly understand their dissolution characteristics. Irrespective of the method of preparation, solid dispersions can be described in two broad classes based on the magnitude of dispersion, oligomolecular level, or molecular level (Figure 17.6) [74].
Statistics You Need
Published in Saif Aldeen Saleh AlRyalat, Shaher Momani, A Beginner's Guide to Using Open Access Data, 2019
Descriptive statistics are used to describe the basic features of the data in a study. They provide simple summaries about a sample to help us to simplify large amounts of data in a sensible way. There are three major characteristics of a single variable that we tend to look at in descriptive statistics: distribution, central tendency, and dispersion. Distribution is a summary of the frequency of individual values or ranges of values for a variable. Central tendency is an estimate of the center of distribution of values by using mean, median, and mode. Dispersion is the spread of the values around the central tendency, represented by range, variance, and standard deviation (SD). These will be further discussed later in this chapter in the section “Statistical Analyses.”
Development of topical thymoquinone loaded polymer–lipid hybrid vesicular gel: in-vitro and ex-vivo evaluation
Published in Journal of Liposome Research, 2022
Sagar Trivedi, Kamlesh Wadher, Milind Umekar
The release profiles of drugs from the investigated dispersions were demonstrated in Figure 3. The results reveal that the release of TH from the formulation was biphasic. This can be elucidated by the statement that the lipidic drug TH is mostly encapsulated between polymer and the lipid bilayers of PLH vesicles. During the following 2 h, further 15–29% of TH was released from various preparations as well as from optimized TH PLHV gel. Figure 3 suggests a comprehensive release of plain TH and plain TH gel at a very quick rate and comparatively the entrapped drug in PLHV showed a sustained release profile. The significant differences (p < 0.05) in the in-vitro release pattern were observed, it may be due to factors such as lamellarity, vesicle size, and membrane fluidity which depends on the chain length of polymer (Kim et al.2019, Mirab et al.2019).
Remdesivir powders manufactured by jet milling for potential pulmonary treatment of COVID-19
Published in Pharmaceutical Development and Technology, 2022
Xiaoying Ruan, Jiaqi Yu, Hao Miao, Renjie Li, Zhenbo Tong
Overall, various factors affect the drug dispersion performance, including the particle size and size distribution, particle shape, drug-carrier ratio, and ternary components (Ganderton 1992). For one thing, we have found that drug content would prominently improve the aerosol performance of drug-carrier formulations. The increasing drug content would effectively increase pulmonary deposition, which could be attributed to that increasing APIs would directly increase the quantities of fine particles deposited in the deep lung. At 70% drug content, FPF would reach the equilibrium at 67.00 ± 1.99%, indicating that the increase of the drug loading could not further improve the aerosol performance. Because the increase of the drug loading would simultaneously increase the dispersion difficulty, when drug loading excesses 70%, the energy to overcoming the cohesive forces between drug-drug particles would become a dominant obstacle to further improve the aerosol performance.
Associations of TLR4 and IL-8 genes polymorphisms with age-related macular degeneration (AMD): a systematic review and meta-analysis
Published in Ophthalmic Genetics, 2021
Nasrin Roshanipour, Elham Shahriyari, Maryam Ghaffari Laleh, Leila Vahedi, Sousan mirjand Gerami, Amin Khamaneh
Z-test with P < .05 was used to authenticate the statistical significance of effect size. Sensitivity analysis was used to investigate the cause of dispersion. Then, outlier studies were removed and analysis was recomputed. In the case of a significant reduction in dispersion (I2 index), this study was considered as a dispersion factor. Concerning the heterogeneity of the studies, Cochran’s Q test (P-value [phet] < 0.10 was considered as statistically significant heterogeneity) and I2 statistics (75 ≤ I2 < 100 as extreme heterogeneity, 50 ≤ I2 < 75 as high heterogeneity, 25 ≤ I2 < 50 as moderate heterogeneity, and I2 < 25 as no heterogeneity) were used to assess the degree of heterogeneity (14). The random-effects model was used for this meta-analysis because it accounts for random variability both within and among studies. Publication bias was investigated by the Funnel plot and Egger’s test. To avoid Type I Error, Bonferroni correction was conducted. For correction, 0.05 was divided by the numbers of comparisons. The output from the equation is α-value, which is a new threshold that must be reached for a single test to be considered statistically significant. In the present study, due to the multiple comparisons, five tests for IL-8 [c.251A>T and c.781 C > T] and three tests for TLR4 [c.896A>G and c.1196 C > T], the Bonferroni corrected P-value≤0.01 and P′-value<0.02, respectively, were considered statistically significant.