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In the era of favoring environment-friendly approaches for pharmaceutical synthesis, “green synthesis” is expanding. Green-based nanomedicine (NM), being less toxic and if having biomedical acceptable activities, thence, the chemical methods of synthesis are to be replaced by plants for reductive synthesis. Iron oxide nanoparticles (IONPs) exhibited remarkable anti-microbial and anti-cancer properties, besides being a drug delivery tool. However, owing to limitations related to the chemical synthetic method, plant-mediated green synthesis has been recognized as a promising alternative synthetic method. This systematic review (SR) is addressing plant-based IONPs green synthesis, characteristics, and toxicity studies as well as their potential biomedical applications. Furthermore, the plant-based green-synthesized IONPs in comparison to nanoparticles (NPs) synthesized via other conventional methods, characteristics, and efficacy or toxicity profiles would be mentioned (if available). Search strategy design utilized electronic databases including Science Direct, PubMed, and Google Scholar search. Selection criteria included recent clinical studies, available in the English language, published till PROSPERO registration. After screening articles obtained by first electronic database search, by title, abstract and applying the PICO criteria, the search results yielded a total of 453 articles. After further full text filtrations only 48 articles were included. In conclusion, the current SR emphasizes the perspective of the IONPs plant-mediated green synthesis advantage(s) when utilized in the biomedical pharmaceutical field, with less toxicity.

Quality by Design (QbD) and chemometric models are different sides of the same coin. While QbD models utilize experimentally designed settings for optimization of some quality attributes, these settings can also be utilized for chemometric prediction of the same attributes. We aimed to synchronize optimization of comparative dissolution results of carvedilol immediate release tablets with chemometric prediction of dissolution profile and content uniformity of the product. As an industrial application, selection of variables for optimization was done by performing risk assessment utilizing the archived product records at the pharmaceutical site. Experimental tablets were produced with 20 different settings with the variables being contents of sucrose, sodium starch glycolate, lactose monohydrate, and avicel Ph 101. Contents of the excipients were modelled with F1 dissimilarity factor and F2 similarity factor in HCL, acetate, and USP dissolution media to determine the design space. We initiatively utilized Partial Least Square based Structural Equation Modelling (PLS-SEM) to explore how the excipients and their NIR records explained dissolution of the product. Finally, the optimized formula was utilized with varied content of carvedilol for chemometric prediction of the content uniformity.

BACKGROUND Pancreatic cystic lesions (PCLs) are common in clinical practice. The accurate classification and diagnosis of these lesions are crucial to avoid unnecessary treatment of benign lesions and missed opportunities for early treatment of potentially malignant lesions. AIM To evaluate the role of cyst fluid analysis of different tumor markers such as cancer antigens [ e.g. , cancer antigen (CA)19-9, CA72-4], carcinoembryonic antigen (CEA), serine protease inhibitor Kazal-type 1 (SPINK1), interleukin 1 beta (IL1-β), vascular endothelial growth factor A (VEGF-A), and prostaglandin E2 (PGE2)], amylase, and mucin stain in diagnosing pancreatic cysts and differentiating malignant from benign lesions. METHODS This study included 76 patients diagnosed with PCLs using different imaging modalities. All patients underwent endoscopic ultrasound (EUS) and EUS-fine needle aspiration (EUS-FNA) for characterization and sampling of different PCLs. RESULTS The mean age of studied patients was 47.4 ± 11.4 years, with a slight female predominance (59.2%). Mucin stain showed high statistical significance in predicting malignancy with a sensitivity of 87.1% and specificity of 95.56%. It also showed a positive predictive value and negative predictive value of 93.1% and 91.49%, respectively ( P < 0.001). We found that positive mucin stain, cyst fluid glucose, SPINK1, amylase, and CEA levels had high statistical significance ( P < 0.0001). In contrast, IL-1β, CA 72-4, VEGF-A, VEGFR2, and PGE2 did not show any statistical significance. Univariate regression analysis for prediction of malignancy in PCLs showed a statistically significant positive correlation with mural nodules, lymph nodes, cyst diameter, mucin stain, and cyst fluid CEA. Meanwhile, logistic multivariable regression analysis proved that mural nodules, mucin stain, and SPINK1 were independent predictors of malignancy in cystic pancreatic lesions. CONCLUSION EUS examination of cyst morphology with cytopathological analysis and cyst fluid analysis could improve the differentiation between malignant and benign pancreatic cysts. Also, CEA, glucose, and SPINK1 could be used as promising markers to predict malignant pancreatic cysts.

Background and Aims SARS-CoV-2 infection has been recorded in 230 countries to date. Obesity has a negative impact on one’s quality of life and is one of the main causes of mortality globally. Obesity affects the immune system, making the host more susceptible to infectious infections. Also, obesity commonly provokes the severity of respiratory diseases so the correlation of LEP rs7799039 Polymorphism in corpulent patients with COVID-19 infection was clearly investigated in the current study. Methods A total of 232 patients were recruited, 116 patients were obese with COVID-19 infection, and 116 patients were non obese COVID-19. Fasting blood glucose test (FBG), hemoglobin A1C (HbA1C), complete blood count (CBC), international normalized ratio (INR), urea, alanine transaminase (ALT), aspartate aminotransferase (AST), D dimer and C-reactive protein (CRP) were estimated. C.T. scan was performed for each patient, and C.T. severity score was calculated. Genotyping for the leptin rs7799039 SNPs was performed by TaqMan ® (Applied Biosystems Step One TM Real-time PCR). Results Regarding LEP polymorphism, all individuals of non-obese groups significantly had the homozygous allele GG (100%), whereas only 56% of obese groups had GG alleles (P = 0.001). The severity scores significantly (P = 0.001) varied regarding LEP polymorphism regarding Rs7799039, where the largest proportion of those with Grade IV had the homozygous allele AA (57.1%). Conclusion There was a correlation between the leptin gene allelic discrimination and COVID-19 CT brutality in obese patients. The A allele was considered a risk factor for severity in COVID-19 patients while the G allele contributes to decreasing that risk.