Prediction of Bioactive Peptides From Chicken Feather and Pig Hair Keratins Using In Silico Analysis Based on Fragmentomic Approach

Background: Keratin is among the most abundant structural proteins of animal origin, however it remains broadly underutilized. Objective: Bioinformatic investigation was performed to evaluate selected keratins originating from mass-produced waste products, i.e., chicken feathers and pig hair, as potential sources of bioactive peptides. Methods: Pepsin, trypsin, chymotrypsin, papain, and subtilisin were used for in silico keratinolysis with the use of “Enzyme(s) action” and fragmentomic analysis of theoretical products was performed using “Profiles of potential biological activity” in BIOPEP-UWM database of bioactive peptides. Bioactivity probability calculation and toxicity prediction of the peptides obtained were estimated using PeptideRanker and ToxinPred tools, respectively. Results: Our results showed that the keratins are a potential source of a variety of biopeptides, including dipeptidyl peptidase IV, angiotensin converting enzyme, prolyl endopeptidase inhibitory and antioxidative. Papain and subtilisin were found to be the most appropriate enzymes for keratin hydrolysis. This study presents possible structures of keratin-derived bioactive peptides that have not been previously described. Conclusion: Our data suggest additional in vitro and in vivo studies to verify theoretical predictions and further investigate the possibility of using keratin-rich waste as a source of peptide nutraceuticals.

PLANT BASED BIOAVAILABILITY ENHANCERS

Many of the synthetic as well as herbal drugs despite of their notable in vitro finding demonstrate insignificant in vivo activity majority of times due to poor bioavailability. As per Biopharmaceutical Classification System (BCS) one of the main concern is low solubility and/or permeation of drugs resulting in reduced absorption and poor bioavailability. To overcome these issues the various strategies have been adopted including use of permeation enhancers which are also known as bioenhancers. Bioenhancers are synthetic or natural compounds that increases the bioavailability of drugs and nutrients such as vitamins, amino acids, minerals, etc. into the systemic circulation and at the site of action for exhibiting improved therapeutic action. By improving bioavailability, bioenhancers can lead to reduction in drug dose, decrease in the treatment period and can circumvent the problem of drug resistance. Numerous studies have reported application of synthetic bioenhancers. On the other hand, owing to the natural origin, plant based bioenhancer can serve as better alternative. Literature review have revealed that the plant-based bioenhancers have been used in with a wide varieties of drugs including antibiotics, antiviral and anti-cancer. These can be categorized based on their sources and the mechanism of activity. This review will provide a systematic and detailed overview of the various plant based bioenhancers and applications.

Hydrogels for modified-release drug delivery systems

Hydrogels for the modified-release drug delivery systems is a continuously growing area of interest for the pharmaceutical industry. According to the global market, the use of polymers in this area is projected to reach $31.4 million by 2027. This review discusses the recent advances and perspectives of hydrogel in drug delivery systems for oral, parenteral, nasal, topical, and ophthalmic. The search strategy did in January 2021, and it conducted an extensive database to identify studies published from January 2010 to December 2020.We described the main characteristic of the polymers to obtain an ideal hydrogel for a specific route of administration and the formulations that was a highlight in the literature. It concluded that the hydrogels are a set useful to decrease the number of doses, side effects, promote adhesion of patient and enhances the bioavailability of the drugs improving the safety and efficacy of the treatment.

Sodium tanshinone IIA sulfonate improves adverse ventricular remodeling post MI by reducing myocardial necrosis, modulating inflammation and promoting angiogenesis

Background and Objective: Myocardial infarction (MI) leads to pathological cardiac remodeling and heart failure. Sodium tanshinone IIA sulfonate (STS) shows therapeutic values. The present study aimed to explore the potential role of STS in ventricular remodeling post-MI Methods: Mice were randomly divided into sham, MI + normal saline (NS) and MI + STS (20.8 mg/kg/day intraperitoneally) groups. MI was established following left anterior descending artery ligation. Cardiac function was evaluated using echocardiography. Scar size and myocardial fibrosis-associated markers were detected using Masson’s trichrome staining and western blot analysis (WB). Necrosis and inflammation were assessed using H&E staining, lactate dehydrogenase (LDH) detection, ELISA, immunohistochemical staining, and WB. Furthermore, angiogenesis markers and associated proteins were detected using immunohistochemical staining and WB. Results: Mice treated with STS exhibited significant improvements in cardiac function, smaller scar size, and low expression levels of α-smooth muscle actin and collagen I and III at 28 days following surgery, compared with the NS-treated group. Moreover, treatment with STS reduced eosinophil necrosis, the infiltration of inflammatory cells, plasma levels of LDH, high mobility group protein B1, interleukin-1β and tumor necrosis factor-α, and protein expression of these cytokines at 3 days. Macrophage infiltration was also decreased in the STS group in the early phase. Additionally, CD31+ vascular density, protein levels of hypoxia-inducible factor-1α, and vascular endothelial growth factor were elevated in the STS-treated mice at 28 days. Conclusion: STS improved pathological remodeling post-MI, and the associated therapeutic effects may result from a decrease in myocardial necrosis, modulation of inflammation, and an increase in angiogenesis.

Drug Delivery Systems and Strategies to Overcome the Barriers of Brain

The transport of drugs to the central nervous system is the most challenging task for conventional drug delivery systems. Reduced permeability of drugs through the blood-brain barrier is a major hurdle in delivering drugs to the brain. Hence, various strategies for improving drug delivery through the blood-brain barrier are currently being explored. Novel drug delivery systems (NDDS) offer several advantages, including high chemical and biological stability, suitability for both hydrophobic and hydrophilic drugs, and can be administered through different routes. Furthermore, the conjugation of suitable ligands with these carriers tend to potentiate targeting to the endothelium of the brain and could facilitate the internalization of drugs through endocytosis. Further, the intranasal route has also shown potential, as a promising alternate route, for the delivery of drugs to the brain. This can deliver the drugs directly to the brain through the olfactory pathway. In recent years, several advancements have been made to target and overcome the barriers of the brain. This article deals with a detailed overview of the diverse strategies and delivery systems to overcome the barriers of the brain for effective delivery of drugs.

Transfer Learning of The ResNet-18 and DenseNet-121 Model Used to Diagnose Intracranial Hemorrhage in CT Scanning

Objective: To verify the ability of the deep learning model in identifying five subtypes and normal images in noncontrast enhancement CT of intracranial hemorrhage. Method: A total of 351 patients (39 patients in the normal group, 312 patients in the intracranial hemorrhage group) performed with intracranial hemorrhage noncontrast enhanced CT were selected, with 2768 images in total (514 images for the normal group, 398 images for the epidural hemorrhage group, 501 images for the subdural hemorrhage group, 497 images for the intraventricular hemorrhage group, 415 images for the cerebral parenchymal hemorrhage group, and 443 images for the subarachnoid hemorrhage group). Based on the diagnostic reports of two radiologists with more than 10 years of experience, the ResNet-18 and DenseNet-121 deep learning models were selected. Transfer learning was used. 80% of the data was used for training models, 10% was used for validating model performance against overfitting, and the last 10% was used for the final evaluation of the model. Assessment indicators included accuracy, sensitivity, specificity, and AUC values. Results: The overall accuracy of ResNet-18 and DenseNet-121 models were 89.64% and 82.5%, respectively. The sensitivity and specificity of identifying five subtypes and normal images were above 0.80. The sensitivity of DenseNet-121 model to recognize intraventricular hemorrhage and cerebral parenchymal hemorrhage was lower than 0.80, 0.73, and 0.76 respectively. The AUC values of the two deep learning models were above 0.9. Conclusion: The deep learning model can accurately identify the five subtypes of intracranial hemorrhage and normal images, and it can be used as a new tool for clinical diagnosis in the future.