Photoclick Reaction Constructs Glutathione-Responsive Theranostic System for Anti-Tuberculosis

Brewers spent grain protein hydrolysate as a functional ingredient for muffins: Antioxidant, antidiabetic, and sensory evaluation

This study assessed the fortification of muffins with 2, 4, and 6 % of brewer's spent grain protein hydrolysates to enhance their in vitro antioxidant, α-glucosidase, and α-amylase inhibitory activities. In addition, oxidative stability, hardness, color and sensory properties of fortified muffins were investigated. The fortification of muffin formulations with 6 % hydrolysates increased antioxidant activity six times higher than that of the control sample. As the hydrolysate increased to 6 %, the α-amylase and α-glucosidase inhibition also increased to 88 and 40 %, respectively. The 6 % fortified muffins exhibited lower peroxide and thiobarbituric acid values during a 14 day storage than the control muffins, while higher hydrolysate levels darkened the color and softened the texture. Sensory evaluation indicated that muffins with 2% hydrolysates achieved similar overall acceptance as the control. It can be concluded that brewer's spent grain hydrolysate is suitable for functional bakery products.

The sequestosome 1 protein: therapeutic vulnerabilities in ovarian cancer

Ovarian cancer (OC) is the most deadly tumor that may develop in a woman's reproductive system. It is also one of the most common causes of death among those who have been diagnosed with cancer in women. An adapter protein known as sequestosome 1(SQSTM1) or p62 is primarily responsible for the transportation, degradation, and destruction of a wide variety of proteins. This adapter protein works in conjunction with the autophagy process as well as the ubiquitin proteasome degradation pathway. In addition, the ability of SQSTM1 to interact with multiple binding partners link SQSTM1 to various pathways in the context of antioxidant defense system and inflammation. In this review, we outline the processes underlying the control that SQSTM1 has on these pathways and how their dysregulation contributes to the development of OC. At the final, the therapeutic approaches based on SQSTM1 targeting have been discussed.

A Survey of COVID-19 Diagnosis Using Routine Blood Tests with the Aid of Artificial Intelligence Techniques

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), causing a disease called COVID-19, is a class of acute respiratory syndrome that has considerably affected the global economy and healthcare system. This virus is diagnosed using a traditional technique known as the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. However, RT-PCR customarily outputs a lot of false-negative and incorrect results. Current works indicate that COVID-19 can also be diagnosed using imaging resolutions, including CT scans, X-rays, and blood tests. Nevertheless, X-rays and CT scans cannot always be used for patient screening because of high costs, radiation doses, and an insufficient number of devices. Therefore, there is a requirement for a less expensive and faster diagnostic model to recognize the positive and negative cases of COVID-19. Blood tests are easily performed and cost less than RT-PCR and imaging tests. Since biochemical parameters in routine blood tests vary during the COVID-19 infection, they may supply physicians with exact information about the diagnosis of COVID-19. This study reviewed some newly emerging artificial intelligence (AI)-based methods to diagnose COVID-19 using routine blood tests. We gathered information about research resources and inspected 92 articles that were carefully chosen from a variety of publishers, such as IEEE, Springer, Elsevier, and MDPI. Then, these 92 studies are classified into two tables which contain articles that use machine Learning and deep Learning models to diagnose COVID-19 while using routine blood test datasets. In these studies, for diagnosing COVID-19, Random Forest and logistic regression are the most widely used machine learning methods and the most widely used performance metrics are accuracy, sensitivity, specificity, and AUC. Finally, we conclude by discussing and analyzing these studies which use machine learning and deep learning models and routine blood test datasets for COVID-19 detection. This survey can be the starting point for a novice-/beginner-level researcher to perform on COVID-19 classification.

Reprogramming tumor-associated macrophages as a unique approach to target tumor immunotherapy

In the last ten years, it has become increasingly clear that tumor-infiltrating myeloid cells drive not just carcinogenesis via cancer-related inflammatory processes, but also tumor development, invasion, and metastasis. Tumor-associated macrophages (TAMs) in particular are the most common kind of leucocyte in many malignancies and play a crucial role in establishing a favorable microenvironment for tumor cells. Tumor-associated macrophage (TAM) is vital as the primary immune cell subset in the tumor microenvironment (TME).In order to proliferate and spread to new locations, tumors need to be able to hide from the immune system by creating an immune-suppressive environment. Because of the existence of pro-tumoral TAMs, conventional therapies like chemotherapy and radiotherapy often fail to restrain cancer growth. These cells are also to blame for the failure of innovative immunotherapies premised on immune-checkpoint suppression. Understanding the series of metabolic changes and functional plasticity experienced by TAMs in the complex TME will help to use TAMs as a target for tumor immunotherapy and develop more effective tumor treatment strategies. This review summarizes the latest research on the TAMs functional status, metabolic changes and focuses on the targeted therapy in solid tumors.

Apoptotic, cytotoxic, antioxidant, and antibacterial activities of biosynthesized silver nanoparticles from nettle leaf

Here, we reported the biosynthesis of silver nanoparticles (AgNPs) using Urtica dioica (nettle) leaf extract as green reducing and capping agents and investigate their anticancer and antibacterial, activity. The Nettle-mediated biosynthesized AgNPs was characterized by UV-Vis a spectrophotometer. Their size, shape and elemental analysis were determined with the using of SEM and TEM. The crystal structure was determined by XRD and the biomolecules responsible for the reduction of Ag⁺ were determined using FTIR analysis. Nettle-mediated biosynthesis AgNPs indicated strong antibacterial activity against pathogenic microorganisms. Again, the antioxidant activity of AgNPs is quite high when compared to ascorbic acid. Anticancer effect of AgNPs, IC₅₀ dose was determined by XTT analysis using MCF-7 cell line and the IC₅₀ value was found to be 0.243 ± 0.014 μg/mL (% w/v).

Nano-enabled agglomerates and compact: Design aspects of challenges

Nanoscale medicine confers passive and active targeting potential. The development of nanomedicine is however met with processing, handling and administration hurdles. Excessive solid nanoparticle aggregation and caking result in low product yield, poor particle flowability and inefficient drug administration. These are overcome by converting the nanoparticles into a microscale dosage form via agglomeration or compaction techniques. Agglomeration and compaction nonetheless predispose the nanoparticles to risks of losing their nanogeometry, surface composition or chemistry being altered and negating biological performance. This study reviews risk factors faced during agglomeration and compaction that could result in these changes to nanoparticles. The potential risk factors pertain to materials choice in nanoparticle and microscale dosage form development, and their interplay effects with process temperature, physical forces and environmental stresses. To render the physicochemical and biological behaviour of the nanoparticles unaffected by agglomeration or compaction, modes to modulate the interplay effects of material and formulation with processing and environment variables are discussed.

Constructing an Intelligent Model Based on Support Vector Regression to Simulate the Solubility of Drugs in Polymeric Media

This study constructs a machine learning method to simultaneously analyze the thermodynamic behavior of many polymer–drug systems. The solubility temperature of Acetaminophen, Celecoxib, Chloramphenicol, D-Mannitol, Felodipine, Ibuprofen, Ibuprofen Sodium, Indomethacin, Itraconazole, Naproxen, Nifedipine, Paracetamol, Sulfadiazine, Sulfadimidine, Sulfamerazine, and Sulfathiazole in 1,3-bis[2-pyrrolidone-1-yl] butane, Polyvinyl Acetate, Polyvinylpyrrolidone (PVP), PVP K12, PVP K15, PVP K17, PVP K25, PVP/VA, PVP/VA 335, PVP/VA 535, PVP/VA 635, PVP/VA 735, Soluplus analyzes from a modeling perspective. The least-squares support vector regression (LS-SVR) designs to approximate the solubility temperature of drugs in polymers from polymer and drug types and drug loading in polymers. The structure of this machine learning model is well-tuned by conducting trial and error on the kernel type (i.e., Gaussian, polynomial, and linear) and methods used for adjusting the LS-SVR coefficients (i.e., leave-one-out and 10-fold cross-validation scenarios). Results of the sensitivity analysis showed that the Gaussian kernel and 10-fold cross-validation is the best candidate for developing an LS-SVR for the given task. The built model yielded results consistent with 278 experimental samples reported in the literature. Indeed, the mean absolute relative deviation percent of 8.35 and 7.25 is achieved in the training and testing stages, respectively. The performance on the largest available dataset confirms its applicability. Such a reliable tool is essential for monitoring polymer–drug systems’ stability and deliverability, especially for poorly soluble drugs in polymers, which can be further validated by adopting it to an actual implementation in the future.

In-Vitro, In-Vivo, Molecular Docking and ADMET Studies of 2-Substituted 3,7-Dihydroxy-4H-chromen-4-one for Oxidative Stress, Inflammation and Alzheimer's Disease

Plants' bioactives are well-known safe drugs for vital diseases. Flavones and Flavonoid-rich dietary supplements are known to exhibit neuroprotective potential. In this study, we isolated a flavone 2-(3,4-dimethoxyphenyl)-3,7-dihydroxy-4H-chromen-4-one from Notholirion thomsonianum and it was evaluated against various targets of the oxidative stress-related neurological disorders. The compound showed excellent acetyl and butyrylcholinesterase inhibitions in its profile, giving IC50 values of 1.37 and 0.95 μM, respectively. Similarly, in in-vitro MAO-B assay, our flavone exhibited an IC50 value of 0.14 μM in comparison to the standard safinamide (IC50 0.025 μM). In in-vitro anti-inflammatory assay, our isolated compound exhibited IC50 values of 7.09, 0.38 and 0.84 μM against COX-1, COX-2 and 5-LOX, respectively. The COX-2 selectivity (SI) of the compound was 18.70. The compound was found safe in animals and was very effective in carrageenan-induced inflammation. Due to the polar groups in the structure, a very excellent antioxidant profile was observed in both in-vitro and in-vivo models. The compound was docked into the target proteins of the respective activities and the binding energies confirmed the potency of our compound. Furthermore, absorption, distribution, metabolism, excretion, and toxicity (ADMET) results showed that the isolated flavone has a good GIT absorption ability and comes with no hepatic and cardiotoxicity. In addition, the skin sensitization test, in-vitro human cell line activation test (h-CLAT) and KeratinoSens have revealed that isolated flavone is not skin sensitive with a confidence score of 59.6% and 91.6%. Herein, we have isolated a natural flavone with an effective profile against Alzheimer's, inflammation and oxidative stress. The exploration of this natural flavone will provide a baseline for future research in the field of drug development.

Synthesis characterization of Zn-based MOF and their application in degradation of water contaminants

Metal-organic frameworks (MOFs) are currently popular porous materials with research and application value in various fields such as medicine and engineering. Aiming at the application of MOFs in photocatalysis, this paper mainly reviews the main synthesis methods of ZnMOFs and the latest research progress of Zn MOF-based photocatalysts to degrade organic pollutants in water, such as organic dyes. This nanomaterial is being used to treat wastewater and has proven to be very efficient because of its exceptionally large surface area and porous nature. The results show that Zn-MOFs are capable of high degradation of the above pollutants and over 90% of degradation was observed in publications. In addition, the reusability percentage was examined and studies showed that the Zn-MOF nanostructure has very good stability and can continue to degrade a high percentage of pollutants after several cycles. This review focuses on Zn-MOFs and their composites. First, the methods of synthesis and characterization of these compounds are given. Finally, the application of these composites in the process of photocatalytic degradation of dye pollutants such as methylene blue, methyl orange, crystal violet, rhodamine B, etc. is explained.

Multiple Cell Cultures for MRI Analysis

Magnetic resonance imaging (MRI) is an imaging method that enables diagnostics. In recent years, this technique has been widely used for research using cell cultures used in pharmaceutical science to understand the distribution of various drugs in a variety of biological samples, from cellular models to tissues. MRI's dynamic development in recent years, in addition to diagnostics, has allowed the method to be implemented to assess response to applied therapies. Conventional MRI imaging provides anatomical and pathological information. Due to advanced technology, MRI provides physiological information. The use of cell cultures is very important in the process of testing new synthesized drugs, cancer research, and stem cell research, among others. Two-dimensional (2D) cell cultures conducted under laboratory conditions, although they provide a lot of information, do not reflect the basic characteristics of the tumor. To replicate the tumor microenvironment in science, a three-dimensional (3D) culture of tumor cells was developed. This makes it possible to reproduce in vivo conditions where, in addition, there is a complex and dynamic process of cell-to-cell communication and cell-matrix interaction. In this work, we reviewed current research in 2D and 3D cultures and their use in MRI studies. Articles for each section were collected from PubMed, ScienceDirect, Web of Science, and Google Scholar.