Alginate as a Promising Biopolymer in Drug Delivery and Wound Healing: A Review of the State-of-the-Art.

Biopolymeric nanoparticulate systems hold favorable carrier properties for active delivery. The enhancement in the research interest in alginate formulations in biomedical and pharmaceutical research, owing to its biodegradable, biocompatible, and bioadhesive characteristics, reiterates its future use as an efficient drug delivery matrix. Alginates, obtained from natural sources, are the colloidal polysaccharide group, which are water-soluble, non-toxic, and non-irritant. These are linear copolymeric blocks of α-(1→4)-linked l-guluronic acid (G) and ß-(1→4)-linked d-mannuronic acid (M) residues. Owing to the monosaccharide sequencing and the enzymatically governed reactions, alginates are well-known as an essential bio-polymer group for multifarious biomedical implementations. Additionally, alginate's bio-adhesive property makes it significant in the pharmaceutical industry. Alginate has shown immense potential in wound healing and drug delivery applications to date because its gel-forming ability maintains the structural resemblance to the extracellular matrices in tissues and can be altered to perform numerous crucial functions. The initial section of this review will deliver a perception of the extraction source and alginate's remarkable properties. Furthermore, we have aspired to discuss the current literature on alginate utilization as a biopolymeric carrier for drug delivery through numerous administration routes. Finally, the latest investigations on alginate composite utilization in wound healing are addressed.

Nanoparticle-Based Drug Delivery System: The Magic Bullet for the Treatment of Chronic Pulmonary Diseases.

Chronic pulmonary diseases encompass different persistent and lethal diseases, including chronic obstructive pulmonary disease (COPD), idiopathic pulmonary fibrosis (IPF), cystic fibrosis (CF), asthma, and lung cancers that affect millions of people globally. Traditional pharmacotherapeutic treatment approaches (i.e., bronchodilators, corticosteroids, chemotherapeutics, peptide-based agents, etc.) are not satisfactory to cure or impede diseases. With the advent of nanotechnology, drug delivery to an intended site is still difficult, but the nanoparticle's physicochemical properties can accomplish targeted therapeutic delivery. Based on their surface, size, density, and physical-chemical properties, nanoparticles have demonstrated enhanced pharmacokinetics of actives, achieving the spotlight in the drug delivery research field. In this review, the authors have highlighted different nanoparticle-based therapeutic delivery approaches to treat chronic pulmonary diseases along with the preparation techniques. The authors have remarked the nanosuspension delivery via nebulization and dry powder carrier is further effective in the lung delivery system since the particles released from these systems are innumerable to composite nanoparticles. The authors have also outlined the inhaled particle's toxicity, patented nanoparticle-based pulmonary formulations, and commercial pulmonary drug delivery devices (PDD) in other sections. Recently advanced formulations employing nanoparticles as therapeutic carriers for the efficient treatment of chronic pulmonary diseases are also canvassed.

Effect of Non-Newtonian Dynamics on the Clearance of Mucus From Bifurcating Lung Airway Models.

Mucus hypersecretion is a common pathophysiological manifestation of several obstructive airway diseases in which the mucociliary clearance is impaired, and the airflow generated by a cough or a forced expiratory maneuver called the huff is primarily responsible for clearing mucus. This airflow driven clearance of mucus is a complex process that is affected by the mucus rheology, airflow rate, airway geometry, and gravity. This study examines the role of mucus rheology in the transport and distribution of mucus in idealized 3D airway geometries. The complex air-mucus interface was tracked by the volume-of-fluid (VOF) model, and the turbulence in the core airflow was modeled using the k-ω shear stress transport (SST) model. Mucus was modeled as a shear-thinning liquid by using a power-law model. The computational model was validated using in vitro experimental data available in the literature. Gravity-dominated eccentric core-annular flow was observed with the core biased toward the outer wall in the inclined daughter branches of the bifurcation models, which transitions into concentric core-annular flow in the trachea. The increase in tangential shear at the interface due to the secondary flow structures developed in the flow divider location resulted in a region of enhanced mucus clearance with reduced mucus layer thickness. Secondary flow developed due to the curvature in the airway geometry resulted in a local redistribution of mucus that reduced the eccentricity. The accumulation of mucus around the carinal ridges and the regions with reduced clearance are sites with the potential for microbial growth.

Monitoring Cardiovascular Problems in Heart Patients Using Machine Learning.

The World Health Organization reports that heart disease is the most common cause of death globally, accounting for 17.9 million fatalities annually. The fundamentals of a cure, it is thought, are important symptoms and recognition of the illness. Traditional techniques are facing many challenges, ranging from delayed or unnecessary treatment to incorrect diagnoses, which can affect treatment progress, increase the bill, and give the disease more time to spread and harm the patient's body. Such errors could be avoided and minimized by employing ML and AI techniques. Many significant efforts have been made in recent years to increase computer-aided diagnosis and detection applications, which is a rapidly growing area of research. Machine learning algorithms are especially important in CAD, which is used to detect patterns in medical data sources and make nontrivial predictions to assist doctors and clinicians in making timely decisions. This study aims to develop multiple methods for machine learning using the UCI set of data based on individuals' medical attributes to aid in the early detection of cardiovascular disease. Various machine learning techniques are used to evaluate and review the results of the UCI machine learning heart disease dataset. The proposed algorithms had the highest accuracy, with the random forest classifier achieving 96.72% and the extreme gradient boost achieving 95.08%. This will assist the doctor in taking appropriate actions. The proposed technology will only be able to determine whether or not a person has a heart issue. The severity of heart disease cannot be determined using this method.

Poly(N-isopropylacrylamide)-Based Hydrogels for Biomedical Applications: A Review of the State-of-the-Art.

A prominent research topic in contemporary advanced functional materials science is the production of smart materials based on polymers that may independently adjust their physical and/or chemical characteristics when subjected to external stimuli. Smart hydrogels based on poly(N-isopropylacrylamide) (PNIPAM) demonstrate distinct thermoresponsive features close to a lower critical solution temperature (LCST) that enhance their capability in various biomedical applications such as drug delivery, tissue engineering, and wound dressings. Nevertheless, they have intrinsic shortcomings such as poor mechanical properties, limited loading capacity of actives, and poor biodegradability. Formulation of PNIPAM with diverse functional constituents to develop hydrogel composites is an efficient scheme to overcome these defects, which can significantly help for practicable application. This review reports on the latest developments in functional PNIPAM-based smart hydrogels for various biomedical applications. The first section describes the properties of PNIPAM-based hydrogels, followed by potential applications in diverse fields. Ultimately, this review summarizes the challenges and opportunities in this emerging area of research and development concerning this fascinating polymer-based system deep-rooted in chemistry and material science.

Mucus transport and distribution by steady expiration in an idealized airway geometry.

Mucus clearance from the airways is vital to reduce the risk of infection and to improve pulmonary function. The removal of mucus is propelled either by a rhythmic ciliary motion or shear-induced by turbulent expiratory airflow. However, in chronic airway diseases, the mucociliary motion is impaired due to mucus hypersecretion and altered biophysical properties. As a result, the ciliary motion is insufficient to remove mucus from the airways and expiratory airflow plays the more dominant role. In this work, the role of expiratory airflow in pathologic mucus clearance was investigated in a three-dimensional idealized bifurcating lung geometry. The two-phase air-mucus annular flow was investigated using a homogeneous flow approach and the complex interface (free-surface) was tracked by employing the volume-of-fluid (VOF) method. Flow turbulence was modeled using the k - ω shear stress transport (SST) model to examine the role of mucus viscosity, airflow rate, gravity, and airway branching on mucus distribution. It was observed that a gravity dominated eccentric core-annular flow developed in the daughter branches. The eccentricity varied with the angle of inclination, curvature, airflow rate, and mucus viscosity, which affected the merging location of airflow from the daughter branches. A mucus secondary flow developed due to the curvature in the airways and caused a local redistribution of mucus reducing the eccentricity. In addition, it was also observed that the thickness of the mucus layer was affected by the secondary airflow in the parent branch. These results emphasize the importance of accurate modeling of mucus-lined airways and indicate that an effective clearance therapy can be devised by analyzing the distribution of mucus in the airway tree.