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.

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 β-(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.

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.

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.

Effective ventilation systems are essential to control the transmission of airborne aerosol particles, such as the SARS-CoV-2 virus in aircraft cabins, which is a significant concern for people commuting by airplane. Validated computational fluid dynamic models are frequently and effectively used to investigate air distribution and pollutant transport. In this study, the effectiveness of different ventilation systems with varying outlet vent locations were computationally compared to determine the best ventilation system for minimizing the risk of airborne transmission. The cabin air conditioning system was optimized to determine how design variables (air inlet temperature, outlet valve width and location, and mass flow rate) affect output parameters, including particle residence time, age of air, and thermal comfort conditions. Inlet mass flow rate was observed to be an influential variable impacting all output parameters, especially on age of air, where it was the most influential. In contrast, the least effective variable was width of the outlet valve, which only affected the particle residence time. Also, Predicted Mean Vote and Predicted Percentage Dissatisfied indices were the most affected by air inlet temperature, which had an inverse relation, while the outlet valve location had the greatest effect on particle residence time.