Water-borne emerging pollutants are among the greatest concern of our modern society. Many of these pollutants are categorized as endocrine disruptors due to their environmental toxicities. They are harmful to humans, aquatic animals, and plants, to the larger extent, destroying the ecosystem. Thus, effective environmental remediations of these pollutants became necessary. Among the various remediation techniques, adsorption and photocatalytic degradation have been single out as the most promising. This review is devoted to the compilations and analysis of the role of metal-organic frameworks (MOFs) and their composites as potential materials for such applications. Emerging organic pollutants, like dyes, herbicides, pesticides, pharmaceutical products, phenols, polycyclic aromatic hydrocarbons, and perfluorinated alkyl substances, have been extensively studied. Important parameters that affect these processes, such as surface area, bandgap, percentage removal, equilibrium time, adsorption capacity, and recyclability, are documented. Finally, we paint the current scenario and challenges that need to be addressed for MOFs and their composites to be exploited for commercial applications.

Polycyclic aromatic hydrocarbons (PAHs) and phenolic compounds had been widely recognized as priority organic pollutants in wastewater with toxic effects on both plants and animals. Thus, the remediation of these pollutants has been an active area of research in the field of environmental science and engineering. This review highlighted the advantage of adsorption technology in the removal of PAHs and phenols in wastewater. The literature presented on the applications of various porous carbon materials such as biochar, activated carbon (AC), carbon nanotubes (CNTs), and graphene as potential adsorbents for these pollutants has been critically reviewed and analyzed. Under similar conditions, the use of porous polymers such as Chitosan and molecularly imprinted polymers (MIPs) have been well presented. The high adsorption capacities of advanced porous materials such as mesoporous silica and metal-organic frameworks have been considered and evaluated. The preference of these materials, higher adsorption efficiencies, mechanism of adsorptions, and possible challenges have been discussed. Recommendations have been proposed for commercialization, pilot, and industrial-scale applications of the studied adsorbents towards persistent organic pollutants (POPs) removal from wastewater.

While the consumption of seaweed and seaweed-based products is very common amongst East Asian nations, forming a notable component of the daily diet, relatively very few studies have concerned the concentrations of heavy metals in these together with potential effects on human health. The present study analyses the concentrations of 17 elements in locally resourced seaweed, also assessing potential noncarcinogenic and carcinogenic risks. The samples were ground, homogenized, and quantified using the ICP-OES technique. It has been found that the essential elements K, Ca, Mg, Zn, and Na typically show concentrations somewhat greater than a number of potentially toxic metals, in particular, Cd, Pb, Ag, and As, with exceptions being Ni, Cr-VI, and Si. Statistical analysis indicates all of the latter to have similar origin, with increased concentration of these metals within the marine ecosystem. While the daily estimated intake of most metals is seen to be within the daily dietary allowance level recommended by various international organizations, the noncarcinogenic risk shows a value greater than unity, estimated via the hazard quotient. This indicates a potential for adverse effects to health arising from consumption of the sampled seaweed. The carcinogenic risk resulting from nonessential elements shows values greater than the United States Environmental Protection Agency (US-EPA) reference limit of 10⁻⁴. Considering the nonbiodegradability of heavy metals and metalloids and their potential accumulation in seaweed, there is need for critical examination of metal levels in the seaweeds obtained from the present study locations, together with the introduction of practices of removal of heavy metals via bio-adsorbent techniques.

The disease may be an explicit status that negatively affects human health. Cardiopathy is one of the common deadly diseases that is attributed to unhealthy human habits compared to alternative diseases. With the help of machine learning (ML) algorithms, heart disease can be noticed in a short time as well as at a low cost. This study adopted four machine learning models, such as random forest (RF), decision tree (DT), AdaBoost (AB), and K-nearest neighbor (KNN), to detect heart disease. A generalized algorithm was constructed to analyze the strength of the relevant factors that contribute to heart disease prediction. The models were evaluated using the datasets Cleveland, Hungary, Switzerland, and Long Beach (CHSLB), and all were collected from Kaggle. Based on the CHSLB dataset, RF, DT, AB, and KNN models predicted an accuracy of 99.03%, 96.10%, 100%, and 100%, respectively. In the case of a single (Cleveland) dataset, only two models, namely RF and KNN, show good accuracy of 93.437% and 97.83%, respectively. Finally, the study used Streamlit, an internet-based cloud hosting platform, to develop a computer-aided smart system for disease prediction. It is expected that the proposed tool together with the ML algorithm will play a key role in diagnosing heart diseases in a very convenient manner. Above all, the study has made a substantial contribution to the computation of strength scores with significant predictors in the prognosis of heart disease.

Numerous factors can contribute to the development of neurodegenerative disorders (NDs), such as Alzheimer’s disease, Parkinson’s disease, amyotrophic lateral sclerosis, Huntington’s disease, and multiple sclerosis. Oxidative stress (OS), a fairly common ND symptom, can be caused by more reactive oxygen species being made. In addition, the pathological state of NDs, which includes a high number of protein aggregates, could make chronic inflammation worse by activating microglia. Carotenoids, often known as “CTs”, are pigments that exist naturally and play a vital role in the prevention of several brain illnesses. CTs are organic pigments with major significance in ND prevention. More than 600 CTs have been discovered in nature, and they may be found in a wide variety of creatures. Different forms of CTs are responsible for the red, yellow, and orange pigments seen in many animals and plants. Because of their unique structure, CTs exhibit a wide range of bioactive effects, such as anti-inflammatory and antioxidant effects. The preventive effects of CTs have led researchers to find a strong correlation between CT levels in the body and the avoidance and treatment of several ailments, including NDs. To further understand the connection between OS, neuroinflammation, and NDs, a literature review has been compiled. In addition, we have focused on the anti-inflammatory and antioxidant properties of CTs for the treatment and management of NDs.