Research into the application of nanocarriers in the delivery of cancer-fighting drugs has been a promising research area for decades. On the other hand, their cytotoxic effects on cells, low uptake efficiency, and therapeutic resistance have limited their therapeutic use. However, the urgency of pressing healthcare needs has resulted in the functionalization of nanoparticles’ (NPs) physicochemical properties to improve clinical outcomes of new, old, and repurposed drugs. This article reviews recent research on methods for targeting functionalized nanoparticles to the tumor microenvironment (TME). Additionally, the use of relevant engineering techniques for surface functionalization of nanocarriers (liposomes, dendrimers, and mesoporous silica) and their critical roles in overcoming the current limitations in cancer therapy—targeting ligands used for targeted delivery, stimuli strategies, and multifunctional nanoparticles—were all reviewed. The limitations and future perspectives of functionalized nanoparticles were also finally discussed. Using relevant keywords, published scientific literature from all credible sources was retrieved. A quick search of the literature yielded almost 400 publications. The subject matter of this review was addressed adequately using an inclusion/exclusion criterion. The content of this review provides a reasonable basis for further studies to fully exploit the potential of these nanoparticles in cancer therapy.

Background. The rising incidence of hypertension and diabetes calls for a global response. Hypertension and diabetes will rise in Ghana as the population ages, urbanization increases, and people lead unhealthy lives. Our goal was to create a time series algorithm that effectively predicts future increases to help preventative medicine and health care intervention strategies by preparing health care practitioners to control health problems. Methods. Data on hypertension and diabetes from January 2016 to December 2020 were obtained from three health facilities. To detect patterns and predict data from a particular time series, three forecasting algorithms (SARIMAX (seasonal autoregressive integrated moving average with exogenous components), ARIMA (autoregressive integrated moving average), and LSTM (long short‐term memory networks)) were implemented. We assessed the model’s ability to perform by calculating the root mean square error (RMSE), mean absolute error (MAE), mean square error (MSE), and mean absolute percentage error (MAPE). Results. The RMSE, MSE, MAE, and MAPE for ARIMA (5, 2, 4), SARIMAX (1, 1, 1) × (1, 1, 1, 7), and LSTM was 28, 769.02, 22, and 7%, 67, 4473, 56, and 14%, and 36, 1307, 27, and 8.6%, respectively. We chose ARIMA (5, 2, 4) as a more suitable model due to its lower error metrics when compared to the others. Conclusion. All models had promising predictability and predicted a rise in the number of cases in the future, and this was essential for administrative and management planning. For appropriate and efficient strategic planning and control, the prognosis was useful enough than would have been possible without it.