Methods: Data were collected from private hospitals in Klang Valley. A total of 379 responses from patients were analysed using the structural equation modelling approach.
Results: The findings revealed that administrative behaviour, nurse's services and Shariah amenities have a highly significant impact on satisfaction. The healthcare technicality, hospital environment and physician's services have a significant relationship with patient satisfaction. Patient satisfaction has a significant impact on patient loyalty to healthcare services at the hospital. Administrative behaviour, physicians' services and healthcare technicality have a direct and positive relationship with loyalty intention, while Shariah amenity has a negative significant relationship with loyalty.
Conclusion: The results have important implications for product development and managerial considerations in hospitals. Service providers need to be mindful that all aspects, including Shariah amenities and generic healthcare service delivery, are important and need to be balanced and delivered satisfactorily to ensure customer satisfaction.
SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s43546-022-00408-x.
METHODS: The produced formulations were evaluated based on particle size and shape (particle size distribution (PSD), scanning electron microscope (SEM)), incompatibility (differential scanning calorimetry (DSC), Fourier-transform infrared (FTIR)), drug release pattern, permeation behavior, in vivo hypoglycemic effects, and in vitro anticancer potential.
RESULTS: Compatibility studies concluded that there was minimal interaction between metformin HCl and the polymer, whereas SEM images revealed smoother, more spherical nanoparticles than microspheres. Drug release from the formulations was primarily controlled by the non-Fickian diffusion process, except for A1 and A4 by Fickian, and B3 by Super case II. Korsmeyer-Peppas was the best-fit model for the maximum formulations. The best formulations of microspheres and nanoparticles, based on greater drug release, drug entrapment, and compatibility characteristics, were attributed to the study of drug permeation by non-everted intestinal sacs, in vivo anti-hyperglycemic activity, and in vitro anticancer activity.
CONCLUSION: This study suggests that the proposed metformin HCl formulation can dramatically reduce hyperglycemic conditions and may also have anticancer potential.
METHOD: For designing and modeling the DSPN severity grading systems for MNSI, 19 years of data from Epidemiology of Diabetes Interventions and Complications (EDIC) clinical trials were used. Different Machine learning-based feature ranking techniques were investigated to identify the important MNSI features associated with DSPN diagnosis. A multivariable logistic regression-based nomogram was generated and validated for DSPN severity grading using the best performing top-ranked MNSI features.
RESULTS: Top-10 ranked features from MNSI features: Appearance of Feet (R), Ankle Reflexes (R), Vibration perception (L), Vibration perception (R), Appearance of Feet (L), 10-gm filament (L), Ankle Reflexes (L), 10-gm filament (R), Bed Cover Touch, and Ulceration (R) were identified as important features for identifying DSPN by Multi-Tree Extreme Gradient Boost model. The nomogram-based prediction model exhibited an accuracy of 97.95% and 98.84% for the EDIC test set and an independent test set, respectively. A DSPN severity score technique was generated for MNSI from the DSPN severity prediction model. DSPN patients were stratified into four severity levels: absent, mild, moderate, and severe using the cut-off values of 17.6, 19.1, 20.5 for the DSPN probability less than 50%, 75%-90%, and above 90%, respectively.
CONCLUSIONS: The findings of this work provide a machine learning-based MNSI severity grading system which has the potential to be used as a secondary decision support system by health professionals in clinical applications and large clinical trials to identify high-risk DSPN patients.