The Saudi population is at high risk of multimorbidity. The risk of these morbidities can be reduced by identifying common modifiable behavioural risk factors. This study uses statistical and machine learning methods to predict factors for multimorbidity in the Saudi population. Data from 23,098 Saudi residents were extracted from the “Sharik” Health Indicators Surveillance System 2021. Participants were asked about their demographics and health indicators. Binary logistic models were used to determine predictors of multimorbidity. A backpropagation neural network model was further run using the predictors from the logistic regression model. Accuracy measures were checked using training, validation, and testing data. Females and smokers had the highest likelihood of experiencing multimorbidity. Age and fruit consumption also played a significant role in predicting multimorbidity. Regarding model accuracy, both logistic regression and backpropagation algorithms yielded comparable outcomes. The backpropagation method (accuracy 80.7%) was more accurate than the logistic regression model (77%). Machine learning algorithms can be used to predict multimorbidity among adults, particularly in the Middle East region. Different testing methods later validated the common predicting factors identified in this study. These factors are helpful and can be translated by policymakers to consider improvements in the public health domain.

In the present study, biocompatible manganese nanoparticles have been linked with zinc and iron molecules to prepare different derivatives of Mn_0.5Zn_0.5Er_xY_xFe_2−2xO₄ NPs (x = 0.02, 0.04, 0.06, 0.08, 0.10), using an ultrasonication approach. The structure, surface morphology, and chemical compositions of Mn_0.5Zn_0.5Er_xY_xFe_2−2xO₄ NPs were elucidated by X-ray diffractometer (XRD), High-resolution transmission electron microscopy (HR-TEM), scanning electron microscope (SEM), and Energy Dispersive X-Ray Analysis (EDX) techniques. The bioactivity of Mn_0.5Zn_0.5Er_xY_xFe_2−2xO₄ NPs on normal (HEK-293) and (HCT-116) colon cancer cell line was evaluated. The Mn_0.5Zn_0.5Er_xY_xFe_2−2xO₄ NPs treatment post 48 h resulted in a significant reduction in cells (via MTT assay, having an IC₅₀ value between 0.88 µg/mL and 2.40 µg/mL). The specificity of Mn_0.5Zn_0.5Er_xY_xFe_2−2xO₄ NPs were studied by treating them on normal cells line (HEK-293). The results showed that Mn_0.5Zn_0.5Er_xY_xFe_2−2xO₄ NPs did not incur any effect on HEK-293, which suggests that Mn_0.5Zn_0.5Er_xY_xFe_2−2xO₄ NPs selectively targeted the colon cancerous cells. Using Candida albicans, antifungal activity was also studied by evaluating minimum inhibitory/fungicidal concentration (MIC/MFC) and the effect of nanomaterial on the germ tube formation, which exhibited that NPs significantly inhibited the growth and germ tube formation. The obtained results hold the potential to design nanoparticles that lead to efficient bioactivity.