Effect of birth weight and nutritional status on transverse maxillary growth: Implications for maternal and infant health

A computational model to analyze the impact of birth weight-nutritional status pair on disease development and disease recovery

Purpose

The purpose of this work is to analyse the combined impacts of birth weight and nutritional status on development and recovery of various types of diseases. This work aims to computationally establish the facts about the effects of individual birth weight-nutritional status pairs on disease development and disease recovery.

Methods

This work designs a computational model to analyze the impact of birth weight-nutritional status pairs on disease development and disease recovery. Our model works in two phases. The first phase finds the best machine learning model to predict birth weight from "Child Birth Weight Dataset" available at IEEE Dataport (https://dx.doi.org/10.21227/dvd4-3232). The second phase combines the predicted birth weight labels with nutritional status labels and establishes the effects using differential equations.

Results

The experimental results find Gradient boosting (GB) to work the best with Information gain (IGT) and Support Vector Machine (SVM) with Chi-square test (CST) for predicting the birth weights. The simulated results establish that "normal birth weight and normal nutritional status" is the best pair for resisting disease development as well as enhancing disease recovery. The results also depict that "low birth weight and malnutrition" is the worst pair for disease development while "high birth weight and malnutrition" is the worst combination for disease recovery.

Conclusion

The findings computationally establish the facts about the effects of birth weight-nutritional status pairs on disease development and disease recovery. As a social implication, this study can spread awareness about the importance of birth weight and nutritional status. The outcome can be helpful for the concerned authority in making decisions on healthcare cost and expenditure.

Assessment of risk factors and molecular biomarkers in children with supernumerary teeth: a single-center study

Background

Supernumerary teeth are considered one of the commonly observed dental anomalies in children. Several theories have been proposed to explain the presence of supernumerary teeth, including environmental and genetic factors. This study aimed to identify the different risk factors and molecular biomarkers in patients presented with supernumerary teeth.

Methods

This case–control study included 240 children, 6 to 12-year-old. They were divided into a test group (n = 120 children presented with supernumerary teeth) and a control group (n = 120 children with no supernumerary teeth). Questionnaires were distributed to assess demographics and exposure to several environmental factors. Ten extracted supernumerary teeth from the test group were processed for histopathological analysis.

Results

Male gender, dental history of severe oral infection or medical history of chemotherapy treatment, previous history of taking medication or illness during pregnancy, family history of neoplastic disorders, use of electronic devices, and living beside agricultural fields or industrial areas were found to be statistically significant associated with the risk of supernumerary teeth development. Immunohistochemistry panel revealed that supernumerary teeth showed enhanced expression of wingless (Wnt) and sonic hedgehog (SHH) proteins as well as a reduced expression of adenomatous polyposis coli (APC) protein, denoting molecular derangement in a group of pathways classically believed to be involved in its pathogenesis.

Conclusions

Males were more frequently affected by supernumerary teeth than females. Several risk factors were notably correlated with the existence of supernumerary teeth. Additionally, molecular biomarkers assessment demonstrated a high expression level of pro-tumorigenic proteins such as Wnt and SHH in patients with supernumerary teeth.