An Introduction to Statistical Learning-with Applications in R by Gareth James, Daniela Witten, Trevor Hastie & Robert Tibshirani

The Influence of Household and Community Food Environments on Food Insecurity in Limpopo Province, South Africa

Insufficient access to enough quality food remains a public health challenge in South Africa. This study aimed to report the influence of community and the household environment, food system inventories, and food procurement on household food security. The findings alluded that food insecurity is prevalent among the rural areas in South Africa. Large household sizes and a limited access to and use of water and food storage facilities for food preservation are the major household determinants. A multi-sectoral nutrition-planning approach that accelerates the achievement of the Sustainable Development Goals should be reinforced. We recommend the promotion of food production for consumption in rural communities.

Developing a multi-institutional nomogram for assessing lung cancer risk in patients with 5-30 mm pulmonary nodules: a retrospective analysis

Background

The diagnosis of benign and malignant solitary pulmonary nodules based on personal experience has several limitations. Therefore, this study aims to establish a nomogram for the diagnosis of benign and malignant solitary pulmonary nodules using clinical information and computed tomography (CT) results.

Methods

Retrospectively, we collected clinical and CT characteristics of 1,160 patients with pulmonary nodules in Guang'an People's Hospital and the hospital affiliated with North Sichuan Medical College between 2019 and 2021. Among these patients, data from 773 patients with pulmonary nodules were used as the training set. We used the least absolute shrinkage and selection operator (LASSO) to optimize clinical and imaging features and performed a multivariate logistic regression to identify features with independent predictive ability to develop the nomogram model. The area under the receiver operating characteristic curve (AUC), C-index, decision curve analysis, and calibration plot were used to evaluate the performance of the nomogram model in terms of predictive ability, discrimination, calibration, and clinical utility. Finally, data from 387 patients with pulmonary nodules were utilized for validation.

Results

In the training set, the predictors for the nomogram were gender, density of the nodule, nodule diameter, lobulation, calcification, vacuole, vascular convergence, bronchiole, and pleural traction, selected through LASSO and logistic regression analysis. The resulting model had a C-index of 0.842 (95% CI [0.812-0.872]) and AUCs of 0.842 (95% CI [0.812-0.872]). In the validation set, the C-index was 0.856 (95% CI [0.811-0.901]), and the AUCs were 0.844 (95% CI [0.797-0.891]). Results from the calibration curve and clinical decision curve analyses indicate that the nomogram has a high fit and clinical benefit in both the training and validation sets.

Conclusion

The establishment of a nomogram for predicting the benign or malignant diagnosis of solitary pulmonary nodules by this study has shown good efficacy. Such a nomogram may help to guide the diagnosis, follow-up, and treatment of patients.

Prediction of diabetes prescription volumes of various geographies using regression techniques

Background: Increasing diabetes prevalence is a major public health concern. In this study we ask whether linked open data can be used to predict prescription volumes of drugs used in the treatment of diabetes across small geographies of England. Methods: We propose and demonstrate a methodology of utilising publicly available open data to infer the geo-spatial distribution of prescribed drugs for diabetes, at the lower layer super output area level. Multiple datasets are acquired, processed, and linked together, enabling a more in-depth analysis. Combining these linked datasets with published deprivation factors of geographies across England, we build highly predictive regression models. Results: Regression models were trained and are capable of accurately predicting diabetes prescribing volumes based on deprivation indicators of various geographies across England. Models built with data covering the city of Bradford, England, produced a predicted against actual correlation value of R2 = 0.672 using multiple linear regression and 0.775 using Least Absolute Shrinkage and Selection Operator (LASSO). Median age and air quality factors proved to be significant markers for diabetes prescribing. Conclusions: The results of this study suggest our methodology is robust and accurate. Such predictive models are useful to health authorities in light of increasing costs and increasing prevalence of diabetes. While using publicly available open data negates any issues of data privacy.

Artificial intelligence in cancer target identification and drug discovery

Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.