In this first Maltese national mammography survey, the effectiveness of direct digital (DR) mammography in breast cancer screening has been confirmed. Patient data were made available from three clinics out of the participating nine. A dose survey of mean glandular dose (MGD) calculated for 759 patients examined in the state-owned mammography facilities was performed. An MGD national diagnostic reference level was set at 1.87 mGy for patients with breast compression thicknesses (BCT) between 5.0 and 7.0 cm. This range was selected since patient data were retrieved from three clinics only and the results showed that other international BCT reference levels may be unsuitable for the Maltese population. In fact, the overall average BCT was 5.75 ± 1.4 cm. The survey results have shown that the technical standard of mammographic equipment in the Malta National Breast Screening Programme is on a par with other countries, including its Western European counterparts.

The aim of the study was to investigate age and sex differences in performance in an 11‐plus selective examination. The sample was made up of 3460 pupils who were born in 1981 and who had sat the examination for the first time. Results based on a data set consisting of raw scores in Maltese, English, mathematics, social studies and religious knowledge showed a strong and consistent age effect in all five attainment measures: performance decreased with age from the ‘oldest’ pupils to the youngest’. In regard to sex differences, girls outperformed boys in Maltese, English and religion, with differences being most marked in the two languages. The performance of the two sexes in mathematics and social studies was very similar, with the boys’ performance being slightly, but not significantly, better. These age and sex effects were also evident in the proportions of pupils who passed or failed the examination. Older pupils tended to be more successful than younger pupils, and girls were more likely to pass the examination than boys.

Monitoring plantar foot temperatures is essential for assessing foot health, particularly in individuals with diabetes at increased risk of complications. Traditional thermographic imaging measures foot temperatures in unshod individuals lying down, which may not reflect thermal characteristics of feet in shod, active, real-world conditions. These controlled settings limit understanding of dynamic foot temperatures during daily activities. Recent advancements in wearable technology, such as insole-based sensors, overcome these limitations by enabling continuous temperature monitoring. This study leverages a data-driven clustering approach, independent of pre-selected foot regions or models like the angiosome concept, to explore normative thermal patterns in shod feet with insole-based sensors. Data were collected from 27 healthy participants using insoles embedded with 21 temperature sensors. The data were analysed using clustering algorithms, including k-means, fuzzy c-means, OPTICS, and hierarchical clustering. The clustering algorithms showed a high degree of similarity, with variations primarily influenced by clustering granularity. Six primary thermal patterns were identified, with the “butterfly pattern” (elevated medial arch temperatures) predominant, representing 51.5% of the dataset, aligning with findings in thermographic studies. Other patterns, like the “medial arch + metatarsal area” pattern, were also observed, highlighting diverse yet consistent thermal distributions. This study shows that while normative thermal patterns observed in thermographic imaging are reflected in insole data, the temperature distribution within the shoe may better represent foot behaviour during everyday activities, particularly when enclosed in a shoe. Unlike thermal imaging, the proposed in-shoe system offers the potential to capture dynamic thermal variations during ambulatory activities, enabling richer insights into foot health in real-world conditions.

In this work, we describe the use of time series features extracted from foot temperature data obtained from a wearable in-shoe system to discriminate between feet from individuals affected by diabetes, and feet from healthy individuals. We identify a set of features that are statistically significant in discriminating between the two classes and that thus can serve as input to machine learning classifiers.