Computer image analysis with artificial intelligence: a practical introduction to convolutional neural networks for medical professionals.

Artificial intelligence tools, particularly convolutional neural networks (CNNs), are transforming healthcare by enhancing predictive, diagnostic, and decision-making capabilities. This review provides an accessible and practical explanation of CNNs for clinicians and highlights their relevance in medical image analysis. CNNs have shown themselves to be exceptionally useful in computer vision, a field that enables machines to 'see' and interpret visual data.

Deep Transfer Learning-Based Approach for Glucose Transporter-1 (GLUT1) Expression Assessment.

Glucose transporter-1 (GLUT-1) expression level is a biomarker of tumour hypoxia condition in immunohistochemistry (IHC)-stained images. Thus, the GLUT-1 scoring is a routine procedure currently employed for predicting tumour hypoxia markers in clinical practice. However, visual assessment of GLUT-1 scores is subjective and consequently prone to inter-pathologist variability.

Using Personalized Avatars as an Adjunct to an Adult Weight Loss Management Program: Randomized Controlled Feasibility Study.

Background: Obesity is a global public health concern. Interventions rely predominantly on managing dietary intake and increasing physical activity; however, sustained adherence to behavioral regimens is often poor. The lack of sustained motivation, self-efficacy, and poor adherence to behavioral regimens are recognized barriers to successful weight loss.

Social distancing enhanced automated optimal design of physical spaces in the wake of the COVID-19 pandemic.

As the COVID-19 pandemic unfolds, manually enhanced ad-hoc solutions have helped the physical space designers and decision makers to cope with the dynamic nature of space planning. Due to the unpredictable nature by which the pandemic is unfolding, the standard operating procedures also change, and the protocols for physical interaction require continuous reconsideration. Consequently, the development of an appropriate technological solution to address the current challenge of reconfiguring common physical environments with prescribed physical distancing measures is much needed.

A Study of Deep Learning-Based Face Recognition Models for Sibling Identification.

Accurate identification of siblings through face recognition is a challenging task. This is predominantly because of the high degree of similarities among the faces of siblings. In this study, we investigate the use of state-of-the-art deep learning face recognition models to evaluate their capacity for discrimination between sibling faces using various similarity indices.

The role of mobile phones as a possible pathway for pathogen movement, a cross-sectional microbial analysis.

Introduction: Mobile phones are used the world over, including in healthcare settings. This study aimed to investigate the viable microbial colonisation of mobile phones used by healthcare personnel.

Methods: Swabs collected on the same day from 30 mobile phones belonging to healthcare workers from three separate paediatric wards of an Australian hospital were cultured on five types of agar plate, then colonies from each phone were pooled, extracted and sequenced by shotgun metagenomics.

Facial Age Synthesis Using Sparse Partial Least Squares (The Case of Ben Needham).

Automatic facial age progression (AFAP) has been an active area of research in recent years. This is due to its numerous applications which include searching for missing. This study presents a new method of AFAP.

The use of thermographic imaging to evaluate therapeutic response in human tumour xenograft models.

Non-invasive methods to monitor tumour growth are an important goal in cancer drug development. Thermographic imaging systems offer potential in this area, since a change in temperature is known to be induced due to changes within the tumour microenvironment. This study demonstrates that this imaging modality can be applied to a broad range of tumour xenografts and also, for the first time, the methodology's suitability to assess anti-cancer agent efficacy.

Elastic-plastic contact law for simulation of tablet crushing using the biharmonic equation.

This work presents a technique for shape modelling of cylindrical and spherical tablets subject to compression. This technique is based on the use of partial differential equations (PDEs), the biharmonic equation in particular. The deformation of the compressed elastic-plastic tablet of both shapes was obtained using the existing contact models found in literature.

Method of modelling the compaction behaviour of cylindrical pharmaceutical tablets.

The mechanisms involved for compaction of pharmaceutical powders have become a crucial step in the development cycle for robust tablet design with required properties. Compressibility of pharmaceutical materials is measured by a force-displacement relationship which is commonly analysed using a well known method, the Heckel model. This model requires the true density and compacted powder mass value to determine the powder mean yield pressure.

Modelling of oedemous limbs and venous ulcers using partial differential equations.

Background: Oedema, commonly known as tissue swelling, occurs mainly on the leg and the arm. The condition may be associated with a range of causes such as venous diseases, trauma, infection, joint disease and orthopaedic surgery. Oedema is caused by both lymphatic and chronic venous insufficiency, which leads to pooling of blood and fluid in the extremities.