Improved perfusion pattern score association with type 2 diabetes severity using machine learning pipeline: Pilot study

Application of machine learning in measurement of ageing and geriatric diseases: a systematic review

BACKGROUND

As the ageing population continues to grow in many countries, the prevalence of geriatric diseases is on the rise. In response, healthcare providers are exploring novel methods to enhance the quality of life for the elderly. Over the last decade, there has been a remarkable surge in the use of machine learning in geriatric diseases and care. Machine learning has emerged as a promising tool for the diagnosis, treatment, and management of these conditions. Hence, our study aims to find out the present state of research in geriatrics and the application of machine learning methods in this area.

METHODS

This systematic review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and focused on healthy ageing in individuals aged 45 and above, with a specific emphasis on the diseases that commonly occur during this process. The study mainly focused on three areas, that are machine learning, the geriatric population, and diseases. Peer-reviewed articles were searched in the PubMed and Scopus databases with inclusion criteria of population above 45 years, must have used machine learning methods, and availability of full text. To assess the quality of the studies, Joanna Briggs Institute's (JBI) critical appraisal tool was used.

RESULTS

A total of 70 papers were selected from the 120 identified papers after going through title screening, abstract screening, and reference search. Limited research is available on predicting biological or brain age using deep learning and different supervised machine learning methods. Neurodegenerative disorders were found to be the most researched disease, in which Alzheimer's disease was focused the most. Among non-communicable diseases, diabetes mellitus, hypertension, cancer, kidney diseases, and cardiovascular diseases were included, and other rare diseases like oral health-related diseases and bone diseases were also explored in some papers. In terms of the application of machine learning, risk prediction was the most common approach. Half of the studies have used supervised machine learning algorithms, among which logistic regression, random forest, XG Boost were frequently used methods. These machine learning methods were applied to a variety of datasets including population-based surveys, hospital records, and digitally traced data.

CONCLUSION

The review identified a wide range of studies that employed machine learning algorithms to analyse various diseases and datasets. While the application of machine learning in geriatrics and care has been well-explored, there is still room for future development, particularly in validating models across diverse populations and utilizing personalized digital datasets for customized patient-centric care in older populations. Further, we suggest a scope of Machine Learning in generating comparable ageing indices such as successful ageing index.

MemAID: Memory advancement with intranasal insulin vs. placebo in type 2 diabetes and control participants: a randomized clinical trial

BACKGROUND

This study aimed at assessing the long-term effects of intranasal insulin (INI) on cognition and gait in older people with and without type 2 diabetes mellitus (T2DM).

METHODS

Phase 2 randomized, double-blinded trial consisted of 24 week treatment with 40 IU of INI (Novolin® R, off-label use) or placebo (sterile saline) once daily and 24 week follow-up. Primary outcomes were cognition, normal (NW), and dual-task (DTW) walking speeds. Of 244 randomized, 223 completed baseline (51 DM-INI, 55 DM-Placebo, 58 Control-INI, 59 Control-Placebo; 109 female, 65.8 ± 9.1; 50-85 years old); 174 completed treatment (84 DM, 90 Controls); 156 completed follow-up (69 DM).

RESULTS

DM-INI had faster NW (~ 7 cm/s; p = 0.025) and DTW on-treatment (p = 0.007; p = 0.812 adjusted for baseline difference) than DM-Placebo. Control-INI had better executive functioning on-treatment (p = 0.008) and post-treatment (p = 0.007) and verbal memory post-treatment (p = 0.004) than Control-Placebo. DM-INI increased cerebral blood flow in medio-prefrontal cortex (p < 0.001) on MRI. Better vasoreactivity was associated with faster DTW (p < 0.008). In DM-INI, plasma insulin (p = 0.006) and HOMA-IR (p < 0.013) decreased post-treatment. Overall INI effect demonstrated faster walking (p = 0.002) and better executive function (p = 0.002) and verbal memory (p = 0.02) (combined DM-INI and Control-INI cohort, hemoglobin A1c-adjusted). INI was not associated with serious adverse events, hypoglycemic episodes, or weight gain.

CONCLUSION

There is evidence for positive INI effects on cognition and gait. INI-treated T2DM participants walked faster, showed increased cerebral blood flow and decreased plasma insulin, while controls improved executive functioning and verbal memory. The MemAID trial provides proof-of-concept for preliminary safety and efficacy and supports future evaluation of INI role to treat T2DM and age-related functional decline.

Conventional and Recent Trends of Scaffolds Fabrication: A Superior Mode for Tissue Engineering

Tissue regeneration is an auto-healing mechanism, initiating immediately following tissue damage to restore normal tissue structure and function. This falls in line with survival instinct being the most dominant instinct for any living organism. Nevertheless, the process is slow and not feasible in all tissues, which led to the emergence of tissue engineering (TE). TE aims at replacing damaged tissues with new ones. To do so, either new tissue is being cultured in vitro and then implanted, or stimulants are implanted into the target site to enhance endogenous tissue formation. Whichever approach is used, a matrix is used to support tissue growth, known as ‘scaffold’. In this review, an overall look at scaffolds fabrication is discussed, starting with design considerations and different biomaterials used. Following, highlights of conventional and advanced fabrication techniques are attentively presented. The future of scaffolds in TE is ever promising, with the likes of nanotechnology being investigated for scaffold integration. The constant evolvement of organoids and biofluidics with the eventual inclusion of organ-on-a-chip in TE has shown a promising prospect of what the technology might lead to. Perhaps the closest technology to market is 4D scaffolds following the successful implementation of 4D printing in other fields.

Conventional and Recent Trends of Scaffolds Fabrication: A Superior Mode for Tissue Engineering

Tissue regeneration is an auto-healing mechanism, initiating immediately following tissue damage to restore normal tissue structure and function. This falls in line with survival instinct being the most dominant instinct for any living organism. Nevertheless, the process is slow and not feasible in all tissues, which led to the emergence of tissue engineering (TE). TE aims at replacing damaged tissues with new ones. To do so, either new tissue is being cultured in vitro and then implanted, or stimulants are implanted into the target site to enhance endogenous tissue formation. Whichever approach is used, a matrix is used to support tissue growth, known as ‘scaffold’. In this review, an overall look at scaffolds fabrication is discussed, starting with design considerations and different biomaterials used. Following, highlights of conventional and advanced fabrication techniques are attentively presented. The future of scaffolds in TE is ever promising, with the likes of nanotechnology being investigated for scaffold integration. The constant evolvement of organoids and biofluidics with the eventual inclusion of organ-on-a-chip in TE has shown a promising prospect of what the technology might lead to. Perhaps the closest technology to market is 4D scaffolds following the successful implementation of 4D printing in other fields.