Artificial intelligence for dementia research methods optimization

Artificial intelligence (AI) and machine learning (ML) approaches are increasingly being used in dementia research. However, several methodological challenges exist that may limit the insights we can obtain from high-dimensional data and our ability to translate these findings into improved patient outcomes. To improve reproducibility and replicability, researchers should make their well-documented code and modeling pipelines openly available. Data should also be shared where appropriate. To enhance the acceptability of models and AI-enabled systems to users, researchers should prioritize interpretable methods that provide insights into how decisions are generated. Models should be developed using multiple, diverse datasets to improve robustness, generalizability, and reduce potentially harmful bias. To improve clarity and reproducibility, researchers should adhere to reporting guidelines that are co-produced with multiple stakeholders. If these methodological challenges are overcome, AI and ML hold enormous promise for changing the landscape of dementia research and care. HIGHLIGHTS: Machine learning (ML) can improve diagnosis, prevention, and management of dementia. Inadequate reporting of ML procedures affects reproduction/replication of results. ML models built on unrepresentative datasets do not generalize to new datasets. Obligatory metrics for certain model structures and use cases have not been defined. Interpretability and trust in ML predictions are barriers to clinical translation

Artificial intelligence for neurodegenerative experimental models

INTRODUCTION: Experimental models are essential tools in neurodegenerative disease research. However, the translation of insights and drugs discovered in model systems has proven immensely challenging, marred by high failure rates in human clinical trials. METHODS: Here we review the application of artificial intelligence (AI) and machine learning (ML) in experimental medicine for dementia research. RESULTS: Considering the specific challenges of reproducibility and translation between other species or model systems and human biology in preclinical dementia research, we highlight best practices and resources that can be leveraged to quantify and evaluate translatability. We then evaluate how AI and ML approaches could be applied to enhance both cross-model reproducibility and translation to human biology, while sustaining biological interpretability. DISCUSSION: AI and ML approaches in experimental medicine remain in their infancy. However, they have great potential to strengthen preclinical research and translation if based upon adequate, robust, and reproducible experimental data. Highlights: There are increasing applications of AI in experimental medicine. We identified issues in reproducibility, cross-species translation, and data curation in the field. Our review highlights data resources and AI approaches as solutions. Multi-omics analysis with AI offers exciting future possibilities in drug discovery.</p

Artificial intelligence for neurodegenerative experimental models

INTRODUCTION: Experimental models are essential tools in neurodegenerative disease research. However, the translation of insights and drugs discovered in model systems has proven immensely challenging, marred by high failure rates in human clinical trials. METHODS: Here we review the application of artificial intelligence (AI) and machine learning (ML) in experimental medicine for dementia research. RESULTS: Considering the specific challenges of reproducibility and translation between other species or model systems and human biology in preclinical dementia research, we highlight best practices and resources that can be leveraged to quantify and evaluate translatability. We then evaluate how AI and ML approaches could be applied to enhance both cross-model reproducibility and translation to human biology, while sustaining biological interpretability. DISCUSSION: AI and ML approaches in experimental medicine remain in their infancy. However, they have great potential to strengthen preclinical research and translation if based upon adequate, robust, and reproducible experimental data. HIGHLIGHTS: There are increasing applications of AI in experimental medicine. We identified issues in reproducibility, cross-species translation, and data curation in the field. Our review highlights data resources and AI approaches as solutions. Multi-omics analysis with AI offers exciting future possibilities in drug discovery

Telomere elongation involves intra-molecular DNA replication in cells utilizing alternative lengthening of telomeres

Alternative lengthening of telomeres (ALT) is a telomere length maintenance mechanism based on recombination, where telomeres use other telomeric DNA as a template for DNA synthesis. About 10% of all human tumors depend on ALT for their continued growth, and understanding its molecular details is critically important for the development of cancer treatments that target this mechanism. We have previously shown that telomeres of ALT-positive human cells can become lengthened via inter-telomeric copying, i.e. by copying the telomere of another chromosome. The possibility that such telomeres could elongate by using other sources of telomeric DNA as copy templates has not been investigated previously. In this study, we have determined whether a telomere can become lengthened by copying its own sequences, without the need for using another telomere as a copy template. To test this, we transduced an ALT cell line with a telomere-targeting construct and obtained clones with a single tagged telomere. We showed that the telomere tag can be amplified without the involvement of other telomeres, indicating that telomere elongation can also occur by intra-telomeric DNA copying. This is the first direct evidence that the ALT mechanism involves more than one method of telomere elongation

Artificial intelligence for dementia prevention

INTRODUCTION: A wide range of modifiable risk factors for dementia have been identified. Considerable debate remains about these risk factors, possible interactions between them or with genetic risk, and causality, and how they can help in clinical trial recruitment and drug development. Artificial intelligence (AI) and machine learning (ML) may refine understanding.// METHODS: ML approaches are being developed in dementia prevention. We discuss exemplar uses and evaluate the current applications and limitations in the dementia prevention field.// RESULTS: Risk-profiling tools may help identify high-risk populations for clinical trials; however, their performance needs improvement. New risk-profiling and trial-recruitment tools underpinned by ML models may be effective in reducing costs and improving future trials. ML can inform drug-repurposing efforts and prioritization of disease-modifying therapeutics.// DISCUSSION: ML is not yet widely used but has considerable potential to enhance precision in dementia prevention

Prescriptive variability of drugs by general practitioners

<div><p>Prescription drug spending is growing faster than any other sector of healthcare. However, very little is known about patterns of prescribing and cost of prescribing between general practices. In this study, we examined variation in prescription rates and prescription costs through time for 55 GP surgeries in Northern Ireland Western Health and Social Care Trust. Temporal changes in variability of prescribing rates and costs were assessed using the Mann–Kendall test. Outlier practices contributing to between practice variation in prescribing rates were identified with the interquartile range outlier detection method. The relationship between rates and cost of prescribing was explored with Spearman's statistics. The differences in variability and mean number of prescribing rates associated with the practice setting and socioeconomic deprivation were tested using t-test and <i>F</i>-test respectively. The largest between-practice difference in prescribing rates was observed for Apr-Jun 2015, with the number of prescriptions ranging from 3.34 to 8.36 per patient. We showed that practices with outlier prescribing rates greatly contributed to between-practice variability. The largest difference in prescribing costs was reported for Apr-Jun 2014, with the prescription cost per patient ranging from £26.4 to £64.5. In addition, the temporal changes in variability of prescribing rates and costs were shown to undergo an upward trend. We demonstrated that practice setting and socio-economic deprivation accounted for some of the between-practice variation in prescribing. Rural practices had higher between practice variability than urban practices at all time points. Practices situated in more deprived areas had higher prescribing rates but lower variability than those located in less deprived areas. Further analysis is recommended to assess if variation in prescribing can be explained by demographic characteristics of patient population and practice features. Identification of other factors contributing to prescribing variability can help us better address potential inappropriateness of prescribing.</p></div

Artificial intelligence for dementia research methods optimization

Artificial intelligence (AI) and machine learning (ML) approaches are increasingly being used in dementia research. However, several methodological challenges exist that may limit the insights we can obtain from high-dimensional data and our ability to translate these findings into improved patient outcomes. To improve reproducibility and replicability, researchers should make their well-documented code and modeling pipelines openly available. Data should also be shared where appropriate. To enhance the acceptability of models and AI-enabled systems to users, researchers should prioritize interpretable methods that provide insights into how decisions are generated. Models should be developed using multiple, diverse datasets to improve robustness, generalizability, and reduce potentially harmful bias. To improve clarity and reproducibility, researchers should adhere to reporting guidelines that are co-produced with multiple stakeholders. If these methodological challenges are overcome, AI and ML hold enormous promise for changing the landscape of dementia research and care

Intra-operative gallbladder scoring predicts conversion of laparoscopic to open cholecystectomy: a WSES prospective collaborative study

Abstract Introduction Laparoscopic cholecystectomy, the gold-standard approach for cholecystectomy, has surprisingly variable outcomes and conversion rates. Only recently has operative grading been reported to define disease severity and few have been validated. This multicentre, multinational study assessed an operative scoring system to assess its ability to predict the need for conversion from laparoscopic to open cholecystectomy. Methods A prospective, web-based, ethically approved study was established by WSES with a 10-point gallbladder operative scoring system; enrolling patients undergoing elective or emergency laparoscopic cholecystectomy between January 2016 and December 2017. Gallbladder surgery was considered easy if the G10 score < 2, moderate (2 ≦ 4), difficult (5 ≦ 7) and extreme (8 ≦ 10). Demographics about the patients, surgeons and operative procedures, use of cholangiography and conversion rates were recorded. Results Five hundred four patients, mean age 53.5 (range 18–89), were enrolled by 55 surgeons in 16 countries. Surgery was performed by consultants in 70% and was elective in (56%) with a mean operative time of 78.7 min (range 15-400). The mean G10 score was 3.21, with 22% deemed to have difficult or extreme surgical gallbladders, and 71/504 patients were converted. The G10 score was 2.98 in those completed laparoscopically and 4.65 in the 71/504 (14%) converted. (p <  0.0001; AUC 0.772 (CI 0.719–0.825). The optimal cut-off point of 0.067 (score of 3) was identified in G10 vs conversion to open cholecystectomy. Conversion occurred in 33% of patients with G10 scores of ≥ 5. The four variables statistically predictive of conversion were GB appearance—completely buried GB, impacted stone, bile or pus outside GB and fistula. Conclusion The G10 operative scores provide simple grading of operative cholecystectomy and are predictive of the need to convert to open cholecystectomy. Broader adaptation and validation may provide a benchmark to understand and improve care and afford more standardisation in global comparisons of care for cholecystectomy

The Deep Dementia Phenotyping (DEMON) Network: A global platform for innovation using data science and artificial intelligence.

This is the final version. Available from Wiley via the DOI in this record. BACKGROUND: The increasing availability of large high-dimensional data from experimental medicine, population-based and clinical cohorts, clinical trials, and electronic health records has the potential to transform dementia research. Our ability to make best use of this rich data will depend on utilisation of advanced machine learning and artificial intelligence (AI) techniques and collaboration across disciplinary and geographic boundaries. METHOD: The Deep Dementia Phenotyping (DEMON) Network launched in 20191 to support the growing interest in machine learning and AI. Led by Director Prof David Llewellyn and Deputy Director Dr Janice Ranson, the leadership team additionally includes 5 Theme Leads and 14 Working Group Leads, supported by an international Steering Committee of world-leading academics. Core funding is provided by Alzheimer's Research UK, the Alan Turing Institute and the University of Exeter, with additional support from strategic partners including the UK Dementia Research Institute and the Alzheimer's Society. Grand Challenges were established at a National Strategy Workshop in June 2020. Multidisciplinary Working Groups were formed to coordinate practical activities in seven key areas: Genetics and omics, experimental medicine, drug discovery and trials optimisation, biomarkers, imaging, dementia prevention, and applied models and digital health. Additional Special Interest Groups coordinate topic specific collaborations. RESULT: Membership on 4th February 2022 comprised 1,321 individuals from 61 countries across 6 continents (see Figure). Areas of expertise include dementia research (904; 68%), data science (692; 52%), clinical practice (244; 18%), industry (162; 12%), and regulation (26; 2%). Individual membership is free, and regular knowledge transfer events are provided including a monthly seminar series, talks and workshops, training, networking, and early career development. Each Working Group meets monthly, with multiple grants, reviews, and original research articles in progress. Eight state of the science position papers are in preparation, resulting from a Symposium held in April 2021. In January 2022, 110 early career researchers participated in the Network's flagship event 'NEUROHACK', a 4-day competitive global hackathon, with pilot grants awarded to those generating the most innovative solutions. CONCLUSION: The DEMON Network is a rapidly growing global platform for innovation that is supporting the global dementia research community to collaborate. Find out more at demondementia.com

Taming the tiger by the tail: modulation of DNA damage responses by telomeres

Telomeres are by definition stable and inert chromosome ends, whereas internal chromosome breaks are potent stimulators of the DNA damage response (DDR). Telomeres do not, as might be expected, exclude DDR proteins from chromosome ends but instead engage with many DDR proteins. However, the most powerful DDRs, those that might induce chromosome fusion or cell-cycle arrest, are inhibited at telomeres. In budding yeast, many DDR proteins that accumulate most rapidly at double strand breaks (DSBs), have important functions in physiological telomere maintenance, whereas DDR proteins that arrive later tend to have less important functions. Considerable diversity in telomere structure has evolved in different organisms and, perhaps reflecting this diversity, different DDR proteins seem to have distinct roles in telomere physiology in different organisms. Drawing principally on studies in simple model organisms such as budding yeast, in which many fundamental aspects of the DDR and telomere biology have been established; current views on how telomeres harness aspects of DDR pathways to maintain telomere stability and permit cell-cycle division are discussed