Frailty Trajectories Preceding Dementia in the US and UK.

Importance: An accessible marker of both biological age and dementia risk is crucial to advancing dementia prevention and treatment strategies. Although frailty is a candidate for that role, the nature of the relationship between frailty and dementia is not well understood.

Objective: To clarify the temporal relationship between frailty and incident dementia by investigating frailty trajectories in the years preceding dementia onset.

Aortic reservoir-excess pressure parameters are associated with worse cognitive function in people with untreated stage II/III hypertension.

Objective: Hypertension is a recognized risk factor for the development of cognitive impairment and dementia in older adults. Aortic stiffness and altered haemodynamics could promote the transmission of detrimental high pressure pulsatility into the cerebral circulation, potentially damaging brain microvasculature and leading to cognitive impairment. We determined whether reservoir-excess pressure parameters were associated with cognitive function in people with hypertension (HT) and normotension (NT).

Harnessing transcriptomic signals for amyotrophic lateral sclerosis to identify novel drugs and enhance risk prediction.

Introduction: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease. This study integrates common genetic association results from the latest ALS genome-wide association study (GWAS) summary statistics with functional genomic annotations with the aim of providing mechanistic insights into ALS risk loci, inferring drug repurposing opportunities, and enhancing prediction of ALS risk and clinical characteristics.

Methods: Genes associated with ALS were identified using GWAS summary statistic methodology including SuSiE SNP-based fine-mapping, and transcriptome- and proteome-wide association study (TWAS/PWAS) analyses.

Frailty trajectories preceding dementia: an individual-level analysis of four cohort studies in the United States and United Kingdom.

Frailty may represent a modifiable risk factor for dementia, but the direction of that association remains uncertain. We investigated frailty trajectories in the years preceding dementia onset using data from 23,672 participants (242,760 person-years of follow-up, 2,906 cases of incident dementia) across four cohort studies in the United States and United Kingdom. Bayesian non-linear models revealed accelerations in frailty trajectories 4-9 years before incident dementia.

Anemia, hemoglobin concentration and cognitive function in the Longitudinal Ageing Study in India-Harmonized Diagnostic Assessment of Dementia (LASI-DAD) and the Health and Retirement Study.

Background: In India, anemia is widely researched in children and women of reproductive age, however, studies in older populations are lacking. Given the adverse effect of anemia on cognitive function and dementia this older population group warrants further study. The Longitudinal Ageing Study in India - Harmonized Diagnostic Assessment of Dementia (LASI-DAD) dataset contains detailed measures to allow a better understanding of anaemia as a potential risk factor for dementia.

Longer Photoperiod Substantially Increases Indoor-Grown Cannabis' Yield and Quality: A Study of Two High-THC Cultivars Grown under 12 h vs. 13 h Days.

Indoor-grown is commonly transitioned to a 12 h daily photoperiod to promote flowering. However, our previous research has shown that some indoor-grown cannabis cultivars can initiate strong flowering responses under daily photoperiods longer than 12 h. Since longer photoperiods inherently provide higher daily light integrals (DLIs), they may also increase growth and yield.

Risk Factors for Young-Onset Dementia in the UK Biobank.

Importance: There is limited information on modifiable risk factors for young-onset dementia (YOD).

Objective: To examine factors that are associated with the incidence of YOD.

Design, Setting, And Participants: This prospective cohort study used data from the UK Biobank, with baseline assessment between 2006 and 2010 and follow-up until March 31, 2021, for England and Scotland, and February 28, 2018, for Wales.

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.

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.

Artificial intelligence for biomarker discovery in Alzheimer's disease and dementia.

With the increase in large multimodal cohorts and high-throughput technologies, the potential for discovering novel biomarkers is no longer limited by data set size. Artificial intelligence (AI) and machine learning approaches have been developed to detect novel biomarkers and interactions in complex data sets. We discuss exemplar uses and evaluate current applications and limitations of AI to discover novel biomarkers.

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.

Admission plasma potassium and length of hospital stay: a meta-analysis.

Objective: Hypokalaemia and hyperkalaemia ('dyskalaemia') are commonly seen in patients requiring emergency hospital admission. The adverse effect of dyskalaemia on mortality is well described but there are few data for the effect on hospital length of stay. We sought to determine the association of serum potassium concentration with in-hospital length of stay.

Artificial intelligence for dementia genetics and omics.

Genetics and omics studies of Alzheimer's disease and other dementia subtypes enhance our understanding of underlying mechanisms and pathways that can be targeted. We identified key remaining challenges: First, can we enhance genetic studies to address missing heritability? Can we identify reproducible omics signatures that differentiate between dementia subtypes? Can high-dimensional omics data identify improved biomarkers? How can genetics inform our understanding of causal status of dementia risk factors? And which biological processes are altered by dementia-related genetic variation? Artificial intelligence (AI) and machine learning approaches give us powerful new tools in helping us to tackle these challenges, and we review possible solutions and examples of best practice. However, their limitations also need to be considered, as well as the need for coordinated multidisciplinary research and diverse deeply phenotyped cohorts.

Artificial intelligence for dementia drug discovery and trials optimization.

Drug discovery and clinical trial design for dementia have historically been challenging. In part these challenges have arisen from patient heterogeneity, length of disease course, and the tractability of a target for the brain. Applying big data analytics and machine learning tools for drug discovery and utilizing them to inform successful clinical trial design has the potential to accelerate progress.

Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: A systematic review.

Introduction: Artificial intelligence (AI) and neuroimaging offer new opportunities for diagnosis and prognosis of dementia.

Methods: We systematically reviewed studies reporting AI for neuroimaging in diagnosis and/or prognosis of cognitive neurodegenerative diseases.

Results: A total of 255 studies were identified.

Is Twelve Hours Really the Optimum Photoperiod for Promoting Flowering in Indoor-Grown Cultivars of ?

("cannabis" hereafter) is a valuable recent addition to Canada's economy with the legalization for recreational use in 2018. The vast majority of indoor cannabis cultivators use a 12-h light/12-h dark photoperiod to promote flowering. To test the hypothesis that robust flowering initiation responses can be promoted in indoor-grown cannabis cultivars under longer photoperiods, clones of ten drug-type cannabis cultivars were grown under six photoperiod treatments.

Artificial intelligence for dementia-Applied models and digital health.

Introduction: The use of applied modeling in dementia risk prediction, diagnosis, and prognostics will have substantial public health benefits, particularly as "deep phenotyping" cohorts with multi-omics health data become available.

Methods: This narrative review synthesizes understanding of applied models and digital health technologies, in terms of dementia risk prediction, diagnostic discrimination, prognosis, and progression. Machine learning approaches show evidence of improved predictive power compared to standard clinical risk scores in predicting dementia, and the potential to decompose large numbers of variables into relatively few critical predictors.

Socioeconomic Deprivation, Genetic Risk, and Incident Dementia.

Introduction: Socioeconomic factors and genetic predisposition are established risk factors for dementia. It remains unclear whether associations of socioeconomic deprivation with dementia incidence are modified by genetic risk.

Methods: Participants in the UK Biobank aged ≥60 years and of European ancestry without dementia at baseline (2006-2010) were eligible for the analysis, with the main exposures area-level deprivation based on the Townsend Deprivation Index and individual-level socioeconomic deprivation based on car and home ownership, housing type and income, and polygenic risk of dementia.

Mediterranean diet adherence is associated with lower dementia risk, independent of genetic predisposition: findings from the UK Biobank prospective cohort study.

Background: The identification of effective dementia prevention strategies is a major public health priority, due to the enormous and growing societal cost of this condition. Consumption of a Mediterranean diet (MedDiet) has been proposed to reduce dementia risk. However, current evidence is inconclusive and is typically derived from small cohorts with limited dementia cases.

Artificial Intelligence for Dementia Research Methods Optimization.

Introduction: Machine learning (ML) has been extremely successful in identifying key features from high-dimensional datasets and executing complicated tasks with human expert levels of accuracy or greater.

Methods: We summarize and critically evaluate current applications of ML in dementia research and highlight directions for future research.

Results: We present an overview of ML algorithms most frequently used in dementia research and highlight future opportunities for the use of ML in clinical practice, experimental medicine, and clinical trials.

Harnessing the potential of machine learning and artificial intelligence for dementia research.

Progress in dementia research has been limited, with substantial gaps in our knowledge of targets for prevention, mechanisms for disease progression, and disease-modifying treatments. The growing availability of multimodal data sets opens possibilities for the application of machine learning and artificial intelligence (AI) to help answer key questions in the field. We provide an overview of the state of the science, highlighting current challenges and opportunities for utilisation of AI approaches to move the field forward in the areas of genetics, experimental medicine, drug discovery and trials optimisation, imaging, and prevention.

Foliar Symptomology, Nutrient Content, Yield, and Secondary Metabolite Variability of Cannabis Grown Hydroponically with Different Single-Element Nutrient Deficiencies.

In controlled environment production systems, (hereafter cannabis) is a commodity with high nutrient demands due to prolific growth under optimized environmental conditions. Since nutrient deficiencies can reduce yield and quality, cultivators need tools to rapidly detect and evaluate deficiency symptoms so corrective actions can be taken quickly to minimize losses. We grew cannabis plants in solution culture with different individual nutrient elements withheld from the solutions to identify deficiency symptoms.

Harnessing Transcriptomic Signals for Amyotrophic Lateral Sclerosis to Identify Novel Drugs and Enhance Risk Prediction.

Introduction: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease. This study integrates the latest ALS genome-wide association study (GWAS) summary statistics with functional genomic annotations with the aim of providing mechanistic insights into ALS risk loci, inferring drug repurposing opportunities, and enhancing prediction of ALS risk and clinical characteristics.

Methods: Genes associated with ALS were identified using GWAS summary statistic methodology including SuSiE SNP-based fine-mapping, and transcriptome- and proteome-wide association study (TWAS/PWAS) analyses.