Evidence that the Kennedy and polyamine pathways are dysregulated in human brain in cases of dementia with Lewy bodies

Risk prediction models for postoperative delirium in elderly patients with hip fracture: a systematic review

Objectives

To systematically evaluate the risk prediction models for postoperative delirium in older adult hip fracture patients.

Methods

Risk prediction models for postoperative delirium in older adult hip fracture patients were collected from the Cochrane Library, PubMed, Web of Science, and Ovid via the internet, covering studies from the establishment of the databases to March 15, 2023. Two researchers independently screened the literature, extracted data, and used Stata 13.0 for meta-analysis of predictive factors and the Prediction Model Risk of Bias Assessment Tool (PROBAST) to evaluate the risk prediction models for postoperative delirium in older adult hip fracture patients, evaluated the predictive performance.

Results

This analysis included eight studies. Six studies used internal validation to assess the predictive models, while one combined both internal and external validation. The Area Under Curve (AUC) for the models ranged from 0.67 to 0.79. The most common predictors were preoperative dementia or dementia history (OR = 3.123, 95% CI 2.108-4.626, p < 0.001), American Society of Anesthesiologists (ASA) classification (OR = 2.343, 95% CI 1.146-4.789, p < 0.05), and age (OR = 1.615, 95% CI 1.387-1.880, p < 0.001). This meta-analysis shows that these were independent risk factors for postoperative delirium in older adult patients with hip fracture.

Conclusion

Research on the risk prediction models for postoperative delirium in older adult hip fracture patients is still in the developmental stage. The predictive performance of some of the established models achieve expectation and the applicable risk of all models is low, but there are also problems such as high risk of bias and lack of external validation. Medical professionals should select existing models and validate and optimize them with large samples from multiple centers according to their actual situation. It is more recommended to carry out a large sample of prospective studies to build prediction models.

Systematic review registration

The protocol for this systematic review was published in the International Prospective Register of Systematic Reviews (PROSPERO) under the registered number CRD42022365258.

Dementia with Lewy bodies post-mortem brains reveal differentially methylated CpG sites with biomarker potential

Dementia with Lewy bodies (DLB) is a common form of dementia with known genetic and environmental interactions. However, the underlying epigenetic mechanisms which reflect these gene-environment interactions are poorly studied. Herein, we measure genome-wide DNA methylation profiles of post-mortem brain tissue (Broadmann area 7) from 15 pathologically confirmed DLB brains and compare them with 16 cognitively normal controls using Illumina MethylationEPIC arrays. We identify 17 significantly differentially methylated CpGs (DMCs) and 17 differentially methylated regions (DMRs) between the groups. The DMCs are mainly located at the CpG islands, promoter and first exon regions. Genes associated with the DMCs are linked to "Parkinson's disease" and "metabolic pathway", as well as the diseases of "severe intellectual disability" and "mood disorders". Overall, our study highlights previously unreported DMCs offering insights into DLB pathogenesis with the possibility that some of these could be used as biomarkers of DLB in the future.

Omics Data and Data Representations for Deep Learning-Based Predictive Modeling

Medical discoveries mainly depend on the capability to process and analyze biological datasets, which inundate the scientific community and are still expanding as the cost of next-generation sequencing technologies is decreasing. Deep learning (DL) is a viable method to exploit this massive data stream since it has advanced quickly with there being successive innovations. However, an obstacle to scientific progress emerges: the difficulty of applying DL to biology, and this because both fields are evolving at a breakneck pace, thus making it hard for an individual to occupy the front lines of both of them. This paper aims to bridge the gap and help computer scientists bring their valuable expertise into the life sciences. This work provides an overview of the most common types of biological data and data representations that are used to train DL models, with additional information on the models themselves and the various tasks that are being tackled. This is the essential information a DL expert with no background in biology needs in order to participate in DL-based research projects in biomedicine, biotechnology, and drug discovery. Alternatively, this study could be also useful to researchers in biology to understand and utilize the power of DL to gain better insights into and extract important information from the omics data.

The Influence of Orthopedic Surgery on Circulating Metabolite Levels, and their Associations with the Incidence of Postoperative Delirium

The mechanisms underlying the occurrence of postoperative delirium development are unclear and measurement of plasma metabolites may improve understanding of its causes. Participants (n = 54) matched for age and gender were sampled from an observational cohort study investigating postoperative delirium. Participants were ≥65 years without a diagnosis of dementia and presented for primary elective hip or knee arthroplasty. Plasma samples collected pre- and postoperatively were grouped as either control (n = 26, aged: 75.8 ± 5.2) or delirium (n = 28, aged: 76.2 ± 5.7). Widespread changes in plasma metabolite levels occurred following surgery. The only metabolites significantly differing between corresponding control and delirium samples were ornithine and spermine. In delirium cases, ornithine was 17.6% higher preoperatively, and spermine was 12.0% higher postoperatively. Changes were not associated with various perioperative factors. In binary logistic regression modeling, these two metabolites did not confer a significantly increased risk of delirium. These findings support the hypothesis that disturbed polyamine metabolism is an underlying factor in delirium that warrants further investigation.